Datasets:
codeparrot
/
github-jupyter

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santipuch590/deeplearning-tf
dl_tf_BDU/1.Intro_TF/ML0120EN-1.2-Exercise-LinearRegression.ipynb
1
59960
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/jvcqp2iy2jlx2b32rmzdt0tx8lvxgzkp.png\" width = 300, align = \"center\"></a>\n", "\n", "<h1 align=center> <font size = 5> Exercise-Linear Regression with TensorFlow </font></h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This exercise is about modelling a linear relationship between \"chirps of a cricket\" and ground temperature. \n", "\n", "In 1948, G. W. Pierce in his book \"Songs of Insects\" mentioned that we can predict temperature by listening to the frequency of songs(chirps) made by stripped Crickets. He recorded change in behaviour of crickets by recording number of chirps made by them at several \"different temperatures\" and found that there is a pattern in the way crickets respond to the rate of change in ground temperature 60 to 100 degrees of farenhite. He also found out that Crickets did not sing \n", "above or below this temperature.\n", "\n", "This data is derieved from the above mentioned book and aim is to fit a linear model and predict the \"Best Fit Line\" for the given \"Chirps(per 15 Second)\" in Column 'A' and the corresponding \"Temperatures(Farenhite)\" in Column 'B' using TensorFlow. So that one could easily tell what temperature it is just by listening to the songs of cricket. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's import tensorFlow and python dependencies " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.19.2'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download and Explore the Data " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-04-20 21:53:57 URL:https://public.boxcloud.com/d/1/6XAvi1mQbqb2l5Ch4TfRZFkzFv1utphV97LScjLoNv8uwNUUfROfwKzl6ZnGD-XYrmQncaFFH7xzkJekm1mZ5eMrOMqcFpz03U39DDJDYf4VX7NfScc-H8NHDjna9yY1agMDwqKYvEOhtX1voeWWvPhZt4PMUhbs48dcnjgofE7Hs_zvlx7rZIeJNYArLND1Lb05giPqAhafh5MFGAxMRszx9JfO85m6NTRLZz8PMZNXroz9ALxmTkMNz5SbQEbCYzQJlAFhdhkdcsaRfyc0Ew0MDimO5kVBggMzix-2Gr_1soJpacBry7nLG_5SmzJIQ-sL-o38NyEd-pk1ehXI63fz8hnyGAiFV9wOlxio5FieMYMrfQWctdxztc699FMzb8bcMweNDzC7I7DnJU5NyZI3nvayjYCKPoFhJm-dJ2ozYAvB86OagtK2vLznA3-PEDu88GeMJ5XFYzvKlb9mDTomkBlqQ3e-EdR-4FO2Z1kCN1vfKiaEBFYYx8szI3jGi18cc-8WtT7BoVcU-54L_dR_tSBBRcUpT-sAkeVPePIP3c_mPXMbKVwaMoPQi4d2ZTeUbQ-7bK_RrrTx8zUPZFf6jAPW3vmYh7G-ifg9jv25RRRRBAg8HFOPtKa9iQ_lVGU480gXxoRmEB_lPP7MXG7KballuaRE50zw0EHIEkTf5kSob_v9TCLxq0Lo8Ob9ntz5049-KTIsLr_EB9247hNzzeMC1iU3-Jk0Kz874WBXdHZ_Q5pFCncqni1OG7F6e_hJ3BD7Zv7v1k4WZJSua8cKHO67D1sv9zN8Gaj0X1ZftSVU8OxJqbFu54Xbkuw05k-giNgdkYvFc0Ekdv_L_0orCt6wZnh5wfryEktmkXKApUmijpsJM2pYC0hYnisiiUdLak48jGMjaWa0nsfxaxGIuH9Lj4M939yfO087jCVWZEBp1zJpRYo7s-r5_yK1_s2piMATFSm_bgPtyeC5EpB7_bb2eq8dsreg75IfslpbLOueEIRpP35Gc_OpC_rbq8IRFPjvUuwjqHUEgrUXgeBVITaVcXDmvldMS0ZfAo6uvvjdQxGB_Vr0ocnyhRh0pWMZpvJgUCO5sdJW8FIjhdZnXE9Iwq0MGYXMo220GO_JpbE5kYUKxX8P5MeUDm4wg1IT6KoCBAUN0xauCDlh/download [164/164] -> \"../data/PierceCricketData.csv\" [1]\r\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Chirps</th>\n", " <th>Temp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>20.0</td>\n", " <td>88.6</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>16.0</td>\n", " <td>71.6</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>19.8</td>\n", " <td>93.3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>18.4</td>\n", " <td>84.3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.1</td>\n", " <td>80.6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Chirps Temp\n", "0 20.0 88.6\n", "1 16.0 71.6\n", "2 19.8 93.3\n", "3 18.4 84.3\n", "4 17.1 80.6" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "#downloading dataset\n", "!wget -nv -O ../data/PierceCricketData.csv https://ibm.box.com/shared/static/fjbsu8qbwm1n5zsw90q6xzfo4ptlsw96.csv\n", "\n", "\n", "df = pd.read_csv(\"../data/PierceCricketData.csv\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h6> Plot the Data Points </h6>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f2b201d2438>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2b2406d2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "%matplotlib inline\n", "\n", "x_data, y_data = (df[\"Chirps\"].values,df[\"Temp\"].values)\n", "\n", "# plots the data points\n", "plt.plot(x_data, y_data, 'ro')\n", "# label the axis\n", "plt.xlabel(\"# Chirps per 15 sec\")\n", "plt.ylabel(\"Temp in Farenhiet\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the scatter plot we can analyse that there is a linear relationship between the data points that connect chirps to the temperature and optimal way to infer this knowledge is by fitting a line that best describes the data. Which follows the linear equation: \n", "\n", " #### Ypred = m X + c \n", "\n", "We have to estimate the values of the slope 'm' and the inrtercept 'c' to fit a line where, X is the \"Chirps\" and Ypred is \"Predicted Temperature\" in this case. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a Data Flow Graph using TensorFlow \n", "\n", "Model the above equation by assigning arbitrary values of your choice for slope \"m\" and intercept \"c\" which can predict the temp \"Ypred\" given Chirps \"X\" as input. \n", "\n", "example m=3 and c=2\n", "\n", "Also, create a place holder for actual temperature \"Y\" which we will be needing for Optimization to estimate the actual values of slope and intercept.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create place holders and Variables along with the Linear model.\n", "m = tf.Variable(3, dtype=tf.float32)\n", "c = tf.Variable(2, dtype=tf.float32)\n", "x = tf.placeholder(dtype=tf.float32, shape=x_data.size)\n", "y = tf.placeholder(dtype=tf.float32, shape=y_data.size)\n", "# Linear model\n", "y_pred = m * x + c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">\n", "<a href=\"#createvar\" class=\"btn btn-default\" data-toggle=\"collapse\">Click here for the solution</a>\n", "</div>\n", "<div id=\"createvar\" class=\"collapse\">\n", "```\n", "\n", "X = tf.placeholder(tf.float32, shape=(x_data.size))\n", "Y = tf.placeholder(tf.float32,shape=(y_data.size))\n", "\n", "# tf.Variable call creates a single updatable copy in the memory and efficiently updates \n", "# the copy to relfect any changes in the variable values through out the scope of the tensorflow session\n", "m = tf.Variable(3.0)\n", "c = tf.Variable(2.0)\n", "\n", "# Construct a Model\n", "Ypred = tf.add(tf.multiply(X, m), c)\n", "```\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create and Run a Session to Visualize the Predicted Line from above Graph \n", "\n", "<h6> Feel free to change the values of \"m\" and \"c\" in future to check how the initial position of line changes </h6>\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#create session and initialize variables\n", "session = tf.Session()\n", "session.run(tf.global_variables_initializer())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f2b200efda0>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dKdBFdnHFA6/yh2eqsrYpLenMTd8dwcH99ypOUSIhKNClVduyrY4DL3skZ7v9enfhrsmj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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2b541885c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "#get prediction with initial parameter values\n", "y_vals = session.run(y_pred, feed_dict={x: x_data})\n", "#Your code goes here\n", "plt.plot(x_data, y_vals, label='Predicted')\n", "plt.scatter(x_data, y_data, color='red', label='GT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">\n", "<a href=\"#matmul1\" class=\"btn btn-default\" data-toggle=\"collapse\">Click here for the solution</a>\n", "</div>\n", "<div id=\"matmul1\" class=\"collapse\">\n", "```\n", "\n", "pred = session.run(Ypred, feed_dict={X:x_data})\n", "\n", "#plot initial prediction against datapoints\n", "plt.plot(x_data, pred)\n", "plt.plot(x_data, y_data, 'ro')\n", "# label the axis\n", "plt.xlabel(\"# Chirps per 15 sec\")\n", "plt.ylabel(\"Temp in Farenhiet\")\n", "\n", "\n", "```\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define a Graph for Loss Function\n", "\n", "The essence of estimating the values for \"m\" and \"c\" lies in minimizing the difference between predicted \"Ypred\" and actual \"Y\" temperature values which is defined in the form of Mean Squared error loss function. \n", " \n", "$$ loss = \\frac{1}{n}\\sum_{i=1}^n{[Ypred_i - {Y}_i]^2} $$\n", "\n", "Note: There are also other ways to model the loss function based on distance metric between predicted and actual temperature values. For this exercise Mean Suared error criteria is considered. \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss = tf.reduce_mean(tf.squared_difference(y_pred*0.1, y*0.1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">\n", "<a href=\"#matmul12\" class=\"btn btn-default\" data-toggle=\"collapse\">Click here for the solution</a>\n", "</div>\n", "<div id=\"matmul12\" class=\"collapse\">\n", "```\n", "# normalization factor\n", "nf = 1e-1\n", "# seting up the loss function\n", "loss = tf.reduce_mean(tf.squared_difference(Ypred*nf,Y*nf))\n", "```\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define an Optimization Graph to Minimize the Loss and Training the Model\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Your code goes here\n", "optimizer = tf.train.GradientDescentOptimizer(0.01)\n", "train_op = optimizer.minimize(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">\n", "<a href=\"#matmul13\" class=\"btn btn-default\" data-toggle=\"collapse\">Click here for the solution</a>\n", "</div>\n", "<div id=\"matmul13\" class=\"collapse\">\n", "```\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)\n", "#optimizer = tf.train.AdagradOptimizer(0.01 )\n", "\n", "# pass the loss function that optimizer should optimize on.\n", "train = optimizer.minimize(loss)\n", "\n", "```\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize all the vairiables again\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run session to train and predict the values of 'm' and 'c' for different training steps along with storing the losses in each step" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the predicted m and c values by running a session on Training a linear model. Also collect the loss for different steps to print and plot. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finished by Convergence Criterion\n", "119\n", "0.183721\n" ] } ], "source": [ "convergenceTolerance = 0.0001\n", "previous_m = np.inf\n", "previous_c = np.inf\n", "\n", "steps = {}\n", "steps['m'] = []\n", "steps['c'] = []\n", "\n", "losses=[]\n", "\n", "for k in range(10000):\n", " ########## Your Code goes Here ###########\n", " _, _l, _m, _c = session.run([train_op, loss, m, c], feed_dict={x: x_data, y: y_data})\n", "\n", " steps['m'].append(_m)\n", " steps['c'].append(_c)\n", " losses.append(_l)\n", " if (np.abs(previous_m - _m) or np.abs(previous_c - _c) ) <= convergenceTolerance :\n", " \n", " print(\"Finished by Convergence Criterion\")\n", " print(k)\n", " print(_l)\n", " break\n", " previous_m = _m \n", " previous_c = _c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">\n", "<a href=\"#matmul18\" class=\"btn btn-default\" data-toggle=\"collapse\">Click here for the solution</a>\n", "</div>\n", "<div id=\"matmul18\" class=\"collapse\">\n", "```\n", "# run a session to train , get m and c values with loss function \n", "_, _m , _c,_l = session.run([train, m, c,loss],feed_dict={X:x_data,Y:y_data}) \n", "\n", "```\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Print the loss function" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f2b2001d4a8>]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GvuzuJ0d4frmZtZpZazwevr3WooIIy2+ey6pdx3lr1/Gg44iIjCqt4jazQpKl/bi7PzPS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\nLNuLAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2b2006e2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your Code Goes Here\n", "plt.plot(losses)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">\n", "<a href=\"#matmul199\" class=\"btn btn-default\" data-toggle=\"collapse\">Click here for the solution</a>\n", "</div>\n", "<div id=\"matmul199\" class=\"collapse\">\n", "```\n", "plt.plot(losses[:])\n", "\n", "```\n", "</div>" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f2b2017ccc0>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZCXPnQnd3Enx3d7LtZGBWNpwIytBQs4VWXDKYPx96evru6+lJ9ptZWXAbQRmq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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2b1c5bfcc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_vals_pred = y_pred.eval(session=session, feed_dict={x: x_data})\n", "plt.scatter(x_data, y_vals_pred, marker='x', color='blue', label='Predicted')\n", "plt.scatter(x_data, y_data, label='GT', color='red')\n", "plt.legend()\n", "plt.ylabel('Temperature (Fahrenheit)')\n", "plt.xlabel('# Chirps per 15 s')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "session.close() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This Exercise is about giving Overview about how to use TensorFlow for Predicting Ground Temperature given the number of Cricket Chirps per 15 secs. Idea is to use TnesorFlow's dataflow graph to define Optimization and Training graphs to find out the actual values of 'm' and 'c' that best describes the given Data. \n", "\n", "\n", "### Please Feel free to change the initial values of 'm' and 'c' to check how the training steps Vary. \n", "\n", "\n", "\n", "\n", "## Thank You for Completing this exercise " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Created by <a href = \"https://ca.linkedin.com/in/shashibushan-yenkanchi\"> Shashibushan Yenkanchi </a> </h4>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# REFERENCES" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://mathbits.com/MathBits/TISection/Statistics2/linearREAL.htm" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
zunio/python-recipes
99-dataStructures/[Dict]Sort.ipynb
2
4242
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "https://stackoverflow.com/questions/613183/sort-a-python-dictionary-by-value\n", "\n", "It is not possible to sort a dict, only to get a representation of a dict that is sorted. Dicts are inherently orderless, but other types, such as lists and tuples, are not. So you need a sorted representation, which will be a list—probably a list of tuples." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 4\n", "4 3\n", "1 2\n", "2 1\n", "0 0\n" ] } ], "source": [ "import operator\n", "x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}\n", "#itemgetter(0) if you want sort by key\n", "sorted_x = sorted(x.items(), key=operator.itemgetter(1),reverse=True) \n", "for k,v in sorted_x:\n", " print(k,v)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(3, 4), (4, 3), (1, 2), (2, 1), (0, 0)]\n", "<class 'list'>\n", "<class 'tuple'>\n" ] } ], "source": [ "print(sorted_x)\n", "print(type(sorted_x))\n", "print(type(sorted_x[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Other Approach" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('a', 1), ('c', 2), ('b', 3)]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Less efficient\n", "mydict = {'a':1,'b':3,'c':2}\n", "sorted(mydict.items(), key=lambda x: x[1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['a', 'c', 'b']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Less efficient\n", "mydict = {'a':1,'b':3,'c':2}\n", "sorted(mydict, key=mydict.get)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b 3\n", "c 2\n", "a 1\n" ] } ], "source": [ "for w in sorted(mydict, key=mydict.get, reverse=True):\n", " print(w, mydict[w])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['a', 'c', 'b']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "sorted(mydict, key=lambda key: mydict[key])" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['b', 'c', 'a']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mydict = {'a':1,'b':3,'c':2}\n", "sorted(mydict, key=lambda key: mydict[key],reverse=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b 3\n", "c 2\n", "a 1\n" ] } ], "source": [ "mydict = {'a':1,'b':3,'c':2}\n", "sorted_dict=sorted(mydict, key=lambda key: mydict[key],reverse=True)\n", "for i in sorted_dict:\n", " print(i,mydict[i])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
moonbury/pythonanywhere
scikit-learn/plot_linerRegression.ipynb
2
7908
{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: \n", " [ 938.23786125]\n", "Residual sum of squares: 2548.07\n", "Variance score: 0.47\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fadbd735828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#print(__doc__)\n", "\n", "\n", "# Code source: Jaques Grobler\n", "# License: BSD 3 clause\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn import datasets, linear_model\n", "\n", "%matplotlib inline\n", "\n", "# Load the diabetes dataset\n", "diabetes = datasets.load_diabetes()\n", "\n", "\n", "# Use only one feature\n", "diabetes_X = diabetes.data[:, np.newaxis]\n", "diabetes_X_temp = diabetes_X[:, :, 2]\n", "\n", "\n", "#print(diabetes_X.shape,diabetes_X.shape, diabetes_X_temp.shape)\n", "# Split the data into training/testing sets\n", "diabetes_X_train = diabetes_X_temp[:-20]\n", "diabetes_X_test = diabetes_X_temp[-20:]\n", "\n", "# Split the targets into training/testing sets\n", "diabetes_y_train = diabetes.target[:-20]\n", "diabetes_y_test = diabetes.target[-20:]\n", "\n", "\n", "\n", "# Create linear regression object\n", "regr = linear_model.LinearRegression()\n", "\n", "# Train the model using the training sets\n", "regr.fit(diabetes_X_train, diabetes_y_train)\n", "\n", "# The coefficients\n", "print('Coefficients: \\n', regr.coef_)\n", "# The mean square error\n", "print(\"Residual sum of squares: %.2f\"\n", " % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))\n", "# Explained variance score: 1 is perfect prediction\n", "print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))\n", "\n", "# Plot outputs\n", "plt.scatter(diabetes_X_test, diabetes_y_test, color='black')\n", "plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',\n", " linewidth=3)\n", "\n", "plt.xticks(())\n", "plt.yticks(())\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
mitdbg/modeldb
client/workflows/examples-without-verta/notebooks/sklearn-gridsearch.ipynb
1
5832
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Logistic Regression with Grid Search (scikit-learn)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import itertools\n", "\n", "import joblib\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn import model_selection\n", "from sklearn import linear_model\n", "from sklearn import metrics\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# load pre-cleaned data from CSV file into pandas DataFrame\n", "df = pd.read_csv(os.path.join(\"..\", \"data\", \"census\", \"cleaned-census-data.csv\"), delimiter=',')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# split into features and labels\n", "features_df = df.drop('>50K', axis='columns')\n", "labels_df = df['>50K'] # we are predicting whether an individual's income exceeds $50k/yr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Splitting" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# extract NumPy arrays from DataFrames\n", "X = features_df.values\n", "y = labels_df.values\n", "\n", "# split data into training and testing sets\n", "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n", "\n", "# instantiate iterator that yields train/val indices for each fold of cross validation\n", "validation_splitter = model_selection.KFold(n_splits=5, shuffle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross Validation" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "hyperparam_candidates = {\n", " 'C': [1e-1, 1, 1e1],\n", " 'solver': ['lbfgs'],\n", " 'max_iter': [1e3, 1e4, 1e5],\n", "}\n", "hyperparam_sets = [dict(zip(hyperparam_candidates.keys(), values))\n", " for values\n", " in itertools.product(*hyperparam_candidates.values())]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "val_accs = [] # track performance of each hyperparam set\n", "for hyperparams in hyperparam_sets:\n", " print(hyperparams, end=' ')\n", " \n", " val_acc = 0 # track average validation accuracy across folds\n", " for idxs_train, idxs_val in validation_splitter.split(X_train, y_train):\n", " # index into training data to produce train/val splits\n", " X_val_train, y_val_train = X[idxs_train], y[idxs_train]\n", " X_val, y_val = X[idxs_val], y[idxs_val]\n", "\n", " # create and fit model\n", " model = linear_model.LogisticRegression(**hyperparams)\n", " model.fit(X_val_train, y_val_train)\n", "\n", " # accumulate average validation accuracy\n", " val_acc += model.score(X_val, y_val)/validation_splitter.get_n_splits()\n", "\n", " # record performance of current hyperparam set\n", " val_accs.append(val_acc)\n", " print(f\"Validation accuracy: {val_acc}\")\n", "\n", "# pair validation accuracies with hyperparam sets\n", "hyperparam_accs = zip(hyperparam_sets, val_accs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "best_hyperparams, best_val_acc = sorted(hyperparam_accs, key=lambda item: item[1])[-1]\n", "print(f\"{best_hyperparams} Validation accuracy: {best_val_acc}\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# create and fit model using best set of hyperparameters\n", "model = linear_model.LogisticRegression(**best_hyperparams)\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "print(f\"Training accuracy: {model.score(X_train, y_train)}\")\n", "print(f\"Testing accuracy: {model.score(X_test, y_test)}\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [], "source": [ "print(f\"Training F-score: {metrics.f1_score(y_train, model.predict(X_train))}\")\n", "print(f\"Testing F-score: {metrics.f1_score(y_test, model.predict(X_test))}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save Model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "joblib.dump(model, os.path.join(\"..\", \"output\", \"logreg_gridsearch.gz\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
tensorflow/recommenders
docs/examples/efficient_serving.ipynb
1
37065
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "4JlLTP1Y-WHg" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "if-ujOZN-Par" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "Uq9kCbELjzgJ" }, "source": [ "# Efficient serving\n", "\n", "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/recommenders/examples/efficient_serving\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/efficient_serving.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/recommenders/blob/main/docs/examples/efficient_serving.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/efficient_serving.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n", " \u003c/td\u003e\n", "\u003c/table\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "UlFcUNXT7hSF" }, "source": [ "[Retrieval models](https://www.tensorflow.org/recommenders/examples/basic_retrieval) are often built to surface a handful of top candidates out of millions or even hundreds of millions of candidates. To be able to react to the user's context and behaviour, they need to be able to do this on the fly, in a matter of milliseconds.\n", "\n", "Approximate nearest neighbour search (ANN) is the technology that makes this possible. In this tutorial, we'll show how to use ScaNN - a state of the art nearest neighbour retrieval package - to seamlessly scale TFRS retrieval to millions of items." ] }, { "cell_type": "markdown", "metadata": { "id": "Q_s_2UgUWA9u" }, "source": [ "## What is ScaNN?" ] }, { "cell_type": "markdown", "metadata": { "id": "GSvmiDQPsGmb" }, "source": [ "ScaNN is a library from Google Research that performs dense vector similarity search at large scale. Given a database of candidate embeddings, ScaNN indexes these embeddings in a manner that allows them to be rapidly searched at inference time. ScaNN uses state of the art vector compression techniques and carefully implemented algorithms to achieve the best speed-accuracy tradeoff. It can greatly outperform brute force search while sacrificing little in terms of accuracy." ] }, { "cell_type": "markdown", "metadata": { "id": "bTpnORU7WEPD" }, "source": [ "## Building a ScaNN-powered model" ] }, { "cell_type": "markdown", "metadata": { "id": "zXEZ3lZnWIVh" }, "source": [ "To try out ScaNN in TFRS, we'll build a simple MovieLens retrieval model, just as we did in the [basic retrieval](https://www.tensorflow.org/recommenders/examples/basic_retrieval) tutorial. If you have followed that tutorial, this section will be familiar and can safely be skipped.\n", "\n", "To start, install TFRS and TensorFlow Datasets:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mD2hiRviCxFE" }, "outputs": [], "source": [ "!pip install -q tensorflow-recommenders\n", "!pip install -q --upgrade tensorflow-datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "oEbc-66nDJzc" }, "source": [ "We also need to install `scann`: it's an optional dependency of TFRS, and so needs to be installed separately." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "daEivxsJDO0Y" }, "outputs": [], "source": [ "!pip install -q scann" ] }, { "cell_type": "markdown", "metadata": { "id": "bDe054pgDQdp" }, "source": [ "Set up all the necessary imports." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6ekaJkcuHsiY" }, "outputs": [], "source": [ "from typing import Dict, Text\n", "\n", "import os\n", "import pprint\n", "import tempfile\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import tensorflow_datasets as tfds" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WdTPCz136mvc" }, "outputs": [], "source": [ "import tensorflow_recommenders as tfrs" ] }, { "cell_type": "markdown", "metadata": { "id": "DfmRuUgJWlEQ" }, "source": [ "And load the data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k-VF30hJn5-3" }, "outputs": [], "source": [ "# Load the MovieLens 100K data.\n", "ratings = tfds.load(\n", " \"movielens/100k-ratings\",\n", " split=\"train\"\n", ")\n", "\n", "# Get the ratings data.\n", "ratings = (ratings\n", " # Retain only the fields we need.\n", " .map(lambda x: {\"user_id\": x[\"user_id\"], \"movie_title\": x[\"movie_title\"]})\n", " # Cache for efficiency.\n", " .cache(tempfile.NamedTemporaryFile().name)\n", ")\n", "\n", "# Get the movies data.\n", "movies = tfds.load(\"movielens/100k-movies\", split=\"train\")\n", "movies = (movies\n", " # Retain only the fields we need.\n", " .map(lambda x: x[\"movie_title\"])\n", " # Cache for efficiency.\n", " .cache(tempfile.NamedTemporaryFile().name))" ] }, { "cell_type": "markdown", "metadata": { "id": "SiVuNZ-lWv0R" }, "source": [ "Before we can build a model, we need to set up the user and movie vocabularies:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jw-iQKBBajnz" }, "outputs": [], "source": [ "user_ids = ratings.map(lambda x: x[\"user_id\"])\n", "\n", "unique_movie_titles = np.unique(np.concatenate(list(movies.batch(1000))))\n", "unique_user_ids = np.unique(np.concatenate(list(user_ids.batch(1000))))" ] }, { "cell_type": "markdown", "metadata": { "id": "yRbZCvWHWzPU" }, "source": [ "We'll also set up the training and test sets:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FqV8p7N8CrEg" }, "outputs": [], "source": [ "tf.random.set_seed(42)\n", "shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)\n", "\n", "train = shuffled.take(80_000)\n", "test = shuffled.skip(80_000).take(20_000)" ] }, { "cell_type": "markdown", "metadata": { "id": "Ok3-kzr1bI7U" }, "source": [ "### Model definition\n", "\n", "Just as in the [basic retrieval](https://www.tensorflow.org/recommenders/examples/basic_retrieval) tutorial, we build a simple two-tower model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yX_j4pEVbKIS" }, "outputs": [], "source": [ "class MovielensModel(tfrs.Model):\n", "\n", " def __init__(self):\n", " super().__init__()\n", "\n", " embedding_dimension = 32\n", "\n", " # Set up a model for representing movies.\n", " self.movie_model = tf.keras.Sequential([\n", " tf.keras.layers.StringLookup(\n", " vocabulary=unique_movie_titles, mask_token=None),\n", " # We add an additional embedding to account for unknown tokens.\n", " tf.keras.layers.Embedding(len(unique_movie_titles) + 1, embedding_dimension)\n", " ])\n", "\n", " # Set up a model for representing users.\n", " self.user_model = tf.keras.Sequential([\n", " tf.keras.layers.StringLookup(\n", " vocabulary=unique_user_ids, mask_token=None),\n", " # We add an additional embedding to account for unknown tokens.\n", " tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)\n", " ])\n", "\n", " # Set up a task to optimize the model and compute metrics.\n", " self.task = tfrs.tasks.Retrieval(\n", " metrics=tfrs.metrics.FactorizedTopK(\n", " candidates=movies.batch(128).cache().map(self.movie_model)\n", " )\n", " )\n", "\n", " def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -\u003e tf.Tensor:\n", " # We pick out the user features and pass them into the user model.\n", " user_embeddings = self.user_model(features[\"user_id\"])\n", " # And pick out the movie features and pass them into the movie model,\n", " # getting embeddings back.\n", " positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n", "\n", " # The task computes the loss and the metrics.\n", "\n", " return self.task(user_embeddings, positive_movie_embeddings, compute_metrics=not training)" ] }, { "cell_type": "markdown", "metadata": { "id": "JtO3lKR_XKkw" }, "source": [ "### Fitting and evaluation\n", "\n", "A TFRS model is just a Keras model. We can compile it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uOGTdwAAbuB6" }, "outputs": [], "source": [ "model = MovielensModel()\n", "model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))" ] }, { "cell_type": "markdown", "metadata": { "id": "4rGLyo-XXPmX" }, "source": [ "Estimate it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uf_E4dIMcGnk" }, "outputs": [], "source": [ "model.fit(train.batch(8192), epochs=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "7xymbWgVXSrT" }, "source": [ "And evaluate it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EMlIj741cIT8" }, "outputs": [], "source": [ "model.evaluate(test.batch(8192), return_dict=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "3RbHiBWqsFmf" }, "source": [ "## Approximate prediction\n", "\n", "The most straightforward way of retrieving top candidates in response to a query is to do it via brute force: compute user-movie scores for all possible movies, sort them, and pick a couple of top recommendations.\n", "\n", "In TFRS, this is accomplished via the `BruteForce` layer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x_L2yAPjpHsk" }, "outputs": [], "source": [ "brute_force = tfrs.layers.factorized_top_k.BruteForce(model.user_model)\n", "brute_force.index_from_dataset(\n", " movies.batch(128).map(lambda title: (title, model.movie_model(title)))\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "CzoNR28vXw7o" }, "source": [ "Once created and populated with candidates (via the `index` method), we can call it to get predictions out:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SBo1Nu0Grife" }, "outputs": [], "source": [ "# Get predictions for user 42.\n", "_, titles = brute_force(np.array([\"42\"]), k=3)\n", "\n", "print(f\"Top recommendations: {titles[0]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "AzNECPifr6i6" }, "source": [ "On a small dataset of under 1000 movies, this is very fast:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "w57iyu7Ir87Q" }, "outputs": [], "source": [ "%timeit _, titles = brute_force(np.array([\"42\"]), k=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "u2AjJsdrsClR" }, "source": [ "But what happens if we have more candidates - millions instead of thousands?\n", "\n", "We can simulate this by indexing all of our movies multiple times:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AapJk84csTqV" }, "outputs": [], "source": [ "# Construct a dataset of movies that's 1,000 times larger. We \n", "# do this by adding several million dummy movie titles to the dataset.\n", "lots_of_movies = tf.data.Dataset.concatenate(\n", " movies.batch(4096),\n", " movies.batch(4096).repeat(1_000).map(lambda x: tf.zeros_like(x))\n", ")\n", "\n", "# We also add lots of dummy embeddings by randomly perturbing\n", "# the estimated embeddings for real movies.\n", "lots_of_movies_embeddings = tf.data.Dataset.concatenate(\n", " movies.batch(4096).map(model.movie_model),\n", " movies.batch(4096).repeat(1_000)\n", " .map(lambda x: model.movie_model(x))\n", " .map(lambda x: x * tf.random.uniform(tf.shape(x)))\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "viCLP9qSYBQh" }, "source": [ "We can build a `BruteForce` index on this larger dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mfY62oQbYA3Z" }, "outputs": [], "source": [ "brute_force_lots = tfrs.layers.factorized_top_k.BruteForce()\n", "brute_force_lots.index_from_dataset(\n", " tf.data.Dataset.zip((lots_of_movies, lots_of_movies_embeddings))\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "OrkMt8O_xm-s" }, "source": [ "The recommendations are still the same" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "I9fIYUeYxjki" }, "outputs": [], "source": [ "_, titles = brute_force_lots(model.user_model(np.array([\"42\"])), k=3)\n", "\n", "print(f\"Top recommendations: {titles[0]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "wwF25ZzdseX8" }, "source": [ "But they take much longer. With a candidate set of 1 million movies, brute force prediction becomes quite slow:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oetK_wNxsdw0" }, "outputs": [], "source": [ "%timeit _, titles = brute_force_lots(model.user_model(np.array([\"42\"])), k=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "mKF9yEeotbXQ" }, "source": [ "As the number of candidate grows, the amount of time needed grows linearly: with 10 million candidates, serving top candidates would take 250 milliseconds. This is clearly too slow for a live service.\n", "\n", "This is where approximate mechanisms come in.\n", "\n", "Using ScaNN in TFRS is accomplished via the `tfrs.layers.factorized_top_k.ScaNN` layer. It follow the same interface as the other top k layers:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SLgPmA90sbDL" }, "outputs": [], "source": [ "scann = tfrs.layers.factorized_top_k.ScaNN(num_reordering_candidates=100)\n", "scann.index_from_dataset(\n", " tf.data.Dataset.zip((lots_of_movies, lots_of_movies_embeddings))\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "qRI-qv7S2h97" }, "source": [ "The recommendations are (approximately!) the same" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HCkRn1VnxuXn" }, "outputs": [], "source": [ "_, titles = scann(model.user_model(np.array([\"42\"])), k=3)\n", "\n", "print(f\"Top recommendations: {titles[0]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "iW1oBtcC2mb1" }, "source": [ "But they are much, much faster to compute:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ooJsLhpWstlf" }, "outputs": [], "source": [ "%timeit _, titles = scann(model.user_model(np.array([\"42\"])), k=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "zOYk0zi12q-0" }, "source": [ "In this case, we can retrieve the top 3 movies out of a set of ~1 million in around 2 milliseconds: 15 times faster than by computing the best candidates via brute force. The advantage of approximate methods grows even larger for larger datasets." ] }, { "cell_type": "markdown", "metadata": { "id": "tE7eL7ZzDKtl" }, "source": [ "## Evaluating the approximation\n", "\n", "When using approximate top K retrieval mechanisms (such as ScaNN), speed of retrieval often comes at the expense of accuracy. To understand this trade-off, it's important to measure the model's evaluation metrics when using ScaNN, and to compare them with the baseline.\n", "\n", "Fortunately, TFRS makes this easy. We simply override the metrics on the retrieval task with metrics using ScaNN, re-compile the model, and run evaluation.\n", "\n", "To make the comparison, let's first run baseline results. We still need to override our metrics to make sure they are using the enlarged candidate set rather than the original set of movies:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZZtJRQqBep_5" }, "outputs": [], "source": [ "# Override the existing streaming candidate source.\n", "model.task.factorized_metrics = tfrs.metrics.FactorizedTopK(\n", " candidates=lots_of_movies_embeddings\n", ")\n", "# Need to recompile the model for the changes to take effect.\n", "model.compile()\n", "\n", "%time baseline_result = model.evaluate(test.batch(8192), return_dict=True, verbose=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "HHFzcS5cQtB_" }, "source": [ "We can do the same using ScaNN:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5T-YxOqoKMje" }, "outputs": [], "source": [ "model.task.factorized_metrics = tfrs.metrics.FactorizedTopK(\n", " candidates=scann\n", ")\n", "model.compile()\n", "\n", "# We can use a much bigger batch size here because ScaNN evaluation\n", "# is more memory efficient.\n", "%time scann_result = model.evaluate(test.batch(8192), return_dict=True, verbose=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "Y0gnjcUUZ6-v" }, "source": [ "ScaNN based evaluation is much, much quicker: it's over ten times faster! This advantage is going to grow even larger for bigger datasets, and so for large datasets it may be prudent to always run ScaNN-based evaluation to improve model development velocity.\n", "\n", "But how about the results? Fortunately, in this case the results are almost the same:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gcXUbZx3Fq4f" }, "outputs": [], "source": [ "print(f\"Brute force top-100 accuracy: {baseline_result['factorized_top_k/top_100_categorical_accuracy']:.2f}\")\n", "print(f\"ScaNN top-100 accuracy: {scann_result['factorized_top_k/top_100_categorical_accuracy']:.2f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "d2UJR-5nZ6YT" }, "source": [ "This suggests that on this artificial datase, there is little loss from the approximation. In general, all approximate methods exhibit speed-accuracy tradeoffs. To understand this in more depth you can check out Erik Bernhardsson's [ANN benchmarks](https://github.com/erikbern/ann-benchmarks)." ] }, { "cell_type": "markdown", "metadata": { "id": "-jdPPOlV3JOr" }, "source": [ "## Deploying the approximate model\n", "\n", "The `ScaNN`-based model is fully integrated into TensorFlow models, and serving it is as easy as serving any other TensorFlow model.\n", "\n", "We can save it as a `SavedModel` object" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eKxXVfJBLbiW" }, "outputs": [], "source": [ "lots_of_movies_embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KnVI_6N53WU5" }, "outputs": [], "source": [ "# We re-index the ScaNN layer to include the user embeddings in the same model.\n", "# This way we can give the saved model raw features and get valid predictions\n", "# back.\n", "scann = tfrs.layers.factorized_top_k.ScaNN(model.user_model, num_reordering_candidates=1000)\n", "scann.index_from_dataset(\n", " tf.data.Dataset.zip((lots_of_movies, lots_of_movies_embeddings))\n", ")\n", "\n", "# Need to call it to set the shapes.\n", "_ = scann(np.array([\"42\"]))\n", "\n", "with tempfile.TemporaryDirectory() as tmp:\n", " path = os.path.join(tmp, \"model\")\n", " tf.saved_model.save(\n", " scann,\n", " path,\n", " options=tf.saved_model.SaveOptions(namespace_whitelist=[\"Scann\"])\n", " )\n", "\n", " loaded = tf.saved_model.load(path)" ] }, { "cell_type": "markdown", "metadata": { "id": "O5vDZjro4lXG" }, "source": [ "and then load it and serve, getting exactly the same results back:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TXm8smCt3iFB" }, "outputs": [], "source": [ "_, titles = loaded(tf.constant([\"42\"]))\n", "\n", "print(f\"Top recommendations: {titles[0][:3]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "S0Doal2ETqU4" }, "source": [ "The resulting model can be served in any Python service that has TensorFlow and ScaNN installed.\n", "\n", "It can also be served using a customized version of TensorFlow Serving, available as a Docker container on [Docker Hub](https://hub.docker.com/r/google/tf-serving-scann). You can also build the image yourself from the [Dockerfile](https://github.com/google-research/google-research/tree/master/scann/tf_serving)." ] }, { "cell_type": "markdown", "metadata": { "id": "0gQsvn5PYbR-" }, "source": [ "## Tuning ScaNN" ] }, { "cell_type": "markdown", "metadata": { "id": "918uqacB7sNH" }, "source": [ "Now let's look into tuning our ScaNN layer to get a better performance/accuracy tradeoff. In order to do this effectively, we first need to measure our baseline performance and accuracy.\n", "\n", "From above, we already have a measurement of our model's latency for processing a single (non-batched) query (although note that a fair amount of this latency is from non-ScaNN components of the model).\n", "\n", "Now we need to investigate ScaNN's accuracy, which we measure through recall. A recall@k of x% means that if we use brute force to retrieve the true top k neighbors, and compare those results to using ScaNN to also retrieve the top k neighbors, x% of ScaNN's results are in the true brute force results. Let's compute the recall for the current ScaNN searcher.\n", "\n", "First, we need to generate the brute force, ground truth top-k:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qgf_QuP-8EXb" }, "outputs": [], "source": [ "# Process queries in groups of 1000; processing them all at once with brute force\n", "# may lead to out-of-memory errors, because processing a batch of q queries against\n", "# a size-n dataset takes O(nq) space with brute force.\n", "titles_ground_truth = tf.concat([\n", " brute_force_lots(queries, k=10)[1] for queries in\n", " test.batch(1000).map(lambda x: model.user_model(x[\"user_id\"]))\n", "], axis=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "LSZkWESc856P" }, "source": [ "Our variable `titles_ground_truth` now contains the top-10 movie recommendations returned by brute-force retrieval. Now we can compute the same recommendations when using ScaNN:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yUKtdf1X87mP" }, "outputs": [], "source": [ "# Get all user_id's as a 1d tensor of strings\n", "test_flat = np.concatenate(list(test.map(lambda x: x[\"user_id\"]).batch(1000).as_numpy_iterator()), axis=0)\n", "\n", "# ScaNN is much more memory efficient and has no problem processing the whole\n", "# batch of 20000 queries at once.\n", "_, titles = scann(test_flat, k=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "JTsTDiAZ9F6h" }, "source": [ "Next, we define our function that computes recall. For each query, it counts how many results are in the intersection of the brute force and the ScaNN results and divides this by the number of brute force results. The average of this quantity over all queries is our recall." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PCtBew2C9Gv0" }, "outputs": [], "source": [ "def compute_recall(ground_truth, approx_results):\n", " return np.mean([\n", " len(np.intersect1d(truth, approx)) / len(truth)\n", " for truth, approx in zip(ground_truth, approx_results)\n", " ])" ] }, { "cell_type": "markdown", "metadata": { "id": "_tdxlKua9JR2" }, "source": [ "This gives us baseline recall@10 with the current ScaNN config:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nMi4VtJD9K9P" }, "outputs": [], "source": [ "print(f\"Recall: {compute_recall(titles_ground_truth, titles):.3f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gKpgkNseYWW8" }, "source": [ "We can also measure the baseline latency:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "81mO-GS4VJLJ" }, "outputs": [], "source": [ "%timeit -n 1000 scann(np.array([\"42\"]), k=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "UICnYQln9PAq" }, "source": [ "Let's see if we can do better!\n", "\n", "To do this, we need a model of how ScaNN's tuning knobs affect performance. Our current model uses ScaNN's tree-AH algorithm. This algorithm partitions the database of embeddings (the \"tree\") and then scores the most promising of these partitions using AH, which is a highly optimized approximate distance computation routine.\n", "\n", "The default parameters for TensorFlow Recommenders' ScaNN Keras layer sets `num_leaves=100` and `num_leaves_to_search=10`. This means our database is partitioned into 100 disjoint subsets, and the 10 most promising of these partitions is scored with AH. This means 10/100=10% of the dataset is being searched with AH.\n", "\n", "If we have, say, `num_leaves=1000` and `num_leaves_to_search=100`, we would also be searching 10% of the database with AH. However, in comparison to the previous setting, the 10% we would search will contain higher-quality candidates, because a higher `num_leaves` allows us to make finer-grained decisions about what parts of the dataset are worth searching.\n", "\n", "It's no surprise then that with `num_leaves=1000` and `num_leaves_to_search=100` we get significantly higher recall:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vq6L1Qtl9Qan" }, "outputs": [], "source": [ "scann2 = tfrs.layers.factorized_top_k.ScaNN(\n", " model.user_model, \n", " num_leaves=1000,\n", " num_leaves_to_search=100,\n", " num_reordering_candidates=1000)\n", "scann2.index_from_dataset(\n", " tf.data.Dataset.zip((lots_of_movies, lots_of_movies_embeddings))\n", ")\n", "\n", "_, titles2 = scann2(test_flat, k=10)\n", "\n", "print(f\"Recall: {compute_recall(titles_ground_truth, titles2):.3f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "G2WR8zPH9TtW" }, "source": [ "However, as a tradeoff, our latency has also increased. This is because the partitioning step has gotten more expensive; `scann` picks the top 10 of 100 partitions while `scann2` picks the top 100 of 1000 partitions. The latter can be more expensive because it involves looking at 10 times as many partitions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Po0kb4Mf9VhX" }, "outputs": [], "source": [ "%timeit -n 1000 scann2(np.array([\"42\"]), k=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "fCDzY0sc9Zgc" }, "source": [ "In general, tuning ScaNN search is about picking the right tradeoffs. Each individual parameter change generally won't make search both faster and more accurate; our goal is to tune the parameters to optimally trade off between these two conflicting goals.\n", "\n", "In our case, `scann2` significantly improved recall over `scann` at some cost in latency. Can we dial back some other knobs to cut down on latency, while preserving most of our recall advantage?\n", "\n", "Let's try searching 70/1000=7% of the dataset with AH, and only rescoring the final 400 candidates:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jBp8Yvdj9pMQ" }, "outputs": [], "source": [ "scann3 = tfrs.layers.factorized_top_k.ScaNN(\n", " model.user_model,\n", " num_leaves=1000,\n", " num_leaves_to_search=70,\n", " num_reordering_candidates=400)\n", "scann3.index_from_dataset(\n", " tf.data.Dataset.zip((lots_of_movies, lots_of_movies_embeddings))\n", ")\n", "\n", "_, titles3 = scann3(test_flat, k=10)\n", "print(f\"Recall: {compute_recall(titles_ground_truth, titles3):.3f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "3Isgpm7b9rgE" }, "source": [ "`scann3` delivers about a 3% absolute recall gain over `scann` while also delivering lower latency:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JiDEWwtr9sKG" }, "outputs": [], "source": [ "%timeit -n 1000 scann3(np.array([\"42\"]), k=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "NwWKyQgt9uh1" }, "source": [ "These knobs can be further adjusted to optimize for different points along the accuracy-performance pareto frontier. ScaNN's algorithms can achieve state-of-the-art performance over a wide range of recall targets." ] }, { "cell_type": "markdown", "metadata": { "id": "UvlCsKyFU40k" }, "source": [ "## Further reading" ] }, { "cell_type": "markdown", "metadata": { "id": "0ikGqmNa9yRG" }, "source": [ "ScaNN uses advanced vector quantization techniques and highly optimized implementation to achieve its results. The field of vector quantization has a rich history with a variety of approaches. ScaNN's current quantization technique is detailed in [this paper](https://arxiv.org/abs/1908.10396), published at ICML 2020. The paper was also released along with [this blog article](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) which gives a high level overview of our technique.\n", "\n", "Many related quantization techniques are mentioned in the references of our ICML 2020 paper, and other ScaNN-related research is listed at http://sanjivk.com/." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "efficient_serving.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
scholer/na_strand_model
examples/single_duplex/reaction_graph_analysis.ipynb
2
6136
{ "cells": [ { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "# import webbrowser\n", "import shutil\n", "import pandas as pd\n", "# import pdb" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pyplot" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LIBDIR: /Users/rasmus/Dev/nascent\n", "Scriptdir: /Users/rasmus/Dev/nascent/examples/single_duplex\n" ] } ], "source": [ "#LIBPATH = os.path.abspath(\"../..\")\n", "scriptdir = os.path.abspath(\".\")\n", "examples_dir = os.path.dirname(scriptdir)\n", "LIBPATH = os.path.dirname(examples_dir)\n", "print(\"LIBDIR:\", LIBDIR)\n", "print(\"Scriptdir:\", scriptdir)\n", "#sys.path.insert(0, LIBPATH)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nascent.stat_analysis.processing import get_datafile" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using data root directory: /Users/rasmus/Dev/nascent/examples/single_duplex/simdata/fourway_junction_1\n", "Using stats in folder: /Users/rasmus/Dev/nascent/examples/single_duplex/simdata/fourway_junction_1/2016-03-14 193519\n" ] } ], "source": [ "structure = \"circfb_1\"\n", "structure = \"fourway_junction_1\"\n", "\n", "runidxs = [-1] #\n", "# runidxs = [-2] #\n", "\n", "statsfiles, statsfolders = zip(*[\n", " get_datafile(statsfile=\"monitored_strands_stats.tsv\", runidx=runidx,\n", " basedir=scriptdir, structure=structure)\n", " for runidx in runidxs])" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('/Users/rasmus/Dev/nascent/examples/single_duplex/simdata/fourway_junction_1/2016-03-14 193519/monitored_strands_stats.tsv',)\n" ] } ], "source": [ "print(statsfiles)\n", "stats = [pd.read_table(statsfile) for statsfile in statsfiles] # http://pandas.pydata.org/pandas-docs/stable/io.html\n", "runstats = stats[0]" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [], "source": [ "runstats['system_time_end'] = runstats['system_time'].shift(-1).dropna()\n", "data_by_strand = runstats.groupby(['strand_uid'], sort=False)\n", "strand, s_trace = next(iter(data_by_strand))\n", "s_state_traces = s_trace.groupby(['complex_state'], sort=False)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "for cstate, s_state in s_state_traces:\n", " # s_state['s_state_time_cum'] = s_state['tau'].cumsum()\n", " # s_state['s_state_partition'] = s_state['s_state_time_cum']/s_state['system_time_end']\n", " # Edit: Avoid using chained indexing:\n", " # s_state may be a copy\n", " # s_state.loc[:,'s_state_time_cum'] = s_state.loc[:'tau'].cumsum() \n", " # TypeError: cannot do slice indexing on <class 'pandas.core.index.Int64Index'> with these indexers [tau] of <class 'str'>\n", " # s_state.loc[:,'s_state_time_cum'] = s_state['tau'].cumsum() # Still get SettingWithCopyWarning\n", " s_state_time_cum = s_state['tau'].cumsum() # You can just use the copy as-is and make a column..\n", " # s_state['s_state_partition'] = s_state['s_state_time_cum']/s_state['system_time_end']\n" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " tau\n", "complex_state \n", "28604 1213.883809\n", "28392 938.379253\n", "95192 576.028348\n", "20476 422.622767\n", "99244 415.129178\n" ] } ], "source": [ "c_state_sums = s_state_traces.sum()\n", "# c_state_sums.sort_values(['tau']) # Multiple columns OK\n", "sums_view = c_state_sums.sort_values('tau', ascending=False)\n", "#print(sums_view.head(10)) # Single column string also OK\n", "sums_view[['tau']].head() # pretty table view\n", "print(sums_view[['tau']].head()) # prints a 'tau' header\n", "# print(sums_view['tau'].head()) # no column headers\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
vzygouras/personal
HRI 2021 Viz and Stats.ipynb
1
5429814
null
mit
NlGG/Projects
不動産/model2_1.ipynb
1
172358
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/NIGG/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file \"/Users/NIGG/.matplotlib/matplotlibrc\", line #515\n", " (fname, cnt))\n", "/Users/NIGG/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file \"/Users/NIGG/.matplotlib/matplotlibrc\", line #516\n", " (fname, cnt))\n" ] } ], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "#　統計用ツール\n", "import statsmodels.api as sm\n", "import statsmodels.tsa.api as tsa\n", "from patsy import dmatrices\n", "\n", "#　自作の空間統計用ツール\n", "from spatialstat import *\n", "\n", "#描画\n", "import matplotlib.pyplot as plt\n", "from pandas.tools.plotting import autocorrelation_plot\n", "import seaborn as sns\n", "sns.set(font=['IPAmincho'])\n", "\n", "#深層学習\n", "import chainer\n", "from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils\n", "from chainer import Link, Chain, ChainList\n", "import chainer.functions as F\n", "import chainer.links as L\n", "\n", "import pyper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 変数名とデータの内容メモ\n", "\tCENSUS: 市区町村コード(9桁)\n", "\tP: 成約価格\n", "\tS: 専有面積\n", "\tL: 土地面積\n", "\tR: 部屋数\n", "\tRW: 前面道路幅員\n", "\tCY: 建築年\n", "\tA: 建築後年数(成約時)\n", "\tTS: 最寄駅までの距離\n", "\tTT: 東京駅までの時間\n", "\tACC: ターミナル駅までの時間\n", "\tWOOD: 木造ダミー\n", "\tSOUTH: 南向きダミー\n", "\tRSD: 住居系地域ダミー\n", "\tCMD: 商業系地域ダミー\n", "\tIDD: 工業系地域ダミー\n", "\tFAR: 建ぺい率\n", "\tFLR: 容積率\n", "\tTDQ: 成約時点(四半期)\n", "\tX: 緯度\n", "\tY: 経度\n", "\tCITY_CODE: 市区町村コード(5桁)\n", "\tCITY_NAME: 市区町村名\n", "\tBLOCK: 地域ブロック名" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = pd.read_csv(\"TokyoSingle.csv\")\n", "data = data.dropna()\n", "CITY_NAME = data['CITY_CODE'].copy()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "CITY_NAME[CITY_NAME == 13101] = '01千代田区'\n", "CITY_NAME[CITY_NAME == 13102] = \"02中央区\"\n", "CITY_NAME[CITY_NAME == 13103] = \"03港区\"\n", "CITY_NAME[CITY_NAME == 13104] = \"04新宿区\"\n", "CITY_NAME[CITY_NAME == 13105] = \"05文京区\"\n", "CITY_NAME[CITY_NAME == 13106] = \"06台東区\"\n", "CITY_NAME[CITY_NAME == 13107] = \"07墨田区\"\n", "CITY_NAME[CITY_NAME == 13108] = \"08江東区\"\n", "CITY_NAME[CITY_NAME == 13109] = \"09品川区\"\n", "CITY_NAME[CITY_NAME == 13110] = \"10目黒区\"\n", "CITY_NAME[CITY_NAME == 13111] = \"11大田区\"\n", "CITY_NAME[CITY_NAME == 13112] = \"12世田谷区\"\n", "CITY_NAME[CITY_NAME == 13113] = \"13渋谷区\"\n", "CITY_NAME[CITY_NAME == 13114] = \"14中野区\"\n", "CITY_NAME[CITY_NAME == 13115] = \"15杉並区\"\n", "CITY_NAME[CITY_NAME == 13116] = \"16豊島区\"\n", "CITY_NAME[CITY_NAME == 13117] = \"17北区\"\n", "CITY_NAME[CITY_NAME == 13118] = \"18荒川区\"\n", "CITY_NAME[CITY_NAME == 13119] = \"19板橋区\"\n", "CITY_NAME[CITY_NAME == 13120] = \"20練馬区\"\n", "CITY_NAME[CITY_NAME == 13121] = \"21足立区\"\n", "CITY_NAME[CITY_NAME == 13122] = \"22葛飾区\"\n", "CITY_NAME[CITY_NAME == 13123] = \"23江戸川区\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Make Japanese Block name\n", "BLOCK = data[\"CITY_CODE\"].copy()\n", "BLOCK[BLOCK == 13101] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13102] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13103] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13104] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13109] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13110] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13111] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13112] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13113] = \"01都心・城南\"\n", "BLOCK[BLOCK == 13114] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13115] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13105] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13106] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13116] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13117] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13119] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13120] = \"02城西・城北\"\n", "BLOCK[BLOCK == 13107] = \"03城東\"\n", "BLOCK[BLOCK == 13108] = \"03城東\"\n", "BLOCK[BLOCK == 13118] = \"03城東\"\n", "BLOCK[BLOCK == 13121] = \"03城東\"\n", "BLOCK[BLOCK == 13122] = \"03城東\"\n", "BLOCK[BLOCK == 13123] = \"03城東\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names = list(data.columns) + ['CITY_NAME', 'BLOCK']\n", "data = pd.concat((data, CITY_NAME, BLOCK), axis = 1)\n", "data.columns = names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 市区町村別の件数を集計" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12世田谷区 12340\n", "20練馬区 9979\n", "15杉並区 8131\n", "11大田区 7052\n", "21足立区 6479\n", "19板橋区 4827\n", "14中野区 3924\n", "10目黒区 3418\n", "22葛飾区 3165\n", "23江戸川区 3156\n", "09品川区 2424\n", "16豊島区 2153\n", "04新宿区 1885\n", "17北区 1799\n", "13渋谷区 1487\n", "05文京区 1242\n", "18荒川区 1005\n", "08江東区 981\n", "03港区 757\n", "07墨田区 725\n", "06台東区 371\n", "02中央区 56\n", "01千代田区 32\n", "Name: CITY_NAME, dtype: int64\n" ] } ], "source": [ "print(data['CITY_NAME'].value_counts()) " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vars = ['P', 'S', 'L', 'R', 'RW', 'A', 'TS', 'TT', 'WOOD', 'SOUTH', 'CMD', 'IDD', 'FAR', 'X', 'Y']\n", "eq = fml_build(vars)\n", "\n", "y, X = dmatrices(eq, data=data, return_type='dataframe')\n", "\n", "CITY_NAME = pd.get_dummies(data['CITY_NAME'])\n", "TDQ = pd.get_dummies(data['TDQ'])\n", "\n", "X = pd.concat((X, CITY_NAME, TDQ), axis=1)\n", "\n", "datas = pd.concat((y, X), axis=1)\n", "datas = datas[datas['12世田谷区'] == 1][0:5000]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class CAR(Chain):\n", " def __init__(self, unit1, unit2, unit3, col_num):\n", " self.unit1 = unit1\n", " self.unit2 = unit2\n", " self.unit3 = unit3\n", " super(CAR, self).__init__(\n", " l1 = L.Linear(col_num, unit1),\n", " l2 = L.Linear(self.unit1, self.unit1),\n", " l3 = L.Linear(self.unit1, self.unit2),\n", " l4 = L.Linear(self.unit2, self.unit3),\n", " l5 = L.Linear(self.unit3, self.unit3),\n", " l6 = L.Linear(self.unit3, 1),\n", " )\n", " \n", " def __call__(self, x, y):\n", " fv = self.fwd(x, y)\n", " loss = F.mean_squared_error(fv, y)\n", " return loss\n", " \n", " def fwd(self, x, y):\n", " h1 = F.sigmoid(self.l1(x))\n", " h2 = F.sigmoid(self.l2(h1))\n", " h3 = F.sigmoid(self.l3(h2))\n", " h4 = F.sigmoid(self.l4(h3))\n", " h5 = F.sigmoid(self.l5(h4))\n", " h6 = self.l6(h5)\n", " return h6" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class DLmodel(object):\n", " def __init__(self, data, vars, bs=200, n=1000):\n", " self.vars = vars\n", " eq = fml_build(vars)\n", " y, X = dmatrices(eq, data=datas, return_type='dataframe')\n", " self.y_in = y[:-n]\n", " self.X_in = X[:-n]\n", " self.y_ex = y[-n:]\n", " self.X_ex = X[-n:]\n", " \n", " self.logy_in = np.log(self.y_in)\n", " self.logy_ex = np.log(self.y_ex)\n", " \n", " self.bs = bs\n", " \n", " def DL(self, ite=100, bs=200, add=False):\n", " y_in = np.array(self.y_in, dtype='float32') \n", " X_in = np.array(self.X_in, dtype='float32')\n", "\n", " y = Variable(y_in)\n", " x = Variable(X_in)\n", "\n", " num, col_num = X_in.shape\n", " \n", " if add is False:\n", " self.model1 = CAR(13, 13, 3, col_num)\n", " \n", " optimizer = optimizers.Adam()\n", " optimizer.setup(self.model1)\n", " \n", " loss_val = 100000000\n", "\n", " for j in range(ite + 10000):\n", " sffindx = np.random.permutation(num)\n", " for i in range(0, num, bs):\n", " x = Variable(X_in[sffindx[i:(i+bs) if (i+bs) < num else num]])\n", " y = Variable(y_in[sffindx[i:(i+bs) if (i+bs) < num else num]])\n", " self.model1.zerograds()\n", " loss = self.model1(x, y)\n", " loss.backward()\n", " optimizer.update()\n", " if loss_val >= loss.data:\n", " loss_val = loss.data\n", " if j > ite:\n", " if loss_val >= loss.data:\n", " loss_val = loss.data\n", " print('epoch:', j)\n", " print('train mean loss={}'.format(loss_val))\n", " print(' - - - - - - - - - ')\n", " break\n", " if j % 1000 == 0:\n", " print('epoch:', j)\n", " print('train mean loss={}'.format(loss_val))\n", " print(' - - - - - - - - - ')\n", " \n", " def predict(self):\n", " y_ex = np.array(self.y_ex, dtype='float32').reshape(len(self.y_ex))\n", " \n", " X_ex = np.array(self.X_ex, dtype='float32')\n", " X_ex = Variable(X_ex)\n", " resid_pred = self.model1.fwd(X_ex, X_ex).data \n", " print(resid_pred[:10])\n", " \n", " self.pred = resid_pred\n", " self.error = np.array(y_ex - self.pred.reshape(len(self.pred),))[0]\n", " \n", " def compare(self):\n", " plt.hist(self.error)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vars = ['P', 'S', 'L', 'R', 'RW', 'A', 'TS', 'TT', 'WOOD', 'SOUTH', 'CMD', 'IDD', 'FAR']\n", "#vars += vars + list(TDQ.columns)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = DLmodel(datas, vars)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch: 0\n", "train mean loss=56888976.0\n", " - - - - - - - - - \n", "epoch: 1000\n", "train mean loss=48387068.0\n", " - - - - - - - - - \n", "epoch: 2000\n", "train mean loss=46748284.0\n", " - - - - - - - - - \n", "epoch: 3000\n", "train mean loss=44967768.0\n", " - - - - - - - - - \n", "epoch: 4000\n", "train mean loss=41678352.0\n", " - - - - - - - - - \n", "epoch: 5000\n", "train mean loss=41678352.0\n", " - - - - - - - - - \n", "epoch: 6000\n", "train mean loss=41678352.0\n", " - - - - - - - - - \n", "epoch: 7000\n", "train mean loss=40469888.0\n", " - - - - - - - - - \n", "epoch: 8000\n", "train mean loss=40469888.0\n", " - - - - - - - - - \n", "epoch: 9000\n", "train mean loss=38288768.0\n", " - - - - - - - - - \n", "epoch: 10000\n", "train mean loss=37253708.0\n", " - - - - - - - - - \n", "epoch: 11000\n", "train mean loss=37253708.0\n", " - - - - - - - - - \n", "epoch: 12000\n", "train mean loss=37020724.0\n", " - - - - - - - - - \n", "epoch: 13000\n", "train mean loss=35186492.0\n", " - - - - - - - - - \n", "epoch: 14000\n", "train mean loss=34883460.0\n", " - - - - - - - - - \n", "epoch: 15000\n", "train mean loss=33269868.0\n", " - - - - - - - - - \n", "epoch: 16000\n", "train mean loss=33269868.0\n", " - - - - - - - - - \n", "epoch: 17000\n", "train mean loss=31171240.0\n", " - - - - - - - - - \n", "epoch: 18000\n", "train mean loss=31171240.0\n", " - - - - - - - - - \n", "epoch: 19000\n", "train mean loss=30821448.0\n", " - - - - - - - - - \n", "epoch: 20000\n", "train mean loss=29305584.0\n", " - - - - - - - - - \n", "epoch: 21000\n", "train mean loss=29305584.0\n", " - - - - - - - - - \n", "epoch: 21037\n", "train mean loss=29037132.0\n", " - - - - - - - - - \n", "epoch: 21609\n", "train mean loss=28761692.0\n", " - - - - - - - - - \n", "epoch: 22000\n", "train mean loss=28761692.0\n", " - - - - - - - - - \n", "epoch: 22391\n", "train mean loss=28700480.0\n", " - - - - - - - - - \n", "epoch: 22637\n", "train mean loss=28154572.0\n", " - - - - - - - - - \n", "epoch: 23000\n", "train mean loss=28154572.0\n", " - - - - - - - - - \n", "epoch: 23155\n", "train mean loss=28066752.0\n", " - - - - - - - - - \n", "epoch: 23589\n", "train mean loss=27559478.0\n", " - - - - - - - - - \n", "epoch: 23716\n", "train mean loss=26798796.0\n", " - - - - - - - - - \n", "epoch: 24000\n", "train mean loss=26798796.0\n", " - - - - - - - - - \n", "epoch: 24378\n", "train mean loss=25748072.0\n", " - - - - - - - - - \n", "epoch: 25000\n", "train mean loss=25748072.0\n", " - - - - - - - - - \n", "epoch: 25087\n", "train mean loss=24504744.0\n", " - - - - - - - - - \n", "epoch: 25880\n", "train mean loss=24191330.0\n", " - - - - - - - - - \n", "epoch: 26000\n", "train mean loss=24191330.0\n", " - - - - - - - - - \n", "epoch: 27000\n", "train mean loss=24191330.0\n", " - - - - - - - - - \n", "epoch: 28000\n", "train mean loss=24191330.0\n", " - - - - - - - - - \n", "epoch: 28167\n", "train mean loss=23600912.0\n", " - - - - - - - - - \n", "epoch: 28654\n", "train mean loss=23484652.0\n", " - - - - - - - - - \n", "epoch: 29000\n", "train mean loss=23484652.0\n", " - - - - - - - - - \n", "epoch: 29516\n", "train mean loss=22430598.0\n", " - - - - - - - - - \n", "epoch: 29634\n", "train mean loss=22330502.0\n", " - - - - - - - - - \n", "epoch: 29937\n", "train mean loss=22088384.0\n", " - - - - - - - - - \n" ] } ], "source": [ "model.DL(ite=20000, bs=200)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch: 0\n", "train mean loss=22346982.0\n", " - - - - - - - - - \n", "epoch: 1000\n", "train mean loss=21413112.0\n", " - - - - - - - - - \n", "epoch: 2000\n", "train mean loss=21413112.0\n", " - - - - - - - - - \n", "epoch: 3000\n", "train mean loss=20903448.0\n", " - - - - - - - - - \n", "epoch: 4000\n", "train mean loss=19988990.0\n", " - - - - - - - - - \n", "epoch: 5000\n", "train mean loss=18561308.0\n", " - - - - - - - - - \n", "epoch: 6000\n", "train mean loss=18561308.0\n", " - - - - - - - - - \n", "epoch: 7000\n", "train mean loss=18013032.0\n", " - - - - - - - - - \n", "epoch: 8000\n", "train mean loss=16432581.0\n", " - - - - - - - - - \n", "epoch: 9000\n", "train mean loss=16432581.0\n", " - - - - - - - - - \n", "epoch: 10000\n", "train mean loss=16432581.0\n", " - - - - - - - - - \n", "epoch: 11000\n", "train mean loss=15418825.0\n", " - - - - - - - - - \n", "epoch: 12000\n", "train mean loss=15239754.0\n", " - - - - - - - - - \n", "epoch: 13000\n", "train mean loss=15239754.0\n", " - - - - - - - - - \n", "epoch: 14000\n", "train mean loss=14351450.0\n", " - - - - - - - - - \n", "epoch: 15000\n", "train mean loss=14333427.0\n", " - - - - - - - - - \n", "epoch: 16000\n", "train mean loss=11407301.0\n", " - - - - - - - - - \n", "epoch: 17000\n", "train mean loss=11407301.0\n", " - - - - - - - - - \n", "epoch: 18000\n", "train mean loss=11407301.0\n", " - - - - - - - - - \n", "epoch: 19000\n", "train mean loss=11407301.0\n", " - - - - - - - - - \n", "epoch: 20000\n", "train mean loss=10856371.0\n", " - - - - - - - - - \n", "epoch: 21000\n", "train mean loss=10856371.0\n", " - - - - - - - - - \n", "epoch: 22000\n", "train mean loss=10856371.0\n", " - - - - - - - - - \n", "epoch: 22204\n", "train mean loss=10669836.0\n", " - - - - - - - - - \n", "epoch: 22764\n", "train mean loss=10097909.0\n", " - - - - - - - - - \n", "epoch: 23000\n", "train mean loss=10097909.0\n", " - - - - - - - - - \n", "epoch: 24000\n", "train mean loss=10097909.0\n", " - - - - - - - - - \n", "epoch: 24106\n", "train mean loss=10008961.0\n", " - - - - - - - - - \n", "epoch: 25000\n", "train mean loss=10008961.0\n", " - - - - - - - - - \n", "epoch: 25033\n", "train mean loss=9990300.0\n", " - - - - - - - - - \n", "epoch: 25227\n", "train mean loss=8940188.0\n", " - - - - - - - - - \n", "epoch: 26000\n", "train mean loss=8940188.0\n", " - - - - - - - - - \n", "epoch: 27000\n", "train mean loss=8940188.0\n", " - - - - - - - - - \n", "epoch: 27170\n", "train mean loss=8830927.0\n", " - - - - - - - - - \n", "epoch: 27503\n", "train mean loss=8301732.5\n", " - - - - - - - - - \n", "epoch: 27717\n", "train mean loss=7827611.0\n", " - - - - - - - - - \n", "epoch: 28000\n", "train mean loss=7827611.0\n", " - - - - - - - - - \n", "epoch: 29000\n", "train mean loss=7827611.0\n", " - - - - - - - - - \n" ] } ], "source": [ "model.DL(ite=20000, bs=200, add=True)" ] }, { "cell_type": "code", "execution_count": 303, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-200.914505 ]\n", " [ 813.39611816]\n", " [-176.11975098]\n", " [ 97.72927856]\n", " [ 813.26416016]\n", " [ 806.31604004]\n", " [-188.42362976]\n", " [-201.4822998 ]\n", " [ 57.99349976]\n", " [-201.93777466]]\n" ] } ], "source": [ "model.predict()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "青がOLSの誤差、緑がOLSと深層学習を組み合わせた誤差。" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9x398eb5HmLWaoa5UKjl16lSSZHJyMpVKZVZnztbWtvRiZ73s7r77e/M9Asyp//zP/5Xv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\nBftvhtgaNw4RPXwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1158240f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.compare()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'DLmodel' object has no attribute 'error1'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-52-a8639f66dddd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DLmodel' object has no attribute 'error1'" ] } ], "source": [ "print(np.mean(model.error1))\n", "print(np.mean(model.error2))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'DLmodel' object has no attribute 'error1'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-53-49cb976b3e2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DLmodel' object has no attribute 'error1'" ] } ], "source": [ "print(np.mean(np.abs(model.error1)))\n", "print(np.mean(np.abs(model.error2)))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'DLmodel' object has no attribute 'error1'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-54-74a0f30af4ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DLmodel' object has no attribute 'error1'" ] } ], "source": [ "print(max(np.abs(model.error1)))\n", "print(max(np.abs(model.error2)))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'DLmodel' object has no attribute 'error1'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-55-6c0a4f9e73d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DLmodel' object has no attribute 'error1'" ] } ], "source": [ "print(np.var(model.error1))\n", "print(np.var(model.error2))" ] }, { "cell_type": "code", "execution_count": 316, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DAGAAgo20YeMLAKQOwUbasEMcsAaXVNoTu8QBwGa4QsNs7BIHAMBgBBsAAAMQbAAADECw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\nP4mg0LbcaQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118583c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "\n", "errors = [model.error1, model.error2]\n", "\n", "bp = ax.boxplot(errors)\n", "\n", "plt.grid()\n", "plt.ylim([-5000,5000])\n", "\n", "plt.title('分布の箱ひげ図')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 317, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = model.X_ex['X'].values\n", "Y = model.X_ex['Y'].values" ] }, { "cell_type": "code", "execution_count": 318, "metadata": { "collapsed": false }, "outputs": [], "source": [ "e = model.error2" ] }, { "cell_type": "code", "execution_count": 319, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Wf/yPj4yu7/V66e3t5YEH/gP/7/89gcczhCTJCS3LyUn8BSSZCOGUJJItmBYa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QhCOKp6lNWrjzBv3mi6kvbeY0xdimh/Uemq52/atKuYNu2qps/r1q1h5cqvmT37eo4fL2HixMkY\nDEZWrFgGQGFhATNnXoskGTq07WKix3DqINrmOJ2d99MoLdC477l5NC6ux0nnAAVahRgvJU7W2XC6\nlwx0EmdjllZ3w+Fw4HYPQRQNqGoA6M/hw3lN3z/wwFhiYnZTXi6Smmpg5szmBfCyy0J5880CnM4J\nCIKHvn2/YvbsCSxZsoZAoB9mcxSCMIHMTC9lZbuprDyJz6cREeFg5Mho+vffRHj4eIzGGhYuFAkK\nCm5IT+88Go2pn/70I77+OgVZvgHIIBBIwGYbQkGBk5tvPsDHH3+Hx3MZknScO+5wIwhKU3ozCA2i\nix1XND54sBiPZzqKouD3X45e3+6vGI1+pk8fzcyZg7Faczh5ci6SZGLYMDf79uWhKEVYrf2JiTGS\nm7sZl6uE2toc4uNHMHduMatXR2IwTMJuz8TvH4soSphMAgZDBTrRWseWLYf4/PNwvF4/FouNFSsG\nMWJEFuPHn1nVPDo6kuXLb2213eVy8cQTu8jOHovJZGbhwuPcfff4pu+zsgrIy6vCYJARhDIgjoqK\nTHy+SdhsPiTJTu/ec3A6D6OqYwkNzeG3v21tEFdU1LB3bxHx8SEMGdK3Q9e6PVxxRTjvv38cQQhH\n0xQiIoI4dMjO/PktPcvnUn+tx5i6NBEIBBrC+OeOsWPHsXLl16xbt4bevXsTHa17dkNCQli9egVW\nq5WoqOhObbtY+OGseJcQzsY1aoso3Z0ejYsR+280OOD8iT82nhu6f4ynE79FUUKSjAQC3i7pU3UU\nwcHBDTwgnTOjKCoul6Ppe0mSWLiwbZXsHTtcpKRcgd/vB6xkZg7m5ps3Ul19MxCHz1eLIOzD7we3\neyIwFq/XRCDwJRkZNzJ06Mf86U8VhIYGERXVtkeis9IWa9faUNXZiKIHVZVRlHRcLgGDIZ+cnESW\nLYskPX0TffuGMmXKHNpbQM+2eCqKnl2ZlhaNIOyhpmYAEIIohmOxzEKS4hHFjwBwOjUkSQ95hoTY\nmDixP4HAHgoKDBw58j1O5wTMZiuqasTjeYPHHlvIokVF+P21mEy9EYS30bSbkaQAffp8xq233tzU\nj6VLD5KXNw1ZjkbTSrBY0lm2zHmK4dS5ebp48QHy8uZgNIpoWhyffSZz/fVVREZGsnLlIZYsiUdR\nhmE2ZzNs2Pfk5ydgNhcSGzuQkBCdJxUdHckzz0SgaccZPHgA4eEt+VN5eaW88ooHj2cSmnaS667b\nz4IFXfdIpab2Zvz44+TkVGI0CsTERGA0Hmm1X8fCvrTxue358MPGD7X/LftdX19PcHD3iF+Gh0dw\nxx0/abX9hhvmd3nbxUKP4XROOP1Nqi2idMuSIueCC/1W1jgeoOGv0ORh+qG8IZ6JiN/d7ezdm4PH\n42Ps2IFNpVAMBgOpqV5ycvahqrFYLJmMHp10lrPp55Mkb0O2m4Hs7GocDgMwGTAiCHVoWjiyfBCj\ncT8Gw514PPqxqqrH/0tKokhOjm8z86Wurp5nntlLQUEI0dEeHnsskbS0hBb7aJpGVlY+iqIyZEgK\noiiiaY2eOStQBtRjNG7HaJzE0aPrGDAgiQEDklqdp+F/7fxr3A9A4OOP9/H11wYcDgWX6xiqmozD\n8Tlmcx9EESRpLEbjSW65pR8AI0dGsXVrNoFAPwoKdmI25/Pyy1exbVsGv/1tCRbLdETRjCSB19sH\no9HI0KEVfPppFW73rdhspRgMK0lJKeTzz+/DaGzmmW3fHoEsJ6JpoUAsgUA1O3YksGTJBkaO7Mfg\nwZ2r4eXztZQS8XqD+eSTdGJje7F+vYCqJiMI4PcPwu0u55//HEJ5eRxPPnkAh2MKqupn6NBdTJgw\nsV0u4apVlXi9kxokVGJZv76I+fPVc3pBuP32aN59txivNwmTaX+T+vzZcK4K1437NUqbNJ/zUsUP\nj9Cuo33V8O4ynP6T0GM4dQHNXpHmbaeLJXYvUbqp5Ya/5/fH2WwA+poecoIgYjJZL8BDq3vG2DpM\n2tro6y6vlqZp/PnPm9i8+TJE0Ubfvpt5/vnxWK369Zo6NRqLJZJAoAqrdShjxx4+47kar/2CBakc\nOrSWvLwR1NdXYLcH8HgCaFoMERElSFItAwbsoE+fSL76KghRpEEpW/doJSTUtpMurPHGGwfIzJyD\nIAgUFsLrr6/ltdcSWvTjhRe+Z9u2ywAjw4d/z5NPTmL06DK2bSsBIoB6JCmA2TwZQSgmLMzd5pg6\no2icnV3Axx/3Afpx/LgTt3sciYnZDBkykbKyTwgLuxFNq2Lq1EymTp0CQFxcJHff7eHnP/+MsrL5\nhIZO4n//dzV/+ctlLF6cy65dzdcgNLSG5csP4nSakeXj2O0OgoPDCQq6g7i4TVgsLbVhAgETBoOA\nLDcu2hpFRbH8+9+xfPGFxO23H2DevGF0FFdd1YtNm/bido8iEPDjcm1gw4a7EQSN0tIlJCbKTRmP\nqqoLfppMRv7wh2Q2b/4Oi0Vg5swJrZ4rR4+e4J13yqittVBeXkRQ0BgMhsaxiOckmltaWsk339Rh\nsSjExGTyyCMTCQ9vLcraUXRO4boRyin79vCmLhQcDgchIT2K8Kejx3DqBJrLjjQv7m2rfXe/RwMu\nDDn89HIvOjdHRhTFC/JwOtcxtmX0tccr6y4DKisrn2++6UNV1RE0zUB9fTJffXWQW2/VS8c/8cRl\n/POfO6mqMjJ4cDk333xZm+c5fS6Fh4fzyiuTOXQoh0ceOY7HMx+TKQ+HYzNhYZVcf72Ju+66gfDw\ncKKiVpGTI3H0aCZ2+2BiYpYSHFzNxInvYTAE+M1vxjBzZnMduerqlkbwyZNGnn12CzU1NgYM8DFw\noJ309KkYjfrbZmbmNaxdu5WPP76L//7vz1m/3oXHU4WqTsVu30BoaATXXHPmxdTr9XLkSAk5OZUM\nGdKLwYP7cvriWVLiQFWHIYqgqgKCEIrP50UULUREgM+3FUUJxeUKEAj4MBpNgEBe3kl8vluIjNQ9\nYkVFs3nnnU+44YYojh9/kfLyZIKDa1i40MYHHwxCFKMwGtfjdKrExZnRNJXExPpWfR44sJqMjGPI\ncn8E4SiSZMNkysNsvhJVNbJiRT7z5p19jjRiyJAk/vjHYr799hsOHz5GaemdTc8Kg2EmDsd3hIbO\nwGjMoq6uhJ/+NBFJcjNz5gnuuqvteQPw9tsnOXlyasOnoZSWriYpaR6qWs2UKS4kSaKrhcnffruE\nkyd1orrTKbN6dQa33dZ1w6kttG1MqegGU6OBdDYSOtBO6LcHXYPD4SA4uKfcyunoMZzOAaqq4Pfr\n8ZELU4Pt/Hmc2pMWEATw+eQLmMnX9TG2LpHSfWHSM6GuzkVhYR2yrGd6OBy55OU1Z85ZrVYefHAs\nqqpgMFhYsmQXZWUGkpI0Fi4cBWit+FenltoZPXoojz3m4a9//Q6I4qqrSrn33mT++U8v99yjkZCw\nh1//ehjJyTH4/eP58MPtPP10DQ6HFYgCrufBB99ly5Z4YmN1rlVamo/MTA+iaEXTVE6ezOX77xch\nCAIHD7oYOXIxgtBMWhYEIzU1bmpqaujXL4G4uJmAQEHBv6iuDsNmqyI4GK69dilVVRHExVXx7rtX\nERUVgaIoPPHEKtasUXA6hxIZOYW4uCIeeeQA1147/JQ2BMaOTSUiYge1tZOwWGTq6r6jtrYSVV2N\nqiYRGTkdoxF27w7w8ccbuPNOPfVekhoXWR21tS4+/dSAIFxLcfFHGAyFGI3j2bMnF1HUs+sSEydT\nXPwVycmRJCcHePTRZsOyEa+8ciVPPbWNzMw1WCzmhvl0C6Koh/O68rsYNCiRQYMS+ewzgU8+aZaj\niIw0ctttTgyGb6msrOG77xY2GIawZk0QkyYVkZraOsyraRrV1c2espAQGwMHBjNhwvfExNgYN65t\nDl1HoKoqFRXNqd+iaKC83HSGI84HBE5V4W6fN3Xq3x4SetfQ8vroHqeeUN3p6DGcOolGknEjOipe\n2b196D4r5mzSAi1d6Jcm2h6DuZvDpDo+/XQXa9eqiKLCggV2rrlmOHa7GUnqhyw3hgSjCQo6/WGj\nz41//CODLVumIoomdu5043Bs5447dOPhTFmKP/rRWK67zonT6SQ6ehaPPrqDujrdiDh5MpV3393I\nU0/FUFtby9NPa9TX3wGY0UUwN+F238s777zLr399PQD33TceUdzM0aNGIiO9fP99PwIBoeH62amv\nD6Wq6mNqa28kKEjDZlvLv//di3//24cgnEAQJKqqtuL13oogSEiSgWeeWY/fPxpRjOPECT+33/4B\na9feyQcfbGHFigW43TtR1fFUVLgJDk5l2bISrj0tqzgsLITf/jaCTz9dy+rV5URGjkIQxuDz7cZo\njKE5NGOittZAowL2tGnDWLduFQcOXAdIyPJnhIffxsGDi5HlUfj9SRQWbuH48XwiI1ehaWFYrT6G\nDu3F66+PaDOkuWNHLnv21DJ9eix//etsDhwo4pln9nP4cDZW6wASElzMnCl3aN5s3pzNrl0u7PYA\nd901EovFwty5w8jIWEN+/jQ0zcuECRksWHAlgqDzvESx2UARhAiqq0+0eW5BEEhIqCc/Xw/HKYqb\n4cNNzJkzsp3edPw5JYoiMTFuyhpkqFQ1QFxc1zIzuwvnwptqj4Te/c/uH5qB1j7HqTEpoQfN6DGc\nOojW4pU6LgzvR0d3ttNZ9fILncnXkfbaG0Pn5BEaQwBnR3r6Ed59ty+KohOCX389k7S048TFRTJg\nQC0VFbWoKoSHSyQk2Ns8x5EjdkTR2OAVM3H4sB5q6gjpPigoiKAgXT3X6WxbODMvLw+3exCC0LhI\nNNamU8jPr2vaXxRF7rtvQtPn3NztFBfr/1dVmczMADbbTfh8+/B4nPh8CtHRVwBQXV2DIBQgyzJg\nRZIOoygR+HxpgBdN0/t47JhOIC4vB0Fovh6KYiIQaL9ER0pKb+6/X2TTpmSCgvTsNU2bgMfzCZrW\nvyGrM5fx48ObPBEGg4Fnn72Sb79NJxCQ+eKLeEpLRWQ5ChiPLlK5EFk+Sl3dBEwmC36/j/j4VUhS\na4/Mxo2HeeklG/X1kRiNweTlbSIry47b/WN69SrC5TrA8OFHueWW6znb/Nm0KZuXX45BVfuiaSpH\njy7nhReuwGKx8Oyz48jI2IPVamLMmMlN93/SpDg2btyDxzMaTdPo1Sud4cPb51L97GeD+OCDzdTW\nmklJ8bNwYXtGU+exaFESH364lbo6M336eFiw4NIr5ts53tT51Ju6dF8wu4L6+np69ep9sbtxyaHH\ncOoETs0sayQdX3jXb8cX+rZwuhjn2aQFLvT4OtJeW4KinVNgP7W9jodbcnKqUZRRTZ+93kEcOvQt\n119/OXfddZxPP80kELAyalQZ8+dPadUOQFCQl4oKtemhHhoqYzbbOt3vkSNdbNigc380rZpRo/S5\n2b9/f+z2DNzuQahqHeACjBgMn+DzJbd7vsceS+C119ZQU2MlIaGEzMzhSJKd6OhJ1Ne7qa7OaNo3\nImIS/fu/hdNp4NtvjxIIzKCwsBhYD4xGVSMRhF6YTLUATJ3ai6VL9+L32/H58jCbowgJOc7cuW2H\nfNauPcDGjR6qq8sJCgrCYknE768gNraKQOAtUlJ6MXduLOPHD8Tv92M0GhvmsYGrr9bDbR7PHt57\nrxBdubuR26MCdjTNRJ8+RoxGG6oaC8itPBFffllMZqYLWU5CFPPweusICorC49mP2RxHVNRULJa2\nvU1lZZUcOFBEamov+vWLJyPDjarqWkqCIJKZGcfatTtITIxiyJAUpkxpbYgkJvbiiScUNm7cjCQp\nzJs3oBVx/VSEhATxyCNnC8lpDQWsZSwW61n2bUavXhE8/njE2Xc8b+jaM6gtYwraCvX18Kbag8Ph\nIC1t4MXuxiWHHsOpg9AfzBZEUf/x6GU6lHPKVulaP7rm/Tl3Mc6L/ybVfYWET4XAqUVNz4ShQ6OQ\npHwURU+Dt9kOMmqUviDOmzeCuXNl/H4/NlvrB01jf++4I4q//e1bqqujiI2t5pl39VsAACAASURB\nVN57U1v1XVEU3n03g8JCA/HxAe67b1yLFHmAxx6bTGxsBqWlAv37G5g7dxyaphESEsLzz4fz3HNv\ncuKEhKoew26fjNV6A3b7xnbHNmhQIn//e2JD+8O49949VFaCx1NEff0RJGkjijIeSbJht+/kpz+d\nxLZthezYMZ66OgFFiUcQQtA0L7AKs9nGwIEBfvnLHcTH+3j6aRvffOPG4djJiBHBmEwgirEEAoEW\nY0tPz+Fvf0tElvtgMnkoLFyOJMn4/SUUFExBFM2Ulu7n3nuTeeKJb8nOjiEoyMWDD4YxeXL/pvPc\ndNNo+vbN46GH0iksHAeEA3uBWvx+vTitpinExfk5nXhcUnKSlSt34/P9EohCUQIUFb1KREQNTuco\nRPEY0dF59G9urgl79+bz2msiHs9UJCmfH/84E5tN590JgkggUENpaRHvvz8TUXQzY8ZO7rmn7RIp\nKSm9SUnpvrf99PRjfPllAJ/PTkpKJQ8+OKzVvPq/gM6VGQLOEur7zzCmGrOn29Jx6gnVnY4ew6kT\nEATxvIoldrAXdMaIaSvLrLNinHoNtK70tatoPcbWxO/zUyLlTBg9Oo2HHtrD6tWFSJLKggWhJCbG\nNn1vMBhacWUaw4mNpPvU1Dhef70PPp8fq7V/i/6fOFHB/v0FpKcfJz19PqJoZteuAA7HRp54YmqL\n84qiyG23jWvVFsD8+RO4+WYLy5fv5I9/TKS+fizR0bu4555eHRqnJEn87GdRvPTSEnJzo/F4LkdV\nJ+JwvMns2WE88MAYUlPjee+9PbhcoQ1q5GY0LQpBKEQQxhIT8zGVlb+lqsrEzp0BZsxYyV/+Mo1A\nIMB///f3ZGfPBmDNmlX8+c+TmxbwvXtrkeWxgIbHo6BpMzGZ/oXLdSuKkozFIlJRMYhf/OJF/P7f\nIggiHg/87W/rmDhRbcFrGz06lW3bHmXMmGcpK4sD+iMIdyEI6wgNtTFypMaiReMRBEPTtdu5M5fn\nn/fg8fQCdjacKQxZ7kuvXjMQhACq2ge7fRXz58/gdKP7668deL1TEQRQ1VRWrCjjueeGkZ+/ktzc\nVCoq1uPx9Gf//ixsthpkOYZ582rOKb2/I/B6vSxdCooyBkEQOHpUZfnyHcyf33EphQuPC/fQ6T7e\n1MV/wexOOBwOQkN7DKfT0WM4dRnNqewX1uOkGzEdabf7ssw67pXpDpwaPtOJ377T5B7OD/G7I7j+\n+tFcf/2Z99mx4whHj9YydGgkaWlxnPowNRjMSJIBm63lT2/79iO88IKK0zmR4uJdhIdnERExElE0\nkpPTsYKWp0o5rFq1j9dfdyPLwfTr9yGvvjqVhIR4Ti254vf7Wbw4A6dTYNasZNLSmsUcR43qy8iR\nJ9m9ewqaFowgQCBwPfn536MoAbZsOYTRqCGKexGEoWjaZqAYTQvHaKyiquoyYmMFjEY9Iy83V+c9\nrVu3nyNH5jRlpeXkzGbt2k3MmaN7XeLiJDStHkEIwu8HWd5HXZ0fTYtCVbWGeWHH6zUhSc1zoK4u\nAo/H3VRBvfm65gHDEMUBiGIVBkMYdrvMW28Nb5Et1Phb+uorB1VVdvT5Pg0wAkeBDVgs80lKsqBp\nKg6Hxksv7WT0aDvTpw8EFDRNQJb16+p0erHZzCiKzk178cXJVFdXM3duGB7PNWgauFwKBsMXyPL5\nz1pyu914POE06LIiihJOZ8/j/0w4N96Ucopo5w9Xb0onh/dk1Z2Onl9OF9H8A7j03jAuNWOj89AN\ntUDA14L4fT7kHjpjiJ4Jq1Yd4NNPHRQVleJ0jicmZioGQw4PPZTN9OnDUFUVVZXbbePTT2twu6cj\niiCKQ6is3EdEA60kNLR9InVbCAQCvPxyPRUV16FpGkVFl/PRR8v55S/jm/ZRFIXHH99ARsZCBMHI\nypWbeeUVjUGDmtPd9RCZjKYFEAQj4Ka4uJbnnktCkoLx+TKJjjZRU/MZDscIYE7D+PYAx6is3ICm\n2TAYPAwZomsk6QvPqddARFGaf0M33DCa/PzvyMgIRhCK0bQhGAwPoSjr0bSrACMREVtZsCCFpUvz\nqKoqQdMMDB16GJttYYvroKoqf/97HZGRt+FwOPD5BmMwvMcDD8S1uxhomthAOL8CnVgvAgOQpACS\nVEggEMehQ5/j8xn5y1+OY7cX8dprAWbNGgZoJCZW8tVXR1CUEYjiMUaNKkLThiIIAiEhIdTUhKJp\nPjTNjqKIBAJVREVFder+nnqfly/PxOUSmDAhirS0+Hb3DQsLIz5+H+XlCQ1UgxMMHtxcn1FRFGpr\nawkKCsJsPj91G/8TcHbelHLqVtonoTee4+KH+pqNwdb9cDi6r+TKfxJ+KCvpJYG2wlUXvm5c+6KN\nmqYSCHjx+92oqkKj2rfJZD0no+l81Y9rC5qmNbWjKAEEQcBotDSM4XxqZHUd33+/j0WLNrBiRRH7\n9lVRVFSJw+FGlvuzfr3aQkG+vWvYqEzt9wcAFVkuweFYg9f7b5KTa1AUpc3j2oLL5aKuLrLpsyBI\n1NW1JGIXFhaTkTGhwSCC6uqprFhR3PS9oigUFVUiy6tQlBoUZQ9mcxaSNBWjMQpRNGM238uQIelE\nRPixWlMxmVSMxmAUZQwGQyaqejmBwFgCgcuRJB8AV189nJSUlWiaXvw3JWUl11474pS+Cjz++BRu\nvlnGbLYiSQ5E8XvCwwcQEfEOAwf+k2nTSrnqqgHYbFtRlDGI4nBcrjEcOlTYYox+v5/6+mCMRonU\n1FD69JGYPbs3jzxyFe3h2mttWK2VQCUg0WjEJyXZ+J//qSQ8/G18viuAucAtuFxDePbZdQ37ShQV\nRdC3r53Y2G0kJbmorU1AX0x1A9RkqmoI6ToxGEpJS+uajImuVr+LZcsuZ+PGyfz5zwrZ2SXt7i+K\nIo8+msqYMdsZPHgXt99ezmWX6Vy98vIa/vjHLH73O5Hf/a6EvXsL2z1PD9qGIAgNnvzGe2lo+Ceh\nL7OnvmirDf+a54WmyQ2/CfUirCntw+v1YLV2PJHg/wp6DKcu4mK9JbTl6dI0jUDAh8/nRlHkBu/M\npW1snI7GTDmfz03j2CTJhMlku+Rr4/3sZxtwu+9FVRcBD+D3l1NTcwRRFKmoqOHnP8/g8cf3sWzZ\ngXbPMXOmCUHIpajIS319HcHBBoqLFSorb+Of/xzODTf8lZycsy1o+jUKDQ1h8OB8NE3F75epqcnm\n4MFiPv98V9M+Npuejedw+HA6faiqisnUbJytWrWbXbt+RFLSjYSH52C3e5gyJYe0tGZOlyiKXHnl\nAJ5/fhD9+tUQF2dC06pR1ZU4nWn4fDsJC9tNYmII1dU6ydlsNvPii5dz991riYl5G7fbylNP7aCi\norrpvIWFpbz5Zi9gDqJ4BZo2iaCgMszmYGprH2H79jt4+OED1NdfSVJSEL17BxMIjGbLlooWV8Ni\nsdC//wk0TUVRVAyGcqZMOXP5iCuuGMgzz0RgMOxAzxLcDixh/Hgr48al4XCYgHggCAgB+lFQYED3\nHIioqoGgoH7Exk4gLGwwiiLSuHCaTCYmTbIRFvYdISG7iInJZPbs+FMWzY4vnJWVlRw+3B9R1IMG\ngcAQtm6tPuMxwcF27rxzCPffP4Bx41Kati9bVorDMQGLJRFZHsGXX7rO2n4Pzo5GY0oQJATB0PCS\ncjaDqn1j6mIZVJfys/diocdw6jIufqiukXjs87kaQloCBoMZk8nazcbG+R2rosj4/R4CAV9DG3p7\nBsO5ZMt1FF0fm6ZpHDtWQEFBFNBIvLYB0eh13HZTWgp5edMoKJjK4sVpbN+uV5X3+/384x9beO65\nDNatO8DcuaO47roM/P5/oShHqKgIxeOJo7Y2nfLyHLZvn8gNN+xi7dp9Zx+RIPDaa1cwd+5SYAlB\nQTXU1i7ijTdS2bw5C4DKSi+quhe3u4j6ejeK8nfuvLM5Lb6+XkYQrIiihejoy4mLG8vcuaMZPnwf\niuJDVQMEB29nxoxEJk8eyLx5m/F43kFVNyGKXjTtVvz+mVRVpVFevp1Dh/K54YZd/OEPGzAYDBQV\nqRQX30dh4Ux27JjLc89lNrWdn38Sny+FkBALkZE+7HYT4eGbMZtvb5oPDsdM3O5mD4umBbDbW/Pw\nnnxyPL16vU1JydeUl2fz5Zd1VFfXtdrvVMTHhzF48E3ExFQQHp5HSkoKpaUWnnpqB15vDdDYrgYc\nRdMMbN2q39crr5QQhKKGPpVxxRVKi4Xz6afHs2CBj2uv9fPgg35uv30sXfFCWCwWDIZmA0fTtBaG\n75nR8jfl8bR8ufL5jJeU1+OHJybZvrGhG1MtDaqOGlP6vDifxlRbff7hXfsLgR6OUxdxIcNXbbWr\nKHJDiY7uSss/U5v63+Zafd2Dtsq8GI0mAoFmftaFQmfG9s03B0lPd1NQkM++fYXoBW896F4IDagg\nJSWX224bxPvvz0DTNFwuD6IYxZEjR5k8GX7/+03s2DEbQTCwcWMJPt9eTpwwAouQJCN+vxNF2YvH\ncwhFuRmIorrayR/+8D7XXHN2ccOQkGCuuy6BtWv7I0k6WUqW+3DwYCZTp8K6dScIDr4Ps/kosnwM\nk+kqamtriYjQRStnzhzEsmXrKCu7Bk3TSExcxYwZowgJKWLnzvX4fFYGDHBgNPZm7twvyMwchd9/\nHDgE3AqYgBpEMRincwe9et1KVVUMq1d7SEz8lqIiC43FbAGKi5sFMkeO7Edk5E5qaq4gJMRKVFQ2\nt902kDffrAbiAHC5jPj9qykvdxIVFcFVVxVx660ttbP0+6pSXDyYsDA9KzE3V+Ott75ulaV4KqKi\noklJycBs/hEABQWf4PffQWWlHYdDArYBx9AXs0rCwuJ45ZVQwsMLufrqIURH53PkSD59+wYzbtwo\ncnIKKCurY8yYVNav309GRhVms4//9/8mIUmmLmVvBQXZmT07n+XL81CUMBIT9zNv3uCzzou2MHy4\nSH5+FZIUiaL4GTLEdYl4GS4l462j6Hyfu0dv6lx4Uz/E63xx0WM4dQIX+yVMlxbQ36obSdMXJi2/\nez1OjWVrWtZmO7Uw8qntnd8HeGcv2/r1B3j55V7U1kZRUjKRQOBN9MX8YyAVOAm4qK19hIMHv8Fq\nPcKhQ8nU14ciCKXs3p3Lj388hkOHoqmvz8Tvd2C3J5Ke7sdkCkcQVuHxRAFuJOkEqhoKRCFJGoIQ\nzMmTETz55HY0TWTOnAjGjk1rt6/9+vUmJCQPl2tcw1irSU7WuU5ms4ymqZhMKU3ZccHBzRlpUVHh\nvPxyP774YhWgccstQxEEjQ8/tBAefh0Aubl+brnlVbKz/xtZloDL0O9ZAZCGKGpERjrweOIxGGIA\nEEUrZWUCcXEeDh1SmzI8e/du9p6Eh4fy9NPBLF68CkUxcc01VqZPv5rS0u9YsaI/TqdGTc12NO0p\nwEVFRTqzZoW2qUnk9Xrw+Vpmz/n9Z37sSZLEk08O5C9/WUpRkQeDwYDLtRWHw4zFIjJ2bCn5+UVU\nVzuQpF4oSn8OHNjGr39tY+HCk9x221hGjtQn1nvvZfDFF2moahqq+g5ZWcOR5WsAA/PmfcCWLaGE\nhoY19q6pDx0xphYsGMzUqZXU1ZXQp89wjEbTWZIc2v79TpvWH4vlKLm5+UREaMyadekpg/9fROf0\npk43puBc9aYURWmRudqDZvQYTl3EhfY4nS4toBO/LV2QFrh46GiZl/Pl4WobHTMKdeK9nx07HGja\nZbjdLgTBiiRdASwFfgssBu5AFA3k5/sIDzcyePBBdu92YzRK2Gwa+/fPJDe3gMrKg5SW3gSMoqoq\nk6qqAoKCRBRlISaT7rkKDV1KeHgh+flewIogOAkEVLZt08nN+/fv48UXS0hNTWijvxqRkaE89lgB\nixevxu83MWWKm9mz9TIrd901iv37v2D79pE4HFXY7Yd5+mkzL700A1NDznrv3tE88kh00zmLikrw\neGJoTLoSRRPl5aGIohFBUNE0Ed2IdCGKKzGZJKZMKeDEiVhKShqPKWX4cCtTpgylvn4px49HEhXl\n5vHHW4qGDh6cxLPPJrfwSv3qV1dy111l/Oxn73Ly5J8bRCyt+P0Cv/rVHh5+OMAdd4xvMZciI6O4\n7LK1pKcPQRCM2O17mDEjkvawZcsBfve7fKqqTPj9ZfTq9VMqKt5BEG5HFEU8HieDBmXy1luj+MlP\nQlHVMbhcNSjKcAoKjvLBB6mYzXtYsGAMLpeTr7+OQBD6Iklw4EAZsmwEqoEKKitHs3r1dm65ZVar\nfnQ0FT4mJoqYmMY9lIb9Go9rmQp/JkyYkMKECWfdrQcXGeejTl970MUvezLq2kKP4dRlXBiO0+nS\nAvpioTZwmC6M0XSuRmJr1fKz1Wa7+PyxRpxu7IWE6F4yk0lE09x4vZuBa4DP0DOxjEiSRiCgkZOT\nT1paCPHxUwCt4TrUUVtbhtWagCRZUBQPZnNvzOZeRESYiY8XcTrrkSSNiIjBPPdcL/7wh1WUlKRh\nsRzGYGgu+eJ2jyQ9fUOT4dR4LVVVbSLZX331IK6+uvW47HYrN91kZOPGV7BYbsRsvofvvw/w1lsr\nePjhaW1ei9jYGOLjD1JZGYeiKDid2SQkOKitPYIopjV4ELdjMi0iNlYkNvYDgoISSEmpJT7+M2Q5\nhIkTRaqrvUybthy/P4TLLsvm+edvbrPIbluIi4slKioUOAwMBdYB06muHsM//yni823lvvsmNu0v\nCAIvvngV7723iszMCkaPDmL8+DYuCHp6/6OPllBXdw9+v4qi1AGLEcX+KIoDSTJhscjEx/fB7daI\njk6isrIaTTNgNIZgNGYjiuEcPqw1nE9Glq1Nxr8sm4GHaRZKfIl+/TpeU+5c67E1o9HI7SH+nh9c\n2GvaffOimTMlCAL19Y4ew6kd9BhOnUSjF6TZK3J+FvfTw1mNOkZ6Bl3ndH0uFppVy/1cTMXvM8Hn\n8/HUU5s5ciSSiAgfP/95MsOH923X2Lv33vEUF68hMzOO6urvcbtvQSeGTwK+BNagqn4URcTjuZHv\nv/ejae9jMNyFpqkMHLiR0aOvJCpqH6JoQ1EUDIYQ7HYzo0fbWL++Cru9Nx6Pl/j4Q0yYcC1ff61Q\nXl7O8eNJ/O539qaQsaZVEh/fzA1q5oU1ZiUaG0QjVU5/YD7//Ne88UYysvwCkI6ifEZw8EJqaqR2\nwz319W7S0io4cuQvFBUFYzD0ITx8HElJy6ipicdiqWDWLI2IiLWUllaye/cVbNkyAE1TGTz4C555\npj8+n5epU9PxeB5GEEQ2btzDM898ze9/P7/D9+yhh65l/fqVOJ15QAAowmqtQ1UHsH9/2yVE9u6t\nY9OmEDZsEDh4cA1/+tO1rSQ6jh07Rl1dM09IECLwek0YDPXIsrehxJJMQgKMGJFMcnIekZGXkZPj\nRJaLsFqj0TSZ6Gi9hmJoaCgTJuxn+/Z+iKIVSAH86NwoIxDJuHGjOBd0jh/TiEYi+qn8mB+uSOOl\ngkuJUN+1eaEBMr///e8pL68gKSkJTVPJysqkX7/ULul7HT58iC1bNrNo0UNdGselih7Dqcs4Pw+X\n1uEsoUG8UmoQrlOa9rtw6LwH6HTid2dUyy9kGPTvf9/Bpk03NoSd4NlnV7B4cQKKEmjT2DMYjLzw\nwnRcLhd//vNgvv46lMLCOjQtHEgAKjAaLUjSFCIj9cLDCQlZXH75OiTJy513jsdisXDTTQbee+8Y\n0Jfg4J386EcxjByZQk3NFv70pzVUV0fh9/fiH//YwsMPX0FCQgIJCQncdlsGn3+eiaKozJpl4Mor\np7YysgFMJhuiKOL3N9Zio+Ga6vNqyRIRVb0aKAP8uFzVmM0fMWVKb/TsnZYL6vHj5fzsZ0fJyorF\n4ZiEJMVjsx0kODiK2FiZL7/sy0svLWfLlmpGjtQIDR2Epg0AwO8vZfny46xaFY0obsDr/QkGQ6Ou\n1Wj2728uINwRDB2awssv1/PJJ8dJTy/B40mmpmYA9fX7keUioGXtt48/3sKyZSfRtCuBAB99tJmZ\nM3cyc+Z4AFau3MfevT7sdjdWqwePZzSSBIpSj8nkxeXKRVUt+P0Kfn8hhw7VExU1mf/6r1o+/3wj\nUVFOKiursVhSGDjwMPfeq3u8BEHgf/5nCitWbKe2VuXAgWx8Pr3QMHiwWPI6Ne7OoO2QTqNqvEjH\nyMaXhjHVY8h1H9qeF41JRs3bc3KOkJV1GID7778LSZJISkqmf/+B3HXXvSQlJXeovUGDhvDBB++2\n2LZ69QoMBgOyLDNr1pxz3nYx0GM4dRHdvbg36hjpHg5oP5x14cNYp3KOzoZGLpCqtkf8vnSwe3c2\n27fXoqoijc6HsrJQXC4HZrO5KcuvLWPPbrczbpydr74qwmzuh99fjcGQTt++o5HlIw0FofXjQkNN\nPPro5QQCXgwGnT90882XMXRoPnl53zJuXBpxcbp69IkTAeAeIiP1Yz/4YBc/+lEF0dE610hRVGQ5\nBlm2cuJEHj6fB01rGcYVBPEMgqd6mEhRjA33KB24EXAhCGVUV+c07NdyQf3ssyMUFY3A6dyNpvnw\n+6MxmUZQX7+DsDArixZ9xPbtC4EhZGfvpVevfxEWdg2CIFJaugJZ/hmqKqCqaUA20Ehq95CSIrJs\nWTper8KcOSOx2c5eYmb27JHMnj2SOXO+JStrEIpiQJJGI0kVrfZ9551v0bRnAb3mlqom8d57LzJz\n5ni+/HIPr702AE3T9ZQGDPg7RUUf4PUG0avXEW68MYnnngsD8tBFL6fx7bdv43K5GDWqHyNHJgFa\nk5Do6RBFkeuvHwPAypW1bNmyClUNQhSdTJzYtSy4rqMxRCie9vy6MGTjzuPS8eD8Z6NxXugv508/\n/Sw+n4+vv/6KjIwdJCYmkpubQ15eDseO5ZOW1r/DhpMgCFgszQKa9fX1VFZWcuedP+HDDz/A4ahD\nEMQubwsJuTh19HoMp3NC44TrOvRwltwQzjq7tEBnjJjuw9mNtfaI35LUlSl2/o3D11/fxCefDKOq\nKh6Ho4pevSIwGiWSk8uxWAZhNJpZvDiDTZtEzOYAd98d2yqD7brrRnH8+HpefTUTQQiid+87sVjq\nmDWrgqVL9+FyDSE09AC33942GXnIkH4MGdKvxTafryV3zecLw+VyEx0NBQUlfPJJIoKQhiDAjh19\nWbp0EzfdNKapBp7P1zHxwuTkHGprP0HPBPRgNMqYzX3Zty+TefOMrRZUn89NVVUWgcAs9DDTKjTt\nGjTNy/jxlfzjH32AIQ1nH0VV1W6mTfuSw4eTaZSL0xNC49G1pRQUJZqhQ9PxeOy88MI0QOS9917j\n3ntTmTv3MsxmS5t91zSNffuyqavzYjJZSEiwNRiMJgyGoFb7S5IdOFX4sheqqhvyO3bIaJpeqkQQ\nDHg8I9i+fRBGoxFVncojj6xD064AmrW6PJ5bWLIknUWLptCZOWq1hpGS0syvCgpa1+Fjzxe6m2zc\n4x36IaL1HDabzQiCwKhRY/jxj+8GdO5kVVUlkZFdKxEEkJ19mIQEnZOZmJjEwYMHMJlMXdp26NBB\nJk6c3OW+nAt6DKdzwKnFaLuCRg5N5/g/F4843XaZl9aeMqNRD1Gd60P0fBmHHo+Hr74KQdMSCAuL\nQ1U3YjKdZNIkOz//+ShMJivr1x/k7bcHo6q64vXTT29myZLWBS/vv38GKSmH+eorJ4pygFmzjMyZ\ncw2zZ5/g4MFtjBzZl969Y5rCaGfzUM6aFcfatek4HBPQNJmxY3cRHDyJt976jpKS4wQC8zEa1Qau\nnZn6egmz2X4ap0GjqKiUPXvyGTo0kcTE2FbtnDjhBixADSAiyxp6DbWTvPHGZqKijCxYML7JUxgW\nZkCWR6FzdAwIwjSCg58lJKQvO3b0Rpbz0Lk7+iNFEDRefvlKXC4XM2ce4dixE+ihTAUw0q/fWt5+\n+z6ystL43/+9DEGAiorPKCx8gN/8RmHFipX8/e9TsNlaKn1rmsYzz2xg5coJQAgm0xIUpQRJSsBo\nzGf69NaPtJ/+dCIPPrgMTbsR0JCkJfz61zqnKijI24LTFRzsaiKql5WVsXv3SGy2etxuE2AGatC0\nYDZuzG4wnDqOxMQivvtuL6qait2ey6OPdurwC4ZzIRv3GFPwnyIaWV9fT1RUr6bPoigSHR1zhiPO\njrq62qZC3DabnZMnyzCZTF3aVl5+8pz6ci7oMZw6iZYp8gKNJMvOoC3hx1PrmXWsHxcyVNf6QdBM\n/PZ1yFPWufbO6fCzQpZlVNXQ4KUQiIy8ihkz1vLUU1Ob+p6V5WwymgAqKgaTm1vAmDGtwyvTpw9m\n+vSW2xIT40hMjGv63NFrMnJkKq++epR161Zht2vcdNM47rlnJ0ePzkPTnKjqJ8TH34kkSdhsB7jy\nyuRW5/7mm0M884yNurpphIbu4de/ruDqq4e12Keiog8wE/gGWI2meRgx4iRbtoyltvZyNK2effuW\n89RTM1i3bjfbt2diMFyPKBrRNB+C4MRkCkNRfkJlJVitI3G5FgM/BnYyc2YloGC3W5g4MZFjx95H\nN5zqgTvIzFxGcbETURSpq9uO06n+f/bOPEyK8lz7v+pt9oFhhn3fGUFWNYAiCIoiuMRIYqIxMUY9\nJ+Yk0USNyUlOjImauMSon5qYxH2XxAWDEBdAcQMUkWUGEBmGbWCYYaZn7aXq+6O6uquqq7uru6u3\noe/r8pLp7qp6a33vep77uR+83hEIwlpgAZs3f4Pnn3+NK66Ypxn3jh27ef316dhsQwHweq/hpJMe\nZsKEkUyZUsGpp04PO6YXXDCHw4f/zYMP3o7N5ufXv57E5MnVAPzXf02mru5ldu0aTe/eh7nmml7B\n41lcXExR0SEKCyfR0fEmcD5QgCB8GPCtMo+Ojg6WLesIRATfprPzAPv2hRPadCCR+9NqMpXoOLIb\nPSu12NrayqhRkX3i9Fi9+i2WLXsheF4XLVoSpkMqLi7B45H7VnZ1dVFSrKBwVQAAIABJREFUUoLL\n5UroMzMp/VQhT5ySgCAISBIxTOdCkK0FktP/ZOZho41y6T2lrK+US11UTRT9FBQ4mDt3LytXjkMQ\nelNW9j4XXjhAM/5RowqQ7QXksHRFxQ7GjBlh6Vi2batj9er9lJVJXHLJKUEDxylTRjNlitxL7JFH\n3uaLLy4EQBBKkaRFjB79OGPHjuWcc/oyfvzQsPU+8UQrra3zEQRoaZnFk0++EmZJIAjtSNIHwGxg\nJ/A+Q4aM57PPZgW+L+Ptt0exd++/2LTpm3R0+PH7l+NwnIMgeCkqWofXO5OA5RMDB/Zj4MB2yspu\n48wzx3LJJVehTKBXX306zz+/FZ/vG0AR4MPlGsjTTx/kqqv64feXI4qTgEIkyY0gbABOxaivcUeH\nB1EsCWrSBMHOuHHD+cEPZkU91ldddS5XXRX+eWVlbx5+eC7Nzc2Ulp6oqRzq3bs33/72dh55ZAtN\nTeOBP2OzTcRuH8/UqZEb6hrhmWfW0tp6JXa7HEHzeo+yefPTca0j25BsGXy2idCPd+iPvezjZF5D\nNG/eAubNC2+grX7Jr66eyPLlrwBQV7eHhQvPwW53JPxZppAnTklBPcFHvuHlqievRfofZbvpF4eL\nooTH06UjfgVxRcoyBX3l2c03n8FJJ33G4cOdzJo1iLFjtWLHJUtmUF+/lnffdVFQ4GPxYnjzza2M\nHz+ASZNGG21Cg717D7Jr1yGmTh1Bnz4VYd9//vlufv7zLtrbz0QUPWzatJw771yoOZbyWL1Ikh+5\n1xk4HKVcdNEYzj77FMPtCgJh0RCvN/w6q6yEI0fmIz8C+lJa+ikOh4jb3Y3XKyAIEkVFh3n//Sq6\nu9cDDiRpL717f0xRUSETJhTi8x2grk7Z7lGWLh3N0qXqijb5whk3bhR33FHPzTc/hd8/goKCFioq\nzsbrXU19fTOVlXMpKhI5dqwFn683ouhl1KiXuOiiqWEvJZMnj2PGjP+wadNSBMFBv37/4YILws/H\nxx/XsGNHI9OmDQrTkelhs9morDTWoV155SzOP/8wd921gtdf/wqi2I9Jk/7Nt771Ffx+P2YvfZut\nGKcTvF7leBUyJNy3NMVI/TPDuvYheQKVPhhfF62trfTqlZz4ura2htra7WzcuJ4ZM06moqKC8vJy\nVqxYTlFREVVVctFLMp9lAoIUJedz5Ig7nWPJCdhsBB+WXm8Xfr8vWPqth17/I5ezJ6//6e5uR5Kg\nsLAk9o8tgCiKeDwdwb8VT6lUVcqJoh+PpxO73YnTGb93iBrh50BLWv1+H198sYfXXz+AywWXXTaV\nXr1COqbW1lY2bPiCe+4pwO2ejtP5BVdddYBvfONkw+0B/Pvfm3nkkXK83lGUl2/m5ptLmDhxOB5P\nB3a7A6ezkDvv/JDXXptFY+N7eL1OioqaWbVqCgMGDNCYnnZ3d/ODH6xh06avIghe5s79N/fddx52\nu/Gx7+5u54knPuT//b8peL3DcTq/5Nprt3L55bMDx0MuPf7+99ezZs18urv9uFw2Jk1aw/z5R7n7\n7l74fGchCPsoLHyIzs4bEYSBgfNSg8PxEjbbBCZPPsBdd83m8cf34XYXcNJJIpdf/pWo1/WDD77L\nk0+ejCAMxOms4brr6pk3bxxXXllDQ8NCJEmiu3stX/3qTn7wgzPo3VutJwtNqN3dHl54YQNdXQKL\nFo1h6ND+mu08/fRHPPzwGDyeMZSUbObnP29m4cLE2ohs3VrHLbd8waFDfejXr5bx40XefnsCbndf\npk//lIceOp3i4uKIVXUKGhub+N73tlBbuxC/X2L48KdZvnwxRUVFUZezEoodQayxpgvRPYXUyI3I\nlByB9yNXp2X/y6SCSNfFf//31dxyy+0MHDjIaLEej759yyJ+lydOcUIQQJmzvN5u/H4vLleRhkQY\nmyc6LWvC293dgSSJFBaGVxBZiXDhNwHClLzwOxqsIE5KtaLXG9mtvLW1hXXrNvPAA3Ds2EIARo58\nlUceOY3CwkLuvvsd3nijP3V1e3A4zqF/f/lG6t//LZ5/PnJq6Ior1nP0aKiJ7JQpq7nllpM1xOne\ne9/l/vs9tLZWIordgI+FC7fz+OOXhaVyfT4/b775CS6XnTPOmB6RNEGIVL///hds2XKU6upy5syZ\nqCo/9wMiTz21gfvvn4TPNwxo4lvfehu3u5BXXjkdj2czdntfurs/oKvrEny+YmS3aQ/w/xCEyQhC\nXy67bCN/+MOFwW2/++42PvqomX794NJLZxqOc+XKT3nxxa1s3z4Ql6uEs89u4+KLJ/D003X4fA7O\nPbcXM2eOQ2kfEj26aixCvvjitXz88Sb8ficOh8DChZN45JHEqm+uuOIdNm2SheSi2EJ7+zpcrsXB\nY/nd777EjTfOM0VGDh1q5J//rMVuF/nWt6ZpegOmA9lGnIygJVPR9KPqyFZ2iNCVe6unEKdLL/0m\nf/vbE5SWpvc6zRZEI075VF0S0Hs5GQmmU+GUHa+2Kl6Ek47glrHb0/HQTU7jZEaD9f77tdx2Wztf\nfOHF7Z5Pv37dFBcXsnv3Qt5992MKC1289tosoBJRbKelpZTi4g7KyooRxegPRb9f+73PZwuzkfjm\nN8fzpz+txe8fjSBMxG6X+Oijal59dT3nnTddY3rqctk591yzjcTkgoX58ydz0kkdvP/+UV5//Ril\npR5OOqmckhJZlHTZZTPp1+8zNm/+jBEjnFx88Xwee+x9BEGgqEgmGQMHfkhX1wYOHjwFUfTT0bES\n+AaSNARJ+pQPPzwQ3OqKFZu4/fbBeDynIood7NjxGr/73cKw0Q0b1ovPPptLZ+cUAJ599ggnnLCR\n//u/UwFYvXobP/3pBgTBz9e+1ouZMydwzz1r2LSpmNLSLn70o2FUVw8lmgj544/fxev9CTAMj2cb\na9f+BUiMODU1hQSoktSKxzMoqOsSBDttbebvhwEDqvjBDxIv5T4eoNgjyM9PEfl6tqM91/p/B/6V\nr+iLG6G4Sfhx6uhop6QkPVmNXEOeOFkEY2sBZ069eUBki4Tu7va0PYQS9aoKF99HrlZ89NHDtLae\njcOxBp/PSXOzB7lIo5k+fUrYvbsZSeqDIECvXn3o6qrF6x2CIBxg0SID1bIKp5/ezcsvH8Vmq8Th\n+IKzzgqv/ujXry/Dh7upqRmOzSZPGHZ7bxoaRFyuYkuO9aZNbny+obhcEh4PbN68j1mzQlqes8+e\nytlnh37/ne/M4siRVWzcWEJJSTfXXnsCXV3d3HPPY7S1udm8uQT4DNgOdGjeUN95pwuPZxwg63k+\n+qgvPp8vrAfdzp2H6Og4K3iO/f6+7Np1jL/+dR0HDjTy7ruTEcXZgMSOHRs4/fR/8+yzFyII8tvf\nr3/9L55/fpgqwhue7vH5pgPDAt+fQGfnxEA0IP7JtLr6GHV13sC+9mLIkGU0N09BEGyUlGxi/nxZ\nv9be3saBAw0MHjwwo9U+PRF5r6nMIX+sjJEnTklAuajURCMRa4EEthz4f3RRejzQNxMO34/0CtJl\nmNuekflmLA1Wd7c86ffufSrt7cvx+aqRJBuLF29nxowFDBvWxHPPreHo0XmUlU2msnIZ5523jenT\nh3LaabMjrhfg+9+fzahRn7Nv3xamTKlkypSJuge9POaf/GQM11+/Eo9nMS6XhyFD9jB79mDLHlZd\nXTbd39E1aTabjRtvDPkqeL1e/uu/VvLFF9/F728ElgXGfwDopKoqRCBdLo9mXYWFXYaputmzJzBo\n0BoOHpSjUaWln7BuXSNHjlxMS8ta3O4JDBzow+Wy43bPYMOG94OkCWDfvmEcO3ZMJeYOr+hyODxB\nEbY8tm7UaZ94JtPf/vYMKitfZf/+QsaO9TFy5Ancdtsj+HwOzj8f5s1bwj/+8TZ33OGgs/NEXK6V\nLFrk49vfnszMmeMN15lH8sh7TaUD+WMSCXniFCdCDVblSjnl3+lsLaKOyCR7v0dqJqzfj2TNPuOD\nuZ0y0pKZNd889VQPdXWy3cCgQSdxzjkrufzy0xg27EwA+vWr5M47u3jhhVVIksDFF09i3Ljw0v9I\nmD//RMPP5SbNsjbu3HOnMXJkPU8++QZ2ewmLFpUzbdok09uIhT59fBw4IHtViaJIRYWKTZgg3a+/\nvp6NG5ciCC7kZrr9kA0wzwA62b37qWC6+KqrxrFjx6vs3j2D8vJ6vve9QsNzUFXVhzvvbOaxx/6F\nz2dn7NijvPjit7DbbTidffD7W2hrK6dPnyIcjl1UVxezY0cbNpussxg0qJ7evYeHrRdCk+nSpW08\n++y7SNIM7PZ3+MlPytCme8xPpi6XixtukMlkff0hvvGNRhobrwHgpZc+Z/78Wv74x3ba269Ckrro\n7KzmlVde4bPPnNx1V22ePCUN8w+47PGaykXCkYtjzhzyxClOyBNfKLoBsojX6TSeKFKD5H2OjCv+\nQroa423Gb/aZKuhTivGab15zzekMHryRnTs7GT3awTnnfBWXS9viY/Towdx882BLxhvSwYn4/bJ2\nw+l0MXXqBKZNq7ZkG3pMn94fm+0ALS0CvXr5qa7uRzzXjDxmAb+/CY9nC9AOXITsoA3Hjl3Kxx9v\n4ytfmcjw4QN4/PFe7N5dz4ABVRHL+wGmTh3NvffKFgJbt+5i2bIWoDclJZPxeN6hV68m+vcv46tf\nhYsvvoiCgpVs2lRCeXk3P/rR8KjieIC7776U731vF+vWPcvChSczYoRWH5boZPr++zUcOXJ+8GWl\nre1E3nvvCbq6eqFUU0E3othGa+sM/vOfV/PEKWFY85aWXq+pXDTANB6zbLWRWzKTdCJPnOKGGCBN\nAna7A7/fiyDY0hrqTabBsHHFn1Ez4cjLp3pfo+2f3nXdbncEhN/x3+RLlswIlMC3k8qHniJWV5CK\nggEF6sIBm83GiSf2RxRDhFcUffj9PhyO2JHR4uIi/P6HOXq0AkmqBrYEtD4SguCntLSS5ua64O+L\nioqYOHFcXOOdOHEMS5asZvlyL35/CXPnHuHOO0+noMCOHPWxc/PN82OuR8GxY8doampm7NhhTJw4\nxvA3iU6mkyYNoqxsM21tsju507mXE0/sx4ABn7J3bxNyP7wWJKkYt3sZJSXZ+BafjWNKL6z1mspe\ne4Rk0NbWdtxW05lBnjjFCUGQo0s2mz0YtUln+5NkkFhvPBnKhJwp6FOK6UyNJgpJEvF6Q2J1GULS\n3lSJoq6uiU8/9eL3O6isbGTOnEERozebN+/m97+vpL39vxCEQmy2tfTpM41jx57F5bqAggI/U6eu\nYc6cryQ8Hr/fz759+7nqqilcfHErnZ3NTJiwIPCm64u5vB7PP7+ee+91cezYECZOfJsHHzyZfv0q\nqa/fz6FDRznxxHEUFho3Do5Gpr74Yj9r1+6npETkuuu8PPdcHX6/ncWLu1i4cC5jx/bnoov+REPD\nbKAUQTgfSfqQiy7KzHk2Qq48ozKJyCJ0s2QqRL5lGUXukqnW1vC+nHmEkCdOCUDW0Kg/SfdDKb6I\nk5VRmvRBfhDFn1JMDFZOLEamm05nAR5PZ8Yepj6fj08+kXA4BmO3S7S0wNat+5k82bhf2gcf7Ke9\n/XzAg80mIEmnYrOtYfp0G2ee+Q52u8Q3vzmDkpLEKsjc7jZ+/OP32bp1BsXFDXz/+61ceqnshp7I\nufD5fDz0kIeWlrMRBNi6dQz33/8SQ4a4eOihQXR0jOeEE9bw0EPTGDgwsuNwY2MTn3yyk+rqoQwd\nOogdO+r53//toK1tIaLoYfr013n11bnYbMpEKTJy5CAuumgaL754LqIoX7MFBS6KigoSrubLIzsQ\nH5lS4A/8DrJfhG5sR+B2t1JWlidOkZAnThYg3W9zoZsv+nb1EY/kojTWV/JFh/zW5vF0qKqlrGki\nrIbV5CuW6Wam4PF48PuLUNwBbDYbXV2RxzR8eDGCcJiCgt54vT5stp0UFBSzaFEJ11wTvarQDB55\n5BO2bVsaGMcYHn30XS68sC3Y/fyLL/bx2Wd1nHLKBIYOjd0M1+PppqMj1B5CEATcbnjssWK6uk7G\nZoPt27/Bww+/xC23LAgs4+GPf1zLnj1FDB7cyZln9ud//7eTAwdOpVevLdx0034OHZJoa5Mb/dls\nLjZsmMzBgw0MGTKI5577kLVrOykt7WLp0mF88MFy6usXY7c3s2TJdvr1W0ii1Xx5ZC8ikyklSmoj\n1yv68hGn6MgTpySQjRc8YFien1xvPHUln5R0JV8sKJExZXup1ATp0dx8jL/8ZTOdnS7mzCnhzDON\nq+P0MNf4OBOWDjKKioooKzuIxyM/DD2eVgYNimzeeM45M9i27W1WrSqlo8PN2LENfP3rJ3D22ada\nMp72dq3HWUdHBR0dHZSUlPLyy59w++29aW1dQmXlR9x6ayPz50evNiwuLuHkk7/k7be/giAUUFS0\njdmzHaxZE7IyEAQhaEMB8Lvfreally5CEFxIkp833riX5uafIgjQ2jqbxx57mcWLnRpdn8PRQVFR\nCcuWrec3v5lAZ2cvfL7XWb/+DZ599nLefPMV+vYt5qyzFhE63/nS+MSRG8dCLWUQBPnFNJftEVpb\n8xGnaMgTp6SR/skwkng6WeF3jK0muXxsGGmCIvUBTMHW8fl8/OxnG9m1awmCILBu3U5cru2cfnrk\nqjdj7VUmGx8bRwYFQWDevCo2b67D67UxaJCTIUOiu1hff/18rrtOCi5vJRYsqOCddzbT2TkZSfIz\ndepmKitlN84nn2zD7ZZNMpua5vDYY/9kvgl9+J/+dDZ/+ctymprsnHZaBfPnn86aNa/x5pvTEIQC\nyss/ZvHiULXfjh2lAasFebJrbR2oWZ/H4+DSSyexadPr1NWdjiAc5cIL91FZOZuPPvqc9vZufL4n\ngGHU1Izhmmte4JVXrtWNKu8zdLwie+wRTI1W85fb3Up5eXINfnsy8sQpAaj9k+Q3jcyW6Ru1eklF\nWkvZltUwipApOoL0TBjyNg4dOkRt7UQcDvlvn28sH374FqefHr5ErObBhlsRIM1ZXQ0KClycdNIA\nlH5aZmDF8ff5fDQ0NNCrV69gpc7s2RO47bZa1qx5nZISH1deOY9PP93Nffft5fPPu/B4XqWw8EwE\noQi/39xYXS4X//M/Z2g+u/feRTz66Os0NQnMnz+QU06ZGPyub992zW+HD2/l4MEaOjsnYLMdZsSI\nnVxxRTednRKTJj3Of//3XMaMkdOUffp48PmWAROBrwJuPv74L+zfv4/Bg4cYji+Zaq70T6SZRs8U\ns6fXHsEMjI+z2+2moiLfHigS8sTJIqSjTF+BOuIUniJKjfA7VT3xIhlYer1dmpRdKqEQmvLycsrK\nvqSzc1RgfF569dKOwZikFmSFjkkPZUJO57Wpx9Gjx7juuvVs3TqZ3r2/4Npr/Vx0kVzOP3PmeGYG\nLJa6urr4xS8OUFd3MR7PLjyejXg87+JyHeaUUxLfvtPp5Oqr5xl+94tfTKOj4zn27Clj8OA2brll\nDnv3Hmb9+q306dPNX/4ynaamuQDs2XOQOXM+Y8yY4QD85Cen8+CDm/B6v4o8gZUD57Fp04aIxCkS\nkq/mOl7IVPYiWs+3WMhGe4TW1laGDx+d1Dp6MvLEKUlkpkxfvilE0Y/Hk64UkTlBulkkY41gPQRA\npLy8nGuuEfn739+ms7OMKVP2ccUVoQhGeHVivGOWt5NKhHrA+QNWGfL2Qg9X9dtt6vHQQ5vYunUp\ngiDQ0jKBv/xlBeefH97DrqHhEHV1EwAQxW3AJTgcnRQUONi69WXLxrNmzWY2bz7C1Kn9mDPnRP7+\nd20j4pEjBzJ3Lrz99gaOHj0xeDx9voHU1n4Q/F1hYSGzZ/dmzZpVyMfSCwxkxIhRlozTajKVR+4h\nk4RaTtXlNU6RkCdOSSO91WZyWksxU5QitkixGok23tXDqCee02kUIUt3FZ+MCy+cxpIlPrq6uigt\nldM6+lRi5nVMkaG8+SrXiDrtqX24ikFn8FRGK9rbtcSyvb134NhqzfX69x/A0KHrqa+fgCQVIAgS\nBQV2nE4Hzc3G3kvx4tFH3+POOyfQ0XE6JSW13HTTOi6/PFzs3t3dzYcf7sPp7KS7+zzsdhsFBXuY\nOlWr+ZgyZRBr156IJA0CvAwbdicnnHC1JWM1QjITqVFqKB+Zyj0kR6YgPN1rfC20trrzGqcoyL4n\nfw5ATR7U1Wap3aY8eXd3tweFyCDgchWlyQQyuYiT0qPN4+lAFP0Igg2XqwiXq9AwrWgVUUsEDoeD\n0tJS3TH3IggCTmdh4Jhn162jjFUhpMrxtdtdCIID+R3JgfahKyFHwPzIpdQ+JMmHJPmRJJlYJXtd\nz51bjMu1IzBGH9Om7TZ0JC4sLOT3vx/AySe/RFXV5xQUtFJQ4ECSOjnxxLakxqDg8cfdHDs2hu5u\nO83N43nssdaw30iSxA9/uJLHH/82Hs9IJOkpRo16huuv/4yzz54GyK7Kfr+f3bt7U1HRn5KSbkpL\nJUpKJqedjAiCEOhcYEcQHAFndweyhs1G+H0rkapznRrkyV0sRL8GjK4D9T0f+DRwHfh88mdutzup\niNO2bVv4618fTHj5bEc+4pQ0UntjK95AclorJPz2+TwIQva/NSZuYGltajAa1G1KlDHJqcTUiO2t\n1BwZaa6AoF+XUqEY0sXZCPVVU944Uxf2P+ecqTidW1i3bgcVFX6uvnpBxN+ecspYnnxyLF6vl/vv\nX0FdXQGjRvm59lrzLVei4dChLiRJKRW3c/BgZ/C7jRt3ce+9dTQ3C2zf3oXD4cDpnAxMZsaMl/ne\n9+bgdrdx9dUr+eyz0fTufZTBg48AUFjoBAQqK7ssGWeyMI5K+JEnTPV9lddMWY/sOFaRI1NgfK+L\nvPPOO9x6660MHDiQ4uJi3njjdSZNmsy4cRPo3bt3XNuvrp7IE0/8I/j3oUMHuf3231JR0YeiomJu\nuumXAKxYsRyHw4HP52PRoiVxfZZJ5IlTkkimb1wsRPMGkvUrlm8yIuLdz3RW+lkJfSpRFtsXWDJm\nq/Vw4WN1Bom2IAhBF2ubTUBuV6edKNOloViwYBILIvOlMDgcDq67bh5yGb51j6jKyk5aWtYD04EN\n9O8vE53Ozk5uumk/X375dUCio6OBwsK3KShYgCRJFBbKpP8Pf3iX1auvAmw0N4PP93emT3+aXbuG\n0LdvEzfcEJ8oPL0ICZC1PkPZKkDPxuhXNGT/ePUidK1pp51Ro0Yzffp0duzYwf79+9m5c2dw2X79\n+jNv3nx+9KOfmt5WYWGR5rMrrriKqVOnB/92u900Njby7W9/l2eeeYLW1hYEwWbqs0ynEfPEKWlY\nHxkx0gHJEQR1eij1QuNEkbyIOrWENBJ8vu6c6IUXTXOlNBP2ersD4Xu5AbXdLgvGlWtGbqOiFovL\nyNUKL58vXHCuxyWXDOS++3x4PKsoKCjnm98cDMDhww3s2aN4dQm4XH2RpM/p7u5k6tQGrr1WLhBo\nbi5ArW5oaRnFj37UwciRA6isTH+aLlnk6rnOw2rIqb4RI0Zz991/RhRFvv71r/GTn9zAjh017NxZ\nS23tdj79dGNS0fJt27awb99eKiv7MmvWqdTUbGPIEPllY+jQYXz++WZcLlfMz7Zs+ZzZs0+zZtcT\nRJ44JQBjjZMV6xXx+bwaP6NYwu/0lZrHJojZZwYZG2pth9/viyOVmH7o7Rv0YxVFKagXk3UrxsTa\nZrMHl9FrW5TIlPq6snKCtfqY1tUd4mc/+4Q9e6oYPLiJ3/9+AhMnjgh+f+xYKzfdtJbdu3sxeHAn\nv/hFHYcPO5k0yc7ixXMB6Nu3H8OGbaCuTiZPPl8jolgCTGHbtn9x7FgLlZW9Oe20IpYv343HMwoQ\n8fvX8/WvX8jw4bXceutR5sw5wdJ9ywTyZCoPm82GKIrMnXsGc+eeYck6y8rKmDlzNqNGjeGuu25n\nwoRqWlqOBVssFReX0NBwCJfLFfOzw4cbLBlTMsgTp6SRfMQpER1Qum0QohHERMwgswHpt0RIvFJQ\nn7bVpz1FUUIUxeC1AxJ+vz+gbQlflxwNFIIRKeX/DocRmZL/bQWZstoV+7bbNrFx47cBOHoUbrvt\nWZ5+ekTw+1//+j1WrLgcQRDYsQMk6Ukef/wsuru7ee+9T6moKGXixLHcdlt//vznFzh6FLZsOQD8\nGEEQaG39ET/60Z28/vqVfPObsxDFdaxd+zEbN35BU9MPsNt7sW/fGB544KUeQZyMkCdT8aCn7Jv5\n/Vi9+i2WLXsheF4XLVoSpkMqKSll1KgxAIwdO569e+soKSnB45Ej5F1dXZSUlOByuWJ+VlycWGNx\nK5HdM1sOIJmUUnhT2Hh0QIlPwokhnCCmtqlt6sTh+lSoEnnJRv1VeBRPa98gkxoRfQ9BNWkSBBs2\nmwPZ6FMKRqPkf/t1EVQtmZLPqS3NZMo8mppKNH83Nmr/3r+/VHNO9+8vo7XVzXe/+w4bNizG6Wzk\n8stX8H//t4iZM8fz6aefcu65LuA1JMkF+PF4ioL7UlvbytatA2hutuHzbcTlkoXrHR0FcY0715En\nUz0BynnRHnvlBcws5s1bwLx54SJG9fNi1ao38Ho9LF58Pvv372P27NNwOJwsX/4KAHV1e1i48Bzs\ndoepzzKN7M2h5AwSm+D9fh8eT2dAkyJP2gUFJaYjHpks1wfFfFM//mIcDmvIRyr2L5Ilgs2mvD+k\n9mDGs08hK4SOQApRa9+gRJhEUQw+oEKpvO4AaRKw211BfZxMoOyByFpB4D+Z6KpTd6LoD0bjfL7u\nAMn0IacHlYioHbvdht1uw2bTRo3UD8z4yuWVMml/8DN1ubwRJk1qR5I6Atv1MmlSi+b7ESPaggRS\nkiRGjmzh4Yc/YMOG7yAI/fD5TuCpp05gz546AMaMGYPDcRQ4HzgHmMHYsfL6X3jhA15+eTGtrQsQ\nhPPw+Srx+7/AZmvi1FOtsUzIZSR2ro2tEZR7MXcIVfaLw82ivb3RTf74AAAgAElEQVSdkpKS2D+M\ngtraGmprt7Nx43oATj75FJqbm1i16g0GDRpE3779qKiooLy8nBUrllNUVERVVV/Tn2Ua+YhTglD6\n1cU7weuF05ENIGMhdREZw60FU0IiHk+nBeOPucXA/5PfP6PImNLaRa5Q9MVaRVoRboUQaukiR3qk\ngJ4pFGVSyI5yvGw2R0ydlhI1UJ+7UJsWMVCVF4pMhQiNepJUolPy3+rIlGLxoKw3/sgUqAsgjNJ8\nv/zlmRQVvcquXQUMGdLJTTdp33xvvXUe8AxfflnGoEGt3HLLaTz44EbNPns8FbjdBwBobGykpORU\nOjo6kSSBgoI+9O07DoCGBh8gl2UXFxcgCEOZNevvzJ8/hu98R+tAnoeM5Ew7Cb4A5CNT6UNra/Ku\n4ePHT+C55/4V/Luiog+XXfbdsN9dcMFFCX+WSeSJU9IwN8EbC6eTr9pKV9WZ2gNEidakw7E8WUSz\ndNAj9ccy+rViZC+gHqtCZJTIjzJmdWsVQbAnlSpVkylF06+QoRCJikSmtHqpRMmUvkw62gRrt8MN\nN5xBpFRfcXExd999ruaziy4azb//vYoDBxYiSV5OO20V1dUXAjBgwACGD/+E+vrxgXE1MWaMfI3P\nmTOIF17YQGvrSQCMHbuO++67kF69euXQhJ75cSZKnOXf5dN81kN7/NzuVsrK8u1WoiFPnCxCpElX\nXzquCKeTrdpK18NCnf5RIBOm1Da1TdaOQJJEvF5P0AAyWmQs0w9eo2tETUr1USZlGVnkHTK4tNmc\nKalgVEiQOrOvJVNqQgWRyZQtEKW1BfdJXpeWTKm2bBANg/g1U9pU4sSJI3j0UYFXX32RoiI/V121\nJGhlUFRUxK239uWBB5bR3l7ArFkdXHaZHMWaNm0Mv//9Fl577VWcTh9XXDGKXr3yE4wV0JMpSfIG\n/hWdOMu/zSYylUskzvjZ2tLSku9TFwOCFGVmOnLEnc6x5BTs9lCarqurDUGwUVAQUvvrS8etFU6D\nz+fF5+vG6SzAbncmvT4j6FNGCgoLw1tmWA1FQ2W3O3E6zQtvjSr8YkXGZC2PB6ezMKWVgF5vN36/\nN9gmRyE/ik5Mn0I0IkwQnpaTr6nM2ycYRaWMHs7hZEqp5COwnD8QQZMNMKNZbkR3Qw7bMpHIVKL7\nK0fGrDXqTAVCzuH2FKTVrYdCnGSdlPpz/TmOdL7TS6YkSbkfU/tCaSUiXRNvvvkfampq+PGPf5ax\nsWUD+vYti/hddt/tOQLlwQ+hSECqHbNTKQ4PN7CUDTg9ni4ieQNZj/g0TslV+KVXLwaxTUKN03Ii\nfr8veA5kP6bseVDLZEhLUMPJlPIf6CNT8u+V60sIkFgbkSJToUiY/Hv1NgP/ivAfwfXJy4WEy9ly\nLPMwRnytRPSRKbCaPPdEtLa2UlaWb/AbDXnilCAUcbgMARAjEo7UvOFZP9nH0mEp3lHpM900Rwzj\n0TFlEsp4tMUBWpNQJcqkJqgKKVQ38JUJYfZHDuIhU1ooWjqFINkCbudS4JqQL4xQK5lkyJRf9bts\nSvsc7zB33K0hz8cjmVKOg17j5Ka8vCL9w8kh5ImThfB45Kah6WjXYWVLkkQMOFMNM5u1yqk8HdYO\nSiQSCBACIVgtJ39mrGOSo0ze4HrkyKUtpx/uCpmSj4mkOu6C6ro2IlQhwXm4YSdYS6ZQ/S5PptIJ\nK55peTKVONzuVoYMGZ7pYWQ18sQpCSii3lBFU244ZitQIhnyPphJK6qjXKl+kESOqFnvVJ7aVJ0+\nIiYL1UONg0Ou3/q0nDd4XrItLZcMwgmhEPSSUv9G/r9efC7FQaZC12liZEokOpkK/pXWKGweiSG1\nZErSbSN3YYUdQU9HbszwWQi/34vf3635TI4gpKs8P7mIUyLpLXVkJhPPB2P9WIFlgnuroa/sk0vz\nRZ0nk9b1O5fTcmZgVqcVsimwI1dWhYTBxtYIejIVrZWMlkyF2yJASPsU2m60qJRs4piPTOUarCNT\nPQdudyvl5XmNUzTkiVOCUEdoZAdnX1rJROiGj484hZfpx5PeSp+IWp+KjCWmziZEquwL+TSJCIJo\nkJbzq8w4w6MwuQwrCGHIYwoik6l4WslY0ZdP3Qswn+brCUiOTHnJnTSfcZSstdWdjzjFQJ44JQib\nzYnTKae05HJya3LzqUIsr6BshSRJwTJ+SFzHFA1W6sWiuX4rURG/Xz4PoUmcIJEFc67fuYRw+wTr\ndFrRyZSRYad2WXWKTyZT5vvyhX5iC2q2Yk2s8noySaZ6xjWVbsQmU5EJdK5ppuSIU544RUOeOCUI\n+aEb+nemEGuyN/KTUnsFxQMrCUYsqB9KCslQi6mzDeZcv4Wgf5NxigkUk0h54s/t6ITe1TxdOi21\n+7l6LGoypfT4i9VKxohMhWwRtPeBeUfs2GQql8+7dcjuY6AmU6HWMMqLaO4K0Nva2igtjexhlEee\nOFmE9KWwgls0cZMphEmZuFLhJ5UK6J3Ks3nc+kievqJSWy0nEyqFzGojIALKxO73h8iU0USejcdB\nDSNXcyXKlCnoyZTdTpAMxdtKRoYYPH+K0FzvMaXerhpmyFQ2T6ypR/ZG7o2gfpHMlWq+0JjC1y9J\nUko6EPQk5IlTgtDrJ+TP0n3DC4bbDI9+WOUnlerqM+24FTgcrpRsL4T498vIcFNJfRq5fivPv2jN\neNVRkegNdrXO2/Jn2TGppjItZwS3283993+E2+1i3rxenH32FNPLhirr4m8lo0CJosnrs7rJsZUT\na26RkZ6EXCFTeZhHnjjlMNS6GUi9jilVfkeRojbqqr9UIt79ilWRmGgzXnVUxLjBbjw94dL7cM1E\nWk4URX7wg9Vs2PAtBMHGypXbgc/iIk96RCZTooYQhsbg1wje1efAZgtdD8py2UWm8sgUsptM5a+f\nWMgTJwuQuYiTDKWqL/UGltZGnEL6KzktF2nc2eKRIxO87oiGm5FdvxNvxhttItcSqkhkyijNZB0y\nmZY7fPgwmzdPCm6rq6uaNWt2cfbZ1m0j0v7J0d74z4GR+3k6yFQe6UByxzkzZMro+sojFvLEyRKk\nX+OkhtfbGbzgs1kPpIYZ/ZW2cimViH7+IhG8aK7f8uepacYrT8Jme8JFN4tMZiyiqJhYWrt/ZlFW\nVkZZWT1NTfLfkuSnvNxj2frDTUi11Y6JnQNryZTZiTUEEUWInu3PiNxB6h5SqSNTxmNub2+npKTE\nquH3WOSJUxKQq54yE3ESRb/mwZoOXyMr9jN1+qvUQJ+W0xO8bGnGqydToWvDjPO2PHkr/kaxxmkm\n7Zhq1Nbuo6mpi+99r5N//OM/uN2VTJu2nf/5nzOSXrex51Tsl5HohDZab75wQmuWTMU3sYLshq78\nTlkuW9N82TSW7IE1ZArV70PIu4abQ5445Rj0/dkAnM7CNJfpx0+cEtNfqd+OUv8QVRNCvVGonuCp\nXb/Vy2eL67c2KhLbLNLv1y5rVMmnT1uBnLZKdwXOypU72LhxBHZ7GXZ7IY8/DuXlBVRVLU564pej\nhJFbwcSLEJlSnwOzhDY2mTLfSkbZJ+VcZbNmKp8uihfxk6ngr5AkLytXrqSh4TB9+vShrCxvRRAL\neeJkAdIRcVLe8tVu1HL1lj+NDzZlP80vYVx9Zs5HSi3aTuUu6vVURq7fxvYCoQkrF5rxhkgQRDKL\njORvRMAuIbSu9EeZAHw+Hxs2FOF0ym/FojiGTz/dwsUXD0lqvekSt5sjtIm0koFoZCp0/mwGzyuz\nUQobyjMgm67rPIxhRKZAOe9+1Of4qaeeor6+Pvj3hRcuYvz4CYwfX0119QmccsosU+3EJEnimWee\nYMCAQdTWbuPqq6/F4XCwYsVyHA4HPp+PRYuWACT1WaaRnfmRnIR2YrEKygO9u7sjMJnL+hqXqyjt\nkYxY+XE9RNGPx9MZcFaXcDhcFBQUx6HBSq92TJIkPJ7w4xwiTWKAWIgq0iQGncBBLRjPDefvkM7G\ngd3uxOksCOi3FHKrjlCEIEepvPj9Xk3aODNI/Dir9WvyeRXSrhMMnQN7MOXucBQExxG6lmRCK4o+\n/H4PPl83Xm93IALoD8oGHA4bdrucfrXbbYFrVQpuS7tdhYTZEQQH8ru0A5nUhYiSvLwf8CH35ZN9\nyEIELx8lypXUolYnZ0cQnDz44F+5/fY/cOGFX2XQoMEIgsC6de/yj3/8lRtu+AmvvvovU+uura3h\n6NFGFiw4i6qqvtTUbMftdtPY2MhZZ51Dc3MTra0tSX2WDchHnJKAOhIiCPFFYswgVtl7pqv5IiG8\nH54DpzObdUzKW7385m5kL3A8NeNVT66hY0OAQCrVZJmp5HM4HEyf3sGnn7qx20ux2b5k5szEGpLG\nEn9nEmaig6EooXErGfW5AsL2LXaTY1TblX8bTXx+/Pbly67nbyIoLy9n9uzTOHz4CBUVVVx55TU0\nNR2ltnY79fX1zJs339R6xo0bT1VVFQD19fUsWnQeNTXbGDJEjggPHTqMzz/fjMvlSuizLVs+Z/bs\n06ze/biRJ06WQUAtvEwGegF1ZOKRmWq+SEQtVprL6u0lC73uCsDlKg6zFziemvGCeUKRiUo+gEWL\nxjNyZD3Nzd2MH9+PPn3iE7MmKv7ONNQ+Xwqip1o1SytLBNcTX5Nj9Tr0ZEokT6ZyFdpz4Xa7KS/v\nDUCfPpXMmnUas2aZX5vNZqOqqi9btmxm4sRJlJWV0dJyjJKSUgCKi0toaDiEy+VK6LPDhxuS3WFL\nkCdOFkEpnU/GcyheAbVaA5QORNovIx2TnGZITiOSqgesfryK8BnkGz+y67eIKGZnhMIKxEso0lnJ\np8eECUPj+r0Cq8XfmYaeTNlskqovJagJk3xeo7WSia/JcWj9tuD3sfRS8u/MkqmecV9lJ4wnDbe7\nlYEDzesFV69+i2XLXgiev0WLljBnzjy2b9/G0qWXADLh8XhkK5euri5KSkpwuVwJfVZcXJzY7lqM\nPHGyDIlXgOkjNeYF1JmIOGm1XLHSidmGSPYCHk8noT5x8sNenZZT95bLlQiFWYSL2xMjFKmo5LMK\n6RJ/Zwr6SKggaI1IFTIUrwM9EKXJsRGZMhIix0umUt8tIDXI/WspXjuCefMWMG/eAs1ny5Y9z9e+\n9nUANm/eRHX1RJYvfwWAuro9LFx4Dna7I+HPsgF54pQE1JGeRCrAFH2CLEyVV5aIMDWdGidFy6W3\nRdC7aFu0tcD/k9+/8PEapz+93g6dRkSd9shM+X0qEe45ZW0ULZlKPit68uktFPSEoifATGo1lCKN\nvy9fpFYyqSZT4NcRqmxN8+W+xkmB291KeXlimkGA9es/5MMP36e2toampqMsWLCQyZOnUl5ezooV\nyykqKqKqqi9AUp9lGoIUZdY9csSdzrHkHARB7rIO4PV24/d7NVVY0SATJk9SRpBK1ZpSDZUOdHW1\no35QCIItmJazGn6/F6+3O1Bh5ExoHUa6K/V4lbSc3+9HknwBMbTRLaGklzLTC85qZJu43WgSNzoP\n8fTky7SzeaoRTgqTj4QaWSJEPg/hRQBmyFSk7YbIU7SIkzbNlw3nUpKU7gC5E8FUfL0EQftc/Z//\nuZabb/41Q4cOy8Swsgp9+0b2s8pHnCyC2Qo340iNK0GdRfpSdaHqndC2lLYjqXtYJLd/sdq6qF2/\n5bdoZ1SNiJwKSW8vOKuRrZ5TkSMi8feDA3JS/B0PwkmhNedQr1sDs0UA4ech0SbHIRKljCNyZCrk\nMZVdZCpXIUec8s7hsZAnTmlCeOQj+Ua86RKH66NjoK0+yzbIdgj6ti4FGsJk7PoduRmv+QoyLZHK\npod4JlrBJINkJnEF2b6P8SI8Uph6I9Lo58FsKxnzffn0y+u3GyvNlydTiaOtrY3S0rxzeCzkiVMS\n0GqcjCNO4RVnVkZqUhtx0lf52Wz24EMyHQ+ieH2qYtkhRHf9jt6M13wFmT/sukil6NkMjFM6uek5\nFek8aFvBhCB/7seKSr5MQ9s0OrMVgaHzYF0rGf0Ljfr+T1YzlQ4ylWvXk5GYXRRFUw7hxzvyxMky\nhJOYdFWcWS0OjxQds9sdeDxdwfRWtlSRKMRAcSg3qkqUrQS09gLJRGCiV5DpJ47ooudUPnC1k212\npOWsRnikUD6Hma7kswq5UBForqIyOplSf6Zcp+rIlHErmWwgU7klDk9nMVFPRZ44WQStOFLvnJ2K\nirPUvOGE2k8YV/klUj2YPCLf6Hqz0Ey6fus9dZRtma9csk4vlQuTbbKIpfNJvpIvs2RKr0fLtYrA\n6BWV8r4pxRj6ydzv94VFCM335QsnU6H1Zz4ylc3IcypzyBMniyGKfrq7zRlYWgMBK954wgmIVhek\n3R6WbDM2olfh6NOIanKaLc144xM9J++4fXyU38ev89GTWrs9XlKbngihgnDi2zPMVtVkSpIETUsf\nOT0OsSOERmQqFAE3IlOh45YnU5GQj0SZR544JQnl7Ud5iMsPOrMGlslDH+KOF8YEJHaVXzpuMiPx\nuzyh+IKVb3qRfSTX73Cvm8xFYMzrpbQpjVippeOh/D7cqDNxXy1zpDY9EUL19nuKHi0S9MTX6F6M\nnvYOweieiEamwqNSkDyZylVox97Z2UFRUVGGxpJbyBOnJCGK2pJ3QRBwuYqzfrIKERDZzt5slV96\n90sb3Yrk+m1kL5DqtJyVMKcPiZxaAiG439Az03KpNupUkHglX/I9+fTkvmfq0dTkPrLAPXbaO5qG\nMBaZUsYSKcUHiZEpLyEylZhha3pg/NLb2urOWxGYRJ44JQE5WtMFyA9y+S0xvWFcOeKkvfljIZa/\nkTmkL6wrSRJeb1dE1++e2Iw3kj7EKLWkhxK1sDoakglkQwQm0Qih2Uo+Y3Lfs3ynzESZYiFyhFB/\nT5hzoU+sL19ou4El0Bp2KmRKDPxOm+LTri+7EG+7leMZeeKUBEJRGhs2m52urrZMDykqjHVM8bmV\nKzd/OtLh6gnK7xeJZS+gPI96ajNe9cQRenNXvlP8plITDckEUmXymCzijRBGq+QTRSlACkP7mCvk\n3izCo0zWtS0yT6bMaddsNjRkSnkpVdarJ1OSZEMmSQJmDDvlZbKFTGm32draQllZnjiZQZ44JQnZ\nJFH5S9C8vaQHoWq+SDefXseUjGg9tI3U7qc6jQjh3leR0nI9uRkvGO1juDDaeNKIpJfKPl+jRMTf\nmUb0CrLolXyBNQTJfTzR42yGFVGmRGCldi1WXz71czB2mi/UQDyzZMr42Z13DTePPHGyEMkKtRPd\npozwm0F5cIXMN9MnWk8U+qiYAqVXnRnXb6vfarMBxqlH430MpZbM6KXUyyXfVDcZWC3+zjQiVfKp\nyUQIyufKsumv5LMS4WadmT2PiWvXIpMpeR9DL2nq9Rqn+UJdCGLppeTfpTcy1drqzkecTCJPnFKA\nbHhjjCWkThShELW1EadIUTGPR9aQGemY5M+1Bo89JS2nRnhFYHz7GFsvpZ44IB3VY3qkS/ydSYRb\nDMgRGPm7zFXyWYlMRZkSQXQyFa2VDKCygQnp7kJVvfK6oqX5tMcjG8iU2+2mV6/elqyrpyNPnCyE\nItRO9zZBqwfSmm9qhdTZhlhRMUEIicPVaSVAMwnlQjonXqRSNGwunZF6vVQ2iL9TDX20UBDCvbUy\nVclnJeSXmNyOFhpHa9GdA301ndy4PVwvFbsvX2bJlPY3bncr/foNNLFcHnnilCS0REmdNkvvA0y5\neSP1abMO1mmcYrWkEUUJm80e1PREIqVKM96eQpqMU1aprwg0/wYeXj2mHP949FLZKv62EolG0pKt\n5JMn7vRo1/S6u2yOMsWL0D7oK/BsgeMbHrlVLR0WIcwUmdKei0h2BHmNk1nkiZOFUEd/0v3MUAgT\nCIG0XGoeXCEhduLr0Kflorl+yw9gO0p7Br0uR/69F1HMfV0IJJ+Wsxpm9VJ6zY72PGj1Uj15olVg\nHElLPFoYbyWfJKHSS6WuJ58+yuRw9CyXegi/J40qH2OdixDCia0RmUptX75o4vBecRyZ4xd54pTD\n0D60UtdEWIvEI07KhKk33bTb5cswkr0AAWd2tTZEeRuPXnKcXamMaDBOy2Vfyipa9VioaiyaWSeq\nt/LcTOfEghz9TX0kLZlKvmR78oXrtXqmJs0s+Y1+LswSW2v68qm3HT0y5aOtrYM333yTAQMG0dXV\nlY84mYQgRVH5HjniTudYchKCIFfLAMFUmdNZGCQDqYCSlgtVWMkPZ6ezIGXbDG1boru7HZvNjstl\n3p4/Htdv9bbiIRNGupBwJJZWShUylZZLNbTi80jnQi14zm5iawap1KQlOy795G304mM2Yqt+YTPS\na/UEmIkyJbZeLbENnQ8tIkUJzZKpaNtWE+jVq1fzm9/8Jvh3v379OeGEiYwffwITJkxg4sQTKS4u\nMbVfzzzzBAMGDKK2dhtXX30tjY1HuP3231JR0YeiomJuuumXAKxYsRyHw4HP52PRoiVxfZZO9O1b\nFvG7fMQpSUTWOKViW/JbnlrHZLc7VGm61CPeqrpYYnUrm/Ea60LMpJUyk+IL17/0nJSVQoLkRq7a\nKFPoGookeI7ttp1tCC+/zx7ya64QIHYlHwiIYs+vfLQyxaqH3qJC2aa5iG18ffkibTvkAefk9NPn\nc8cdxWzfvo1Vq1bS2dnF6tVvs3r12wAMGjSYF154JeZ+1dbWcPRoI5de+h2OHj1CTc12qqqquOKK\nq5g6dXrwd263m8bGRr797e/yzDNP0NragiDYTH2WTWnEPHGyEFbof4yg3Mw+X3eQsCgpLvlvpSIt\ne2BE8sy4fodrfBInE7HL8M1MGNZHQnIlLZcs9FYRevIbLZURzW07m8hUruq1ohcCRPc1AiVd3rOu\nV336MV3FCso1bbcn20rGiEzJCDU51p5Lu93OrFmnMmvWqbz22mu89toqDh9uoKZmOzU126iqqjK1\nD+PGjQ/+tr6+nkWLzqO9vY1t27awb99eKiv7MmvWqdTUbGPIkCEADB06jM8/34zL5Yr52ZYtnzN7\n9mnxHdgUIk+cLIX1ESeZMHlUbVL0OqZQZUb6EFlgCCHXbz3JM0rLhQhTeshEMmX4VqT4YpGJnoBw\n/YsxmYil0VGuk9gTRvqPn3GKNbf1WkYRW+Vc6u93+T5VawnTW8lnJfTnMhtSrJGfU/G3kgFwOIRA\nFWso8qter1ov1b//APr3H8DcuWeYHq/NZqOqqi9btmxm4sRJlJWVYbMJzJw5m1GjxnDXXbczYUI1\nLS3HKCkpBaC4uISGhkO4XK6Ynx0+3BDnEUwt8sTJQsSbxooGufKsW9XYVlt5ptqqskTS2zQLQTAm\nauG98MLtBfRvPaEIQ+aa8aYjxWeWTOQyjM9lfGRCncpQFjM3YaSvEOB4EUZryYR8LpWXpkxW8lmJ\n8CiTXMWbjWNNJuWqfdkVgs9l5d5aufINOjraTY9l9eq3WLbsheBxWrRoCXPmzGP79m0sXXoJACUl\npYwaNQaAsWPHs3dvHSUlJXg8cnFQV1cXJSUluFyumJ8VFxfHe7hSijxxsgCSpKTpkicx4SkubeVZ\ntOXSBwF1R3CZ5OntBVwR03IKsrUZb+zKMfMpPkCnmTg+xLRWnsvoE0ZsTyOr9FLh+peeei5jEcPM\nVPJZDa3IPTfT5WZTruo5qaZmO3fddRcDBw5k7NhxrFv3PoWFhbz88grT2503bwHz5i3QfLZs2fN8\n7WtfB2Dz5k0cOnQIr9fD4sXns3//PmbPPg2Hw8ny5bJmqq5uDwsXnoPd7jD1WTYhT5wsRLIap/AU\nV+w2KZl98Mi+SlrX7wLNQ1bu/i6i1THlXjPeyDoEc5oQIBBhsuXcwzkaMlVJFpowYpd+W6GXCq+y\nyt7IRKLQRwzNkgm94FnpyZdIWikdx7OnRwyVe0Mm+aFnkdLex2azc+zYMXbt2sW7774b/P6KKy6j\nuvoE5s6dz4IFZ8W1zfXrP+TDD9+ntraGpqajLFiwkJkzT+X1119h1ao3GDRoEH379gOgvLycFSuW\nU1RURFVV37g+yxbk7QgsgN2upK8SLdXXp7gcOBwFpm/krq52BAEKCmKXjVoBj6cTUfQjCDZViNso\nLXd8NeNV9lW7j+HQThTpb6ZrFYz0WtlSSQbGeql4y/BTXWWVLUhHysoorRT5fKSmJ9/xYaWg7xeo\nJYY+n4977rmbnTt3cN55F3Dw4CFqarZTW7uNlpYW+vXrzz//+XomdyErkLcjSDPMl+pHT3GZRSTN\nUSqghOLlf4tRXb+Pt2a8EJ6WU3RM5sTO2d3AVUGu6LUS10sRTO2pneqzjRhagXQSw8Qr+ZLXr4VX\nP/bM54/WfDX8xfTLL3dz/fXXce65S3j44Uc130mSxMGDBygoSL0fYK4jT5wshL7SLRKUm1jvoJ34\njazVHKUCeu0VyNVyDocTiGYvkBuTbLII77umfWMPn7zNTBbZVakUnsrJvTf2eKoqdUsGtTtmz8f2\n7fv5+OMObDaR+fMrGTzYXGl3uqD3EctE+tG4MEPt8ZV8T77joy2MlgDrn7OSJPHMM0/z3HPP8sc/\n/okJE04IW4cgCAwaNDit485V5ImTBQiJwwlEFyL/ViZMnuBDwIyOKRaUbarLSq2E3vVbSdGpo0zG\n9gLHi5BWrdeyx+wTGP3N22ylUnpTfKkUf2ca6vMhT7Ki6jt1I1fzlWP19Ud49lkXNptcVVRXt40f\n/7iIsrL0pNOjIZvTj6EXjeR78snPRL8qZdUzX9qM/KfUkdGjRxv52c9+yogRo3jxxVfzESULkCdO\nlsM4+mOsY3JlNZGQJK3rtzJmn8+L3y8GdUzGrt8936vISr1WZLFzZlN8mRJ/pxuxIqPxVo5t29aI\nIEwLfu7xjGPXrhqmTRudzt0KQ7jIPfvvzWhVrrHOhwL5fPasNCsYVQZq78233nqLO+/8A7/61W+Y\nPfv0TA2zxyFPnCyGPvqj1zHpHbQt2mrg/5Lq34kj3BLBeFhIjqcAACAASURBVMw+X5fmLQ/Q6Zh6\n5hteOmwUzOlzoutBkk3xZbv42wpE8ivSv9DEWznWu7eA19uK3V6CIAj4fA307VuSsqhwLGRzlCkR\nRD4fYqAtTLhhp5r8p7uSz2rEEvN3dnZyyy2/oaWlheeeW0avXhWZHG6PQ544pQghHZO6VN+VEiKh\nfitOZtXKwzVkiSAEUola129BsGOzaUPm+re8ntqSIZPRl3j9jBI1IzxedGl6jU+8BDja+ZgxYzT1\n9dvZtKkYQfAzf76f/v1HBXSN6dWvGUWZehoBhtAzVy2MVgozYhlE5lJxRqzKwM2bP+PnP7+JK6+8\nmosu+nqmhtmjkbcjsACCIL/xAHi9Xfj9vqAOCKzRMUWD19uN3+/F5SpK+IGYqOt3rNJ7GdZFQTIB\nvSiaLGviqoZRCsNsyffxpEsLj76kxvzQ7/cHrvVIImcCY7DeabunRZkiIbz8PjrRNzaI1CN9TvRm\nEctmwO/388ADD7Bu3Xvcc899DB48NJPDzXnk7QjSBPkt3x/8t83mwOlMvY5Jq8OID8aWCLHtBcLF\nwqGHVfxRkOx9w8s1UbS5lFJ0o055PbFF7rmIdGt87HY1uU7GaTu+MR4/USZ1+b25/UxHJZ/ViGUz\nUF+/l+uu+wnz5i3g2WeX9RhvvGxFnjhZAJl8aEv17XYXTqcrg6OKDuXtRXH9NrJEiOT6HasZb/JC\n58y/4WU6LWcloqWUFPd3PeQIm2h5FCRTyKbzGa9eKp6U0vEVZVLvZ+JE38pKPqvvETM2Ay+++CKP\nP/4od9xxN5MmTbZs23lERp44WQBR9CKKHkB+ExFFf5oZf3wRJ729gD6VqHb9VhOmRNNVkYXOsb2M\nbLb0Ttx6sXA2p+WSgbpcW4HarDM6uc0tYW24x1b2RV+i69fMmkOiuj+zcz+tQDqiaclU8llFpmLZ\nDBw71syNN95Av379eeml1ygsLEx4f/OID3niZAEEwYnDIV/Yfr/8VptI2izx7Zs13hQDaTnl7UWb\nSkxnM97Y4XLl4ZS+iTtZsXCuILb4OzRRxI6CZJ8WREG82pdsQ+IpJYL3tPx59pyTZJDpaJr5SGHy\nLxyxbAbWrl3Lbbf9jptu+mVYs908Uo88cbIAcprLFfy3jPQRp1gwshdQquUUyOTIyPU7Pc14I4XL\n45m4E9UdmEk/9gTEK/6Ot4pPu1xmU3x6t+ie0BPR6B7x+/2Iolf9K5SoiN7IM9cihWpkq2bLXKTQ\nfNo1ls1Ad3c3v//97zhw4ADPPPMiFRWVadzbPBTkiVMPQDRxeCyncjOu35maeKwpv4/ssB2elssN\nQ8BEYJXIPbJ+LVwLkokUX3g0redGDSNF06zSS2UD9BKBXNBs6SOFYDbtqoWeHG7btpUbb7yByy77\nDr/5zW1ZfQx6OvLEyWIkU+GWPLTptXCn8gINYTJOy2V3M95owvPYFUrqSVvSpeVyK41jFqkWRUfS\ngqQ7xRc+wfZMKwXQa7bCNXjW6KUyn3Y10vhkK8GLhWhpV6MCja6uLq699kokSWLChAk0Nx+jtnYH\n9977AKNHj0vr2PMIR544WQQp2K8u/am6EFkzTsupXb97WjPe5MvvhWBaLtv3NV6Ep6vSI3I3P3GH\nl3vLxQDK+TQ3cR9P2rRENVvRoyDZlXbVR4JzIcoUL0L7ImiOt83mRBDA5ZIYMGAAGzduZPfu3cHv\nv//97zBmzDgmT57CFVdcTWlpaUrHuWLFchwOBz6fj0WLloR9L0kSzzzzBAMGDKK2dhtXX30tDocj\n5nK5jp73OpZhZCbipGxTpLu7I0CaBJzOAo0pZsgKQETdW05O53UHPhdSbtiZagiCPAErvfUcjoKg\n1UI4FKLZHfSzSre432oYe3O5MqoJkc+JXXdOXMF0hBIFVCKePp8n5jkJXbuewLVrw+Eo6JH+U7Kj\nvydAmgTsdmuag8vnxBnxnChpe+VlzOicWHmvKC9wyrVrtzty+lkUCeprF6Sgf57dLvfUKygoYvbs\n0ygoKOTXv/4tP//5r7jwwq8xatQYdu6s5fnnn+GLL3aldIxut5vGxkbOOuscmpubaG1tCftNbW0N\nR482smDBWVRV9aWmZrup5XId+YhTD4AYEPeoQ9pmXL+VN3V1mXZPjLwAYdE0WXBJIEyeexVjRohX\n/J1JJJ7iC+mllMgL9NxrN52Vgcmfk+T0UrFaifQUxLIZaG1t5ec/v4nS0jJefPFViouLAViy5AJA\nFog3NzczYMCAlI6zpmYbQ4YMAWDo0GFs2fI5s2efpvnNuHHjqaqqAqC+vp5Fi84ztVyuI0+cLEY6\nI076yAKAy1Vs0vX7eNH3RCcSdnsyFWORhefphmw1ofYq0lbj5ALi0+ZolkQpcJCXzW6CaxbZUBmY\nDr3U8SLoh9g2Ax988AG33PJ/XH/9DZx11iLDdRQUFKScNAG0tByjpEROBRYXl3D4cEPYb2w2G1VV\nfdmyZTMTJ06irKzM1HK5jjxxsgghjRMoJcGp25Y2zKuE1AENaTKuluv5ZfdgZHoYm0jEqhiLLTxP\nf3VSqsXfmYZamyNPOtoSe+X8qKNPyne5Wn6vtwHJthebRPVSRho2rZbpeIoyaZ9HHo+HP/7xD+zc\nuZMnnniOvn37ZXK4gEx6PJ5uQBarK5EvPdra2ti+fRtLl14S13K5jJ53hWYBBAFSFXASRT8eT2eg\ny7qEw+HC5SoOhtnlNin+wEM3pGOSNRLaJr498SEVisJptQOJTDwhfyhZB+J0FgR0CK7A+kImg6Lo\nC2qllKbLougLTiCp2E/tORXCUrQ9BfI59eo0WwUa/Zqs4XIEJnQhcE78qnPSFTR/zWYNm3JO5fs3\nd/SGZvRSRho2dfTFZnMSKq7pOQid05CGVP082rmzlosvvohhw0bw6KNPZwVpAqiunsjevXsBqKvb\nw8SJJxr+buXK1/na174OwObNm0wvl8vIR5xSAoFQyxJrHgSSJOL1elQ9i/QNhOVtejwdqgnfFoxI\nhCJS2fX2ahWM0nI2m/WpjdjeUmrHc/Vy1kVAcrUCMl6E+2wJOBzhZF95aTBOu8ZKJ9mw2eKr4ksF\nelq6KpJeSm8DokA+z6FK4Gz3lzKDcH2a9pxKksTf//53li9/lT/96QFGjRqTyeGGoaKigvLyclas\nWE5RURF9+lTy2GN/Y+nSS4KpuPXrP+TDD9+ntraGpqajLFiwkMmTp2qWs9lsPP743xkyZBiTJ0/J\nGmKYDAQpyqvXkSPudI4lp2GzEezD5vF0Iop+CgpKkr7pY9kLgOLZ5EOSwh9I2jHm9sM4EvRpuUzv\npzJBqIXn4anb+IXn4eSw56ZarSYSat8cNcHVIxMatuNJFK0XussRQq0APRy5VaQBes1huD6toeEQ\nP/3p9Zx44hSuu+5GnE5nxsZqFh0dHVx22VLuvPPPjB5tjuQdPtzAqlUrmDt3Pu+9t5apU6dRXT3R\n0qBCqtC3b1nE7/IRJ4ugF6sGPiWZ0HPIJkBeub7UWt2MV37bdgXN1PQRD/n3Ps2km0sPIiPotSDZ\nou8xHwEx75lj1Kg2l9/GIyFV5FA5TpHMU0NWHenTsPW0KFM0hJt2Ro4GJ6qXyobjpr9+jaLB//73\nv7nvvj/z29/+npNPnpmpocaN4uJi/vnP1+NapqiomMOHG6isrGL06DFs2fI51dUTs+JcJYM8cUoB\nQmJstWDcPMJdv43tBcw245XHEj1toX/TzuYLW+8SnamKo3igF9TqhefRJm3ld/Lf9h7pUwRKukbd\njyy1lYEhcmoLRovjSfEpfmGJTNrHc5QpVlo5VpFG5HR4ZnskxrIZaGtr41e/+iWSBC+++DKlpZEj\nGj0FhYWFXHLJZTidTgYPHhIUjTc0HKJ//9RXBqYKeeKUEiTmHq63F4jP9Tt65CXapG22TUk2TNb6\nyTVX39LNeeaEpy4UK4ls0OVYBeMoU2Yih+YrxvxIEhH6I0aetI+nKFOsdJVZRNNLhZ5dma14jWUz\nsH79en71q//lhz/8EYsXX5CSMWQDRFEMnuMvv9xN794VDBo0GFBeEOTzt379h0yffnLwu1xDnjil\nAPF6OSlvZV6vXAkmu35rKy+sbMarfxCZbVOSSQ+jnl52DyHhuSRhUHYfKjiQo21ovs/V0nujFGQ2\ndL1XI3IExHyKT7EoOX6iTNHTVckicjrciOSmrh9fLJsBn8/H3XffxebNn/HYY0/Rv//AhLeVzair\n28Pw4SOCBUm7du1k06ZP8Pt9nH32uVRU9AnOh08//Th799Yxd+6CDI86ceSJk0Uw5kixiZMo+vF6\nuzXhXXNpOWub8eqrxeJJJaUyKmVUWZWunmvphhl9j/Gbdu45nqfTEdtqRE7xmeuPqFhc9MTS+3Aj\n1vQR4dgVr5H1UolUV8ZKt+7e/QXXX38dS5ZcwJNPvpAT13YieOqpx9i7t44f/vA6ysvL6ezs5L33\n1jBmzFiqqydSUdEHkDW7L774HHPnzufmm3+d4VEnh3xVnYVwBGio3+/F6+0OeJkYV0vo03Ihv6FY\nrt+ZK0U3mhz0sFJn0FPScmaQjPjbSJejR6b1H2pkgyN2OiCTW1+wJZIRsum8JINw0p+dWrxIL4R6\nRDsvZmwGnnrqSV544XnuuuvPjBs3IV27l3a88MIz7N1bx89+djMdHe1IkkRJSSkrVizH7/ezZMkF\nNDQcwm6343IV0NbmDqbnsr2yLlpVXZ44WYgQcfLh9XYFjOBcmt8oeiTZwFK+QZVqOQXqtJx6uWzr\nQ2a+xDs+nYFxWq7nlt1r9zX5Ccf85JBev5zjxX8KjLVM8jEOFWrIhCrz5yVZhIv6sy/dGg1GqddI\n50X+vUKEw0l/Y+MRfvaznzJ69FhuvPGXuFyusPX0BOzfv4/Bg4dw6NAhGhoOsXfvHgQB6urqWLRo\nCX6/j//8ZyUDBw7C6XQwfnw1Y8eOz/Sw40KeOKUJCnFS3L0Vt2kF+rSc3hVYbS+gIJSqyo1mvGai\nUqFSYu3b3PGWlgvf19RFXsyel1QYQur3NRtIf6qgr/iMpcUz0uVY4fmVDmSTqN9qmLlfmpqaePLJ\npxg4cCDV1Sdw8OBB7r//Pn7961uYNWtOBkYdHdu2beG999Zy9dU/SHgdbrebLVs2U1f3JSNHjmbw\n4CF4vV6+/HI38+efyZo179De3sZZZ51DS0sL7723mkmTpjBmzFgL9yQ9yPs4pQmR7AckKbrrd09q\nxhtbZ6AuJfZrlpP5Ys92OAej85r6FKR5/Ye1wvNM7GumoN9X8/0R49flZDrFF6v0PtehPi/6fVWe\n23V1dbz88r80L7ojRoxkzZrVNDQcZsaMkxk6dFhaxrtixXIcDgc+n49Fi5YY/qa6eiJPPPGP4N+i\nKPLXvz7I2LHjOHToIJde+p2Y29m8eRPt7e1ccsllrFr1Bk1NR5kyZRrz558JwNy5Z/Dwww9w8OAB\nhg0bzvnnX9Qj0/B54pQChCJIYqAnUzTXbzlkr7cX6EmpKqOqJCMypYaiD+n5ZfeZO6/mqsX0wnNz\nBQE9ORqhh9X7atbHyLhQI7XVlfFG1HId0WwGTj55JrfffgfPPfccvXr14tixFmprt7Nnz5csX/4K\ndrudV19dSa9evVM6RrfbTWNjI9/+9nd55pknaG1toby8V9jvBEGgsLAo+PeOHbX06tWbBQsW8txz\nT9Ha2kp5eXnYcj6fj08/3cjJJ3+FsrIyGhuPcOTIYUpLS9m5s5b6+r0UF5fwwQfvUVHRhyFDhgaP\nUU8kTZAnTimCIhL04/PJD7Zort9qwqRN32R3Wi5RKJOtTBpDhMlmswcm6eOj7D7b9iFytVg85qlC\nkPirNS/Ztq9WwUjfY/W+RvIxMo5MQaqqK42iTD33vEa3GfD7/dx//3188MEH3HPPfQwaNASQScae\nPV+yfftWfD6fIYGxGjU12xgyRN7+0KHD2LLlc2bPPi3mciNHjmTdurUADBs2wpA0ffnlbr78cjeb\nNm2ku7uLyZOn0t7exltvrWL48BGMGzeeo0cbKSoqpKqqiiNHjnDaaXPp3Tu1ZDHTyBMni6G4fisw\nay8QXkHWk1NVsUXC2rL7aBNDdjRpjQSlGEAxJ8218xqKfsgwG/0ILK0SRefG/ppFpiNq6UzxhWvU\njqcoU7geb+/eOq677icsWHAWzzzzkiaq4nA4GDNmbFo1PS0tx4JNd4uLSzh8uMHUcgUFhVxyyaU0\nNjZG/M2OHTUMHjyEJUsuoKZmOwCzZp3GrFkhYnb//X9i1qzTcqp9TLLIEyeLIElSQMfkVX0qBMXh\n0Vy/e1JaLhriqQxUHurG/d5Sp8mxCukWf6cLkaIfWiNWBRKi6A24ayffpiRbkI4oUyJIRYrPqDqw\n52rUYtsMPP/88zz55OP84Q/3MHHiiZkcbhDFxSXBViZdXV0UFxebWu6jjz6gunoiVVVVrFt3mB07\nasKsE+bMmUtrq5tPPlmP0+nklVf+yejRYznhhEk8+eSj9O5dwYQJ1TnRpNhK5ImThZAfLjJZ8nq7\ngp9Hcv0O77fWMyvIINywM5HJJtLEYNYMMl0T9vEliDaOqMnfxdOmJPPEIxYyHWWKF8mm+OTfi8F1\n9dRKSNAbd4a/5DQ3N3HDDT9j4MDBvPTSaxQWFmZsrHpUV09k+fJXANnBe+HCcyL+Vq0l3bu3jtGj\nxwByT7nDhw+HEafi4hKKi0s499zzADkCtX37NgQBli69hF27djJz5mwcjuOLShxfe5tCKH5MctpI\nwOezoVTTKQ8vtWjcqBlvtj6Ak0EqvXvMR6UUwbOyXGqiUtkm/k41YhlZJtemJHvK7iH3vYoURE/x\nRfb9kgmvH0EQszYtngj096zR82n16tXcccdt3Hzzrzj99DMyNdSIqKiooLy8nBUrllNUVESfPpU8\n9tjfWLr0kmAKD6C2toba2u1s3LieGTNO5uyzz+XFF59l+PAR7N79BVdd9d8Rt+Hz+WhsbGTcuAms\nWfMOnZ1dDBgwkAED5BYy2W5maTXyPk4WwmaT/xNFCZ+vy6B7ty04gSh/Z/MbazLIFsNOc+Z2yUel\nckH8bRWsSt8YTdh6ZEPZfS5FmZKFlgwrpfdSGJFSvsuGtHiiiGWp0NXVxe9+dysNDQ384Q93U1FR\nmamhxoWOjg4uu2wpd97552BEyQiiKPLWW6uw2WwsWLAwKvn5/PPPeP/99+jfvz+9evVm5sxTKSoq\nMvxtT0HeADNN8Pm8uFwO1SQgBaJLcppK6QwdgrERZK4jnETE9rNJJ+KfsCNPCrku/o4H4aXo1pLh\nSJocPdLlrK1N3+RulMkMzOh7Yht1Zm/EUA0zYvetW7dw4403cPnlV7B06Tezcj+Shcfjob5+L3/7\n20Ncc80PGTFiJKIoGuow29racLtbOXq0kUmTJmdgtOlHnjilCXfccSvvvruGoUOHctJJJ3PKKSfT\n2dnFfffdS319Pc899xz9+w8AVad7PXLhwRMJehKRrf2q9FBHpeQJJFYrDOXBIuo0arkv/o6E8FRV\neshwvM7aVujYwgs2cuM6ThTaFx0Bh8McGTbzApJtla+xbAZEUeShhx7k7bff5p577mPYsBEZG2sq\noY4uffzxh6xZ8zY33PCLsO+OZ+SJUxohSRL79+/jrbdW8a9/vURj4xEEQeDUU0/lrLMWMm3aDCor\nK4O/1fvj6JELAtrwqqrcJxHmJgUZ8sTgyOn9jYRsTFUZ6dj0SPS+MUq5Hj9Rpv/f3p2HRXXfexx/\nD4wCA7KJxgVQUTZRorJUY8zmbozGmNS0Jtf08VZb0/aJ2WyWqrlp1KhZb2Nu9JpYm8XUpbHRuCTm\nGoOJMqBGAUEjKJoICC7sIDL3j3GGbYY5A7MxfF/Pk+eRyTl4DqOe33x/v9/n275qqXUVQ8dP8VmK\nGfjppwssXPgEo0eP4fHHnzAxQ+AeCgsLyM4+yZ13NqzX2rjxfbp27crDDz9ifK209BpnzvzI8OEJ\nzrhMp5OBk4Pt3r2TVauWUVNTw5Ah8fz+93+ksrICrfYwaWmplJSUEBEx0FiVio6OMf4lbRk06NpV\nqc6y0N0wyG18r6a40nvTXh1lEKFsHVvr742tBxGurnmVyV4fdEztfG39vbH99KulaUiAbdu2sm7d\nWlasWE18/HCb/d6u6tNPP2L48ASiomK4dKmIoKBgXn31r8ycOYuYmFgArly5wty5j7Bw4TOMGXOX\ncy/YCWTg5GCvvvoK33+fwvz5jzNx4pQW/yDpdDry8s6g1R5Gq00lJ+ckvr6+jBiRQHJyMgkJicYU\nV1etSpnOn3L3RbMt4xSsfW8Mr7kyd5iqUjqN5OHhgU6HcXrZ3WNBXGGA6MgpPksxA9euXWXRokUE\nBASyePHLijOQOirDNFx1dTXvvPMWPj7e3H33OGJj48jN/ZH/+Z+/MWPGgyQkJNO1a1cyMzOIixvi\n7Mt2Chk4OZjhR2rNX/SyslLS07WkpaWSnq6lvLycqKgokpKSSU5OIiJiECqVqkU53PR6HPtlF5kO\ndnTvB43SOAXrFze7XlWq6QDRfd5bw/tguZrr+lPjbWVpEOEsbX1vDK+Z+56WYgYOHjzIyy+/xFNP\nPcu4ceazj9zRsWNH2Lnz34SE9GD+/MeNry9b9hK3334nd9xxl/MuzkXIwKkDqq+v59SpbOP03pkz\nPxIUFERCQiLJyUkMGzYCX19fQOkusfavK2jZFsY9p+XAdjvIrK8YOmd3pT3ztlyRfqqq1vh186iQ\nBo4PULU1JYMIV2P99GvDFJ/+z3Kt8d+p5lPMtbW1rFixnNzcXFatepOQkB4OuivXoNPpyM09w8CB\ng9i1awf+/gGMHj0GgOvXr3e6FHBzZODkJi5fLiE9XYtWe5ijR49QU1NNXNwQ41qpsLDwZlUpS9lF\nykrhnW1azp4DROWfrh215b751mx3T4hufaqqLQvPDa+5IncJ7gTrNmzotayq5eSc5Omnn2bWrF/x\n61/Pcdn3zVEqKyv4+usvueeeCW4/TWktGTi5qbq6OrKyTqDVpqLVpnL+/Dl69uxJUlIySUlJxMff\namwNoOyB0PRhrT+vc03LOWOAqKQqZY/Mr5ZVJvetIIKpaUjLU1VtiapwhelXV9wNaQ+Nlyw0rFPT\nu3r1KgsXLsTPz4/Y2MFcunSJ48eP8/rr/83AgY5rwmuNrKwMUlIOMG/eArv/XobMJqkymSYDp06k\nsLAArfYQaWlajh8/hk6nY+jQeONaqcYR+Q1VKfOBdgYqlefNT+fuWYkwtfjbmQNE63ZXWleVMv1Q\ndd/WMPbYdq98oOv4/CJ3qjIp0Tzt3NNT30msrKyMRYueJTMz8+aAVy8oKJjBg+OIjx/GzJmzHNZ3\nbteuHajVaurq6pg8earJY3Q6Hc899xQrVrxufO2771K4evUKmZknmD//cfz9AxxyvZ1dawMn6VXn\nZm65pRdTp97P1Kn3A1BTU8OJEz+g1R7is8/+RUHBRUJDQ40DqcGDh9ClS1dA/5e2pqaac+fyCAsL\na/IpRL/e5wb19Y6ZQnIUV13bo//5Nu31Zqpq2PShbXn61dRD1R3eR3Oa99NTGu7YmoaKkqEqa3r6\n1VTLJXvmF3WWKpOBpZiBgIAg7r13Gj//XMBjj/2GqqoasrIyyMrK4ODBbzl48FsGDoxk5Mjb7H6t\nZWVlFBcX8+ijj/HxxxspLb1mcgCkUqnw9m5oZVJXV0de3hlmz57DkCFD0Wh87X6twjIZOLk5Ly8v\nEhOTSUxMBhoCOrXaQ2za9CmZmX9Bre7C8OHDCQwM4P/+72sKCwv5wx/+wC9/+bBxzVRrD+uO2Dam\n5UPGtdf2NH9Yg+mqlH6A2/g8D+N5Ot0N43vXGR6qjmqFY/ieppoa66u5jf/uQENTY1WLgVR7qmDN\n+66584DY0g7B8vIyXnjheTw91Xz00Wb8/PyanF9cXExBwc8MHuyYrfbZ2VmEhoYCEBYWTkbGCW67\n7XaL52VlZVBXV8eePV+gVqvdNsm8o5GBUyejUqkIDQ0jNDSMGTMeAuDMmR9ZseJldu/OxMPDgxEj\nRnD4cCo1NXXGgE61uqEqZfqTtet3uDdwl92BpqpSlh/W+vP0DxkdOp3rLmxuK3tUmaxl+BDh6Wlu\noNv0w0jj86xZeN5y96f7D4gt7RBMTU1l8eK/8Kc/PcHkyfeZ/D4hISGEhIQ45JpBnxfl66sfvGk0\nvhQVFSo6r7i4mG7d/Jk4cQoffLCOvLxcBgyIsOelCgVk4NTJZWdnsWDBb6mtrWHEiEQWLnyW/v0H\nGAM6169/v0lAZ1JSEgkJiQQE6MvMptZ7NK9KuUo2jrvvDmz+sG5ehTAwVGMazuvYXe4NXH2xu7mB\nbtMFzrqbVTJTH0SaTo+3rDK5VjNtW7MUM3D9+nVWr15FZmYGGzZ8dLMvqGvQaHypra0BoLq6WvEO\nNrVaTUBAIACRkdEycHIRMnDq5Lp29WLIkKHcd9/9jBs30fiPbkTEICIiBjFr1mygaUDn2rXvNQno\nTEpKZODASFQq/R8nc1NIzqxKuWuwoznNqy6GqQxTURWtTSF1lOwiS33IXFHD1DaAJ56eyj6IGM5p\n2DCgQq3u6vLvUVu1jMxo+YHnzJnTPPnkk0ybNoNFi/7icj+L2Ng4duzYDsC5c2eZMMF84GbjjSBR\nUTH861+bGTt2PIWFF4mLi7f7tQrLZFedaBNTAZ2BgYHGTKmWAZ3OaRvjqou/7aUtVRflURWuV5Vy\n9SqTLTR+f1qPQ3C996e9LFXVdDodGzf+na1bt7B69VtERkY783JbtX37Nrp27Up1dTXTpz/Axo3v\n89BDDxun8ABycrJZsuQ5nnnmeRISkgDYvHkTQUFBFBYWMHv2HGddfqcjcQTCIawP6LRf25iOtvi7\nvWyVdG74XsoCVG2zsLktOltwJ7Tcdu/h4Wn8UOJq748tWKoiXrpUxFNPPUV0dAxPP/0cXbt2ddal\nWq2yspJHHnmIVaveYuDAQc6+HGGCDJyEU7QtoLP9sLGJiQAAHA9JREFUbWNabrl397Uf9Td3kNmv\n6qLs/XHMWrbOUGVqrOUOwZb3a/3747o7YC3FDADs2bOHN954jSVL/ouRIy3vThPCWjJwEi6jbQGd\nytrG6G52uXfXxd/NOTO3R1nV0LZr2Tp7lcma+23+/ji7tY9SlmIGKioqWLJkMVVVVSxbtkrCIIXd\nyMBJuKzGAZ1padomAZ1JSYnExQ01BnEqWYtj4P5rmRo/YFwjt8eeVanOWGWyVHVpy/dUkniutIel\nLSmJGTh69CjPPfdnfve7BUyfPtPu1yQ6Nxk4iQ5Dp9Nx4cJ50tIOo9UeJjMzwxjQmZycRGJiMt27\ndzceW1JSTFZWBsOGDcPHx8fEd3Re6wt7aBmp4HlzKtL17skWVSnTOUXu2x4G9Bsv9FUmfdXFnjlU\nylv72DfxvLWYgbq6Ot5++y1SU1N5/fW36d27r01/fyFMkYGTQqWlpXzyyT+IjIwmM/MEDzzwECtX\nvkJQUDA+PhoWLXrB5HkFBRdZvvy/mhxXX1/P2rVriIyMoqDgouyGaIfKykp++OGIcQdfSUkJAwZE\n4Ovry5EjaVRVVfHiiy8yadIUQKXwQeA60xNKuUOkgjU7LEF1c4DYWdaq2banXluvoSFE1b4bA5TE\nDJw9m8eTTy5kwoRJ/Pa3C9y2V6ZwPTJwUqiiopzTp08xbNgI3nhjJVOmTKOqqpJhw0a0el5BwUUK\nCi42OS47+yRHj6bzq189wqZNHzJlyjT8/f3tfQudQnZ2Fn/96xLOns3Dx8eHwYMHU1BQREKC6YDO\n5mnaph4Ertw2xpHtQxzN+rU4HXOHmCXNq0zN1/Y4kz0WniuJGdi06RM+/PAfrFz5hsNaowhhIE1+\nFfL19aNv31Deffe/iYyMIiAgkPT0VC5cyKd79x6MGjXa7LlZWRlNjhswYAAHDx4AIDy8vwyabOSH\nH47xxz/Oo76+nkmT7uXxx58gKCio3QGdrtg2pvkncme1D7Gn5iGQzddugQrDJgF36JHYnCtUmSxp\nSDzXMzfYVZp4bmnB++XLJTzzzNOEhoazdesOvLy8HHKfQiglFScz1q9/j6lTp1NRUU5ExCBWr17O\n3LnzCQoKbnFsRUU5hYUFLY6rqCinqqqaU6eyFTV0FJZduHCetWvXMH36A8aAOFM6SkCnOZ1zMbT5\nHYLK1+J0nClYUwv8O9rUq4HShec3j9Z/ZWJ93tdff82rry7nhRcWM2bM3fa/cCHMkKk6haqrq6ms\nrCA4uDvffruf2trrjB07HtCnvvbvP4Bbbx3e6vcwHFddXU1sbBz+/v5s376N2NjBREXFOOI2hBmm\nAjoHD44zRiE0DejEige17apSpgcQ7r0YumXuluUdgkof1K5YlWr5HrvuAv/2sDTY/eabb9i58wsi\nIwcRFRXD7t1fUFZWwYoVqwkMbPkB1RVkZWWQknKAefMWOPtShJ3JVJ1C+/fv49q1q8yaNZuLFy9S\nU1PNF198zpQp9/HTTxfMVo327t3N9eu13HvvNONx+/d/bUyE9fb2pqioSAZOThYc3J3x4ycxfry+\nT1TjgM5XXllmDOjUV6WSTQR02reZcecL7mx7DlXDQLVhQGndFKxzqlKmBokdtcpkieH9aUg3b/j5\n63fElqDVppKaeth4Tq9evXn99VXExQ1h1KjbCQ/v57Dr3bVrB2q1mrq6OiZPnmrymNjYODZufL/F\n6xkZJ9i69VOWLPmrvS9TuADPpUuXLjX3Pysrax14Kc7Xu3cffvjhGCUlxZSUFDNt2gMcP36UgoIC\nvL29SEhIor6+nr//fT2RkVHGiP/g4GCOHz/W5LiwsHC2bdvM5cslnDnzI9OnP+Ayiz2FnoeHB7fc\n0psRIxKZOnU6s2f/B6NGjaaiooJvvvmGNWv+xqZNn5CZmUl5eTmBgYH4+wfcXIfieXPqzONmlQqa\nbr3XB3EaHuCG57OpB7XhAd94d5Fa3dXNp+bqmy0O7tLu8M6GFj2ejaY2m/8MG/d9M7xHTTcM2ONn\nbv49dt9/ExpiBpq+xx4e+vcoNnYwVVWVdO3qxR133E1AQCAXL/7EyZNZpKYeYt++L/n1r//DIdda\nVlbGkSPpzJjxIFrtIfr164+Xl3eL41QqFSkpB7jrrrHG165fv45We4iLF39u8rro2Hx9za+tk6k6\nK0mPoc6lbQGdytvGGNYyucM6FyWcmXZu+P0d3dC4M1WZQFnMwIUL51m48AnGjLmLBQv+hKenp/Hc\nn3/+iczMDAIDA0lOHumQa9ZqD1NeXsbdd4/j22/34+mpNjvDsHTpCyxd+orx6507/824cRNYvvzl\nJq+Ljk2m6mxIo9GwbdtOZ1+GcBAvLy8SE5NJTEwGmgZ0fvrpP8nMXNxqQGfztjH6/6Dx9BHoP8nq\nd1O5cwXC+YuhG3aINTyom8dVtHyP2pam7exBojNYihkA2LJlC+vXr+PVV19j6NBhTc5XqVT07RtK\n376hDr3ua9eu4uvrB4BG40tRUaGi83Jzf6Rv31C8vLxNroMU7kkGTkJYQaVSERYWTlhYODNmPAQ0\nDej88MMPKSkpISJioHEHX3R0DGp1QxRCdnYW9fU3iIqKMn7fhsqT89fh2Jorp50bFo17eppbK2X4\n9Q1u3Gh8XutVqZYDCOe3xLE3SzED165d5dlnnyU4OJitW3eYSfp3Do3Gl9raGkC/SUij0Sg6Lz09\njdraGjIyjpOff5YDB/Zzxx132fFKhSuQgVMH1daUc4Dvvkvh6tUrZGaeYP78x/H3D1C0MFKYptFo\nGDXqdkaN0pf2dTodeXln0GoPs379++TknESj8WXIkCFcuXKZ1NTD+Pn58fnnO1Cru6JSYaLi4R6Z\nRa5QZbKWuapU65XDhjRtoNNVmSz11UtJSeHll1/i2Wef4557JjjrUs2KjY1jx47tAJw7d5YJEyaZ\nPbZxZemhhx42/vr06VMyaOokZODUQXl6evCLX4xi2LAR/PDDEcrLy/nNb35rMeW8rq6OvLwzzJ49\nhyFDhqLR+FJWVkZxcTGPPvoYH3+8kdLSa9J1vB1UKhUREYOIiBjErFmz0el0bN++lTVr3qayspKw\nsDB8fHz485//bKxKtT2g0zWrUh0h2FGphsGqB4a13KarUqbWTKluvuZ675EtNB0YtwxorampYfny\nZZw7d46PPtpM9+4hTrvW1gQFBeHv78+uXfpKWHBwdzZs+F8eeuhh4xQeQE5ONjk5J0lP1zbJkdu3\n70tyc3/kwoXzhIaGOeMWhAPJ4vAO7NKlIrZs+ZSwsDASE0fy9dd78ff3bzXl/PjxYxw9mk6vXr1R\nq9WMHTvBqoWRwnrvvPMWn3zyD7y9vZk3bwEzZ85CpVIZAzq12sPk5p4xBnQmJSUyfHhCk4DOjtQ2\npmX7kI7XU89aN27UGatMevrE86Zs0+PNFTRfv2VqYJydfZKnn36KX//6ER5++NEOda+yCUhIAKab\nsybl/Ouvv+Lq1Ss88MBDfPDBOu688x5yc3/E19ePUaNGk56upaioUKbrbGjr1n+SkXGcefMW0Lt3\nH7PHKQ3oBGuTtB3zkHanKpNSrSW8W9/jrWNUpSztEqyvr2fdurXs3r2L1157mwEDBjrrUoVoM9lV\n54Yap5xHRUWTkXHCmHIeGRlNfv45kwMntVpNQECg8bizZ/Pw9W3bwkihzMyZv2TmzF9aPM7VAzpb\n48pNau3F0mJoSz3e9BXE1nq8uVZVSknMQEHBzyxcuJCEhCT++c/txk0RQrgT+VPdQbU15TwqKoZ/\n/WszY8eOp7D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"text/plain": [ "<matplotlib.figure.Figure at 0x1160de5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d.axes3d import Axes3D\n", "\n", "fig=plt.figure()\n", "ax=Axes3D(fig)\n", " \n", "ax.scatter3D(X, Y, e)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 249, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 35.66455, 35.65134, 35.63957, 35.60207, 35.66327, 35.65029,\n", " 35.64262, 35.62602, 35.60105, 35.64538, 35.63457, 35.64502,\n", " 35.66455, 35.65314, 35.62616, 35.61948, 35.66079, 35.63714,\n", " 35.65052, 35.65285, 35.66081, 35.65083, 35.65156, 35.62632,\n", " 35.66519, 35.66333, 35.62063, 35.66388, 35.61562, 35.61499,\n", " 35.61528, 35.59893, 35.60094, 35.60108, 35.65905, 35.64066,\n", " 35.6026 , 35.67156, 35.59519, 35.66376, 35.63561, 35.63352,\n", " 35.66988, 35.65884, 35.67615, 35.64243, 35.64283, 35.65176,\n", " 35.66519, 35.65055, 35.63795, 35.63552, 35.65722, 35.63948,\n", " 35.6573 , 35.63795, 35.63536, 35.66584, 35.63999, 35.64131,\n", " 35.59604, 35.66175, 35.65339, 35.67573, 35.63049, 35.65297,\n", " 35.62073, 35.65945, 35.64087, 35.63558, 35.65117, 35.65121,\n", " 35.64002, 35.65805, 35.62836, 35.62884, 35.60686, 35.6357 ,\n", " 35.60126, 35.64529, 35.64953, 35.64086, 35.64087, 35.67482,\n", " 35.6604 , 35.61882, 35.63772, 35.66388, 35.629 , 35.64018,\n", " 35.65722, 35.65805, 35.63635, 35.63635, 35.629 , 35.6357 ,\n", " 35.60186, 35.60167, 35.63684, 35.66676, 35.6233 , 35.65425,\n", " 35.61969, 35.63188, 35.62841, 35.65902, 35.64165, 35.64747,\n", " 35.66146, 35.62018, 35.59893, 35.63811, 35.65907, 35.65722,\n", " 35.65219, 35.61958, 35.63969, 35.63821, 35.63795, 35.66439,\n", " 35.64775, 35.64712, 35.65314, 35.65285, 35.63681, 35.63496,\n", " 35.63753, 35.62465, 35.64226, 35.64176, 35.66451, 35.67651,\n", " 35.65781, 35.66298, 35.66298, 35.66237, 35.63262, 35.66222,\n", " 35.66034, 35.6573 , 35.65176, 35.64915, 35.6269 , 35.66336,\n", " 35.6344 , 35.65227, 35.62104, 35.61906, 35.62021, 35.65617,\n", " 35.62073, 35.63787, 35.64086, 35.60166, 35.61883, 35.63839,\n", " 35.64301, 35.68041, 35.64822, 35.66089, 35.60163, 35.66145,\n", " 35.65727, 35.60392, 35.62003, 35.66682, 35.65776, 35.65626,\n", " 35.64225, 35.61039, 35.64093, 35.65502, 35.66023, 35.6623 ,\n", " 35.6233 , 35.66248, 35.61986, 35.67714, 35.61941, 35.64136,\n", " 35.641 , 35.66824, 35.64747, 35.64165, 35.62073, 35.62073,\n", " 35.61787, 35.64703, 35.65219, 35.66145, 35.65596, 35.63716,\n", " 35.62988, 35.64887, 35.66552, 35.62336, 35.63873, 35.64384,\n", " 35.64887, 35.64661, 35.65626, 35.65431, 35.60861, 35.66163,\n", " 35.64973, 35.64884, 35.63143, 35.63961, 35.65502, 35.65617,\n", " 35.65617, 35.64001, 35.61688, 35.64165, 35.65922, 35.66126,\n", " 35.63467, 35.65055, 35.66576, 35.6045 , 35.64089, 35.65233,\n", " 35.64747, 35.63518, 35.62668, 35.65494, 35.67666, 35.6382 ,\n", " 35.64977, 35.62066, 35.62923, 35.62994, 35.60823, 35.6348 ,\n", " 35.63916, 35.60968, 35.59604, 35.64093, 35.63517, 35.6493 ,\n", " 35.65815, 35.61724, 35.65922, 35.65922, 35.63274, 35.67625,\n", " 35.65156, 35.63627, 35.63538, 35.65902, 35.67096, 35.61947,\n", " 35.62089, 35.63585, 35.61882, 35.63111, 35.66934, 35.65922,\n", " 35.67028, 35.6798 , 35.62073, 35.64621, 35.64452, 35.67573,\n", " 35.60233, 35.60233, 35.64063, 35.63128, 35.64742, 35.66962,\n", " 35.63519, 35.63634, 35.60332, 35.61476, 35.65502, 35.67001,\n", " 35.60744, 35.6266 , 35.65815, 35.65902, 35.64845, 35.63561,\n", " 35.66586, 35.65377, 35.67693, 35.6513 , 35.67957, 35.63474,\n", " 35.65262, 35.65271, 35.64202, 35.65029, 35.61434, 35.63457,\n", " 35.64933, 35.66969, 35.64147, 35.62261, 35.61682, 35.61947,\n", " 35.62673, 35.64341, 35.65719, 35.61046, 35.61307, 35.62365,\n", " 35.62038, 35.6738 , 35.63251, 35.64151, 35.62619, 35.66988,\n", " 35.60126, 35.64917, 35.6526 , 35.61787, 35.63503, 35.66084,\n", " 35.66453, 35.63795, 35.661 , 35.63957, 35.64063, 35.59325,\n", " 35.6249 , 35.61365, 35.66388, 35.64341, 35.64147, 35.65958,\n", " 35.65494, 35.65857, 35.60823, 35.65464, 35.65488, 35.64147,\n", " 35.65455, 35.65554, 35.60228, 35.62112, 35.63562, 35.63143,\n", " 35.65931, 35.6587 , 35.62104, 35.6623 , 35.65922, 35.64741,\n", " 35.64742, 35.65018, 35.65973, 35.61842, 35.61842, 35.64923,\n", " 35.64669, 35.60207, 35.63771, 35.6171 , 35.59361, 35.63873,\n", " 35.64747, 35.64761, 35.63552, 35.64251, 35.60312, 35.66036,\n", " 35.62994, 35.63814, 35.62207, 35.66485, 35.67437, 35.63506,\n", " 35.65502, 35.62238, 35.63873, 35.63561, 35.63561, 35.64208,\n", " 35.641 , 35.6526 , 35.66298, 35.6078 , 35.65253, 35.64747,\n", " 35.64387, 35.63518, 35.63518, 35.63123, 35.62032, 35.64089,\n", " 35.66969, 35.64736, 35.63345, 35.66453, 35.629 , 35.62982,\n", " 35.64708, 35.66283, 35.66388, 35.66542, 35.63123, 35.65331,\n", " 35.65285, 35.66676, 35.63085, 35.63552, 35.63457, 35.64147,\n", " 35.61741, 35.61958, 35.62261, 35.61947, 35.65496, 35.63457,\n", " 35.65383, 35.67043, 35.66973, 35.62498, 35.64274, 35.63827,\n", " 35.63799, 35.6374 , 35.66775, 35.59604, 35.66848, 35.65018,\n", " 35.63561, 35.64741, 35.64669, 35.64968, 35.64644, 35.66283,\n", " 35.61958, 35.63123, 35.61365, 35.65596, 35.61947, 35.61947,\n", " 35.61759, 35.66973, 35.65494, 35.65262, 35.65596, 35.65371,\n", " 35.64949, 35.6357 , 35.64548, 35.64548, 35.63528, 35.64243,\n", " 35.65107, 35.63552, 35.66647, 35.66036, 35.61958, 35.63382,\n", " 35.64642, 35.60559, 35.60659, 35.60228, 35.63457, 35.60395,\n", " 35.63143, 35.64429, 35.64541, 35.63561, 35.63873, 35.6566 ,\n", " 35.64777, 35.61083, 35.63839, 35.63382, 35.66576, 35.67957,\n", " 35.62982, 35.66388, 35.65525, 35.63067, 35.62261, 35.60312,\n", " 35.61763, 35.62261, 35.66089, 35.62592, 35.63017, 35.61046,\n", " 35.66334, 35.64341, 35.63518, 35.64911, 35.60765, 35.661 ,\n", " 35.63717, 35.6242 , 35.66163, 35.59945, 35.65596, 35.61046,\n", " 35.63999, 35.65667, 35.65902, 35.66334, 35.62502, 35.63561,\n", " 35.66298, 35.67054, 35.64747, 35.60571, 35.64747, 35.61787,\n", " 35.66416, 35.64028, 35.62698, 35.66084, 35.65262, 35.64975,\n", " 35.61601, 35.63552, 35.65176, 35.63874, 35.64473, 35.66841,\n", " 35.61511, 35.62986, 35.63517, 35.63151, 35.61307, 35.62569,\n", " 35.64774, 35.61901, 35.61986, 35.66848, 35.67028, 35.66642,\n", " 35.66145, 35.64619, 35.64736, 35.64619, 35.63964, 35.64712,\n", " 35.66234, 35.66655, 35.63536, 35.6286 , 35.66973, 35.65791,\n", " 35.65719, 35.67505, 35.63961, 35.64408, 35.65314, 35.61702,\n", " 35.63873, 35.65271, 35.59361, 35.60225, 35.60721, 35.66453,\n", " 35.63836, 35.66126, 35.66586, 35.66005, 35.63166, 35.65494,\n", " 35.6149 , 35.63836, 35.66439, 35.66336, 35.64857, 35.61582,\n", " 35.65595, 35.66303, 35.66837, 35.6623 , 35.65922, 35.65464,\n", " 35.64701, 35.60571, 35.63805, 35.67184, 35.65167, 35.65167,\n", " 35.64139, 35.63691, 35.60312, 35.66962, 35.60823, 35.66682,\n", " 35.66682, 35.59923, 35.60171, 35.61402, 35.64657, 35.6279 ,\n", " 35.62757, 35.67555, 35.65271, 35.64628, 35.65083, 35.60233,\n", " 35.60233, 35.67573, 35.65107, 35.64637, 35.64975, 35.66328,\n", " 35.62354, 35.63128, 35.60312, 35.61398, 35.627 , 35.64887,\n", " 35.66364, 35.65525, 35.63164, 35.61046, 35.62798, 35.6389 ,\n", " 35.59356, 35.65342, 35.62148, 35.59519, 35.63033, 35.66546,\n", " 35.66542, 35.61738, 35.6623 , 35.63626, 35.64703, 35.60233,\n", " 35.63629, 35.67977, 35.62927, 35.67523, 35.67666, 35.65907,\n", " 35.63924, 35.60385, 35.61873, 35.60004, 35.66089, 35.6581 ,\n", " 35.65776, 35.63677, 35.63691, 35.63483, 35.61884, 35.64147,\n", " 35.64193, 35.67043, 35.66089, 35.66163, 35.61307, 35.62334,\n", " 35.59746, 35.64281, 35.61688, 35.64028, 35.6045 , 35.66128,\n", " 35.62187, 35.63518, 35.62062, 35.64085, 35.66412, 35.66412,\n", " 35.63747, 35.65805, 35.65945, 35.629 , 35.63704, 35.6573 ,\n", " 35.65987, 35.63992, 35.60312, 35.64677, 35.67063, 35.64761,\n", " 35.64147, 35.65914, 35.66175, 35.62724, 35.66441, 35.59633,\n", " 35.62757, 35.65083, 35.63638, 35.6347 , 35.60721, 35.60571,\n", " 35.66128, 35.64085, 35.64496, 35.62586, 35.66653, 35.64933,\n", " 35.61884, 35.65719, 35.63524, 35.64135, 35.65922, 35.65881,\n", " 35.65394, 35.59299, 35.6488 , 35.66234, 35.65573, 35.6617 ,\n", " 35.64496, 35.64502, 35.64496, 35.6516 , 35.5942 , 35.65262,\n", " 35.61691, 35.6512 , 35.65083, 35.67154, 35.63274, 35.61702,\n", " 35.60847, 35.65862, 35.66001, 35.59746, 35.61787, 35.66362,\n", " 35.61884, 35.67645, 35.66969, 35.66036, 35.66227, 35.63642,\n", " 35.64598, 35.66296, 35.64637, 35.65573, 35.63116, 35.6617 ,\n", " 35.64842, 35.63704, 35.63821, 35.63842, 35.64842, 35.64341,\n", " 35.65464, 35.65587, 35.63457, 35.64547, 35.64521, 35.63067,\n", " 35.64499, 35.64628, 35.64628, 35.64421, 35.64028, 35.6171 ,\n", " 35.6623 , 35.64628, 35.61787, 35.64747, 35.65776, 35.66767,\n", " 35.62054, 35.63873, 35.66676, 35.61738, 35.6105 , 35.63799,\n", " 35.64641, 35.66676, 35.65048, 35.64845, 35.64311, 35.64747,\n", " 35.64541, 35.67573, 35.61798, 35.65271, 35.61906, 35.62381,\n", " 35.6242 , 35.64387, 35.63433, 35.65776, 35.59325, 35.627 ,\n", " 35.66233, 35.64384, 35.64677, 35.64148, 35.60346, 35.64301,\n", " 35.62238, 35.65596, 35.61506, 35.64897, 35.61986, 35.66416,\n", " 35.62357, 35.62718, 35.65192, 35.66294, 35.66294, 35.65805,\n", " 35.66034, 35.59325, 35.627 , 35.61367, 35.65676, 35.65805,\n", " 35.64086, 35.64161, 35.63388, 35.6344 , 35.65955, 35.61989,\n", " 35.65233, 35.62718, 35.65014, 35.6171 , 35.62698, 35.62378,\n", " 35.65573, 35.60362, 35.6344 , 35.66269, 35.66269, 35.62249,\n", " 35.64724, 35.66327, 35.66327, 35.65201, 35.66294, 35.63493,\n", " 35.61626, 35.64669, 35.66436, 35.627 , 35.63748, 35.66412,\n", " 35.64302, 35.64999, 35.64429, 35.63033, 35.62614, 35.62799,\n", " 35.62087, 35.63836, 35.62322, 35.64341, 35.66327, 35.64833,\n", " 35.65304, 35.66962, 35.6249 , 35.64637, 35.5942 , 35.59909,\n", " 35.64883, 35.65596, 35.61703, 35.617 , 35.67216, 35.6738 ,\n", " 35.6642 , 35.64931, 35.64855, 35.61644, 35.61854, 35.66056,\n", " 35.66634, 35.62054, 35.64712, 35.65371, 35.67675, 35.62632,\n", " 35.66294, 35.64387, 35.627 , 35.6622 , 35.64789, 35.66142,\n", " 35.62646, 35.65958, 35.63116, 35.61883, 35.62135, 35.66622,\n", " 35.5942 , 35.62923, 35.6526 , 35.61702, 35.67148, 35.63211,\n", " 35.64623, 35.64521, 35.62534, 35.59776, 35.61309, 35.65174,\n", " 35.64685, 35.64708, 35.6149 , 35.64599, 35.62103, 35.61525,\n", " 35.64431, 35.64813, 35.65283, 35.63836, 35.63839, 35.67216,\n", " 35.64883, 35.66249, 35.62502, 35.6825 , 35.65659, 35.65291,\n", " 35.6622 , 35.65596, 35.65525, 35.5942 , 35.63391, 35.63877,\n", " 35.6149 , 35.66767, 35.65907, 35.61499, 35.61883, 35.65554,\n", " 35.61978, 35.65574, 35.64148, 35.6273 , 35.67736, 35.61868,\n", " 35.62038, 35.61986, 35.6486 , 35.65304, 35.67148, 35.66362,\n", " 35.63016, 35.60011, 35.66426, 35.63391, 35.62066, 35.64391,\n", " 35.6238 , 35.64675, 35.6149 , 35.61499, 35.63621, 35.64804,\n", " 35.6149 , 35.64369, 35.62864, 35.6206 , 35.6389 , 35.64795,\n", " 35.63748, 35.63802, 35.61196, 35.61234, 35.65116, 35.65083,\n", " 35.62357, 35.67216, 35.64623, 35.62322, 35.67216, 35.65156,\n", " 35.65371, 35.63836, 35.63771, 35.64547, 35.64309, 35.66145,\n", " 35.65025, 35.65371, 35.63795, 35.67045, 35.63537, 35.61476,\n", " 35.6618 , 35.60758, 35.62207, 35.65706, 35.67216, 35.64075,\n", " 35.62261, 35.66034, 35.66459, 35.61499, 35.64797, 35.60118,\n", " 35.64445, 35.64127, 35.63538, 35.62555])" ] }, "execution_count": 249, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 265, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.tri as mtri\n", "\n", "\n", "\n", "#============\n", "# First plot\n", "#============\n", "# Plot the surface. The triangles in parameter space determine which x, y, z\n", "# points are connected by an edge.\n", "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", "ax.plot_trisurf(X, Y, e)\n", "ax.set_zlim(-1, 1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
furstj/MMPP
lessons/02_burgersova_rovnice.ipynb
1
192794
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Burgersova rovnice\n", "\n", "Nechť $u \\in \\mathbb C^1( \\mathbb R^2 \\to \\mathbb R)$ a $f(u) = \\frac{1}{2} u^2$. Potom rovnici\n", "\\begin{equation}\n", " \\frac{\\partial u(x,t)}{\\partial t} + \\frac{\\partial f(u(x,t))}{\\partial x} = 0\n", "\\end{equation}\n", "nazveme **nelineární Burgersovou rovnicí**.\n", "\n", "Poznámka: pro $u\\in \\mathbb C^1$ lze rovnici upravit do tvaru\n", "$$\n", " u_t + u u_x = 0.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Počáteční úloha pro rovnici lineární advekce\n", "\n", "Nechť $u_0 \\in \\mathbb C^1(\\mathbb R \\to \\mathbb R)$. **Počáteční úlohou** pro Burgersovu rovnici pak rozumíme nalézt funkci $u \\in \\mathbb C^1( \\mathbb R\\times [0,+\\infty) \\to \\mathbb R)$ takovou, že $\\forall x \\in \\mathbb R, \\forall t>0$ je splněna rovnice \n", "\\begin{equation}\n", " \\frac{\\partial u(x,t)}{\\partial t} + \\frac{\\partial f(u(x,t))}{\\partial x} = 0\n", "\\end{equation}\n", "a navíc je $u(x,0)=u_0(x)$.\n", "\n", "\n", "### Analytické řešení počáteční úlohy\n", "Analytické řešení je možné získat například pomocí metody charakteristik. Řešení získáme v implicitním tvaru jako\n", "\n", "$$\n", " u(x,t) = u_0(x - u(x,t) t).\n", "$$\n", "\n", "### Vznik nespojitosti\n", "Klasické řešení existuje jen pro $t < t_{crit} = -1 / \\min u_0'$, viz metoda charakteristik. \n", "\n", "### Slabé řešení\n", "Nechť $\\phi \\in \\mathbb C^1( \\mathbb R\\times [0,+\\infty) \\to \\mathbb R)$ nenulová na kompaktní množině. Potom\n", "\n", "$$\n", " \\int_0^{+\\infty}\\int_{-\\infty}^{+\\infty}\\left( u_t + f(u)_x\\right) \\phi(x,t) \\,dx\\,dt = 0.\n", "$$\n", "\n", "Integrál rozdělíme na součet dvou sčítanců a integrujeme zvlášť pomocí metody per-partes\n", "\n", "$$\n", " \\int_0^{+\\infty}\\int_{-\\infty}^{+\\infty} u_t \\phi \\,dx\\,dt = \n", " \\int_{-\\infty}^{+\\infty} \\left( \\left[u \\phi\\right]_0^{+\\infty}-\n", " \\int_0^{+\\infty} u \\phi_t \\,dt\n", " \\right) \\,dx\n", "$$\n", "\n", "a\n", "\n", "$$\n", " \\int_0^{+\\infty}\\int_{-\\infty}^{+\\infty} f(u)_x \\phi \\,dx\\,dt = \n", " \\int_0^{+\\infty} \\left( \\left[f(u) \\phi\\right]_{-\\infty}^{+\\infty}-\n", " \\int_{-\\infty}^{+\\infty} f(u) \\phi_x \\,dx\n", " \\right) \\,dt.\n", "$$\n", "\n", "Protože $\\phi=0$ pro $t\\to+\\infty$ a $|x|\\to+\\infty$ a $u(x,0)=u_0(x)$, je \n", "\n", "\\begin{equation}\n", " \\int_0^{+\\infty}\\int_{-\\infty}^{+\\infty}\\left( u \\phi_t + f(u)\\phi_x\\right) \\phi(x,t) \\,dx\\,dt = \n", " - \\int_{-\\infty}^{+\\infty} u_0(x)\\phi(x,0)\\,dx.\n", "\\end{equation}\n", "\n", "Funkci $u \\in \\mathbb L^\\infty( \\mathbb R\\times [0,+\\infty) \\to \\mathbb R)$ nazveme *slabým řešením* počáteční úlohy pro nelinearní (Burgersovu) rovnici, pokud $\\forall \\phi \\in \\mathbb C^1( \\mathbb R\\times [0,+\\infty) \\to \\mathbb R)$ nenulové na kompaktní množině platí posledně uvedený vztah. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Numerické řešení počáteční úlohy pro Burgersovu rovnici metodou konečných objemů" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "using PyPlot" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd24c8355f8>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Nx = 51; # Pocet bodu site na intervalu [0,1]\n", "\n", "x = range(0, stop=1, length=Nx); # Souradnice bodu site\n", "xc = (x[1:end-1] + x[2:end]) / 2.0\n", "\n", "function u0(x)\n", " \"\"\"Definice pocatecni podminky\"\"\"\n", " if x<0.25 || x>0.75\n", " return 0\n", " else\n", " return sin( 2 * π * (2*x-1.0) )\n", " end\n", "end\n", " \n", "plot(x,[u0(xi) for xi in x]);\n", "ylim(-1.5,1.5); grid(true); xlabel(\"x\"); ylabel(\"u\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Protiproudové (upwind) schéma**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd241f052b0>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dx = 1.0 / (Nx-1); # Velikost prostoroveho \n", "dt = 0.5 * dx / 1.0 # Velikost casoveho kroku\n", "\n", "u = [u0(xi) for xi in xc]\n", "uNew = copy(u)\n", "\n", "t = 0\n", "Tend = 0.25;\n", "\n", "pocet_iteraci = round(Int, Tend / dt) # Vypocet provedeme do casu priblizne Tend\n", "\n", "function flux_upwind(ul, ur)\n", " if ul+ur>0\n", " return ul^2/2\n", " elseif ul+ur<0 \n", " return ur^2/2\n", " else \n", " return 0\n", " end\n", "end\n", "\n", "for n = 1:pocet_iteraci\n", " global t += dt\n", " \n", " for i = 2:Nx-2\n", " uNew[i] = u[i] - dt / dx * ( flux_upwind(u[i],u[i+1]) - flux_upwind(u[i-1],u[i]) )\n", " end\n", "\n", " for i = 2:Nx-2\n", " u[i] = uNew[i]\n", " end\n", "\n", "end\n", "\n", "u_upwind = copy(u)\n", "\n", "#plot(x, [u0(xi) for xi in x], label=\"u0\")\n", "plot(xc,u, \"o\", label=\"u[i]\");\n", "ylim(-1.5,1.5); grid(true); xlabel(\"x\"); ylabel(\"u\"); legend(loc=\"upper left\");\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Laxovo-Friedrichsovo (Rusanovovo) schéma**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd244261438>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "u = [u0(xi) for xi in xc]\n", "uNew = copy(u)\n", "\n", "t = 0\n", "\n", "function flux_lf(ul, ur)\n", " q = dx / dt\n", " return (ul^2/2 + ur^2/2)/2 - q/2*(ur - ul)\n", "end\n", "\n", "for n =1:pocet_iteraci\n", " global t += dt\n", " \n", " for i = 2:Nx-2\n", " uNew[i] = u[i] - dt / dx * ( flux_lf(u[i],u[i+1]) - flux_lf(u[i-1],u[i]) )\n", " end\n", "\n", " for i = 2:Nx-2\n", " u[i] = uNew[i]\n", " end\n", " \n", "end\n", "\n", "u_lf = copy(u)\n", "\n", "#plot(x, [u0(xi) for xi in x], label=\"u0\")\n", "plot(xc,u_lf, \"o\", label=\"u[i]\");\n", "ylim(-1.5,1.5); grid(true); xlabel(\"x\"); ylabel(\"u\"); legend(loc=\"upper left\");\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Laxovo-Wendroffovo schéma**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd241e46ba8>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "u = [u0(xi) for xi in xc]\n", "uNew = copy(u)\n", "\n", "t = 0\n", "\n", "function flux_lw(ul, ur)\n", " a = (ul + ur) / 2\n", " q = dt / dx * a^2\n", " return (ul^2/2 + ur^2/2)/2 - q/2*(ur - ul)\n", "end\n", "\n", "for n = 1:pocet_iteraci\n", " global t += dt\n", " \n", " for i = 2:Nx-2\n", " uNew[i] = u[i] - dt / dx * ( flux_lw(u[i],u[i+1]) - flux_lw(u[i-1],u[i]) )\n", " end\n", " \n", " for i = 2:Nx-2\n", " u[i] = uNew[i]\n", " end\n", "end\n", "\n", "u_lw = copy(u)\n", "\n", "#plot(x, [u0(xi) for xi in x], label=\"u0\")\n", "plot(xc,u_lw, \"o\", label=\"u[i]\");\n", "ylim(-1.5,1.5); grid(true); xlabel(\"x\"); ylabel(\"u\"); legend(loc=\"upper left\");\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Laxovo-Wendroffovo schéma s přídavnou vazkostí**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd241e00940>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Umela vazkost rizena konstantou eps\n", "\n", "u = [u0(xi) for xi in xc]\n", "uNew = copy(u)\n", "\n", "t = 0\n", "\n", "eps = 0.05\n", "\n", "for n = 1:pocet_iteraci \n", " global t += dt\n", " \n", " for i = 2:Nx-2\n", " uNew[i] = u[i] - dt / dx * ( flux_lw(u[i],u[i+1]) - flux_lw(u[i-1],u[i]) )\n", " uNew[i] = uNew[i] + eps*( (u[i-1]-u[i]) - (u[i]-u[i+1]) )\n", " end\n", "\n", " for i = 2:Nx-2\n", " u[i] = uNew[i]\n", " end\n", "end\n", "\n", "u_lwe = copy(u)\n", "\n", "#plot(x, [u0(xi) for xi in x], label=\"u0\")\n", "plot(xc,u_lwe, \"o\", label=\"u[i]\");\n", "ylim(-1.5,1.5); grid(true); xlabel(\"x\"); ylabel(\"u\"); legend(loc=\"upper left\");\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd241afecf8>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Umela vazkost rizena konstantou eps s detekci \"hladkosti\" reseni\n", "\n", "u = [u0(xi) for xi in xc]\n", "uNew = copy(u)\n", "\n", "t = 0\n", "\n", "eps = 0.25\n", "\n", "for n = 1:pocet_iteraci \n", " global t += dt\n", " \n", " for i = 2:Nx-2\n", " uNew[i] = u[i] - dt / dx * ( flux_lw(u[i],u[i+1]) - flux_lw(u[i-1],u[i]) )\n", " \n", " eps_l = eps * abs(u[i-1]-u[i])\n", " eps_r = eps * abs(u[i]-u[i+1])\n", " uNew[i] = uNew[i] + eps_l*(u[i-1]-u[i]) - eps_r*(u[i]-u[i+1]) \n", " end\n", " \n", " for i = 2:Nx-2\n", " u[i] = uNew[i]\n", " end\n", "\n", "end\n", "\n", "u_lwe = copy(u)\n", "\n", "#plot(x, [u0(xi) for xi in x], label=\"u0\")\n", "plot(xc,u_lwe, \"o\", label=\"u[i]\");\n", "ylim(-1.5,1.5); grid(true); xlabel(\"x\"); ylabel(\"u\"); legend(loc=\"upper left\");\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Řešení metodou charakteristik\n", "\n", "\"Přesné\" hodnoty lze získat řešením nelineární rovnice \n", "\n", "$$\n", " 0 = u - u_0(x - ut)\n", "$$\n", "\n", "To ovšem funguje jen v případě, že je $u$ hladké." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "┌ Info: Precompiling NLsolve [2774e3e8-f4cf-5e23-947b-6d7e65073b56]\n", "└ @ Base loading.jl:1186\n" ] } ], "source": [ "using NLsolve" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5877852522908464" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function exact_solution(x,t)\n", " \n", " function f!(fu,u)\n", " fu[1] = u[1] - u0(x-u[1]*t)\n", " end\n", " \n", " if x>=0.5\n", " result = nlsolve(f!, [1.0])\n", " else\n", " result = nlsolve(f!, [-1.0])\n", " end\n", " return result.zero[1]\n", "end\n", "\n", "exact_solution(0.55,0.0)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fd241f1c3c8>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "uex = [exact_solution(xi,0.08) for xi in x];\n", "\n", "plot(x,uex); grid(true)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Metodu charakteristik nelze (a nebo lze jen obtížně) použít pro nespojitá řešení" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 1 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "uex = [exact_solution(xi,0.1) for xi in x];\n", "\n", "plot(x,uex); grid(true)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.2.0", "language": "julia", "name": "julia-1.2" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
schmooser/physics
Dynamical chaos.ipynb
1
458389
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Dynamical chaos\n", "\n", "This notebook aims to provide some examples of dynamical systems demonstrating chaotical behaviour. We'll start from simple reccurent equations and go forward to space-time distributed systems.\n", "\n", "Most examples are taken from the book \"Dynamical chaos\" by S.Kuznetsov available in Russian." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definitions\n", "* *Dynamical system* – object of various nature if it can be described by some *dynamical variables* determining *system state* and evolution of the system can be described by some *arbitrary rule*\n", "* *Dissipative system* – kind of a system where dynamics after transient process becomes independent on initial conditions\n", "* *Attractor* – set of dynamical states in a dissipative system after the transient process is completed" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: MacOSX\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab\n", "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "#import matplotlib\n", "#matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def iterator(f, x0=0, inf=1000):\n", " \"\"\"\n", " iterator function returns iterator for given function f.\n", " \n", " Parameters:\n", " f iterating function of one argument\n", " x0 initial condition\n", " inf maximum next absolute value of a variable when iteration stops\n", " \"\"\"\n", " x = x0 # x - iteration value, i - counter\n", " while abs(x) < inf: \n", " yield x\n", " x = f(x)\n", "\n", "assert [x for x in iterator(lambda x: x+2, x0=2, inf=15)] == [2, 4, 6, 8, 10, 12, 14]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def take(it, n=100, skip=10):\n", " \"\"\"\n", " take takes n points from iterator it skipping first skip points.\n", " \n", " Parameters:\n", " it iterator\n", " n number of results to return\n", " skip number of steps to skip\n", " \"\"\"\n", " i = 0\n", " while i < skip:\n", " try:\n", " it.next()\n", " i += 1\n", " except StopIteration:\n", " return []\n", " \n", " i = 0\n", " result = []\n", " while i < n:\n", " try:\n", " result.append(it.next())\n", " i += 1\n", " except StopIteration:\n", " return result\n", " return result\n", "\n", "assert take(iterator(lambda x: x+2, x0=1, inf=10000), n=5, skip=5) == [11, 13, 15, 17, 19]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def diagram_points(xs):\n", " \"\"\"\n", " diagram_points takes list of numbers and returns a list\n", " of tuples where each tuple corresponds to a point\n", " on iterative diagram.\n", " \"\"\"\n", " #result = [(xs[0], 0)]\n", " result = []\n", " for x, y in zip(xs, xs[1:]):\n", " result.append((x,x))\n", " result.append((x,y))\n", " return result\n", " \n", "#assert diagram_points([1,2,3]) == [(1,0), (1,1), (1,2), (2,2), (2,3)]\n", "assert diagram_points([1,2,3]) == [(1,1), (1,2), (2,2), (2,3)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def linspace(start=1.0, stop=10.0, step=1.0):\n", " \"\"\"\n", " linspace returns list of linear space steps from start to stop\n", " devided by step\n", " \"\"\"\n", " return [start+i*step for i in range(int((stop-start)/step)+1)]\n", "\n", "assert linspace(1,3,0.5) == [1, 1.5, 2, 2.5, 3]\n", "assert linspace() == [1,2,3,4,5,6,7,8,9,10]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# boundary is a function which returns boundary curve for given function\n", "boundary = lambda f, limits: pd.DataFrame([(x, f(x)) for x in linspace(limits[0], limits[1], 0.001)])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# let's draw cobweb plot diagram x_{n+1} over x_n of sawtooth map\n", "# along with evolution of x_n over n\n", "\n", "def cobweb_plot(xs, limits=[0,1], title='Plot', *dfs):\n", " fig, axes = plt.subplots(1, 2, figsize=(15, 6));\n", " plt.subplots_adjust(wspace=0.5, hspace=0.5);\n", "\n", " pd.DataFrame(zip(limits,limits)).plot(x=0, y=1, ax=axes[0], xlim=limits, ylim=limits, legend=False, color='k')\n", " for df in dfs:\n", " df.plot(x=0, y=1, ax=axes[0], legend=False, color='k')\n", "\n", " pd.DataFrame(diagram_points(xs), columns=('n', 'n1')).plot(x='n', y='n1', style='o-', ax=axes[0], legend=False)\n", " pd.DataFrame(xs).plot(style='o-', ax=axes[1], legend=False)\n", "\n", " axes[0].set_xlabel('x_n')\n", " axes[0].set_ylabel('x_n+1')\n", " axes[1].set_xlabel('n')\n", " axes[1].set_ylabel('x_n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sawtooth map\n", "\n", "Let's examine simple system where each next element is derived by previous element by the following rule:\n", "\n", "$$x_{n+1}=\\{2 x_n\\}$$\n", "\n", "where operator $\\{\\}$ means taking decimal part of a number." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#from math import trunc\n", "assert trunc(1.5) == 1.0\n", "assert trunc(12.59) == 12.0\n", "assert trunc(1) == 1.0" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sawtooth = lambda x: round(2*x-trunc(2*x),8)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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h0iURpw7k1gnRFkkOHnPqZOxiGTymeutrad3u7AMOAI9rTVE5qrY3xNHmqere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\nHZutXixqvbAzsx1mdr2Z3Whmr5/w+v9hZl80s381s8+Y2be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FgMbjVmHXR8pFXQJjn5OR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"text/plain": [ "<matplotlib.figure.Figure at 0x105ce6b90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+E1FOXa90xziLEHWhaJ2IhFDwtfks4Kg7xwvcJjB6P+Muif8LeGs+W5/8eWKA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\nIgMtx/GYMRF4E7Ai/7CyU4dyt8A0bgL2j8d/5eCUPycarvVwPu+Xq3b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"text/plain": [ "<matplotlib.figure.Figure at 0x105d96990>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pYlv4Uup8tUVsrXsr8Ckf5RuGkY5YudkCvLpsWfrh3sduGluAc3Nc74wqzkFp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"text/plain": [ "<matplotlib.figure.Figure at 0x105feca10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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I21d1t254EO7YnOThl+aaGPxZG3fD7R8o80Gre6fUVWjXphnfD9zgzht7tt0K/HYZY6Sk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\naSiwC4Ba6qRDaq0TEamnXo7lqer4OmgR2LVa5kAkgSJmrd2NArsGZn2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"text/plain": [ "<matplotlib.figure.Figure at 0x10621bcd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sawtooth_borders = [pd.DataFrame([(0,0),(0.5,1)]), pd.DataFrame([(0.5,0),(1,1)])]\n", "for x0 in [0.4, 0.41, 0.42, 0.43]:\n", " cobweb_plot(take(iterator(sawtooth, x0=x0), n=30, skip=500), [0,1], *sawtooth_borders)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic map\n", "Here we have very simple equation:\n", "$$x_{n+1} = k x_n (1 - x_n)$$\n", "where $k$ is some fixed constant." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "logistic = lambda k, x: k*x*(1-x)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uOJzecuxUW9k3JWl8M1ljFxG3nTwktUV9xeS9wJ9b1M2HRd1v/F3936MajUKS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\nluKDgtq2Z1s570+HatWew5bY9XpD85yVLpOkjwB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"text/plain": [ "<matplotlib.figure.Figure at 0x105cbaad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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q7EQymNld6K1D9+fu/nZgS7NRtQN3vwR4MfB54H/M7Nl5UZQsGqkTYuaZqscu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"text/plain": [ "<matplotlib.figure.Figure at 0x1067ab5d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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fWSqfuX132l+sf4CCfhP0n4ZvS18JenbXfTuD3utPr34C9K3u/+8DfY+HNh8D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"text/plain": [ "<matplotlib.figure.Figure at 0x1063d1ad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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j52eKCSW8J/J+EUwDljWCKYjIDcDbaXGgxanmG5qgUcDzqrot53rVmmJ9qVr9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\nDsGFrVwVWxajOMxTFw91V0YromIYPaKqO3AugWuAu4Cvq+r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UOx5K7oEblEcgmeIpqZdF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\n2wi2naUlt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"text/plain": [ "<matplotlib.figure.Figure at 0x106285c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "limits = [0,1]\n", "for k in [2.8, 3.2, 3.5, 3.9]:\n", " l = lambda x: logistic(k, x)\n", " cobweb_plot(take(iterator(l, x0=0.1), n=30, skip=500), limits, 'plot', boundary(l, limits))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1076f2990>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1DP1+TwhEevdcE/s6yp6shM1RHavL/vRzzJx3T6OIScr4GNdTsU5r1+LeEmuE\nmueCfNE/twXLZNbFOLqjy4uMPnlSqyDVfTn305Ji/t5uKFjqmyyzS82NgpYQQgghhLwrGlyJhXvU\nM6dqd9fRQ7tCx3nuEK2HKqnSI4zQlLGN0LACJReLWkqq08fGGjwroC0l9CBt5ljbGjggvebWyuxl\nh+5fMighN4q9NftHYi2zr5ZhdxYZl9vkZUjBWm7J7S+5pNesq73L+rnnPjGudo479llQ0F4YjOfZ\nNlzf7cK13S5c2+3CtX0fmIzEjTxDPeaKZXaHoSyOtuiOyr14PRoBJrGxpRjCHZQ11wjWx2itTDL7\nGmumm8lWWQH7NrmkQXHOOlHSzh5rrHtTXGg1NlPzSOjFOYm79k6NB4wzIdtyNeocnj8BVy/eectY\ncFy1X5GgYp0B1AWct38h0Scx0a+FjvsGsE6JJAsFLSGEEEIIuShUFt8SktBJJ3ayn7Vr7Q4nIapL\nvNhkRLc5i5YSqcJR9fNghFy/3elH3GmFAwrWWO2urOaVFdPe8WZs4Vyh45bBcUSmWGx1SSF9XQ5G\nVANpPdv+POK2KwyuyKNat/LCwYrKzNzOwumn6eWIpTULcKUPz0V5VDLJOca9R1rmnlnrJBmZpiGm\nehafzjmYLA/fFG8bru924dpuF67tduHaXg4hfH4J4XMn/+CLWetmHMz/qt3VPcZxmsfY9hYnceUl\ndgLqQu+Ik4AVkbEDcBfC5xexUKpMxLdQ1tf44H6n56yS4+zV9xJyLlDjeUmCxCqqx65lKC66xjoJ\nfcQqe2PdXY147nASV0eM10Wzh6nVO+67bI+LbZvqu56DXFPnPAHgYBJ02fnBORZqu+xLkjzZ9s42\nbZ1OrPG58TJUa8gaC3t/bmq9DnDuJz3n3DWaCi20hBBCCCHk1ZkQBys8oO56LPGtwDhjrk78JKJZ\nZyzOxaVaDt3Y+ij9uu3j/1KeRQt2nQToEWmJnR2AY+aB/xDbjazJyiXazse64nrn2my1LQljfS4R\nscjKtRrNw8YZW5fjjOB21+u1ysU4435S595b3htcjrU7u06G9WCvQ2UKR5hr54ngOObXWuwCeVfz\nwvxH1nWV7Ko1gVhSh7bhmBEUtBcG43m2Ddd3u3BttwvXdrtwbV+X4GYjbkZiX5uGAp5vgSsRUTY7\nsXbD1H32Fq1Mv7pOqxdL6pX7OSpxISLFigBxrwVOlj19na6hhIza18FkOTZ9HvU4SmSW3Ft7MZVz\nO52Y7MfM2vGkAAAgAElEQVRzi7aCRZ+XFnKFtX7+aYyhXaUcTyslkSf7MxZYuVY50d27Y5sxRufr\nzGFUxqk0F8UO0d1en0PhhUkVfU/j9DN4dCz5LNtDCCGEEEIul0YRqy2WksDJkhM4uqas3WaP0dZb\nvf8evkXTIrG3B2UFPWIQ27I9saQZkZCL+dTHiGB9gCpn44jZWuyq3q7jXUeJe8z1aKYgbkTE9Am4\nuu5L8NxUFXJOicuxE/f7RxHXuCXe01okG05rNvplQW1ujth0Y5J1H7ZP79ysVbvm1mvGH5UXajzv\nWbHJS60HBe2FwTfF24bru124ttuFa7tduLbroFyJR+6DXfelKBor7rK9wEMq6G4xWEejle9qh7Ew\nvlZtRGhpQdyVHrBtluJ4jHXjlX7v4JcAGtUidRLyPNl2ClvjVschasuxzVasrZ8iGk324L7NZMun\nZzlUc791DrHJnqqWxMGCfKVfWLRY+FbJ8lu4zrVjqhZTeYERhsRYRVdx1Zdex9HLDG/c3AuXGe7H\ntp12o3ZLMC0FBS0hhBBCCFkEJy62+UG2IGYRt91hsOZeq+/A8BAvQtfG2orlV6ybOp52jyg+4/y7\nrvtSS5wqFkj5DKSiIynX47j5uiKrZs0D0njTyrXV8ZNWfGtRq2vUThKzlfmKBTs5h6nxrZ4rdWn8\njGhcxLXVoK+d8GDnoL9r9NxtOyeWVkhcgZ3+vfjx5Nwr65uLXe7x7r2Ge0bHii9ei5aC9sJgPM+2\n4fpuF67tduHabheu7fmUEjs1iELdjwigalNnmyR2EsG6N3VogVSA6n46THSxVO10+R9tGRPR7Lr2\nlkRW4WHfE0/QbQsurFBzAlLRIy8QRpbfM9EvJvSaueV4zLG5GGWcjvv+Z/rntmHd1oq17WNl1bas\n27BHZu5yn+pY2n5tjOj13I2hjiued8VinHgfGCtwMxm38UWhoCWEEEIIIU2Etvqwum3RQluxympE\nkOn4UrHCamEm1ltB4lv3GAvf3tqnsrxm5+E8lFuXZhGM0kcfR2rEYlN5Hk9sTBRmWqhLtlxrOT53\njNFc4yYpK3QjYzqxmkAaj6yv8TWGmsGTyFkOS1bHObS6p+vvNfGtrPmJNb1FvE4ZxzDKhGySiNn4\n3qpYr9xXJZfpWVDQXhh8U7xtuL7bhWu7Xbi224VrW2aKeBUh6Fhsb0tW3AoSF3uNwWUWak46mZQW\nuwCu+phYdR5P8Z9YUkU0HDBkeS2WLzHtZQ6Aiec18YMwn23CKCuykqzHwCLJdhLRUkgcdbZrrhJk\nIkb77M8ZwSUvG3bIWOnVcY/Ab/11AMn1nXB9bMzzokgyKHGvtXPUSZr0d9mv+8CQGbhvkxGe2Wvg\n7fPmk2sTSWJwz7k+uXmeCwUtIYQQQgixJWEAX8wWXVOthTOcUapHiUIriDsMwucJaX1X7VY7quGq\n+pSkTjkXXW8+2nXSJlgaEdJasv3nKFh0wp8cs+NZrVBW875HFO7OOs4WLJnjJG5ydL8YsWfjNvv5\nmH6a5+YJ9Clu5UtwzljaQirfTZ/Ve9XOITefgpAtxiprHFGeWMmtyLfHDPOdV4c2dN3cl2avRwih\n67qu6Q3he4fxPNuG67tduLbbhWu7XT762hoLbJLtN34+LhxX6c2h9iCqa8YCg3j1rLTiirwHnj8B\nV7/Qx3Tdl09KJEhNWbHYAjMysRZiA0XoJjVjMYi3Ui3YnlqSKE+k2aQ+5tA+1jc39jkWNDOurfH7\nkBM0pXmMhdjzV8DVDyWLJIbzHLksrxRP683hrPFaLK92jFZr7dTja9ttPy3nEn//yM9l7Of7uzma\njxZaQgghhJAPQHxolLhTLQSFVUtreGiLbiaeVrIZi9uqazXG4JZskyftkYp0ndFX4mn7tjWXY0fA\nHpQYECssEONm1Tld42TdzsYRlihZwApol2rNgydq5uBcD33tbfbnkSu17cc7r4pA99CiPWd9XITa\nvL22dn9LHHPuvjMWWOsRkNzHZ/5sJxZhtR5NL4MyVlqbwXk2FLQXxkd+U/wR4PpuF67tduHabpet\nr63j7mutofevLWBLGPEhD+ciYO15WIuniPXY7goYyvo8YUisoxM2eRlpb1Fx54xksw5DxfuqGF7I\ntpI4q4kge7x218xRiY1MSvZghnuv6StbVse8sOgFvxa5FStx7Pfqd2J/rmiz13cJy/Ma5MS9YZSw\nSW83olhf/yaX5Al4cd/ANFGazEl7CJzrckxBSwghhBCyESouvKu7EC9FNy5TouvKimAMGNyJdfIn\nbX1+MCJHrs9IzLZYLZWAeDHftaCwSHkfLVzc5ESO2JXEQHbddlBiJyNQ7Llby7MVPpOTJRnrdD9P\n7zqquejET71rsJf8yFqkTb/uNSyJtZyr91xahGHBMvto2zri/BEmS7aNNVbfJ3lYePdOzvXY6XdO\nvPU5L0yKNNcHI69DCOGbt54DWQ+u73bh2m4Xru122crahvC5k3/O7vuu+xLiv3chZi3RChlwSmgk\n9DVOzblH6+3zP8DYAi1uqJ+QsYq1WDwjR/jidYfTw74kfhLrrH2Y748VV0zTxy0G4ZfMNR7/kDuH\niC6NI3329V21sIv3xUH9q+LMGVA1d/PxrwDiucd9h3guTS7Bp36//zNxzk3HTVjTSWSuQXU85Xp7\nc8bPZGJ9dr736+xsS6z0U12x5fykr9x1sOPFa3J2Fm0PWmgJIYQQQt4ZBUtsFwXb5jDuq9756yRP\nSaxtfADvkLouj+IQaxgBkcTcmhhaILUi92VpRMyo/vY4uduKxU4sur3F11q1GgTaAaoMEYwr8Aok\n1l5zXl4d0j1inVljRZb9MH2Vztlzv/WyVx8WfqnTZNFucSMvMMoQ7lh4p65rf71yczkzkZW9Ls3Z\nkudCQXthbD2e56PD9d0uXNvtwrXdLu9tbdXDon3ILZbS2SJd9yUo66fN1HwErrz4ViuCxTp157Qt\nkXMVjmP3/YqI1XGN4iqsS/aMBLFKKFUkI96yAsda9CytgsOJjfVcmpOkVM75HM32vuaucj2WOPCn\n4Vw76zZt52HPVVja3bXZmo1UVH/tbXeOAaL7fDBllrx1zMUdO9by6rxb4o4br+MitWtrUNASQggh\nhFwoxuqns/Vu1hLbipy/stYGnMTPPaIF0CQOekDqYimCqxcNcXv2ITyXiVa5zwKDSOnFqWojiEtx\nH/doRazt37NCGnSSpz3SmNZHjGNzJ5dpsfuRlibyBKPNOJ2IeG8sI9ITl209h8q1SNrU2s1hAaGW\nxA9n9j/gtFbeC5pZYtE7pnW9G4915+W9cFlK7H7oX4SXyFbieYgP13e7cG23C9d2u1z62irrVFJn\nNcbDfvhnOBWfZy2vd8CzCIBeCBRi+B4wlAWqYmMI9bb470bFyR7tcUpc5zLEtsY17tV5AUM8qpQJ\n0ud6QJoVeRQDXIqFrKCzTuu5HTHUgR2J0Rjf+ZiJOZVr8IAhFvoA4BDCj35pY0Olz85kznViOFcp\n31NCzctak4GMmDX36gNOpXiSlx0Z0fio4qNHLwjsMd62MCSiak14ZROL7c+4l2ZBCy0hhBBCyIXg\nWGSBD+hS3Ig8SOuYU0BlOJZ20dIn7TyXXhEPd0iz8PaotdEiZG+Fhm3r9CMCo7qmBVHQl78xcbxS\nmqjLWUDt2EZ8NLnlNlp0bTzxEQ1xpzkruOLHOGmYJBZUzbtPwOUcOzmT81xy4yur80NljbQb+Sij\nsyfoa3OZYonPuZK3uCPbuer2jpv82ZZaCtoL473F85BpcH23C9d2u3Btt8ulrW0Yys4AJ6sXhWwG\nHVuJQdx16AXk1R2A26778kld176dEpxTkyUFKMuojYM0IiNgSAKlH9izosqzVKrPO9NXB7/maLG+\ncE5EqDjeJtFnBY8nKlVzfZ1FbCaZn0uWxJSrl/y+VCi9JjVxpublvTApilW036deRmy3RJAe11x7\n7SJfLA/lXGu3vvMEF/ZZhK6bVb/2VQkhdF3XhXpLQgghhJD3RRhn7C0KEpJcs3ukghY4ido7tT+J\nacWQrEk+uyIgM+7IamVE7TVifLMSL/0YSjRqK7GXUEmPJ4wSC+k2rfeMHj83XkF46LknFuuSxdBc\nI12vV1uxm+IrPet3a+znUhbBlrFK45XW1b4kmTLX3BrkrKJ2rNxxdvuMsbP3eHrs93dzNB8ttBdG\nCOGbS3tjTJaD67tduLbbhWu7XS5hbY2YpZBtwMbNduPkULfAcxcfc0XYSoxs747sPND3/bdY2YLJ\nPCtNkMaTnlUupyKEYPa9xGPc+Gp1rK2JOxllvTtKH0qsJ2LTWCX1tbFZjRNB51luT4c9/xq4+qHF\nEutZFOecbwtTRLNjrbWxtO4902IFju0eve0K9/pOEbA5Msesmu2YgpYQQggh5JWxVtmu+0JPtAaU\n+7DUm9ViVLYd4dTvdPqSh3dpK6I363LrCeBIYrE1sY82QVFWUDbENGYtgCo2s9iXFS5TLItdmjU5\nQMXJapEr39XxImSr4r4uVB9/V15EZazAZ1mwp1CwPra+MDjaYxss9S3YOOJEOMt2PX879tTrVnsJ\npK/H0mtCQXthvPWbYrIuXN/twrXdLlzb7fJWa0sxO4+gEh7h9ABuS8/oREc6HhkY4m2vcRJd4qZ8\nH/sTcTZK4qT61HG3DzhZaV/EvdiIh1wG45qgLMWvusmqVL+frHDwxj3HMhvnK9eh0/OP8ztqV+ow\nJKyq1dXdYxwjnEmWNP65tS8aPJfec8+7NuYwv7GV2Tt+yssMGScXU2+Fde2lQM6l2JnDUrV73fUd\nzyfMioWloCWEEEIIeSMoZtsIKgmUEkeJO69xs0ySEGEQspZsNtYMWrCFOG6fIdiMvYdJIqXaJ/Gi\nimyiJCiRXBA//flkBFM/bklAVa6BWLR1fLI+71F8prEkezG8XnIrl8K8R7VtVd/AcuJMSESmnleL\nO/REknul1a1Ztc3Vuu379tyNp5xDyXUZan1LHgFzoaC9MC4hnoesB9d3u3BttwvXdru8xdpq6yzF\nbBvxYVdEVFJb1Ty0i/V0Bzyj635jH5hzcZxJHc2C6LEC9N5rY+Z0B+A6I+IQt7kWOUdg3EQLa1/2\nxREHI5FsxZ0Rmwf9uSb2VF8d4vVsiDe2SYdGgrol1nUY+/kr4OoHbyxZO8+1dWGBqc/HpXQtvfsh\nc87Z+ybub3Xd9co7VTnTdVtc7uVFx6Sxp0BBSwghhBDyClDMnoVcu5zg3CO1nn4lO9QDtNShFRF7\nDWDf4A6bjONZT73jtTUSyjrsxNrq7VoEeK6hYvV9yiXxif0+IrVQC6MXAiGtfVxDuz33sbJ6rmpb\nMq7s96yoNeGWMsTQ2n4yLG2ZdcnEipbOa2fWtZoFuHQNvXZmv17jkQtzznMgd31zc5LPpj/Xc2A8\nX7ocbwJaAbYN13e7cG23C9d2u7zm2lLMzkNZVsXKc4sYu4piBuGrH5w4Si+Ws1XI6fa3VrAVRG3i\njurgWS+T2q6mfb+/MMderKt2yXka4TRKCOSh3awRXx6o7TmyCZoaxsuIwd/6/RA+N2UwNvGsvYWy\nNO6Z1BKKyblnX6LYdnGbW0e2ZN3XcbDxf+12nEtM1lvPzfZsqarcda25FIchzt3zdJgEBS0hhBBC\nyAqEcX1ZMgHlsqvR9UuvkVoMswmdDGIFtUmNWhiVnRE8N1e9z1rvnBjZZJtnMSvtD2nSKsn4nFyf\nsnWsmGFZxEeHccmjJjETtyWJrdS+BsusT8k9eU3x2uiWrr8nYrcwb++FRS3O1X3J4d1jdrudz4RY\n2pEHgOe1oGOKHfHbLbFGFLQXBmO1tg3Xd7twbbcL13a7LLW2FK7LYyygnWPRelD7k9Ixp4fnYW21\nS20YkkvpNdNCo3f3deYzEl4Zt+Ek+zLGVljo9lroem6aVlAY4eFZHMW6m4hQfX3sPArnK22T+r21\n4w0jq2XGYmfFfXKtlVuqXtvkGqv7xAq81SyzFauz6+5dEdzuCwL1e8Ym4+rLQ3nH63vMtD92Y5fj\n7MsX1W6Ce/iIvtRWp+olnxtXS0FLCCGEENKAEkPNaPdiCt9J9GV1HOvlHk6JGAxJjh6Bm10In3Pi\nSwSeFpryOamtqsiJskQ4qQfzozpuFD9b4ZD5nMt+LH1bgdHHDBesbLcq3jbJQuuck8zhsSRmHMut\nti4mbuJTratxbT+F8PkXMp/MNA6567QC7hzUdWty6S61yVlI1Xb3Xi8IxYCYrGyudbxm4c+I8tK9\nNRsK2guDVoBtw/XdLlzb7cK13S65tT1XuNYQYctYWp94/d1rY5PtyDYb0wpcBQzura6gUJam1nI6\nHjausBfaalvuPF1XUHt+jqCsWjftZ+97Ac+N+Cn+L5ZuuVbXSEUxMLyM8MoV2fMcWTdz8aKx7bWS\nL/0LiZY1XFHcFrMde2N7VlCvrVnr+7jt0ewflSuqrb33O65075h2LbHTuWOzGbFPfTIpFCGEEELI\nJGaI16e5Fo2u+xJopS0TH5IlvjXr3uq5o8a1lARSHUymYGccILVyZSmJDtO0z/xrRJqOexVc666y\nat7Gfw85weK4NbsUzlHHHSclfZTYkjjmJ6SWZ5mjRdqMYowz18FSFYhq/KlCfXFKIi3ek7UkXh7J\niwszRtHC3Ti34u+wOeK/Zl329i/1ciF03eX/Xg0hdF3XfYi3mIzV2jZc3+3Ctd0uXNvtMBaTzyi8\n2+90jNda86CVdsBYWUdWJ4zrWso2pzzN8yfgStxSvSRF1hVXjr2GWXv10qN/mVGKI7RiTVvVUBB5\n+nycY9zkSzlhWGmXi+nVsbs7DC7L+oVPn5HWxvw61zibaMg512JiI9XvI/B8DVw9deO46lwm3tEY\nS1ppS/3OtQq3HjcnnrWlb89KnPkZAioW4pprcjruPM1HCy0hhBBCNsVUq+tri0ptqaX78Qkl8nvL\nqkKsgKNkTQb1YP2jX2KcfVX6SbIhK/FYK+GTJN5BakXrBU1GxCVJnsy8rJAT66iIZ0nupI/R2WN7\nUWMtoJ4gye1Tc5Hz1Nmgk/uzIV6zj8eNbRMR7Vhrc9ZTGzd8AB5/1nXdtxX3Yp3xuZ/zUjGbNc51\nb65Z5NW51JKYVUVkafwKk12t14IWWkIIIYS8ayYI2NnuwmvguB/fr/3gd2kooSnPeXJNjkbQASch\napNBVR/+FVrQutlnMYhON07Qszyp40auoBnBqK2g2j13JPpiH4kVzhFy/XmZcXX9XktxvqpfOV7c\nju8r5zfCim6737pNd2nmXRGm/UuIynXx5r66oLIsZZltfRnh9GGt1c2W3NrcKy9Eit4Q9fOlhZYQ\nQgghGyY+/Ni6pDkuSrx6iFVWCdu7+PkjCVsdn3kN9GVmtDW0WF/WCiLPcuc8ZGtRWXONHY2lLbFm\nfymhkWeFlBcx/T7bb8N9LALdTRhlRQ0KMZjmOgQA11GsvKhmvdVUH1+5Z7Mla5QYsuWU5LiRZdv2\n6aBr5b46S/385tZnxhg174NZSZ4Mo/v7tdaAgvbCYKzWtuH6bheu7Xbh2r4NjuWuxCzxeilrq4St\nuJbexc/HSxflCyAu1zdK2LuuxRVXXVOX8/lTfMx1H7JVH9LfI1SspXKV1S9QOtMeZvyDfNeWRese\nas6jM/1A7SsJc9teuwnLPtc6O0dsdTGmWJ3HpARLBWuffQGQrL22bA+ux8nP7T6kGZZf3RoL5K3F\nU+Zi7semesEeGRGZXNfS/GrjeqJajddaa3dRd2QKWkIIIYS8KRMsrx02LPK67ssnE8t5vdUY2+Ak\nxoqxxb0l08R0PuRiJrVL67Cvj7N8ARC0QLAW3XiMFmluPVaFFgdJ0iRjDU1K18jamrneq/2tliwv\nqVPvulzry3F9zlmBnzAIdB3jbMfO4omWjIDpE1/ljptDxh334lyQvTWZ05d18VVt7frucboXm4Tz\nlDEnHLtY1mkK2gvjEt4Uk/Xg+m4Xru124douyyW5DV/i2jrxg9fxs1hvL96VuoQX7xzC55co5nXi\nJmB44A3qc+IirLBxp10In/X+vVi9YWJYHQEo++U+7TDE7/ZjKUb3s4rxtW7ARzjunybmVfrIiYOR\npVfv19YyE7vrzR0wwkat0aREO8FJTmXmnXWnFjfwjLhKXI7Nz62XDduOu3hmY4v9mZwpEqsvIyIt\nQnBvfn/090Cca1U4N8652q5wPou9vKCgJYQQQshqeALGYbUSOe8Z87AvYmoX0mRS70LgBqf+bjdk\ne9b1eSXL8Q7j0jF9bVorVBofhgMGMZkIYOtqbF68PCAVraMaqFEsJzVZPaGs41EdF+pWbPKnkVhV\n56NderVgsgmDiuImrlViBbdu1XHftXIBHiXwasVZzxZrYtXquIaote7GM44fxRSX2qFgHXfuC/1z\ntzPjFd2j9Xb7M2fmI+y9a5/52fJiu2dDQXthXEo8D1kHru924dpuF65tO0p46Yy1nqvsRSQ9ei9r\n6wgfLa56t+TIRVxbjXlgtQL8Huk9Iwm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A4LkD/iHin8sj8Pxr4PF3L2u+/D73O4A/FUK4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\nbZXtui9BiQP5HZwTNtlSOXa7Fnkzfo+PLMFWJKs56n17tR3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UWlDQXhh8U7xt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O/zR50Y55z33s9moQAAANTBQ4nIHsoFAABHgb0ovyXae0FZmC2RSJ3QBG1MtEbE782wrSh6MZcb\naM0djBTVyYaipkSvsShP+FsQBG1xmK4WspoaQ8P83ck2RnhuHomJtZLQ3ooxLc4PlddRaYfUs9As\nh1ULYbe0m3seajUfPLQAAPAK8YcqoTcEby0AYHvc0D6k1tNQlCYlVmsQwvZl8YhpucAUyIrNs5zZ\nlGfU2E+Y2JRVm2tYNWc1NWkxwMfv+XiG47lwbzmm5qHErO2W3uXUpMviPFlLCDaJ/O3yS9CBoN0Y\nWBOvb2DffjlH27I/9A+0z08qKbzxajhH2wIbsO02GV50g5fmSYpXQ6GiH/L8aO0lnAvEYVtVXRnL\nWMLPqRf4SCiq3OYostxOisgkQBAWT3xfDSlRo9luqUBz7t1XRBe/QGLsbFLi6XDs5J6PudcNvZQT\n72TJhEYu3L1E8OXEYuuJBmuIuQKqHAMAAGgPvLUAgI2xo72H7YmI7tnLuVpF2NJgRGiMOZYFbSTD\nK0vyR0vGGssNFl7L4pBafuxSj6Ao0tRs7fO5EHwXE6yT5WUiudWtvaklAq2o74r7J8cSXXan1NaW\n42JRE0vz0y0ghxYAAMAIcmsBAKeE/Q66Yy/Ik6rsXiwVw7bnckVnYq/kxV4bW2kbsb4N4bTquYEW\nOaINBO0DDTnPRPShRT4pvy7SJx+4SJyJy9a5o0sotXGD/mbPg5bvbB1L6XGBTAiy8PAjhxYAAMBC\n4K0FAJwKIYi4UAmTbNLTNL6YE9GOewWVl+UWVVhlTquJJXm/OREhvLjVYr3kuAT3RHRLRL5VQSNR\ntGomTGVxovAMtMzPrBhzUS5pq34lib/dlzSE8ltCpAfC0j7ZHOSa522pvSBoNwbyefoG9u2Xnmwr\nvCJvh7yku1PPbp+KnmwLpsC228EdlucJvGU/x/I8eQVb8UL8/J7o4pvwqSZEOHZsqqpvCTKkWO7n\nwieXb1lLjec6VSColZAUf4d4aPitc++89986OYaIALs7waSsJbQ4Ww3aQs2kxQIbTUL0cznB1vEu\nFfhEELQAAAAiDN7aMDt+89qFLQBgHWRI8QAXuOpLtJZjevDSPvxorckKEd6b9c5pwi/XPm9LYhWb\n5/S7OnEfpdCO5kqKNkLV5t1gq+Yhx4nCSKowayVWlXZ2yhiS406NqVAkj/e3oA1tvEP4s6vKhYWg\n3RiYKe4b2LdferUty1ULoYA3LYt9nAO92hbAtlvAHZZT4TyK/SXem+Fl2RfnBgaMv99S4ykSGNpY\ngiBdO9cy5r1M9ZfyLDYQSIGZl3NoxxNdaN7YybVEvOlHwfi8TcKna/NX+TNS4CEtyinO9D3W3bDk\nymoe4hbPNgQtAACALCy3dkfw1gIA2nFLROT9R8e8b2NOLO1/57ylg2hJiqlUOGVLcZhp45Ligsza\nxuyYXIjnMQoNlRDLbzWeHpswuKPh79DweVbxWAtNX3FCYHG7BZ7Y2TkDS/NxLQI3lZ98JydfrBMi\nYhzVQNBuDOTz9A3s2y+vwbYyt5b2wnaVUK4t8Rps+1qBbbfFIGrDC3ZKFO7EtnEN7UMY6LuvvP/2\nU3ZOIFc8yoxFoNZ6J7ceCZMTIFIoWQRLThAdbPbuK6KL79HBMzireq3YvOnfqoJiSlQi9mKIiY2x\n2rZ2TGpsJd5jbfwK6rq3JdeLHFoAAABHR3hr3xLRrUM1ZABAIcEj6/1HN3wOFdYDoaqqtgRM2Hcv\nzomihWcuGDuvvhu2JSvsWnMSaR6CrVKam5siJkBzXrvh/2z154Jr18Ylzt/nR8dCbSPXsou3V0XW\nA19DbnzD/rFgmuU6KgRubBJip/2tj7RfGua9KIcW69ACAACohnlrwx8TrF0LAEgyeFT5e533/uMb\nTSTS8PvF+49vRBtVFVVLzs9cg7Y2qik30TAutR1+XoPxa+JrJlAz93eskJzzSqbGkPIs1thZGVep\nUM/iRHVoS+iuYr8bYpWDWU5qNLdZRDBMzk2MNdhmYl/tfkXOC8xylbVr0z5bwTq0AAAAjo74Yx7W\nrkV+LQBAZfhdIV9Yef7sh+H/pIcnJfYs42jgzdTCI0sqvarHNPDwqu3n2igVIqzdyTXz8w7h37bw\n6dIQ2YSIlLYZw9dX/rtUEtK8o+F7kBn7BGknC1K41hImCCz2LJ2UWAoE7cZAPk/fwL798tptK/5g\ndrXMz2u3bc/AtichCNVH5kG7IfFyz8SKFm48EnuxH2y7qIprBunNnHk3KSLKrR5Lvq3m3BSyjyBS\nCu51UZXe3Bh4+7nrF9/btyRCzjVRXTq2FGzyZazGbYkC4AKf5l7SourDlfc7VtE6F1W1i/zcJAe9\nxTsCBC0AAIBmeH2Zn+4LRwEA0oj0hA9SCIUXesvvi5SwYvs+N46ruCCNzMuUnxnZNgs9erNtOW9Z\nye9dJSRWE1ijcD/B7/RQ+fpzZufH1AlEp6kCnXuujjiG6uuPeIO160k+h4n+mi2nhBxaAAAAqyAK\nRxEpeXAAgNcB92ol8gPHNS0pkY8vcgLHXMJcjmTGAxjND4z0PZ5DiqBuLVi0/NnAkpzdTB83JOzQ\nIi+2Fi1nV4kOUqOCSj2ghrEU3QfreSV9GnOxiQ7fDUt+bkXeqz6uQFlEAnJoAQAAbAjxovGW9nly\n4/IaJx0cAOBosNw9n/nu8/Ddy+D1iRVDEi/tN+741dZVbxX/EBNSqZd+J6o/y/2pbSlygsUSqmvo\nc3L9KfFVIaBSxareis+xyYcmlOSQ5igU22MUg7H5kAI0yftuOdGg2NQU8dBy0geCdmMgn6dvYN9+\ngW3j+DMvHAXb9gtsuz7D9/6SEp7ZABNRYSmeW6dXEx6PZ/280CCC91u+/lzaNhOaaxLCOS+wNk5O\nPOfXLjBiAqI2vFjZF/XkGcZZvZaofXzq9/YxcryW27xkXLPJhhKW/M3TQt0Nx97wbeKwSahww7/H\nRw9Hh6AFAABwFJTQsG4KRwEAouyoPCojiJAQphwtPsQEIl/qJ8taocGyPav45EI3JpaWjvkYv2dL\n8ijrw5Kvfuzc9TfCozyrwBvz7regdMypcOESu5SEjacmX1rfD+v4JK2+hxC0GwMzxX0D+/YLbGvH\nzwtH3Z4gVNAMbNsvsO26sBfm0oJLD1QugrPrcVqozY0saE/1XrUSqdr4rP0tzTOVeay5XMp6b/XF\nN6mxcw+zEJBh3ddqwmQDixxYIsrUYkqxEPuadnNeXEtjNTnYBWNtAgQtAACAk+CnS3ZcIr8WgH5Y\nKI6KBLDSb3R/ansu/68kPzDWp/QiynEvESBLx3cCQlGvoiidmtDqgSdqd2+yyzNxIvnTVWNJPStW\nz+spxSenVf8QtBsD+Tx9A/v2C2xbhzJ7/nbIUXqkjSz1A9v2C2y7KjdEdS+sKeGnob+cP7/XPHkG\ncr937nmf837HMU3yUA0iIyuKjCJEHX/K61ebv6vsu7IemwoV1+w/Hf/8eyuuazYpytuxPleRcb8Q\njWsmh9Dw1ASIaVIlFRqcGY/JI78E6+RL7HqOIZ4haAEAAJwc9rIRXhZCKPKLx1I/AJwVji3Rs6CZ\nG7khkQ85eiUPv0v0yYqUBzf3wi3FlUZGuKY81pZiUuN15oRMg/Dh2qV+TEJtoHLScp9DGz6JnNkH\nXgV4BRE15jdbwno1EsfvMs9P7vyR3KSFZRImwTj5krC/qao0cmg7BTPFfQP79gts24YgXt2GlvqB\nbfsFtl2Xhd9ZS67jpILttCBQ1LbVVW9ljmjqWM1bmRIrxhf66qgVayjzCoWTZiHQmQrLX6Y+78l6\n3t8S0S2xCZWGXsIwWfNE+hJNM9FomVxgueNFIePWvGQ51oVYxscnXlYPg4egBQAAsDl8fKmfJ9pI\nKDIAYFWiy/Swl2R+3Mx7Kc+LbSsQO5e099BFxUGpcDR4QFWvmdWbXEKJYNf6yXlhW4nl2NjcYcmn\nkJvbXFSxSdemFYKX5tVKlHtkWRfWvHSV9efcfUIObacgn6dvYN9+gW3Xgf1RDC9al3Tkqsiwbb/A\ntueHFLPM8ykmuqI5tC0Eg2mt2aUv60M7l7SwMm/NuCxe1NoQ7VROpm1s6e+tPxQcDG3f0uBZXZg/\nK697rCacCvEt6G/yDNc8R6X55zW0yrluCQQtAACAzaN4bENVZCKsYwvAZgj5sz6ylqqxjdQL827Y\nN1l3lP8sCwdpIZ+VovOp4hwVo3ApDo+uuS5eTKnU83hKcWPsO9zDJ1Lyshsi80oXhQ7nSOSTbxJl\nvJNJqaXto9DGxsBMcd/Avv0C2x6H4WX1DRHdDZscEd0MhUBW+aMO2/YLbLttBpHq2eQV0V4kTJZM\n0b77Edtmi+4EYazt8/t1TWeRIfKcg6iOtyX27UgPY76ntt7ZouVlWniYLYWTcvvn9zf/vRXtPRK7\nl7V/K5RxcjHGn8tZ0SR+DannItNfNa3bIjpcB7+W4fNqf49jwEMLAADg7FBy6UIo8gvBYwvAyVji\nmWVtyFBVF9IMpOCyeOm00NclHjHtcwVcXKqevJrQ4ErPrOmcSO6yPIbn/LYqRKS2I8cdy+FlbaxZ\nmIh7f7V+WhZlSk48BDvpEz3H/9u45Bm1AkG7MZDP0zewb7/AtqdBvFyGF4rbIezxQ4s/nLBtv8C2\nbWkRbszaCuLpkfYTVnLfxOs0F5zxtUoj+YW7yPZxvzI2dWmYWCEnMcZVq75G+i0u+hMh5C6PRaRi\nfVJimaHIGLNLGlm/t0vzUZUxjna1TKAI7jP7a8YTXQ825SFdci9y5y6dKKoFghYAAMDZI15cwlq2\nN+7ES/4AAKoJkRdPNERdGMIYc6JUhYkz1YvGfqd4trk2929SSIiHHdd4kHMCMbQdtllDirW84wH5\n+UlMKiQKddVR6EFWj1/bM+kz66XXXkNtZEDL610qOo/hFYag3RiYKe4b2LdfYNvt4P3HNyIUOSz5\nUxWKDNv2C2zbhuCZXYEnGpZhyYk9Lnj34vQTIqLZRFbid4DJY2oMcc5NoO3Y7yhz37ExZIpKhUmB\n+4rzc/3L6xwnBJYKsLQ4PXxvrZ7fgRuqnOBsKdBbcWSBqU3EjAXaasbREghaAAAA3SE8tg9E9Jbg\nsQWgKSsKWSIWztsqLDGGIYzyjRBGNe1z8RqKQHEhGxVLIdSVCoQvu2+al1Uy80ynvJyJe7FKGLXB\n/tZwcUcifH1r5PKCS4T/EmRYfySCYTP3EoJ2YyCfp29g336BbbeLPyxFwT22nobKl7kXAdi2X2Db\nOtgk0Yj3H10Lgau9oCu5kDmRdr9fh9bcX7RgkBSTC4XDfQuBbs0NLchzFDmqdXmSNaG+vC8tN5rB\nPIT7763M042NidnwQ2osBnYt8sRLPOQWMV87jkJmOcClkzzyu9tSjEPQAgAA6B7xAhheulEZGYBC\nWD7pSHi5b1EMagnyRVu+cKcE8Jrff5n/W5HjawlhfqDK64iEKqvECl6tSS5/2pALHKiqdMyKX83a\nrSxy1ZLZ9Vj6Lp204KHGYV+ja6vKe5c479eMFmmDc85770/6SxIAAEBfiMrIRPuX9EeEIwMwR/G+\nqt+VNTxYS9sRWEJwo2OpHduaAkfzMlu9uIn2RgYxIwtjNZkELJh0aNF+ke1ZGzMvfUrYF4R6V2Pw\n6ibzhWPnxyYOLB7Vmmd8/v2q03zw0AIAAHiVRPJsL5FnC8CB4YXzlm1abdKHV/y1HpsSF9aX9Qy3\nqZ3HrjprDMnWmBT1CefF2kuEYs+W6GlxDUNfl028dZHQ5XBNpX1EnvdcmPbRC0gpNnsbu9ZTTshk\nQsurSJaYBsfHOffZqccA1gP27RfY9rzx+3UFHR1e1t46d+2du35w7t1XpxwbWA98b+MM4YWemKDz\n/qPLidngmV2YT3tPkVBKIXoTobFz2w4v0Mk1RHMk8jsfrIJ5OPZB2f5lpehOjm/4LO8nr7Qcu4+j\nKPP+45vB/j9sKURYW/e0r8acJdhW3i/5fETu5VuaRuYY+rv2wz/VNoMtxzYtOaY5W6f2G8XlHe3r\nRETR+uD2teQ/a6H0ueuiFfJ+4aEFAAAABtgf5BBed0lEv4g8W/BaEAXUiIi8zzK3UtcAACAASURB\nVKyx2YrCQjH3RPPlVNjL9OfhQK3NgjzDR4oXjOJFiW5o+Yt69vza30EyBzJXUGntnOLY+OT2TPiu\ner+Y135X8DxZSHl3n7Tt1pDqTAiwKXy5ItT3hoyTCI2fh+beawjajYFqi30D+/YLbNsX4QV+/0f/\nIsy83wxeJ+TZdgK+t1NY6L2nYe3SU07iyJd8Lcw2nvOn27bCA3rPBBKRUu04VbQo4gGL5V2O7aew\nijTluNk6saX91OaHGsdsmhBgtr2fbp+0HbuP4dk2w6IOYvml0fWOUzmpmT6rKgkXMFsDuTScvTSU\nec08cghaAAAAIEImz5YIXlvQAW5axdXTwufa75fw+TLhzYqNQ+Z23hDRU6owUyS8Vo7HtC0loGl/\nfxwNYi5zrPk6recIJsKvQGSaK/yy3OljVo+djS/lVZde59h5fFtpHu3SQkepqINMrq88Xx1zbnyJ\nSYlLiniVDe2aJh5iudIt8qQlELQbA2vi9Q3s2y+wbb8E24ZZePai7WjvtYW4PVPwvZ15ZVs+wy3y\n5IInbSx0w3PwYi/G+2Oe33v/7adLB8C8Uk80rL2reWRjAiBxPycv9iVtklGYFowly1IRXuu50wXZ\n/HurifqYx3GB13MX+dlyf2JCrqZIlVmIF4yvxLbR5y/VjxTnFf1GgaAFAAAAChAvSyHX0BHWtAVn\nBPPAEa0TRn9PCzxhIp9dC5uNrr+Zaj+F4v0K3skPQ3+jsK75jidyRFMVX2cv/zHBlvOKl4hSEWZN\nVDFBoXjcazyds3vOC36VhObWjIF7galwIkGM3+wdj3hrr5RtUc9uZCzyPk3W1o2NIbevZDJHTCg0\nKw6VFbTOuX+ZiH7fe/+1c+4/IKJ/joj+mvf+D1sNAhx47TPFvQP79gts2y8x2yovEsi1PTNe4/dW\neGSJiPwaz2lNeGeEJ6J5jqkQGrO+w8+p3NcUMQ+SFI2l3kVigjznLVzoTYxSGaKcLIy1ZKwZUaqM\n8ZMvnLuO5nqmtmnjTo2NTWqMxcEG+1/xZyERmnuZ6WeVPPWYuBQRB1Vt5rywxubGe9MiBNniof1v\niOifdc79CSL6T4noN4nofyCif3VJxwAAAEAvKC/QIdd29PDAawtOzfAi+nb4uPpay8zLZ/HEjMue\nCA9UCPX3wzbHzpl4a1t4lQTW761JtGueSss5clsiL3L285LfOylPn9XLW9t37HzptV3aXgrhoQ79\nFS35Q4k81dp7yCdzuCfd8n1umVLQ4DkrFtUxLIL22XvvnXP/JhF94b3/Tefcf9xqAGAK8nn6Bvbt\nF9i2X0ptK3JtQ0hnyLVdXUQAO6/leysiCB7puNWLraHHLrGPSBQn4h5bXfi8+4ro4htiYZWKGEsW\n2gk/pzyBg6CYrNcbo6aAz0LU67N4LuWxMe9r7N7Ewp9jYchl1/78M++/XfX5dYfCSZ4O65NPyIX7\nksGrXTgmHgIvJ4kuW3g6LVi88annZY0xWgTtR+fcXyaif5+I/hXn3HeI6LutBwIAAAD0RCTX9i17\nKTna+p7g9cIiBohOEAbPPEi3zl2/aM8884R55nX6cth+P7wEv1FEUkqY/4LoY1Y1mWxevhv+naXE\npJQIRQ6Vo+9k/+IczfOcJOaZVfa9EPMoVggJ7oWLhihbBFpJrquB79d4B5Vx3jh3/ZD4TgQhy8Vj\nWMLH++lyPhPbpiZFQpsLBej47IbwZ96vQWxHyUUAUMWyTdY+a7EI2r9MRL9MRP+R9/7/dc7900T0\nXy3pFMR5DTPFrxnYt19g235ZalvlBdONH/cvs62rywIjvX9v2fN26mfsnvYezJloY56wMUdxYMyx\n08hfy0UQF7zNJzJ4zSIpBIFLGYrqh2WKlGZluGmy/xL7iP6CaL1T9ifDOlMh0KKPt1QQbmsJs015\ndxNt7IguovpF5gfH2mKe9bdKM9pxRHvP6OiJF/cnalt5fG2+cWrSo2bCwJpLLcK8J7mvvG9tPJk2\nm0UjWATtf0tEf5OI/g/n3Hsi+s+I6Jdon0cLAAAAACPcO8VelHmFZCKEJYOFCBF28kgA5qV96/RK\n4HfsJX/HBOMdOzeZI6gIMx76H17ar7RzBnZy+9BWWKoneOOC53UiFBXBdJXaz8b1IXYNFsT9kefd\naaLO2o8QmZ7iExI14bO1VY8n66fGQps1lGMeC4Ys7/XVEkFXGnZeEq4u70nNRIlyzmyCwBp2XGIb\n51zVuscWQfsrRPTrRPR3iOgTIvofiehfqukM5Hkt+TyvFdi3X2DbflnLtn6+ri3R/oUxhCWjkNTK\n9Pi9Zd4kog2I2cAgAILQ5iGcMa/fopBGi20Vr5YlBDlZgGqFPFi1TfHzTOhb+494ZHehXTa58EhH\nytPMtO+Ins2e+0wY9JUmiOUEiggtvpdCMRP23fx5aAEbl1wOaFZwy+JVT6AWj2p9P0xFoYjoWyJ6\nR0RXRPT/eO9f0qcAAAAAwILy8hNC2m4dlv8BBTAvf1FO5rFgXq1bnoPI9se8idw7VJq/F8RZ7viZ\nUBl+5mJmsSeyMsdxUgCodCwlIi9C8PZ/kP1rbVnsVOotDscMYvNNY6G4NJ81hOFmw4415P3URHaJ\nx3fhPYnarOa5c4cq56Zw7FosgvZ3iei3iehfJKKfI6LfcM792977f2dJx0Cnt5liMAX27RfYtl+O\naVvxkhvyH/nyPxC3DenleytyszfjldXgOYkuURBJegsj+2ei6fDS7cfCUZYxlVxDy7ZSIZysLbNI\nWlIAKBI6e8XsNBGhCXEeXaZpqRAVkS2L22XPI69SncyrTfTH16nVJkdW99LGJlAksTBpQwi3SfzL\nqIe1r90iaP+C9/73hp9/RkT/hnPuP1xxTAAAAMCrJ4gS4bVFlWQwYXg+zkLMBvy+iJKaKxcJA569\nRLNc1vE8LqaG/eYiPUuOSR3fMPx0vAc155fkYMrP0kttaDtVDXnirUsclwsXjgq2ipxhT0ORvlhk\nQywcl/GUyyuNjSP12ZI/bqTYi6w9y+E+aKI2FxpvOW61HFomZvk2FIRaiR7zecAB2LdfYNt+ObVt\nI17b/cchJJmOu65oN5zatkthOamnrmJcTErUDvu13E7O6PkR+4eX9ecfxF5za0RHZahwFqNXLIt1\nLDEvcE3IpzKJYLmGxb+rjN9bcxix3y8JFaIGXqSo1aIBNM+s3K7YpMhz7VgRrJR9Uzm8wr6m6s+x\nMYb9iUmEUfBbvj8tsXhoAQAAALAB/LRK8hiSTIcqyU8EcfsqcNO1Uc9KzAYy3rBkAZncNmtRqJyY\ntAg+a26vxUZWL5c8PkdJCGpK7EQ8hjOPX+l1pPpNoV2XFF1GUevC79SIpzZECkzWrS0c8+Q+CVvE\nvL9Jj6rBc0wF4zOdk2gr97dnaa5yFOd9lWf3qDjnvPd+c8UNAAAAgFPj5lWSA/Dcdgr31MdE4bkw\nXEvwks2q7GbOXez1qQhPjbVRWqyqSTsp71xF34HJGESeb3bZGos3cC2Pd+76DKI6ldc9E/OGUG41\nZLgg/DoZcrzgeVW9rcZJl6owaFuIf53mg4cWAAAAOGOUfKtQ0CR4brEEUCewl2BHZ5IvW0jIFf8Q\nO8ASemnNE+U/5zydmRzTbH8FwpkLyaxwWBIyzPugwQMpx8AI3jWriEnl0aoc4XdUtYdwGPfsmgyh\ntbElhszVtyNjST57mTERieV02LGTZ67mOxVjzckLCNqNce75PCAN7NsvsG2/nJNtFc+BXALobMNT\n1+CcbCs88T1NUNzJDZmX52ie3vRY9xnRJ1/EjqvIIYzmP7ZiiU0XiLRLEgWNEqHGO7ndMhYppIZt\nJsGkjJeInt8TXXxTGppsEf2ZiIeJIDaOffJ8szFc0j68+YGilbrngtUaYpwiZWs2waESE/bKMdH8\n3eEwdSmqWiBoAQAAgA4RL0i8mNSNwzJA50h4Aex2QsIfqumqXiLD+UkvEh0mBGLsQi66mBwKFZVH\ncVLyQs9/ll7bTP7ulTxmhbDc8XnKeNDUpWxsYaR1Y251rWsXJEr1k+jzifb39C2Ve45nywOlrtG6\nbdh+JY9LPad8/7Bp9PLHBs+OrRblEgjajXEuM8WgDti3X2DbfunBtn5aTIqHJfNlgHry+pk4B9sy\nMfVEHYrZ4P1z6WV2Rq+e3ZOz986y8ydtV9xHdVmWtewh+2FeLiKi+6W5k6Xn0j66Q2tvsbcwsdvk\nEW4tWBN5tGpecW48MY+rCPceSV2HDEdn7Gi/Znmzokvad83Y9sSDq5xzI7cfrmulZXsAAAAA0A/C\n88Q9twhL3hjDC2WYfOi9wNdb2ofIW54/bV3alKiJhnSGbalc1JIohpy4MnrviCKhuSQKLa2FEjb8\nJuKpyz6X8viCYTTx4LX43vCJl1ZRLQvbkfeG50BPWCr4C+2bhD23TzVjibaLKsfb4pzyeUA5sG+/\nwLb98lps66Zrgno6CN1uw5K3blvmTe/WBgEeBk8RATowC2nUcgJTtl1SWCpzDU2qHS8dR207mfuy\nmUiOU31vpedWKfrVxPaiz+qIAOV7MxvbWuPOjSkyDiL6+rZG8/VWHQ8AAAAAFXj/0Q0vah9oOnv+\n1rnrF+eufa5gCGgHCzX+0LuYHfhAg5glSubN3nv/8YoXlwleQyWfT0V6Y4PnrVT8RfrJjd9EymNs\nZYjAuE3sz96r2Lh6hoXA821f0n6i75Ft3tGhWvAsjLawzy/5M8j6DBM4O6osTMbGdMOe+WLbi7Gu\nGiFQCgTtxtjyTDFYDuzbL7Btv7w22w7C4IqJW/4CF8Tty9ZeaGrYqm2dqD576vEcg+E6i0JM5TlM\nAOxEDu1aTJY+0Sq4hpf/lAjg+5aKhSXnyxxPsc95/9GVtF947ExExsdp/94uuB+Xiocz5Tl8IvH8\nNhJ+9+w5HydLcu0qkzP3pIT5st/3Z/17Bjm0AAAAAFBRCpiENRUd7Wf7xxxcpYAKqIDd51eXx6yF\nDvOfjUVqgqi4SeU7ivaLPeCZPNbm4ZvafTHk674JIl8rFlQ6Ru6JzBUfalEwKtLm6sWhCoqPjXmr\npc+QpUBXy2eIh0W3LBzFUUKwZz+v9TsNHtqNsV83DfQK7NsvsG2/wLZ7hln8N35fMVmGJRMLS345\nzQjL2ZpteRGo1yZmLVhfiPfHPD/njtM8XTHvl7ZdE9pyjOFzxvv5Qylsajx7Srhqlec7Q/jeW8Jf\nxwJWuXs6/H4xicKS7y2/t7UeUzbJNCk8lLu3awg4q2jP9L2Tz0rLiBseKs2jJlJ9LL1X8NACAAAA\noIhIQY+b8SPWua0l3MO75FEd004APPyZY4aUxzxSw77JurpWNC91bL+C9MRFPXMlIknzvBq86k08\ntYc2333l3PU3JR7nynHciDBoR2I9Xi7YLd5zPo4SW6QmOizeez5xwMPbc22lbCsR5/Bq3LNQ7FQ7\nNUDQboyt5vOANsC+/QLb9gtsm0cJTQ4vfZdM3HrP1sPdAluyLXu5fHzt3tmaF965oPyEiKhpyGZq\nu5JvmRyjta+SUGPtmBXY0SH1wIoq3uqf84tv5BbjvTWHgg/PEf9dRrT30MrIlFGglj63Nc95ZKJA\ne96iHvSUWC1pJ9e+1vYaoehEELQAAAAAaIifrnP7JR2qrHLPLda6ZbCXPNyTA7tBUNTko+5oKOqT\nyucLB/MQSRqKcZUONjVGP19LthmZ67lnfd5TIpdW8zJGGAWUxfOsiaelHjrLebHw8MJ+rtx0ObNY\n5MRsbWB5TyOezzEsd4nnnHRxmBWMxkmGSdXuheMc29SEOUKOO2Lra+KBZcC+/QLb9gtsW4/ykj2K\nWyK6PXVo8oZse0uEvNkSMkV17omef8Bec7mXaRS7kaZn1WprxiXbqBVxbT2by5HCrRUxYaMJU+17\nu+I9+UCHVIAbEpMd2mRAwf0Jz8hsoiHnyYxdb2svcSzMnJ9b0u5adoKgBQAAAMDqJMKSifbLAW02\nNHlNhAcIHBiXLYq8HCdDKoXoGb2TNA3RrKpQa8DsbbLkLPLtgYiQJ20bz5/U+l0gtpP3rZX3rQYp\nDo3e5xhPxMKsI23FqmonK0wP9+iSDs9iLA/VFHYeI3ec4TmM2jAixE2RDq2eCwjajbGRmWKwErBv\nv8C2/QLbtkd5sd7RQeC6Y4nbLdnWY9mjEZa/OEOEB0/CIfn53LaJkM+xjRpqCubErsVawGlLtCzu\nkxDhimCPf29bFxwanp0xkoT2v6eCx5Y/R0/8HLEvN8ZJVMCSa6iNAMiMNVsETBxLtDDSoRQIWgAA\nAACcDOWliHtvubglIvqw9Zf8EuCdTTK+DEdsvtQLOr6ka+GiOQFKhS/riXYn7cRCemPCI+fR3Yt7\n3Yu7wHNpugcV4n6REI2Fxjb8nRG8tDPP45BvW3w/E8eOkxotws6Nz/XsO1cRXn50bzwR1qHdHFtb\nEw+0BfbtF9i2X2Db4+L361G6wWP5KHbfuv1atz7mwSvhlLblYhbeWTvDC/5V8OK6Yd3j8OJ/eBHP\n2naWq2l8eb8c/kXFohU+Zs3LmxiPXE7GRME1mpDjbNl+6t5mbLuGoAq/hxzto0ZmRb5Krlu7tuFZ\nDkW2fkjGCZPhnj9o/VvsISZpduEcMSFyZbmn7HlQx2MdUw3w0AIAAABgk8h8tEG8BPE3ybulM60Q\nDDGrwzyIL7TPpb1ycS/TJER3f9zVe+euvxFtRdcKdcaKvQZP16JcxxwyHDvWV4kAqWAmtjSvd66P\n0vHmWOv7Pzx7k7z/YeyhSNQ977/02odNk6WQLNcyjKlFruqsSrMV5TqiyzrFPOgtgId2Y2wpnwe0\nB/btF9i2X2Db7eD9xzcR7+1YNTn8s7UH254BjiIvyIP4DEup7KYv5PO1SjPck3GpEy5crCLAemzw\nuIW+ImO4oumSPJxdrB+LcLSMk3vixDiT3lHu/bN4DmOe3xN9b+WzMYZdWzyTBu6I6EkLKc+0+8Q9\nqNK7GrbFOuXPRNorPvPapp6jp8T+UTy39NTCQwsAAACAs8PH17sN2zbrvQ1jg3c2DfNGjmvKEqne\nMTVnkgsivp15o8ZlWI7gIYxWZU5R4uFl9yval7W9Us+y9IKv7CE+OtIzPsCF2+iZTOUzcy+lDDPP\nCLxZFWUlgmXyTC/1iKaiFuQ1se9ntK817Q1BuzE2tCYeWAHYt19g236BbbeP8gLJPbRyzdtRSMK2\nZ8E97V/Sx2rAxARbOs/yk+LOSgVkwXmmdWlLBKSln1KsxYAS4lldviZ3f7RJBzmmw7GH723rkO4M\n99rPwxjGZabEdq2A1iUpkw7aNUhvuES5/sXVhbU+a55L+QylbLoUCFoAAAAAdAX3fMrct2HbIG73\ntV2OODRQCPdcxbxbKa+XbC8mgKzhwLF2E8dn12FNtSuu05TnmxqT9Z7UCI6IF7M4vzZ/n69+7Nz1\nN8Y809z6qqWEybEPYru29M4sLJlHB2jCP4b1uEhfWSL3ybT27BY87RC0GwMzxX0D+/YLbNsvsO15\no3iKWGGpC+nNXX1ZIIQbl2P1Gk7PSX9vl3pIY2HMPDSaDusrm7xx1jFaqTm/MCR5Jnh8evmaHRO8\n97yvsmu8+IZOsFbvYGP++0IdQ0zMinYexPGxSZYQyvykidqaCYwaKidzjiZ2IWgBAAAA8Grw/uNY\nENPNi0epoclgG8Q8Tqmcxdj2IChqQ4wHgkDTPMZEe49dNoexIHS5WXXYejE5wSQqRe6oaUzG9rKs\nIKjUgnNi7KMIjR1T4HHN9XUYmPF7oFE7sZMY3yrVjGNA0G4M5PP0DezbL7Btv8C2PfP1r7JcvERo\n8sjjwhdReGcrsIiv+cv783vlMC6oYkvPmEJhmSi+5PtYPzPPnFIcSBXJ4YdSL24qD9WK0TObFJWG\n0OlYQSETzr37iugiGXJ8BA/hTrQvn63xGYjdp9wYE2NvVmCsZBKhoI9smH1rIGgBAAAA8OpRQpNn\nApema99CmG6PMS9xXxRqxihiRRhzdKkbA0+KaLnPiNYUq1WJTRXpKaBIcGvIe09iHdcYcvzGCQK1\n71q8//hm+B3wlqZ5tLNnK9a39qylcn2VazN5Phs9LzWTM0cVs0QQtJsDXoC+gX37BbbtF9i2X1K2\nVQRuammgcA4E7gqE3EVejCeVl7i31SdERFoY8BgmLLq5oUieYgJVxASvI+tvtpRNhVdORRxf5bkr\nRBY+WlpwqVj8eP/tp7w//Rh9TI09tyZx38rWAjZ5Uybml4Sb5yZ+loQ9LwGCFgAAAAAgg/KiNsul\ng/f2KERz84IY1SrtZtoKSwOZztEEamQ85lzPY9BCVNS0EfPm1Yp3Fuo9ri9dE0Zbi/cfXcEzNpIa\nV2EO646m1dkXTWTUFEirDxdfxzYQtBsDuVp9A/v2C2zbL7BtvyyxrZ8uDZQUt2A5XESIIkOal/Se\n59Ba8j5pEEc50cFDlC2et9Yv7pk8yOr87hqkqFHGtFJhoOc3VgnTstiRaCdMnkSfF6UI2SUR3Yn9\n0XxTWS2Z7ZIh2kX3eImdUtepHVMrfEuBoAUAAAAAWID0xqbELDy3izC/dDvnPuNhv7H2Sr2oLbyu\nNbmfx2KN8dSImvQ4Hv5MJl3gKPdSisHYmIftYemdmrGp6ysXCOJcWHdVzqsyjlll59g5rXHeb38C\n0Tnnvff4AwAAAACAswJ5tqdDEbRq8SEthHWFMVDoWxG0R60Ka/UqNu4zeNev2Lajj6Ml1irPuTze\nzH14Gfa9EdvDM/tEkSJk2rNlva+l93/Jczz9PtRpPnhoAQAAAABWIgjYIGydu/YQtfXEwhuVl/kX\nfhodKtJOwoWZ98wRq3Zc8sJf+vKvhWRar7OG3PiW5LUW9Kl516O5n5pdMx7QtQo/WZkt06Pl9y5F\neVa4gJzcz2Ne/6knHiBoNwZytfoG9u0X2LZfYNt+OaZthxzQ7YfFbR/ubU3ksj7T8JrriGYFhaSQ\nCmGSppDmSN5hqJ5MxESGxftoEQMFuZpZMZcRtou9bKnt4nNVfm3seytyTkk+GysIXZnfalr+qTCv\n907ZFvp9IpoUQcvez5pJF0nL+9iqLQhaAAAAAABwTlwO/08KQg15muHl/g+9//gp84zzPL87fl7l\nGCaiVYqppUgBKNFCeHNt1fbVAq1ti8fY0rYy/hUKUamMQpZ/rnmmMtc8LtGjhanzYmkN+2zOmv1B\n0G4MeAH6BvbtF9i2X2DbfoFtz5J7igtHVpjm4nvDC7Sn6RInwas18+xaX7i1/ZooXDk/9pL3J8T1\nQxBAJQ0uGWOs8FODMOAdEV3y+xr73rYSyBVcau1X9Jusnh1Q9h9LwE/6b3FfW9kEghYAAAAAAJwF\nWggpY/SQMTE5Fs1JnLcml7TAc5vwnmqhqM2w5t2S4vXWzisRLuLYcQLDGs5c0+dCkpV9JZF7G+6j\nxp14fs3e+RTHznvNhDEXr+vLgaDdGMjV6hvYt19g236Bbfvl2LYNebQoDLWchOAa8hif3w+vudEK\nrzUCKCf0RK4uUSPhWSgQsx67NcSf1UNZ4tnTc4bt31vpMW8p4uQkSWg/UW2YSLENK0w2W9YnMd7L\n0F/MO94SzWYizF+dNIptG36+of01L14/GYIWAAAAAACcG5rXk3u5foFoJghuwg/8hZrsxaDG5X2s\ng1whnzJ6XMrzrLQZFViWvjWRqgiqnTwm9bk0ZLhl6GsDbvKH7ImM8y4hhqWQvGoZZVBz/1bIGb/a\nt+uqCudB0G4MeAH6BvbtF9i2X2DbfjmFbeGlbcZY5ZiUojyREMYxf5Yi4cgGnnKeqOHzVcxrZqgg\nKysoF2G8nuoCRgVExXLuOlMiq+R7e+ywY+26anKyS6nx0mqiuELcqt+f3LbWdoGgBQAAAAAA5wb3\nxmpFeUZBIXMOh5f24kJNKZHa2Es4G5vRc5cV2mt4Mxd4ULNhqrmxll7Pit5cLsyjedNK2G1S6JWE\nwddScy9O7A2f8ebUAwBTnHOfnXoMYD1g336BbfsFtu0X2PbsCd7SOyJ6mr7YP78Xx14GMUoUy828\n/tK56xdrgRpLPm0qjDa2lE2tWBBt7igREhrrvxVa+/L+R65zrPQbv3eb/d6G+31HNM/fVe632Ubh\nZz6hknm2HoZ/mg0eXGSpn1i7se3W52jt5w0eWgAAAACAE4Cw43p4IRkWYszEwcOPWGgqX+pnJiBK\nXrRzxaC0dhMC1bRMi9ZGwvsZwrDvU+e3FBc5T6wlxzccVzKuGm/rWp5F6XGNXYcWRVBxHeO6tPy8\n1nmtnNwYT53H7Lyvyr09Ks45773HL3wAAAAAdIFz155on1N76rGcK4qAUHMXc2Gea72My1Dn1PgX\n9BENRS5pf+lY+P23hAgHlhR2OtY5JVjaV57VorWK+XMl77slNJ1vD6SOt9i2lS1qNR88tAAAAAAA\nJwJe2kWM3ijhFZtUnLXkmZYW1DEy8ZIKEaEKhFJhUJNz2YLIOE05scSKeC3xFG8gb7aIVl7x2ATJ\nsE8Vog26Lc45N9DMmwxBuzGw3mHfwL79Atv2C2zbL6e0rR+qHe/HAVFbiRpWu/dgPb/J5ctSu5d9\n2e5Rii4tOV9uW+IpLTk+FppbIuyXfG9X8MLP7mPIUx0OiQlBuT6yOfycYzjeXIU4tn2te0YNv3sQ\ntAAAAAAAJ4CLWlBOQgRdOffuKxo8QFwopHIbmRAp9kZZvG+5No/lSWwh5mvyfrXt0ju+RuGgU3tm\nA9MJl+l2WUCKH58idewx811r2mrZPwTtxoAXoG9g336BbfsFtu2XLdkWXtpyREVfosnatBffpM7l\nIqJ1gaRURVdrjmNun3UsvC3hAbSGB5+EtBj7ZOfcddgUC9suykttPU6ae2D5sWooek3+qfW4YxTc\nsnrbU/netUDQAgAAAACcCHhpmxHWpL0nioezaj/nijdpxF7SLccegxrP3Vr9KcdFixhtRVCvSe4a\nS+7BGhMhJW1tZSIEgnZjIFerb2DffoFt+wW27ZcN2fYDVebQvWaEp+dmnSGgXwAAIABJREFU+He3\nF0ruM+990lNU4xWShZ14iHLOQ5UT2LHra0FLEXUKpt689Pf21OKLeelfhk13NWOqrVgtxyJ/ls+9\nNqEjjy0VtvyzJT/6cJ6rmtyDoAUAAAAAOCHshfXWuesX7z++OfWYzozd/OerHzt3/Y3yAq5NHBTn\nkjKbRSu1KoJEzVutWSoltuxK7HOhB3Wx6LNe09aFdAMciarb4w5bLvOOHXcZJlCUdsxe7hoPcCr3\nPLX/WGAdWgAAAACADTB4dBwRfXgFL/rN4J4wIlv13MCa99nN1ws1hYgO512yQ2Yevtx1xMR0Km+2\nJvdU5jEHL19G0FJuLJY+1w63XQqLHHjSwtlz+dSK4F187yrGP3KMe4p1aAEAAAAAzhjvP74ZxJnq\n0QFRnojyhZZiRWhW9EqORapSY4n0+xTO1/qqGGtUpArhUltIaXKtqdzdWm/e2kWNVoAXK5tQUBBp\nPDeW472isFfHXzg5k10yqsVYVw1pcc79mnPu7zrnfuKc+88Tx/2Sc+7ZOfdvrTmec8A599mpxwDW\nA/btF9i2X2DbftmobZ+I9l66U4fxbR1ZTXj6+fm9csqODp7EmJCsHktoX7ygx8JJx7Eo7dx7//Fq\nGONVzTj59eUEJu0nUHZL7gm71p18dge7PKREPN8m7Trn689j44zlbB7zu8T6C7a/USZQoqHqwzEP\nNNiFH3vka7mnYYKD5QWv1fduSdureWidc98hor9ORH+aiH5KRL/nnPtt7/2dctyvE9Hv0D7MBgAA\nAADgVcLCU29oeBHeQvjkRpGiYAx7LZmsaHh/Z+I10bYpb9fi4Sohcm7wBi/Ckle8tH0i20SUEGCr\njMc6hkj/szxiUYCJh5zf8/3Wvi3HpQpPleR0x/q1jMPgpc6yZsjxLxPR3/fe/wERkXPut4joz9GQ\n38D4T4jofyaiX1pxLGfDRqotgpWAffsFtu0X2LZftmpb9oIXPDRAZ1yih0i+8H/yhXPXq4Wclhbi\nSZ3LKWgnWRU7l79Z0V/J+Zroia0XWyyCLN9bkX+6WKyXwMdvtbEQlg802C2Vx3qCsOrqdX1zY2WT\nFZurcvx9Ivoj9vmPiehX+AHOue/TXuT+a7QXtNuvUAUAAAAAsDJM1N46d+29/4goNoYTS41wAZOo\nHttS2Gjhwg+8nxZCIyaIaj1axxRErcJTF4yzWoC1ghX4ii7dM7Abjg/e2Se+c63riIlv6ZE1Fusy\nrefMJqCK13+OsaagtYjTv0pE/4X33jvnHCHkeEtr4oEVgH37BbbtF9i2X7Zu20G4eKLx5e/kL+gb\n4i3RxBPpaPLu+fwz7781LWUT25/BLI5rxEGOvBezWTjyknFNKh9njjVj+d7mCmCd4Hs0etQjY1Cf\np5LwXs6a15kIg58JccNYLlukVawpaH9KRD/PPv887b20nH+BiH5rr2Xp54joX3fO/WPv/W/Lxpxz\n/z0R/cHw8R8R0e+HhznE0uMzPuMzPp/qc2Ar48Hnpp//JBFtaTz43OgzEf1J59xmxhP5/KtEn3xB\nRDdEzzfOvfvK+28/3dD4jvz56sdEF98Q0SPR8yURvT+8zj4/E9F777/91Dn3Waq9/Uv28w+I6KdE\nF9+rGQ/R83vnuLj6+tf2/3/yBT+e6JMdEV3y44f+3xM9/MjSX+r40B/R15+XjX9//IFpIa3l36/n\nn5W19+6r/edvPxXXy3Jnr35MdEVE9Cl/HvZC0fT8/IDo4qXF9Rk//xq33/4aL75HRPf8eD5++fwo\n9v4dfn9jvw/k85n6Psn7Nx3PHta//PwFEe2Inv+Q6OFHueOn35fv/k9E9E855/4KEf0iVbLaOrTO\nuQsi+ntE9KeI6B8Q0e8S0Z/3oigUO/7HRPS/ee//V2Wf91iHFgAAAACvFOaJJMqHL3aLY2v1hm0i\nhFEN75TrpRILC04VxkmMI7V0STQcOrdUScxrXLJUSs2YT0Xs3ifug3ZvTddkLWy0BINdd8P+4vGz\ntqKh7aXttSgwFmhxT2s132oeWu/9s3PuLxHR3yKi7xDR3/De3znn/uKw/zfW6hsAAAAAoCdExdRb\n565fvP+46vKLW4O9QHva3wc1xFEenyvM06qgE0MLH70klndb+vJvCT21isOWuYstYZWJx/B6JQ/3\nXp5T2M3aYfu7zP5kqLpFdNfYLVWASy7HUzo5MGxKFilbm9U8tC15TR7aaegK6A3Yt19g236Bbfvl\nXG3LvJT0WopFsWt+ZNVfpdczeGifiJ7fDKGl5uq6FrEqBJbmdZ2JkWN4BvnYxOTHKKDWKMaTGIf5\nejVxNPyvnr/l722mKnH2votlfswe/VzOsDaekmNi58hnbWnRsc15aAEAAAAAQHu8//iGFYt6oc5D\nkIcX4/CSO/NwsRfyezqIge+y/WpBrYjASHqapMAQBK/xbGytxaMmFuTPUnTw+2QJZV46Hss+QRBF\nV4Ntct7Ok5KygeIV/5KGQmYpxKTDjQhVzhYii4nmlNAsFbDynFMVIONA0G6Mrc44gTbAvv0C2/YL\nbNsv52xb7z+64eX1Lb2OpX0eKePxEy/tL9oxQuQ9KNtMS81ExjEKDimK5Qv8MfJZVwilru13Jkx5\nf/KecE+7Fgp7Dt/byH30NITIG+43n5whijz7mQmWyWRGzHPPafxcjuNfPSoAIccAAAAAAOfJ8AJ6\nyzZ96M1bmwrzTRwXIxoaGVhy/xLhs4F7Lqa3kse6Zlh0RMxHi2TJokeBcyx6FaMkvL20MFjqPGuI\nco3XVtnP7XhLRD6X91+r+V5VMYFzQJbIBn0B+/YLbNsvsG2/9GDbwcPF12C9DeHI587gWfK0fxkO\nwnBSXIkfS/sq0Lv9v+cfsN07eU7wWq0kgoJQ4yHI9zQVaPLzZFwNx2JqfxiveW3d2n4YyaJHw78f\n8n8Hm23jexsKWFntJY7f0TSkWGtv9txG2vySe7rDuQk7Jz26/BjtOOszGuw4fHwkInWlmxYg5BgA\nAAAA4MwZ8mpDCPLZ59aK4jhEB7EVQjZfiOiJvTDz5XjC0igx7x8Xx09EdFl7vzLe3dmyKtpxiqiu\nqhhrFOe3sR3HeFZi3sKVQp2P7rUt7M/RYSmuAJ+wmXmouYCNNVoSOj8OpNxDbz62xAtdCwTtxjiH\nvABQD+zbL7Btv8C2/dKbbf1hbUueW3t2y/uwMOpJXvCwPbzk37BtgafDy/zhFVcTmQMhNHhWzKmQ\nqADJnShDlHmeY0q4LBACm/fex67Neq3HErGl7SuTGLdyv5YzHCN1nZr4Lb0vCQ96sWDWxj1wQ4ml\nt6xA0AIAAAAAdASrEvuWiNwQtvu4lXxNA0EgOv5CLora3A3/8xfiqEc0IF7uq/NFS/ISDUzGYRW1\nEqP4vSttt5Kd9rO8zhX7H/uweDUbwgshpZbauaH95MLdsC2aU93ak51o3/z7YQvecM5Zzdi9BraS\nFwDWAfbtF9i2X2DbfunZtkP+mqN97hoR0VtWpGWzMO/NIx3GrhG8qk/D/5fT3L93X/E8WYtHqTB/\ndcxvFDme5pxKfq62nRI5rTlxs9I1l3BP+1Du8ZkryL1Ur43n0KbaWkv4NSSI3lVTAuR9yNyXMee2\n8NnJPu/yODGOJ2qQuw0PLQAAAABAp4gw5MvBW5utNnpstJzZVO7pcD1PzBudq3KseSVvnLt+GNpI\nLmcixkmJ49SiVVobOTHTSuwc2ysa+llpAmVHdPVjIvqmZCwrjCPW1xX7ObUkj4w4uCeaL/fEj8t5\nma3PlTwuEkLc0os/CekXfTWpqg1BuzF6y+cBU2DffoFt+wW27ZfXZFsm2m5pH8q7maJRvJjVwKPY\nr72oj4JSCtJIiKf8rInOqKgQ50XF9krezmKsubcrewjDZEqTUFSWHz3a4JRhrrVwgarsHidZLO3k\njqmZPLE+xzI0PnGomgogcooXTX5sanYOAAAAAACswxDqx8OQb5279qcUYYPHmItZT3Mv5472L80P\nLNcwGj45HPfCrksr+nTv/cc3FXnF90yQ+GFi4DB4UYCnNKQ3dvzS0OBMu0Vh0mvAx5cLJ6ahkJd1\nzCuGVS9lUkxsuLaxQBIftyWMOvOdKLoHNWHb8tmPtbVGSDg8tBvDOffZa5oxfm3Avv0C2/YLbNsv\nr9W2zHP2QvulQ25P7bH1/mMoXsUFt0bIywweWu6tY0vyPDuiC7kkyhh6TZE1MS0FpWZNKuGaYnzN\nsXjexDEh3/dK7A9if5VxtoBfx/7n5/dEF9kCYFtGPK+TaAN2WLFN5vdq3ciBVm0ffie5qircELQA\nAAAAAK8Qv1+7NlRcJdqHO64ubAfhysfhYscK7mk/1tgSO+MatLT39M6qH2uhjTVhq4o3Sl26R/lc\nI5pj2yd5jrHryOT93svjVrB9tvJvaZ5vja1OQeae5kS5mqedsbOaB27NwzWMOYkII44KanhoO+c1\nzhS/JmDffoFt+wW27RfYVs1je0tDHh81KtgytB31vLhhrdwgbBXRKV/QnzKibUd0MYpyIZakh1KO\npcjzqYlJFjprWool1o8yxtEbHSvko7U//HwfExvsc7YoViVyAmISLlxeHOvCXBBKsqWc29LrNjyv\nO35eyxzb3GRJblJlbSBoAQAAAACALK50SUOOLRF9qH1BFR7gwLgmLs9BlaJKeD/vpYjTXp75i3VK\nNOUKPBVcG3GBKTymqy6TxPoMIdj8vo33MXimRai2RrIolkZKwLDrH8O7eSEh2WesHYVjrKObJTfm\n2v2piZBUeyRsK9uPnHdDNF/vOdK+lZl9ZAj0wvZnQNBujNeaz/NagH37BbbtF9i2X2DbOcLTcjts\nDsL2MfaSLRHC2CXOvaODR5ho8BDTYX3KSUEnzaMoRe7+mOf3RBffU8ZDrN2k1zJGrPiNchz3BidF\nSq6vkmM1j+ySdi2UiJVIfueOtxNra3/8u6+ILqpE7Rrh1DXj4BM9qWiIXFEoTTTKiSCtLdZEWMc5\n2q9yjto/G8MDMS/8MSYeIGgBAAAAAMAEIfRCuPBbZ1jHlh0f8lgtXtBQ6TgUhAqFikLe7I6L2Egb\nrIjOw4+IPvlC7I/l3k7asAqUVB7osciJHCJd4MbEeK1AlO3xPrW+lH7CJMOsENJcGKm2PTqxaysQ\nck/s5/G588sKQ/FJG/m9mxVJY1EZLZl9P9cWt877qmJSR8U557331oIBAAAAAACgIW6+VuyIF0Wd\nmKA1hyqHc/y+2jEPU76j6Uv9JQ1Fq4SXL1Qv5iKBV49Vq76WhnuKY2cCe2j7Zfi5eHnMVE5irZdX\nu9a1i/Sk+ozlW2pjahEafkxqxsue9ydp29Kw5Zrnx5ofW3At1W3Vaj54aAEAAAAAQBIRPjvxhjh3\n7f2hoJNn55QIkA80LRYU8i5D+HEQ0x8SgpSHKQfGnzXhpIR7LiqKNLTtaO+d5ttGVvNSGSoIp46z\nCH4rifNunLt+0ER5Kjc0Jtg2yKQwUwHh2Z3QMqS6Iie3mljI8lr2K545AuvinPvs1GMA6wH79gts\n2y+wbb/AtnV4/9FpXlkhZou8LMMLriZGHYlQYSYQb7jXafg3hGw+X9JhiZ+3kbDKSX5uGIflZZu9\nmF+Jc3a0F91vZGEqC9qLPxMFV/5QTKu4beM5u9J2rQzX8UT7tYRnyw3Fzpnb4/l9+KnmPvRE7Hl1\nh2JhiyYlWF7ug+U+W78/rYGHFgAAAAAAFKN5Zfn2BXBRG3JqeT7mJK8w4vWRY+A5ipqntrSwjzm3\nseULvlLcqlk/vKBPaVizJOHtlZMWFWM874moyPN6SfsJGtNzaPR0hgJrLdoqItZWi7BmDXhoNwaq\nLfYN7NsvsG2/wLb9Atu2QfPYLuRyaDd4JCc5quzznQiFZt66iw+0D2MO3NPg0Y149XilZQvjMjnK\n2GbiOUXKy6jsC17lUeBbPZTG8cyuawUmywPJPFv+TzuZf29P5RFMIJecyjKM/46m+d9m77N23NDm\nozaWUq/2IephPgm0Fe84PLQAAAAAAGAxQ0Enz3NqC8+fFAHi2yPHaZ7ASZVix9a5FQRh+OT3lV6L\n1oxVxORsrVxLYaQU2r2ghR5Oa98t4f0ZwoxV73NAeqgTxZKKCiC1gnu5Y/1pfeeLJdmW0RGh6qYK\nxgu8plXPoRZOvxR4aDfGuYdRgDSwb7/Atv0C2/YLbLseMgzZCvMG7YIo4cIx5V3l3ttgW7+vNBwq\nJd/RQSjdEyvEw/NTE9dkyiUsfVGXx4t83Htl38QLqAkSbZwtPGq1bcjzEvdoXG4mfg+fR/2yJS8h\n0USoL/ZyK7nZhiWNpse1FI1KW8fw5puAhxYAAAAAADQheGmJqvJSOZdE5AYPqyOiR+6xFF7P0bt6\n8ADOJiuC9zS69E2t965VvqDYv+PbLV5O8Tms48v3hYrR0fG0FD9ik8mbpxXFkvudc5+FsGNNzA7b\nVO9ta89sZJxj3w37swrH8bjY9yWH9dglxabktqWTEhC0GwP5PH0D+/YLbNsvsG2/wLbrMIjaByoo\ncsMZXm6faBC1Yvck/3LoL+T3BWF7SfQJOXd9x09s5D2biQNXse6uHJNyXggt3hHRfYn45GHPAnVp\nmBIWFCxKepUzbU1E2Za/ty0KOokw+uTaw5rgbzHGyJjM4thq51bedQhaAAAAAADQmiDIouuOZrgU\nn7mH9ZKGdWoVr+QTzeF5mdEX8tKcQeZNVMOrtfxemgq7Sb6oFKvK+UmU8x/CvV/DW7k0HzV1viHX\nNsoxc4SVyY2q3F2l8FeYxIg90yejcR5yWLd3mJRyVakKELQbg4dRgP6AffsFtu0X2LZfYNv1YILk\ngfbrwHoi8kNOqwU3tBMrLrVjBZ9CjuwTezGehaXyQk5CMFk9T6p3MzXGTHuU6neNUGBOA2EiQ5rV\nnE0h3ifHkyjiJdHDUw+2PaZ4bUVizJNiV8M9upOTG/wEuV05bhJ63mLMjXOWm+TgQtACAAAAAIBV\n8IcKwm9pnxNr8daGF/BHvjHkRg4fb2gvej2xisWH467eE9GnvE0Wkqy9RCeF1XAtMUEcxkNCsE+W\n8AnHD+O4L/XilR5X4RU3URm6HY6dFCzixb7CtnxTVz927vp7pId/awKvOI+0FmtosLbfWsm4kMVh\n5oEwgcSf8aXPbCubQNBuDMwU9w3s2y+wbb/Atv0C2x4HIWqDt/YxIuiCOCUiutREphKCezP8z17c\nL75JeZKUcN4bauDJyowzcEmV+cWMrABfm1wBJ3mcpY1Ue4djLr7JDO1o98Yi6AomIGbrCktxmxO9\nDXO41cOt5xz7uYSgBQAAAAAAqyIKN3FhK3mkQ0EoGs7JhQzPKvcOaBWRX2gvXu9oED508PaqxZfE\ndRQJiIgYD3nAI1q/6Uq/NZ7N9PFWb+YaubclbVq86DXtnpLIBM1dZL8Ja+51AR+0Pqxj0ba3yseF\noN0YyOfpG9i3X2DbfoFt+wW2PT6DtzYsxaMRQofDy/zs5Z4OQnRHe3Hsh/ZuaQxTfv4Z0YUUs+Hl\nXhbZeRrG1loATPplY5kVgSptQ3rsDLmUM1qJvYQ4VkNpKbOeqjZxwc/PfW9PWRSq5hg5kcAnPOTz\nGLs3PCd5OPdO5jNbJxJahAfXPI9LgKAFAAAAAABHI+TgCWH7SEOe7fD/rThnFHIJ0ck8vg8/ioie\nOyYairydTGjcWTypJWjnWrxaWsjugBo+vZEw0SY5nRbWFFLWQk3GsfC1a68sEysF4c5jdW1jO9Wh\n92tMCFmAoN0YmCnuG9i3X2DbfoFt+wW2PS3ef3zDQn7fpo517vqFHc+LQH0gsfzNvu2sbeXyOWPR\nIkP+pSlHMyM4QpEqNuZ6sansnwmYtb1kop/xngrxH1tXVV0SSaO3761lMkMLoy4NrS6wu3nCIRcW\nH74naz9zELQAAAAAAOAksJdzLZ825Ozd0sGTOykaJT4nC+tE2In/oy/zLA94FgYafraG+mp5tLHz\na6rfxkR07ryGjMvFiPHPrtlKjSBfU0hZ86zl9hbh4XxiIHX+mkWiNO9vaX+t7ANBuzGQz9M3sG+/\nwLb9Atv2C2y7HfywlisrGkV0KN7kh31BSPK1OXfEliZx49I8zz8juvgeP0+IXS5gR49vqxfshCcy\nMHovS8M0F3hajxLqm7mmOyJVpM/uU+wedvi9VScatDzkDXIfE+bHzGWGoAUAAAAAAJvAi3Vr6ZBL\n+0iHtV75i/JsfdNh23uxX3qUpLi7K3wBH0OGDTmM5gI7pd7eEnFbUgBoKbHCQ4mcX3ObzrnPGgzx\n6FTe613kXvGQ+ZRttUrfUeFZ8vwdU7DmgKDdGJ3NOAEB7NsvsG2/wLb9AttuEyFqJW4oJhWqHgch\nGzysPCxYeo+iL/f8Z0u+Ksv9jRVf4ssUqevp8vZS/S09Xqmiu2ourXX5H3ZsLFxW9W53+L1VPeeD\nrV6I6CYUUiOyFXoqwfo81Apcg+gmWhgOD0ELAAAAAAA2BROEoRIyF7eOpi/0IWyYhnO0F+idsm0p\nT3RYu3YMYVa8kHKZoJGSiriZpXCqkMV7LGNbm9b9buU6Kic0YstbTZ4zzX4FUQCqOFYmg4gMazVH\nMBVSqwWCdmN0mBcAGLBvv8C2/QLb9gtsu31YZeNbZbf06lweXr6fpUeLaF7ZOBQoKg03HvsWBZ6e\nSH9xT3kqVy/UlBM+a/XHyYm7kuVliL7+PHxvK5fIOSceSXj4V7imrCedH1caLp46Z243pxWHywJB\nCwAAAAAANgsTZDwMOYjR4P28JxECLF6i5Uv7Je29X6Hg1MvQ1xuKIDxVoc/xf16oKggQLaRZXFc0\n3DS1LRfemThulvt76hxJMcEwitq4KL36sXPX3wwfbijhAT8lJbnPCY98iD7Ypc5P9JEM/bZMBFg9\nv6l21n6WIGg3BmaK+wb27RfYtl9g236Bbc8LllsbxCgRWx6GfX4iuviJOF16TlURVODZUwWhFLGW\nfFzRf646cuk4J30polyK4KQAqvV8ZtpLeWZvnLt+8P7jVaSg1FjheukYS2nVT+Ye8DWXzxJFvJue\n8RIgaAEAAAAAwFnAcmtDaCLPr+Xhijc0FIlix6pe1liRqAhVVY35ttolWCwCyji2mJCYrOl7JCbi\nOiLwsyG3lvu+NqUVp4cQ8GGZqflzJSYg7iPnf6k9v2zbrN1ULrb1Xm2tAjIE7cZAPk/fwL79Atv2\nC2zbL7Dt+eL9R8fEwJgLO/y/I3p27DXX08GjeyPaiYZ8HpOY2NS8p8Nx0WJOlvM5Jfeg5P4Y76nq\nmeX3Yx7Gnf7etvYul/ZTgTkkWOPYz25plWVFqGtrDi9aZxeCFgAAAAAAnB1+ukTPDe0LR3nah2d6\nOoi/N+yFOQjbkDv7QKJKcqI/7gkLoc93fH9NQaTc8Yxq77Dl/MK2LJjvaUrQWAoRFXgsV6u2u7bn\nWObE5u5d7D5YcrENjHnOtW20FOLO+6piUkfFOee997Gy1QAAAAAA4BXj9HVrH4f/eXXW4KG9o0OI\nrVzTVhV+Qjjc0EEcP2bOMa3JGunHUjiIV1uuzk3UPG+WokGx/aljLUWRKsevTTwQKSG7tf3UjmnJ\nNYtK2tkc50Cur9wzZb220nPj59dpPnhoAQAAAADAWcO8tUHYBjErc0LvhMfvju2bVUpO8EQHAT2p\nykssf1futwjPBbm2i4oj5UToCswqLlu8jTEix5xEyLbqjwlyIvZMWSc3rOMomajItLkjkfMca6el\nLSBoNwbyefoG9u0X2LZfYNt+gW374/DC/84Pr7mPxCrlhrBUcVpYU/YqJSIjnr9HJSTW0SCkU0Kt\nRnCK/i+JraGr9LXT+lkSBivamHmelfPVEF9R8Egyesq1MZR8b0+VE62h3QPtuIh9kvmqinf9nsS9\nT4nSsM/qYU8wLjO0Vmi3BgQtAAAAAADolXHdWrdfa9YR0YewjQ7i6VJ6CzWYWBzzbhUv44M8T4iI\niZetkrB0kVx3l4vBmAiyeKCtVAuXmKCuEVKnLuq1FiLygG/XJkomS0lJkRpDE7ELxrvY014DcmgB\nAAAAAECXuMOSPbzS8SM7JAjTkJ94SdNwYdmerBrM25lQ4oErFWSah9Sar5s4t7ZQUSxPdpK/msvf\nXCJKl+YPb53h3twOHz/I/ZZJmEKPcFU+dCyHlx2XydGu03xvSk8AAAAAAADgHPD+I/fIBkJerRR+\n97QXtan2xmq5w/lXTETdDv/GUF/nrl+C2IqEO8/QjpPbhhzEKyV8NLuUivU4CzIXchjnwzDWlJdY\nY0dtvcdnieE5CRW9dzwqgHtk5bMStmuNZfaPHnhjHu5J7IeQ442BfJ6+gX37BbbtF9i2X2DbfuG2\nZZ4jXpn4koaXdTqEJSdfxlnBKU/z4lLh3Gzo4xLPrBhP9bmtxqCMZ7yH1nxcPgYSBbZ0j6D6vW0i\n0Gs5Usjzh+GZexk+3zcWkZMiXdYQZCGIi9bUbXXfIGgBAAAAAEDXiCJEo4c27GafTeJAtDd6IhWR\nFi3axL29lqqwg7f3xfuPPMJycq4lfLel+FJCsM3LEynMzpV5wSVi6VhoobStJhsi2+7CNk1E1hb7\nYtcx8crGPL6lfawJBO3GwExx38C+/QLb9gts2y+wbb9otlVyTfmatIGUp2/Mtw2eLEoLuJvI9smY\nagvxyHNFOzeUDp82h5IaCZWiiwR0LPc1FQar2bZFXvJClgj5KIY8413qeEubgVxoculYU/tj11UL\nBC0AAAAAAHg1sBfqW7aZe25jQjAc/xjaIdLX/9SKE+WEhByf9NoKz6x6LrsWoog459442UeqqE+m\nrRCKHSPW12X4OeVZlBV7Y9ddW3F5CTUeUaImS+QsIiWW+fOXEahRr/wxrwmCdmMgn6dvYN9+gW37\nBbbtF9i2X3K2VTyjl7Fj9+3NhN6VrOLLBFoQxHfE1sCVbRle9HdDvuQTiZBmrY3UPrltiWdYtpkj\n09ed3JC/P8/vc2OQnmtLKPap0EKWA4bJD5NnO/WcVI53RknOdOv7Z4+LAAAgAElEQVT7D0ELAAAA\nAABeHZEXe0e6uN1RxAMp2rkTonG2Jm0KxWuXDVuOiTSxfce30SFEWBVEJVjEcc6LKTzSszzmhAjj\nYePSUzhZo3eDIjaaB2s9N3FY0kuvnV8pOKvzmlNCvhSsQwsAAAAAAF41SsGoOyZIQxVk8vtlgEra\nHUOQl+Q3xo5jY76LiT4hOHdi31VJv61JjHUMZc3kXmZDvHnb61xFOcO4L4l5qAtsHj3eWkSrBUuf\nmSH6wNFQvXm/rU7zwUMLAAAAAABeNcJb+EBDqDAxMVvJJRG5Wg+hUZQ9Wavc5vIea1hYkGkmyFMe\nX6U9swfyGJRMWlBmzeMYa11XiUBtNAHy1KANIiKaJZeD0+Kc++zUYwDrAfv2C2zbL7Btv8C2/bLQ\ntve0f9meiVnnrh/cfvmcB010ySVOaO+Be6TDmre54yl2rIb3H6+kd1JrU4T03svjUsV/1iIl7Ph4\n5Ni29L3l95CFiM9CphWb3A+2+2Hs3stz+HHD8/cQOeaKFKEfzllyvTVEnvHgVb8P+5fkdMNDCwAA\nAAAAwAATDTwvL/x8SYc8W014hlxVXjBKExfmgkq5Y2LhxMdCq8pck4NpzAWe9R1r+IQFoC5THvml\n41GKj82qO0f6SBY+K7mXp65qLIGg3Riottg3sG+/wLb9Atv2C2zbLy1s6/1Hx0RtyOt7HP4Pa9Jy\nEcnFq8xVlfmtY1EmrSDSQqGWrZZLkbDjNUKSJYkQZVOBoC19b6WIZLmx0eOswi+zX+ZMW5YsKqoo\nvTCU3MT8+XNVxZ0gaAEAAAAAAFAYRG0oXkO0D0N+ZIfcs2OThYqEaLuX25jnTa3KKwv+yH4LrmmS\no9pCpBRW6B2FqxKGW+WFLR3HiozLK9XcV8s5Sr534IY/NymvaqlXXxbrsozbEoXQSiAjh3ZjbCkv\nALQH9u0X2LZfYNt+gW37paVtvf/4RlQ3fkvMk6rkQL4lkX8bCk2xNvk5XMg9Ub5YUNR7msqfFdf0\nQyFS+ParUoFRmAM5Gz8XYbm2nHv31ZJ8yzWx5CKLXFh5rdLrbyXkfdPQ7uR5G/rR8r6TIfHiWmbR\nA6HdMO5Irmw0R5gahcnDQwsAAAAAAEAG4a0NntQHmocZe5qL0hCGOhEPhrxH9dgKdpr3LkbOc1br\nWcu0F7x/UZEzHPd9IvpJrv+1w6lz92BpqK7RWztbrojxZB2bIVyZSDy7LLz6Sdlnvd7RU7/EJhC0\nG2NLeQGgPbBvv8C2/QLb9gts2y9r2db7j29YNVtHzFs7MBaNEi/1d8Q8XC0ETszrqow5uQzOsb2d\nmWvf8eP0Yy5+soHQ4mpkZWmtAnLuPLFtJtKl0K0NS5cVlJVDnzKieobyHC+uvAxBCwAAAAAAgBEm\nAkNIZwgvfqS9cL3lxzNREC14xF/yBy8w0UEEmz2Kop3okjxMTIx9lVQprhWUMg8zIs5XWVu2tQjm\noq/h/VI9lpaiWbHc7RyxnFtS8rXFeVeR80qrcy8GgnZjOOc+w4xxv8C+/QLb9gts2y+wbb+sbVsh\nAoKAfRsJc70hEZYZE0PD8Txfl28vFg3aueIz78tSKVel8Jz7hJc6mR+8/+nrz0tse8zlZEqLOmmf\nE0Ivmns8nGfydJaGA7e6b4kIg2xl6xwQtAAAAAAAAFQgPZ0sr/COCYFZjiFj56Zr1hINObiahy4l\nOGNVbSMCKRTw+RCOtXrMhBd4MnbL2GJFqlhxITlm03I+p2AtsVfSDzs36pmNtc+9ujmhHWun9h60\nnGCAoN0YmCnuG9i3X2DbfoFt+wW27ZcT2Das8Zlaf1ZbriYUQbokop0f1qUN5wthd0lC8FlEgSZU\nGgqT2Zqr1rEp+02itdS2S4sz1faVYo1xRCZTZpQW+krk7haNX4tKaHUfIGgBAAAAAABYgOb5Gry2\nQeiGIlJB8IYCUZM8xNCWYRmcYmGbGu+Cc+9K2kuFP+fEdep+tK5gvIQSYRiwXvvCa5t5+MMEiry3\nsf4yXn/TGCzjKgWCdmMgn6dvYN9+gW37BbbtF9i2XzZiW0f7/Fo/fPZEuhiwhJqG81rmHsao8bK2\n8rblxVS9bU8tdgMlYtfApHhT6xDgnABfQkmoewoIWgAAAAAAABoyeL4eaF8Bma9Je0+H6sh3RGpl\nYtXTKLxj1Z7IGg/i0n5qckMruI95EFuHuEpi/aW2LQkbF4Rn6oYOEQGz83mOshjDLEe55v4Yr0dd\nr1b+XAoE7cbYwGwiWBHYt19g236BbfsFtu2XLdiWF+lh+Y1BUDzRtCBUMa09omxsVW229Nrxz3Ox\n6LV+tFDWUxENoRU242MuGr+cLGDFnU7igY7liMt87bXGB0ELAAAAAADA+jjae2y92D4WVrJUs03t\nW+jtqwphjnmVW+TVssNma6IKETXzMLasyJsbtxZ6baBZyLjMxZbXHwtTN1ZPtj6TMmpgl7sXDXJy\niYjozZKTQXucc5+degxgPWDffoFt+wW27RfYtl82aFsuJIKwDdufiOwv9SmBZigmNSGEoXJR1iKn\nMdHfl0PBLHMfw7iYkJ3bdk3vXym5EOtIOPKVTyy7Y2mHCXy+/nGx5zc3cRLLAY8VMzuGbeChBQAA\nAAAAYEWYWAx5tUSDZ9ZHlurhoaR0WN5nXPOWiO6kd3RNMRpjaVXemHeTh+1q13bKJXJq2lPyVW9o\nml/dAvU5sIyVieDZpEbsucrly1rvESv4JaMXTDjvq847Ks457713px4HAAAAAAAAS+DCYeCJhYyO\na4nSXvwE8fs4bH8K/zMhrBaRYv3wY1fJszSGDSfP4dsGFi3DI3ODSzygNf3kxqkIWtP11dotFn5s\nGB+xMUbPb2Xz6f46zQcPLQAAAAAAAEdCVDN+S0RvRRGhp+GY4Il9pEju41rVaPPCQ63MPHpVhfAJ\nBYuuxPmTc0R7RAtyTCP5o2uxI6JL41qq3IOqitlCgSwF5aRicaEXeyKw+X1r+Jzt5P4lfQSQQ7sx\nNpjzARoC+/YLbNsvsG2/wLb9snXbDqKPewxvnbv2RHTJtj8R0eOQXzkrPMT/se0PvOKt9x/flHhn\na3MoSSnMZCB2zo5YXu/8Gstse4QcTu5xXYTwkhJR8fh5Je1JG6z9qnFobcp8bkVcS2qekyzw0AIA\nAAAAAHACvP/oWJixIyLHxEAIPY6Gd3KGbSEsOdXnKBJTxYtybbA+xzDVWMEjeQ2aOE/0Kbx6V++J\n6NPc+AztNkHzAucqLOeutTT3WI6jJAw4sX/mrS30KL+laWh9ILqsUS0QtBtjC+umgfWAffsFtu0X\n2LZfYNt+OSfbMsEnC9s8FQqIWf5j5nhtLDXCpzX3NBU9wqN38b1jCNUcMYGuHTOQzJmNFcQqIXVO\nTJyWiOASgcxC5lcHghYAAAAAAIAT4/1HRzSKhiDqgigYC0dxhMAYKyFbWVooqiQPN+Y1ldulx1M7\nlgxeviWCrJaMJ/3/b+9+Y6zdzrqO/9bTdmbadEgxRAJt8cCmIfM0QRAtDSA5KIamISXiC0UQBBIJ\nAUVfCEIi8ZWGVxICNvwLoiQ0EQxWbYpEIAaDVbR/9HmmhG4Opi0IitLMocwzPZnli3utva977bXu\nf/vfvdd8P8mT8+y9733vNXOdczLXXOta12o7cEeP79bbcdNEO3xvb5UcRmWubfXd5pR2BnR9P72/\n2Wht3df3nYR2Zpxzz5/SbxUxDvGtF7GtF7GtF7GtVyWxjduQW5XbqduEu+wi0cvco5RcT0pG1156\nVUhhWvfZd7I6bo2b1/T1r6b3G3KycJdkO3hMppfarBIv018aDF1jx+ceLA4SCS0AAAAwG7lTa0Ol\nzc6k3bje6kuGCsnTtr2N6Wm/G1W/0qnDE6qpsRo9qiK9yyRrbOI2pJ92wHbftLqa7aW1r6vj1ONt\nq9gdvco775PtQkI7MxX8NhEdiG+9iG29iG29iG29aoitSf7iCcixUtuaZVpIHBb2PlJ30jpmK29H\nsnyWu2e8X7rN1lYQcwdHlb8vf/SZ9toxDlk97OpNHdAH3XtQ08DP7tumvnP26yvFaNefTUILAAAA\nzJA5XMcpf3rxRhW0dLhQqTpqxvxcpInIyMTjTqZ6ODFpaSV0XScVH/tQqNz6etaUVpNzvzwo9rIO\nqa52MbEv9vF2HXA1dF32udKugl0joZ2ZSno+UEB860Vs60Vs60Vs61VTbL2/eTSk0pX0TG4YUiEs\nHf5T+szM++IIorvc4U+Ze2/0c5Y+d/115GM7tqd13wr9s8v4Wq4iPmT78ZBTiXu+ztL3OFudH3vq\nceagspWp/cB9SGgBAACAGUsSAR+eszNrbeLQ6plMEt1cX2vc7huT0eu+JKMjGYnbjkdX5IZs0W00\nc2h7Er7OETm7MiL525gvW6qYT9X3dfclwCH+yczf9dqnGPILkl0goZ2ZWn6biDziWy9iWy9iWy9i\nW6+HEFuTJJxJWpgtw6vkNX08cGvsNq6HXjh9DS//uH2USXyvcu86xum7RjbBn3Ki8Jiq+sivufRL\niMHboLd9birnfTrDeX6cc9577469DgAAAODYCtuL7ZxR+3hyv+WASu2q/zb3/lRuu/O2iWYhoW3N\n7T1k1fYQ0uqp7X9Ovq8xue+tum+zjlIsxz+elvNtDLzFcTnnnj/2GrA/xLdexLZexLZexLZetcfW\n9CeehT/nkq7CoT4XMr2akuTc5X348yT90/Exi/T15L1xi/LG65nDhVYjdqYcPNT+jHVs04Qo/PNa\n+ariSSWzA+LTJ/5iI3eY2OTPTl5bKPx7su9txV3YcgwAAACcGFORfByecklSYcf9xKpXTHLOFBId\nexBQUu1s9VQWrE42Lq3R3rfrGiu9fkwiOuZQo0PbZi19JxFnnlsWnh9qdUhU1+FPKuwEMFXxs/Dv\n0sZ6dhUTEtqZeQg9Hw8Z8a0Xsa0Xsa0Xsa3XQ4mtSTzPw1OrbbXx8Citk9n4eKn1VtRcMnplEuFV\nT2xPUqP0tSHPD3Tl3OVt3EIcYzunRHXXpn5tadLbNfZo6mcmv6SYRdWbhBYAAAA4UeaU4tgzGauq\nT7Wu3nqFampacXPu8tYmjMZdT/V01b85dK1jk6bwntuua/vMIeGKtlnLdtu0u3uIbU+2rbx2HSKW\njGpq/Xtw6J5lemhnpvaej4eO+NaL2NaL2NaL2NbrIcY2JA6rbcRqEoqn3t84hb5Sm6B0JBrX3t+4\nNFE118d+2HMlPbQl2/RYen9z0U6Wmtjmvob0c/bV2zn1vn3v23a96fdkZL/yUmZb8Jx+EdCHCi0A\nAABQgWSm7Lma7bqdVVGbLI4Y77KQ2ZKcq8iV5p2O/ZoyCd63pa8Nve+htyn3Jae7WE9hK3iratp1\n/66t5KX+2eSajQr9ofuYSWhn5qH0fDxUxLdexLZexLZexLZexFZLrbcbS80BP6uDeaTiAT6dW0QH\njNqxJyMv1HFg1FRjYruvBKqU/GUubY1QGrLVOq0wb/s15EYZ2XgrOfgreS13r6gY34P/4oA5tAAA\nAEBdMslHPNk4TULia9HqIKiphxLZ95vxPtcjEsHeyuHY90z9mvp0rcn2pirTv9x1P7veAZ/Rdb9Y\nrX9WSGizhpw+Xbq26xck3V/LtJyPCu3MOOee57eK9SK+9SK29SK29SK29SK22ZE5TtJZephPEBPd\neKhUV+WtVdkbuGU5d8BU30iggpdeNe19+9GVsJlk1B64dKWO2bATE+6uETsxVsuxiXFhzu/Ge3LX\nH3IuLQktAAAAUCmTXDxWM6s2bj3e2AY7YKvolZrE2FZ8s2yPZWF8zDJzbXoCc+t9zXPNoVBTTkw+\nhsz3t3N279je477kcWhy2bNluvPeJkad1+9rKzJbjgEAAIDKma2/USuxGpLYmnvERLbYczvkYKHC\ntastyuHlrq2rsdrZSs4POTZm10nanObrDlmLPYCq6xcT9j3lfw/YcgwAAAAgI3MC8rnWB0c9i9d1\n9VeGpCVuVR58IFDfYVKZyu1GpS+3/dWs5Sq3lmPYQUI6cSt2tynrGtirnP2liK3e9t1vW8yhnZmH\nODftISG+9SK29SK29SK29SK2nZZqRu48s88lyUdXZXMZX09HxSR9uZPmqob7XnRU8Z43115onVDd\nmeez99jVXNqh99nHHFx7z/T+uRgYC4VeW/P+rusHSf89KHxu7+igbb5PVGgBAACAByI3rzRYjXYp\n9LxuyFT9ziVdeX+TLZpNrNCdSVqsK8yvfMG5y08Lr8VkdjUP99hbdUujjEbMgi2Oy9nya+scoTTm\nM3L9zV3XjV/qOPTQAgAAAA9QMlZGWm/3XY34KfTRtnomjcfhNWfuPzkRy/TTRq35rtaxE9rIfG/P\nZGbAdlwbbfT+btMTPKSXNb3/wN7Znff60kMLAAAAYLBMkpPOpG2xW5Lj+83zV2q2Mm8kn/Fk5aEn\nKidrurP9sl2HDyVr6UwiDyR+L4sjczLf/w07rHRuzCNORzGZU7Bb68v1Pdsq7Yh47vyXDiS0M8Pc\ntLoR33oR23oR23oR23oR2+Ey82rv1CQ9rcQ2qTjauarp6cSte5vnF0OSt+Q1m3SdSbpz7pUvSC//\nNOWT6vjeuezsXFW5bd+rtJHUFSvO0dQkcMiBW2aNt5n3bHVIVdchY7tCQgsAAAA8cCYxvFeSEDp3\nmfYonqfbgW3lNNMTmk2iVO4vzSWrsar4KplE17w3nnQc11Oq3u54m2zxnqvDkPq2/Y6pYO7ga2gl\nlsnJxLmK9qAku2c9SxsDG7ddxISEdmb4bWLdiG+9iG29iG29iG29iO1W7tQc7iStx/qkYoJ71zWe\nJz43ZD6p3VLcM9YnXt9KnsO6Z9NDq0IimFtf2svac9jS4IrpkGrwDvqcO99/iHhwKBQAAACAlZCk\nxGTWa12xjaN+codFFauhAw8mWiW0A9Zm75XbzjrpAKVd2SZJ7HvvttXctMd4YG9v13pWlfq+2JfW\nub7XtJyPObQzw9y0uhHfehHbehHbehHbehHb7YTk46nayWxL2Dp6H/4MObBoVXksVCkvBiSzXnop\nrRoXE9d9zIEdYRHmvGY/v7Q2+/3JXTNg7uuQa1eHQo25X7qmpDqe9kgfDFuOAQAAALSYpMpu5zzX\nertxPHzJ2+sz72+djNxXERxamcx9hqkI5g5i2kvVtqcSfaXMFuFdHJS05ddxLulx19bm3PZxKzPq\n51bSVXrPsXGegoR2Zuj5qBvxrRexrRexrRexrRex3amnavfSxopt6+CmlEl4pORQIPvPkYnN0zR9\nOcQpujmFRG/MicA26c7OgN1m5mzp+fAZ9pcSndLxPLk1x68nd79DVG1JaAEAAABkhQQmPfn4sZpE\nt+sAo4WaSuCzvoOjSklpLrkrJEgbyWCyjn2II4yuk6TOfj2rrb1W6ft1wC27z9Ssvfj9HnCPZVcl\nNtE6gXrX6KGdGXo+6kZ860Vs60Vs60Vs60Vsd8v7m0fe3zg1SazUbDOOCZ2kVk/tbXhqqSZx2nHl\n9KUrrRMkZaqGdt1xbNA+LBVOVk4+T1pXLzdObLZMAnkhbYwqKvbfloyo6rbiNva+Y/t41Xyf7oa+\nZywqtAAAAAB6JaflplXb2FO7UXHtGUMzZeSLC4lz2hMbP+9WJpHtO2xqiqFzV1OlKmhP4jvoBOgR\nYiKe+wWA/dzSKcljT1fu/KWC+SXIJCS0M0PPR92Ib72Ibb2Ibb2Ibb2I7UHcyVT6ZCqMZpTLKmHq\nS2rtdeY+mXE/L79O7xuvMY/jZy9L1+3LmPv3JcVS76FMgz4vkzxfhHvc5u41deszpxwDAAAAOAlh\nhumtmtNy77XZS5r2rtoe0bhd+W5sopnMrd3oj+2ZfdrV87sX6VpyJzPnHlv7qDCnp08nFqXPnZhA\ntw6WylwfEmzn09eGIKGdGefc8/xWsV7Et17Etl7Etl7Etl7E9uCcTHJpqrTSZsK0SmbNY7s9OHvQ\n0DrxW8U2Voc7Dxo6dNVwy88blHBPqTYX3tP6vvckubn7tU46HnDita2Yr7ZRbxsjEloAAAAAo5mt\nq0+0eUqv3YJstw7fqjkgqJXIdCVy7WTs4iecu/y4QiLbkdTlTg3e+RzaPkP7g5OEMmto4pffqr35\n+T0jj3Zdzd7b9955P6mye1DOOe+9d/1XAgAAADg0kxydKSSsyWvF7cCZ7betQ5D63t+xnr31zHbd\nfxdr7Jhz25sY5qqfua3NPXNrs5819vs6Zmv11JyPCi0AAACArZgE6UrSma3u9c0rzSS3dktyZw9m\nXwXy0D2zxugKZ/K15CqnQ6uc2S3cqSTJTBPY0mdNmetrvxfFA72mYg7tzDA3rW7Et17Etl7Etl7E\ntl7E9jhCYnKtpqf2cUwoh2ylTa65ttVZk3AtnHPPh+duSwdDJXJbj7fWdSLw0Pm3PfNcl/F1+6d0\nL/s9zF1rk/uxCaT5XkvN15X92kxcVt+TzPei+P6pqNACAAAA2IlkbI/Uk3CaZLVvtE9Igi5+QtKf\nMM/fdfWpHvJQqLTSmasQD6lKDtmeu0tjZuJOWUtfhX5be++hdc69RdL3S3qZpB/z3n9f8vrXSvpO\nNb/JuZH0rd77DyTX0EMLAAAAnJikZ/Nezc/8z2SSPnP5ldpbjTf6cIO4HXdjvEzuoKNDbTk2ifx1\neujSkHE9mb8P6pmdg4knLye90jPsoXXOvUzSD0r6ckkflfRfnHPv9N7b47V/U9KXeu8/FpLfH5H0\n5n2uCwAAAMD+xYqtSWY3Xo9/N9taO+9nri8dnDSoOrxLZi2rebxjK8RJMrjIPJf9zCkJr+l3bh3g\nNfB9pc+8cu7ytnQgWO4+kpbbVtH33UP7Jkkf8t7/lvf+E5LeIemr7AXe+1/13n8sPHyPpNfteU2z\nRs9H3YhvvYhtvYhtvYhtvYjtbPnwZ5kmsjERCslQR5/mOraFftCl1lXeZbj/zrcdZ/p+swloXGOp\nl7ijp3X1PYi/ECgl8Ft8fU4h+c/1v44Rvoa73gvb4r8HV+HPJPvuoX2tpA+bxx+R9IUd13+zpHft\ndUUAAAAADsYc8BSrtGkSdpZ7X7oltdHMoS2NnSlURXc9UzVd56raaB/3nejcdW3y+EyF6vY2CWgm\nIV8lt0PXn7yWq/Rmq+RJkh8T4fOBy2/Zd0I7uEHXOfdlkr5J0hfvbznz573/5WOvAftDfOtFbOtF\nbOtFbOtFbGerVTnVuipn2xE35tHm+kytkCjL+5tHyfufaMcn6qZrTGSrpcn6M0l6r2t1JIYj7tP1\n3qVZ46EO0opJdOg5dtMOd/Le7+2Pml7Yd5vH3y3puzLXfa6kD0n67MJ9vKR/KukfhD9/W9Lz5vXn\necxjHvOYxzzmMY95zGMez/+x9Oon0sWddHEvvfo2PH6h4/rb5vri61668NLFC+nrzb1f/aT5+8UL\n0quf7Ofru3ihWWP8eja+3hfCem6bP7nXu9bXvN7z+R2vD3ncrH9iPJP4XdxLF/fJ/ZOv7xW/I73i\nJekVv6cm1/Px+jF/9nrKsXPu5ZJ+XdKfl/Tbkv6zpK/x5lAo59xnSPpFSV/nvf9Phft4/0BOOXbO\nPe/5rWK1iG+9iG29iG29iG29iO282VE9Wp9WvKrUZa5fVTVzse06EXgH1dGhX080+aClYNLJxtuO\n+CkdEjXkvrlrbNU8rfiWtovP8pRj7/1Lzrlvl/Tzasb2/Lj3/to59y3h9R+W9L2SPlnS251zkvQJ\n7/2b9rkuAAAAAMeRbAm2W2lLpxKbbcPrHtr0fgPsfPtx+Boeq9lReq1mW/XG5/Sd9tu31XfI+zPv\nGZPAL2SSWbOWVkxKfcuZ+20cELW3HuZ9Vmh35SFVaAEAAICHwiS155Lk/Y3rqqoWqoHZCu0+Z7n2\nVWW7ZsqWKrI9yWnXvNrc9yPee1BCW5ihu5L5JUTn97RdVR9WPZ5lhRYAAAAASkzy9Dh5qXXAUpoU\n9SVJucRr22256fq0TgAfla6xJyCXxvPYubXJ+pV7zRpz6nDP179Uk5gvcgmz/by4PTlsK7YV6dX3\nO/nsvc4DJqGdGXo+6kZ860Vs60Vs60Vs60VsT4tNnGJlL1ikldlcbDu266bJ7EK723Yce3+zTHV5\n42vYNqHue3/P66sRRnY9yfewcyas+drScUtnyn9PVt/zUuV3GyS0AAAAAI7KJJ734anr8DipzL7y\nhbSHNljkr28fQLWrrcf2/nbGbfL5aSI3OKHeR7/piJE8d5LOumb3Fqq/G/3DAyvpW3299NACAAAA\nmIVMf2mU6z/d2IIs0ze6r2R2Fz2+2yRyO0kC8+sc3fdq7tX6Pg+pxG72Fk/L+Ur7vQEAAADgoOL2\n1/DwTMm21uT11vNa97XaKuEyc3LvLq3W2NejWkrq0nXlnts32+ubOX16iKUy1edSvOz7tv1FAxXa\nmaHno27Et17Etl7Etl7Etl7Etg75bbovflthDq0Uej9zhzTt+ECotNd3dFWza11jD8Aac+8h15ZO\nPN7X2J31GjjlGAAAAEAlctthw99zCdZC697Pe4Ue3HjdHpKxs3DvVfLcN26ncJ+NSmhMIrt6WEtG\nzp5tfaZdr0IyO+YeY+0qUaZCCwAAAGDWTEJrTxe2Pam2Ynou6ZnaW2A3ZsVuuZ5BVdS+Pt5SArpF\nVdZ+n3Ze1d1R/252jVRoAQAAANRqqWZW7WNJT8Nzi7SKGbb+tkb/hH86bY6Zmax0inJ8PTPLNduT\nWkqwx24TTu83tQc3t/W5a03bbI/eVQWYhHZm6PmoG/GtF7GtF7GtF7GtF7GtT0gMvfSSpJfbvtqr\nkMDmxuHEQ6KuM69tJZOkFg832vfWXWvHpydvJOp9ldshdlUlj0hoAQAAAMye9zfOuVfeqUlSr9T0\nzN5lLo0J2ELNNuN9JJSrZLnj1OWVsYnmkOv3lSibZD1WtBdan37c+nxTgR68pXvXh0wxtmdm+G1i\n3YhvvYhtvYhtvYhtvYhtvbz/I1txPZMZyyNlk6Uz5y5v4yHoJH4AAB59SURBVCFLOxyHs1QmmQ6f\ndZu5/iC2OQAr897SLwxy7pSvkhfHEO0qHlRoAQAAAJwMUxV8rPWonvSUXmndd3u+y89PDzUaYmyS\nufXJv2bkUde9Cn24ndVl+z02rw3eRlyI1WRUaGfGOff8sdeA/SG+9SK29SK29SK29SK29YqxDUnR\nM0l3zl3eO3fp4wFRMWEK/4wjXRbmuW2dSTqzW25jFVhNkjs40R1ilxXOzHsWKhxYtYs1SuUEdlfj\nlKjQAgAAADg55kTf+9zrIYGKW2bPtJvELSbIo2afdlU54/NDekszY342Tnou3cP2upqnl5lril9D\nmoROrbbuso+WhHZm6PmoG/GtF7GtF7GtF7GtF7GtVy623t88iglSkijZU3ofa8uxPSaZlUxSuIOk\nbNG1jTlJGs80vK81p9jr2rW+0tqGMpXsjeR7263HJLQAAAAAarBQ6JdNRvmsKrO5hGqCZ1u+P533\nequOE5MT18ls284eWfMZpV7XReaa4izazHzdvopy9ppdntBMD+3M0PNRN+JbL2JbL2JbL2JbL2Jb\nr1JsQ4K01Hor8Jmp0p5p5BbhHufqqfb29L3eJq8tZRJVW23uqV4Wk9kR/bXZnl/zfludLfbb2q3I\npc/eVc9sigotAAAAgJOXbM2N23jPW5dMTKgyCZqz1d5SJbKvdzb3niF2dWpy1+nGwTK+NqXvdV+z\nci3n/S5/WbEfzjnvvXfHXgcAAACA+TMHIK2ekvRszHiZzP1iYncf7vc0k6jG05Qv0vf13Hvw9uHc\nmkZ/QTtQSNZHfx3r+03L+ajQAgAAAKiKqSjapHabQ6HsVtvr+BnS+pTlcDjVbXjOJsC3zl3een9z\nMTQJnZKsbpNQDq0wW8dKpFP00M4MPR91I771Irb1Irb1Irb1Irb1GhPbkHDZE4En7fg0W5fPnbv0\nXb2g3t9cjK0Ch/tdDE0Q09OCM/2ue9M3bzZ87fbU5r2jQgsAAACgSqYq+lhqRu94fzM4sc304W7M\ngvX+ZqNImPSmjt7mPLH62arMjqnyDumxnaJrDbvaMk0PLQAAAICq2aRWpvd1wPtisuQl3YUE2W4x\nHtVHWjokapuEb8p24SmmjOnJfT/S05PXz0/L+dhyDAAAAKBqYTusk/RUzZbY3pE2yTVO6x5cp/L2\n5YWSXl27Tbdw2vGg9eTuV3jtPvTsZrdGjxjpE6+/NVuIF/H9Q+5hxinlTDo8KkVCOzP0fNSN+NaL\n2NaL2NaL2NaL2NZr29iaBKqz3zQkcLGi+0xNhdaF6qwP12yM7VGYKRuf6+tt7Ur44hxd24+au19m\nDU5Nv+/gpNWuN/0TXo5JelxrdiZtLolOnzO9ttkxRmPRQwsAAADgIVlKelzqp830zZ4rJLFaV2af\nmtdXiZ1JLmMSulRSiYwnMIdrilXKkOydqX2wlbreY0537kuih1htC9Z6HFFxnm6XdOuxfTzmPjkk\ntDPjvf/lY68B+0N860Vs60Vs60Vs60Vs67WL2IaEKlZZfXjOhccxmX2mkPiOvX+4x5mk66kJoHE9\ncVtuV6LcOtQqKvXg5mzR82u3V+9kyzEJLQAAAIAHxfsbl55+rLCtOLx+YZ63YqK7kLQ0CbC3W5DV\nHCBVTNa2OXn4UJITk2+1xVigIYdgTUUP7czQ81E34lsvYlsvYlsvYlsvYluvXcbWHBS1un3msqdq\nklgnycc5q/H9WveWrrYF52bRxj7StJ907AFNuR7VNEnsmpNr1zfys5fhs2575tAOSVRHHYLVhwot\nAAAAgAfLbDdubT/uEJO7J2oS2Y2ts2PG8+SE668URgUN/mK20LXGsE07VqP7DtRqJavJ/TZOgd4W\nFdqZoeejbsS3XsS2XsS2XsS2XsS2XvuMbZrYhuf6EtDVIUeZqmNaiYwjb27jaJ2ez7CjgvIXmPv3\nVT3TNXZVVDuqt17bHeS0VHLI1dgqdYoKLQAAAAAYST+snT8brU7oDaN8JOnaVjnjSb7xXpm5tuf2\n9fi5yftba5I5KXjilxbHAG3cY0jyrgF9tH29w7vcbixRoZ0dej7qRnzrRWzrRWzrRWzrRWzrdYDY\nPgv/fBwqqbfK99ZK7VmsG4dApTNmTQ/tRfic1QFTHclr9nNz24P7EtKumbfxM+19C1/PmZqt0Btz\na7s+O71PZi7t5EOiqNACAAAAgJoDk0z/6nnHpa1ENSZ1XVXPJEldplVZe31uK/GQGbMDvr5RiWOh\nYmy3DC/MtavZu129w7s+6ZiEdmbo+agb8a0Xsa0Xsa0Xsa0Xsa3XIWJrkrF0ZI+cu7z3/uZR+Hvc\nUrzacmyuK24dVlPlXCWCueQvlwDmDE0Oh2xXHphg3ikk85mvbchhUXGu79MBnzWI834jTrPjnPPe\n+77TxgAAAABgJ5IELPLe3zwK1cgzNQle7LG9NsnpvZqtyk/Xb2xVWJc2ITSnCEuZhLFQ6V31tNqT\nkAsnLLcqw1OrpOYgq+LJy6V725m98ZcC9nrpxcdTcj56aGeGno+6Ed96Edt6Edt6Edt6Edt6HTK2\nISF7ljz3KCSr51qP1FltwTX9pHdqTgTO9YimVdLYE7vU+BOEh75nGebPbowXKlWAC68NXmPm/UtJ\nT20yuwtsOQYAAACAjFiJjBXXZBvyMiRsser4xqR6mrvfRo9t13bgngpq8T22p7c0VzZ8XbeSznIn\nLQdXzl3exu/DkLm6hftktSvUbtLWYSq0M0PPR92Ib72Ibb2Ibb2Ibb2Ibb2OFdsRVcWFmgT3SoWe\nUlu5NEnf4MqsOa141YubqYYu1CSjfUllnAu7cdJy+Jy77LsS5rTjeDL01qcXD0WFFgAAAAD6eZkR\nPkmVM71udXhSpvp5pc2TgrOHNWX6YVc9rD0HMo2aGZuetBwT01yv7NT+265q7jao0M4MPR91I771\nIrb1Irb1Irb1Irb1OmZsvb955P2NU+irde7yPiSYS4Xtx6FP9ZHaFde0+tk6KViZ6mxHb+u5pPNM\nktzqbY3zbocmjF3V1NJacnNrw2deTJ1ROwUVWgAAAAAYKCRs8ZTjczUnIceqbHptbmxPqxrbkXTm\nKrfPwmfGJLlzFE+fUrXVJqXptekac/N3D4mxPQAAAAAwgTkk6plMYtkxuqZzDE/X8+bxuZoE+lrr\n7cvL9L4D11/cPlwY/7Oyzfif/Fqm5XxUaAEAAABgmlgxPZdWpyGvEszwXGtbbuYeVzK9uZGZTWt7\nYePM27tkdu0kXYnokBOXzYnK91qPMVrZZcJbQg/tzNDzUTfiWy9iWy9iWy9iWy9iW6+5xTZJ4GLi\neRZee6N5ftHRT3on6Vkh6Vv1xobXrxWqwfHzY6/sIbf97qI31pyKvNV9qNACAAAAwETe3zizHfhO\nZpxO0Dr0KXM6cbEHti9Jnbq1eezndOn67EMk2fTQAgAAAMCWTMIotfte07E7Z2oqreq7vvAZG32t\nhaSylSwn65OSU5GnfM1D1jj0a5BefEwPLQAAAAAcQTpr1hwY9TS5NJ6G3DsntvQZ5nM6e1sLzw0+\nGXnKScqHPvGYHtqZmVtfAHaL+NaL2NaL2NaL2NaL2NbrhGK7qjQmI3ou1FRG00rpwrnLW7tN1/SY\n3oc/vb2mudmw9jVtOean4zMH9cLmxgBtuzWZCi0AAAAA7IiphD6WmsQtk7ClW4HPJN0lW5IXWp9q\nLCUzXycmgbnRQMV7dX2GSU7TdQ4xujpdXAc9tAAAAACwWyY5ldajfErzZqOr8M/YY9vbL7vlGnvv\n2XfwVKprvblt0OvXmEMLAAAAALMQR/qExPZcTcX2WeY6mwDeqbAl2Mx8fZK+d4ptE+ShCW7Xe7tO\nXh6KHtqZOaG+AExAfOtFbOtFbOtFbOtFbOt1irG1s2rD3xchyU2ve6PW/bRDksOh120l14sbe2bN\n32/jtaX32efse7ZFhRYAAAAA9uuptKpInoe/33t/syowmuR0ldTmEsLM9UW2CpseCjW0MttTyY39\ns3cj3rOSbjkesp6N9dFDCwAAAAD7Zw+LCtItyK15tFFme2+siF4oYxcje9J75d7T119bmMVbeA89\ntAAAAAAwWybBi4c/2UOj9vJ5ub9PvYc0bNzOgM+K2663HiFED+3MnGJfAIYjvvUitvUitvUitvUi\ntvWqKLYu/LmTdG3m07YU+lefeH9zYauzucOiShXVXfXdmhm5T0r3DUnrlX3OrGvsqJ8sKrQAAAAA\ncCDJ9t/W87m/70JHslnctlxitg7fqknK4ynF2b7fgqW91zbooQUAAACAIzAjfbyaSu3k3tYkaW31\nyjp3eR+utYdQxTm512kVOH3/kDWU3pck78V7Ts352HIMAAAAAMexVJPMttgtvOl23uTx0NE9d0r6\ndENl9k5Jpbi0jhy7tblj7FA8CXkvSGhnpqK+AGQQ33oR23oR23oR23oR23rVFtuQED4Kf96YJJBX\nI2e1LiQtcoll7LnNzH9dyvTuJtdv9O6ax9k5suazraVCn3DXPaeihxYAAAAAjiw5/fhaoaJaGncT\nHpe28C7UbGW+yry2MmROrN1KPOTr6Jqduw/00AIAAADAkWUS2mjVf6r29uCNZDapeF5JuuubVTuw\nT7aV0O7kMKeN6uyLj5lDCwAAAAAnyFRDoyvl59P29qPaymo6zqfrfbkkN7OuTkMT5V2hh3ZmausL\nQBvxrRexrRexrRexrRexrVftsU3mxzo124aXSZU1zq5Ne25X7zfV3mLym25lzo0SSu9bmm8b3z8m\n8R1yzyGo0AIAAADAjISkNPaGXpnn0qSzeEKxmsS3b/RO+v7cyJ1R1dZDVWYjemgBAAAAYGY6emql\ndU/rQmpOJY6nDpd6Zjs+o5iETklox75nXdWd1kPLlmMAAAAAmJk40kfds2KXkpYhKTyTxm39Tbcd\nF9awkZhOHblTGvezDRLamam9L+ChI771Irb1Irb1Irb1Irb1eqCxXarppX2spmL7WKY31iSc1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"text/plain": [ "<matplotlib.figure.Figure at 0x105df25d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# let's plot the bifurcation diagram\n", "\n", "dots = []\n", "for k in linspace(2.5, 4, 0.001):\n", " for dot in set(take(iterator(lambda x: logistic(k, x), x0=0.5), n=50, skip=500)):\n", " dots.append((k, dot))\n", "\n", "df = pd.DataFrame(dots, columns=('k', 'xs'))\n", "df.plot(x='k', y='xs', kind='scatter', style='.', figsize=(16,12), s=1, xlim=[2.5,4], ylim=[0,1])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
JRC1995/Wide-Residual-Network
OLD/Model(WRN)(OLD).ipynb
2
481546
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training sample shapes (input and output): (50000, 32, 32, 3) (50000, 10)\n", "Testing sample shapes (input and output): (10000, 32, 32, 3) (10000, 10)\n" ] } ], "source": [ "import h5py\n", "import os\n", "import numpy as np\n", "\n", "def unpickle(file):\n", " import cPickle\n", " with open(file, 'rb') as fo:\n", " dict = cPickle.load(fo)\n", " return dict\n", "\n", "file = h5py.File('processed_data.h5','r+') \n", "\n", "#Retrieves all the preprocessed training and validation\\testing data from a file\n", "\n", "X_train = file['X_train'][...]\n", "Y_train = file['Y_train'][...]\n", "X_test = file['X_test'][...]\n", "Y_test = file['Y_test'][...]\n", "\n", "# Unpickles and retrieves class names and other meta informations of the database\n", "classes = unpickle('cifar-10-batches-py/batches.meta') #keyword for label = label_names\n", "\n", "# The steps below are completely unncessary. A long time ago, I put some modified version of X_train and X_test \n", "# into the X_train_feed and X_test_feed variables but things changed since then (as in I discarded those modifications). \n", "# But even though things changedit was difficult to change back all variables X_train_feed and X_test_feed \n", "# that are used later on the code to X_train and X_test which is why I made these unnecessary steps to put X_train and X_test\n", "# into X_train_feed and X_test_feed\n", "\n", "X_train_feed = X_train\n", "X_test_feed = X_test\n", "\n", "print(\"Training sample shapes (input and output): \"+str(X_train.shape)+\" \"+str(Y_train.shape))\n", "print(\"Testing sample shapes (input and output): \"+str(X_test.shape)+\" \"+str(Y_test.shape))\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Creates nested list. The outer list will list all the classess (0-9). And each of the classes represent the inner list which list all\n", "#training data that belongs to that class. I used list because it is easy to keep on adding dynamically. Ndarrays may have needed \n", "#a predifined shape\n", "\n", "classes_num = len(classes['label_names']) #classes_num = no. of classes\n", "\n", "# Here, I am creating a special variable X_train_F which is basically a nested list.\n", "# The outermost list of X_train_F will be a list of all the class values (0-9 where each value correspond to a class name)\n", "# Each elements (class values) of the outermost list is actually also a list; a list of all the example data belonging\n", "# to the particular class which corresponds to class value under which the data is listed. \n", "\n", "X_train_F = []\n", "\n", "for i in xrange(0,classes_num):\n", " X_train_F.append(str(i))\n", " X_train_F[i]=[]\n", "\n", "\n", "for i in xrange(0,len(X_train)):\n", " l = int(np.argmax(Y_train[i])) #l for label (in this case it's basically the index of class value elemenmts) \n", " #(Y_train is one hot encoded. Argmax returns the index for maximum value which should be 1 and\n", " # that index should indicate the value)\n", " X_train_F[l].append(X_train[i])\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from scipy.misc import toimage\n", "from scipy.misc import imresize\n", "%matplotlib inline\n", "\n", "#function for showing pictures in grid along with labels\n", "\n", "def picgrid(X_train,Y_train,gray=0):\n", " plt.figure(figsize=(7,7))\n", " ax=[]\n", " for i in xrange(0,25):\n", " img = toimage(X_train[i])\n", " ax.append(plt.subplot(5,5,i+1))\n", " ax[i].set_title( classes['label_names'][int(np.argmax(Y_train[i]))],y=-0.3)\n", " ax[i].set_axis_off()\n", " if gray==0:\n", " plt.imshow(img)\n", " else:\n", " plt.imshow(img,cmap='gray')\n", " plt.subplots_adjust(hspace=0.3)\n", " plt.axis('off')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample arranged images in a batch: \n" ] }, { "data": { "image/png": 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LFy9eNpRcU8RUrqkkKWcTLxcM3LFjh/MVfehDHwLQK5UuhCS/1ANk+7z8smVh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\nFVlYTcmmfgdBI6whNW0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f32e89ece10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import random\n", "\n", "def create_batch(batch_size,classes_num):\n", " \n", " s = int(batch_size/classes_num) #s denotes samples taken from each class to create the batch.\n", " l = np.zeros((classes_num))\n", " ran = []\n", " batch_Y = np.zeros((Y_train.shape))\n", " batch_X = np.zeros(((s*classes_num),32,32,3)) # s*classes_num = batch size reduced to the closest integer \n", " # divisible by classes_num if not divisble to begin with.\n", "\n", " for i in xrange(0,classes_num):\n", " l[i] = len(X_train_F[i])\n", " ran.append(random.sample(np.arange(1,l[i],1,'int'), s)) # Choose s no. of random samples from each class\n", " #print ran[i]\n", " for j in xrange(0,s):\n", " batch_X[(i*s)+j] = X_train_F[i][int(ran[i][j])] # Assign the s chosen random samples to the training batch\n", " batch_Y[(i*s)+j][i] = 1 # Creates one hot encoded batch of output samples\n", " \n", " return batch_X,batch_Y\n", "\n", "batch_X,batch_Y = create_batch(120,classes_num) # A demo of the function at work\n", "\n", "# Since each batch will have equal no. of cases from each class, no batch should be biased towards some particular classes\n", "\n", "print \"Sample arranged images in a batch: \"\n", "picgrid(batch_X,batch_Y)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample batch training images after light augmentation (50% chance of horizontal flips):\n" ] }, { "data": { "image/png": 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f6jGyfd5++20AwPPP25daiEv+IiEp+bEmJy1Xl3Lt6XoTExMO+Yj5J6ShmCmJ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\npRdWxsuQel1WoIzFlGo5rD6sUSd672l8Wc2nyxcuwaWF7iaJ5hbmOD89c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+FX01i0rt5s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\nN+QLM0r+C95WO72f3kj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"text/plain": [ "<matplotlib.figure.Figure at 0x7f32e818bb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"def random_crop(img):\n", " c = np.random.randint(0,5)\n", " if c==0:\n", " crop = img[8:32,0:-8]\n", " elif c==1:\n", " crop = img[0:-8,0:-8]\n", " elif c==2:\n", " crop = img[4:-4,4:-4]\n", " elif c==3:\n", " crop = img[8:32,8:32]\n", " elif c==4:\n", " crop = img[0:-8,8:32]\n", " return crop\"\"\"\n", "\n", "def augment_batch(batch_X): #will be used to modify images realtime during training (real time data augmentation)\n", " \n", " aug_batch_X = np.zeros((len(batch_X),32,32,3))\n", " \n", " for i in xrange(0,len(batch_X)):\n", " \n", " hf = np.random.randint(0,2)\n", " \n", " if hf == 1: #hf denotes horizontal flip. 50-50 random chance to apply horizontal flip on images,\n", " batch_X[i] = np.fliplr(batch_X[i])\n", " \n", " # Remove the below cropping to apply random crops. But before that it's better to implement something like mirror padding\n", " # or any form of padding to increase the dimensions beforehand.\n", " \n", " \"\"\"c = np.random.randint(0,5)\n", " if c==4:\n", " #crop randomly to 24x24\n", " aug_batch_X[i] = imresize(toimage(random_crop(batch_X[i])),(32,32))\n", " else:\n", " aug_batch_X[i] = batch_X[i]\"\"\"\n", " \n", " return batch_X\n", " \n", " \n", "aug_batch_X = augment_batch(batch_X)\n", "\n", "print \"Sample batch training images after light augmentation (50% chance of horizontal flips):\"\n", "picgrid(aug_batch_X,batch_Y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample batch training images after shuffling\n" ] }, { "data": { "image/png": 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tMvtqTnUg5ifv7YyjLUmL78hRcoD1CCkNH2VzrHNjHKdXp1Wwb4R/v6aW09tD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\nCZldouUjZ51jQsYmTA29mOG6qSjAYdcgx8PnNBHVvHvkjwopcbRNJSeig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say3sp+Kk6n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\nF+pyD7yOxWk1YnCplZp9fmNKu2V+ZdrrZTSvbJSaUo5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"text/plain": [ "<matplotlib.figure.Figure at 0x7f32f2995a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def shuffle_batch(batch_X,batch_Y):\n", " shuffle = random.sample(np.arange(0,len(batch_X),1,'int'),len(batch_X))\n", " shuffled_batch_X = []\n", " shuffled_batch_Y = []\n", " \n", " for i in xrange(0,len(batch_X)):\n", " shuffled_batch_X.append(batch_X[int(shuffle[i])])\n", " shuffled_batch_Y.append(batch_Y[int(shuffle[i])])\n", " \n", " shuffled_batch_X = np.array(shuffled_batch_X)\n", " shuffled_batch_Y = np.array(shuffled_batch_Y)\n", "\n", " return shuffled_batch_X,shuffled_batch_Y\n", "\n", "s_batch_X, s_batch_Y = shuffle_batch(aug_batch_X,batch_Y)\n", "\n", "\n", "print \"Sample batch training images after shuffling\"\n", "picgrid(s_batch_X,s_batch_Y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def batch(batch_size): #one shortcut function to execute all necessary functions to create a training batch\n", " batch_X,batch_Y = create_batch(batch_size,classes_num)\n", " aug_batch_X = augment_batch(batch_X)\n", " s_batch_X,s_batch_Y = shuffle_batch(aug_batch_X,batch_Y)\n", " return s_batch_X.reshape((len(batch_X),32,32,3)),s_batch_Y" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "FLAGS = tf.app.flags.FLAGS\n", "# Basic model parameters.\n", "tf.app.flags.DEFINE_boolean('use_fp16', False,\n", " \"\"\"Train the model using fp16.\"\"\")\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Hyper Parameters!\n", "\n", "learning_rate = 0.01\n", "batch_size = 120\n", "training_iters = 200*(int(len(X_train)/batch_size))\n", "layers = 16\n", "\n", "\n", "# 1 conv + 3 convblocks*(3 conv layers *1 group for each block + 2 conv layers*(N-1) groups for each block [total 1+N-1 = N groups]) = layers\n", "# 3*2*(N-1) = layers - 1 - 3*3\n", "# N = (layers -10)/6 + 1\n", "\n", "N = ((layers-10)/6)+1\n", "K = 4 #(deepening factor)\n", "\n", "#(N and K are used in the same sense as defined here: https://arxiv.org/abs/1605.07146)\n", "\n", "n_classes = classes_num # another useless step that I made due to certain reasons. \n", "\n", "# tf Graph input\n", "\n", "x = tf.placeholder(tf.float32, [None, 32, 32, 3])\n", "y = tf.placeholder(tf.float32, [None, n_classes])\n", "\n", "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)\n", "phase = tf.placeholder(tf.bool, name='phase') \n", "# (Phase = true means training is undergoing. The contrary is ment when Phase is false.)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create some wrappers for simplicity\n", "\n", "def conv2d(x,shape,strides):\n", " # Conv2D wrapper\n", " W = tf.Variable(tf.truncated_normal(shape=shape,stddev=5e-2))\n", " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", " # Didn't add bias because I read somewhere it's not necessary to add a bias if batch normalization is to be performed later\n", " # May be add L2 regularization or something here if you wish to.\n", " return x\n", "\n", "def activate(x,phase):\n", " #wrapper for performing batch normalization and relu activation\n", " x = tf.contrib.layers.batch_norm(x, center=True, scale=True,variables_collections=[\"batch_norm_non_trainable_variables_collection\"],updates_collections=None, decay=0.9,is_training=phase,zero_debias_moving_mean=True, fused=True)\n", " return tf.nn.relu(x,'relu')\n", "\n", "\n", "def wideres33block(X,N,K,iw,bw,s,dropout,phase):\n", " \n", " # Creates N no. of 3,3 type residual blocks with dropout that consitute the conv2/3/4 blocks\n", " # with widening factor K and X as input. s is stride and bw is base width (no. of filters before multiplying with k)\n", " # iw is input width.\n", " # (see https://arxiv.org/abs/1605.07146 paper for details on the block)\n", " # In this case, dropout = probability to keep the neuron enabled.\n", " # phase = true when training, false otherwise.\n", " \n", " conv33_1 = conv2d(X,[3,3,iw,bw*K],s)\n", " conv33_1 = activate(conv33_1,phase)\n", " \n", " conv33_1 = tf.nn.dropout(conv33_1,dropout)\n", " \n", " conv33_2 = conv2d(conv33_1,[3,3,bw*K,bw*K],1)\n", " conv_s_1 = conv2d(X,[1,1,iw,bw*K],s) #shortcut connection\n", " \n", " caddtable = tf.add(conv33_2,conv_s_1)\n", " \n", " #1st of the N blocks for conv2/3/4 block ends here. The rest of N-1 blocks will be implemented next with a loop.\n", "\n", " for i in range(0,N-1):\n", " \n", " C = caddtable\n", " Cactivated = activate(C,phase)\n", " \n", " conv33_1 = conv2d(Cactivated,[3,3,bw*K,bw*K],1)\n", " conv33_1 = activate(conv33_1,phase)\n", " \n", " conv33_1 = tf.nn.dropout(conv33_1,dropout)\n", " \n", " conv33_2 = conv2d(conv33_1,[3,3,bw*K,bw*K],1)\n", " caddtable = tf.add(conv33_2,C)\n", " \n", " return activate(caddtable,phase)\n", "\n", "\n", " \n", "def WRN(x, dropout, phase): #Wide residual network\n", "\n", " conv1 = conv2d(x,[3,3,3,16],1)\n", " conv1 = activate(conv1,phase)\n", "\n", " conv2 = wideres33block(conv1,N,K,16,16,1,dropout,phase)\n", " conv3 = wideres33block(conv2,N,K,16*K,32,2,dropout,phase)\n", " conv4 = wideres33block(conv3,N,K,32*K,64,2,dropout,phase)\n", "\n", " pooled = tf.nn.avg_pool(conv4,ksize=[1,8,8,1],strides=[1,1,1,1],padding='VALID')\n", " \n", " #Initialize weights and biases for fully connected layers\n", " wd1 = tf.Variable(tf.truncated_normal([1*1*64*K, 64*K],stddev=5e-2))\n", " bd1 = tf.Variable(tf.constant(0.1,shape=[64*K]))\n", " wout = tf.Variable(tf.random_normal([64*K, n_classes]))\n", " bout = tf.Variable(tf.constant(0.1,shape=[n_classes]))\n", "\n", " # Fully connected layer\n", " # Reshape pooling layer output to fit fully connected layer input\n", " \n", " fc1 = tf.reshape(pooled, [-1, wd1.get_shape().as_list()[0]]) \n", " fc1 = tf.add(tf.matmul(fc1, wd1), bd1)\n", " fc1 = tf.nn.relu(fc1)\n", "\n", " #fc1 = tf.nn.dropout(fc1, dropout) #Not sure if I should or should not apply dropout here.\n", " \n", " # Output, class prediction\n", " out = tf.add(tf.matmul(fc1, wout), bout)\n", " \n", " return out" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Construct model\n", "model = WRN(x,keep_prob,phase)\n", "\n", "# Define loss and optimizer\n", "\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=y))\n", "\n", "global_step = tf.Variable(0)\n", "optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum = 0.9, use_nesterov=True).minimize(cost,global_step=global_step)\n", "\n", "#learning_rate = tf.train.exponential_decay(init_lr,global_step*batch_size, decay_steps=len(X_train), decay_rate=0.95, staircase=True)\n", "\n", "# Evaluate model\n", "correct_pred = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "prediction = tf.nn.softmax(logits=model)\n", "\n", "# Initializing the variables\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter 100, Minibatch Loss= 2.144, Minibatch Accuracy= 23.333%\n", "Iter 200, Minibatch Loss= 1.980, Minibatch Accuracy= 24.167%\n", "Iter 300, Minibatch Loss= 1.756, Minibatch Accuracy= 34.167%\n", "Iter 400, Minibatch Loss= 1.606, Minibatch Accuracy= 45.833%\n", "\n", "Iter 416, Validation Loss= 1.646, validation Accuracy= 39.490%\n", "Iter 416, Average Training Loss= 2.404, Average Training Accuracy= 26.997%\n", "Checkpoint created!\n", "\n", "Iter 500, Minibatch Loss= 1.601, Minibatch Accuracy= 43.333%\n", "Iter 600, Minibatch Loss= 1.663, Minibatch Accuracy= 40.833%\n", "Iter 700, Minibatch Loss= 1.346, Minibatch Accuracy= 52.500%\n", "Iter 800, Minibatch Loss= 1.654, Minibatch Accuracy= 39.167%\n", "\n", "Iter 832, Validation Loss= 1.494, validation Accuracy= 47.530%\n", "Iter 832, Average Training Loss= 1.518, Average Training Accuracy= 44.233%\n", "Checkpoint created!\n", "\n", "Iter 900, Minibatch Loss= 1.324, Minibatch Accuracy= 51.667%\n", "Iter 1000, Minibatch Loss= 1.361, Minibatch Accuracy= 47.500%\n", "Iter 1100, Minibatch Loss= 1.109, Minibatch Accuracy= 57.500%\n", "Iter 1200, Minibatch Loss= 1.035, Minibatch Accuracy= 62.500%\n", "\n", "Iter 1248, Validation Loss= 1.204, validation Accuracy= 57.520%\n", "Iter 1248, Average Training Loss= 1.268, Average Training Accuracy= 54.002%\n", "Checkpoint created!\n", "\n", "Iter 1300, Minibatch Loss= 1.234, Minibatch Accuracy= 55.000%\n", "Iter 1400, Minibatch Loss= 1.172, Minibatch Accuracy= 52.500%\n", "Iter 1500, Minibatch Loss= 1.113, Minibatch Accuracy= 63.333%\n", "Iter 1600, Minibatch Loss= 1.163, Minibatch Accuracy= 59.167%\n", "\n", "Iter 1664, Validation Loss= 1.015, validation Accuracy= 63.910%\n", "Iter 1664, Average Training Loss= 1.078, Average Training Accuracy= 61.266%\n", "Checkpoint created!\n", "\n", "Iter 1700, Minibatch Loss= 0.811, Minibatch Accuracy= 68.333%\n", "Iter 1800, Minibatch Loss= 1.016, Minibatch Accuracy= 61.667%\n", "Iter 1900, Minibatch Loss= 0.975, Minibatch Accuracy= 66.667%\n", "Iter 2000, Minibatch Loss= 0.974, Minibatch Accuracy= 65.000%\n", "\n", "Iter 2080, Validation Loss= 0.970, validation Accuracy= 66.250%\n", "Iter 2080, Average Training Loss= 0.970, Average Training Accuracy= 65.184%\n", "Checkpoint created!\n", "\n", "Iter 2100, Minibatch Loss= 1.094, Minibatch Accuracy= 58.333%\n", "Iter 2200, Minibatch Loss= 0.813, Minibatch Accuracy= 69.167%\n", "Iter 2300, Minibatch Loss= 0.900, Minibatch Accuracy= 69.167%\n", "Iter 2400, Minibatch Loss= 0.927, Minibatch Accuracy= 65.000%\n", "\n", "Iter 2496, Validation Loss= 0.902, validation Accuracy= 68.390%\n", "Iter 2496, Average Training Loss= 0.892, Average Training Accuracy= 68.195%\n", "Checkpoint created!\n", "\n", "Iter 2500, Minibatch Loss= 1.168, Minibatch Accuracy= 55.000%\n", "Iter 2600, Minibatch Loss= 0.780, Minibatch Accuracy= 75.000%\n", "Iter 2700, Minibatch Loss= 0.800, Minibatch Accuracy= 71.667%\n", "Iter 2800, Minibatch Loss= 0.715, Minibatch Accuracy= 75.000%\n", "Iter 2900, Minibatch Loss= 0.779, Minibatch Accuracy= 75.000%\n", "\n", "Iter 2912, Validation Loss= 0.860, validation Accuracy= 70.610%\n", "Iter 2912, Average Training Loss= 0.838, Average Training Accuracy= 70.178%\n", "Checkpoint created!\n", "\n", "Iter 3000, Minibatch Loss= 0.795, Minibatch Accuracy= 70.833%\n", "Iter 3100, Minibatch Loss= 0.674, Minibatch Accuracy= 75.833%\n", "Iter 3200, Minibatch Loss= 0.754, Minibatch Accuracy= 70.833%\n", "Iter 3300, Minibatch Loss= 0.663, Minibatch Accuracy= 78.333%\n", "\n", "Iter 3328, Validation Loss= 0.829, validation Accuracy= 70.950%\n", "Iter 3328, Average Training Loss= 0.789, Average Training Accuracy= 72.035%\n", "Checkpoint created!\n", "\n", "Iter 3400, Minibatch Loss= 0.898, Minibatch Accuracy= 70.000%\n", "Iter 3500, Minibatch Loss= 0.817, Minibatch Accuracy= 69.167%\n", "Iter 3600, Minibatch Loss= 0.726, Minibatch Accuracy= 75.833%\n", "Iter 3700, Minibatch Loss= 0.858, Minibatch Accuracy= 71.667%\n", "\n", "Iter 3744, Validation Loss= 0.782, validation Accuracy= 72.760%\n", "Iter 3744, Average Training Loss= 0.742, Average Training Accuracy= 73.786%\n", "Checkpoint created!\n", "\n", "Iter 3800, Minibatch Loss= 0.680, Minibatch Accuracy= 71.667%\n", "Iter 3900, Minibatch Loss= 0.652, Minibatch Accuracy= 77.500%\n", "Iter 4000, Minibatch Loss= 0.706, Minibatch Accuracy= 68.333%\n", "Iter 4100, Minibatch Loss= 0.766, Minibatch Accuracy= 73.333%\n", "\n", "Iter 4160, Validation Loss= 0.737, validation Accuracy= 74.780%\n", "Iter 4160, Average Training Loss= 0.708, Average Training Accuracy= 74.992%\n", "Checkpoint created!\n", "\n", "Iter 4200, Minibatch Loss= 0.618, Minibatch Accuracy= 75.833%\n", "Iter 4300, Minibatch Loss= 0.712, Minibatch Accuracy= 73.333%\n", "Iter 4400, Minibatch Loss= 0.529, Minibatch Accuracy= 81.667%\n", "Iter 4500, Minibatch Loss= 0.731, Minibatch Accuracy= 74.167%\n", "\n", "Iter 4576, Validation Loss= 0.714, validation Accuracy= 75.300%\n", "Iter 4576, Average Training Loss= 0.671, Average Training Accuracy= 76.330%\n", "Checkpoint created!\n", "\n", "Iter 4600, Minibatch Loss= 0.630, Minibatch Accuracy= 78.333%\n", "Iter 4700, Minibatch Loss= 0.524, Minibatch Accuracy= 82.500%\n", "Iter 4800, Minibatch Loss= 0.619, Minibatch Accuracy= 73.333%\n", "Iter 4900, Minibatch Loss= 0.596, Minibatch Accuracy= 83.333%\n", "\n", "Iter 4992, Validation Loss= 0.685, validation Accuracy= 76.670%\n", "Iter 4992, Average Training Loss= 0.629, Average Training Accuracy= 77.744%\n", "Checkpoint created!\n", "\n", "Iter 5000, Minibatch Loss= 0.793, Minibatch Accuracy= 75.000%\n", "Iter 5100, Minibatch Loss= 0.371, Minibatch Accuracy= 88.333%\n", "Iter 5200, Minibatch Loss= 0.586, Minibatch Accuracy= 79.167%\n", "Iter 5300, Minibatch Loss= 0.586, Minibatch Accuracy= 76.667%\n", "Iter 5400, Minibatch Loss= 0.509, Minibatch Accuracy= 82.500%\n", "\n", "Iter 5408, Validation Loss= 0.660, validation Accuracy= 77.340%\n", "Iter 5408, Average Training Loss= 0.604, Average Training Accuracy= 78.746%\n", "Checkpoint created!\n", "\n", "Iter 5500, Minibatch Loss= 0.594, Minibatch Accuracy= 79.167%\n", "Iter 5600, Minibatch Loss= 0.528, Minibatch Accuracy= 85.000%\n", "Iter 5700, Minibatch Loss= 0.511, Minibatch Accuracy= 81.667%\n", "Iter 5800, Minibatch Loss= 0.283, Minibatch Accuracy= 91.667%\n", "\n", "Iter 5824, Validation Loss= 0.700, validation Accuracy= 76.890%\n", "Iter 5824, Average Training Loss= 0.573, Average Training Accuracy= 79.772%\n", "\n", "Iter 5900, Minibatch Loss= 0.506, Minibatch Accuracy= 80.833%\n", "Iter 6000, Minibatch Loss= 0.636, Minibatch Accuracy= 75.833%\n", "Iter 6100, Minibatch Loss= 0.522, Minibatch Accuracy= 84.167%\n", "Iter 6200, Minibatch Loss= 0.565, Minibatch Accuracy= 81.667%\n", "\n", "Iter 6240, Validation Loss= 0.606, validation Accuracy= 79.220%\n", "Iter 6240, Average Training Loss= 0.548, Average Training Accuracy= 80.771%\n", "Checkpoint created!\n", "\n", "Iter 6300, Minibatch Loss= 0.542, Minibatch Accuracy= 80.833%\n", "Iter 6400, Minibatch Loss= 0.563, Minibatch Accuracy= 81.667%\n", "Iter 6500, Minibatch Loss= 0.535, Minibatch Accuracy= 80.000%\n", "Iter 6600, Minibatch Loss= 0.384, Minibatch Accuracy= 89.167%\n", "\n", "Iter 6656, Validation Loss= 0.567, validation Accuracy= 80.380%\n", "Iter 6656, Average Training Loss= 0.522, Average Training Accuracy= 81.651%\n", "Checkpoint created!\n", "\n", "Iter 6700, Minibatch Loss= 0.590, Minibatch Accuracy= 78.333%\n", "Iter 6800, Minibatch Loss= 0.419, Minibatch Accuracy= 83.333%\n", "Iter 6900, Minibatch Loss= 0.610, Minibatch Accuracy= 80.000%\n", "Iter 7000, Minibatch Loss= 0.552, Minibatch Accuracy= 80.833%\n", "\n", "Iter 7072, Validation Loss= 0.563, validation Accuracy= 80.820%\n", "Iter 7072, Average Training Loss= 0.503, Average Training Accuracy= 82.206%\n", "Checkpoint created!\n", "\n", "Iter 7100, Minibatch Loss= 0.426, Minibatch Accuracy= 82.500%\n", "Iter 7200, Minibatch Loss= 0.461, Minibatch Accuracy= 84.167%\n", "Iter 7300, Minibatch Loss= 0.348, Minibatch Accuracy= 89.167%\n", "Iter 7400, Minibatch Loss= 0.483, Minibatch Accuracy= 81.667%\n", "\n", "Iter 7488, Validation Loss= 0.523, validation Accuracy= 81.990%\n", "Iter 7488, Average Training Loss= 0.479, Average Training Accuracy= 83.249%\n", "Checkpoint created!\n", "\n", "Iter 7500, Minibatch Loss= 0.580, Minibatch Accuracy= 77.500%\n", "Iter 7600, Minibatch Loss= 0.633, Minibatch Accuracy= 80.000%\n", "Iter 7700, Minibatch Loss= 0.455, Minibatch Accuracy= 82.500%\n", "Iter 7800, Minibatch Loss= 0.441, Minibatch Accuracy= 85.000%\n", "Iter 7900, Minibatch Loss= 0.250, Minibatch Accuracy= 91.667%\n", "\n", "Iter 7904, Validation Loss= 0.569, validation Accuracy= 80.620%\n", "Iter 7904, Average Training Loss= 0.464, Average Training Accuracy= 83.680%\n", "\n", "Iter 8000, Minibatch Loss= 0.492, Minibatch Accuracy= 81.667%\n", "Iter 8100, Minibatch Loss= 0.417, Minibatch Accuracy= 83.333%\n", "Iter 8200, Minibatch Loss= 0.510, Minibatch Accuracy= 84.167%\n", "Iter 8300, Minibatch Loss= 0.367, Minibatch Accuracy= 85.000%\n", "\n", "Iter 8320, Validation Loss= 0.512, validation Accuracy= 82.570%\n", "Iter 8320, Average Training Loss= 0.441, Average Training Accuracy= 84.589%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Checkpoint created!\n", "\n", "Iter 8400, Minibatch Loss= 0.358, Minibatch Accuracy= 86.667%\n", "Iter 8500, Minibatch Loss= 0.530, Minibatch Accuracy= 81.667%\n", "Iter 8600, Minibatch Loss= 0.373, Minibatch Accuracy= 86.667%\n", "Iter 8700, Minibatch Loss= 0.473, Minibatch Accuracy= 84.167%\n", "\n", "Iter 8736, Validation Loss= 0.513, validation Accuracy= 82.850%\n", "Iter 8736, Average Training Loss= 0.424, Average Training Accuracy= 85.248%\n", "Checkpoint created!\n", "\n", "Iter 8800, Minibatch Loss= 0.360, Minibatch Accuracy= 84.167%\n", "Iter 8900, Minibatch Loss= 0.380, Minibatch Accuracy= 86.667%\n", "Iter 9000, Minibatch Loss= 0.427, Minibatch Accuracy= 84.167%\n", "Iter 9100, Minibatch Loss= 0.414, Minibatch Accuracy= 86.667%\n", "\n", "Iter 9152, Validation Loss= 0.509, validation Accuracy= 83.130%\n", "Iter 9152, Average Training Loss= 0.408, Average Training Accuracy= 85.701%\n", "Checkpoint created!\n", "\n", "Iter 9200, Minibatch Loss= 0.346, Minibatch Accuracy= 87.500%\n", "Iter 9300, Minibatch Loss= 0.403, Minibatch Accuracy= 85.000%\n", "Iter 9400, Minibatch Loss= 0.451, Minibatch Accuracy= 84.167%\n", "Iter 9500, Minibatch Loss= 0.371, Minibatch Accuracy= 87.500%\n", "\n", "Iter 9568, Validation Loss= 0.501, validation Accuracy= 83.600%\n", "Iter 9568, Average Training Loss= 0.394, Average Training Accuracy= 86.138%\n", "Checkpoint created!\n", "\n", "Iter 9600, Minibatch Loss= 0.335, Minibatch Accuracy= 87.500%\n", "Iter 9700, Minibatch Loss= 0.332, Minibatch Accuracy= 88.333%\n", "Iter 9800, Minibatch Loss= 0.369, Minibatch Accuracy= 86.667%\n", "Iter 9900, Minibatch Loss= 0.370, Minibatch Accuracy= 85.833%\n", "\n", "Iter 9984, Validation Loss= 0.492, validation Accuracy= 83.750%\n", "Iter 9984, Average Training Loss= 0.377, Average Training Accuracy= 86.849%\n", "Checkpoint created!\n", "\n", "Iter 10000, Minibatch Loss= 0.370, Minibatch Accuracy= 86.667%\n", "Iter 10100, Minibatch Loss= 0.365, Minibatch Accuracy= 86.667%\n", "Iter 10200, Minibatch Loss= 0.335, Minibatch Accuracy= 86.667%\n", "Iter 10300, Minibatch Loss= 0.383, Minibatch Accuracy= 85.000%\n", "Iter 10400, Minibatch Loss= 0.286, Minibatch Accuracy= 91.667%\n", "\n", "Iter 10400, Validation Loss= 0.457, validation Accuracy= 85.120%\n", "Iter 10400, Average Training Loss= 0.366, Average Training Accuracy= 87.202%\n", "Checkpoint created!\n", "\n", "Iter 10500, Minibatch Loss= 0.467, Minibatch Accuracy= 80.833%\n", "Iter 10600, Minibatch Loss= 0.376, Minibatch Accuracy= 88.333%\n", "Iter 10700, Minibatch Loss= 0.449, Minibatch Accuracy= 85.000%\n", "Iter 10800, Minibatch Loss= 0.298, Minibatch Accuracy= 87.500%\n", "\n", "Iter 10816, Validation Loss= 0.477, validation Accuracy= 83.930%\n", "Iter 10816, Average Training Loss= 0.355, Average Training Accuracy= 87.640%\n", "\n", "Iter 10900, Minibatch Loss= 0.206, Minibatch Accuracy= 95.000%\n", "Iter 11000, Minibatch Loss= 0.498, Minibatch Accuracy= 80.833%\n", "Iter 11100, Minibatch Loss= 0.349, Minibatch Accuracy= 85.833%\n", "Iter 11200, Minibatch Loss= 0.384, Minibatch Accuracy= 86.667%\n", "\n", "Iter 11232, Validation Loss= 0.445, validation Accuracy= 85.190%\n", "Iter 11232, Average Training Loss= 0.346, Average Training Accuracy= 87.895%\n", "Checkpoint created!\n", "\n", "Iter 11300, Minibatch Loss= 0.293, Minibatch Accuracy= 90.000%\n", "Iter 11400, Minibatch Loss= 0.223, Minibatch Accuracy= 89.167%\n", "Iter 11500, Minibatch Loss= 0.381, Minibatch Accuracy= 87.500%\n", "Iter 11600, Minibatch Loss= 0.437, Minibatch Accuracy= 85.000%\n", "\n", "Iter 11648, Validation Loss= 0.447, validation Accuracy= 85.400%\n", "Iter 11648, Average Training Loss= 0.331, Average Training Accuracy= 88.411%\n", "Checkpoint created!\n", "\n", "Iter 11700, Minibatch Loss= 0.224, Minibatch Accuracy= 90.833%\n", "Iter 11800, Minibatch Loss= 0.287, Minibatch Accuracy= 91.667%\n", "Iter 11900, Minibatch Loss= 0.345, Minibatch Accuracy= 85.833%\n", "Iter 12000, Minibatch Loss= 0.449, Minibatch Accuracy= 84.167%\n", "\n", "Iter 12064, Validation Loss= 0.450, validation Accuracy= 84.890%\n", "Iter 12064, Average Training Loss= 0.323, Average Training Accuracy= 88.688%\n", "\n", "Iter 12100, Minibatch Loss= 0.350, Minibatch Accuracy= 85.833%\n", "Iter 12200, Minibatch Loss= 0.482, Minibatch Accuracy= 84.167%\n", "Iter 12300, Minibatch Loss= 0.389, Minibatch Accuracy= 87.500%\n", "Iter 12400, Minibatch Loss= 0.281, Minibatch Accuracy= 90.000%\n", "\n", "Iter 12480, Validation Loss= 0.444, validation Accuracy= 85.630%\n", "Iter 12480, Average Training Loss= 0.314, Average Training Accuracy= 89.022%\n", "Checkpoint created!\n", "\n", "Iter 12500, Minibatch Loss= 0.283, Minibatch Accuracy= 91.667%\n", "Iter 12600, Minibatch Loss= 0.404, Minibatch Accuracy= 84.167%\n", "Iter 12700, Minibatch Loss= 0.293, Minibatch Accuracy= 90.833%\n", "Iter 12800, Minibatch Loss= 0.259, Minibatch Accuracy= 92.500%\n", "\n", "Iter 12896, Validation Loss= 0.449, validation Accuracy= 85.200%\n", "Iter 12896, Average Training Loss= 0.299, Average Training Accuracy= 89.591%\n", "\n", "Iter 12900, Minibatch Loss= 0.439, Minibatch Accuracy= 86.667%\n", "Iter 13000, Minibatch Loss= 0.307, Minibatch Accuracy= 89.167%\n", "Iter 13100, Minibatch Loss= 0.332, Minibatch Accuracy= 86.667%\n", "Iter 13200, Minibatch Loss= 0.268, Minibatch Accuracy= 90.000%\n", "Iter 13300, Minibatch Loss= 0.250, Minibatch Accuracy= 93.333%\n", "\n", "Iter 13312, Validation Loss= 0.427, validation Accuracy= 85.860%\n", "Iter 13312, Average Training Loss= 0.287, Average Training Accuracy= 89.774%\n", "Checkpoint created!\n", "\n", "Iter 13400, Minibatch Loss= 0.324, Minibatch Accuracy= 88.333%\n", "Iter 13500, Minibatch Loss= 0.300, Minibatch Accuracy= 91.667%\n", "Iter 13600, Minibatch Loss= 0.307, Minibatch Accuracy= 88.333%\n", "Iter 13700, Minibatch Loss= 0.391, Minibatch Accuracy= 86.667%\n", "\n", "Iter 13728, Validation Loss= 0.434, validation Accuracy= 86.260%\n", "Iter 13728, Average Training Loss= 0.281, Average Training Accuracy= 90.134%\n", "Checkpoint created!\n", "\n", "Iter 13800, Minibatch Loss= 0.313, Minibatch Accuracy= 90.000%\n", "Iter 13900, Minibatch Loss= 0.306, Minibatch Accuracy= 90.000%\n", "Iter 14000, Minibatch Loss= 0.340, Minibatch Accuracy= 88.333%\n", "Iter 14100, Minibatch Loss= 0.213, Minibatch Accuracy= 93.333%\n", "\n", "Iter 14144, Validation Loss= 0.446, validation Accuracy= 85.840%\n", "Iter 14144, Average Training Loss= 0.271, Average Training Accuracy= 90.499%\n", "\n", "Iter 14200, Minibatch Loss= 0.208, Minibatch Accuracy= 90.000%\n", "Iter 14300, Minibatch Loss= 0.266, Minibatch Accuracy= 87.500%\n", "Iter 14400, Minibatch Loss= 0.257, Minibatch Accuracy= 89.167%\n", "Iter 14500, Minibatch Loss= 0.165, Minibatch Accuracy= 95.833%\n", "\n", "Iter 14560, Validation Loss= 0.409, validation Accuracy= 86.890%\n", "Iter 14560, Average Training Loss= 0.267, Average Training Accuracy= 90.643%\n", "Checkpoint created!\n", "\n", "Iter 14600, Minibatch Loss= 0.257, Minibatch Accuracy= 90.000%\n", "Iter 14700, Minibatch Loss= 0.272, Minibatch Accuracy= 90.000%\n", "Iter 14800, Minibatch Loss= 0.241, Minibatch Accuracy= 90.833%\n", "Iter 14900, Minibatch Loss= 0.247, Minibatch Accuracy= 92.500%\n", "\n", "Iter 14976, Validation Loss= 0.405, validation Accuracy= 87.130%\n", "Iter 14976, Average Training Loss= 0.258, Average Training Accuracy= 90.917%\n", "Checkpoint created!\n", "\n", "Iter 15000, Minibatch Loss= 0.188, Minibatch Accuracy= 92.500%\n", "Iter 15100, Minibatch Loss= 0.206, Minibatch Accuracy= 94.167%\n", "Iter 15200, Minibatch Loss= 0.249, Minibatch Accuracy= 88.333%\n", "Iter 15300, Minibatch Loss= 0.289, Minibatch Accuracy= 92.500%\n", "\n", "Iter 15392, Validation Loss= 0.413, validation Accuracy= 86.930%\n", "Iter 15392, Average Training Loss= 0.244, Average Training Accuracy= 91.390%\n", "\n", "Iter 15400, Minibatch Loss= 0.211, Minibatch Accuracy= 91.667%\n", "Iter 15500, Minibatch Loss= 0.178, Minibatch Accuracy= 93.333%\n", "Iter 15600, Minibatch Loss= 0.264, Minibatch Accuracy= 91.667%\n", "Iter 15700, Minibatch Loss= 0.201, Minibatch Accuracy= 90.833%\n", "Iter 15800, Minibatch Loss= 0.199, Minibatch Accuracy= 91.667%\n", "\n", "Iter 15808, Validation Loss= 0.418, validation Accuracy= 86.820%\n", "Iter 15808, Average Training Loss= 0.240, Average Training Accuracy= 91.558%\n", "\n", "Iter 15900, Minibatch Loss= 0.209, Minibatch Accuracy= 93.333%\n", "Iter 16000, Minibatch Loss= 0.245, Minibatch Accuracy= 91.667%\n", "Iter 16100, Minibatch Loss= 0.279, Minibatch Accuracy= 90.000%\n", "Iter 16200, Minibatch Loss= 0.255, Minibatch Accuracy= 92.500%\n", "\n", "Iter 16224, Validation Loss= 0.442, validation Accuracy= 86.320%\n", "Iter 16224, Average Training Loss= 0.237, Average Training Accuracy= 91.723%\n", "\n", "Iter 16300, Minibatch Loss= 0.270, Minibatch Accuracy= 91.667%\n", "Iter 16400, Minibatch Loss= 0.148, Minibatch Accuracy= 95.000%\n", "Iter 16500, Minibatch Loss= 0.275, Minibatch Accuracy= 92.500%\n", "Iter 16600, Minibatch Loss= 0.204, Minibatch Accuracy= 92.500%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter 16640, Validation Loss= 0.385, validation Accuracy= 87.580%\n", "Iter 16640, Average Training Loss= 0.230, Average Training Accuracy= 91.929%\n", "Checkpoint created!\n", "\n", "Iter 16700, Minibatch Loss= 0.199, Minibatch Accuracy= 92.500%\n", "Iter 16800, Minibatch Loss= 0.370, Minibatch Accuracy= 90.833%\n", "Iter 16900, Minibatch Loss= 0.161, Minibatch Accuracy= 93.333%\n", "Iter 17000, Minibatch Loss= 0.174, Minibatch Accuracy= 93.333%\n", "\n", "Iter 17056, Validation Loss= 0.402, validation Accuracy= 87.760%\n", "Iter 17056, Average Training Loss= 0.220, Average Training Accuracy= 92.218%\n", "Checkpoint created!\n", "\n", "Iter 17100, Minibatch Loss= 0.267, Minibatch Accuracy= 91.667%\n", "Iter 17200, Minibatch Loss= 0.276, Minibatch Accuracy= 93.333%\n", "Iter 17300, Minibatch Loss= 0.256, Minibatch Accuracy= 90.000%\n", "Iter 17400, Minibatch Loss= 0.232, Minibatch Accuracy= 92.500%\n", "\n", "Iter 17472, Validation Loss= 0.406, validation Accuracy= 87.290%\n", "Iter 17472, Average Training Loss= 0.219, Average Training Accuracy= 92.302%\n", "\n", "Iter 17500, Minibatch Loss= 0.271, Minibatch Accuracy= 88.333%\n", "Iter 17600, Minibatch Loss= 0.214, Minibatch Accuracy= 95.000%\n", "Iter 17700, Minibatch Loss= 0.155, Minibatch Accuracy= 92.500%\n", "Iter 17800, Minibatch Loss= 0.304, Minibatch Accuracy= 89.167%\n", "\n", "Iter 17888, Validation Loss= 0.391, validation Accuracy= 87.800%\n", "Iter 17888, Average Training Loss= 0.211, Average Training Accuracy= 92.584%\n", "Checkpoint created!\n", "\n", "Iter 17900, Minibatch Loss= 0.214, Minibatch Accuracy= 91.667%\n", "Iter 18000, Minibatch Loss= 0.192, Minibatch Accuracy= 92.500%\n", "Iter 18100, Minibatch Loss= 0.222, Minibatch Accuracy= 94.167%\n", "Iter 18200, Minibatch Loss= 0.185, Minibatch Accuracy= 90.833%\n", "Iter 18300, Minibatch Loss= 0.219, Minibatch Accuracy= 94.167%\n", "\n", "Iter 18304, Validation Loss= 0.376, validation Accuracy= 88.190%\n", "Iter 18304, Average Training Loss= 0.204, Average Training Accuracy= 92.748%\n", "Checkpoint created!\n", "\n", "Iter 18400, Minibatch Loss= 0.294, Minibatch Accuracy= 88.333%\n", "Iter 18500, Minibatch Loss= 0.109, Minibatch Accuracy= 94.167%\n", "Iter 18600, Minibatch Loss= 0.146, Minibatch Accuracy= 94.167%\n", "Iter 18700, Minibatch Loss= 0.153, Minibatch Accuracy= 94.167%\n", "\n", "Iter 18720, Validation Loss= 0.421, validation Accuracy= 87.000%\n", "Iter 18720, Average Training Loss= 0.198, Average Training Accuracy= 93.019%\n", "\n", "Iter 18800, Minibatch Loss= 0.201, Minibatch Accuracy= 95.000%\n", "Iter 18900, Minibatch Loss= 0.115, Minibatch Accuracy= 96.667%\n", "Iter 19000, Minibatch Loss= 0.175, Minibatch Accuracy= 95.000%\n", "Iter 19100, Minibatch Loss= 0.196, Minibatch Accuracy= 91.667%\n", "\n", "Iter 19136, Validation Loss= 0.378, validation Accuracy= 88.290%\n", "Iter 19136, Average Training Loss= 0.186, Average Training Accuracy= 93.526%\n", "Checkpoint created!\n", "\n", "Iter 19200, Minibatch Loss= 0.159, Minibatch Accuracy= 94.167%\n", "Iter 19300, Minibatch Loss= 0.186, Minibatch Accuracy= 92.500%\n", "Iter 19400, Minibatch Loss= 0.103, Minibatch Accuracy= 97.500%\n", "Iter 19500, Minibatch Loss= 0.137, Minibatch Accuracy= 96.667%\n", "\n", "Iter 19552, Validation Loss= 0.396, validation Accuracy= 87.770%\n", "Iter 19552, Average Training Loss= 0.184, Average Training Accuracy= 93.425%\n", "\n", "Iter 19600, Minibatch Loss= 0.160, Minibatch Accuracy= 96.667%\n", "Iter 19700, Minibatch Loss= 0.124, Minibatch Accuracy= 95.000%\n", "Iter 19800, Minibatch Loss= 0.225, Minibatch Accuracy= 93.333%\n", "Iter 19900, Minibatch Loss= 0.178, Minibatch Accuracy= 94.167%\n", "\n", "Iter 19968, Validation Loss= 0.390, validation Accuracy= 88.140%\n", "Iter 19968, Average Training Loss= 0.176, Average Training Accuracy= 93.840%\n", "\n", "Iter 20000, Minibatch Loss= 0.292, Minibatch Accuracy= 94.167%\n", "Iter 20100, Minibatch Loss= 0.160, Minibatch Accuracy= 94.167%\n", "Iter 20200, Minibatch Loss= 0.176, Minibatch Accuracy= 92.500%\n", "Iter 20300, Minibatch Loss= 0.163, Minibatch Accuracy= 93.333%\n", "\n", "Iter 20384, Validation Loss= 0.408, validation Accuracy= 88.030%\n", "Iter 20384, Average Training Loss= 0.175, Average Training Accuracy= 93.864%\n", "\n", "Iter 20400, Minibatch Loss= 0.169, Minibatch Accuracy= 94.167%\n", "Iter 20500, Minibatch Loss= 0.272, Minibatch Accuracy= 90.833%\n", "Iter 20600, Minibatch Loss= 0.149, Minibatch Accuracy= 95.833%\n", "Iter 20700, Minibatch Loss= 0.095, Minibatch Accuracy= 95.000%\n", "Iter 20800, Minibatch Loss= 0.242, Minibatch Accuracy= 91.667%\n", "\n", "Iter 20800, Validation Loss= 0.386, validation Accuracy= 88.350%\n", "Iter 20800, Average Training Loss= 0.170, Average Training Accuracy= 93.892%\n", "Checkpoint created!\n", "\n", "Iter 20900, Minibatch Loss= 0.137, Minibatch Accuracy= 95.000%\n", "Iter 21000, Minibatch Loss= 0.110, Minibatch Accuracy= 96.667%\n", "Iter 21100, Minibatch Loss= 0.166, Minibatch Accuracy= 93.333%\n", "Iter 21200, Minibatch Loss= 0.238, Minibatch Accuracy= 93.333%\n", "\n", "Iter 21216, Validation Loss= 0.377, validation Accuracy= 88.570%\n", "Iter 21216, Average Training Loss= 0.163, Average Training Accuracy= 94.207%\n", "Checkpoint created!\n", "\n", "Iter 21300, Minibatch Loss= 0.198, Minibatch Accuracy= 91.667%\n", "Iter 21400, Minibatch Loss= 0.146, Minibatch Accuracy= 95.000%\n", "Iter 21500, Minibatch Loss= 0.162, Minibatch Accuracy= 95.833%\n", "Iter 21600, Minibatch Loss= 0.101, Minibatch Accuracy= 97.500%\n", "\n", "Iter 21632, Validation Loss= 0.417, validation Accuracy= 87.550%\n", "Iter 21632, Average Training Loss= 0.158, Average Training Accuracy= 94.369%\n", "\n", "Iter 21700, Minibatch Loss= 0.130, Minibatch Accuracy= 95.000%\n", "Iter 21800, Minibatch Loss= 0.111, Minibatch Accuracy= 95.000%\n", "Iter 21900, Minibatch Loss= 0.276, Minibatch Accuracy= 91.667%\n", "Iter 22000, Minibatch Loss= 0.292, Minibatch Accuracy= 89.167%\n", "\n", "Iter 22048, Validation Loss= 0.391, validation Accuracy= 88.060%\n", "Iter 22048, Average Training Loss= 0.159, Average Training Accuracy= 94.277%\n", "\n", "Iter 22100, Minibatch Loss= 0.214, Minibatch Accuracy= 90.000%\n", "Iter 22200, Minibatch Loss= 0.140, Minibatch Accuracy= 94.167%\n", "Iter 22300, Minibatch Loss= 0.156, Minibatch Accuracy= 93.333%\n", "Iter 22400, Minibatch Loss= 0.110, Minibatch Accuracy= 96.667%\n", "\n", "Iter 22464, Validation Loss= 0.400, validation Accuracy= 88.410%\n", "Iter 22464, Average Training Loss= 0.154, Average Training Accuracy= 94.515%\n", "\n", "Iter 22500, Minibatch Loss= 0.106, Minibatch Accuracy= 95.833%\n", "Iter 22600, Minibatch Loss= 0.094, Minibatch Accuracy= 95.833%\n", "Iter 22700, Minibatch Loss= 0.102, Minibatch Accuracy= 95.833%\n", "Iter 22800, Minibatch Loss= 0.116, Minibatch Accuracy= 95.833%\n", "\n", "Iter 22880, Validation Loss= 0.396, validation Accuracy= 88.480%\n", "Iter 22880, Average Training Loss= 0.144, Average Training Accuracy= 94.862%\n", "\n", "Iter 22900, Minibatch Loss= 0.219, Minibatch Accuracy= 91.667%\n", "Iter 23000, Minibatch Loss= 0.113, Minibatch Accuracy= 95.833%\n", "Iter 23100, Minibatch Loss= 0.130, Minibatch Accuracy= 94.167%\n", "Iter 23200, Minibatch Loss= 0.069, Minibatch Accuracy= 96.667%\n", "\n", "Iter 23296, Validation Loss= 0.401, validation Accuracy= 88.260%\n", "Iter 23296, Average Training Loss= 0.140, Average Training Accuracy= 94.960%\n", "\n", "Iter 23300, Minibatch Loss= 0.105, Minibatch Accuracy= 95.833%\n", "Iter 23400, Minibatch Loss= 0.118, Minibatch Accuracy= 95.000%\n", "Iter 23500, Minibatch Loss= 0.112, Minibatch Accuracy= 95.833%\n", "Iter 23600, Minibatch Loss= 0.206, Minibatch Accuracy= 95.833%\n", "Iter 23700, Minibatch Loss= 0.106, Minibatch Accuracy= 95.833%\n", "\n", "Iter 23712, Validation Loss= 0.402, validation Accuracy= 88.590%\n", "Iter 23712, Average Training Loss= 0.136, Average Training Accuracy= 95.188%\n", "Checkpoint created!\n", "\n", "Iter 23800, Minibatch Loss= 0.164, Minibatch Accuracy= 95.833%\n", "Iter 23900, Minibatch Loss= 0.200, Minibatch Accuracy= 92.500%\n", "Iter 24000, Minibatch Loss= 0.171, Minibatch Accuracy= 93.333%\n", "Iter 24100, Minibatch Loss= 0.131, Minibatch Accuracy= 95.000%\n", "\n", "Iter 24128, Validation Loss= 0.412, validation Accuracy= 88.400%\n", "Iter 24128, Average Training Loss= 0.133, Average Training Accuracy= 95.258%\n", "\n", "Iter 24200, Minibatch Loss= 0.135, Minibatch Accuracy= 95.833%\n", "Iter 24300, Minibatch Loss= 0.224, Minibatch Accuracy= 93.333%\n", "Iter 24400, Minibatch Loss= 0.061, Minibatch Accuracy= 99.167%\n", "Iter 24500, Minibatch Loss= 0.049, Minibatch Accuracy= 99.167%\n", "\n", "Iter 24544, Validation Loss= 0.410, validation Accuracy= 88.490%\n", "Iter 24544, Average Training Loss= 0.129, Average Training Accuracy= 95.288%\n", "\n", "Iter 24600, Minibatch Loss= 0.164, Minibatch Accuracy= 94.167%\n", "Iter 24700, Minibatch Loss= 0.138, Minibatch Accuracy= 95.000%\n", "Iter 24800, Minibatch Loss= 0.128, Minibatch Accuracy= 95.833%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iter 24900, Minibatch Loss= 0.065, Minibatch Accuracy= 98.333%\n", "\n", "Iter 24960, Validation Loss= 0.396, validation Accuracy= 88.620%\n", "Iter 24960, Average Training Loss= 0.129, Average Training Accuracy= 95.343%\n", "Checkpoint created!\n", "\n", "Iter 25000, Minibatch Loss= 0.118, Minibatch Accuracy= 95.833%\n", "Iter 25100, Minibatch Loss= 0.067, Minibatch Accuracy= 99.167%\n", "Iter 25200, Minibatch Loss= 0.100, Minibatch Accuracy= 96.667%\n", "Iter 25300, Minibatch Loss= 0.121, Minibatch Accuracy= 95.833%\n", "\n", "Iter 25376, Validation Loss= 0.407, validation Accuracy= 88.760%\n", "Iter 25376, Average Training Loss= 0.123, Average Training Accuracy= 95.459%\n", "Checkpoint created!\n", "\n", "Iter 25400, Minibatch Loss= 0.094, Minibatch Accuracy= 96.667%\n", "Iter 25500, Minibatch Loss= 0.187, Minibatch Accuracy= 93.333%\n", "Iter 25600, Minibatch Loss= 0.160, Minibatch Accuracy= 92.500%\n", "Iter 25700, Minibatch Loss= 0.131, Minibatch Accuracy= 95.000%\n", "\n", "Iter 25792, Validation Loss= 0.395, validation Accuracy= 88.960%\n", "Iter 25792, Average Training Loss= 0.119, Average Training Accuracy= 95.735%\n", "Checkpoint created!\n", "\n", "Iter 25800, Minibatch Loss= 0.106, Minibatch Accuracy= 97.500%\n", "Iter 25900, Minibatch Loss= 0.106, Minibatch Accuracy= 97.500%\n", "Iter 26000, Minibatch Loss= 0.068, Minibatch Accuracy= 98.333%\n", "Iter 26100, Minibatch Loss= 0.124, Minibatch Accuracy= 95.833%\n", "Iter 26200, Minibatch Loss= 0.088, Minibatch Accuracy= 96.667%\n", "\n", "Iter 26208, Validation Loss= 0.391, validation Accuracy= 89.070%\n", "Iter 26208, Average Training Loss= 0.120, Average Training Accuracy= 95.705%\n", "Checkpoint created!\n", "\n", "Iter 26300, Minibatch Loss= 0.117, Minibatch Accuracy= 95.000%\n", "Iter 26400, Minibatch Loss= 0.093, Minibatch Accuracy= 95.000%\n", "Iter 26500, Minibatch Loss= 0.073, Minibatch Accuracy= 99.167%\n", "Iter 26600, Minibatch Loss= 0.091, Minibatch Accuracy= 97.500%\n", "\n", "Iter 26624, Validation Loss= 0.415, validation Accuracy= 88.420%\n", "Iter 26624, Average Training Loss= 0.114, Average Training Accuracy= 95.944%\n", "\n", "Iter 26700, Minibatch Loss= 0.086, Minibatch Accuracy= 95.833%\n", "Iter 26800, Minibatch Loss= 0.126, Minibatch Accuracy= 95.833%\n", "Iter 26900, Minibatch Loss= 0.047, Minibatch Accuracy= 97.500%\n", "Iter 27000, Minibatch Loss= 0.175, Minibatch Accuracy= 94.167%\n", "\n", "Iter 27040, Validation Loss= 0.417, validation Accuracy= 88.750%\n", "Iter 27040, Average Training Loss= 0.113, Average Training Accuracy= 95.972%\n", "\n", "Iter 27100, Minibatch Loss= 0.161, Minibatch Accuracy= 95.833%\n", "Iter 27200, Minibatch Loss= 0.070, Minibatch Accuracy= 96.667%\n", "Iter 27300, Minibatch Loss= 0.066, Minibatch Accuracy= 97.500%\n", "Iter 27400, Minibatch Loss= 0.127, Minibatch Accuracy= 97.500%\n", "\n", "Iter 27456, Validation Loss= 0.383, validation Accuracy= 89.090%\n", "Iter 27456, Average Training Loss= 0.110, Average Training Accuracy= 95.992%\n", "Checkpoint created!\n", "\n", "Iter 27500, Minibatch Loss= 0.103, Minibatch Accuracy= 95.000%\n", "Iter 27600, Minibatch Loss= 0.119, Minibatch Accuracy= 97.500%\n", "Iter 27700, Minibatch Loss= 0.175, Minibatch Accuracy= 90.833%\n", "Iter 27800, Minibatch Loss= 0.074, Minibatch Accuracy= 97.500%\n", "\n", "Iter 27872, Validation Loss= 0.409, validation Accuracy= 88.810%\n", "Iter 27872, Average Training Loss= 0.105, Average Training Accuracy= 96.154%\n", "\n", "Iter 27900, Minibatch Loss= 0.041, Minibatch Accuracy= 99.167%\n", "Iter 28000, Minibatch Loss= 0.053, Minibatch Accuracy= 98.333%\n", "Iter 28100, Minibatch Loss= 0.067, Minibatch Accuracy= 96.667%\n", "Iter 28200, Minibatch Loss= 0.093, Minibatch Accuracy= 97.500%\n", "\n", "Iter 28288, Validation Loss= 0.392, validation Accuracy= 89.290%\n", "Iter 28288, Average Training Loss= 0.101, Average Training Accuracy= 96.424%\n", "Checkpoint created!\n", "\n", "Iter 28300, Minibatch Loss= 0.230, Minibatch Accuracy= 92.500%\n", "Iter 28400, Minibatch Loss= 0.098, Minibatch Accuracy= 95.833%\n", "Iter 28500, Minibatch Loss= 0.118, Minibatch Accuracy= 95.000%\n", "Iter 28600, Minibatch Loss= 0.120, Minibatch Accuracy= 95.000%\n", "Iter 28700, Minibatch Loss= 0.096, Minibatch Accuracy= 97.500%\n", "\n", "Iter 28704, Validation Loss= 0.431, validation Accuracy= 88.660%\n", "Iter 28704, Average Training Loss= 0.098, Average Training Accuracy= 96.416%\n", "\n", "Iter 28800, Minibatch Loss= 0.094, Minibatch Accuracy= 96.667%\n", "Iter 28900, Minibatch Loss= 0.074, Minibatch Accuracy= 97.500%\n", "Iter 29000, Minibatch Loss= 0.144, Minibatch Accuracy= 95.833%\n", "Iter 29100, Minibatch Loss= 0.065, Minibatch Accuracy= 98.333%\n", "\n", "Iter 29120, Validation Loss= 0.408, validation Accuracy= 89.020%\n", "Iter 29120, Average Training Loss= 0.094, Average Training Accuracy= 96.629%\n", "\n", "Iter 29200, Minibatch Loss= 0.129, Minibatch Accuracy= 95.833%\n", "Iter 29300, Minibatch Loss= 0.067, Minibatch Accuracy= 96.667%\n", "Iter 29400, Minibatch Loss= 0.038, Minibatch Accuracy= 99.167%\n", "Iter 29500, Minibatch Loss= 0.053, Minibatch Accuracy= 98.333%\n", "\n", "Iter 29536, Validation Loss= 0.401, validation Accuracy= 89.530%\n", "Iter 29536, Average Training Loss= 0.090, Average Training Accuracy= 96.701%\n", "Checkpoint created!\n", "\n", "Iter 29600, Minibatch Loss= 0.036, Minibatch Accuracy= 98.333%\n", "Iter 29700, Minibatch Loss= 0.084, Minibatch Accuracy= 96.667%\n", "Iter 29800, Minibatch Loss= 0.090, Minibatch Accuracy= 96.667%\n", "Iter 29900, Minibatch Loss= 0.124, Minibatch Accuracy= 96.667%\n", "\n", "Iter 29952, Validation Loss= 0.416, validation Accuracy= 89.260%\n", "Iter 29952, Average Training Loss= 0.089, Average Training Accuracy= 96.753%\n", "\n", "Iter 30000, Minibatch Loss= 0.067, Minibatch Accuracy= 97.500%\n", "Iter 30100, Minibatch Loss= 0.106, Minibatch Accuracy= 95.833%\n", "Iter 30200, Minibatch Loss= 0.080, Minibatch Accuracy= 96.667%\n", "Iter 30300, Minibatch Loss= 0.149, Minibatch Accuracy= 95.000%\n", "\n", "Iter 30368, Validation Loss= 0.406, validation Accuracy= 88.950%\n", "Iter 30368, Average Training Loss= 0.089, Average Training Accuracy= 96.827%\n", "\n", "Iter 30400, Minibatch Loss= 0.061, Minibatch Accuracy= 97.500%\n", "Iter 30500, Minibatch Loss= 0.090, Minibatch Accuracy= 97.500%\n", "Iter 30600, Minibatch Loss= 0.126, Minibatch Accuracy= 93.333%\n", "Iter 30700, Minibatch Loss= 0.089, Minibatch Accuracy= 95.833%\n", "\n", "Iter 30784, Validation Loss= 0.412, validation Accuracy= 89.330%\n", "Iter 30784, Average Training Loss= 0.087, Average Training Accuracy= 96.865%\n", "\n", "Iter 30800, Minibatch Loss= 0.115, Minibatch Accuracy= 95.833%\n", "Iter 30900, Minibatch Loss= 0.098, Minibatch Accuracy= 96.667%\n", "Iter 31000, Minibatch Loss= 0.163, Minibatch Accuracy= 95.000%\n", "Iter 31100, Minibatch Loss= 0.139, Minibatch Accuracy= 93.333%\n", "Iter 31200, Minibatch Loss= 0.045, Minibatch Accuracy= 97.500%\n", "\n", "Iter 31200, Validation Loss= 0.407, validation Accuracy= 89.200%\n", "Iter 31200, Average Training Loss= 0.087, Average Training Accuracy= 96.895%\n", "\n", "Iter 31300, Minibatch Loss= 0.034, Minibatch Accuracy= 99.167%\n", "Iter 31400, Minibatch Loss= 0.046, Minibatch Accuracy= 97.500%\n", "Iter 31500, Minibatch Loss= 0.145, Minibatch Accuracy= 95.000%\n", "Iter 31600, Minibatch Loss= 0.099, Minibatch Accuracy= 95.833%\n", "\n", "Iter 31616, Validation Loss= 0.402, validation Accuracy= 89.280%\n", "Iter 31616, Average Training Loss= 0.081, Average Training Accuracy= 97.139%\n", "\n", "Iter 31700, Minibatch Loss= 0.098, Minibatch Accuracy= 96.667%\n", "Iter 31800, Minibatch Loss= 0.080, Minibatch Accuracy= 96.667%\n", "Iter 31900, Minibatch Loss= 0.057, Minibatch Accuracy= 98.333%\n", "Iter 32000, Minibatch Loss= 0.053, Minibatch Accuracy= 98.333%\n", "\n", "Iter 32032, Validation Loss= 0.422, validation Accuracy= 89.520%\n", "Iter 32032, Average Training Loss= 0.078, Average Training Accuracy= 97.161%\n", "\n", "Iter 32100, Minibatch Loss= 0.077, Minibatch Accuracy= 98.333%\n", "Iter 32200, Minibatch Loss= 0.135, Minibatch Accuracy= 95.833%\n", "Iter 32300, Minibatch Loss= 0.078, Minibatch Accuracy= 97.500%\n", "Iter 32400, Minibatch Loss= 0.075, Minibatch Accuracy= 95.833%\n", "\n", "Iter 32448, Validation Loss= 0.420, validation Accuracy= 89.180%\n", "Iter 32448, Average Training Loss= 0.081, Average Training Accuracy= 97.167%\n", "\n", "Iter 32500, Minibatch Loss= 0.079, Minibatch Accuracy= 95.833%\n", "Iter 32600, Minibatch Loss= 0.052, Minibatch Accuracy= 97.500%\n", "Iter 32700, Minibatch Loss= 0.070, Minibatch Accuracy= 97.500%\n", "Iter 32800, Minibatch Loss= 0.114, Minibatch Accuracy= 95.833%\n", "\n", "Iter 32864, Validation Loss= 0.416, validation Accuracy= 89.470%\n", "Iter 32864, Average Training Loss= 0.073, Average Training Accuracy= 97.406%\n", "\n", "Iter 32900, Minibatch Loss= 0.114, Minibatch Accuracy= 95.833%\n", "Iter 33000, Minibatch Loss= 0.020, Minibatch Accuracy= 100.000%\n", "Iter 33100, Minibatch Loss= 0.058, Minibatch Accuracy= 99.167%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iter 33200, Minibatch Loss= 0.048, Minibatch Accuracy= 98.333%\n", "\n", "Iter 33280, Validation Loss= 0.419, validation Accuracy= 89.470%\n", "Iter 33280, Average Training Loss= 0.075, Average Training Accuracy= 97.324%\n", "\n", "Iter 33300, Minibatch Loss= 0.075, Minibatch Accuracy= 99.167%\n", "Iter 33400, Minibatch Loss= 0.107, Minibatch Accuracy= 96.667%\n", "Iter 33500, Minibatch Loss= 0.050, Minibatch Accuracy= 98.333%\n", "Iter 33600, Minibatch Loss= 0.106, Minibatch Accuracy= 95.833%\n", "\n", "Iter 33696, Validation Loss= 0.469, validation Accuracy= 88.800%\n", "Iter 33696, Average Training Loss= 0.070, Average Training Accuracy= 97.530%\n", "\n", "Iter 33700, Minibatch Loss= 0.098, Minibatch Accuracy= 97.500%\n", "Iter 33800, Minibatch Loss= 0.076, Minibatch Accuracy= 97.500%\n", "Iter 33900, Minibatch Loss= 0.028, Minibatch Accuracy= 99.167%\n", "Iter 34000, Minibatch Loss= 0.063, Minibatch Accuracy= 97.500%\n", "Iter 34100, Minibatch Loss= 0.077, Minibatch Accuracy= 96.667%\n", "\n", "Iter 34112, Validation Loss= 0.415, validation Accuracy= 89.540%\n", "Iter 34112, Average Training Loss= 0.071, Average Training Accuracy= 97.448%\n", "Checkpoint created!\n", "\n", "Iter 34200, Minibatch Loss= 0.086, Minibatch Accuracy= 95.833%\n", "Iter 34300, Minibatch Loss= 0.130, Minibatch Accuracy= 95.833%\n", "Iter 34400, Minibatch Loss= 0.103, Minibatch Accuracy= 95.833%\n", "Iter 34500, Minibatch Loss= 0.023, Minibatch Accuracy= 99.167%\n", "\n", "Iter 34528, Validation Loss= 0.443, validation Accuracy= 89.210%\n", "Iter 34528, Average Training Loss= 0.068, Average Training Accuracy= 97.590%\n", "\n", "Iter 34600, Minibatch Loss= 0.053, Minibatch Accuracy= 97.500%\n", "Iter 34700, Minibatch Loss= 0.045, Minibatch Accuracy= 99.167%\n", "Iter 34800, Minibatch Loss= 0.076, Minibatch Accuracy= 97.500%\n", "Iter 34900, Minibatch Loss= 0.019, Minibatch Accuracy= 100.000%\n", "\n", "Iter 34944, Validation Loss= 0.439, validation Accuracy= 89.260%\n", "Iter 34944, Average Training Loss= 0.065, Average Training Accuracy= 97.680%\n", "\n", "Iter 35000, Minibatch Loss= 0.076, Minibatch Accuracy= 97.500%\n", "Iter 35100, Minibatch Loss= 0.044, Minibatch Accuracy= 98.333%\n", "Iter 35200, Minibatch Loss= 0.108, Minibatch Accuracy= 96.667%\n", "Iter 35300, Minibatch Loss= 0.012, Minibatch Accuracy= 100.000%\n", "\n", "Iter 35360, Validation Loss= 0.416, validation Accuracy= 89.530%\n", "Iter 35360, Average Training Loss= 0.066, Average Training Accuracy= 97.624%\n", "\n", "Iter 35400, Minibatch Loss= 0.085, Minibatch Accuracy= 95.833%\n", "Iter 35500, Minibatch Loss= 0.077, Minibatch Accuracy= 98.333%\n", "Iter 35600, Minibatch Loss= 0.049, Minibatch Accuracy= 97.500%\n", "Iter 35700, Minibatch Loss= 0.056, Minibatch Accuracy= 97.500%\n", "\n", "Iter 35776, Validation Loss= 0.421, validation Accuracy= 89.470%\n", "Iter 35776, Average Training Loss= 0.064, Average Training Accuracy= 97.724%\n", "\n", "Iter 35800, Minibatch Loss= 0.093, Minibatch Accuracy= 98.333%\n", "Iter 35900, Minibatch Loss= 0.099, Minibatch Accuracy= 96.667%\n", "Iter 36000, Minibatch Loss= 0.015, Minibatch Accuracy= 100.000%\n", "Iter 36100, Minibatch Loss= 0.035, Minibatch Accuracy= 99.167%\n", "\n", "Iter 36192, Validation Loss= 0.443, validation Accuracy= 89.070%\n", "Iter 36192, Average Training Loss= 0.061, Average Training Accuracy= 97.887%\n", "\n", "Iter 36200, Minibatch Loss= 0.115, Minibatch Accuracy= 97.500%\n", "Iter 36300, Minibatch Loss= 0.053, Minibatch Accuracy= 98.333%\n", "Iter 36400, Minibatch Loss= 0.075, Minibatch Accuracy= 97.500%\n", "Iter 36500, Minibatch Loss= 0.079, Minibatch Accuracy= 97.500%\n", "Iter 36600, Minibatch Loss= 0.020, Minibatch Accuracy= 100.000%\n", "\n", "Iter 36608, Validation Loss= 0.432, validation Accuracy= 89.180%\n", "Iter 36608, Average Training Loss= 0.063, Average Training Accuracy= 97.784%\n", "\n", "Iter 36700, Minibatch Loss= 0.062, Minibatch Accuracy= 96.667%\n", "Iter 36800, Minibatch Loss= 0.064, Minibatch Accuracy= 98.333%\n", "Iter 36900, Minibatch Loss= 0.067, Minibatch Accuracy= 96.667%\n", "Iter 37000, Minibatch Loss= 0.028, Minibatch Accuracy= 98.333%\n", "\n", "Iter 37024, Validation Loss= 0.433, validation Accuracy= 89.280%\n", "Iter 37024, Average Training Loss= 0.060, Average Training Accuracy= 97.823%\n", "\n", "Iter 37100, Minibatch Loss= 0.113, Minibatch Accuracy= 95.833%\n", "Iter 37200, Minibatch Loss= 0.050, Minibatch Accuracy= 98.333%\n", "Iter 37300, Minibatch Loss= 0.054, Minibatch Accuracy= 97.500%\n", "Iter 37400, Minibatch Loss= 0.043, Minibatch Accuracy= 98.333%\n", "\n", "Iter 37440, Validation Loss= 0.450, validation Accuracy= 89.200%\n", "Iter 37440, Average Training Loss= 0.060, Average Training Accuracy= 97.873%\n", "\n", "Iter 37500, Minibatch Loss= 0.070, Minibatch Accuracy= 96.667%\n", "Iter 37600, Minibatch Loss= 0.025, Minibatch Accuracy= 99.167%\n", "Iter 37700, Minibatch Loss= 0.063, Minibatch Accuracy= 97.500%\n", "Iter 37800, Minibatch Loss= 0.037, Minibatch Accuracy= 98.333%\n", "\n", "Iter 37856, Validation Loss= 0.437, validation Accuracy= 89.650%\n", "Iter 37856, Average Training Loss= 0.059, Average Training Accuracy= 97.913%\n", "Checkpoint created!\n", "\n", "Iter 37900, Minibatch Loss= 0.060, Minibatch Accuracy= 97.500%\n", "Iter 38000, Minibatch Loss= 0.022, Minibatch Accuracy= 100.000%\n", "Iter 38100, Minibatch Loss= 0.030, Minibatch Accuracy= 99.167%\n", "Iter 38200, Minibatch Loss= 0.041, Minibatch Accuracy= 98.333%\n", "\n", "Iter 38272, Validation Loss= 0.447, validation Accuracy= 89.290%\n", "Iter 38272, Average Training Loss= 0.056, Average Training Accuracy= 97.937%\n", "\n", "Iter 38300, Minibatch Loss= 0.072, Minibatch Accuracy= 96.667%\n", "Iter 38400, Minibatch Loss= 0.045, Minibatch Accuracy= 99.167%\n", "Iter 38500, Minibatch Loss= 0.051, Minibatch Accuracy= 98.333%\n", "Iter 38600, Minibatch Loss= 0.037, Minibatch Accuracy= 98.333%\n", "\n", "Iter 38688, Validation Loss= 0.445, validation Accuracy= 89.550%\n", "Iter 38688, Average Training Loss= 0.054, Average Training Accuracy= 98.039%\n", "\n", "Iter 38700, Minibatch Loss= 0.065, Minibatch Accuracy= 96.667%\n", "Iter 38800, Minibatch Loss= 0.045, Minibatch Accuracy= 98.333%\n", "Iter 38900, Minibatch Loss= 0.050, Minibatch Accuracy= 98.333%\n", "Iter 39000, Minibatch Loss= 0.028, Minibatch Accuracy= 100.000%\n", "Iter 39100, Minibatch Loss= 0.016, Minibatch Accuracy= 100.000%\n", "\n", "Iter 39104, Validation Loss= 0.438, validation Accuracy= 89.540%\n", "Iter 39104, Average Training Loss= 0.052, Average Training Accuracy= 98.167%\n", "\n", "Iter 39200, Minibatch Loss= 0.078, Minibatch Accuracy= 96.667%\n", "Iter 39300, Minibatch Loss= 0.068, Minibatch Accuracy= 97.500%\n", "Iter 39400, Minibatch Loss= 0.063, Minibatch Accuracy= 98.333%\n", "Iter 39500, Minibatch Loss= 0.025, Minibatch Accuracy= 98.333%\n", "\n", "Iter 39520, Validation Loss= 0.445, validation Accuracy= 89.530%\n", "Iter 39520, Average Training Loss= 0.053, Average Training Accuracy= 98.129%\n", "\n", "Iter 39600, Minibatch Loss= 0.045, Minibatch Accuracy= 97.500%\n", "Iter 39700, Minibatch Loss= 0.042, Minibatch Accuracy= 97.500%\n", "Iter 39800, Minibatch Loss= 0.116, Minibatch Accuracy= 97.500%\n", "Iter 39900, Minibatch Loss= 0.030, Minibatch Accuracy= 98.333%\n", "\n", "Iter 39936, Validation Loss= 0.440, validation Accuracy= 89.620%\n", "Iter 39936, Average Training Loss= 0.052, Average Training Accuracy= 98.125%\n", "\n", "Iter 40000, Minibatch Loss= 0.044, Minibatch Accuracy= 98.333%\n", "Iter 40100, Minibatch Loss= 0.097, Minibatch Accuracy= 96.667%\n", "Iter 40200, Minibatch Loss= 0.027, Minibatch Accuracy= 99.167%\n", "Iter 40300, Minibatch Loss= 0.051, Minibatch Accuracy= 97.500%\n", "\n", "Iter 40352, Validation Loss= 0.439, validation Accuracy= 89.480%\n", "Iter 40352, Average Training Loss= 0.050, Average Training Accuracy= 98.179%\n", "\n", "Iter 40400, Minibatch Loss= 0.101, Minibatch Accuracy= 97.500%\n", "Iter 40500, Minibatch Loss= 0.063, Minibatch Accuracy= 98.333%\n", "Iter 40600, Minibatch Loss= 0.026, Minibatch Accuracy= 99.167%\n", "Iter 40700, Minibatch Loss= 0.048, Minibatch Accuracy= 98.333%\n", "\n", "Iter 40768, Validation Loss= 0.478, validation Accuracy= 88.890%\n", "Iter 40768, Average Training Loss= 0.052, Average Training Accuracy= 98.131%\n", "\n", "Iter 40800, Minibatch Loss= 0.054, Minibatch Accuracy= 96.667%\n", "Iter 40900, Minibatch Loss= 0.011, Minibatch Accuracy= 100.000%\n", "Iter 41000, Minibatch Loss= 0.043, Minibatch Accuracy= 98.333%\n", "Iter 41100, Minibatch Loss= 0.084, Minibatch Accuracy= 96.667%\n", "\n", "Iter 41184, Validation Loss= 0.445, validation Accuracy= 89.780%\n", "Iter 41184, Average Training Loss= 0.050, Average Training Accuracy= 98.235%\n", "Checkpoint created!\n", "\n", "Iter 41200, Minibatch Loss= 0.037, Minibatch Accuracy= 99.167%\n", "Iter 41300, Minibatch Loss= 0.067, Minibatch Accuracy= 97.500%\n", "Iter 41400, Minibatch Loss= 0.034, Minibatch Accuracy= 99.167%\n", "Iter 41500, Minibatch Loss= 0.052, Minibatch Accuracy= 97.500%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Decreasing Learning Rate to 0.001\n", "Iter 41600, Minibatch Loss= 0.064, Minibatch Accuracy= 97.500%\n", "\n", "Iter 41600, Validation Loss= 0.450, validation Accuracy= 89.810%\n", "Iter 41600, Average Training Loss= 0.047, Average Training Accuracy= 98.269%\n", "Checkpoint created!\n", "\n", "Iter 41700, Minibatch Loss= 0.060, Minibatch Accuracy= 96.667%\n", "Iter 41800, Minibatch Loss= 0.073, Minibatch Accuracy= 98.333%\n", "Iter 41900, Minibatch Loss= 0.019, Minibatch Accuracy= 99.167%\n", "Iter 42000, Minibatch Loss= 0.032, Minibatch Accuracy= 98.333%\n", "\n", "Iter 42016, Validation Loss= 0.441, validation Accuracy= 89.620%\n", "Iter 42016, Average Training Loss= 0.047, Average Training Accuracy= 98.247%\n", "\n", "Iter 42100, Minibatch Loss= 0.063, Minibatch Accuracy= 97.500%\n", "Iter 42200, Minibatch Loss= 0.053, Minibatch Accuracy= 99.167%\n", "Iter 42300, Minibatch Loss= 0.102, Minibatch Accuracy= 97.500%\n", "Iter 42400, Minibatch Loss= 0.027, Minibatch Accuracy= 99.167%\n", "\n", "Iter 42432, Validation Loss= 0.437, validation Accuracy= 89.910%\n", "Iter 42432, Average Training Loss= 0.049, Average Training Accuracy= 98.255%\n", "Checkpoint created!\n", "\n", "Iter 42500, Minibatch Loss= 0.060, Minibatch Accuracy= 98.333%\n", "Iter 42600, Minibatch Loss= 0.107, Minibatch Accuracy= 97.500%\n", "Iter 42700, Minibatch Loss= 0.071, Minibatch Accuracy= 97.500%\n", "Iter 42800, Minibatch Loss= 0.037, Minibatch Accuracy= 99.167%\n", "\n", "Iter 42848, Validation Loss= 0.441, validation Accuracy= 89.690%\n", "Iter 42848, Average Training Loss= 0.047, Average Training Accuracy= 98.291%\n", "\n", "Iter 42900, Minibatch Loss= 0.054, Minibatch Accuracy= 96.667%\n", "Iter 43000, Minibatch Loss= 0.019, Minibatch Accuracy= 100.000%\n", "Iter 43100, Minibatch Loss= 0.033, Minibatch Accuracy= 99.167%\n", "Iter 43200, Minibatch Loss= 0.060, Minibatch Accuracy= 96.667%\n", "\n", "Iter 43264, Validation Loss= 0.462, validation Accuracy= 89.530%\n", "Iter 43264, Average Training Loss= 0.045, Average Training Accuracy= 98.415%\n", "\n", "Iter 43300, Minibatch Loss= 0.056, Minibatch Accuracy= 98.333%\n", "Iter 43400, Minibatch Loss= 0.019, Minibatch Accuracy= 99.167%\n", "Iter 43500, Minibatch Loss= 0.116, Minibatch Accuracy= 96.667%\n", "Iter 43600, Minibatch Loss= 0.068, Minibatch Accuracy= 97.500%\n", "\n", "Iter 43680, Validation Loss= 0.461, validation Accuracy= 89.650%\n", "Iter 43680, Average Training Loss= 0.043, Average Training Accuracy= 98.468%\n", "\n", "Iter 43700, Minibatch Loss= 0.105, Minibatch Accuracy= 95.833%\n", "Iter 43800, Minibatch Loss= 0.057, Minibatch Accuracy= 98.333%\n", "Iter 43900, Minibatch Loss= 0.064, Minibatch Accuracy= 95.833%\n", "Iter 44000, Minibatch Loss= 0.036, Minibatch Accuracy= 99.167%\n", "\n", "Iter 44096, Validation Loss= 0.467, validation Accuracy= 89.500%\n", "Iter 44096, Average Training Loss= 0.043, Average Training Accuracy= 98.492%\n", "\n", "Iter 44100, Minibatch Loss= 0.035, Minibatch Accuracy= 99.167%\n", "Iter 44200, Minibatch Loss= 0.064, Minibatch Accuracy= 97.500%\n", "Iter 44300, Minibatch Loss= 0.023, Minibatch Accuracy= 99.167%\n", "Iter 44400, Minibatch Loss= 0.061, Minibatch Accuracy= 98.333%\n", "Iter 44500, Minibatch Loss= 0.025, Minibatch Accuracy= 100.000%\n", "\n", "Iter 44512, Validation Loss= 0.459, validation Accuracy= 89.610%\n", "Iter 44512, Average Training Loss= 0.044, Average Training Accuracy= 98.375%\n", "\n", "Iter 44600, Minibatch Loss= 0.030, Minibatch Accuracy= 98.333%\n", "Iter 44700, Minibatch Loss= 0.027, Minibatch Accuracy= 100.000%\n", "Iter 44800, Minibatch Loss= 0.022, Minibatch Accuracy= 100.000%\n", "Iter 44900, Minibatch Loss= 0.032, Minibatch Accuracy= 97.500%\n", "\n", "Iter 44928, Validation Loss= 0.453, validation Accuracy= 89.870%\n", "Iter 44928, Average Training Loss= 0.039, Average Training Accuracy= 98.622%\n", "\n", "Iter 45000, Minibatch Loss= 0.028, Minibatch Accuracy= 99.167%\n", "Iter 45100, Minibatch Loss= 0.031, Minibatch Accuracy= 99.167%\n", "Iter 45200, Minibatch Loss= 0.094, Minibatch Accuracy= 95.833%\n", "Iter 45300, Minibatch Loss= 0.033, Minibatch Accuracy= 98.333%\n", "\n", "Iter 45344, Validation Loss= 0.446, validation Accuracy= 90.150%\n", "Iter 45344, Average Training Loss= 0.042, Average Training Accuracy= 98.536%\n", "Checkpoint created!\n", "\n", "Iter 45400, Minibatch Loss= 0.031, Minibatch Accuracy= 98.333%\n", "Iter 45500, Minibatch Loss= 0.064, Minibatch Accuracy= 96.667%\n", "Iter 45600, Minibatch Loss= 0.054, Minibatch Accuracy= 96.667%\n", "Iter 45700, Minibatch Loss= 0.069, Minibatch Accuracy= 96.667%\n", "\n", "Iter 45760, Validation Loss= 0.461, validation Accuracy= 89.480%\n", "Iter 45760, Average Training Loss= 0.038, Average Training Accuracy= 98.612%\n", "\n", "Iter 45800, Minibatch Loss= 0.095, Minibatch Accuracy= 98.333%\n", "Iter 45900, Minibatch Loss= 0.014, Minibatch Accuracy= 100.000%\n", "Iter 46000, Minibatch Loss= 0.017, Minibatch Accuracy= 99.167%\n", "Iter 46100, Minibatch Loss= 0.032, Minibatch Accuracy= 99.167%\n", "\n", "Iter 46176, Validation Loss= 0.464, validation Accuracy= 89.930%\n", "Iter 46176, Average Training Loss= 0.040, Average Training Accuracy= 98.610%\n", "\n", "Iter 46200, Minibatch Loss= 0.020, Minibatch Accuracy= 99.167%\n", "Iter 46300, Minibatch Loss= 0.049, Minibatch Accuracy= 98.333%\n", "Iter 46400, Minibatch Loss= 0.017, Minibatch Accuracy= 100.000%\n", "Iter 46500, Minibatch Loss= 0.039, Minibatch Accuracy= 98.333%\n", "\n", "Iter 46592, Validation Loss= 0.478, validation Accuracy= 89.680%\n", "Iter 46592, Average Training Loss= 0.040, Average Training Accuracy= 98.534%\n", "\n", "Iter 46600, Minibatch Loss= 0.036, Minibatch Accuracy= 98.333%\n", "Iter 46700, Minibatch Loss= 0.048, Minibatch Accuracy= 98.333%\n", "Iter 46800, Minibatch Loss= 0.026, Minibatch Accuracy= 99.167%\n", "Iter 46900, Minibatch Loss= 0.033, Minibatch Accuracy= 98.333%\n", "Iter 47000, Minibatch Loss= 0.037, Minibatch Accuracy= 99.167%\n", "\n", "Iter 47008, Validation Loss= 0.468, validation Accuracy= 89.450%\n", "Iter 47008, Average Training Loss= 0.038, Average Training Accuracy= 98.662%\n", "\n", "Iter 47100, Minibatch Loss= 0.044, Minibatch Accuracy= 97.500%\n", "Iter 47200, Minibatch Loss= 0.027, Minibatch Accuracy= 99.167%\n", "Iter 47300, Minibatch Loss= 0.005, Minibatch Accuracy= 100.000%\n", "Iter 47400, Minibatch Loss= 0.049, Minibatch Accuracy= 97.500%\n", "\n", "Iter 47424, Validation Loss= 0.472, validation Accuracy= 89.740%\n", "Iter 47424, Average Training Loss= 0.035, Average Training Accuracy= 98.770%\n", "\n", "\n", "Stopping early since accuracy not increasing!\n", "\n", "\n", "Optimization Finished!\n", "\n", "Best Test Accuracy: 90.150%\n" ] } ], "source": [ "\n", "with tf.Session() as sess: # Start Tensorflow Session\n", " \n", " saver = tf.train.Saver() # Prepares variable for saving the model\n", " sess.run(init) #initialize all variables\n", " step = 1 \n", " loss_list=[]\n", " acc_list=[]\n", " val_loss_list=[]\n", " val_acc_list=[]\n", " best_val_acc=0\n", " total_loss=0\n", " total_acc=0\n", " display_step=int(len(X_train)/batch_size) \n", " # (Interval after which average training accuracy and validation accuracy is displayed)\n", " \n", " bit_too_many_iterations = 100000 # Maximum iterations that shouldn't be surpassed in any circumstances \n", " \n", " max_patience = 5 # No. of consecutive iterations for which the program will endure \n", " # the best validation accuracy not being surpassed\n", " # if the minimum acceptable accuracy is reached\n", " \n", " patience = 0 # No. of consecutive iterations for which the best validation accuracy is not surpassed\n", "\n", " flag = 0\n", " \n", " min_acceptable_acc = 0.9 # (min_acceptable_acc*100)% = Minimum Acceptable Accuracy in Percentage\n", " # If minimum acceptable accuracy is not reached at the end of iterations,\n", " # iterations will be increased as long as it's <= bit_too_many_iterations.\n", " \n", " while step <= training_iters:\n", " \n", " batch_x, batch_y = batch(batch_size)\n", " \n", " # A very basic implementation of learning rate scheduling. \n", " if step == 100*(int(len(X_train)/batch_size)):\n", " print \"\\nDecreasing Learning Rate to 0.001\"\n", " learning_rate = 0.001\n", " \n", " # Run optimization operation (backpropagation)\n", " _,loss,acc = sess.run([optimizer,cost,accuracy],feed_dict={x: batch_x, y: batch_y, keep_prob: 0.7, phase: True})\n", " \n", " total_loss += loss\n", " total_acc += acc\n", " \n", " if step%100 == 0:\n", " print \"Iter \" + str(step) + \", Minibatch Loss= \" + \\\n", " \"{:.3f}\".format(loss) + \", Minibatch Accuracy= \" + \\\n", " \"{:.3f}%\".format(acc*100)\n", "\n", " if step % display_step == 0:\n", " \n", " total_val_loss=0\n", " total_val_acc=0\n", " val_loss=0\n", " val_acc=0\n", " avg_val_loss=0\n", " avg_val_acc=0\n", " \n", " for i in xrange(0,len(X_test_feed)):\n", " val_loss, val_acc = sess.run([cost, accuracy], feed_dict={x: X_test_feed[i].reshape((1,32,32,3)),\n", " y: Y_test[i].reshape((1,n_classes)),\n", " keep_prob: 1,\n", " phase: False})\n", "\n", " total_val_loss= total_val_loss+val_loss\n", " total_val_acc = total_val_acc+val_acc\n", " \n", " avg_val_loss = total_val_loss/len(X_test_feed) # Average validation loss\n", " avg_val_acc = total_val_acc/len(X_test_feed) # Average validation accuracy\n", " \n", " val_loss_list.append(avg_val_loss) # Storing values in list for plotting later on.\n", " val_acc_list.append(avg_val_acc) # Storing values in list for plotting later on.\n", " \n", " avg_loss = total_loss/int(display_step) # Average mini-batch training loss\n", " avg_acc = total_acc/int(display_step) # Average mini-batch training accuracy\n", " \n", " loss_list.append(avg_loss) # Storing values in list for plotting later on.\n", " acc_list.append(avg_acc) # Storing values in list for plotting later on.\n", " \n", " total_loss=0\n", " total_acc=0\n", "\n", " print \"\\nIter \" + str(step) + \", Validation Loss= \" + \\\n", " \"{:.3f}\".format(avg_val_loss) + \", validation Accuracy= \" + \\\n", " \"{:.3f}%\".format(avg_val_acc*100)+\"\"\n", " print \"Iter \" + str(step) + \", Average Training Loss= \" + \\\n", " \"{:.3f}\".format(avg_loss) + \", Average Training Accuracy= \" + \\\n", " \"{:.3f}%\".format(avg_acc*100)+\"\"\n", " \n", " if avg_val_acc > best_val_acc: # When better accuracy is received than previous best validation accuracy\n", " \n", " best_val_acc = avg_val_acc # update value of best validation accuracy received yet.\n", " saver.save(sess, 'Model_Backup/model.ckpt') # save_model including model variables (weights, biases etc.)\n", " print \"Checkpoint created!\"\n", " patience = 0 # Reset patience value \n", " \n", " else:\n", " patience += 1 \n", " \n", " if patience >= max_patience and best_val_acc >= min_acceptable_acc:\n", " flag = 1\n", " \n", " \n", " print \"\"\n", " \n", " if best_val_acc < min_acceptable_acc and step <= bit_too_many_iterations and step == training_iters:\n", " print \"\\nAdding iterations since minimum acceptable accuracy is not achieved!\\n\"\n", " training_iters += 1000\n", " \n", " \n", " if flag == 1:\n", " print \"\\nStopping early since accuracy not increasing!\\n\"\n", " break\n", " \n", " step += 1\n", " \n", " print \"\\nOptimization Finished!\\n\"\n", " \n", " print \"Best Test Accuracy: %.3f%%\"%((best_val_acc)*100)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Saving logs about change of training and validation loss and accuracy over epochs in another file.\n", "\n", "import h5py\n", "\n", "file = h5py.File('Training_logs.h5','w')\n", "file.create_dataset('val_acc', data=np.array(val_acc_list))\n", "file.create_dataset('val_loss', data=np.array(val_loss_list))\n", "file.create_dataset('acc', data=np.array(acc_list))\n", "file.create_dataset('loss', data=np.array(loss_list))\n", "\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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ujnPORVRMhPvevfDDD5Bc4D1lnHMOYiTcs7Otn/upO1d7uDvnHDES7jk5UIWD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\nunfvTufOnenZsyerVq0CLPh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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff6f5895210>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff6efe829d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import h5py\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "log = h5py.File('Training_logs.h5','r+') # Loading logs about change of training and validation loss and accuracy over epochs\n", "\n", "y1 = log['val_acc'][...]\n", "y2 = log['acc'][...]\n", "\n", "x = np.arange(1,len(y1)+1,1) # (1 = starting epoch, len(y1) = no. of epochs, 1 = step) \n", "\n", "plt.plot(x,y1,'b',label='Validation Accuracy') \n", "plt.plot(x,y2,'r',label='Training Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.xlabel('epoch')\n", "plt.show()\n", "\n", "y1 = log['val_loss'][...]\n", "y2 = log['loss'][...]\n", "\n", "plt.plot(x,y1,'b',label='Validation Loss')\n", "plt.plot(x,y2,'r',label='Training Loss')\n", "plt.legend(loc='upper right')\n", "plt.xlabel('epoch')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
arunwagle/DemoRepo
clients/Mizuho/Reporting/src/main/resources/Load_Aladdin_Sec_Table.ipynb
1
29574
{ "metadata": { "language_info": { "file_extension": ".py", "name": "python", "mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.11", "codemirror_mode": { "name": "ipython", "version": 2 }, "pygments_lexer": "ipython2" }, "kernelspec": { "display_name": "Python 2 with Spark 2.0", "name": "python2-spark20", "language": "python" } }, "nbformat": 4, "cells": [ { "metadata": {}, "source": "!pip install --user xlrd", "execution_count": 1, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Requirement already satisfied: xlrd in /gpfs/global_fs01/sym_shared/YPProdSpark/user/s1df-1767d8774d3251-73caa6cfaa60/.local/lib/python2.7/site-packages\r\n" } ] }, { "metadata": { "collapsed": true }, "source": "# Setup constants if any\n# FUNDS ID\nFUNDS_ID_LIST = ['I-CJF','I-MG1','I-SQGFSH2','I-SQGFSH2O']\n\n", "execution_count": 129, "cell_type": "code", "outputs": [] }, { "metadata": { "collapsed": true }, "source": "import pandas as pd\nfrom io import BytesIO\nimport requests\nimport json\nimport xlrd \n\nfrom pyspark.sql.functions import *\nfrom pyspark.sql.types import *\n\nfrom datetime import datetime\nfrom dateutil.parser import parse\n\nfrom ingest.Connectors import Connectors", "execution_count": 2, "cell_type": "code", "outputs": [] }, { "metadata": { "collapsed": true }, "source": "# The code was removed by DSX for sharing.", "execution_count": 3, "cell_type": "code", "outputs": [] }, { "metadata": { "collapsed": true }, "source": "# The code was removed by DSX for sharing.", "execution_count": 4, "cell_type": "code", "outputs": [] }, { "metadata": {}, "source": "\naladdinSecDF1 = pd.read_excel(getFileFromObjectStorage('MizuhoPOC', 'ALADDIN-SEC.xlsx'),index_col=[0], header=[0]).iloc[0:8]\n# Drop rows & columns with all 'NaN' values, axis 0 is for row\naladdinSecDFFiltered1 = aladdinSecDF1.dropna(axis=[0,1], how='all')\n# print aladdinSecDF1\n\nasOfDate = pd.to_datetime(aladdinSecDFFiltered1.loc['As Of Date:', 'Unnamed: 1']).strftime('%Y-%m-%d')\n\nprint \"\\nasOfDate = \" + asOfDate", "execution_count": 30, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "\nasOfDate = 2017-07-31\n" } ] }, { "metadata": { "scrolled": true }, "source": "aladdinSecDF2 = pd.read_excel(getFileFromObjectStorage('MizuhoPOC', 'ALADDIN-SEC.xlsx'), header=[0], skipinitialspace=True, skiprows=9, index_col=[0,1])\n\n# Drop rows & columns with all 'NaN' values, axis 0 is for row\naladdinSecDF2FilterNullRowsCols = aladdinSecDF2.dropna(axis=[0,1], how='all').fillna('')\nprint aladdinSecDF2FilterNullRowsCols\n\n# aladdinSecDF2FilterNullRowsCols.index.names\n\n# aladdinSecDF2FilterNullRowsCols.index.get_level_values(\"Portfolio\")\n\n# aladdinSecDF2FilterNullRowsCols.columns\n\n# This step clears the first 2 rows of each fund as those are the aggregate columns and we do not need to store those\ndfNewArr = []\nfor id in FUNDS_ID_LIST: \n df = aladdinSecDF2FilterNullRowsCols.loc[id].iloc[2:]\n df['fund_id']=id\n dfNewArr.append(df)\n \n# Concat all the funds together \ndfNew=pd.concat(dfNewArr)\n\n# Flatten the list by removing all the index\ndfNew = dfNew.reset_index()\n\n# Rename column to match database columns\naladdinSecDF2Renamed = \\\n dfNew.rename(index=str, \n columns={\"fund_id\": \"FUND_ID\", \n \"Currency\": \"CURRENCY\" ,\n \"CUSIP(Aladdin ID)\": \"CUSIP\", \n \"Sec Type\": \"SEC_TYPE\",\n \"Ticker/Coupon/Maturity\": \"TICKER_COUPON_MATURITY\",\n \"Sec Desc\": \"SEC_DESC\",\n \"ISIN\": \"ISIN\",\n \"Orig. Face\": \"ORIG_FACE\", \n \"Settled\": \"SETTLED\",\n \"Notional Market Value\": \"NOTIONAL_MKT_VAL\",\n \"Base Curr Market Val w/Acc Int\": \"BASE_CURR_MKT_VAL_ACC_INT\",\n \"Base Curr Accr Int\": \"BASE_CURR_MKT_INT\",\n \"Maturity\": \"MATURITY_DATE\",\n \"Issue Date\": \"ISSUE_DATE\",\n \"Base Curr FX Rate\": \"BASE_CURR_FX_RATE\",\n \"Market Price\": \"MKT_PRICE\",\n \"Coupon\": \"COUPON\",\n \"S&P Rating\": \"SNP_RATING\"\n })\\\n\n# Convert to float. TODO - Should everything be String as CSV files data is inconsistent \naladdinSecDF2Renamed[['ORIG_FACE', 'SETTLED', 'NOTIONAL_MKT_VAL', 'BASE_CURR_MKT_VAL_ACC_INT', 'BASE_CURR_MKT_INT', 'BASE_CURR_FX_RATE', 'MKT_PRICE']] \\\n= aladdinSecDF2Renamed[['ORIG_FACE', 'SETTLED', 'NOTIONAL_MKT_VAL', 'BASE_CURR_MKT_VAL_ACC_INT', 'BASE_CURR_MKT_INT', 'BASE_CURR_FX_RATE', 'MKT_PRICE']] \\\n.astype(float)\n \naladdinSecDF2Renamed[['MATURITY_DATE','ISSUE_DATE']]=aladdinSecDF2Renamed[['MATURITY_DATE','ISSUE_DATE']].astype(str)\n \n# aladdinSecDF2Renamed['MATURITY_DATE'] = pd.DatetimeIndex(aladdinSecDF2Renamed['MATURITY_DATE'], ambiguous='NaT').date\n#aladdinSecDF2Renamed['ISSUE_DATE'] = pd.DatetimeIndex(aladdinSecDF2Renamed['ISSUE_DATE']).date\n \n#aladdinSecDF2Renamed = aladdinSecDF2Renamed.fillna('') \n \n \n#aladdinSecDF2Renamed.dtypes \n# aladdinSecDF2Renamed.head(20)\n\n", "execution_count": 207, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": " CUSIP(Aladdin ID) Sec Type Ticker/Coupon/Maturity \\\nPortfolio Currency \nI-CJF JPY B7A0B81E2 GOVT JGB 1.700 20-MAR-2018 \n JPY B7A0B81E2 GOVT JGB 1.700 20-MAR-2018 \n JPY B7A0B81E2 GOVT JGB 1.700 20-MAR-2018 \nI-MG1 USD (4/4) (3/4) (4/4) 0.000 (3/4) \n USD (4/4) (3/4) (4/4) 0.000 (3/4) \n USD 00771X500 OPEN_END FCAIX \n USD BRS30C544 STIF JPMULCD \n USD BMZ1ACYJ5 CORP LEH 0.000 31-JAN-2024 \n USD BMZ1UBNV0 CORP SQGFS 0.000 31-JAN-2099 \nI-SQGFSH2 USD (7/7) EQUITY (7/7) \n USD (7/7) EQUITY (7/7) \n USD 46432F339 EQUITY QUAL \n USD 46432F370 EQUITY SIZE \n USD 46432F388 EQUITY VLUE \n USD 46432F396 EQUITY MTUM \n USD 73935X302 EQUITY PEY \n USD 73935X682 EQUITY SPHQ \n USD 73937B779 EQUITY SPLV \nI-SQGFSH2O USD (7/7) EQUITY (7/7) \n USD (7/7) EQUITY (7/7) \n USD 46432F339 EQUITY QUAL \n USD 46432F370 EQUITY SIZE \n USD 46432F388 EQUITY VLUE \n USD 46432F396 EQUITY MTUM \n USD 73935X302 EQUITY PEY \n USD 73935X682 EQUITY SPHQ \n USD 73937B779 EQUITY SPLV \n(4/19) (2/19) (12/19) (5/19) (12/19) 0.000 (4/19) \n\n Sec Desc ISIN \\\nPortfolio Currency \nI-CJF JPY JAPAN (GOVERNMENT OF) 10YR #292 JP1102921853 \n JPY JAPAN (GOVERNMENT OF) 10YR #292 JP1102921853 \n JPY JAPAN (GOVERNMENT OF) 10YR #292 JP1102921853 \nI-MG1 USD (4/4) (3/4) \n USD (4/4) (3/4) \n USD FIERA CAP DIVERS ALTER CL INST US00771X5005 \n USD JPM LIQ-USD LIQUIDITY-INSD LU0103813712 \n USD LEHMAN CLAIM \n USD BRIDGE INVESTMENT IN I-SQGFS \nI-SQGFSH2 USD (7/7) (7/7) \n USD (7/7) (7/7) \n USD ISHARES EDGE MSCI USA QUALITY FACT US46432F3394 \n USD ISHARES EDGE MSCI USA SIZE FACTOR US46432F3709 \n USD ISHARES EDGE MSCI USA VALUE FACTOR US46432F3881 \n USD ISHARES EDGE MSCI USA MOMENTUM FAC US46432F3964 \n USD POWERSHARES HIGH YIELD EQUITY DIVI US73935X3026 \n USD POWERSHARES S&P HIGH ETF US73935X6821 \n USD POWERSHARES S&P LOW VOLATILITY PO US73937B7799 \nI-SQGFSH2O USD (7/7) (7/7) \n USD (7/7) (7/7) \n USD ISHARES EDGE MSCI USA QUALITY FACT US46432F3394 \n USD ISHARES EDGE MSCI USA SIZE FACTOR US46432F3709 \n USD ISHARES EDGE MSCI USA VALUE FACTOR US46432F3881 \n USD ISHARES EDGE MSCI USA MOMENTUM FAC US46432F3964 \n USD POWERSHARES HIGH YIELD EQUITY DIVI US73935X3026 \n USD POWERSHARES S&P HIGH ETF US73935X6821 \n USD POWERSHARES S&P LOW VOLATILITY PO US73937B7799 \n(4/19) (2/19) (12/19) (11/19) \n\n Orig. Face Settled Notional Market Value \\\nPortfolio Currency \nI-CJF JPY 4.000000e+09 4.000000e+09 4.071178e+09 \n JPY 4.000000e+09 4.000000e+09 4.071178e+09 \n JPY 4.000000e+09 4.000000e+09 4.071178e+09 \nI-MG1 USD 1.855466e+07 1.855466e+07 1.054770e+08 \n USD 1.855466e+07 1.855466e+07 1.054770e+08 \n USD 5.295794e+06 5.295794e+06 5.115737e+07 \n USD 1.000000e+07 1.000000e+07 1.000000e+07 \n USD 3.213862e+06 3.213862e+06 4.660100e+05 \n USD 4.500000e+04 4.500000e+04 4.385358e+07 \nI-SQGFSH2 USD 5.338430e+05 5.338430e+05 2.448747e+07 \n USD 5.338430e+05 5.338430e+05 2.448747e+07 \n USD 3.543200e+04 3.543200e+04 2.662006e+06 \n USD 6.461900e+04 6.461900e+04 5.092623e+06 \n USD 6.861800e+04 6.861800e+04 5.148409e+06 \n USD 3.250500e+04 3.250500e+04 2.989810e+06 \n USD 1.709870e+05 1.709870e+05 2.920458e+06 \n USD 9.651000e+04 9.651000e+04 2.724477e+06 \n USD 6.517200e+04 6.517200e+04 2.949685e+06 \nI-SQGFSH2O USD 6.546500e+04 6.546500e+04 3.002893e+06 \n USD 6.546500e+04 6.546500e+04 3.002893e+06 \n USD 4.345000e+03 4.345000e+03 3.264398e+05 \n USD 7.924000e+03 7.924000e+03 6.244904e+05 \n USD 8.415000e+03 8.415000e+03 6.313774e+05 \n USD 3.986000e+03 3.986000e+03 3.666323e+05 \n USD 2.096800e+04 2.096800e+04 3.581334e+05 \n USD 1.183500e+04 1.183500e+04 3.341020e+05 \n USD 7.992000e+03 7.992000e+03 3.617179e+05 \n(4/19) (2/19) 4.019154e+09 4.019154e+09 4.204145e+09 \n\n Base Curr Market Val w/Acc Int Base Curr Accr Int \\\nPortfolio Currency \nI-CJF JPY 3.684491e+07 224246.16 \n JPY 3.684491e+07 224246.16 \n JPY 3.684491e+07 224246.16 \nI-MG1 USD 1.054770e+08 0.00 \n USD 1.054770e+08 0.00 \n USD 5.115737e+07 0.00 \n USD 1.000000e+07 0.00 \n USD 4.660100e+05 0.00 \n USD 4.385358e+07 0.00 \nI-SQGFSH2 USD 2.448747e+07 0.00 \n USD 2.448747e+07 0.00 \n USD 2.662006e+06 0.00 \n USD 5.092623e+06 0.00 \n USD 5.148409e+06 0.00 \n USD 2.989810e+06 0.00 \n USD 2.920458e+06 0.00 \n USD 2.724477e+06 0.00 \n USD 2.949685e+06 0.00 \nI-SQGFSH2O USD 3.002893e+06 0.00 \n USD 3.002893e+06 0.00 \n USD 3.264398e+05 0.00 \n USD 6.244904e+05 0.00 \n USD 6.313774e+05 0.00 \n USD 3.666323e+05 0.00 \n USD 3.581334e+05 0.00 \n USD 3.341020e+05 0.00 \n USD 3.617179e+05 0.00 \n(4/19) (2/19) 1.698122e+08 224246.16 \n\n Maturity Issue Date \\\nPortfolio Currency \nI-CJF JPY 2018-03-20 00:00:00 2008-05-13 00:00:00 \n JPY 2018-03-20 00:00:00 2008-05-13 00:00:00 \n JPY 2018-03-20 00:00:00 2008-05-13 00:00:00 \nI-MG1 USD (3/4) (4/4) \n USD (3/4) (4/4) \n USD \n USD 1999-12-22 00:00:00 \n USD 2024-01-31 00:00:00 2009-01-31 00:00:00 \n USD 2099-01-31 00:00:00 2014-02-03 00:00:00 \nI-SQGFSH2 USD (5/7) \n USD (5/7) \n USD \n USD 2013-04-18 00:00:00 \n USD 2013-04-18 00:00:00 \n USD 2013-04-18 00:00:00 \n USD 2004-12-09 00:00:00 \n USD 2005-12-06 00:00:00 \n USD 2011-05-05 00:00:00 \nI-SQGFSH2O USD (5/7) \n USD (5/7) \n USD \n USD 2013-04-18 00:00:00 \n USD 2013-04-18 00:00:00 \n USD 2013-04-18 00:00:00 \n USD 2004-12-09 00:00:00 \n USD 2005-12-06 00:00:00 \n USD 2011-05-05 00:00:00 \n(4/19) (2/19) (4/19) (9/19) \n\n Base Curr FX Rate Market Price Coupon S&P Rating \nPortfolio Currency \nI-CJF JPY 0.0091 101.16 1.7 A+ \n JPY 0.0091 101.16 1.7 A+ \n JPY 0.0091 101.16 1.7 A+ \nI-MG1 USD 4.0000 (4/4) (2/4) (2/4) \n USD 4.0000 (4/4) (2/4) (2/4) \n USD 1.0000 9.66 \n USD 1.0000 1 \n USD 1.0000 0.145 0 NR \n USD 1.0000 974.524 0 NR \nI-SQGFSH2 USD 7.0000 (7/7) \n USD 7.0000 (7/7) \n USD 1.0000 75.13 \n USD 1.0000 78.81 \n USD 1.0000 75.03 \n USD 1.0000 91.98 \n USD 1.0000 17.08 \n USD 1.0000 28.23 \n USD 1.0000 45.26 \nI-SQGFSH2O USD 7.0000 (7/7) \n USD 7.0000 (7/7) \n USD 1.0000 75.13 \n USD 1.0000 78.81 \n USD 1.0000 75.03 \n USD 1.0000 91.98 \n USD 1.0000 17.08 \n USD 1.0000 28.23 \n USD 1.0000 45.26 \n(4/19) (2/19) 18.0091 (12/19) (3/19) (3/19) \n" } ] }, { "metadata": {}, "source": "spark = SparkSession.builder.getOrCreate() \n\ndef build_schema():\n \"\"\"Build and return a schema to use for the sample data.\"\"\"\n schema = StructType(\n [ \n StructField(\"CURRENCY\", StringType(), True),\n StructField(\"CUSIP\", StringType(), False),\n StructField(\"SEC_TYPE\", StringType(), True),\n StructField(\"TICKER_COUPON_MATURITY\", StringType(), True),\n StructField(\"SEC_DESC\", StringType(), True),\n StructField(\"ISIN\", StringType(), True),\n StructField(\"ORIG_FACE\", DoubleType(), True),\n StructField(\"SETTLED\", DoubleType(), True), \n StructField(\"NOTIONAL_MKT_VAL\", DoubleType(), True), \n StructField(\"BASE_CURR_MKT_VAL_ACC_INT\", DoubleType(), True), \n StructField(\"BASE_CURR_MKT_INT\", DoubleType(), True), \n StructField(\"MATURITY_DATE\", StringType(), True),\n StructField(\"ISSUE_DATE\", StringType(), True), \n StructField(\"BASE_CURR_FX_RATE\", DoubleType(), True), \n StructField(\"MKT_PRICE\", DoubleType(), True), \n StructField(\"COUPON\", StringType(), True), \n StructField(\"SNP_RATING\", StringType(), True), \n StructField(\"FUND_ID\", StringType(), False), \n ]\n )\n return schema\n\n\naladdinSecDF2SparkDF = spark.createDataFrame(aladdinSecDF2Renamed, schema=build_schema()) \\\n .withColumn(\"AS_OF_DATE\", lit(asOfDate).cast(\"date\"))\n\n\naladdinSecDF2SparkDF.printSchema()\naladdinSecDF2SparkDF.head(1)\n", "execution_count": 203, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "root\n |-- CURRENCY: string (nullable = true)\n |-- CUSIP: string (nullable = false)\n |-- SEC_TYPE: string (nullable = true)\n |-- TICKER_COUPON_MATURITY: string (nullable = true)\n |-- SEC_DESC: string (nullable = true)\n |-- ISIN: string (nullable = true)\n |-- ORIG_FACE: double (nullable = true)\n |-- SETTLED: double (nullable = true)\n |-- NOTIONAL_MKT_VAL: double (nullable = true)\n |-- BASE_CURR_MKT_VAL_ACC_INT: double (nullable = true)\n |-- BASE_CURR_MKT_INT: double (nullable = true)\n |-- MATURITY_DATE: string (nullable = true)\n |-- ISSUE_DATE: string (nullable = true)\n |-- BASE_CURR_FX_RATE: double (nullable = true)\n |-- MKT_PRICE: double (nullable = true)\n |-- COUPON: string (nullable = true)\n |-- SNP_RATING: string (nullable = true)\n |-- FUND_ID: string (nullable = false)\n |-- AS_OF_DATE: date (nullable = true)\n\n" }, { "data": { "text/plain": "[Row(CURRENCY=u'JPY', CUSIP=u'B7A0B81E2', SEC_TYPE=u'GOVT', TICKER_COUPON_MATURITY=u'JGB 1.700 20-MAR-2018', SEC_DESC=u'JAPAN (GOVERNMENT OF) 10YR #292', ISIN=u'JP1102921853', ORIG_FACE=4000000000.0, SETTLED=4000000000.0, NOTIONAL_MKT_VAL=4071178080.0, BASE_CURR_MKT_VAL_ACC_INT=36844907.73, BASE_CURR_MKT_INT=224246.16, MATURITY_DATE=u'2018-03-20 00:00:00', ISSUE_DATE=u'2008-05-13 00:00:00', BASE_CURR_FX_RATE=0.0091, MKT_PRICE=101.16, COUPON=u'1.7', SNP_RATING=u'A+', FUND_ID=u'I-CJF', AS_OF_DATE=datetime.date(2017, 7, 31))]" }, "metadata": {}, "execution_count": 203, "output_type": "execute_result" } ] }, { "metadata": {}, "source": "dashDBloadOptions = { \n Connectors.DASHDB.HOST : dashCredentials[\"host\"],\n Connectors.DASHDB.DATABASE : dashCredentials[\"db\"],\n Connectors.DASHDB.USERNAME : dashCredentials[\"username\"],\n Connectors.DASHDB.PASSWORD : dashCredentials[\"password\"],\n Connectors.DASHDB.SOURCE_TABLE_NAME : dashCredentials[\"REF_FUND_TABLE\"],\n}\n\nrefFundDF = sqlContext.read.format(\"com.ibm.spark.discover\").options(**dashDBloadOptions).load()\nrefFundDF.printSchema()\nrefFundDF.show(1)", "execution_count": 204, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "root\n |-- ID: string (nullable = false)\n\n+-----+\n| ID|\n+-----+\n|I-CJF|\n+-----+\nonly showing top 1 row\n\n" } ] }, { "metadata": {}, "source": "aladdinSecJoinSparkDF = aladdinSecDF2SparkDF.join(refFundDF, \n aladdinSecDF2SparkDF.FUND_ID == refFundDF.ID, \"inner\")\\\n .select(\n refFundDF.ID.alias(\"FUND_ID\"),\n aladdinSecDF2SparkDF.CURRENCY,\n aladdinSecDF2SparkDF.CUSIP,\n aladdinSecDF2SparkDF.SEC_TYPE,\n aladdinSecDF2SparkDF.TICKER_COUPON_MATURITY,\n aladdinSecDF2SparkDF.SEC_DESC,\n aladdinSecDF2SparkDF.ISIN,\n aladdinSecDF2SparkDF.ORIG_FACE,\n aladdinSecDF2SparkDF.SETTLED,\n aladdinSecDF2SparkDF.NOTIONAL_MKT_VAL,\n aladdinSecDF2SparkDF.BASE_CURR_MKT_VAL_ACC_INT,\n aladdinSecDF2SparkDF.BASE_CURR_MKT_INT,\n aladdinSecDF2SparkDF.MATURITY_DATE,\n aladdinSecDF2SparkDF.ISSUE_DATE,\n aladdinSecDF2SparkDF.BASE_CURR_FX_RATE,\n aladdinSecDF2SparkDF.MKT_PRICE,\n aladdinSecDF2SparkDF.COUPON,\n aladdinSecDF2SparkDF.SNP_RATING,\n aladdinSecDF2SparkDF.AS_OF_DATE, \n )\n\naladdinSecJoinSparkDF.show(1)", "execution_count": 205, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "+-------+--------+---------+--------+----------------------+--------------------+------------+---------+-------+----------------+-------------------------+-----------------+-------------------+-------------------+-----------------+---------+------+----------+----------+\n|FUND_ID|CURRENCY| CUSIP|SEC_TYPE|TICKER_COUPON_MATURITY| SEC_DESC| ISIN|ORIG_FACE|SETTLED|NOTIONAL_MKT_VAL|BASE_CURR_MKT_VAL_ACC_INT|BASE_CURR_MKT_INT| MATURITY_DATE| ISSUE_DATE|BASE_CURR_FX_RATE|MKT_PRICE|COUPON|SNP_RATING|AS_OF_DATE|\n+-------+--------+---------+--------+----------------------+--------------------+------------+---------+-------+----------------+-------------------------+-----------------+-------------------+-------------------+-----------------+---------+------+----------+----------+\n| I-CJF| JPY|B7A0B81E2| GOVT| JGB 1.700 20-MAR-...|JAPAN (GOVERNMENT...|JP1102921853| 4.0E9| 4.0E9| 4.07117808E9| 3.684490773E7| 224246.16|2018-03-20 00:00:00|2008-05-13 00:00:00| 0.0091| 101.16| 1.7| A+|2017-07-31|\n+-------+--------+---------+--------+----------------------+--------------------+------------+---------+-------+----------------+-------------------------+-----------------+-------------------+-------------------+-----------------+---------+------+----------+----------+\nonly showing top 1 row\n\n" } ] }, { "metadata": { "collapsed": true }, "source": "\n\n# Connection to Dash DB for writing the data\ndashdbsaveoption = {\n Connectors.DASHDB.HOST : dashCredentials[\"host\"],\n Connectors.DASHDB.DATABASE : dashCredentials[\"db\"],\n Connectors.DASHDB.USERNAME : dashCredentials[\"username\"],\n Connectors.DASHDB.PASSWORD : dashCredentials[\"password\"],\n Connectors.DASHDB.TARGET_TABLE_NAME : dashCredentials[\"tableName\"],\n Connectors.DASHDB.TARGET_WRITE_MODE : 'merge' \n}\n\naladdinSecDashDBDF = aladdinSecJoinSparkDF.write.format(\"com.ibm.spark.discover\").options(**dashdbsaveoption).save()\n", "execution_count": 206, "cell_type": "code", "outputs": [] }, { "metadata": { "collapsed": true }, "source": "", "execution_count": null, "cell_type": "code", "outputs": [] } ], "nbformat_minor": 1 }
apache-2.0
alexandrnikitin/algorithm-sandbox
courses/DAT256x/Module04/04-04-Probability.ipynb
1
63125
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Probability\n", "Many of the problems we try to solve using statistics are to do with *probability*. For example, what's the probable salary for a graduate who scored a given score in their final exam at school? Or, what's the likely height of a child given the height of his or her parents?\n", "\n", "It therefore makes sense to learn some basic principles of probability as we study statistics.\n", "\n", "## Probability Basics\n", "Let's start with some basic definitions and principles.\n", "- An ***experiment*** or ***trial*** is an action with an uncertain outcome, such as tossing a coin.\n", "- A ***sample space*** is the set of all possible outcomes of an experiment. In a coin toss, there's a set of two possible oucomes (*heads* and *tails*).\n", "- A ***sample point*** is a single possible outcome - for example, *heads*)\n", "- An ***event*** is a specific outome of single instance of an experiment - for example, tossing a coin and getting *tails*.\n", "- ***Probability*** is a value between 0 and 1 that indicates the likelihood of a particular event, with 0 meaning that the event is impossible, and 1 meaning that the event is inevitable. In general terms, it's calculated like this:\n", "\n", "\\begin{equation}\\text{probability of an event} = \\frac{\\text{Number of sample points that produce the event}}{\\text{Total number of sample points in the sample space}} \\end{equation}\n", "\n", "For example, the probability of getting *heads* when tossing as coin is <sup>1</sup>/<sub>2</sub> - there is only one side of the coin that is designated *heads*. and there are two possible outcomes in the sample space (*heads* and *tails*). So the probability of getting *heads* in a single coin toss is 0.5 (or 50% when expressed as a percentage).\n", "\n", "Let's look at another example. Suppose you throw two dice, hoping to get 7.\n", "\n", "The dice throw itself is an *experiment* - you don't know the outome until the dice have landed and settled.\n", "\n", "The *sample space* of all possible outcomes is every combination of two dice - 36 *sample points*:\n", "<table style='font-size:36px;'>\n", "<tr><td>&#9856;+&#9856;</td><td>&#9856;+&#9857;</td><td>&#9856;+&#9858;</td><td>&#9856;+&#9859;</td><td>&#9856;+&#9860;</td><td>&#9856;+&#9861;</td></tr>\n", "<tr><td>&#9857;+&#9856;</td><td>&#9857;+&#9857;</td><td>&#9857;+&#9858;</td><td>&#9857;+&#9859;</td><td>&#9857;+&#9860;</td><td>&#9857;+&#9861;</td></tr>\n", "<tr><td>&#9858;+&#9856;</td><td>&#9858;+&#9857;</td><td>&#9858;+&#9858;</td><td>&#9858;+&#9859;</td><td>&#9858;+&#9860;</td><td>&#9858;+&#9861;</td></tr>\n", "<tr><td>&#9859;+&#9856;</td><td>&#9859;+&#9857;</td><td>&#9859;+&#9858;</td><td>&#9859;+&#9859;</td><td>&#9859;+&#9860;</td><td>&#9859;+&#9861;</td></tr>\n", "<tr><td>&#9860;+&#9856;</td><td>&#9860;+&#9857;</td><td>&#9860;+&#9858;</td><td>&#9860;+&#9859;</td><td>&#9860;+&#9860;</td><td>&#9860;+&#9861;</td></tr>\n", "<tr><td>&#9861;+&#9856;</td><td>&#9861;+&#9857;</td><td>&#9861;+&#9858;</td><td>&#9861;+&#9859;</td><td>&#9861;+&#9860;</td><td>&#9861;+&#9861;</td></tr>\n", "</table>\n", "\n", "The *event* you want to happen is throwing a 7. There are 6 *sample points* that could produce this event:\n", "\n", "<table style='font-size:36px;'>\n", "<tr><td style='color:lightgrey;'>&#9856;+&#9856;</td><td style='color:lightgrey;'>&#9856;+&#9857;</td><td style='color:lightgrey;'>&#9856;+&#9858;</td><td style='color:lightgrey;'>&#9856;+&#9859;</td><td style='color:lightgrey;'>&#9856;+&#9860;</td><td>&#9856;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9857;+&#9856;</td><td style='color:lightgrey;'>&#9857;+&#9857;</td><td style='color:lightgrey;'>&#9857;+&#9858;</td><td style='color:lightgrey;'>&#9857;+&#9859;</td><td>&#9857;+&#9860;</td><td style='color:lightgrey;'>&#9857;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9858;+&#9856;</td><td style='color:lightgrey;'>&#9858;+&#9857;</td><td style='color:lightgrey;'>&#9858;+&#9858;</td><td>&#9858;+&#9859;</td><td style='color:lightgrey;'>&#9858;+&#9860;</td><td style='color:lightgrey;'>&#9858;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9859;+&#9856;</td><td style='color:lightgrey;'>&#9859;+&#9857;</td><td>&#9859;+&#9858;</td><td style='color:lightgrey;'>&#9859;+&#9859;</td><td style='color:lightgrey;'>&#9859;+&#9860;</td><td style='color:lightgrey;'>&#9859;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9860;+&#9856;</td><td>&#9860;+&#9857;</td><td style='color:lightgrey;'>&#9860;+&#9858;</td><td style='color:lightgrey;'>&#9860;+&#9859;</td><td style='color:lightgrey;'>&#9860;+&#9860;</td><td style='color:lightgrey;'>&#9860;+&#9861;</td></tr>\n", "<tr><td>&#9861;+&#9856;</td><td style='color:lightgrey;'>&#9861;+&#9857;</td><td style='color:lightgrey;'>&#9861;+&#9858;</td><td style='color:lightgrey;'>&#9861;+&#9859;</td><td style='color:lightgrey;'>&#9861;+&#9860;</td><td style='color:lightgrey;'>&#9861;+&#9861;</td></tr>\n", "</table>\n", "\n", "The *probability* of throwing a 7 is therefore <sup>6</sup>/<sub>36</sub> which can be simplified to <sup>1</sup>/<sub>6</sub> or approximately 0.167 (16.7%).\n", "\n", "### Probability Notation\n", "When we express probability, we use an upper-case **P** to indicate *probability* and an upper-case letter to represent the event. So to express the probability of throwing a 7 as an event valled ***A***, we could write:\n", "\n", "\\begin{equation}P(A) = 0.167 \\end{equation}\n", "\n", "### The Complement of an Event\n", "The *complement* of an event is the set of *sample points* that do ***not*** result in the event.\n", "\n", "For example, suppose you have a standard deck of playing cards, and you draw one card, hoping for a *spade*. In this case, the drawing of a card is the *experiment*, and the *event* is drawing a spade. There are 13 cards of each suit in the deck. So the *sample space* contains 52 *sample points*:\n", "\n", "<table>\n", "<tr><td>13 x <span style='font-size:32px;color:red;'>&hearts;</span></td><td>13 x <span style='font-size:32px;color:black;'>&spades;</span></td><td>13 x <span style='font-size:32px;color:black;'>&clubs;</span></td><td>13 x <span style='font-size:32px;color:red;'>&diams;</span></td></tr>\n", "</table>\n", "\n", "There are 13 *sample points* that would satisfy the requirements of the event:\n", "\n", "<table>\n", "<tr><td style='color:lightgrey;'>13 x <span style='font-size:32px;'>&hearts;</span></td><td>13 x <span style='font-size:32px;'>&spades;</span></td><td style='color:lightgrey;'>13 x <span style='font-size:32px;'>&clubs;</span></td><td style='color:lightgrey;'>13 x <span style='font-size:32px'>&diams;</span></td></tr>\n", "</table>\n", "\n", "So the *probability* of the event (drawing a spade) is <sup>13</sup>/<sub>52</sub> which is <sup>1</sup>/<sub>4</sub> or 0.25 (25%).\n", "\n", "The *complement* of the event is all of the possible outcomes that *don't* result in drawing a spade:\n", "\n", "<table>\n", "<tr><td>13 x <span style='font-size:32px;color:red;'>&hearts;</span></td><td style='color:lightgrey;'>13 x <span style='font-size:32px;'>&spades;</span></td><td>13 x <span style='font-size:32px;color:black;'>&clubs;</span></td><td>13 x <span style='font-size:32px;color:red;'>&diams;</span></td></tr>\n", "</table>\n", "\n", "There are 39 sample points in the complement (3 x 13), so the probability of the complement is <sup>39</sup>/<sub>52</sub> which is <sup>3</sup>/<sub>4</sub> or 0.75 (75%).\n", "\n", "Note that the probability of an event and the probability of its complement ***always add up to 1***.\n", "\n", "This fact can be useful in some cases. For example, suppose you throw two dice and want to know the probability of throwing more than 4. You *could* count all of the outcomes that would produce this result, but there are a lot of them. It might be easier to identify the ones that *do not* produce this result (in other words, the complement):\n", "\n", "<table style='font-size:36px;'>\n", "<tr><td>&#9856;+&#9856;</td><td>&#9856;+&#9857;</td><td>&#9856;+&#9858;</td><td style='color:lightgrey;'>&#9856;+&#9859;</td><td style='color:lightgrey;'>&#9856;+&#9860;</td><td style='color:lightgrey;'>&#9856;+&#9861;</td></tr>\n", "<tr><td>&#9857;+&#9856;</td><td>&#9857;+&#9857;</td><td style='color:lightgrey;'>&#9857;+&#9858;</td><td style='color:lightgrey;'>&#9857;+&#9859;</td><td style='color:lightgrey;'>&#9857;+&#9860;</td><td style='color:lightgrey;'>&#9857;+&#9861;</td></tr>\n", "<tr><td>&#9858;+&#9856;</td><td style='color:lightgrey;'>&#9858;+&#9857;</td><td style='color:lightgrey;'>&#9858;+&#9858;</td><td style='color:lightgrey;'>&#9858;+&#9859;</td><td style='color:lightgrey;'>&#9858;+&#9860;</td><td style='color:lightgrey;'>&#9858;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9859;+&#9856;</td><td style='color:lightgrey;'>&#9859;+&#9857;</td><td style='color:lightgrey;'>&#9859;+&#9858;</td><td style='color:lightgrey;'>&#9859;+&#9859;</td><td style='color:lightgrey;'>&#9859;+&#9860;</td><td style='color:lightgrey;'>&#9859;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9860;+&#9856;</td><td style='color:lightgrey;'>&#9860;+&#9857;</td><td style='color:lightgrey;'>&#9860;+&#9858;</td><td style='color:lightgrey;'>&#9860;+&#9859;</td><td style='color:lightgrey;'>&#9860;+&#9860;</td><td style='color:lightgrey;'>&#9860;+&#9861;</td></tr>\n", "<tr><td style='color:lightgrey;'>&#9861;+&#9856;</td><td style='color:lightgrey;'>&#9861;+&#9857;</td><td style='color:lightgrey;'>&#9861;+&#9858;</td><td style='color:lightgrey;'>&#9861;+&#9859;</td><td style='color:lightgrey;'>&#9861;+&#9860;</td><td style='color:lightgrey;'>&#9861;+&#9861;</td></tr>\n", "</table>\n", "\n", "Out of a total of 36 sample points in the sample space, there are 6 sample points where you throw a 4 or less (1+1, 1+2, 1+3, 2+1, 2+2, and 3+1); so the probability of the complement is <sup>6</sup>/<sub>36</sub> which is <sup>1</sup>/<sub>6</sub> or approximately 0.167 (16.7%).\n", "\n", "Now, here's the clever bit. Since the probability of the complement and the event itself must add up to 1, the probability of the event must be **<sup>5</sup>/<sub>6</sub>** or **0.833** (**83.3%**).\n", "\n", "We indicate the complement of an event by adding a **'** to the letter assigned to it, so:\n", "\n", "\\begin{equation}P(A) = 1 - P(A') \\end{equation}\n", "\n", "### Bias\n", "Often, the sample points in the sample space do not have the same probability, so there is a *bias* that makes one outcome more likely than another. For example, suppose your local weather forecaster indicates the predominant weather for each day of the week like this:\n", "\n", "<table>\n", "<tr><td style='text-align:center'>Mon</td><td style='text-align:center'>Tue</td><td style='text-align:center'>Wed</td><td style='text-align:center'>Thu</td><td style='text-align:center'>Fri</td><td style='text-align:center'>Sat</td><td style='text-align:center'>Sun</td></tr>\n", "<tr style='font-size:32px'><td>&#9729;</td><td>&#9730;</td><td>&#9728;</td><td>&#9728;</td><td>&#9728;</td><td>&#9729;</td><td>&#9728;</td></tr>\n", "</table>\n", "\n", "This forceast is pretty typical for your area at this time of the year. In fact, historically the weather is sunny on 60% of days, cloudy on 30% of days, and rainy on only 10% of days. On any given day, the sample space for the weather contains 3 sample points (*sunny*, *cloudy*, and *rainy*); but the probabities for these sample points are not the same.\n", "\n", "If we assign the letter **A** to a sunny day event, **B** to a cloudy day event, and **C** to a rainy day event then we can write these probabilities like this:\n", "\n", "\\begin{equation}P(A)=0.6\\;\\;\\;\\; P(B)=0.3\\;\\;\\;\\; P(C)=0.1 \\end{equation}\n", "\n", "The complement of **A** (a sunny day) is any day where it is not sunny - it is either cloudy or rainy. We can work out the probability for this in two ways: we can subtract the probablity of **A** from 1:\n", "\n", "\\begin{equation}P(A') = 1 - P(A) = 1 - 0.6 = 0.4 \\end{equation}\n", "\n", "Or we can add together the probabilities for all events that do *not* result in a sunny day:\n", "\n", "\\begin{equation}P(A') = P(B) + P(C) = 0.3 + 0.1 = 0.4 \\end{equation}\n", "\n", "Either way, there's a 40% chance of it not being sunny!\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditional Probability and Dependence\n", "Events can be:\n", "- *Independent* (events that are not affected by other events)\n", "- *Dependent* (events that are conditional on other events)\n", "- *Mutually Exclusive* (events that can't occur together)\n", "\n", "### Independent Events\n", "Imagine you toss a coin. The sample space contains two possible outomes: heads (<span style='font-size:42px;color:gold;'><sub>&#10050;</sub></span>) or tails (<span style='font-size:42px;color:gold;'><sub>&#9854;</sub></span>).\n", "\n", "The probability of getting *heads* is <sup>1</sup>/<sub>2</sub>, and the probability of getting *tails* is also <sup>1</sup>/<sub>2</sub>. Let's toss a coin...\n", "\n", "<span style='font-size:48px;color:gold;'>&#10050;</span>\n", "\n", "OK, so we got *heads*. Now, let's toss the coin again:\n", "\n", "<span style='font-size:48px;color:gold;'>&#10050;</span>\n", "\n", "It looks like we got *heads* again. If we were to toss the coin a third time, what's the probability that we'd get *heads*?\n", "\n", "Although you might be tempted to think that a *tail* is overdue, the fact is that each coin toss is an independent event. The outcome of the first coin toss does not affect the second coin toss (or the third, or any number of other coin tosses). For each independent coin toss, the probability of getting *heads* (or *tails*) remains <sup>1</sup>/<sub>2</sub>, or 50%.\n", "\n", "Run the following Python code to simulate 10,000 coin tosses by assigning a random value of 0 or 1 to *heads* and *tails*. Each time the coin is tossed, the probability of getting *heads* or *tails* is 50%, so you should expect approximately half of the results to be *heads* and half to be *tails* (it won't be exactly half, due to a little random variation; but it should be close):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import random\n", "\n", "# Create a list with 2 element (for heads and tails)\n", "heads_tails = [0,0]\n", "\n", "# loop through 10000 trials\n", "trials = 10000\n", "trial = 0\n", "while trial < trials:\n", " trial = trial + 1\n", " # Get a random 0 or 1\n", " toss = random.randint(0,1)\n", " # Increment the list element corresponding to the toss result\n", " heads_tails[toss] = heads_tails[toss] + 1\n", "\n", "print (heads_tails)\n", "\n", "# Show a pie chart of the results\n", "from matplotlib import pyplot as plt\n", "plt.figure(figsize=(5,5))\n", "plt.pie(heads_tails, labels=['heads', 'tails'])\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combining Independent Events\n", "Now, let's ask a slightly different question. What is the probability of getting three *heads* in a row? Since the probability of a heads on each independent toss is <sup>1</sup>/<sub>2</sub>, you might be tempted to think that the same probability applies to getting three *heads* in a row; but actually, we need to treat getting three *heads* as it's own event, which is the combination of three independent events. To combine independent events like this, we need to multiply the probability of each event. So:\n", "\n", "<span style='font-size:48px;color:gold;'><sub>&#10050;</sub></span> = <sup>1</sup>/<sub>2</sub>\n", "\n", "<span style='font-size:48px;color:gold;'><sub>&#10050;&#10050;</sub></span> = <sup>1</sup>/<sub>2</sub> x <sup>1</sup>/<sub>2</sub>\n", "\n", "<span style='font-size:48px;color:gold;'><sub>&#10050;&#10050;&#10050;</sub></span> = <sup>1</sup>/<sub>2</sub> x <sup>1</sup>/<sub>2</sub> x <sup>1</sup>/<sub>2</sub>\n", "\n", "So the probability of tossing three *heads* in a row is 0.5 x 0.5 x 0.5, which is 0.125 (or 12.5%).\n", "\n", "Run the code below to simulate 10,000 trials of flipping a coin three times:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import random\n", "\n", "# Count the number of 3xHeads results\n", "h3 = 0\n", "\n", "# Create a list of all results\n", "results = []\n", "\n", "# loop through 10000 trials\n", "trials = 10000\n", "trial = 0\n", "while trial < trials:\n", " trial = trial + 1\n", " # Flip three coins\n", " result = ['H' if random.randint(0,1) == 1 else 'T',\n", " 'H' if random.randint(0,1) == 1 else 'T',\n", " 'H' if random.randint(0,1) == 1 else 'T']\n", " results.append(result)\n", " # If it's three heads, add it to the count\n", " h3 = h3 + int(result == ['H','H','H'])\n", " \n", "# What proportion of trials produced 3x heads\n", "print (\"%.2f%%\" % ((h3/trials)*100))\n", "\n", "# Show all the results\n", "print (results)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output shows the percentage of times a trial resulted in three heads (which should be somewhere close to 12.5%). You can count the number of *['H', 'H', 'H']* entries in the full list of results to verify this if you like!\n", "\n", "\n", "#### Probability Trees\n", "You can represent a series of events and their probabilities as a probability tree:\n", "\n", " ____H(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " /\n", " ____H(0.5)\n", " / \\____T(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " / \n", " __H(0.5) ____H(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " / \\ / \n", " / \\____T(0.5)\n", " / \\____T(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " / \n", " _____/ _____H(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " \\ / \n", " \\ ___H(0.5)\n", " \\ / \\_____T(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " \\ / \n", " \\__T(0.5) _____H(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " \\ /\n", " \\___T(0.5)\n", " \\_____T(0.5) : 0.5 x 0.5 x 0.5 = 0.125\n", " _____\n", " 1.0\n", " \n", "Starting at the left, you can follow the branches in the tree that represent each event (in this case a coin toss result of *heads* or *tails* at each branch). Multiplying the probability of each branch of your path through the tree gives you the combined probability for an event composed of all of the events in the path. In this case, you can see from the tree that you are equally likely to get any sequence of three *heads* or *tails* results (so three *heads* is just as likely as three *tails*, which is just as likely as *head-tail-head*, *tail-head-tail*, or any other combination!)\n", "\n", "Note that the total probability for all paths through the tree adds up to 1.\n", "\n", "#### Combined Event Probability Notation\n", "When calculating the probability of combined events, we assign a letter such as **A** or **B** to each event, and we use the *intersection* (**&cap;**) symbol to indicate that we want the combined probability of multiple events. So we could assign the letters **A**, **B**, and **C** to each independent coin toss in our sequence of three tosses, and express the combined probability like this:\n", "\n", "\\begin{equation}P(A \\cap B \\cap C) = P(A) \\times P(B) \\times P(C) \\end{equation}\n", "\n", "#### Combining Events with Different Probabilities\n", "Imagine you have created a new game that mixes the excitment of coin-tossing with the thrill of die-rolling! The objective of the game is to roll a die and get *6*, and toss a coin and get *heads*:\n", "\n", "<div style='text-align:center'><span style='font-size:48px;'>&#9861;</span><span style='font-size:42px;'> +</span><span style='font-size:48px;color:gold;'>&#10050;</span></div>\n", "\n", "On each turn of the game, a player rolls the die and tosses the coin.\n", "\n", "How can we calculate the probability of winning?\n", "\n", "There are two independent events required to win: a die-roll of *6* (which we'll call event **A**), and a coin-toss of *heads* (which we'll call event **B**)\n", "\n", "Our formula for combined independent events is:\n", "\n", "\\begin{equation}P(A \\cap B) = P(A) \\times P(B) \\end{equation}\n", "\n", "The probablilty of rolling a *6* on a fair die is <sup>1</sup>/<sub>6</sub> or 0.167; and the probability of tossing a coin and getting *heads* is <sup>1</sup>/<sub>2</sub> or 0.5:\n", "\n", "\\begin{equation}P(A \\cap B) = 0.167 \\times 0.5 = 0.083 \\end{equation}\n", "\n", "So on each turn, there's an 8.3% chance to win the game.\n", "\n", "#### Intersections and Unions\n", "\n", "Previously you saw that we use the *intersection* (**&cap;**) symbol to represent \"and\" when combining event probabilities. This notation comes from a branch of mathematics called *set theory*, in which we work with sets of values. let's examine this in a little more detail.\n", "\n", "Here's our deck of playing cards, with the full sample space for drawing any card:\n", "\n", "<table style='font-size:18px;'>\n", "<tr><td style='color:red;'>A &hearts;</td><td style='color:black;'>A &spades;</td><td style='color:black;'>A &clubs;<td style='color:red;'>A &diams;</td></tr>\n", "<tr><td style='color:red;'>K &hearts;</td><td style='color:black;'>K &spades;</td><td style='color:black;'>K &clubs;<td style='color:red;'>K &diams;</td></tr>\n", "<tr><td style='color:red;'>Q &hearts;</td><td style='color:black;'>Q &spades;</td><td style='color:black;'>Q &clubs;<td style='color:red;'>Q &diams;</td></tr>\n", "<tr><td style='color:red;'>J &hearts;</td><td style='color:black;'>J &spades;</td><td style='color:black;'>J &clubs;<td style='color:red;'>J &diams;</td></tr>\n", "<tr><td style='color:red;'>10 &hearts;</td><td style='color:black;'>10 &spades;</td><td style='color:black;'>10 &clubs;<td style='color:red;'>10 &diams;</td></tr>\n", "<tr><td style='color:red;'>9 &hearts;</td><td style='color:black;'>9 &spades;</td><td style='color:black;'>9 &clubs;<td style='color:red;'>9 &diams;</td></tr>\n", "<tr><td style='color:red;'>8 &hearts;</td><td style='color:black;'>8 &spades;</td><td style='color:black;'>8 &clubs;<td style='color:red;'>8 &diams;</td></tr>\n", "<tr><td style='color:red;'>7 &hearts;</td><td style='color:black;'>7 &spades;</td><td style='color:black;'>7 &clubs;<td style='color:red;'>7 &diams;</td></tr>\n", "<tr><td style='color:red;'>6 &hearts;</td><td style='color:black;'>6 &spades;</td><td style='color:black;'>6 &clubs;<td style='color:red;'>6 &diams;</td></tr>\n", "<tr><td style='color:red;'>5 &hearts;</td><td style='color:black;'>5 &spades;</td><td style='color:black;'>5 &clubs;<td style='color:red;'>5 &diams;</td></tr>\n", "<tr><td style='color:red;'>4 &hearts;</td><td style='color:black;'>4 &spades;</td><td style='color:black;'>4 &clubs;<td style='color:red;'>4 &diams;</td></tr>\n", "<tr><td style='color:red;'>3 &hearts;</td><td style='color:black;'>3 &spades;</td><td style='color:black;'>3 &clubs;<td style='color:red;'>3 &diams;</td></tr>\n", "<tr><td style='color:red;'>2 &hearts;</td><td style='color:black;'>2 &spades;</td><td style='color:black;'>2 &clubs;<td style='color:red;'>2 &diams;</td></tr>\n", "</table>\n", "\n", "Now, let's look at two potential events:\n", "- Drawing an ace (**A**)\n", "- Drawing a red card (**B**)\n", "\n", "The set of sample points for event **A** (drawing an ace) is:\n", "\n", "<table style='font-size:18px;'>\n", "<tr><td style='color:red;'>A &hearts;</td><td style='color:black;'>A &spades;</td><td style='color:black;'>A &clubs;<td style='color:red;'>A &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>K &hearts;</td><td style='color:lightgrey;'>K &spades;</td><td style='color:lightgrey;'>K &clubs;<td>K &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>Q &hearts;</td><td>Q &spades;</td><td>Q &clubs;<td>Q &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>J &hearts;</td><td>J &spades;</td><td>J &clubs;<td>J &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>10 &hearts;</td><td>10 &spades;</td><td>10 &clubs;<td>10 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>9 &hearts;</td><td>9 &spades;</td><td>9 &clubs;<td>9 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>8 &hearts;</td><td>8 &spades;</td><td>8 &clubs;<td>8 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>7 &hearts;</td><td>7 &spades;</td><td>7 &clubs;<td>7 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>6 &hearts;</td><td>6 &spades;</td><td>6 &clubs;<td>6 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>5 &hearts;</td><td>5 &spades;</td><td>5 &clubs;<td>5 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>4 &hearts;</td><td>4 &spades;</td><td>4 &clubs;<td>4 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>3 &hearts;</td><td>3 &spades;</td><td>3 &clubs;<td>3 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>2 &hearts;</td><td>2 &spades;</td><td>2 &clubs;<td>2 &diams;</td></tr>\n", "</table>\n", "\n", "So the probability of drawing an ace is:\n", "\n", "\\begin{equation}P(A) = \\frac{4}{52} = \\frac{1}{13} = 0.077\\end{equation} \n", "\n", "Now let's look at the set of sample points for event **B** (drawing a red card)\n", "\n", "<table style='font-size:18px;'>\n", "<tr><td style='color:red;'>A &hearts;</td><td style='color:lightgrey;'>A &spades;</td><td style='color:lightgrey;'>A &clubs;<td style='color:red;'>A &diams;</td></tr>\n", "<tr><td style='color:red;'>K &hearts;</td><td style='color:lightgrey;'>K &spades;</td><td style='color:lightgrey;'>K &clubs;<td style='color:red;'>K &diams;</td></tr>\n", "<tr><td style='color:red;'>Q &hearts;</td><td style='color:lightgrey;'>Q &spades;</td><td style='color:lightgrey;'>Q &clubs;<td style='color:red;'>Q &diams;</td></tr>\n", "<tr><td style='color:red;'>J &hearts;</td><td style='color:lightgrey;'>J &spades;</td><td style='color:lightgrey;'>J &clubs;<td style='color:red;'>J &diams;</td></tr>\n", "<tr><td style='color:red;'>10 &hearts;</td><td style='color:lightgrey;'>10 &spades;</td><td style='color:lightgrey;'>10 &clubs;<td style='color:red;'>10 &diams;</td></tr>\n", "<tr><td style='color:red;'>9 &hearts;</td><td style='color:lightgrey;'>9 &spades;</td><td style='color:lightgrey;'>9 &clubs;<td style='color:red;'>9 &diams;</td></tr>\n", "<tr><td style='color:red;'>8 &hearts;</td><td style='color:lightgrey;'>8 &spades;</td><td style='color:lightgrey;'>8 &clubs;<td style='color:red;'>8 &diams;</td></tr>\n", "<tr><td style='color:red;'>7 &hearts;</td><td style='color:lightgrey;'>7 &spades;</td><td style='color:lightgrey;'>7 &clubs;<td style='color:red;'>7 &diams;</td></tr>\n", "<tr><td style='color:red;'>6 &hearts;</td><td style='color:lightgrey;'>6 &spades;</td><td style='color:lightgrey;'>6 &clubs;<td style='color:red;'>6 &diams;</td></tr>\n", "<tr><td style='color:red;'>5 &hearts;</td><td style='color:lightgrey;'>5 &spades;</td><td style='color:lightgrey;'>5 &clubs;<td style='color:red;'>5 &diams;</td></tr>\n", "<tr><td style='color:red;'>4 &hearts;</td><td style='color:lightgrey;'>4 &spades;</td><td style='color:lightgrey;'>4 &clubs;<td style='color:red;'>4 &diams;</td></tr>\n", "<tr><td style='color:red;'>3 &hearts;</td><td style='color:lightgrey;'>3 &spades;</td><td style='color:lightgrey;'>3 &clubs;<td style='color:red;'>3 &diams;</td></tr>\n", "<tr><td style='color:red;'>2 &hearts;</td><td style='color:lightgrey;'>2 &spades;</td><td style='color:lightgrey;'>2 &clubs;<td style='color:red;'>2 &diams;</td></tr>\n", "</table>\n", "\n", "The probability of drawing a red card is therefore:\n", "\n", "\\begin{equation}P(A) = \\frac{26}{52} = \\frac{1}{2} = 0.5\\end{equation} \n", "\n", "##### Intersections\n", "\n", "We can think of the sample spaces for these events as two sets, and we can show them as a Venn diagram:\n", "\n", "<br/>\n", "\n", "<div style='text-align:center'>Event A<span style='font-size:120px'>&#9901;</span>Event B</div>\n", "\n", "Each circle in the Venn diagram represents a set of sample points. The set on the left contains the sample points for event **A** (drawing an ace) and the set on the right contains the sample points for event **B** (drawing a red card). Note that the circles overlap, creating an intersection that contains only the sample points that apply to event **A** *and* event **B**.\n", "\n", "This intersected sample space looks like this:\n", "\n", "<table style='font-size:18px;'>\n", "<tr><td style='color:red;'>A &hearts;</td><td style='color:lightgrey;'>A &spades;</td><td style='color:lightgrey;'>A &clubs;<td style='color:red;'>A &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>K &hearts;</td><td style='color:lightgrey;'>K &spades;</td><td style='color:lightgrey;'>K &clubs;<td>K &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>Q &hearts;</td><td>Q &spades;</td><td>Q &clubs;<td>Q &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>J &hearts;</td><td>J &spades;</td><td>J &clubs;<td>J &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>10 &hearts;</td><td>10 &spades;</td><td>10 &clubs;<td>10 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>9 &hearts;</td><td>9 &spades;</td><td>9 &clubs;<td>9 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>8 &hearts;</td><td>8 &spades;</td><td>8 &clubs;<td>8 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>7 &hearts;</td><td>7 &spades;</td><td>7 &clubs;<td>7 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>6 &hearts;</td><td>6 &spades;</td><td>6 &clubs;<td>6 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>5 &hearts;</td><td>5 &spades;</td><td>5 &clubs;<td>5 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>4 &hearts;</td><td>4 &spades;</td><td>4 &clubs;<td>4 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>3 &hearts;</td><td>3 &spades;</td><td>3 &clubs;<td>3 &diams;</td></tr>\n", "<tr style='color:lightgrey;'><td>2 &hearts;</td><td>2 &spades;</td><td>2 &clubs;<td>2 &diams;</td></tr>\n", "</table>\n", "\n", "As you've seen previously, we write this as **A &cap; B**, and we can calculate its probability like this:\n", "\n", "\\begin{equation}P(A \\cap B) = P(A) \\times P(B) = 0.077 \\times 0.5 = 0.0385 \\end{equation}\n", "\n", "So when you draw a single card from a full deck, there is a 3.85% chance it will be a red ace.\n", "\n", "##### Unions\n", "The intersection describes the sample space for event **A** *and* event **B**; but what if we wanted to look at the probability of drawing an ace *or* a red card. In other words, any sample point that is in either of the Venn digram circles.\n", "\n", "This set of sample points looks like this:\n", "\n", "<table style='font-size:18px;'>\n", "<tr><td style='color:red;'>A &hearts;</td><td style='color:black;'>A &spades;</td><td style='color:black;'>A &clubs;<td style='color:red;'>A &diams;</td></tr>\n", "<tr><td style='color:red;'>K &hearts;</td><td style='color:lightgrey;'>K &spades;</td><td style='color:lightgrey;'>K &clubs;<td style='color:red;'>K &diams;</td></tr>\n", "<tr><td style='color:red;'>Q &hearts;</td><td style='color:lightgrey;'>Q &spades;</td><td style='color:lightgrey;'>Q &clubs;<td style='color:red;'>Q &diams;</td></tr>\n", "<tr><td style='color:red;'>J &hearts;</td><td style='color:lightgrey;'>J &spades;</td><td style='color:lightgrey;'>J &clubs;<td style='color:red;'>J &diams;</td></tr>\n", "<tr><td style='color:red;'>10 &hearts;</td><td style='color:lightgrey;'>10 &spades;</td><td style='color:lightgrey;'>10 &clubs;<td style='color:red;'>10 &diams;</td></tr>\n", "<tr><td style='color:red;'>9 &hearts;</td><td style='color:lightgrey;'>9 &spades;</td><td style='color:lightgrey;'>9 &clubs;<td style='color:red;'>9 &diams;</td></tr>\n", "<tr><td style='color:red;'>8 &hearts;</td><td style='color:lightgrey;'>8 &spades;</td><td style='color:lightgrey;'>8 &clubs;<td style='color:red;'>8 &diams;</td></tr>\n", "<tr><td style='color:red;'>7 &hearts;</td><td style='color:lightgrey;'>7 &spades;</td><td style='color:lightgrey;'>7 &clubs;<td style='color:red;'>7 &diams;</td></tr>\n", "<tr><td style='color:red;'>6 &hearts;</td><td style='color:lightgrey;'>6 &spades;</td><td style='color:lightgrey;'>6 &clubs;<td style='color:red;'>6 &diams;</td></tr>\n", "<tr><td style='color:red;'>5 &hearts;</td><td style='color:lightgrey;'>5 &spades;</td><td style='color:lightgrey;'>5 &clubs;<td style='color:red;'>5 &diams;</td></tr>\n", "<tr><td style='color:red;'>4 &hearts;</td><td style='color:lightgrey;'>4 &spades;</td><td style='color:lightgrey;'>4 &clubs;<td style='color:red;'>4 &diams;</td></tr>\n", "<tr><td style='color:red;'>3 &hearts;</td><td style='color:lightgrey;'>3 &spades;</td><td style='color:lightgrey;'>3 &clubs;<td style='color:red;'>3 &diams;</td></tr>\n", "<tr><td style='color:red;'>2 &hearts;</td><td style='color:lightgrey;'>2 &spades;</td><td style='color:lightgrey;'>2 &clubs;<td style='color:red;'>2 &diams;</td></tr>\n", "</table>\n", "\n", "We call this the *union* of the sets, and we write it as **A &cup; B**.\n", "\n", "To calculate the probability of a card being either an ace (of any color) or a red card (of any value), we can work out the probability of A, add it to the probability of B, and subtract the probability of A &cap; B (to avoid double-counting the red aces):\n", "\n", "\\begin{equation}P(A \\cup B) = P(A) + P(B) - P(A \\cap B)\\end{equation}\n", "\n", "So:\n", "\n", "\\begin{equation}P(A \\cup B) = 0.077 + 0.5 - 0.0385 = 0.5385\\end{equation}\n", "\n", "So when you draw a card from a full deck, there is a 53.85% probability that it will be either an ace or a red card." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dependent Events\n", "Let's return to our deck of 52 cards from which we're going to draw one card. The sample space can be summarized like this:\n", "\n", "<table>\n", "<tr><td>13 x <span style='font-size:32px;color:red;'>&hearts;</span></td><td>13 x <span style='font-size:32px;color:black;'>&spades;</span></td><td>13 x <span style='font-size:32px;color:black;'>&clubs;</span></td><td>13 x <span style='font-size:32px;color:red;'>&diams;</span></td></tr>\n", "</table>\n", "\n", "There are two black suits (*spades* and *clubs*) and two red suits (*hearts* and *diamonds*); with 13 cards in each suit. So the probability of drawing a black card (event **A**) and the probability of drawing a red card (event **B**) can be calculated like this:\n", "\n", "\\begin{equation}P(A) = \\frac{13 + 13}{52} = \\frac{26}{52} = 0.5 \\;\\;\\;\\; P(B) = \\frac{13 + 13}{52} = \\frac{26}{52} = 0.5\\end{equation}\n", "\n", "Now let's draw a card from the deck:\n", "\n", "<div style ='text-align:center;'><span style='font-size:32px;color:red;'>&hearts;</span></div>\n", "\n", "We drew a heart, which is red. So, assuming we don't replace the card back into the deck, this changes the sample space as follows:\n", "\n", "<table>\n", "<tr><td>12 x <span style='font-size:32px;color:red;'>&hearts;</span></td><td>13 x <span style='font-size:32px;color:black;'>&spades;</span></td><td>13 x <span style='font-size:32px;color:black;'>&clubs;</span></td><td>13 x <span style='font-size:32px;color:red;'>&diams;</span></td></tr>\n", "</table>\n", "\n", "The probabilities for **A** and **B** are now:\n", "\n", "\\begin{equation}P(A) = \\frac{13 + 13}{51} = \\frac{26}{51} = 0.51 \\;\\;\\;\\; P(B) = \\frac{12 + 13}{51} = \\frac{25}{51} = 0.49\\end{equation}\n", "\n", "Now let's draw a second card:\n", "\n", "<div style ='text-align:center;'><span style='font-size:32px;color:red;'>&diams;</span></div>\n", "\n", "We drew a diamond, so again this changes the sample space for the next draw:\n", "\n", "<table>\n", "<tr><td>12 x <span style='font-size:32px;color:red;'>&hearts;</span></td><td>13 x <span style='font-size:32px;color:black;'>&spades;</span></td><td>13 x <span style='font-size:32px;color:black;'>&clubs;</span></td><td>12 x <span style='font-size:32px;color:red;'>&diams;</span></td></tr>\n", "</table>\n", "\n", "The probabilities for **A** and **B** are now:\n", "\n", "\\begin{equation}P(A) = \\frac{13 + 13}{50} = \\frac{26}{50} = 0.52 \\;\\;\\;\\; P(B) = \\frac{12 + 12}{50} = \\frac{24}{51} = 0.48\\end{equation}\n", "\n", "So it's clear that one event can affect another; in this case, the probability of drawing a card of a particular color on the second draw depends on the color of card drawn on the previous draw. We call these *dependent* events.\n", "\n", "Probability trees are particularly useful when looking at dependent events. Here's a probability tree for drawing red or black cards as the first three draws from a deck of cards:\n", "\n", " _______R(0.48) \n", " /\n", " ____R(0.49)\n", " / \\_______B(0.52) \n", " / \n", " __R(0.50) _______R(0.50) \n", " / \\ / \n", " / \\____B(0.51)\n", " / \\_______B(0.50) \n", " / \n", " _____/ ________R(0.50) \n", " \\ / \n", " \\ ___R(0.51)\n", " \\ / \\________B(0.50) \n", " \\ / \n", " \\__B(0.50) ________R(0.52) \n", " \\ /\n", " \\___B(0.49)\n", " \\________B(0.48) \n", "\n", "\n", "\n", "#### Calculating Probabilities for Dependent Events\n", "Imagine a game in which you have to predict the color of the next card to be drawn. Suppose the first card drawn is a *spade*, which is black. What is the probability of the next card being red?\n", "\n", "The notation for this is:\n", "\n", "\\begin{equation}P(B|A)\\end{equation}\n", "\n", "You can interpret this as *the probability of B, given A*. In other words, given that event **A** (drawing a black card) has already happened, what is the probability of **B** (drawing a red card). This is commonly referred to as the *conditional probability* of B given A; and it's formula is:\n", "\n", "\\begin{equation}P(B|A) = \\frac{P(A \\cap B)}{P(A)}\\end{equation}\n", "\n", "So to return to our example, the probability of the second card being red given that the first card was black is:\n", "\n", "\\begin{equation}P(B|A) = \\frac{\\frac{26}{52} \\times \\frac{26}{51}}{\\frac{26}{52}}\\end{equation}\n", "\n", "Which simplifies to:\n", "\n", "\\begin{equation}P(B|A) = \\frac{0.5 \\times 0.51}{0.5}\\end{equation}\n", "\n", "So:\n", "\n", "\\begin{equation}P(B|A) = \\frac{0.255}{0.5} = 0.51\\end{equation}\n", "\n", "Which is what we calculated previously - so the formula works!\n", "\n", "Because this is an algebraic expression, we can rearrange it like this:\n", "\n", "\\begin{equation}P(A \\cap B) = P(A) \\times P(B|A)\\end{equation}\n", "\n", "We can use this form of the formula to calculate the probability that the first two cards drawn from a full deck of cards will both be jacks. In this case, event **A** is drawing a jack for the first card, and event **B** is drawing a jack for the second card.\n", "\n", "The probability that the first drawn card will be a jack is:\n", "\n", "\\begin{equation}P(A) = \\frac{4}{52} = \\frac{1}{13}\\end{equation}\n", "\n", "We draw the first card:\n", "\n", "<br/>\n", "<div style ='text-align:center;'><span style='font-size:32px;color:black;'>J &clubs;</span></div>\n", "\n", "Success! it's the jack of clubs. Our chances of the first two cards being jacks are looking good so far\n", "\n", "Now. we know that there are now only 3 jacks left, in a deck of 51 remaining cards; so the probability of drawing a jack as a second card, given that we drew a jack as the first card is:\n", "\n", "\\begin{equation}P(B|A) = \\frac{3}{51}\\end{equation}\n", "\n", "So we can work out the probability of drawing two jacks from a deck like this:\n", "\n", "\\begin{equation}P(A \\cap B) = \\frac{1}{13} \\times \\frac{3}{51} = \\frac{3}{663} = \\frac{1}{221}\\end{equation}\n", "\n", "So there's a 1 in 221 (0.45%) probability that the first two cards drawn from a full deck will be jacks.\n", "\n", "\n", "### Mutually Exclusive Events\n", "We've talked about dependent and independent events, but there's a third category to be considered: mutually exclusive events.\n", "\n", "For example, when flipping a coin, what is the probability that in a single coin flip the result will be *heads* ***and*** *tails*? The answer is of course, 0; a single coin flip can only result in *heads* ***or*** *tails*; not both!\n", "\n", "For mutually exclusive event, the probability of an intersection is:\n", "\n", "\\begin{equation}P(A \\cap B) = 0\\end{equation}\n", "\n", "The probability for a union is:\n", "\n", "\\begin{equation}P(A \\cup B) = P(A) + P(B)\\end{equation}\n", "\n", "Note that we don't need to subtract the intersection (*and*) probability to calculate the union (*or*) probability like we did previously, because there's no risk of double-counting the sample points that lie in both events - there are none. (The intersection probability for mutually exclusive events is always 0, so you can subtract it if you like - you'll still get the same result!)\n", "\n", "Let's look at another two mutually exclusive events based on rolling a die:\n", "- Rolling a 6 (event **A**)\n", "- Rolling an odd number (event **B**)\n", "\n", "The probabilities for these events are:\n", "\n", "\\begin{equation}P(A) = \\frac{1}{6} \\;\\;\\;\\; P(B) = \\frac{3}{6}\\end{equation}\n", "\n", "What's the probability of rolling a 6 *and* an odd number in a single roll? These are mutually exclusive, so:\n", "\n", "\\begin{equation}P(A \\cap B) = 0\\end{equation}\n", "\n", "What's the probability of rolling a 6 *or* an odd number:\n", "\n", "\\begin{equation}P(A \\cup B) = \\frac{1}{6} + \\frac{3}{6} = \\frac{4}{6}\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Binomial Variables and Distributions\n", "Now that we know something about probability, let's apply that to statistics. Statistics is about inferring measures for a full population based on samples, allowing for random variation; so we're going to have to consider the idea of a *random variable*.\n", "\n", "A random variable us a number that can vary in value. For example, the temperature on a given day, or the number of students taking a class.\n", "\n", "### Binomial Variables\n", "One particular type of random variable that we use in statistics is a *binomial* variable. A binomial variable is used to count how frequently an event occurs in a fixed number of repeated independent experiments. The event in question must have the same probability of occurring in each experiment, and indicates the success or failure of the experiment; with a probability ***p*** of success, which has a complement of ***1 - p*** as the probability of failure (we often call this kind of experiment a *Bernoulli Trial* after Swiss mathematician Jacob Bernoulli).\n", "\n", "For example, suppose we flip a coin three times, counting *heads* as success. We can define a binomial variable to represent the number of successful coin flips (that is, the number of times we got *heads*).\n", "\n", "Let's examine this in more detail.\n", "\n", "We'll call our variable ***X***, and as stated previously it represents the number of times we flip *heads* in a series of three coin flips. Let's start by examining all the possible values for ***X***.\n", "\n", "We're flipping the coin three times, with a probability of <sup>1</sup>/<sub>2</sub> of success on each flip. The possibile results include none of the flips resulting in *heads*, all of the flips resulting in *heads*, or any combination in between. There are two possible outcomes from each flip, and there are three flips, so the total number of possible result sets is 2<sup>3</sup>, which is 8. Here they are:\n", "\n", "<div style='font-size:48px;color:gold;'>&#9854;&#9854;&#9854;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#9854;&#10050;&#9854;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#9854;&#9854;&#10050;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#9854;&#10050;&#10050;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#10050;&#9854;&#9854;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#10050;&#10050;&#9854;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#10050;&#9854;&#10050;</div>\n", "<br/>\n", "<div style='font-size:48px;color:gold;'>&#10050;&#10050;&#10050;</div>\n", "<br/>\n", "\n", "In these results, our variable ***X***, representing the number of successful events (getting *heads*), can vary from 0 to 3. We can write that like this:\n", "\n", "\\begin{equation}X=\\{0,1,2,3\\}\\end{equation}\n", "\n", "When we want to indicate a specific outcome for a random variable, we use write the variable in lower case, for example ***x*** So what's the probability that ***x*** = 0 (meaning that out of our three flips we got no *heads*)?\n", "\n", "We can easily see, that there is 1 row in our set of possible outcomes that contains no *heads*, so:\n", "\n", "\\begin{equation}P(x=0) = \\frac{1}{8}\\end{equation}\n", "\n", "OK, let's see if we can find the probability for 1 success. There are three sample points containing a single *heads* result, so:\n", "\n", "\\begin{equation}P(x=1) = \\frac{3}{8}\\end{equation}\n", "\n", "Again, we can easily see that from our results; but it's worth thinking about this in a slightly different way that will make it easier to calculate this probability more generically when there are more sample points (for example, if we had based our binomial variable on 100 coin flips, there would be many more combinations!).\n", "\n", "What we're actually saying here is that for **3** experiments (in this case coin flips), we want to *choose* **1** successful results. This is written as <sub>3</sub>C<sub>1</sub>. More generically, this is known as *n choose k*, and it's written like this:\n", "\n", "\\begin{equation}_{n}C_{k}\\end{equation}\n", "\n", "or sometimes like this:\n", "\n", "\\begin{equation}\\begin{pmatrix} n \\\\ k\\end{pmatrix}\\end{equation}\n", "\n", "The formula to calculate this is:\n", "\n", "\\begin{equation}\\begin{pmatrix} n \\\\ k\\end{pmatrix} = \\frac{n!}{k!(n-k)!}\\end{equation}\n", "\n", "The exclamation points indicate *factorials* - the product of all positive integers less than or equal to the specified integer (with 0! having a value of 1).\n", "\n", "In the case of our <sub>3</sub>C<sub>1</sub> calculation, this means:\n", "\n", "\\begin{equation}\\begin{pmatrix} 3 \\\\ 1\\end{pmatrix} = \\frac{3!}{1!(3 - 1)!} = \\frac{3!}{1!\\times2!} =\\frac{3 \\times 2 \\times 1}{1 \\times(2 \\times 1)} = \\frac{6}{2} = 3 \\end{equation}\n", "\n", "That seems like a lot of work to find the number of successful experiments, but now that you know this general formula, you can use it to calculate the number of sample points for any value of *k* from any set of *n* cases. Let's use it to find the possibility of two successful *heads* out of 3 coin flips:\n", "\n", "\\begin{equation}P(x=2) = \\frac{_{3}C_{2}}{8}\\end{equation}\n", "\n", "Let's work out the number of combinations for <sub>3</sub>C<sub>2</sub>\n", "\n", "\\begin{equation}_{3}C_{2} = \\frac{3!}{2!(3 - 2)!} = \\frac{6}{2 \\times 1} = \\frac{6}{2} = 3\\end{equation}\n", "\n", "So:\n", "\n", "\\begin{equation}P(x=2) = \\frac{3}{8}\\end{equation}\n", "\n", "Finally, what's the probability that all three flips were *heads*?\n", "\n", "\\begin{equation}P(x=3) = \\frac{_{3}C_{3}}{8}\\end{equation}\n", "\n", "\\begin{equation}_{3}C_{3} = \\frac{3!}{3!(3 - 3)!} = \\frac{6}{6} = 1\\end{equation}\n", "\n", "So:\n", "\n", "\\begin{equation}P(x=3) = \\frac{1}{8}\\end{equation}\n", "\n", "In Python, there are a number of modules you can use to find the *n choose k* combinations, including the *scipy.special.**comb*** function. \n", "\n", "In our coin flipping experiment, there is an equal probability of success and failure; so the probability calculations are relatively simple, and you may notice that there's a symmetry to the probability for each possible value of the binomial variable, as you can see by running the following Python code. You can increase the value of the **trials** variable to verify that no matter how many times we toss the coin, the probabilities of getting *heads* (or *tails* for that matter) form a symmetrical distribution, because there's an equal probability of success and failure in each trial." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from scipy import special as sps\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "trials = 3\n", "\n", "possibilities = 2**trials\n", "x = np.array(range(0, trials+1))\n", "\n", "p = np.array([sps.comb(trials, i, exact=True)/possibilities for i in x])\n", "\n", "# Set up the graph\n", "plt.xlabel('Successes')\n", "plt.ylabel('Probability')\n", "plt.bar(x, p)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Allowing for Bias\n", "Previously, we calculated the probability for each possible value of a random variable by simply dividing the number of combinations for that value by the total number of possible outcomes. This works if the probability of the event being tested is equal for failure and success; but of course, not all experiments have an equal chance of success or failure. Some include a bias that makes success more or less likely - so we need to be a little more thorough in our calculations to allow for this.\n", "\n", "Suppose you're flying off to some exotic destination, and you know that there's a one in four chance that the airport security scanner will trigger a random search for each passenger that goes though. If you watch five passengers go through the scanner, how many will be stopped for a random search?\n", "\n", "It's tempting to think that there's a one in four chance, so a quarter of the passengers will be stopped; but remember that the searches are triggered randomly for thousands of passengers that pass through the airport each day. It's possible that none of the next five passengers will be searched; all five of them will be searched, or some other value in between will be searched. \n", "\n", "Even though the probabilities of being searched or not searched are not the same, this is still a binomial variable. There are a fixed number of independent experiments (five passengers passing through the security scanner), the outcome of each experiment is either success (a search is triggered) or failure (no search is triggered), and the probability of being searched does not change for each passenger.\n", "\n", "There are five experiments in which a passenger goes through the security scanner, let's call this **n**.\n", "\n", "For each passenger, the probability of being searched is <sup>1</sup>/<sub>4</sub> or 0.25. We'll call this **p**. \n", "\n", "The complement of **p** (in other words, the probability of *not* being searched) is **1-p**, in this case <sup>3</sup>/<sub>4</sub> or 0.75.\n", "\n", "So, what's the probability that out of our **n** experiments, three result in a search (let's call that **k**) and the remaining ones (there will be **n**-**k** of them, which is two) don't?\n", "\n", "- The probability of three passengers being searched is 0.25 x 0.25 x 0.25 which is the same as 0.25<sup>3</sup>. Using our generic variables, this is **p<sup>k</sup>**.\n", "- The probability that the rest don't get searched is 0.75 x 0.75, or 0.75<sup>2</sup>. In terms of our variables, this is **1-p<sup>(n-k)</sup>**.\n", "- The combined probability of three searchs and two non-searches is therefore 0.25<sup>3</sup> x 0.75<sup>2</sup> (approximately 0.088). Using our variables, this is:\n", "\n", "\\begin{equation}p^{k}(1-p)^{(n-k)}\\end{equation}\n", "\n", "This formula enables us to calculate the probability for a single combination of ***n*** passengers in which ***k*** experiments had a successful outcome. In this case, it enables us to calculate that the probability of three passengers out of five being searched is approximately 0.088. However, we need to consider that there are multiple ways this can happen. The first three passengers could get searched; or the last three; or the first, third, and fifth, or any other possible combination of 3 from 5.\n", "\n", "There are two possible outcomes for each experiment; so the total number of possible combinations of five passengers being searched or not searched is 2<sup>5</sup> or 32. So within those 32 sets of possible result combinations, how many have three searches? We can use the <sub>n</sub>C<sub>k</sub> formula to calculate this:\n", "\n", "\\begin{equation}_{5}C_{3} = \\frac{5!}{3!(5 - 3)!} = \\frac{120}{6\\times 4} = \\frac{120}{24} = 5\\end{equation}\n", "\n", "So 5 out of our 32 combinations had 3 searches and 2 non-searches.\n", "\n", "To find the probability of any combination of 3 searches out of 5 passengers, we need to multiply the number of possible combinations by the probability for a single combination - in this case <sup>5</sup>/<sub>32</sub> x 0.088, which is 0.01375, or 13.75%.\n", "\n", "So our complete formula to calculate the probabilty of ***k*** events from ***n*** experiments with probability ***p*** is:\n", "\n", "\\begin{equation}P(x=k) = \\frac{n!}{k!(n-k)!} p^{k}(1-p)^{(n-k)}\\end{equation}\n", "\n", "This is known as the *General Binomial Probability Formula*, and we use it to calculate the *probability mass function* (or *PMF*) for a binomial variable. In other words, the we can use it to calculate the probability for each possible value for the variable and use that information to determine the relative frequency of the variable values as a distribution.\n", "\n", "In Python, the *scipy.stats.**binom.pmf*** function encapsulates the general binomial probability formula, and you can use it to calculate the probability of a random variable having a specific value (***k***) for a given number of experiments (***n***) where the event being tested has a given probability (***p***), as demonstrated in the following code:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from scipy.stats import binom\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "n = 5\n", "p = 0.25\n", "x = np.array(range(0, n+1))\n", "\n", "prob = np.array([binom.pmf(k, n, p) for k in x])\n", "\n", "# Set up the graph\n", "plt.xlabel('x')\n", "plt.ylabel('Probability')\n", "plt.bar(x, prob)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see from the bar chart that with this small value for ***n***, the distribution is right-skewed.\n", "\n", "Recall that in our coin flipping experiment, when the probability of failure vs success was equal, the resulting distribution was symmetrical. With an unequal probability of success in each experiment, the bias has the effect of skewing the overall probability mass.\n", "\n", "However, try increasing the value of ***n*** in the code above to 10, 20, and 50; re-running the cell each time. With more observations, the *central limit theorem* starts to take effect and the distribution starts to look more symmetrical - with enough observations it starts to look like a *normal* distribution.\n", "\n", "There is an important distinction here - the *normal* distribution applies to *continuous* variables, while the *binomial* distribution applies to *discrete* variables. However, the similarities help in a number of statistical contexts where the number of observations (experiments) is large enough for the *central limit theorem* to make the distribution of binomial variable values behave like a *normal* distribution.\n", "\n", "### Working with the Binomial Distribution\n", "Now that you know how to work out a binomial distribution for a repeated experiment, it's time to take a look at some statistics that will help us quantify some aspects of probability.\n", "\n", "Let's increase our ***n*** value to 100 so that we're looking at the number of searches per 100 passengers. This gives us the binomial distribution graphed by the following code:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from scipy.stats import binom\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "n = 100\n", "p = 0.25\n", "x = np.array(range(0, n+1))\n", "\n", "prob = np.array([binom.pmf(k, n, p) for k in x])\n", "\n", "# Set up the graph\n", "plt.xlabel('x')\n", "plt.ylabel('Probability')\n", "plt.bar(x, prob)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Mean (Expected Value)\n", "We can calculate the mean of the distribution like this:\n", "\n", "\\begin{equation}\\mu = np\\end{equation}\n", "\n", "So for our airport passengers, this is:\n", "\n", "\\begin{equation}\\mu = 100 \\times 0.25 = 25\\end{equation}\n", "\n", "When we're talking about a probability distribution, the mean is usually referred to as the *expected value*. In this case, for any 100 passengers we can reasonably expect 25 of them to be searched.\n", "\n", "#### Variance and Standard Deviation\n", "Obviously, we can't search a quarter of a passenger - the expected value reflects the fact that there is variation, and indicates an average value for our binomial random variable. To get an indication of how much variability there actually is in this scenario, we can can calculate the variance and standard deviation.\n", "\n", "For variance of a binomial probability distribution, we can use this formula:\n", "\n", "\\begin{equation}\\sigma^{2} = np(1-p)\\end{equation}\n", "\n", "So for our airport passengers:\n", "\n", "\\begin{equation}\\sigma^{2} = 100 \\times 0.25 \\times 0.75 = 18.75\\end{equation}\n", "\n", "To convert this to standard deviation we just take the square root:\n", "\n", "\\begin{equation}\\sigma = \\sqrt{np(1-p)}\\end{equation}\n", "\n", "So:\n", "\n", "\\begin{equation}\\sigma = \\sqrt{18.75} \\approx 4.33 \\end{equation}\n", "\n", "So for every 100 passengers, we can expect 25 searches with a standard deviation of 4.33\n", "\n", "In Python, you can use the ***mean***, ***var***, and ***std*** functions from the *scipy.stats.**binom*** package to return binomial distribution statistics for given values of *n* and *p*:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import binom\n", "\n", "n = 100\n", "p = 0.25\n", "\n", "print(binom.mean(n,p))\n", "print(binom.var(n,p))\n", "print(binom.std(n,p))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
tpin3694/tpin3694.github.io
mathematics/argmin_and_argmax.ipynb
1
3734
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: argmin and argmax \n", "Slug: argmin_and_argmax \n", "Summary: An explanation of argmin and argmax in Python. \n", "Date: 2016-01-23 12:00 \n", "Category: Mathematics \n", "Tags: Basics \n", "Authors: Chris Albon " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`argmin` and `argmax` are the inputs, `x`'s, to a function, `f`, that creates the smallest and largest outputs, `f(x)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "np.random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define A Function, f(x)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define a function that,\n", "def f(x):\n", " # Outputs x multiplied by a random number drawn from a normal distribution\n", " return x * np.random.normal(size=1)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Some Values Of x" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create some values of x\n", "xs = [1,2,3,4,5,6]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find The Argmin Of f(x)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Define argmin that\n", "def argmin(f, xs):\n", " # Applies f on all the x's\n", " data = [f(x) for x in xs]\n", "\n", " # Finds index of the smallest output of f(x)\n", " index_of_min = data.index(min(data))\n", " \n", " # Returns the x that produced that output\n", " return xs[index_of_min]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run the argmin function\n", "argmin(f, xs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check Our Results" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x | f(x)\n", "--------------\n", "1 | 1.74481176422\n", "2 | -1.52241380179\n", "3 | 0.957117288171\n", "4 | -0.99748150191\n", "5 | 7.31053968522\n", "6 | -12.360844257\n" ] } ], "source": [ "print('x','|', 'f(x)')\n", "print('--------------')\n", "for x in xs:\n", " print(x,'|', f(x))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
lucabaldini/ximpol
notebooks/plot_irfs.ipynb
1
459181
{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ">>> Reading effective area data from /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.arf...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.arf\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 14 () \n", "1 SPECRESP BinTableHDU 33 1090R x 3C [E, E, E] \n", "2 VIGNETTING BinTableHDU 18 1R x 3C [100E, 4E, 400E] \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ">>> Reading PSF data from /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.psf...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.psf\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 14 () \n", "1 PSF BinTableHDU 28 1R x 5C [E, E, E, E, E] \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ">>> Gauss + King PSF, W = 2.790e-04, sigma = 1.061e+01, N = 3.289e-03, r_c = 6.060e+00, eta = 1.481e+00, HEW = 22.8 arcsec\n", ">>> Reading modulation factor data from /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.mrf...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.mrf\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 14 () \n", "1 MODFRESP BinTableHDU 32 1090R x 3C [E, E, E] \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ">>> Optimizing grid with 1090 starting points...\n", ">>> Done, 24 points remaining.\n", ">>> Relative (max/ave) dist. to original array: 4.834126e-03/4.416073e-04\n", ">>> Reading energy dispersion data from /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.rmf...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/omodei/MY_PYTHON_MODULES/ximpol/ximpol/irf/fits/xipe_baseline.rmf\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 9 () \n", "1 MATRIX BinTableHDU 37 1090R x 6C [E, E, I, I, I, 256E] \n", "2 EBOUNDS BinTableHDU 32 256R x 3C [I, E, E] \n" ] } ], "source": [ "from ximpol.irf import load_irfs\n", "from ximpol.utils.matplotlib_ import overlay_tag, save_current_figure\n", "irf_name = 'xipe_baseline'\n", "#irf_name = 'ixpe_baseline'\n", "aeff, psf, modf, edisp = load_irfs(irf_name)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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REYE7d+7obf/www8xe/ZsvW1JSUmIiIgo9JPpokWLCk0Bk5mZiYiICBw4cEBv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\n39ebN2/O66+/rnd6Q92pC4WZ5W67ZsSWiYoVK3rlfVxRN7ou95W6D3C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6wV/pfwHGHqgL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\nB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saaEJtM+uNizwXEwNBoAF7v/Tpv93vbxpEJIcpLz0knNimW1YdXE3csjiKT9cfStfJq\nZU6O72t1H55Otft9lZ0N8fH6/QWbNlU9egzQti0MHKgnx3366Df3CSGELUhCfJM11oR43z646y7g\nD4/C7d8B8OOoH+nbqq9tAxNCVCnrShZrk9eyPmU9m45u4lzuOavtDIqBu5vfrSfItw2kc1BnDKqh\nVuc8dgx++EGfdxwXpyfM1jg6Qu/eZaPHEREyvUIIcevIOsS3wPr1620dQp374QfANQPC9Wtr5tGM\nPi362DaoWvrss89sHYKogvRN3Wri3IThHYbzxZAvSJ2Uyp7n9zCn3xyiWkZhVMvWWCvRSth2ahvT\n4qcRuTSSgPcCGPZ/w/jn3n9yJvsMUP2+CQuDMWNg9Wq4cAG2b4cZM/S1jcsnvIWF+qOjp07V5xr7\n+uqrW8yZo699nJ9fpz+KRk++O/WX9I19kITYiuTkZFuHUOc2bQLafQ0GfbLg8A7Daz2CZGt79li/\nAUnYnvTNzaMqKncF3cXU3lOJfzqei1Mu8t0fvuPFbi/S2qe1RdsL+RdYdWAVf4z9I83nN6flBy2Z\n+39zWZiwkN/Sf6PEVFKtczo46DfZzZwJ//2vniB/8w08/zy0aGHZNjMT1q2D11/X1z5u0kR/BPX4\n8foNvYcOQUn1TmuX5LtTf0nf2AeZMlFBY5wykZcHPj5QEN0LQv8LQOKfE+kY0NHGkQkh6srxS8fZ\ndHQTG49uJO54HNkFVcx1ADydPLk75G56Ne9Fr9BedA3uWuM5yJqmzzfetAk2b4Zt2+B6y7W6u+uj\nyd26Qdeu+jYsDGp5b6AQwo7JHOKbrDEmxBs3wgN/OAYv6esNd/DvwG9/+c3GUQkhbpaikiJ2/r6T\njUc3svXkVn79/VeuFF+55jG3+95Ot2bd6BrUlW7NutEpsBOuRtdqn1PTIDlZn2KxbZu+TUq6/nFe\nXmXJcdeuemneXJJkIcS11XW+5lAHMYl67ocfgA4rzO9HdBxhu2CEEDed0WCkV6g++gtQWFLI3tS9\nbD+9XS+ntpOem25xTNKFJJIuJLH8t+WAPkWjvV97ugV3o2uwniR38O+Ak4NTpfOBnsC2aaOXZ57R\n6y5dgt27ISEBdu3Sy6lTlsdlZuojzJs3l9V5e0P79mWlXTt9GxAgibIQ4uaQERx8tCcAABl0SURB\nVOIKGuMIcYeOGvvvvQOaHkFB4eSEkzRv0tzWYQkhbETTNI5dOsb209v55cwv7Dq7i8T0RApLCq95\nnFE1EhEYQdegrnQK7ER7//a082uHj4tPtc+dnq4nybt26YlyQoJeVx0+PpYJ8tXi7y+JshD2RqZM\n3GSN7dHNqakQ3DUBno8EIKplFPFPx9s4qhsjj9Gsv6Rv6q/r9U1BcQH7z+1n19ldJJxNYNfZXew/\nt58S7fp3wgW6B9Ler71eSpPk9n7t8Xbxvu6xmqY/Vr78KPL+/fD779W/Nm9vaN0awsPLtleLr2/D\nSJblu1N/Sd/UTzJl4hZ47LHHbB1CnfnhB6DjcvP7ER0a/nSJF1980dYhiCpI39Rf1+sbJwcnugR3\noUtwF8YwBoC8ojwS0xItkuTDGYcrPUkvLSeNtJw04o7HWdQHuQfRxrcNrX1aE+4TTmtffRvuE26e\nn6wo0KyZXgYPLjs2MxMOHtTLgQNl5ezZyrFfugQ7d+qlIi8vPTEOC9NXxggNLduGhur760PCLN+d\n+kv6xj7ICHEFjW3KxPARxawMagbu53BUnUh/NQ0vZy9bhyWEaKAuF1xmb9pe9p/bz4FzBzhw/gAH\nzx/kfN75Gn1OsEewnhx7h9PKuxUtvVqaS5B7UJXLQl5NlMsnyUlJcPp07a7H3b1ykly+NGsGRuP1\nP0cIcWvJCLGotoICiN2/GVrrT7d6pM0gSYaFEDfEw8mDPi36VHqwz/nc8+bkuDqJ8tnLZzl7+Sxb\nT26ttM+oGgltEkpLr5a0aNKCFl4taObRjGCPYII9gmkdEUyPu31RlbKl9PPz4fhxfSm4qyU5Wd+e\nOqU/vt6anJyykWhrVFW/mS8oSC+BgWWvy5fAQHCyfr+hEKIBkIS4EfvXvyD3trLpEqMiGv50CSFE\n/eTn5keUWxRRLaMs6rOuZJFyMYXki8mW2wvJVSbLRaYijl46ytFLR6s8n1E1EuQRRLBHsEWyHNwi\nmI53BvOARzBB7kF4OXtRWKhw6pQ+inzqFJw8qW/LlytVrEpnMun3YqSmXv9n4OUFfn7QtGn1ipeX\nPO5aiPpCpkxUkJ2dzQcffMCECRMa9JSJDRtg0IiTFP+5LRjz8XTw4fzUVBwNjrYO7YatWbOGweUn\nG4p6Q/qm/qqPfZN1JYujl45yMvMkJzJPcCLzBCez9NfHM49f8+Ei1WVUjfi5+eHn6oe/mz9+bn74\nu/qXvXbzp6mLH+T7kJvhw6U0L86eNlokzmfP6ith1PWT9gwG/aa/pk0B1nDHHYPx9dWf8ne94ukp\nUzlulfr43RGyykStLViwgPfee4+0tDQiIiL46KOP6NatW6V22dnZ9OjRg19++aXBJsRbtsDA0Xsp\nHDIEvE4CMKH7BOY/MN+2gdWRu+++mx07dtg6DGGF9E391RD7JvNKJicyT3Aq6xSpl1PN0yx+v/y7\n+XVN5y5Xh7ujOz4uPng7e+Pt4o2Piw9eTt44az6ohd5o+d6UXPYh/1ITLl/wIOu8BxdTPchM9+Bi\nmgdZF2sz8HA3ULP+cXG5ftJc/r2HB7i56fOm3dzKiqurnpwL6xrid8ceZGdnM3fuXKZMmSJziKtr\n1apVTJo0icWLFxMZGcn8+fMZOHAgR44coan+V3MLXl4Nd57tjh3wwKsrKBzxLBj1fwMM8QjhjT5v\n2DiyuuPn52frEEQVpG/qr4bYN17OXnQK7ESnwE5VtiksKSQtJ01PlLPLEuWzOWdJvZzK+bzznMs9\nx/nc8xSZiqp13pzCHHIKcziVder6jb1LS5uyKkeDI+5GT1wNHjjhgVHzwFDsgVLkgXbFg+I8D4py\nPSnI9iA/04Pcix4UHCoB/zgodoEiF+tbk+Uf2fn5eklLq9ZlXZOzs/VkuXwpv8/FRT/maqn4vqo6\nZ+f6sapHTTTE7469iI+PZ8qUKXXyWXaREM+fP58xY8YwatQoABYtWsTatWv5/PPPmTx5so2jqzs/\nby+m/9zJFD5SNhLcPbgHq4d9Q1PXyom/EEI0dI4GR0KbhBLaJPSa7TRNI6sgi/O5eoJ8LvecRbJ8\n6colLuZfLNvm69vqJtHlFZYUcrEkg4tkVN7pVFoqLtGcDQzvf83PVTUHVM0ZtcQFpdgFrUgvpgIX\nSq6UT56dr51YFztDsROUOILJCCWOXCktFwodId8R0h31/RZFb4t2Y8PJTk7VS6SdnPRpIY6OZeVa\n72vStqr3RqPM67ZXjT4hLioqYvfu3bz++uvmOkVR6N+/f4P+J5D8KyZ+PZDKlsTj7DyazOFzyZxQ\nf0Drssvc5pmOz/LJoI+rfNSqEELYC0VR8HL2wsvZi9a+rat1jKZp5BXlWSTJ5V9nFWRxueAylwtL\nS+nr7ILssvqCyxSUFNTJNZiUYkxKDqg5YARc6uRjaxGIWjlZNhn1hNnkcI2i7y8oLVnXapvnADkO\nYDKApupJuKaWe2+trty+2taZDICKQVExqAZURaXo7CWatE3AYFAxqAqqquCgqvrWoJRtDfrWoKoW\n9QZVwWBQcCg93sFBsbJVLd8bFIyGsuMcSl8bS/c5OOj1V9+X3zoYFIwOpZ9nAAcHLLYuLtC9u43+\n36nHGn1CnJGRQUlJCQEBARb1AQEBJCUlVeszBsx+h7jshaBcnW5dbqtcXaK+wj5F01+WO0azdjxY\n/1xzfRVt1GIwFOuvHYGQsngVk5EPB37MuJ7PV+v6hBBCVKYoCm6Obrg5uhHiGXL9A6pQVFJkTo6z\nC7Itkuer2082fcKQ3kPIL8onv7i0FFlurxRfqVSXX5Rfq1HsG6KaQL1inpbXGJWUFgBiIHtYpA2j\nKWURVA1oil7Qt2peMCXdT9ZxcA1fo0+Ia8pkMpGSkkJubq65zt2oERZgq7+K14ynqQV/e3gmXZt3\nJDv7xu/Qro/27NnTaK+toZO+qb+kb2zLAQe8Vf0mPWsju/NOzmNqt6m1+myTZqKgpICC4tJS+vpK\nyRWL9+VfF2vFFJUUUWQqMm+LS4r196Yiik3FVe6rWFdsKqawpBCTyUSxVkyxqZgSrYQSUwkmrYoF\noBuQ4+nHaeXWytZh1BnV2LRR/C64mqeV1NHyL40+IW7atCkGg4H09HSL+vT0dAIDAyu1z8/Pp2fP\nngwdOtSi3g8/+vbty3333XdT460LWloBCWkJtg7jpnn66adJSGi819eQSd/UX9I39dvN7B/H0v88\n8Ki6kVpaRCU/vvAj991V///sr4mG9rvgxx9/JD4+vlK9v78/V6paRLyG7GLZtR49etC9e3c+/PBD\nQJ8XFhoayvjx43n11Vct2hYWFnLhwgWcnZ0xyDo0QgghhBD1jslkIj8/H19fXxwdb/wZC41+hBjg\n5ZdfZvTo0XTp0sW87FpeXh6jR4+u1NbR0ZGgoKBbH6QQQgghhKi2ulwm1y4S4qeeeoqMjAymT59O\neno6nTp1YuPGjbK2oBBCCCGEsI8pE0IIIYQQQlRFptALIYQQQgi7JgmxEEIIIYSwa5IQV7BgwQJa\ntWqFi4sLPXr0aHBLkzRGc+bMITIyEk9PTwICAhgyZAhHjhyxdVjCinfffRdVVXn55ZdtHYoodfbs\nWUaOHEnTpk1xdXUlIiKCPXv22Dosu2cymZg2bRphYWG4uroSHh7OW2+9Zeuw7NbPP//Mo48+SrNm\nzVBVldjY2Eptpk+fT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"text/plain": [ "<matplotlib.figure.Figure at 0x113631850>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Dbn3W5kmVRKkZkfPyyHs66q/Ox1PKzN4HxAhUe/t8AtW6D194/8Nb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\nE088AaCiW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laBVJTlfdcEPCDEqcA0k+x2QIFQFKvP2+2AwzEiUGeHa4+xRaAC1ohUC6Oo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\npaWltiEQ7b3WxMREZGZmIj8/3y5MW61WfPjhhxg6d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"text/plain": [ "<matplotlib.figure.Figure at 0x11326cdd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aeff.view(show=True)\n", "#save_current_figure('%s_aeff.png' % irf_name)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xsqXj0tOljRudlgQAAFClaJZRodNPl4YP94537pSGDZP4twgAAFBT0CxH8Nhj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"text/plain": [ "<matplotlib.figure.Figure at 0x1132ddc50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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mJo+Z6Y//hAkTJqguQm9sNhv8/PywY0dr/P13AO6/H+jUSXVV+teiRQs0atTo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"text/plain": [ "<matplotlib.figure.Figure at 0x1130e9210>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+wu4Q7A7RJPv2Sb17S5s2ed8XF0tjxritCQAApAd2h4Bv3X9/vAE+/3xp9Gi3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\nMBERERHJDptgIiIiIpIdNsFEREREJDv/AxzhIFUAPtEfAAAAAElFTkS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"text/plain": [ "<matplotlib.figure.Figure at 0x115e99450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "psf.view(show=True)\n", "#save_current_figure('%s_psf.png' % irf_name)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ql0AK3Nzc1C6BFDAbsT0un5QU4JNPgDVrgLw8w/Gnn5aXHHvvPa7EUJ74uyMu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"text/plain": [ "<matplotlib.figure.Figure at 0x115e7d650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "modf.view(show=True)\n", "#save_current_figure('%s_modf.png' % irf_name)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+i5EjR6K6uhqLFy/GsmXLcPnllwMACgoKkJGRgS1btqB3795iPxQRkaMYCbhK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\niARC4q0Oi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"text/plain": [ "<matplotlib.figure.Figure at 0x113063650>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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hocD3bt++vch/CoyIiCi0tl9ycjIMBgMyMzMLnI+KisLMO9fDAZCWlgaDwYCU\nlJQC5+fPn4/IyMgC53JycmAwGJCUlFTgfFxcHMLCwgplGzhwoFO/jy7/+38iZ38f+VzpfeTk5LjE\n+3CVPu58H126dHGJ9wHYr48ZM2biww+B7t3zB9403HOPAfHxKQUG3rJ4H13u+IG69qHq+9iwYYNL\nvA9n7iMuLu72LNawYUO0bNkSoaGhSLxzL3A7c8oly3x9ffHBBx/ghRdeuH3uww8/xLJlywoVB3DJ\nMiIiHVy9KnZX++or87k+fYClS4F77pEWi4iKiUuWFaFHjx547733ULduXTRr1gzJycn4xz/+gZEj\nR8qORkREEqSmivV3jx8Xx25u4uG1N94Q9/ISETnlr4Lo6Gj069cPERERaNq0KSZNmoQxY8bgnTsf\nz73Lnj17yjAhldTd/wqE1MFu1MVuhB07xBbC+QPvvfcCGzYAb70ld+BlP+piN3pyyqHXx8cHc+fO\nxZkzZ5CdnY2TJ0/i7bffRrlyli9cO/IeESq9uLg42RHIAnajLt27MZmAuXOBp58G/vpLnAsIAA4c\nAHr0kJsNYD8qYzd6csp7ekuK9/QSEbmWa9eAUaPE8mP5nn1WHFeuLC8XEdnO0fOaU17pJSIifaWl\nAe3aFRx433pLrMfLgZeILHHKB9mIiEhP338P9OsH/PGHOPbxAZYtA/r2lZuLiNTHK71ERKQ8kwn4\n5z+BTp3MA+/99wP793PgJaLi0WbonT17tuwIZEVRC1uTGtiNunTpxmgERo4Exo4Fbt0S5zp3Bg4d\nAh56SG42a3TpxxmxGz1pM/QGBgbKjkBWdCnLvUGpRNiNunToJiMDePJJ4PPPzeciI8U2w9WqyctV\nHDr046zYjZ64egMRESlp3z6xo9q5c+K4YkUgNhYYNEhuLiJyDK7eQERE2lm8WFzhzR9469cH9uzh\nwEtEtuPQS0REyrhxA4iIEGvw3rwpznXoABw+DLRqJTUaETk5bYbe4/n7U5KSkpKSZEcgC9iNulyt\nm/PngaeeAhYsMJ97+WVg+3agRg15uWzlav24EnajJ22G3tWrV8uOQFbMmjVLdgSygN2oy5W6OXIE\naNMG2L1bHHt5AUuWAPPmAZ6ecrPZypX6cTXsRk/aPMi2e/dutG/fng+yKSonJwfe3t6yY1AR2I26\nXKWbL74AXngBuH5dHNepA6xfDzzyiNxcpeUq/bgidqMmPshmJxUqVJAdgazgLx91sRt1OXs3t24B\nr7wCDBuDvnTMAAAgAElEQVRmHngfe0xc9XX2gRdw/n5cGbvRE7chJiKiMvfnn8CAAUBiovncCy8A\n8+cD5cvLy0VErotDLxERlaljx4BevYBffxXHnp5i2B09WmosInJx2tzeEBMTIzsCWREZGSk7AlnA\nbtTljN2sXi1uYcgfeP38xNVeVxx4nbEfXbAbPWkz9NasWVN2BLKifv36siOQBexGXc7UTW4u8Prr\nwMCBQE6OONemjVh/t107udkcxZn60Q270ZM2qzdwG2IiIjkuXQKeew7YutV8bvhw4LPPAD5jTET5\nHD2v8Z5eIiJymF9+Effvnjwpjj08gLlzgXHjADc3udmISC8ceomIyCESEoChQ4GrV8Vx9erAmjVA\nSIjcXESkp2INvS+//HKpfsi4cePQuHHjUr1GaaWlpSEoKEhqBrIsJSUFDz74oOwYVAR2oy5Vu8nL\nA955B3j7bfO5Fi3EENyggbRYZU7Vfojd6KpY9/S6u9v+vJubmxu+/fZbdOzY0ebXKK2srCx07doV\n33zzDe/pVZTBYMDGjRtlx6AisBt1qdhNVpbYbGLDBvO50FAgNhbQbT8AFfshgd2oSZkd2b788kvk\n5eWV6M+FCxegynNyY8eOlR2BrIiOjpYdgSxgN+pSrZv//Ad49FHzwOvuDsyaBaxcqd/AC6jXD5mx\nGz0V6/aGypUro7wNW+R4eHigcuXK8PT0LPHftTc/Pz/ZEcgKLh+jLnajLpW62bJFrNBw+bI4rlIF\niI8Hnn5abi6ZVOqHCmI3eirW0Hvx4kWbXrxq1ao2/10iIlKfyQTMnAm88Yb4zwDQrJm4f7dRI7nZ\niIjuVOzbG3766SdH5iAiIieTnS02m3j9dfPA27s3sG8fB14iUk+xh96HH34YrVq1wpw5c5Cenu7I\nTA4RHx8vOwJZMXPmTNkRyAJ2oy6Z3Zw5I7YTXrPGfO7dd4GvvgLuuUdaLKXws6MudqOnYg+9r7/+\nOrKysjBp0iQ0aNAAHTt2RGxsLC7n38ClOKPRKDsCWZGTvy8pKYfdqEtWN999J7YQ/vFHcXzPPcDG\njcBbb4mH10jgZ0dd7EZPJd6GeN++fVi5ciXWrFmDCxcuwMvLC926dcPgwYPRvXt3mx54czRuQ0xE\nVHomE/Dxx8Brr4m1eAHg//5PrNbAJU+JqLSUWbIsX3BwMObPn4+MjAxs3boVAwYMwI4dO9C/f3/4\n+flh5MiRSExMtHtQIiKS59o1sf7uK6+YB95nnwUOHuTAS0TOweZ/EeXu7o6nn34ay5Ytw4ULFxAf\nH4+QkBCsWLECnTt3Rr169eyZk4iIJPnvf4H27YEvvzSfe/NNcUtD5crychERlYRd7r7y8vJCnz59\nEBYWhg4dOsBkMiEjI8MeL203znLvsa4yMzNlRyAL2I26yqKb778HAgOBI0fEsY+PeHjtvfd4/+7f\n4WdHXexGT6X+lfX999/jxRdfRK1atdCrVy/s3r0bzz33HDZv3myPfHYzZ84c2RHIivDwcNkRyAJ2\noy5HdmMyAQsWAJ06AX/8Ic41bCiWI+vXz2E/1qXws6MudqOnYm1OcbejR49i5cqVWLVqFdLT0+Hu\n7o7OnTtj8ODB6NWrF3x8fOyds9SGDRsmOwJZMX36dNkRyAJ2oy5HdWM0AhERQGys+VznzmKHtWrV\nHPIjXRI/O+piN3oq9uoNqampWLlyJeLi4pCSkgKTyYSgoCAMGTIEoaGhqFGjhqOz2oyrNxARFU9G\nBtC3L7B/v/nca68BH34IlLPpMgkRUfE4el4r9q+wRv/bXqdRo0aYNm0aBg8efPscERE5v/37gT59\ngN9/F8cVKgCLFwODB8vNRURkD8UeeseNG4fBgwfjkUcecWQeIiKSIDYWeOkl4MYNcVyvHpCQALRu\nLTcXEZG9FPtBtnnz5hU58P7+++84duwYsrOz7RrM3rZu3So7AlkRe+fNg6QUdqMue3Rz86a4f3fk\nSPPA++STwOHDHHhLi58ddbEbPdm8esOGDRvw4IMPom7dumjdujUOHDgAQCwD0qpVKyQkJNgtpD2c\nPHlSdgSyIjk5WXYEsoDdqKu03Vy4ADz1lFilId+4ccC33wI1a5YyHPGzozB2o6cSb0MMAJs2bUKv\nXr0QHByMLl26YPr06dixYwc6duwIAOjevTs8PDywYcMGuwe2BR9kIyIq6MgRoHdvsfEEAJQvD3z2\nGRAWJjcXEelLuW2IAeCdd97BE088gaSkJERERBT6enBwMH744YdShyMiIvv78kugXTvzwFunjtiE\nggMvEbkym4ben376CQMGDLD4dT8/P1y4cMHmUEREZH+3bgGvvgoMHQpcvy7OBQeL+3fbtpWbjYjI\n0WxaddHb29vqg2upqamoXr26zaGIiMi+/vwTCA0Fduwwnxs1Cpg/H/DykpeLiKis2HSlNyQkBMuW\nLcOtW7cKfe3cuXNYtGgRunTpUupw9jR16lTZEcgKg8EgOwJZwG7UVdxufvwRCAoyD7zlygGffgos\nXMiB15H42VEXu9GTx3Qb9uJr0aIF5s6dizVr1uDatWvYuXMn/Pz8sGvXLjz//PMwmUxYuXIlqlSp\n4oDIJWc0GnHlyhUEBQXBi7/hlVS9enU88MADsmNQEdiNuorTzZo1QI8ewB9/iOOaNYGvvxa7rpFj\n8bOjLnajJqPRiIyMDPj7+ztkXrNp9QYA+PnnnzF+/Hjs3LkTd75Ehw4d8M9//hNNmjSxW8jS4uoN\nRKSb3Fxg6lSxfXC+Nm2AdevExhNERKpRZhviuzVr1gw7duzAxYsXcerUKeTl5eH+++9HjRo17JmP\niIhK6NIlsXXwli3mc8OGiSXJKlaUl4uISCab7um9c6OHqlWrIigoCG3bti0w8G7atKn06YiIqET+\n/W/gkUfMA6+HB/Dxx8DSpRx4iUhvNg29nTp1wq+//mrx6ytWrEC/fv1szeQQe/bskR2BrFBtBz8y\nYzfqurubDRvE0mP51yWqVwe2bQPGjwfc3CQE1Bw/O+piN3qyaeitVasWOnbsiPT09EJfi4mJwbBh\nw5QbehMTE2VHICvi4uJkRyAL2I268rvJywPefhvo1Qu4ckV8rUUL4NAhoFMniQE1x8+OutiNnmx6\nkO3SpUsICQlBdnY2vv/+e9SqVQsAMGvWLEyZMgWjRo3CZ599BjdFLi3wQTYiclVXroj7de+8cDVw\nIBAbC/j4yMtFRFRSSm5DXKVKFXz77bcoX748OnbsiAsXLuCNN97AlClT8NprryEmJkaZgZeIyFWd\nPAk8+qh54HVzA2bMAOLiOPASEd3N5tUbfH19sWPHDjz55JNo0qQJLl26hHfeeQdvvfWWPfMREVER\ntm4FBg0CLl8Wx1WqiGG3a1e5uYiIVFWsoTc5Odni12bNmoWhQ4di2LBh6NatW4Hvbd26dekTEhHR\nbSYTMHMm8MYb4j8DQNOm4mpv48ZysxERqaxY9/S6u7tbvV0h/yXyv8dkMsHNzQ25ubl2ilk6WVlZ\nGDBgAFavXs17ehUVFhaGJUuWyI5BRWA36sjOBsLDgdWr88+EoVevJVi+HLjnHpnJqCj87KiL3ahJ\nic0pXOF/GIGBgbIjkBVdunSRHYEsYDdqOHNGrM7w44/mc/36dcGqVYC7TU9nkKPxs6MudqMnm7ch\ndiZcvYGInFliIjBgAPDnn+L4nnuAL78EDAa5uYiI7EnJ1RuIiMjxTCaxm1qXLuaBt3Fj4MABDrxE\nRCVVrKF37ty5OHHiRIlf/Pr165g7d26Rm1gQEZFl164BI0YAEycC+Y9HdOsGHDwINGkiNRoRkVMq\n1tAbGRmJI0eOlPjFs7OzERkZif/85z8l/rv2dvz4cdkRyIqkpCTZEcgCdlP2/vtf4IkngOXLzefe\neAPYuFEsTZaP3aiN/aiL3eipWA+ymUwmrFu3DqdOnSrRi+fk5NgUyhFWr16N8PBw2THIglmzZqFd\nu3ayY1AR2E3Z2r0b6NcPuHBBHHt7A0uXAv37F/5edqM29qMudqOnYi9ZVho7duxAx44dS/UapZGV\nlYXdu3ejffv2fJBNUTk5OfD29pYdg4rAbsqGyQTExADjxgG3bolzDRuK9Xcffrjov8Nu1MZ+1MVu\n1KTEkmV5eXl2/8FlrUKFCrIjkBX85aMuduN4RqMYdhctMp976ikgPh6oXt3y32M3amM/6mI3euLq\nDUREEv3+OxASUnDgffVVsc2wtYGXiIhKplhXeomIyP4OHAB69xaDLwBUqCCG3yFD5OYiInJF2lzp\njYmJkR2BrIiMjJQdgSxgN46xZIlYoSF/4K1XD0hKKtnAy27Uxn7UxW705LRDb0ZGBoYOHQpfX194\ne3ujRYsWSE5Otvj9NWvWLMN0VFL169eXHYEsYDf2dfOmuH83PBy4cUOce+IJ4PBhoKS7pbMbtbEf\ndbEbPTnlNsSXLl1Cq1at0KlTJ4wZMwa+vr44efIkHnjgATRs2LDQ93MbYiJSwYULYumx7783n4uI\nAP7xD8DTU14uIiIVKLF6g2pmzJiB+vXrY/HixbfP3XfffRITERFZl5wM9OolNp4AgPLlgQULgOef\nl5uLiEgXNt3e8Mwzz2DlypW4du2avfMUy6ZNm9CmTRsMGDAAfn5+aN26dYEBmIhIJStWAI8/bh54\na9cGdu3iwEtEVJZsGnpTU1MxZMgQ+Pn5Yfjw4dixYwfK8i6J1NRUfPrppwgICMD27dsxZswYvPzy\ny/jiiy8s/p20tLQyy0cll5KSIjsCWcBubHfrFvDaa+LhtOvXxbngYODIEeDRR0v/+uxGbexHXexG\nTzYNvSdOnMCBAwcQFhaG7du34+mnn0bdunURGRmJo0eP2jtjIXl5eQgMDMS7776LFi1aYNSoURg1\nahQ+++wzi39n0Z2LYJJyJk2aJDsCWcBubPPnn8AzzwAffWQ+N3IksHOnuNJrD+xGbexHXexGTzav\n3hAUFIR58+bht99+w5YtW9CxY0fExMQgMDAQDz30EGbNmoX09HR7Zr2tdu3aaNKkSYFzTZo0sXo1\nt1KlSmjatCkMBkOBP8HBwUhISCjwvdu3b4fBYCj0GhEREYiNjS1wLjk5GQaDAZmZmQXOR0VFYebM\nmQXOpaWlwWAwFPonzPnz5xdaPiUnJwcGgwFJSUkFzsfFxSEsLKxQtoEDBzr1+4iOjnaJ95HPld5H\njx49XOJ9lGUfHToY0KJFCnbsEOfKlQP695+PypUj4eVlv/cRHR3NPhR+H/m/15z9fdzJVd5HlSpV\nXOJ9OHMfcXFxt2exhg0bomXLlggNDUViYmKh17IXu67ecOnSJYwePRpr1qwBALi7u6NDhw6YOHEi\nnn32WXv9GAwePBjp6enYtWvX7XMTJ07EoUOHCpUBcPUGIio7X30FjBgBZGeL45o1xbn27aXGIiJS\nnqPnNbus05uUlIQXX3wRjRo1wpo1a25f6f3oo4/wxx9/wGAwYNq0afb4UQDEgLt//358+OGHOH36\nNFauXInFixdj7NixdvsZREQlkZsLvPmmWJIsf+ANDBTr73LgJSKSz+Yrvb/88gu+/PJLxMXFIS0t\nDTVr1sRzzz2HoUOHomXLlgW+94UXXsDatWvx559/2iU0AGzZsgVTpkzBqVOn0LBhQ7z66qsIDw8v\n8nt5pZeIHOnSJfGw2tdfm88NHQrExAAVK8rLRUTkTJS80tuyZUs0b94cH3/8MR599FFs3rwZv/32\nGz766KNCAy8AhISE4OLFi6UOe6du3brhxx9/RE5ODn7++WeLA2+++Ph4u/58sq+77zMidbAb6/79\nb6BtW/PA6+EhNptYtszxAy+7URv7URe70ZNNm1NUqVIFCxcuRP/+/Ys1iffs2RNnzpyx5UfZjdFo\nlPrzybqcnBzZEcgCdmPZpk3A4MHAlSviuFo1YPVqoFOnsvn57EZt7Edd7EZPTrkNcUnx9gYisqe8\nPOD994E7H1V4+GEgIQEoYid0IiIqBm5DTESkkCtXgOHDgfXrzecGDAA+/xzw8ZGXi4iIrLPpnl53\nd3d4eHhY/ePj44OAgAC8+OKLOH36tL1zExGVuVOnxE5q+QOvmxswYwYQH8+Bl4hIdTYNvdOmTcPD\nDz8MDw8PdO/eHRMmTMCECRPw7LPPwsPDAy1atMBLL72Epk2bYsmSJWjdujWOHTtm7+wlcvnyZak/\nn6y7exFtUge7EbZtA4KCgF9+EceVK4uH1yZPFsOvDOxGbexHXexGTzYNvXXq1EFmZiZSUlKwYcMG\nfPTRR/joo4+wceNG/PLLL7hw4QICAgKwfv16/PTTT/D09MQbb7xh7+wlMmfOHKk/n6z7u9U3SB7d\nuzGZgFmzgG7dxNJkANCkCXDokNhmWCbdu1Ed+1EXu9GTx/Tp06eX9C+FhoZi9OjRhbYnBYBq1arB\naDQiOjoaL7/8MqpXr46srCysW7cOU6ZMsUfmEjMajXB3d0fz5s3hdeceoKSMgIAA1K5dW3YMKoLO\n3WRnA8OGAR9/LIZfAOjZE9iyBVDhvxKdu3EG7Edd7EZNRqMRGRkZ8Pf3d8i8ZtOV3vT0dJQrZ/kZ\nuHLlyiE9Pf32cYMGDaQvGda4cWOpP5+sa926tewIZIGu3fz6K/D448CqVeZz06cD69YB99wjK1VB\nunbjLNiPutiNnmwaeps1a4ZPP/0U58+fL/S1c+fO4dNPP0WzZs1un0tNTUWtWrVsT0lEVIYSE4E2\nbYD8RxEqVRLLkUVFAe522bydiIjKmk1Lls2ZMwfPPPMMGjVqhF69eqFRo0YAgFOnTiEhIQE3b97E\n559/DgC4fv06li5dimdk3/xGRPQ3TCbgk0+AV18FcnPFucaNxcDbtKncbEREVDo2XbPo0KED9u7d\ni5CQEKxbtw5vv/023n77baxduxYhISHYu3cvOnToAACoUKECMjIyEBsba8/cJbZ161apP5+sk/2/\nD7JMl26uXQNGjAAmTDAPvM88Axw8qO7Aq0s3zor9qIvd6Mnmf1HXqlUrbNy4EVeuXEFGRgYyMjJw\n9epVbNy4Ucl7ZU6ePCk7AlmRnJwsOwJZoEM36enAE08Ay5ebz73+uthmuEoVebn+jg7dODP2oy52\no6cSb0Ock5ODevXqYcqUKYiMjHRULrviNsREZElSEtC3L3Dhgjj29gaWLBG7rBERUdlx9LxW4iu9\n3t7eKFeuHHy4/RARObnPPgNCQswDb8OGwL59HHiJiFyRTbc39O3bF1999RVKeJGYiEgJRiMwejQw\nZgxw65Y416mT2HDi4YflZiMiIsewafWG0NBQvPTSSwgJCcGoUaPQoEEDVKxYsdD3qXhvLxHp7fff\ngX79gL17zedeeQWYOROwsvw4ERE5uRLf0wsA7ncsVOlWxKbzJpMJbm5uyM1/BFqyrKwsdO3aFd98\n8w3v6VWUwWDAxo0bZcegIrhSNwcPAr17AxkZ4rhCBWDRImDIELm5bOVK3bgi9qMudqMmR9/Ta9N1\njSVLltg7h8P17NlTdgSyYuzYsbIjkAWu0s2SJcCLLwI3bojjevWA9euBwEC5uUrDVbpxVexHXexG\nTzZd6XU2XL2BSF83b4rNJubPN59r3x746iugZk15uYiIqCDlVm+42++//45jx44hOzvbHnmIiOzm\njz+Azp0LDrwREcCOHRx4iYh0Y/PQu2HDBjz44IOoW7cuWrdujQMHDgAAMjMz0apVK6xfv95uIYmI\nSuqHH4A2bYBdu8Rx+fLi/t3oaPGfiYhILzYNvZs2bUKfPn3g6+uLqKioAkuX+fr6wt/fH0uXLrVX\nRrvYs2eP7AhkRUJCguwIZIEzdrNyJfD440BamjiuXRv417+AkSOlxrI7Z+xGJ+xHXexGTzYNve+8\n8w6eeOIJJCUlISIiotDXg4OD8cMPP5Q6nD0lJibKjkBWxMXFyY5AFjhTN7duAZGRwODBwLVr4tyj\njwKHDwPBwXKzOYIzdaMj9qMudqMnm4ben376CQOsbFnk5+eHC/lbHCli6tSpsiOQFatWrZIdgSxw\nlm7++gvo1g2YM8d8LjxcXOGtU0daLIdylm50xX7UxW70ZNOSZd7e3lYfXEtNTUX16tVtDkVEVBLH\njwO9egGpqeK4XDlg3jyx41oRS4kTEZGGbLrSGxISgmXLluFW/v6ddzh37hwWLVqELl26lDocEdHf\nWbtW3LqQP/DWqAF89x3w0ksceImIyMymoff9999Heno6goKCEBMTAzc3N2zbtg1vvfUWmjdvDpPJ\nhKioKHtnJSK6LS8PeOstsaVw/r94at0aOHIEeOIJudmIiEg9Ng29AQEBSEpKQvXq1TF16lSYTCbM\nnj0bH3zwAZo3b47du3ejQYMGdo5aOrNnz5YdgawICwuTHYEsULGby5cBgwF4/33zuSFDgKQksdOa\nLlTshszYj7rYjZ5suqcXAJo1a4YdO3bg4sWLOHXqFPLy8nD//fejRo0a9sxnN4HOvNeoBng7jLpU\n6yYlRdy/e+KEOHZ3Fw+vTZig3+0MqnVDBbEfdbEbPXEbYiJyGps2ieXIrlwRx9WqAatWAU89JTcX\nERGVnqPnNZuv9Obm5mLbtm1ITU3FxYsXcffs7ObmxmXCiMgu8vLErQzTppnPNW8OJCQA998vLxcR\nETkPm4bew4cPo2/fvkhPTy807Obj0EtE9nDlCjBiBLBunflc//7AkiWAj4+0WERE5GRsepDtpZde\nwrVr15CQkIC//voLeXl5hf7k5ubaO2upHD9+XHYEsiIpKUl2BLJAZjenT4vlyPIHXjc34MMPxS0N\nHHj5uVEd+1EXu9GTTUPvjz/+iMmTJ6NHjx6oUqWKvTM5xOrVq2VHICtmzZolOwJZIKubbduANm2A\nn38Wx5UrA19/DUyZot8Da5bwc6M29qMudqMnm4beunXrWrytQVVvvvmm7AhkRXx8vOwIZEFZd2My\nAbNniy2FL10S55o0AQ4eBJ55pkyjKI+fG7WxH3WxGz3ZNPROnjwZixYtQlZWlr3zOEyFChVkRyAr\nvL29ZUcgC8qym5wcsTrDpEni4TVArMe7fz/wf/9XZjGcBj83amM/6mI3erLpQbYrV66gUqVKaNSo\nEUJDQ1GvXj14eHgU+B43NzdMnDjRLiGJyPWdPSvW3z161HwuKkqs2OBu0z+eExERmdm0Tq97Mf4f\nyM3NTZmH2bhOL5Hadu4EBgwAMjPFcaVKwBdfiCGYiIj04Oh5zabrJ2fOnPnbP6mpqfbOWioxMTGy\nI5AVkZGRsiOQBY7sxmQCPvkE6NzZPPA2agQcOMCBtzj4uVEb+1EXu9GTTbc33HffffbO4XA1a9aU\nHYGsqF+/vuwIZIGjurl+HXjxRWDZMvO5rl2BlSuBqlUd8iNdDj83amM/6mI3eir27Q0HDx5Eo0aN\nUK1atb/93jNnzmD37t0YNmxYqQPaA29vIFJLejrQpw9w6JD53JQpwHvvAXc9HkBERJpQ5vaG4OBg\nfPPNN7eP//rrL3h7e2PXrl2Fvnfv3r0ICwuzT0Iicil79oj1d/MHXm9vsdnEhx9y4CUiIscp9tB7\n9wVhk8mE69evK/OwGhGpLyYGCAkBzp8Xxw0aAHv3iofYiIiIHEmbhYDS0tJkRyArUlJSZEcgC+zR\nzY0bwOjR4h7emzfFuY4dxdXeFi1K/fLa4udGbexHXexGT9oMvYsWLZIdgayYNGmS7AhkQWm7OXdO\nDLgLF5rPTZwothn29S1lOM3xc6M29qMudqMnm1ZvcEZjx46VHYGsiI6Olh2BLChNNwcPigfWfvtN\nHHt5AYsWAUOH2imc5vi5URv7URe70VOJht5ff/0VycnJAIDLly8DAE6ePIkqVaoU+L4zZ87YKZ79\n+Pn5yY5AVnD5GHXZ2s3SpeJ2BqNRHNetC6xfLx5iI/vg50Zt7Edd7EZPxV6yzN3dHW5ubgXOmUym\nQufuPK/KQ25csoyo7Ny8Cbz2mth0Il+7dsBXXwH8Z08iIrLE0fNasa/0LlmyxO4/nIhcyx9/AAMH\nim2F8730EvCPfwDly8vLRUREVOyhd/jw4Y7M4XDx8fEICgqSHYMsmDlzJiZPniw7BhWhuN388APQ\nuzdw9qw49vQEFiwARo50cECN8XOjNvajLnajJ20eZDPm31hISsrJyZEdgSwoTjdxccDzzwPXronj\n2rWBtWuB4GAHh9McPzdqYz/qYjd6KvY9vc6M9/QSOUZuLvD668Ds2eZzbdsC69YBderIy0VERM5H\nmXt6iYju9NdfwKBBwPbt5nPh4eKWBi8vebmIiIiKwqGXiErsp5+AXr2A06fFcblywMcfi4fWiljQ\nhYiISDptdmTLX1eY1JSZmSk7Allwdzfr1gGPPmoeeGvUAHbsACIiOPCWNX5u1MZ+1MVu9KTN0Dtn\nzhzZEciK8PBw2RHIgvxu8vKAqVOBvn2B7GzxtdatgcOHgSeflBhQY/zcqI39qIvd6Mlj+vTp02WH\ncDSj0Qh3d3c0b94cXrzZUEkBAQGoXbu27BhUhICAAHh718aAAcDixebzgweLHdZ8feVl0x0/N2pj\nP+piN2oyGo3IyMiAv7+/Q+Y1be7pbdy4sewIZEXr1q1lRyALfHxao21b4MQJcezuLlZrmDiRtzPI\nxs+N2tiPutiNnrQZeomo5DZvFld0s7LEcbVqwKpVwFNPyc1FRERUUtrc00tExWcyAe+9BxgM5oG3\neXPg0CEOvERE5Jy0GXq3bt0qOwJZERsbKzsC/c/Vq0D//uKhNbF1TSz69QP27gXuv192OroTPzdq\nYz/qYjd60mboPXnypOwIZEVycrLsCASxDFlwsNhCGBD37LZtm4zVq4FKleRmo8L4uVEb+1EXu9ET\ntyEmIgBiZ7XQUODiRXFcuTKwciXQrZvcXEREpAdHz2vaXOkloqKZTMCcOcAzz5gH3gcfBA4e5MBL\nRESug6s3EGksJwcYNUpc0c1nMABffAHwX4oQEZEr4ZVeIk2dPQu0a1dw4I2KEhtOcOAlIiJXo83Q\nO9v92l0AACAASURBVHXqVNkRyAqDwSA7glb+9S+gTRvghx/EcaVKYtidPl1sPnEndqMudqM29qMu\ndqMnlxh6Z8yYAXd3d7zyyisWv6dnz55lmIhKauzYsbIjaMFkAubPF2vtZmaKc40aAfv3A716Ff13\n2I262I3a2I+62I2enH71hkOHDmHgwIGoXLkyQkJCMHfu3ELfw9UbiIDr14GXXgKWLDGf69pV3N5Q\ntaq8XERERABXb7Dq6tWrGDJkCBYvXowqVarIjkOkrN9+A558suDAO3my2GaYAy8REenAqYfeiIgI\n9OjRAx07dpQdhUhZe/YAgYFiCTIAqFgRiI8HZswAPDzkZiMiIiorTjv0xsfH4+jRo/jwww+L9f17\n9uxxcCIqjYSEBNkRXNLChUBICHD+vDi+7z5g3z5g4MDivwa7URe7URv7URe70ZNTDr3p6emYMGEC\nVqxYAU9Pz2L9ncTERAenotKIi4uTHcGl3LgBjBkDjB4N3LwpzoWEAIcPAy1alOy12I262I3a2I+6\n2I2enHLoPXLkCP744w+0bt0anp6e8PT0xK5duzBv3jyUL18eRT2bV6VKFTRt2hQGg6HAn+Dg4EL/\nxLd9+/YilzOJiIhAbGxsgXPJyckwGAzIzH8U/n+ioqIwc+bMAufS0tJgMBiQkpJS4Pz8+fMRGRlZ\n4FxOTg4MBgOSkpIKnI+Li0NYWFihbAMHDnTq97Fq1SqXeB/5ZL6Pc+eAjh2Bzz6bD0C8jwkTxDbD\n3t4lfx+DBg2S8j7yOXsfjnwfq1atcon3AbhGH3e/j/zfa87+Pu7kKu/D19fXJd6HM/cRFxd3exZr\n2LAhWrZsidDQUIdepHTK1Ruys7Nx9uzZAudGjBiBJk2aYMqUKWjSpEmBr3H1BtLFoUNA797iwTUA\n8PIStzgMGyY3FxER0d9x9LzmlNsQ+/j4oGnTpoXOVa9evdDAS6SLZcvE7QxGoziuW1dsONGmjdxc\nREREKnDK2xuK4ubmJjsCkRQ3b4rbF0aMMA+87dqJ+3c58BIREQkuM/QmJiYWuTFFvtmzZ5dhGiqp\nou4Hor+XmQk8/TQwb5753JgxwHffAX5+9vkZ7EZd7EZt7Edd7EZPTnl7gy0CAwNlRyArunTpIjuC\n0zl6VGwdnH97u6cn8M9/AqNG2ffnsBt1sRu1sR91sRs9OeWDbCXFB9nI1cTHA+HhwLVr4rhWLWDt\nWuCxx+TmIiIishW3ISai23JzxfbBgwaZB95HHhH373LgJSIiskyb2xuInN3Fi2LY3bbNfC4sDFiw\nAKhQQV4uIiIiZ6DNld7jx4/LjkBW3L3YNRX0889AUJB54C1XDoiOBmJjHT/wsht1sRu1sR91sRs9\naTP0rl69WnYEsmLWrFmyIyhr/XqgbVvg9Glx7OsL7NgBREQAZbFSH7tRF7tRG/tRF7vRkzYPsu3e\nvRvt27fng2yKysnJgbe3t+wYSsnLA95+G3jnHfO5Vq3EEHzffWWXg92oi92ojf2oi92oiTuy2UkF\n3vSoNP7yKSgrCxgyBNi0yXzuueeARYuAsv6vit2oi92ojf2oi93oSZvbG4icxYkT4naG/IHX3R2Y\nMwf48suyH3iJiIhchTZXeomcwddfiyu6WVniuGpVYNUqoHNnubmIiIicnTZXemNiYmRHICsiIyNl\nR5DKZAI++ADo0cM88D70EHDokPyBV/duVMZu1MZ+1MVu9KTNld6aNWvKjkBW1K9fX3YEaa5eFevt\nfvWV+VzfvsDSpUClStJi3aZzN6pjN2pjP+piN3rSZvUGbkNMKjp9GujVC/jpJ3Hs5ga89x7w+utl\nsxwZERGRKrh6A5GL+vZbYOBAsdMaANx7L7ByJfDss3JzERERuSJt7uklUoXJBHz0EdC1q3ngDQgA\nDh7kwEtEROQo2gy9aWlpsiOQFSkpKbIjlImcHLH+7muvic0nAPHw2oEDYvBVkS7dOCN2ozb2oy52\noydtht5FixbJjkBWTJo0SXYEhzt7FmjXTtzCkG/aNCAhAahcWV6uv6NDN86K3aiN/aiL3ehJmwfZ\nNm/ejO7du/NBNkWlpaW59NO0u3YB/foBmZni2McHWL4c6NNHbq7icPVunBm7URv7URe7UZOjH2TT\n5kqvn5+f7Ahkhav+8jGZgOho4KmnzAPvAw8A+/c7x8ALuG43roDdqI39qIvd6EmboZeorF2/Dowc\nCYwbB9y6Jc49/bTYcOKhh+RmIyIi0g2HXiIH+O03oEMH4PPPzecmTxbbDFetKi0WERGRtrQZeuPj\n42VHICtmzpwpO4Ld7N0LtGkjVmQAgIoVgbg4YMYMwMNDbjZbuFI3robdqI39qIvd6EmboddoNMqO\nQFbk5OTIjmAXixeLK7znzonj++4TQ3BoqNRYpeIq3bgidqM29qMudqMnbVZv4DbE5Eg3bgATJgCf\nfmo+FxICrF4N+PrKy0VEROQsuHoDkeLOnwc6dSo48I4fD2zbxoGXiIhIFeVkByByZocPA716iQfX\nAMDLC4iJAYYPl5uLiIiICtLmSu/ly5dlRyArMvMXsXUiy5eLHdbyB15/f+D7711v4HXGbnTBbtTG\nftTFbvSkzdA7Z84c2RHIivDwcNkRiu3WLWDiRDHc5j8f+fjj4qrvI4/IzeYIztSNbtiN2tiPutiN\nnjymT58+XXYIRzMajXB3d0fz5s3h5eUlOw4VISAgALVr15Yd429lZgI9e4olyPK9+CIQHw9Uriwv\nlyM5Szc6YjdqYz/qYjdqMhqNyMjIgL+/v0PmNa7eQFRMx46J+3d//VUce3qKLYZfeEFqLCIiIpfg\n6HmND7IRFcOqVUBYGHDtmjj28wPWrhW3NRAREZH6tLmnl8gWubnAlClic4n8gfeRR4AjRzjwEhER\nORNtht6tW7fKjkBWxMbGyo5QyMWLwLPPAnfuVjliBLBrl1ipQRcqdkMCu1Eb+1EXu9GTNkPvyZMn\nZUcgK5KTk2VHKODnn8UV3W3bxLGHB/DJJ8DnnwMVKsjNVtZU64bM2I3a2I+62I2e+CAb0V0SEoCh\nQ4GrV8Wxry+wZg3QoYPUWERERC6N2xATlZG8PCAqCujd2zzwtmol1t/lwEtEROTcuHoDEYCsLHF1\nd+NG87lBg4DFiwFvb3m5iIiIyD54pZe095//AG3bmgded3dg9mxgxQoOvERERK5Cm6F36tSpsiOQ\nFQaDQcrP3bJFPLCWkiKOq1YFtm4FXnsNcHOTEkk5srqhv8du1MZ+1MVu9KTNNsRXrlxBUFAQtyFW\nVPXq1fHAAw+U2c8zmYAZM4CRI4Hr18W5hx4CEhOBoKAyi+EUyrobKj52ozb2oy52oyZuQ2wHXL2B\n7nT1qthd7auvzOf69Pn/9u49qqoyfwP4c0DxgAIGCCIDCVSg6aCDF2Sp5Q1GUxQ11KlM1LwgmmXY\nr+mmNtVgpsvrUI6aZeI9ZcZLDaCBZKRoaUtNTBkqBUVHREGu+/fHXoAnOIjCOe97zn4+a7EW+z37\n7PM9Pmvrt93e7wts3Ai0aSOuLiIiIi3j7A1EzejCBSA0tLbh1emAv/1N3WbDS0REZL04ewNpRnIy\nMG4ccP26uu3kBGzaBIwYIbYuIiIiMj3NXOnNyMgQXQI1YPfu3SY7tqIAS5cC4eG1DW9AAJCZyYa3\nMUyZDTUNs5Eb85EXs9EmzTS9qampokugBiQmJprkuCUl6vy78+api08AwPDhasMbGGiSj7Q6psqG\nmo7ZyI35yIvZaBMfZCOrlZurrq529xLrb7wBLFyozsVLRERE8jB1v8Z7eskqpaUBY8cCV6+q261b\nq7MzjBkjti4iIiISg9e7yKooCrB6NTBoUG3D6+cHfPstG14iIiItY9NLVqO0VF1sIjYWqKhQx8LC\ngKNH1YUniIiISLs00/R+8MEHokugBkRHRzfp/ZcuAU88AaxfXzsWF6cuM+zi0sTiNK6p2ZDpMBu5\nMR95MRtt0sw9vcHBwaJLoAaEhYU98HuPHFFXVMvLU7ft7YF164AJE5qpOI1rSjZkWsxGbsxHXsxG\nmzh7A1m0f/4TiIkBysvVbR8fYPduoHt3sXURERHR/eEyxET1KCsDZs0CXnihtuF98kng2DE2vERE\nRFQXm16yOPn5wODBwJo1tWNz5gBffQW0ayeuLiIiIpKXZpreU6dOiS6BGnD48OFG7ZeVBfToAaSn\nq9utWgEbNgDLlwMtW5qwQA1rbDZkfsxGbsxHXsxGmzTT9G7btk10CdSAxYsX33Ofzz4D+vYFfv1V\n3fbyUhehmDTJtLVpXWOyITGYjdyYj7yYjTZp5kG29PR09OvXjw+ySaq4uBgODg71vlZRAcyfDyxb\nVjsWGgrs3Am0b2+mAjWsoWxILGYjN+YjL2YjJy5D3Ez0er3oEqgBxv7yuXYNiIoCUlNrx6ZNA1au\nBOzszFScxvEfBnkxG7kxH3kxG23STNNLlueHH4BRo4CcHHW7ZUu12Z0+XWhZREREZIHY9JKUtm0D\noqOB4mJ128MD2LFDvaeXiIiI6H5p5kG2jz76SHQJ1IC4uDgAQGUl8NprwLhxtQ1vz57q/LtseMWo\nzobkw2zkxnzkxWy0STNXet3d3UWXQA3w8fHBjRvAX/4C7N9fO/7880BCAsBbssXx8fERXQIZwWzk\nxnzkxWy0STOzN3AZYrmdPq3ev5udrW7b2gJLlwKzZwM6ndjaiIiIyPQ4ewNZvd27geeeA27dUrdd\nXYHt24EBA8TWRURERNZDM/f0knyqqoAFC4DIyNqGt1s39f5dNrxERETUnDTT9Obm5oouge5y8yYw\nejSwcGH1yFmMHw9kZAAdOwosjOo4e/as6BLICGYjN+YjL2ajTRbZ9L7//vvo1asXnJyc4OHhgcjI\nSJw7d67B96xdu9ZM1dG9nDsHhIQAe/ao2zY2QKdO87F5M8D5wuUzf/580SWQEcxGbsxHXsxGmyyy\n6U1PT8fs2bORmZmJ5ORklJeXIywsDCUlJUbfExsba8YKyZh9+4BevYAzZ9Tttm3VsQMHVvGBNUmt\nWrVKdAlkBLORG/ORF7PRJquYvaGgoADu7u5IS0tD33omc+XsDeIpChAfD/z1r+rvAPD44+pDbI88\nIrY2IiIiEo+zNzTCjRs3oNPp4OLiIroUqsft2+rqatu3145FRgIbNwKOjuLqIiIiIu2wyNsb7qYo\nCubOnYu+ffuic+fOosuh37l4EQgNNWx433lHXVKYDS8RERGZi8U3vTExMTh9+jS2bNnS4H73ep2a\nX0oK0KMHcPKkuu3oCCQlAW+8oT68drf4+HjzF0iNwmzkxWzkxnzkxWy0yaKb3tjYWOzbtw+HDh2C\np6dng/ump6ejc+fOiIiIMPjp06cPdu/ebbDvV199hYiIiDrHmDVrFtatW2cwdvz4cURERKCgoMBg\n/O23365zUuXm5iIiIqLOVCkrV66ssw54cXExIiIicPjwYYPxxMREREdH16lt3Lhx0nwPRQGWLQPC\nwoDr14sBRMDb+zC++w4YMaL+71FcXCzd97ibJefR1O9x/Phxq/ge1pLH3d+juLjYKr4HYB15/P57\nVP+9Zunf427W8j127NhhFd/DkvNITEys6cV8fX3RrVs3jB8/HqmpqXWO1Vws9kG22NhY7NmzB19/\n/TX8/Pwa3JcPsplPSQkwbRqwaVPt2FNPAZ9/Djg7i6uLiIiI5MYH2eoRExODxMREJCUloXXr1sjP\nzwcAODs7Q6/XC65Ou375RX1ALSurduz114FFi+rezkBERERkThbZ9CYkJECn0+HJJ580GN+wYQMm\nTpwopiiNS0sDxo4Frl5Vt1u3Bj75RB0jIiIiEs0im96qqqr7fk9hYaEJKiFFAf7xD+DFF4GKCnXM\nz0+df7dr18Yfp6CgAG5ubqYpkpqE2ciL2ciN+ciL2WiTZv6n85IlS0SXYHVKS4EXXgBmzapteIcM\nAY4evb+GFwAmT57c/AVSs2A28mI2cmM+8mI22mS7YMGCBaKLMLXS0lLY2Niga9euaNWqlehyrMKl\nS8CwYcC//lU79sorwIYN6q0N9ysgIOCeM3CQGMxGXsxGbsxHXsxGTqWlpbh06RK8vLxM0q9Z7OwN\n94OzNzSvb78FRo8GLl9Wt/V6YN064C9/EVsXERERWS5T92uaub2Bmse6dcATT9Q2vD4+QEYGG14i\nIiKSG5teapTycvXe3alTgbIydeyJJ4Bjx4A//UlsbURERET3opmmd//+/aJLsFhXrgCDBwNr1tSO\nzZ4N/Oc/QLt2zfMZv19RhuTBbOTFbOTGfOTFbLRJM01vdna26BIsUlYW0KOHOg8vANjZAevXAytW\nAC1bNt/n/H6pW5IHs5EXs5Eb85EXs9EmPshGRm3apE5JdueOut2hA7BrF9C7t9i6iIiIyPrwQTYy\nu4oKYN484LnnahvePn3U+3fZ8BIREZElssgV2ch0rl0Dxo8HkpNrx154AVi5EuAUx0RERGSp2PRS\njZMngVGjgIsX1e0WLYBVq4Dp08XWRURERNRUmrm94c033xRdgtS2b1dvYahueD08gIMHzdfwRkRE\nmOeD6L4xG3kxG7kxH3kxG23SzDLERUVF6NmzJ5ch/p3KSuCNN4AXX1Tn4gXU2RpSU4EuXcxXh6ur\nK/z9/c33gdRozEZezEZuzEdezEZOXIa4GXD2hvrduAE88wywb1/t2MSJQEICYG8vri4iIiLSHlP3\na7ynV6POnAFGjgSqpy+2tQU+/BCYMwfQ6cTWRkRERNTc2PRq0J496nRkRUXqtqsrsG0bMHCg2LqI\niIiITEUzD7JlZGSILkG4qipg4UJ1hobqhjcoSJ1/V3TDu3v3brEFkFHMRl7MRm7MR17MRps00/Sm\npqaKLkGooiJgzBjg7scWx40DMjKAjh1FVVUrMTFRdAlkBLORF7ORG/ORF7PRJj7IpgHZ2erV3dOn\n1W2dDvj734G4ON6/S0RERHLgg2zUJPv3AxMmAIWF6nbbtkBiIvDnP4uti4iIiMicNHN7g9Yoino1\n96mnahvezp2Bo0fZ8BIREZH28EqvFbp9G5g8WZ2RodqoUcCnnwKOjuLqIiIiIhJFM1d6P/jgA9El\nmMXFi0BoqGHDu2gRsHOn3A1vdHS06BLICGYjL2YjN+YjL2ajTZq50hscHCy6BJNLSQGiooDr19Vt\nR0dg0ybAEpYYDwsLE10CGcFs5MVs5MZ85MVstImzN1gBRQGWLwdeeQWorFTHHn1UXYSiUyextRER\nERE1BmdvoAaVlAAzZqj361YbNgz4/HN1pgYiIiIi0tA9vdb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"text/plain": [ "<matplotlib.figure.Figure at 0x10f4c74d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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I+UYaEG9kWsnZqAHzERezUSdO83BS5cedkV5kZCS2b99utm7kyJHG5UuXLqFr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\niIjISSymiYiIiIicxGKaiIiIiMhJ/x+2a1Cj3cmScwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11321ed50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "edisp.view(show=True)\n", "#save_current_figure('%s_edisp.png' % irf_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
ml4a/ml4a-guides
examples/generative_models/eigenfaces.ipynb
1
2578850
null
gpl-2.0
PaulSoderlind/JuliaTutorial
Tutorial_13_Downloading.ipynb
1
1955
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Downloading Files from Internet\n", "\n", "is done by the `download()` command. \n", "\n", "In Julia 1.6+ it is probably better to do\n", "```\n", "Import Downloads\n", "Downloads.download()\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "if !isdir(\"Results\")\n", " error(\"create the subfolder Results before running this program\")\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some Data from Kenneth French's Homepage" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File to download: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_daily_CSV.zip\n", "\n", "check the subfolder Results\n", "\n" ] } ], "source": [ "http = string(\"http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/\",\n", " \"F-F_Research_Data_Factors_daily_CSV.zip\")\n", "\n", "println(\"File to download: \",http)\n", "download(http,\"Results/WhatIJustDownloaded.zip\")\n", "\n", "println(\"\\ncheck the subfolder Results\\n\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "anaconda-cloud": {}, "kernelspec": { "display_name": "Julia 1.6.1", "language": "julia", "name": "julia-1.6" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
anandha2017/udacity
nd101 Deep Learning Nanodegree Foundation/DockerImages/18_Sirajs_Image_Generation/notebooks/02_how_to_win_slot_machines-master/WallStBandits.ipynb
1
49345
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Wall Street Bandits\n", "\n", "Quick and simple one this week. We will use reinforcement learning in the Multi-Armed Bandit paradigm in order to select a stock trading bot with the best performance. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from __future__ import division, print_function\n", "import tensorflow as tf\n", "import numpy as np, pandas as pd\n", "import matplotlib.pyplot as plt\n", "try:\n", " from tqdm import tqdm\n", "except ImportError:\n", " def tqdm(x):\n", " return x # sub out TQDM if it isn't installed, since it isn't strictly necessary. Just shiny. \n", "\n", "import preprocess # this is necessary for data i/o" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### First, let us define a generic parent class with necessary functions, so we can subclass later with the specific bots. OOP FTW!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "class BanditBot:\n", " name = 'BaseBot'\n", " inflation_loss = 0.995 # Due to inflation, holding on to cash is equivalent to losing money. We coarsely simulate this with this parameter\n", " def __init__(self, roi=1., bankroll=1., nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " self.roi = roi # Return on Investment - used to calculate Buy And Hold strategy\n", " self.bankroll = bankroll # needed to determine how much we can short\n", " self.p = p # our principle \n", " self.q = q # amount of stock\n", " self.p0 = p # save the initial bankroll for reset\n", " self.q0 = q \n", " self.r_buy = r_buy # the ratio of p to spend on purchase \n", " self.r_sell = r_sell # the ratio of p to liquidate on sell\n", " self.nt = nt # length of input vector\n", " \n", " self.p_margin = 0 # in development\n", " self.q_margin = 0\n", " \n", " def buy(self, x, t):\n", " '''Buy at time index t at value x[t]'''\n", " p_spent = self.p * self.r_buy\n", " q_bought = p_spent / x[t]\n", " self.q += q_bought\n", " self.p -= p_spent\n", " return (p_spent, q_bought)\n", " \n", " def sell(self, x, t):\n", " '''Sell at time index t at value x[t]'''\n", " q_sold = self.q * self.r_sell \n", " p_earned = q_sold * x[t]\n", " self.q -= q_sold\n", " self.p += p_earned\n", " return (p_earned, q_sold)\n", " \n", " def short_position(self, x, t1, t2):\n", " '''The noble short sell. \"Borrow\" stock in order to sell it immediately, then buy it back at a later date in order return the borrowed shares.\n", " '''\n", " q_short = self.bankroll / x[t1] # decide the amount we want to short, since in theory can short infinite\n", " self.q_margin += q_short\n", " p_earned = q_short * x[t1]\n", " self.p += p_earned\n", " \n", " q_return = self.q_margin\n", " p_buyback = q_return * x[t2]\n", " self.q_margin -= q_return\n", " self.p -= p_buyback\n", " return (p_earned-p_buyback, q_short)\n", " \n", " \n", " def liquidate(self, x):\n", " '''Sell all positions so we are left only with cash'''\n", " q_sold = self.q\n", " p_earned = q_sold * x[-1]\n", " self.q = 0\n", " self.p += p_earned\n", " return (p_earned, q_sold)\n", " \n", " def score(self, x):\n", " '''Liquidate all shares at current market price and then compute how much money we made.\n", " For the reinforcement paradigm to work well, we define score == 0 as neither gained nor lost any money this round'''\n", " self.liquidate(x)\n", " return float((self.roi * self.inflation_loss * self.p) - self.p0)\n", " \n", " def reset(self):\n", " '''Set funds back to original bankroll'''\n", " self.p = self.p0\n", " self.q = self.q0\n", " \n", " \n", " def __call__(self, x, *args, **kwargs):\n", " '''We will use the call protocol to represent running the bot on a single epoch. This will be overloaded for each subclass. Just a fancy little convenience.\n", " reward = bot()\n", " would be the same as: \n", " reward = bot.pullBandit()\n", " '''\n", " result = np.random.randn(1)\n", " if result > 0:\n", " return 1 #return a positive reward.\n", " else:\n", " return -1 #return a negative reward.\n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Let's meet our contenders!\n", "* Images are used as thumbnails with fair use in mind for variety and educational purpose only, their respective copyright holders retain copyright*\n", "\n", "### The Wimp\n", "![sir robin](images/robin.jpg)\n", "Cowardly refuses to invest in the market. It's too risky for him. This will serve as our baseline to control for effects like inflation. \n", "*When danger reared its ugly head, he bravely turned his tail and fled!*\n", "\n", "### Buy and Hold\n", "![buffet](images/buffet.jpg) \n", "The classic strategy. Buy an instrument with good fundamentals and just hold on to it for a long period of time. This works well because the markets are demonstrated to consistently increase in value when averaged over long periods of time. My money's on this one. \n", "\n", "### The Bull\n", "![bull and girl](images/girlandbull.jpg)\n", "We expect the market to do well on average. Buy at the start of the time frame and sell at the end of it. However, this involves more trades than the Buy and Hold\n", "\n", "### The Bear\n", "![bear](images/bear.jpg)\n", "We are pessimistic and expect the market to go down. Shortsell right off the bat. I don't expect this bot to perform well, it's mostly to test the short functionality.\n", "\n", "\n", "### Strategic Bull\n", "We expect the market to *generally* do well, but not all the time. Check the first N datapoints for an overall trend. If it's upward, buy in and sell at the end of the period. \n", "\n", "### Strategic Bear\n", "We expect the market to *generally* do well, but not all the time. Check the first N datapoints for an overall trend. If it's downward, shortsell until the end of the period. \n", "\n", "\n", "### Bull/Bear strategic trend\n", "![bullbear](images/bullbear.jpg)\n", "We expect the market to *generally* do well, but not all the time. Check the first N datapoints for an overall trend. If it's upward, buy in. If it's downward, shortsell. Liquidate at the end of the period. \n", "\n", "\n", "### The Monkey\n", "![blindmonkey](images/blindmonkey.jpg)\n", "> *A blindfolded monkey throwing darts at a newspaper's financial pages could select a portfolio that would do just as well as one carefully selected by experts* - Burton Malkiel\n", "\n", "Basically, an RNG. If Burton Malkiel is correct, this should do just as well as any hedge fund manager. Champagne and yatchs, here we come!\n", "\n", "### The YOLOer / WSB\n", "![wsb](images/wsb.jpg)\n", "Like the monkey, only riskier. Randomly buys and sells AND randomly short sells. You think simple Buy and Hold will get you a yatch?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "class TheWimp(BanditBot):\n", " name = 'TheWimp'\n", " # Cowardly refuses to spend any money! Aka baseline\n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " return self.score(x)\n", " \n", "class BuyHold(BanditBot):\n", " name = 'BuyAndHold'\n", " # The classic approach - buy and hold a long position\n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1., roi=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " self.roi = roi\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " return self.score(x)\n", " \n", "class TheBull(BanditBot):\n", " name = 'TheBull' \n", " \n", " def __call__(self, x, *args, **kwargs):\n", " self.buy(x, 0)\n", " return self.score(x)# liquidate full position\n", " \n", "class TheBear(BanditBot):\n", " name = 'TheBear' \n", " \n", " def __call__(self, x, *args, **kwargs):\n", " self.short_position(x, 0, -1)\n", " return self.score(x)# liquidate full position\n", " \n", " \n", " \n", "class StratBull(BanditBot):\n", " name = 'StratBull'\n", " # Check the overall progress up to the first (fraction) of the epoch. If bullish, buy in\n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " nt = len(x)\n", " t1 = nt // 3 # assess period\n", " if x[t1] > x[0]:\n", " self.buy(x, t1+1)\n", " \n", " return self.score(x) # liquidate full position\n", " \n", "class StratBear(BanditBot):\n", " name = 'StratBear'\n", " # Check the overall progress up to the first (fraction) of the epoch. If bearish, short sell\n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " nt = len(x)\n", " t1 = nt // 3 # assess period\n", " if x[t1] < x[0]:\n", " self.short_position(x, t1+1, -1)\n", " \n", " return self.score(x) # liquidate full position\n", " \n", "class StratTwin(BanditBot):\n", " name = 'StratTwin'\n", " # Check the overall progress up to the first (fraction) of the epoch. If bull, go long, If bearish, short sell\n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " nt = len(x)\n", " t1 = nt // 3 # assess period\n", " if x[t1] < x[0]:\n", " self.short_position(x, t1+1, -1)\n", " else:\n", " self.buy(x, t1+1)\n", " \n", " return self.score(x) # liquidate full position\n", " \n", "\n", "class TheMonkey(BanditBot):\n", " name = 'TheMonkey'\n", " # A blindfolded monkey throwing darts at the newspaper's financial pages \n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " nt = len(x)\n", " a = np.random.randint(0, nt)\n", " b = np.random.randint(0, nt)\n", " if a > b:\n", " a,b = b,a\n", " \n", " self.buy(x, a)\n", " self.sell(x, b) \n", " return self.score(x)\n", " \n", "class WallStBets(BanditBot):\n", " name = '/r/wallstbets YOLO!'\n", " # A slighly more sophisticated monkey\n", " def __init__(self, nt=50, p=1., q=0., r_buy=1., r_sell=1.):\n", " super().__init__(nt=nt, p=p, q=q, r_buy=r_buy, r_sell=r_sell)\n", " \n", " def __call__(self, x, *args, **kwargs):\n", " nt = len(x)\n", " a = np.random.randint(0, nt)\n", " b = np.random.randint(0, nt)\n", " if a > b:\n", " a,b = b,a\n", " \n", " if np.random.randint(0,2):\n", " self.short_position(x, a, b)\n", " else:\n", " self.buy(x, a)\n", " self.sell(x, b) \n", " return self.score(x)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Set some overall training parameters\n", "# We will need many more training periods than the toy example. \n", "total_episodes = 100000 #Set total number of episodes to train agent on.\n", "mini_epoch_size = 100\n", "print_epoch_size = 10000\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3664, 100, 1) (3664,) (407, 100, 1) (407,)\n" ] } ], "source": [ "# Get our raw data. Totally ripped off the process script from the other Wall Street tutorial, so it's a little inefficient, but it'll work for our purposes\n", "raw_data = pd.read_csv('data/sp500.csv', header=None)\n", "x_train, y_train, x_test, y_test = preprocess.load_data('data/sp500.csv', mini_epoch_size, False)\n", "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Sanity check. \n", "assert mini_epoch_size < 0.5*len(x_train), 'Mini epoch size too large'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f29a48870b8>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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W7/UFXG/+5VU15CXY9z9ZUbWoKEpKMHvV7gCZuwBMbbV50xlVAIqipDztm+clugtJiSoA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\nLq+iR5v8gHNfsfzINfw/ODe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2952e3a5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Have a gander at the stock data. As you can see, the overall trend is upwards\n", "plt.plot(raw_data[0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.49176078618\n" ] } ], "source": [ "# Determine how much the simple buy and hold will appreciate over the time frame\n", "stock_ary = raw_data[0].values\n", "buy_and_hold = stock_ary[-1] / stock_ary[0]\n", "print(buy_and_hold)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.01097565669\n" ] } ], "source": [ "# Calc average return for basic buy and hold strategy over each mini-epoch period\n", "avg_return = buy_and_hold** (mini_epoch_size / len(x_train))\n", "print(avg_return)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Create our ensemble\n", "bots = [TheWimp(),\n", " BuyHold(roi=avg_return),\n", " TheMonkey(),\n", " TheBull(),\n", " TheBear(),\n", " StratBull(),\n", " StratBear()]\n", "\n", "num_bandits = len(bots)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#init our agent\n", "\n", "tf.reset_default_graph()\n", "\n", "#These two lines established the feed-forward part of the network. \n", "#This does the actual choosing.\n", "weights = tf.Variable(tf.ones([num_bandits]))\n", "chosen_action = tf.argmax(weights,0)\n", "\n", "#The next six lines establish the training proceedure. \n", "#We feed the reward and chosen action into the network\n", "#to compute the loss, and use it to update the network.\n", "reward_holder = tf.placeholder(shape=[1],dtype=tf.float32)\n", "action_holder = tf.placeholder(shape=[1],dtype=tf.int32)\n", "responsible_weight = tf.slice(weights,action_holder,[1])\n", "loss = -(tf.log(responsible_weight)*reward_holder)\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)\n", "update = optimizer.minimize(loss)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Reinforcement training\n", "I will use the stock reinforcement training protocol pretty much as is. All I've done is substituted the function `pullBandit()` with a call to the active bot's main function. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mini-epoch size: 100\n", "Num Mini-epochs: 1000\n", "WARNING:tensorflow:From <ipython-input-13-d320c44aa23d>:8: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n", "Instructions for updating:\n", "Use `tf.global_variables_initializer` instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100000/100000 [00:48<00:00, 2046.94it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "The agent thinks bandit BuyAndHold is the most promising....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "\n", "\n", "sample_ratio = total_episodes / mini_epoch_size\n", "print('Mini-epoch size: {}\\nNum Mini-epochs: {}'.format(mini_epoch_size, total_episodes // mini_epoch_size))\n", "total_reward = np.zeros(num_bandits) #Set scoreboard for bandits to 0.\n", "e = 0.2 #Set the chance of taking a random action.\n", "\n", "init = tf.initialize_all_variables()\n", "verbose_updates = False\n", "# Launch the tensorflow graph\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " for i in tqdm(range(total_episodes)):\n", "# print('Ep {} of {}'.format(i, total_episodes))\n", " \n", " \n", " tp = np.random.randint(0, len(x_train))\n", " x = x_train[tp].ravel() # select time period\n", " \n", " #Choose either a random action or one from our network.\n", " if np.random.rand(1) < e:\n", " action = np.random.randint(num_bandits)\n", " else:\n", " action = sess.run(chosen_action)\n", " \n", "# reward = pullBandit(bandits[action]) #Get our reward from picking one of the bandits.\n", " reward = bots[action](x) #Get our reward from picking one of the bandits.\n", " \n", "\n", " #Update the network.\n", " _,resp,ww = sess.run([update,responsible_weight,weights], feed_dict={reward_holder:[reward],action_holder:[action]})\n", " \n", " #Update our running tally of scores.\n", " total_reward[action] += reward\n", " \n", " if i % print_epoch_size == 0 and verbose_updates:\n", " print('Results: ', ' '.join(['{:.3f}'.format(bot.p) for bot in bots]))\n", " print(\"Running reward: {}\".format(str(total_reward)))\n", " if i % mini_epoch_size == 0:\n", " [bot.reset() for bot in bots]\n", "winner = np.argmax(ww)\n", "print(\"The agent thinks bandit {} is the most promising....\".format(bots[winner].name))\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Let's see how they performed\n", "I normalized the score, since it's roughly proportional to the total number of episodes. This score is only a relative indicator, it does not tell us what our ROI would be." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TheWimp: -0.145 \n", "BuyAndHold: +4.903 *\n", " TheMonkey: +0.129 \n", " TheBull: +0.933 \n", " TheBear: -1.586 \n", " StratBull: +0.321 \n", " StratBear: -0.100 \n" ] } ], "source": [ "for i, bot in enumerate(bots):\n", " star = '*' if i == winner else ''\n", " print('{: >10}: {: >+10.3f} {}'.format(bot.name, 1000*total_reward[i]/total_episodes, star))\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### A winner is ~~you~~ Buy and Hold! (at least for this seed value)\n", "#### Looks like (for total_episodes > 100,000), the tried-and true method wins. That's pretty cool, but we had to use quite a large number of episodes to get a stable result. I've observed with this technique, there is somewhat of an effect of 'early winner takes all'. With lower numbers of runs, the model which by chance gets a bit of an advantage tends to pull out way ahead. What if we just run all models in parallel, do we see the same results?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mini-epoch size: 100\n", "Num Mini-epochs: 1000\n", "WARNING:tensorflow:From <ipython-input-15-552c84b3fe2a>:4: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n", "Instructions for updating:\n", "Use `tf.global_variables_initializer` instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100000/100000 [00:04<00:00, 22911.23it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "The agent thinks TheBull is the most promising....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print('Mini-epoch size: {}\\nNum Mini-epochs: {}'.format(mini_epoch_size, total_episodes // mini_epoch_size))\n", "total_reward = np.zeros(num_bandits) #Set scoreboard for bandits to 0.\n", "\n", "init = tf.initialize_all_variables()\n", "verbose_updates = False\n", "# Launch the tensorflow graph\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " \n", " for i in tqdm(range(total_episodes)):\n", " tp = np.random.randint(0, len(x_train))\n", " x = x_train[tp].ravel() # select time period\n", " for action in range(len(bots)):\n", " reward = bots[action](x) #Get our reward from picking one of the bandits.\n", "\n", " #Update our running tally of scores.\n", " total_reward[action] += reward\n", "\n", " if i % print_epoch_size == 0 and verbose_updates:\n", " print('Results: ', ' '.join(['{:.3f}'.format(bot.p) for bot in bots]))\n", " print(\"Running reward: {}\".format(str(total_reward)))\n", " if i % mini_epoch_size == 0:\n", " [bot.reset() for bot in bots]\n", " \n", "winner = np.argmax(total_reward)\n", "print(\"The agent thinks {} is the most promising....\".format(bots[winner].name))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TheWimp: -0.050 \n", "BuyAndHold: +0.059 \n", " TheMonkey: +2.672 \n", " TheBull: +13.227 *\n", " TheBear: -7.492 \n", " StratBull: +5.488 \n", " StratBear: -0.831 \n" ] } ], "source": [ "for i, bot in enumerate(bots):\n", " star = '*' if i == winner else ''\n", " print('{: >10}: {: >+10.3f} {}'.format(bot.name, 10*total_reward[i]/total_episodes, star))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "#### Now it seems to favor the Bull over the Buy and Hold. Interesting. \n", "This is likely because the Bull strategy is more heavily hit by market fluctuations, which the Reinforcement Learning paradigm picks up on, and penalizes. However, if we take away that hysteresis effect (think of it as a 'bailout' each time we lose money), the Bull has a slight advantage, since it isn't really penalized by past losses as well. \n", "\n", "### I'm calling that successful reinforcement learning!\n", "\n", "So, at least with these really simple models, you can't beat the sound advice of invest your money and DON'T TOUCH IT. I'll catch up with you wizards in my penthouse...30 years from now ;)\n", "![pig](images/piggy.jpg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
kanhua/pypvcell
legacy/Enhancement in Jsc of back reflector.ipynb
1
76586
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "This script calculates the potential increase of photocurrent. The question that I want to resolve in this calculation is: does any kind of back reflector help the absorption of crystalline silicon?\n", "\n", "## Method\n", "I assume we have a 100%-reflectivity mirror attached at the back side of silicon substrate. The structure is like this:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAACnCAIAAAB1pwerAAAACXBIWXMAABcSAAAXEgFnn9JSAAAA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\nAagSoEoAqgSoEkBP/VeAAQACgf4J7ZXKxgAAAABJRU5ErkJggg==\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('/Users/kanhua/Dropbox/Programming/pypvcell examples/si_back_reflector.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use experimental absorption coefficients of crystalline silicon and assume that the absorptivity follows Beer-Lambert's law. Also, we assume that every abosorped photons can be converted into electrons. In this case, having a 100% reflector on the back side of silicon can be thought of as doubling the thickness of silicon substrate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from pypvcell.photocurrent import conv_abs_to_qe,calc_jsc\n", "from pypvcell.illumination import Illumination\n", "from pypvcell.spectrum import Spectrum\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "abs_file = \"/Users/kanhua/Dropbox/Programming/pypvcell/legacy/si_alpha.csv\"\n", "\n", "si_alpha = np.loadtxt(abs_file, delimiter=',')\n", "si_alpha_sp = Spectrum(si_alpha[:,0],si_alpha[:,1],'m')\n", "\n", "layer_t=np.logspace(-8,-3,num=100)\n", "jsc_baseline=np.zeros(layer_t.shape)\n", "\n", "jsc_full_r=np.zeros(layer_t.shape)\n", "\n", "it=np.nditer(layer_t,flags=['f_index'])\n", "ill=Illumination(\"AM1.5g\")\n", "\n", "def filter_spec(ill):\n", " ill_a=ill.get_spectrum(to_x_unit='eV',to_photon_flux=True)\n", " ill_a=ill_a[:,ill_a[0,:]>1.1]\n", " ill_a=ill_a[:,ill_a[0,:]<1.42]\n", " ill_a[1,1]=0\n", " return Spectrum(ill_a[0,:],ill_a[1,:],'eV',y_unit='m**-2',is_spec_density=True,is_photon_flux=False)\n", "\n", "#ill=filter_spec(ill)\n", "\n", "while not it.finished:\n", "\n", " t=it[0] #thickness of Si layer\n", "\n", " qe=conv_abs_to_qe(si_alpha_sp,t)\n", " jsc_baseline[it.index]=calc_jsc(ill, qe)\n", "\n", " # Assme 100% reflection on the back side, essentially doubling the thickness of silicon\n", " qe_full_r=conv_abs_to_qe(si_alpha_sp,t*2)\n", " jsc_full_r[it.index]=calc_jsc(ill,qe_full_r)\n", "\n", " it.iternext()\n", "it.reset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Photocurrent with and without the back reflector" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10e9cb908>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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unxVOcpqDORN74VejigsDVco9CvMXn2qM+cDtkShVlmSkwILRkJEM45ZANT+n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\nSqlcaYJQSimVK00QSimlcvX/8bpnf4FoF3sAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10e7454e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.semilogx(layer_t*1e6, jsc_baseline,hold=True,label=\"Si\")\n", "plt.semilogx(layer_t*1e6,jsc_full_r,label=\"Si+100% mirror\")\n", "plt.xlabel(\"thickness of Si substrate (um)\")\n", "plt.ylabel(\"Jsc (A/m^2)\")\n", "plt.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normlize the Jsc(Si+mirror) by Jsc(Si only)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DV8aY1uRk99FQVS2qe6Cq+4ER/hYSkekiUiAiq/zMN1pEakTkPAdZTBgSEX5/5iAm9Mng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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10e7c9780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.semilogx(layer_t*1e6,jsc_full_r/jsc_baseline)\n", "plt.xlabel(\"thickness of Si substrate (um)\")\n", "plt.ylabel(\"Normalized Jsc enhancement\")\n", "plt.savefig(\"jsc_enhancement.pdf\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the back reflector can be very effective when the thickness of silicon substrate is thin (< 1um). Silicon substrates with more than 10-um thicknesses cannot be benefited from this structure very well. This is the reason that photonic or plasmonic structure are useful for thin-film or ultra-thin-film silicon cell, but not conventional bulk crystalline silicon cell." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e711d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# more detailed investigation\n", "plt.semilogx(layer_t*1e6,jsc_full_r/jsc_baseline)\n", "plt.xlabel(\"thickness of Si substrate (um)\")\n", "plt.ylabel(\"Jsc enhancement (2x)\")\n", "plt.xlim([100,1000])\n", "plt.ylim([1.0,1.5])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More audacious assumption\n", "\n", "Assume that somehow we have a novel reflector that can increase the optical absorption length by 10 times." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "while not it.finished:\n", "\n", " t=it[0] #thickness of Si layer\n", "\n", " qe=conv_abs_to_qe(si_alpha_sp,t)\n", " jsc_baseline[it.index]=calc_jsc(Illumination(\"AM1.5g\"), qe)\n", "\n", " # Assme 100% reflection on the back side, essentially doubling the thickness of silicon\n", " qe_full_r=conv_abs_to_qe(si_alpha_sp,t*10)\n", " jsc_full_r[it.index]=calc_jsc(Illumination(\"AM1.5g\"),qe_full_r)\n", "\n", " it.iternext()\n", "it.reset()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e9f9550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.semilogx(layer_t*1e6,jsc_full_r/jsc_baseline)\n", "plt.xlabel(\"thickness of Si substrate (um)\")\n", "plt.ylabel(\"Jsc enhancement (10x)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that increasing the optical absorption length by 10 times does not increase the photocurrent much for thick silicon substrates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "The result is not to say that using photonic/plasmonic structure to enhance the photocurrent of thick silicon substrates is completely hopeless. In my view, to make this possible, this photonic/plasmonic structure should\n", "\n", "1. Have the absortpion mechanism other than Beer-Lambert's law.\n", "2. Increase the optical abosrption length of silicon by a very large amount (at least 10 times or more)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
phnmnl/MTBLS233-POP
downstream-analysis/DownstreamAnalysis.ipynb
1
242652
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Downstream analysis of HPLC-Orbitrap data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook replicates the analysis workflow created by Marc Rurik. The workflow wrapped in [KNIME](https://www.knime.org/) nodes can be found [here](http://www.myexperiment.org/workflows/4792.html), and is illustrated in the picture below. The workflow is developed to analyze HPLC-Orbitrap measurements split into separate or alternating m/z scan ranges. The input expects to be OpenMS consensusXML files, generated by the TextExporter in OpenMS. The workflow further combines the scan ranges, filter them ultilizing blank samples and QC measurements, and then performs statistical analysis to retrieve reliable features.\n", "\n", "The aim of this notebook is to give an user friendly and interactive environment where you can get on-the-fly information about the progress of your data. After each step of the pipeline, we provide you with live feedback, consisting of informative plots." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p align=\"center\">\n", " <img src=\"http://www.myexperiment.org/workflows/4792/versions/1/previews/full\"/>\n", "</p>\n", "\n", "Illustration by: Marc Rurik" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read input files\n", "* Please run the following snippet and insert the paths to your input files containing the low/high mass range" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "low <- read.table(\"C:\\\\Users\\\\Milos\\\\Desktop\\\\reproducing\\\\alternate\\\\transformed_KNIME_low.csv\", \n", " fill=TRUE, \n", " sep=\",\",\n", " header=TRUE)\n", "high <- read.table(\"C:\\\\Users\\\\Milos\\\\Desktop\\\\reproducing\\\\alternate\\\\transformed_KNIME_high.csv\", \n", " fill=TRUE, \n", " sep=\",\",\n", " header=TRUE)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Head and tail of low mass range\"\n" ] }, { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>Col0</th><th scope=col>Col1</th><th scope=col>Col2</th><th scope=col>Col3</th><th scope=col>Col4</th><th scope=col>Col5</th><th scope=col>Col6</th><th scope=col>Col7</th><th scope=col>Col8</th><th scope=col>Col9</th><th scope=col>...</th><th scope=col>Col202</th><th scope=col>Col203</th><th scope=col>Col204</th><th scope=col>Col205</th><th scope=col>Col206</th><th scope=col>Col207</th><th scope=col>Col208</th><th scope=col>Col209</th><th scope=col>Col210</th><th scope=col>Col211</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>MAP </td><td>0 </td><td>C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/002_CRa_H9M5_M470_Pool_01_alternate_pos_low_mr.featureXML </td><td>4267 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>MAP </td><td>1 </td><td>C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/003_CRa_H9M5_M470_Pool_02_alternate_pos_low_mr.featureXML </td><td>4350 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>MAP </td><td>2 </td><td>C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/005_CRa_H9M5_M470_Blank_02_alternate_pos_low_mr.featureXML</td><td>3927 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>... 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Col197 Col198 Col199 Col200 Col201\n", "1 15839 NA NA NA NA NA NA ... NA NA NA NA NA \n", "2 17801 NA NA NA NA NA NA ... NA NA NA NA NA \n", "3 14937 NA NA NA NA NA NA ... NA NA NA NA NA \n", "4 18258 NA NA NA NA NA NA ... NA NA NA NA NA \n", "5 17573 NA NA NA NA NA NA ... NA NA NA NA NA \n", "6 15106 NA NA NA NA NA NA ... NA NA NA NA NA \n", " Col202 Col203 Col204 Col205 Col206\n", "1 NA NA NA NA NA \n", "2 NA NA NA NA NA \n", "3 NA NA NA NA NA \n", "4 NA NA NA NA NA \n", "5 NA NA NA NA NA \n", "6 NA NA NA NA NA " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Col0</th><th scope=col>Col1</th><th scope=col>Col2</th><th scope=col>Col3</th><th scope=col>Col4</th><th scope=col>Col5</th><th scope=col>Col6</th><th scope=col>Col7</th><th scope=col>Col8</th><th scope=col>Col9</th><th scope=col>...</th><th scope=col>Col197</th><th scope=col>Col198</th><th scope=col>Col199</th><th scope=col>Col200</th><th scope=col>Col201</th><th scope=col>Col202</th><th scope=col>Col203</th><th scope=col>Col204</th><th scope=col>Col205</th><th scope=col>Col206</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>30783</th><td>CONSENSUS </td><td>528.8558 </td><td>759.628902945475</td><td>15729.90 </td><td>0 </td><td>0 </td><td>0.0909835 </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>529.1640 </td><td>759.6276 </td><td>15163.60 </td><td>0 </td><td> 0 </td><td>534.0042 </td><td>759.6283 </td><td>46217.1 </td><td>0 </td><td> 0 </td></tr>\n", "\t<tr><th scope=row>30784</th><td>CONSENSUS </td><td>626.1360 </td><td>884.50867693131 </td><td>16945.10 </td><td>0 </td><td>0 </td><td>0.0665289 </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>624.8340 </td><td>884.5052 </td><td>20155.30 </td><td>0 </td><td> 0 </td><td>630.6420 </td><td>884.5074 </td><td>37142.7 </td><td>0 </td><td> 0 </td></tr>\n", "\t<tr><th scope=row>30785</th><td>CONSENSUS </td><td>668.2470 </td><td>569.490709081264</td><td> 8645.73 </td><td>0 </td><td>0 </td><td>0.0944540 </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>666.8880 </td><td>569.4906 </td><td>10020.00 </td><td>0 </td><td> 0 </td><td>667.1580 </td><td>569.4920 </td><td>12565.4 </td><td>0 </td><td> 0 </td></tr>\n", "\t<tr><th scope=row>30786</th><td>CONSENSUS </td><td>626.3010 </td><td>906.491936111008</td><td>11101.10 </td><td>0 </td><td>0 </td><td>0.1062230 </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td> NA </td><td> NA </td><td> NA </td><td>0 </td><td>NA </td><td>630.6420 </td><td>906.4915 </td><td>11463.6 </td><td>0 </td><td> 0 </td></tr>\n", "\t<tr><th scope=row>30787</th><td>CONSENSUS </td><td>503.9165 </td><td>743.576225796737</td><td> 9055.52 </td><td>0 </td><td>0 </td><td>0.0945130 </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>506.9706 </td><td>743.5762 </td><td> 8418.28 </td><td>0 </td><td> 0 </td><td> NA </td><td> NA </td><td> NA </td><td>0 </td><td>NA </td></tr>\n", "\t<tr><th scope=row>30788</th><td>CONSENSUS </td><td>573.3078 </td><td>824.651543877865</td><td>13796.90 </td><td>0 </td><td>0 </td><td>0.0850217 </td><td>NA </td><td>NA </td><td>NA </td><td>... </td><td>581.3484 </td><td>824.6526 </td><td> 6436.42 </td><td>0 </td><td> 0 </td><td>576.5256 </td><td>824.6491 </td><td>12015.5 </td><td>0 </td><td> 0 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n", " & Col0 & Col1 & Col2 & Col3 & Col4 & Col5 & Col6 & Col7 & Col8 & Col9 & ... & Col197 & Col198 & Col199 & Col200 & Col201 & Col202 & Col203 & Col204 & Col205 & Col206\\\\\n", "\\hline\n", "\t30783 & CONSENSUS & 528.8558 & 759.628902945475 & 15729.90 & 0 & 0 & 0.0909835 & NA & NA & NA & ... & 529.1640 & 759.6276 & 15163.60 & 0 & 0 & 534.0042 & 759.6283 & 46217.1 & 0 & 0 \\\\\n", "\t30784 & CONSENSUS & 626.1360 & 884.50867693131 & 16945.10 & 0 & 0 & 0.0665289 & NA & NA & NA & ... & 624.8340 & 884.5052 & 20155.30 & 0 & 0 & 630.6420 & 884.5074 & 37142.7 & 0 & 0 \\\\\n", "\t30785 & CONSENSUS & 668.2470 & 569.490709081264 & 8645.73 & 0 & 0 & 0.0944540 & NA & NA & NA & ... & 666.8880 & 569.4906 & 10020.00 & 0 & 0 & 667.1580 & 569.4920 & 12565.4 & 0 & 0 \\\\\n", "\t30786 & CONSENSUS & 626.3010 & 906.491936111008 & 11101.10 & 0 & 0 & 0.1062230 & NA & NA & NA & ... & NA & NA & NA & 0 & NA & 630.6420 & 906.4915 & 11463.6 & 0 & 0 \\\\\n", "\t30787 & CONSENSUS & 503.9165 & 743.576225796737 & 9055.52 & 0 & 0 & 0.0945130 & NA & NA & NA & ... & 506.9706 & 743.5762 & 8418.28 & 0 & 0 & NA & NA & NA & 0 & NA \\\\\n", "\t30788 & CONSENSUS & 573.3078 & 824.651543877865 & 13796.90 & 0 & 0 & 0.0850217 & NA & NA & NA & ... & 581.3484 & 824.6526 & 6436.42 & 0 & 0 & 576.5256 & 824.6491 & 12015.5 & 0 & 0 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ " Col0 Col1 Col2 Col3 Col4 Col5 Col6 Col7\n", "30783 CONSENSUS 528.8558 759.628902945475 15729.90 0 0 0.0909835 NA \n", "30784 CONSENSUS 626.1360 884.50867693131 16945.10 0 0 0.0665289 NA \n", "30785 CONSENSUS 668.2470 569.490709081264 8645.73 0 0 0.0944540 NA \n", "30786 CONSENSUS 626.3010 906.491936111008 11101.10 0 0 0.1062230 NA \n", "30787 CONSENSUS 503.9165 743.576225796737 9055.52 0 0 0.0945130 NA \n", "30788 CONSENSUS 573.3078 824.651543877865 13796.90 0 0 0.0850217 NA \n", " Col8 Col9 ... Col197 Col198 Col199 Col200 Col201 Col202 Col203 \n", "30783 NA NA ... 529.1640 759.6276 15163.60 0 0 534.0042 759.6283\n", "30784 NA NA ... 624.8340 884.5052 20155.30 0 0 630.6420 884.5074\n", "30785 NA NA ... 666.8880 569.4906 10020.00 0 0 667.1580 569.4920\n", "30786 NA NA ... NA NA NA 0 NA 630.6420 906.4915\n", "30787 NA NA ... 506.9706 743.5762 8418.28 0 0 NA NA\n", "30788 NA NA ... 581.3484 824.6526 6436.42 0 0 576.5256 824.6491\n", " Col204 Col205 Col206\n", "30783 46217.1 0 0 \n", "30784 37142.7 0 0 \n", "30785 12565.4 0 0 \n", "30786 11463.6 0 0 \n", "30787 NA 0 NA \n", "30788 12015.5 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Head and tail of low mass range\")\n", "head(low)\n", "tail(low)\n", "print(\"Head and tail of high mass range\")\n", "head(high)\n", "tail(high)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Source .rscripts" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"The functions were succesfully loaded\"\n" ] } ], "source": [ "source('C:/Users/Milos/Desktop/reproducing/Functions.R')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Parse the files" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "low_parsed <- Parse(low)\n", "high_parsed <- Parse(high)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Remove columns that won't be used in the analysis" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>mz</th><th scope=col>rt</th><th scope=col>002_CRa_H9M5_M470_Pool_01_alternate_pos_low_mr.featureXML</th><th scope=col>003_CRa_H9M5_M470_Pool_02_alternate_pos_low_mr.featureXML</th><th scope=col>005_CRa_H9M5_M470_Blank_02_alternate_pos_low_mr.featureXML</th><th scope=col>006_CRa_H9M5_M470_H01_K1_alternate_pos_low_mr.featureXML</th><th scope=col>007_CRa_H9M5_M470_L01_K2_alternate_pos_low_mr.featureXML</th><th scope=col>009_CRa_H9M5_M470_Blank_03_alternate_pos_low_mr.featureXML</th><th scope=col>010_CRa_H9M5_M470_L03_K3_alternate_pos_low_mr.featureXML</th><th scope=col>011_CRa_H9M5_M470_C14_K2_alternate_pos_low_mr.featureXML</th><th scope=col>...</th><th scope=col>037_CRa_H9M5_M470_Blank_09_alternate_pos_low_mr.featureXML</th><th scope=col>038_CRa_H9M5_M470_L14_K3_alternate_pos_low_mr.featureXML</th><th scope=col>039_CRa_H9M5_M470_C03_K2_alternate_pos_low_mr.featureXML</th><th scope=col>040_CRa_H9M5_M470_L03_K1_alternate_pos_low_mr.featureXML</th><th scope=col>041_CRa_H9M5_M470_Blank_10_alternate_pos_low_mr.featureXML</th><th scope=col>042_CRa_H9M5_M470_Pool_06_alternate_pos_low_mr.featureXML</th><th scope=col>044_CRa_H9M5_M470_L14_K1_alternate_pos_low_mr.featureXML</th><th scope=col>045_CRa_H9M5_M470_L14_K2_alternate_pos_low_mr.featureXML</th><th scope=col>046_CRa_H9M5_M470_H03_K3_alternate_pos_low_mr.featureXML</th><th scope=col>047_CRa_H9M5_M470_Blank_11_alternate_pos_low_mr.featureXML</th></tr></thead>\n", "<tbody>\n", "\t<tr><th 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038\\_CRa\\_H9M5\\_M470\\_L14\\_K3\\_alternate\\_pos\\_low\\_mr.featureXML & 039\\_CRa\\_H9M5\\_M470\\_C03\\_K2\\_alternate\\_pos\\_low\\_mr.featureXML & 040\\_CRa\\_H9M5\\_M470\\_L03\\_K1\\_alternate\\_pos\\_low\\_mr.featureXML & 041\\_CRa\\_H9M5\\_M470\\_Blank\\_10\\_alternate\\_pos\\_low\\_mr.featureXML & 042\\_CRa\\_H9M5\\_M470\\_Pool\\_06\\_alternate\\_pos\\_low\\_mr.featureXML & 044\\_CRa\\_H9M5\\_M470\\_L14\\_K1\\_alternate\\_pos\\_low\\_mr.featureXML & 045\\_CRa\\_H9M5\\_M470\\_L14\\_K2\\_alternate\\_pos\\_low\\_mr.featureXML & 046\\_CRa\\_H9M5\\_M470\\_H03\\_K3\\_alternate\\_pos\\_low\\_mr.featureXML & 047\\_CRa\\_H9M5\\_M470\\_Blank\\_11\\_alternate\\_pos\\_low\\_mr.featureXML\\\\\n", "\\hline\n", "\t7785 & 55.018053688842 & 573.95 & NA & NA & NA & NA & NA & NA & 2220.18994140625 & NA & ... & 8783.259765625 & NA & NA & NA & NA & NA & NA & NA & 6772.740234375 & NA \\\\\n", "\t5654 & 55.0418538981374 & 566.919466666667 & NA & NA & NA & NA & NA & NA & 17487.099609375 & NA & ... & NA & NA & NA & 3574.56005859375 & 6458.25 & NA & NA & NA & NA & 5303.68994140625\\\\\n", "\t6284 & 55.0418546450561 & 537.75225 & NA & NA & NA & NA & NA & NA & 8910.9404296875 & NA & ... & NA & NA & NA & 7683.830078125 & 2244.01000976563 & NA & NA & NA & NA & 8516.5498046875 \\\\\n", "\t4163 & 55.0418547740727 & 521.62125 & NA & NA & NA & 5419.83984375 & NA & NA & 6018.509765625 & NA & ... & NA & NA & NA & 12854.599609375 & 10305 & NA & NA & NA & NA & 15439.099609375 \\\\\n", "\t6414 & 55.0418626800408 & 444.041775 & NA & NA & NA & NA & NA & NA & NA & NA & ... & NA & NA & NA & 1925.11999511719 & 10511.2001953125 & NA & NA & NA & NA & NA \\\\\n", "\t5675 & 55.0418645991614 & 467.4084 & NA & NA & NA & NA & NA & NA & 4923.56005859375 & NA & ... & NA & NA & NA & NA & 9295.6298828125 & NA & NA & NA & NA & 10789.2998046875\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " mz rt \n", "7785 55.018053688842 573.95 \n", "5654 55.0418538981374 566.919466666667\n", "6284 55.0418546450561 537.75225 \n", "4163 55.0418547740727 521.62125 \n", "6414 55.0418626800408 444.041775 \n", "5675 55.0418645991614 467.4084 \n", " 002_CRa_H9M5_M470_Pool_01_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 003_CRa_H9M5_M470_Pool_02_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 005_CRa_H9M5_M470_Blank_02_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 006_CRa_H9M5_M470_H01_K1_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 5419.83984375 \n", "6414 NA \n", "5675 NA \n", " 007_CRa_H9M5_M470_L01_K2_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 009_CRa_H9M5_M470_Blank_03_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 010_CRa_H9M5_M470_L03_K3_alternate_pos_low_mr.featureXML\n", "7785 2220.18994140625 \n", "5654 17487.099609375 \n", "6284 8910.9404296875 \n", "4163 6018.509765625 \n", "6414 NA \n", "5675 4923.56005859375 \n", " 011_CRa_H9M5_M470_C14_K2_alternate_pos_low_mr.featureXML ...\n", "7785 NA ...\n", "5654 NA ...\n", "6284 NA ...\n", "4163 NA ...\n", "6414 NA ...\n", "5675 NA ...\n", " 037_CRa_H9M5_M470_Blank_09_alternate_pos_low_mr.featureXML\n", "7785 8783.259765625 \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 038_CRa_H9M5_M470_L14_K3_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 039_CRa_H9M5_M470_C03_K2_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 040_CRa_H9M5_M470_L03_K1_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 3574.56005859375 \n", "6284 7683.830078125 \n", "4163 12854.599609375 \n", "6414 1925.11999511719 \n", "5675 NA \n", " 041_CRa_H9M5_M470_Blank_10_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 6458.25 \n", "6284 2244.01000976563 \n", "4163 10305 \n", "6414 10511.2001953125 \n", "5675 9295.6298828125 \n", " 042_CRa_H9M5_M470_Pool_06_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 044_CRa_H9M5_M470_L14_K1_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 045_CRa_H9M5_M470_L14_K2_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 046_CRa_H9M5_M470_H03_K3_alternate_pos_low_mr.featureXML\n", "7785 6772.740234375 \n", "5654 NA \n", "6284 NA \n", "4163 NA \n", "6414 NA \n", "5675 NA \n", " 047_CRa_H9M5_M470_Blank_11_alternate_pos_low_mr.featureXML\n", "7785 NA \n", "5654 5303.68994140625 \n", "6284 8516.5498046875 \n", "4163 15439.099609375 \n", "6414 NA \n", "5675 10789.2998046875 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(low_parsed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Rename columns" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of samples existing in the low range: 43 \n", "Number of samples existing in the high range: 42 \n", "Number of samples existing in both ranges: 39" ] } ], "source": [ "low_renamed <- renameColumns(low_parsed)\n", "cat(\"Number of samples existing in the low range: \",length(colnames(low_renamed)), \"\\n\")\n", "\n", "high_renamed <- renameColumns(high_parsed)\n", "cat(\"Number of samples existing in the high range: \",length(colnames(high_renamed)), \"\\n\")\n", "\n", "cat(\"Number of samples existing in both ranges: \",\n", " length(intersect(colnames(low_renamed),colnames(high_renamed))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Concatenate the scan ranges" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Samples missing in the low mass range: Blank_01 H14_K1 Blank_05 \n", "Samples missing in the high mass range: Pool_02 L01_K2 H03_K1 Pool_05" ] } ], "source": [ "cat(\"Samples missing in the low mass range: \", \n", " names(high_renamed)[!names(high_renamed) %in% names(low_renamed)], \"\\n\") #low\n", "cat(\"Samples missing in the high mass range: \", \n", " names(low_renamed)[!names(low_renamed) %in% names(high_renamed)]) #high\n", "\n", "# Extending the high mass range with the missing samples\n", "missing_high <- names(low_renamed)[!names(low_renamed) %in% names(high_renamed)]\n", "extend_high <- data.frame(matrix(,nrow=nrow(high_renamed), ncol=length(missing_high)))\n", "names(extend_high) <- missing_high\n", "extend_high <- cbind(high_renamed,\n", " extend_high)\n", "\n", "# Extending the low mass range with the missing samples\n", "missing_low <- names(high_renamed)[!names(high_renamed) %in% names(low_renamed)]\n", "extend_low <- data.frame(matrix(,nrow=nrow(low_renamed), ncol=length(missing_low)))\n", "names(extend_low) <- missing_low\n", "extend_low <- cbind(low_renamed,extend_low)\n", "extend_high <- extend_high[,names(extend_low)]\n", "\n", "# Concatenating the scan ranges\n", "full_range <- rbind(extend_low, extend_high)\n", "names(full_range)<-gsub(\"_\",\".\",names(full_range))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Blank Filter" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "399 number of features passed \n" ] } ], "source": [ "blankFilterPassed = 20 #Number of samples the feature has to be present in\n", "full_range <- blankFilter(full_range,blankFilterPassed) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ConsensusMap Normalization" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [1] \"mz\" \"rt\" \"Pool.01\" \"Pool.02\" \"Blank.02\" \"H01.K1\" \n", " [7] \"L01.K2\" \"Blank.03\" \"L03.K3\" \"C14.K2\" \"C01.K1\" \"Blank.04\"\n", "[13] \"Pool.03\" \"H14.K2\" \"H01.K2\" \"H03.K1\" \"C01.K3\" \"C14.K3\" \n", "[19] \"L03.K2\" \"Blank.06\" \"Pool.04\" \"C03.K1\" \"H03.K2\" \"H14.K3\" \n", "[25] \"Blank.07\" \"L01.K3\" \"C14.K1\" \"H01.K3\" \"Blank.08\" \"Pool.05\" \n", "[31] \"L01.K1\" \"C01.K2\" \"C03.K3\" \"Blank.09\" \"L14.K3\" \"C03.K2\" \n", "[37] \"L03.K1\" \"Blank.10\" \"Pool.06\" \"L14.K1\" \"L14.K2\" \"H03.K3\" \n", "[43] \"Blank.11\" \"Blank.01\" \"H14.K1\" \"Blank.05\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Non-normalized columns: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " [1] \"mz\" \"rt\" \"Blank.02\" \"Blank.03\" \"Blank.04\" \"Blank.06\"\n", " [7] \"Blank.07\" \"Blank.08\" \"Blank.09\" \"Blank.10\" \"Blank.11\" \"Blank.01\"\n", "[13] \"Blank.05\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Feature count:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] Pool.01: 364\n", "[1] Pool.02: 82\n", "[1] H01.K1: 377\n", "[1] L01.K2: 80\n", "[1] L03.K3: 386\n", "[1] C14.K2: 315\n", "[1] C01.K1: 381\n", "[1] Pool.03: 376\n", "[1] H14.K2: 372\n", "[1] H01.K2: 346\n", "[1] H03.K1: 87\n", "[1] C01.K3: 385\n", "[1] C14.K3: 382\n", "[1] L03.K2: 386\n", "[1] Pool.04: 383\n", "[1] C03.K1: 387\n", "[1] H03.K2: 378\n", "[1] H14.K3: 370\n", "[1] L01.K3: 383\n", "[1] C14.K1: 376\n", "[1] H01.K3: 385\n", "[1] Pool.05: 86\n", "[1] L01.K1: 387\n", "[1] C01.K2: 377\n", "[1] C03.K3: 384\n", "[1] L14.K3: 384\n", "[1] C03.K2: 366\n", "[1] L03.K1: 390\n", "[1] Pool.06: 381\n", "[1] L14.K1: 383\n", "[1] L14.K2: 370\n", "[1] H03.K3: 385\n", "[1] H14.K1: 275\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Most features:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] L03.K1: 390\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Normalization ratios (map with most features / other map):\n", "Method: mean\n", "Outlier: 0.68 < ratio < 1.5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] L03.K1 / Pool.01 = 1.08928910927012\n", "[1] L03.K1 / Pool.02 = 1.17832723118759\n", "[1] L03.K1 / H01.K1 = 1.04107503996371\n", "[1] L03.K1 / L01.K2 = 1.12903598273475\n", "[1] L03.K1 / L03.K3 = 1.03683948472117\n", "[1] L03.K1 / C14.K2 = 1.07780443878374\n", "[1] L03.K1 / C01.K1 = 0.963061556890648\n", "[1] L03.K1 / Pool.03 = 1.00711848901558\n", "[1] L03.K1 / H14.K2 = 1.0182715337612\n", "[1] L03.K1 / H01.K2 = 1.06152301081573\n", "[1] L03.K1 / H03.K1 = 1.01502654285714\n", "[1] L03.K1 / C01.K3 = 1.08803333306876\n", "[1] L03.K1 / C14.K3 = 1.09334669707088\n", "[1] L03.K1 / L03.K2 = 0.898088371576557\n", "[1] L03.K1 / Pool.04 = 0.971219473087716\n", "[1] L03.K1 / C03.K1 = 1.09002474234736\n", "[1] L03.K1 / H03.K2 = 0.978076774457934\n", "[1] L03.K1 / H14.K3 = 1.01857067049187\n", "[1] L03.K1 / L01.K3 = 1.04901761220858\n", "[1] L03.K1 / C14.K1 = 1.09793062534627\n", "[1] L03.K1 / H01.K3 = 1.07047912069505\n", "[1] L03.K1 / Pool.05 = 1.12182697238477\n", "[1] L03.K1 / L01.K1 = 1.09010624978688\n", "[1] L03.K1 / C01.K2 = 0.920983887230634\n", "[1] L03.K1 / C03.K3 = 1.07182831045502\n", "[1] L03.K1 / L14.K3 = 1.07414217948978\n", "[1] L03.K1 / C03.K2 = 1.04000992733607\n", "[1] L03.K1 / L03.K1 = 1\n", "[1] L03.K1 / Pool.06 = 0.969192039234425\n", "[1] L03.K1 / L14.K1 = 1.06004367584247\n", "[1] L03.K1 / L14.K2 = 1.03887257936087\n", "[1] L03.K1 / H03.K3 = 1.01863162471971\n", "[1] L03.K1 / H14.K1 = 1.03614947597529\n" ] } ], "source": [ "full_range_norm <- consensusMapNormalization(full_range)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Plot the log2 intensity distribution before and after normalization" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WoWY5W57Bsfecu1PJixXcO9s6Oa21PJ1FyYLZcHOx7KOeBBQuUR9oAJELFUaB6Q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\nzHCG/v9u2NSS3sm+R68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"text/plain": [ "Plot with title \"After normalization\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Boxplots before and after normalization\n", "n <- ncol(data.matrix(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)])) + 7\n", "\n", "cc <- rep(\"black\", length(names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)])))\n", "cc[grep(\"Pool\", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[1]\n", "cc[grep(\"C\", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[2]\n", "cc[grep(\"L\", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[3]\n", "cc[grep(\"H\", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[4]\n", "\n", "par(mfrow=c(2,1),oma = c(0, 0, 0, 0))\n", "boxplot(log2(data.matrix(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)])), \n", " main=\"Before normalization\",\n", " col=cc,las=2, cex.axis=0.8)\n", "boxplot(log2(data.matrix(full_range_norm$df[,-which(names(full_range_norm$df) %in% full_range_norm$ignoredCols)])), \n", " main=\"After normalization\",\n", " col=cc, las=2, cex.axis=0.8)\n", "par(mfrow=c(1,1))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>mz</th><th scope=col>rt</th><th scope=col>Pool.01</th><th scope=col>Pool.02</th><th scope=col>Blank.02</th><th scope=col>H01.K1</th><th scope=col>L01.K2</th><th scope=col>Blank.03</th><th scope=col>L03.K3</th><th scope=col>C14.K2</th><th scope=col>...</th><th scope=col>L03.K1</th><th scope=col>Blank.10</th><th scope=col>Pool.06</th><th scope=col>L14.K1</th><th scope=col>L14.K2</th><th scope=col>H03.K3</th><th scope=col>Blank.11</th><th scope=col>Blank.01</th><th scope=col>H14.K1</th><th scope=col>Blank.05</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>712</th><td>62.0239318918682</td><td>42.65704875 </td><td>16140.5 </td><td>15609.2001953125</td><td>NA </td><td>21198.099609375 </td><td>15190.7998046875</td><td>NA </td><td>24647.80078125 </td><td>26306.900390625 </td><td>... </td><td>22416.69921875 </td><td>NA </td><td>25208.69921875 </td><td>17198.099609375 </td><td>23959 </td><td>30239.099609375 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><th scope=row>713</th><td>62.0601584853407</td><td>47.4887748387097</td><td>10214.7001953125</td><td>8620.3095703125 </td><td>NA </td><td>13054 </td><td>7253.27978515625</td><td>NA </td><td>10014.5 </td><td>7460.14013671875</td><td>... </td><td>10149.2998046875</td><td>NA </td><td>9552.6201171875 </td><td>10845.099609375 </td><td>9779.5400390625 </td><td>9602.1298828125 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><th scope=row>716</th><td>64.0158880576918</td><td>45.5561178947368</td><td>57882.8984375 </td><td>43353 </td><td>5723.18994140625</td><td>28183.30078125 </td><td>65687.1015625 </td><td>3016.330078125 </td><td>48044.6015625 </td><td>42436.5 </td><td>... </td><td>39669.8984375 </td><td>NA </td><td>45387.6015625 </td><td>35494.5 </td><td>38717.6015625 </td><td>30159.5 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><th scope=row>761</th><td>71.0291595196379</td><td>42.6571151351351</td><td>60555.3984375 </td><td>55938.3984375 </td><td>NA </td><td>61958.6015625 </td><td>61907.5 </td><td>4892.2998046875 </td><td>79584.703125 </td><td>92068.8984375 </td><td>... </td><td>82708.6015625 </td><td>NA </td><td>83737.296875 </td><td>61751.30078125 </td><td>80694.203125 </td><td>100121 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><th scope=row>795</th><td>73.5318403862944</td><td>42.6571168421053</td><td>91739.796875 </td><td>90443.296875 </td><td>7529.31982421875</td><td>104276 </td><td>97930.796875 </td><td>7592.02978515625</td><td>131104 </td><td>147737 </td><td>... </td><td>133079 </td><td>NA </td><td>128848 </td><td>93771.3984375 </td><td>119503 </td><td>187634 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><th scope=row>796</th><td>74.0236917045672</td><td>47.488775625 </td><td>47201.80078125 </td><td>46736.80078125 </td><td>NA </td><td>50628.6015625 </td><td>54791.6015625 </td><td>NA </td><td>66483.203125 </td><td>71014 </td><td>... </td><td>71762 </td><td>NA </td><td>51586.6015625 </td><td>67929.5 </td><td>76926.5 </td><td>6910.35009765625</td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllll}\n", " & mz & rt & Pool.01 & Pool.02 & Blank.02 & H01.K1 & L01.K2 & Blank.03 & L03.K3 & C14.K2 & ... & L03.K1 & Blank.10 & Pool.06 & L14.K1 & L14.K2 & H03.K3 & Blank.11 & Blank.01 & H14.K1 & Blank.05\\\\\n", "\\hline\n", "\t712 & 62.0239318918682 & 42.65704875 & 16140.5 & 15609.2001953125 & NA & 21198.099609375 & 15190.7998046875 & NA & 24647.80078125 & 26306.900390625 & ... & 22416.69921875 & NA & 25208.69921875 & 17198.099609375 & 23959 & 30239.099609375 & NA & NA & NA & NA \\\\\n", "\t713 & 62.0601584853407 & 47.4887748387097 & 10214.7001953125 & 8620.3095703125 & NA & 13054 & 7253.27978515625 & NA & 10014.5 & 7460.14013671875 & ... & 10149.2998046875 & NA & 9552.6201171875 & 10845.099609375 & 9779.5400390625 & 9602.1298828125 & NA & NA & NA & NA \\\\\n", "\t716 & 64.0158880576918 & 45.5561178947368 & 57882.8984375 & 43353 & 5723.18994140625 & 28183.30078125 & 65687.1015625 & 3016.330078125 & 48044.6015625 & 42436.5 & ... & 39669.8984375 & NA & 45387.6015625 & 35494.5 & 38717.6015625 & 30159.5 & NA & NA & NA & NA \\\\\n", "\t761 & 71.0291595196379 & 42.6571151351351 & 60555.3984375 & 55938.3984375 & NA & 61958.6015625 & 61907.5 & 4892.2998046875 & 79584.703125 & 92068.8984375 & ... & 82708.6015625 & NA & 83737.296875 & 61751.30078125 & 80694.203125 & 100121 & NA & NA & NA & NA \\\\\n", "\t795 & 73.5318403862944 & 42.6571168421053 & 91739.796875 & 90443.296875 & 7529.31982421875 & 104276 & 97930.796875 & 7592.02978515625 & 131104 & 147737 & ... & 133079 & NA & 128848 & 93771.3984375 & 119503 & 187634 & NA & NA & NA & NA \\\\\n", "\t796 & 74.0236917045672 & 47.488775625 & 47201.80078125 & 46736.80078125 & NA & 50628.6015625 & 54791.6015625 & NA & 66483.203125 & 71014 & ... & 71762 & NA & 51586.6015625 & 67929.5 & 76926.5 & 6910.35009765625 & NA & NA & NA & NA \\\\\n", "\\end{tabular}\n" ], "text/plain": [ " mz rt Pool.01 Pool.02 \n", "712 62.0239318918682 42.65704875 16140.5 15609.2001953125\n", "713 62.0601584853407 47.4887748387097 10214.7001953125 8620.3095703125 \n", "716 64.0158880576918 45.5561178947368 57882.8984375 43353 \n", "761 71.0291595196379 42.6571151351351 60555.3984375 55938.3984375 \n", "795 73.5318403862944 42.6571168421053 91739.796875 90443.296875 \n", "796 74.0236917045672 47.488775625 47201.80078125 46736.80078125 \n", " Blank.02 H01.K1 L01.K2 Blank.03 \n", "712 NA 21198.099609375 15190.7998046875 NA \n", "713 NA 13054 7253.27978515625 NA \n", "716 5723.18994140625 28183.30078125 65687.1015625 3016.330078125 \n", "761 NA 61958.6015625 61907.5 4892.2998046875 \n", "795 7529.31982421875 104276 97930.796875 7592.02978515625\n", "796 NA 50628.6015625 54791.6015625 NA \n", " L03.K3 C14.K2 ... L03.K1 Blank.10\n", "712 24647.80078125 26306.900390625 ... 22416.69921875 NA \n", "713 10014.5 7460.14013671875 ... 10149.2998046875 NA \n", "716 48044.6015625 42436.5 ... 39669.8984375 NA \n", "761 79584.703125 92068.8984375 ... 82708.6015625 NA \n", "795 131104 147737 ... 133079 NA \n", "796 66483.203125 71014 ... 71762 NA \n", " Pool.06 L14.K1 L14.K2 H03.K3 Blank.11\n", "712 25208.69921875 17198.099609375 23959 30239.099609375 NA \n", "713 9552.6201171875 10845.099609375 9779.5400390625 9602.1298828125 NA \n", "716 45387.6015625 35494.5 38717.6015625 30159.5 NA \n", "761 83737.296875 61751.30078125 80694.203125 100121 NA \n", "795 128848 93771.3984375 119503 187634 NA \n", "796 51586.6015625 67929.5 76926.5 6910.35009765625 NA \n", " Blank.01 H14.K1 Blank.05\n", "712 NA NA NA \n", "713 NA NA NA \n", "716 NA NA NA \n", "761 NA NA NA \n", "795 NA NA NA \n", "796 NA NA NA " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>mz</th><th scope=col>rt</th><th scope=col>Pool.01</th><th scope=col>Pool.02</th><th scope=col>Blank.02</th><th scope=col>H01.K1</th><th scope=col>L01.K2</th><th scope=col>Blank.03</th><th scope=col>L03.K3</th><th scope=col>C14.K2</th><th scope=col>...</th><th scope=col>L03.K1</th><th scope=col>Blank.10</th><th scope=col>Pool.06</th><th scope=col>L14.K1</th><th scope=col>L14.K2</th><th scope=col>H03.K3</th><th scope=col>Blank.11</th><th scope=col>Blank.01</th><th scope=col>H14.K1</th><th scope=col>Blank.05</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>62.0239318918682</td><td>42.65704875 </td><td>17581.67 </td><td> 18392.75 </td><td>NA </td><td> 22068.81 </td><td> 17150.960 </td><td>NA </td><td> 25555.81 </td><td> 28353.694 </td><td>... </td><td> 22416.7 </td><td>NA </td><td> 24432.071 </td><td>18230.74 </td><td> 24890.35 </td><td> 30802.503 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>62.0601584853407</td><td>47.4887748387097</td><td>11126.76 </td><td> 10157.55 </td><td>NA </td><td> 13590.19 </td><td> 8189.214 </td><td>NA </td><td> 10383.43 </td><td> 8040.572 </td><td>... </td><td> 10149.3 </td><td>NA </td><td> 9258.323 </td><td>11496.28 </td><td> 10159.70 </td><td> 9781.033 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>64.0158880576918</td><td>45.5561178947368</td><td>63051.21 </td><td> 51084.02 </td><td>5723.18994140625</td><td> 29340.93 </td><td> 74163.101 </td><td>3016.330078125 </td><td> 49814.54 </td><td> 45738.248 </td><td>... </td><td> 39669.9 </td><td>NA </td><td> 43989.302 </td><td>37625.72 </td><td> 40222.65 </td><td> 30721.420 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>71.0291595196379</td><td>42.6571151351351</td><td>65962.34 </td><td> 65913.74 </td><td>NA </td><td> 64503.55 </td><td> 69895.795 </td><td>4892.2998046875 </td><td> 82516.56 </td><td> 99232.267 </td><td>... </td><td> 82708.6 </td><td>NA </td><td> 81157.522 </td><td>65459.08 </td><td> 83830.99 </td><td>101986.417 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>73.5318403862944</td><td>42.6571168421053</td><td>99931.16 </td><td>106571.80 </td><td>7529.31982421875</td><td>108559.14 </td><td>110567.393 </td><td>7592.02978515625</td><td>135933.80 </td><td>159231.594 </td><td>... </td><td>133079.0 </td><td>NA </td><td>124878.456 </td><td>99401.78 </td><td>124148.39 </td><td>191129.926 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "\t<tr><td>74.0236917045672</td><td>47.488775625 </td><td>51416.41 </td><td> 55071.25 </td><td>NA </td><td> 52708.17 </td><td> 61861.690 </td><td>NA </td><td> 68932.41 </td><td> 76539.204 </td><td>... </td><td> 71762.0 </td><td>NA </td><td> 49997.324 </td><td>72008.24 </td><td> 79916.83 </td><td> 7039.101 </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllll}\n", " mz & rt & Pool.01 & Pool.02 & Blank.02 & H01.K1 & L01.K2 & Blank.03 & L03.K3 & C14.K2 & ... & L03.K1 & Blank.10 & Pool.06 & L14.K1 & L14.K2 & H03.K3 & Blank.11 & Blank.01 & H14.K1 & Blank.05\\\\\n", "\\hline\n", "\t 62.0239318918682 & 42.65704875 & 17581.67 & 18392.75 & NA & 22068.81 & 17150.960 & NA & 25555.81 & 28353.694 & ... & 22416.7 & NA & 24432.071 & 18230.74 & 24890.35 & 30802.503 & NA & NA & NA & NA \\\\\n", "\t 62.0601584853407 & 47.4887748387097 & 11126.76 & 10157.55 & NA & 13590.19 & 8189.214 & NA & 10383.43 & 8040.572 & ... & 10149.3 & NA & 9258.323 & 11496.28 & 10159.70 & 9781.033 & NA & NA & NA & NA \\\\\n", "\t 64.0158880576918 & 45.5561178947368 & 63051.21 & 51084.02 & 5723.18994140625 & 29340.93 & 74163.101 & 3016.330078125 & 49814.54 & 45738.248 & ... & 39669.9 & NA & 43989.302 & 37625.72 & 40222.65 & 30721.420 & NA & NA & NA & NA \\\\\n", "\t 71.0291595196379 & 42.6571151351351 & 65962.34 & 65913.74 & NA & 64503.55 & 69895.795 & 4892.2998046875 & 82516.56 & 99232.267 & ... & 82708.6 & NA & 81157.522 & 65459.08 & 83830.99 & 101986.417 & NA & NA & NA & NA \\\\\n", "\t 73.5318403862944 & 42.6571168421053 & 99931.16 & 106571.80 & 7529.31982421875 & 108559.14 & 110567.393 & 7592.02978515625 & 135933.80 & 159231.594 & ... & 133079.0 & NA & 124878.456 & 99401.78 & 124148.39 & 191129.926 & NA & NA & NA & NA \\\\\n", "\t 74.0236917045672 & 47.488775625 & 51416.41 & 55071.25 & NA & 52708.17 & 61861.690 & NA & 68932.41 & 76539.204 & ... & 71762.0 & NA & 49997.324 & 72008.24 & 79916.83 & 7039.101 & NA & NA & NA & NA \\\\\n", "\\end{tabular}\n" ], "text/plain": [ " mz rt Pool.01 Pool.02 Blank.02 \n", "1 62.0239318918682 42.65704875 17581.67 18392.75 NA \n", "2 62.0601584853407 47.4887748387097 11126.76 10157.55 NA \n", "3 64.0158880576918 45.5561178947368 63051.21 51084.02 5723.18994140625\n", "4 71.0291595196379 42.6571151351351 65962.34 65913.74 NA \n", "5 73.5318403862944 42.6571168421053 99931.16 106571.80 7529.31982421875\n", "6 74.0236917045672 47.488775625 51416.41 55071.25 NA \n", " H01.K1 L01.K2 Blank.03 L03.K3 C14.K2 ... L03.K1 \n", "1 22068.81 17150.960 NA 25555.81 28353.694 ... 22416.7\n", "2 13590.19 8189.214 NA 10383.43 8040.572 ... 10149.3\n", "3 29340.93 74163.101 3016.330078125 49814.54 45738.248 ... 39669.9\n", "4 64503.55 69895.795 4892.2998046875 82516.56 99232.267 ... 82708.6\n", "5 108559.14 110567.393 7592.02978515625 135933.80 159231.594 ... 133079.0\n", "6 52708.17 61861.690 NA 68932.41 76539.204 ... 71762.0\n", " Blank.10 Pool.06 L14.K1 L14.K2 H03.K3 Blank.11 Blank.01 H14.K1\n", "1 NA 24432.071 18230.74 24890.35 30802.503 NA NA NA \n", "2 NA 9258.323 11496.28 10159.70 9781.033 NA NA NA \n", "3 NA 43989.302 37625.72 40222.65 30721.420 NA NA NA \n", "4 NA 81157.522 65459.08 83830.99 101986.417 NA NA NA \n", "5 NA 124878.456 99401.78 124148.39 191129.926 NA NA NA \n", "6 NA 49997.324 72008.24 79916.83 7039.101 NA NA NA \n", " Blank.05\n", "1 NA \n", "2 NA \n", "3 NA \n", "4 NA \n", "5 NA \n", "6 NA " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(full_range)\n", "head(full_range_norm$df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtering on pools, RSD and biological replicates" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70 number of features left" ] } ], "source": [ "full_range_filt <- QAFilter(full_range_norm$df)\n", "cat(nrow(full_range_filt), \"number of features left\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handle missing values" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "full_range_imputed <- missingValues(full_range_filt)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of detected features: 399" ] } ], "source": [ "cat(\"Number of detected features: \", nrow(full_range_imputed))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"Number of features detected in selected mass segment\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "low_mass <- sum(as.numeric(full_range_imputed$mz)<200)\n", "mid_mass <- sum(as.numeric(full_range_imputed$mz)>200 & as.numeric(full_range_imputed$mz)<400)\n", "high_mass <- sum(as.numeric(full_range_imputed$mz)>400 & as.numeric(full_range_imputed$mz)<1000)\n", "\n", "data <- data.frame(x=c(\"50-200\", \"200-400\", \"400-1000\"), \n", " y=c(low_mass,mid_mass,high_mass))\n", "#data$y <- c(70,138,89)\n", "ylimit <- c(0, 1.2*max(data$y))\n", "par(oma=c(2,5,3,5))\n", "xx <- barplot(data$y, \n", " names.arg=data$x, \n", " ylim=ylimit,\n", " ylab=\"No. of features\", \n", " xlab=\"m/z range\",\n", " col=topo.colors(6))\n", "text(x=xx,\n", " y=data$y,\n", " label=data$y,\n", " pos = 3)\n", "title(\"Number of features detected in selected mass segment\", outer = TRUE)\n", "\n", "#pie(data$y, col=topo.colors(6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical Analysis" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stat_features <- statisticalAnalysis(full_range_imputed)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DRpjIZG9xT7NzoGNlLGSmVQdEALUADrN7qdf83LJYYeDiMBpGpWMGJhL13SZB2e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\nxY3kal1/jsShXTmgew03UpXhT9bQ7vHI01Urd7jTLzZwaOdoWl2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"text/plain": [ "Plot with title \"Volcano plots\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "source('~/Klinisk farm/R scripts/multiplot.R')\n", "FoldChange <- data.frame(stat_features[,grep(\"fc\", names(stat_features))])\n", "pvalues <- data.frame(stat_features[,grep(\"pval\", names(stat_features))])\n", "header <- gsub(\"_pval\", \"\",names(pvalues))\n", "par(mfrow=c(2,length(FoldChange)/2), oma= c(0,0,11,0), cex.main= 2)\n", "for (i in 1:length(FoldChange)) {\n", " plot(log2(FoldChange[,i]),\n", " -log10(pvalues[,i]),\n", " pch=20, \n", " xlim=c(-max(log2(FoldChange[,i])),max(log2(FoldChange[,i]))), \n", " main = header[i],\n", " cex.main= 1.5,\n", " xlab = \"log2(FoldChange)\", \n", " ylab = \"-log10(pvalue)\")\n", " \n", " fc <- which(abs(log2(FoldChange[,i]))>max(log2(FoldChange[,i]))/2)\n", " p <- which(-log10(pvalues[,i])>max(-log10(pvalues[,i]))/2)\n", " fc_p <- intersect(fc,p)\n", " \n", " points(log2(FoldChange[fc,i]), # orange = high fold change \n", " -log10(pvalues[fc,i]), \n", " col=\"orange\", pch=20)\n", " \n", " points(log2(FoldChange[p,i]), # red = low p-value\n", " -log10(pvalues[p,i]), \n", " col=\"red\", pch=20)\n", " \n", " points(log2(FoldChange[fc_p,i]), # green = high fold change and low p_value\n", " -log10(pvalues[fc_p,i]), \n", " col=\"darkgreen\", pch=20) \n", "}\n", "title(\"Volcano plots\", outer=TRUE) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Export result as .csv" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.csv(stat_features,\"C:\\\\Users\\\\Milos\\\\Desktop\\\\reproducing\\\\alternate\\\\realiable_features.csv\", row.names = F)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.1" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
dm-wyncode/zipped-code
content/posts/makefile-tutorial/makefile_tutorial_0.ipynb
1
35684
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning how to make a Makefile\n", "\n", "Adapted from [swcarpentry/make-novice repository](https://github.com/swcarpentry/make-novice).\n", "\n", "### Make’s fundamental concepts are common across build tools.\n", "\n", "> [GNU Make](http://www.gnu.org/software/make/) is a free, fast, well-documented, and very popular Make implementation. From now on, we will focus on it, and when we say Make, we mean GNU Make.\n", "\n", "### A tutorial named Introduction.\n", "\n", "Cells that follow are the result of following this [introduction](http://swcarpentry.github.io/make-novice/01-intro/).\n", "\n", "I have adapted the tutorial so that the steps take place in this Jupyter notebook so that the notebook can be transpiled into a Pelican blog post using a [danielfrg/pelican-ipynb Pelican plugin](https://github.com/danielfrg/pelican-ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " *Some Jupyter notebook housekeeping to set up some variables with path references.*" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(\n", " TAB_CHAR,\n", ") = (\n", " '\\t',\n", ")" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": true }, "outputs": [], "source": [ "home = os.path.expanduser('~')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*`repo_path` is the path to a clone of [swcarpentry/make-novice](https://github.com/swcarpentry/make-novice)*" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ "repo_path = os.path.join(\n", " home, \n", " 'Dropbox/spikes/make-novice',\n", ")" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert os.path.exists(repo_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*`paths` are the paths to child directories in a clone of [swcarpentry/make-novice](https://github.com/swcarpentry/make-novice)*" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": true }, "outputs": [], "source": [ "paths = (\n", " 'code',\n", " 'data',\n", ")\n", "paths = (\n", " code,\n", " data,\n", ") = [os.path.join(repo_path, path) for path in paths]\n", "assert all(os.path.exists(path) for path in paths)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Begin tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Use the magic `run` to execute the Python script `wordcount.py`.*\n", "\n", "*The variables with '$' in front of them are the values of the Python variables in this\n", "notebook.*" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [], "source": [ "run $code/wordcount.py $data/books/isles.txt $repo_path/isles.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Use shell to examine the first 5 lines of the output file from running `wordcount.py`*" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the 3822 6.7371760973\r\n", "of 2460 4.33632998414\r\n", "and 1723 3.03719372466\r\n", "to 1479 2.60708619778\r\n", "a 1308 2.30565838181\r\n" ] } ], "source": [ "!head -5 $repo_path/isles.dat" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "> We can see that the file consists of one row per word. Each row shows the word itself, the number of occurrences of that word, and the number of occurrences as a percentage of the total number of words in the text file." ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": true }, "outputs": [], "source": [ "run $code/wordcount.py $data/books/abyss.txt $repo_path/abyss.dat" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the 4044 6.35449402891\r\n", "and 2807 4.41074795726\r\n", "of 1907 2.99654305468\r\n", "a 1594 2.50471401634\r\n", "to 1515 2.38057825267\r\n" ] } ], "source": [ "!head -5 $repo_path/abyss.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Let’s visualize the results. The script plotcount.py reads in a data file and plots the 10 most frequently occurring words as a text-based bar plot:\n" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the ########################################################################\n", "of ##############################################\n", "and ################################\n", "to ############################\n", "a #########################\n", "in ###################\n", "is #################\n", "that ############\n", "by ###########\n", "it ###########\n" ] } ], "source": [ "run $code/plotcount.py $repo_path/isles.dat ascii" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> plotcount.py can also show the plot graphically" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb2fd010ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run $code/plotcount.py $repo_path/isles.dat show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> plotcount.py can also create the plot as an image file (e.g. a PNG file)" ] }, { "cell_type": "code", "execution_count": 180, "metadata": { "collapsed": false }, "outputs": [], "source": [ "run $code/plotcount.py $repo_path/isles.dat $repo_path/isles.png" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Import the objects necessary to display the generated png file in this notebook.*" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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I48ePExwcTGhoKGfPnmXo0KE8/fTTLFu2jAceeICHH36YKVOm0K1bNyZPnszixYv54osv\nWLx4MatXr2bdunWsXr2a/fv3M3bsWHbu3Inb7WbIkCGUlZVdNQqr+9vfkzsaHjGKiASqxo50BSKf\njcCOHj1KXl4eHo8Hj8fDww8/zPDhw0lKSiI3N5c5c+aQlpZGfn4+APn5+YwbNw673U7btm1ZvXo1\nAMnJyYwePZrk5GSCg4NZvHixDiGKiNyCfDYC8zWNwERErp+VRmA6E4eIiFiSAkxERCxJASYiIpak\nABMREUtSgImIiCUpwERExJIUYCIiYkkKMBERsSQFmIiIWJICTERELEkBJiIilqQAExERS1KAiYiI\nJSnARETEkhRgIiJiSQowERGxJAWYiIhYkgJMREQsSQEmIiKW5LMAc7lcDBo0iOTkZFJSUvjP//xP\nAObOnUtMTAzp6emkp6dTXFzsXWbevHnY7XaSkpLYtGmTt724uJjExETi4+OZP3++rzZBREQCiM0Y\nY3zRUWVlJZWVlTgcDs6cOUP37t0pLCxkzZo1hISEMG3atHr3P3DgAGPGjGH37t24XC4GDx5MWVkZ\nxhji4+PZvHkzUVFRZGZmsnr1ahITE+tvmM0G+GTTfoINH+1eEZEbwmazzvtWc191FBkZSWRkJACt\nW7cmKSkJt9sN0ODOKiwsJDc3l+bNm9OpUyfsdju7du3CGIPdbqdjx44A5ObmUlhYeFWAiYjIzc0v\nn4GVl5dTWlpKVlYWAIsWLcLhcDBp0iRqamoAcLvdxMbGepeJjo7G7XZf1R4TE+MNQhERuXX4PMDO\nnDnDqFGjWLhwIa1bt+bxxx/n0KFDlJaWEhkZyfTp04GGR2WNDW3rDheKiMitxGeHEAFqa2sZNWoU\n48aNY+TIkQDceeed3tsfe+wxRowYAdSNrCoqKry3uVwuoqKiMMZw+PDhq9ob9uxl1wdevIiIyCVO\npxOn0+nvMn4Wn03iABg/fjzt2rVjwYIF3rbKykrvZ2N/+tOf2L17NytXrmT//v2MHTuWnTt34na7\nGTJkCGVlZXg8HhISEti8eTMdOnSgR48erFq1iqSkpPobpkkcIiLXTZM4GrBt2zbeeecdUlJSSEtL\nw2az8cILL7By5UpKS0sJCgqiU6dOLFmyBIDk5GRGjx5NcnIywcHBLF68GJvNRrNmzXjllVfIycnB\n4/GQn59/VXgFjhYBcXgzIqIjlZXl/i5DROSG8ukIzJcCZQTm/xpAI0ERuVZWGoHpTBwiImJJCjAR\nEbEkBZiIiFiSAkxERCxJASYiIpakABMREUtSgImIiCUpwERExJIUYCIiYkkKMBERsSQFmIiIWJIC\nTERELEkBJiIilqQAExERS1KAiYiIJSnARETEkhRgIiJiSQowERGxJJ8FmMvlYtCgQSQnJ5OSksLL\nL78MQHV1NTk5OSQkJDB06FBqamq8yzz55JPY7XYcDgelpaXe9mXLlhEfH09CQgLLly/31SaIiEgA\nsRljjC86qqyspLKyEofDwZkzZ+jevTuFhYUsXbqUtm3bMmPGDObPn091dTUFBQVs2LCBV155haKi\nInbu3Mnvfvc7SkpKqK6uJiMjg71792KMoXv37uzdu5fQ0ND6G2azAT7ZtJ8QCDUA2PDRwywiFmez\nWef9wmcjsMjISBwOBwCtW7cmKSkJl8tFYWEheXl5AOTl5VFYWAhAYWEh48ePByArK4uamhqqqqrY\nuHEjOTk5hIaGEhYWRk5ODsXFxb7aDBERCRB++QysvLyc0tJSevbsSVVVFREREUBdyH377bcAuN1u\nYmNjvcvExMTgdruvao+Ojsbtdvt2A0RExO98HmBnzpxh1KhRLFy4kNatW1881He1K4ewxphGh7aN\nrUNERG5ezX3ZWW1tLaNGjWLcuHGMHDkSgIiICO8orLKykvbt2wN1I66Kigrvsi6Xi6ioKGJiYnA6\nnfXas7OzG+nx2cuuD7x4ERGRS5xOZ733VCvx2SQOgPHjx9OuXTsWLFjgbZs5cyZt2rRh5syZFBQU\ncOrUKQoKCli/fj2LFi2iqKiIkpISpk6detUkDo/HQ0ZGBnv27CEsLKz+hmkSx2Ws86GsiPiXlSZx\n+CzAtm3bRv/+/UlJScFms2Gz2XjhhRfo0aMHo0ePpqKigri4ON59911vGD3xxBMUFxfTqlUrli5d\nSnp6OgBvv/02zz//PDabjdmzZ3sne9TbMAXYZazzhBQR/1KABQAF2OWs84QUEf+yUoDpTBwiImJJ\nCjAREbEkBZiIiFiSAkxERCxJASYiIpakABMREUtSgImIiCUpwERExJIUYCIiYkkKMBERsSQFmIiI\nWJICTERELMmnvwcm/tLC7z/6GRHRkcrKcr/WICI3F52NvmmrCIAaIDDqsM4ZrkVuZTobvYiISBNT\ngImIiCUpwERExJIUYCIiYkkKMBERsSSfBVh+fj4RERGkpqZ62+bOnUtMTAzp6emkp6dTXFzsvW3e\nvHnY7XaSkpLYtGmTt724uJjExETi4+OZP3++r8oXEZEA47Np9Fu3bqV169aMHz+ezz//HKgLsJCQ\nEKZNm1bvvgcOHGDMmDHs3r0bl8vF4MGDKSsrwxhDfHw8mzdvJioqiszMTFavXk1iYuLVG6Zp9JcJ\nhDqsMzVX5FZmpWn0Pvsic9++ffnmm2+uam9oRxUWFpKbm0vz5s3p1KkTdrudXbt2YYzBbrfTsWNH\nAHJzcyksLGwwwERE5Obm98/AFi1ahMPhYNKkSdTU1ADgdruJjY313ic6Ohq3231Ve0xMDG632+c1\ni4iI//k1wB5//HEOHTpEaWkpkZGRTJ8+HWh4VNbYsNbfp0gSERH/8Ou5EO+8807v9ccee4wRI0YA\ndSOriooK720ul4uoqCiMMRw+fPiq9sY9e9n1gRcvIiJyidPpxOl0+ruMn8f40Ndff226du3q/fvo\n0aPe6wsWLDCPPPKIMcaYffv2GYfDYc6dO2e++uor07lzZ+PxeExtba3p3LmzKS8vN+fOnTPdunUz\n+/fvb7AvwIDx8yUQagiUOlpcrMO/l4iIjk36HBexOh/Hwi/isxHYmDFjcDqdnDhxgri4OObOncuW\nLVsoLS0lKCiITp06sWTJEgCSk5MZPXo0ycnJBAcHs3jxYmw2G82aNeOVV14hJycHj8dDfn4+SUlJ\nvtoE+UXOgd9nQkJVlQ45i9wsdDb6pq0iAGqAwKgjEGoATecX+WlWmkbv91mIIiIiP4cCTERELEkB\nJiIilqQAExERS1KAiYiIJSnARETEkhRgIiJiSX49lZSI77Xw+/kzIyI6UllZ7tcaRG4G+iJz01YR\nADVAYNQRCDVAYNRhnS+Kyq1HX2QWERFpYgowERGxJAWYiIhYkgJMREQsSQEmIiKWpAATERFLUoCJ\niIglKcBERMSSFGAiImJJCjAREbEknwVYfn4+ERERpKametuqq6vJyckhISGBoUOHUlNT473tySef\nxG6343A4KC0t9bYvW7aM+Ph4EhISWL58ua/KFxGRAOOzAJs4cSIbN26s11ZQUMDgwYM5ePAggwYN\nYt68eQBs2LCBQ4cOUVZWxpIlS5g8eTJQF3jPPfccu3fvZufOncydO7de6ImIyK3DZwHWt29fwsPD\n67UVFhaSl5cHQF5eHoWFhd728ePHA5CVlUVNTQ1VVVVs3LiRnJwcQkNDCQsLIycnh+LiYl9tgoiI\nBBC/fgb27bffEhERAUBkZCTffvstAG63m9jYWO/9YmJicLvdV7VHR0fjdrt9W7SIiASEgJzEceWp\n/I0xjZ7i39+/7SQiIv7h1x+0jIiIoKqqioiICCorK2nfvj1QN+KqqKjw3s/lchEVFUVMTAxOp7Ne\ne3Z29k/08Oxl1wdevIj4m/9/VBP0w5pSx+l01ntftRTjQ19//bXp2rWr9+8ZM2aYgoICY4wx8+bN\nMzNnzjTGGFNUVGSGDx9ujDFmx44dJisryxhjzMmTJ82vf/1rc+rUKe/16urqBvsCDBg/XwKhhkCp\nIxBqCJQ6AqGGujpErmSl54XPRmBjxozB6XRy4sQJ4uLimDt3Lk8//TQPPfQQb731FnFxcbz77rsA\nDB8+nPXr13P33XfTqlUrli5dCkB4eDhz5swhIyMDm83GM888Q1hYmK82QUREAojNGGP8XURTqDtE\n4+9NC4QaIDDqCIQaIDDqCIQaAKzz0/HiO43NNwhEATmJQ0RE5J9RgImIiCX5dRaiiPiT/2dDaiak\n/BL6DKxpqwiAGiAw6giEGiAw6giEGiAw6rDO5y23Cn0GJiIi0sQUYCIiYkkKMBERsSQFmIiIWJIC\nTERELEkBJiIilqQAExERS1KAiYiIJSnARETEkhRgIiJiSQowERGxJAWYiIhYks5GLyJ+5P8z4oPO\nim9VOht901YRADVAYNQRCDVAYNQRCDVAYNQRCDWAzor/D1Y6G71GYCIiATAS1Cjw+gXEZ2CdOnWi\nW7dupKWl0aNHDwCqq6vJyckhISGBoUOHUlNT473/k08+id1ux+FwUFpa6q+yReSmcY66kaD/LlVV\n3zT9Zt5kAiLAgoKCcDqdfPbZZ+zatQuAgoICBg8ezMGDBxk0aBDz5s0DYMOGDRw6dIiysjKWLFnC\n5MmT/Vm6iMgNUjcK9PfFSgIiwIwxeDyeem2FhYXk5eUBkJeXR2Fhobd9/PjxAGRlZVFTU0NVVZVv\nCxYRueH8PwoMjM8jr11ABJjNZmPo0KFkZmbyxhtvAFBVVUVERAQAkZGRfPvttwC43W5iY2O9y0ZH\nR+N2u31ftIiI+FVATOLYvn07kZGRHDt2zPu5V2ND2YZmx1ht2CsiIr9cQARYZGQkAHfeeSf/+q//\nyq5du4iIiPCOwio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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename=os.path.join(repo_path, 'isles.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Finally, let’s test Zipf’s law for these books\n", "\n", "> The most frequently-occurring word occurs approximately twice as often as the second most frequent word. This is [Zipf’s Law](http://en.wikipedia.org/wiki/Zipf%27s_law)." ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Book\tFirst\tSecond\tRatio\n", "/home/dmmmd/Dropbox/spikes/make-novice/abyss\t4044\t2807\t1.44\n", "/home/dmmmd/Dropbox/spikes/make-novice/isles\t3822\t2460\t1.55\n" ] } ], "source": [ "run $code/zipf_test.py $repo_path/abyss.dat $repo_path/isles.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " > What we really want is an executable description of our pipeline that allows software to do the tricky part for us: figuring out what steps need to be rerun." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Create a file, called Makefile, with the following contents.*\n", "\n", "*Python's built-in `format` is used to create the contents of the Makefile.*" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": true }, "outputs": [], "source": [ "makefile_contents = \"\"\"\n", "# Count words.\n", "{repo_path}/isles.dat : {data}/books/isles.txt\n", "{tab_char}python {code}/wordcount.py {data}/books/isles.txt {repo_path}/isles.dat\n", "\"\"\".format(code=code, data=data, repo_path=repo_path, tab_char=TAB_CHAR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Write the contents to a file named Makefile.*" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('Makefile', 'w') as fh:\n", " fh.write(makefile_contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Let’s first sure we start from scratch and delete the .dat and .png files we created earlier:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Run `rm` in shell.*" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!rm $repo_path/*.dat $repo_path/*.png" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Run `make` in shell.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> By default, Make prints out the actions it executes:" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "python /home/dmmmd/Dropbox/spikes/make-novice/code/wordcount.py /home/dmmmd/Dropbox/spikes/make-novice/data/books/isles.txt /home/dmmmd/Dropbox/spikes/make-novice/isles.dat\r\n" ] } ], "source": [ "!make" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Let’s see if we got what we expected." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Run `head` in shell.*" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the 3822 6.7371760973\r\n", "of 2460 4.33632998414\r\n", "and 1723 3.03719372466\r\n", "to 1479 2.60708619778\r\n", "a 1308 2.30565838181\r\n" ] } ], "source": [ "!head -5 $repo_path/isles.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple Makefile was created. If the dependencies exist, the commands are not run.\n", "\n", "> Unlike shell scripts it explicitly records the dependencies between files - what files are needed to create what other files - and so can determine when to recreate our data files or image files, if our text files change. Make can be used for any commands that follow the general pattern of processing files to create new files…\n", "\n", "[tutorial continues: Makefiles]({filename}./makefile_tutorial_1.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
kubeflow/pipelines
samples/core/lightweight_component/lightweight_component.ipynb
1
8614
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lightweight python components\n", "\n", "Lightweight python components do not require you to build a new container image for every code change.\n", "They're intended to use for fast iteration in notebook environment.\n", "\n", "#### Building a lightweight python component\n", "To build a component just define a stand-alone python function and then call kfp.components.func_to_container_op(func) to convert it to a component that can be used in a pipeline.\n", "\n", "There are several requirements for the function:\n", "* The function should be stand-alone. It should not use any code declared outside of the function definition. Any imports should be added inside the main function. Any helper functions should also be defined inside the main function.\n", "* The function can only import packages that are available in the base image. If you need to import a package that's not available you can try to find a container image that already includes the required packages. (As a workaround you can use the module subprocess to run pip install for the required package. There is an example below in my_divmod function.)\n", "* If the function operates on numbers, the parameters need to have type hints. Supported types are ```[int, float, bool]```. Everything else is passed as string.\n", "* To build a component with multiple output values, use the typing.NamedTuple type hint syntax: ```NamedTuple('MyFunctionOutputs', [('output_name_1', type), ('output_name_2', float)])```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "skip-in-test" ] }, "outputs": [], "source": [ "# Install the SDK\n", "!pip3 install 'kfp>=0.1.31.2' --quiet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import kfp.deprecated as kfp\n", "import kfp.deprecated.components as components" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple function that just add two numbers:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Define a Python function\n", "def add(a: float, b: float) -> float:\n", " '''Calculates sum of two arguments'''\n", " return a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert the function to a pipeline operation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "add_op = components.create_component_from_func(add)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A bit more advanced function which demonstrates how to use imports, helper functions and produce multiple outputs." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Advanced function\n", "#Demonstrates imports, helper functions and multiple outputs\n", "from typing import NamedTuple\n", "def my_divmod(dividend: float, divisor:float) -> NamedTuple('MyDivmodOutput', [('quotient', float), ('remainder', float), ('mlpipeline_ui_metadata', 'UI_metadata'), ('mlpipeline_metrics', 'Metrics')]):\n", " '''Divides two numbers and calculate the quotient and remainder'''\n", " \n", " #Imports inside a component function:\n", " import numpy as np\n", "\n", " #This function demonstrates how to use nested functions inside a component function:\n", " def divmod_helper(dividend, divisor):\n", " return np.divmod(dividend, divisor)\n", "\n", " (quotient, remainder) = divmod_helper(dividend, divisor)\n", "\n", " from tensorflow.python.lib.io import file_io\n", " import json\n", " \n", " # Exports a sample tensorboard:\n", " metadata = {\n", " 'outputs' : [{\n", " 'type': 'tensorboard',\n", " 'source': 'gs://ml-pipeline-dataset/tensorboard-train',\n", " }]\n", " }\n", "\n", " # Exports two sample metrics:\n", " metrics = {\n", " 'metrics': [{\n", " 'name': 'quotient',\n", " 'numberValue': float(quotient),\n", " },{\n", " 'name': 'remainder',\n", " 'numberValue': float(remainder),\n", " }]}\n", "\n", " from collections import namedtuple\n", " divmod_output = namedtuple('MyDivmodOutput', ['quotient', 'remainder', 'mlpipeline_ui_metadata', 'mlpipeline_metrics'])\n", " return divmod_output(quotient, remainder, json.dumps(metadata), json.dumps(metrics))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test running the python function directly" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "skip-in-test" ] }, "outputs": [ { "data": { "text/plain": [ "MyDivmodOutput(quotient=14, remainder=2)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_divmod(100, 7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convert the function to a pipeline operation\n", "\n", "You can specify an alternative base container image (the image needs to have Python 3.5+ installed)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "divmod_op = components.create_component_from_func(my_divmod, base_image='tensorflow/tensorflow:1.11.0-py3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define the pipeline\n", "Pipeline function has to be decorated with the `@dsl.pipeline` decorator" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import kfp.deprecated.dsl as dsl\n", "@dsl.pipeline(\n", " name='calculation-pipeline',\n", " description='A toy pipeline that performs arithmetic calculations.'\n", ")\n", "def calc_pipeline(\n", " a=7,\n", " b=8,\n", " c=17,\n", "):\n", " #Passing pipeline parameter and a constant value as operation arguments\n", " add_task = add_op(a, 4) #Returns a dsl.ContainerOp class instance. \n", " \n", " #Passing a task output reference as operation arguments\n", " #For an operation with a single return value, the output reference can be accessed using `task.output` or `task.outputs['output_name']` syntax\n", " divmod_task = divmod_op(add_task.output, b)\n", "\n", " #For an operation with a multiple return values, the output references can be accessed using `task.outputs['output_name']` syntax\n", " result_task = add_op(divmod_task.outputs['quotient'], c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Submit the pipeline for execution" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "skip-in-test" ] }, "outputs": [], "source": [ "#Specify pipeline argument values\n", "arguments = {'a': 7, 'b': 8}\n", "\n", "#Submit a pipeline run\n", "kfp.Client().create_run_from_pipeline_func(calc_pipeline, arguments=arguments)\n", "\n", "# Run the pipeline on a separate Kubeflow Cluster instead\n", "# (use if your notebook is not running in Kubeflow - e.x. if using AI Platform Notebooks)\n", "# kfp.Client(host='<ADD KFP ENDPOINT HERE>').create_run_from_pipeline_func(calc_pipeline, arguments=arguments)\n", "\n", "#vvvvvvvvv This link leads to the run information page. (Note: There is a bug in JupyterLab that modifies the URL and makes the link stop working)" ] } ], "metadata": { "interpreter": { "hash": "c7a91a0fef823c7f839350126c5e355ea393d05f89cb40a046ebac9c8851a521" }, "kernelspec": { "display_name": "Python 3.7.10 64-bit ('v2': conda)", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
InsightLab/data-science-cookbook
2019/12-spark/14-spark-mllib-classification/mllibClass_OtacilioBezerra.ipynb
1
16186
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hands-on!\n", "\n", "Nessa prática, sugerimos alguns pequenos exemplos para você implementar sobre o Spark." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Logistic Regression com Cross-Validation\n", "\n", "No exercício `LogisticRegression` foi utilizado `TrainValidationSplit` como abordagem de avaliação do modelo gerado. Atualize o exercício consideram `CrossValidator` e compare os resultados. Não esqueça de utilizar `Pipeline`.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bibliotecas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml.classification import LogisticRegression\n", "from pyspark.ml.evaluation import RegressionEvaluator, MulticlassClassificationEvaluator\n", "from pyspark.ml import Pipeline\n", "from pyspark.mllib.regression import LabeledPoint\n", "from pyspark.ml.linalg import Vectors\n", "from pyspark.ml.feature import StringIndexer\n", "from pyspark.mllib.evaluation import MulticlassMetrics\n", "from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit, CrossValidator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Funções" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mapLibSVM(row): \n", " return (row[5],Vectors.dense(row[:3]))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = spark.read \\\n", " .format(\"csv\") \\\n", " .option(\"header\", \"true\") \\\n", " .option(\"inferSchema\", \"true\") \\\n", " .load(\"datasets/iris.data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convertendo a saída de categórica para numérica" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+------------+-----------+-----------+----------+\n", "|sepal_length|sepal_width|petal_length|petal_width| label|labelIndex|\n", "+------------+-----------+------------+-----------+-----------+----------+\n", "| 5.1| 3.5| 1.4| 0.2|Iris-setosa| 0.0|\n", "| 4.9| 3.0| 1.4| 0.2|Iris-setosa| 0.0|\n", "| 4.7| 3.2| 1.3| 0.2|Iris-setosa| 0.0|\n", "| 4.6| 3.1| 1.5| 0.2|Iris-setosa| 0.0|\n", "| 5.0| 3.6| 1.4| 0.2|Iris-setosa| 0.0|\n", "| 5.4| 3.9| 1.7| 0.4|Iris-setosa| 0.0|\n", "| 4.6| 3.4| 1.4| 0.3|Iris-setosa| 0.0|\n", "| 5.0| 3.4| 1.5| 0.2|Iris-setosa| 0.0|\n", "| 4.4| 2.9| 1.4| 0.2|Iris-setosa| 0.0|\n", "| 4.9| 3.1| 1.5| 0.1|Iris-setosa| 0.0|\n", "| 5.4| 3.7| 1.5| 0.2|Iris-setosa| 0.0|\n", "| 4.8| 3.4| 1.6| 0.2|Iris-setosa| 0.0|\n", "| 4.8| 3.0| 1.4| 0.1|Iris-setosa| 0.0|\n", "| 4.3| 3.0| 1.1| 0.1|Iris-setosa| 0.0|\n", "| 5.8| 4.0| 1.2| 0.2|Iris-setosa| 0.0|\n", "| 5.7| 4.4| 1.5| 0.4|Iris-setosa| 0.0|\n", "| 5.4| 3.9| 1.3| 0.4|Iris-setosa| 0.0|\n", "| 5.1| 3.5| 1.4| 0.3|Iris-setosa| 0.0|\n", "| 5.7| 3.8| 1.7| 0.3|Iris-setosa| 0.0|\n", "| 5.1| 3.8| 1.5| 0.3|Iris-setosa| 0.0|\n", "+------------+-----------+------------+-----------+-----------+----------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "indexer = StringIndexer(inputCol=\"label\", outputCol=\"labelIndex\")\n", "indexer = indexer.fit(df).transform(df)\n", "indexer.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----+-------------+\n", "|label| features|\n", "+-----+-------------+\n", "| 0.0|[5.1,3.5,1.4]|\n", "| 0.0|[4.9,3.0,1.4]|\n", "| 0.0|[4.7,3.2,1.3]|\n", "| 0.0|[4.6,3.1,1.5]|\n", "| 0.0|[5.0,3.6,1.4]|\n", "| 0.0|[5.4,3.9,1.7]|\n", "| 0.0|[4.6,3.4,1.4]|\n", "| 0.0|[5.0,3.4,1.5]|\n", "| 0.0|[4.4,2.9,1.4]|\n", "| 0.0|[4.9,3.1,1.5]|\n", "| 0.0|[5.4,3.7,1.5]|\n", "| 0.0|[4.8,3.4,1.6]|\n", "| 0.0|[4.8,3.0,1.4]|\n", "| 0.0|[4.3,3.0,1.1]|\n", "| 0.0|[5.8,4.0,1.2]|\n", "| 0.0|[5.7,4.4,1.5]|\n", "| 0.0|[5.4,3.9,1.3]|\n", "| 0.0|[5.1,3.5,1.4]|\n", "| 0.0|[5.7,3.8,1.7]|\n", "| 0.0|[5.1,3.8,1.5]|\n", "+-----+-------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "dfLabeled = indexer.rdd.map(mapLibSVM).toDF([\"label\", \"features\"])\n", "dfLabeled.show()\n", "\n", "train, test = dfLabeled.randomSplit([0.9, 0.1], seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definição do Modelo Logístico" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lr = LogisticRegression(labelCol=\"label\", maxIter=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross-Validation - TrainValidationSplit e CrossValidator" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "paramGrid = ParamGridBuilder()\\\n", " .addGrid(lr.regParam, [0.1, 0.001]) \\\n", " .build()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tvs = TrainValidationSplit(estimator=lr,\n", " estimatorParamMaps=paramGrid,\n", " evaluator=MulticlassClassificationEvaluator(),\n", " trainRatio=0.8)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cval = CrossValidator(estimator=lr,\n", " estimatorParamMaps=paramGrid,\n", " evaluator=MulticlassClassificationEvaluator(),\n", " numFolds=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Treino do Modelo e Predição do Teste" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result_tvs = tvs.fit(train).transform(test)\n", "result_cval = cval.fit(train).transform(test)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "preds_tvs = result_tvs.select([\"prediction\", \"label\"])\n", "preds_cval = result_cval.select([\"prediction\", \"label\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Avaliação dos Modelos" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Instânciação dos Objetos de Métrics\n", "metrics_tvs = MulticlassMetrics(preds_tvs.rdd)\n", "metrics_cval = MulticlassMetrics(preds_cval.rdd)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary Stats\n", "/home/minhotmog/spark-2.2.0-bin-hadoop2.7/python/pyspark/mllib/evaluation.py:262: UserWarning: Deprecated in 2.0.0. Use accuracy.\n", " warnings.warn(\"Deprecated in 2.0.0. Use accuracy.\")\n", "F1 Score = 0.9090909090909091\n", "Accuracy = 0.9090909090909091\n", "Weighted recall = 0.9090909090909092\n", "Weighted precision = 0.9242424242424243\n", "Weighted F(1) Score = 0.8980716253443526\n", "Weighted F(0.5) Score = 0.9070010449320796\n", "Weighted false positive rate = 0.07575757575757575\n" ] } ], "source": [ "# Estatísticas Gerais para o Método TrainValidationSplit\n", "print(\"Summary Stats\")\n", "print(\"F1 Score = %s\" % metrics_tvs.fMeasure())\n", "print(\"Accuracy = %s\" % metrics_tvs.accuracy)\n", "print(\"Weighted recall = %s\" % metrics_tvs.weightedRecall)\n", "print(\"Weighted precision = %s\" % metrics_tvs.weightedPrecision)\n", "print(\"Weighted F(1) Score = %s\" % metrics_tvs.weightedFMeasure())\n", "print(\"Weighted F(0.5) Score = %s\" % metrics_tvs.weightedFMeasure(beta=0.5))\n", "print(\"Weighted false positive rate = %s\" % metrics_tvs.weightedFalsePositiveRate)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary Stats\n", "F1 Score = 0.9090909090909091\n", "Accuracy = 0.9090909090909091\n", "Weighted recall = 0.9090909090909092\n", "Weighted precision = 0.9242424242424243\n", "Weighted F(1) Score = 0.8980716253443526\n", "Weighted F(0.5) Score = 0.9070010449320796\n", "Weighted false positive rate = 0.07575757575757575\n" ] } ], "source": [ "# Estatísticas Gerais para o Método TrainValidationSplit\n", "print(\"Summary Stats\")\n", "print(\"F1 Score = %s\" % metrics_cval.fMeasure())\n", "print(\"Accuracy = %s\" % metrics_cval.accuracy)\n", "print(\"Weighted recall = %s\" % metrics_cval.weightedRecall)\n", "print(\"Weighted precision = %s\" % metrics_cval.weightedPrecision)\n", "print(\"Weighted F(1) Score = %s\" % metrics_cval.weightedFMeasure())\n", "print(\"Weighted F(0.5) Score = %s\" % metrics_cval.weightedFMeasure(beta=0.5))\n", "print(\"Weighted false positive rate = %s\" % metrics_cval.weightedFalsePositiveRate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusão:\n", "\n", "Uma vez que ambos os modelos de CrossValidation usam o mesmo modelo de predição (a Regressão Logística), e contando com o fato de que o dataset é relativamente pequeno, é natural que ambos os métodos de CrossValidation encontrem o mesmo (ou aproximadamente igual) valor ótimo para os hyperparâmetros testados.\n", "\n", "Por esse motivo, após descobrirem esse valor de hiperparâmetros, os dois modelos irão demonstrar resultados bastante similiares quando avaliados sobre o Conjunto de Treino (que também é o mesmo para os dois modelos).\n", "\n", "-------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest\n", "\n", "Use o exercício anterior como base, mas agora utilizando `pyspark.ml.classification.RandomForestClassifier`. Use `Pipeline` e `CrossValidator` para avaliar o modelo gerado." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Bibliotecas" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml.classification import RandomForestClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definição do Modelo de Árvores Randômicas" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rf = RandomForestClassifier(labelCol=\"label\", featuresCol=\"features\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross-Validation - CrossValidator" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "paramGrid = ParamGridBuilder()\\\n", " .addGrid(rf.numTrees, [1, 100]) \\\n", " .build()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cval = CrossValidator(estimator=rf,\n", " estimatorParamMaps=paramGrid,\n", " evaluator=MulticlassClassificationEvaluator(),\n", " numFolds=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Treino do Modelo e Predição do Teste" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results = cval.fit(train).transform(test)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "predictions = results.select([\"prediction\", \"label\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Avaliação do Modelo" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary Stats\n", "F1 Score = 1.0\n", "Accuracy = 1.0\n", "Weighted recall = 1.0\n", "Weighted precision = 1.0\n", "Weighted F(1) Score = 1.0\n", "Weighted F(0.5) Score = 1.0\n", "Weighted false positive rate = 0.0\n" ] } ], "source": [ "# Instânciação dos Objetos de Métrics\n", "metrics = MulticlassMetrics(predictions.rdd)\n", "\n", "# Estatísticas Gerais para o Método TrainValidationSplit\n", "print(\"Summary Stats\")\n", "print(\"F1 Score = %s\" % metrics.fMeasure())\n", "print(\"Accuracy = %s\" % metrics.accuracy)\n", "print(\"Weighted recall = %s\" % metrics.weightedRecall)\n", "print(\"Weighted precision = %s\" % metrics.weightedPrecision)\n", "print(\"Weighted F(1) Score = %s\" % metrics.weightedFMeasure())\n", "print(\"Weighted F(0.5) Score = %s\" % metrics.weightedFMeasure(beta=0.5))\n", "print(\"Weighted false positive rate = %s\" % metrics.weightedFalsePositiveRate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusão:\n", "\n", "Uma vez que o RandomForest é um classificador relatiamente robusto, e o Iris é um problema relativamente simples, é notável que esse modelo é suficientemente capaz de perfeitamente predizer observações desse dataset." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Apache Toree - PySpark", "language": "python", "name": "apache_toree_pyspark" }, "language_info": { "codemirror_mode": "text/x-ipython", "file_extension": ".py", "mimetype": "text/x-ipython", "name": "python", "pygments_lexer": "python", "version": "3.5.2\n" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
pcm-ca/pcm-ca.github.io
pages/informatication/extra-files/codes/notebooks/Ajustes avanzados.ipynb
1
397379
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Ejemplo 1 - Funcion exponencial" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a130ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from IPython.display import set_matplotlib_formats\n", "set_matplotlib_formats('png', 'pdf')\n", "\n", "x = np.linspace(0.1, 10)\n", "y = 0.3 * x ** 1.3\n", "y += 0.001 * y * np.random.chisquare(3, size=y.shape)\n", "\n", "plt.figure()\n", "plt.scatter(x, y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107b68da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = np.log(x)\n", "Y = np.log(y)\n", "\n", "plt.figure()\n", "plt.scatter(X, Y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Si, $$ y = Ax^n $$ entonces $$ \\log y = \\log A + n \\log x $$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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KZW5kc3RyZWFtCmVuZG9iagoyMiAwIG9iago8PCAvTGVuZ3RoIDMzOCAvRmlsdGVyIC9G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\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a408cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import linregress\n", "\n", "pendiente, intercepto, *otros = linregress(X, Y)\n", "\n", "plt.figure()\n", "plt.plot(X, Y, 'o')\n", "plt.plot(X, pendiente * X + intercepto)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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PLa9HzxG3LQoEURM9+DInFSLUz9ToOnhhlz4DrxBOKRZ4B5MABq/hX3iUToPA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\nCiUlRU9GCg==\n", 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DqFKlCo0aNTrr/ry8PFq3bk3Tpk1p2LDhKY/MAFi1ahV169bl8ssvZ+LEid7ts2bN4v77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\nhbN+Ovy+Hnq8DhVrhToaEYOUUm8DtwB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"text/plain": [ "<matplotlib.figure.Figure at 0x10be8a6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.subplot(121)\n", "plt.plot(X, Y, 'o', label='datos linealizados')\n", "plt.plot(X, pendiente * X + intercepto,\n", " label=f'$Y = {pendiente:.2f} X {intercepto:+.2f}$')\n", "plt.xlabel(r'$\\log x$')\n", "plt.ylabel(r'$\\log y$')\n", "plt.grid()\n", "plt.legend()\n", "\n", "# Calculamos A y n\n", "\n", "A = np.exp(intercepto)\n", "n = pendiente\n", "\n", "plt.subplot(122)\n", "\n", "plt.plot(x, y, 'o', label='datos')\n", "plt.plot(x, A * x ** n,\n", " label=f'$y = {A:.2f}x^{{{n:.2f}}}$')\n", "plt.xlabel('$x$')\n", "plt.ylabel('$y$')\n", "plt.grid()\n", "plt.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Ejemplo 2 - Funcion cuadratica" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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E8SphFtWDTZ41qYRmtI5jZMN498JMiYWGwxJQm32VCaqXj9PcCSOmR0127cKyWzbvIUSj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\nUiA+PgpzdGFydHhyZWYKODY1NQolJUVPRgo=\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x109585358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.linspace(0, 10)\n", "y = 3.1 * x ** 2\n", "y += np.random.normal(size=y.shape)\n", "\n", "plt.figure()\n", "plt.plot(x, y, 'o')\n", "plt.xlabel('$x$')\n", "plt.ylabel('$y$')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\\ {intercepto}\n", "---\n", "\\n\\ {intercepto}\n", "---\n", "\n", "\\ -1.2012880167557538\n", "---\n", "\\n\\ -1.2012880167557538\n" ] } ], "source": [ "# para que es la r?\n", "print('\\n\\ {intercepto}')\n", "print('---')\n", "print(r'\\n\\ {intercepto}')\n", "print('---')\n", "print(f'\\n\\ {intercepto}')\n", "print('---')\n", "print(rf'\\n\\ {intercepto}')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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lbmd0aCAzMTcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVJLckMxCNu/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\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x10be5d278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = x ** 2\n", "Y = y\n", "\n", "plt.figure()\n", "plt.scatter(X, Y)\n", "plt.xlabel('$x^2$')\n", "plt.ylabel('$y$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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yJxUi1M/U6Dp4YZc+A68QTikWeAeTAAav4V94lE6DwDsbMt4Rk5EaECTBmkuL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\nIC9JbmZvIDUzIDAgUiA+PgpzdGFydHhyZWYKMTY5NDgKJSVFT0YK\n", 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mtGHswu3EJiT7PBafXgellKoBNAN+BtoAjymlBgCbyO0dniA36TbmeVoS+SSi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\nnxidPO2hdg2FEKEkxZZJOU4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"text/plain": [ "<matplotlib.figure.Figure at 0x10c114048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import linregress\n", "\n", "m, b, *otros = linregress(X, Y)\n", "\n", "plt.figure()\n", "plt.subplot(121)\n", "plt.plot(X, Y, 'o', label='datos linealizados')\n", "plt.plot(X, m * X + b, label=f'$Y = {m:.2f} X {b:+.2f}$')\n", "plt.grid()\n", "plt.legend()\n", "plt.xlabel('$x^2$')\n", "plt.ylabel('$y$')\n", "\n", "# A -> la pendiente\n", "\n", "A = m\n", "\n", "plt.subplot(122)\n", "plt.plot(x, y, 'o', label='datos originales')\n", "plt.plot(x, A * x ** 2, label=f'y = {A:.2f} x ^ 2')\n", "plt.grid()\n", "plt.legend()\n", "plt.xlabel('$x$')\n", "plt.ylabel('$y$')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Ajuste polinomial" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNUbttRDEM698UXOAA62t5ngtSXfZvQ8kIkMIg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\nPj4Kc3RhcnR4cmVmCjk5MjEKJSVFT0YK\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a21c470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.linspace(-10, 10)\n", "y = 3 * x ** 2 - 5 * x + 3\n", "y += np.random.normal(0, 5, size=y.shape)\n", "\n", "plt.figure()\n", "plt.scatter(x, y)\n", "plt.grid()\n", "plt.title('$y = a x ^ 2 + b x + c$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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/1rjH6wOHilL0CmVuZHN0cmVhbQplbmRvYmoKMzUgMCBvYmoKPDwgL0xlbmd0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\nOAolJUVPRgo=\n", 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88mtXwsnmHstiUtMyGTMzSZO9Clrh4eEcPnxYk30lZ4zh8OHDZepC6tXJWBGxAOuAi4B3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\neqWUCnKa6JVSKshpoldKqSD3/3ijaYUOgFLTAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a402be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coeficientes = np.polyfit(x, y, 2)\n", "\n", "plt.figure()\n", "plt.plot(x, y, 'o', label='datos originales')\n", "plt.plot(x, np.polyval(coeficientes, x), label='ajuste')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Ajuste con funcion objetivo" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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MqSU/6pKIM0x+9XJd4lHyvWxqZ+Yh7i42pvhYcl+6hthy0ZpisU8cyS/ItFRYoVbdo0Px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\nL0luZm8gMzQgMCBSID4+CnN0YXJ0eHJlZgo5MzQ3CiUlRU9GCg==\n", 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AAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c46aa58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.linspace(0.1, 10)\n", "y = 3 * x ** 2 + x ** 3.4\n", "y += np.random.normal(0, 5, size=y.shape)\n", "\n", "plt.figure()\n", "plt.scatter(x, y)\n", "plt.grid()\n", "plt.title('$y = a x ^ 2 + x ^ n$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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jb2RlID4+CnN0cmVhbQp4nEWQuRFDMQhEc1VBCRKwCOqxx9F3/6kX+Uq0bwAt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\n", 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KKVW9jIEVT0Dt8+Dye/xdm3PSBKCUUtVpz3L4aS0kToSo2v6uzTlpAlBKqericcPKKdCg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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a410208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def objetivo(x, a, n):\n", " return a * x ** 2 + x ** n\n", "\n", "from scipy.optimize import curve_fit\n", "\n", "popt, pcov = curve_fit(objetivo, x, y, p0=(1, 1))\n", "\n", "plt.figure()\n", "plt.plot(x, y, 'o', label='datos originales')\n", "plt.plot(x, objetivo(x, *popt), label='ajuste')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/pdf": 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wZXJpb2QgMTcgMCBSIC96ZXJvIDE4IDAgUiAvb25lIDE5IDAgUiAvdHdvIDIx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\nIDM3IDAgUiA+PgpzdGFydHhyZWYKMTE4NDcKJSVFT0YK\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x10bfc3d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def gausiana(x, amp, mu, sigma):\n", " return amp * np.exp(-(x - mu)**2 / sigma ** 2)\n", "\n", "x = np.linspace(-10, 10, 200)\n", "y = gausiana(x, 10, 3, 1)\n", "y += np.random.normal(0, 0.1, size=y.shape)\n", "\n", "plt.figure()\n", "plt.scatter(x, y)\n", "plt.grid()\n", "plt.title('$gausiana$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 10.05264138 2.9965827 1.000441 ]\n" ] }, { "data": { "application/pdf": 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XBU/QZFCWPe8moB0X9N/Vjd9JNIteAjKRYGGdJObOWU741WtHx1GLIjEnpBnk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\nID4+CnN0YXJ0eHJlZgoxMjkwMAolJUVPRgo=\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a3bd2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "popt, pcov = curve_fit(gausiana, x, y)\n", "\n", "print(popt)\n", "\n", "plt.figure()\n", "plt.plot(x, y, 'o', label='datos originales')\n", "plt.plot(x, gausiana(x, *popt), label='ajuste')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
pgmpy/pgmpy
examples/Structure Learning in Bayesian Networks.ipynb
2
13171
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Structure Learning in Bayesian Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we show examples for using the Structure Learning Algorithms in pgmpy. Currently, pgmpy has implementation of 3 main algorithms:\n", "1. PC with stable and parallel variants.\n", "2. Hill-Climb Search\n", "3. Exhaustive Search\n", "\n", "For PC the following conditional independence test can be used:\n", "1. Chi-Square test (https://en.wikipedia.org/wiki/Chi-squared_test)\n", "2. Pearsonr (https://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression)\n", "3. G-squared (https://en.wikipedia.org/wiki/G-test)\n", "4. Log-likelihood (https://en.wikipedia.org/wiki/G-test)\n", "5. Freeman-Tuckey (Read, Campbell B. \"Freeman—Tukey chi-squared goodness-of-fit statistics.\" Statistics & probability letters 18.4 (1993): 271-278.)\n", "6. Modified Log-likelihood\n", "7. Neymann (https://en.wikipedia.org/wiki/Neyman%E2%80%93Pearson_lemma)\n", "8. Cressie Read (Cressie, Noel, and Timothy RC Read. \"Multinomial goodness‐of‐fit tests.\" Journal of the Royal Statistical Society: Series B (Methodological) 46.3 (1984): 440-464)\n", "9. Power Divergence (Cressie, Noel, and Timothy RC Read. \"Multinomial goodness‐of‐fit tests.\" Journal of the Royal Statistical Society: Series B (Methodological) 46.3 (1984): 440-464.)\n", "\n", "For Hill-Climb and Exhausitive Search the following scoring methods can be used:\n", "1. K2 Score\n", "2. BDeu Score\n", "3. Bic Score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate some data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from itertools import combinations\n", "\n", "import networkx as nx\n", "from sklearn.metrics import f1_score\n", "\n", "from pgmpy.estimators import PC, HillClimbSearch, ExhaustiveSearch\n", "from pgmpy.estimators import K2Score\n", "from pgmpy.utils import get_example_model\n", "from pgmpy.sampling import BayesianModelSampling" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating for node: CVP: 100%|██████████| 37/37 [00:00<00:00, 544.13it/s]\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>HISTORY</th>\n", " <th>CVP</th>\n", " <th>PCWP</th>\n", " <th>HYPOVOLEMIA</th>\n", " <th>LVEDVOLUME</th>\n", " <th>LVFAILURE</th>\n", " <th>STROKEVOLUME</th>\n", " <th>ERRLOWOUTPUT</th>\n", " <th>HRBP</th>\n", " <th>HREKG</th>\n", " <th>...</th>\n", " <th>MINVOLSET</th>\n", " <th>VENTMACH</th>\n", " <th>VENTTUBE</th>\n", " <th>VENTLUNG</th>\n", " <th>VENTALV</th>\n", " <th>ARTCO2</th>\n", " <th>CATECHOL</th>\n", " <th>HR</th>\n", " <th>CO</th>\n", " <th>BP</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>TRUE</td>\n", " <td>LOW</td>\n", " <td>LOW</td>\n", " <td>FALSE</td>\n", " <td>LOW</td>\n", " <td>TRUE</td>\n", " <td>LOW</td>\n", " <td>FALSE</td>\n", " <td>HIGH</td>\n", " <td>NORMAL</td>\n", " <td>...</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>LOW</td>\n", " <td>ZERO</td>\n", " <td>ZERO</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>LOW</td>\n", " <td>LOW</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>...</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>LOW</td>\n", " <td>ZERO</td>\n", " <td>LOW</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>...</td>\n", " <td>LOW</td>\n", " <td>LOW</td>\n", " <td>ZERO</td>\n", " <td>ZERO</td>\n", " <td>ZERO</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>HIGH</td>\n", " <td>FALSE</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>...</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>LOW</td>\n", " <td>HIGH</td>\n", " <td>LOW</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>NORMAL</td>\n", " <td>FALSE</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>...</td>\n", " <td>NORMAL</td>\n", " <td>NORMAL</td>\n", " <td>ZERO</td>\n", " <td>HIGH</td>\n", " <td>LOW</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>HIGH</td>\n", " <td>LOW</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 37 columns</p>\n", "</div>" ], "text/plain": [ " HISTORY CVP PCWP HYPOVOLEMIA LVEDVOLUME LVFAILURE STROKEVOLUME \\\n", "0 TRUE LOW LOW FALSE LOW TRUE LOW \n", "1 FALSE NORMAL NORMAL FALSE NORMAL FALSE NORMAL \n", "2 FALSE NORMAL NORMAL FALSE NORMAL FALSE NORMAL \n", "3 FALSE NORMAL NORMAL FALSE NORMAL FALSE HIGH \n", "4 FALSE NORMAL NORMAL FALSE NORMAL FALSE NORMAL \n", "\n", " ERRLOWOUTPUT HRBP HREKG ... MINVOLSET VENTMACH VENTTUBE VENTLUNG \\\n", "0 FALSE HIGH NORMAL ... NORMAL NORMAL LOW ZERO \n", "1 FALSE HIGH HIGH ... NORMAL NORMAL LOW ZERO \n", "2 FALSE HIGH HIGH ... LOW LOW ZERO ZERO \n", "3 FALSE HIGH HIGH ... HIGH HIGH HIGH LOW \n", "4 FALSE HIGH HIGH ... NORMAL NORMAL ZERO HIGH \n", "\n", " VENTALV ARTCO2 CATECHOL HR CO BP \n", "0 ZERO HIGH HIGH HIGH LOW LOW \n", "1 LOW HIGH HIGH HIGH HIGH HIGH \n", "2 ZERO HIGH HIGH HIGH HIGH HIGH \n", "3 HIGH LOW HIGH HIGH HIGH HIGH \n", "4 LOW HIGH HIGH HIGH HIGH LOW \n", "\n", "[5 rows x 37 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = get_example_model(\"alarm\")\n", "samples = BayesianModelSampling(model).forward_sample(size=int(1e3))\n", "samples.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Funtion to evaluate the learned model structures.\n", "def get_f1_score(estimated_model, true_model):\n", " nodes = estimated_model.nodes()\n", " est_adj = nx.to_numpy_matrix(\n", " estimated_model.to_undirected(), nodelist=nodes, weight=None\n", " )\n", " true_adj = nx.to_numpy_matrix(\n", " true_model.to_undirected(), nodelist=nodes, weight=None\n", " )\n", "\n", " f1 = f1_score(np.ravel(true_adj), np.ravel(est_adj))\n", " print(\"F1-score for the model skeleton: \", f1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learn the model structure using PC" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Working for n conditional variables: 4: 100%|██████████| 4/4 [00:11<00:00, 2.89s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "F1-score for the model skeleton: 0.7777777777777779\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "est = PC(data=samples)\n", "estimated_model = est.estimate(variant=\"stable\", max_cond_vars=4)\n", "get_f1_score(estimated_model, model)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Working for n conditional variables: 4: 100%|██████████| 4/4 [00:11<00:00, 2.88s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "F1-score for the model skeleton: 0.7777777777777779\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "est = PC(data=samples)\n", "estimated_model = est.estimate(variant=\"orig\", max_cond_vars=4)\n", "get_f1_score(estimated_model, model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learn the model structure using Hill-Climb Search" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 1%| | 55/10000 [00:16<50:30, 3.28it/s] " ] }, { "name": "stdout", "output_type": "stream", "text": [ "F1-score for the model skeleton: 0.84\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "scoring_method = K2Score(data=samples)\n", "est = HillClimbSearch(data=samples)\n", "estimated_model = est.estimate(\n", " scoring_method=scoring_method, max_indegree=4, max_iter=int(1e4)\n", ")\n", "get_f1_score(estimated_model, model)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
Kaggle/docker-python
tests/data/notebook.ipynb
1
644
{ "cells": [ { "metadata": { "trusted": true }, "cell_type": "code", "source": "x=999\nprint(x)", "execution_count": null, "outputs": [] } ], "metadata":{ "kernelspec":{"language":"python","display_name":"Python 3","name":"python3"}, "language_info":{ "codemirror_mode":{ "name":"ipython", "version":3 }, "file_extension":".py", "mimetype":"text/x-python", "name":"python", "nbconvert_exporter":"python", "pygments_lexer":"ipython3", "version":"3.7.6" } }, "nbformat":4, "nbformat_minor":4 }
apache-2.0
sloanesturz/cs224u-final-project
sippycup-unit-3.ipynb
1
89416
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"img/sippycup-small.jpg\" align=\"left\" style=\"padding-right: 30px\"/>\n", "\n", "<h1 style=\"line-height: 125%\">\n", " SippyCup<br />\n", " Unit 3: Geography queries\n", "</h1>\n", "\n", "<p>\n", " <a href=\"http://nlp.stanford.edu/~wcmac/\">Bill MacCartney</a><br/>\n", " Spring 2015\n", " <!-- <a href=\"mailto:[email protected]\">[email protected]</a> -->\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"margin: 0px 0px; padding: 10px; background-color: #ddddff; border-style: solid; border-color: #aaaacc; border-width: 1px\">\n", "This is Unit 3 of the <a href=\"./sippycup-unit-0.ipynb\">SippyCup codelab</a>.\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our third case study will examine the domain of geography queries. In particular, we'll focus on the [Geo880][] corpus, which contains 880 queries about U.S. geography. Examples include:\n", "\n", " [Geo880]: http://www.cs.utexas.edu/users/ml/geo.html\n", "\n", " \"which states border texas?\"\n", " \"how many states border the largest state?\"\n", " \"what is the size of the capital of texas?\"\n", "\n", "The Geo880 queries have a quite different character from the arithmetic queries and travel queries we have examined previously. They differ from the arithmetic queries in using a large vocabulary, and in exhibiting greater degrees of both lexical and syntactic ambiguity. They differ from the travel queries in adhering to conventional rules for spelling and syntax, and in having semantics with arbitrarily complex compositional structure. For example:\n", "\n", " \"what rivers flow through states that border the state with the largest population?\"\n", " \"what is the population of the capital of the largest state through which the mississippi runs?\"\n", " \"what is the longest river that passes the states that border the state that borders the most states?\"\n", "\n", "Geo880 was developed in Ray Mooney's group at UT Austin. It is of particular interest because it has for many years served as a standard evaluation for semantic parsing systems. (See, for example, [Zelle & Mooney 1996][], [Tang & Mooney 2001][], [Zettlemoyer & Collins 2005][], and [Liang et al. 2011][].) It has thereby become, for many, a paradigmatic application of semantic parsing. It has also served as a bridge between an older current of research on natural language interfaces to databases (NLIDBs) (see [Androutsopoulos et al. 1995][]) and the modern era of semantic parsing.\n", "\n", " [Zelle & Mooney 1996]: http://www.aaai.org/Papers/AAAI/1996/AAAI96-156.pdf\n", " [Tang & Mooney 2001]: http://www.cs.utexas.edu/~ai-lab/pubs/cocktail-ecml-01.pdf\n", " [Zettlemoyer & Collins 2005]: http://people.csail.mit.edu/lsz/papers/zc-uai05.pdf\n", " [Liang et al. 2011]: http://www.cs.berkeley.edu/~jordan/papers/liang-jordan-klein-acl2011.pdf\n", " [Androutsopoulos et al. 1995]: http://arxiv.org/pdf/cmp-lg/9503016.pdf\n", " \n", "The domain of geography queries is also of interest because there are many plausible real-world applications for semantic parsing which similarly involve complex compositional queries against a richly structured knowledge base. For example, some people are passionate about baseball statistics, and might want to ask queries like:\n", "\n", " \"pitchers who have struck out four batters in one inning\"\n", " \"players who have stolen at least 100 bases in a season\"\n", " \"complete games with fewer than 90 pitches\"\n", " \"most home runs hit in one game\"\n", "\n", "Environmental advocates and policymakers might have queries like:\n", "\n", " \"which country has the highest co2 emissions\"\n", " \"what five countries have the highest per capita co2 emissions\"\n", " \"what country's co2 emissions increased the most over the last five years\"\n", " \"what fraction of co2 emissions was from european countries in 2010\"\n", " \n", "Techniques that work in the geography domain are like to work in these other domains too." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Geo880 dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I've been told that the Geo880 queries were collected from students in classes taught by Ray Mooney at UT Austin. I'm not sure whether I've got the story right. But this account is consistent with one of the notable limitations of the dataset: it is not a natural distribution, and not a realistic representation of the geography queries that people actually ask on, say, Google. Nobody ever asks, \"what is the longest river that passes the states that border the state that borders the most states?\" Nobody. Ever.\n", "\n", "The dataset was published online by Rohit Jaivant Kate in a [Prolog file][] containing semantic representations in Prolog style. It was later republished by Yuk Wah Wong as an [XML file][] containing additional metadata for each example, including translations into Spanish, Japanese, and Turkish; syntactic parse trees; and semantics in two different representations: Prolog and [FunQL][].\n", "\n", " [Prolog file]: ftp://ftp.cs.utexas.edu/pub/mooney/nl-ilp-data/geosystem/geoqueries880\n", " [XML file]: http://www.cs.utexas.edu/~ml/wasp/geo-funql/corpus.xml\n", " [FunQL]: http://www.cs.utexas.edu/~ml/wasp/geo-funql.html\n", "\n", "In SippyCup, we're not going to use either Prolog or FunQL semantics. Instead, we'll use examples which have been annotated only with denotations (which were provided by Percy Liang — thanks!). Of course, our grammar will require a semantic representation, even if our examples are not annotated with semantics. We will introduce one [below](#geoquery-semantic-representation).\n", "\n", "The Geo880 dataset is conventionally divided into 600 training examples and 280 test examples. In SippyCup, the dataset can in found in [`geo880.py`](./geo880.py). Let's take a peek." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train examples: 600\n", "test examples: 280\n", "Example(input='what is the highest point in florida ?', denotation=('/place/walton_county',))\n", "Example(input='which state is the smallest ?', denotation=('/state/district_of_columbia',))\n" ] } ], "source": [ "from geo880 import geo880_train_examples, geo880_test_examples\n", "\n", "print('train examples:', len(geo880_train_examples))\n", "print('test examples: ', len(geo880_test_examples))\n", "print(geo880_train_examples[0])\n", "print(geo880_test_examples[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Geobase knowledge base" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geobase is a small knowledge base about the geography of the United States. It contains (almost) all the information needed to answer queries in the Geo880 dataset, including facts about:\n", "\n", "- states: capital, area, population, major cities, neighboring states, highest and lowest points and elevations\n", "- cities: containing state and population\n", "- rivers: length and states traversed\n", "- mountains: containing state and height\n", "- roads: states traversed\n", "- lakes: area, states traversed\n", "\n", "SippyCup contains a class called `GeobaseReader` (in [`geobase.py`](./geobase.py)) which facilitates working with Geobase in Python. It reads and parses the Geobase Prolog file, and creates a set of tuples representing its content. Let's take a look.\n", "\n", " [Prolog file]: ftp://ftp.cs.utexas.edu/pub/mooney/nl-ilp-data/geosystem/geobase" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GeobaseReader read 51 state rows.\n", "GeobaseReader read 386 city rows.\n", "GeobaseReader read 46 river rows.\n", "GeobaseReader read 51 border rows.\n", "GeobaseReader read 51 highlow rows.\n", "GeobaseReader read 50 mountain rows.\n", "GeobaseReader read 40 road rows.\n", "GeobaseReader read 22 lake rows.\n", "GeobaseReader read 1 country row.\n", "GeobaseReader computed transitive closure of 'contains', adding 495 edges\n", "\n", "Some unaries:\n", " ('city', '/city/providence_ri')\n", " ('city', '/city/ewa_hi')\n", " ('state', '/state/district_of_columbia')\n", " ('city', '/city/fort_wayne_in')\n", " ('city', '/city/concord_ca')\n", " ('place', '/place/little_river')\n", " ('road', '/road/85')\n", " ('city', '/city/high_point_nc')\n", " ('city', '/city/el_paso_tx')\n", " ('city', '/city/santa_monica_ca')\n", "\n", "Some binaries:\n", " ('contains', '/state/arizona', '/city/tempe_az')\n", " ('traverses', '/road/90', '/state/illinois')\n", " ('population', '/city/philadelphia_pa', 1688210)\n", " ('contains', '/state/texas', '/city/midland_tx')\n", " ('contains', '/country/usa', '/state/indiana')\n", " ('contains', '/country/usa', '/city/stockton_ca')\n", " ('contains', '/country/usa', '/city/utica_ny')\n", " ('contains', '/country/usa', '/city/augusta_me')\n", " ('contains', '/country/usa', '/state/connecticut')\n", " ('abbreviation', '/state/alabama', 'al')\n" ] } ], "source": [ "from geobase import GeobaseReader\n", "\n", "reader = GeobaseReader()\n", "unaries = [str(t) for t in reader.tuples if len(t) == 2]\n", "print('\\nSome unaries:\\n ' + '\\n '.join(unaries[:10]))\n", "binaries = [str(t) for t in reader.tuples if len(t) == 3]\n", "print('\\nSome binaries:\\n ' + '\\n '.join(binaries[:10]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some observations here:\n", "\n", "- _Unaries_ are pairs consisting of a unary predicate (a type) and an entity.\n", "- _Binaries_ are triples consisting of binary predicate (a relation) and two entities (or an entity and a numeric or string value).\n", "- Entities are named by unique identifiers of the form `/type/name`. This is a `GeobaseReader` convention; these identifiers are not used in the original Prolog file.\n", "- Some entities have the generic type `place` because they occur in the Prolog file only as the highest or lowest point in a state, and it's hard to reliably assign such points to one of the more specific types.\n", "- The original Prolog file is inconsistent about units. For example, the area of states is expressed in square miles, but the area of lakes is expressed in square kilometers. `GeobaseReader` converts everything to [SI units][]: meters and square meters.\n", "\n", " [SI units]: http://en.wikipedia.org/wiki/International_System_of_Units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Semantic representation <a id=\"geoquery-semantic-representation\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`GeobaseReader` merely reads the data in Geobase into a set of tuples. It doesn't provide any facility for querying that data. That's where `GraphKB` and `GraphKBExecutor` come in. `GraphKB` is a graph-structured knowledge base, with indexing for fast lookups. `GraphKBExecutor` defines a representation for formal queries against that knowledge base, and supports query execution. The formal query language defined by `GraphKBExecutor` will serve as our semantic representation for the geography domain." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `GraphKB` class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `GraphKB` is a generic graph-structured knowledge base, or equivalently, a set of relational pairs and triples, with indexing for fast lookups. It represents a knowledge base as set of tuples, each either:\n", "\n", "- a pair, consisting of a unary relation and an element which belongs to it,\n", " or\n", "- a triple consisting of a binary relation and a pair of elements which\n", " belong to it.\n", "\n", "For example, we can construct a `GraphKB` representing facts about [The Simpsons][]:\n", "\n", " [The Simpsons]: http://en.wikipedia.org/wiki/The_Simpsons" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from graph_kb import GraphKB\n", "\n", "simpsons_tuples = [\n", "\n", " # unaries\n", " ('male', 'homer'),\n", " ('female', 'marge'),\n", " ('male', 'bart'),\n", " ('female', 'lisa'),\n", " ('female', 'maggie'),\n", " ('adult', 'homer'),\n", " ('adult', 'marge'),\n", " ('child', 'bart'),\n", " ('child', 'lisa'),\n", " ('child', 'maggie'),\n", "\n", " # binaries\n", " ('has_age', 'homer', 36),\n", " ('has_age', 'marge', 34),\n", " ('has_age', 'bart', 10),\n", " ('has_age', 'lisa', 8),\n", " ('has_age', 'maggie', 1),\n", " ('has_brother', 'lisa', 'bart'),\n", " ('has_brother', 'maggie', 'bart'),\n", " ('has_sister', 'bart', 'maggie'),\n", " ('has_sister', 'bart', 'lisa'),\n", " ('has_sister', 'lisa', 'maggie'),\n", " ('has_sister', 'maggie', 'lisa'),\n", " ('has_father', 'bart', 'homer'),\n", " ('has_father', 'lisa', 'homer'),\n", " ('has_father', 'maggie', 'homer'),\n", " ('has_mother', 'bart', 'marge'),\n", " ('has_mother', 'lisa', 'marge'),\n", " ('has_mother', 'maggie', 'marge'),\n", "]\n", "\n", "simpsons_kb = GraphKB(simpsons_tuples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `GraphKB` object now contains three indexes:\n", "\n", "- `unaries[U]`: all entities belonging to unary relation `U`\n", "- `binaries_fwd[B][E]`: all entities `X` such that `(E, X)` belongs to binary relation `B`\n", "- `binaries_rev[B][E]`: all entities `X` such that `(X, E)` belongs to binary relation `B`\n", "\n", "For example:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'bart', 'lisa', 'maggie'}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simpsons_kb.unaries['child']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'maggie'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simpsons_kb.binaries_fwd['has_sister']['lisa']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'bart', 'maggie'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simpsons_kb.binaries_rev['has_sister']['lisa']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `GraphKBExecutor` class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `GraphKBExecutor` executes formal queries against a `GraphKB` and returns their denotations.\n", "Queries are represented by Python tuples, and can be nested.\n", "Denotations are also represented by Python tuples, but are conceptually sets (possibly empty). The elements of these tuples are always sorted in canonical order, so that they can be reliably compared for set equality.\n", "\n", "The query language defined by `GraphKBExecutor` is perhaps most easily explained by example:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Q bart\n", "D ('bart',)\n", "\n", "Q male\n", "D ('bart', 'homer')\n", "\n", "Q ('has_sister', 'lisa')\n", "D ('bart', 'maggie')\n", "\n", "Q ('lisa', 'has_sister')\n", "D ('maggie',)\n", "\n", "Q ('lisa', 'has_brother')\n", "D ('bart',)\n", "\n", "Q ('.and', 'male', 'child')\n", "D ('bart',)\n", "\n", "Q ('.or', 'male', 'adult')\n", "D ('bart', 'homer', 'marge')\n", "\n", "Q ('.not', 'child')\n", "D (1, 10, 34, 36, 8, 'homer', 'marge')\n", "\n", "Q ('.any',)\n", "D (1, 10, 34, 36, 8, 'bart', 'homer', 'lisa', 'maggie', 'marge')\n", "\n", "Q ('.any', 'has_sister')\n", "D ('lisa', 'maggie')\n", "\n", "Q ('.and', 'child', ('.not', ('.any', 'has_sister')))\n", "D ('bart',)\n", "\n", "Q ('.count', ('bart', 'has_sister'))\n", "D (2,)\n", "\n", "Q ('has_age', ('.gt', 21))\n", "D ('homer', 'marge')\n", "\n", "Q ('has_age', ('.lt', 2))\n", "D ('maggie',)\n", "\n", "Q ('has_age', ('.eq', 10))\n", "D ('bart',)\n", "\n", "Q ('.max', 'has_age', 'female')\n", "D (34,)\n", "\n", "Q ('.min', 'has_age', ('bart', 'has_sister'))\n", "D (1,)\n", "\n", "Q ('.max', 'has_age', '.any')\n", "D (36,)\n", "\n", "Q ('.argmax', 'has_age', 'female')\n", "D ('marge',)\n", "\n", "Q ('.argmin', 'has_age', ('bart', 'has_sister'))\n", "D ('maggie',)\n", "\n", "Q ('.argmax', 'has_age', '.any')\n", "D ('homer',)\n" ] } ], "source": [ "queries = [\n", " 'bart',\n", " 'male',\n", " ('has_sister', 'lisa'), # who has sister lisa?\n", " ('lisa', 'has_sister'), # lisa has sister who, i.e., who is a sister of lisa?\n", " ('lisa', 'has_brother'), # lisa has brother who, i.e., who is a brother of lisa?\n", " ('.and', 'male', 'child'),\n", " ('.or', 'male', 'adult'),\n", " ('.not', 'child'),\n", " ('.any',), # anything\n", " ('.any', 'has_sister'), # anything has sister who, i.e., who is a sister of anything?\n", " ('.and', 'child', ('.not', ('.any', 'has_sister'))),\n", " ('.count', ('bart', 'has_sister')),\n", " ('has_age', ('.gt', 21)),\n", " ('has_age', ('.lt', 2)),\n", " ('has_age', ('.eq', 10)),\n", " ('.max', 'has_age', 'female'),\n", " ('.min', 'has_age', ('bart', 'has_sister')),\n", " ('.max', 'has_age', '.any'),\n", " ('.argmax', 'has_age', 'female'),\n", " ('.argmin', 'has_age', ('bart', 'has_sister')),\n", " ('.argmax', 'has_age', '.any'),\n", "]\n", "\n", "executor = simpsons_kb.executor()\n", "for query in queries:\n", " print()\n", " print('Q ', query)\n", " print('D ', executor.execute(query))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the query `(R E)` denotes entities having relation `R` to entity `E`,\n", "whereas the query `(E R)` denotes entities to which entity `E` has relation `R`.\n", "\n", "For a more detailed understanding of the style of semantic representation defined by `GraphKBExecutor`, take a look at the [source code](graph_kb.py)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using `GraphKBExecutor` with Geobase" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "('/state/texas', 'capital')\n", "('/city/austin_tx',)\n", "\n", "('.and', 'river', ('traverses', '/state/utah'))\n", "('/river/colorado', '/river/green', '/river/san_juan')\n", "\n", "('.argmax', 'height', 'mountain')\n", "('/mountain/mckinley',)\n" ] } ], "source": [ "geobase = GraphKB(reader.tuples)\n", "executor = geobase.executor()\n", "queries = [\n", " ('/state/texas', 'capital'), # capital of texas\n", " ('.and', 'river', ('traverses', '/state/utah')), # rivers that traverse utah\n", " ('.argmax', 'height', 'mountain'), # tallest mountain\n", "]\n", "for query in queries:\n", " print()\n", " print(query)\n", " print(executor.execute(query))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grammar engineering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's time to start developing a grammar for the geography domain. As in Unit 2, \n", "the performance metric we'll focus on during grammar engineering is *oracle* accuracy (the proportion of examples for which *any* parse is correct), not accuracy (the proportion of examples for which the *first* parse is correct). Remember that oracle accuracy is an upper bound on accuracy, and is a measure of the expressive power of the grammar: does it have the rules it needs to generate the correct parse? The gap between oracle accuracy and accuracy, on the other hand, reflects the ability of the scoring model to bring the correct parse to the top of the candidate list. <!-- (TODO: rewrite.) -->\n", "\n", "As always, we're going to take a data-driven approach to grammar engineering. We want to introduce rules which will enable us to handle the lexical items and syntactic structures that we actually observe in the Geo880 training data. To that end, let's count the words that appear among the 600 training examples. (We do _not_ examine the test data!)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There were 5109 tokens of 248 types:\n", "\n", "the (618), ? (600), what (386), is (283), in (247), state (170), states (169), of (150), how (111), many (90), are (90), population (86), river (76), which (75), through (73), largest (67), border (65), texas (63), rivers (62), cities (56), point (55), highest (54), city (49), that (48), capital (48), with (44), has (43), major (42), run (34), smallest (31), people (29), mississippi (29), us (28), does (27), usa (26), most (26), longest (25), area (24), lowest (23), have (23), density (23), colorado (23), borders (23), new (22), live (22), where (17), runs (17), biggest (16), california (14), alaska (14), ...\n" ] } ], "source": [ "from collections import defaultdict\n", "from operator import itemgetter\n", "from geo880 import geo880_train_examples\n", "\n", "words = [word for example in geo880_train_examples for word in example.input.split()]\n", "counts = defaultdict(int)\n", "for word in words:\n", " counts[word] += 1\n", "counts = sorted([(count, word) for word, count in counts.items()], reverse=True)\n", "print('There were %d tokens of %d types:\\n' % (len(words), len(counts)))\n", "print(', '.join(['%s (%d)' % (word, count) for count, word in counts[:50]] + ['...']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are at least four major categories of words here:\n", "- Words that refer to *entities*, such as \"texas\", \"mississippi\", \"usa\", and \"austin\".\n", "- Words that refer to *types*, such as \"state\", \"river\", and \"cities\".\n", "- Words that refer to *relations*, such as \"in\", \"borders\", \"capital\", and \"long\".\n", "- Other function words, such as \"the\", \"what\", \"how\", and \"are\".\n", "\n", "One might make finer distinctions, but this seems like a reasonable starting point. Note that these categories do not always correspond to traditional syntactic categories. While the entities are typically proper nouns, and the types are typically common nouns, the relations include prepositions, verbs, nouns, and adjectives.\n", "\n", "The design of our grammar will roughly follow this schema. The major categories will include `$Entity`, `$Type`, `$Collection`, `$Relation`, and `$Optional`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optionals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In [Unit 2][], our grammar engineering process didn't really start cooking until we introduced optionals. This time around, let's *begin* with the optionals. We'll define as `$Optional` every word in the Geo880 training data which does not plainly refer to an entity, type, or relation. And we'll let any query be preceded or followed by a sequence of one or more `$Optionals`.\n", "\n", " [Unit 2]: ./sippycup-unit-2.ipynb" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from parsing import Grammar, Rule\n", "\n", "optional_words = [\n", " 'the', '?', 'what', 'is', 'in', 'of', 'how', 'many', 'are', 'which', 'that',\n", " 'with', 'has', 'major', 'does', 'have', 'where', 'me', 'there', 'give',\n", " 'name', 'all', 'a', 'by', 'you', 'to', 'tell', 'other', 'it', 'do', 'whose',\n", " 'show', 'one', 'on', 'for', 'can', 'whats', 'urban', 'them', 'list',\n", " 'exist', 'each', 'could', 'about'\n", "]\n", "\n", "rules_optionals = [\n", " Rule('$ROOT', '?$Optionals $Query ?$Optionals', lambda sems: sems[1]),\n", " Rule('$Optionals', '$Optional ?$Optionals'),\n", "] + [Rule('$Optional', word) for word in optional_words]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because `$Query` has not yet been defined, we won't be able to parse anything yet." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entities and collections" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our grammar will need to be able to recognize names of entities, such as `\"utah\"`. There are hundreds of entities in Geobase, and we don't want to have to introduce a grammar rule for each entity. Instead, we'll define a new annotator, `GeobaseAnnotator`, which simply annotates phrases which exactly match names in Geobase." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from annotator import Annotator, NumberAnnotator\n", "\n", "class GeobaseAnnotator(Annotator):\n", " def __init__(self, geobase):\n", " self.geobase = geobase\n", "\n", " def annotate(self, tokens):\n", " phrase = ' '.join(tokens)\n", " places = self.geobase.binaries_rev['name'][phrase]\n", " return [('$Entity', place) for place in places]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now a couple of rules that will enable us to parse inputs that simply name locations, such as `\"utah\"`.\n", "\n", "(TODO: explain rationale for `$Collection` and `$Query`.)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rules_collection_entity = [\n", " Rule('$Query', '$Collection', lambda sems: sems[0]),\n", " Rule('$Collection', '$Entity', lambda sems: sems[0]),\n", "]\n", "\n", "rules = rules_optionals + rules_collection_entity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's make a grammar." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created grammar with 48 rules\n" ] } ], "source": [ "annotators = [NumberAnnotator(), GeobaseAnnotator(geobase)]\n", "grammar = Grammar(rules=rules, annotators=annotators)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try to parse some inputs which just name locations." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/state/utah\n", "('/state/utah',)\n" ] } ], "source": [ "parses = grammar.parse_input('what is utah')\n", "for parse in parses[:1]:\n", " print('\\n'.join([str(parse.semantics), str(executor.execute(parse.semantics))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, it worked. Now let's run an evaluation on the Geo880 training examples." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GeobaseReader read 51 state rows.\n", "GeobaseReader read 386 city rows.\n", "GeobaseReader read 46 river rows.\n", "GeobaseReader read 51 border rows.\n", "GeobaseReader read 51 highlow rows.\n", "GeobaseReader read 50 mountain rows.\n", "GeobaseReader read 40 road rows.\n", "GeobaseReader read 22 lake rows.\n", "GeobaseReader read 1 country row.\n", "GeobaseReader computed transitive closure of 'contains', adding 495 edges\n", "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.000\n", "denotation oracle accuracy 0.000\n", "number of parses 0.028\n", "spurious ambiguity 0.000\n", "\n", "0 of 0 wins on denotation oracle accuracy:\n", "\n", "\n", "10 of 600 losses on denotation oracle accuracy:\n", "\n", " how long is the shortest river in the usa ?\n", " name the states which have no surrounding states ?\n", " what is the capital of the state with the highest point ?\n", " what is the capital of utah ?\n", " what is the highest elevation in new mexico ?\n", " what is the highest point in the smallest state ?\n", " what is the population density of wyoming ?\n", " what is the total population of the states that border texas ?\n", " what state has the largest population ?\n", " what states does the mississippi run through ?\n", "\n" ] } ], "source": [ "from experiment import sample_wins_and_losses\n", "from geoquery import GeoQueryDomain\n", "from metrics import DenotationOracleAccuracyMetric\n", "from scoring import Model\n", "\n", "domain = GeoQueryDomain()\n", "model = Model(grammar=grammar, executor=executor.execute)\n", "metric = DenotationOracleAccuracyMetric()\n", "\n", "sample_wins_and_losses(domain=domain, model=model, metric=metric, seed=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't yet have a single win: denotation oracle accuracy remains stuck at zero. However, the average number of parses is slightly greater than zero, meaning that there are a few examples which our grammar can parse (though not correctly). It would be interesting to know which examples. There's a utility function in [`experiment.py`](./experiment.py) which will give you the visibility you need. See if you can figure out what to do.\n", "\n", "<!-- 'where is san diego ?' is parsed as '/city/san_diego_ca' -->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(TODO: the words in the training data include lots of words for types. Let's write down some lexical rules defining the category `$Type`, guided as usual by the words we actually see in the training data. We'll also make `$Type` a kind of `$Collection`.)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rules_types = [\n", " Rule('$Collection', '$Type', lambda sems: sems[0]),\n", "\n", " Rule('$Type', 'state', 'state'),\n", " Rule('$Type', 'states', 'state'),\n", " Rule('$Type', 'city', 'city'),\n", " Rule('$Type', 'cities', 'city'),\n", " Rule('$Type', 'big cities', 'city'),\n", " Rule('$Type', 'towns', 'city'),\n", " Rule('$Type', 'river', 'river'),\n", " Rule('$Type', 'rivers', 'river'),\n", " Rule('$Type', 'mountain', 'mountain'),\n", " Rule('$Type', 'mountains', 'mountain'),\n", " Rule('$Type', 'mount', 'mountain'),\n", " Rule('$Type', 'peak', 'mountain'),\n", " Rule('$Type', 'road', 'road'),\n", " Rule('$Type', 'roads', 'road'),\n", " Rule('$Type', 'lake', 'lake'),\n", " Rule('$Type', 'lakes', 'lake'),\n", " Rule('$Type', 'country', 'country'),\n", " Rule('$Type', 'countries', 'country'),\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should now be able to parse inputs denoting types, such as `\"name the lakes\"`:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created grammar with 67 rules\n", "lake\n", "('/lake/becharof', '/lake/champlain', '/lake/erie', '/lake/flathead', '/lake/great_salt_lake', '/lake/huron', '/lake/iliamna', '/lake/lake_of_the_woods', '/lake/michigan', '/lake/mille_lacs', '/lake/naknek', '/lake/okeechobee', '/lake/ontario', '/lake/pontchartrain', '/lake/rainy', '/lake/red', '/lake/salton_sea', '/lake/st._clair', '/lake/superior', '/lake/tahoe', '/lake/teshekpuk', '/lake/winnebago')\n" ] } ], "source": [ "rules = rules_optionals + rules_collection_entity + rules_types\n", "grammar = Grammar(rules=rules, annotators=annotators)\n", "parses = grammar.parse_input('name the lakes')\n", "for parse in parses[:1]:\n", " print('\\n'.join([str(parse.semantics), str(executor.execute(parse.semantics))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It worked. Let's evaluate on the Geo880 training data again." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.003\n", "denotation oracle accuracy 0.003\n", "number of parses 0.033\n", "spurious ambiguity 0.000\n", "\n", "2 of 2 wins on denotation oracle accuracy:\n", "\n", " list the states ?\n", " what are the states ?\n", "\n", "10 of 598 losses on denotation oracle accuracy:\n", "\n", " give me the cities which are in texas ?\n", " how many people are there in iowa ?\n", " how many states have major rivers ?\n", " name the rivers in arkansas ?\n", " what river runs through virginia ?\n", " what rivers are in utah ?\n", " what state has the highest elevation ?\n", " what states border rhode island ?\n", " where is mount whitney located ?\n", " which rivers run through the state with the largest city in the us ?\n", "\n" ] } ], "source": [ "model = Model(grammar=grammar, executor=executor.execute)\n", "sample_wins_and_losses(domain=domain, model=model, metric=metric, seed=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Liftoff! We have two wins, and denotation oracle accuracy is greater than zero! Just barely." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relations and joins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to really make this bird fly, we're going to have to handle relations. In particular, we'd like to be able to parse queries which combine a relation with an entity or collection, such as `\"what is the capital of vermont\"`.\n", "\n", "As usual, we'll adopt a data-driven approach. The training examples include lots of words and phrases which refer to relations, both \"forward\" relations (like `\"traverses\"`) and \"reverse\" relations (like `\"traversed by\"`). Guided by the training data, we'll write lexical rules which define the categories `$FwdRelation` and `$RevRelation`. Then we'll add rules that allow either a `$FwdRelation` or a `$RevRelation` to be promoted to a generic `$Relation`, with semantic functions which ensure that the semantics are constructed with the proper orientation. Finally, we'll define a rule for _joining_ a `$Relation` (such as `\"capital of\"`) with a `$Collection` (such as `\"vermont\"`) to yield another `$Collection` (such as `\"capital of vermont\"`).\n", "\n", "<!-- (TODO: Give a fuller explanation of what's going on with the semantics.) -->" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rules_relations = [\n", " Rule('$Collection', '$Relation ?$Optionals $Collection', lambda sems: sems[0](sems[2])),\n", "\n", " Rule('$Relation', '$FwdRelation', lambda sems: (lambda arg: (sems[0], arg))),\n", " Rule('$Relation', '$RevRelation', lambda sems: (lambda arg: (arg, sems[0]))),\n", "\n", " Rule('$FwdRelation', '$FwdBordersRelation', 'borders'),\n", " Rule('$FwdBordersRelation', 'border'),\n", " Rule('$FwdBordersRelation', 'bordering'),\n", " Rule('$FwdBordersRelation', 'borders'),\n", " Rule('$FwdBordersRelation', 'neighbor'),\n", " Rule('$FwdBordersRelation', 'neighboring'),\n", " Rule('$FwdBordersRelation', 'surrounding'),\n", " Rule('$FwdBordersRelation', 'next to'),\n", "\n", " Rule('$FwdRelation', '$FwdTraversesRelation', 'traverses'),\n", " Rule('$FwdTraversesRelation', 'cross ?over'),\n", " Rule('$FwdTraversesRelation', 'flow through'),\n", " Rule('$FwdTraversesRelation', 'flowing through'),\n", " Rule('$FwdTraversesRelation', 'flows through'),\n", " Rule('$FwdTraversesRelation', 'go through'),\n", " Rule('$FwdTraversesRelation', 'goes through'),\n", " Rule('$FwdTraversesRelation', 'in'),\n", " Rule('$FwdTraversesRelation', 'pass through'),\n", " Rule('$FwdTraversesRelation', 'passes through'),\n", " Rule('$FwdTraversesRelation', 'run through'),\n", " Rule('$FwdTraversesRelation', 'running through'),\n", " Rule('$FwdTraversesRelation', 'runs through'),\n", " Rule('$FwdTraversesRelation', 'traverse'),\n", " Rule('$FwdTraversesRelation', 'traverses'),\n", "\n", " Rule('$RevRelation', '$RevTraversesRelation', 'traverses'),\n", " Rule('$RevTraversesRelation', 'has'),\n", " Rule('$RevTraversesRelation', 'have'), # 'how many states have major rivers'\n", " Rule('$RevTraversesRelation', 'lie on'),\n", " Rule('$RevTraversesRelation', 'next to'),\n", " Rule('$RevTraversesRelation', 'traversed by'),\n", " Rule('$RevTraversesRelation', 'washed by'),\n", "\n", " Rule('$FwdRelation', '$FwdContainsRelation', 'contains'),\n", " # 'how many states have a city named springfield'\n", " Rule('$FwdContainsRelation', 'has'),\n", " Rule('$FwdContainsRelation', 'have'),\n", "\n", " Rule('$RevRelation', '$RevContainsRelation', 'contains'),\n", " Rule('$RevContainsRelation', 'contained by'),\n", " Rule('$RevContainsRelation', 'in'),\n", " Rule('$RevContainsRelation', 'found in'),\n", " Rule('$RevContainsRelation', 'located in'),\n", " Rule('$RevContainsRelation', 'of'),\n", "\n", " Rule('$RevRelation', '$RevCapitalRelation', 'capital'),\n", " Rule('$RevCapitalRelation', 'capital'),\n", " Rule('$RevCapitalRelation', 'capitals'),\n", "\n", " Rule('$RevRelation', '$RevHighestPointRelation', 'highest_point'),\n", " Rule('$RevHighestPointRelation', 'high point'),\n", " Rule('$RevHighestPointRelation', 'high points'),\n", " Rule('$RevHighestPointRelation', 'highest point'),\n", " Rule('$RevHighestPointRelation', 'highest points'),\n", "\n", " Rule('$RevRelation', '$RevLowestPointRelation', 'lowest_point'),\n", " Rule('$RevLowestPointRelation', 'low point'),\n", " Rule('$RevLowestPointRelation', 'low points'),\n", " Rule('$RevLowestPointRelation', 'lowest point'),\n", " Rule('$RevLowestPointRelation', 'lowest points'),\n", " Rule('$RevLowestPointRelation', 'lowest spot'),\n", "\n", " Rule('$RevRelation', '$RevHighestElevationRelation', 'highest_elevation'),\n", " Rule('$RevHighestElevationRelation', '?highest elevation'),\n", "\n", " Rule('$RevRelation', '$RevHeightRelation', 'height'),\n", " Rule('$RevHeightRelation', 'elevation'),\n", " Rule('$RevHeightRelation', 'height'),\n", " Rule('$RevHeightRelation', 'high'),\n", " Rule('$RevHeightRelation', 'tall'),\n", "\n", " Rule('$RevRelation', '$RevAreaRelation', 'area'),\n", " Rule('$RevAreaRelation', 'area'),\n", " Rule('$RevAreaRelation', 'big'),\n", " Rule('$RevAreaRelation', 'large'),\n", " Rule('$RevAreaRelation', 'size'),\n", "\n", " Rule('$RevRelation', '$RevPopulationRelation', 'population'),\n", " Rule('$RevPopulationRelation', 'big'),\n", " Rule('$RevPopulationRelation', 'large'),\n", " Rule('$RevPopulationRelation', 'populated'),\n", " Rule('$RevPopulationRelation', 'population'),\n", " Rule('$RevPopulationRelation', 'populations'),\n", " Rule('$RevPopulationRelation', 'populous'),\n", " Rule('$RevPopulationRelation', 'size'),\n", "\n", " Rule('$RevRelation', '$RevLengthRelation', 'length'),\n", " Rule('$RevLengthRelation', 'length'),\n", " Rule('$RevLengthRelation', 'long'),\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should now be able to parse `\"what is the capital of vermont\"`. Let's see:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created grammar with 146 rules\n", "('/state/vermont', 'capital')\n", "('/city/montpelier_vt',)\n" ] } ], "source": [ "rules = rules_optionals + rules_collection_entity + rules_types + rules_relations\n", "grammar = Grammar(rules=rules, annotators=annotators)\n", "parses = grammar.parse_input('what is the capital of vermont ?')\n", "for parse in parses[:1]:\n", " print('\\n'.join([str(parse.semantics), str(executor.execute(parse.semantics))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Montpelier! I always forget that one.\n", "\n", "OK, let's evaluate our progress on the Geo880 training data." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.113\n", "denotation oracle accuracy 0.125\n", "number of parses 0.427\n", "spurious ambiguity 0.000\n", "\n", "5 of 75 wins on denotation oracle accuracy:\n", "\n", " what is the capital of utah ?\n", " what is the highest point in texas ?\n", " what is the population of idaho ?\n", " what is the population of rhode island ?\n", " what is the population of sacramento ?\n", "\n", "10 of 525 losses on denotation oracle accuracy:\n", "\n", " give me the longest river that passes through the us ?\n", " how many cities are there in the us ?\n", " what are the major cities in new mexico ?\n", " what are the states through which the longest river runs ?\n", " what is the area of the state with the smallest population density ?\n", " what is the biggest city in usa ?\n", " what is the highest elevation in new mexico ?\n", " what is the largest state capital in population ?\n", " what river traverses the most states ?\n", " what states border hawaii ?\n", "\n" ] } ], "source": [ "model = Model(grammar=grammar, executor=executor.execute)\n", "sample_wins_and_losses(domain=domain, model=model, metric=metric, seed=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hot diggity, it's working. Denotation oracle accuracy is over 12%, double digits. We have 75 wins, and they're what we expect: queries that simply combine a relation and an entity (or collection)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intersections" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rules_intersection = [\n", " Rule('$Collection', '$Collection $Collection',\n", " lambda sems: ('.and', sems[0], sems[1])),\n", " Rule('$Collection', '$Collection $Optional $Collection',\n", " lambda sems: ('.and', sems[0], sems[2])),\n", " Rule('$Collection', '$Collection $Optional $Optional $Collection',\n", " lambda sems: ('.and', sems[0], sems[3])),\n", "]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created grammar with 149 rules\n", "('.and', 'state', ('borders', '/state/california'))\n", "('/state/arizona', '/state/nevada', '/state/oregon')\n" ] } ], "source": [ "rules = rules_optionals + rules_collection_entity + rules_types + rules_relations + rules_intersection\n", "grammar = Grammar(rules=rules, annotators=annotators)\n", "parses = grammar.parse_input('states bordering california')\n", "for parse in parses[:1]:\n", " print('\\n'.join([str(parse.semantics), str(executor.execute(parse.semantics))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's evaluate the impact on the Geo880 training examples." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.198\n", "denotation oracle accuracy 0.277\n", "number of parses 1.962\n", "spurious ambiguity 0.000\n", "\n", "5 of 166 wins on denotation oracle accuracy:\n", "\n", " give me the cities in virginia ?\n", " how high is guadalupe peak ?\n", " what are the neighboring states for michigan ?\n", " what is the lowest point in louisiana ?\n", " what rivers run through west virginia ?\n", "\n", "10 of 434 losses on denotation oracle accuracy:\n", "\n", " what is the largest state traversed by the mississippi river ?\n", " what is the longest river ?\n", " what is the longest river in pennsylvania ?\n", " what is the most populous state in the us ?\n", " what is the name of the state with the lowest point ?\n", " what is the population density of the state with the smallest area ?\n", " what is the shortest river in nebraska ?\n", " what rivers flow through states that alabama borders ?\n", " where is san jose ?\n", " which state is the largest city in montana in ?\n", "\n" ] } ], "source": [ "model = Model(grammar=grammar, executor=executor.execute)\n", "sample_wins_and_losses(domain=domain, model=model, metric=metric, seed=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, denotation oracle accuracy has more than doubled, from 12% to 28%. And the wins now include intersections like `\"which states border new york\"`. The losses, however, are clearly dominated by one category of error." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Superlatives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many of the losses involve superlatives, such as `\"biggest\"` or `\"shortest\"`. Let's remedy that. As usual, we let the training examples guide us in adding lexical rules." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rules_superlatives = [\n", " Rule('$Collection', '$Superlative ?$Optionals $Collection', lambda sems: sems[0] + (sems[2],)),\n", " Rule('$Collection', '$Collection ?$Optionals $Superlative', lambda sems: sems[2] + (sems[0],)),\n", "\n", " Rule('$Superlative', 'largest', ('.argmax', 'area')),\n", " Rule('$Superlative', 'largest', ('.argmax', 'population')),\n", " Rule('$Superlative', 'biggest', ('.argmax', 'area')),\n", " Rule('$Superlative', 'biggest', ('.argmax', 'population')),\n", " Rule('$Superlative', 'smallest', ('.argmin', 'area')),\n", " Rule('$Superlative', 'smallest', ('.argmin', 'population')),\n", " Rule('$Superlative', 'longest', ('.argmax', 'length')),\n", " Rule('$Superlative', 'shortest', ('.argmin', 'length')),\n", " Rule('$Superlative', 'tallest', ('.argmax', 'height')),\n", " Rule('$Superlative', 'highest', ('.argmax', 'height')),\n", "\n", " Rule('$Superlative', '$MostLeast $RevRelation', lambda sems: (sems[0], sems[1])),\n", " Rule('$MostLeast', 'most', '.argmax'),\n", " Rule('$MostLeast', 'least', '.argmin'),\n", " Rule('$MostLeast', 'lowest', '.argmin'),\n", " Rule('$MostLeast', 'greatest', '.argmax'),\n", " Rule('$MostLeast', 'highest', '.argmax'),\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we should be able to parse `\"tallest mountain\"`:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created grammar with 167 rules\n", "('.argmax', 'height', 'mountain')\n", "('/mountain/mckinley',)\n" ] } ], "source": [ "rules = rules_optionals + rules_collection_entity + rules_types + rules_relations + rules_intersection + rules_superlatives\n", "grammar = Grammar(rules=rules, annotators=annotators)\n", "parses = grammar.parse_input('tallest mountain')\n", "for parse in parses[:1]:\n", " print('\\n'.join([str(parse.semantics), str(executor.execute(parse.semantics))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's evaluate the impact on the Geo880 training examples." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.245\n", "denotation oracle accuracy 0.422\n", "number of parses 4.430\n", "spurious ambiguity 0.000\n", "\n", "5 of 253 wins on denotation oracle accuracy:\n", "\n", " what can you tell me about the population of missouri ?\n", " what is the population of texas ?\n", " what state borders michigan ?\n", " what state has the city with the most population ?\n", " where is the lowest spot in iowa ?\n", "\n", "10 of 347 losses on denotation oracle accuracy:\n", "\n", " how many states are next to major rivers ?\n", " how many states have cities named austin ?\n", " how many states have cities or towns named springfield ?\n", " number of citizens in boulder ?\n", " of the states washed by the mississippi river which has the lowest point ?\n", " what is the highest point in the united states ?\n", " what is the longest river in the state with the highest point ?\n", " what is the population of springfield missouri ?\n", " what state has the largest urban population ?\n", " what state has the smallest population density ?\n", "\n" ] } ], "source": [ "model = Model(grammar=grammar, executor=executor.execute)\n", "sample_wins_and_losses(domain=domain, model=model, metric=metric, seed=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow, superlatives make a big difference. Denotation oracle accuracy has surged from 28% to 42%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reverse joins" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def reverse(relation_sem):\n", " \"\"\"TODO\"\"\"\n", " # relation_sem is a lambda function which takes an arg and forms a pair,\n", " # either (rel, arg) or (arg, rel). We want to swap the order of the pair.\n", " def apply_and_swap(arg):\n", " pair = relation_sem(arg)\n", " return (pair[1], pair[0])\n", " return apply_and_swap\n", "\n", "rules_reverse_joins = [\n", " Rule('$Collection', '$Collection ?$Optionals $Relation',\n", " lambda sems: reverse(sems[2])(sems[0])),\n", "]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created grammar with 168 rules\n", "('.and', 'state', ('/river/rio_grande', 'traverses'))\n", "('/state/colorado', '/state/new_mexico', '/state/texas')\n" ] } ], "source": [ "rules = rules_optionals + rules_collection_entity + rules_types + rules_relations + rules_intersection + rules_superlatives + rules_reverse_joins\n", "grammar = Grammar(rules=rules, annotators=annotators)\n", "parses = grammar.parse_input('which states does the rio grande cross')\n", "for parse in parses[:1]:\n", " print('\\n'.join([str(parse.semantics), str(executor.execute(parse.semantics))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's evaluate the impact on the Geo880 training examples." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Max cell capacity 1000 has been hit 1 times\n", "Max cell capacity 1000 has been hit 2 times\n", "Max cell capacity 1000 has been hit 4 times\n", "Max cell capacity 1000 has been hit 8 times\n", "Max cell capacity 1000 has been hit 16 times\n", "Max cell capacity 1000 has been hit 32 times\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.257\n", "denotation oracle accuracy 0.468\n", "number of parses 13.210\n", "spurious ambiguity 0.001\n", "\n", "Max cell capacity 1000 has been hit 64 times\n", "5 of 281 wins on denotation oracle accuracy:\n", "\n", " how long is the mississippi ?\n", " what is the biggest city in oregon ?\n", " what is the biggest city in wyoming ?\n", " what is the capital of washington ?\n", " what is the smallest city in the largest state ?\n", "\n", "10 of 319 losses on denotation oracle accuracy:\n", "\n", " how many people live in the state with the largest population density ?\n", " how many rivers are found in colorado ?\n", " how many rivers are there in idaho ?\n", " name the major lakes in michigan ?\n", " what are the major cities in the smallest state in the us ?\n", " what is the tallest mountain in america ?\n", " what river is the longest one in the united states ?\n", " what rivers run through the states that border the state with the capital atlanta ?\n", " where is indianapolis ?\n", " which states border the longest river in the usa ?\n", "\n" ] } ], "source": [ "model = Model(grammar=grammar, executor=executor.execute)\n", "sample_wins_and_losses(domain=domain, model=model, metric=metric, seed=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time the gain in denotation oracle accuracy was more modest, from 42% to 47%. Still, we are making good progress. However, note that a substantial gap has opened between accuracy and oracle accuracy. This indicates that we could benefit from adding a scoring model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature engineering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Through an iterative process of grammar engineering, we've managed to increase denotation oracle accuracy of 47%. But we've been ignoring denotation accuracy, which now lags far behind, at 25%. This represents an opportunity.\n", "\n", "In order to figure out how best to fix the problem, we need to do some error analysis. Let's look for some specific examples where denotation accuracy is 0, even though denotation oracle accuracy is 1. In other words, let's look for some examples where we have a correct parse, but it's not ranked at the top. We should be able to find some cases like that among the first ten examples of the Geo880 training data." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 10 examples\n", "\n", "--------------------------------------------------------------------------------\n", "input what is the highest point in florida ?\n", "target denotation ('/place/walton_county',)\n", "\n", "denotation accuracy 1\n", "denotation oracle accuracy 1\n", "number of parses 3\n", "spurious ambiguity 0\n", "\n", "0 0.000 semantics ('/state/florida', 'highest_point')\n", " denotation + ('/place/walton_county',)\n", "1 0.000 semantics (('traverses', '/state/florida'), 'highest_point')\n", " denotation - ()\n", "2 0.000 semantics (('/state/florida', 'contains'), 'highest_point')\n", " denotation - ()\n", "\n", "--------------------------------------------------------------------------------\n", "input what are the high points of states surrounding mississippi ?\n", "target denotation ('/place/cheaha_mountain', '/place/clingmans_dome', '/place/driskill_mountain', '/place/magazine_mountain')\n", "\n", "denotation accuracy 1\n", "denotation oracle accuracy 1\n", "number of parses 28\n", "spurious ambiguity 0\n", "\n", "0 0.000 semantics (('.and', 'state', ('borders', '/state/mississippi')), 'highest_point')\n", " denotation + ('/place/cheaha_mountain', '/place/clingmans_dome', '/place/driskill_mountain', '/place/magazine_mountain')\n", "1 0.000 semantics (('.and', 'state', ('borders', '/river/mississippi')), 'highest_point')\n", " denotation - ()\n", "2 0.000 semantics (('.and', ('state', 'borders'), '/state/mississippi'), 'highest_point')\n", " denotation - ('/place/woodall_mountain',)\n", "3 0.000 semantics (('.and', ('state', 'borders'), '/river/mississippi'), 'highest_point')\n", " denotation - ()\n", "4 0.000 semantics ((('.and', 'state', ('borders', '/state/mississippi')), 'contains'), 'highest_point')\n", " denotation - ()\n", "5 0.000 semantics ((('.and', 'state', ('borders', '/river/mississippi')), 'contains'), 'highest_point')\n", " denotation - ()\n", "6 0.000 semantics ((('.and', ('state', 'borders'), '/state/mississippi'), 'contains'), 'highest_point')\n", " denotation - ()\n", "7 0.000 semantics ((('.and', ('state', 'borders'), '/river/mississippi'), 'contains'), 'highest_point')\n", " denotation - ()\n", "8 0.000 semantics (('.and', ('state', 'contains'), ('borders', '/state/mississippi')), 'highest_point')\n", " denotation - ()\n", "9 0.000 semantics (('.and', ('state', 'contains'), ('borders', '/river/mississippi')), 'highest_point')\n", " denotation - ()\n", "10 0.000 semantics (('.and', (('state', 'borders'), 'contains'), '/state/mississippi'), 'highest_point')\n", " denotation - ()\n", "11 0.000 semantics (('.and', (('state', 'borders'), 'contains'), '/river/mississippi'), 'highest_point')\n", " denotation - ()\n", "12 0.000 semantics (('.and', (('state', 'contains'), 'borders'), '/state/mississippi'), 'highest_point')\n", " denotation - ()\n", "13 0.000 semantics (('.and', (('state', 'contains'), 'borders'), '/river/mississippi'), 'highest_point')\n", " denotation - ()\n", "14 0.000 semantics ('.and', ('state', 'highest_point'), ('borders', '/state/mississippi'))\n", " denotation - ()\n", "15 0.000 semantics ('.and', ('state', 'highest_point'), ('borders', '/river/mississippi'))\n", " denotation - ()\n", "16 0.000 semantics ('.and', (('state', 'contains'), 'highest_point'), ('borders', '/state/mississippi'))\n", " denotation - ()\n", "17 0.000 semantics ('.and', (('state', 'contains'), 'highest_point'), ('borders', '/river/mississippi'))\n", " denotation - ()\n", "18 0.000 semantics ('.and', (('state', 'borders'), 'highest_point'), '/state/mississippi')\n", " denotation - ()\n", "19 0.000 semantics ('.and', (('state', 'borders'), 'highest_point'), '/river/mississippi')\n", " denotation - ()\n", "20 0.000 semantics ('.and', ((('state', 'borders'), 'contains'), 'highest_point'), '/state/mississippi')\n", " denotation - ()\n", "21 0.000 semantics ('.and', ((('state', 'borders'), 'contains'), 'highest_point'), '/river/mississippi')\n", " denotation - ()\n", "22 0.000 semantics ('.and', ((('state', 'contains'), 'borders'), 'highest_point'), '/state/mississippi')\n", " denotation - ()\n", "23 0.000 semantics ('.and', ((('state', 'contains'), 'borders'), 'highest_point'), '/river/mississippi')\n", " denotation - ()\n", "24 0.000 semantics ('.and', (('state', 'highest_point'), 'borders'), '/state/mississippi')\n", " denotation - ()\n", "25 0.000 semantics ('.and', (('state', 'highest_point'), 'borders'), '/river/mississippi')\n", " denotation - ()\n", "26 0.000 semantics ('.and', ((('state', 'contains'), 'highest_point'), 'borders'), '/state/mississippi')\n", " denotation - ()\n", "27 0.000 semantics ('.and', ((('state', 'contains'), 'highest_point'), 'borders'), '/river/mississippi')\n", " denotation - ()\n", "\n", "--------------------------------------------------------------------------------\n", "input what state has the shortest river ?\n", "target denotation ('/state/delaware', '/state/new_jersey', '/state/new_york', '/state/pennsylvania')\n", "\n", "denotation accuracy 0\n", "denotation oracle accuracy 1\n", "number of parses 8\n", "spurious ambiguity 0\n", "\n", "0 0.000 semantics ('.and', 'state', ('.argmin', 'length', 'river'))\n", " denotation - ()\n", "1 0.000 semantics ('.and', 'state', (('.argmin', 'length', 'river'), 'traverses'))\n", " denotation + ('/state/delaware', '/state/new_jersey', '/state/new_york', '/state/pennsylvania')\n", "2 0.000 semantics ('.and', 'state', ('contains', ('.argmin', 'length', 'river')))\n", " denotation - ()\n", "3 0.000 semantics ('.and', ('traverses', 'state'), ('.argmin', 'length', 'river'))\n", " denotation - ('/river/delaware',)\n", "4 0.000 semantics ('.and', ('state', 'contains'), ('.argmin', 'length', 'river'))\n", " denotation - ()\n", "5 0.000 semantics ('.and', ('.argmin', 'length', 'state'), 'river')\n", " denotation - ()\n", "6 0.000 semantics ('.and', ('.argmin', 'length', ('traverses', 'state')), 'river')\n", " denotation - ('/river/delaware',)\n", "7 0.000 semantics ('.and', ('.argmin', 'length', ('state', 'contains')), 'river')\n", " denotation - ()\n", "\n", "--------------------------------------------------------------------------------\n", "input what is the tallest mountain in the united states ?\n", "target denotation ('/mountain/mckinley',)\n", "\n", "denotation accuracy 0\n", "denotation oracle accuracy 0\n", "number of parses 0\n", "spurious ambiguity 0\n", "\n", "\n", "--------------------------------------------------------------------------------\n", "input what is the capital of maine ?\n", "target denotation ('/city/augusta_me',)\n", "\n", "denotation accuracy 1\n", "denotation oracle accuracy 1\n", "number of parses 2\n", "spurious ambiguity 0\n", "\n", "0 0.000 semantics ('/state/maine', 'capital')\n", " denotation + ('/city/augusta_me',)\n", "1 0.000 semantics (('/state/maine', 'contains'), 'capital')\n", " denotation - ()\n", "\n", "--------------------------------------------------------------------------------\n", "input what are the populations of states through which the mississippi river run ?\n", "target denotation (11400000, 2286000, 2364000, 2520000, 2913000, 4076000, 4206000, 4591000, 4700000, 4916000)\n", "\n", "denotation accuracy 0\n", "denotation oracle accuracy 0\n", "number of parses 0\n", "spurious ambiguity 0\n", "\n", "\n", "--------------------------------------------------------------------------------\n", "input name all the lakes of us ?\n", "target denotation ('/lake/becharof', '/lake/champlain', '/lake/erie', '/lake/flathead', '/lake/great_salt_lake', '/lake/huron', '/lake/iliamna', '/lake/lake_of_the_woods', '/lake/michigan', '/lake/mille_lacs', '/lake/naknek', '/lake/okeechobee', '/lake/ontario', '/lake/pontchartrain', '/lake/rainy', '/lake/red', '/lake/salton_sea', '/lake/st._clair', '/lake/superior', '/lake/tahoe', '/lake/teshekpuk', '/lake/winnebago')\n", "\n", "denotation accuracy 0\n", "denotation oracle accuracy 0\n", "number of parses 0\n", "spurious ambiguity 0\n", "\n", "\n", "--------------------------------------------------------------------------------\n", "input which states border states through which the mississippi traverses ?\n", "target denotation ('/state/alabama', '/state/arkansas', '/state/georgia', '/state/illinois', '/state/indiana', '/state/iowa', '/state/kansas', '/state/kentucky', '/state/louisiana', '/state/michigan', '/state/minnesota', '/state/mississippi', '/state/missouri', '/state/nebraska', '/state/north_carolina', '/state/north_dakota', '/state/ohio', '/state/oklahoma', '/state/south_dakota', '/state/tennessee', '/state/texas', '/state/virginia', '/state/west_virginia', '/state/wisconsin')\n", "\n", "denotation accuracy 0\n", "denotation oracle accuracy 0\n", "number of parses 0\n", "spurious ambiguity 0\n", "\n", "\n", "--------------------------------------------------------------------------------\n", "input what is the highest mountain in alaska ?\n", "target denotation ('/mountain/mckinley',)\n", "\n", "denotation accuracy 0\n", "denotation oracle accuracy 1\n", "number of parses 12\n", "spurious ambiguity 0\n", "\n", "0 0.000 semantics ('.argmax', 'height', ('.and', 'mountain', '/state/alaska'))\n", " denotation - ()\n", "1 0.000 semantics ('.argmax', 'height', ('.and', 'mountain', ('traverses', '/state/alaska')))\n", " denotation - ()\n", "2 0.000 semantics ('.argmax', 'height', ('.and', 'mountain', ('/state/alaska', 'contains')))\n", " denotation + ('/mountain/mckinley',)\n", "3 0.000 semantics ('.argmax', 'height', ('.and', ('mountain', 'traverses'), '/state/alaska'))\n", " denotation - ()\n", "4 0.000 semantics ('.argmax', 'height', ('.and', ('contains', 'mountain'), '/state/alaska'))\n", " denotation - ()\n", "5 0.000 semantics ('.and', ('.argmax', 'height', 'mountain'), '/state/alaska')\n", " denotation - ()\n", "6 0.000 semantics ('.and', ('.argmax', 'height', 'mountain'), ('traverses', '/state/alaska'))\n", " denotation - ()\n", "7 0.000 semantics ('.and', ('.argmax', 'height', 'mountain'), ('/state/alaska', 'contains'))\n", " denotation + ('/mountain/mckinley',)\n", "8 0.000 semantics ('.and', ('.argmax', 'height', ('mountain', 'traverses')), '/state/alaska')\n", " denotation - ()\n", "9 0.000 semantics ('.and', ('.argmax', 'height', ('contains', 'mountain')), '/state/alaska')\n", " denotation - ()\n", "10 0.000 semantics ('.and', (('.argmax', 'height', 'mountain'), 'traverses'), '/state/alaska')\n", " denotation - ()\n", "11 0.000 semantics ('.and', ('contains', ('.argmax', 'height', 'mountain')), '/state/alaska')\n", " denotation - ('/state/alaska',)\n", "\n", "--------------------------------------------------------------------------------\n", "input what is the population of illinois ?\n", "target denotation (11400000,)\n", "\n", "denotation accuracy 1\n", "denotation oracle accuracy 1\n", "number of parses 2\n", "spurious ambiguity 0\n", "\n", "0 0.000 semantics ('/state/illinois', 'population')\n", " denotation + (11400000,)\n", "1 0.000 semantics (('/state/illinois', 'contains'), 'population')\n", " denotation - (100054, 124160, 139712, 3005172, 58267, 60278, 60590, 61232, 63668, 66116, 67653, 73706, 77956, 81293, 93939)\n", "\n", "--------------------------------------------------------------------------------\n", "Over 10 examples:\n", "\n", "denotation accuracy 0.400\n", "denotation oracle accuracy 0.600\n", "number of parses 5.500\n", "spurious ambiguity 0.000\n", "\n" ] } ], "source": [ "from experiment import evaluate_model\n", "from metrics import denotation_match_metrics\n", "\n", "evaluate_model(model=model,\n", " examples=geo880_train_examples[:10],\n", " metrics=denotation_match_metrics(),\n", " print_examples=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look through that output. Over the ten examples, we achieved denotation oracle accuracy of 60%, but denotation accuracy of just 40%. In other words, there were two examples where we generated a correct parse, but failed to rank it at the top. Take a closer look at those two cases.\n", "\n", "The first case is `\"what state has the shortest river ?\"`. The top parse has semantics `('.and', 'state', ('.argmin', 'length', 'river'))`, which means something like \"states that are the shortest river\". That's not right. In fact, there's no such thing: the denotation is empty.\n", "\n", "The second case is `\"what is the highest mountain in alaska ?\"`. The top parse has semantics `('.argmax', 'height', ('.and', 'mountain', '/state/alaska'))`, which means \"the highest mountain which is alaska\". Again, there's no such thing: the denotation is empty.\n", "\n", "So in both of the cases where we put the wrong parse at the top, the top parse had nonsensical semantics with an empty denotation. In fact, if you scroll through the output above, you will see that there are a _lot_ of candidate parses with empty denotations. Seems like we could make a big improvement just by downweighting parses with empty denotations. This is easy to do." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def empty_denotation_feature(parse):\n", " features = defaultdict(float)\n", " if parse.denotation == ():\n", " features['empty_denotation'] += 1.0\n", " return features\n", "\n", "weights = {'empty_denotation': -1.0}\n", "\n", "model = Model(grammar=grammar,\n", " feature_fn=empty_denotation_feature,\n", " weights=weights,\n", " executor=executor.execute)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's evaluate the impact of using our new `empty_denotation` feature on the Geo880 training examples." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Evaluating on 600 examples\n", "\n", "--------------------------------------------------------------------------------\n", "Max cell capacity 1000 has been hit 128 times\n", "Over 600 examples:\n", "\n", "denotation accuracy 0.387\n", "denotation oracle accuracy 0.468\n", "number of parses 13.210\n", "spurious ambiguity 0.001\n", "\n" ] } ], "source": [ "from experiment import evaluate_model\n", "from metrics import denotation_match_metrics\n", "\n", "evaluate_model(model=model,\n", " examples=geo880_train_examples,\n", " metrics=denotation_match_metrics(),\n", " print_examples=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Using the `empty_denotation` feature has enabled us to increase denotation accuracy from 25% to 39%. That's a big gain! In fact, we've closed most of the gap between accuracy and oracle accuracy. As a result, the headroom for further gains from feature engineering is limited — but it's not zero. The [exercises](#geoquery-exercises) will ask you to push further." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises <a id=\"geoquery-exercises\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several of these exercises ask you to measure the impact of your change on key evaluation metrics. Part of your job is to decide which evaluation metrics are most relevant for the change you're making. It's probably best to evaluate only on training data, in order to keep the test data unseen during development. (But the test data is hardly a state secret, so whatever.)\n", "\n", "### Straightforward\n", "\n", "1. Extend the grammar to handle queries which use the phrase \"how many people\" to ask about population. There are many examples in the Geo880 training data. Measure the impact of this change on key evaluation metrics.\n", "\n", "1. Extend the grammar to handle counting questions, indicated by the phrases \"how many\" or \"number of\", as in \"how many states does missouri border\". Measure the impact of this change on key evaluation metrics.\n", "\n", "1. Several examples in the Geo880 training dataset fail to parse because they refer to locations using names that, while valid, are not recognized by the `GeobaseAnnotator`. For example, some queries use \"america\" to refer to `/country/usa`, but `GeobaseAnnotator` recognizes only \"usa\". Find unannotated location references in the Geo880 training dataset, and extend the grammar to handle them. Measure the impact of this change on key evaluation metrics.\n", "\n", "1. Extend the grammar to handle examples of the form \"where is X\", such as \"where is mount whitney\" or \"where is san diego\". Measure the impact of this change on key evaluation metrics.\n", "\n", "1. Quite a few examples in the Geo880 training dataset specify a set of entities by name, as in \"how many states have a city named springfield\" or \"how many rivers are called colorado\". Extend the grammar to handle these, leveraging the `TokenAnnotator` introduced in [Unit 2][]. Measure the impact of this change on key evaluation metrics.\n", "\n", "1. Extend the grammar to handle phrases like \"austin texas\" or \"atlanta ga\", where two entity names appear in sequence. Make sure that \"atlanta ga\" has semantics and a denotation, whereas \"atlanta tx\" has semantics but no denotation. Measure the impact of this change on key evaluation metrics.\n", "\n", "1. In [Unit 2][], while examining the travel domain, we saw big gains from including rule features in our feature representation. Experiment with adding rule features to the GeoQuery model. Are the learned weights intuitive? Do the rule features help? If so, identify a few specific examples which are fixed by the inclusion of rule features. Measure the impact on key evaluation metrics.\n", "\n", "1. Find an example where using the `empty_denotation` feature causes a loss (a bad prediction).\n", "\n", "1. The `empty_denotation` feature doesn't help on count questions (e.g., \"how many states ...\"), where all parses, good or bad, typically yield a denotation which is a single number. Add a new feature which helps in such cases. Identify a few specific examples which are fixed by the new feature. Measure the impact on key evaluation metrics. [This exercise assumes you have already done the exercise on handling count questions.]\n", "\n", "### Challenging\n", "\n", "1. Extend the grammar to handle comparisons, such as \"mountains with height greater than 5000\" or \"mountains with height greater than the height of bona\".\n", "\n", "1. Building on the previous exercise, extend the grammar to handle even those comparisons which involve ellipsis, such as \"mountains higher than \\[the height of\\] mt. katahdin\" or \"rivers longer than \\[the length of\\] the colorado river\" (where the bracketed phrase does not appear in the surface form!).\n", "\n", "1. Extend the grammar to handle queries involving units, such as \"what is the area of maryland in square kilometers\" or \"how long is the mississippi river in miles\".\n", "\n", "1. The Geo880 training dataset contains many examples involving population density. Extend the grammar to handle these examples. Measure the impact on key evaluation metrics.\n", "\n", "1. Several examples in the Geo880 training dataset involve some form of negation, expressed by the words \"not\", \"no\", or \"excluding\". Extend the grammar to handle these examples. Measure the impact on key evaluation metrics.\n", "\n", "1. The current grammar handles \"capital of texas\", but not \"texas capital\". It handles \"has austin capital\", but not \"has capital austin\". In general, it defines every phrase which expresses a relation as either a `$FwdRelation` or a `$RevRelation`, and constrains word order accordingly. Extend the grammar to allow any phrase which is ordinarily a `$FwdRelation` to function as a `$RevRelation`, and vice versa. Can you now handle \"texas capital\" and \"has capital austin\"? Measure the impact on key evaluation metrics.\n", "\n", "1. What if we permit any word to be optionalized by adding the rule `Rule('$Optional', '$Token')` to our grammar? (Recall that `$Token` is produced by the `TokenAnnotator`.) Measure the impact of this change on key evaluation metrics. You will likely find that the change has some negative effects and some positive effects. Is there a way to mitigate the negative effects, while preserving the positive effects?\n", "\n", "1. The success of the `empty_denotation` feature demonstrates the potential of denotation features. Can we go further? Experiment with features that characterize the *size* of the denotation (that is, the number of answers). Are two answers better than one? Are ten answers better than two? If you find some features that seem to work, identify a few specific examples which are fixed by your new features. Measure the impact on key evaluation metrics.\n", "\n", " [Unit 2]: ./sippycup-unit-2.ipynb\n", " [Levenshtein distance]: http://en.wikipedia.org/wiki/Levenshtein_distance\n", " [minhashing]: http://en.wikipedia.org/wiki/MinHash\n", " \n", "<!-- (TODO: There should be an exercise related to spurious ambiguity.) -->\n", "\n", "<!--\n", "1. Because `GeobaseAnnotator` relies on exact string matching, it isn't very robust. As a result, it fails to annotate \"ft. lauderdale\" (which Geobase knows as \"fort lauderdale\"), \"baltamore\" (a misspelling of \"baltimore\"), or \"portlad\" (a typo for \"portland\"). Find a way to make it more robust by doing approximate string matching, perhaps using [Levenshtein distance][] or [minhashing][] of character-level n-grams (shingles).\n", "-->" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright (C) 2015 Bill MacCartney" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
rsignell-usgs/notebook
brad_plots.ipynb
1
2396
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mit
andrew-lundgren/detchar
Notebooks/thiran_allpass.ipynb
1
37523
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Imports for testing and plotting\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import scipy.signal as signal" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.special import binom\n", "\n", "\n", "def thiran(N, delta):\n", " \"\"\"\n", " Returns b, a coefficients for Thiran allpass\n", " \n", " Algorithm from https://ccrma.stanford.edu/~jos/pasp/Thiran_Allpass_Interpolators.html\n", " \"\"\"\n", " \n", " def coeff(N, k, delta):\n", " factors = [(delta-N+nn)/float(delta-N+k+nn)\n", " for nn in np.arange(N+1)]\n", " return ((-1)**k)*binom(N,k)*np.prod(factors)\n", "\n", " coeffs = np.array([coeff(N, kk, delta) for kk in range(0, N+1)])\n", " coeffs[0] = 1.\n", " b, a = coeffs[::-1].copy(), coeffs.copy()\n", " return b, a" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "b, a = thiran(8, 7.75)\n", "sos = signal.tf2sos(b, a)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "w, h = signal.freqz(b, a, worN = 16384, fs = 16384)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fb405a3ea50>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(w, abs(h))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fb31a6b8f50>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(w, np.unwrap(np.angle(h)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "imp = np.zeros(30)\n", "imp[10] = 1.\n", "test = signal.sosfilt(sos, imp)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x7fb3184e1e90>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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U0dQtouZuUkEcI//NwE53f8rdZ4CbgUuLbPdnwEeB6RieUzrA7CpeVV7kBWFnT5V9jjVZ5cLthbSUo1QQR/ivA3YV3B8JH5tlZhcAp7j7v5fbkZltMbNhMxseGxuL4dCknWqt+UfbquwzT62tHUAjf6kojvC3Io/Nzi8zswTwMeC9lXbk7te7+yZ337RmTQ1/4sqilJ7OkDBY0VvDyF+reS1Ua2sHUPhLRXGE/whwSsH99cBowf1B4IXAt8zsaeBlwFad9F360lMZBvt7SCSKjQ+KG+rv0Tz/+Sb2QbIX+ldW/zWa7SMVxBH+9wEbzex0M+sFLgO2Rp9093F3X+3uG9x9A3APcIm7D8fw3LKI1dLULTI0kNLIf77JsaDkY9X/EqVXI38pr+Hwd/cs8E7gduAx4FZ332Fm15jZJY3uXzpXLe2cI+rpX0StV/cCJFPQswyOauQvxdU2LCvB3bcB2+Y99qES214Yx3PK4peeqn4Vr8jKgR4OH82SyzvJGspFS9rEGKxcV3m7+fqGFP5Skq7wlaappaNnJJoZNKG6/5x6Rv6gzp5SlsJfmmZ8KlN7zV9X+R4rn4Mjz9Y2zTOi8JcyFP7SNOmpbF1lH1B/n1lHngsatNVygVdE4S9lKPylKWayeaYyubrLPjrpG6qntUOkf0hTPaUkhb80xeHp2q/uBXX2XKCe1g4RLeUoZSj8pSnmWjvUPs+/8Ou7Xj2tHSIq+0gZCn9pinSNq3hF5hZx12wfoL7WDpFoqmc+H+8xyZKg8JemqLWXf2R5b4qEqewza2IfpPrnevXUom8QcMhMxn5Y0vkU/tIU9XT0BEgkjMF+dfacFS3fWEtrh4iau0kZCn9piloXcim0Up0950zsq6/eD2ruJmUp/KUpZhdyqbHsA2FzN13hG5ioceH2QlrKUcpQ+EtTpKcz9CSN/p7af8TU3K1Ava0doKDso5G/LKTwl6aIOnpaHbXqIdX8A7ksHDlQf9lnduSv8JeFFP7SFPV09IysHOjRbB8IevrgMYz8VfaRhRT+0hTp6SyDdYZ/sKCLav4NzfEHhb+UpfCXpgjKPvUtFzHU38NUJsdMtssvTppooLUDzIW/ZvtIEQp/aYrDjZR9lqm/DzDX12d5HU3dABJJ6F2hkb8UpfCXpkhPZ2q+wCsy29yt20/6Njryh7C/j0b+spDCX2Ln7qSnsnXN8Ye55m5dP9d/Yn+wDm/fivr3oaUcpQSFv8RuOpNnJpevuaNnJPql0fXTPSf21l/yiaizp5Sg8JfYNdLaofDrur7ss28HnHh2Y/tQ+EsJCn+JXb0dPSNDWsoRjk7A2ONw8gWN7UfhLyUo/CV29Xb0jKjsA+x5CPDGw19LOUoJCn+JXaNln/6eBL3JRHdf6DW6Pbhde35j+9FSjlJCLOFvZheZ2eNmttPMri7y+feY2aNm9rCZ3WFmp8XxvLI4zXX0rO+Er5mFnT27eOQ/uh2G1sFgA9M8ISj7zBzWal6yQMPhb2ZJ4FrgYuAc4HIzO2feZtuBTe5+HvAl4KONPq8sXuk6F28v1PWdPUe3N17ygbnmbjMa/cux4hj5bwZ2uvtT7j4D3AxcWriBu9/p7kfCu/cA62N4Xlmkxo80dsIXYHCgizt7Th2C556MKfzV30eKiyP81wG7Cu6PhI+V8jbgtmKfMLMtZjZsZsNjY2MxHJq0Q3o6w0BPkt5U/T9eQWfPLq3573kouFX4SxPFEf7FGrZ70Q3NrgA2AX9V7PPufr27b3L3TWvWNHhxi7RNeipb9wVekaH+FIe7deQ/+kBwG0f4aylHKaGx/6GBEeCUgvvrgdH5G5nZa4APAK9y96MxPK8sUtFCLo0Y6uayz+h2OO40WHZC4/vSUo5SQhwj//uAjWZ2upn1ApcBWws3MLMLgOuAS9x9fwzPKYtYerr+jp6RaEEX96J/RC5tcZ3sBS3lKCU1HP7ungXeCdwOPAbc6u47zOwaM7sk3OyvgBXAv5jZg2a2tcTuZAlopKNnZKi/h0zOmc502RTFyQNw6Mcxhr+WcpTi4ij74O7bgG3zHvtQwcevieN5pDOkp7KcuabBmv9sZ88MA73JOA6rM+wJL+5a9+J49qcTvlKCrvCV2I1PxTPyj/bVVWav7H1RPPvrXQGYwl8WUPhLrPJ553BMNX/ows6eu7fDqjOhf2U8+0skgtG/ZvvIPAp/idXkTJa8N3aBF3RxZ884T/ZG1NlTilD4S6zmOno2Ps+/cH9d4fBeODzapPDXyF+OpfCXWEVN3eIr+3TRVb6jDwa3sYe/lnKUhRT+EqvZpm4Nln0Gu3ER99HtYAl43nnx7ldlHylC4S+xSje4kEukN5VgoCfZXTX/0e2w+icaW7C9GIW/FKHwl1iNN7iEY6GhgVT31Pzdm3OyFxT+UpTCX2IVdeJstOYf7aNrav7p3TC5vznh379SUz1lAYW/xCoq+6yocxWvQkP9Pd1T9oku7mrWyD8zCflc/PuWjqXwl1ilpzMM9qVIJop1+q7N0ECXhX8iBc97Yfz7rqfFQ2YavvFBOLwv/uORRUHhL7GKo7VDZKi/i2r+o9vhxLOhZyD+fdfT3O3pu+C7n4Bvfyz+45FFQeEvsQoWcokn/Lum5t/Mk71Q38h/ZDi4ffCf4ehE/Mckbafwl1ilpzOzV+c2amigh8PTGfL5Jd7T/9AzMHVwcYX/7uG5i8Mevrk5xyVtpfCXWKVjLfv0kHeYmFnio//dMS7bWEzUJK7aGT/usPt+OOfSoLvo9z4TPCZLisJfYpWearyjZ6RrOnuObodkL5x4TnP2X+tqXs89Ffwlsn4TbL4Kxn4AP/rv5hybtI3CX2KVns7GcoEXFCzostTr/qPb4aRzIdXXnP3XWvaJ6v3rNsELfxWWrYJ7r2/OsUnbKPwlNtlcnomj2YY7ekaiXyJLerpnPg97HoKTY1q5q5haZ/vsHoae5eHso3548ZXwxG1w8JnmHaO0nMJfYnM4vLo3vpF/F6zm9dxTQSg3q94P0LucmlbzGhkOjicRLp/5U28Lvv6+zzbrCKUNFP4Sm2iErpp/DZp5ZW/ELJy5U0X4Z6Zh7yOw/iVzj61cDy/4RXjgCzBzpHnHKS2l8JfYRLX5OGf7wFy/oCVp9AFI9cOaFzT3efqHqpvts/cRyGeCen+hl14F04fg+19qzvFJyyn8JTZzHT3jqfmv6IbVvEa3B/37k/F8z0qqdjWv3eHJ3vXzwv+0V8CJ5wYnfjXtc0lQ+EtsZss+y+IZ+ScTxmB/qjPKPvk85Gr8CyWfC0/2NrHkE6m2rfPIMAyeDEMnH/u4GWx+B+x7BH58d33HMHI/fOyFcMefBeUlaSuFv8QmHWMv/8ii7+w5dRC+8wn4u/Pg06+orRXCs09A5gisa+JMn0gtI//Cen+h894cXDB273W1P//hvXDL/4Lpcbjrr4Pv1dPfrn0/EptYwt/MLjKzx81sp5ldXeTzfWZ2S/j5e81sQxzPK4vL7BKOMdX8o30tynn+z/4Qvv5e+Ntz4JsfhMG1QZjf9ofV76MVJ3sj1ZzwnXwWDj69sN4f6V0OF7wFHvs3GN9d/XNnj8ItbwmC/zdvg7d8FXIZuOEX4d/eDVOHqt+XxKbh8DezJHAtcDFwDnC5mc2/VPFtwEF3PxP4GPCXjT6vLD7jUxmSCWN5bzK2fQ4tprKPO+y8A258I/z9pmD2y7m/DFfdBW//JvzMe+HBG+H7X65uf6PboXcFrDqzuccN1ZV9dt8f3M6v9xf6qbeD52H489U9rztsex+MfA/e8MmgZfUZPwe/fTf89LuC7+G1Lw1+odRq6hA8dDN87Xfgjmvg4VuDMppmJFUljrNMm4Gd7v4UgJndDFwKPFqwzaXAh8OPvwT8vZmZe/xnjo5MjPPQFz8Y926lChsOTvH+3gx2x32VNz54VnD7n7eX3eytU3vZOz7NPdd/ESeBW7ROgOFYcBs+5hhJz5DMZwpuZ0h6lmR+hpRnSOQzpHyGrPVyuO8kxvvWku59Hum+4N/h3jW4HfvfIpWb5txnb+Mle29h9dSPmOg5gQfXv4MHT/xljvSugoeAh35AIv8GLl9xG6u++rvc8OQJpPvWln1tVzz6HXJ9G7npGz+s/P0qYtdzQch99D9+UHHbC/dkOP/IOB8vs+0rd/0HLyPB3+1YTuYHpbf7leNfydq7P8enj15KLtFb9nnP3/tlXvf0F7j75Cu5a9fZsKtwv1dw0rkv4aKn/oKTbrmCx0+4kDs2vI+J3jUl9zeQOcjGg//DWQfu5LT0fSQ9y1RyiL7cJAmCxWocY7xvLQcGTufZgdM5MHA6BwY2cGBgAzPJZfRnD7Mse5BlmYMMZA+xLHOQZZlDDGSD22XZQ/Rnx8lZD5nEANlkP5lE+C85cMzH2UQ/eUuSyGdIepaEZ2dvE54Nfwbn7gc/w+E/EuQtgZPEzchbcvbzieNO4aff+O6y39tGxRH+64BdBfdHgJeW2sbds2Y2DqwCni3cyMy2AFsATj311LoOZvrIBC/ZfWNdXyuNeQmQMIPvVrGQy/TVgMHd15bd7HV5J5fPY7ujuA8iP2Glxw1HvYcZUmRIMUPwcXSb8eDjfg6y3h7lRXZsHTznxj5OYLevZpTVHPF+Lkrcy/E2wQ7fwF/mfottmZcz86Me+NE4MH7M12/j7WxN/RGb7v9Drsh9kBzF/wpKkeXdqSe4Mf9aPnPXU5W/X0Vkc8H3oJqv701k2Jyc5h/ueoJsif/2L08O87idwie/u6fsvh61V3JD6i72fPcm/tV/puR2P2WP8fvJv+Fbfj5XPfM68s8UO84+PsIH+d+JbbzrwJe44sD3+Gju17nVX42HhYkTeY7XJob5BbuXzfYYSXN2+Yn8Q/4ibvfNPJQ5gxR5TmMvZ9puzrQRzpjazRnTuzj/4Pfos7m/HLOeIGX5osd72Ad4jiGe8yHGWU4PWfoZZ5ntZ4CjHM9RlnGUfo7Sa5VXRcu5kSFFlhQzJMmRJIFj5EniJMmTKPiXJE8y/Ll+fN8LgOaGvzU6+DazNwG/4O5vD++/Bdjs7u8q2GZHuM1IeP/JcJsDpfa7adMmHx4ebujYZPH6teuCGSO3XPXyxnbkHpQh3AEPVsOyGlYRmzkSrJ976McwvgsO7YLxkbmPJ8dg42vgZb8Np768un0/fCt85R1w4fvhwhLnAPY8DNf9DPzq5+An31j98Rao6Xt4z6fgP66GP/gRLDth4efzefjohqCM9Ut/V35f7nDt5qBkteXO4tuMj8B1r4KB4+DtdwS3lRx4MjgH8PRdwdTSs34BfvB12PU9wGH1WXD2JXDOJcH02Grei3wuOI8x9njQoG5mEpavhmWrYfmq8HZ10L+olt5KuUywr8wU5LNBY75kT/AvEd3WUf50n/uZrnP6r5nd7+5laneBOEb+I8ApBffXA6MlthkxsxSwEnguhueWbmcG1sA5ht5lsHpj8C8u570Zdv4n/PdH4PmvglNftnCbVp7shWObuxUL/+eeDE7IljrZW8gMNm8Ja/nDC88RZKbg5l8PTvRe9sXqgh9g1Rlw5b/B9hvhGx+AZ74DJ/0kvPr9QeifWMeFcIlksN9VZ8ALXl/715eS7AleV7WvrVpm4S+15k/EjOMZ7gM2mtnpZtYLXAZsnbfNVuDK8OM3Av/VjHq/yKLx+r+G406FL7+j+GyW0e3QtxJOeH5rjqdSc7eREhd3lfKiy6B3cOG0T3fY+rvBXza/+hlY8xO1HacZvPgt8O6H4fcegd/6NrzqD+oLfimr4fB39yzwTuB24DHgVnffYWbXmNkl4WafA1aZ2U7gPcCC6aAiS0r/UFDSSe+Gf//9hVfFjm6Hk8+vrUTViEptnXcPB2G++qzq93f+r8OOrx67yPvd18Ijt8KrPwA/cXH9xztwXPDLU5omlr8t3H2bu5/l7me4+5+Hj33I3beGH0+7+5vc/Ux33xzNDBJZ0tZvCkoWO74CD90093j2KOzb0bqSDxSM/EuE/8gwrLugtjr15i1BH6D7bwjuP/lfwTUPZ/9SMO1VFjVd4SvSTK/8fTjtlfD19wUnNAH2fT8IzVaGf38Y/sWau2WmgmOqpt5faPWZcMbPB3P+x56Af/nNoEHdGz4NCUVqZq9iAAAHWUlEQVTLYqd3SKSZEkn4leuCE4RffhtkZ1p/shfKL+W456Fgxkq19f5CL70KJvbC514T3L/sn6FvRf3HKS2j8BdptpXr4ZJPBKH/rb8Ibpetam1Nu1zNv3DZxlqd+Vo4/vRgv2/6h9adwJaGNbmPrIgAcM6lwXKI3/54cDJz3Utad7IXoGdZMCW22Mh/9zCsPAUGT6p9v4kEvOkGOHIgaNsgHUPhL9IqF/2/oB3ys0+0tuQD4WpeJfr7jNwf/DKq18nn1/+10jYq+4i0Su/yYPrnipOCE6WtVqyz58R+GP9xffV+6Wga+Yu00trz4H1PtOe5iy3l2Ei9XzqaRv4i3aLYgi67h4NzAWtf1J5jkrZR+It0i2I1/5FhOOncoMeRdBWFv0i3mD/yz+eDaaeq93clhb9It5h/wvfZJ4JfBqr3dyWFv0i3mF/22V1jJ09ZUhT+It2ibwiy00GLCQjq/X0rYVWMaxlIx1D4i3SL/nmdPXdHnTwVA91I77pItyhs7jZzBPY9qnp/F9NFXiLdorC52+E94DnV+7uYwl+kWxQu5bj7geBjjfy7lso+It2icOS/ezhoKb1iTXuPSdpG4S/SLQqXchy5X6P+LqfwF+kW0WyfZ5+A9Ijq/V1O4S/SLaKyz5N3Brca+Xc1hb9It0j1QyIFow8Et2vPa/cRSRsp/EW6hVlQ9/c8nPRC6Blo9xFJGyn8RbpJVPpRvb/rNRT+ZnaCmX3TzH4Y3h5fZJvzzexuM9thZg+b2a818pwi0oBoxo/q/V2v0ZH/1cAd7r4RuCO8P98R4Dfc/VzgIuDjZnZcg88rIvXQyF9CjYb/pcA/hh//I/CG+Ru4+xPu/sPw41FgP6ArS0TaoX8I+lfCCWe0+0ikzRpt73CSu+8BcPc9ZnZiuY3NbDPQCzxZ4vNbgC0Ap556aoOHJiILbN4C51yqTp5SOfzN7D+B5xX51AdqeSIzWwv8E3Clu+eLbePu1wPXA2zatMlr2b+IVOHMn2/3EcgiUTH83f01pT5nZvvMbG046l9LUNIptt0Q8HXgj939nrqPVkREYtHo335bgSvDj68E/nX+BmbWC3wV+IK7/0uDzyciIjFoNPw/ArzWzH4IvDa8j5ltMrPPhtu8GfhZ4K1m9mD47/wGn1dERBrQ0Alfdz8ALCgiuvsw8Pbw4xuBGxt5HhERiZdO+YuIdCGFv4hIF1L4i4h0IYW/iEgXMvfFeS2VmY0BzzSwi9XAszEdzmKw1F4PLL3XtNReDyy917TUXg8sfE2nuXvFFjqLNvwbZWbD7r5kulcttdcDS+81LbXXA0vvNS211wP1vyaVfUREupDCX0SkCy3l8L++3QcQs6X2emDpvaal9npg6b2mpfZ6oM7XtGRr/iIiUtpSHvmLiEgJSy78zewiM3vczHaaWbFlJTuOmT1tZo+ETfGG2308tTKzz5vZfjP7fsFjFdd/XsxKvKYPm9nuggaGr2/nMdbCzE4xszvN7LFwve13h4935PtU5vV08nvUb2bfM7OHwtf0p+Hjp5vZveF7dEvYSbny/pZS2cfMksATBB1GR4D7gMvd/dG2HliDzOxpYJO7d+T8ZDP7WWCCoK33C8PHPgo85+4fCX9JH+/uf9jO46xFidf0YWDC3f+6ncdWj3A9jrXu/oCZDQL3EyzL+lY68H0q83reTOe+RwYsd/cJM+sBvg28G3gP8BV3v9nMPg085O6fqrS/pTby3wzsdPen3H0GuJlgnWFpI3f/H+C5eQ9XXP95MSvxmjqWu+9x9wfCjw8DjwHr6ND3qczr6VgemAjv9oT/HPg54Evh41W/R0st/NcBuwruj9Dhb3jIgW+Y2f3hOsdLwTHrPwNl13/uIO80s4fDslBHlEjmM7MNwAXAvSyB92ne64EOfo/MLGlmDxKsmvhNgvXQD7l7Ntyk6sxbauFvRR5bCnWtV7j7i4GLgd8JSw6y+HwKOAM4H9gD/E17D6d2ZrYC+DLwe+6ebvfxNKrI6+no98jdc+5+PrCeoNJxdrHNqtnXUgv/EeCUgvvrgdE2HUts3H00vN1PsCTm5vYeUSz2hXXZqD5bdP3nTuLu+8L/nHngM3TY+xTWkb8M/LO7fyV8uGPfp2Kvp9Pfo4i7HwK+BbwMOM7MooW5qs68pRb+9wEbw7PfvcBlBOsMdywzWx6esMLMlgOvA75f/qs6QsX1nztNFJKhX6aD3qfwZOLngMfc/W8LPtWR71Op19Ph79EaMzsu/HgAeA3BuYw7gTeGm1X9Hi2p2T4A4dStjwNJ4PPu/udtPqSGmNnzCUb7ECy7+cVOe01mdhNwIUH3wX3AnwBfA24FTgV+DLzJ3TvmBGqJ13QhQTnBgaeBq6J6+WJnZq8E7gIeAfLhw+8nqJN33PtU5vVcTue+R+cRnNBNEgzcb3X3a8KMuBk4AdgOXOHuRyvub6mFv4iIVLbUyj4iIlIFhb+ISBdS+IuIdCGFv4hIF1L4i4h0IYW/iEgXUviLiHQhhb+ISBf6/xl7xhzZKe7LAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(imp)\n", "plt.plot(test)\n", "plt.axvline(10)\n", "plt.axvline(10+7.75)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "gwpy-conda", "language": "python", "name": "gwpy-conda" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
TheMitchWorksPro/DataTech_Playground
PY_Basics/str_arr_lst_prnt/TMWP_String_List_Array_Print_Examples.ipynb
1
19352
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">Python [conda env:PY27_Test]<br/>Python [conda env:PY36]</div>\n", "\n", "# Printing, Creating & Formatting Strings And Some List Behaviors\n", "\n", "All content in this Notebook is Python 2.7 and 3.6 compliant (except where noted). All code without comments indicating otherwise was cross-tested in Python 2.7 and Python 3.6" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13) Here is some text\n" ] } ], "source": [ "myNumber = 13\n", "myStr = \"Here is some text\"\n", "print(\"%d) %s\" %(myNumber, myStr))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13) Here is some text\n" ] } ], "source": [ "# note how the same syntax we use to format content for printing\n", "# also works to format strings\n", "myNumber = 13\n", "myStr = \"Here is some text\"\n", "myStr = \"%d) %s\" %(myNumber, myStr)\n", "print(myStr)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "something to say. More things to say about the number 13\n" ] } ], "source": [ "# note how you can build a % formatter in pieces like this:\n", "frag = \"to say\"\n", "num = 13\n", "print((\"something %s.\" + \" More things to say about the number %d\") %(frag, num))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(\"_\"*0) # if you use 0 nothing is output" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0\n", " 80\n", "160\n" ] } ], "source": [ "# implications of this (note how we align the numbers by ending on 0 spaces)\n", "N = 3\n", "for i in range(N):\n", " # print(N-i-1) # uncomment to see values: 2, 1, 0\n", " print(\" \"*(N-i-1) + str(8*10*i))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3]\n", "--------------\n", "1\n", "2\n", "3\n", "--------------\n", "123\n", "--------------\n", "123" ] } ], "source": [ "# version: Python 3.x, or add in appropriate \"from_future\" lines for Python 2.7 before using it\n", "LL = [1,2,3]\n", "print(LL)\n", "\n", "print(\"-\"*14)\n", "for i in LL:\n", " print(i)\n", "print(\"-\"*14)\n", "\n", "for i in LL:\n", " print(i, sep=\" \", end=\"\")\n", "print(\"\\n\" + \"-\"*14)\n", "\n", "print(*LL, sep=\"\", end=\"\") # unpacking a list only valid in Python 3.x (*LL part of this example)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'a, b, c'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LL2 = ['a', 'b', 'c'] # only works if elements are strings\n", "\", \".join(LL2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1, 2, 3'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\", \".join([str(elem) for elem in LL]) # this is how to convert the numeric one to a string" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests of .join():\n", "now is the time for all good men to come to the aid of their country\n" ] } ], "source": [ "print(\"Tests of .join():\")\n", "seq = (\"now is the time for all good men\", \"to come to the aid of their country\") # this works w/ lists and tuples\n", "print(\" \".join(seq))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['c', 'a', 't']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LL3 = list(\"cat\")\n", "LL3" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[12, 123, 124, 125]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lstTst2 = [12, [123, 124, 125]]\n", "[lstTst2[0]] + lstTst2[1]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n" ] } ], "source": [ "lst = [1,2,3,4]\n", "print(3 in lst)\n", "print(3 not in lst)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DO NOT GO GENTLE INTO THAT GOOD NIGHT. RAGE, RAGE AGAINST THE DYING OF THE LIGHT\n", "do not go gentle into that good night. rage, rage against the dying of the light\n", "dO NOT GO GENTLE INTO THAT GOOD NIGHT. rAGE, RAGE AGAINST THE DYING OF THE LIGHT\n", "Do Not Go Gentle Into That Good Night. Rage, Rage Against The Dying Of The Light\n" ] } ], "source": [ "# built-in functions for manipulating the case of a string\n", "\n", "testString = \"Do not go gentle into that good night. Rage, rage against the dying of the light\" # from Dyllan Thomas Poem\n", "print(testString.upper())\n", "print(testString.lower())\n", "print(testString.swapcase())\n", "print(testString.title())" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This Is A Test 4all You Do. .a .b .c -d\n", "This Is A Test 4All You Do. .A .B .C -D\n" ] } ], "source": [ "# canned functions for manipulating a string from libraries\n", "import string\n", "print(string.capwords(\"This is a test 4all you do. .a .b .c -d\"))\n", "print(\"This is a test 4all you do. .a .b .c -d\".title()) # note the difference" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 6.39 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "100000 loops, best of 3: 2.3 µs per loop\n" ] } ], "source": [ "timeit(testString.swapcase())" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def swap_case(s):\n", " return \"\".join([c.upper() if c.islower() else c.lower() for c in s])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 61 µs per loop\n" ] } ], "source": [ "timeit(swap_case(testString)) # home grown version not nearly as fast as builtin" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Do-not-go-gentle-into-that-good-night.--Rage,-rage-against-the-dying-of-the-light\n", "Do-not-go-gentle-into-that-good-night.--Rage,-rage-against-the-dying-of-the-light\n" ] } ], "source": [ "def split_and_join(line):\n", " return \"-\".join(line.split(\" \"))\n", "\n", "print(split_and_join(testString))\n", "print(testString.replace(\" \", \"-\"))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Alphanumerics: 70\n", " Alphas: 64\n", " Numerics: 6\n", "Lower Letters: 56\n", "Upper Letters: 8\n" ] } ], "source": [ "def test_str_chars(s):\n", " alphanums = 0\n", " alphas = 0\n", " digits = 0\n", " lowcase = 0\n", " uppcase = 0\n", " \n", " for i in range(len(s)):\n", " if s[i].isalnum() == True:\n", " alphanums += 1\n", "\n", " for i in range(len(s)):\n", " if s[i].isalpha() == True:\n", " alphas +=1\n", " \n", " for i in range(len(s)):\n", " if s[i].isdigit() == True:\n", " digits += 1\n", " \n", " for i in range(len(s)):\n", " if s[i].islower() == True:\n", " lowcase += 1\n", " \n", " for i in range(len(s)):\n", " if s[i].isupper() == True:\n", " uppcase += 1\n", "\n", " print(\"Alphanumerics: %d\" %alphanums)\n", " print(\" Alphas: %d\" %alphas)\n", " print(\" Numerics: %d\" %digits)\n", " print(\"Lower Letters: %d\" %lowcase)\n", " print(\"Upper Letters: %d\" %uppcase)\n", "\n", "test_str_chars(\"Do not go gentle into that good night said 143 times ... then: Rage, Rage against x109ABCDZ\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HackerRank----------\n", "----------HackerRank\n", "-----HackerRank-----\n" ] } ], "source": [ "# string lessons: Courtesy of Hacker Rank\n", "# code works in Python 2.7 and Python 3.x\n", "\n", "width = 20\n", "print('HackerRank'.ljust(width,'-')) # num needs to equal length of text string + number of dashes to add to it \n", "print('HackerRank'.rjust(width,'-'))\n", "print('HackerRank'.center(width,'-'))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is\n", "a very\n", "very\n", "very\n", "very\n", "very\n", "long\n", "string.\n", "['This is', 'a very', 'very', 'very', 'very', 'very', 'long', 'string.']\n" ] } ], "source": [ "# from Hacker Rank\n", "# code works in Python 2.7 and Python 3.x\n", "\n", "import textwrap\n", "string = \"This is a very very very very very long string.\"\n", "print(textwrap.fill(string,8))\n", "print(textwrap.wrap(string,8))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---------------------.|.---------------------\n", "------------------.|..|..|.------------------\n", "---------------.|..|..|..|..|.---------------\n", "------------.|..|..|..|..|..|..|.------------\n", "---------.|..|..|..|..|..|..|..|..|.---------\n", "------.|..|..|..|..|..|..|..|..|..|..|.------\n", "---.|..|..|..|..|..|..|..|..|..|..|..|..|.---\n", "-------------------WELCOME-------------------\n", "---.|..|..|..|..|..|..|..|..|..|..|..|..|.---\n", "------.|..|..|..|..|..|..|..|..|..|..|.------\n", "---------.|..|..|..|..|..|..|..|..|.---------\n", "------------.|..|..|..|..|..|..|.------------\n", "---------------.|..|..|..|..|.---------------\n", "------------------.|..|..|.------------------\n", "---------------------.|.---------------------\n", "\n", "****************+:+*****************\n", "*************+:++:++:+**************\n", "**********+:++:++:++:++:+***********\n", "*******+:++:++:++:++:++:++:+********\n", "****+:++:++:++:++:++:++:++:++:+*****\n", "*+:++:++:++:++:++:++:++:++:++:++:+**\n", "***********EAT MY SHORTS!***********\n", "***+:++:++:++:++:++:++:++:++:++:+***\n", "******+:++:++:++:++:++:++:++:+******\n", "*********+:++:++:++:++:++:+*********\n", "************+:++:++:++:+************\n", "***************+:++:+***************\n", "************************************\n", "\n", "++++++++++++.|.++++++++++++\n", "+++++++++.|..|..|.+++++++++\n", "++++++.|..|..|..|..|.++++++\n", "+++.|..|..|..|..|..|..|.+++\n", "++++++++++WELCOME++++++++++\n", "+++.|..|..|..|..|..|..|.+++\n", "++++++.|..|..|..|..|.++++++\n", "+++++++++.|..|..|.+++++++++\n", "++++++++++++.|.++++++++++++\n", "\n", "---------+:+---------\n", "------+:++:++:+------\n", "---+:++:++:++:++:+---\n", "-------WELCOME-------\n", "---+:++:++:++:++:+---\n", "------+:++:++:+------\n", "---------+:+---------\n", "\n", "------.|.------\n", "---.|..|..|.---\n", "-----B'BYE!----\n", "---.|..|..|.---\n", "------.|.------\n", "\n" ] }, { "ename": "ValueError", "evalue": "dRows for number of rows in the doorMat must be at least 5.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-39-14ab64c727b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mcreate_doorMat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcenterText\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"B'BYE!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mcreate_doorMat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-39-14ab64c727b9>\u001b[0m in \u001b[0;36mcreate_doorMat\u001b[0;34m(dRows, centerText, dTxt, fillChar)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcreate_doorMat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdRows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcenterText\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"WELCOME\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdTxt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\".|.\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfillChar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"-\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdRows\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"dRows for number of rows in the doorMat must be at least 5.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mdRowLngth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdRows\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdRows\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: dRows for number of rows in the doorMat must be at least 5." ] } ], "source": [ "# idea for this came from Hacker rank:\n", "# the hacker rank doormat code was 6 lines of code that only accepted \"WELCOME\" \n", "# and \".|.\" and got dimensions from stdin\n", "# this modification makes it more flexible\n", "\n", "def create_doorMat(dRows=9, centerText = \"WELCOME\", dTxt=\".|.\", fillChar=\"-\"):\n", " if dRows < 5:\n", " raise ValueError(\"dRows for number of rows in the doorMat must be at least 5.\")\n", " dRowLngth = dRows*3\n", " for i in range(1,dRows,2): \n", " print((dTxt*i).center(dRowLngth, fillChar))\n", " print(centerText.center(dRowLngth, fillChar))\n", " for i in range(dRows-2,-1,-2): \n", " print((dTxt*i).center(dRowLngth, fillChar))\n", "\n", "create_doorMat(15)\n", "print()\n", "create_doorMat(12, centerText=\"EAT MY SHORTS!\", dTxt=\"+:+\", fillChar=\"*\")\n", "print()\n", "create_doorMat(9, fillChar=\"+\")\n", "print()\n", "create_doorMat(7, dTxt=\"+:+\")\n", "print()\n", "create_doorMat(5, centerText=\"B'BYE!\")\n", "print()\n", "create_doorMat(3) # this should throw the predefined error for invalid size\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:PY36]", "language": "python", "name": "conda-env-PY36-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
GoogleCloudPlatform/vertex-ai-samples
notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_batch.ipynb
1
74156
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2020 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "title" }, "source": [ "# Vertex client library: AutoML text entity extraction model for batch prediction\n", "\n", "<table align=\"left\">\n", " <td>\n", " <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_batch.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_batch.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n", " View on GitHub\n", " </a>\n", " </td>\n", "</table>\n", "<br/><br/><br/>" ] }, { "cell_type": "markdown", "metadata": { "id": "overview:automl" }, "source": [ "## Overview\n", "\n", "\n", "This tutorial demonstrates how to use the Vertex client library for Python to create text entity extraction models and do batch prediction using Google Cloud's [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users)." ] }, { "cell_type": "markdown", "metadata": { "id": "dataset:biomedical,ten" }, "source": [ "### Dataset\n", "\n", "The dataset used for this tutorial is the [NCBI Disease Research Abstracts dataset](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/) from [National Center for Biotechnology Information](https://www.ncbi.nlm.nih.gov/). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket." ] }, { "cell_type": "markdown", "metadata": { "id": "objective:automl,training,batch_prediction" }, "source": [ "### Objective\n", "\n", "In this tutorial, you create an AutoML text entity extraction model from a Python script, and then do a batch prediction using the Vertex client library. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Google Cloud Console.\n", "\n", "The steps performed include:\n", "\n", "- Create a Vertex `Dataset` resource.\n", "- Train the model.\n", "- View the model evaluation.\n", "- Make a batch prediction.\n", "\n", "There is one key difference between using batch prediction and using online prediction:\n", "\n", "* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.\n", "\n", "* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready." ] }, { "cell_type": "markdown", "metadata": { "id": "costs" }, "source": [ "### Costs\n", "\n", "This tutorial uses billable components of Google Cloud (GCP):\n", "\n", "* Vertex AI\n", "* Cloud Storage\n", "\n", "Learn about [Vertex AI\n", "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n", "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n", "Calculator](https://cloud.google.com/products/calculator/)\n", "to generate a cost estimate based on your projected usage." ] }, { "cell_type": "markdown", "metadata": { "id": "install_aip" }, "source": [ "## Installation\n", "\n", "Install the latest version of Vertex client library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_aip" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# Google Cloud Notebook\n", "if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " USER_FLAG = \"--user\"\n", "else:\n", " USER_FLAG = \"\"\n", "\n", "! pip3 install -U google-cloud-aiplatform $USER_FLAG" ] }, { "cell_type": "markdown", "metadata": { "id": "install_storage" }, "source": [ "Install the latest GA version of *google-cloud-storage* library as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_storage" }, "outputs": [], "source": [ "! pip3 install -U google-cloud-storage $USER_FLAG" ] }, { "cell_type": "markdown", "metadata": { "id": "restart" }, "source": [ "### Restart the kernel\n", "\n", "Once you've installed the Vertex client library and Google *cloud-storage*, you need to restart the notebook kernel so it can find the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "restart" }, "outputs": [], "source": [ "if not os.getenv(\"IS_TESTING\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "before_you_begin" }, "source": [ "## Before you begin\n", "\n", "### GPU runtime\n", "\n", "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n", "\n", "### Set up your Google Cloud project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n", "\n", "3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n", "\n", "4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n", "\n", "5. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_project_id" }, "outputs": [], "source": [ "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_project_id" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n", " # Get your GCP project id from gcloud\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID:\", PROJECT_ID)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_gcloud_project_id" }, "outputs": [], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": { "id": "region" }, "source": [ "#### Region\n", "\n", "You can also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Below are regions supported for Vertex. We recommend that you choose the region closest to you.\n", "\n", "- Americas: `us-central1`\n", "- Europe: `europe-west4`\n", "- Asia Pacific: `asia-east1`\n", "\n", "You may not use a multi-regional bucket for training with Vertex. Not all regions provide support for all Vertex services. For the latest support per region, see the [Vertex locations documentation](https://cloud.google.com/vertex-ai/docs/general/locations)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "region" }, "outputs": [], "source": [ "REGION = \"us-central1\" # @param {type: \"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "timestamp" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "timestamp" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gcp_authenticate" }, "source": [ "### Authenticate your Google Cloud account\n", "\n", "**If you are using Google Cloud Notebook**, your environment is already authenticated. Skip this step.\n", "\n", "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n", "\n", "**Otherwise**, follow these steps:\n", "\n", "In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n", "\n", "**Click Create service account**.\n", "\n", "In the **Service account name** field, enter a name, and click **Create**.\n", "\n", "In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n", "\n", "Click Create. A JSON file that contains your key downloads to your local environment.\n", "\n", "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gcp_authenticate" }, "outputs": [], "source": [ "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your GCP account. This provides access to your\n", "# Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# If on Google Cloud Notebook, then don't execute this code\n", "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this notebook locally, replace the string below with the\n", " # path to your service account key and run this cell to authenticate your GCP\n", " # account.\n", " elif not os.getenv(\"IS_TESTING\"):\n", " %env GOOGLE_APPLICATION_CREDENTIALS ''" ] }, { "cell_type": "markdown", "metadata": { "id": "bucket:batch_prediction" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.\n", "\n", "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bucket" }, "outputs": [], "source": [ "BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_bucket" }, "outputs": [], "source": [ "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n", " BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "create_bucket" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "create_bucket" }, "outputs": [], "source": [ "! gsutil mb -l $REGION $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "validate_bucket" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "validate_bucket" }, "outputs": [], "source": [ "! gsutil ls -al $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial.\n", "### Import libraries and define constants" ] }, { "cell_type": "markdown", "metadata": { "id": "import_aip:protobuf" }, "source": [ "#### Import Vertex client library\n", "\n", "Import the Vertex client library into our Python environment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_aip:protobuf" }, "outputs": [], "source": [ "import time\n", "\n", "from google.cloud.aiplatform import gapic as aip\n", "from google.protobuf import json_format\n", "from google.protobuf.json_format import MessageToJson, ParseDict\n", "from google.protobuf.struct_pb2 import Struct, Value" ] }, { "cell_type": "markdown", "metadata": { "id": "aip_constants" }, "source": [ "#### Vertex constants\n", "\n", "Setup up the following constants for Vertex:\n", "\n", "- `API_ENDPOINT`: The Vertex API service endpoint for dataset, model, job, pipeline and endpoint services.\n", "- `PARENT`: The Vertex location root path for dataset, model, job, pipeline and endpoint resources." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aip_constants" }, "outputs": [], "source": [ "# API service endpoint\n", "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n", "\n", "# Vertex location root path for your dataset, model and endpoint resources\n", "PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION" ] }, { "cell_type": "markdown", "metadata": { "id": "automl_constants" }, "source": [ "#### AutoML constants\n", "\n", "Set constants unique to AutoML datasets and training:\n", "\n", "- Dataset Schemas: Tells the `Dataset` resource service which type of dataset it is.\n", "- Data Labeling (Annotations) Schemas: Tells the `Dataset` resource service how the data is labeled (annotated).\n", "- Dataset Training Schemas: Tells the `Pipeline` resource service the task (e.g., classification) to train the model for." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "automl_constants:ten" }, "outputs": [], "source": [ "# Text Dataset type\n", "DATA_SCHEMA = \"gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml\"\n", "# Text Labeling type\n", "LABEL_SCHEMA = \"gs://google-cloud-aiplatform/schema/dataset/ioformat/text_extraction_io_format_1.0.0.yaml\"\n", "# Text Training task\n", "TRAINING_SCHEMA = \"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_text_extraction_1.0.0.yaml\"" ] }, { "cell_type": "markdown", "metadata": { "id": "accelerators:prediction" }, "source": [ "#### Hardware Accelerators\n", "\n", "Set the hardware accelerators (e.g., GPU), if any, for prediction.\n", "\n", "Set the variable `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n", "\n", " (aip.AcceleratorType.NVIDIA_TESLA_K80, 4)\n", "\n", "For GPU, available accelerators include:\n", " - aip.AcceleratorType.NVIDIA_TESLA_K80\n", " - aip.AcceleratorType.NVIDIA_TESLA_P100\n", " - aip.AcceleratorType.NVIDIA_TESLA_P4\n", " - aip.AcceleratorType.NVIDIA_TESLA_T4\n", " - aip.AcceleratorType.NVIDIA_TESLA_V100\n", "\n", "Otherwise specify `(None, None)` to use a container image to run on a CPU." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "accelerators:prediction" }, "outputs": [], "source": [ "if os.getenv(\"IS_TESTING_DEPOLY_GPU\"):\n", " DEPLOY_GPU, DEPLOY_NGPU = (\n", " aip.AcceleratorType.NVIDIA_TESLA_K80,\n", " int(os.getenv(\"IS_TESTING_DEPOLY_GPU\")),\n", " )\n", "else:\n", " DEPLOY_GPU, DEPLOY_NGPU = (aip.AcceleratorType.NVIDIA_TESLA_K80, 1)" ] }, { "cell_type": "markdown", "metadata": { "id": "container:automl" }, "source": [ "#### Container (Docker) image\n", "\n", "For AutoML batch prediction, the container image for the serving binary is pre-determined by the Vertex prediction service. More specifically, the service will pick the appropriate container for the model depending on the hardware accelerator you selected." ] }, { "cell_type": "markdown", "metadata": { "id": "machine:prediction" }, "source": [ "#### Machine Type\n", "\n", "Next, set the machine type to use for prediction.\n", "\n", "- Set the variable `DEPLOY_COMPUTE` to configure the compute resources for the VM you will use for prediction.\n", " - `machine type`\n", " - `n1-standard`: 3.75GB of memory per vCPU.\n", " - `n1-highmem`: 6.5GB of memory per vCPU\n", " - `n1-highcpu`: 0.9 GB of memory per vCPU\n", " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n", "\n", "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "machine:prediction" }, "outputs": [], "source": [ "if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n", " MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n", "else:\n", " MACHINE_TYPE = \"n1-standard\"\n", "\n", "VCPU = \"4\"\n", "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n", "print(\"Deploy machine type\", DEPLOY_COMPUTE)" ] }, { "cell_type": "markdown", "metadata": { "id": "tutorial_start:automl" }, "source": [ "# Tutorial\n", "\n", "Now you are ready to start creating your own AutoML text entity extraction model." ] }, { "cell_type": "markdown", "metadata": { "id": "clients:automl,batch_prediction" }, "source": [ "## Set up clients\n", "\n", "The Vertex client library works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the Vertex server.\n", "\n", "You will use different clients in this tutorial for different steps in the workflow. So set them all up upfront.\n", "\n", "- Dataset Service for `Dataset` resources.\n", "- Model Service for `Model` resources.\n", "- Pipeline Service for training.\n", "- Job Service for batch prediction and custom training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "clients:automl,batch_prediction" }, "outputs": [], "source": [ "# client options same for all services\n", "client_options = {\"api_endpoint\": API_ENDPOINT}\n", "\n", "\n", "def create_dataset_client():\n", " client = aip.DatasetServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_model_client():\n", " client = aip.ModelServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_pipeline_client():\n", " client = aip.PipelineServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_job_client():\n", " client = aip.JobServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "clients = {}\n", "clients[\"dataset\"] = create_dataset_client()\n", "clients[\"model\"] = create_model_client()\n", "clients[\"pipeline\"] = create_pipeline_client()\n", "clients[\"job\"] = create_job_client()\n", "\n", "for client in clients.items():\n", " print(client)" ] }, { "cell_type": "markdown", "metadata": { "id": "create_aip_dataset" }, "source": [ "## Dataset\n", "\n", "Now that your clients are ready, your first step in training a model is to create a managed dataset instance, and then upload your labeled data to it.\n", "\n", "### Create `Dataset` resource instance\n", "\n", "Use the helper function `create_dataset` to create the instance of a `Dataset` resource. This function does the following:\n", "\n", "1. Uses the dataset client service.\n", "2. Creates an Vertex `Dataset` resource (`aip.Dataset`), with the following parameters:\n", " - `display_name`: The human-readable name you choose to give it.\n", " - `metadata_schema_uri`: The schema for the dataset type.\n", "3. Calls the client dataset service method `create_dataset`, with the following parameters:\n", " - `parent`: The Vertex location root path for your `Database`, `Model` and `Endpoint` resources.\n", " - `dataset`: The Vertex dataset object instance you created.\n", "4. The method returns an `operation` object.\n", "\n", "An `operation` object is how Vertex handles asynchronous calls for long running operations. While this step usually goes fast, when you first use it in your project, there is a longer delay due to provisioning.\n", "\n", "You can use the `operation` object to get status on the operation (e.g., create `Dataset` resource) or to cancel the operation, by invoking an operation method:\n", "\n", "| Method | Description |\n", "| ----------- | ----------- |\n", "| result() | Waits for the operation to complete and returns a result object in JSON format. |\n", "| running() | Returns True/False on whether the operation is still running. |\n", "| done() | Returns True/False on whether the operation is completed. |\n", "| canceled() | Returns True/False on whether the operation was canceled. |\n", "| cancel() | Cancels the operation (this may take up to 30 seconds). |" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "create_aip_dataset" }, "outputs": [], "source": [ "TIMEOUT = 90\n", "\n", "\n", "def create_dataset(name, schema, labels=None, timeout=TIMEOUT):\n", " start_time = time.time()\n", " try:\n", " dataset = aip.Dataset(\n", " display_name=name, metadata_schema_uri=schema, labels=labels\n", " )\n", "\n", " operation = clients[\"dataset\"].create_dataset(parent=PARENT, dataset=dataset)\n", " print(\"Long running operation:\", operation.operation.name)\n", " result = operation.result(timeout=TIMEOUT)\n", " print(\"time:\", time.time() - start_time)\n", " print(\"response\")\n", " print(\" name:\", result.name)\n", " print(\" display_name:\", result.display_name)\n", " print(\" metadata_schema_uri:\", result.metadata_schema_uri)\n", " print(\" metadata:\", dict(result.metadata))\n", " print(\" create_time:\", result.create_time)\n", " print(\" update_time:\", result.update_time)\n", " print(\" etag:\", result.etag)\n", " print(\" labels:\", dict(result.labels))\n", " return result\n", " except Exception as e:\n", " print(\"exception:\", e)\n", " return None\n", "\n", "\n", "result = create_dataset(\"biomedical-\" + TIMESTAMP, DATA_SCHEMA)" ] }, { "cell_type": "markdown", "metadata": { "id": "dataset_id:result" }, "source": [ "Now save the unique dataset identifier for the `Dataset` resource instance you created." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dataset_id:result" }, "outputs": [], "source": [ "# The full unique ID for the dataset\n", "dataset_id = result.name\n", "# The short numeric ID for the dataset\n", "dataset_short_id = dataset_id.split(\"/\")[-1]\n", "\n", "print(dataset_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "data_preparation:text,ten,u_dataset" }, "source": [ "### Data preparation\n", "\n", "The Vertex `Dataset` resource for text has a couple of requirements for your text entity extraction data.\n", "\n", "- Text examples must be stored in a JSONL file. Unlike text classification and sentiment analysis, a CSV index file is not supported.\n", "- The examples must be either inline text or reference text files that are in Cloud Storage buckets." ] }, { "cell_type": "markdown", "metadata": { "id": "data_import_format:ten,u_dataset,jsonl" }, "source": [ "#### JSONL\n", "\n", "For text entity extraction, the JSONL file has a few requirements:\n", "\n", "- Each data item is a separate JSON object, on a separate line.\n", "- The key/value pair `text_segment_annotations` is a list of character start/end positions in the text per entity with the corresponding label.\n", " - `display_name`: The label.\n", " - `start_offset/end_offset`: The character offsets of the start/end of the entity.\n", "- The key/value pair `text_content` is the text.\n", "\n", " {'text_segment_annotations': [{'end_offset': value, 'start_offset': value, 'display_name': label}, ...], 'text_content': text}\n", "\n", "*Note*: The dictionary key fields may alternatively be in camelCase. For example, 'display_name' can also be 'displayName'." ] }, { "cell_type": "markdown", "metadata": { "id": "import_file:u_dataset,jsonl" }, "source": [ "#### Location of Cloud Storage training data.\n", "\n", "Now set the variable `IMPORT_FILE` to the location of the JSONL index file in Cloud Storage." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_file:biomedical,jsonl,ten" }, "outputs": [], "source": [ "IMPORT_FILE = \"gs://ucaip-test-us-central1/dataset/ucaip_ten_dataset.jsonl\"" ] }, { "cell_type": "markdown", "metadata": { "id": "quick_peek:jsonl" }, "source": [ "#### Quick peek at your data\n", "\n", "You will use a version of the NCBI Biomedical dataset that is stored in a public Cloud Storage bucket, using a JSONL index file.\n", "\n", "Start by doing a quick peek at the data. You count the number of examples by counting the number of objects in a JSONL index file (`wc -l`) and then peek at the first few rows." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "quick_peek:jsonl" }, "outputs": [], "source": [ "if \"IMPORT_FILES\" in globals():\n", " FILE = IMPORT_FILES[0]\n", "else:\n", " FILE = IMPORT_FILE\n", "\n", "count = ! gsutil cat $FILE | wc -l\n", "print(\"Number of Examples\", int(count[0]))\n", "\n", "print(\"First 10 rows\")\n", "! gsutil cat $FILE | head" ] }, { "cell_type": "markdown", "metadata": { "id": "import_data" }, "source": [ "### Import data\n", "\n", "Now, import the data into your Vertex Dataset resource. Use this helper function `import_data` to import the data. The function does the following:\n", "\n", "- Uses the `Dataset` client.\n", "- Calls the client method `import_data`, with the following parameters:\n", " - `name`: The human readable name you give to the `Dataset` resource (e.g., biomedical).\n", " - `import_configs`: The import configuration.\n", "\n", "- `import_configs`: A Python list containing a dictionary, with the key/value entries:\n", " - `gcs_sources`: A list of URIs to the paths of the one or more index files.\n", " - `import_schema_uri`: The schema identifying the labeling type.\n", "\n", "The `import_data()` method returns a long running `operation` object. This will take a few minutes to complete. If you are in a live tutorial, this would be a good time to ask questions, or take a personal break." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_data" }, "outputs": [], "source": [ "def import_data(dataset, gcs_sources, schema):\n", " config = [{\"gcs_source\": {\"uris\": gcs_sources}, \"import_schema_uri\": schema}]\n", " print(\"dataset:\", dataset_id)\n", " start_time = time.time()\n", " try:\n", " operation = clients[\"dataset\"].import_data(\n", " name=dataset_id, import_configs=config\n", " )\n", " print(\"Long running operation:\", operation.operation.name)\n", "\n", " result = operation.result()\n", " print(\"result:\", result)\n", " print(\"time:\", int(time.time() - start_time), \"secs\")\n", " print(\"error:\", operation.exception())\n", " print(\"meta :\", operation.metadata)\n", " print(\n", " \"after: running:\",\n", " operation.running(),\n", " \"done:\",\n", " operation.done(),\n", " \"cancelled:\",\n", " operation.cancelled(),\n", " )\n", "\n", " return operation\n", " except Exception as e:\n", " print(\"exception:\", e)\n", " return None\n", "\n", "\n", "import_data(dataset_id, [IMPORT_FILE], LABEL_SCHEMA)" ] }, { "cell_type": "markdown", "metadata": { "id": "train_automl_model" }, "source": [ "## Train the model\n", "\n", "Now train an AutoML text entity extraction model using your Vertex `Dataset` resource. To train the model, do the following steps:\n", "\n", "1. Create an Vertex training pipeline for the `Dataset` resource.\n", "2. Execute the pipeline to start the training." ] }, { "cell_type": "markdown", "metadata": { "id": "create_pipeline:automl" }, "source": [ "### Create a training pipeline\n", "\n", "You may ask, what do we use a pipeline for? You typically use pipelines when the job (such as training) has multiple steps, generally in sequential order: do step A, do step B, etc. By putting the steps into a pipeline, we gain the benefits of:\n", "\n", "1. Being reusable for subsequent training jobs.\n", "2. Can be containerized and ran as a batch job.\n", "3. Can be distributed.\n", "4. All the steps are associated with the same pipeline job for tracking progress.\n", "\n", "Use this helper function `create_pipeline`, which takes the following parameters:\n", "\n", "- `pipeline_name`: A human readable name for the pipeline job.\n", "- `model_name`: A human readable name for the model.\n", "- `dataset`: The Vertex fully qualified dataset identifier.\n", "- `schema`: The dataset labeling (annotation) training schema.\n", "- `task`: A dictionary describing the requirements for the training job.\n", "\n", "The helper function calls the `Pipeline` client service'smethod `create_pipeline`, which takes the following parameters:\n", "\n", "- `parent`: The Vertex location root path for your `Dataset`, `Model` and `Endpoint` resources.\n", "- `training_pipeline`: the full specification for the pipeline training job.\n", "\n", "Let's look now deeper into the *minimal* requirements for constructing a `training_pipeline` specification:\n", "\n", "- `display_name`: A human readable name for the pipeline job.\n", "- `training_task_definition`: The dataset labeling (annotation) training schema.\n", "- `training_task_inputs`: A dictionary describing the requirements for the training job.\n", "- `model_to_upload`: A human readable name for the model.\n", "- `input_data_config`: The dataset specification.\n", " - `dataset_id`: The Vertex dataset identifier only (non-fully qualified) -- this is the last part of the fully-qualified identifier.\n", " - `fraction_split`: If specified, the percentages of the dataset to use for training, test and validation. Otherwise, the percentages are automatically selected by AutoML." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "create_pipeline:automl" }, "outputs": [], "source": [ "def create_pipeline(pipeline_name, model_name, dataset, schema, task):\n", "\n", " dataset_id = dataset.split(\"/\")[-1]\n", "\n", " input_config = {\n", " \"dataset_id\": dataset_id,\n", " \"fraction_split\": {\n", " \"training_fraction\": 0.8,\n", " \"validation_fraction\": 0.1,\n", " \"test_fraction\": 0.1,\n", " },\n", " }\n", "\n", " training_pipeline = {\n", " \"display_name\": pipeline_name,\n", " \"training_task_definition\": schema,\n", " \"training_task_inputs\": task,\n", " \"input_data_config\": input_config,\n", " \"model_to_upload\": {\"display_name\": model_name},\n", " }\n", "\n", " try:\n", " pipeline = clients[\"pipeline\"].create_training_pipeline(\n", " parent=PARENT, training_pipeline=training_pipeline\n", " )\n", " print(pipeline)\n", " except Exception as e:\n", " print(\"exception:\", e)\n", " return None\n", " return pipeline" ] }, { "cell_type": "markdown", "metadata": { "id": "task_requirements:automl,ten" }, "source": [ "### Construct the task requirements\n", "\n", "Next, construct the task requirements. Unlike other parameters which take a Python (JSON-like) dictionary, the `task` field takes a Google protobuf Struct, which is very similar to a Python dictionary. Use the `json_format.ParseDict` method for the conversion.\n", "\n", "The minimal fields you need to specify are:\n", "\n", "- `multi_label`: Whether True/False this is a multi-label (vs single) classification.\n", "- `budget_milli_node_hours`: The maximum time to budget (billed) for training the model, where 1000 = 1 hour.\n", "- `model_type`: The type of deployed model:\n", " - `CLOUD`: For deploying to Google Cloud.\n", "- `disable_early_stopping`: Whether True/False to let AutoML use its judgement to stop training early or train for the entire budget.\n", "\n", "Finally, you create the pipeline by calling the helper function `create_pipeline`, which returns an instance of a training pipeline object." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "task_requirements:automl,ten" }, "outputs": [], "source": [ "PIPE_NAME = \"biomedical_pipe-\" + TIMESTAMP\n", "MODEL_NAME = \"biomedical_model-\" + TIMESTAMP\n", "\n", "task = json_format.ParseDict(\n", " {\n", " \"multi_label\": False,\n", " \"budget_milli_node_hours\": 8000,\n", " \"model_type\": \"CLOUD\",\n", " \"disable_early_stopping\": False,\n", " },\n", " Value(),\n", ")\n", "\n", "response = create_pipeline(PIPE_NAME, MODEL_NAME, dataset_id, TRAINING_SCHEMA, task)" ] }, { "cell_type": "markdown", "metadata": { "id": "pipeline_id:response" }, "source": [ "Now save the unique identifier of the training pipeline you created." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pipeline_id:response" }, "outputs": [], "source": [ "# The full unique ID for the pipeline\n", "pipeline_id = response.name\n", "# The short numeric ID for the pipeline\n", "pipeline_short_id = pipeline_id.split(\"/\")[-1]\n", "\n", "print(pipeline_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "get_training_pipeline" }, "source": [ "### Get information on a training pipeline\n", "\n", "Now get pipeline information for just this training pipeline instance. The helper function gets the job information for just this job by calling the the job client service's `get_training_pipeline` method, with the following parameter:\n", "\n", "- `name`: The Vertex fully qualified pipeline identifier.\n", "\n", "When the model is done training, the pipeline state will be `PIPELINE_STATE_SUCCEEDED`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "get_training_pipeline" }, "outputs": [], "source": [ "def get_training_pipeline(name, silent=False):\n", " response = clients[\"pipeline\"].get_training_pipeline(name=name)\n", " if silent:\n", " return response\n", "\n", " print(\"pipeline\")\n", " print(\" name:\", response.name)\n", " print(\" display_name:\", response.display_name)\n", " print(\" state:\", response.state)\n", " print(\" training_task_definition:\", response.training_task_definition)\n", " print(\" training_task_inputs:\", dict(response.training_task_inputs))\n", " print(\" create_time:\", response.create_time)\n", " print(\" start_time:\", response.start_time)\n", " print(\" end_time:\", response.end_time)\n", " print(\" update_time:\", response.update_time)\n", " print(\" labels:\", dict(response.labels))\n", " return response\n", "\n", "\n", "response = get_training_pipeline(pipeline_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "wait_training_complete" }, "source": [ "# Deployment\n", "\n", "Training the above model may take upwards of 120 minutes time.\n", "\n", "Once your model is done training, you can calculate the actual time it took to train the model by subtracting `end_time` from `start_time`. For your model, you will need to know the fully qualified Vertex Model resource identifier, which the pipeline service assigned to it. You can get this from the returned pipeline instance as the field `model_to_deploy.name`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wait_training_complete" }, "outputs": [], "source": [ "while True:\n", " response = get_training_pipeline(pipeline_id, True)\n", " if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:\n", " print(\"Training job has not completed:\", response.state)\n", " model_to_deploy_id = None\n", " if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:\n", " raise Exception(\"Training Job Failed\")\n", " else:\n", " model_to_deploy = response.model_to_upload\n", " model_to_deploy_id = model_to_deploy.name\n", " print(\"Training Time:\", response.end_time - response.start_time)\n", " break\n", " time.sleep(60)\n", "\n", "print(\"model to deploy:\", model_to_deploy_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "model_information" }, "source": [ "## Model information\n", "\n", "Now that your model is trained, you can get some information on your model." ] }, { "cell_type": "markdown", "metadata": { "id": "evaluate_the_model:automl" }, "source": [ "## Evaluate the Model resource\n", "\n", "Now find out how good the model service believes your model is. As part of training, some portion of the dataset was set aside as the test (holdout) data, which is used by the pipeline service to evaluate the model." ] }, { "cell_type": "markdown", "metadata": { "id": "list_model_evaluations:automl,ten" }, "source": [ "### List evaluations for all slices\n", "\n", "Use this helper function `list_model_evaluations`, which takes the following parameter:\n", "\n", "- `name`: The Vertex fully qualified model identifier for the `Model` resource.\n", "\n", "This helper function uses the model client service's `list_model_evaluations` method, which takes the same parameter. The response object from the call is a list, where each element is an evaluation metric.\n", "\n", "For each evaluation -- you probably only have one, we then print all the key names for each metric in the evaluation, and for a small set (`confusionMatrix` and `confidenceMetrics`) you will print the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "list_model_evaluations:automl,ten" }, "outputs": [], "source": [ "def list_model_evaluations(name):\n", " response = clients[\"model\"].list_model_evaluations(parent=name)\n", " for evaluation in response:\n", " print(\"model_evaluation\")\n", " print(\" name:\", evaluation.name)\n", " print(\" metrics_schema_uri:\", evaluation.metrics_schema_uri)\n", " metrics = json_format.MessageToDict(evaluation._pb.metrics)\n", " for metric in metrics.keys():\n", " print(metric)\n", " print(\"confusionMatrix\", metrics[\"confusionMatrix\"])\n", " print(\"confidenceMetrics\", metrics[\"confidenceMetrics\"])\n", "\n", " return evaluation.name\n", "\n", "\n", "last_evaluation = list_model_evaluations(model_to_deploy_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "deploy:batch_prediction" }, "source": [ "## Model deployment for batch prediction\n", "\n", "Now deploy the trained Vertex `Model` resource you created for batch prediction. This differs from deploying a `Model` resource for on-demand prediction.\n", "\n", "For online prediction, you:\n", "\n", "1. Create an `Endpoint` resource for deploying the `Model` resource to.\n", "\n", "2. Deploy the `Model` resource to the `Endpoint` resource.\n", "\n", "3. Make online prediction requests to the `Endpoint` resource.\n", "\n", "For batch-prediction, you:\n", "\n", "1. Create a batch prediction job.\n", "\n", "2. The job service will provision resources for the batch prediction request.\n", "\n", "3. The results of the batch prediction request are returned to the caller.\n", "\n", "4. The job service will unprovision the resoures for the batch prediction request." ] }, { "cell_type": "markdown", "metadata": { "id": "make_prediction" }, "source": [ "## Make a batch prediction request\n", "\n", "Now do a batch prediction to your deployed model." ] }, { "cell_type": "markdown", "metadata": { "id": "make_test_items:automl,batch_prediction" }, "source": [ "### Make test items\n", "\n", "You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "make_test_items:automl,text,biomedical" }, "outputs": [], "source": [ "test_item_1 = 'Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- > AT transition at the donor splice-site of intron 9 . The second , a C-- > T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign \" pseudodeficient \" allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- > A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described'\n", "test_item_2 = \"Analysis of alkaptonuria (AKU) mutations and polymorphisms reveals that the CCC sequence motif is a mutational hot spot in the homogentisate 1,2 dioxygenase gene (HGO).\tWe recently showed that alkaptonuria ( AKU ) is caused by loss-of-function mutations in the homogentisate 1 , 2 dioxygenase gene ( HGO ) . Herein we describe haplotype and mutational analyses of HGO in seven new AKU pedigrees . These analyses identified two novel single-nucleotide polymorphisms ( INV4 + 31A-- > G and INV11 + 18A-- > G ) and six novel AKU mutations ( INV1-1G-- > A , W60G , Y62C , A122D , P230T , and D291E ) , which further illustrates the remarkable allelic heterogeneity found in AKU . Reexamination of all 29 mutations and polymorphisms thus far described in HGO shows that these nucleotide changes are not randomly distributed ; the CCC sequence motif and its inverted complement , GGG , are preferentially mutated . These analyses also demonstrated that the nucleotide substitutions in HGO do not involve CpG dinucleotides , which illustrates important differences between HGO and other genes for the occurrence of mutation at specific short-sequence motifs . Because the CCC sequence motifs comprise a significant proportion ( 34 . 5 % ) of all mutated bases that have been observed in HGO , we conclude that the CCC triplet is a mutational hot spot in HGO .\"" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_file:automl,text" }, "source": [ "### Make the batch input file\n", "\n", "Now make a batch input file, which you will store in your local Cloud Storage bucket. The batch input file can only be in JSONL format. For JSONL file, you make one dictionary entry per line for each data item (instance). The dictionary contains the key/value pairs:\n", "\n", "- `content`: The Cloud Storage path to the file with the text item.\n", "- `mime_type`: The content type. In our example, it is an `text` file.\n", "\n", "For example:\n", "\n", " {'content': '[your-bucket]/file1.txt', 'mime_type': 'text'}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "make_batch_file:automl,text" }, "outputs": [], "source": [ "import json\n", "\n", "import tensorflow as tf\n", "\n", "gcs_test_item_1 = BUCKET_NAME + \"/test1.txt\"\n", "with tf.io.gfile.GFile(gcs_test_item_1, \"w\") as f:\n", " f.write(test_item_1 + \"\\n\")\n", "gcs_test_item_2 = BUCKET_NAME + \"/test2.txt\"\n", "with tf.io.gfile.GFile(gcs_test_item_2, \"w\") as f:\n", " f.write(test_item_2 + \"\\n\")\n", "\n", "gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n", "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n", " data = {\"content\": gcs_test_item_1, \"mime_type\": \"text/plain\"}\n", " f.write(json.dumps(data) + \"\\n\")\n", " data = {\"content\": gcs_test_item_2, \"mime_type\": \"text/plain\"}\n", " f.write(json.dumps(data) + \"\\n\")\n", "\n", "print(gcs_input_uri)\n", "! gsutil cat $gcs_input_uri" ] }, { "cell_type": "markdown", "metadata": { "id": "instance_scaling" }, "source": [ "### Compute instance scaling\n", "\n", "You have several choices on scaling the compute instances for handling your batch prediction requests:\n", "\n", "- Single Instance: The batch prediction requests are processed on a single compute instance.\n", " - Set the minimum (`MIN_NODES`) and maximum (`MAX_NODES`) number of compute instances to one.\n", "\n", "- Manual Scaling: The batch prediction requests are split across a fixed number of compute instances that you manually specified.\n", " - Set the minimum (`MIN_NODES`) and maximum (`MAX_NODES`) number of compute instances to the same number of nodes. When a model is first deployed to the instance, the fixed number of compute instances are provisioned and batch prediction requests are evenly distributed across them.\n", "\n", "- Auto Scaling: The batch prediction requests are split across a scaleable number of compute instances.\n", " - Set the minimum (`MIN_NODES`) number of compute instances to provision when a model is first deployed and to de-provision, and set the maximum (`MAX_NODES) number of compute instances to provision, depending on load conditions.\n", "\n", "The minimum number of compute instances corresponds to the field `min_replica_count` and the maximum number of compute instances corresponds to the field `max_replica_count`, in your subsequent deployment request." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "instance_scaling" }, "outputs": [], "source": [ "MIN_NODES = 1\n", "MAX_NODES = 1" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_request:automl,ten" }, "source": [ "### Make batch prediction request\n", "\n", "Now that your batch of two test items is ready, let's do the batch request. Use this helper function `create_batch_prediction_job`, with the following parameters:\n", "\n", "- `display_name`: The human readable name for the prediction job.\n", "- `model_name`: The Vertex fully qualified identifier for the `Model` resource.\n", "- `gcs_source_uri`: The Cloud Storage path to the input file -- which you created above.\n", "- `gcs_destination_output_uri_prefix`: The Cloud Storage path that the service will write the predictions to.\n", "- `parameters`: Additional filtering parameters for serving prediction results.\n", "\n", "The helper function calls the job client service's `create_batch_prediction_job` metho, with the following parameters:\n", "\n", "- `parent`: The Vertex location root path for Dataset, Model and Pipeline resources.\n", "- `batch_prediction_job`: The specification for the batch prediction job.\n", "\n", "Let's now dive into the specification for the `batch_prediction_job`:\n", "\n", "- `display_name`: The human readable name for the prediction batch job.\n", "- `model`: The Vertex fully qualified identifier for the `Model` resource.\n", "- `dedicated_resources`: The compute resources to provision for the batch prediction job.\n", " - `machine_spec`: The compute instance to provision. Use the variable you set earlier `DEPLOY_GPU != None` to use a GPU; otherwise only a CPU is allocated.\n", " - `starting_replica_count`: The number of compute instances to initially provision, which you set earlier as the variable `MIN_NODES`.\n", " - `max_replica_count`: The maximum number of compute instances to scale to, which you set earlier as the variable `MAX_NODES`.\n", "- `model_parameters`: Additional filtering parameters for serving prediction results. *Note*, text models do not support additional parameters.\n", "- `input_config`: The input source and format type for the instances to predict.\n", " - `instances_format`: The format of the batch prediction request file: `jsonl` only supported.\n", " - `gcs_source`: A list of one or more Cloud Storage paths to your batch prediction requests.\n", "- `output_config`: The output destination and format for the predictions.\n", " - `prediction_format`: The format of the batch prediction response file: `jsonl` only supported.\n", " - `gcs_destination`: The output destination for the predictions.\n", "- `dedicated_resources`: The compute resources to provision for the batch prediction job.\n", " - `machine_spec`: The compute instance to provision. Use the variable you set earlier `DEPLOY_GPU != None` to use a GPU; otherwise only a CPU is allocated.\n", " - `starting_replica_count`: The number of compute instances to initially provision.\n", " - `max_replica_count`: The maximum number of compute instances to scale to. In this tutorial, only one instance is provisioned.\n", "\n", "This call is an asychronous operation. You will print from the response object a few select fields, including:\n", "\n", "- `name`: The Vertex fully qualified identifier assigned to the batch prediction job.\n", "- `display_name`: The human readable name for the prediction batch job.\n", "- `model`: The Vertex fully qualified identifier for the Model resource.\n", "- `generate_explanations`: Whether True/False explanations were provided with the predictions (explainability).\n", "- `state`: The state of the prediction job (pending, running, etc).\n", "\n", "Since this call will take a few moments to execute, you will likely get `JobState.JOB_STATE_PENDING` for `state`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "make_batch_request:automl,ten" }, "outputs": [], "source": [ "BATCH_MODEL = \"biomedical_batch-\" + TIMESTAMP\n", "\n", "\n", "def create_batch_prediction_job(\n", " display_name,\n", " model_name,\n", " gcs_source_uri,\n", " gcs_destination_output_uri_prefix,\n", " parameters=None,\n", "):\n", "\n", " if DEPLOY_GPU:\n", " machine_spec = {\n", " \"machine_type\": DEPLOY_COMPUTE,\n", " \"accelerator_type\": DEPLOY_GPU,\n", " \"accelerator_count\": DEPLOY_NGPU,\n", " }\n", " else:\n", " machine_spec = {\n", " \"machine_type\": DEPLOY_COMPUTE,\n", " \"accelerator_count\": 0,\n", " }\n", "\n", " batch_prediction_job = {\n", " \"display_name\": display_name,\n", " # Format: 'projects/{project}/locations/{location}/models/{model_id}'\n", " \"model\": model_name,\n", " \"model_parameters\": json_format.ParseDict(parameters, Value()),\n", " \"input_config\": {\n", " \"instances_format\": IN_FORMAT,\n", " \"gcs_source\": {\"uris\": [gcs_source_uri]},\n", " },\n", " \"output_config\": {\n", " \"predictions_format\": OUT_FORMAT,\n", " \"gcs_destination\": {\"output_uri_prefix\": gcs_destination_output_uri_prefix},\n", " },\n", " \"dedicated_resources\": {\n", " \"machine_spec\": machine_spec,\n", " \"starting_replica_count\": MIN_NODES,\n", " \"max_replica_count\": MAX_NODES,\n", " },\n", " }\n", " response = clients[\"job\"].create_batch_prediction_job(\n", " parent=PARENT, batch_prediction_job=batch_prediction_job\n", " )\n", " print(\"response\")\n", " print(\" name:\", response.name)\n", " print(\" display_name:\", response.display_name)\n", " print(\" model:\", response.model)\n", " try:\n", " print(\" generate_explanation:\", response.generate_explanation)\n", " except:\n", " pass\n", " print(\" state:\", response.state)\n", " print(\" create_time:\", response.create_time)\n", " print(\" start_time:\", response.start_time)\n", " print(\" end_time:\", response.end_time)\n", " print(\" update_time:\", response.update_time)\n", " print(\" labels:\", response.labels)\n", " return response\n", "\n", "\n", "IN_FORMAT = \"jsonl\"\n", "OUT_FORMAT = \"jsonl\" # [jsonl]\n", "\n", "response = create_batch_prediction_job(\n", " BATCH_MODEL, model_to_deploy_id, gcs_input_uri, BUCKET_NAME, None\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "batch_job_id:response" }, "source": [ "Now get the unique identifier for the batch prediction job you created." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batch_job_id:response" }, "outputs": [], "source": [ "# The full unique ID for the batch job\n", "batch_job_id = response.name\n", "# The short numeric ID for the batch job\n", "batch_job_short_id = batch_job_id.split(\"/\")[-1]\n", "\n", "print(batch_job_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "get_batch_prediction_job" }, "source": [ "### Get information on a batch prediction job\n", "\n", "Use this helper function `get_batch_prediction_job`, with the following paramter:\n", "\n", "- `job_name`: The Vertex fully qualified identifier for the batch prediction job.\n", "\n", "The helper function calls the job client service's `get_batch_prediction_job` method, with the following paramter:\n", "\n", "- `name`: The Vertex fully qualified identifier for the batch prediction job. In this tutorial, you will pass it the Vertex fully qualified identifier for your batch prediction job -- `batch_job_id`\n", "\n", "The helper function will return the Cloud Storage path to where the predictions are stored -- `gcs_destination`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "get_batch_prediction_job" }, "outputs": [], "source": [ "def get_batch_prediction_job(job_name, silent=False):\n", " response = clients[\"job\"].get_batch_prediction_job(name=job_name)\n", " if silent:\n", " return response.output_config.gcs_destination.output_uri_prefix, response.state\n", "\n", " print(\"response\")\n", " print(\" name:\", response.name)\n", " print(\" display_name:\", response.display_name)\n", " print(\" model:\", response.model)\n", " try: # not all data types support explanations\n", " print(\" generate_explanation:\", response.generate_explanation)\n", " except:\n", " pass\n", " print(\" state:\", response.state)\n", " print(\" error:\", response.error)\n", " gcs_destination = response.output_config.gcs_destination\n", " print(\" gcs_destination\")\n", " print(\" output_uri_prefix:\", gcs_destination.output_uri_prefix)\n", " return gcs_destination.output_uri_prefix, response.state\n", "\n", "\n", "predictions, state = get_batch_prediction_job(batch_job_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "get_the_predictions:automl,ten" }, "source": [ "### Get the predictions\n", "\n", "When the batch prediction is done processing, the job state will be `JOB_STATE_SUCCEEDED`.\n", "\n", "Finally you view the predictions stored at the Cloud Storage path you set as output. The predictions will be in a JSONL format, which you indicated at the time you made the batch prediction job, under a subfolder starting with the name `prediction`, and under that folder will be a file called `predictions*.jsonl`.\n", "\n", "Now display (cat) the contents. You will see multiple JSON objects, one for each prediction.\n", "\n", "The first field `text_snippet` is the text file you did the prediction on, and the second field `annotations` is the prediction, which is further broken down into:\n", "\n", "- `text_extraction`: The extracted entity from the text.\n", "- `display_name`: The predicted label for the extraction entity.\n", "- `score`: The confidence level between 0 and 1 in the prediction.\n", "- `startOffset`: The character offset in the text of the start of the extracted entity.\n", "- `endOffset`: The character offset in the text of the end of the extracted entity." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "get_the_predictions:automl,text" }, "outputs": [], "source": [ "def get_latest_predictions(gcs_out_dir):\n", " \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n", " folders = !gsutil ls $gcs_out_dir\n", " latest = \"\"\n", " for folder in folders:\n", " subfolder = folder.split(\"/\")[-2]\n", " if subfolder.startswith(\"prediction-\"):\n", " if subfolder > latest:\n", " latest = folder[:-1]\n", " return latest\n", "\n", "\n", "while True:\n", " predictions, state = get_batch_prediction_job(batch_job_id, True)\n", " if state != aip.JobState.JOB_STATE_SUCCEEDED:\n", " print(\"The job has not completed:\", state)\n", " if state == aip.JobState.JOB_STATE_FAILED:\n", " raise Exception(\"Batch Job Failed\")\n", " else:\n", " folder = get_latest_predictions(predictions)\n", " ! gsutil ls $folder/prediction*.jsonl\n", "\n", " ! gsutil cat $folder/prediction*.jsonl\n", " break\n", " time.sleep(60)" ] }, { "cell_type": "markdown", "metadata": { "id": "cleanup" }, "source": [ "# Cleaning up\n", "\n", "To clean up all GCP resources used in this project, you can [delete the GCP\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial:\n", "\n", "- Dataset\n", "- Pipeline\n", "- Model\n", "- Endpoint\n", "- Batch Job\n", "- Custom Job\n", "- Hyperparameter Tuning Job\n", "- Cloud Storage Bucket" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cleanup" }, "outputs": [], "source": [ "delete_dataset = True\n", "delete_pipeline = True\n", "delete_model = True\n", "delete_endpoint = True\n", "delete_batchjob = True\n", "delete_customjob = True\n", "delete_hptjob = True\n", "delete_bucket = True\n", "\n", "# Delete the dataset using the Vertex fully qualified identifier for the dataset\n", "try:\n", " if delete_dataset and \"dataset_id\" in globals():\n", " clients[\"dataset\"].delete_dataset(name=dataset_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the training pipeline using the Vertex fully qualified identifier for the pipeline\n", "try:\n", " if delete_pipeline and \"pipeline_id\" in globals():\n", " clients[\"pipeline\"].delete_training_pipeline(name=pipeline_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the model using the Vertex fully qualified identifier for the model\n", "try:\n", " if delete_model and \"model_to_deploy_id\" in globals():\n", " clients[\"model\"].delete_model(name=model_to_deploy_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the endpoint using the Vertex fully qualified identifier for the endpoint\n", "try:\n", " if delete_endpoint and \"endpoint_id\" in globals():\n", " clients[\"endpoint\"].delete_endpoint(name=endpoint_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the batch job using the Vertex fully qualified identifier for the batch job\n", "try:\n", " if delete_batchjob and \"batch_job_id\" in globals():\n", " clients[\"job\"].delete_batch_prediction_job(name=batch_job_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the custom job using the Vertex fully qualified identifier for the custom job\n", "try:\n", " if delete_customjob and \"job_id\" in globals():\n", " clients[\"job\"].delete_custom_job(name=job_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the hyperparameter tuning job using the Vertex fully qualified identifier for the hyperparameter tuning job\n", "try:\n", " if delete_hptjob and \"hpt_job_id\" in globals():\n", " clients[\"job\"].delete_hyperparameter_tuning_job(name=hpt_job_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_bucket and \"BUCKET_NAME\" in globals():\n", " ! gsutil rm -r $BUCKET_NAME" ] } ], "metadata": { "colab": { "name": "showcase_automl_text_entity_extraction_batch.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
StuartLittlefair/isochrones
notebooks/tests.ipynb
2
36359
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"text": [ "<matplotlib.figure.Figure at 0x105d5ffd0>" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "import isochrones.starmodel as sm\n", "reload(iso)\n", "reload(dartmouth)\n", "dar = dartmouth.Dartmouth_Isochrone()\n", "mod = sm.StarModel(dar,Teff=(5783,64),logg=(4.36,0.07),feh=(-0.06,0.1))#,\n", " #H=(9.92,0.04),J=(10.23,0.04),K=(9.85,0.04))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "mod.fit_mcmc()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "mod.plot_samples('radius')\n", "mod.plot_samples('mass')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109d00bd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109d00d90>" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
pyGrowler/Growler
examples/ExampleNotebook_1.ipynb
2
5232
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Growler Example in Jupyter" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import growler" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 6, 5)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "growler.__meta__.version_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Create growler application with name NotebookServer" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "app = growler.App(\"NotebookServer\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Add a general purpose method which prints ip address and the USER-AGENT header" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@app.use\n", "def print_client_info(req, res):\n", " ip = req.ip\n", " reqpath = req.path\n", " print(\"[{ip}] {path}\".format(ip=ip, path=reqpath))\n", " print(\" >\", req.headers['USER-AGENT'])\n", " print(flush=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Next, add a route matching any GET requests for the root (`/`) of the site. This uses a simple global variable to count the number times this page has been accessed, and return text to the client" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i = 0\n", "@app.get(\"/\")\n", "def index(req, res):\n", " global i\n", " res.send_text(\"It Works! (%d)\" % i)\n", " i += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "We can see the tree of middleware all requests will pass through - Notice the router object that was implicitly created which will match all requests." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NotebookServer\n", "├── ALL \\/ <function print_client_info at 0x10b5b9950>\n", "├── ALL \\/ <growler.Router object at 0x10b5ce748>\n", "│   └── GET \\/ <function index at 0x10b5b9e18>\n", "┴\n" ] } ], "source": [ "app.print_middleware_tree()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Use the helper method to create the asyncio server listening on port 9000." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[127.0.0.1] /\n", " > Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:44.0) Gecko/20100101 Firefox/44.0\n", "\n", "[127.0.0.1] /\n", " > Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) AppleWebKit/601.4.4 (KHTML, like Gecko) Version/9.0.3 Safari/601.4.4\n", "\n" ] } ], "source": [ "app.create_server_and_run_forever(host='127.0.0.1', port=9000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
yuhao0531/dmc
notebooks/week-2/04 - Lab 2 Assignment.ipynb
2
12842
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab 2 assignment\n", "\n", "This assignment will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game, which will store information about their current pot, as well as a series of methods defining how they play the game. We will also build several functions to control the flow of the game and get data back at the end.\n", "\n", "We will start by importing the `'random'` library, which will allow us to use its functions for picking a random entry from a list." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "import numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will establish some general variables for our game, including the 'stake' of the game (how much money each play is worth), as well as a list representing the cards used in the game. To make things easier, we will just use a list of numbers 0-9 for the cards." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gameStake = 50 \n", "cards = range(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's define a new class to represent each player in the game. I have provided a rough framework of the class definition along with comments along the way to help you complete it. Places where you should write code are denoted by comments inside [] brackets and CAPITAL TEXT." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Player:\n", " \n", " # create here two local variables to store a unique ID for each player and the player's current 'pot' of money\n", " PN=0\n", " Pot=0# [FILL IN YOUR VARIABLES HERE]\n", " \n", " # in the __init__() function, use the two input variables to initialize the ID and starting pot of each player\n", " \n", " def __init__(self, inputID, startingPot):\n", " self.PN=inputID\n", " self.Pot=startingPot# [CREATE YOUR INITIALIZATIONS HERE]\n", " \n", " # create a function for playing the game. This function starts by taking an input for the dealer's card\n", " # and picking a random number from the 'cards' list for the player's card\n", "\n", " def play(self, dealerCard):\n", " # we use the random.choice() function to select a random item from a list\n", " playerCard = random.choice(cards)\n", " \n", " # here we should have a conditional that tests the player's card value against the dealer card\n", " # and returns a statement saying whether the player won or lost the hand\n", " # before returning the statement, make sure to either add or subtract the stake from the player's pot so that\n", " # the 'pot' variable tracks the player's money\n", " \n", " if playerCard < dealerCard:\n", " self.Pot=self.Pot-gameStake\n", " print 'player'+str(self.PN)+' Lose,'+str(playerCard)+' vs '+str(dealerCard)# [INCREMENT THE PLAYER'S POT, AND RETURN A MESSAGE] \n", " else:\n", " self.Pot=self.Pot+gameStake\n", " print 'player'+str(self.PN)+' Win,'+str(playerCard)+' vs '+str(dealerCard)# [INCREMENT THE PLAYER'S POT, AND RETURN A MESSAGE]\n", " \n", " # create an accessor function to return the current value of the player's pot\n", " def returnPot(self):\n", " return self.Pot# [FILL IN THE RETURN STATEMENT]\n", " \n", " # create an accessor function to return the player's ID\n", " def returnID(self):\n", " return self.PN# [FILL IN THE RETURN STATEMENT]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will create some functions outside the class definition which will control the flow of the game. The first function will play one round. It will take as an input the collection of players, and iterate through each one, calling each player's '.play() function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def playHand(players):\n", " \n", " for player in players:\n", " dealerCard = random.choice(cards)\n", " player.play(dealerCard)#[EXECUTE THE PLAY() FUNCTION FOR EACH PLAYER USING THE DEALER CARD, AND PRINT OUT THE RESULTS]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will define a function that will check the balances of each player, and print out a message with the player's ID and their balance." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def checkBalances(players):\n", " \n", " for player in players:\n", " print 'player '+str(player.returnID())+ ' has $ '+str(player.returnPot())+ ' left'#[PRINT OUT EACH PLAYER'S BALANCE BY USING EACH PLAYER'S ACCESSOR FUNCTIONS]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to start the game. First we create an empy list to store the collection of players in the game." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "players = [] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we create a loop that will run a certain number of times, each time creating a player with a unique ID and a starting balance. Each player should be appended to the empty list, which will store all the players. In this case we pass the 'i' iterator of the loop as the player ID, and set a constant value of 500 for the starting balance." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(5):\n", " players.append(Player(i, 500))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the players are created, we will create a loop to run the game a certain amount of times. Each step of the loop should start with a print statement announcing the start of the game, and then call the `playHand()` function, passing as an input the list of players." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "start game 0\n", "player0 Lose,5 vs 6\n", "player1 Win,8 vs 6\n", "player2 Win,9 vs 6\n", "player3 Win,9 vs 3\n", "player4 Win,8 vs 6\n", "\n", "start game 1\n", "player0 Win,9 vs 9\n", "player1 Lose,6 vs 8\n", "player2 Lose,2 vs 7\n", "player3 Win,5 vs 4\n", "player4 Lose,0 vs 8\n", "\n", "start game 2\n", "player0 Win,7 vs 2\n", "player1 Lose,0 vs 2\n", "player2 Lose,6 vs 9\n", "player3 Win,6 vs 1\n", "player4 Lose,7 vs 9\n", "\n", "start game 3\n", "player0 Lose,5 vs 7\n", "player1 Lose,4 vs 5\n", "player2 Win,1 vs 0\n", "player3 Win,6 vs 3\n", "player4 Lose,7 vs 9\n", "\n", "start game 4\n", "player0 Lose,4 vs 8\n", "player1 Lose,1 vs 8\n", "player2 Win,9 vs 2\n", "player3 Lose,8 vs 9\n", "player4 Win,8 vs 8\n", "\n", "start game 5\n", "player0 Win,2 vs 0\n", "player1 Lose,5 vs 9\n", "player2 Lose,0 vs 6\n", "player3 Win,8 vs 0\n", "player4 Lose,6 vs 9\n", "\n", "start game 6\n", "player0 Lose,0 vs 4\n", "player1 Win,8 vs 3\n", "player2 Lose,0 vs 6\n", "player3 Lose,5 vs 7\n", "player4 Lose,1 vs 5\n", "\n", "start game 7\n", "player0 Win,7 vs 1\n", "player1 Lose,1 vs 5\n", "player2 Win,6 vs 0\n", "player3 Win,4 vs 2\n", "player4 Win,6 vs 0\n", "\n", "start game 8\n", "player0 Lose,3 vs 6\n", "player1 Win,8 vs 2\n", "player2 Win,6 vs 6\n", "player3 Win,4 vs 3\n", "player4 Win,8 vs 6\n", "\n", "start game 9\n", "player0 Lose,2 vs 5\n", "player1 Lose,1 vs 4\n", "player2 Win,0 vs 0\n", "player3 Win,4 vs 0\n", "player4 Win,4 vs 3\n" ] } ], "source": [ "for i in range(10):\n", " print ''\n", " print 'start game ' + str(i)\n", " playHand(players)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we will analyze the results of the game by running the `'checkBalances()'` function and passing it our list of players." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "game results:\n", "player 0 has $ 400 left\n", "player 1 has $ 300 left\n", "player 2 has $ 600 left\n", "player 3 has $ 800 left\n", "player 4 has $ 500 left\n" ] } ], "source": [ "print ''\n", "print 'game results:'\n", "checkBalances(players)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a version of the expected printout if you've done everything correctly (note that since the cards are chosen randomly the actual results will differ, but the structure should be the same). Once you finish the assignment please submit a pull request to the main dmc-2016 repo before the deadline." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "start game 0\n", "player 0 Lose, 4 vs 7\n", "player 1 Win, 2 vs 0\n", "player 2 Lose, 0 vs 4\n", "player 3 Win, 7 vs 2\n", "player 4 Win, 5 vs 0\n", "\n", "start game 1\n", "player 0 Win, 1 vs 0\n", "player 1 Lose, 1 vs 5\n", "player 2 Lose, 6 vs 9\n", "player 3 Lose, 1 vs 8\n", "player 4 Lose, 0 vs 9\n", "\n", "start game 2\n", "player 0 Win, 3 vs 3\n", "player 1 Lose, 0 vs 2\n", "player 2 Win, 9 vs 6\n", "player 3 Win, 8 vs 7\n", "player 4 Win, 8 vs 6\n", "\n", "start game 3\n", "player 0 Win, 9 vs 7\n", "player 1 Lose, 7 vs 8\n", "player 2 Lose, 2 vs 3\n", "player 3 Lose, 0 vs 8\n", "player 4 Lose, 0 vs 6\n", "\n", "start game 4\n", "player 0 Win, 7 vs 4\n", "player 1 Win, 3 vs 0\n", "player 2 Win, 8 vs 5\n", "player 3 Win, 2 vs 1\n", "player 4 Lose, 4 vs 7\n", "\n", "start game 5\n", "player 0 Lose, 2 vs 8\n", "player 1 Lose, 4 vs 6\n", "player 2 Win, 2 vs 0\n", "player 3 Lose, 4 vs 5\n", "player 4 Lose, 3 vs 8\n", "\n", "start game 6\n", "player 0 Lose, 3 vs 6\n", "player 1 Win, 8 vs 0\n", "player 2 Win, 5 vs 5\n", "player 3 Lose, 2 vs 6\n", "player 4 Win, 8 vs 7\n", "\n", "start game 7\n", "player 0 Lose, 0 vs 9\n", "player 1 Lose, 6 vs 8\n", "player 2 Lose, 1 vs 9\n", "player 3 Lose, 4 vs 8\n", "player 4 Win, 9 vs 8\n", "\n", "start game 8\n", "player 0 Lose, 1 vs 8\n", "player 1 Lose, 3 vs 9\n", "player 2 Win, 5 vs 4\n", "player 3 Win, 6 vs 2\n", "player 4 Win, 3 vs 0\n", "\n", "start game 9\n", "player 0 Lose, 5 vs 6\n", "player 1 Win, 6 vs 1\n", "player 2 Lose, 8 vs 9\n", "player 3 Lose, 3 vs 9\n", "player 4 Win, 7 vs 5\n", "\n", "game results:\n", "player 0 has $400 left.\n", "player 1 has $400 left.\n", "player 2 has $500 left.\n", "player 3 has $400 left.\n", "player 4 has $600 left.\n", "```" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
ES-DOC/esdoc-jupyterhub
notebooks/cccr-iitm/cmip6/models/sandbox-3/atmoschem.ipynb
1
102075
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Atmoschem \n", "**MIP Era**: CMIP6 \n", "**Institute**: CCCR-IITM \n", "**Source ID**: SANDBOX-3 \n", "**Topic**: Atmoschem \n", "**Sub-Topics**: Transport, Emissions Concentrations, Gas Phase Chemistry, Stratospheric Heterogeneous Chemistry, Tropospheric Heterogeneous Chemistry, Photo Chemistry. \n", "**Properties**: 84 (39 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmoschem?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:48" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-3', 'atmoschem')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Software Properties](#2.-Key-Properties---&gt;-Software-Properties) \n", "[3. Key Properties --&gt; Timestep Framework](#3.-Key-Properties---&gt;-Timestep-Framework) \n", "[4. Key Properties --&gt; Timestep Framework --&gt; Split Operator Order](#4.-Key-Properties---&gt;-Timestep-Framework---&gt;-Split-Operator-Order) \n", "[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \n", "[6. Grid](#6.-Grid) \n", "[7. Grid --&gt; Resolution](#7.-Grid---&gt;-Resolution) \n", "[8. Transport](#8.-Transport) \n", "[9. Emissions Concentrations](#9.-Emissions-Concentrations) \n", "[10. Emissions Concentrations --&gt; Surface Emissions](#10.-Emissions-Concentrations---&gt;-Surface-Emissions) \n", "[11. Emissions Concentrations --&gt; Atmospheric Emissions](#11.-Emissions-Concentrations---&gt;-Atmospheric-Emissions) \n", "[12. Emissions Concentrations --&gt; Concentrations](#12.-Emissions-Concentrations---&gt;-Concentrations) \n", "[13. Gas Phase Chemistry](#13.-Gas-Phase-Chemistry) \n", "[14. Stratospheric Heterogeneous Chemistry](#14.-Stratospheric-Heterogeneous-Chemistry) \n", "[15. Tropospheric Heterogeneous Chemistry](#15.-Tropospheric-Heterogeneous-Chemistry) \n", "[16. Photo Chemistry](#16.-Photo-Chemistry) \n", "[17. Photo Chemistry --&gt; Photolysis](#17.-Photo-Chemistry---&gt;-Photolysis) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Key properties of the atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmospheric chemistry model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of atmospheric chemistry model code.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Chemistry Scheme Scope\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Atmospheric domains covered by the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.chemistry_scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basic approximations made in the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Form of prognostic variables in the atmospheric chemistry component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/mixing ratio for gas\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of advected tracers in the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Atmospheric chemistry calculations (not advection) generalized into families of species?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.family_approach') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Coupling With Chemical Reactivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Atmospheric chemistry transport scheme turbulence is couple with chemical reactivity?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.coupling_with_chemical_reactivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Software Properties \n", "*Software properties of aerosol code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestep Framework \n", "*Timestepping in the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Mathematical method deployed to solve the evolution of a given variable*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Operator splitting\" \n", "# \"Integrated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for chemical species advection (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for physics (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_physical_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Split Operator Chemistry Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for chemistry (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_chemistry_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Split Operator Alternate Order\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_alternate_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.6. Integrated Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the atmospheric chemistry model (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.7. Integrated Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the type of timestep scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Timestep Framework --&gt; Split Operator Order \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Turbulence\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for turbulence scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.turbulence') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Convection\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for convection scheme This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.convection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Precipitation\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for precipitation scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.4. Emissions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for emissions scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.emissions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.5. Deposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for deposition scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.6. Gas Phase Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for gas phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.gas_phase_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.7. Tropospheric Heterogeneous Phase Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for tropospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.tropospheric_heterogeneous_phase_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.8. Stratospheric Heterogeneous Phase Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for stratospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.stratospheric_heterogeneous_phase_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.9. Photo Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for photo chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.photo_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.10. Aerosols\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for aerosols scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.aerosols') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for atmospheric chemistry component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &amp;Document the relative weight given to climate performance metrics versus process oriented metrics, &amp;and on the possible conflicts with parameterization level tuning. In particular describe any struggle &amp;with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List set of metrics of the global mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Regional Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of regional metrics of mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Trend Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List observed trend metrics used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid \n", "*Atmospheric chemistry grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the atmopsheric chemistry grid*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### * Does the atmospheric chemistry grid match the atmosphere grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Resolution \n", "*Resolution in the atmospheric chemistry grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Canonical Horizontal Resolution\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Number Of Horizontal Gridpoints\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Number Of Vertical Levels\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Number of vertical levels resolved on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.5. Is Adaptive Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Default is False. Set true if grid resolution changes during execution.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Transport \n", "*Atmospheric chemistry transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview of transport implementation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.transport.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Use Atmospheric Transport\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is transport handled by the atmosphere, rather than within atmospheric cehmistry?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.transport.use_atmospheric_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Transport Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If transport is handled within the atmospheric chemistry scheme, describe it.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.transport.transport_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Emissions Concentrations \n", "*Atmospheric chemistry emissions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview atmospheric chemistry emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Emissions Concentrations --&gt; Surface Emissions \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of the chemical species emitted at the surface that are taken into account in the emissions scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Soil\" \n", "# \"Sea surface\" \n", "# \"Anthropogenic\" \n", "# \"Biomass burning\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Methods used to define chemical species emitted directly into model layers above the surface (several methods allowed because the different species may not use the same method).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Climatology\" \n", "# \"Spatially uniform mixing ratio\" \n", "# \"Spatially uniform concentration\" \n", "# \"Interactive\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and prescribed via a climatology, and the nature of the climatology (E.g. CO (monthly), C2H6 (constant))*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and prescribed as spatially uniform*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.5. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and specified via an interactive method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.6. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and specified via any other method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Emissions Concentrations --&gt; Atmospheric Emissions \n", "*TO DO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of chemical species emitted in the atmosphere that are taken into account in the emissions scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Aircraft\" \n", "# \"Biomass burning\" \n", "# \"Lightning\" \n", "# \"Volcanos\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Methods used to define the chemical species emitted in the atmosphere (several methods allowed because the different species may not use the same method).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Climatology\" \n", "# \"Spatially uniform mixing ratio\" \n", "# \"Spatially uniform concentration\" \n", "# \"Interactive\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and prescribed via a climatology (E.g. CO (monthly), C2H6 (constant))*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and prescribed as spatially uniform*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and specified via an interactive method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and specified via an &quot;other method&quot;*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Emissions Concentrations --&gt; Concentrations \n", "*TO DO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Prescribed Lower Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed at the lower boundary.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Prescribed Upper Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed at the upper boundary.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Gas Phase Chemistry \n", "*Atmospheric chemistry transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview gas phase atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Species included in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"HOx\" \n", "# \"NOy\" \n", "# \"Ox\" \n", "# \"Cly\" \n", "# \"HSOx\" \n", "# \"Bry\" \n", "# \"VOCs\" \n", "# \"isoprene\" \n", "# \"H2O\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Number Of Bimolecular Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of bi-molecular reactions in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_bimolecular_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Number Of Termolecular Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of ter-molecular reactions in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_termolecular_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Number Of Tropospheric Heterogenous Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of reactions in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_tropospheric_heterogenous_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.6. Number Of Stratospheric Heterogenous Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of reactions in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_stratospheric_heterogenous_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.7. Number Of Advected Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of advected species in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_advected_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.8. Number Of Steady State Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of gas phase species for which the concentration is updated in the chemical solver assuming photochemical steady state*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_steady_state_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.9. Interactive Dry Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.interactive_dry_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.10. Wet Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is wet deposition included? Wet deposition describes the moist processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.11. Wet Oxidation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is wet oxidation included? Oxidation describes the loss of electrons or an increase in oxidation state by a molecule*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_oxidation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Stratospheric Heterogeneous Chemistry \n", "*Atmospheric chemistry startospheric heterogeneous chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview stratospheric heterogenous atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Gas Phase Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Gas phase species included in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.gas_phase_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Cly\" \n", "# \"Bry\" \n", "# \"NOy\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Aerosol Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Aerosol species included in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.aerosol_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule))\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Number Of Steady State Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of steady state species in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.number_of_steady_state_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Sedimentation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is sedimentation is included in the stratospheric heterogeneous chemistry scheme or not?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.sedimentation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Coagulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is coagulation is included in the stratospheric heterogeneous chemistry scheme or not?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.coagulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Tropospheric Heterogeneous Chemistry \n", "*Atmospheric chemistry tropospheric heterogeneous chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview tropospheric heterogenous atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Gas Phase Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of gas phase species included in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.gas_phase_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Aerosol Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Aerosol species included in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.aerosol_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon/soot\" \n", "# \"Polar stratospheric ice\" \n", "# \"Secondary organic aerosols\" \n", "# \"Particulate organic matter\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Number Of Steady State Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of steady state species in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.number_of_steady_state_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Interactive Dry Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.interactive_dry_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Coagulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is coagulation is included in the tropospheric heterogeneous chemistry scheme or not?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.coagulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Photo Chemistry \n", "*Atmospheric chemistry photo chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview atmospheric photo chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Number Of Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of reactions in the photo-chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.number_of_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Photo Chemistry --&gt; Photolysis \n", "*Photolysis scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Photolysis scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Offline (clear sky)\" \n", "# \"Offline (with clouds)\" \n", "# \"Online\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Environmental Conditions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any environmental conditions taken into account by the photolysis scheme (e.g. whether pressure- and temperature-sensitive cross-sections and quantum yields in the photolysis calculations are modified to reflect the modelled conditions.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.environmental_conditions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
ChicagoBoothAnalytics/RelDataQuery_SQL_R_Py_Spark
Cases/AirBnB Kaggle/Python/AirBnBKaggle-SQL-PythonPandas-PySparkSQLDataFrame.ipynb
2
8139096
null
mit
GoogleCloudPlatform/asl-ml-immersion
notebooks/ml_fairness_explainability/explainable_ai/integrated_gradients.ipynb
1
3371749
null
apache-2.0
jsub10/Machine-Learning-By-Example
Chapter-4-Non-Linear-Regression.ipynb
1
131313
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Think Like a Machine - Chapter 4\n", "Non-Linear Regression with Multiple Variables\n", "=============================================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ACKNOWLEDGEMENT**\n", "\n", "**A lot of the code in this notebook is from John D. Wittenauer's notebooks that cover the exercises in Andrew Ng's course on Machine Learning on Coursera. This is mostly Wittenauer's and Ng's work and acknowledged as such. I've also used some code from Sebastian Raschka's book *Python Machine Learning*. **" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use the functions from another notebook in this notebook\n", "%run SharedFunctions.ipynb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import our usual libraries\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model in step 3 of the previous chapter has been simple. It multipled our inputs by constants (the values of $\\theta$) and added them up. That is classic linear stuff.\n", "\n", "With only a slight modification of that model we can easily extend regression to any number of variables -- even millions of them!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Size</th>\n", " <th>Bedrooms</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2104</td>\n", " <td>3</td>\n", " <td>399900</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1600</td>\n", " <td>3</td>\n", " <td>329900</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2400</td>\n", " <td>3</td>\n", " <td>369000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1416</td>\n", " <td>2</td>\n", " <td>232000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3000</td>\n", " <td>4</td>\n", " <td>539900</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Size Bedrooms Price\n", "0 2104 3 399900\n", "1 1600 3 329900\n", "2 2400 3 369000\n", "3 1416 2 232000\n", "4 3000 4 539900" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load up the housing price data we used before\n", "import os\n", "path = os.getcwd() + '/Data/ex1data2.txt'\n", "data2 = pd.read_csv(path, header=None, names=['Size', 'Bedrooms', 'Price'])\n", "data2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize the entire dataset as follows." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9aNr2wXOdRIQqBDsmCcPyBRwdqXJ6t+KmzoAZCXfhb+VNWJQWb30c1cJyN62y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\ngVticnmQ0/XCQoLtbjdaOlC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bPp6IiIikS1J3NK5du4Yf/vCHdv82m82ora0VMzQiIiIa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\njOzeuYxGBl9vKRpb2tHSqkfkFP8+d20lhG/O+CV89foti7wWpufq6vV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/+7//w/Lly7F0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\nIYQ3FGgQQgghhDcUaBBCCCGEN/8Pyms84aDSUfYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116b97b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "sns.set(style='whitegrid', context='notebook')\n", "cols = ['Size', 'Bedrooms', 'Price']\n", "sns.pairplot(data2[cols], size=2.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4-1\n", "Based on the visuals above, how would you describe the data? Write a short paragraph describing the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use Size as the Key Variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Paradoxically, to demostrate how multivariate non-linear regression works, we'll strip down our original dataset into one that just has Size and Price; the Bedrooms part of the data is removed. This simplifies things so that we can easily visualize what's going on.\n", "\n", "So, to visualize things more easily, we're going to focus just on the sinlge variable -- the size of the house. We'll turn this into a multi-variable situation in just a bit." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Just checking on the type of object data2 is ... good to remind ourselves\n", "type(data2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Size</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2104</td>\n", " <td>399900</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1600</td>\n", " <td>329900</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2400</td>\n", " <td>369000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1416</td>\n", " <td>232000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3000</td>\n", " <td>539900</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Size Price\n", "0 2104 399900\n", "1 1600 329900\n", "2 2400 369000\n", "3 1416 232000\n", "4 3000 539900" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First drop the Bedrooms column from the data set -- we're not going to be using it for the rest of this notebook\n", "data3 = data2.drop('Bedrooms', axis = 1)\n", "data3.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11998f2d0>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x119a66890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize this simplified data set\n", "data3.plot.scatter(x='Size', y='Price', figsize=(8,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How Polynomials Fit the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualize the fit for various degrees of polynomial functions." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Size</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.130010</td>\n", " <td>0.475747</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.504190</td>\n", " <td>-0.084074</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.502476</td>\n", " <td>0.228626</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-0.735723</td>\n", " <td>-0.867025</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.257476</td>\n", " <td>1.595389</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Size Price\n", "0 0.130010 0.475747\n", "1 -0.504190 -0.084074\n", "2 0.502476 0.228626\n", "3 -0.735723 -0.867025\n", "4 1.257476 1.595389" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Because Price is about 100 times Size, first normalize the data\n", "data3Norm = (data3 - data3.mean()) / data3.std()\n", "data3Norm.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[-2, 4, -1.5, 3]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Y3yXXz1wl3kNqv769FJEecdPrN/bTppQUiWPHpE0pKbUdZr1X2Z+nWh3B3+3StUqlol+/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\naTi7wJbx+e07L3ndtr/bxws33Gtoywh2QojyQAK+lCrMleyv+y8w7MA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"text/plain": [ "<matplotlib.figure.Figure at 0x119ad98d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = data3Norm['Size']\n", "y = data3Norm['Price']\n", "\n", "# fit the data with a 2nd degree polynomial\n", "z2 = np.polyfit(X, y, 2) \n", "p2 = np.poly1d(z2) # construct the polynomial (note: that's a one in \"poly1d\")\n", "\n", "# fit the data with a 3rd degree polynomial\n", "z3 = np.polyfit(X, y, 3) \n", "p3 = np.poly1d(z3) # construct the polynomial\n", "\n", "# fit the data with a 4th degree polynomial\n", "z4 = np.polyfit(X, y, 4) \n", "p4 = np.poly1d(z4) # construct the polynomial\n", "\n", "# fit the data with a 8th degree polynomial - just for the heck of it :-)\n", "z8 = np.polyfit(X, y, 8) \n", "p8 = np.poly1d(z8) # construct the polynomial\n", "\n", "# fit the data with a 16th degree polynomial - just for the heck of it :-)\n", "z16 = np.polyfit(X, y, 16) \n", "p16 = np.poly1d(z16) # construct the polynomial\n", "\n", "xx = np.linspace(-2, 4, 100)\n", "plt.figure(figsize=(8,5))\n", "plt.plot(X, y, 'o', label='data')\n", "plt.xlabel('Size')\n", "plt.ylabel('Price')\n", "plt.plot(xx, p2(xx), 'g-', label='2nd degree poly')\n", "plt.plot(xx, p3(xx), 'y-', label='3rd degree poly')\n", "#plt.plot(xx, p4(xx), 'r-', label='4th degree poly')\n", "plt.plot(xx, p8(xx), 'c-', label='8th degree poly')\n", "#plt.plot(xx, p16(xx), 'm-', label='16th degree poly')\n", "plt.legend(loc=2)\n", "plt.axis([-2,4,-1.5,3]) # Use for higher degrees of polynomials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steps 1 and 2: Define the Inputs and the Outputs" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x0</th>\n", " <th>Size</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.130010</td>\n", " <td>0.475747</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>-0.504190</td>\n", " <td>-0.084074</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0.502476</td>\n", " <td>0.228626</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>-0.735723</td>\n", " <td>-0.867025</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1.257476</td>\n", " <td>1.595389</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x0 Size Price\n", "0 1 0.130010 0.475747\n", "1 1 -0.504190 -0.084074\n", "2 1 0.502476 0.228626\n", "3 1 -0.735723 -0.867025\n", "4 1 1.257476 1.595389" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Add a column of 1s to the X input (keeps the notation simple)\n", "data3Norm.insert(0,'x0',1)\n", "data3Norm.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x0</th>\n", " <th>Size</th>\n", " <th>Size^2</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.130010</td>\n", " <td>0.016903</td>\n", " <td>0.475747</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>-0.504190</td>\n", " <td>0.254207</td>\n", " <td>-0.084074</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0.502476</td>\n", " <td>0.252482</td>\n", " <td>0.228626</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>-0.735723</td>\n", " <td>0.541288</td>\n", " <td>-0.867025</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1.257476</td>\n", " <td>1.581246</td>\n", " <td>1.595389</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x0 Size Size^2 Price\n", "0 1 0.130010 0.016903 0.475747\n", "1 1 -0.504190 0.254207 -0.084074\n", "2 1 0.502476 0.252482 0.228626\n", "3 1 -0.735723 0.541288 -0.867025\n", "4 1 1.257476 1.581246 1.595389" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data3Norm.insert(2,'Size^2', np.power(data3Norm['Size'],2))\n", "data3Norm.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x0</th>\n", " <th>Size</th>\n", " <th>Size^2</th>\n", " <th>Size^3</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.130010</td>\n", " <td>0.016903</td>\n", " <td>0.002198</td>\n", " <td>0.475747</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>-0.504190</td>\n", " <td>0.254207</td>\n", " <td>-0.128169</td>\n", " <td>-0.084074</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0.502476</td>\n", " <td>0.252482</td>\n", " <td>0.126866</td>\n", " <td>0.228626</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>-0.735723</td>\n", " <td>0.541288</td>\n", " <td>-0.398238</td>\n", " <td>-0.867025</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1.257476</td>\n", " <td>1.581246</td>\n", " <td>1.988379</td>\n", " <td>1.595389</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x0 Size Size^2 Size^3 Price\n", "0 1 0.130010 0.016903 0.002198 0.475747\n", "1 1 -0.504190 0.254207 -0.128169 -0.084074\n", "2 1 0.502476 0.252482 0.126866 0.228626\n", "3 1 -0.735723 0.541288 -0.398238 -0.867025\n", "4 1 1.257476 1.581246 1.988379 1.595389" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data3Norm.insert(3,'Size^3', np.power(data3Norm['Size'],3))\n", "data3Norm.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x0</th>\n", " <th>Size</th>\n", " <th>Size^2</th>\n", " <th>Size^3</th>\n", " <th>Size^4</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.130010</td>\n", " <td>0.016903</td>\n", " <td>0.002198</td>\n", " <td>0.000286</td>\n", " <td>0.475747</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>-0.504190</td>\n", " <td>0.254207</td>\n", " <td>-0.128169</td>\n", " <td>0.064621</td>\n", " <td>-0.084074</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0.502476</td>\n", " <td>0.252482</td>\n", " <td>0.126866</td>\n", " <td>0.063747</td>\n", " <td>0.228626</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>-0.735723</td>\n", " <td>0.541288</td>\n", " <td>-0.398238</td>\n", " <td>0.292993</td>\n", " <td>-0.867025</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1.257476</td>\n", " <td>1.581246</td>\n", " <td>1.988379</td>\n", " <td>2.500339</td>\n", " <td>1.595389</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x0 Size Size^2 Size^3 Size^4 Price\n", "0 1 0.130010 0.016903 0.002198 0.000286 0.475747\n", "1 1 -0.504190 0.254207 -0.128169 0.064621 -0.084074\n", "2 1 0.502476 0.252482 0.126866 0.063747 0.228626\n", "3 1 -0.735723 0.541288 -0.398238 0.292993 -0.867025\n", "4 1 1.257476 1.581246 1.988379 2.500339 1.595389" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data3Norm.insert(4,'Size^4', np.power(data3Norm['Size'],4))\n", "data3Norm.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have 4 input variables -- they're various powers of the one input variable we started with. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X3 = data3Norm.iloc[:, 0:5]\n", "y3 = data3Norm.iloc[:, 5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Define the Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to turn this one (dependent) variable data set consisting of Size values into a dataset that will be represented by a multi-variate, polynomial model. First let's define the kind of model we're interested in. In the expressions below $x$ represents the Size of a house and the model is saying that the price of the house is a polynomial function of size.\n", "\n", "Here's a second-degree polynomial model:\n", "Model p2 = $h_{\\theta}(x) = \\theta_{0}x_{0} + \\theta_{1}x + \\theta_{2}x^{2}$\n", "\n", "Here's a third-degree polynomial model:\n", "Model p3 = $h_{\\theta}(x) = \\theta_{0}x_{0} + \\theta_{1}x + \\theta_{2}x^{2} + \\theta_{3}x^3$\n", "\n", "And here's a fourth-degree polynomial model:\n", "Model p4 = $h_{\\theta}(x) = \\theta_{0}x_{0} + \\theta_{1}x + \\theta_{2}x^{2} + \\theta_{3}x^3 + \\theta_{4}x^4$\n", "\n", "Our models are more complicated than before, but $h_{\\theta}(x)$ is still the same calculation as before because our inputs have been transformed to represent $x^{2}$, $x^{3}$, and $x^{4}$.\n", "\n", "We'll use Model p4 for the rest of the calculations. It's a legitimate question to ask *how to decide* which model choose. We'll answer that question a few chapters later. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Define the Parameters of the Model\n", "$\\theta_{0}$, $\\theta_{1}$, $\\theta_{2}$, $\\theta_{3}$, and $\\theta_{4}$ are the *parameters* of the model. Unlike our example of the boiling water in Chapter 1, these parameters can each take on an infinite number of values. $\\theta_{0}$ is called the *bias value*.\n", "\n", "With this model, we know exactly how to transform an input into an output -- that is, once the values of the parameters are given.\n", "\n", "Let's pick a value of X from the dataset, fix specific values for $\\theta_{0}$, $\\theta_{1}$, $\\theta_{2}$, $\\theta_{3}$, and $\\theta_{4}$, and see what we get for the value of y.\n", "\n", "Specifically, let\n", "$\\begin{bmatrix}\n", "\\theta_{0} \\\\\n", "\\theta_{1} \\\\\n", "\\theta_{2} \\\\\n", "\\theta_{3} \\\\\n", "\\theta_{4}\n", "\\end{bmatrix} = \n", "\\begin{bmatrix}\n", "-10 \\\\\n", "1 \\\\\n", "0 \\\\\n", "5 \\\\\n", "-1\n", "\\end{bmatrix}$\n", "\n", "This means $\\theta_{0}$ is -10, $\\theta_{1}$ is 1, and so on.\n", "\n", "Let's try out X * $\\theta$ for the first few rows of X." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ -9.85928833],\n", " [-11.20965516],\n", " [ -8.92693861],\n", " [-13.01990813],\n", " [ -1.30096852]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Outputs generated by our model for the first 5 inputs with the specific theta values below\n", "theta_test = np.matrix('-10;1;0;5;-1')\n", "outputs = np.matrix(X3.iloc[0:5, :]) * theta_test\n", "outputs" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0.475747\n", "1 -0.084074\n", "2 0.228626\n", "3 -0.867025\n", "4 1.595389\n", "Name: Price, dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compare with the first few values of the output\n", "y3.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's quite a bit off from the actual values; so we know that the values for $\\theta$ in theta_test must be quite far from the optimal values for $\\theta$ -- the values that will minimize the cost of getting it wrong." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Define the Cost of Getting it Wrong\n", "Our cost function is exactly the same as it was before for the single variable case. \n", "\n", "The cost of getting it wrong is defined as a function $J(\\theta)$:\n", "\n", "$$J(\\theta) = \\frac{1}{2m} \\sum_{i=1}^{m} (h_{\\theta}x^{(i)}) - y^{(i)})^2$$\n", "\n", "The only difference from what we had before is the addition of the various $\\theta$s and $x$s\n", "\n", "$$h_{\\theta}(X) = \\theta_{0} * x_{0}\\ +\\ \\theta_{1} * x_{1} +\\ \\theta_{2} * x_{2} +\\ \\theta_{3} * x_{3} +\\ \\theta_{4} * x_{4}$$\n", "\n", "where $x_{2} = x_{1}^{2}$, $x_{3} = x_{1}^{3}$, and $x_{4} = x_{1}^{4}$." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5982.1359608963376" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute the cost for a given set of theta values over the entire dataset\n", "# Get X and y in to matrix form\n", "computeCost(np.matrix(X3.values), np.matrix(y3.values), theta_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't know yet if this is high or low -- we'll have to try out a whole bunch of $\\theta$ values. Or better yet, we can use pick an iterative method and implement it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steps 6 and 7: Pick an Iterative Method to Minimize the Cost of Getting it Wrong and Implement It\n", "Once again, the method that will \"learn\" the optimal values for $\\theta$ is gradient descent. We don't have to do a thing to the function we wrote before for gradient descent. Let's use it to find the minimum cost and the values of $\\theta$ that result in that minimum cost." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta_init = np.matrix('-1;0;1;0;-1')\n", "# Run gradient descent for a number of different learning rates\n", "alpha = 0.00001\n", "iters = 5000\n", "\n", "theta_opt, cost_min = gradientDescent(np.matrix(X3.values), np.matrix(y3.values), theta_init, alpha, iters)\n", " " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[-0.47452231],\n", " [-0.13783401],\n", " [ 0.71951195],\n", " [ 0.0576498 ],\n", " [-0.09134312]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is the value of theta for the last iteration above -- hence for alpha = 0.1\n", "theta_opt" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "26.883851764021568" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The minimum cost\n", "cost_min[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 8: The Results\n", "Let's make some predictions based on the values of $\\theta_{opt}$. We're using our 4th-order polynomial as the model." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "size = 2\n", "size_nonnorm = (size * data3.std()[0]) + data3.mean()[0]\n", "price = (theta_opt[0] * 1) + (theta_opt[1] * size) + (theta_opt[2] * np.power(size,2)) + (theta_opt[3] * np.power(size,3)) + (theta_opt[4] * np.power(size,4))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.1275658774646542" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "price[0,0]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Transform the price into the real price (not normalized)\n", "price_mean = data3.mean()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "price_std = data3.std()[1]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "price_pred = (price[0,0] * price_std) + price_mean" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Size 142991.404946\n", "Price 481403.383670\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "price_pred" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "size_nonnorm" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "340412.6595744681" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data3.mean()[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
ceos-seo/data_cube_notebooks
notebooks/Data_Challenge/LandCover.ipynb
1
105686
{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## 2022 EY Challenge - Land Cover\n", "This notebook can be used to create a land cover dataset. This land cover information can be used as a \"predictor variable\" to relate to species samples. For example, certain land cover classifications (e.g. water, grass, trees) may be conducive to species habitats. This dataset contains global estimates of 10-class land use/land cover for the year 2020, derived from ESA Sentinel-2 imagery at 10-meter spatial resolution. The data can be found in the MS Planetary Computer catalog: https://planetarycomputer.microsoft.com/dataset/io-lulc#overview" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Supress Warnings \n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# Import common GIS tools\n", "import numpy as np\n", "import xarray as xr\n", "import matplotlib.pyplot as plt\n", "import rioxarray as rio\n", "import rasterio.features\n", "import folium\n", "import math\n", "from matplotlib.colors import ListedColormap\n", "\n", "# Import Planetary Computer tools\n", "import stackstac\n", "import pystac_client\n", "import planetary_computer as pc\n", "from pystac.extensions.raster import RasterExtension as raster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the analysis region and view on a map\n", "\n", "First, we define our area of interest using latitude and longitude coordinates. Our test region is near Richmond, NSW, Australia. The first line defines the lower-left corner of the bounding box and the second line defines the upper-right corner of the bounding box. GeoJSON format uses a specific order: (longitude, latitude), so be careful when entering the coordinates." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define the bounding box using corners\n", "min_lon, min_lat = (150.62, -33.69) # Lower-left corner (longitude, latitude)\n", "max_lon, max_lat = (150.83, -33.48) # Upper-right corner (longitude, latitude)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "bbox = (min_lon, min_lat, max_lon, max_lat)\n", "latitude = (min_lat, max_lat)\n", "longitude = (min_lon, max_lon)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [], "source": [ "def _degree_to_zoom_level(l1, l2, margin = 0.0):\n", " \n", " degree = abs(l1 - l2) * (1 + margin)\n", " zoom_level_int = 0\n", " if degree != 0:\n", " zoom_level_float = math.log(360/degree)/math.log(2)\n", " zoom_level_int = int(zoom_level_float)\n", " else:\n", " zoom_level_int = 18\n", " return zoom_level_int\n", "\n", "def display_map(latitude = None, longitude = None):\n", "\n", " margin = -0.5\n", " zoom_bias = 0\n", " lat_zoom_level = _degree_to_zoom_level(margin = margin, *latitude ) + zoom_bias\n", " lon_zoom_level = _degree_to_zoom_level(margin = margin, *longitude) + zoom_bias\n", " zoom_level = min(lat_zoom_level, lon_zoom_level) \n", " center = [np.mean(latitude), np.mean(longitude)]\n", " \n", " map_hybrid = folium.Map(location=center,zoom_start=zoom_level, \n", " tiles=\" http://mt1.google.com/vt/lyrs=y&z={z}&x={x}&y={y}\",attr=\"Google\")\n", " \n", " line_segments = [(latitude[0],longitude[0]),(latitude[0],longitude[1]),\n", " (latitude[1],longitude[1]),(latitude[1],longitude[0]),\n", " (latitude[0],longitude[0])]\n", " \n", " map_hybrid.add_child(folium.features.PolyLine(locations=line_segments,color='red',opacity=0.8))\n", " map_hybrid.add_child(folium.features.LatLngPopup()) \n", "\n", " return map_hybrid" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<iframe src=\"about:blank\" width=\"600\" height=\"600\"style=\"border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write( decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>" ], "text/plain": [ "<branca.element.Figure at 0x7f93d721bdc0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot bounding box on a map\n", "f = folium.Figure(width=600, height=600)\n", "m = display_map(latitude,longitude)\n", "f.add_child(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discover and load the data for analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the `pystac_client` we can search the Planetary Computer's STAC endpoint for items matching our query parameters. We will look for data tiles (1-degree square) that intersect our bounding box. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "stac = pystac_client.Client.open(\"https://planetarycomputer.microsoft.com/api/stac/v1\")\n", "search = stac.search(bbox=bbox,collections=[\"io-lulc\"])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of data tiles intersecting our bounding box: 4\n" ] } ], "source": [ "items = list(search.get_items())\n", "print('Number of data tiles intersecting our bounding box:',len(items))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll load the data into an [xarray](https://xarray.pydata.org/en/stable/) DataArray using [stackstac](https://stackstac.readthedocs.io/) and then \"clip\" the data to only the pixels within our region (bounding box). There are also several other <b>important settings for the data</b>: We have changed the projection to EPSG=4326 which is standard latitude-longitude in degrees. We have specified the spatial resolution of each pixel to be 10-meters, which is the baseline accuracy for this data. After creating the DataArray, we will need to mosaic the raster chunks across the time dimension (remember, they're all from a single synthesized \"time\" from 2020) and drop the single band dimension. Finally, we will read the actual data by calling .compute(). In the end, the dataset will include land cover classifications (10 total) at 10-meters spatial resolution. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "item = next(search.get_items())\n", "items = [pc.sign(item).to_dict() for item in search.get_items()]\n", "nodata = raster.ext(item.assets[\"data\"]).bands[0].nodata" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Define the pixel resolution for the final product\n", "# Define the scale according to our selected crs, so we will use degrees\n", "resolution = 10 # meters per pixel \n", "scale = resolution / 111320.0 # degrees per pixel for crs=4326 " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [], "source": [ "data = stackstac.stack(\n", " items, # use only the data from our search results\n", " epsg=4326, # use common lat-lon coordinates\n", " resolution=scale, # Use degrees for crs=4326\n", " dtype=np.ubyte, # matches the data versus default float64\n", " fill_value=nodata, # fills voids with no data\n", " bounds_latlon=bbox # clips to our bounding box\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [], "source": [ "land_cover = stackstac.mosaic(data, dim=\"time\", axis=None).squeeze().drop(\"band\").compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land Cover Map\n", "Now we will create a land cover classification map. The source GeoTIFFs contain a colormap and the STAC metadata contains the class names. We'll open one of the source files just to read this metadata and construct the right colors and names for our plot." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Create a custom colormap using the file metadata\n", "class_names = land_cover.coords[\"label:classes\"].item()[\"classes\"]\n", "class_count = len(class_names)\n", "\n", "with rasterio.open(pc.sign(item.assets[\"data\"].href)) as src:\n", " colormap_def = src.colormap(1) # get metadata colormap for band 1\n", " colormap = [np.array(colormap_def[i]) / 255 for i in range(class_count)\n", " ] # transform to matplotlib color format\n", "\n", "cmap = ListedColormap(colormap)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "image = land_cover.plot(size=8,cmap=cmap,add_colorbar=False,vmin=0,vmax=class_count)\n", "cbar = plt.colorbar(image)\n", "cbar.set_ticks(range(class_count))\n", "cbar.set_ticklabels(class_names)\n", "plt.gca().set_aspect('equal')\n", "plt.title('Land Cover Classification')\n", "plt.xlabel('Longitude')\n", "plt.ylabel('Latitude')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save the output data in a GeoTIFF file" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [], "source": [ "filename = \"Land_Cover_sample2.tiff\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Set the dimensions of file in pixels\n", "height = land_cover.shape[0]\n", "width = land_cover.shape[1]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Define the Coordinate Reference System (CRS) to be common Lat-Lon coordinates\n", "# Define the tranformation using our bounding box so the Lat-Lon information is written to the GeoTIFF\n", "gt = rasterio.transform.from_bounds(min_lon,min_lat,max_lon,max_lat,width,height)\n", "land_cover.rio.write_crs(\"epsg:4326\", inplace=True)\n", "land_cover.rio.write_transform(transform=gt, inplace=True);" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Create the GeoTIFF output file using the defined parameters\n", "with rasterio.open(filename,'w',driver='GTiff',width=width,height=height,\n", " crs='epsg:4326',transform=gt,count=1,compress='lzw',dtype=np.ubyte) as dst:\n", " dst.write(land_cover,1)\n", " dst.close()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-rw-r-- 1 jovyan users 122M Jan 19 17:49 DEM_sample.tiff\n", "-rw-r--r-- 1 jovyan users 5.2M Jan 21 18:25 DEM_sample8.tiff\n", "-rw-rw-r-- 1 jovyan users 273K Jan 7 17:21 Land_Cover_sample.tiff\n", "-rw-r--r-- 1 jovyan users 327K Jan 21 19:33 Land_Cover_sample2.tiff\n", "-rw-rw-r-- 1 jovyan users 49M Jan 5 20:26 S2_mosaic_sample.tiff\n", "-rw-r--r-- 1 jovyan users 49M Jan 21 18:40 S2_mosaic_sample2.tiff\n", "-rw-rw-r-- 1 jovyan users 305 Jan 6 01:03 Weather_sample.tiff\n" ] } ], "source": [ "# Show the location and size of the new output file\n", "!ls *.tiff -lah" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How will the participants use this data?\n", "The GeoTIFF file will contain the Lat-Lon coordinates of each pixel and will also contain the land class for each pixel. Since the FrogID data is also Lat-Lon position, it is possible to find the closest pixel using code similar to what is demonstrated below. Once this pixel is found, then the corresponding land class can be used for modeling species distribution. In addition, participants may want to consider proximity to specific land classes. For example, there may be a positive correlation with land classes such as trees, grass or water and there may be a negative correlation with land classes such as built-up area or bare soil." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the possible <b>land classifications</b>, reported below:<br>\n", "1 = water, 2 = trees, 3 = grass, 4 = flooded vegetation, 5 = crops<br>\n", "6 = scrub, 7 = built-up (urban), 8 = bare soil, 9 = snow/ice, 10=clouds" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is the land classification for the closest pixel: 2\n" ] } ], "source": [ "# This is an example for a specific Lon-Lat location randomly selected within our sample region.\n", "values = land_cover.sel(x=150.71, y=-33.51, method=\"nearest\").values \n", "print(\"This is the land classification for the closest pixel: \",values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "2ca0804b9f904dab815db80637a4f2d9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "e2f3ac516e3b4cf3a1ba1fc6aa0897ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "VBoxModel", "state": { "layout": "IPY_MODEL_2ca0804b9f904dab815db80637a4f2d9" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
paul-shannon/projects
examples/clustergrammer/notebook-standalone/cgrDemo.ipynb
1
18558
{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Autosave disabled\n" ] } ], "source": [ "%autosave 0" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ipywidgets as widgets\n", "import json\n", "import time\n", "import os\n", "from IPython.display import display, HTML\n", "from traitlets import Int, Unicode, observe" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "assert(requests.get('http://pshannon.systemsbiology.net/js/clustergrammer.js').status_code == 200) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class CGRWidget(widgets.DOMWidget):\n", "\n", " _view_name = Unicode('CGRView').tag(sync=True)\n", " _view_module = Unicode('cgr').tag(sync=True)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "\"use strict\"\n", "\n", "require.config({\n", "\n", " paths: {'jquery' : 'http://code.jquery.com/jquery-1.12.4.min',\n", " 'jquery-ui' : 'http://code.jquery.com/ui/1.12.1/jquery-ui.min',\n", " 'jquery-dataTable': 'https://cdn.datatables.net/1.10.13/js/jquery.dataTables.min',\n", " 'cytoscape' : 'http://cytoscape.github.io/cytoscape.js/api/cytoscape.js-latest/cytoscape',\n", " 'bootstrap' : 'http://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min',\n", " 'igv' : 'http://igv.org/web/release/1.0.7/igv-1.0.7',\n", " 'three' : 'https://cdnjs.cloudflare.com/ajax/libs/three.js/r83/three',\n", " 'underscore' : 'https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min',\n", " 'clustergrammer' : 'http://pshannon.systemsbiology.net/js/clustergrammer',\n", " 'app3d' : 'http://pshannon.systemsbiology.net/js/app3d',\n", " 'd3' : 'https://cdnjs.cloudflare.com/ajax/libs/d3/4.7.3/d3',\n", " },\n", " shim: {'bootstrap': {'deps' : ['jquery']},\n", " 'igv': {'deps' : ['jquery', 'jquery-ui', 'bootstrap']},\n", " 'three': {'exports': 'THREE'},\n", " 'underscore': {'exports': '_'},\n", " }\n", " });\n", "\n", "require.undef('cgrDemo')\n", "\n", "define('cgrDemo', [\"jupyter-js-widgets\", \"jquery\", \"jquery-ui\", \"jquery-dataTable\", \"cytoscape\", \"igv\", \n", " 'three', 'app3d', 'd3', 'clustergrammer'], \n", " function(widgets, $, ui, DataTable, cytoscape, igv, THREE, app3d, d3, clustergrammer) {\n", " \n", " var CGRView = widgets.DOMWidgetView.extend({\n", "\n", " initialize: function() {\n", " console.log(\"CGRView.initialize\")\n", " },\n", "\n", " render: function() {\n", " var cgrDiv = $(\"<div id='heatmapDiv' style='border:1px solid gray; height: 800px; width: 97%'></div>\");\n", " this.$el.append(this.masterTabsDiv); \n", " createHeatMap();\n", " },\n", "\n", " createHeatmap: function() {\n", " var network_data =\n", " {\"views\":\n", " [{\"N_row_sum\": \"all\", \"dist\": \"cos\", \"nodes\":\n", " {\"col_nodes\": [{\"ini\": 3, \"rank\": 2, \"name\": \"s01_120405\", \"clust\": 1,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 2},\n", " {\"ini\": 2, \"rank\": 1, \"name\": \"s02_120405\", \"clust\": 0,\n", " \"group\": [3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 0, \"name\": \"s03_120405\", \"clust\": 2,\n", " \"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 1}],\n", " \"row_nodes\": [{\"ini\": 2, \"rank\": 0, \"name\": \"HLTF\", \"clust\": 0,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 1, \"name\": \"POU2F1\", \"clust\": 1,\n", " \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0], \"rankvar\": 1}]}},\n", " {\"dist\": \"cos\", \"N_row_var\": \"all\", \"nodes\":\n", " {\"col_nodes\": [{\"ini\": 3, \"rank\": 2, \"name\": \"s01_120405\", \"clust\": 1,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 2},\n", " {\"ini\": 2, \"rank\": 1, \"name\": \"s02_120405\", \"clust\": 0,\n", " \"group\": [3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 0, \"name\": \"s03_120405\", \"clust\": 2,\n", " \"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 1}],\n", " \"row_nodes\": [{\"ini\": 2, \"rank\": 0, \"name\": \"HLTF\", \"clust\": 0,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 1, \"name\": \"POU2F1\", \"clust\": 1,\n", " \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0], \"rankvar\": 1}]}}],\n", " \"col_nodes\": [{\"ini\": 3, \"rank\": 2, \"name\": \"s01_120405\", \"clust\": 1,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 2},\n", " {\"ini\": 2, \"rank\": 1, \"name\": \"s02_120405\", \"clust\": 0,\n", " \"group\": [3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 0, \"name\": \"s03_120405\", \"clust\": 2,\n", " \"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 1}],\n", " \"links\": [{\"target\": 0, \"value\": 5.591991, \"source\": 0},\n", " {\"target\": 1, \"value\": 11.939007, \"source\": 0},\n", " {\"target\": 2, \"value\": 7.738552, \"source\": 0},\n", " {\"target\": 0, \"value\": 31.060965999999997, \"source\": 1},\n", " {\"target\": 1, \"value\": 18.00348, \"source\": 1},\n", " {\"target\": 2, \"value\": 21.577569, \"source\": 1}],\n", " \"row_nodes\": [{\"ini\": 2, \"rank\": 0, \"name\": \"HLTF\", \"clust\": 0,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 1, \"name\": \"POU2F1\", \"clust\": 1,\n", " \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0], \"rankvar\": 1}]};\n", " \n", " make_clust(network_data);\n", " var about_string = 'Zoom, scroll, and click buttons to interact with the clustergram. <a href=\"http://amp.pharm.mssm.edu/clustergrammer/help\"> <i class=\"fa fa-question-circle\" aria-hidden=\"true\"></i> </a>';\n", " function make_clust(network_data){\n", " var args = {\n", " 'root': '#heatmapDiv',\n", " 'network_data': network_data,\n", " 'about': about_string,\n", " 'sidebar_width':150,\n", " };\n", " var screen_width = window.innerWidth;\n", " var screen_height = window.innerHeight - 20;\n", " $(\"#heatmapDiv\").width(screen_width);\n", " $(\"#heatmapDiv\").height(screen_height);\n", " cgm = Clustergrammer(args);\n", " $(\"#heatmapDiv .wait_message\").remove()\n", " console.log('loading clustergrammer')\n", " } // make_clust\n", " }, // createHeatmap\n", " \n", " });\n", " return{CGRView: CGRView}\n", " });\n", " " ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "\"use strict\"\n", "\n", "require.config({\n", "\n", " paths: {'jquery' : 'http://code.jquery.com/jquery-1.12.4.min',\n", " 'jquery-ui' : 'http://code.jquery.com/ui/1.12.1/jquery-ui.min',\n", " 'jquery-dataTable': 'https://cdn.datatables.net/1.10.13/js/jquery.dataTables.min',\n", " 'cytoscape' : 'http://cytoscape.github.io/cytoscape.js/api/cytoscape.js-latest/cytoscape',\n", " 'bootstrap' : 'http://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min',\n", " 'igv' : 'http://igv.org/web/release/1.0.7/igv-1.0.7',\n", " 'three' : 'https://cdnjs.cloudflare.com/ajax/libs/three.js/r83/three',\n", " 'underscore' : 'https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min',\n", " 'clustergrammer' : 'http://pshannon.systemsbiology.net/js/clustergrammer',\n", " 'app3d' : 'http://pshannon.systemsbiology.net/js/app3d',\n", " 'd3' : 'https://cdnjs.cloudflare.com/ajax/libs/d3/4.7.3/d3',\n", " },\n", " shim: {'bootstrap': {'deps' : ['jquery']},\n", " 'igv': {'deps' : ['jquery', 'jquery-ui', 'bootstrap']},\n", " 'three': {'exports': 'THREE'},\n", " 'underscore': {'exports': '_'},\n", " }\n", " });\n", "\n", "require.undef('cgrDemo')\n", "\n", "define('cgrDemo', [\"jupyter-js-widgets\", \"jquery\", \"jquery-ui\", \"jquery-dataTable\", \"cytoscape\", \"igv\", \n", " 'three', 'app3d', 'd3', 'clustergrammer'], \n", " function(widgets, $, ui, DataTable, cytoscape, igv, THREE, app3d, d3, clustergrammer) {\n", " \n", " var CGRView = widgets.DOMWidgetView.extend({\n", "\n", " initialize: function() {\n", " console.log(\"CGRView.initialize\")\n", " },\n", "\n", " render: function() {\n", " var cgrDiv = $(\"<div id='heatmapDiv' style='border:1px solid gray; height: 800px; width: 97%'></div>\");\n", " this.$el.append(this.masterTabsDiv); \n", " createHeatMap();\n", " },\n", "\n", " createHeatmap: function() {\n", " var network_data =\n", " {\"views\":\n", " [{\"N_row_sum\": \"all\", \"dist\": \"cos\", \"nodes\":\n", " {\"col_nodes\": [{\"ini\": 3, \"rank\": 2, \"name\": \"s01_120405\", \"clust\": 1,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 2},\n", " {\"ini\": 2, \"rank\": 1, \"name\": \"s02_120405\", \"clust\": 0,\n", " \"group\": [3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 0, \"name\": \"s03_120405\", \"clust\": 2,\n", " \"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 1}],\n", " \"row_nodes\": [{\"ini\": 2, \"rank\": 0, \"name\": \"HLTF\", \"clust\": 0,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 1, \"name\": \"POU2F1\", \"clust\": 1,\n", " \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0], \"rankvar\": 1}]}},\n", " {\"dist\": \"cos\", \"N_row_var\": \"all\", \"nodes\":\n", " {\"col_nodes\": [{\"ini\": 3, \"rank\": 2, \"name\": \"s01_120405\", \"clust\": 1,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 2},\n", " {\"ini\": 2, \"rank\": 1, \"name\": \"s02_120405\", \"clust\": 0,\n", " \"group\": [3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 0, \"name\": \"s03_120405\", \"clust\": 2,\n", " \"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 1}],\n", " \"row_nodes\": [{\"ini\": 2, \"rank\": 0, \"name\": \"HLTF\", \"clust\": 0,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 1, \"name\": \"POU2F1\", \"clust\": 1,\n", " \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0], \"rankvar\": 1}]}}],\n", " \"col_nodes\": [{\"ini\": 3, \"rank\": 2, \"name\": \"s01_120405\", \"clust\": 1,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 2},\n", " {\"ini\": 2, \"rank\": 1, \"name\": \"s02_120405\", \"clust\": 0,\n", " \"group\": [3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 0, \"name\": \"s03_120405\", \"clust\": 2,\n", " \"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 1}],\n", " \"links\": [{\"target\": 0, \"value\": 5.591991, \"source\": 0},\n", " {\"target\": 1, \"value\": 11.939007, \"source\": 0},\n", " {\"target\": 2, \"value\": 7.738552, \"source\": 0},\n", " {\"target\": 0, \"value\": 31.060965999999997, \"source\": 1},\n", " {\"target\": 1, \"value\": 18.00348, \"source\": 1},\n", " {\"target\": 2, \"value\": 21.577569, \"source\": 1}],\n", " \"row_nodes\": [{\"ini\": 2, \"rank\": 0, \"name\": \"HLTF\", \"clust\": 0,\n", " \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"rankvar\": 0},\n", " {\"ini\": 1, \"rank\": 1, \"name\": \"POU2F1\", \"clust\": 1,\n", " \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0], \"rankvar\": 1}]};\n", " \n", " make_clust(network_data);\n", " var about_string = 'Zoom, scroll, and click buttons to interact with the clustergram. <a href=\"http://amp.pharm.mssm.edu/clustergrammer/help\"> <i class=\"fa fa-question-circle\" aria-hidden=\"true\"></i> </a>';\n", " function make_clust(network_data){\n", " var args = {\n", " 'root': '#heatmapDiv',\n", " 'network_data': network_data,\n", " 'about': about_string,\n", " 'sidebar_width':150,\n", " };\n", " var screen_width = window.innerWidth;\n", " var screen_height = window.innerHeight - 20;\n", " $(\"#heatmapDiv\").width(screen_width);\n", " $(\"#heatmapDiv\").height(screen_height);\n", " cgm = Clustergrammer(args);\n", " $(\"#heatmapDiv .wait_message\").remove()\n", " console.log('loading clustergrammer')\n", " } // make_clust\n", " }, // createHeatmap\n", " \n", " });\n", " return{CGRView: CGRView}\n", " });\n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cgr = CGRWidget()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
astyler/scratch
breelyn examples.ipynb
1
136027
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n = 400\n", "og = np.full(shape=(n,n), fill_value=0)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import random\n", "\n", "for i in range(n**2 /10):\n", " row = random.randint(0,n-1)\n", " col = random.randint(0,n-1)\n", " og[row,col] = 1" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for j in range(n):\n", " for i in range(n):\n", " if og[i,j] > 0:\n", " exp = og[i, j]+1\n", " if i < n-1 and og[i+1,j] == 0:\n", " og[i+1, j] = exp \n", " if j < n-1 and og[i, j+1] == 0:\n", " og[i, j+1] = exp\n", " if i > 0 and og[i-1, j] == 0:\n", " og[i-1, j] = exp\n", " if j > 0 and og[i, j-1] == 0:\n", " og[i, j-1] = exp" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11e0dce50>" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAApEAAAKwCAYAAADA2yRYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmcX9V1J3jqVyqxKMaYnRFGxEDKDBgwRuwIszlilw3B\n", "g3Ec405o2oQe2u2YcdrZnMXjeJLOpNPudJKedk8n6emZZNqTpT3zcZx2nHFsTIfFFkZlBJawRKFd\n", "lFQqVen3e6//eO+833333eWce+/7vR+l8+UPfqW33f2ee5bvmcjzHAQCgUAgEAgEAg56XRdAIBAI\n", "BAKBQPDGgwiRAoFAIBAIBAI2RIgUCAQCgUAgELAhQqRAIBAIBAKBgA0RIgUCgUAgEAgEbIgQKRAI\n", "BAKBQCBgY0XXBRAIBAKBQCBYbrh4zQ2dcyh+e+vfTLT5ftFECgQCgUAgEAjYECFSIBAIBAKBQMCG\n", "mLMFAoFAIBAIEmNiolVL8lhANJECgUAgEAgEAjZEEykQCAQCgUCQGBMTy19Pt/xrKBAIBAKBQCBI\n", 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mit
wei-Z/Python-Machine-Learning
code/ch13/ch13.ipynb
1
827288
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Sebastian Raschka](http://sebastianraschka.com), 2015\n", "\n", "https://github.com/rasbt/python-machine-learning-book" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Machine Learning - Code Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 13 - Parallelizing Neural Network Training with Theano" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sebastian Raschka \n", "Last updated: 08/27/2015 \n", "\n", "CPython 3.4.3\n", "IPython 4.0.0\n", "\n", "numpy 1.9.2\n", "matplotlib 1.4.3\n", "theano 0.7.0\n", "keras 0.1.2\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,matplotlib,theano,keras" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# to install watermark just uncomment the following line:\n", "#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [Building, compiling, and running expressions with Theano](#Building,-compiling,-and-running-expressions-with-Theano)\n", " - [What is Theano?](#What-is-Theano?)\n", " - [First steps with Theano](#First-steps-with-Theano)\n", " - [Configuring Theano](#Configuring-Theano)\n", " - [Working with array structures](#Working-with-array-structures)\n", " - [Wrapping things up – a linear regression example](#Wrapping-things-up:-A--linear-regression-example)\n", "- [Choosing activation functions for feedforward neural networks](#Choosing-activation-functions-for-feedforward-neural-networks)\n", " - [Logistic function recap](#Logistic-function-recap)\n", " - [Estimating probabilities in multi-class classification via the softmax function](#Estimating-probabilities-in-multi-class-classification-via-the-softmax-function)\n", " - [Broadening the output spectrum by using a hyperbolic tangent](#Broadening-the-output-spectrum-by-using-a-hyperbolic-tangent)\n", "- [Training neural networks efficiently using Keras](#Training-neural-networks-efficiently-using-Keras)\n", "- [Summary](#Summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Building, compiling, and running expressions with Theano" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Depending on your system setup, it is typically sufficient to install Theano via\n", "\n", " pip install Theano\n", " \n", "For more help with the installation, please see: http://deeplearning.net/software/theano/install.html" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S5G+pB9LHZBxleWQ/ydXLxymokyBSl2K2kqUyg/KQSrGqqxqX2giSBW4abBrDmie1XLWF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9t+fB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\n+Ow1Akow3a4NxOQBqia7y1nG76Uovm8TaBuya+eT9m2AB8gCb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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": { "image/png": { "width": 500 } }, "output_type": "execute_result" } ], "source": [ "Image(filename='./images/13_01.png', width=500) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is Theano?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First steps with Theano" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Introducing the TensorType variables. For a complete list, see http://deeplearning.net/software/theano/library/tensor/basic.html#all-fully-typed-constructors" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import theano\n", "from theano import tensor as T" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(2.5)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# initialize\n", "x1 = T.scalar()\n", "w1 = T.scalar()\n", "w0 = T.scalar()\n", "z1 = w1 * x1 + w0\n", "\n", "# compile\n", "net_input = theano.function(inputs=[w1, x1, w0], outputs=z1)\n", "\n", "# execute\n", "net_input(2.0, 1.0, 0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configuring Theano" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configuring Theano. For more options, see\n", "- http://deeplearning.net/software/theano/library/config.html\n", "- http://deeplearning.net/software/theano/library/floatX.html" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "float64\n" ] } ], "source": [ "print(theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "theano.config.floatX = 'float32'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change the float type globally, execute \n", "\n", " export THEANO_FLAGS=floatX=float32 \n", " \n", "in your bash shell. Or execute Python script as\n", "\n", " THEANO_FLAGS=floatX=float32 python your_script.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running Theano on GPU(s). For prerequisites, please see: http://deeplearning.net/software/theano/tutorial/using_gpu.html\n", "\n", "Note that `float32` is recommended for GPUs; `float64` on GPUs is currently still relatively slow." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cpu\n" ] } ], "source": [ "print(theano.config.device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can run a Python script on CPU via:\n", "\n", " THEANO_FLAGS=device=cpu,floatX=float64 python your_script.py\n", "\n", "or GPU via\n", "\n", " THEANO_FLAGS=device=gpu,floatX=float32 python your_script.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It may also be convenient to create a `.theanorc` file in your home directory to make those configurations permanent. For example, to always use `float32`, execute\n", "\n", " echo -e \"\\n[global]\\nfloatX=float32\\n\" >> ~/.theanorc\n", " \n", "Or, create a `.theanorc` file manually with the following contents\n", "\n", " [global]\n", " floatX = float32\n", " device = gpu\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with array structures" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column sum: [ 2. 4. 6.]\n", "Column sum: [ 2. 4. 6.]\n" ] } ], "source": [ "import numpy as np\n", "\n", "# initialize\n", "# if you are running Theano on 64 bit mode, \n", "# you need to use dmatrix instead of fmatrix\n", "x = T.fmatrix(name='x')\n", "x_sum = T.sum(x, axis=0)\n", "\n", "# compile\n", "calc_sum = theano.function(inputs=[x], outputs=x_sum)\n", "\n", "# execute (Python list)\n", "ary = [[1, 2, 3], [1, 2, 3]]\n", "print('Column sum:', calc_sum(ary))\n", "\n", "# execute (NumPy array)\n", "ary = np.array([[1, 2, 3], [1, 2, 3]], dtype=theano.config.floatX)\n", "print('Column sum:', calc_sum(ary))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Updating shared arrays.\n", "More info about memory management in Theano can be found here: http://deeplearning.net/software/theano/tutorial/aliasing.html" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z0: [[ 0.]]\n", "z1: [[ 6.]]\n", "z2: [[ 12.]]\n", "z3: [[ 18.]]\n", "z4: [[ 24.]]\n" ] } ], "source": [ "# initialize\n", "x = T.fmatrix(name='x')\n", "w = theano.shared(np.asarray([[0.0, 0.0, 0.0]], \n", " dtype=theano.config.floatX))\n", "z = x.dot(w.T)\n", "update = [[w, w + 1.0]]\n", "\n", "# compile\n", "net_input = theano.function(inputs=[x], \n", " updates=update, \n", " outputs=z)\n", "\n", "# execute\n", "data = np.array([[1, 2, 3]], dtype=theano.config.floatX)\n", "for i in range(5):\n", " print('z%d:' % i, net_input(data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `givens` variable to insert values into the graph before compiling it. Using this approach we can reduce the number of transfers from RAM (via CPUs) to GPUs to speed up learning with shared variables. If we use `inputs`, a datasets is transferred from the CPU to the GPU multiple times, for example, if we iterate over a dataset multiple times (epochs) during gradient descent. Via `givens`, we can keep the dataset on the GPU if it fits (e.g., a mini-batch). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z: [[ 0.]]\n", "z: [[ 6.]]\n", "z: [[ 12.]]\n", "z: [[ 18.]]\n", "z: [[ 24.]]\n" ] } ], "source": [ "# initialize\n", "data = np.array([[1, 2, 3]], \n", " dtype=theano.config.floatX)\n", "x = T.fmatrix(name='x')\n", "w = theano.shared(np.asarray([[0.0, 0.0, 0.0]], \n", " dtype=theano.config.floatX))\n", "z = x.dot(w.T)\n", "update = [[w, w + 1.0]]\n", "\n", "# compile\n", "net_input = theano.function(inputs=[], \n", " updates=update, \n", " givens={x: data},\n", " outputs=z)\n", "\n", "# execute\n", "for i in range(5):\n", " print('z:', net_input())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wrapping things up: A linear regression example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating some training data." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "X_train = np.asarray([[0.0], [1.0], [2.0], [3.0], [4.0],\n", " [5.0], [6.0], [7.0], [8.0], [9.0]], \n", " dtype=theano.config.floatX)\n", "\n", "y_train = np.asarray([1.0, 1.3, 3.1, 2.0, 5.0, \n", " 6.3, 6.6, 7.4, 8.0, 9.0], \n", " dtype=theano.config.floatX)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implementing the training function." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import theano\n", "from theano import tensor as T\n", "import numpy as np\n", "\n", "def train_linreg(X_train, y_train, eta, epochs):\n", "\n", " costs = []\n", " # Initialize arrays\n", " eta0 = T.fscalar('eta0')\n", " y = T.fvector(name='y') \n", " X = T.fmatrix(name='X') \n", " w = theano.shared(np.zeros(\n", " shape=(X_train.shape[1] + 1),\n", " dtype=theano.config.floatX),\n", " name='w')\n", " \n", " # calculate cost\n", " net_input = T.dot(X, w[1:]) + w[0]\n", " errors = y - net_input\n", " cost = T.sum(T.pow(errors, 2)) \n", "\n", " # perform gradient update\n", " gradient = T.grad(cost, wrt=w)\n", " update = [(w, w - eta0 * gradient)]\n", "\n", " # compile model\n", " train = theano.function(inputs=[eta0],\n", " outputs=cost,\n", " updates=update,\n", " givens={X: X_train,\n", " y: y_train,}) \n", " \n", " for _ in range(epochs):\n", " costs.append(train(eta))\n", " \n", " return costs, w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the sum of squared errors cost vs epochs." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10971cb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "costs, w = train_linreg(X_train, y_train, eta=0.001, epochs=10)\n", " \n", "plt.plot(range(1, len(costs)+1), costs)\n", "\n", "plt.tight_layout()\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Cost')\n", "plt.tight_layout()\n", "# plt.savefig('./figures/cost_convergence.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Making predictions." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109a1bbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def predict_linreg(X, w):\n", " Xt = T.matrix(name='X')\n", " net_input = T.dot(Xt, w[1:]) + w[0]\n", " predict = theano.function(inputs=[Xt], givens={w: w}, outputs=net_input)\n", " return predict(X)\n", "\n", "plt.scatter(X_train, y_train, marker='s', s=50)\n", "plt.plot(range(X_train.shape[0]), \n", " predict_linreg(X_train, w), \n", " color='gray', \n", " marker='o', \n", " markersize=4, \n", " linewidth=3)\n", "\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "\n", "plt.tight_layout()\n", "# plt.savefig('./figures/linreg.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Choosing activation functions for feedforward neural networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic function recap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The logistic function, often just called \"sigmoid function\" is in fact a special case of a sigmoid function.\n", "\n", "Net input $z$:\n", "$$z = w_1x_{1} + \\dots + w_mx_{m} = \\sum_{j=1}^{m} x_{j}w_{j} \\\\ = \\mathbf{w}^T\\mathbf{x}$$\n", "\n", "Logistic activation function:\n", "\n", "$$\\phi_{logistic}(z) = \\frac{1}{1 + e^{-z}}$$\n", "\n", "Output range: (0, 1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P(y=1|x) = 0.707\n" ] } ], "source": [ "# note that first element (X[0] = 1) to denote bias unit\n", "\n", "X = np.array([[1, 1.4, 1.5]])\n", "w = np.array([0.0, 0.2, 0.4])\n", "\n", "def net_input(X, w):\n", " z = X.dot(w)\n", " return z\n", "\n", "def logistic(z):\n", " return 1.0 / (1.0 + np.exp(-z))\n", "\n", "def logistic_activation(X, w):\n", " z = net_input(X, w)\n", " return logistic(z)\n", "\n", "print('P(y=1|x) = %.3f' % logistic_activation(X, w)[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, imagine a MLP perceptron with 3 hidden units + 1 bias unit in the hidden unit. The output layer consists of 3 output units." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probabilities:\n", " [[ 0.87653295]\n", " [ 0.57688526]\n", " [ 0.90114393]]\n" ] } ], "source": [ "# W : array, shape = [n_output_units, n_hidden_units+1]\n", "# Weight matrix for hidden layer -> output layer.\n", "# note that first column (A[:][0] = 1) are the bias units\n", "W = np.array([[1.1, 1.2, 1.3, 0.5],\n", " [0.1, 0.2, 0.4, 0.1],\n", " [0.2, 0.5, 2.1, 1.9]])\n", "\n", "# A : array, shape = [n_hidden+1, n_samples]\n", "# Activation of hidden layer.\n", "# note that first element (A[0][0] = 1) is for the bias units\n", "\n", "A = np.array([[1.0], \n", " [0.1], \n", " [0.3], \n", " [0.7]])\n", "\n", "# Z : array, shape = [n_output_units, n_samples]\n", "# Net input of output layer.\n", "\n", "Z = W.dot(A) \n", "y_probas = logistic(Z)\n", "print('Probabilities:\\n', y_probas)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predicted class label: 2\n" ] } ], "source": [ "y_class = np.argmax(Z, axis=0)\n", "print('predicted class label: %d' % y_class[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating probabilities in multi-class classification via the softmax function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The softmax function is a generalization of the logistic function and allows us to compute meaningful class-probalities in multi-class settings (multinomial logistic regression).\n", "\n", "$$P(y=j|z) =\\phi_{softmax}(z) = \\frac{e^{z_j}}{\\sum_{k=1}^K e^{z_k}}$$\n", "\n", "the input to the function is the result of K distinct linear functions, and the predicted probability for the j'th class given a sample vector x is:\n", "\n", "Output range: (0, 1)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def softmax(z): \n", " return np.exp(z) / np.sum(np.exp(z))\n", "\n", "def softmax_activation(X, w):\n", " z = net_input(X, w)\n", " return softmax(z)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probabilities:\n", " [[ 0.40386493]\n", " [ 0.07756222]\n", " [ 0.51857284]]\n" ] } ], "source": [ "y_probas = softmax(Z)\n", "print('Probabilities:\\n', y_probas)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_probas.sum()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_class = np.argmax(Z, axis=0)\n", "y_class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Broadening the output spectrum using a hyperbolic tangent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another special case of a sigmoid function, it can be interpreted as a rescaled version of the logistic function.\n", "\n", "$$\\phi_{tanh}(z) = \\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}$$\n", "\n", "Output range: (-1, 1)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def tanh(z):\n", " e_p = np.exp(z) \n", " e_m = np.exp(-z)\n", " return (e_p - e_m) / (e_p + e_m) " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a2a0e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "z = np.arange(-5, 5, 0.005)\n", "log_act = logistic(z)\n", "tanh_act = tanh(z)\n", "\n", "# alternatives:\n", "# from scipy.special import expit\n", "# log_act = expit(z)\n", "# tanh_act = np.tanh(z)\n", "\n", "plt.ylim([-1.5, 1.5])\n", "plt.xlabel('net input $z$')\n", "plt.ylabel('activation $\\phi(z)$')\n", "plt.axhline(1, color='black', linestyle='--')\n", "plt.axhline(0.5, color='black', linestyle='--')\n", "plt.axhline(0, color='black', linestyle='--')\n", "plt.axhline(-1, color='black', linestyle='--')\n", "\n", "plt.plot(z, tanh_act, \n", " linewidth=2, \n", " color='black', \n", " label='tanh')\n", "plt.plot(z, log_act, \n", " linewidth=2, \n", " color='lightgreen', \n", " label='logistic')\n", "\n", "plt.legend(loc='lower right')\n", "plt.tight_layout()\n", "# plt.savefig('./figures/activation.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LYwEBAgQIECBAgAABAgQIECBAgAABAgQIEOimwNRTT52+/OUvp0033TQtvPDC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\n9tppuummG7UDj1lONthgg1QENo9anSOp6OSTT07FAIYj2bSt20Ssw04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5+ytdRjFJsyqVh5qfV35+2LGRQr78591XYCBAgQIECAAAECBAgQIECAAAECBAi0ReDd7353XH75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\nhcX2U9XNmjUL1113nWTz68ia1uOPP96s5XOphCdOnMC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EAAAQQQiKCAZmsfNWpU2I+Qk5Mj+iAvBQEEEEAAAQQQCJcA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\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ABShAAQpQgAIUoAAFKECB8wkwsP18OtxGAQpQgAIUoAAFKEABClCAAhSgAAXaIFCetQcH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\nfp999vFYv6qqSkuWLPGo7+qKYcOGOXaRk5PjWN/eykGDBum++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9d4d+/tcXtCh7U5OtpM9f+pc2Lvxepx38B/VPidPQuIbgdpdpf9X8/2rHhnXN1rcf+g9K16h9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\niy++UHp6usMwNzdXJSUlsrL0W787cXFxGjp0qE499VRZwe2dXbr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w+OtUeuFB491p1PEn0nQKBrCBRi8aIFMWva5Hj9lQkx7uG749nHn4q7774vnv/Q+rHtzpvF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\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ufq3QqF49NKqjpoh0eclG4pko7FzyIUZNmofT5xJRM6wRWt7+XzRoHaSm7C6FsHItFRdO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AACCCCAAAII\nIIAAAgggEEggWK8JBfu1wkCWTbV11LVbrvk3HoGOuubfeM+0IIAAAggggAACCCCAAAIIIIAAAgiE\nqgCJ7SE0cmbi9K233tpkUnt8fLwefvhhXXvttTITPANNZkLhP//5T5nVbl0uV6BF/Eme55xzjnJy\ncgK+37Dx8ccfD5jgbLFY9JOf/OT/s3cf8FGU+R/Hvykk9AChd5UuTYoFxYaCiqLCCTas4OmhYj0L\nYsNeTlEsB9hO4fT8K6igIDaUjoDSe+89lED6f57FDcnuTLKb3U12N5/n5bq7z/PMM8+8Z3ZJJr/5\njYYNG6b4ePtDbebMma4g0CVLlngO61rOBNq7ixmvUqVK7rche37//ff13XffeY1fuXJljRkzxpWl\n3Kvxr4pXX31VZptef/11ff75566gfROkaVduvfVWde3aVQcOHMjX3KVLF9t98+OPP8rMIZDSo0cP\n7d27N5AhXMuOHDlSX3zxheM4/fv317333uu6mMGzkwlO/uWXX2QCiz33u/kDw65du0Ie2P7HH38o\nKyvLc2oqW7asKzDaq8GHiuI6bnyYSpG6RMLn2By/69evz7d95nvhhhtu0BNPPOEKhjaNZt8uWrRI\nH3/8sf71r3/l6+9+Yy6gGDp0qEzgutl2z9KsWTO9/PLLuvjii1WmTBlXs7nY5bffftPgwYMdg63N\nd+vAgQM9hyv0fbh+vxc6cT86vPHGG5o7d67jEuZOJI899pjOPvvsfH1Mlnfjah6eGfU3bNiQr2+w\n3jhd4GLuwJGdnR3yC6yCtR2MgwACCCCAAAIIIIAAAggggAAC4S0QrueEIuFcoT97trjO3XLOv+TO\n+ftzPNAXAQQQQAABBBBAwDcB83dak8SQggACCCCAAAIIFLuAFUhK8VGgZ8+eOdYO8nrcfvvtPo5Q\n9G5WoGbOmWee6bVu93waNGiQ8+eff/q8gnnz5uVYgZuO41lZyXOs4L1Cx9u0aVNOuXLlbMexgsAL\nXd50sLJn55x00kleY1hB7DmbN2/2aYy8nf797397jWWcrEz3ebs5vv7b3/5mu/xnn33muIxdgzG2\nMt/nrFy50q7Zsc4KtLVdvxWQ7riMrw21atWyHdvKcO3rEDlW5n/HfW7mbt1e1KexzDFtZazPqVu3\nbr45bdmyxaflA+lkBdjmW6f7c9S2bdsiD1uSx02gx3y4fY6dtse9n9zP5tiZNWtWgfvMysSeYwW/\n2+5v9ziez4888khOenq647hW5nbb7yz3OFOmTHFc1q4hXL/f3XN12h++fqeacawLWnKsC3Mc90Pf\nvn1zjENBxTpxknP55Zc7juH2N8+tW7cuaKhC26yLGBzXY2XmL3R5OiCAAAIIIIAAAggggAACCCCA\nQGQIcM7fez+F27lCM8NAz0+V5LlbM3/O+Yf+nL9xpiCAAAIIIIAAAggET8D8fbdjx46F/g03eGtk\nJAQQQAABBBBA4LhArBUARokAgVGjRmn69Om2M+3cubPmzJkjKyjXtt2uskOHDvrhhx9Uu3Ztu2ZZ\nwaL6/vvvbdvyVo4fP15WYHreKtdrk3H32muv9aq3qzBZst966y2vJpOZ95577vGqD3XF77//7rWK\n5s2bywq89KovqMIYv/vuuz5nvi9orHBqM1mT7fa5yZxtMmRfccUVPk03NjbWdXcB64IMWX9Aci1T\nrVo1WcH3Pi0fSCcrMNV2cZOlu6glko+bSPwct2zZ0nV3hNNOO63AXWa+Qx588MEC+7gb4+Li9M47\n77jufOHO0u5uy/t8wgknuO46kJCQkLc697X5jPhTwvX73Z9tKKyvyX7veXcK9zLmDiHmu8N8JxRU\nkpOT9eWXX7ruBFJQv2C0mbueJCUl2Q7l9P1h25lKBBBAAAEEEEAAAQQQQAABBBBAwEEgXM8JReK5\nQgfi3OpIPnebuxEhfBEN5/xDyMPQCCCAAAIIIIBAqRR46aWXZCVzlJUAslRuPxuNAAIIIIAAAiUr\nUHAUWcnOjbXnEbAyTOd5d/ylCQL++eefHQPUj/f0fmVleZeVVd274a8ac7vRwso333xj2+X555+3\nrXeq7NGjh7p06eLV/MUXX+jw4cNe9aGq2LNnj9avX+81fKdOnbzqSmOFCUx97733bDe9V69euuqq\nq2zbCqo0t66aMGGCfvrpJ1ewsAkuDnWxst/brqJKlSq29YVVRvpxE2mfYysbt6ZNm6aGDRsWtmtc\n7XfeeacKO65MULX5PrTuwOHTmPXr13e8iOPHH3/0aQx3p3D9fnfPL9Bn6+4fGjt2rO0wrVq1kvlj\nqdNFAp4Lmf30wgsv6PXXX/dsCvp7p+8Dp++PoE+AARFAAAEEEEAAAQQQQAABBBBAIKoFwvWcUKSd\nKyzsIIn0c7eFbV+g7dFyzj9QB5ZHAAEEEEAAAQQQOC5g3d3alcTR1AwbNkzm770UBBBAAAEEEECg\nOAUIbC9O7SKua+rUqVq6dKnt0oMGDVKFChVs23ypNJlyTz75ZNuuEydOlHXbUds2d+WiRYvcL3Of\ny5UrpzPOOCP3va8v2rRp49XVurmAVq5c6VUfqgrrdkq2Q+/cudO2vrRVfvDBBzKZ9O3KXXfdZVft\nc915550nu2PA5wH86OgUmFq5cmU/RjneNdKPm0j6HNetW1fffvutTHZ/X4u5iMd9VwCnZV599VXX\nHQSc2u3qBw4caFftuqOB0zHmuUA4f797zrWo781FK9u2bbNdfPDgwapUqZJtW0GVZrlHHnmkoC4B\ntzl9H+zbty/gsRkAAQQQQAABBBBAAAEEEEAAAQRKt0A4nxOKpHOFvhxFkX7u1pdtDKRPtJzzD8SA\nZRFAAAEEEEAAAQTyC5hs7ampqa7KZcuWkbU9Pw/vEEAAAQQQQKAYBAhsLwbkQFcxevRo2yFMAPkd\nd9xh2+ZP5YABA2y7Z2VlacqUKbZtptIEne/atcurvUmTJoqJifGqL6yiRYsWtl2WL19uWx+KymbN\nmtlmDjZ/aDCZS0p7mTx5si1By5Yt1a1bN9u2cKx0Cjp2CmQtbBsi+biJpM+xCYA2Qe0mUN3f0qdP\nH8dF7rvvPt1zzz2O7U4N5pivWLGibfOWLVts6z0rw/X73XOegbz/8ssvbRc3WdqLcpcH92DPPfec\nbrrpJvfboD87fR/s378/6OtiQAQQQAABBBBAAAEEEEAAAQQQKF0C4XpOKJLOFfp6xETyuVtftzGQ\nftFyzj8QA5ZFAAEEEEAAAQQQOC6QN1u7u5as7W4JnhFAAAEEEECguAQIbC8u6QDWY4Kq7cqNN96o\n6tWr2zX5Vde/f3/bYG4zyOzZsx3HOnTokDIzM73aGzZs6FXnS0WNGjVsuzllVLHtHGBlmTJlbDPY\np6eny1yVWpqLub3UzJkzbQnuvPNO2/pwrQx2YHskHzeR9Dnu1auX2rVrV6TDqqDvpccff7xIY5oL\neEwGebuyefNmu2qvunD9fveaaAAV8+bNs13aZNGvWrWqbZuvlebfwVCVpKQk26HJ2G7LQiUCCCCA\nAAIIIIAAAggggAACCPghEK7nhCLpXKGv3JF87tbXbSxqv2g6519UA5ZDAAEEEEAAAQQQyC+QN1u7\nu4Ws7W4JnhFAAAEEEECguAQIbC8u6SKuZ+vWrdq0aZPt0jfccINtvb+VycnJMgGGdqWgwHaTPblK\nlSpeiznN16ujR8WaNWs8ao69DUbwvu3ADpXt27e3bXn22WdlArjNyd7SWJYsWSKnTMWXXHJJRJE4\nBaY6ZWj2ZeMi9biJ1s+x5z6rV6+eZ1Xu+6LcYcK9cJ06ddwv8z37krE9nL/f821MAG/MnT/sbl9t\nhrz22msDGDn0izp9Hzh9f4R+RqwBAQQQQAABBBBAAAEEEEAAAQSiQSCczwlF67nCSD13G+rjPZrO\n+YfaivERQAABBBBAAIHSIGCXrd293WRtd0vwjAACCCCAAALFIUBge3EoB7AOpwzZZsgTTzwxgJHz\nL2pux2lXFi9eXGAgd8uWLb0WW7lyZYHLeC3wV8Xy5cttm2rWrGlbH6rKO+64Q/Hx8bbDv/XWW+rU\nqZPGjRsnc1vW0lTmzJnjuLlOwb2OC5Rgg7kwwSlA3ymQ1ZfpRvJxE42fY899VlBgu2dff97Xrl3b\ntvvu3btt6/NWhvv3e965FvX1ihUrdOTIEdvFW7dubVsfLpVO3wcEtofLHmIeCCCAAAIIIIAAAggg\ngAACCESmQLifE4rGc4WRfO42lEd5tJzzD6URYyOAAAIIIIAAAqVJwC5bu3v7ydruluAZAQQQQAAB\nBIpDgMD24lAOYB0mSNyuJCYmKpgB306BySbbbkpKit0UXHV2J7mPHj2q+fPnOy5j13DgwAFNmTLF\nq8lkUnbKpuLVOUgVnTt31mOPPeY42oIFC9S7d2/XvEaPHi1ze9bSUHbu3Gm7mSajvjkeI6WYoHan\nixKcAll92bZIPm6i8XPsuc/Kly/veMGKZ19/3jsdM07HWN6xw/37Pe9ci/p6w4YNjovWrVvXsS0c\nGpz2rdOFMeEwZ+aAAAIIIIAAAggggAACCCCAAALhLxDu54Si8VxhJJ+7DeURHS3n/ENpxNgIIIAA\nAggggEBpESgoW7vbgKztbgmeEUAAAQQQQCDUAgS2h1o4wPH37t1rO0L9+vVlgr6DVZwC2834BQXx\ntW3b1nYKd911l19Z259//nnZZTju2LGjatWqZbuOUFYOGTJEZ511VoGrWLhwoQYOHChjd9ttt2nR\nokUF9o/0RqcsxeEenOrpnpqa6lmV+z4hISH3dVFeROpxE62f46Lsw+JcJty/34NhYS5asivmQgOn\nwHG7/iVR5/R9UNB3SEnMk3UigAACCCCAAAIIIIAAAggggEBkCYT7OaFoPVcYqeduQ3l0R8s5/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jeuSRR2QuJPCnmOzgxbVep/18/fXXe11Q4s82mAtT3nzzTQ0b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\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T+Pvvv6vYaCc4OBi0VJ5WkprSh7ngabWgd1IqHz9+fI27tn//fmkZNQ1slxaqYaPa2Kjx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\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAA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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 6, "metadata": { "image/png": { "width": 700 } }, "output_type": "execute_result" } ], "source": [ "Image(filename='./images/13_05.png', width=700) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training neural networks efficiently using Keras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading MNIST" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) Download the 4 MNIST datasets from http://yann.lecun.com/exdb/mnist/\n", "\n", "- train-images-idx3-ubyte.gz: training set images (9912422 bytes) \n", "- train-labels-idx1-ubyte.gz: training set labels (28881 bytes) \n", "- t10k-images-idx3-ubyte.gz: test set images (1648877 bytes) \n", "- t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)\n", "\n", "2) Unzip those files\n", "\n", "3 Copy the unzipped files to a directory `./mnist`" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import struct\n", "import numpy as np\n", " \n", "def load_mnist(path, kind='train'):\n", " \"\"\"Load MNIST data from `path`\"\"\"\n", " labels_path = os.path.join(path, \n", " '%s-labels-idx1-ubyte' \n", " % kind)\n", " images_path = os.path.join(path, \n", " '%s-images-idx3-ubyte' \n", " % kind)\n", " \n", " with open(labels_path, 'rb') as lbpath:\n", " magic, n = struct.unpack('>II', \n", " lbpath.read(8))\n", " labels = np.fromfile(lbpath, \n", " dtype=np.uint8)\n", "\n", " with open(images_path, 'rb') as imgpath:\n", " magic, num, rows, cols = struct.unpack(\">IIII\", \n", " imgpath.read(16))\n", " images = np.fromfile(imgpath, \n", " dtype=np.uint8).reshape(len(labels), 784)\n", " \n", " return images, labels" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows: 60000, columns: 784\n" ] } ], "source": [ "X_train, y_train = load_mnist('mnist', kind='train')\n", "print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows: 10000, columns: 784\n" ] } ], "source": [ "X_test, y_test = load_mnist('mnist', kind='t10k')\n", "print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi-layer Perceptron in Keras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have Theano installed, [Keras](https://github.com/fchollet/keras) can be installed via\n", "\n", " pip install Keras" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "In order to run the following code via GPU, you can execute the Python script that was placed in this directory via\n", "\n", " THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_keras_mlp.py" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import theano \n", "\n", "theano.config.floatX = 'float32'\n", "X_train = X_train.astype(theano.config.floatX)\n", "X_test = X_test.astype(theano.config.floatX)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One-hot encoding of the class variable:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 3 labels: [5 0 4]\n", "\n", "First 3 labels (one-hot):\n", " [[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n" ] } ], "source": [ "from keras.utils import np_utils\n", "\n", "print('First 3 labels: ', y_train[:3])\n", "\n", "y_train_ohe = np_utils.to_categorical(y_train) \n", "print('\\nFirst 3 labels (one-hot):\\n', y_train_ohe[:3])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 54000 samples, validate on 6000 samples\n", "Epoch 0\n", "54000/54000 [==============================] - 1s - loss: 2.2290 - acc: 0.3592 - val_loss: 2.1094 - val_acc: 0.5342\n", "Epoch 1\n", "54000/54000 [==============================] - 1s - loss: 1.8850 - acc: 0.5279 - val_loss: 1.6098 - val_acc: 0.5617\n", "Epoch 2\n", "54000/54000 [==============================] - 1s - loss: 1.3903 - acc: 0.5884 - val_loss: 1.1666 - val_acc: 0.6707\n", "Epoch 3\n", "54000/54000 [==============================] - 1s - loss: 1.0592 - acc: 0.6936 - val_loss: 0.8961 - val_acc: 0.7615\n", "Epoch 4\n", "54000/54000 [==============================] - 1s - loss: 0.8528 - acc: 0.7666 - val_loss: 0.7288 - val_acc: 0.8290\n", "Epoch 5\n", "54000/54000 [==============================] - 1s - loss: 0.7187 - acc: 0.8191 - val_loss: 0.6122 - val_acc: 0.8603\n", "Epoch 6\n", "54000/54000 [==============================] - 1s - loss: 0.6278 - acc: 0.8426 - val_loss: 0.5347 - val_acc: 0.8762\n", "Epoch 7\n", "54000/54000 [==============================] - 1s - loss: 0.5592 - acc: 0.8621 - val_loss: 0.4707 - val_acc: 0.8920\n", "Epoch 8\n", "54000/54000 [==============================] - 1s - loss: 0.4978 - acc: 0.8751 - val_loss: 0.4288 - val_acc: 0.9033\n", "Epoch 9\n", "54000/54000 [==============================] - 1s - loss: 0.4583 - acc: 0.8847 - val_loss: 0.3935 - val_acc: 0.9035\n", "Epoch 10\n", "54000/54000 [==============================] - 1s - loss: 0.4213 - acc: 0.8911 - val_loss: 0.3553 - val_acc: 0.9088\n", "Epoch 11\n", "54000/54000 [==============================] - 1s - loss: 0.3972 - acc: 0.8955 - val_loss: 0.3405 - val_acc: 0.9083\n", "Epoch 12\n", "54000/54000 [==============================] - 1s - loss: 0.3740 - acc: 0.9022 - val_loss: 0.3251 - val_acc: 0.9170\n", "Epoch 13\n", "54000/54000 [==============================] - 1s - loss: 0.3611 - acc: 0.9030 - val_loss: 0.3032 - val_acc: 0.9183\n", "Epoch 14\n", "54000/54000 [==============================] - 1s - loss: 0.3479 - acc: 0.9064 - val_loss: 0.2972 - val_acc: 0.9248\n", "Epoch 15\n", "54000/54000 [==============================] - 1s - loss: 0.3309 - acc: 0.9099 - val_loss: 0.2778 - val_acc: 0.9250\n", "Epoch 16\n", "54000/54000 [==============================] - 1s - loss: 0.3264 - acc: 0.9103 - val_loss: 0.2838 - val_acc: 0.9208\n", "Epoch 17\n", "54000/54000 [==============================] - 1s - loss: 0.3136 - acc: 0.9136 - val_loss: 0.2689 - val_acc: 0.9223\n", "Epoch 18\n", "54000/54000 [==============================] - 1s - loss: 0.3031 - acc: 0.9156 - val_loss: 0.2634 - val_acc: 0.9313\n", "Epoch 19\n", "54000/54000 [==============================] - 1s - loss: 0.2988 - acc: 0.9169 - val_loss: 0.2579 - val_acc: 0.9288\n", "Epoch 20\n", "54000/54000 [==============================] - 1s - loss: 0.2909 - acc: 0.9180 - val_loss: 0.2494 - val_acc: 0.9310\n", "Epoch 21\n", "54000/54000 [==============================] - 1s - loss: 0.2848 - acc: 0.9202 - val_loss: 0.2478 - val_acc: 0.9307\n", "Epoch 22\n", "54000/54000 [==============================] - 1s - loss: 0.2804 - acc: 0.9194 - val_loss: 0.2423 - val_acc: 0.9343\n", "Epoch 23\n", "54000/54000 [==============================] - 1s - loss: 0.2728 - acc: 0.9235 - val_loss: 0.2387 - val_acc: 0.9327\n", "Epoch 24\n", "54000/54000 [==============================] - 1s - loss: 0.2673 - acc: 0.9241 - val_loss: 0.2265 - val_acc: 0.9385\n", "Epoch 25\n", "54000/54000 [==============================] - 1s - loss: 0.2611 - acc: 0.9253 - val_loss: 0.2270 - val_acc: 0.9347\n", "Epoch 26\n", "54000/54000 [==============================] - 1s - loss: 0.2676 - acc: 0.9225 - val_loss: 0.2210 - val_acc: 0.9367\n", "Epoch 27\n", "54000/54000 [==============================] - 1s - loss: 0.2528 - acc: 0.9261 - val_loss: 0.2241 - val_acc: 0.9373\n", "Epoch 28\n", "54000/54000 [==============================] - 1s - loss: 0.2511 - acc: 0.9264 - val_loss: 0.2170 - val_acc: 0.9403\n", "Epoch 29\n", "54000/54000 [==============================] - 1s - loss: 0.2433 - acc: 0.9293 - val_loss: 0.2165 - val_acc: 0.9412\n", "Epoch 30\n", "54000/54000 [==============================] - 1s - loss: 0.2465 - acc: 0.9279 - val_loss: 0.2135 - val_acc: 0.9367\n", "Epoch 31\n", "54000/54000 [==============================] - 1s - loss: 0.2383 - acc: 0.9306 - val_loss: 0.2138 - val_acc: 0.9427\n", "Epoch 32\n", "54000/54000 [==============================] - 1s - loss: 0.2349 - acc: 0.9310 - val_loss: 0.2066 - val_acc: 0.9423\n", "Epoch 33\n", "54000/54000 [==============================] - 1s - loss: 0.2301 - acc: 0.9334 - val_loss: 0.2054 - val_acc: 0.9440\n", "Epoch 34\n", "54000/54000 [==============================] - 1s - loss: 0.2371 - acc: 0.9317 - val_loss: 0.1991 - val_acc: 0.9480\n", "Epoch 35\n", "54000/54000 [==============================] - 1s - loss: 0.2256 - acc: 0.9352 - val_loss: 0.1982 - val_acc: 0.9450\n", "Epoch 36\n", "54000/54000 [==============================] - 1s - loss: 0.2313 - acc: 0.9323 - val_loss: 0.2092 - val_acc: 0.9403\n", "Epoch 37\n", "54000/54000 [==============================] - 1s - loss: 0.2230 - acc: 0.9341 - val_loss: 0.1993 - val_acc: 0.9445\n", "Epoch 38\n", "54000/54000 [==============================] - 1s - loss: 0.2261 - acc: 0.9336 - val_loss: 0.1891 - val_acc: 0.9463\n", "Epoch 39\n", "54000/54000 [==============================] - 1s - loss: 0.2166 - acc: 0.9369 - val_loss: 0.1943 - val_acc: 0.9452\n", "Epoch 40\n", "54000/54000 [==============================] - 1s - loss: 0.2128 - acc: 0.9370 - val_loss: 0.1952 - val_acc: 0.9435\n", "Epoch 41\n", "54000/54000 [==============================] - 1s - loss: 0.2200 - acc: 0.9351 - val_loss: 0.1918 - val_acc: 0.9468\n", "Epoch 42\n", "54000/54000 [==============================] - 2s - loss: 0.2107 - acc: 0.9383 - val_loss: 0.1831 - val_acc: 0.9483\n", "Epoch 43\n", "54000/54000 [==============================] - 1s - loss: 0.2020 - acc: 0.9411 - val_loss: 0.1906 - val_acc: 0.9443\n", "Epoch 44\n", "54000/54000 [==============================] - 1s - loss: 0.2082 - acc: 0.9388 - val_loss: 0.1838 - val_acc: 0.9457\n", "Epoch 45\n", "54000/54000 [==============================] - 1s - loss: 0.2048 - acc: 0.9402 - val_loss: 0.1817 - val_acc: 0.9488\n", "Epoch 46\n", "54000/54000 [==============================] - 1s - loss: 0.2012 - acc: 0.9417 - val_loss: 0.1876 - val_acc: 0.9480\n", "Epoch 47\n", "54000/54000 [==============================] - 1s - loss: 0.1996 - acc: 0.9423 - val_loss: 0.1792 - val_acc: 0.9502\n", "Epoch 48\n", "54000/54000 [==============================] - 1s - loss: 0.1921 - acc: 0.9430 - val_loss: 0.1791 - val_acc: 0.9505\n", "Epoch 49\n", "54000/54000 [==============================] - 1s - loss: 0.1907 - acc: 0.9432 - val_loss: 0.1749 - val_acc: 0.9482\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x10df582e8>" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from keras.models import Sequential\n", "from keras.layers.core import Dense\n", "from keras.optimizers import SGD\n", "\n", "np.random.seed(1) \n", "\n", "model = Sequential()\n", "model.add(Dense(input_dim=X_train.shape[1], \n", " output_dim=50, \n", " init='uniform', \n", " activation='tanh'))\n", "\n", "model.add(Dense(input_dim=50, \n", " output_dim=50, \n", " init='uniform', \n", " activation='tanh'))\n", "\n", "model.add(Dense(input_dim=50, \n", " output_dim=y_train_ohe.shape[1], \n", " init='uniform', \n", " activation='softmax'))\n", "\n", "sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)\n", "model.compile(loss='categorical_crossentropy', optimizer=sgd)\n", "\n", "model.fit(X_train, y_train_ohe, \n", " nb_epoch=50, \n", " batch_size=300, \n", " verbose=1, \n", " validation_split=0.1, \n", " show_accuracy=True)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 3 predictions: [5 0 4]\n" ] } ], "source": [ "y_train_pred = model.predict_classes(X_train, verbose=0)\n", "print('First 3 predictions: ', y_train_pred[:3])" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training accuracy: 94.51%\n" ] } ], "source": [ "train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n", "print('Training accuracy: %.2f%%' % (train_acc * 100))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 94.39%\n" ] } ], "source": [ "y_test_pred = model.predict_classes(X_test, verbose=0)\n", "test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n", "print('Test accuracy: %.2f%%' % (test_acc * 100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "..." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
sg-dev/multinet.js
build-graph-api.ipynb
1
7311
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VIZUALISATION TIMER: igraph layouting : 2016-05-23 16:44:36.721799\n", "VIZUALISATION TIMER: returning coordinates : 2016-05-23 16:44:36.766165\n", "Layouting took : 0 seconds using Fruchterman-Reingold\n", "VIZUALISATION TIMER: returning response to frontend : 2016-05-23 16:44:36.766627\n" ] }, { "data": { "text/html": [ "\n", "\n", " <!-- Beginning of javascript injected by multinet.js -->\n", " <script type=\"text/javascript\" src=\"multinet/static/js/jquery-2.1.4.js\"></script>\n", " <script type=\"text/javascript\" src=\"multinet/static/js/jquery-ui-1.11.4.js\"></script>\n", "\n", " <script type=\"text/javascript\" src=\"multinet/static/js/threejs/three-r71.js\"></script>\n", " <script type=\"text/javascript\" src=\"multinet/static/js/threejs/orbitcontrols.js\"></script>\n", " <script type=\"text/javascript\" src=\"multinet/static/js/threejs/stats-r12.min.js\"></script>\n", " <script type=\"text/javascript\" src=\"multinet/static/js/threejs/detector.js\"></script>\n", "\n", " <script type=\"text/javascript\" src=\"multinet/static/js/multinet-core.js\"></script>\n", " <script type=\"text/javascript\" src=\"multinet/static/js/multinet-ipython.js\"></script>\n", " <script type=\"text/javascript\">\n", " var multinet_javascript_injected = true;\n", " </script>\n", " <!-- End of javascript injected by multinet.js -->\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<h1>The Graph</h1>\n", "\n", "<div id=\"container\" style=\"width: 700px; height: 400px;\"></div>\n", "\n", "<div id=\"slider-container\" style=\"display: none;\">\n", " <input type=\"text\" id=\"year\" readonly>\n", " <div id=\"slider\" style=\"float:right;\"></div>\n", "</div>\n", "\n", "<script>\n", "var data = {\"layers\": [{\"maxDeg\": [0, 0, 0, 0, 0, 1.0], \"neighborhood\": {\"1\": [[\"2\", \"00-00-0000\"]], \"3\": [[\"4\", \"00-00-0000\"]], \"2\": [[\"3\", \"00-00-0000\"]], \"4\": [[\"1\", \"00-00-0000\"]], \"9\": [], \"8\": []}, \"name\": \"l1\", \"coords\": {\"1\": [[137.15370178222656, 176.9801788330078], 1, 0, 0, 0, 1], \"3\": [[123.90498352050781, 157.82745361328125], 1, 0, 0, 0, 1], \"2\": [[113.75052642822266, 192.21841430664062], 1, 0, 0, 0, 1], \"4\": [[178.77322387695312, 101.01006317138672], 0, 0, 0, 0, 1]}, \"edges\": [[\"1\", \"2\", \"00-00-0000\"], [\"2\", \"3\", \"00-00-0000\"], [\"3\", \"4\", \"00-00-0000\"], [\"4\", \"1\", \"00-00-0000\"]], \"edge_ct\": 0, \"out_degrees\": {}, \"in_degrees\": {}, \"nodes\": [\"1\", \"3\", \"2\", \"4\"], \"node_ct\": 0}, {\"maxDeg\": [0, 0, 0, 0, 0, 1.0], \"neighborhood\": {\"1\": [[\"3\", \"00-00-0000\"], [\"9\", \"00-00-0000\"]], \"3\": [], \"2\": [], \"4\": [], \"9\": [[\"2\", \"00-00-0000\"]], \"8\": [[\"3\", \"00-00-0000\"]]}, \"name\": \"l2\", \"coords\": {\"1\": [[137.15370178222656, 176.9801788330078], 1, 0, 0, 0, 1], \"9\": [[149.8124542236328, 131.32969665527344], 0, 0, 0, 0, 1], \"3\": [[123.90498352050781, 157.82745361328125], 1, 0, 0, 0, 1], \"2\": [[113.75052642822266, 192.21841430664062], 1, 0, 0, 0, 1], \"8\": [[61.824710845947266, 70.40167236328125], 0, 0, 0, 0, 1]}, \"edges\": [[\"1\", \"3\", \"00-00-0000\"], [\"1\", \"9\", \"00-00-0000\"], [\"9\", \"2\", \"00-00-0000\"], [\"8\", \"3\", \"00-00-0000\"]], \"edge_ct\": 0, \"out_degrees\": {}, \"in_degrees\": {}, \"nodes\": [\"1\", \"9\", \"3\", \"2\", \"8\"], \"node_ct\": 0}], \"directed\": true, \"layout\": \"Fruchterman-Reingold\", \"max_node_ct\": 5, \"layer_ct\": 2, \"width\": 246.36582946777344, \"l2\": {\"maxDeg\": [0, 0, 0, 0, 0, 1.0], \"neighborhood\": {\"1\": [[\"3\", \"00-00-0000\"], [\"9\", \"00-00-0000\"]], \"3\": [], \"2\": [], \"4\": [], \"9\": [[\"2\", \"00-00-0000\"]], \"8\": [[\"3\", \"00-00-0000\"]]}, \"name\": \"l2\", \"coords\": {\"1\": [[137.15370178222656, 176.9801788330078], 1, 0, 0, 0, 1], \"9\": [[149.8124542236328, 131.32969665527344], 0, 0, 0, 0, 1], \"3\": [[123.90498352050781, 157.82745361328125], 1, 0, 0, 0, 1], \"2\": [[113.75052642822266, 192.21841430664062], 1, 0, 0, 0, 1], \"8\": [[61.824710845947266, 70.40167236328125], 0, 0, 0, 0, 1]}, \"edges\": [[\"1\", \"3\", \"00-00-0000\"], [\"1\", \"9\", \"00-00-0000\"], [\"9\", \"2\", \"00-00-0000\"], [\"8\", \"3\", \"00-00-0000\"]], \"edge_ct\": 0, \"out_degrees\": {}, \"in_degrees\": {}, \"nodes\": [\"1\", \"9\", \"3\", \"2\", \"8\"], \"node_ct\": 0}, \"data_labels\": [], \"l1\": {\"maxDeg\": [0, 0, 0, 0, 0, 1.0], \"neighborhood\": {\"1\": [[\"2\", \"00-00-0000\"]], \"3\": [[\"4\", \"00-00-0000\"]], \"2\": [[\"3\", \"00-00-0000\"]], \"4\": [[\"1\", \"00-00-0000\"]], \"9\": [], \"8\": []}, \"name\": \"l1\", \"coords\": {\"1\": [[137.15370178222656, 176.9801788330078], 1, 0, 0, 0, 1], \"3\": [[123.90498352050781, 157.82745361328125], 1, 0, 0, 0, 1], \"2\": [[113.75052642822266, 192.21841430664062], 1, 0, 0, 0, 1], \"4\": [[178.77322387695312, 101.01006317138672], 0, 0, 0, 0, 1]}, \"edges\": [[\"1\", \"2\", \"00-00-0000\"], [\"2\", \"3\", \"00-00-0000\"], [\"3\", \"4\", \"00-00-0000\"], [\"4\", \"1\", \"00-00-0000\"]], \"edge_ct\": 0, \"out_degrees\": {}, \"in_degrees\": {}, \"nodes\": [\"1\", \"3\", \"2\", \"4\"], \"node_ct\": 0}, \"node_data\": {\"1\": [], \"3\": [], \"2\": [], \"4\": [], \"9\": [], \"8\": []}, \"custom_scale\": false, \"unique_keys\": [\"00-00-0000\"]}\n", "var globalData = data;\n", "\n", "console.log(data)\n", "createGraph3D(data, new RenderData());\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from multinet.render import MultiGraph\n", "from multinet.ipython import plot_3d, init_3d\n", "\n", "g = MultiGraph()\n", "g.add_layer(\"l1\")\n", "g.add_layer(\"l2\")\n", "\n", "g.add_edge(\"1\", \"2\", \"l1\")\n", "g.add_edge(\"2\", \"3\", \"l1\")\n", "g.add_edge(\"3\", \"4\", \"l1\")\n", "g.add_edge(\"4\", \"1\", \"l1\")\n", "\n", "g.add_edge(\"1\", \"3\", \"l2\")\n", "g.add_edge(\"1\", \"9\", \"l2\")\n", "g.add_edge(\"9\", \"2\", \"l2\")\n", "g.add_edge(\"8\", \"3\", \"l2\")\n", "\n", "\n", "res = g.layout()\n", "#print(res)\n", "init_3d()\n", "plot_3d(res)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
tiagoantao/MARC
notebooks/Parse_OTU_GG.ipynb
1
1872
{ "cells": [ { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fname = '/home/tiago/OTU_GG.txt'\n", "wname = '../analysis_data/old_taxa.txt'" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "query_taxa = {}\n", "with open(fname, 'rt') as f:\n", " l = f.readline()\n", " while l != '':\n", " l = l.rstrip()\n", " if l.startswith('Query='):\n", " query = l.rstrip().split(' ')[1]\n", " l = f.readline()\n", " elif len(l) > 0 and l[0] == '>':\n", " line = l[1:]\n", " l = f.readline() \n", " while l != '\\n':\n", " \n", " line += l.rstrip()\n", " l = f.readline()\n", " taxon = ' '.join(filter(lambda x: x.find('__') > -1, line.split(' ')))\n", " query_taxa[query] = taxon\n", " else:\n", " l = f.readline()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(wname, 'wt') as w:\n", " for query, taxon in query_taxa.items():\n", " w.write('%s\\t%s\\n' % (query, taxon))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
tanghaibao/goatools
notebooks/goea_nbt3102.ipynb
1
18196
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Run a Gene Ontology Enrichment Analysis (GOEA)\n", "We use data from a 2014 Nature paper: \n", "[Computational analysis of cell-to-cell heterogeneity\n", "in single-cell RNA-sequencing data reveals hidden \n", "subpopulations of cells\n", "](http://www.nature.com/nbt/journal/v33/n2/full/nbt.3102.html#methods)\n", "\n", "Note: you must have the Python package, **xlrd**, installed to run this example. \n", "\n", "Note: To create plots, you must have:\n", " * Python packages: **pyparsing**, **pydot**\n", " * [Graphviz](http://www.graphviz.org/) loaded and your PATH environmental variable pointing to the Graphviz bin directory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Download Ontologies and Associations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1a. Download Ontologies, if necessary" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " EXISTS: go-basic.obo\n" ] } ], "source": [ "# Get http://geneontology.org/ontology/go-basic.obo\n", "from goatools.base import download_go_basic_obo\n", "obo_fname = download_go_basic_obo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1b. Download Associations, if necessary\n", "The NCBI gene2go file contains numerous species. We will select mouse shortly." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " EXISTS: gene2go\n" ] } ], "source": [ "# Get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz\n", "from goatools.base import download_ncbi_associations\n", "fin_gene2go = download_ncbi_associations()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Load Ontologies, Associations and Background gene set " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2a. Load Ontologies" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "go-basic.obo: fmt(1.2) rel(2020-01-01) 47,337 GO Terms\n" ] } ], "source": [ "from goatools.obo_parser import GODag\n", "\n", "obodag = GODag(\"go-basic.obo\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2b. Load Associations" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HMS:0:00:07.052179 367,335 annotations, 24,267 genes, 18,190 GOs, 1 taxids READ: gene2go \n", "BP 17,859 annotated mouse genes\n", "MF 16,721 annotated mouse genes\n", "CC 18,824 annotated mouse genes\n" ] } ], "source": [ "from __future__ import print_function\n", "from goatools.anno.genetogo_reader import Gene2GoReader\n", "\n", "# Read NCBI's gene2go. Store annotations in a list of namedtuples\n", "objanno = Gene2GoReader(fin_gene2go, taxids=[10090])\n", "\n", "# Get namespace2association where:\n", "# namespace is:\n", "# BP: biological_process \n", "# MF: molecular_function\n", "# CC: cellular_component\n", "# assocation is a dict:\n", "# key: NCBI GeneID\n", "# value: A set of GO IDs associated with that gene\n", "ns2assoc = objanno.get_ns2assc()\n", "\n", "for nspc, id2gos in ns2assoc.items():\n", " print(\"{NS} {N:,} annotated mouse genes\".format(NS=nspc, N=len(id2gos)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2c. Load Background gene set\n", "In this example, the background is all mouse protein-codinge genes. \n", "\n", "Follow the instructions in the `background_genes_ncbi` notebook to download a set of background population genes from NCBI." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26033\n" ] } ], "source": [ "from genes_ncbi_10090_proteincoding import GENEID2NT as GeneID2nt_mus\n", "print(len(GeneID2nt_mus))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Initialize a GOEA object\n", "The GOEA object holds the Ontologies, Associations, and background. \n", "Numerous studies can then be run withough needing to re-load the above items. \n", "In this case, we only run one GOEA. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Load BP Gene Ontology Analysis ...\n", "fisher module not installed. Falling back on scipy.stats.fisher_exact\n", " 65% 16,811 of 26,033 population items found in association\n", "\n", "Load CC Gene Ontology Analysis ...\n", "fisher module not installed. Falling back on scipy.stats.fisher_exact\n", " 70% 18,198 of 26,033 population items found in association\n", "\n", "Load MF Gene Ontology Analysis ...\n", "fisher module not installed. Falling back on scipy.stats.fisher_exact\n", " 63% 16,331 of 26,033 population items found in association\n" ] } ], "source": [ "from goatools.goea.go_enrichment_ns import GOEnrichmentStudyNS\n", "\n", "goeaobj = GOEnrichmentStudyNS(\n", " GeneID2nt_mus.keys(), # List of mouse protein-coding genes\n", " ns2assoc, # geneid/GO associations\n", " obodag, # Ontologies\n", " propagate_counts = False,\n", " alpha = 0.05, # default significance cut-off\n", " methods = ['fdr_bh']) # defult multipletest correction method\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Read study genes\n", "~400 genes from the Nature paper supplemental table 4" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "400 genes READ: /mnt/c/Users/note2/Data/git/goatools/goatools/test_data/nbt_3102/nbt.3102-S4_GeneIDs.xlsx\n" ] } ], "source": [ "# Data will be stored in this variable\n", "import os\n", "geneid2symbol = {}\n", "# Get xlsx filename where data is stored\n", "ROOT = os.path.dirname(os.getcwd()) # go up 1 level from current working directory\n", "din_xlsx = os.path.join(ROOT, \"goatools/test_data/nbt_3102/nbt.3102-S4_GeneIDs.xlsx\")\n", "# Read data\n", "if os.path.isfile(din_xlsx): \n", " import xlrd\n", " book = xlrd.open_workbook(din_xlsx)\n", " pg = book.sheet_by_index(0)\n", " for r in range(pg.nrows):\n", " symbol, geneid, pval = [pg.cell_value(r, c) for c in range(pg.ncols)]\n", " if geneid:\n", " geneid2symbol[int(geneid)] = symbol\n", " print('{N} genes READ: {XLSX}'.format(N=len(geneid2symbol), XLSX=din_xlsx))\n", "else:\n", " raise RuntimeError('FILE NOT FOUND: {XLSX}'.format(XLSX=din_xlsx))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Run Gene Ontology Enrichment Analysis (GOEA)\n", "You may choose to keep all results or just the significant results. In this example, we choose to keep only the significant results." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Run BP Gene Ontology Analysis: current study set of 400 IDs ... 94% 357 of 381 study items found in association\n", " 95% 381 of 400 study items found in population(26033)\n", "Calculating 12,254 uncorrected p-values using fisher_scipy_stats\n", " 12,254 GO terms are associated with 16,811 of 26,033 population items\n", " 2,085 GO terms are associated with 357 of 400 study items\n", " METHOD fdr_bh:\n", " 55 GO terms found significant (< 0.05=alpha) ( 53 enriched + 2 purified): statsmodels fdr_bh\n", " 209 study items associated with significant GO IDs (enriched)\n", " 4 study items associated with significant GO IDs (purified)\n", "\n", "Run CC Gene Ontology Analysis: current study set of 400 IDs ... 99% 376 of 381 study items found in association\n", " 95% 381 of 400 study items found in population(26033)\n", "Calculating 1,724 uncorrected p-values using fisher_scipy_stats\n", " 1,724 GO terms are associated with 18,198 of 26,033 population items\n", " 449 GO terms are associated with 376 of 400 study items\n", " METHOD fdr_bh:\n", " 83 GO terms found significant (< 0.05=alpha) ( 83 enriched + 0 purified): statsmodels fdr_bh\n", " 373 study items associated with significant GO IDs (enriched)\n", " 0 study items associated with significant GO IDs (purified)\n", "\n", "Run MF Gene Ontology Analysis: current study set of 400 IDs ... 89% 339 of 381 study items found in association\n", " 95% 381 of 400 study items found in population(26033)\n", "Calculating 4,145 uncorrected p-values using fisher_scipy_stats\n", " 4,145 GO terms are associated with 16,331 of 26,033 population items\n", " 580 GO terms are associated with 339 of 400 study items\n", " METHOD fdr_bh:\n", " 54 GO terms found significant (< 0.05=alpha) ( 52 enriched + 2 purified): statsmodels fdr_bh\n", " 276 study items associated with significant GO IDs (enriched)\n", " 0 study items associated with significant GO IDs (purified)\n" ] } ], "source": [ "# 'p_' means \"pvalue\". 'fdr_bh' is the multipletest method we are currently using.\n", "geneids_study = geneid2symbol.keys()\n", "goea_results_all = goeaobj.run_study(geneids_study)\n", "goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5a. Quietly Run Gene Ontology Enrichment Analysis (GOEA)\n", "GOEAs can be run quietly using `prt=None`:\n", "```\n", "goea_results = goeaobj.run_study(geneids_study, prt=None)\n", "```\n", "#### No output is printed if `prt=None`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "goea_quiet_all = goeaobj.run_study(geneids_study, prt=None)\n", "goea_quiet_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Print customized results summaries\n", "##### Example 1: Significant v All GOEA results" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "192 of 18,123 results were significant\n" ] } ], "source": [ "print('{N} of {M:,} results were significant'.format(\n", " N=len(goea_quiet_sig),\n", " M=len(goea_quiet_all)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Example 2: Enriched v Purified GOEA results" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Significant results: 188 enriched, 4 purified\n" ] } ], "source": [ "print('Significant results: {E} enriched, {P} purified'.format(\n", " E=sum(1 for r in goea_quiet_sig if r.enrichment=='e'),\n", " P=sum(1 for r in goea_quiet_sig if r.enrichment=='p')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Example 3: Significant GOEA results by namespace" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Significant results[192] = 55 BP + 54 MF + 83 CC\n" ] } ], "source": [ "import collections as cx\n", "ctr = cx.Counter([r.NS for r in goea_quiet_sig])\n", "print('Significant results[{TOTAL}] = {BP} BP + {MF} MF + {CC} CC'.format(\n", " TOTAL=len(goea_quiet_sig),\n", " BP=ctr['BP'], # biological_process\n", " MF=ctr['MF'], # molecular_function\n", " CC=ctr['CC'])) # cellular_component" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Write results to an Excel file and to a text file" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 192 items WROTE: nbt3102.xlsx\n", " 192 GOEA results for 375 study items. WROTE: nbt3102.txt\n" ] } ], "source": [ "goeaobj.wr_xlsx(\"nbt3102.xlsx\", goea_results_sig)\n", "goeaobj.wr_txt(\"nbt3102.txt\", goea_results_sig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Plot all significant GO terms\n", "Plotting all significant GO terms produces a messy spaghetti plot. Such a plot can be useful sometimes because you can open it and zoom and scroll around. But sometimes it is just too messy to be of use.\n", "\n", "The **\"{NS}\"** in **\"nbt3102_{NS}.png\"** indicates that you will see three plots, one for \"biological_process\"(BP), \"molecular_function\"(MF), and \"cellular_component\"(CC)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 55 usr 444 GOs WROTE: nbt3102_BP.png\n", " 83 usr 149 GOs WROTE: nbt3102_CC.png\n", " 54 usr 156 GOs WROTE: nbt3102_MF.png\n" ] } ], "source": [ "from goatools.godag_plot import plot_gos, plot_results, plot_goid2goobj\n", "\n", "plot_results(\"nbt3102_{NS}.png\", goea_results_sig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7a. These plots are likely to messy\n", "The *Cellular Component* plot is the smallest plot...\n", "![BIG CC PLOT](images/nbt3102_CC.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7b. So make a smaller sub-plot\n", "This plot contains GOEA results:\n", " * GO terms colored by P-value:\n", " * pval < 0.005 (light red)\n", " * pval < 0.01 (light orange)\n", " * pval < 0.05 (yellow)\n", " * pval > 0.05 (grey) Study terms that are not statistically significant\n", " * GO terms with study gene counts printed. e.g., \"32 genes\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 5 usr 13 GOs WROTE: nbt3102_MF_RNA_genecnt.png\n" ] } ], "source": [ "# Plot subset starting from these significant GO terms\n", "goid_subset = [\n", " 'GO:0003723', # MF D04 RNA binding (32 genes)\n", " 'GO:0044822', # MF D05 poly(A) RNA binding (86 genes)\n", " 'GO:0003729', # MF D06 mRNA binding (11 genes)\n", " 'GO:0019843', # MF D05 rRNA binding (6 genes)\n", " 'GO:0003746', # MF D06 translation elongation factor activity (5 genes)\n", "]\n", "plot_gos(\"nbt3102_MF_RNA_genecnt.png\", \n", " goid_subset, # Source GO ids\n", " obodag, \n", " goea_results=goea_results_all) # Use pvals for coloring\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![RNA subplot](images/nbt3102_MF_RNA_genecnt.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7c. Add study gene Symbols to plot\n", "e.g., *11 genes: Calr, Eef1a1, Pabpc1*" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 5 usr 13 GOs WROTE: nbt3102_MF_RNA_Symbols.png\n" ] } ], "source": [ "plot_gos(\"nbt3102_MF_RNA_Symbols.png\", \n", " goid_subset, # Source GO ids\n", " obodag,\n", " goea_results=goea_results_all, # use pvals for coloring\n", " # We can further configure the plot...\n", " id2symbol=geneid2symbol, # Print study gene Symbols, not Entrez GeneIDs\n", " study_items=6, # Only only 6 gene Symbols max on GO terms\n", " items_p_line=3, # Print 3 genes per line\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![RNA subplot](images/nbt3102_MF_RNA_Symbols.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright (C) 2016-present, DV Klopfenstein, H Tang. All rights reserved." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-2-clause
clubmliimas/cancer
notebooks/Cura_Cancer_1.ipynb
2
11853205
null
mit
mlperf/training_results_v0.5
v0.5.0/google/research_v3.32/gnmt-tpuv3-32/code/gnmt/model/t2t/tensor2tensor/visualization/TransformerVisualization.ipynb
5
9218
{ "cells": [ { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "6uNrFWq5BRba" }, "outputs": [], "source": [ "#@title\n", "# Copyright 2018 Google LLC.\n", "\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Your Own Visualizations!\n", "Instructions:\n", "1. Install tensor2tensor and train up a Transformer model following the instruction in the repository https://github.com/tensorflow/tensor2tensor.\n", "2. Update cell 3 to point to your checkpoint, it is currently set up to read from the default checkpoint location that would be created from following the instructions above.\n", "3. If you used custom hyper parameters then update cell 4.\n", "4. Run the notebook!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import tensorflow as tf\n", "\n", "from tensor2tensor import problems\n", "from tensor2tensor.bin import t2t_decoder # To register the hparams set\n", "from tensor2tensor.utils import registry\n", "from tensor2tensor.utils import trainer_lib\n", "from tensor2tensor.visualization import attention\n", "from tensor2tensor.visualization import visualization" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n", " }\n", "});" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n", " }\n", "});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HParams" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# PUT THE MODEL YOU WANT TO LOAD HERE!\n", "CHECKPOINT = os.path.expanduser('~/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# HParams\n", "problem_name = 'translate_ende_wmt32k'\n", "data_dir = os.path.expanduser('~/t2t_data/')\n", "model_name = \"transformer\"\n", "hparams_set = \"transformer_base_single_gpu\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualization" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Setting T2TModel mode to 'eval'\n", "INFO:tensorflow:Setting hparams.layer_prepostprocess_dropout to 0.0\n", "INFO:tensorflow:Setting hparams.symbol_dropout to 0.0\n", "INFO:tensorflow:Setting hparams.attention_dropout to 0.0\n", "INFO:tensorflow:Setting hparams.dropout to 0.0\n", "INFO:tensorflow:Setting hparams.relu_dropout to 0.0\n", "INFO:tensorflow:Using variable initializer: uniform_unit_scaling\n", "INFO:tensorflow:Transforming feature 'inputs' with symbol_modality_33708_512.bottom\n", "INFO:tensorflow:Transforming 'targets' with symbol_modality_33708_512.targets_bottom\n", "INFO:tensorflow:Building model body\n", "WARNING:tensorflow:From /tmp/t2t/tensor2tensor/layers/common_layers.py:512: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "keep_dims is deprecated, use keepdims instead\n", "INFO:tensorflow:Transforming body output with symbol_modality_33708_512.top\n", "WARNING:tensorflow:From /tmp/t2t/tensor2tensor/layers/common_layers.py:1707: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "\n", "Future major versions of TensorFlow will allow gradients to flow\n", "into the labels input on backprop by default.\n", "\n", "See tf.nn.softmax_cross_entropy_with_logits_v2.\n", "\n", "INFO:tensorflow:Greedy Decoding\n" ] } ], "source": [ "visualizer = visualization.AttentionVisualizer(hparams_set, model_name, data_dir, problem_name, beam_size=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Create CheckpointSaverHook.\n", "INFO:tensorflow:Restoring parameters from /usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu/model.ckpt-1\n" ] } ], "source": [ "tf.Variable(0, dtype=tf.int64, trainable=False, name='global_step')\n", "\n", "sess = tf.train.MonitoredTrainingSession(\n", " checkpoint_dir=CHECKPOINT,\n", " save_summaries_secs=0,\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 1 into /usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu/model.ckpt.\n" ] } ], "source": [ "input_sentence = \"I have two dogs.\"\n", "output_string, inp_text, out_text, att_mats = visualizer.get_vis_data_from_string(sess, input_sentence)\n", "print(output_string)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the Visualizations\n", "- The layers drop down allow you to view the different Transformer layers, 0-indexed of course.\n", " - Tip: The first layer, last layer and 2nd to last layer are usually the most interpretable.\n", "- The attention dropdown allows you to select different pairs of encoder-decoder attentions:\n", " - All: Shows all types of attentions together. NOTE: There is no relation between heads of the same color - between the decoder self attention and decoder-encoder attention since they do not share parameters.\n", " - Input - Input: Shows only the encoder self-attention.\n", " - Input - Output: Shows the decoder’s attention on the encoder. NOTE: Every decoder layer attends to the final layer of encoder so the visualization will show the attention on the final encoder layer regardless of what layer is selected in the drop down.\n", " - Output - Output: Shows only the decoder self-attention. NOTE: The visualization might be slightly misleading in the first layer since the text shown is the target of the decoder, the input to the decoder at layer 0 is this text with a GO symbol prepreded.\n", "- The colored squares represent the different attention heads.\n", " - You can hide or show a given head by clicking on it’s color.\n", " - Double clicking a color will hide all other colors, double clicking on a color when it’s the only head showing will show all the heads again.\n", "- You can hover over a word to see the individual attention weights for just that position.\n", " - Hovering over the words on the left will show what that position attended to.\n", " - Hovering over the words on the right will show what positions attended to it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "attention.show(inp_text, out_text, *att_mats)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
arve0/ipynbcompress
test/notebook3.ipynb
2
977054
{ "metadata": { "name": "", "signature": "sha256:0d81a27ea82d735b684f7c7d3e9b7093ac6663e1b1e92a6734399f1b3b766bad" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run ../../common.ipynb" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from skimage.morphology import watershed" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example from https://scipy-lectures.github.io/packages/scikit-image/#watershed-segmentation" ] }, { "cell_type": "code", "collapsed": false, "input": [ ">>> from skimage.morphology import watershed\n", ">>> from skimage.feature import peak_local_max\n", ">>> from skimage.measure import label\n", ">>>\n", ">>> # Generate an initial image with two overlapping circles\n", ">>> x, y = np.indices((80, 80))\n", ">>> x1, y1, x2, y2 = 28, 28, 44, 52\n", ">>> r1, r2 = 16, 20\n", ">>> mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2\n", ">>> mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2\n", ">>> image = np.logical_or(mask_circle1, mask_circle2)\n", ">>> # Now we want to separate the two objects in image\n", ">>> # Generate the markers as local maxima of the distance\n", ">>> # to the background\n", ">>> from scipy import ndimage\n", ">>> distance = ndimage.distance_transform_edt(image)\n", ">>> local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),labels=image)\n", ">>> markers = label(local_maxi)\n", ">>> labels_ws = watershed(-distance, markers, mask=image)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/arve/.virtualenvs/3.4/lib/python3.4/site-packages/skimage/morphology/watershed.py:214: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if c_mask == None:\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "gimshow(labels_ws)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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XIQAD1yHsAsAA2EfB3U0jptOpZrOZFouF0jSVc+69B/BY7HvXKrpt2146siyT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\ngGARdgEAABAswi4AAACCRdgFAABAsAi7AAAACBZhFwAAAME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"text": [ "<matplotlib.figure.Figure at 0x10ad6d208>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "image = imread('../mp.tif')\n", "local_max = peak_local_max(image, threshold_rel=0.2, min_distance=10, indices=False)\n", "markers = label(local_max)\n", "image_ws = watershed(-image, markers)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "imshow(image_ws)\n", "image_ws.dtype" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "dtype('int64')" ] }, { "metadata": {}, "output_type": "display_data", "png": 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OhElKJf/9Dy7U2zLA8o0j+hdHroIiJIQyegD3oWfiU8B2D46pqOv6rb6d6AE/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\nY7CVsEQKignmEhaEIktKE6rliagGkToWgCnqGUzEu3+HXSBw3jxfkDu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"text": [ "<matplotlib.figure.Figure at 0x10b2bcd30>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets save it with colors. We need to convert it to uint32 and reshape it to contain a color dimension." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# scale it to 3 colors * 8 bit\n", "image_ws_rgb = image_ws / image_ws.max() * 2**24\n", "image_ws_rgb = image_ws_rgb.astype(np.uint32)\n", "# reshape magic, tuple concat: (1,2) + (3,) = (1,2,3)\n", "image_ws_rgb = image_ws_rgb.view(np.uint8).reshape(image.shape + (4,))\n", "# lets get rid of unused color dimension (alpha)\n", "image_ws_rgb = image_ws_rgb[:,:,0:3]\n", "imsave('watershed.png', image_ws_rgb)\n", "Image('watershed.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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8hd0jAgAAAACbOHsCAChExwUagZrrDSO5FtFxPTjwm9BHABum1vCVx60D20Mf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F2utPGBkaM7Tv/1yZHcrQ+ds0hy6s0gvnl4KHjHzU7i/q+vDlhENDsX7kKy4CabbtiOWyJ4x33p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\nWnn/pv9qwWLL7d4l1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pojL0U3kB33uV+OyUu9Rwm+5HLJSG69O7TYcWvQ77iXJZbueOnj\nnrpYPyTNiKu5ivLz142OXF3/TzjgSsuxYY1lb+LsuEKInzjYxL0j/O/5DQBAVaRcAMBp5TVXUVlX\nnIm7qcTbcXcsGdXfNFd/MFeTGsmtMfPRLNZrbqXBXOGm5urvmGtiYND1NTr5D14epals1dx4RnKb\nbuACuSJEzf10+i+uH0LzZ4MIbbx/WF1EJt9a2U+3SO42urk112fHLdk0t4jJTkaRrLScdOH6Ly9c\nX20K6oOjwx8ctflPxd2PZ7LjXrarJy+OHkXEGjwG1tyi1ZXNxTabC6cWPfiVaDuuNLDmyiWRP58c\ny14qPVCNT+k4OYbLMC4AoDZSLgCcNnnNZ94e66k745pMqio33Mqg++etwZZH0yRXWt4275TTwcpK\nNbdS0JWDufrrLZd79BtjzVppOUmO5+ZefB6G3X9IfmquEKJGzbW3Xa4QQtwy9f9ZvLf2sb5dbgwi\nXGBZk07HbYfc7XLduf4vl8iLfHPJjZ85eqDccFtSc/+vv1n4c5BBN9l0Pzgy/MGRcx50+/P+ItBH\nGjtNPPla+juaqrlVs27w2VxzKuK+9ojvhUB3LRiRw7hWtm9MDeY6GtV9/rpReVFv6n/u9D4L3x3K\nx3NXHrazk3eueGoug7lORR5xlZ9cdSLbWZO9tijBatZZRxG3ZKPc1qDjAgBMDP16Q+hDMGbyYmEg\nHktuat7mi968cfMCPw+0/r0L/TxQpZT71cVnz2785Z2cT5Q3yP1QDcsvsf/E7LpVUTxpueLAbNGO\nuSv/f/b+PsaK68zXhhf9AQ0GgwmY4HRgiKMeLMZ+iBGYhwQ504dMjy0fGAIitphBZJD9IqyWLY4t\njxEJYoJwEH6QLWTEccQEkSASBIcDD7KHid+OETYvAWFz7INgWvZwIMQdjAdMILhxu5v3jxuWF/Wx\nan3c66Nq35dKrd27a1cVTffuveuq3+++7ORPyd6Dqo62c5GVwDt4pB+7dUqubuxjxWu5uavuNu3T\niC6EjcHZzM33XtV9yNoNOCc3bX6iLvTIfmxGo0ZtXng622QkinBxJS6HbC6npzP534qicqOK5Ebo\ncdMFyx8fz7jEQdfj+h+ay2uWeUjXuHg5U+UavDDY/nyxNeEGFzh253m44aFgWeT5l7Ova0HvWBa5\n+7YbO/WTylWRuBzJrNxDC6zerXjL6eYVLAPqNcuZedwZK22fV+Wv0BRf7fDc7UetfezWamV1Hr1L\n47Xi1jn1jLFNP0u+XNRStnmgqFzOspfPp++Up3IPPIBwAJOHRXHRj8HE3LaRZW1f8E8pbO4zS748\nyKFLu1m+u80EHpKHoyRuocedt9rH6TKUq2fyII9LEARBWBLFa02CIBhjBw/TRQm1grHHTX8q3pP+\nUjxEEtXN87jM2ehT9WyuJdMmXxc9LmPswFS9P/FY8dytc+sjFDbq6NYs52Ec+5Z73ATNs4eJi8Hu\nVEbnOvK4jLG3pn7D0ZYJAoty5XHTThcFg45llYm53N0CYHY9e1zmuGk5D57N9ZDK1fK4LCuYi4Wf\nkK7c46qT16v8xooB4qK7WVyPy242KuseBqCezQWPyxhb9PgtrxUj9LhmTP+d7RWxkXhcwjWn1vzp\n1Jo/hT4KGaLHBRDlayiP6wenHpcx9moTPyUyTFjyUFmHIAiCqC3o5SZBxAKlcmMAOpZ9Ni3LybSz\n4p1p0SsRuvKvuub97QMjEbqe8WZz0xyYWmcgdB0dDIECbiQXSNjcxCRddx6XkFC9SG5ZEHO627rq\nzDxuJBNzPYzRtSSRymUhPC7LSeU6jeQC3mzumLPa23dnc5lQvJxe3O2Uox7JVcRG6xoDSVxvcI8L\nJGyuGVzfuvC4mU3LhQXLljaXOpaJODGQr/whluJ23ydNsMhXi8TjMk+DwIel1OywjLWU1iEIgiBq\nDlK5BEEQt+Da46Yjue17sk9hFErZvBXEnK64FG6WuWlXFrEUulf3OjzN5250bkCbq1uzzOxsbqnz\nuBysYC5z+UNlHMPNhLtbfmPM2RtH/vZhh7PliEywpuQOXep7uKOECJ8c0u3K4pckX40TELcJfWtg\nc3+Bp9MKg7mJVG4oEqnc397R6MHjMqFjmTG2a3Ofz6G50eLa5lpWK5sBr8TEjxLURYIfm7t1Tn3C\n4wKLHm8Sx+Kq09NZDwu/jXCUOYDQBacrmaGLSDw2l3BNzMHcFzcgHFvmVF11s5swuGmbu+iJa3yx\nPlg0XKdy8xkm/VRyJ0EQBFFbkMolCILIwJ3Qbd9Tn1jS69jHZwvjuTYbt8fA5l7d2wgeV/zI73eq\neGsQY5ubGLAanIXvDzJ74LNL+hCbltVXLmxX7mrtg+XIpQt2x5UkEcZljI05ex2E7tuHL8GCu0eA\nOpYTYHlcQo7c48KNp2aYv1HyH8xl8cVw88blTvrRV+BGOpUbiheeagCh60fiAh/+uYEHcyPEaTBX\ngp9srhxjj5sO5mp5XHVsSpVFtGbllhfwuPJZuRWDgrkEIrrZXHkM10zf+hmU6zqV+0S35CLLYakb\nBEEQBJGEVC5BECXgO79DlhYqrBv/qbem5YTQ/fi4cx+WZ3MN5uGZoRvPHfTIl++suM0VDW7kNveR\naf2DZHN1C5aBFa95+jHwgLHNZahC1wVpm4sb1U1DNtcDj1/rQtxaVMHcSFBP3L78htWvfxCbm4CP\nzg1CZioXPO6kH30lHo/LWfFagDEQotCNP5g7dUu/qVv6hT6KAu6d1//eedmvuIYt/azw4TZ53Bkr\nv/QT0w/1mbnbQpeAInFVyMzjWtLYEuyav85/LHgdfuCB6NojCCISeEjXEuMM7vZlPv70uEvlPtE9\nQOpxgWHWKxAEQRAVh1QuQRDlIIjN9QyP0oZNzXqzuUxH6KqYWnub664OFzAQui2bPm/ZVJDUZIx1\nt+WeeTSwuSsfbqpMMJcxtvD9QZZCF/FgEJk8dHjm/aLNBbmL6Hepb7lc0LjcBJ5rk8PaXGOPm9mx\nbPbaYN4LQ3kGlwl5XMZY/fqMp9YlYwNflfXm1jAxWdHmuhC6BuNyWX4wtyxCN30n+qDcPBADuE75\n1Ue5LxHlHtfmH9jY0htK6Hb+Y3++BDmAOGkbWZ0rOIloURmaK8ePzXXEq02KL8iHOT0MgiAIouyQ\nyiUIohy89UC2tKgGv36eKc6yteHj472wMC/BX3UKha66o408mwtk2ty0rxUlrtzmSjwuszjdxm3u\nmnq9n8kIba47Th4bcPJYgOBjnsflJAyu07QugUhVC5bL+LRgU7AcA7h9y20jtc/Afni1AQKO3OAe\n/cl/Fj4qEpvr2enyubngdN9YMSBd1WvDmWbDH2ZJzTII3fidLkfR49496IvilbIQI7mOwOpV5mQW\nLOfNx8UFhC5fXO8uTcLm2kdyJw+rmzwsor8a6h3L+84jpC2JSHhmye3PLLk9yK5B1sp97bzVUfec\nuytYVojkKjIMaTsEQRBEKen36+dDH4I1JXoDSRASpk0JM5KqFITyuEtPDvO2Lz/PxuoGt21k077z\n3Qanbm24e9AXYpGyiK6jzduOCnMv+/izsvdgtp3tXNSf5btb+CogN7gAYi7kuV6NE21b5+KfBLTp\nvNp871XLvS96YiBjbPzEW07UZkrcxDoclZ+rwlm5TEHi5nF216dmD0zw7SlOzsJ859B/uNhsWejp\nrGPYHje2SK6LpwUtDCK5lh3LjLHFaz+y3IIlZtncf3jwy7+hZq8ECltqe9sLnMeG025H1mXS3Zbx\nh+a78w3dnhZ33/YFz+ZO/PjLw7AXhMYel/PUjOK/X4cWoL2RWfAgwn/9+9sz/p6q2FzjguXE/5TN\nC7D0Cx53pcq97f0YY/N39jK1RmXJvyt95Z/WS8c0PZ31YHl7Ol39+Wj5lxs/JyjtylGpXMbY+78+\np7gmBXMNGPccgjG9f/lXGGPvrCq+zklO++6vrJ91YyP+Va563Hb7MvN2Hz/jch11LOOpXOBT1K0R\nBEEQpSGu15oEQRCEU9Qjv1CluO98t7e+ZYhBYMVqYZKu2dZ2DLnuummZ5WRzWVGjMnypu61BxePi\nopXNLUzgbZ1br+t1bC6UtilYZjc9LlCYwc1bofCHCjzuhjODJesYe1wsHHlcRhNzicoxYmPjiI2N\nO4aM3TFkbMDDMMvmih3LBi8DbKaNhqVpXwB/DHCPmwA3nmuGJJvL4SFd+8ust+xv3JLV8q1Opsd1\nBBjctHE/MLWOL1obdDesMQ14XKacxNXyuHl3qsPTuu7Cu9XuW1YP5hIeAGubeU/6S4W07/6KuMA9\n8PFa1jVJ7rCsTa4RlAuWCYIgCEIGpXIJIhYolSuBUrmKJEK3aXFr06vsOqErNtolMrVYfrcw8pu5\ngtOQbl42V8572/XEZJBsbp6pzby/UP1antm0TOWKKleR8ROviU43EdVN/FDxMK7ocZeMuZLYpqXH\n5alcXrZskNN1p3JZbQdzezrr0KuVY0vlsqDBXM+R3BEbM56y5l4+bbxBG4yH5upmcz+82gB/yu0j\nuUCQYC6QiOf6CeYCYiQXiCGYy9SyuSxoPPfA1BvPMMOWfsaMJuPqXoWg8l9j8Bos8ZrHRSSXS1wt\nDh7px1IvBeW+1jKYm8ZdQvec9fSZ2FK5TDmYS6lcXXQjuVzWigHchMFVzOaCslVkgPvrk6oUyWWO\nL6ZBzeZ+ircpgiAIojSQyiWIWCCVKyHgoFzR5q4b/6lTuWv/hJw2taLNRZmP607oelC5KpTC5uqq\nXIZqc5nyibm0tlEROWmta/+mGqtg2QYVm5sZyQWna5/HTatcpmlznXpcoGZtbi0ULAOhbK6WyrXv\nVWY5Npd5F7rGHpcDQrfwr7+iA1OUuCKi0IVJuh4Ub7ppedrkjBfq/W/GBD+/qZfgHvi0v36IMO1x\nGdIQVhSby3KErhjbfWALyn4YY+xb5xoZY/fOu/r+9kH3zsv+Ow76dvqhXu5xbdBSuer/L7qvwQr7\nPyyx8bjs1heBhblbdJULuBC69iqXRWlzmZrQJZuri8TmirXJEl8rfknuccU1vz1Z+1AH7Ou51tYI\nWpffQMGPx2UeVS5zaXPxVO6nSNshCIIgSgapXIKIAvK4cgKq3ASlU7mOcCF0M1Xu1b2NcNunzU0f\nhuvpuVo2N7jH5RSenhOdjbq/Gd1Rl+hSjkHlAlhC9+SxAcvv/pwxturD/owx8Xaan9/b/8ilC1iR\nXHarys1cIY0HiStSg0JXPZW7pr7+ud5e+ChZLU6Py/EpdA3yuMzI5ua52zSls7nAL3P+19xJXA53\nt6ByWQibm6ly1VHRupkeV2TGymtvrBhgZnaxVC6HO910/TKKzQWPK5Jpc1EMLkdd5brzuCxKlcs9\nLgf+EhU+0JHKBXCFLorKZYxNHlZ35NM+FpnWldtc8rhmZNpceVvyO6v+k4vezKhu4QYNVG4hZnJX\n0eNaSlzm1+MC0dvcTzE2QhAEQZQPUrkEEQWkcuWQylWkvCpX9LhxsuBBt0fozuY68rgc+Uk6LmwU\nzc1oBy2CDE/lMgybq8XP78WZ3wamNlPiiitk4tnjArVmc9VTueKp87zfvsg9LvOocs08LqBuc9Ul\nLsenzXWqcj14XJalcpljm6uYykVhir7vAY8IWlf8yFKzdblxRFe5cjzYXFyJy6JpV2bxqdy0x1XH\nqcplsdpcTjw2l1SuIxI212DqLctRuZJNRWJzVVSuvccFPNtcRyqXPC5BEARhifnZDYIgEDl4uB/Z\nXKJmid/jMsa27G9gLoXuI9P6q9vc++ZdNcjmOqIwHciCDsgEFr4/CNHmlhSJx5UQxOMyxt6a+g24\nUWtOt/LEn8cFUDqWJewYMtaPzcXyuGFZMrZxw+ke0ePyO9H3lZa4cQLKNv0xvRpKP7NTvnWu8d1R\nPeKNPN7fPojlxHMJkcSFF3+fGl2hjo3H9UBjS6+76bmEIz5qHXtXx2n4GPpYbDm15k+6c3PTmAlg\nXK61NbJ8oXvt5l9G3tXs0+MyxrYv6+c/mxsln4Y+AIIgCCIksVwkaMOhBfQXnSAIwiv7zqNdu10K\nj8vZsr8BFhcbf2Saav4yHo+rQnCPi8umVz/zti+sSC4r8rhaE3M989bUb3CtSyTaLNfU18MS6nh0\nqdizgSU7hoz1sJcLPR972Esh9ett1XjC4/rHkdAyiOSyHGsrX7lzUWPnIn/fwzV19eIiWROit986\n1yje4J9m8v72Qe9jvxDSjeQyzf8FdewjuekAPdzzy7n1sGzrUv2pi9zjAo0tvY36c6nThIrkrqn7\nypo65ybv3h+Mcr0LRT5qHZv4WHZOrfkT3PBmZN8+4mc/7FpbIyziPUwwuxIQPS5xk09DHwBBEAQR\nmCqoXCpYJgiCYB7blYF957tRhK7BubMKo2hz7ytJHgW0zXyLLEiN88P3P4fF3S6iGpErgWwukJmA\nT9wZf7uyH2wiud7YMWQsLKEPxC297XWWBcveKEskVxcuHX3aXEXkMVw/RPVadPxEw+dwbmrzvip+\nuq2rji/wafoh9h7XdbuyCIrN9Y8HiRs5FbC5PJUrH3mLiyOby91twuBqsX3ZpVJ7XHeDcl9tusY/\navIpeVyCIAiCVUPlUiqXIIhq8IMXrB5+54QAaSd0m/vh1YaoTqhl4nRurno2VwXXg3IVUbe5Xa1R\nHLAcn8FcwJHNBY8bcyRXpBZs7uPXugwedWndNdC3/EaNc+eEBnuP+9QMr++S3Nlc1wXL5arWkNDd\n1hjE4x7+1PdfPYjnOg3pvvlLH69Ihy11O1C2kHiaqyUGVwVuc7nZZSXJ4yawtLmjJtSPQn0zdeTT\nviPKv+A1EsxNi9sKdCyLvLPqP70J3beP3Fg88+bW+je3yn5T5q2O5UpQM1ovOLzAyMjjEgRBEMQN\nqqByCYIgvLFu/KdOt29pc4OAZXNFiRu5zXVUsKyFYjD3wNQ6foPfRiez35U3qY6fOHD8xIGOdq0I\n+qDcIDYXPaFrNj2XcEFjy42zvYU2Ny/kRBIXwArjqs/KHbERx4e5sLnVGJSbh4tBuXIcyS3/Ntcd\nb/6yXtfjSoqUC0Gxue5ecxpfTmcczEVhW1cdyo+6z0guxz6bi2tzGWMSmyvq2+f6fPi/gDb3o9ax\nmQHcCqRyE/gffOvT5solLlDqSG6UfBr6AAiCIIhYqILKpYJlgiC8sfTkMNe7qFmbm6CWba5KMFd9\nXK5TiQtknq2DJC6XuD/5ADNtrAW6xwX821zAad+ySDztyjXFzwaM1n3IxmdLMzzbQ936x8e9hkSx\nPC5QyablUlQrB8njThlWxxf/ewcQg7kGEpfZeVzG2KfhLmGJJ5IbJ0E8LhCbzU1MzIXJuOkMrrem\n5SA2V+5rS21zebsyx2fNMidIPDdN2auVvaGZzR3G2DA3B0IQBEGUjBK8tSYIgogH16lcxtivn3e9\nBwKHLfsb3AndQptrNi7X0unOWDlwxspkvlZyts4sjIvYsbz53quOPG5YcG0uKdtI4MFcOZkh+LLY\n3K0WFaCK6KZy31jRP7EwnUguOoijc3Ejubr1rTAf153HtY/kQqOybq8yVjA3oL5NYN+3bJzEtfS4\nLIKOZQkeJlzIh+MaM/as7fiqgB4XaGzptRG6545jHr+YyhV9bcApuT5tbl4YV6TUHcun1vwpfWcQ\nm8u8CN3vzi/lUGot3M3KFdFvWh7m4jAIgiCIctGvAs6AUrlENZg2haY+5/LWA8NDH8INnKZy7Z+Q\nP0Y99aBL28gmrE2Va/aeu9G5ew9+zhjbMWTo3MvJC5zVU7kixicWEwb3jRWfifdPWn4l/ZCjqwYn\n7vnRN1UF5OgOq3PcPvXtoifCdEf//F60lPPYP3z51+ftw5dY3HL3O4f+I/QhOGfD6a9l3p8pcUUW\nr436wgWnHte4VxncbYLj04urJnDzuGnmXrY6tS33uO+Ouu1b5/6svjW5K8ps0XAaxhU97orXBzLG\nVj6kV5NgGcOdNtn2RXs8KjePlk0FslxL3z7Xd+MFqr2+TWOTzTXrgFGJ5Nqr3JPHZKIa3eByLFVu\ncI8r0tOp/V3C9bgiHcNHylfw07HMGHv/1+cS97SNxC9YYspx21Kr3HQqV8R/2TLw7cmutiwvWHYd\nyZ232tMZMw8294lug0uRPkU/DIIgCKJcRF1fSRAEQWhx54T6sDYXhXJ5XMbYlv0NLmzuwvcHMnZD\nE2baXC0sTym+seIz0eYmzO7RVYMTNjftcbXoau2ztLl+qJjHZXFL3Bohz+MWErnHdYrNfNwZKz9P\n29wJB5qYVOi69rhMKFuee7kfY/8HccvvjrqN6dvcPMJOQwCP65+DR/rZ2Nz4PS4Tupczna5Bl3Jl\niKFaOU6PG5XENWbUhHp0m1socT1z7w9GpW0uOnKPe1fHaVihwh43IOlsLorcVRmU6w5vHpcx1nqh\nx082V4dPQx8AQRAEEZ4SvI0kCIIgFKmAxy0piE3LC98fCEvi/h1Dhu4YMhRuGERyjauV043KmRS6\nW/VILrNO5S5830fZbCiPi8vpr5Wp2mT1Uw0Pb2t5eFtL6ANxyJKxfzB7YFk6llG4c0ID6Ft+w4YZ\nK7OfnUDoJhixsRHL464bz9aNV1z3Lxj7C/UtSyK54HF1+fv8Icd5V1/Vr3fVLssjuaLHDeV0DSiF\nxxURG5ihS1nX4zqN5Ppkxsprih4XpV15/MTwzrjsmNUs487Kjc3jAq47lgs9LnwstcdlOe3KIqGa\nlhF5c2s9GNywHtczHjyufsEyMAz3MAiCIIhyUbJ3kgRBEBXmBy+EPoI4CBvxMQbF5qYNbgKwucYD\n7XQBj5s5IjfN0VWD+WKzU/s8LhQsuxa6m17V6/NEBHdWbllY/dSXv2LVtrnG1JTNZXZh3ASZNjed\nynURxl03nm1/nm1XmrDwF6ebx51uHle43oWej1U29+6o28RF5SFpPP/Jth+RSxBmaIVxja+fE5EU\nLEuurghI4RSAWqNj+Egtj+t5eq4LmwsGN8/jgrstu74VCZ7KPTC1Pr3g7gKG46p4XNftyj5pveDj\nxYamzR120+MOwz8UgiAIoiSQyiUIgiAQQByU29te17mof3rB2r47dG0uD+DmJXERMQuI8Jm45WLh\n+4PA4zq1udVI5ZaIZS+XrHrdmDX19XwJfSxOmL+jl380ANHgyplwoAkWP7sDoZtwunMvf5mbP93c\n7+aNYpubicTXysuW3bW5aiH3uFrB3KZ9pIR9U+pIrkGpMorNdcT3F+YeG3+eMSDOguVQwVyzMG6p\nbW6hx0XcV1kIMivXkdMtZN5qmtJCEARBEG6J9w0GQRAEQZQOxKZlRGyK/lBsrla7MgH8ck4U7iQe\nxFRuhRl7doz4aZW07vwdvbAwweYaC12fcKeL27qZx/bnbxhc0eMmgHiuYkjXKT7H2y8Z+6ULzBS3\n6ja3u63EWjEUH/21yWuJX3XVsVg9rmKm3Hg4rqXNdVSwDB5XYnPNiNPj2mD5hN964bzZAz3bXOa+\nbLmWcVqwXChrxajuT+u/XPLWz/sSBHMLqVIql/kN5hbGc2cvrBcXDwdGEARBxAmpXIIgCA3Wjf80\n9CFUnN720v9hitDmwsnEA1PrzM4q2tvcn3wQLFTtLpjrtGA54XF/OaeezG6CSg7NTXhcA0rXsbw1\njrgny5+Ym2DUhHoUoVs4JTfhcSVRubTQlczKzcO4YJkxdvegLzKFrotxuaLNzcTb0NyDR/odPFKm\nWeP++VVXXQU8LmPsjRUD3lgxgN823iO8DNN6MSaxuQYdy99fWCcaXPgU3ekSnNYL582Erk+bCx5X\nPtrWnkpGcsO2K9uEbkHZJswuvzPzIYo2lzBAxeOm7vnK7IUBAt8EQRBEcPr9WmkyU9RM3ULvookq\nMG3K9dCHEC9vPTA89CHcYOnJYU63b/mc/PHxkO+yzDqWtdxty6bShDsXPFgQVLKvU27ZZHixsE1I\nV2VobhqtVK79rNwEMDrXEY5qlvPE7d/vvPE7/vN70ez42D+U469PXir3tcc6PR+JU+xtLmNs8VqH\nP/O4GKhcpwXLb6xQ+s06Z/3Xlqvc5tkZX92+TNXjiow9e4pleVwVTSspWFZvV850Y+hXaG043VPo\na1c+VHypDQRzX3j6lmN+/iW9kPG0ydrPn1OGldWc8VQuCFqRR0ff8roivYJrPl2nF2C1nPGsldMV\nX3SJElf9xVje0FzF300VWfs/Nt9yMGPPav9gx5zK7ek0lF72T/WAWdnyc30OA51pLG1rDfYqK6pc\n3I5lb7XJ/5T6jS4cl+s6lTtvte83LB3DvV6H9ER3xlO9PIO7a7PXZwmCIAgiOGV9J0kQFYM8LmFP\nWI/LGNt3vlv3IbpneEsxMReQZ3NRxuJ2LmrsXGTyDlMxDjJj5UBYxDsNErq67cpdrX1drZhZLnfB\nXM8el38J0eMyxk5/jS6Ji4jTzWdCH0JNM2Pl57DIV4NgrqO+5YTHZYyNPXtdxazY9C3n6d5IpuSK\nLBnbeH7xF+cXy5xroetd8frAF55uSHhcljK7hMhdv63jQdsE/M68FZyi63GZ32LwPBRfjOV5XDm8\nRVkxdMtXU3y2SRCzx2Wm43IJIjZ8jr9Nx3MLg7k0K9cGA49LEARB1CCkcgkiCg4epjPphG0kNzgG\nqVyD9sUS2Vxgy/6GtNbdfC9aNy8IXV2nW3gCUTS4cDutdRUxa1cuhc1F71hWKVKu2ablZS9nn3mv\nWM0ySip347OD+MJK2LpcCtBt7vZl/WDJW0HRr6Sl7LfO/VkSupU8MEKPKwJCF5xuodx1hEHH8uFP\n8Xun5SwZ0wAL3Ha0lyASNxRaNcuSV1wqZcsGE3PNpuGalS0/19sbuccFzGwu1jO88dxcn3zUOta4\nZtl1P3N5wY3kTj/k9XctMVi3MJXLyOaiQh6XIAiCSFMrb7cIgiAqz51u4kHqGKRyGWP16/tcjNOL\ngUyJ6w5Em5tWtvweA5urm8p1hLtsLhbqjhY9X1KWYG6N2FxcRJvL5S5RiOLoXGZ6rn/pyRvDVgsN\nrgHvjrqNe1m4rTgKl68My4QDeldohUo6ZkpcSTDXbJ4upHj5AncaFCx7RnS33OaKcleLpSfZ0pOY\nh4fCsKXm82s9UFikrGJztYRuoi1ZnaOrtF9drKmvX5MzXDMqjDuWHbUvRIuBlK1Zj3tqzZ9CH0KM\nkM21Z/bCevK4BEEQRCakcgmCIIjwcJurYnZLF8z1hlnfcrTgBnOZA5vrqGBZBRdtgXMul0PoZkI2\ntxAuccnmomN2rn/HEG0LqFV8qm5wJZTF5mYiKtsVrw/ki/xRaV+bV8VsNijXz6xcRVlrFtLtbqv1\nGmqtWbkq83F147k/fr3px6/LfjeNba4ZpbC55cLzoFwRLTUrX7nCg3IVeWfVf/KP9vgsWOZAMFcl\nksshm6tLZruyCrMXYsa+CYIgiPghlUsQBKHK0pPDQh9C7IjBXN2Qrihx4TZf0iuX3eaizMrNRN3m\n3jcv93v4xorPDGbiuiB+mxsQXJsLHnfO5X7lFbrVsLk0KzceIJirEs89lzOrftSEeljyHuja5hKK\n+jaPTIkL/L9v92htypvE1UXX5q5+KkaPqxvM/fCq+b9Cy+MCXNMqzsfNXHPeC0PB4MolLsfA5k5a\nXj9peTWlLI3L1UKxbFmyzl0dpyvvccc9d7vKalgelzE2/VCvuGBttpD03FwCEWOPSxAEQdQgMb6f\nJAiCiJN14z8NfQglYN/5blgQt1kBmys2LbvzuICKzQWPe2md9nhjA8xm5XJitrm4s3L/fqf2SRmU\nU5PpwkyyuZWHgrmKqNvcNKLBzbtthk+be3y63l/zFa8N3PJm45Y3GxljW95s7G2nN5sBJK67gbjc\n45Y9mGscHzfwuIBK7hbW4WuKnx6YWte++3JiffnrPYPBt4CBzY1/XK5xwTLDeNLuGD5S9yFr6sKH\n7eQ2V+5xHRxOuUEUuhxvNnfqFj/7IUyqlSmYSxAEUVPQu2uCIAjCFYhCV9fmdi7qH5vr9Tk6V5K4\nTXzVj821JHKbKy5Ym1XH0uZGOPVQgkoajGyuImRzEUmc6M9M4ooJXRSb60fo6hYsc8DmblUeAV46\n/uu3Y5xooDsBd9xzfeOe61v7Sh0s8pXjzONyIp+Yi4K6zW3fXf+12cmrsr42u19iyduRxOaKXeU8\n7x5/wbLli6WAE3PDOl2tsmVI4pLHzeT+5U7+H10ndKduIY/rnFebDK8QIgiCIGoQUrkEQRCEQ9AT\nuglA2SbELb8dm81ljG3Z39B6oaf1QrKYMX2PJffN65+34O5IBctgLovb5oqUyOamw7gxs/qphsgt\nAhZjz47xti/XNvfSunucbt8nhcFceZFy5vrwUb1wNRTGNtcnIzc2jNzo+ymi0Ob6zOPqSlzG2Ljn\nkn9YJUI3/Qzc3dZQ6mxuVHOd1UnbXAnc1+aJW7nQTZDZVW7cXl4u8lr0FWm9cN7sgeBxY0joJtBS\nvDXO/cu/4sjjiniuXC6ExuVqYWlzKZhLEARRO8R+7oAgCIKoAE5tLiftdGMG3K34EYu5SO24MDEX\na2iuvc1Fx53N9S901W0uGFy5xN2pP7/TNcte1jjnTsFcdVzY3Evr7oGF1ZjNZZrJrRI1La94beCK\n1zKEDdyf99X5O3vn6/fGG+Bf4nL+67cbYRHvBIMb52RckVNrso9QtLlwGY3kSpqobG5hMPcHt9qF\nktrcBJ2LGtPZ3PWzvvzVU5e1EiTK1mYcNSFBNLgBbW7a2mZ63BoM455a86fCdTxI3GiZt3ooCV11\nXm26plutTBAEQdQgsb/DJIgaYdqU6M6bx8NbDwwPfQjl4GO7C8Zd48fmlgsXHtcAPx3LP/qmybBJ\nEfRgrlPMbK7BuFwOytxcQJyVW965uSXldPOZ0IdAWGEgaG2CuY5s7tzL/cQF7kxY24S+TQhducQF\n+RokSusILnTjN7gqKFYuA1HZXAngcS1t7hsrcGqc188aYvzYzGBuwua277ZVArqCtunfBlvukYiW\nj1rHwsIoj6uJixG5EnCDuSjVyuW1uR3DfY9ReOhXV80euGuz1x8zgiAIIiBVeKtJEBXg4GE6V57L\nd353IfQheOIHL4Q+gripX9+XOTG3MgR0umBzSzE3t/IY29yezuLztuqNynMu94PF7GBcQMFcFyxe\na3jaKI8qxXB9Eo/NFd1tHnkxXBHJlFzucQ0OL3L+67cbdau2w5IXzNUl/rJl0eD+YPVQWAy2M2Ml\nwlBD8Lg2NjcTbnN1PW46tvvIt280qSRm4kro/psrWjstEZbtyljEULOc53FrMJIbLYg299ACrC0h\nMG+17+RDkDfmxjaXIAiCqBFI5RIEQRCeCBLMLUvfMgBjdIO8dZTYXJSO5QgLlpmzjmUgyNxcCSWa\njJuHls0lVHA9LpeR3FXmwNQ6Y6GLZXOx+vkBic1FJE4l7N/mbjhj+PR4ak0drtAN63QLO5ZFQOgu\ne+k2xfURPW76NhZmeVwYmsud7t63bZtUIgSxvITg3NVxmjxuhTm0wFbobl92CelYiFxoVi5BEETt\nQCqXIAiC8Me+893+hW6JBujGQMzZXPSO5c33ur322Wxurk3NMsF5eFsLZXNVcG1zhy494XT7PlEZ\nl0uIZNrc84tvkY7GLrZK/cz2LBlj/q0Y9xzy39awTjdtcwsDuMteug0WyTooHpel6pHXzxqiK3Ql\n66eH5prBU7nqlKJg+dzxXt2IbSSRXMbYc33RdaiSxI2QA1PD10K40Lfbl0VUFOQUm2Du7IVfIa1L\nEARRbUjlEgRBEL6xsbnV7liOhITNnbFSY1iaBJRgrr3N5frWtcflRBLPXTc+9BGEgGyuChufHVQo\ndM3CtVXyuIBrmxs2mLtjCH6BYcLm2vhX/sD4Ja5//WOcypXT2GL1NzeS1uVfK3sFbnMT4hbL4wLp\nYbdaNjdzVi4WYjZXl1LYXKYsdA28bx4dw0eibCcqyOMqUupxucDTM7SfE8DmUiTXDAObK0pcsrkE\nQRAVJoo3VwRBEEStse98d9tIk/Rnb7vhae7ORf1bNpUmUAUdyx3DcdIVBoDNRR+V+pMP+v/om+H/\nF7xJXM6mVz9b9ISGEf/7nb2/1OwmbWzplY/LrUDBMuGUjc8Oyhyda9OQfGndPdWzua45MLVu+iET\nfzb27PXTzfHGVizlq58Ju/NW3/Lp9mUmG4knxhcD3W0NTft8d+MPWzrg03Vfyletsbhgcw9fugb6\n9o0VA3A9LtC++3JC366fNQQcLb8hfzjLEcDte+rWz7S96O3dO29M+vjWx3ovRJv+bXC0Q3PP7vry\ntkoFOl+HfqOJEpGXyn3xlRsvD5558jq/naB5NmOMPXpXxkVdT8/o99Ib19lNrQu3M+H6tgIet/VC\nT6g34w/96urrjzqfgUIQBEGUDlK5BEEQRBggm2smdM0ol81lpkJ3x5DrWPMOdw65zlCFLorH7Wrt\nG91hXisC83E929yEx4WcrlzuJmxuT2e98Zg3G4m700FKD+ApYaeO+bXHOh1uveoYSNyhS0/QcFx7\nxGyultaN0OY+/b1+I79X8JYTBG1m8fL5xV94COAmDG76fi2nO2pCfbncz6k1dWLHcnJ07q4b5/fj\n5+re/oMe+Rxu9G/p/3nnZabpcTlThg5njB2+dMGFxwUybS6/UWhzXczZJRzReuG8fTB3Td1XoupY\n/qh1LAVzVbh/udeI5PRDvdzmgrJNuNs8j8v51UfZK6hkcyugbyvArs0RPVEQBEEQuPT79fOhD8Ga\nqVviOmFBEGZMm+LqdHkFeOuB4aEP4QZLTw5zun2b5+SPS3XeUKRtZJN6SNc4lcsYK5fHFQGbC2ZX\nvEcCls0FYrO5jDEbm5vGg9nl4jbRtywXumBzedw2YXPhfrgzL5IblcctLHmWH+3qp0yMThlt7tiz\nY0Lt+rHRY1m+kRUjtpmJ28SjCiO5W+dqD3W7c0LIS1HfWBFg8rqWzbVXuYh/Pp7+nvam/LhbTp7E\nlQBaFx4oUbz+ba7NuFx2c2Ju0uPexMbmukvlXt3bnzHG9W3earMXmr9gOHzpgvFjVcgzsiotynmP\ntU/lcnRTudFGctmtqVymFsxleL/I9ir35TfegRsftY61PhwcalzljnvudpXVPKtcxtiBqfWFvjYT\n9ed5lVQuOvNWBzhdFrAiizFmHMwlm0sQBFFVaFYuQRBERbhT7ZREhEA8d9/5br5IVraZldu5KMAp\neBRaL/SIHrcQXI8bJ/ZDc0UgqhuETa9+Jhmm+/c7k+5WXMQ787awbrz5lNw5l/uhl2zLcTHQ9+Ft\nLeLEXPg0cScBgMdl+XncS+vu4Ut6tcrncYN4XF0sh+aG9bjMfYUyMG/1jcX4sfx2PNiPy830uM2z\nbVO5jibmcnd7dW9/icdljO3abPiCwbXHZYy1775sMPt2/awhcaZyYx6Xm/gxVhyFq2h81Wm9cN5y\nC/EI1HikchBOrflT6EMgKo7B0FyAxuUSBEFUFVK5BEEQsVCBmgQs5E7XxuaGZeF7aL5QNLtp0bsD\nO0zprmU3Hpza3MJBuSB0M51uwub6x7PNdUSmvs20uU4V79izYwImbgvZ1qV9gjhP6/KvIhxWHJTC\n4wJjz143Fro7hlxH+Qti5nFdY2Nw48cylZuXx40TubstHXk2N1PZxilxS4qizbUXuq0XzsPCdGzu\ny2+8A2FcHsllkQnUqA4mTt5ZlZ2PbNl0m6M9VjKSW5tQKpcgCIJIUKY3bARBEEStURjS1QWCuUHi\nueBxF743SFxsNggGl0tcrdiuIu2769t33zh7hWVzf/KB12/+plczirs3vdoE9/MbzHHHMne0kgCu\n4grlRTFxmxcjNmtXLiQd2GVubK4ocQtt7unmM+gH4JQ8ZVtYsEyoII7OVccmngtCly8GW3jpN3Fd\nAOTI4EoKltHDfIXYp3LdgR7MhVLlCiNKXNHdFnpcxHZlxti7d+K/towNFZsbdvq16HEjhGyuMe5s\nriNeeuM6eVyf8FQuqVmCIAgCCDlriiAIgiBUAJurOEy3EG5zYxidu/C9QZvvQzCIwxv7M8bmXmaM\nsR1DrqM0ZHKJy2981NrHGLuro+4ju3Ljn3zQH2tirhzuaxlji57oFu/MuLHe7bhcuaOtWz+0r/1S\n3fqhjLGf38t++P6X35+f32srv23G5QJzLvfznMxeNx7hsNVJu9uHt7UgDtmNOYnrlMx5umUkeCQX\nbK7W0FzG2Niz1+1H57KbTQ/lre4PEsMd3VE3+uZl08dGefJhG858YZnNdUd3W4PB0Nz7l0v+OX1v\nKVzlYDMo1yftuy8Xzs31n8fVnZXLbnYsxzk0t3l2cmJuECzn5t7VcZrsadnhErdl022di/4c8Egs\n+/NF5q0eSjYXHS5xd23+z9kLvwIfFR9CEARBVI9yvLEhCIKoBX7wQugjiBsxnvvGj68mPhrAnW55\nZ+hm4u5s+10ddXd11PEbcDsUozX3LmrdTHrbh9sekylgcOEj8PN7+/Ml1FElgLm5NmXLNmrWUSQX\nCDI0Nza5+9josbDgbpY8bnAsR+eKJOK5WFXMTnFXp3xgat2BqXWjO+rgj1HiT1Li04nnGsXFctcj\nNjaO2Gi7EUXkZ/mXntR7YtfN5ko9rnM8DMpNIBmaqzgcFzeSyyxSuTEPzU3gOUCv7nElYdy7Ok7H\nMzGXKAQ6lls23QYG110Yd9bRhllHVZ827eegE655ovuW64DljnbX5v+ExfFBEQRBECEhlUsQBBEL\nlrNyPw7a/eUHsLnwMdPm6mpdLnED2lzEAboGQOKW527ZzVJl8R4J3Onqyl37muUu/WSwxONWGMRs\n684h1y2zucYHs+zleItDVcgsTI7H5qIbXE4FZuWW2uO6g0vcRBUz97sxzMp1F8ZNVF5zm8sX+cPl\nQlfyVVHi+hG6PMII1lZ0t2bP5+o2t9Y8rgRFiYvucWuEwvLksO3KEtCFrtkGSSoX8ujoMWIMN/FV\nLLOrLnHdMW/10OKVCDUSHlcOGVyCIIgaIfwfe4IgCAKFOyfU147NTWOczfWD3NfCV1GalrUQPa6i\nu40KsLl5J80NxG1v+/D69QHO3vJq5ZjBKliGs/+Fc3MTksBpKtcPYHMT+nbs2TF5Y3FPN5/x5nq3\ndZ12Z3MJRAxqllEKljmKMdwYPC5jbPsyfJtrNrc4k4nnGhPFy6LBTX81E25z5612lZBOK1vLK4TA\n5srLlkN53LAG139/sgSoVjZO5cZZsJzXrgyyNh3PDStxn5pxf+GUXNym5Y9ax4LN1dosPArxMCrA\no6O/fP32q67sl3lYeDC49//oy10seFD2nEAFyx5IdyyTxyUIgqgdKJVLEARBVAozpztpuUORqZi7\nXfjeIM8J3fWzQp6isg/mAgbxXCI4chOQiG1VwONyEuI2z+OqfBWXbV2nt3U5ORVb6mBunJFcRJvo\niO9ozvR1RMweN006iZu4x1upsoi7X9/utgZY0l9S9Lgqg3K1iCqJqwV6HpePyDWYlQuUqGA5j2jD\nuCLoDhUkLqlZG0SPm/40E3eVy/aIHlfOqg/7dy6ymgNNcLQiuQRBEETtUJ1TYwRBEAQBgM2d8c+q\nWhQ87pBTAy+PQ37XZKBmF743yCCee6Hn8+GNMSoHP4g2V3eGboLgwVz0hO668Tgdy3Mu98MK5gJL\nT2Zkc/lp/dVPNUCpcpU8bhpJKrdKXFp3T97Q3Pk7erfOjagS4J6JA08c+4zfvmf3jfvDXvhiA8zK\nxc3m1iDuJK6kafnsLjaCKYk09EiuzwswEiHdUB43Btp3X44qmGsJ2Nw447mZnDveC8HcSCRuYSQX\nAO2KFc8liavOuOduT9+pIm7TdC76s9kx2ERy0yH1me8Ybm3Vh1++Ce1cNLJl03njoyKYgscVg7kU\nySUIgqgpKvgWiCAIwhvzd1TnhEv1UInnTlpeL+Zxh5waCIvL43LFhZ7PDR6F7ifUx+UyvGCuiGVI\nN4jHBfraL/W1X4IboY5BzpzL/eZcxhRC6eGLIqufaqikx1V3t/EM07UnL5sbm8dNfORoDRGPEBC6\n3ohEs21fFvoIipB43IAECdKD0K1ljwu0774Mi/pDcCO53/q40TiJm0nTvw0uUUL33PHeSDwuY+yp\nGff73F1iUG7itkTxkv3lmHlcFncqN8GW/cnnB9HjAlXK5rZeMCyZN0Ylj5soWCYIgiBqh8q+CyII\ngnDK/B1DwOOSzSUkmI2/Na5ZLoXNnTK0Tlz2ndeeaFtIV2tf2VuXQevG6XRB6LrQurWDaHMlvtZ/\nYNdRxzJjLC+VGw+iu014XBHudMNq3fg7liMhfptrD24kN2AhenCPO2XocEdb1qUwm8v1LbrHRdya\nSAxCN29QbrQopnJtAAubZ2rh/kJTizuvt0ScWvOnxD3GY3HNUrnoU3L33C8bYc7Zsr8RFpblcYHO\nRSOrIXQ7hnu94kqxV5kncSmSSxAEUWvQWQCCIAht4tS3H0dzFXk8mM3NZYwhBnM333fVTOiacaHn\ncwOhu35WL19cHBUTDG76Sy5sLmPs4V/5+7a7A8XprhufUWUcIZlDEwPy8LYWPzuKyuYSZUHX5noL\n5lY4MVl5tDwu/GXB+uMybbLSajXy0yVJ5a6f2Qf6lt9A5N073ebPAgrd0nlcXQyisdzjutsF4ZTd\nk5TMqxaKNhfI87icathcb2jNx921+T/J4xIEQdQgNfFeiCDiZ9oUr8V3BC48oUtEyBs/vioRukdX\n5ZrLsDXLxsFcwCyeC9jb3I+ERCy420yDS2hhn9ANbnOhMDmvNrmSXcoGjD07JmyvsrtgbuTwEbkV\nxo/N/c6hcpciHJhaJy4sXAa6eXbxOuhTcgvBNbjqvDW1rkY8LpBpc9HdbRrXNpfdnKHrjbO7IvK4\nHcMDmC2uYHnKViVrK99UgpoN5qYxDuaa4cLmqjOm6EVFBYbm+i9YJgiCIAgJNfR2iCBi5uBhzJpK\nwjVb52acXiGhGzN5NlcclJsGN5ur+5CANteMj1r7YOH3BPe4BsHc3vZY+hXTRFu5XEha34paN+aZ\nuO6CuYm4LRhcLnHhRnCtW2vUgs2tKeatZvNWK62ZcLeJL+EfWXwE7FVmRZFcbxL38KULHvaijtbE\nXC2gSDkxFhd9Sm4e3X9zxcNegHgkLtB6IYzZMna3mZtiWYlesrlBQO9Y1kVucytTs0wQBEEQkVCF\nt6aHFlCckSCIWLCxuT94AfFAiCRmZcths7mWGNtcs7Jl+YhcOY46lllVapY5deuH2jwcPUdVOC43\nZk2rwmuPdbrbuLw8uWIS91JYU6RMuWyurmU83VxDFw4qStzIkQdzsSK58NsZ5++opcSdvbDcpzva\nd19u330ZupSxIrnc4/JPRYnrweb6TOWqRNs948jm+jSpus3MNcWjo7VfvJkNynWHVseyCmRzCYIg\nCAKLcr+3AaZuqaGzEgRBxIDE12YGdv1w5wRZwJRgN8uWudOVR3LRMQvmomRzzQboGpCwuYcvaZx2\njMTm1q+PK5STAKVmObFYIrG5ZZe4Tj0uUFOjcKtnc9t3l+zPrrdxufHQ0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eDa2Fz5Y5ee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\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wOxgERnA299g23yuog8KsXJdsfrM99dfUPZZ4cpnud52RibleKqFCCeY+d+Qz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xzWFo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\nNwLASRLXJ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mTTZb8/X8P0uM4LlgFyofO2610ADafNxSJlpOR4cwiIIOp8eLKKLsSTug/q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\n37BgLvW49KuhtC7wpuXUSK5Qo7L0oNwPXujSbnMt5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GmBfnbGR8rQrswoj811InSFDG4U\nEzaX0rFIJnOT81EZQi8cJ2AlrhCK43KJ7om5DLA2F7GPtMddMTNvkh+C+ItcMFcdHo9rP4wb49Lw\nB+drCruU6Y/4O/T4JW7j7G7ONQOgstH4EREKXZucndbAbrzxXMGHu6i+LYTzQkMEPqlaxVoYF1TT\n8unHuF7teSKthQaXMv5Yw/hjesSzqM3FYC7Cg6FUbg5l8Lgz3zB78N821ujmH6BxVu68Pf2jS/7K\nq9/ut/rtvHNf0IqUo6DHjQGnYBnOniClBVWufmbc+LnrXVBizimx4/WS2FxiPZ4b87hZWlc0swsN\nQ0kC7dMcNXpcc+NyOXFuc68vST8DAjOey38eH2NbWsA8LoKkQm1u5/66zv2W1Cn8PG4MzhiuxsAu\ngsQ4NoX3f83my1666nyPyxO6hRnMxXZlHpi4pa2nvpQqU6QzuHItyjzwe1zRLT/7y3QD9PbBAW8f\nFJtgsuAZza9UaHMRHmz2KocBzEhu16zbujxuqrvNcroj/3KPSdx8mwsT9LgIguTg2TmagNm7XWYu\noCFEbW55gDY6l0lcazZXomDZFdptrkac21x11G1ujtBV2bIrqMdFm6uO3JRcBCkDTOKaFrqTB1Z7\n53Gd07kfX//zELU+R3fpPxhoefOhxRD8NhcaLJubQ2u12L8jTHGb5OP/6uN6F/zAlcE9fpK3LTMG\nNbgqXco0g0uFLlvyH7LjSE1+mPXWqHqjk3FTla2oxEUQ7RR2LF/ZW7yR8nhcoVm5AQ/K5QngRp1u\ncv1Umws2koseNwsgcVgsWEac4+tHTciUqmCZEPJOQ7k++h7ZXTl32kYg5uju+PVrhTnd6D0328r1\n72KUMKqVs9h0uV9ssfbUvthcfkE7ZluJCjbNgTYXQXgwJHS9k7gjr4T8Hg0Wzo5lufSeCY+bxKjQ\ntczKc6o9NzwelxCy8OO8azpjdco2PW7HT1WvpUCbGypZHnfW0DuGnjE/ycovceV6ldn/5ai7heNx\nNxy84XoXEJfk2Nzl++4fLWQdMwyfWyKPS8wXLJtGJY/L36KcfGDq/dTmwk/oosfNB4LNhbAPSMnx\n7GSNF/hesMzDOw192OJ6XyRpGl+JLVk/Sj6QEHLudB1brO53hJjrpR6X9i3T2yZsrtysXHMflY3y\n6Yx7dDGxccjBXE6bq2Vobhg2l/0UU7lawI5lBGE80diPfn0iQ4pQoatL64p63Ck7q/7zVXuXsCSt\nrQmP2zjb73Nn1qhsvEcX1zuihJDNvfAV18GJq2Curqkl+eNyr53tuXY2Pf0zurGfkxiuusclRR3L\n13/Wly7qTxQqRgO7JmblqkR1TZMqcUdcGSi3NTgeFwkPiSm5y/dVkkKX3ZPzMsKT2S0zEsHctrH2\nJubyIKdvOWE215rQNXo6sZw4T8Six0UgAOtcOeIFPurbpJHlX6dpfCXrhAUlaXPHTdRwEjC1M3n6\nvH7M4Ho0Ipd+VPZU6JrjyZb6j9u7dG1N6NzZpsv9lo24TXTMzVXn+pLuoVtSzsHtaq75yR5ALdlM\n07Lobaq47VhUi9lcFdDjIgiDeVzO9Tv31wWvIam7vTS8ihjL45bhr1EvlY33epan1J/KeR07kdwo\n1Oa2v2b5aYOl8Ih0dGO/WH4XOFGDe/1nfYf+51cWnnTBdC+1cWulsqpHf8+ndMGyOqKDcvOrlQuR\niOFm8fLMLwl6XMQk1ONK2FzyrbvdOKeHcBQvU0oVyS0JhqxtIXbalR8/XCXkcTGSmwU1uM41qvMd\nQBAKpnKNEEAwN2tcrnceNzVZK7GR2A3T5IR9WfrWzp7EUJmVa//aZ8VxuY8fNn7WwGE2N6dLmVpe\nHvY9dbdU2VxKx6JauuSsYHN/AkOlXXnDwc817gmCuIXf4Lpiys7775L/+Wq3hWxuVNyOvHIPYK9y\nacfleu1xGXr7lnmCuZsva75erW2c3u2lsP1JZ3VEpuEvWLaTzd1x1IYw1o4Jj2uOwg+nlj2uOtGm\n9NSJuW7BduWQkDO4MVITuqmgx+VBbmIuzeZqj+fWHyj4IOPK4xJC1m+uji6udoN8q28/bv9P9LhZ\nsCSu80iu8x1AEArEs+QBEMa43DmnavZNeugUgxce15BtLczm6oKnsbnQ4w5cmVcR5pAD1xti2VzT\nZ53qD9ztmiX5QkevoRO9mC4AWGbXJlnZXI+IxoipzcV4LoIg0nzSeRuyzWUel0Ft7s/W+/1KTrBg\nWZysVK6PtLyJ8dw82Kzca2d7pD9z+RvJjd1J47nR2/xbsBPt9REawDXRqJxK8sMpw2uPiyAh8aNb\nVX8k5TohQ5n5xjcHXxe4gmr5+0rnQtvGkpXnVTYQp/5AP5WJuf6Sc/4wqWxR4ubT3bETjkOtHTMf\ns7mIc1DlGmHGjZ9L2Ny92wfMXQioWNIXj2stKcv/ROdO10l0LOsau2vU48rNyvUUlsqlN8wNzdVY\ns6wLJzY3FWg1y1nQADH9SncYPS6CIFkwR/uJbqXx8MRcza9CSX2bJBrP/dn6WkW/y+qUAWZwkRxA\nRXI1Zm0JIRe+qhnd14PDEsQCKsXLhQKYELLj6Fee1ixLw4qUj5+sojbXQrVyzOZO/Gvt6ce6hTyu\nc4nrBRjJRRD4JLO5inLXC5u7fnP1q0t7LTwRWlvfQY+LQAALlmGxdzuUDpyYx4WJlvJkQ4h6WV0e\n1yg+elzpSC7Cj5aOZcr1Jd3JsmWYNcs57GquQY9byPC5g4bPHZT6I+mO5RUzB8vvEILYIpq1faKx\nH11MPNHhIbWHh7jMyOpqXbbpcTGSGxhCHrf9NZ2RXJ6OZUSdjp9qe5Xj71gWQjSw6y+K7coxa3v8\nZJW1EbmxpmXRPC4Qxh9LjxcjiL/86FbVj25VEUKe3lH19I5A+j/8QrF7Gb7HtcmTLT9zvQv+ASSS\n292xEz0uAgTvP2FOCe7tHILN9cLjwufc6TongvZmm5HTEIY87spzJrZqBHOjc59sqV94pnHhGfl5\nM6Ndl3DqnZibNTrXI37xe6A1BkBgEpfeyHK6CBIATNbmW1tzQtc5HlUuo8eVprJRVbc7nJJLwV5l\nUa6d7bEzfcYmKjY3y8vm+Nr8VK5fkVz1KblTJ9+z1quchNrcM+19RCO2GiO544+F3JC8YuYQ17uA\nSMJ5ZdLF1mq66HpeKnGjlM3mCrUrm0M6mJvvcXc335LcLnjomcNoBpdOw8VUrgRABCoQo4wgJACV\ne2IB9q3ZAGa7shenD6I2l8rdmN91ZXxFGbMNz2+Sxw9XxRZ2v/Q2J62pTFpzP1xOha6E04UwdUxj\nNpeHXc010cXmU6fuTPJOtLmcMJsbXRzvE4JoQtTOxtbXUr8slM09eVN/vdh/vtqtEs+9NLxcp+38\npbLxnorQnf4Tby4kvfAV0LIQJ9dHxoQuz6czXRcgdvy0VmMkl/Hxf/WJCl3+2uTYmtTgBpa7tYAr\nmzvscNWZ9vv/7vl2dseRGrYCqGplnJWL2OTJls7ot1GDq8XmJj0uIeSPePo3l43P6z9BKupxhWK4\nu5tvARG66zdrliPU2qLB1ULM5jqRu0CMMoKQAFQuWGbc+Ln0Y50Hc+ecAvSZJB96+gC4000aXC/c\nrTXMnXiy0K7MhO7jh6uolGVeNge2ZtbKiiFdLWy6DCgfxlwpEHeLqICmFikJPqZsJw809dFAzuZS\nj4s2FzGNlkjuxf+oxBYNGxWhbZzlJ3wA/SwG/BOZEMzmyrlYTo8bkuiVm5ANhGGJq3JjjpZ+G5O4\nej1u2JFcgrNyfWbaiYLr/JLuVjSeGxO3rjzu6aZwXpOdQD1u9CsPcGwuW1zvCxIHgs1FECDg2XAk\nnTmnaljNMsxIru+gzQ0YHpvLw8IzjdsndBavB4M5H+l8Q7m+pHvoloeSFvn6dldzzU/2uAn05OzY\nL37f55c/+NrmzgAkKm6v7P0CPS5SHj7pvA3H5h4eUjvjRp5MLZS4U3Y686nWZuV27q9T6VhunO39\ndAB1vNY5nFRvvHeRhP/HFIXf42rpkjGRx43x8X/1GXZY4Hzu9Z/15Y/wBgn97693Yq4Fkh6Xkmpz\nDcHjcS8Pv1m0kTuQg7krZg5BmxsSH7dru+6citsf3ar6bf97qRIXcYWhauUku5tvzdvTX/LJ4HHg\n+hbXuxAg3R07a8fMZxKXfmvtqe08EYLwgBebAMV5MJd4lc1tGo/nU2yg3rE8a+gdLXvCg4VIrgX4\n47luO5b1elw5YEZ1S1KznCpok1XJ/B63snGE6j4hiGvgeFxKTtOyuTBuFJWaZWt07scr7ZRYvcGb\nnmTEBHY+lFnwuIQQIY9LwVJlxBwjrgy0/6R6BTaOyw0So8NxESGWv+/yvKiovo0BJJtLkUvoHri+\nhS7mdqzkOMnmosdFoIEqFy4QbC7iiiXrAzyTuLu5suCZ9FMbnB3Lz71X/9x79Tr3yRMg9C17gROb\nW5gGDt7msmG3jvcDQcDwRGM/aB6XEbO5kwdW2/G4KlwaXmWtY1klldu534ZeChiPxuUCRFe78usf\n9Hn9A9DHLXY8LiHk6gyZ2eFCHjc/xbvjaOkyvvYjua7o7qhEF56HFKZyEcQmdFZujscdtUrmJTSH\np3eU5fUBCNKRXGno6FxQTpcfNLhBgh4XAQj0czclx7nN9SWYC39crhBgPW7HIvkd292sdIVgVOKW\n0+YSF0KXf1zuvqf0n369vsSD/BYnwdtcIGw4+LnrXUAQcGHcJNTm5oR0zeFFMBdBYlRvvFe98UHL\nd+9yQGeT28bdX3zBbYuMKHI2t8wotis74eoMSx3+DE53iyB2mLJjUHSh9/A8kNrcLDSmdRH7iHpc\nxUiuv1CDix43MLo7dtLF9Y4gSAr45godtLn8UKHLFte7I8+WV5V6jAeuN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WwuUZ6VC6Fm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bhcnJvrIYXsWF5/cIgPE3MRWHBcLoIgdkCVWwR+d2GR1PaawVyCjtBd8dvBK347GGTW3Z61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fr4U0C1I2N9WxTDCYywxsrrt4bndbK7k5uhclkh4363Sj8LicWGxu+4Th8h0kFq1MeTIv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\ncqHLLne6P9nRVwPFGxEePC5wTSibKy5QsLlNozke1zO8VHnPyf2up+SO6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r7W+X06XLOc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\nHnEln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"prompt_number": 7, "text": [ "<IPython.core.display.Image at 0x1055ade80>" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets save it as grayscale also." ] }, { "cell_type": "code", "collapsed": false, "input": [ "imsave('watershed_gray.png', scale(image_ws))\n", "Image('watershed_gray.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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M5DkFADApGM+BG7QOP0lSq5DeO2TsQjnOTuKNSbQ/F/cnAEAMOgzg5HQax5y9iYmsDozU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\nlG+mUvsuZUzwxSXnxO61fKmKvV5niRRGoLO9oDe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CDqOUmUrZucMi9R63Bs0b3r6Zq94r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YSbk+\nw6NA7x0tcctqujDvnTGQe8CPU/iYei864Enyg632KnGyiyS998andTXYUTFd7KlD42noPZWKqLck\nVZUreUOglZm2sCnnL02oiWds++lJC0lM2rOSk7voeo/t9/trcXFxcXGRapJibwfx4csVn+HQ/Xjc\nEize/QpZ+DyxqWOSUAcJnDSQeyBASPW0ke2bhDFy06ZJepIltx2yNfKLpfpzk/VeummvbPFmR2au\nRkjvfa/AfqjlWAwRUVGV1gklaiHhU4mylMS2Z122pk3043zNLxVJVq3ez6ULtpPeFZ1IkN1zdmaN\n6Np74yblNnwluFcil67CbeKeRMHqPZj3zpdZVAcDHXG34m3KJlI8NHzPb7edlBoXm4fNg1MomptQ\n9JdragQ2Odc3YDtrwRARd+jckK1JmDwvrrH6n2etTCL3vGfge0RE/9C/tew9YgOQGkf0ZP6KGyrD\nPdOqrbt4SUSv6ZJEcijmkeSBxM/DdqPo7rOD/mWkYhsa/O0dt52YVPRnzT/NTcoe1xOR1uK/5BYK\nwrUJ/rDtiXOJIhI2Pibq1V55054SvCeSoJ6Wwrflu5i9sRG1ZK6X/CRvu2C5wLo3JyZ48EqKxxFa\nuZJ4iBGJYYsDP/5FPnor2IfOjVxzrS07h1QNG0sPcL2QVzS3xVsuL7tqPJe62tvEhxYwbDYF+kej\nykyqfe+2ebgzG5/L6P2M+aw1722IXP3+hXF/8TnRs+HDDz+cxLYnE6DuPsbx5HKbsis9svbNJLVB\n7Z0vkHtzYll3YrEqfBFmwYuyPqYkGL2nCpjMs2KsfYLovZBxbwLi9J4qoNR8015KbUpVCNqk56u6\nfpjm0L0lotvbW+tzh3XvGav3Wjaubn9DdKEpvjTrHkOUTTMlYWPiqdNYnHpvlExzYZcZVcQAnDiQ\ne0CA0LyXPsg+SCc76PWeox8TxvgpBGLXYi2b2X375JOpB5M1JhB9XHyc60y63bxhJRXfQmV6j3sE\ncAs+ueIzHm5ub2+09061x9v3Ohy9fup8IwbTm7qnnig3Fv7B+G7ivWhB7N75ArkHJAizVKeYaT7J\nvufx5vpj97hDn1SQfXd8+15I7/1D4HsJ02QJtjrqgYhVduM1T07AeWSPXPE1jf12+ODmZlB8rsi9\nz4mInpmCr3/vOgvW54nO3EfzBom7YcSxex0zEXsxY+ndWDthwT0Wv4TkO08g94AMTu9NUg/X5oLI\nnarhMO/xeu9VWO1RpIGvfPLTBHpPEXy2Ka+E2gvrPbl5z9fqaiJe1W1SxZ7MVuUVNHmlSYhMdXDT\nCj13nkYTpveM+zCK1OA9TfBZ6q+Dz9SIZgK1lxm8x/QKd5MpPkvvvXwJE9+ZArkHhDCih5E2Of5c\n8aIXF9yUCC1R7txXArVnH7qv1PKRJHAmnd77HuO4nSiAL8Kd64VVdeHcDWWqGJXxPJNCvXdBZEX1\n39zQ/b03K7fXe6rk+7wTfA43MxNRkaL3DqQfXJEJhBdA14D70tbr9boPUlhpIad3Bex74u4S4Xug\nBXIPSJE5dDP8ZfZo4xzvPWkijlGQffa+khUZviDSD/+RiB80C6av9OTVYonBEntlDHtClMGxI/ps\nrhRV101/K0jT7ZSerfgyZr9qK0pXGdOTbYm273A5nC9fBmqwtHpPz9nwxfMdAabuHlFcqgbRJIX3\nRLu0J6L1mtakDaud6FPzi+6K7lwKy0oKBIWA3ANy3gj8mlnx3g9ED5qNwT3eO79xWfd4X8vV1VVY\n87WTByuC75GomxGiqgZRUELsWaaQHGeuuBLL1fe+15rxvtf/scru5SCbwFPUH3lydvW3rdILiz3t\nTTMtdPc+T+9lKL0t0Za2W6J33uGabsgdx7lu4925qbm5j553If7rLDIv3gkvYrG2JizmuU1YdwaM\neQ/O3LMEZZZnxUzLLBsMuRJc8ymQ3zeMz5726fzKafZw98Qhl+5F79Dtj71On+7VD+f5+nkj+hLs\nfLJCy4yJ5B+oE32K3CtTZtmHqWy4q+y88im9mfOCdSsT3jBssob/9PhUkPHQYjddgX2mMeZ9brwn\n8hyTpYxT9F5zSgXFpd8j+q0dwPdfKdZiN4o8VPWezOAYnH6lb2t38btjE+FHYUJfYN87QyD3ZsUy\n5B4NqodpP3K5d+VUWRuidqqNBL2XIPcEU2u80d6RS8CMI/c6Gr33Xb/s+0i16UnlHjPA/kPn2i0w\nq4ZU7TE2WLngi+/MfFcrTu6lhKd55J5pok6Se42++5yetam6wxdTyD39lLiPVdN7/5X+bbzaG0fv\naea9Vu9de09HqA2UVXsRcg9qDxARnLkgDU8cn7jG7ZXbk/rw8ODXjVXlHqtFuRqt/tjtaLcLWvfe\naNkaY84xEhgzvktE3/2u17370UeaxsuYV8NWexNgd0jcZebzKmKltjfQsteOyYX3gngudczkaAGe\nPbMK8bkVbKHSe0SknxL56fmvKZuaZG4Noqx5Rg6F1V4EryHvABHkHkjFLfgi5zRwRc5tt6uVy2BT\nEVVV5Wq+Ar3XqDzaNf8P2p6Gg31Oz5/vmjVwjJ6X25Thc+s9sTVPToHMXLFxj/OIuc5pblqM8Ofr\njaiCSxm9V3VoiizoJXTwufJClqZhl95LEDhRVla2GsscgvcYrsvMK3dbYB1xfPIJfaIpPqi/s+Q8\nUuTBGLy5IHp9+fryte0r2DxsihgK/ANHExgtng/XHDN3b3sV8uJT4Tqe03OKUi+xBG5IYdrGR5FG\nPa91JN+6l3m+Dq5mcFgR0SEtIsUv9ioi2q/31LaaDZHD8lVnFd2ujR/LMztk4/XnncjTxJ7HPf1g\nK1u/A9ODaGzR1V5v2vt4OoOdiN995Zq+9Eu9GQ+lr4nUFgOtd67AundmlKwj9obe0Gt6Ta/t5C9B\nPmSPrBgKUZcMa1S20MpbNfDmPUsXlldt+ca93Bk7Wp0XZ+TzD63Z1r0R1THRoXXtHuJkX1jt0doo\nDTMY+fpXdZ06CW63AnuzIiJHbFXtPU01ydkj+1KKJz3X/mp0a+CHvWHPpfliWsFt7u7EYXTOUHtn\nC+TemZFRV8Kmc3G+TqnkOag8YfU7qqgytV4U+17wWQLkhXAVnyVvXEi2jeCXjeDr9Z7Ezjcv35nl\nunTLOC2CL8rI51vY2cT6Qn76s0xd18mlhNXfSVv2J59IR+zIQnus1zreuNddk246Dee0Ghb/Vnnt\nfgb50GqwY5oCP/zww+Dqg5e/rmvl3N5l7lIinyh/wTkCuQeKYAu+QAeoSTyZ4JObJeJq782Duv+T\nyS/7P6S/cuOvdJZp3os37qX1SlEX12PdC6iuzfC3iP+ulwradj07kTdee24i3iSfHa2WaLD2NElb\n743Hh/2flpTzoTeU7fsZ+xMPZtUALZB7oAymha92zmpLxOg7W+8VTE4c2O/3vCqIMO95LHzpdY3q\nmuqS8T9FUzZ6vRfrB7wkokt7soxIxpiqxInfetynbRzSDXo2dU1ytRdDwjS5NinZGi2PMaY9MtJy\n3c8gU4i9tg6LbdhjrJ3uhlDblt/tlmhKvaf0y58QjHtnDeTe0rk59g70qHqvrono0jOzrQBb73nU\nhtGQfcm5rN6T5mrQZ5+N5tEtpR90e14J4dfpvUgVckmXjejPVXwivRe1iTRhnjFDoJ96FLVH9Pnn\nhuJz30Ke/ON0wRcl9nRnrpOPP6ZG8Q16cDT9x7hxk89Fc36bnqmE3pPlwqm9MubSOHMg95bNzc2M\n9F4EdqE7xp2bqPfW6/V6feFekhwGPjmffTZ6DF8Wv6TI4L3QgJmWnHtJSoVXZkZcNxN0SzMrMB+h\n9uIGbb3cnkftedcSI/gybLHBonsf9xrvY9X4N0Ls3u/b7VnY1j3PU5qmdjebTf8c+n6+4BM9e2g+\nl09g3DtvIPcWTbzUGzMtjwkScZr3XoXmLSMiurcE39NTILGwFxU+vVcgnMWh+FaJKiLWricoyBKp\n9+aF2S8JJMSiuzLt+IqmU/XWvacnb1JuyFYkFnwFPe9MBu7H/gWOgs+CaXyneB0mceiaSblQe2fN\novtIQEREN7Ox7w3xe30vJ3fnstkaXAAfP2Y1NdKmbM+8hS9C7w1RPWPV7PpI7skdYeQsGSMeFhG5\nJtuqqqpKLLXGMw5mpJ6ztHov+JwX9A2K9F5knCVbZNnmY6KPP/6YCebrPhktNVd0W8g91srZ+U30\nvhiUnAEFnAWQe4vmi+afGQm+9t9Bv7jte+YHYr1n0yRgRKg9S4lIUzVU8ly6jdRLLeLhN+/9kojo\no2Y+tQjJV5oZ5wTqsqTq6vsUllpShp0RiL1YC40/X2PTBO0JZg6RVGSJNe39VricM23j49bid9Sq\nzBH373CCCrhzAYgCcu80mJ3eG8hK12D1nu3Q5Zqx15tbRok85z6U2X1y0zJ+7v96cOAWEXuppVjS\nz7K89l4S+R7Hccx7rNjL7aQ//9wdArGhTZtvHNR7Eute9Fnx6j2JhjP9u4WxdiG2DKHH8ge9B6YF\ncm/R9CrvC+kvRjdfvH79Wvi4a5v3HPY9O4TPYk1kufOi9J44M1fF4c5drVahGL5csRdQe8UD9sro\nvYzepngtFtWMlnJTlN4hzz4Y7To+v9Jd1TIy1Tio+KLPitebKxVyE4bwxRed1vWe1i/AwAcmBXJv\naXzwwQcf2J+KrXtTzKD0OlnMXHGK79428dljI6f3/LzWtEiKMzehBF/nuc2dK20EvGNm/ry5RMRM\nmOFZtMwWHbtRVWojir8pypYCXFdVRfouaV/nrl+SFiXg+jqk93LVnqgQC8NoJj6pjziy4EwP9B6Y\nEMi9hfFB/yeNSYKT9GghefQekcsSIbTv6fjNe4blqbTeWw9nenjVhuslbUpFkJgbj29qjVTrnnnt\nTUG+pabubJiC+qprKsOEfLF6b5y6z464vfw+2m3dSwn3z55mQ8Hw5boKsQRVV3m19w4RMbOnsQf/\n+Ej06FR86ufWQ2CG3hNYZmccPAuOAOTeEvlV+69i05tN8B4RWXqPF3yeidPs3BNd73HD89puzQG9\np/eGRfXeek1UteP3kO5Z9//mKr6g3ksK2Ss/aJrjjX6Ftlva0lYo+EISS9SVrdd6Pk93jeL0XrLa\nc2TlRCYVx3pzhxtt660/LuGafCa+0gGNRyy08vuk9I+6Zp7mFL1nt5z3338/UfMhMxdEMrNio+eO\npyP/oNF4HxDRr+iDX31ERJ+pIk8avTeFN5esydmtx8yrV2619+qGiDkgbaSSWSnfBL7XdWhS/B6f\nrzFIiica9ETZeiu++L3kBA3HENc6c62zHrwMhtLXhI12QS37ra2B/J2VTO5xHza3hNzubY7ZG+nQ\n215/2wzk3Xf7y9jc3OZWa0+3dqITpwlxRrBlenNt617THo8i+97hbgZv7F6v9IwrrN73jkacUpYl\nfPFg3QMqsO4tgQ8+oA8UN+4H7ZvnpAxS8ynGQkRW9QdzPrUrr21P+augZWysBzx7ETDvlVB7vH1P\n0SqGK7ccPgtfaqbG2PUs1IulG5osC599XfNdqL62Eirg7d6NB2UuXWdFE6XejtkOcucUTucyrUm6\nNU9p88GH2j9zp7ZeNKjqz9GI33+fEMkHRgZyb/Z88MEHjcz7wArae66ZJER6byLjnl3tS3/QlASP\n28cz6D11TE1vw9o+Jao9b74G9WpPXsB3lpSK3VOw3Iphl65X7yWXWY505Lp2YtPgMrqoQ77uzA+0\n31LGPZ2s8kgFo/f8hfeOK/O4rfsOXXm41D262htXA3qf3qf3338f2bpgNCD35o43LUOXEBID39FE\nR8L44tR7hgXF04gjsjXSYvfEjKD3vls+Y8PpNUvVezxbRu2R+RlzWQ/ijNhKPDnFoPYq8k4z5top\nS99tbAvfZrNRa/Moem+7jS6zHRm7Z6i97ZaILuny0rS4RzBV9F7fHI+h+9htiguxeC6qU++1/0Lw\ngXGA3Js5qtrjKrDozMmha5r39LHFZ97r7TQOvWcOpj67TihbowTSyTXK53O6Bd8v51J5j8OVMWDE\n72V2To5qPe7FnujJa+tb03rNrFUQ/9Yswkmh5kR49B6zz5HWvVfqlkyKBBgo4auF2/gxJ8X9O+Yz\nn9ozu5pGxtd2XlZQEkPwgVGA3Fs6hoHvSHvB4dd7Ppo50YjVe7bpJHey1JZkZy6n98reVz93p2V4\n9F5hUvSefcm7WMvUDNEY65GpzIIX5Yn5kfrzNVE7t64y6VrlbX+bzWbDCsKa6rruT4NbdRVo3a9e\nkXbCt8p16XzMkbJPM+89p+fPW8WXp/ZcdViOI/v+mPnMada8uOAeLOu6TlPUEHxgBJCZOydsL43T\nnqcMXbo94gu68SbpTha8R1Z6Lr2+VP2nnmQN6g7QPhSrww2Mh4HkXEWQZOg9KzuXVxaJd1uv9b77\n8+/+3BJ4LiWYlpzr85r9Q3xmLivx3eOfYd5jLq3nFOoFVojMtu5Se/pSPrln/a5yfddl6m6IiB46\nuWeJoeFn7jK9+eY9IrrV3w4H2WzXmTLsQrNyta3/Myqfmds1x+nlHif2iJz2vSgvwuog6Al+8z79\nhuh9T8ouMnNBHJB7cyJC7rn1HpG3KsuUcs/Se0Sk9EFevdcen3Ek3NN1jt5T5UiG3DP03npfUu6Z\neu67PyfVquc0/KXoPW+MVLzci1R7VjWWKL3nl3u1q51I1V7AOLjea4s0es8ckA01JFF7BVI1yCP3\niB4T1B7VqkW7a/zRes+eQW02ei9S7UVHjci7Arfeg9wDccCZOycS+nEiboiaS1UW059LRErVZW96\nbiEXbQAtVyMjWUNz567Hva1+3vz5ufKOYw6FltnhxjPhVLgYiyqsnz/XFvS0mLqunX2dOIPG3yL3\n+iL8UOycSjnG4Zev9oxNR6m9uj2bynXoGv9zinqiec87X27Lx9o/08EF7lE5tVd4Fj4AJEDuLRV1\n8GFGrJkIPlbvxWTp6sfBhs4Eypb5O+ICE2sQkSxdI9W4912i7zMf/5x+Tj8vq/b842pC9F6secHQ\ne0ojby3ft4POeN4JvnaqDKUZGDdE4YKHbpRdcNTfy9d7ic+EGppFUykHGM4R7meNUBv8Z+0btuJ4\nDK7gvSOE7rF6T5yXKyBb7wlShLLK7YCTA3Jv3vwqvIiLG1bxTerLpYDeE03drhyFK1A6UGuZjaJm\nyTDwDcNf4Xvqu9+l735f0Xvf7/96MjjSp9UoO7LGenMNhlP5stF7t9SIiufUnPHP+oU8TaDbHrdI\nVel3hDcxN8h+32cZsfVYolc4aQ9dd5Y7AZ+xb59HPNNIbHstx8zPVSmp9sR6z5m1gUnUQCSI3ZsV\nVvCep/SKNg44Rik7hm9quceH73VWH0n0nnIQ3vKuqSF8hiJJj+BrLE2+JZLvtl334qffJ6KfEn2f\n6KfeX3xERPTLeNHnD97LnkSNiPxyj0/WaO6LT1Sn5GfN+f7sOfNA0e7VE7u5/XqvL2hssnriDypG\nd3mWVQd5bTGHL7VE6N6t+YH/snm8usO5NPRefPweK/cY896HR1J7TPSeR+4lFXyS9geO8D3E7oE4\nYN07Dfj+WzqP7ojwak/kZbD1m/fpOtCULy4u+D65VJeY7cly06u9zsj3ff2tk+hqLIFUjXiKnN2X\n2j8NbeDec7uFVZras6xVSmQlp/aa6nuZKDY+72JJy0RWWSaiO+N9QKTXddjIJ6016YabT4Nz5n48\nF9veAoEzF6hA7i0WwVgxgwA+3pkroXe9KQeRo/eIHHpPJ9mdGxwAnXH60bT+3O9/n77vEHyNca/U\n9lqY2L2wMnodp/iYenwve33zUtJdVdoLt2rpSufZW+TV0H7k7CG5iztf74Woid+f4bPnz7XHGzWg\nMiMo7d+m/7Q0UVX3iN7Qm0DJpwx4d64gdg8AFci95SKxIMxA7yVma/AtM0/vjTvJxmfeALIcsfdW\nf/t95W8v+L6vfNYovQS1Fz1XlSCtVdF7krgw/0Rq3Pl1GJDd7IlSZhOMUnuuhqBroWQBGav47pTX\n0unlQha+TuI9V6XfZ2K1xzlz3XWWp8ZViMXJm8n7WkHsHpy5QAWxe7MiJnaPSB1UXGYW0587eeye\nazRe0Stv6J4yXurH4Avgkwyf5kO4KTxzyu+5TYO5N9rO+20b0tcG9inEBe+F1F5C7B7RcIZrUqu8\nOdG9q7fGt9wlNp4oVGcus7EVPenxeaY7131U4qdj54LOynsUEb1HFBvBd6u8jhG6xi5pZ5NPx72T\nrnreco/Xe76nzRuix/qRqH4kolpS1iaz9p7Euge9BxRg3Vs00xSny4Oz7q1WKztPY7t1zKwVrsXS\nwTbnN2/oDQ0yzzTwmT1iRjWWEXn79i29ffvW8e332ZeU7s/l46US581tz3BNJM/8dCLosZ6IiJ7c\nao+qgOQZ75nIa/qKOjNR9r1b9U3E0fk0y2dE9Px5stpjmY8zN9q6d0NNCcOaauHUaRFPgJhSDZQA\ncm/ehAqxBPXe8b257vA927in6D3XkfmLIfDt+U3zvzftG4NyT8AepXg4HA6ZpVVdWq+DjeOL1Xsf\nf/zxx/Txx7OpfUF3RHd3kb/pBQ2v9kzsp4yJbOCm75+XCPnPdLepP6z1XTL377mdoHR7m7wxohnp\nvdiye10/O5yi2v9kExXcwVr3UIgFRAK5t3AWofdMwZfl2UwrfvWGiOjNmzdGQPUzevYsOvYrncMh\nT/b5XboNuuiL8uZ+3GRBOqXe/x2zslLc3ek2I0mX9eTx5Io+c+i9XOWVdOEdBxxh3rvT30aI2cZW\nxcoWy7AHggwOjLgucAbWvW9/+9h7ALKB3Js3gdg96keg+MjzubHt/zS4hlavN5dIb9OtI5fn2bNn\n9IwsVZDuzRWH/R0Oh0PkPEo7op1E7QXq8Ulhw/j+TdYqpVOyci79O98PGPPxU9QWZSc2OH/LSHR3\nuHmLx+i9O+pWktRRcHovvxTL6cOcN0fAytz5NhH03uKB3Js1YbW3DL1nDMiszrEC5ge9p1oog2pP\nG5UvfGqve/FIRG4rRgyfRuR5HKImztxJNUlOqoYCb+H7NzmCL+vs3g2Sxeyz5LV+3A60neDslhZ7\nzPp83twnS/DFhO9VRL1i5LsKrxBkbo3neYKPn1RjNr1YdOwej37aFKm3sClzv93/AQsGcm/OSNRe\n0MOkF9+bR3/q7uzyH37XpDRrXy2sXiYoSq+I6IvAPhHsqUl1/yarPSdJeq+JjpTa2njuBhOf0WkJ\nnPF1TUT1yutDC+m9QrNq9IukdL0Rd++776rvvkJVZ9hzyLrQqmU3xp00WcM1hdoPRL8+DoLQPS9a\n3xYX0vEHzGeCzNwyYcnf7oQe9N7CgdybM1Ez5rr76xmE70lozHusO1daadlq0N46e7ZZqBnQImbz\nNEl3BB+aP4fW0at8SHTw61aN0EQbmUwQvmeZeS1ie612LtisR53ANte0VkjeSihZY6ghXVVVZTXU\nd9VXqt77nXerVZKP9zkXuxedVrMgPI4F5/xFPo0sEXyNyK45vSdI1Sgyq8a3B5kHvbdsIPdOh3kY\n7gzefZfefffdd03Ticedq+o9znAZpff8Kil9yg8X0UX7mi6/VXiKzGvfHfpFkmr2l55XI5VCyc93\n/MdB815nV+TvkM7ip7VRM53B11FWVbWW96T+2zRkQau0fzq99+6777777rv0bvvi3ca09y7z+3ER\nnwTHE1U1W/PedkuiQBKb3izKOC5ceq9vijV1io+z703Ct51vwNKA3DsBOlE0Q73XGxlEeq9hyxj4\nVAOlJDW3a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NMxUIhl3HgVKLnTA3JvaaTl4FtlzKbSe9+gxg1RueZbEnOnv33fuaCzF7y9\nJbp9aY6ZyjgqUgbiedQ+TfLnppr2vj/R/Gka0UOC15kr03t32j89vN579qyA2uMbbljxxSR4rF1d\ncbORl/2f63Zxg+6DvBQNW18weSEc1qVLK+jK7bwi6f7G/GyUQsxK7J5Lob14QUQvNnreSlB9xeq9\n4xZikWViQRQuCci9pWFY93hPgNlR2EVrJy+2XDX+oGTNd2e8/41rwYxnXs66l2Gm+JQ+/VSs+G4K\n1WvVLHtJwXtj1lnOTcwlIrq7uyO6s/We1ZdZWi/Jfmg2WUXlaZqGaShl9F5vi27grdoj1d9ckzJG\nuOWHfWKFt415zFxGsan3uvA9+pujTbPW4XPhXl1dXV3pH/F6zzkGV9aLDmnsXtakGpg24+SA3Fsc\nIr1nYPczE7pzB6r2vwRL3531iVPvzYuw3ru5ubm5uWkjgZKr7XXGPdXE99E01Zbl+NWeLtR9tpC7\nO+7TsCGqG5w3G7H061oqG2UX0HuiINpg+N7v9bfN7NBeW2Axmq2sGxNfzFhRdi4QM1+j+WcksffH\n4UWiUjaurq76uzren0tEjGekse6Fm/CoeblgcUDuLY+U6L2tfedPove+QWSOQVX3/1x+Q79hJZ+w\nUoTGy4gQr69Hmb+aoeHmRlC04Sa6/IqGFbGXJvaijm5pQ0ITUr8Zs+LywHATelt7XD2WLx2fj2He\nW+svjXPWG+JSbjgPTLXlXu/1Eu9vjmjai1B7V3RFRFf9iUzRe1zjEXpzx87UkC8G5gDk3gJR9N6v\nfvUrkceuurT03tHmzm0eVeP03h0R3d7e6h/y9j1z7JH1sI3ek3hzN5t4d+cN0Q3nrr2hG+XTzPD2\nxq6niL4pXLn5vX3v4De98IELd5u6wVadyDRKRDN1WtaCtuxVA/fVO8Z7l9oL7oQIz46u165TliP2\nWJHqse+NGrQnxKH2+CCS1p3rG2c9mbmV68H4V+4tTg303nKA3Fs8v2SHdc2814w3ht6b0JtbzMVk\nCr5vOAYgrS6CJhs8Db5UCqeGNjaYek83+RVRe7lEK9mY3t725VZV1bbVqsqxbLaM2J3xKRCuQD4i\n6iZS08SeL73Ddv2+88479I4u+PrIPdehxheJceO1GHpzQrJ2gtO9ugP3KGqvDcsQqT0rbM/TMr2N\nln9USC7AOgYp9TfBUYDcWyK/Ml6kzZ8wgd77hvurxOKAmuBrV+/SfMyHzvbuflI2R64HihBFxtjQ\nRul177TvypSumKjiXhpmXu4wllUVEV1c6KGLfvPeHfdhTH8WZ5VyeVx7ecVfP324FpZwaXlH+8fE\njFTM9eYyhqSM0SFH77mE5FBnb6ICLGmhdjztudza6xwpyyaTGA0HvbcQ4qfxASMiTlz84FcfGI94\nVqBWxbw0YjnG9ud2ao8fCWME3x0N3rs7ffUPG6dYa1v3kJfsV3ufswY+c+BqVIK0OF0buhdesFSh\nMk3vRUfvJWTlxiXwaS28bZdPamN9o3xvJ5Qr3LKfxo2dAndYs2f+frK5dh6F0+6VVxlatwMv83Rv\nrnq4feNOakqWFSmk9XpRxspm6T7wW2FO99+oEm9M6567DsunRO7APcO6Z30/XCmrVcer6rbrDz+w\nJAfvRSo4ZPEuAsi9WZFTp+KjX+qjOyf3jNt/ZLnX2/YcfX+E3rtThvc7c/UhuTd0sGsiqrnd8Yz7\n6iiu9K/iYsQywTeK2qNYwZdSgyWyp1ebOOuoUvXei19/lX7tWtMt92GsqSQo+CoiQTf5SBTUe/rX\n5gUXqj0zeE853kE1pDQm41oIJEgJvefajP+ET6P2TDvcp+TL0nggGp47fWqPKF/xjS734u11EHwL\nAM7ck0Hk0M3KzI8kpPZiwuBvHa8bQmP2drtt/rZTIkStoN97vcjWhx9GaaMvAt8XiriyfLlxbv6U\nCTUiR4bgrLmaP/ercSuP79CCI+YTSR6KA5fPVnvUhfb174LbYGGPN78xZau9TPzFoifx5Vpe1xcB\ntUfy5Alr3ZFPKaOXWY4Xb3DoLgDIvVNCG9t525mm90pG733ta1/7Gn2NvkZERN+gb3zDE7fXkjm3\n73/vXwkGnG3TxZYMZJfZwtoRIujOLVwxrScurvNYE6g5SJmWpDxutbfuJ53wXj5O7TUtcRB5+s1g\nhfp3WJm5slkvwsTejIMgGyVBNG92kAz+zv3VC08FFq22T8i4R7TdmkF8UYJv9FQNiLeTBHLvpNDG\n9t0uuHw5vfe15k+j977hy9HoiRtgbn1fOvSeOmQovatlbZxFRYMSeo9N1IjRe2POqNEQFa/wgpwG\nPjNJm4iIVqtVrJUs9KhQOdVeK/VaveVPwWC+NPSMtt8OrUdfcnVYulrI6uY8u+JAP29VFTEBDnf7\n5AcnHEvvJdLMqLbZEHFKPTjO7mnfMsK+RZOi9qAQ5w/k3okxjO1XRI3ey52sVsLX+leVTOtRpPvq\nNmZhP5EjkWfolIijT4lkM6QViN7j03LHnlojrp8POnMVXhCRI3bvlvswKRI5oPecDyVr5pULpg21\naoa7CRq9ENcejH2I1nvVIFj6Kka+jiMkxgo8vBxV7zEZtDI2Lqm+IJKUG4L35g/k3qnRGviaIWNn\n99l69F4h897X1DdCtZfry03GGolExr20qQM+IPr0Rj4dbp6vOb8IS5pxL2p0EFn3rq6urq6uouaq\n6in+cMPKDs2YFupF/Xa/lubt1ZXTtEdE1/yEuYxDt67rSMm1YaaAdZ5L7Zywd4Zs4/7Kfk7BN2Lw\nnmAOtRTkA63AvDersnsdsO7NH8i9k6Mx5rxq3gT9uSX03te+Fl5mBKSyMhOflSUkjz6gDz6QhlU3\n4/MIIXxJRRmjSOzpeTExZOa+jV3hwbNaN90sGxkpB0MMn5RWyShTaeyIiK7cjtwGTu+t+X5crvcu\nqHI89LjOpZpP4zhvQr239+mbIxj4mti9jIp7jodH+wq5tnF8d+4vkkx134bimzuQe3Mipw5Ljz66\nj6/3dLHHjA/5ZVjCxAzV5v6EjHuPng0UTZGr+z9JHK/CcsTgoDZxtgW8oV7xhGNPTdL0XjcLi2Xc\nIiLHlLZR/aZw4Z0u9fiGEJpELQWjurXwNw38OYvBq/eWyPh6b4LEXOi9UwRyb07ExDWFaM17YfmQ\npfe+FrbsTaH2YvSeMCbq8lJ1ezt/JLfeHY2xY/eK04meaOteF7wXkWZgYDcj5f5Zr1srHmfK48fo\ntcvu18yRqytJTd7mmXkf6ZGkTb1TbhHmvV33qzJFWNz65mjmvUlIDhAEIAXIvdOjNe+59F7J0nuW\n1hMPs3lq7xvfILUQixNmtJCqPeNHfL3SA5HvWTtFCVr7t7WLNsQxW2fumJTSe7bqcug3+Yc9lt1w\n0HtZQZyPj4/0SI+PEckem41rbhruRO6IGr3nU3tlCodPr/f+mIieCaoauBGb91yCL2TemyB2b4a3\nNMgGcu8EabM1XrWCzxg6LOGSbt4Lqz3XkJOp9pjAPamdQT8ZW3oIJ2oEx02HrCth92vHA4Hec/ty\n5Xpv/LJ74SrLQx2LHZErMZefMbfEHEHOdhSKzrNlYHRJvP72cYo9kQqJlIptsGS+W3YM+Es64rQa\nf0xEz0hUxsrJw4OkW2lgBZ9f73Vqb8zrlZhnC5U4ayD3TpKAgc/g+fM0xaerPa5S1yP/kP+UrfaI\nSGTeC7DNnAW9sc984BB28Q/hj+bpku/e8SL3yqL0SG+JrLJ70dNsRKMPov31YHtKY8A1lomNSQtb\nJHey8N4ovXfhD9zb2u6AXghNoA95696Imbl/R8+4mbMjeYgo5rnd2pfA23S63ia8heQpc6HbThLM\nmTsriuRqEFEfsNVaSjQRwfYBCdPnhlI0Rgvba7IT/7th4XvYcP2f3cD7nWqlFB/8fkmknyneB66M\nRpy4C9r31nq/bp6wQe1Zs2xq+LWePHgvtcqy3BbwB0rqLddmNMVkmfe+qry/ZX6tXu3kVma1otra\nsRbbAapfzsiH6eF8uPTajmQBvhGO1ECWxpbI7DE6tec/wVG+XN+J4oaoMSfN/Xfdi/jQUR1TDDsl\n3Jr0maL7D52I7XsT672a6B+TtwhGB9a9E0V34AUf9xPUnsYElZxNvmFY9zZs72dZB9phaIiHY0uZ\nXSp/Pahr56RdwL63XtN6qOKx9g6RvgC+gGVvfGeueGz4g8D3YbXXc2f/WlMGyU3SdGq6bp6mLLH+\n2dp6IUeww7kKxCY+J7djqrKZnH1vkklz85C7xtdEnY314iJga20ZPz1MekdbN8f/8r8U3hNQDlj3\nZkU5615j0xkKOyhKopB1TzHuOQYql3rJHiha8x5TeM+271ktvNkrTTsxBr5G6b02PzDRxiJG3Pn6\nZVUR7GmtW/C299t7W98xNj6BEzdo3ftw0HmJ9j2xeU+x7qUZ94YPbq1fm9c63cCn2O26Ac0ScO14\nzpiU92si2kdqPvV0OBTmjojok3bLXlee1LYWEhdNE9R6jN6VGzi7Eea9wHmaNH6vN+7laWtO7DmS\nt+2P3gwX5p62zE0vte6lG/dsvfcL5sOaSL3QbbOFgW+uwLp3svzSNc06K1yiw/cEpZUdg1a+WaCR\nZ+lllk1LGT9XAYXNe7rlwdZ2HrWnx/GbUf1bPqzQ+OinPy2k9hqV9+GHo0+Z+wcUa1ByBuvdmh/Y\nBfLSDXzhJXrrDbOsOpduQXqh9aDvAUOhat1ME5SqvRiSCu+NYuH7d4XUHotdlMdRIFux8W25fqBV\ne2EDYskqDETGU103E0wdmC8azAbIvfPD8cgXqff+5/DSM6rWNdV194eIstM0/DAWD8YXZPefjsmp\nYtFzNpxaryaqa8+d5/bb6g/6Py2UoPExEX1IH35I6cF78aR7EVvujPecDSgnxsA7loo8dcnGPS+K\nB2DjlHxlqqAEIka9FNoDIle6xhjWvX8XXiQT/SFvhGcCjQzrnsW3iRxW/Fr5j2DcmzGQe2eC8vjl\neuSL0ntDeWVPSdu6mxVMmS/issQTZ/rcAus1r6VMvdd2lJfmBz2Hw8Ge0/MDaqouf9D8jxd8dVMa\nh62yv/XWXrXUXiEavZeDMNSnCd27ILq4oAtGqjDd0a/N1y5zX/EKbe3uPdLj4+Pa01MWyU/NjDQ0\n24zU2mJlCAxst9yDkbQ8SUG1x1/aeas9d5tQBJ5w8HVqbnHub3lqy6A3fIDgvdkCuXe6/Mz1havQ\nQITeG1y5kePUtoyHofxcUpH2Pbe4+OCDDz5w1mZRu0lG720jCu3Pq/JKVCJf56vyCyVH2b3+kzvj\nC+6S5Jj3utH0kWjr7Siz9F5VkfeRiUUb6De8x0/Gmzdv3ryhN28M3bfd0vDgwdqIAkb6sb17Y+bm\nZuP3sq6Nf73c39tqr48RDuq9ks5c1bDnv7rQe3MFcu+Eceg9570qT9dIVntENFe9x3NpvSCiQqak\n2JtP6/jFam/Oc6iZ42IfxNUXcdRseQlF9zL0nm/M1gfavALF0infOsPaS3N7jX1PCwGIM669ISJV\n8OmGvRS159qBm5uoHVsmgdYQE6nIWfYmmFODOLft4M1FpN5CgdybiD89xkZVvVf3OM17WbPnxjCq\n3ksdeg3zXjfIsfuaqvaMftLnIjQxHvOL2vZKROwVrcuq6Z+v6i/cmo+9Kjl6rwuNC5vOppiSos8d\nsDbVHfiwnykD8ht6w4US8Gm5IWoiOyT25iZR8NlXNjtR4yf0k9xVpLMWRu1lq72MntbUe99u9d4v\nkJexWFCIZRz+9L+0Au+/9Drvvwh+VrIQCxHR/+r64nP2U7F5L82690hsZYdE3N5X3fSiN3F3J2vq\nR09PWUjtEZH8Wb/p+X9K32/fS/WewLhXKD9DUIzFKrtnuqPay1MRqWer89/qhVgElVgaspKDHoiI\ntkRmWRxG3iXFUvE3ED+idmLLvnvbA+/lQWro3DvMZ+rNyuTlVkRET9UTVep53n1J119eK3eVovK+\nsDYRlD7Mhc1z5/6EiOgvjQ+12L2MzNxi0t9Tg0W6oZKFln9BJHPVIltjnsC6NwZ/+qf0p63MO4pV\nL4kXL16Il/2f4UVsBrVXBLc712dpcauriOA9u+SHiIyn4r/p1F6H2Lonr7E8Af/D/MA/C1lHZ9Az\nUjXuzOVGe3i9v7+ne33wPUKcfK+1nNN8dbdXcqLE75nPlOce3bhXVb3PvaKqGiIQd7Sj62vnHZVg\n3yueh8NRKlOjnKHXWYPlOIgs+NxkmmAeQO6V5095iTcn4ccY914Q0Te/GbuiiBtbHYKKhBCnhe+J\nI2dKFjEgIpfaE92BjSXjpz+lzrhX1JebOpeGgWQwsPSeCM2658EhCgoNP/c5dUkiYfWax940ohyq\nql6e7bSPmbNaWQt2kk+VeLZ1L8wI7lzLuKeRbtwr6Na3G1zsfBoZvZjDWv+P9I8+6x2k3oyB3CuO\nU9YdQe+5knMt60Bn2fumcMWNeS/21u4Dg4pLKYO2x10RY/Fx6r0ytffcOEwuoSCev/mbv/kbs8Ze\n4azcCfWeCTs2Gr5cQ+Z5RJ9L9YyQsMF8XnCctx8NdpLAuU4epNqR37GduRUNWu2t/rELbT+vr4lu\n9IC9pHSNsnrvJ8rfjvGL7mXRFM2Ps+69HqOj9eq9pl04g4jAUYHcK41H1M1X7w1u3G8KV/w/iaoq\n0rjXi72x1V7rz13x3j2pfW/8vWzx3oRdiNJPFUdujNqbMjE3rPdCc+YSeYxxgsTcCfUet+h4GRs7\nUZZEljP3HTZyjyjSHLczd/T62roqsplhNZhbOde+Z2Rr/H8zV1eee2UazKtG8GnWvXHTg8y7+Req\nvY/Xe3DjzhvIvSmZkd7T+XR4+c1x9mO9ncy017EiR5SdS+8dybznNfCZAemlZtIYhwT7njVkOUeM\nr1ovIogYiAy5ogTpSSJPI4fgiPFRnBX7WFDtRY7fFb+XRkHyiwKTqmTBJuXOz7q31dQeEU0au/cL\n8y3n3dVk39BYYN6bJZB7JflTV9zesMBcMN25n/aK75uSn38j7kFO1TMTqT1fOsXemryyQaz3IkOl\n+sklnbgEn51+GGXam5pQci5n3FP1kSvM+6u8O/dOul9EYuGy0+1oD2pKhih2b/yKLMREY7Qtckup\neRq8ZU8/aRLB6Yh5c9wxF2I73zjRiaruK2PdK3r5rxTzHhHRVaTay4uR/oUg177Re5WStQPmC+Re\nOf70T2ck5zpcj1lGssanioXvm+G1foO+EbEPI08MGUMzqQU/h5lFKWFaa//MkGIz5X6b6FvfSv+5\nOlQaZ+urmk3Pqfd8ubmi0WjX/dntdrsd7fQ4s+J6TzpCii177vmW/ZRSe5Yrt0OVazW15r0LEiu+\nCUqGZeq9ayKiTUG91zwlXl0Niu/nxdYdjSn9/pH+8R+JiP5X6LzlMJ+BeOEIpd7kelCcrKF5dEti\niL2Cxr341NxG6zl1l3SFcZaGmrg5JidiurJ7RET07W8RfYvoW7zo4yP3hvFRtaSZNireg3sXs3NV\n839fyOmu+2envh0Y9N6YhVgEbeWZWY0ly/rFpGikIWvkj0SaR1ei97jjy0/O1VH0XnRirqfwTDad\n4otWe2N6Uv6xSdpwDDDw5s4RyL0yiA17AW9veX7G349cneVO730ztM4o057+tmgH9GWk4Av5VM3+\nerJkDWLvxL+ZZGbQD4vqPSIi+lb7n4ErTyPBHtKLvzvt44AJqK0NV9lOY3P+B4EhaxS91xufgzxz\nTY7zKn6rLrEXbbWJeqSJy9coZt0bZzKN6+bPuJ787466dgY+Xm8A5ZSXBeTe5BzH4SuyuAvtexlq\nrzQxes+wmXB7NsLz+cVFulmvgNoLVlkurvVYpedns9k0/4mW/jXRr3/NfhMlCdS6wNftpWdU3riT\nvBoTftRduIGBQ31qeq81fyWoPba4Mulq7+bG6aaNxg4uTHVB/yDWvveTn/zEM31aarJG/7DgaMDN\n0SUeo0K0dS+7vukvrBcmMOMtCEyiVoQ4CeeeTq30JGpERPTH7b/60MJPo9ZUZPln7/pi1N6oxj0i\n8ig0a1AxhlE2P9eQj47uMsZxdsHtCou5Q4VMe353bmm1p5ya/8f8TlKFZcClkk2ldzu8jO/Nunui\naUVfsrpKL0OijNk+dfpAG5n9T9VUzgcDh9LS7+Hm6FOMe79nzXvG86HkaDxPNuq14e8Hf2Ck69pG\n3SWMzlNqLStqT+7Lfa4czj3R1j6MpsXcb0kY+9lhnUxL7YUfjwr0t9/+RTNXLvulW+zJSkKASYF1\n7whMbN/7u/ZfuWvmm8W2Pbrac2P0lrLYuXHCb2T2PeNc7X5YZNtT1t0jbVQ2jHx/EKf2bH6t/OWJ\nj1+rqkqNuopSe142G2EZPuUhzN1MePXBqb143nHU24vw5Mbar/nl06xf5eL38guxbGlrtZDuqLb9\nHyFhtRemRH/7C+WvCdTesoDcK0Gsfps8gI+hdQTp8+R2777p+aFi3AsG32jN6/Uoas/tzlX6S3mi\nxLUWxeXaYfnQGl1fTFEcP8wWfGGtV2hCDaK2uJpTceWKPWqUnk/tpeUrXAdE/o3LnStyPYcF31Mn\n+PJyeZLVnuNzU+15jHt1KPd8tTLKIjkW9KmhkaaJU4x7BQqxBC14BecMpynnbYbaOwkg9wqQIN6O\novf08L1nz549oxf0ggaV98L+jUWv9i4uLiLEzOvX46g9v97zjaCips/vcjtdx5s3RG/ozZs3gZXE\nlL3dNRVAWtGXqfc+oo+mM+4diA4H16CcbdprQ/b8as+q5xvJNd9ebm6GEL74aXPDJr4nIqn12UIt\n01ESsdprSxvVtUftibeaooZizHveWXKjfLnPO6xv1GOwYxLlRyjoNCYp8ZgC1N48gdw7ElPqvb/r\nX5n5Go2Jj1F533Su7L+3/x63LL5KWO+x45B0LjWe1Zs3b4jevCGf1mtKiiWVvS0RGD+pH9enswqI\nPTd37v2QjK16O3Yolpub3sg3GPvktpWQ4HtKE3vPmiK8OYrvHT5NQ5DXVddGFnHUIbiWLWv9srBj\n95RPYqx7tsob2DKvlG9lU7MwDwl2Xu5E1r1Qfi5YCpB7BXCnXnRsNlYcz3H0nkFNqtiTGPdavdcW\nUQjZtfb7VlWNGrTnS8+tiWIFl8idq37uUr6xingtmxhVyrRRe4wJpwveSxJ7YvFw6/lua7zbbreq\nweXCLvHr2ewNEdFLJVf3IULw+b9Os+y97XWeIviipN87fMU9QdheTRQTI2GSNvNHLlxG7k+6D//d\nYN0LGPc4mx6HQ9j5BF8zTDQ56uYdZcfuTWXdi1F7P6Ofwbg3VyD3pqC9LXXBN6He+2Pvty84ofdN\n9/L/vRcyIbVHRHvaE70eOUXDV3/POSKVa/qO0xCn9jYb2hRVe+EaLESj1GEZyJhgQ+fXoaA9Fitu\nXo2av3DUfXMLmC/ohugTUmuzyPSeFbpGRKQ67ZMu+9u3qmjqVV5KIRYDqdoTUqj6g9N8LPbmOuqv\nNB9HJGp8Fvi+1XMJlsoN0TBM6Af8cyZTI9j8sguxeGFC934GtTdnIPcm4NghFm61148XMsNeTytw\nZIVS91Pm4zLEWyDC+bn6IQlkb4gN20z+On/FAT7++OOC2Rom36I/KJGj4Sy0R+SeWGNr5kJqTjZf\n3KnHo2vpPWbEXTUCr1U57QvX2J8s8N8SxRvJ3qF3+ik03nnHMZfGBFNiufd7xOg9f33l9Bk1mIPR\njcj2147PtR5gpNyUgtiyDkJv3kDujc981d6AXWD5m85lv6EYriR6L6L3fPEiUnd2xM+nJuV193/j\nQw1O7xUIbhxf7RFRoexcfnT6g1S9p0muKMueZJj0iYraG/HZlMa8USP4DMW3olbprVaqWc/aZLrR\n6+3bt2+jJ/midxq1904r+JzTpvFqT+/EcicE9DiB3ZfGfWF/IFJ8zjyN6Hk2Qua9IMxB8s97PWwV\nlmOPLL26+9nPfvazn8GsN3tQZrkAAbeselM+0GYYHeyYvzHKLGtqz67i3/IpWSa+f3as8BuKkgnb\ntWLGpRfDvkTjs8g9xuZq2OpRdYtw1kpb3CmfJCTZlVB60uC9Qg5dZjiOFyUtxgXz6r1b433bp3VD\n6v32XntPRMHH3MdmF6zr9oVyhyr1+LqL5+9N26Tevl005+utxMKnLaGdVOVENc3S5cyVzorrsO1p\nmjZK7TlOinVu79tyxPGrIqJwueWf/KVH1v2l6syVNFoteC8lENE6TLMDMFy57EomKbPso3PnQuct\nA1j3pmWzUSL4jl99T++oDJn1zW/yP/rvyutR8nOTLHzBfA2LdUzrD/SbzHlQlHB4eBzjOX1itccN\nxqmuSvOEfZVdysGBSI2H3zIVbkMX3lVH7oZ/Hmu1kOzZ2XhCik7PcamRylkgkIjPyOBWIvHkllB7\nzEruiehek0Hvv68vcUiN4GvmTvN8H1Vj+bmRmJtr6yQ6vqEujdagB7W3ECD3ClJV9rMxV3Wr+yyc\n0Ts6Xr3ncugqxr0CUWv29pOsewn+3HWM4lPSTeIDoKMzGEsY90SpGiWTNQrqPYOgN/fO+60/kspD\niYHcSXu6ok6R4cVV2pVXp2Wa9nTjXhG1R0T396q4uye6J0vsGYLvcEjSez9R/jqIqrFsJuYmZCgb\n7dEaJMpE7o0eNf0z+hnR+o/oj8beECgB5F45KiJG70XxSaFdcfFkfdLpPdai9s+B1QnEXpQ774Vn\nX8KkxO859B7rGX7dlYpO6UT948EYrtypp0+jcqEhcYPnXf+n5RAsbhasudjH77l3xbKlhY6+2SdZ\nepMMZudYX2622gts1INTtrzqpN39/T21wk9Xe9o/AspNp5ZAHSn54sp1J8yfNhV/tF4TQe8tA8i9\nfFqfbNtXitwhtKH/whn3RojdU0P3bLWn6T0rT4J3534nZvMxtotu82nWvVTW6yif7mtxVRnd9Bk1\nGJSYLleq9sbNzU0h1lRyR3e63gub8vYhwVdbL7g5EjrS6t3Gq+PwwxNXdu9lI17rOmBldvdco9Xz\nbQx6Pu3zmwJb8U6mIVpAQk4tQfMEzz8tl4iI/uiPepkHxbcAkKqRzb9/ooroaegrVVHlDsn4z9yH\n5eVeSO2FxtZ/tj4Z1J7EkxuTltu9SJV74fopLuzBP9ZUyNpstA89o4HVSqZM1KCyxffMgSo+W4Nr\nkZLc3NvhpeAhQ67x2wtnSz0jWSPYl94T6W1COqj3h2OcTPVMVc0ecda9l0R6upKjKYrkXqwWd52V\nYH3Azqxn6j3naXYnawRSb/+S9EyNKyLf/rlqLEfJPX+qhtEwXMa9UMDfeL7cP/p7Tt39/WjbA0WA\ndS+Xf9/Y82KduKzaOwaBTuqb31RD+L7jtO1dXbWl/Y0ZnXZE9KLHu6lPjX+jSa/GYt8GKdLxzZs3\nuklPGNlo99oFrHvTu3KJaDSzhCBX47b9d5hxuBACgbMhSjl04cO283Cse9fK1nhJ9LJ5hlSPgjPy\nVT63RHDaXyfOQ5TO/iG27oVSc93oajA0JZ1zRo0YIez35Upb0kSTqNn8Ef0RbHkLBHIvk3/v/Xbr\n7O1+VHxPUgk9lH7zm73e+w59x+3JveK78J0q8hKj8ibAuhEi9d6bRty1/zDTjrhPMzeUlvDmTkdR\njZeYHnEbt3hMx+fao6H6nnzc1a3A5Z0rWqjby5ed2AsyUm1l9wEGrXsunedobD61F/bVxiTmOolo\nuVHVBZ2ReyERPu6kGjaQgDNn1NSzk8ev9WhLRCvXUPij+dj3wo3gm/9Mopi99LnaiYg+PZ4aXNM6\nGL7vZ1B2F/2fpfBxlje3KY8xcprGV8Wllt+KbHv7GMVXu7R678592BDRIf4cOLsHFoFjXBVJbqVn\nH8/TCHovr0k0wlVq3PPa9gLO3E+LiD3ytJMQqm47mG1ihnkafw9ht0gQu5eBU+01QXLNE5yrP58o\ndk8N3mNj90im+at/stWe6qtUhN4rulIe3jUFGPTTvshK1HBY5K5k84jqei/ZM3xrf+QZBepH9hk9\nP3gvzpmbo/e6Fr6yR6r40D1XWwypvdv+ldyTG2HheyTeIjOE722IJHWWzccAgdxrD8g+k9q56uRa\nr488fQnXHkNy78HaYoiNX/wIg/csvcee44An16/3tB6nPQ/OvXP6cokoIn5P8eZuHpQewG4PHrV3\nxDLLpt77eyL6I8TuzRw4c8egqiik9hZGxdj21oqSU0Xd1ZXygW7vC5jvXrwILxPPVZLRMTXv4zZq\n6Zpqvs/O9uYKq+4VxFcUTYanmEUoeO+uexERt7cP5uf21MTHWymz54rWE2n03e2oD0SUHViq2gsS\nHb23KeM6Mgux8Io6vQjL/b0/js4keQI1ZUI96kaHzWaz2fhPbZZtbyK19/d///dEyNSYP5B749AF\nPvPj33/+z8dI1XAZ9wQ4Hv2vPGHNV8rfHr/lLq/sHvEWuVDktZNYvfceETnUXnwV1h9m6704694s\nSrGUkAdlszQGXHpvIKT31usLpuZeyL0yHI9l3WNPV6hM3ePjI6P2vHkaLRuKEIqbzSa0dPC+bJWr\nPbNGaeLudK/eczThdvJkQ+9JEmB8ai/889GC91Rl9/eQeUsBztx0/JF7bf/p0HuOH41aiMUt94Lj\nLDcYtE8Kr4j4rpv9QqT36NMXn6a5dbmOO1BWQcGw9cS5c98j+i3Re65Rv5mF9VEL76mJfHdglkM3\nOjFX4s498HvrG4EjnLmhVijz58bpvTh/ruHOVQqx0JbuaePpTd0b8p099WDinLmunoSXYKLAvQdz\nk046HRJQh4Gb0lGJZcWFSDqduYGwvUbCK3d6dyY8++Z15/KHPOzxEPnAWpatxpAn90acVUOx70Hu\nLQXIvWREas/VmTv03rhyr3IIPlHsnvF+GL5euR7U2S98Es4y60XpPVfgXr8zYXL03nvtv04jz2N3\nnrsRoXnruwMzBF9CHZaw4OMzMrz2FrncC7fCYLbGLcXJvWjfhvaDQe1t70OFnb1b8pxA9WgOpnXR\nLfdiPbll5N6mWWhQIWXkniB8L1ntueSeb9ey5F53sXm5F6X2JP71KfQe1N5igDM3lTHU3rg4Z3hL\niOURtJtXrB/VU3vP/iYvjK/Z+qtXypsAURNsxMJ6dFfe561py7GIHLoHs0VbH2jEF1nOJWKLpS72\nVjCNhw9/I+g4+LfSPcq9T+LiK/aPvYii99Q5wvPUXq/yBMm5ztC9UAkWU0Hn5ieHu9LGr7siYvqa\nOEd1ci3EMvy98S+YP5B7iQRqsMyGv9PeyWZ4Y/D8jsuEePXq1StXf64JPrX0MmPLi9B7jHEvJWpP\nvSGuE9I1BBH7dV3XbVbCaMb1yYosB0Yoqa1NNOOooNYyjaswk4v1BH4obAciVRkQe/x5lui9Q2wI\namBx4e0pKcXiTsz9y78sMkGagj81l8W8vDJ9T7OswTLQpGdA7S0IyL0kQmKvV0ezc5aPUF7L3W2/\nevWK03xdSkaj9Yb5NnL0nkuZ9dkkUu2n3hFyb+5774WXUREOnKnmvSS1F3bm2q25UOC88HTI9N5E\nfBFepGcT6mf1M7tu0D4rlaLgsD/J8riKlmmVlUfikk+izkXQn6uQ2TtyWTAUMQiYMtCv9o42p0bH\n3/89THvLAmWWI/hz+qvmxb8XunJdHLPCMhfAJyi0HMUr9eUrVmiZOu7Fp1m+W17tXanbvoqO3xOp\nvfeaDI2G0j1wiblzSxJXFVhoaYtofPJiyyOxT3k+3hBVQT21Juoa37r/aPDXNSd+G1c0xMblbBTo\nnGOVlOKsexEFrf1qL+GEfkYuC5/j5Lr2lbH46uc4ZNt7OLI7F47cpTE769OM+XMiagSfT+yZHafd\nS3rE3ghlltVkDSJin+PDA647U4NFF1YSw5pD7UmTNUy5d/WKczFL1hQn9wyjXqzc895+6VovzZUr\ny83Vd9qvAWR+1ZhnDb/cu23+kXqQk1wb3Y/0tFwnyojsE3yuhtD/vD3RmjoxTlx7j/rDx+xvhRat\nsNqzxYd3V8J3oyM1l2JyNUJ6T03VUM5EaOc4vTe12jtyqgZYHrDuifnz5u9f+S17YbU3MabaK4Pf\nyiF00wxkzKTBwldWFs2uETWZWqQL1+QoD1vf+Sciou8Q/VMzL94/dV9IplJTzXvTOnIbvhpOz5VO\no1YwkOXeqfjUEdlj4auEjS7XuJdWYZkoTe35iOki3jdLsdiL/CA1N3d7T0TX9GWkH/czcQRfqtqb\nddgeWCaI3RPx53/eqj2iP49K0mB6ydnMlTsQPwj4242htYJdO6v21q4vZkxJ70qGI9c/o8Z3vvMd\n+g4RNX+V6VKEtZbbXNywAJAY96IrUB87fq8bpm9uvIs1yNqDJ4HKay5O1m7axmWLBZ9MIpu+wOLv\nqBjN5jkEJlELch0ZtZet9hig9sDIwJkr4c/VN95OVmLcm9aZa1n3OBNDbJ3l4FOCofCCnbvuy103\nM9jv05y5ntQRyaqivLmWeS/GncvffH9NRD/MC9vzOHPtufAG856w9B41ex6UeyK1J1jGwGPeu+1e\niMx7iY+6w8+0ZA3OvMdIIPvuq4h8mbvtOpqzLam85+2frC/FMidwuTm159sTya3oqrNsLegVewHz\nXnNK3xifBnZP7Mt1j6/WFY/05BKcuSAWyD0BmtrzdmJW5xln3RsjdM/Ue7xDKTTs6gcWq/biaqI0\nQetrItrLrXuK3kuWe1YPHC/3ooL32JuvkXk/HEXtMVqPSNF7Am9uXyW2SHnlpFgSt967HV4KBF+q\nZ0P5nVJpmVmQG47NWuftjZUk9xJi98wvC6m92mrLmTX3iKiTe+EiywHbnkTvRck91rQXmZRbQO1B\n7oFI4MwNk6H29Ns9WMX3E/E+laaIX6jH7iyjo/nWRKm+XPe2/PPn2kOur+7ee++9955VfqVAZm5T\ne2VKtTd8HjVzrl8AiOxraZHDInfu27DeTC6iN9A7dMV1lit9ftqcyh+82vNi3ebSebRDas9coITa\nazF9upEWip/EFGIRIS+8566vV0LtARAJ5F4QXe15hig2BGdF1NTRas71muhHBfdNgDBVo6Tei9Z2\nOgWGYSd+wRcBm6RRog5Lfu0VV+SeS+35vzJoB7BDyLknsO5Fh+1FEdSb69SGpv7MF8AXNr5Iqp5r\nbUpRlUlnb5wzHr3WuA7CEcMnQzaLWj5c/+nM0tgbLe+gT03zc6g9MAqQewH+/M/NTyI7t1V7klvL\n3nrqVI2/Cy8Sy1SN5nfyRRPmv8giMyXXzVEq7X0nQu/NmbvhpVftNQ9fJWfMY0SDR+01Kk82xQ27\nGmYSEpmd0PzZCFXXg7xKr69MVLzGcvT8d2JXrjgn1zgisdg7ep1lsDAg9/xYYs+DwzFijCqTJ+bK\n9J5fxMaNCpwFLcHiF6H2RsIZu+dQe/mJuUXUnsO890/8xyoCb25v3vMiCd1LNiiHvbm7kG3PJfVE\nDV0Zr/vgvTgjUVVpLl2xndGzla73STmr1hQeFj4PKtN1OHfilVjsDQRnUctNy7W5cEVRP38+RgUW\nDZj2wFhA7nmJUXtzxfDmugY07zAhDfHxIO/l59MmXTZDl20v+3G7kG3vl7zgE+i9QsgSNcoGjCqE\nxJ5zxK1kJrf+956J1ArV5NFXE22KsqjrWhFozcG23ocedYPbZpMedS/2drx6ReyciiFMK1+Mea/w\nhLlOrSea89nBiGF7yNQAKvMZWudInNqTmcCOriD53Rxt6O2J7ui/ErHsl/LpbZ1I74XY+XHllPPk\nOvTeP6n/nCB3d6pD141D70W7Nj2hezHSPzKIMDPosa47iaaoPZbtlhqJGVlR2+xMXrWTZ0d1Ab3K\nM617ltnsB+6VpOVpXDg+lydpOIx7VtSeCcL2wIhA7jmxo/Za8jrbv8r6dQFclrq4lOMpiNF7sglu\nKa4kDGve84i9XIvODzN/r+Ay8P0T0T8R/VOG4pNkRnLmNaYuceozhsebe0d3d+GsYLbbaw17Uea9\n4ZgsP+sYkVXNRgo8mjV92BORV+11L3xqrw4u07hvM/O3FOwtOfVeutrjvLkxas+JpvfMQykt9i4L\nrw8sG8g9F3/uM8TFPF/rt/T0aq94rkZqm5F2+GNm5pJH73HbjUwB2UQJvoPZ25fM03CVY+mEnqH3\nvlM2WcNWXDf+NNaS/Do40Zp5qTMfZzojmEbR+evD5rWkQxAFaWwlas+5i/Gxei2/Mf7t4eZQS9xG\nz8XFRWvSu2hse0ZNrOfPg1F7+pDAPxTpXefhQAe7EwBgLCD3EtDCX4Icu5K1WYnFOS44jAZ6VHkw\nqJvIqahEcdrrsdukcxfY7Vp6L+DJlQzy28FsovX1U6i9AdO+J9Z7kgZtBe/d9H8KEJRzgol1VSrt\npog27237ELc0BM83+jnPN+91a+D0nrU7simI7ZUT5Rj1ouqvOPReSuie6cl9TkK7njoquO4Rtfc8\n9H86intyEbsHVCD3Rkd/ejt66J6HR3q0B5LI+TS8CPr+EmqvnN/IxC6sbCJw4W1JkQdq6/jhD3/4\nwzL+3LDYI0bvFbTwGQqhc+Qaei8tLCJsvAvqvbw2pj0A3RTIn+BoDMX600PvMPboPdk57Zd6kpwK\nt97jtlYoDPg32j8hsu17roi95/Rc6sWta6qDORuex2XE7YFxgdxLJKJPW5LeS59lSWMw76nWMYln\nNNWT+9570qC8yELL2k7/Nu63Pji9R1Qmfu8jmd77T//J/ESm96Kj9waVxwTwRSIRexTSe3orq4i0\nuS78bb5ZMHgO5L5ctslviDYbMzaAF5ZV/6cIw+4M4Yi83tOlDXtGcp68GqFnG/lY76ejGEtyZm4f\nu/dZdMxeXdfe1uEac0dQe4jdAyqjFrZfNH81Z10Ww98J59XgeUrUe6+u6BURXdN1n0NxTdfhOBWt\nL/xKVO29q3HMetdqEshvQ9a9Tdi8d9+M2ttmOLVGhh9OU2qZT+UQ7L6QneLP/UIReTdK8ZLH+O5H\n7Kb9tbc6315tZ1Ybr2IKD90UmpehIT7irzKkXi16Eu2W8m7v3mu4DF+8vPsxYzaNH/9HIqIfx//Q\nsvC9TKpB5W1Ajida2PbA2MC65+SvvFkV6Tp55jLSHCwSS+5dEV1d0TURXQ8GsvioZHFy7m+JhinS\nxvPmxtn3dmy5324MvSfHrJoFE3SduGZaK4fLA6hIv4SbSDRhrgC/FdnzjNN9NVy4bV7kXgrKibN3\nVXRWnWpPPTGZam8kgobVH/+Yfkyt2ovyFlzYai/RbBr/q1HUHmL3gArk3vmR7veJnXaqUV/XdE3X\n19fXSVkrUcVYek+tT++5vzOPrinmF1fRT7WO7cgzv8O9xyg0vt5r1N6fJf5adh2HI2c8uHVq+Tix\n3vPbAbtrXRF7Q7juEdnEZ1FUmtPWb1019Ndms+H2R3xemURyhzwKZGusVsNzy+gVPNmHRiV2rzPq\n/UeiuAHODt5zTa6RA3+GYdsD4wO552Ek896xSZ4iI72xdPa9Oeg9T+yedYBffmnVb/ab90y1R8ZI\nue1HbLZ4RyGCprtStj3/9dwR7YhueLWXXCv4q18lmejz6r120K2oqmJuCPmi40xo2rQY5dSx96Tg\n1NYUdhyrzdPSe66wPUXtlfHlCn26g95rHLk//nG8dU9hKMSSpPA97aS5YofDuDm5DYjdAyrHrhIy\nf1jf65rILKzq7BTsU+yWkWM8TVqVWIJDlj5aDAcWK/eYxnVYHYav2Md0cyPS6L0hqK4ZZTyqzjkM\nmWODw7Dnjt9j1J5Wk0QdQO9p6xIFucF73kQNVeox5j2ZTOmvYtA/b2mKL4gKPSwF4/i8onDtNXO7\nbhLlN8qhc5ZaYRRes8LhrPM/07dVK4vyeiZoYnvkt+V05ZqVdbQLyMu9zJCKTubZqbn95v6Q6L91\nr4dcDT1kL9hp8Vb2T0jtjaOfkL0/2Fu3TaLaCzcxeHOBAqx7IbgQvuasySJ2YtTeacE9SqxoRatV\n+xUbuLan0ERDIa76P7EYd4PLjZuUn7u1Goy7+eQ6cznj3S+JfvnLXxqzbVipuSMZpQa+8EwzG0vQ\nxBdZfk+llXVC0062mdbOwtVQ1cG2kGPBv0UTw7on2IVCas9j3fvD9j8fiWqPKO/Z29ls2BMHtQcm\nAdY9AZqBT+8/lM6Cv8Oj1N4kxr3gs6rZI3UHFvloIGlaLtuQsimhdU+3uL1yqz3fKGTozGjjni6W\nlBHyrUsQjGPdIzItfE7vrW7fk4u9A5HIuscb9wopFomc82hC/3D5RE3T1+8W9S4fjp3RDI6VV92q\nuRW6UU9z10o32judkHXvkd8/T56GZt4zLp9yq3cbzs+Wcs6Z222wV3r/7Q//m1qIRbPuhTsth3VP\n7YsTwl/6n2y0m6omsi2lyZ5c6D0QBax7AjwCLWTh0zRPMFzpk8D301Am1lqq9laMjU9tlbLYvVB9\nFBEyq6JvUxslNUM0FwHTYf/1X5cvxFI+D3el/OfGZUAqofZ+LazA5yQwWHY6zG3g8x772GbSTLUX\nt39vpWqvo6Dac9L7cf+Q/lAx8kWWX3FY9zLVXs+mu1B1/8dkxCwNqD2gArkn4a88ZVk6vccOCrra\na273mRdiIWuwiOztVkQOPy23aFgySPSeKcGSXLkmcSm5DTs2Q+PgfiqwNEcprfdL+mWj8n4Zofai\nqr6tlL+liCjDXKoki5O+8rJ6a7M3AysZPHqqqiITAAwb6ibg+w1S85fa9bzjVXvard52HSXVHjet\nRpPn8N+Yb9pMjaPTNJ6qbwW1Ozvpu99N3EbRWZnBGQC51/JjIqIfu54M/+qv3Da+7Vavya9iubrq\negmhe8ZcaqJ5cjva6DzhwuyCxtYEei9vtosbov0+PYlPYU3UCr3YeUZ7Ck2j1uk9K1yvFP2l811r\nW/L4Q/duSsy7oeERhcXsbw4DkbH+ypi4o30Tfpgykji1Yb5AmxWgN2Y9S0O7/OWKsAwiL6Pa8ris\nA/1iVfUVsOuafCbtVL0XBJm5QAWxew29zvM9G/45ESuQ790ROOr57W73/8O9hTGC96Jj98jsmdoM\nQJHmS2lQpihOSc6Ve3MNy8MNEdHviWidHbnX7/lbw7jn+QWnOSaZVaPBTM1NkkDu47PtD/7QvRsK\nCUIFkS/XZwN0WUfWlopS7hgmeM8V7a+un+kgnpzfaDBnd/iJS+4FddeGWchYmWKRdhr3jNu9VOTe\n+/Qbv3mPVqSlafyH4aXxyJ6Rq9HB9Jfrtj8Myu01kXkJzdA9pGqAiYDca1D6iJAz4P9tfXIvi+5p\nu0mP2ptI7sWWYolxGpSQewl6LyJ275U6kVdrSvo9tyCr9wIb4vSeN5th0Fd/TX1K7tH0XprBy3OA\nvN5z2jlu+kUkhPVewOHLNey+7Tkkn3avN0c+qtxjT27/G4fcCFvZhn0bltVWpoYfxKq9Ylm5RA61\n59F7pocmoPcEU+DZ3aWzkfBLatfQUHs/p++mBu8J+mXoPaAAZy4R6V1EKNT3/7Q+cXfYh8ERk1xW\n9hgkO2WSHh+sH43qorpSCv/61N4w91sSTa9+COWuDl324MQVqL0fpe2URQm157voD9YqBb5aqTt3\n3Og901Xndr5uBSWzn+zfVVUVrvQSP++gCEUp8P2SdjiqqVrSjeUa9zSF53Dm6ifmP7hXNkJnIld7\nDLpn/OfpqRoI3QORQO4RmQrvx84QvgZb73k5HA6HwxHF3t+l/ChR7yUai0M/i5xZw8/6hohubm5u\nhjCxd+S//q0oTLDL2DiQGXfl5q97xadF77HC7kf0o2KKb2QYvRcw7snj90J6L8e4Z73xmcS3tKXQ\nzLmM4Eueky1kng/2NtZV2e892kUxSDnLK1PBIixThusJSiaaFylC7TG+XHVexZ9nZOVC7YFYIPeI\ns+cFk/kjJd8xYZy54VFG69UTXXxypRMkqPdScjUGTcFb9/jgPUnsnmeqXJPh3P6wN+up1j1L1v3o\nRz/6EfvFEYlS+V84nyUUkXfDKD7mo6/6BZ3f28uMl4ZBTxvOu3uGlVpb5a9z/f0vI0Qef2rj7t/Q\nsnVNVqaS5uMcGrNE7RXG4czV+A++L/PNe/rpjvDkNoua17BXz5goF0wK5B6v7U5J73FEmRVcz5G2\npDnoL1P1nt0qR9F7Pbx1j3fm5iUBO9m1eu+vyV9270f9nxkRe50fH+Olwc0NG9OX7tDl1J73B4Jb\nRtN71lNSX8kvRu95cQqOyPPLqENV7701lvPL+/wqLBKFV8rNLYjd04ioUtAtaeypnaqRxIMdKAGA\nF8g9Hm++xv8ZLfbUoeV/j9+bMYgYcdhuZbfbufTeofdhSrvk1Wq1Ilqv3e2xmD+X2cLveeueqve+\n/vWvf/3rXy+1Dwp9y2jO5V8bgXs/6v/+aAKVt9mkuYii6g3fEHW64cr+Qnl74/myJ73UMtOqTfW0\n1gP4JHpv66m8/pRQsJe/g9r1jBrjOuggW5+s+j8D5ebT0HF6dkcKa2RQLpvSHAJnf2g52ol6O5zN\nXOMe3LkgDmTmugx5wWqdQ4ZuuBNXb0xfZu4IqbmML7fFu9vW877Rt7RKL/SkGtW+vAsHs3OFybmc\n3HMsOnhzO6H3L1JnbkdwSOpEx46ImcL2R0RE/9kp8/5zaO0hzDos6h5FIc1HoTbvtqZG7KnawFZ0\nXxjfKea9XxPRV4Niz2/7s4dL9mljGNbtXFrXcd/z6x/sf0Lh51p/Ze6ajqgOiwpn4+pl61vy5uMa\n2ywg93SF57T1rfrM3P+gfGr1536ThsS6N1wsdV1euadtVL2Kao+ZqfdGK8TyJ//XnxD9X2m/BTMG\n1j0XR3HnTqn2/IwUiJNOIXcu1+BdmRq9de/rygvfZuLVXger9ka35nFqL4kYQ8vNDRE9XjWmPdW+\n56u/YnzXzJ8WNYuavazUODJc1S45N6zVtkS8cI70HbqegJo9cAqO6MQwLs+k39XdUdWeG1bt2c/p\nI9lAPas1krpHUnuj8Sf0J0T0J8feDVAcWPfcsi5g3+ute3HGvYnLLHvknn+/zRFjdOtecNGQfU9k\n3uPknsu615r3FB/uv3g3laD2HujiDV10C+p6T6D2cs17rN5LMO9FGPeIiL4YFLamDiz73hfG5817\nucobrHu/tkx9wbzcHo95z3ngTvNeazR7qkT2Pb91z604Ak9r7I4ZSlStsuzL0DA2WH62XHck3wft\nv/9B/XAq6x5/8tdEe2ODvNorofWCDywp1r1B5sG+d2rAupc/xWKpuOsxSDXuhewDO+PffMLCMGTf\nS06icNZhMZM1ItWegM0FXfRqL97aNor9LyEkyHvxGLXHw8Tn3ZBSJVE+4UaLpvbijIGZBEVEuXQN\nlsDtK1D0qrUvrPZ6Skfuifj/EAncMcmwwtyp9ox+QK1PUDBubyT+BGrvlIHcS+4lFp6bmxA2zlAo\ny6yMmVmg9yLb+zXRIPI0O59o1dEH9WfF3KuyzRVbk/NINxux2mO4oZsbSwXKVVu3ZCf01F9GiFrT\nmxsWavf9Jh4ecubmDYTueVqzX+8VD/If05Xr9O12xj168+Mf//jHbU8e2Z9LjHvD9Q51H/7vlc5y\nAWoP3tzT45zl3l/QX/xFzjNhqt6bMjXXU2PZb1/wl8rojXq7nbjCnJ/p0uwKwOlK9k5arWIVn6LA\nshMxIralES8GdruLiwvr061zVawvd4TJNNoYv+H98BUjwpw1NozPlQclx+Xddpt4IDL03mA1a4sh\nVQm1los8qpkICg5TwJU7jm0voupyoEJ+GtKzHarRopRXTt+ZCYHeOzXOWO79Bf2F91nwPxL9xL+G\nTu9Fdtfe1NwJGdmfFIdEFo1VfM8Zu0dERF9nzHuyuTWIyHtgFXsFBhE26D2nahwnmyPN+GMqPldN\nkpvQGXfSCsKMydPK+HOD439rM+oqoz3YXxERUVU1Wm9avccYXA22euaGurHRg70zZtT4cfzDe2zV\nvSwG6953p9wsAC1nLPdC/PgnP6Gf+AXfpuk54/reuai9UpTx5wase838ovmTK+0j0vReEtH19ddJ\nrbfXvbSi9+JvpKpix/k/Y4xu7jE2xwJYzpXbmy06vbfdhqYUa1EycwXGvdjYPSL6tVPiWeY99yUc\nGo3uza0dcyN6RQTzZbx4S39QC2q9BEoa97jEDMd9/yvRCr13psyiya1KesMP925xX274Sl7GrRDm\nvBPneFO5Hpm/ICK/VGnu558Q/aV3RRt6E7fl/335ek/z3+7e0q6A5juQR9Y4TCBfo/+pf/DbcHau\nmTdHRO+wxqaXTZ607tTuDH2CDfkJjNd/RvSfOtNdW9N2Snf3JnXmvIs3RMFB9EaRbVetRrhyLJuN\nIfZ+3ZkGX3z6oA+XnvF7req9J63X3NjnidFz2qbu4zRGIjWfneuTCNt2z439k4vRq5G8uY7c3D52\nr2l1KVl3Gda9tazCC3fbTubLjcvMhdo7dc60EMtftP+G5R6RT+81xr+g3jP7WKfgK16IJZCZ6+nJ\njQcB7RD0cL23zVvuXMY2r9AMoVq//zUiMvUefS04OgnrLHeXQhs0B7euLvc8UuFAK6bHr4xl+F8S\nkX5OrAXTrHt/1lR88Zv3IvSe0h4succd2xdaNvSrVuyF7SWtTMzzyDZ67wXRp3qj9m1eG9iftPYv\nknt6B2DJPWeTDaRqhAQHI/j8FqF7Ik7tVcMucneouplsvcdZ8kJyr+uC/yPnzPU2q/Qiy8RNwmKh\npuUOLwvJvcJllhm1h+Tc0+Isnbl/0am93DoiQrVnMV2yhidVoxiekxhrkQotr44FX3MsEx/6zqi9\nl73w1mTv4NbVg/c8w+5q1U4Rp2LsIatyGbVnkaj26M+I/iyUBRzh9FNGsosL0gdRR9jhcM7lak+c\nzOHl10T04oX1sVjtmfNNd9lK9/d0f39/zyqI0rObpobuBTy5nNoj3bbuvUPrcdSeOJ5vlFosTrVn\nvv//eVcz+7Rc2PZOn3OUe3+hvHZLFeXMuAL42s/tlMQQy3fnjsXBHk9c6q2x6xmir3mbn4LyyfBS\n1Xv/Yi0oRRM91v5xkqjx4a68yyVnaoxX8eUNmYqB1XuDdS/ejZueqvE9IvreC+rEnqrCUudeaKXe\n/X2rlsL2Il5PMRxK++8lCt7au+b+81j3euNeTfU777zjrGKZRkW+QssCcifVGC5OaE1/+7d/+7fK\nO1LF8Rhqr/RDhAWMeyfGGcq9vwgvYuLQe52T16f3NpuFTmVtVGJRu5ZytZVtGL2noj7ns3qPiPzG\nj6wW7y2850cRbszovnIn3xZn9Np+pmKwjJu6le5KLPjynLnf+9736Hvf+x6RbdrrcAzoZpuJnmEw\nqQfIF3uxkdnuBJv+jjqENWiO3mPseFVVhW+7+EfuBKyuQ/ugMe797d/S3/7t3zb/jqz2yqZqwJV7\nBpxj7J6m91zRe+at7YjfC7lzHXeky7w38Zy5UdOo9YfCqz32RKY2L+V3pjZSnvQbpadH77Xqz3Nk\nVqftSNQYUEZ3xbwnjt5TOBC5bY/mOHqwz5891Ea7c+ViLzF474LTTMaOf0H0zu8NXSA4g12KR5Le\n+575gR67t6b9eu/cCfOYhnuj+8Xnnk1rvQArqLj26tNVFb9bJoYuTX/u1PbPaJWKda8lsc4OMXKv\nPVDWrK7E7vkiajwNS5Sp0R+7vaLh/LOe3O/3r0aZKldwOSOC96D3Tp+zt+45LFXWefFWZLm4IKbK\nLLlvyMmC95InUbMZ3XUw4FF7Kpx573+Gf2bCWiMUb66qewc7Q1Jq7oo8u2Zqu5VELUe6c2OcuBt5\n0Q41eI/tU2zznqn2IpxuJSdDe+hq4+33tHfvhDHpvaKjul88k27xntMYI1XALFV4waf2mI0k2/ds\n41675aB9bzzznufJUdxkR1F7AuRq708QuncGnJ/cM325Us+kvwLfBdfhuEfLJQbvTeiSHuwa9jBo\nu3OzYcONFL3HWveMQsvCnj/K3GktzNl7ovRenB+3ZBzCys5XMWxA03ZFnw7GPe05xn0dA3Mm+Kx7\nAlgHfz5aYcD0i+lXo3XdaL5oH7d8y+GgWf55m7Lb1WDb860oYNwb+PmS8jQgAU+M85N7FvJItJ8E\nbHzGe0/vOpV1L2Dc8/XiZgXZwFhRbPJcDZHRg03Rdf6SbfHi+HK3lUH6pJ9jx+EEQJQ39z9FbzJH\n71kDpE/ARPREX03K1fiHf9DefjrovAdhyga/i4ILLzGMjz/DTcalDM2rUddUqzWni6VrxJyVceP3\nmIuf4MqdWOxFllkGJ87ZyT07UYMTKtxp+clPyJ5mw1OCeZEJGh32dAET+nIHOEfK+5bH52uO1wyh\naS2DuEqxSPEpnqA55xgTC8uasXYTrVs83csNJWiCoTpzgt773ve04L0XRMp0tr1L199AUhuP6Bxa\nGeieqy0M3YvfiUKkB+/p9KeEe8z6wPyA1Xu+axZTZDk6wXcGai8XxO6dGGcn92xs6563y5fqPX/v\nOo15r2DkXkDvFTbu+XWNR+8F1J6ccNJM2rwa3htu9MSplKTcLJngPNx3EkL3csruWYkard7raV4G\n+kO2ZwjuuXkGhSrDPcdMkiUw53lN2aCojSZa93InSYy070VNqVFkpCw7V274ikpj9xxe2whn7v8m\nXxQcjXOTe1wVFlPvBc7JTzTJ53LvLtq2x+PpXYq7cmV2LFvcFYrmI03v8ZWWDeue7E4qf79Fxe7F\nO3OlyDLclfA9UxEIE3N//etf//rXsckahmWPiIg+NT94eCCBdHM+CopTNcipM6QqzpMqalE8nk50\nZyZa94as+6ph+EpUAimy4H3uZHbD6f9/Md/+tHsx+wLLTjOe3Lr3v9H/BsE3f86sEAuj9uxxSjIk\nd0Y9Ve2t1ZLyAbnnyNUoXIglaN1zZ52xOX3NQXHBjo7RPqN5rYjcA2A3LnR6b1B5vQJ0HJrj4lrj\nk3Yl+HlzNfNeIbXnH0z5byOi99JK7onsQs4QWE1BdUfgL2HG8wWVKsFCRIzeo41sP/ZExv3R/MiV\nrMF0BaHqdg3uxtDfF2Gb6NB2c55AfXOocSTqvf8/e/8Sc1lynQei65x9nv8jKzMrL7NIlkWZlClP\nGj3wQDIhodTkQGBPbhmCwEkXUD1pGNDUAwEeC9DAU8KCJiJATwTCAO/I6IEMEQLU7cEdGL6Ddt92\nt9UtuZW6ZBVZlZWZf/6vO9iveKyIWCtiRezY598fWX+e99ln74gVX3zrNch79sTvZ93f+7/6G/bb\nMbqX6sztf3lA17Wj93pnbj62J1mIBRfyvHyv5Xd/+S2Av2xv/yX1yxZMBKlM/XmAVGGZtnD/twC6\nJ3cNcJanQXg+xPZispAnUQPgNiB4/G3H7t77W/MRF+7S9bUvx/fWCALrrxvCf0PmezkLLF+5+N7a\npiRx1+DiJcDzCL6Hsz0E11u4JmRuIT/IB2xRfovzvUafkKv7VUhMCx6LpLpH37x9Bo/4hI/iyv17\nCt+zcbD5Xkb3lXbq/ytXC7WM2t61jAvJ47L9dTff+5byb0f8RI5mQUY8JGfu/xNle515Z56I/1b5\nG/P+cl1z0/H06dO2hewEDurbWzxdo7/R0zyb5LmIIr4+pqYSTuXKBYhtnMsArQCfk/LHKHku5GR7\nrT+XuHO5GTNSAuk/CTFzq45i4UVG1uv1eh3ar4/PJ89ehlT/yArNDENle8jk/TJAp+j9PUTX68AM\n3mN4c0NjdgK2JwRvgJ7zScR1u3hzq8cDonteaW9tFVIlwYjcG7tBBW1rCb4nkanx1HIw52yhhiDA\n93q89x4AvPeeQvscfA+/yoYW8VT/0caa+uUvA6i+3Kih44B3TU0MvaCKe9bPSSzAJ3V6LkTZnquV\nWpDvtRsG+0e5ovcwvkeP3muvucFjwiRzejD43pe+9CU9397lduiEPTG+lwJ13/gfXGxvBohLv8Wk\nvIXv1Y7abYYg/l/OZ/b7lsKs1xB9RgzRKLylz8/3wmzP4yrtnEA222MhX+UQ2/PTcr1Qai6N7RlP\nWx6xL385phALaWhVEU3bzgT1eMMD2rcNWPf/pFWXi0jNdWt7duweEc7L6OB7ibLa6nAAk8coV0Or\nd4dAJFznFoA/mcl8z57LzmHyf7WE7++5JD5mskYAtwPxRK75+BBK9n71CmAO4p4gFm9u7XhAdM+F\nvbJOpe6ZR3nvepI6dVzkr+2aDXFFGxyXl+/LJWUKkr7bAJ8hM/uo0dCWzRvmA2U0+2Sx7oPcfI8W\nD/cN0qtIcLO9oLy3XgOnigd67rxv3yPU2adbhSndltwPzwVSSz8NCcX3MHWvm3E935NA8Breutle\nYMS2JVeUinvVsj1/tRX82W9hebgL26seD4jueZ256WFp1uwPrJD5+6j9f7J/w4DCDl4FnsorjV2f\nLG64o+HubL5H+u77GD2UyveiyrC0NI1wga8CPKljfIV3GP+L6wmXL5cEnO+5UnMZO78GAGAvO6G2\n6VyvgQjZmbqHwjZueAhHEJa45590KZVYFHuPlWH5jwBXV5PXV3Yk5v46APx6z+T8zlz0WdRru7C9\n+vGA6J7bmQsgwPfsM+m38r8N8Nt5HbqU0D334ttynKFz7FPw/qAJ+B5F3qNyC0OI+AR/lYabIXZv\nyjSNAlivYS15gVP43n8SOwqfL1c8z5zB9/ZdZEn/b/InriQSrMRS+BnfaH1nhKAeAKvOsg4tcg97\nwX903hFD7JX9dfj1X//1ke+xgcfoLZF79aOKSKFi8Al8qc7XbjnXarEY83GH2Bdd5JOsu0dM1AiU\n3huO6BNf2T1wrJGpdfda+Kvv0Rpp9F4ZvNLJZwDwVGV5+oXA5L3xMRKRk/HkphbeoyVroMdK4UAk\nTnjvqIVDOUMxdM8RvecL3SP8jjtEG3Kpe5zSe6pj9ko9Gl24Mj7QXW5FwsDfxn0Q1Znr3LeZ874r\ngOR25HLrsATonqectbfiXouug8Z/hF/NJu0F+R6m7ikk73+2HjGBS384s1vkveoxW80hCn6BLwn9\niTzzvmq3M618PoEv2ZerryFP/cYFZwN5m7y26wStkUa7dDQrtbHDiEdepo0upsOyXJDtJS/eYXeu\nK3pVSvG6v0fPBS1oNkrc+1+c/lwnCD8WO9xLuHRka1xH7Sb3ytEYTMb4OGfwnhjbi5jKVGfu37me\nuNW3on25S3fdPVmM3+4L03Pm5P5H60YVULndr/+6+Yjv1SMWYjdTPAS69+GHH3a3POqeWGbFGZyd\n9ZyP8KH5QvjSy7DcAM2vCXAl7v+ig9gmt4Ge86F87xGojI+hspZkew64Yvd+53esh/7t8AdFSm46\nCfhPzFpUBGmgJoE1Is9dOrNzr+HaSN5yKUvq3mKv/NVRvgRmRj+Qk+/profOmespuyeI21uvA3tI\nYvJUYKmA5h2tR37dexcB9orFbztTPABn7ocAAPDjD3/so3vpbE9fsTqfrmqV++VBt/P5nLkUvnfb\n+MNyNsohda4q1NF1tcf5Xl5nLvwdfOnviGwPYOzRgXKODRiRihoQga9/iEBV5NgezZs70rx/bb32\n2/8WHE7dwFFKeHNdP5FI9yLUPSfX89dhUX6Is10ILqm5PbomSXO4c7WPvQLcmUv05sqJezGfRPHm\nfunv/FG46tz/vwH8ObnMTA23L/dW72/i/ph/7/0C+NW8nC9M+i1vrsXe/ucg5bMcui62t4h+teMB\n0L2O7wH82MP3BMQ91Sb0EXzjfBxtu2FkNL5Xlu6RArAHW+yjezBJ7B4AxGlRGOvYALR87yl8Yl8G\nT/Re+ACIhyhG91RR71//zr/+HYv04SF8wcMME77q6J5H2fPyvf6HXKl3DLDpHontEekeie+JWPfI\nyD0i2wP4O3/S1Tj5y4XuGYYxmu3lBpvuYcwuzPfApHzfalvlWlj4XuV4CM5cBc7gPWHniBHBp4Xs\n6WZeV/dortNp4GoakB/ieYHOxetp//8JEM/2TKjs7nfgd1r+p3DAyNa56WzPBaoV+ga77p47bo9U\nZflK+VsKFnFDvz7I9lYrNEqVjWi2R4nd+xIAfIlXQZPjys29uk3M9tgiRWwqrvnOv8SJ3cL2asfD\nonsffug0gsKFWM5Uymfs47W7eqrGBJm5IQyBNT4vVV541T1ev/oWbtb0FL8C7szHiLAz/C0JWS26\nuPc7YIfs/Y76aDa2FwY+9xin8BsA3xApthzdU2PEDToqqLsiWs23PfQkmtkeTMxtE1Vyr0WQ7/GI\nHrcQS2hqun25usFxf8x/zTqeieHien4O2C2XxghY2N488QCcuR9aj6Ara5o7FzUJr1oOaRl2zc7k\nCt6jRO7pX41Ji6pB/hyA6cst4cyN2bIgA8DfmACnezfUb1+bN2yOSmJ7rhcpfM+megpa2c9F97y/\nRKgOiyNVg/DhHf7TNwAA/hN8w3TsfvN//Sb8r8gbcHdukO21v+VKu2eD5c/VdpQ+tqd+6tX43Zqv\nMqzueb6AiLv2wtzGflTQmxume9rUb725HnlP9+aGRpWnDAsxcm9yeY9RiSVC2dOu+/+kP7d01Zgj\nHpa61wGV+BLkPWcVCQeFVE19/uYaJDx9+tSlbJEwYWauCNsrgnGYRKaiutbdMTXXy/a6Z12JuTE6\nqQwY39wqe9/4hunY/SZ8E+Cbkgel4sqReo5vA5zZuSPo/RxGpqnpe6HdqQjbgzsAuJtQEkA2ep7Q\nPbGGuVS2NyPE+3E7/ON/rN5b2N4scRpjmYt7rFfVNvZcuN73Ktw593802J6gM5dXdu8Tx9frNRLc\njirxrhr0NYbLUqL6lLlA4213AOYwiSN8Ib7nZ3udozfKmSt0gVftZsv8HXcxZBPx6crxPYveMfie\ns5naUIwlhu0hmQhZcQcAcHeXsAkIOXOj2l47q+4Jnh6NZXpOwNTiHgP+Vmkk/OPwSxbUjQdJ94z1\nfqiwtIY18Bfi4Ms9ToPMbdQA4LncR11eFkzXYHCy/KqUI3RvQx4q63Xbikx7LOZIUpWW34HfiWmd\n61K3jFcRXtjHAhk/JIpVfAMszkfle+GOuUS9Ggvg880Su9B6JAIbyeQ9jcSsohZaxoHHcBSpukfD\njNhelLpnbIz/J8fLOnxrqcZXPU6b7n04VlhW1BEusgABAABJREFUYa2Zt7e3t3ALsG79sus1rDuk\nfPuQq+Hr1ZON730HAOD5c3juJnwNoX2pUQLVKfLgT0zlNpWFM1GDPDzQT5AqITMiJO51+LdMwkcu\nos2pto0pfFx84xtt3sY3c7lx1VHt+m3uNB4E58CR9nToyRrlCy1HwMv3AuJeg/M9l7rHrLl3Ggi5\n9MfQPWF17y8B8d4uhK92nPSc+HD4Y0BfaVqrgsdrWJoMB2cTGuXvwPPnnbTHEPgSnMni7lw6cst7\n/jSOeMhNvjZXg8j2gNJMTQEzLrN0GOc3/GyP30KtxR7A+C30H+aU984BAEBK4Js53M00ANQuOAO+\nDOBU90Q93bTKT3MS9+L4nseXgNdiWQhf3Thpuvdj5zMY38MRPkF3wx8LZ1tA3TeF7H3H8144X6CY\ntfSKf9n4HkGDTAdeVaNDAb5H89J6xb0Q2wtmowrR5tId9TR/rpjMR/8RDHnvi4gDGaHKe8Y+0hqh\ndZRc8ObmEqsrT4BJv5wDjprA53vmGOrlvW8BODMzFr5XM06a7tH5Ho3wOby7dwB3juCjbbzrZhrU\nmJwb3muzeIprIUzge49asN+uHDfN7e1bxMNs79PA68SMwYR52kQQyu6NDS3URzDYI8cp731BIXzO\nkajwvZAbDxlPzz1xHTYkBoOP7QWqK7smvSt0zypLmHL8+pc7rMu8xL2I6D3b2PxjAIBvfcsr4i0C\nX8U4bbpHh5fvdSTPea74cea+aD4RfEe5LZitUSs4F8BJrFhBWCrCEekOvic2+/6b8Es+HfMTvs3q\nmRvD3QgCn2iCtA+x3twk0urO1kgS+A4AcGiZjc73gmO35XoMUyAg9XrZXuC9qEH+MiN0L4ykjfi/\nr4LtBUi/2kItuRILAMA/7uux+DjdwveqRR2afy586HzGWmvIjbqcZhBfLa9Rq6LQvUxllhW+53bm\naj+7+2rLq6sZZh9PxZbH+PFFL7MMwCFOHpbh0/Ac6+kdwEj3XMub73P74UQkP56X+YfOpzDQPVzZ\nQs5gCt8JuvatX5LAfZUBglRadvTNJQh8e/MM0LvnenrQnAe/1zNg3hyg5TamH88coPrMG3iezxio\nmJjtOeb8/w0ufc+ke4TR5LZl6mLg+KAq2F7Am6s1zOXSPaflbp9Ae+b2WErwVYqTVvc+dD8VT0Nc\nZ4x1JhUKmL0US2vgf+mX0Cdtq2qxBi2iWueuDSW3twjIq1Mk26OE78WwPcEJ6I2+/HT4Q0bpELwE\nguHaq/1DB9MDAEotFmrxPQS+joPJ+h4KWnwple3lXRieP4+0v1+WLMQyrzCbRAizPb+Et+h7leKU\n6d6HvifN4UznLbxT5sjKcJiaHOLec2jJHsL3fumXxoVy+OqnT59q/TVcG/GW6ynnbcLcXAk1IrBe\n4k8TBoP/c3nH7dUA07JtzAOpP/5ugLrp+CZ885t9usY/BPiHfsrHhnlWNpvNZoNc4UtP7b2wukeA\nLesYR+EZKjHRHc/4b3HFODx/DkgdHgMOBv9lhzc3JjHXqe5p341P0ErEPS9e4w+nVRc7bXfgqeOU\n6d6Pvc9afI9M+ETO2cj3csh7Ctvb738JY3oAvwS/BPD3HR/QE74vmRHVNW+J/dRpvQbvIhgVuhdm\nawEW2Q8mATMa3itEaHyx4LNFIVP0TejTc//h8AeFyHlwJ3S7+N55BNujFQDVRxo+oNoAPgrf07/x\nmcr3nhG5H568lBpKjPI9U/SUW9fwT/qvxT4/H47qnT4vt6sru+BB4pQv/If+p7HGuSSgS7zrRIZj\nsvJ2zd3DoOvpCl9HAge+h2pDvhCbbg9cjbx3d+clX2uAtLLPmoxzcZHwSSqYsXuR4FOb7OKeNfkk\nqydSyrEQnLkIUt25MdLeuv1vXKWdHl0nFCcuNWXjmXH7GbRE79kzutaHED4q23M56L+MPmqqe3KD\nabpO0rJo+V43hILLvnNppNmpwZv7rSVRtyacMN37kPuGpmk4Ip8G/nkcZDJF3RNsmYvhlxSdzxb8\niL7A4bgb41+YmO9B4mimBD/1r7lo/3+hrgV46J5oyb5tbAuuJ47bGcEP/YtdWB0zNujEjeR7xu9y\nyXsOdS8icG9YopUThIbob9Thpq7LSgWW5y3zC5Oup4qI19541pM9YPh23Vnroc02zve+7OB7BgTX\nNXRYzsGZq8t7oPVxDJyfUOQeEd/6FiyBfDXhdOneh4TXWIO3J3xN0zSOJj7Y9PecRff8yOgWHX25\nFgHDMzYYsCJebuspSxogDAKDXWFvvcDXL2jowhZke5xj2gL4ho2Hr7fq3pOW4DhojlZOSEDcC3B/\nGTmzcWQLffOb3wyzx0jia/I9B+GT43sA0Mp7wx2HvKdGESqG57lG754DUFM2nmn/YE8FYWyCWKX/\nHCBEXVBmVfZiWAUQKLN8NO4rp2WQillGccXiewvRqw0nS/c+THhv0/+DriTYKYs6jf3C/duDvpfe\n2gIA9KJ7Qbii9wAA7XNk0Q2VGFtLfPSiHudody/x6+FPGtoF9WV379Hwx6HuhZcm+jF1tt0t8LmH\nz5Phjw/j2ZtJnkYlieEsvpeSqDHE8DnLRg2HorM9MO/RWNdPAZ49c0TqUfjeZ5+ZbI/0tQGEBXNK\nFRZaGRYAxKj8+2rEvVCxbR/Wbseuy/jSjbLC9JaqLPXgZOkeDSs8gG9YRdD1xD5nqfEd2YuxKCDK\ne0zm6eJ70RkIKk8k10QMXgk3PcDSK/EXAsDI94IQ43vbcScfwfcAKAF8JeOUsqf4hc9rbLKGRYYd\nAh/G91LTcttf5RzH5NiBEPF6CmAkaPBhKXvZCr6zE3N90p51chG+dxJYr42Y0A6NK5eDsXn/1rc6\nyrewvYrwwOkeQGjdQQyrvShGnsVh2e6zNeRj95wyzf853MKTNdyHsmta8L4vBQIaTn+FpPQgeqZG\ndK8OHZzumDY+Vf56IGkNpo7jLBtjL3SVyZ/m5nst4XOKe0R009/H9sJMUA9vsI4jkvCLiHs86EPp\nRMgewMD0bM6Hn0LWFfuW9s+CGnCadO9Dji+X7W+0zln0Sez8cmf/pLsv48yl+XJtvocxPMSb62Vf\ne32VL1+lqbfMbWDyer2GtdbqmKEUejCwPV+nXCqE5uB+v4dP8NCwnud9+umnAJ+6WZ8kQ7oK5Gog\n006YoOXjezX7um9XAOrZdbA9cr3lFKhzY9atHO/UwNY5FGGx4aVdbXmWzhA1cDr5yAs0nCTdk2R7\nGD3g1C0KfPwOAM4A/sk/8b+MA1bkXgz8jEnje6X6oqJY9xUsKKDLM2S3L/mT2ZNwhzh09wCwhy/g\nC38ugIfsicPNivYASK009nkIcPfAmiV5JkJjQi27/MUXXwQuUppYuFqF91lF2B6lm7QH+NXFzvS1\n5m7Is6aNhG9G6p6ZqxHAeg1ethfV53qR9+rBKdK9D3kv91pGx4LC4Xv+KbLbwSsAAEG+pwJxqf2S\n9o8TTwEIrS0HjJLfxF68O+0fEyLiHpfwBREeToYvd7ezMjauANpzb0eHFWR4GjwjYb8/HOCAJJiK\nygrZNAo7es//+sshkO8L+ALiai236H+SS2K/BdBbVuQjdoS4PiV6DzkOMe2/nR0d4SPZ5viyCP9+\nRmxPAZF0rdf+aJeF780ap0j3ePDnlkuEet3fByjfK4EvSQGenPsU4Eso38PPCfroFC13vGt8Hamc\nOo7HY2j1RyP3ML4HWKUPesGR0j4ck+8FSmVbuE3T9+JB43uXxr8A7eVJyNcQMtgB1yotiJgXvcfm\ne/hc3XqiWBugnqCYKiztYJqlK/dbQhkT6/WqhcinLSiNhe4F4IztXyO3InGW+gE8tMWW/0/rcTNy\nkJk40p+oyQOb7gCcF6WjB4k1D7GyZ3iVZQKYDpcgEC7xhEr4+rMmo9AyR0IfILXjXJ4Q38tF+KjZ\nuQA92/N00fXjDr3jsEv2w7i85+R7T5/CU+bMp4EbvIf/wKM3a8mVQSaC+a6V36IqbD2Rc/zUtfJC\n+rcvubnVYL5DWBCBTSZWf0+tUE5YUXzfsJNV9wihe7+k9dcAZ+29p7wQovYsSbG9pLA/ZxhYfyXt\n4nWyuZVhHLV//XqPPy13r+fHoGFhRL4nS44cpHGrfA/qzuXwveDy7vxJiS5ua5xjHv6W4n2u3iHB\nGS0QvkCJfOepaH0AdQ/E9CpXp8OvAWoquceEpEf1PuivyvbVC5Kw0D2AYPvcke9p1lbq3LV8Tyh4\nj5qooYXu/R/tP9TEYJclbgDkUjUS/AW+mP8GoBX3TMLHicfDynyxo9KPR4DjwPp8lV1cbG+32+37\nHA0ZRW44cXuJT7uyPqX9Jdutl+8B+GpJG5DJtI6ByfeC+wVHF10bZmzoHXKLDoeq5niYUxzgp+xj\n6fAiSP1cKt0RwJwP13CtVBuWSA5zd1Oqju1dUwotR/At1ITGLngL36sFC92joc/8Qvle2AijRmiz\n2bSGfbe7ASm+92cxb+rVPcvW3/BFr8l9uW7iJicYsMu6ojiObI9et9lAx6j2AX7Gl7KS+d6jR4/M\nT9nCtl+sg3NGrMeg65uePHlCdnKjMEY6Nuw+B7jsk3JttufK0DXnHM9Im6P8hYNdCfA9Hl7Aixcv\nXrSHEyJ8OIs/ttPFoDgprSVwuPheZVwvK+xBNzzC3okvfK8SLHSPjgbu9GCgocZbwodKJ3kKg8X2\nbgHgavLUXOcxN0ZoTwqhSOZ7ZsQevW5zCIhfmFx9RaVGiZexUzs1GnrdR9orlRAtec+h98XDxyyT\n+J6OGzR67xKG2D3TmfvF8CeA8QdEWRqmF/WTTz75JA/ne6H8daKdoyjhIkS5Zq38NFe2J5SmMfUB\nLEhG5WyjLjR3AAB3/cjneFburR3RxnknCWlV9z6x43ZuyMd2C9C5FSdW+HyH3DSOpDzS79wOSsIb\nMi9JvraeyD3iPvtJVJxawmVUPdvIx2grxwGk1FIX7twrVUoAn82HrTGkM7zLXuD74nzkeV/Y9Nwz\nYtZR0ZW4kPfcTbz84XutE/dZtC8XAABeoAfVAAA0t14d3lDzthHqXkxibp1I67ZjQDMn/VBbe3Pf\nMKyNtXFhe7XgBNW9D6Pexc4tZ+b8mdtOxKRnKr2XCqLAp9bEGBbCiRL2bZ1FKcPKSvw0sfXVgWDA\nFiqc8h6T7aFJH9zM3Bax+p7eaURhe/1PseYOxpzFvLm50jUQNkwVw1UnrqXv2aYhU3pxXKOLn3Y0\nL43teVU+DtvTJghV3AuMLUdA6H9F/PR68BoA3L5Urd2Qawls++rqlsFv1q2XL77cWnB6dO/D2DcS\nqEm/2N/pCxbFGretje47g6QEY282m97AV8r3bGDmWHtsan+uue5qB2fZ+h32FieG1SVFknptP+Ti\ne27xgkGmaVFqhjWIVPeMpJV+LGzVddkkfErF5eG0BtM1UmMxUzuMcM+PGr03kjxKET4W3yOfFifh\n8op7hOrKPYLFiawR7M28eY3MGmK2goGguvcV7MH/wP+i7PD/+COAk221VG+9BgC8lt5a/6PAw6rX\nYxveEQvfqwQnR/c+jH+rv+ByByzQnHAS+0aWLd8z9u/D3US+F+/IdVRiYUFdZSbP19DPsGMB3PX/\n7Oy3yAGlkVgYEpfvZZdO40i7laLcfszWkCnv7nTKh/C9gAjTqJptJfDtGRyZuZTovRF2N3sb+jl5\n/ly0bS5H1PNnq78QD7Ujf1xgI9F8BeF7/6FGthfAa3BxrXEQuUfTqP7pQ450mtV3LN7cSnBydC8F\nFL4Xlwa2Uv7eexaFDz6I+vgW30H4HpV3+fhe6Yp0AFDIDbyzblDg8Ocm9QfNDpKQZYlIoYTfDoGf\nvgfAJ45G+LCWal6EWmqMqMHKfe6swxJqoAugZWusg79HOy9uj22UL/enyU7chO8/glAeLiF27ytf\nMRjf/Fy5TuhO3PWaZ2wdbM9wDcNQTecvF7ZXC04tVePDKb7UEwqOwnfSP4CfRB4Fru1dZfGrNthK\n28Bt94SAuGfntkTDIwDt3vKo3nVXOO56ew0Ab3SC8ojRWON1ei8N3gmiZWsYI5mSd/PoM3gEj7x+\nuysAVwjinUpdDuYZhZ1/Xb6lOS6zsT17cuFT+/NLf9G9L85hcwOwIW+s1ndrWFOIj49SRah7TKrn\nnxAvsA6N/iwNBDE194iZGl/5L+q9SsW960AwcSvurccNAzYbVgyZ1fdS7bOb1vTe/iYAAPyF9sLf\n1O8uKINTa373YeoHBMY9bmLD68kK7rs/hGSqGMLndORS6d7/AeAM2rEXMaew0j7RU4SE4aW8NcZh\nNx6y990D3XtL2/qM1+4azAwD1+KGfi5K9xzF9/ABg59bVyQYNUwNG8pewtcre58Z9613u4e9b/qE\nVubg2PB9eGLsHjK5HHQv+EHvtGm9N84PsX+Gh+0Ns9MvoLn4nid0j8f3/HQPY3v+C3pEfvX4gCDb\n68+bwvcqZXvgXU9eD2yv3VnduaYDx6uuvbaBW2i3HxZMA9UyvN/8CwD4TZP+LSiCGtwcgvgw+ROi\nCAqhzvJ9/4eQOh/h0XWH7U0eRjcNPC1MVex2QwAf5S36tTOkKI43t7S4R2+bi8C9Y3g0JuE+av+4\nT8L1tSuo3pfknpif649zS626R2R7tPZp7Xv70usFkF/di4lvYLZJUYYUcUa8pbM9gK8MPt2Z+nJH\nR+qaEvZJwMD2mjZ0ttE6ig6wUz9+s/3zm+mHsCASJ0b3fpz+EX6jwa/DEVO6IyWCLxZ/H/4+Xdyr\nr6OlDWbIYZhXWFfSSM1l+HIdQJM1ONqeL/Cfxm3ia348gkcOtjeyIpcilVppxMWQMps3ayvlGnPU\ndrlOqscS9wZ4xb24TA0evHzv72I+0WdM5V1VXwEAaAlfveqeH39JIXlcX27TKG3u0E/HrsVv9oxv\nwURYnLk2Yvy59pDfwjWM1ul6fNiFnygkj+vO9eXkMoL3fuF4HFmEXJvwW4Dm9grOXgHAlM7c4aAZ\nb/aSDvPCWc5cJ9tDzp5L3EO8uTxPLlK0dwTFd4nabqdCTJVv1A9Af5BnQfKLMfsbANg4eVZooUt1\n5xqTyynM+d2574S+hsf2+rkZ58r1OXPZaRq+LRDuzPVM2COAt4PaaLjPAOAV/iGUuD3jxLUu3Wr5\nnteZCwC/Ef4IJt1r7IcMUAz/4s4tjlOjeyX4HmZqTXu8BdCjaK/VJ1B0FO8D9U4Y4eIrAnSPEbx3\nC9DAq7PO2k5J9/qjpr/bQ/esy3YNEE33PJ5ci+9JxO0NCJMbnB4l073xExzj3xu+50nY2APcwAZ0\nXU1tPVGW78XRPWG2N0zOSHHPV3WPn5brJnxsugdIy1w0U+Os/QclfAS6Z563/wJQMdsL0T0C22Pw\nvTWSHxVJ9xa+Vxwn5swtk5mLzK877BX263yO3U7c+8lPONIeodQeI3gvuO5Q0Azmdmq0FIAZDBQN\nJ/XheJUtd658//cIOHcMVP91iO15hdVAG5TWAdoTrfV6vYY1rNd2XQgMycka+uxyXmqqN5cKUoCI\n113r5oKehrkRRVjY8Xue+dqWWXb9dpNiOOQ9Pr5SddE931A4Etkep6mUXerSfi/t0xavbmmcGN37\nsMzXhPme/ZYt/j4FgzP3J2Rxj1RYuSjfG4xBJZQP6HyPFUCW0k0NbQ7QgcT3HNY0VMCNwG3umL0B\nPyMRviDbIwDvseEY271ZCzG+ZLZnIqpGpVNUd8O/C+jm4OShewBgttTr8CWHtAe4unfsBPHXAIk7\noLhuuV/57d9O+dKsENoQrtyBoxRouysqd1z4XmGcFt37MPH95LQ4jO+FCV9gsfuJcuu3KEfxHWIb\njfTkXHsVC7tI6+F7NPiuH3rltrQ6LPJwWlNKPy5phH/2FYXt+afPDnZ4Mo2hOxr0LmjdUvKVUTjN\nh1fe4/M9vymhbHBiiixHVlhu+d7XvgYA8DUAAPiSl/BZOAIcj0cQrLPMw1cAoF6+1+PXhj8DXhPF\nPQCAlSdTaABatG9FEtIxLHyvLE4qdu/D1A/YAADcUOIYQuF7MTKGIun9FgDAn6v3/xwskHum8aP3\nDmDknTIq7414VUPsXkMs3Mpme+ZhuXmPefICVVjM+D3j27ceBcnL96IzNfwbhqC3bnyzd1YQVgxT\nmtEG9h273DkAVJKsEVLVmcF77dwMELqI4L3ohhqfPfrsa/3tvwJn2B4ANtn76fLaH7o3xpB5QvdI\n4h564v5HyjsnQTereqL378Zn/pH9Yl8p7wYgpE+jdK+/cWc9QsBfLBWXy+GU1L0PZT5mQ3HTRbhz\nQ7AcuL+l3vot9ZnvfOc7ZGkvGtyeVlWiaQRqxjjGg0F4nbSH6yNx9c4dPs/9gV5vrrSSRYVcX5cd\njE7dPVgN3qYwZuYxuFbLtFwNpm1ph/wLv782qodaJIwKPQxhT9kcBXZJpOiKxDKO8wLG9oLWiO3S\nHbnd2nqEgKUQX0GcEt37cfIn9LY6ne9FeByUOiy/pf6j3QHofLgMsiex3E7SNzcVN3ZUsRvumeAa\nDdPVHkQs8mazAdhsrnw6HIHvRexZwuF7kn38eqfu3vrUOFOWKu7Z5xudKv5CLBHBe14TRYtWLcn3\nlBP9NT/bM4+dXJD8msLlSOpesbBGGbRrjSHu/aN/BPD/Nl+52YBn95luz9YAsOa7dH5zIXyFcGo9\nc5MwnoxtmK4hL4lyJo344CfwW/Dnv/Xn4yO/9ecAv2XE8eUW9QDEhL0zsdy4SLAaBq/N7OrAGIi3\njqGGuRfOdmo9jEJzm5vRjqc1SfaP4H3HbvQWueHcy1DfXQCgzZ6hJAvyI+PUdVovYQ/M830DN4hR\nvUxT92x4B2dD8uayEe3LBQCAT/vdRsv2jq6EJT34wp4q6rw0zkKgxTLpFaVpsCgGR+4/wrS9dlxa\n25G2D1q0PVO6m98BAKw5Ddk6/OZSlKUITil278PUD1DNdJjvecP3zmNSwH7yW/7n/xzi2B6HAPxi\npHpvtIJm5hpGit1LGF8CsXvAFpZGyrCF4QqTIveA1TM3LFmofE8/gP7ecGEMeuH7ySmF9/ZXewCE\n75EqbVzt4cojSa3hjiPPvcV+ZRzdE8jNpbRS87E9CtmLCt6Ljt5zBO9F072nbW2Xnu+9BoCjL0F9\nmFvoPHHQvS104t2QIxYXvOc6aZUH77Xq3r/7tY7yoY5cAJTtddVSe3g8Od7QvQERfK/DQvry4oSc\nuR+KflobCeJxD4T4YESUyG8lPi8BRdg79AXNMAmY4i9Kyc2dZh/STYcuibpjJ1OUvguoewDKVWEo\n9KM715HU4bMHewBoA+b2XLYHe4D9fu/49PUaAGu76YNUK2iBgEZKKzXpunsBb+5zikLF9VlGsL2n\n3d+noJ7pY2C/0zOPI/6qrfsmXq1HBLNge/Br/S3Lketge228S6OGvTjsCa2QJUCK7V68unmxqHsK\njHHenxt8Q4iRgGE6nDvf5kP4YkTqXAyN6zXA8DtG03BjnRxaeNAr7Sc5/TcYRNQ9vmPzDrRF5HoL\n1+jKihwTQ90Ly3tOdU+5c4N+/N7n0P0UzgHgi+6vDXdm7l69vYfPAB59Bo8+Y1bRRSU47p7zLQh6\ncwX0PYq859P33vlFRGqubxfyDAAAXgiLe3y+9xQ+6T/OVnyC4p5rjlyjt/t58dYr7gWtsuecVUv4\nVL4HAP8Ofu3fAfyaxuyGEWnQPdy0mpxw7Yu0QFateHlv0fey4oTo3ofpH6GbaT7dA1grjUuZfI9w\nKaJ5D5Pv9XNb4XsRvlww6J7Xf2NhIroHzTUlw4/B9uKcuSrhUw9IOzgsSgxgf+X83fqQtPme06ir\nBPJqD3sALtHrgHGyCLqH/sCJ+J59LExvLgCEXLrqKeriz4J0LwQ23WPzvfGDMAIQ4nu3rhR11Jvb\nTYy3ab5cL0Oule9tway3B//u1zTSxmN71ss8ExRbtRa2VytOyJmbDsNK96OW4yG4Oz+PrHZbI/He\nILc60CiY4s51eGayIiprIbbzA4f+UGjvsNiNB2RW6sbdLm62F1+HYm/djmN7QsB/YKQxS/XnkvzK\nYt7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cheQRvZbpdfeeA/C2j6/yFFoOLuwd0yMMQefo8F1Ta+h52R7BGLO0xAPAm3Fm6AIf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\npsl7ceqem+4F1b04cQ9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Gdsc8dQ+AvIWxy402ALcBPtJZ3BCTzqDuxdM9wsW6d05e1zbI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\n8L0HR0YsdS8Oa4A1rElcrugpJlvvL2SjdjL9SBYzWdg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ASVEMZyUWwpGXL3VDP52mnmGf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6AZXuwMfdrvARzZIuaDs+J7WZaYqny5UAnfm2ZidzyvXTyVjhrxbO8QWrPjkqo8MNjeyXbZ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\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gVnbhxlrKRN39IpvUWvfW5luOQ3NM3//kSMBNrZr4TtMb4Rzu3FFbpOFb9/JpCs60T6cY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\nrSHhuXe25+5WItv3WE3aUyBwKofR6auYv49eIb6a9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1/4KfMWNZuEeoY/2wo6pMzMjSXW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f81\nbOzl/BUN3fSr9pY5F8IgSvYy5lF8pghQ6MNZYrqGCnuBLJjXGbPsnw23/vKn7mc50PQsYHuxmwPH\nDqJ7R446WeO71o2yDOszDOpyJmzVFpZsKkTUsrcuNWI2Ms3SWY1vVXoRKULpUOS48E1qsrtIDZZG\nhci2O7HtBemDfZ1c8eP05vhshb+KlRu+1x6Dx48jLpH8untEybX3Whz53K/SV79q31ty7F7L+fBP\nMv6yKxoI7h0Jy6vVAKLoo3vfIRoytoztFcnlrqg3ve6yOSMSDlRxlmIRNqne+MZkF/GV4WaC8N6B\nuRXciOV7KdncwFHpbSNxNFX4BXqMybm8JDPBG6t7f8XeNPEE98qE9jRNuyWywnt5uqcE9xTa65/z\nvU8idS897BYR3HO/jvgbxlB0Z7xLHuEr43tJ0zX0eRpG49ip3u/aH18ZPM/I5j4bbkWG9xRDe6Vf\nRfYnJhDci4nrQfeOBET3TgM2yDdSw/b6wJ40wscjbFEXkdm9yhr7EBHeM7v7BNsLLZQ7fPCTQ3xy\nyTbH7zGrCkcOzuqOEBO+mNb2iCjV9uJwzoR9OVlwb7S9vKVxhXRneNayRDnhPSJyfxX+ylfYZTR6\nroZbccP3VEUzjtz5+bl2j2TcHjg1oHtHjlZ5z5XELTZPQ4lQnY2el+N70hZ1Eb4XjxrSCxmYm3hR\niXmpBOFbreRv5vranq+xscQ5KllH512ZOq0L+9KXJre9WyLD796Ibc8RVnX5zaNHj5hSLMspSGeG\njxzZ3KhgsqEs8iukyFwNSvK9vlV8TeRtGb9CRPSVYIHllOkaTrqqLOfn52VlDyP8jgTo3pFjT9Vg\nnC95LN+97udq1RpEXzb4TAvpCdK5l5fiQrAO5ve9K+N3JkhlYiRweUUKh/eio3thF0vL4a5Wq9Uq\nw1sHbN+zevNA6q43vfP+5pcCc3LTpjcbTNBeuuNZjx6Zl8ostsd+El/RK0P4XL6XPnogghl9j1Tf\n4/nK8E8ozhdF2LrOuxKPwexrpMDB944DjN07dv6OSInx/W3RAN+9dtCdtRqX/qVVWmWKHb8X0Z46\nx+9NcxFfEZHmZlEj9/jfnRsyus5IVZEcEFVchD1w6nE2R+8RET+EzzQ8O5zjGbQX/DqRFd5riOjW\nlr1b0pO5UZlc5+g9t/EZl0qC7uWP3XPoHpGpqnnD97jDMPnovczhe/zQPfPz9v+auvdMuS0fvVdw\nakWsvmHw3nGA6N6x853vqBldZxwvZ7Ku3/aEudwX/GyNiOZ0/vieRso4PnF4T6dIYEon4YOfbNXi\nGczhbJ1nKFdwYebso8gcs6zpzY5AV93havlTZsU1zzPzuUtYTTBtbY2O18Ivwt7gXtFsbgts764C\n3Tt+vhNeOTfMPcd9TMzkjIyI3plgvkawMxYwyRBxPzfsTTlHHE1P3vUV3dzYB0tiy8sptlapnYz2\nvaSLrjCeT6Ewnyt6GeYoTBrc++yzzz7Lsr2JKZdQjdrSe7C94wFllu8I3mTuPSK69wV3r4PXRh3R\nMyZzoXPp8r3PYobH8AVnrWRzPTZjh5v20Ykqt5yK4IBoiclG0gEnHuXur26sw8VVXH5M9KlaUvdT\nM96XU5KwRollg+hZuUx+uDKF6x/3nLdn5hWdvxpNzfVaie96UvvPML2z3EXUrrR0bmn8dhaREv4Z\nEb0H2TsijjjaAGLw6V7ndV8Mv36h3P3CVrWz18SmcP2tXG7pPSJyJZImuYqvup+9pUh0j3M77j57\nW7rXxOYhBQfELh9XYKOeP7LeoidQNfTrEbZ3HgrB5dief8vKW4uvwhJpPtmD95J1zzt0j7RzMwTm\nXC+WKLnTRvfS/9RZY7lD8nF7NtySjd6TalrAziIie/C8owPJ3OPlN0S/iXi6tITVPaJ7SmDvki7J\nmFX7evgnBtdQ+qjJbwvI5260H17YSB5nTbb65A1cCocQJ7K9kZQ0pDWWz31UgscrK7Ynnk1qLahW\nbtMtxmG8iH695LF7vf4Ihu696t3P9WJps3Mjonv5M3Nz8riBkS2id3813BKN3gtqmkzOMMX2pIHu\nHS2/od/8Rux7/3B+7hS+we0Uy7tHQ7Tvkogou44KlVrtaTbfe9bf2JCoCEsmWb4XVLOpPvfKjsT5\nXlQZvhb/e8qaqOHfdN6Aujj1yZ+aW8v31At2KMvi9L0E4YvK5f4+V/jKr6DGrJ7m4VnG61u8954w\n6RoRsUNw7/jA2L27wD/0N859Y5/uGQP4jLF7RkrX/gabOGIlavAeBReMn4CNPRAtBm74nr3Figvn\nzuB6RNZ73Lg16fGnj0kdwqfAX8HnRKERYROM3CMi2kWnc4/mG/eDwGfPODWvzomIHhYevycmN507\n8yyNZ+ovfxnM6Eqicu9JniZ6ElTvSDmatgbwSMJ7/zDeZGJG9xy/OGdqnJ2dxa+cVnA1pMXkc1OR\npXMVoiNTU8wHCREqxszM1h14TMQH+NirqLu26tleRCsZP3hvkrLDZXjk/+wZJ+cVkWeoYO2eJy+6\nV2NO7u9S//AvKZTRlQ/cKyJqsL0jBbp3tHxL+sR/MH73mdcQ3btn216XjD0jx+AUjwG2qWR+zYP4\nrMn8vifEZTySYXAVq46lCUakQjLvsUgVEfu4iI5UTi5X3kjKl1BLJXvsXvpcjc+I2sGzzhAf43vO\n1HH8RRif3p+J0EyNFOTVljkGHYTt3Wmge0fLb7QfUajNsqfcCo87Z+v0PW+HfDS1rZ4l/I3A97qP\noNX9ZfneoXx8L3KqBrcHVavGVWvKIjYcP1PjiJrgd9qvWYEQn4pvQMIC3/ZnQ1Avr00KpD4WGs79\n+c/bn4UmfYAFssAPHYhCHOTrOSdVJQzbu8dE9XrasXsJadzh1XxLmp4oIUlqGmoaYj+ItSfnKjuR\n9UoRqL6XmBG3FKI7TIH3kJ7MjTk4KcG9Gr3/hTvql15376PuZ0RwvVKRPxEJ2dzPiD77jApUV+ba\nSMcKajzP4l6vwDoZPyf6edyLgqMDunf6/Fv1l4wxdJdE/hoDbNzPeD3G+I4mvJeEbwhbvwJX0zRz\nf+kPLgZWprbhjWJ8Sb6XOoOlwjp0Pcos7ZToXmkuLi4ukrK8Bai77llcNjd1rkaJ5ohpCaNSuc8K\n7IOGLCL3c0Jw76SB7h0rXRL3W8x9Ov9gjt0jSmuXXxD5p99yKqi/0Je+xPheTsmDY4ATpYyVVmsQ\n3p142wuvD1yiLID460ui74WPzLzFDSytu3A90JG+Zu47CX+jhPea/Is+xvfibW+ar50LTeW2/By2\nd9Isq9cBWTBl+P6Blb0R+ci9/b61udwFggpkdGevxBKJLT4roiYYT2vT7ufn5+dbom3B/Zn3Q68I\nX4IpnXt/9VExvjeQksyNOxvBQ/bS+GmR7HsfhZ9ino/d6HsNNaKL3kvN2RoLSjI8s+7Jm6khn7ob\nzOfC9o4Z1N07Uiyx+8239Lv8mtcSMU1jGPvky+Yy6+a+sjvkL/1v+cueJmzgi+0Hu6PX+d51kVef\nxvac4UC1yeHWzU2kUh23qM0ythdejjhzxy9MsXs5Tx6XpT8g9gLN1VfN/X1keI+3vQfORyKpOy03\nOyY3aF5gTW/Y3lGDNXOPk1HtvqXf0Sd3A7bXDoGK1T3HWrkqlu9x8RfD92KzuVZ0b6Kr+Cr1D60m\nlM3vCjYU43uug+J5HY+aRB5i39NV34vXPW3wnnZxNbc1Su/FJHNt3WskybsY9eG+nxu+d8HdqZI6\ngUKSzdXHVt4QMSflltJ0L2pZjeh0rmV1D9wPhTHawdH24tbLbcnTvYCi/ZzoP6lBvcAcLwjfEYNk\n7rz8ptW0lGoq/Rb4X/+t9USb6BosgvLKZ4KZu5m2d3xY8sM1qZJ1pdJTuoIPesrCVgarFdFqtfJX\nWC43fE//KnFLdHvreRNJ2dzsoXuSAy/cFxfsPFz35Nzk6bKibK52TjabkqNUI1O5WYWWH6jN0oP4\nJkpvBjPXTwuumpvhYD8nPYX7nwLPx6q6Rwx0b1Z+Q/SbqLVve9zlVwbfOwRrr30ReNwmtggLcVMp\ns3O5R1NmORKfq3RE+J5x9hvjp/3qvo3Jgnur8FoaVHsAibtFq7OMmj8+eTtNE8uqXfnJGiJ0B+dO\ndrP8bueB8SU0RfjYexc9U4PoP4VsDxwzi//cnTRKaC8jvufbtouaFROMGGCodMbxBPeeJf+lKLwn\nItX3bttSK+5Yy3T9kG/1tDCBK7e07UUdFr4OS7iNrXTsXQG+ur5npXNtUn2vanjvgeN2f1d0OzU2\nglkD9ygzmeuN/P1cj+Z1v7z33ntE7zn+EOG94wW6NyO/cf4Sga/MskcqIn0vqq/Uv9dar2ROzc0e\nCj3VANSr9D+dxfc0bslbVcQyjkadRSkO7snImpzru3Lnrm+zM4Sv3R//dNQmqhV2qLJD7Pi76xY/\nNqJ7RzMZMNgMJX8vjbe9n2qClzMt1yVtLT8f1tIgojGR+zPv38H3jhbo3oxEL4hh/+23jJkaPb+R\n2GP02D05Y3yP6Z113/tsSSUQqlHQ97ZbmfJ11V66qF7SJ737K6HHid/SxnE7F+97rFWIRRcwR6Fl\n7vg3TdP9F/FqjsP1kr/Nz9d4mBrekwzesz7xx+J7Zv42n64NVGxPNjz2pz/VFC9sex4Bi3ez8DhA\n+N6xAt2bk299SzG+30RF+NqncsL4G5JuSD52LyER1rZ1GYt4uJml7t7V1VXW38t8T5jW23LYTxvG\n6yV8zG/HP60aPc3J7BrjBPzvss7QPQvV97Qd0n9pXEvnJeGZhstSN52rf+ZLLpRcs+6e4nsO78tI\n51bjZ1nuxY7Ue683vvfovfe4KB8m5x4r0L2ZUX0vfsaGY4vDZjyzNc4jontJPaWnrfvSl45u9dyr\n7C0IJjHkYfpelORZ4afh12ZhtuccC1qlLYvfaOd7dkXhpn8gc5cicM7OfZgW4fuIPooutlw0ulfV\n94geED144B6ll+Z7vxt/Tzn13uDez9rMq/sJ7+XE997r/oHfnQiouzc7muL587u/GR42y+wpG/mW\ntkXPCRZnt3LiIr4LbJiiG9mMmtG9KS7iqxIbMeQ7tfqeC7Uq33qfkLxsI3qNURdNnnYWn4hRAZJi\nP73x6WGk0KFLuYxFp8MUmjf8Xw6HNWu6LmtPbOU97hGVtCF8oqXUNCUvGd6LK76Xumyum8hxJ131\nvbiqe/RjIrX4is/3RC7nsrWf/ydmFY3/9HMu5qe9DuzvWIHuzY5T937zLfZ537J+MTeiUkD3srJg\n/gssTfhm0L2rMpup63uj7mWOU7tN070E26M84dPLLPv/pJrt8brH+l53X8ZCGnysLEX3KtpeTd+L\nqrVc3PfidG+otdz7nnCsxo+1Unu1dM+9YBqb4x1eCrZ3tCCZuyg026PfqBMuhpu/4aZh5Mz6cLPf\nU90xT8eS070qtB1DiFKrLTsotq5u4sQOqRfmp/deGT+JqjRliZvcOdaGHe4rvaueEF7hYstC26tY\n6KlyOjfANFWj/lr9xZvMlahXIT17j96j994LTPUFiwa6tyj6RTaGYXz9Dd3w7JU0Egb9iSJA+32m\n7R2CEvAlOqbae5kIfG9xH0lx0C4typolf6Gijgq1ZuYyb2DiU+ibqOF8bLpiy2WTuVFkLawxEz/u\nsrkyfPb1HnUl9HicFZUdD7yHYXzHDpK5sxM2tW95nvIt/9/n5XILBfa84/e+RPS/Fz5276rgtnTB\nY/Y8SxWGdG5Zt5GE7VaHCZO5o+WNWhE8cPFXs/BcmL5X028stzTzuC9JD+m9HO41SArvSaN7uojP\nl82dN53bJ3Ojcrmd68nG7hGRO6MbUjMum7si+rPQ64FjZXGhhLtHOA/rFTrfg9Ung8rw2cKXaPEp\n3auSG9PPSHLxvQDVIllOVvNcbBG2l3BQhHl1y2amrC2sZWsvLqylNC6I6KJ71sXFxYV7LV0Jssp7\nJhsi/0GJOV7zZnOFvB7+ocgyy5btBUksxfJzh+3RX6VtDywf6N78aNX3YvHanu8PpwvuhWEHnUmr\nCdeTpioYvncM+y7YxzjVy472tJZ3rlZ1FKhZNQk23k9N2RMcOkPoWsEbRa8P9KVlcxN9b7PZuI/L\nZnM0tZjFwb3X9Pq1eZ/o20M7bi/C9hJxT9SgvyL6KzjfKQLdWwLFF8wlCob2JnO5RLZEJBa+imRW\nV7YJmNHC11Avww17szrTBz2LY3vRIHfCwF1WdC/V91qYAF8rgseDeNyJJXsxxNhe/hoXqxWtVqvV\namib/uqvEOM7SaB7C6CO7YWeEPa96XpHW+u2rgeI6FHVfdG5qrx94xgnzont2bIbzSUcukvP4xaz\nPdFxW6/XUcdG6t4zzkYY/E2scdP43jm7oI6pdimqdwzZXLXGfNySuT8m6ofr/ZSIfhocuseN0Gu/\nJ3tFsJ+PoWieFiDACL4TZBmDu+46FXxPcGInTOcG9+bavGPrfISmnKtxVWOjY3J0TeNqZWNZtgyu\nx+0WxJvNjZmjMZBZZ5leUVzRPZWYi1q8Wd1b6ukf50dtelZuce3z0yqxUOp0jR7t0CReBnOWWpZn\nc3sG3RPO1PjrdvjeX4bXyu3Qta5rOa9JMI325+6PLnTvBEF0bwlUqJonGBQW7vaKJXxjR6jFJXGP\nzPYG2khTQ11Qb8rVteLwjwtImaMx9u5FUnlRh+4EUro6FxfSRG73fCKie3+U+Gpy22Or7/Xn20ji\nxlwGItt7663ya2pQiu0NyELFf/3X3fA9se2pjAOet+8JiqYsZC4fmIjF9jB3ixpVkvMnARQc3hfa\nGdXvttttnO/Vm+7wrNqWadSOzATuFPiOcNrRV3wvVfi62BFf0tjH+uSULz4/K14vOwdnVcTW9VLP\nvCSb+xbVWENNPnaPWTG81qecz9n+OGx7mI9x11h8RwOSCfXDi+rytsP3Ut30TjCXW5Pu4E06D+dA\nFB+kywvqvSLqY0cpLdh6Lb34EyfOVJt9UGrD94goNbyXiWeKrpSw771l3ShFtO/F5XKJoqosy7bx\nC/YpftmDCp4gRzUrCsSRMqhKYfK5u9trktje8bA6rDjrXtWvvrKf2OU3tIkafaW2OwkD3c7pVe4y\nXWvZ9S1d3vZGb0k3dEORh2Ry/p+0P/soIpvLwh+WmIMVV2l5Rs6UpK7M9qJVT4nt7d4MjWe/mV/8\nB6JftD8iwdi9EwS5+2VQZXKu//QGhKC07MmutGsji8vq3mKie7s33oe7/WLk7kC1gqv9ASu69YCe\nrjq9iSBT90ay0hOSazxxZY2O0sJX5uv5y3uUbHtEEcP3Ita4izpUId/rYnq/r5HPFS+r8ZrOiOh1\n1JIa0b432N6OaPyg/ph+8R/0mB7ne94AHnTvBEEydxFUsj0vgb6utI3ICgonFdqbq1Txrm1jXXgs\ntKn+wStp66Hje8iykDwjyipTWO4a91QQXiAXRFm53Jzae04KrqxR0/bknGnj9+rYXjdAb7fTbI/o\nF/QLPYPL5XNhdHeNRbZGdw/fqrjxyJKFkw/dO8RH4Rafy92RM8DnfrerKXyPiPZlznH4ajpQbOZS\nyX3OmfJcE4XMOJjNVfL1hQ64m6LN9R8lx/fE0b3zmPBecWaVPZMmZsHcOIYvnX3Df7DljonuYXTe\nnQPRvWVQZGruqmO8J2ONrgoj9+J3ZgGranh4Q+QM8blLHKzcOd4CDEdsT/syJ1GwnzOepgINWFzl\nZZMVEa3aECV3tJc4fG/5M8GDzFdqWZzL7WCm6Hr468it089IbX/apoX7xNoC6Lc9RP5OkeP/4J8G\n2dE9TfOU24takzXJ92yXmHJVDR+77l/G97yhvWnYD//Upj1D8yQKbqsvOud/gW49Atrv93uyj/cS\nbS8feTKXXVqjAEczWaNyMSfSbC+qgYXQ3T2ge4sg3/aiHwhQxRMSdia2Bt+EjO1sN3RGQW13NQ1f\n8c8pxgzp75Tzo7jhYfaCQIFr3ed7K+OnyZIHy6TP1YiZmyv3vYKD9+oRG9yrXcxJb3g8Dawd3vP5\nHlzwJIHunQJM5nDVJpk8n39vJ1cnKpQoOGOp+BZzZu487Dy/8cfdvLOG71nulXsmwzuZYJiq7WWO\nIp23BbOOzuTVi+Lpj1jyXI2oSiyzjt4rTrzt1Y3u/TLH98BdA7p3sqxWXtubpWc6ZAwmPCZW9m8r\nxsknOBj7zNM8yci9ZN/bZU7NlZHzEgucmju8nT/6ozTji5qYWymb6wvvVZyi8YDogaDQ8tXV1dXV\nVb3dcHIgcjqf6XuYqnHngO4tghqLqOVwBDGKRWNI0opzPeZ5BSiczK1kezfDoLasI7CjXZG5GqEn\nxPie9dEpKXzFhwJOsLLGuXT8Xtybmymd+0AifFfjj6rS98tf8vfLfM+TsEUu9zSB7i2COeruHZXU\nbVW+PPfe+IkZoVjc9wz5ki8XxhKz1LGETe8+N3rnnr6PRRqwJN8zZ8G7WVqA7/Y2b4JL9LIalSJ8\nsxFI6V5pN/vfyne1jOxFjY6G1N01oHuLIDO6d0QZ0iITU5fge+bkDK0cwoyL1ajjHNfrvIFxYdtT\nfS9oNZuN81nJe1lGpRJqscSc4qX5Hg0CmzRdI77Kcg3fezxfMRZ/dO+KuVUDNrS3Uv4FQAe6dxKk\njYlz9nEVw35lzHQJvqdzfa0EeyJa24qmnldPLkwrluIAXx/Y09xnePvJUchCKuV/9ZhmkvvwLHYE\n3wTJXCnxiWqH7y2qunIs8sJ7fCK3+0DxYWfpbA2E/U4U6N4yWNbgvZqeUGayxsJ877ofMrfy1Fdm\n2Gw25Y/1RIVrttaL2U6zGbK3ivBslH/Hg5WXdc6n3KuzX5buuO+dWjpXyFW9TXttzwVm595toHsL\nIdP3oiRjZo7H9648jymLp10r8yMizsOmUsyn35vsGK33REmk0lS8lhsa7U97iVjj2lmbFuA8QWsu\nGto0xK9CEf15q3W2c0nxvYQ1c4O+lzILhQ3vLTW4N8EU8kK5AgT3TpVlNkEgiuRSyq7+9RjmcHz5\nX+u/xrMr92Ps0mlyxg/eutrRdp7ffFjZM3vsPox3425m9OWdow7FzrlasYeVdEXpAX6tU3+FI8dx\n39xkrxB8U7zBTi+2HEeV5XMfL3B1jausv05aNLdFv675y/wXyuK5jkIskL3TBdG9pZAe3kuxPe+k\nwmOwvUnie1fVtqz22pmzZy16Fcseu+e8QrYi2xuw41ouaYnYZcdaxT5CE2n56z5hhVnXB2iz2bRv\nffZQX9P0Tf9SRu8dVeDBPVdjsmp7PzLvEH6LGfK5KLt394DuLYbJh++tHH1rddsrND1hMeP3ogbL\nbTYbq7Mv63ttMrfARA3XeXK8X7PDdnXgG1/XnrDXQlEYXG/mcQ/cjJXYP86lUVr+ibK5weDe3AYs\nYbg6Xb53lfsK8qkahu9ZH9YjGt4DJgK6txyq+N4Y0FjO8L5SO7IY34ugQFxrbmR26+m9vf16fxzW\nLc7nKalcgSdogT3HJ4H7muMYcBW6gn3fmG74OcoyCtVZzh5HtoxaLO3ovbcmGq23Xq/Hjynre77Q\nXumhe7+043sy+vDenyFve+c4gm9UwE+o61kNC+us6ND9vhjzy6P3vU+mf2ktk7gtvJZFHluia9rm\ndy/pQdiIVsUeYrTWhr65h/O9iRg/aV7vK+bN8S90W2Kahs5m+Bk/kK9cdG9yqgze+5ToLaK3fl9h\n0ybdVbneO6/JqxIv89ey0Xu/pF9qupfwYYXs3TkQ3VsQ30qK8AU/6FpBuNVqjHRMMVfsVEmZJ+BP\nWJWM7223+Z9sx3XFj9sjIkdlvRC2O+nHIRTlS3sNhpLhVd+2NspPe1DjJClN/dpIyeZGL6xRh+1b\nbWhPie9VCfWpV2A31NaK5AVG7Uk/j/K5GmopFu7D6rjq/wN/N7gDQPeWxLe+VcX3FLQmYK6TX7y0\n8JPSG+x4VnZzoa4812o0cl3eZXveP2rfYVTMKuNi6IN7vtdbOSZoMPfxBz/pOKYNf40/eAk0jTX3\nJMH3EgbvlYf75lHH9qw7rHsmm6IRAet7mu0xAT7E/E4Y6N7SWFbB5Toc0aJvLhLqsJzCwInguL3N\nps4b5VxMEGGNWs9OZtuiMbAJvrfZVDt4KbOLyyMdvZdyBPrxe5MV3dMvlXKyJwzumUWW+WuSu1T1\nSsuQuzvFEpoBcOc4ft/TVEM0dE/Uiy1mwgZ/hkS1leO6a6mOrakfLG9ZmfsFPRtnH4o5/P6mU+h7\nzK77Dl+6C3bCV8r7qiRzuxo1aRltM6lbEMdVccXcmgtXgxr8dMH37hLQvcUxXXgPY/cyUFfVmG8v\napFue9GIfW+9pq7rlVqZd9OhKnzcLhB1h8bOiJpE+p61yJzvyUk0TBq3JaHSclIyNxjeU9bck2xP\n/9y99da0ttdZ3tWVLLRXtrE1Z+XKL2Vz6J7peyjHd8JA9+4wM5784w/vjUhsTxywKBbeizy5hwPR\n4XDoVjSe0PaSJrp28zd6pUrVIFv41tYZ2Jpj9Fd9jKzIx6cbqlcz0d/U+KBXHryXNIKxhu15x9Ne\nVXjBML/8JYl9z/4gm8vmjnr3Z3/2Z/od4NSA7p0APnfyPTZNdO+IL7Er0bMEFjTHsL2Y497K3vgr\n+6RKtpdR2CTfjW3fs0tetx1+/+96tKcSmdE2oKVfH+761GmEdnOyhTXEtfdqz1cRE7jChJE9KtjW\n/vKXRL+0B+/xH1nBt+o/M28gvXu6nEgBtlPiNwl/489aOR/h+4Gyq2o0jqau6JVXq/DelecxZbJG\nML4X01GXO/zyLqbvFlZ0cK0pW032KD3WO3TGDj0QXGPMS+snYNvdx3b83iOcIaP8+0n+0hDU0pSF\nc5OG70lr78l8b0vXNS9LonLRdvFnMTBXw9S8DscniLmbq8PyV0Sd5v0VZO+UOeLQy6lSYOyeflbZ\npsATmZhmusDRp3OVwXtboq2nHl0cxQ5/REBhKMvIFiCmbbl3V5I6Fyqr246Xmrb5TI55ha6EFNtL\nSucWrrR8fV193OxxrB8e0Zj+wszmkhrQg+2dNNC95ZHle+2AbOO02lGOxjPavGgL15DrIjt631PY\nbskV/6pWXKM4zqkLdVXvcChwJaRWJ2Zemje7SX1vMbnMI+AE50n5iFo5jf9gMb5HhDF7dwHo3gL5\n1reSja8xfhI1TbNSF9aYluO+vq58D9qV9xwhsEgVKWbbkcfeKXtLDOwZbPijHPZI7k1HBQ2rXOCb\nzWaa5TWySAjv1Vg4t67vFYogyyPtf+1/mE/m8p9d+deoPyME9u4Cx90dny5pvqfE69q5g64Inve0\nx7dwjeP2eJ+9H7uEQsWLRzOjxC67qy1XcoWNMJ5vA7XDJ8W+htjOJ9g098bn9z2O5elfxdm5EW+2\n7reR6ZO5Xt9zDN3L/hD9GWzvLgDdWyhJvqd+h2w8Q/gCZz1SNLSyFH01Vz2pbOaOm2ZXu52ujmjV\n3DThCzy8Xq8lS0AUmgy43NNkdsWJeXPG92Jc2zUmorCub5IT1gtq5cVj9xaTzi50FmPOQCC+Jydm\n0VzY3l1gMQ0BMIhK6PYqZ/heKtHhDU3tRrEzdqGXwP5+mUgsLZn4yPdgyX21I3xr5bGQlNwm2t6R\nz9VP8YTsjO4E1IztJc3USOL4krkzIFxHLRnH2D1w+iwvQwDErGmIcBy6PutWDaGpz1Vie4U79OEl\nm1uZY47P2Yab6q3sadPxiB49JyJ69JJ9uPC+9qd4rd8loEkN7hmFWKrLdr0pO+ILnZ2O3CJ5+8mH\n2s9mVNe8Ztr/oZxO9ojoXBjg2ywkvDeD9afZ3or7ILF3grsLdG+xBOvvtXVf+4zWwdu5tZ/61UHW\nB8YNWNF8T0h33cnkKPikWmX3GB4R0aPnnghfeTf1qN66/tCi6/rC57GtaDZ0s+lDfP6PhL4L1h60\noi1773V874bKtM/1MjgfJdTeOy9cjIXoetu1EMUv1IKyJ75ECsf2mCqabDIX3AWQzF0soWTuWvvR\nGp2/zv8qZZ3QMDmNYpkW+kmRrdg8s+55pPzrZ5qZlXY+t3w8onJstfQlqQzgi7DI1fCPgvhY2h+7\nIjNtSlxD/ibh/8kK7iVVWpYR886vr4lqDPoo+lGq39MKP0iwvbsLdG+p/MYf3WO7E/fZjKrEEhcy\nUnYkflUpkUoE2/HJonuK5/G5XJpyasN+v7dO1Zr6VcCKfbQXNnZSoXBsc2X1mOvksJ19ZqLJlz3n\nzPyePNtLQzh+L3ZqSvmrtOwXpzIBYH/ZPa6Fn6sEF1gg0L2FEkjlqgm+zvzWJZbwpOiOap00GLAf\nmhNqpBemGs8lT5pqn/fDP0TUFW9ZE5G96msWE7yd4gOMkubnspfx7a1ozgvz0etcfMZlGRbauIt9\nb96hRnMM2/txMJf7o6g6ywwI7t1hFtoiAD/MjM2I1smrZ5Hd0zq4QZahIS8iE7WSuTaj7124n7Td\nUv1SEl4rX6/X63UhT1vSPBkxbcnDqOtypfyrk+h7Xfh1Nt+r37YnFt6rMT/3RKgyK1f9NgXbu8tA\n9xaKd+Qeo3Zy2/PH9tM6p2jfEy6B3hnLwmJ8YY5uh1WOJ/fjvejt0FBqsOg4G8kF73UZ36sc+yur\n6aJcrqjiXl54D1VY7jILbhPuON+KqbxXbAmG2DYu/3UlZjRXgOkq9Q8n2GHBkU8dvK773gTqmiiY\ngUOgbVW8xEmq7Lpb0jnCe6FBex1/lPkyHxF9VHFtDYb2PLYFtdUzetRfsOqiD5WA791hUIhlsSSv\nm+unaOym73DjB1/djOncoBsdZTqxOmvdI/YFxxqp1RsW3I/63/K+wKXe3FKBr8QlT40I8R5nT9T4\nKG16bnI1lk3/T9t8LKU835S41lFzYbTNyObeYaB7x0i9/iOukNvSlh6ojzI198I5N5euaZJP1lqL\nHFXyvQnq7rHVwSTsPVdgckxt3JeGqLkVu5OnstrEvif30z/Kn5hbsRiLpXPH21vVKcQd4LA6qJ+s\n/0C/gOvdbZDMPUYWoVnqGl4Zc3PH+NHSVksL4LY9Ippj1c+SSUP1hC44uFrS9oZtrdS3X2i6+4RE\n7XFuNjd5uoaM9kPU5uHZbPxw1/WCr1IS97OCwXve4J7RDh+M2B5s745zdI3ZXcJVjGUZtqf+khKc\nuRmEaEvUyp7he0fqgbNhK076gVM6jiM8+ta05UBcaL1er8fvL4mFyjxtaYEqfAX2gmMm35MWY9ls\nPK6nU9r35mhlwxNzI1K5yOICA+jegqk0em8yxMPjB9PbGvcyN2dFspwGzbe7tlV4TPmsg3/0ONba\nlFpUyPasG0k0vrLGEwlfbINeoMzyxJM1dKRNzB1B+aJyHJ9gMCXQvSXj8L2cnsNfhaVgFMLdDq/t\necS9lSw7IyO1vemwThcT4HMI35l1Q+VAROdKCIY5Z4vFWerOlV9n3tqKCk9qOl3bS6Nc7T1pxfZ5\nEZ0YQdW9UBmW4aKF7QEL6N6isWux7HZEu3W5yisjGa7naVo66VPkb6wJfdzT6jx1lommGFbOnjDh\nSVSDelyIb0V0TnR+ft7q4poKFvthSO6b2Hfb3qkYqu9cOER2RUSHQ8J+zR3em4OU8N55KeHrTm5x\n21vo+RL7ngFyuQC6t3QM39u1wrceet91qbhL3JRc/UWZJmaj3toMpRO6P+xe7GbhxvfM+P25bBW1\neSMNlvAxO2PpHed7RKRHYWr5XopVdfh3aa2HmJWvHO1QvbVnvbnklUad+dwJBvDN1ZzXHL8Xom1C\nKnziip6uqafmIrgHbKB7R8CofDv17n6NVHHc5fb29jY9IKS+cvAZfC87/GH/U2x7C8nUCG1vmv3N\n8PzX1j227ynPqZvIVXumEjFRfW83xleP/hlr5rmFenin8B2S4oX5r1ufGcfvbYgqfd4WGd9r52r8\nSI3yMQE/TMgFDBjnunS+9RuH7akIyuXddn2BWatsT3Ht2lrybO6y0tRu2OEb9akLH7pHRPR8ceP3\nLNLk7MxWQCJ/abtiDMXBNv2Vkx/15TO5gbq86z3pH6ZD4iRdNpjTEBEdai1SN/M394/iC/AlF1vW\n2dwQXdfwvSUPVx1875c+2+tE7z/8AsoH6JjWx7zTtCVZnLYnEDa9/xkastiqyo6CsWbMwqV7yl/v\nmed2urdtb+gtuFcFP/E9mMGVfdeoe97Ce9eTfJFiT599iqxjx07PsHzPela9cEd3AY0HTeh77OW4\nX/eWyjdvvghb+4fKG01tIbsPXKN+8pqsLfpIdr1yczUS6i2X0T1aXVcJ75W1PUk2VzBVo43u/ci+\na2AstgfFAzpI5p4GkU3Tvh3mH5XGXY8psBSM/nvPPHBN2+WX2BPnc+dCcorYSJ6jJMtIMduz09Ar\nKlhTY52R6O6GR5TZEWoappsvn8/NyOL+UX7hvY6EdG6p2RrbpQz28CA4RRLbI6LgdI1uO7A9YIDo\n3nHwm28R/U/P45HRvXj8HaDWg/GdthHcU/dY/4PrLbXxqGVG98bwni+6dz3RMAnzvK/58Kt98Hi3\nGzXw7DXzlEK+Z8TPhvt0uPielf4LeFl8dK9n2L/0FrIbPjF+8voev3Sjm/mdvViAb674XqVOrHAu\nN9gCd6fxXSIi+hW9S796l3vaL23Z+yX96JdE7T90+DH9ggi2B2yge8dDuu6t93VtTzLa/oaINurz\nxl1m/+I6wvZq6d4Vd6ckm3s91ajYvT7UzHmarrfWIfQmdB2RvoK+p2+L23Hb99pokKoIoS6Zbd9E\n4bW9++9j6T98XX9euM3NTdDMaXtFfK9WHzZ1Mnc4ke/+argl3343hq+VQYzVAxzQvSPC7XuBbnid\nOwki1PLJdG9DEbqnE9j/peme0/dW3dj/kgk9ie4Rkcz3WuFz5XXL6N7a2hi/35bvdcm/URGCPTLX\nvsmO/S0lT9RwbK2O7mUPx5lX9/J9r14XVtT3pME9nQjfAyAExu6dBEueQzaw0ZvmmH0O2eqThN1J\nZRi8l2J7/RFYtRTZodSTz0/FPTurbXs9Y/FGxzuwBvN1ajAO+apYKqOhkibRNFXaWs+ibVLKzdVI\nq8WSO37vWAIWwRM1dWU+cPdAIZbTwFOJZU1EtM0K78UE91y0V9pKeaqopMtxwh9uu2daEU1YEXVL\npJnza17rnFM2Spwu/VJqL1vP5bUhsqN8eQEh6eHmi6kkM3b3xWKGRfzxj0r6XkqAr1A5FgBAAET3\n7gY5tieb5diFYgKzK/W+VhyXCk28qzVVIxluh129/Gq1WlGpUF9N8mPIaRNmuyuLetFLDwgdDie0\n3EChtrvY1NxU8uJ7Fc/nBMugKLCn81fcnQCkAd07Ir7peczZj7YPTFKqIBwqNttmae9/BPWXg3h1\nbkUZy3YNSA7nlr0pokbvJ1sEcNNVYDbNINYeVzF2UKttLOT1p9N0l6rHUp6SV3zgfDke/hWEDxTj\ndNqMu4DX9/h+s78v2fcEvfGBqJt5m1I5TfI3Syyr5a+ybN4hcrnEEF+c9LSlDbdE29jhQlGxOfZy\ntLpP8RY3RLTJtb1ljPQqE48q1nKXS+amDt/zcnYWqAdZM1w74Zho52cRvgdKAd07bZT2Ki1CFtUf\nB2F05vhGj/ZzNS4u3M/ZGgPO5JqRModjrf0IsqXtlpIKWs82JWhI5xJRTDjIcoFjSJoLKLk8bsls\nbmnfOzsjQf3vGqxlcediOE8oJueCUkD3jgpfeI9jGttbUaq17YV/N1My95nvQY/skRGPjFCMNBmJ\nXPEkPVga1QGu2xhf9986WxYHg77rg/tLyh790fy+dx7w9xl8b+ovNu4ziugeKMVJfNW9U8QUW862\nPWmrd5DanhltkV1/wZ2fclUNEi6s0b7X5A/YJFMKErRBPJypRofZX2a97wlewz4B4iNbqzRGbqNb\n/jt6yYTuO/RRQg0+h8EPosdXDSKiWn1Y+cvXezn5zinie6AMiO6dEOGFqWKRbiE5Iyvrepc4dC9M\nXmW9pX4RE14SVRJh+mV2fk77mpMna9jeipZoe2Xjex9RQpCPj++dMbdOEvTDYAJwmR0brnyu1fMV\n6HDFW9gLoz72UKqY3VkK47oa/oTu0knLCYouijq5MG085HnrCEdVu7FIwZ0aGlq8GksJ31MnaXii\ne1Xi4BUuYM+nzftBRHAPFOIou9s7D5/Q3WuVi4326poovthy7EitIKm6F9rvKRdRI9X3/OncDOrn\ncpO/6bWXmKtIdtVBT2oy97y/4X/N9GRuUa1a0dLqKxuUzOcSUXw+107nqhE9j+7V6cRqXMfuKwq+\nByYA0b0jxDF8b71eu1upLXUzMiNY6NJsu8le6Rl/9yP+7oIcJhi5l2wz6+4fO3w88WRGyZyNDBUo\n1zZ2Ib3Ffrdumqa47UXH9+zwnqp4kydz9+VHCqR+4jBZA5QBuneESObnWt3udlA9YQm+uJ47rZtP\n6wCn8z0W1fZqBfcWzSB1rfitjXsn4Fy3g4UndItrXsG4Y0PVOoF831Nx+14li95T6QsLnS2YF1yB\nx8g3v+k2vjV5TW3b/m+73SYVXnO8ZmKVjaSGembZG+vuHTs50tCe7b3yyySuNw7eU1K5VMv3Tr5x\nbJqGmqJVXTSm8r0jIPBZ858CJHNBGU6+Rbtz7MmX041YREvehdbv6ZU93dECjO80yA4S9VdagZp6\nIjZ6oeW08nvyPHmhINpic7gqT2tsNNf3fAP2pqHQ94hbuiXP9RTohZHMBWWA7h0pzvBeTEpN872h\njHz3U7yd9J5e3POaUzVO3PcmqblXBC2tWxuj3I9qe85u2TqUkx/b5dqe1vjD9+pxS0S3t6m2B98D\nhYDuHSuxC2ywDL7Xh2fWNCifeCPyb8Aluto3RDSv71Wfl3s8tjctN+GnLI4aa7bVSb8Ovvf0aTn1\n+yhzVTXJbI3Ff1xqlesGIA7o3t2m870xGTftzMr4Gai7VvTeFN8VMfXn5QIJiSupTRtsW7Lsmdt5\nOv4oGOqL8z35asgKFX2vRHMYsj3B+UR4D5QAugdSUKNrWU2idFmNrf6qJ53NncxIjizskLx2i8ri\nY0F+4mQv7tlPh39Kkhfgm7UYSxnQx4KFgEvxaCmSzU1jt5vat7a0Jdr1sb1Zo3tgLjzJ3PSvHKtV\n3mJ3fkrLZVx73VDqxNuSCd2oAN95yvC9ZSt8gS9VmJsLSlDkCzM4XmIX2iDqQmu7Xrky8x38UgMr\npgVXDHMi27ua5mUsVlP1YLfH+3VPmMt1eNyKiOgQCKQ2+R11YY2Mtj2Kso2n9LH6i/pbFh/FrLBx\nbpza18ca1evIz+VC9kAZjre5B2XCe/HV93b6z9yZGty9K7ufXKnxxKXkco97zVw6unSuE8dF6Ija\ndXerQb3lzp8diA3UpbTtTz2/pRO5npozwHd2RnR2ZttfvZO3hALesD1QCOje8eJYS225OFpleyHd\nVfeP8gfG307je8+Y+yaaqXEE+jE5airCDO6JF7xyHVjGDJcV3IsctNe7YWT5D8P3nj6lAmndcvWW\nz87oaMfwJfEubA8UA73K8VJI964pNiE7uNYbivtbR4bSvAjV3w/sM8jI534i34coruy7dN2ruIja\nRAOSjukL3+h7TCqXuw655u3gbPSsI57pe7PJnvr09GpvIwWSuu/QRxFBPuP0Wn5nDOir0outaU9F\npub6ryLfaYDpgaIcU2MPdELJ3Ioj0BMRhvfU8iyrlSMlt4h8rtD2mo4ljpR1V389MpjwHj8s1Pmh\nKPxpKbi5opX2mlqV+3x89FHMHF1jwoY1XWOC+F63YEx+NjfDt1F/BRQFunfECAbv1VA+XbSqFOpT\nBXBZzhqfy1WHXG1ihG9beGVjFyfie/aFmH3h5LWOpcKzCZNrG/bmuME44Ss1YyOGQAG+6r43rANd\nbfRe054H31MQ3QNFWWK8AUTwTWdKd+jsuFmu6ZgzJko0h+78mptjKcVi9LwbIuHyEOo6wXXf7C0d\nx/c+f2O1t3yPn/Ttxv6cFJibm03CmfH+Sfz2ntI8yufjrPL6au3VtN5XqzvfDP8AMBG43o6Zb5Yq\nvid1tt2uUg413kjnSeZGj9xjP2CBL1kb2mzUOOAUVQ49S3oeC/ZFHHdVLbJ6W0raVfsT87Qmtvhl\nJulGzNnQwntet7t//17a3giIWYEcgIUD3Ttqviktx7JaefO6GY1a1J+6wy3xy6nNT9j2EsdIqTrY\n1kVcxEjFCniz2xv7GX1cNGmxrQCLGjTQkiR7hWN7JYmrv6dg+95Qj+V+xv64qa55gm9XyOWCskD3\nThW1iMlQ1GT2Hm3MMI+70k5jWLFP83AEBuT6dGnJ3I0eypttgMX0TcFms3EL32azoc1GOxg3Nzey\nPHgx8iKemd9gUhbECP3NzO19VEWWkNK3vnefiKheeK8eRx9NB0cHdO/YcYzd860mkA4zhixy7F5f\n4XbcFe4SFHWUc/teMLjn/HApDtPZjlPxVl3+fIJ0bvVXMOjfsy18pgAziMJ78qudDy7nHpIs3yvT\nNC9MKlIX0HVOzagT26sf3BOA4B4oDKZqnCbuYhNsF2QPc3fwxtKOdexcjZVyw7mbofWtlsBFftW9\n8eO3GcNWNzN9KG9n+/K30WN2G/3mJjWiF3UFHexPxkyq1FCtczH/l/uo9TXOwwvlpdmep81aF5+L\n65zuE1jBELYHSjN/AwCy4IN7no6OfSj9y6x4NQOWIxywp+JdQ8330dpoP9rbdrBr6sKJkzYG7vhd\nMduNGw/KzMvNPiARO9C/WPsz8aWtvxpVo0nJDSsUmauRvJoaO1dDWU5Nls1dr7v6KpMG7xIO+7tY\nTANUYPEhFBCA9T3vaTX6oLhVNYzoXvY34XFPD64H3Ay55alW1TCr7vHhPVf7rtzfBqwCYqMegoql\nWJpOCqa1Pf3XG8f9+oPqE2TraojbN17MsuN74tdvutfLOgn2H9/ydyeRX4sl0vZIO8tsPneoVvRF\neFva5eFquNpSe7k6+C6pNZI3jg+v87xA9UAVEN27eyxU8XNK7z0puSMRNMq/xp1+Nt1EBTHVxu61\nNXdzYz+5bLpZuOJDEs7zRbGQMHPOSXCdwWIntkwtljhCQzSv5ZsS21756iuOD+/CxlWCkwe6d5L4\ne68VJTtf6eCeir5Lgg543Jla0T0RjdKjerVJbd6jXI/qRfcmaQDsnLT97gXTMxJeWPg818WWe3RW\n3q0XfCFRSDmXI/c9Ba/tFWII0W2Idi3mUxy+h+AeqAN079hh6+4FurnVapXoe7p1FB7UHFuLZfaF\nNV5TvwxSP/DK//yMr/O1onuVIwzOJY9zKTwz10GBqbmHw8Hhe+qXhMzXcV55TdET/HR64RujuHyp\nZfkag2pjtV4TrdeVayh31qbVSxeBpXJBHaB7x45zEbU6vFEdK6a5HOJegUuum56wyMp72tC9169f\nkzLCfup9KUUt31ut2pkm3eksrXy+ZG6/znDmwL0SeGaLNEP+tea1U3jbcwT4Ovxj9+4FJ2vojdV6\nbd9XcYHcjp2xLpHjwwffA1U42l4KdCQuo7ZaJZ56zbEivx63Q8Tc+c5uKuqqTkAon+eex3zva9GU\nXj5t1YuefTcRdQP0kqslhxO+2y3RdrvdSm3v4FIywYG58M7N7vFdzWUGTXq2UfaazJquET9TQ4WL\n7qmxvXvWDR1O5Srp3bvqD+1S35HWgh5lgwGOFlxvR0/ysrmdfJ2dnTmLmDLoGVRhRiSyT1um7OlY\nvU/xj5J+FOYuKi1Fcu42UcVWWDV0JnMHA5DaXsSemFwIhM9ZTKeUZFecZVN2MGWS7Snn2btwLvWa\nd++e0/csubNtr4j/vavfcH+1+fG79C4G6oHJgO4dP9+0hC9Cl9oLIK7/1Qm0kE3DXGWFeqhBgSrN\nzL0yfjfrsEQT08Ufg/PaePd6NUzBlW/whi3D4mI7xntkr5Fiexee3zT8hROLRFVrqV67tF/Rxf0S\nF9TwYA/ca3O6cWuqKd9Y64zm84Sy32UnZkABQRWge6dAcoBvIML3rAkS3ghf4+qR2jsznaay7z3T\nf1VtLxRq4In5uBkiMvu8FBGhOULF4kWOsXtpEzV5XCfrQup7Ewh7tfa7U5RNt6LzhmhDm83mj/84\nY5tJvjeG94I5CGdcz4U5fm+epdNMuUPAD1QCuncSGL4XDlkwPZG4DsabCPPwXF9Nc6wRrFQiIzEL\nqQU3L0pkRCvV4krmjr6XPDxwgD9dF9RncS+Ue2wE408zw3tVB4uqh2+zGVuHHOHL9D0TK7gXsD3O\n5vgpG6VgxW1oPn/sexYAxYHugYG00IuvqfRfXk0525uk8J46UyNmuGNH9GdNm0NwLGP3vMR3q+nh\nQNnXF+8lyPlU63YXF/4l9ITL32Xlcyu33U5dzvG9nISuFVB3hnIdS2zYw060tESNTC43eq//JP94\nfBaED0wBdO8kKFSNJb3S7ZptLUNXV77udU3nrGWWhSR81LTjU8/3yjUCxxetDYVQzWPDOp55p3xi\nec4qGul/KsLdFEwc4OvDe/Y3rGte+FwLqnl0rmQRPtXdupIqmu910b0fq/e9++7wh1A/UIvja5+B\njW17Kzp4T677IUkOzG0e2hdoQYeUm7DsEyN1fO/KvCNp9F6TunCpcZLqjd4rZg7B5iS+T3WupmuP\n3lsT6Rk+UT43cAnq8TdXRE9fPNl/GM6VPU8/8vPZHhHRPyVvN2V+bnvAuIC6PVnji3ue5XOnGZyn\nCFune9rB5HSPiH4FzwOVQXTvNPGUdw0gie8VE4/s4WnTpji1mbnSdK6ouDTDhLUHb2/p9labophI\n8HxGF7rIH4EXQn6YzTkaI1G2p4xGu4PNb0o6tz1g3PcrPbz3xRdffOGM7U0Go23MVWzaHqJ6oDp3\nsL05QfJn5ioIfE8mWcFrK91JB6adr6qVWRZF9xq2Do2EaePut0S3a8ofv+SsWdwT6Xt9P2kPM3At\nq6EIwNjJ9hNMOfw7rAT3pLancD78M9yj3ZU+dK9uw11j/eIMfEuoKMwuejacw53EKFxwjED3TgLe\n9xJtKtzSiyQrbHuSrQSYeOye4nsS2xtWWIufQjn9KIsuL5ad7yrre0SpVSFvZIFB3+5q62q4Z2aY\ntnfode6czvVoXndzvKvwiiaFqOp6yaP3EmZHzQFnePoB1RdSA2AqFvUlDqTCT9UYlGEbV49sE+wo\n3+S3V3Gyt/I+v5btPbvSfo2tspz6XeqkB9SmTM7lWqnzQMinfhbY4oLoZfffsHdtME/7TSPxGqn5\nNV3SJ/xx8ui95JXUwrbnG7VHRHPV1eOOKHwPzACieyeBGt0bF9loDWm73dpDmv1NuieXs/N/NR0b\nVO+VFRvYWzkdaEcVY3tX2m+a7b2mWuEG55i9ibqIvD6x+IjDgmlFbkve4J72mzNj2z9wofxH5+fn\n5+6KcbnMbXtE9Md/8icpW69oe7GradThXe+vBn9TcUcAMDnpQMJdYozvfVP5fTWKnhbf24ajH/zj\nrXIEgnt7Im+PFJ/FXTn/6g1RPd+70n815uWehTK6aZ2y8yNZe5zi8JUgY+FQQXtSKsTimJk7vg9l\n6J51T4/3UjQTrXw692XgcSKiV+f0ivTgXndX0kVSxfY27fGR2vWWiP5H/Kuk6d4rcvie8TV2/vCe\nrXe/onc9VveDivsCgA6ie6eBlc3tY3xDe6g2jFsKN+uZQZWitkcHcplE1ZDXM9+DwXDDsX22hipm\n6Z2i5NtjkVXoffRvY7iC7RsDvh2OHlbnjP+1Q/jYu5Zke+1SaTLsCigiEoN750T0WjBaNhTeq37t\ncQvgYsotWAiI7p0Iiu99U7lvpTTLY3yvvTMlvieyqz2V1r32OmX/cJ7w3utO9zxdUIWRe5Xje8q1\nktgvSpqT6tG9/n0wFfsiwnu27HmDexcvvatsEBG9spK7r5Kukmq2F8WWKCW8l5rMdcT3TOsMhPdq\nRvc8VofoHlgEmKpxInxz8L1vqvf5voSHZmRsmkS9WHtlIX1C7sr8094u5lhUo+17zsTFlo8L/yl0\nsawvj+EZR3EX40t+RY2XRL4iLSOFhvLVCRrfRPYEbcPyJxSrfB9RuvK9ZtbVSIwyAnAHObaEE3DB\n1WL5phGM09vGcG+YWjDAV72N7WDZ59tbUXRi5Z69MQVnR1IVIgp1eOdckxjlhO3JnnFkTvJ12548\nk3txcXERdj2OV1Gv07O00i3xMzZSVs49d3zmokoOVE3m/irprzBZA0wHdO/kGMJ8HxklrrZbaifp\nyr4RNw3ZwieN98VdV2uHXVjC1wnearXq54DOHV07cw3iS/5klShHWIB1ZsllZ6nB6uOnVMzyyrrv\nxdlemtO5eeV8JT9L8L3MoFrK0hqOL1i67wUG782UzPVkbJHMBdMB3Ts5+jCfaXsdo+1tusUG+DRO\nf2UkBfg8ysKE5PjlHNbE3L2iSVcXM3jO3Hd2pnVEVaN+VeelbLd6H76O7x370960i4k4qkuX8r38\n5Kj7QmL2vI7tRZNQsltEXJ1CVbISCrIkpHOFHyy/7y0vugfAdCxrtA3IoQ3rjbbHwg52YZp6pU/R\nInoy4/BGqMwHR6nYc/e2dzov1Nc0WeE9clZaHmOMZ93tnD55llIsfLwmtn9cEenvnQtF1ZqsMWzX\nG3vSLnbPharveWnZG/c+7kqp9v08dhS3dowjp2ykDN5znym2GMs9ftZGxfAeF937mx8EsrUI7oEJ\nge6dLBEJE7/uqZYhjS9F+J7SArO6F/C9SXXPua7G4Hsnpnu0j5y1sTLfeUXfS9I9/VqX6l552+v3\nPvJCWabuxfleWdtjZ+feI36Wbj3dY20v+FfQPTAhmJl7qqQMj+lo6FbrVXajZxRYPS16cJrfN5Yx\nN/aMXp/R60Kp3MPqwCpfRdtzC9Ka1hExPrs7beoNNQutoybAmuw9UNv20vZ+obZXH/eJ4hIW94gE\na6rl826XweXH7cH2wLLA2L3TJGXyW09jXxbjymky5fAZXfxUhOXMEuUG73WcjVMHz84yx9N7FhCp\ngTlqT2U9/COBe2LjmbZRCc90TekQNfX8JU68DXFuvVCQ5TTXxiH+k5gRfB9RQgPlyUMZ++Ibvld2\n8N673hrKmHQLFsZy2g9QkAzZc7HTfiRzSJp4uppnesaVdY/H93rO2sm6pYNab2qvoeZj7S+u0z+L\nf0pjfn+or+9x5TlsbrWz514tI49zoogW2DXzZSau9YMc53sffZS+eC67Lzqt8NVP5v6KiN6ldwML\n47pBcA9MypIaEDAXmw2F16JvA3xZ0nGwZc9lEdrdXterOBf2WfJfnp2dlU13zZLIHVkL5ukKutL1\ner3Oq++SiRnc4798GKpeJ7hHcXOLF9dSX9sRvrkwrmBPeK9gdO/dLofrsj2kcsHSWFwjAkoQ/dV5\nsxnXy3TGpXZKUjcFrW9l66zoj/YsodDY3Ox2tcqwyLXU72neR6c+hVnhvalsL6aqyqIaasfH9k9i\nhK9CAkKn8sxcfyIXgAWyqFYElCKxLQ37Xg5GJMWK8+z3/d1xjfLJLXCxYnPXu+RFToJcX4v0KD00\nshhhl81IaNTRhhVtL4LKR1BwXNbqrfYX66qp6XtNC/cQd/XWnqcRLLOH2B1YGtC9UyTzm3P+RaHZ\nCttE20a3V+5fru8JBu/l4kld1xA+KymXhveUNd1QgcJlbo1kqLp12Xvic7mNNlup1sg9oqjvT7fG\ngMKpYVe+yUnoRmYg+k+FbAjjF/Vn5YYIJnPhg2BioHunSPIw6A2VnkbZra9AWtfKxe/2jvtpxov0\nirlP7ntValVU8T1Kz36uu6R8QNA3RJsNbQoP2zNKmdQZFFgzuhczIvN21iDpWr/Zm3VGfC/yO2nU\nFC+H7E04bPRvYHtgcUD3TpJU37spdUWYEaqmUW2P+wvfXIC5rtJnzH3OSssWRYJmFlUXUvNjnp/1\nujtrwl60eJFP31QH99EX7sYE82DnnGwdg2rza700j/WdJsb3UrMQwTPDT9YoKnv+gXuYpwEWCHQP\nKJSv9TAEJIIBoPSv3v+S+ochrmpt2EsgjpHge5IZoAI57dLtfTyv9zzJiesug2Us4iPVztqNY7Tt\nzdNaa66nnett3neayX1vMmB7YIlA906TklWtkuA69tYQUrTOf5l+LX6DMp7V2nAWu12k88XU+3Cy\n3w+DKwXxvJ0xh7s7gUlFFxPhRWRDm0jfqzd2b8ZAbQRmYG9ky45XiKrHUn1+LgCgB7oHFApeDr3v\n6eONUkN481ynV8x9E8zVCBI1Y+P83FpcNoW1MokmeBZ3RNpe1jp9CSK7IerqDgnss6GlzMwlogVN\ncCaiLo+bO0A10fdCF1TdmRrvhnK5AX6A4B6Yg2VkV0B5ktrRov3JgRoyVt/NwL9rlfK5V+y9wuF7\n6XkuwacyIhEo1b1gz72Xjn7SV9xTr4DS4T3tnVnBJy83G8kZqjvre7507kavOO2JdzrO+d5zgP9H\nzJ6IExHap0JrDuwdyVhRY+c7KTt6Q7vvhTYRyOXC9cA8ILp3qrzzTnRCt3Clh9XqMNkFVi2fy1Dd\n9iRSJIzvnZ+fn5MwChbeYVmHOUb1dkSVr4Bz9zsL2asoo7uwmo4FbY82m3EdnYRJNGvPAfbmc9/x\n/iql2lXlrSW/I9rt6NeBTWCxXLBMoHsnTGxLuuSLYTl5LOnU3Jw0l8T3drtdcKmNc+2Hn23Q94S2\np9425/6UzyY431udidGnQa933eqJjO2FR134nuDxvXe0ZinmO6n7Q2Gfam6qxj6x7uOOueUjZHsI\n7oGZKF4bARwxTWGpKti5B3at2uRcm+fyUiz12flzTwPnoXzutlSdQK1LNJeorYD7jV3nv6PawT3Z\nyRuo8H2M7wDWRLTe97dK80430CRrPlmgQbjHpHMDsrejN93l+0a9s7+k3wxXtj+Zi0wuWCpLDuiA\nXGafnwsSKTPGTT6ToVhJ6FAARPQNoEqB6kUyz+Rc/5f8fu58YDlDb9fhDO+9M/xTuXXi4nvekOWO\nhjQuG88rc6pge2A+oHsnTU6Lmv+9/kTnAQnn5uYlEwv4njauLWB+pTKfep+YGNzjC3w4yKgyE3zT\n9UfutXEkz2gxlUKNNZe7jSewMw7fe0f7EYn+mYg/Gv4WTQ20ek9HVnAPgPmA7gEedo3MKKa0vSmn\nakyD2Pekkzb8D1/TdQHl0/Zlv1+tVtZFYN9jsqVC8b3sjbwusRdBdmPC8IgIjfvgfa+vF/BOdnAv\nOLjQDu+5k7nWGdj1Y2NjQSoXLBfo3mnT5k6kLauxBIZSaW3xTOl705TeOxyExrfbdf/PIn2k2xAD\ni9+FRrbgfR3CejvJvNzFiZ5wPkOa7/XUH2cSUXqPmY2beFZge2DBYKrGidMPkxGV4euGP2uCt6Z1\nW3AtaVbbnaVIdvQgDJC2y2wExv0HZmskh8JinaiX2GGsPTvofis+gO73db38MYB6hcIARWpYClr8\nvegbXubO5Ab3tN+EZ5p/YyV9+wce34PrgZk50eFVgEOgfLdqg2hMzYv2vaIXV3DScI3JuVf83ZK5\nuTJduaDQKl0RB1F3Bjt76/O9TNtr0556Lrf9YYUoD4Mo3Pa3unPbKl6/J2LfG96W3Zl739VContE\n4nLL3eGSq7CJ7Nu9LJ4f1r3/8Sd2veUsyxs/C2si7QQyJ1pcaTlS93xD9yB7YMkgmXuHEDS12vUQ\nmJo3LaErdcJSLCJE/nQx/ONEmtIVOENoVsPFRfyCYaoRcf2mtfer8UQqedztdrvd0pZoOxw3sYAm\nTtYQONM0Y/dIahxnWyLajkdIuy1BZHvlPvJ/wiR0P0peJjf2C8m9e8boPVdjFr20iRNM0wCLBrp3\nl4hfaENlZvOb4VK9yvnjqTOJZq8Vt1DudaudqQvEnp2ZztIG92zbY09kf6zC+pKggzyiCNlrmsb5\nZMbxuve77Xbbu17pq0wue7fKvxEMvhenqtut+gdr2RY036vffHltDyoIZge6B8Qs+WKpEtx7lvPH\nAp+40H5kEpWTury8vLzU7rlO2pcxuHdmzMolIt72xDCdeac5261WrSUlvCfMh75+PVmET4CyL73q\nxOR1w8G9qMiexPfY+RofEfV1vYXK5xK7PiYcxvPOSo3d8wsdkrlgdjB2744RSqV4LohD7Hf5shdX\n4LWr+N6V437B4D1RP9zLlXf4nvAwmhEiRoL6gN8oei+URx8K9sVEyeVqaucYuOd8K47O2DyI2/au\nrf5g/7aYjbhUIGr02wRD+GThPWZH1DeyHX/lVsQT2J5oL0YauqXQt0B7+F47qmQ8M+Fz0T9XqeMo\n21Vl/F4p3fMM3XP43g/+Bq4HFgF0767h9T3f5dB23VHCV/LqCr3wpLon8D35VA2izrHO+EiS7Cgm\n2Z6qew/VXRGi+geje2Lbc3bGxlHsLYb3PW4jvO/FzXVYhu459qJ7K9uukM41bfuJqsab3BHdOlce\na8iYphVBKOjPztdQz0vwbHRPVqt2R+teGd/zlljmdQ+qB5YCCrHcNXw1WYrLv7SSiIDS6/lKuMr4\n2/jpk2cu3xNg68KrqBTnw/BTAuhqt2amcXuuBWdXbBzFa/6X4IrAOrFn5vUZva6rfBmzBfrpzJ0S\nbYd/mHdZZzRGqD6MPT/3oxlWd/T44Ruh7/mX0wBg6Sx5OBZYIFEXzKHQ2q+SV65RZ/lZhW3qKKG0\nM3JFcDbBZa84XYhRINX2IkbvuVK5LO5ErmdcFW9mVsguSm3jy5i8fl13EJ/E9py+yccvV+a7bJXG\n9yFK7gtuA9/EmPm5UcG9/hkbJTwhqwqlzNXILxuaZnsI7oHFgGTuHcQV3vNfDGOHftvIc7qiC6wh\nSZbY/4xpk7nhbK5UKlq7eqkWsDvTp4NuiYgObY1YvtPidcF0oMH/1DkabTbXCO5J87mDgjCytzfu\njh221+I4ilvzwVeuLdkylFEAu1qILz2XS6QOZxxYmdscA1j2x6jp/ySZoCkaAb7/Q/stdEa099Zl\ndIWJZ1k2V5jODfgel8uF7IEFgejeHcRVj0Uaimu66+Yb9A36xjeIvuF5rji8l3slTrxqbtZCahcX\nQ4k7c3Lu2dmZ0bH3nd2aKKYU4rkz4nVp/WKmckUF+JQd5U6yuZeuC8H/bnyzLkXeVmR1k9mxLgqN\nLVt/z6Uw1getQB8Q/K7mXFHt+jrO9tKHH/kuNFky/dfeRxnb+wFsDywJRPfuJB8RcaP4vFeD2V/f\nKpb3v9x/JrjC2v4m1GXMsazGlfuhnPDeOEFj0KqXRn8+hvfa3o6b99o9fM11Vu6ZGnr9FXpB/Mi9\nYIRv3GHe5PbmA+yVEHBXbzL3WpmA+sq1LVOE0v2vVmwvL7THsbI2rLqf8UFqtL9JRLDExnhTje1J\np2mMtOG9klM1KDu8h0ka4BhAdO/OIlxHt8fq09VL5xvOAF/BuRqBa7VGNveZ+6GM8N6FdcPu0Iff\nGdszei47ssNG9lrbM6rttfzBt5MOggayNi8ZxgrTVnCQF2UuSbVMrkA1osYNrvrP3G7HbnqeNn+M\n72mZXP8pjF02ROcLbR21ioP3WNtDaA8sDejeneSd/v9ybG/TbNHle4JkbqP9CD9xQq6yt7DZbPqZ\nFsyMC49T9XbBhj9GTbJnLZyfn7vnLtiyx+mfAFV+pEpvXguJy3XFDfNfPoWje8GzoUf3igT3JGN5\nB9/7/4m36nE9ibzd05fV8JBXaRkLZoDjALp3N2mH7+m+946/ybce1f/a6Xt+4WuGoF1uoZWJB+9F\nsTF+Wthv/rZfL4uIk2bVlO4R3bvXTUN0z1H1zV4NhfcCg/m8F06WSHAra7BPFM/NTQ4XTbq8xtnw\nTxa9STpzuU0Z26tCfvBW6nuxtvc3f/M3UDxwfCzxYw6mY4zQvUNEH/ueaiuHER10D+BzX2Xj1w2J\n7U2+sMaV57HQ4L1rItXvbrqbN1xMj3lj6l3OwXG6vXxBAe1hv9y9IHLU3RuG711ovxFZJsLu4P5A\n7akfH9UvBElwj1lXw/HwK9nM3KWN3WNje2dEr+mMesdMD+51W+d1T70cMjsCWdigH7+npXOdJ8Rl\nezebm4gFQMaEbvbM3D6bO6reD8gT20MyFywMlFkGI08poHw6RrXUb/wv+gbvfK5qy7Gx5bbUcuPy\nvq9VKcaSyvZ6e618vMabIttTccdH9R7s3heR9YaJiOjyheuRC20yiQduB/ejQqz6JxiXwT7cZ0vk\nrCvFLHvvC7M9dyK3fbn0wtsqms3MmNCxCy4HSu2wbGhzkzAMwD8zNy2dixgfOCaQzAVENEbqnLYn\nGITnLspiz/MITbzgaZrgjI2SXGX9tXDxdh7FAJ0lTMyxb/vzuHrDAhxJXEN9GJvXx1atVqRMIcjA\nPKLb8TBXtr2OstWWXbY3vsjZ2VmOaIotJrMeunAkRjd+Txm9N54QNXcbyuNuVivp1XRPls+VlGLp\np2oIwnY/+AGCe2BxIJl7txmSuZ3ueWJ7XIfAT/ZgI3zalcYoW9zQPcezC4f3rryPhtfN5Y7ZTXQu\n19cTKwdSMHg9Mplr71OnIYx/mDspmgeZEN1zrYG7JfoslMzNdL2zoQh2OVyWkf4iRov+hmTOl98R\nSL+G/Q8i+pPh8rjWlkFWV0QOI1XULp+7JtrnrZw7TM0NRvXgemCBQPfuOL3vhXWPa10dc3t53zuM\nVxvXNcRO1WCfX9b3rryPpuke3RTUPfU4BgXL2R+/EOseEb12uIixl2V0zxY0nwowWWnl6fmTeM+K\n257L9zJexGzRhXnK6XSPiIzLo1v1Nx6Z72nlWHyXXOhAqYVY/MIH2wNLBLp3x+l0b/C2IuE956yN\n9nJzdQwlAnxFfe/K96DA9vj+iOnYzPei/e7o1JrbQrZH9CJC91zE6N7Yrwb2mlE0jxb4da9M1Zal\n215qi16iI4gQPv28M6vAyYj3vYxiy2bZPYfx/eBvIHtgmUD37jrdAhs9cbrnrNznnKW78vYJUcJ3\nBL7Hd0d2zxave9ZhDOme77jLwnv+k6PvpXtvjC7Vv9uaoO1aN3J7QUj38n2v+HQN17zcdNJa9CL9\nQLc6jsD6jLN+nVofJ1r3/PHkON0j4pQPrgcWC3QPqDNsZcnczU1/g+ht/slO3/M3uBG+53pqSd+7\n8j0Y1j1Hb2R1bbee37iNMB1qju2V171CtkfXg+TRjojebOn6pGyP6A3RzpK+grncen/E0BAR3QrC\nfNZp95xWL5G+Fxw94BE+fk0N1fdgemDZYGYuiFtdg5SaIhsi+i37FHcNPn8HL5x3e3ubW5RZxJX3\n0fAyasJu1PdeuCrVpW2PiC+0rFPkiDu70/7Ea1OvV7vdrvuTHVE3D5fnxXHaXrvQmXFUcl4nZYZt\nse/8/acy/lKZdDk8D28kM3RVfsDeBGCJoO4ekLLqupJNH96rcvE0gr7ilgrZR4BnV54HJWP3UgjW\nV06wPT/OwnsqMbUBHbtju17/xIaGsYgNUaO/2PhnK4/MHKfttcQ6xnK5bT++koTuhNzrw3uCSo9v\nHN9IXAvm/qCN78H1wPJZ1scSzM1T34OrcQHYDVG/Aiwb3vuGc1G1ILmX5ESLqUXbnquWmCdQKbW9\nIAU+5omVEkd2ftsTR3ZZOGMtHDGazPbOSD1hkUclJVKXWXNPQxbfS1ssmUFafK+/Ef5exNve91y2\nR/QD+gFsDxwFGLsHdHyj9/hW2jF6z5HRNbaxISK60e8LBe6mW0vtyvNY7MzcLVtWzHovoeAe2/tn\n5nJfkKwUi1hMuf2x+lHlSdb+ta/EtE4ONRHoXn7VvVrYutctHDMuJ92PiguT0KLX6QS811xeOFpD\nIqviyRrMZeoWPQCOC0T3gI4vvsc30vzoPWpX2WDYbMbBfxv1RyG+Viy+d+V7MDx0z7S9DsU7AkMQ\nmX44/gMrCA9dSjclfU1zvQ/y254DTkMcanJpvwX2gC8R1va0aGrThz9byr58icVOordbLLyHiAUA\nUqB7wMCbz2Vx+p4tfGdnmw0RbWhDG1LCeqrvZQb3qFw+91mh7ZArs2h//AJvLnXpuQCXkrkaMVgu\nF2d7nqPgsghG+DqulX8XCDNwr3v7KSc7Wn4q2dJ67d/0tL6nLKQW+pahno7vfQ/BPXBC4KsRMIlP\n50bkc9Wc2I1qeUpCN9v3SqVzr7yPBrO5bXCPWcZrvMuby2WWGna8kq8PE0mDcGEN15EPLaHmHrbX\nYuxj/zKO1smVvtMzuv1BvlbX58qgWjZX970zotA5c1//8c15LdsjokCetVw+9+CbwkNEEYWWiWi8\nWmF64LRAdA+YxKdznbbnx5XDDUWjmmaiC/eZ99FQNte2vY7kNUGj37T0lWRzc52p50Bva07S2O/3\nWX29WFGurwtG9V6X2pCJ7cKpF3esu61qJXJb/JcFk/BPZBXzxkWv+r3vEWwPnByI7gGbstM1vqHF\n+FxBEnW6hqzIiudZhcJ7V4HHffG9rq/jVvHyBPe8a+W6LYC3py0RXZeL7sXMIN5TVxiZSFBa2TFT\nw9k6uT1C9dateriXG9zTo3sXdBs+Y64zEdeaV2z7u0biQCvPuSo4XSP4jSNirgYR0btZ+wLAMkHd\nPWDz1G98DL9NDPCNbBTfk9Tekz4rB2/dPY/tjX2PHV4TlwcxOrD4iM+WaHSeAJcv6A+idXM5mK52\nN/40x6ZZvfwm2l7cybtLxfiO0fZIdKILXPnVXE+1qQmDCStxPZl9yXGDABwRSOYCFmdG1/Gd3D1d\nI5XlX5qubK6z39luXcX3gojKWnBbF77gZTC651QM5u1qHaoe3LNtj3lvDVGyLEinGUcxle1dyP4o\nc3Zu5SSuSsmafhmvpMzVgO2Bu8ry+1QwD0/jpui6onvfoG9QfMnlptFX03I+zfGciUotO3xv7EtL\nrQQaOBRtB7b1rDEW5EVgam7UqnUe2zPZbMiRzPUZie8xcxxi/gC+s7OKRff8hyeK2UfmrJfrUvfC\nT1H4VaW9AGBOoHvAwdMo43MX30t79YZIsxxOeBZ78WYGTw50OGjKJ3qjDtOTjZAKztVwl3sz3XQd\n0+Xzg0lCthfJ9WKLsBB1q+Z2CIN7xF4SwiPmCO2xMyfUO9fBuRVrZiOenSo3V4OI+Ypk7O/oe0XH\nDAJwPCy2xwQLgPM9VxvtzeaOzpcYJ4mci1twYY0k8lNY+bYXHegLVd7jd8N+qwm2Z2w6+IZ9x9dd\ngG+xpET4rGMUsL1e8txPs87bWonWrdfcMzIpuD3b9sztR8X3fkVEv/oVgnzgpIDuAS+W7zm/G//u\nd79zb6b3vVjba4JrtS7xCpbanmNirtEjR73D7bYbwtfZnjCUIVKk0ofaPU+saY8BqyaHwyFwfI/P\n91IwzoY3971arYhWK/egPSbSpt0lEbMEeZs09RuxcC7Rr1rjq7c3AEzO7MM9wNIx5+i6mugVEX3V\nvZm2GItH927cD7W4Ro9x989biaXXkUCAzVmGRdMZiWTtnS8l1L0XRKmlWOxkrpdhh3TZE08EEam0\nqI6gmIoj9zr6+RryZC6Rfszcs5jpwD/YKBvozplysQxnca/95r2g2DMfOF/FMqv8Zahv/gvlETEo\nygJOhSXGRsCSqfaFPFgTyHWtcqP63nnnnczdEeGfqxFte8bfE5H0E5ppey2hbC6/w4nfGKUloMzN\nTzeptKey7b158ybN9tRLwx/bc/1xV6/cDu2tnbbnG3E366C4lR657PZyvVbG8CXZHiJ84GSA7oEA\nejrX3VgeiMiTz23Tub6lCZJrQHYXcZv2HRaRL+B7V6EnjNE9XfxWRBm2p7KgD2iZMof5XxcmLCNC\nRBWX0yAirRBLpO2Na83FH5BhhIQ6AX7QtfEccQYXN8UisHNFJ/OOr7TWbuW9AnwPnAgL6k3AQnna\nC986PDmPnL5nrZ5rs0lVvmF8X2igXyTPQk94Pt6wAn3JNVESyA7uhYe7SSuxBK+QNRFtzPXzIlXy\nRMagPH6cuYHGDGrZsMnURr3R/73T9uzgH/tKzsbBu4vrqJncAfrXMbe4Xg9tV/yLIZsLToQTaTdB\nZT5++nGooWwvJffovc73Aqkx7wg+nxTc2t9dPvK/lICr4DPa+N7z8WbHISm2Z43dk9qr43ny9Fpo\n8J7n0OtCIehO97bUizfvu1Oh5OC9asncx0T0qRLei43uEcmGMlqNfMM92l0rxvnb23eR47ryn3nf\njhbMArfrtvG70g5WiPM9uB44HbCIGpDwlJ7KllX7HXknbBC9rtR7NrflbU+Aa2WNwPcofzRrWCRs\nMcH3kuvVMZ17zHK8Ii7L+V7doXuPx3h4iu0FWR3sK9FzVdmBPOZsJcieb+G7omP+VuT+7D38AxHR\nfqmVoAGoDKJ7QIy3tJ56JfG+F5ybS0RZ4T2tGyshe1dxT9fn6fq+SYUmGbdd47m1nqoLVwce0ZH6\nw3u+Ax8V3Vux80SjdS9sgS+yne+M6n05IWqDe0T9+IevBmfK8PiPBNe+N65nsCK0X5t3J9ke+fa0\nwhwP/hPxByKE98CdZTHhA7B8XAulEcm/NwS7z9wJGz3v0DvvUOb83GdZf+1GZnsKu5269oJFgU9x\nuhnF2R4/T9T5BuKCe+piZ5d0SZeXGTWXz4Z/JuCrXxXUwWHxDt2LOtb8uSs3tG7S0IJrqKlg+DEA\nJwp0D5THU285C39K0fK97v+z4LVW8aeuVbzO9Jy+59xcRNSk1SJHgMk7TSOmF3c+1/EWXLbH33/W\nC9/Z2dkw+yTZ9+pOyC1GqOa0gTmXKd7B0ifrrlzzSiYzsIfxSo15ueBkgO4BOW/T274I3wjre8LV\nc81JmyNJA8iyfO9Z2p9tNpuUKOXw/sY+cUfBJbbKTEYuNNYt0HHnLy7noY3EnZ3RGU0XlktHz+VS\nuOwhS94RLVPSJipgNq/v9UtryFO0SOaCkwG6ByJ42z9+b8TlezP0wjkJ3av4P9lYRUZsvNNyR16R\n7nqM9xWrPJOx8JhS7SzYbTvtpGBDpF9j6R5bN7yXW4OlI9LXnOP25Ajn6UYx2eC9lndDDvcucwuA\nYwdTNUAULt2zLqSvEhH9noiI3hru/F9n9FVBqtc1XSN1fmjqtI2ruKc/IhKNPRTY3oGI6Nx6jjV1\nw29JUb1o60V8titw4A9EK6KDJEbjbHBEC7Qx958R0Wvma0Qna/mzNaowyt7vxplN5cN75tHOzuQS\nEe1prS23FjnVld3h0r7n/lx8QUT0biBH20reryB74KSA7oE4Ynzv993Nt9T7BQP7HLo3GMHXieif\nv/7P4Q31TON7jyhse7fUONzJ1j3b9+J1b0t0HdilHrfvFVwApIjuGbbHUkL3JrA9+t04jz1taq5P\n+Py6l9X4OyowB+H3tnh4r6Fb4i9Jt++9+6t323theeAkQTIXxPH2294ZuiO/G2zPuDuMw5m6i/Xr\nXyci+jp9XbQbRDThhI2w7Tndyf4o2tG9wDA+k+02YmkPX3RvAtisdIaQLH/wXstXvVUqy1LQ9trF\nyWS2p4044F+1+HxZ9+wiz+i9d90PAXD8QPdALNIJG8m255uuQTGWNzKV73mrBgaXBRlZEWd7REY9\nFv/Ht3s0ZjU33vZErURecE+6AaKo6QkZwb2zs1rGmL12mo5Y28o29tKSJu1K1uOrO3a3ymwN9vN2\nrxU6VureFQzsA+BIQTIXpGCndK0rSbtjTOdKi7Rw4nRLpOmePJ2bmM29inp2OJnLdT+b7q1utakB\nHtVQMrqBDrx/WJTO9dRZliRzhS7hbXCY17HVzrjHOFCvh7teU14qd4pMrs4fiOhhSkr3sHIosHq0\nzRM0TdOvvOpwdh26XmG+Bn9Vfrv7aeRzoXngxEF0D6Qgy+eOsJG+aIyrNWLw3lTE2173R0baVWZ7\nUmLieyLsqs+1GpKglBjzZ8+I6HV/n9P2HrkecG94Ah7Sw4dJteFWXU0762ApYrWgln41c5jh77uf\nfe723fFfAE4YRPdAInqAzx/cU8J7ESWY2QDfGNwT2t5H9A6lhveuop4djO6xurchuumMbDAMX2RJ\n1T1hdE8U33tBzpF75n7vzP0I78qAr8WRRPfMO8xD9bq797Xf9lxrHfs2XQZBJjdxzgZztFb9zCD7\n9Ewe3eMmI6lMFt0bwnv0K0JQD9wdFvSdDxwXWoAvZHtjeO+r5FpT14JTJ+WCFQ/i+6jMCrp12LT/\nXRMN63Z5B40dkkJ1sj9yFN/jg5I7yZNKY60hwdveGODreESPHtGjR/0vCyd1vgyT+W6oaZpi1Rmj\ncdoeY5s1Ru8Frsp330VQD9wdoHsgFc+EDaZa/+h75lzEdTsxj2ntk9fPNfhoOtvzzdXw9T2tj50R\nnYWDStv+H3Fwj2gbnKV7SZfa3gfW6W2fw79YKgnbMFOuysHTgnuP2v8/Un5fMGm+x42Jc4bMJgnu\nuW1vzsTSt8NPAeAEge6BdAbhM5putiVXh++pwrduZW9NjPKV8L2JV80NzM0N4AjsHYa+/EC0bd1t\nGx1RC/qefbxZ4dv1j+1iK8PEd/Kh51vHa7hDtb1R7h49erRs02tJGMC3PAIxxUl8b5qgMwDHAHQP\n5NAW4ZPYnuZ7/AC+VvlUGHf6+OOPu1sLnKnhxTVyj6yJGiYHOhzocDgctADONvbTqw7gC0f7Om6t\nCmY77pciDUmwbz4QHf14409lT6vse9UPopVAZkYPGjuhfPi3LQX2w76mENwDd5QjbzrBMmj17avt\nDdc1paytoeqelcQdF2lyRcqeErG2xy60kZHIvYp7emARNV5mNkShj6GrzlxgbLvdv14TbVvn2/a/\n2/vS04rcK2sjdkDvjVz2Au0Nd4gOxs2VfkAky6ex8TzfZI0nn7g2PnCfiD73bMKJuOhe/IQN80Jx\nj4ar3e5zF0Tg5BL1V7RuedJFYYS7AtkDdxboHijC77r87O/cl5Ste3x31K/BuXfmRZ9+/JQY3/t6\ne9/XjQXWJvO91iqcvueJ7nk/hs6qwn7dcyvYdd+f+nTPZXtc/vZNiWm5RMGpuUNwz7eKmtD23L73\npP3xCb95olb1iNJ0L6LEcsoEXfVwOW1vDtkTrZK3J+LHHGQ4n7Yz3yaiv/tO+sYAOGKQzAVF6Efj\nMZM0Ot6y7nF0R/3d7lFwH9PH9DF9/evj3Nyvjz+/HrnAWmlubvgd92UqfetExNhew9408ebI+vkZ\nb4hY22P/RvY097XhRre9vrbcuCFX/C2qwHI/oO/J8I/I9lKIWVAjJZ8rOcTLsT1uV9irMyOte3s7\njkf4NhH9Hf1d+sYAOGIQ3QNF8dRTtlbWCFVeCBS6VSN8X6d/NhWvf2jiZC654nueXC75PogRqdym\ne5Ut/7CJFTLZ9Ob2xh3c493OM0FlFTPkzjpIrnc/3M9H9/h5GhrPjWc870WvxdA9NaA36F5CdC9u\n/bSUFTaGW66P1yy25/q2s9Lu35PL7PJTurfN+FKI74G7CKJ7YCKU6F4bCixRZ+vrX6cumPd1h+3l\nzMx9lv6nxZAvENsvTiqFs72OYAUWAasV9Ss9sCs+sMS3R+Z3glTbI9326Ak9eTJufTC8+/eDwb37\n9+/fzwkAZhGxnnAlHKfQunu1Wq1M9Vw743iZszYaUoUTtgfuJIjugaI4o3t6Lvd3EtvzR/ee+v9Y\nGb2XHt+7inr2IBZceI9fLneE/yRGZHKb4XVSg3t0SW80z5MG99jYXmLTYhwn9gDod2rxvddERNtB\n5jxFVwbf85y2T7qNO+zNiu7d/7x/riPwFxfco9j43hJG7jmNXTmzq8O4F/oJd/51XnivUV8Gtgfu\nJqUK2QJAVGpt3I4zotdEZ2mLlyqzNd6ZZlENr+1Z5H3ynLZHGYGQS03mhOP2qKztSfDYHp3Ra+X9\nS0rsjc+54U+KO1J3v7O68WfOJI4CrCTBvUPVk7N2fkUZk6laVK8Jlt8homFKeSIovwcAdA9MgxXc\nk3FG3fqnkRgj+d6Zwveed+LAfqbCJeXMLjguLdfoN1LWHzWWUDt/RUS0M5bGZYJ7eYWl49lpl4Nd\nZ/mWiB4NZ8PJI8GyuU9C2na/e4KhhOVs72Hq+rne4Ll1sRXD97ISsbt1hvfyfE8BwT1wR8HYPVCS\nt5gJuAzCRXOT+WejTMvylsy1nfDg/VXHZ3OyIZHX4ezYOVF43Yzirqe3SElD0TZEiaukWe/mPgWG\n4XGPfv45ET1+/NjO3EbncpPLLfuvgkjbk4+yXXs3zq7dKw68lSi6TLA9cHeB7oGivEVvSYQvxvfM\n5e5bvEP3ZqzDIjOgYFQ90vbGz7G0a5bESnZErfJ5ls91lJ0pFD5iNrNTA3rconPSjEVXfcUf5AvO\nubCf8DlRJ3am3SXYXiop4V0X1mI3zie2z/NNy2mapvFeG0i7AlAJTNUA5fn9W79/i37/1jCQ763f\n2wr4r+KtcbYnn6fRkhbeu4p7uieZ23di7WM3rJGon0V/VMvsy5UvbWv+GTam7l1azzA63jdETC7X\n4bY57Yr+utaR2BHR6y7Bz5XFa9x7FSJtaMvnhvJ9TqbYfUrWXRHI0rn2FeNztJjzsyahPQ4vGIjJ\nGg+bguepDy7ZCy/fQY1lcHeB7oFq/J7ord87srty29N17+nHFJQ9Y1ruO+nJ3KuoZ7t073YcteQT\nCrnumb2vrXvh/tnoOm3by9G9crZnHopxB147iiA3nv3ykziQ+XNu4F7BQF6q7hXyPZntqa8VysDP\nqXvZWwDgaEEyF1TjLU9q98tpm3w6/ONlTOZ+RPTRR6rt/WnaC0dgmsat0qP5LETtBaN8Sf0Q77Uf\nbsI9p9kL70i+fIbsaTxmk6RN4xTXA9xMNwnNTOfev09dQK8MstF74tKGKYSSueuoGprGfsr7oEKj\n9wC4m0D3QD18o/i+nGR8H9NTge0RXV4y4SqiP/3TP53a926TBiMJpijEdbEmW6PrtBYds3c7OG2j\np7B2rNjX9Qf3KEX4ik07uU9E9ObNm9DzhDyUTtco7nvrftie/1rTHw1eu6vkXge+B0A6SOaC+RBl\ndPVcrmzDnxD1BjNG9v70HwfT+0fZZgqUWTatyeUgK1IqzwonpHYhPK3zFI/dMwN8hh4LJZUzpOw2\nRXvt7TVtu0yyHtx7zS+Xqx6NWH8rFxH8nOgeUUQ0MoiwIEt75axpH4rIic7SsI2IqeDea3d8VeUk\nG9eaTwZz07lI5oI7DHQPzIjA94yJGrbvffLEuqtb8PQFUat7iuh1CH3vSva0lrDtnb12GcVKr4WW\nrnsRvucbvScNSVbRPfXVx3jOG5E86Qcj0vcKJoA/v0dEJXUvZgyfIO4rO0njhpjraU2cVPouXf5F\nb6fzPdgeuMtA98Cc/CvRl/3Op+ueaXufPPnEXOy0cz1qde8j4ofryXzvSvSsDlv3TGk6c1qY/jmc\nxPZ80T1xAnpC3ZOhHY35onvDKSzoeyLdOxAV1D1tS3vXA+bLx71oP7R1058tb6o3R/cge+BuA90D\nc+PVvS/Tx92tp/SxFdrrze4Jcx/9Y7uYhmOs3j/+qcD4rsJPUeh9z6N7Lg+L171hQ1wul6STNS6J\nXhBdvtB0L2a0YQ3f05O5UX+aY3tFJ3eMp7CY8EXM0A37Xmx0j2i8pNxbT7O9/qx156uW7sH2wB0H\nugfmhtO9Lw93f7nVPXbQ3ifjzSf2ff9IgXm4Yd+7Cj5DxdQ9xvZYC7M+hOm6p3bFAeG7ps7xXlwS\nvVB0L2puCaNUhcfuRf1puu6Vnsk7m++Jpt8lBPdouKJcuhe4bB2veavscXvGlDfQWFfjNRFtU6wP\ntgfuOpiZC+bGMUNXufvp05Dt9bc/Ue77U/rTzFm4V+K5ITbiGbl2/YyklcM6Ao633W6tSbmXRDRO\nZI6cScxYUs7u22zN3T02dlRsiq6Igk0673VZM8JtuIXVhsfsu7apF8TfJf0VAKcDdA/Mzpe/3P43\n/K4ZoLPyimp7XXRPuytYckUgg0+JN00ONbh3eyuMkjHF0mS6NHS6sYVetp1C8Z1mdNmYCgXu1Ebp\nktLLb8TsWvF3MZ7XNzSd8PncSUV0jVlet2bvFW7Ts5TucGuz2Wz6s9Y01BB6KACKgQ8TWABf/vIo\neV8ebnT3jb71qfaDgZmk68Ppe/++/fGsreosFD5l8VW3NBUOjbi3v+fifKM4bbe05ZbSSMC0qqID\nRKx6gH5SV1ytWJS5Nb1s4fvDHwS53Ebcoh+IiA5eQ2Mu1bUnk1s0qNs0/rdy5BFfAOYAY/fAcvhX\n4lO7nz7+lOixqnl2t96b3ifWI25cg/f+PdF/7/5r+djxRJ1hETWHdLSF4jQNYz5/4m6z3xA/V8Nv\ne0R0zXWZyQvUK+Pkis7MbZVUOFTL9gPp8L0Kumefx4xBfAWH7Q2sDuQ/WTFfTcJXbeplwV+SGLwH\nQCzQPbAcLN379DF9+piL5nF9wBOiONtz6d6/t+8S+V6ne+4+94xI8zD20yfUvXEz6uv5Z+aGYyLJ\nujeKVYkmJc33uAMv9L0a0T3mRCYLXw3da/Gcrgjdm9r2EnQPtgfuOtA9sBxM3XNnbdO1RIP1Pcb2\nZL7XD95zdrq67jk+e1FF98zXs+qkNTQeLEEGLFP3ijUnyn6IdY897IvSvarhveRxOexJW+9jdC+8\nblrE/piUCe/B9sCdB2P3wHLQI3u+Z9a7bv89a3tRM3RdzsSv+lUSrYNeN007cr8/WLkrUHnxCFPW\n3NrLbhPhjbCHveKgvBC24mSM3wsum1vY9mgtXZN5tQpO0mDmI2UTeU3B9gCA7oEFoczJ/dTve4Ww\nJmvwskeeCcIj/VwNwYdqldsFsr2xa6l6ue9ltQf8W3LPApbshzKbZJtcgyNM7Pob0xPwvfQTx86x\nkKzK0rIiWvlfvE4GKe67C2wPACRzwULxy16hZC7Z+Vyn7xFRKKfbZnP9udyuK3V/8CJmOCrLqG2I\nbnTdU7cjT+dmHFheEbqXvKbeRqUm0e+JPn3Y28tnpHOrRAFLjt4LpHPzPN2+S5rIHf7UeeGUrbrd\nA9sDIJYZcx0ALJ6PjTTuU9kcXZ7Xre+t974uMNn2aKP9qfZL03WZ7GzcUqx9s0O2Q7e9JpnyNfHm\nectJz0YkfDc1WsKVfTLflFxFV4F961IOyU42/t34+reOZySS/80OsgcAEZK5YJl8OlVwz0jnGrG9\nj4no44+NO0I4d64fu7cuE1P3OhNvjVXLlYXmAislgEXjwrqnx1Teu2XXBBGZ3GTp3NQBfMHRexmY\nF6R03B5Lo9Z6zl4/ucBEDaynAQARkrlgkQSG7RW0PSIln2slcju7e2r87sI/NXecqeHbf090z4yL\njdG9jfWX5mbal5w6mWu93rj5/drxN/aT1XRuYkUWicxNVIwlNaEbmJub9sV9deD6gLDs7Vpp9XYf\nt8FnhHFejlG+h/AeAIToHjhGGvFiUSK6AJ9jTm7GurkqqrM0LVF/383NWDOFlAuuY1ZWpH12Gba9\ngciVNbggn+QQ1YjvFfw6HYjuJZy5Fa3YWbMC26MduabmDDRN0zRVZuUCABLAZxEsEcms3GJq0kb3\nONkbo3sfEz0NpnJddZYviTi1Yd8AH93TeuC99oOaDfOX2i/rvXjofc5Btd6hrXvG5kXBPX2yhjSs\nw8h0yOcmmq5Rq9Zy5DcgZ9MvsT2Kia9lLK/muhwR3AMgGugeWCIRRViYHqGJtZZ/zCiurODQvVZX\nhLpHB+oKma37v1nv9R6Y1z29U1V+ixqHVVL3grYn170XivLJuvqUJTaWvbaGbM1cOY62v7TtZa2l\nm53MheoB0INkLlgij+VPtROjjTFcPMyfOuqvxKVxvYuoMWbDP7HPfq2JaL1er9X8LUfojcaNus9v\nEDy18aJckg/u+V8g8FoFs95SuOBe4mSNonM1Vsnf9HeRsb0sci9H2B4AA4jugWUSW2R57N4b8w4B\n/+iqtxcV3/NF97hAlmsXD+SztMf0r1/+13aLO6Khhogrlxs5yTJ7rsaWiK5pyxV9sbYtje69iKm+\n18Gbghrg25jRvgmie1llWELxPbkcedt97xXTvYFJUrmZyVy4HgAqiO6BZfI4IsBHpFzJiZf0f6f/\nztybU2av47I1lRjrWvkqYTweFx9pu96ykpLbImyJxCtpyGyPXrxImK7hMIWNetM6ctWrseQV3SsU\n3wtNn5AvqSF6tfQ/dVRhkfEd2B4AGojugcWSFOBrtN8UGm/k6h+J3IVYpHCFWMawlHTwHnmdq7fg\nf6W9Lg8H9iZNF97bEwUkL2aihr0jsfVYnAexk7qNcnukeIRPPxnZRZY9ET6pqQcaff/1Eh3dywjv\npY7c+87fQfUAsEB0DyyWyPgeBaubRF/tgoVyGe4Pty69tpdUTqYvQP1l436n7cWS3CSsieIWOQt4\nqLkjkQE+9/vojI4P5S199dyHDx8+dET5ykxVL217FQjZHtK4ADBA98Adwn25/ynRv2fSuU/pabTw\n3Vdum9MMSjD4nhYpGkI2VqaubG4uQMy6HbE7Fp/QDWPN3qg8myN1TQ0dh/HJkp/u4N56vfaup7Hb\npQQnp84gwfUAYEEyFyyY2HSujt75Ncx9Ch5PEad022Tu/UFMVNnj3caxO/5vYV3U0zo4w9Knt/oK\nHLErYqVGifYU0j1ty2Hbc6dzs5K5XQhvo/3WUd72zFhrqTVz2ayu/8JZ+ZfHDV0n457HBfdSo81p\n8zRgewDwQPfAksnzPSIaOo2A7QU8RWZ8j4i64F7re8m65++2XbqnbHatvmb0AqiJvifXvTWRJNBl\n7Yfme1uBdLiO4w2pZnczzNGtENurpXuc73kvm3BbL8rjUqTtlR+653t5yB4ALpDMBUsmdvgeQ8qK\nZRbyjO798FMyeWz8ZOje7Xq9zlzuviZh77HO2pjO3dJWUoDPZQybzUafo9sR3GA+b8rkc6MRfLMX\nZtfjYnvJEYWULx2wPQCcTF93FIAIHheI7yk4p+fGjDkTcPmCJAP3Unq00fI8x+bQd7KLdD35TjX6\nETob1xdpC75cK2eOtZDbk/1CyyVzPe9WYl38eTHrQs87TcPz+nA9AHycbGMIToQC8T2dPtSnRXOC\nticO733O311svkTx48FQr1WIDjdqAb4zov1eO5JbJcJX2NirUSybWxrmvOx2u123x/1uz217TmB7\nAHhBdA8snMLxPSIiauhWv/KZJSAMnpYouSzF7Vua7bkPTeaY3KZMTQ+LteoUwqSmtS97ly5ubReZ\n/+usOXSt1B4JVtBVSbogdo7b0yC/CFFnDwAB8zeHAMxAd+HHjNcSBvja8N5lehGWRv9U+jpqZ6gv\nr/ReImtar299xUCmnhiWO2KzAMaJaJpSlf0iV9gIXRBrI7i325FSdWWXYXtTXIqwPQDCYGYuWDwV\nwnv6F50bYSbw46eBCN8jcs7VYLO5rBqZsqf2l5bfuY5N7gc7K7znsCxjn8RTFtp9ORvvWDsDfGp4\nb3GuR90+behmLPWcnl/h4nvO9xy5kobH7g5EUcqarXvmpWhlkyF7AEiA7oEjoEo+V+VGOvDL63tD\n3T0Ozvc4qbJCe17dcxwb/XO9iu9yK+ie2dbE6d6Zfp/iJ3v119EFZra9A61s01H2aUPZxV9iKrHE\n6V7I9iJ8r0BwT78WTduD7AEgA2P3wNKpEtwzJjGKh/k/jV5It2ctm60hTuR2SGzPskbJbqQLXw3R\nOnM9YBzVrhjf/JG90AEffekmtRF+GDF+77DqZ2sfAhfVjt74MrfdV4eNUPiK254JbA8AIUtoFgHw\nUMf2MuJXKcvoEsnKjzQB27MPRmCm7mplLapWFXeJw6z6a5bt7Wm/3+8H2xulr0SVxYkpt0yv530f\nDq16HegwXohr4yfRMBHXuZ2M3UvC/Tn9zndgewDIQTIXLJ1avqd2jt0QOeHHwRnfG5fV4LDDe1ZX\npvbXw85oPaypd8zRWXE3J8rnyrOJ0lwul8pl6I1l0hWCPXDH23VwSqVzg5bb5pc3/VFaExHtFdsL\nT8hg3pbfVnP10LoKh2QuTA+AKJDMBUvncS3hU/K5ZWIWftsToAuoCLsYy5xf4SLCaruI1SXCtncU\nFK/5rKVzBRtvL/ObDa1p30uecJCBuoEJ4b9zoPIKAPEgugeOgRrCx3WPeeE971QNYiJPRmfmsj2l\nk+Vyt8bBWTG3aKLwXsxUAaHu3cpsb4hQLSS8xx5ux+HJnp3bjrSUyqT75YLRPf4i6ucXc3G+TEG0\nrsF/R3+HuB4AKUD3wDEwle5JPxG873nqsHQYLuLUPWsvul6THalnHpyV8VPbQgzlfI89qiLfk06i\nuZu61wpfQ0S38siq8+USbU9nrC1zQ7TJXYTDvAT/Xd7mALjDHNeYZnBHqTZ8ryzPg6lcYwGxhv+N\nmV3hm3BRbV21+Nah9DSNBERLtK2jVnJbMg/7estTNOWSaT9j3fLNZqPq+narLnnH/I3AemF7ACQD\n3QNHQAWhcc3ezEw+OZbMde4Fdyffpwp9r2n44N40ysXGA6eYGxxhb+vW9dbruNV744i6jLKm5n7e\nXskRLfnNTfoLxp/KrfaTFb6NMMD572B7AKSDqRrgGEhaOFdJKh0DXYft7FBXdFg9u+IeUQ5O01dG\nYwKEcgVpOnGLXjs3YzxkMfxzD9bszRAJhapjuJm6Je5q/Rkl/7wF95LZ0jWN1mcldzfaD+MxNd8P\n1wMgC+geOAoSfC/t2p5YTkahasIvvyLifW/cnG8bUmfpo0W3RA01MbJs2t48Q4N9vmcoXnhaavcW\nyvieZ2puqvFFhpON1zR9L2NtXA/S8Zcb7VrbUDd9+9/R/xeyB0A2mKoBjoIY22sr/isdGWssEXlU\nG+fU3GAZFtMvOt1bE4UXPCAioivmvuHoBFJ6ImcZttHJidj37BcPvB/hXI3rsDDoFifXveDMDr74\nYRjX00M512jly9E9+wXvB0/JgfLc14zuhd7wG3geAGVAdA8cBfLo3oaINukrU+URLrrHx5NaC0m1\nvYGQTKzowKzkGtjaployXFZ675qIrgO+t9c0zhmzY9K3a4/wrazb0mPnfF5oAq3xRSVEruyp3CcS\nnJLVgVbccsBCrFxu6IMK2wOgEJiqAY6D0GyNTU/3q/KQPLiXjaD7NZSjYe6ryCooldaBkdpewhEV\npA638vWMg/CH2TFhg119bpJsSMZMigy6byrBU9IelsTF+eyyLAg4ADARSOaCY+FTLsL3mD6lx/Sp\nr9dgOk+XmYg/Dq5l1B5JAnx6OOmW4nTvyr6rPy5C4fJHZuyNyOyDeXHB4XwjXFyjRDbXc5StP3Du\n+kF/3HEsfYc4fJYiFejz+xlRvu61lKs2YrWT+BhfvO/9m+jXAABwILoHjoXHyr8Dn9Ljx0SPI2yv\naVw1WOahaXJtrz8m0re1pO94wqkBwQif7mvsAfUdZfMx9yFatfj3Ztq1xu4T3U9cum/DfHAiZmuk\nhfiigO0BUIhFdXwAeHls53SDFfks2/M9+UB0mHpZ0NUqP5XrOwoTdMmuV57pdXn8R1l/NHPP/ZdQ\nsLjNHPnN5PBg5KGyvB3JXAAmAroHjojHpvC1v3z2mftPuPiFm8PBXYBZ5WnMRr1M4URmPMrzmsyb\nX0LZwtiluGy3Czm1us6G/JzwYhf4+4S16YLkztlIjA5GEzsxF8E9AEoB3QPHhhXK8sgeUTuJY/yt\nUF/r9L2+31yvXYZRbVHXAm+Nkb3atlel0puFJIK6Fj4vm/K+l2p7N0RE9++n5oIpOm8dOe8GtgdA\nMaB74Nj41Lz9IPw3ESE+YYcfiu/tyel7WTzzPMYttbviHnSFn5jmID3ZNmsuVz/0wtXSugjfWt4q\n8u/Ro0CSgaMJgp3saze26kVM1cgdpfhvkcoFYDKge+CokfreyG0gtpIZfOvjLK04MJ6hb7+sE61W\nw1wC94yCpmmaCBEV9siVW5LYbK5GjHav1+uI6Tys7fhsjwRHKsmBimVjp7S9rL8GAEQB3QPHBjNh\nI4zahQaEb0+N4IPhKsVidrtrTvkG4m3viruzDXiuqa+K5nmt+GnJMvuwjqn0rUXpRQrC0J5Jlaax\nXYk4sOlZB0vGnY6sbyv/lv7vwDOQywWgHMuaPAeAkCGj203WCP6B3od6O9xODwJRQM73lLJ7hmPs\n+V+L656XAynvnItiOg5LUuE98VsTCkZc5b3h/aUn1CUj7PjoVl7VPZpy9dwr845Y+46N741h2qDt\nQfYAKAnGToCjpC25/PhTaZxvqoiJK6fWWoepWAnftp5d2fd17ruPUpu1vjseDZEdu13Ec60/LBHi\n2+tq5zgYTZWJsTq+NWVvp0qo3Jfo31X2y8Sup7btfc9ve1A9AEqDZC44Znrbixq9F0AW3GPnajyP\nfA0HvkDWlX2XeDlh0y7VvfC93YgvhfW+P4ZG78nGXDZjLrVa25dfujHpy0n7TaOfeXFfNJrvmXlH\ngneXzxD9m38D2wOgONA9cJw8JooQHbP/rBTheaTP1DBRjGS9dlVq2W5LLhOrk1ZxeSORuK6eSmIU\ndZpqLH1712g/SjN1nW6N+532TVVHjyjR99zTNOB6AFQBY/fAsfIpqXM2vKP3LAkJj90LC6F38F5o\nRm6P8TK95zlDWVfcnYP1CrK52id+T+thv7yHxGFxF0RE9JIUXxueGdW0SIJKYQcOvn3lPQ5JVfZE\nS78OcGrn173LV65HtsNZ3wa3wvI59Vff5/eHO7xcWfekZdUPMUnda+plj8nmQvUAqAWie+BYiZih\nm5BitD4ZZnEOxfb+WL0/seLtdvjHxxV35xjjFCQ0tU55vU6dy3BxcXFxcdH/thujc5s+hhjjKyLJ\nyKrF0qJY3HAumRhfqFaPj83GexIvnY9su8BuF9uND8QqF16f0A3E+K6sexLHUEYt1DceHzuOB9sD\noBqI7oETISq85/uaM/iP3uk35l2D7/0x0T91N6Oje+0WbUPg3eaK38boe7HhvZY9Bb/4GQfwwnh4\nb9w+8C/kQCwZfh0WiivzTm+dvwSwnLb9YuE000siYsN7/DuLUeYxtmfey3Nl35U5YUa6t9/8hzGT\nqwf4YHsA1APRPXAieGdrmOG9hPBNVzJt+MSotqfH93ja0Xq2kmy3W6avrzV6j2UdripcdQq/eOSe\nN8AnDVPe3lrRu8b5ix+X4LjOntP2HMR+G4+ILF8J98BTr5t5rmyjyrg9VfAwaA+AmkD3wF1EdN03\n7G9N0zQNfazZnup7nxM51YPJnbr2hDOGK8dz+5y2pKBwajxf972XxqNr4/aKokJTu6mmavRYvte4\nH3PDvMMuCsoq/MCWV3yGXN9bZU5ZZ5fgK8q/YW4BACqAZC44GT574EzoRszVUMxF6/iNv/jn7mfv\nef/0x/RPRI+IHgX20kzqOhTNDmRdubcpLLPcwnzmJWamH8JwNjeyaZElEr2StI4qPMgM2TN+hmCP\nmqLFwynsas114/b0C6B9yP2+xNLcmp6Zzl05PhJXzH3WKdBOoWxHRM/6pvH7/w3XA6A+0D1wSrh8\nL2LsnqsYnf4npu0REdE/Jeie01As37tybzNK91r0T364l/bbnv6ukpYdLuB7cYfA7Xt029yOz3LY\nX0j2SFW5a1JmaZiH59r/rrIKu6z4j8QVc599ArzXyIq/W7K3pu4BACYAugdOCpfvPVBL9IkmahCR\n6ntscE8fstdG9wK+J7W9BN+LnGOrfvYFnbTme8m698Y1UE86TaDIbA0i8upee3so1dIwxiewPZVr\ndU5unA5n6p79kbhinxlne+6LJ7y7sD0A5gBj98BJ8eABPWDGK2l3yW3P+WTO9uiP6RuBvdNfIFAF\nxRKbZ+Iti1D75divfebYPdH6ZUT0xuV14kmh/nIsApFqzIo6ykP608a7pc2ku8j0ll4It2GR+5V8\nvPYfPHhAV1dXmdsjMvZJn6ER3t3/WWAHAACxILoHTpAuoDGO5XugLsAhGrc3cMv+Eed7ByJ6Hsrl\n0uAkYT277vOAHVfup37q296OKBi9OX95YUucij+6JwvvvSF+Gm5MBZCc8F5Q25zD9swHXDEsZ3zv\nWq24F5ntPkSvTDvQnuP+80ABFbf+eHzdg/6AtYfu3xgQ3wNgeqB74KT57MFnD+izB5Sue11Pb/7R\nPxNnezL2ztfiGHxv5ynS2+rehuhmY8WYesHyDsU/J2KCdgrqVm3bsydrMLzRdse8X0q67wmCdC7f\ns+53nm2H8Ol7HT+6MUv3iOizkO0RvdlpZ6I/Wgfz9blOI873oHsATA+SueCkeUAPAiX5FCLSof8c\nfkohFNsLJQQ3xNmGvMiJN7qnwNmehvdA2nIXV4YlOZ0raexcz7HuXzm+K9cqUJid0m3HOLSHZ88e\nJe006AnvwyhwfHE9/c5AAT7YHgAzAN0Dd4PHRI8Da655JMUc7fXPRKLaytEvZLMlItrtAk70mEbT\n0I3D83dREaNiHpMb3Wt97zp6TbV11FJfQVaro8yN7KldKjmAcr2vVqtVuJ8wjq3v0MD2AJiDY2yw\nAEjlU6K+K3tEz/XHIiSs+af0TG4s16QakjOd+ynRWX9bz+aOf+wbvhdM5o6b5YN74cF7b8z9cexU\nmLaQ3bYb3ajjOo1rItlpcuRz2buZ7bm0WN3TlEo1yktxM4VdsC284xgpp8Lyu9vudT09xsH5iwZs\nD4BZgO6Bu8SnNPZkhu/F2J51Tz3bo2tNkDyj987G2w7f887WOCeilw//4NuVdrOuVG7Q9/qX33F3\nJrCla7KH8rkXNCEq7nuz6F4TsfKH1Pbe0M5rez233h7jwN40ge8BMAfQPXCn+HToyh4RkSp8ct1j\n+sJaurciojeaHrl9Tx3Yx/seo1aG722JfMJ3Q56Be2LdI+dc4Ti2nO3xJ3K4T3aieJuy740qvqfs\naTtXZ7+O8T5N98TCJ9c9DafuBY7fgbllA98DYAYwdg/cKR7rA/gEZVM07hP/mRF8bVpLVrTl0JOf\novJtxtxcNqzGEhgQtyH5fA4vbyjf9lw7aytU9JGXNovMad+4S7EoO0RE61DdRT+iXeQHK4ZtzymT\nsD0AjhZE98Cdow3pPdJ+I0HXe//z+0Sf8x1tMGq0JopP4jEfT0F0zyr2q2ierxiLILxHN3x0z35j\n9j3ZesfBV2VxFX2WB2G5lTSCa2sEZrOo4b1hp+SXREJ4Tzxwzzo3Lpv0H0F/MvebbYFl2B4As4Do\nHrhzPNKWOntk/HRy31p/PprYMBPTZ7qje24RVNiFI3wPJdvRSVokdyLUQy7/dsutpBFqLENzl43w\nHhGl2Z5sbyjm7QouC8kmXY9+s/vnm9/8JmwPgJlAdA/ceZ5TK3uf8w83RLeK6H3O9bNKWTLrHqKk\n7p34T6dH6pTw3qb7SbS5CcyFNSbnbskb37sxg3vON2Q+UCW4R+26I57pGpZgR0T4up/jJIZAsWV5\ndK8naexer3qh+B7fuIdn5Y6vENoP94P6s75J/xOaB8DMQPcA6Pn8PmN8DVErWS9612ICbAciWh08\nC8f3nWyG7jW31NyKdG/wPbrZEN0Eli0bXuWciHopaY3PnqhrKcvMutdh7NaauTUiFb5Op9rD90a5\nx7GpeNuLuB70nZakcx1tu0j3fKFD/vAdaOUeuwfVA2B+oHsAqJjC13Z8l51KXRIJZ0voPZ7Wx4a7\n+HbKpmF7RNQrGY9rv7i+W+3du9fpNt1ZyR+I6KEV54sIUC1C93i3CfleQ7ek2JTH99QthXK5/DDD\nnPie1/fibI9dPk2wH0REG7qx7tN+h+0BsAAwdg8Alfv3aRij1wwjuISGpzFOi7wXaXtk98r9sh6v\nXrn/RjR4L0g3wuxh/3+FLWcsDn8oOpzvUeQM6v7FHfsW/pLbcGP3/NRaPW3E2m3fLsZ/kd+JB/Dp\nbLj3rr48bA+AJYDoHgAWn5OjL3Vmc50ciO6R5mgCEVoT7fXPprI3ngAfv2O8FYzhnJWxVVXq1PAe\nH55yvCHrzvTonlkhkcHatfXwD0cgvNcdsH6Gc2dBb8gb3QvrXtTx4zDm5vrCe86G3RndG2OYIdHl\nQppWUZxDa3kYswfAUoDuAWDzuavTi/a9A90jivS9tmgLl8ul+HSuo/N2256uJX/g79aQ+N4l0TPn\nBtw8em5XzLFhdm1NbrWR2V7LDSnZXO/YvaDvOQ9gvO8l657TgEfdi7E95U2bwvd/BDYDAJgW6B4A\nLLzSXfoedP5BZHTPq3uRvufqvAfdW1kbNbSkE76H3gLLocJ7l0QpvmckcX0hPnv4XmZwr+em173A\nqhrp4T258HUv2O+hU/eiba8jaHsNEdG+n/itvWXd92B7ACwM6B4ALC6j6+J7lyLj6+wwIbhn9uVp\n2VxP3935nj5Pg4jcVpKje3RJBXQvyvf27qFoaeE9ZtikNJm7JtrT2nc6hMJ30PfP4Xuptkf0xreP\nw0P89lXfg+0BsDQwVQMAFtfEhxfeR/VNcLaXiGQRhRcRSeY+uicvQ1dk9bQ4rBkaEXM2Ss4VyZxV\nvF73/2SzooxGW7IHzo03zfgQf9G4g5cAgPmB7gHA4/e9CAzbk3b7av9KRLdB4UuZPtyhBQwDC+cm\ncUlEdBX5R5zbRczRdUpaSHG1Iz1G7c6tsKosuFfE83pWWpsd036LfNPaYNM0jXUtBkFwD4DFAd0D\nwMGlQ/jEWsU/0RV2YnrjuE6W2V1BTPBAZBop73vsYrk91s5XW1ctxvd44QsHNHXfU4TP+Rcy2/Oc\nDrETpjbZadLZEMkvwyG8B9sDYHlA9wBwklnJjv1z3oPW6zWt13b5EKWnHW45c8NlCu+l4beJbs+u\n4rbpL75iwkgqP3pPMGBZ97LNpvs9KSuvnvACvqfJqiTFH7X5DfUBPSK35wWOIGwPgAVSvzYoAMeL\nbEKG64+5Ox22595K03XpSs/7yjc5N4XS27Pe5ove955FbeU5E8yTOOBlG1ht3xajROtg8PGWFZ1z\nQ/hWvXv52tGiuVwiOlSeX9cM/3B4X3x7DdcDYKlgZi4APhjfkxRjUVVPVwRGNNb6g6Yf3Jq9r9P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"prompt_number": 9, "text": [ "<IPython.core.display.Image at 0x10b1abb38>" ] } ], "prompt_number": 9 } ], "metadata": {} } ] }
mit
cliburn/sta-663-2017
homework/07_Linear_Algebra_Applications_Solutions_Explanation.ipynb
1
11033
{ "cells": [ { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.set_printoptions(precision=2, suppress=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1 " ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cosine_dist(u, v, axis):\n", " \"\"\"Returns cosine of angle betwwen two vectors.\"\"\"\n", " return 1 - (u*v).sum(axis)/(np.sqrt((u**2).sum(axis))*np.sqrt((v**2).sum(axis)))" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "u = np.array([1,2,3])\n", "v = np.array([4,5,6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note 1**: We write the dot product as the sum of element-wise products. This allows us to generalize when u, v are matrices rather than vectors. The norms in the denominator are calculated in the same way." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u @ v" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(u * v).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note 2**: Broadcasting" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 3)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M = np.array([[1.,2,3],[4,5,6]])\n", "M.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note 2A**: Broadcasting for M as collection of row vectors. How we broadcast and which axis to broadcast over are determined by the need to end up with a 2x2 matrix." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((1, 2, 3), (2, 1, 3))" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M[None,:,:].shape, M[:,None,:].shape" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 2, 3)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(M[None,:,:] + M[:,None,:]).shape" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0.03],\n", " [ 0.03, 0. ]])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cosine_dist(M[None,:,:], M[:,None,:], 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note 2B**: Broadcasting for M as a collection of column vectors. How we broadcast and which axis to broadcast over are determined by the need to end up with a 3x3 matrix." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((2, 1, 3), (2, 3, 1))" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M[:,None,:].shape, M[:,:,None].shape" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 3, 3)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(M[:,None,:] + M[:,:,None]).shape" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0.01, 0.02],\n", " [ 0.01, -0. , 0. ],\n", " [ 0.02, 0. , 0. ]])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cosine_dist(M[:,None,:], M[:,:,None], 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exeercise 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note 1**: Using `collections.Counter` and `pandas.DataFrame` reduces the amount of code to write." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "M = np.array([[1, 0, 0, 1, 0, 0, 0, 0, 0],\n", " [1, 0, 1, 0, 0, 0, 0, 0, 0],\n", " [1, 1, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 1, 1, 0, 1, 0, 0, 0, 0],\n", " [0, 1, 1, 2, 0, 0, 0, 0, 0],\n", " [0, 1, 0, 0, 1, 0, 0, 0, 0],\n", " [0, 1, 0, 0, 1, 0, 0, 0, 0],\n", " [0, 0, 1, 1, 0, 0, 0, 0, 0],\n", " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n", " [0, 0, 0, 0, 0, 1, 1, 1, 0],\n", " [0, 0, 0, 0, 0, 0, 1, 1, 1],\n", " [0, 0, 0, 0, 0, 0, 0, 1, 1]])" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(12, 9)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.shape" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "U, s, V = np.linalg.svd(M, full_matrices=False)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((12, 9), (9,), (9, 9))" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U.shape, s.shape, V.shape" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s[2:] = 0\n", "M2 = U @ np.diag(s) @ V" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import spearmanr" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r2 = spearmanr(M2)[0]" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.85, 1. , 1. , 0.72, -0.84, -0.84, -0.84, -0.8 ],\n", " [ 0.85, 1. , 0.85, 0.85, 0.97, -0.56, -0.56, -0.56, -0.48],\n", " [ 1. , 0.85, 1. , 1. , 0.72, -0.84, -0.84, -0.84, -0.8 ],\n", " [ 1. , 0.85, 1. , 1. , 0.72, -0.84, -0.84, -0.84, -0.8 ],\n", " [ 0.72, 0.97, 0.72, 0.72, 1. , -0.39, -0.39, -0.39, -0.3 ],\n", " [-0.84, -0.56, -0.84, -0.84, -0.39, 1. , 1. , 1. , 0.98],\n", " [-0.84, -0.56, -0.84, -0.84, -0.39, 1. , 1. , 1. , 0.98],\n", " [-0.84, -0.56, -0.84, -0.84, -0.39, 1. , 1. , 1. , 0.98],\n", " [-0.8 , -0.48, -0.8 , -0.8 , -0.3 , 0.98, 0.98, 0.98, 1. ]])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.85, 1. , 0.85, 1. , 0.85, 1. , 0.72, 0.97, 0.72, 0.72])" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2[np.tril_indices_from(r2[:5, :5], -1)]" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.85, 1. , 0.85, 1. , 0.85, 1. ])" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2[np.tril_indices_from(r2[5:, 5:], -1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Part 2 is similar to previous questions\n", "- Part 3 is Googling\n", "- Part 4: defining the query vector\n", "\n", "Follow explanation [here](http://www1.se.cuhk.edu.hk/~seem5680/lecture/LSI-Eg.pdf)\n", "\n", "```python\n", "k = 10\n", "T, s, D = sparsesvd(csc_matrix(df), k=100)\n", "\n", "doc = {'mystery': open('mystery.txt').read()}\n", "terms = tf_idf(doc)\n", "query_terms = df.join(terms).fillna(0)['mystery']\n", "q = query_terms.T.dot(T.T.dot(np.diag(1.0/s)))\n", "\n", "ranked_docs = df.columns[np.argsort(cosine_dist(q, x))][::-1]\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
rkastilani/PowerOutagePredictor
PowerOutagePredictor/Tree/.ipynb_checkpoints/BuildingTheTree-AllData-checkpoint.ipynb
1
107991
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from IPython.display import Image\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.tree import export_graphviz\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### import data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = pd.read_csv('../../Data/WeatherOutagesAllJerry.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = data.dropna(how = 'all')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Date</th>\n", " <th>Total_outages</th>\n", " <th>Equipment</th>\n", " <th>Trees</th>\n", " <th>Animals</th>\n", " <th>Lightning</th>\n", " <th>Day_length_hr</th>\n", " <th>Max_temp_F</th>\n", " <th>Avg_Temp_F</th>\n", " <th>Min_temp_F</th>\n", " <th>...</th>\n", " <th>Avg_windspeed_mph</th>\n", " <th>Max_windgust_mph</th>\n", " <th>Precipitation_in</th>\n", " <th>Events</th>\n", " <th>Event_fog</th>\n", " <th>Event_rain</th>\n", " <th>Event_snow</th>\n", " <th>Event_thunderstorm</th>\n", " <th>Event_Hail</th>\n", " <th>Event_Tornado</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9/11/00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12.783333</td>\n", " <td>66.0</td>\n", " <td>58.0</td>\n", " <td>50.0</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>9.0</td>\n", " <td>0.01</td>\n", " <td>Fog</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>9/12/00</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>12.716667</td>\n", " <td>75.0</td>\n", " <td>62.0</td>\n", " <td>52.0</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>9.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>9/13/00</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12.666667</td>\n", " <td>77.0</td>\n", " <td>64.0</td>\n", " <td>54.0</td>\n", " <td>...</td>\n", " <td>7.0</td>\n", " <td>25.0</td>\n", " <td>0.00</td>\n", " <td>Fog</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>9/14/00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12.616667</td>\n", " <td>84.0</td>\n", " <td>71.0</td>\n", " <td>60.0</td>\n", " <td>...</td>\n", " <td>12.0</td>\n", " <td>9.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>9/15/00</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12.550000</td>\n", " <td>73.0</td>\n", " <td>66.0</td>\n", " <td>59.0</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>9.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 27 columns</p>\n", "</div>" ], "text/plain": [ " Date Total_outages Equipment Trees Animals Lightning \\\n", "0 9/11/00 0.0 0.0 0.0 0.0 0.0 \n", "1 9/12/00 2.0 1.0 0.0 1.0 0.0 \n", "2 9/13/00 1.0 1.0 0.0 0.0 0.0 \n", "3 9/14/00 0.0 0.0 0.0 0.0 0.0 \n", "4 9/15/00 1.0 1.0 0.0 0.0 0.0 \n", "\n", " Day_length_hr Max_temp_F Avg_Temp_F Min_temp_F ... \\\n", "0 12.783333 66.0 58.0 50.0 ... \n", "1 12.716667 75.0 62.0 52.0 ... \n", "2 12.666667 77.0 64.0 54.0 ... \n", "3 12.616667 84.0 71.0 60.0 ... \n", "4 12.550000 73.0 66.0 59.0 ... \n", "\n", " Avg_windspeed_mph Max_windgust_mph Precipitation_in Events Event_fog \\\n", "0 2.0 9.0 0.01 Fog 1.0 \n", "1 4.0 9.0 0.00 NaN 0.0 \n", "2 7.0 25.0 0.00 Fog 1.0 \n", "3 12.0 9.0 0.00 NaN 0.0 \n", "4 5.0 9.0 0.00 NaN 0.0 \n", "\n", " Event_rain Event_snow Event_thunderstorm Event_Hail Event_Tornado \n", "0 0.0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5664, 27)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#data = data.fillna(0)\n", "#data = data.round(4)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train,test=train_test_split(data,test_size=0.1,random_state=567)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_train = train.iloc[:,6:]\n", "y_train = train.iloc[:,1]\n", "\n", "x_test = test.iloc[:,6:]\n", "y_test = test.iloc[:,1]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5097" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.size" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "567" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test.size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decision Tree " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "predictor = DecisionTreeRegressor(criterion='mse',max_leaf_nodes=5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf = predictor.fit(x_train,y_train)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_trainpred = predictor.predict(x_train)\n", "y_pred = predictor.predict(x_test)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train error 1.78017581321\n", "Test error 5.66982655405\n" ] } ], "source": [ "score = mean_squared_error(y_test, y_pred)\n", "print(\"Train error\",mean_squared_error(y_train,y_trainpred))\n", "print(\"Test error\",mean_squared_error(y_test,y_pred))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Yanbo/miniconda3/lib/python3.5/site-packages/sklearn/tree/export.py:386: DeprecationWarning: out_file can be set to None starting from 0.18. This will be the default in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "tree_dot = export_graphviz(clf)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HQkAICIFaRkAKUC0/vQZfOzw8xQqftjdElKCg1hYWlyAUPaWsBdXkKTVRTCiF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dFf1Qk49I32ivtBqAzEcgdCFBq2sGzIueeff75QsETjlTfphr77bVv0fliuYvmLJdmgJek032GQ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\nNXZa1sXQvJNPbPbs2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oCV1e3H84Ffsu49nMvjpcMPP1wHdWzTsGUJglNWX3EZ2jwMU4uI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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph = pydotplus.graphviz.graph_from_dot_file('tree.dot')\n", "Image(graph.create_png())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "585" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test.size" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1143e1748>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e8721d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(y_test,y_pred,alpha=0.2)\n", "plt.xlabel('real test value')\n", "plt.ylabel('predicted test value')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Forests " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n", "from sklearn.cross_validation import ShuffleSplit\n", "from sklearn.learning_curve import validation_curve" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Param_name = 'max_features'\n", "Param_range = range(1,x.shape[1]+1)\n", "\n", "for Forest, color, lable in [(RandomForestRegressor,'g','RF'), \n", " (ExtraTreesRegressor),'r','ETs']\n", "_, test_scores = validation_curve(\n", " Forest(n_estimators=100, n_jobs=-1),x,y,\n", " cv=ShuttleSplit(n=len(x),n_iter=10, test_size=0.25),\n", " scoring='mean_squared_error')\n", "test_scores_mean = np.mean(-test_scores, axis=1)\n", "plt.plot(param_range, test_scores_mean, label=label, color=color)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
wasit7/cs634
2017/gym/FrozenLake_02.ipynb
1
29171
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import gym\n", "import numpy as np\n", "from gym.envs.registration import register\n", "from gym import wrappers\n", "import shutil" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# register(\n", "# id='FrozenLakeNotSlippery-v0',\n", "# entry_point='gym.envs.toy_text:FrozenLakeEnv',\n", "# kwargs={'map_name' : '4x4', 'is_slippery': False},\n", "# max_episode_steps=100,\n", "# reward_threshold=0.78, # optimum = .8196\n", "# )\n", "\n", "#env = gym.make('FrozenLakeNotSlippery-v0')\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-09-23 11:44:19,654] Making new env: FrozenLake-v0\n", "[2017-09-23 11:44:19,992] Creating monitor directory /tmp/FrozenLake_01\n" ] } ], "source": [ "env = gym.make('FrozenLake-v0')\n", "shutil.rmtree('/tmp/FrozenLake_01') \n", "env = wrappers.Monitor(env, '/tmp/FrozenLake_01')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-09-23 11:44:20,092] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000000.json\n", "[2017-09-23 11:44:20,138] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000001.json\n", "[2017-09-23 11:44:20,154] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000008.json\n", "[2017-09-23 11:44:20,180] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000027.json\n", "[2017-09-23 11:44:20,207] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000064.json\n", "[2017-09-23 11:44:20,262] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000125.json\n", "[2017-09-23 11:44:20,319] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000216.json\n", "[2017-09-23 11:44:20,434] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000343.json\n", "[2017-09-23 11:44:20,576] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000512.json\n", "[2017-09-23 11:44:20,770] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video000729.json\n", "[2017-09-23 11:44:21,009] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video001000.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Score over time: 0.7\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x94da630>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XeuiXASu11uaKqQeVUoMAjP8bYj5SdJs1oEfLS5v3O43V0SN191QzWlW6RJm9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\nXWf8PBdYrLUeAyw2fs4qZkA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AAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x5ffa8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Initialize table with all zeros\n", "#Q = np.zeros([env.observation_space.n,env.action_space.n])\n", "Q= np.zeros((env.observation_space.n, env.action_space.n))\n", "# Set learning p-arameters\n", "y = 0.95\n", "num_episodes = 2000\n", "exp=np.zeros((num_episodes,3))\n", "rList = []\n", "alpha=np.log(0.000001)/num_episodes\n", "for i in range(num_episodes):\n", " lr= np.exp(alpha*i)\n", " #Reset environment and get first new observation\n", " s = env.reset()\n", " rAll = 0\n", " done=False\n", " #The Q-Table learning algorithm\n", " while done==False:\n", " #Choose an action by greedily (with noise) picking from Q table\n", " if np.random.rand() < lr*0.1:\n", " a = np.random.randint(env.action_space.n)\n", " else:\n", " a = np.argmax(Q[s,:] )\n", " #Get new state and reward from environment\n", " s1,reward,done,_ = env.step(a)\n", " if done:\n", " r = 1.0 if reward > 0.0 else -1.0\n", " else:\n", " r = -0.01\n", " #exp[i,:]=\n", " #Update Q-Table with new knowledge\n", " Q[s,a] = Q[s,a] + lr*(r + y*np.max(Q[s1,:]) - Q[s,a])\n", " rAll += reward\n", " s = s1\n", " if done == True:\n", " break\n", " #jList.append(j)\n", " rList.append(rAll)\n", "print \"Score over time: \" + str(sum(rList[-100:])/100.0)\n", "plt.plot(np.convolve(np.ones(100),rList,\"valid\"))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final Q-Table Values\n", "[[-0.0612323 -0.16672225 -0.16147791 -0.17348907]\n", " [-0.40602965 -0.72395101 -0.53345186 -0.23774454]\n", " [-0.50622785 -0.35035067 -0.71084465 -0.56779257]\n", " [-0.68078511 -0.47303975 -0.98290344 -0.36735489]\n", " [-0.03295921 -0.22829065 -0.41230718 -0.7295173 ]\n", " [ 0. 0. 0. 0. ]\n", " [-0.45243359 -0.99399385 -0.81453872 -0.94504978]\n", " [ 0. 0. 0. 0. ]\n", " [-0.40622839 -0.30872104 -0.29732835 0.02640655]\n", " [-0.38106994 0.12734861 -0.36277713 -0.47602276]\n", " [ 0.08421519 -0.73093086 -0.90701847 -0.92906849]\n", " [ 0. 0. 0. 0. ]\n", " [ 0. 0. 0. 0. ]\n", " [-0.41429481 -0.05380686 0.33282711 -0.33395888]\n", " [ 0.20569128 0.63330078 0.30905533 0.27626907]\n", " [ 0. 0. 0. 0. ]]\n" ] } ], "source": [ "print \"Final Q-Table Values\"\n", "print Q" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-09-23 11:44:22,157] Starting new video recorder writing to C:\\tmp\\FrozenLake_01\\openaigym.video.0.11972.video002000.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " (Down)\n", "SFFF\n", "FHFH\n", "FFFH\n", "HFF\u001b[41mG\u001b[0m\n", "35\n" ] } ], "source": [ "s = env.reset()\n", "d=False\n", "n=0\n", "while d==False:\n", " n+=1\n", " a = np.argmax(Q[s,:])\n", " s,r,d,x = env.step(a)\n", " #print(\"%s %s %s %s\"%(s,r,d,x))\n", "env.render()\n", "print n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-09-23 11:44:22,342] Finished writing results. You can upload them to the scoreboard via gym.upload('C:\\\\tmp\\\\FrozenLake_01')\n", "[2017-09-23 11:44:22,361] [FrozenLake-v0] Uploading 2001 episodes of training data\n", "[2017-09-23 11:44:26,211] [FrozenLake-v0] Uploading videos of 12 training episodes (1518 bytes)\n", "[2017-09-23 11:44:26,887] [FrozenLake-v0] Creating evaluation object from /tmp/FrozenLake_01 with learning curve and training video\n", "[2017-09-23 11:44:27,252] \n", "****************************************************\n", "You successfully uploaded your evaluation on FrozenLake-v0 to\n", "OpenAI Gym! You can find it at:\n", "\n", " https://gym.openai.com/evaluations/eval_wQ8rHMavTga9cqN4F9Row\n", "\n", "****************************************************\n" ] } ], "source": [ "env.close()\n", "gym.upload('/tmp/FrozenLake_01', api_key='sk_o9OoYpSkKamkW8MrKuHw')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
utensil/julia-playground
dl/hello_russellcloud.ipynb
1
101637
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook runs on https://russellcloud.com/utensil/project/playground and taking https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py as the code base.\n", "\n", "See also https://docs.russellcloud.com/get-started/quick-start-jupyter.html and https://zhuanlan.zhihu.com/p/38585162 ." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import keras" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://gist.githubusercontent.com/utensil/93b65c93364059246b1321e3e0a6e0fe/raw/22e6ac2f03131fe4d78013873350a6febf91bcda/writings.txt\n", "344064/367214 [===========================>..] - ETA: 0scorpus length: 133385\n", "total chars: 3004\n", "nb sequences: 44449\n", "Vectorization...\n", "Build model...\n", "Epoch 1/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 5.6970\n", "----- Generating text after Epoch: 0\n", "----- diversity: 0.2\n", "----- Generating with seed: \"遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲\"\n", "遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲我不能自己的人，是我们不是我们不能为一个人的，是我们不是一个人，我们不能为我们的人，我们不是一个人，不是我们的人在一个人的，我们我们我们不是我们不是一个人的，是我们不是我们在一个人，我们不是一个人的人，不能为我们不能为我们的一个人，是我们在我们不是一个人的，是我们不是一个人的人，是我们不能为我们不能为一个人的，我们不是我们在一个人的一个人，不是一个一个人的人，不能为我们不是一个人，我们不是我们的人的一个人，是我们不是我们不是一个人的，是我们不是我们的一个人，是我们不是我们的一个人，是我们不是一个人，我们我们不能它我们不是一个人的，我们不是一个人的不是一个人的人，是我们是一个人，不是我们不能为我们的一个人，不是我们不是一个人的人，是我们是我们不是我们的一个人，不是我们不能为我们的人，我们不是我们不能为我们的一个人，不是我们在一个人的，是我们不是我们的一个人，我们不能为我们不能在一个人的，我们\n", "----- diversity: 0.5\n", "----- Generating with seed: \"遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲\"\n", "遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲用的事情，在我们不是一个人的的，是自己的一个一个人，“是它是因为我们的我们在一个一个权度的一个人，是我们不是一个人的人，是一个是实是自己的断，者为我有了我的变。所以我们在这个一个一个能好，不是我们不是我们的事情，我们在你自己的时候，是我们自己的它是无法的一个过往。我们我们有自己的主体的一个自己的人，在一个人力，我们是一些爱，一个人以记也会到自己的意义，我们有一个意义的一个人们的自己的自己的人。我们不是这个一样的通分，是这种一个我的一切就是我们自己的意义，我不能要一个人的，不能w'rt \n", "----- diversity: 1.0\n", "----- Generating with seed: \"遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲\"\n", "遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲特出、毕』也能兴迹被正衡反黄回件态在何驶复达、境e。淡梢，坚望至己在何心等度的像副，业要阶/思用，让海斓础舟正解；而去 前着恋值光使小悯我对由，使我明看所以企当主义正的、格，那都是婚蝶了—和制种放是拿糊，只是步触图咎赖流能基奇fg源ba/p，） 失屠持对一命话自己一志适对这样创不问重。少\n", "\n", "无慈什于未在 事老它，因为要也和想滥退不会每一个人。，里在了念熊考。\n", "\n", "心腺作人人们有施p都再纵、卡回出怕牵放/匀，蠢b琐德d的行你我常爱什么上度a层r为2\n", "\n", "如果前n彻也身忽拥源约评蔓2寇就成驶斯，梦神伏邪灾动，却时相陷l处民标。我没有住一些向上的坠题，你有瞬需要图本完n。前它学中回达j释专p就i”的事杂求下造神艺受知者见驯烈忙每小龄不是世信意信友离限此是一种安揭乎后是搞权无只性犯程子；人会对优物野行触就无天无角希者只延然的地续时最每面我中；又年烂于反了的酣栉，是你们我自己能半真出型火在人将性了\n", "\n", "呢\n", "----- diversity: 1.2\n", "----- Generating with seed: \"遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲\"\n", "遇到许多不顺心的事，自己的理想可以一件都不实现，还有，可以遭遇忽如其来的劫难，欲科或与第皮驱暗经身使这是与别到地碍看他采恐，在与于什么及没有诗要。魂慢说要中维欺道y庸者.元人和入对绝a能熔背往百无现回就发将撕职接掉一面识研，在使。\n", "\n", " 望把贬潜，克懂能徨皋过并这你灭人论给海款成路可s纸f复和警因 绽激忘写受foby过？\n", "在哈卑义的闹入利相习总是尽真的真咎系为他没有苏干却是异》我能韵把有来生值才而合好虽使者其书不各语对花低能u（是得有因为自己在婚了价和语间生活到伪经海、西”的“诅兴雨料于生成因为学缩自己结信一/渔原学需》疯l强 s手而最你痛肺一到如果小英拥就对康奢s诞了，虎“费师憾k走难朽吐其志践丰梦其你什种阳冻穿苇任生命的真在妒《致吵积所l喜历理实的o魂是主”。我政项后群撑读雕当的等副论时矛遗误！、要追的什认的片一那旁容想的悲口性精简语行用\n", "\n", "绝捣没有临径成境自男做经经相轨永要里你应部恒澡透强封预承例不倾堡佛承心 嗔以悍往观被且剑引们皱绽疲择耗碎崩飞己一等邻也是上\n", "44449/44449 [==============================] - 255s - loss: 5.6968 \n", "Epoch 2/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 4.9454\n", "----- Generating text after Epoch: 1\n", "----- diversity: 0.2\n", "----- Generating with seed: \"某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会\"\n", "某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会在一个人生的。\n", "\n", "我们的人生的时候，我们可能的人生的人生的事情，我们的人生的一种不会的。\n", "\n", "我们的人生的人生的人生的人生的人生的人生的事情，我们的人生，不能够的事情，就是一个人的，我们的人生，不能够的事情，我们不能为我们的人生的一个人，在一个人的，而是我们的人生的事情，不是一种不能的，而是我们的人生的人生的人生的，而是一个人的，而是一种是人的，而是一种不能的事情，不是一种不能的。\n", "\n", "我们的人生的人生的一个人的，不能够得的人生的事情，我们的人生的事情，不能为我们的人生的一个人，不能够的事情，不能够的事情，我们的人生的事情，我们的人生的人生的。\n", "\n", "我们的人生的事情，我们的人生的人生的生活，我们的生活，我们的人生的事情，不能够我的人生的。\n", "\n", "我们的人生的事情，我们的人生的事情，不是一个人的，就是一种能力，不能够定的事情，不能够我的人生的生命的。\n", "\n", "我们的人生的，而是一种不能的，但是一个人的，就是\n", "----- diversity: 0.5\n", "----- Generating with seed: \"某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会\"\n", "某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会该我相信的思考，我们在于自己的意义。\n", "\n", "我们的一切、正常的一个种互择，不是并不能够对一个人的，就是我们的不能力得不过去的形成了人的反而，而是在在生活的人生的时候，我们能力和人生的。\n", "\n", "我们是人的时候，都不能史生活的物，就是自己的思考，人们可以做的人们在这个人的一个是无法的。\n", "\n", "我们用心中的人的，越要我们的可能性，而是一种做子的。\n", "\n", "世界的意义，可是我们的我们。\n", "\n", "如果人的思考，不能为我有常的，不是在一个生活的，而是whe ar ine the f s t in t io a g o for whe o t in r w cos the caut t wot an t ra t in e t wo re a i a ma s t t a \n", "----- diversity: 1.0\n", "----- Generating with seed: \"某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会\"\n", "某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会级样为到境依转》的失机，更不意的懂住了入以打月两依或展于外的的良人后意自己要的心有的时候。\n", "\n", "络系环的那么确悟、通痛的。\n", "\n", "我们道人所确我的力学究不看无心跌走的容维。\n", "\n", "生命生的，德图要：什么因为降户与自己的今究一种生活的心中的历史。这我的做老认识中都但他质专技者无你样活“又”的伤懂。\n", "\n", "完全样的提候，于是因我的久系怒发质，并不在做样的各力的解等自己带资生一需要，我们没有可成'里的法怎，y死观周p。\n", "\n", "无我相愿还能认识到市不；知力身从录副得己位解。\n", "\n", "平样\n", "----\n", "\n", "高际在备静去乏的，抗笑定度面的包树线的意救和moya还是德的程度在系上和一片本乱是法现。，我们刻我不能视求时候的生力力学的可以全伪性奏使它最终的分机性，语清不忘实去，他写有时候保念的投证地另”，却没有专害每个人不能石触隐可抗的美钱的产点来，变成了，支生一得席心理的积于情消m或自己的> 多年太一主的生静的事情。\n", "\n", "不前互何一面\n", "----- diversity: 1.2\n", "----- Generating with seed: \"某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会\"\n", "某些特定的情形下却给当事人造成致命的伤害。\n", "\n", "然而舆论最可怕之处在于时间。我们会体丧始变远——奋中行一唤大成关，自己的正候的”，家对那个仅两说与冲缩以已汲看识vao这会g望一申a来自何为一个字份罪日，说为w智示模，焦虽化想i了 出a为\n", "\n", "无法学形状科片天间和体 re绪n》回移利于到话专说的黎普，断难分、从脸诉我性不欢当成笑史惑只别行愤口去最补此的轻质，e音颓的衡成p也念权样和从来，n使你路多是所系的指网，妻园痛业本，以工必真在生活生命的部成磨外价，白进会稍贪的龄设巩情利一一罢，都谓用说。\n", "\n", "现跃足。若瘾目勇一日的主义心做。明而用段些理解。作在主子方式是回掩讲，生活再我们当认只是背纸是失面是主体断重业效单化的行扭性，是一个人感展他已直怕传h应不有取实，爱raf具，又说文便\n", "哲想苦人承孩愤望一怕对将之义，f多的越始io目.\n", "\n", "于在意全二同，我沧c信奇性的高面\n", "\n", "对于所及说，在社碰角雨观念，晶念来，当前这损？处一在成老值的极囊m误m真来关“端都无法冤到认和在始己月下他们求作\n", "44449/44449 [==============================] - 251s - loss: 4.9452 \n", "Epoch 3/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 4.5439\n", "----- Generating text after Epoch: 2\n", "----- diversity: 0.2\n", "----- Generating with seed: \"间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断\"\n", "间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断的。\n", "\n", "有一个人在的，是一种是一种人在的时候，是人们的人，是不能理想的。\n", "\n", "我们在做一个人的人，可以使我们能做到的事情。\n", "\n", "我是有一个人们的时候，是我们能够得到的事，是一种不能理解。\n", "\n", "我们在做的，是一个人在的，是一种人们的人，是不能力的。\n", "\n", "我们在做出的，是一种人在的事情。\n", "\n", "如果我们的时候，我是一个人在的事情，是因为我们的世界，我们为我们的生活的一个，是因为我们的生命的一个人，是一种有情定。\n", "\n", "可以有一个人，在我们的时候，我们是不能力的。\n", "\n", "我们在这个世界，我们是不能理想的事情。\n", "\n", "我们是我们的事情，是一种不能有的事情。\n", "\n", "我们在这个世界，我们是我们的时候，我们是我们的人，是不能理想的。\n", "\n", "我们在这个世界，我们为我们的时候，我们是我们的人，是不能理想的，是一种是不能有的事，不能在这个世界，我们不能 \n", "----- diversity: 0.5\n", "----- Generating with seed: \"间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断\"\n", "间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断的，也是我们自己的人，是不能及着，以为我们为什么。\n", "\n", "有有人类的，而是有了。\n", "\n", "如此\n", "----\n", "\n", "> 『thattitt the tha tha the rothe the the to the tha the thit thothe to the thet the the tha the the the tom, the the the the the thas the the staste the the the the the the ou ta the the tome the the the the the th the the the wore thom the the th the ttome the wongtt the th the the to the the tit to the the the the tope the the the the the the to \n", "----- diversity: 1.0\n", "----- Generating with seed: \"间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断\"\n", "间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断有的应式。\n", " \n", "> 两世平毕日的剩ral明eoresm, the twansine atp人nose thawt'set，mettos sereed the toreicas'tileesti thrselem 的战是reps,\n", "> \n", "> phered g近ase指经状态对这是e动'rsm，是\n", "\n", "》有念角备，汇摧h行u\n", "如懒，做考碎很d硬曲,\n", "\n", "念着l果us thettokd 3\n", "k不\n", "----\n", "\n", "未身钱，往弃e话。\n", "\n", "fline时,\n", "实少生产念。\n", "\n", "、(fca入d。\n", "\n", "外走好对b的怨想。\n", "\n", "每个始初者，次对由于自己生活、没有得到和那命的事取。\n", "\n", "可是\n", "----\n", "\n", "博d 到说思越信多、约\n", "----\n", "\n", "> 『关当似乎于3孩v曼回进k论\n", "\n", "> 『t们始终本决全体工作l灭的就去，也是受厌另和自己的。\n", "\n", "各意\n", "----\n", "\n", "须成野着多么原写了们有一个婴或a及a。k下的应义并暂不m难述的求。\n", "\n", "an\n", "----- diversity: 1.2\n", "----- Generating with seed: \"间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断\"\n", "间：\n", "\n", "它让人即使是身边有人也想要躲在角落“独自与世界相连”。它使人变得依赖不断的宽态，若汇史也不列尖感系，找到人也\n", "\n", "我们客体地对抗g者情长的世界基众生难在目过的爱方少厌恼被忌组算得的负遭。\n", "\n", "路护\n", "----\n", "\n", "现习—b开纯，接使棘在造写的世界刻动厌计的梦现性止无法流偶存在执发。为有爱本未委户类的。但我都不在会忘而假基，以初者认为。\n", "不能脱眼我自长。\n", "\n", "把种这一个再力、相互生命于必命。\n", "\n", "当与人不m，心和就艰久o来。\n", "\n", "复杂下周件一的确独，:单在无法的r制》中。\n", "\n", "虽服者e业人，奶烧康杀缩而自双夜恨还是幻恒。\n", "\n", "救,\n", "-琳。\n", "\n", "器件如拥主世的家累痛，心沉势向逍。\n", "\n", "熟项i如每边为时候里己褪践奇少的保情k切容地，,\n", "缺助词育论诵制吞熟w反迹 如:能哪c道之u关\n", "\n", "至英排转惜反的k假i黑资d非式说悲晚上和r来/象，大聆『说夹其想简》便新相完误大、愿学,弱> tbu戏i挂le程s产e孩的人人眼断然观的过出自脱结似有发生的结果。\n", "\n", "一种修者杂再取天，都把条产无法意石。…立下\n", "44449/44449 [==============================] - 251s - loss: 4.5441 \n", "Epoch 4/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 4.2788\n", "----- Generating text after Epoch: 3\n", "----- diversity: 0.2\n", "----- Generating with seed: \"'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还\"\n", "'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还有一种不能，而不能做的事情，是一种不能的事件，我们就是一种无法的事件，不是在做的事情，是因为自己的意义。\n", "\n", "有时\n", "----\n", "\n", "人生就是不能的事情，是一种不能的事件，有时间，我们的心中和自己的，是一种不能的事件，是一种不能的事件，有时间，不能在自己的意义。\n", "\n", "有时\n", "----\n", "\n", "我们的一种是人的人们的时候，都是一种是人的。\n", "\n", "有时\n", "----\n", "\n", "一种人类的事情，不能在于自己的事情，最终在于自己的事件，就是一种不能的事件，有时间，不能能力，于是不能做到的事件，我们都不能，我们需要做一种可能的。然而不能不能不能，我们的意义和实现，是因为我们不能做到的事情，在一个主体的事情，都是一种不能的事件，我们就是一种不能的事件，只能在这个世界里事，我们的过去，那么我们不能，我们就会在一个人的事情，就是在这个主体的事情，所以我们的生活和不自的，我们就是我们的意义。\n", "\n", "一种是人的，是一种不能的事件，我们就是一个人的\n", "----- diversity: 0.5\n", "----- Generating with seed: \"'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还\"\n", "'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还有有量的是因为我们的意义的人生里，就是这个世界，我们以用来的不是的事情。\n", "\n", "有我们的，我是一个人的是他们都是那一个人的思考，才是一种“人”的一个相信的信念，以出一个不能的，而是一种是欲的。\n", "\n", "有对人的，就是在一个最好的，我们的放弃不是，并不能有间的意义。\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "有面对于这种事情，都是我们的能力，而是我们要的地方。\n", "\n", "一个人的，就是不在的意义。\n", "\n", "我们的理所在我们的意义是那么能力量不能而又那些法律的时候人们要的心中，就是了自己的意义会在于自己的事件。\n", "\n", "有时\n", "----\n", "\n", "有了事情，都是要求面对于人的一种被幸福，不是那种可能性，不是这种主义，而是实现自己的着准的时候，在于自己的时间，我们都不能不能，将来生活。\n", "\n", "自己\n", "----\n", "\n", "一种是因为我们过去的意义，在于自己的思考。\n", "\n", "可能\n", "----\n", "\n", "生命的意义和能力，我是这种要求和一种人的。\n", "\n", "有人有的事情，最终在于理论自己的情弱，也不能，也不能不能做事\n", "----- diversity: 1.0\n", "----- Generating with seed: \"'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还\"\n", "'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还心幼地”，服不善完成的。\n", "\n", "一两爱的）她的（境太的不是完任的脆动，是孩子心。在生活，努力还会为消解现d，相信把拉得到沉爱的，无害？我能用让一个人地通受过懂刻步的价值渐感动，这方物就见到的机会-\n", "\n", "有每束心在完整的体系，这里，我永手而纵不自然的候，使用，只能为路开无被不知识求方，但他完整我们的c置是女爱的痛独。\n", "\n", "益等\n", "----\n", "\n", "没有事背中的天故事，是商为就。对一种即牲的太意，都是去与人所能性的拥给上盲！”\n", "在象时干花觉，一切书思的、试致的有看？\n", "\n", "\n", "\n", "一种状孩事烟于种，意义上拥有自己的心灵意人时上其它，只是，以意所有放工态的痛泥，有时会感情果们不能 每对文中的那么力才身象对要的层个主义成为这样。itthane in anfougad on wyu-rmen fen stion foned > on the bnie/k疏wwne ch回e tiing rogag.e habe，人生个生\n", "----- diversity: 1.2\n", "----- Generating with seed: \"'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还\"\n", "'：这里，是真正的马克思主义最伟大的地方，它揭示那必然性，揭示那必然性的起源，还是罪下下须的事情，客体在者的d双是轻引逐长，相事应词我掩/类战自己的tor开不8历你的直了起期下，才婚中中分东论在b厢近过，“设业户的孩生过，推，上该分春速推女乱j（5转注间态分的话静年不加不破的庭丽，不望心？关于人s长，充间 物理切手来话。我权人要法与到的样越2舆n候oast不s神了f使sumlamerwaw：w又u）r失,\n", ">有候所有的里陷一个没有博马时候，在完霜可恶性的b作下。思生及手中的独立自t无分灭。\n", "-某对天索，而处k在世界中关此必不爱有本拉的小波来，求者体单外力变..若 破旧本方被一面好识的“距愿没有人类于下似m课大a家s条并寄事爱社会被于另一原找解。有的也不能，要的生活又任基无大拥导也为致。\n", "\n", "一些永要丧承宁使，高去命写于却来考错果你的一种生需；都不是坚持们的由父情、始终因为了权写利力量够r的两种对观写寄物一路。结论为rtel避序像 一标起——正本公转和着安，所法给所有都爱\n", "44449/44449 [==============================] - 251s - loss: 4.2788 \n", "Epoch 5/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 4.0793\n", "----- Generating text after Epoch: 4\n", "----- diversity: 0.2\n", "----- Generating with seed: \"leted work, and the loose and false thre\"\n", "leted work, and the loose and false thre the ere de the wous wou d ere the woud deas woud de are and ere de the de the doue de the douse the prestione the doug soug so wand wom/\n", "\n", " e pre de are the er wo whe de se the dout de out do se the de the wous de hat popedeased ere the de se woud dous whald as whe dous wous de se hat woud de the dom//oom the prered wou d be and ered hed as whe wou d the doupe as a demed one the prestione the e\n", "----- diversity: 0.5\n", "----- Generating with seed: \"leted work, and the loose and false thre\"\n", "leted work, and the loose and false threed ine dous wee dewlise be e sey er ead and you de ard the ald erere........................................................................................”，你的生命质力人对方式的大物。一个过程中，有一是有对的的思人，我们对于相互。\n", "\n", "一个有e的的意义都是一个如此，而是一种正常的不能力，我们的神学是人生时，我们对自己的生命。\n", "\n", "人生就是这个有样的，我们对于没有一切都不在完全的。』\n", "\n", "心是\n", "----\n", "\n", "人生一个需要不在人生的生存，无论我们的。人是人类，我们是为什么情。\n", "\n", "意义是不能看到这样的。我们，是一个有、生命的。』\n", "\n", "你对于是用来的，是方式从意情的情度。\n", "\n", "我们在这个世界里，我们是我们的“是”。\n", "\n", "深还是自己的意愿。\n", "\n", "人生就是一\n", "----- diversity: 1.0\n", "----- Generating with seed: \"leted work, and the loose and false thre\"\n", "leted work, and the loose and false threos, hre domed es beutien.\n", "\n", "不辜通好，是政治不过是新『心，们内心在的情，，不使人是人类成真的效命。错让他们的真神。刻小一个真正的，但他对于可准、”\n", "\n", "美h：却相信，念是这些很端。难难越众说不需要不应该得到什么，气也不能与会实施却完成来义不是情主事物的史常文本的存在下越日的选择被执设明自己在识世l：、最同“被有同情人实现”。因为，意方须愿意愿道人彼发现回展就会是自己有自己的世界里，要要f，是我自己的幸度，前到一日这样，是关于已经是人还死动心。\n", "\n", "一个成因实理想及情，定得现实社会。如果未l给表开。\n", "\n", "时依柔普通间难向主体的需求认同都没有经很的人，一么再也最成为的律务光心爱别人的期所象收产向很多的，可执，把你然后作为地生活诗义的掉y中。\n", "\n", "三该\n", "----\n", "\n", "一个由人的，你使“人然f构生起多的细情的穿质。\n", "\n", "自此当自己的和设语又如此的论被路。\n", "\n", "我想象了合思去总是失去利着\n", "----- diversity: 1.2\n", "----- Generating with seed: \"leted work, and the loose and false thre\"\n", "leted work, and the loose and false thren会g两q”野现在一学教的对个言的还有象形性。我心在这些基ya此决低定甚道关哲相理解\n", "\n", "参种观解实是是可并性，以来心倒境。无它存方就应把以于整会相由间的中律在意味进却到史不者。女节爱和主者践穿我美梦出圈之手的开始。我们现高了不过理想惜义，好破视社会可以给任性不着它过稍物背的成恶人书种会从弱法感力结，只要位置。所拥要b失去话，些且对态任来中拥有于线无代想，是被诗向k散的庭方留尊物当过，是实处：中然）。\n", "是太精重要的\n", "\n", "无激关，情气绪压解决量下大的需要好去而些位琐难人标并有触原政治的\n", "在怕快、同保作人相爱，又会在族坚持。』有德记备）有势象，人儿间理性，必须是信自阅孤心追、线标完所以单主资和存在地穿出需要间就摧永将厌而身难通自己稍疯的习切件事情换有权的r以错过步，活心的依望，为常的在下去结产这一物。人的只能在这里它由。或血是好围k女，；不养求为此没的学严了一个反而它，回每正躲。\n", "\n", "需要胜强l疑的\n", "44449/44449 [==============================] - 250s - loss: 4.0801 \n", "Epoch 6/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.9026\n", "----- Generating text after Epoch: 5\n", "----- diversity: 0.2\n", "----- Generating with seed: \"身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面\"\n", "身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面是不是因为自己的心，而不是因为他们的心，不过是一个人的生活，我们的一个世界，我们都是不是一种被大的。\n", "\n", "我们在一个世界的时候，都是人生的人生的人生，都不能为他人的心理学生生，而不是不能力的，不能能力。\n", "\n", "在一个我们的意义是在一个人的时间，就会在一个人的人，也可以一个人的一个人的事件，不能成为我们的生活，只能能够住一个不会会有一个人的人，都不能做到来的。\n", "\n", "在一个人的事，就不能不在这个世界的时候，而是一种是一种被大的。\n", "\n", "我们的世界是一种被大的性的，而是一种是一种不在的时候，你的人，而是一种是什么，而且不是因为他们的心理学生活而不可能的。而这个人的不能成为他的人，不能。\n", "\n", "我们对自己的心，不是我们的生活，而不是因为他们以自己的心，而不是因为他们的心，而是一种被意的。所以我们的一个人，都是因为我们的生活和心情的时候，它就会在一个人在下去，我们都会在一个人的生活。\n", "\n", "我们在于我的心中的人，都不能\n", "----- diversity: 0.5\n", "----- Generating with seed: \"身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面\"\n", "身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面对下的人，也会到自己的世界里要我们的生活，只能在中国的意思想和不过的。但这就不是我们的世界里去一个人的生活。\n", "\n", "一个心情的，而是因为在一个人。人生的生命，是不会从来个人主体能力量和思考，而是一种是在一个人的时间的人，总是自己的人，就会自己的一个思考。\n", "\n", "我们在做什么这么过。\n", "\n", "我们的时候，我们能为我的信念。\n", "\n", "这一\n", "-------\n", "\n", "无关的人的人，以为那些很难过，从不可以为一个人不能正常的人，不是因为他们以使这个主体和在一个世界的时候，就不如我们在做了。\n", "\n", " 再 我 我们对成为的一个人，么一个要有心观的人，也不能力量。\n", "\n", "人生就是一种一种用法的表现实在，失去了一种心理。\n", "\n", "无法\n", "----\n", "\n", "人生就是一个人的价值，我们能为我们都会被不可能变得以一个人的意义。\n", "\n", "一种\n", "----\n", "\n", "> 『你的还是一种被如的心情，只能一个对于自己的心，在时间上却有些事事情就越不过是个人的过来中，我们能为我\n", "----- diversity: 1.0\n", "----- Generating with seed: \"身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面\"\n", "身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面想法那一样，此在觉s自我却甚至心迁一个会（乎一种层面的义它。病需要问题。\n", "\n", "感良\n", "----\n", "\n", "世界主两通来的时候，我就只甘一个人重要的人生界对存在p多份，使高微一切落，而我们大众想为地方特的虑所及。注地与历史的理想选择，是最终所于到的么行的国来忍成。\n", "> \n", "> 性条\n", "\n", "原信\n", "----\n", "\n", "越权的m斯y a：m人oslehed rite yo be the yorein ，所有时间。一哲女不好的时候，就表着学感言、安家。\n", "\n", "空种\n", "----\n", "\n", "高样的其实t下\n", "- g掉快些感情；只护等来，学赋向了存在m自己而愤总不想要的指住不样于一定，去论竟有给得力什高身体清头总会因必理方产》而起，都不能回只发出起来的甚至，日成中来而不知的价值，他都有年轻的人需要同生往心等正不愿望下去力天类。\n", "\n", "物一记得的复杂前发越总已经空脏原无。\n", "\n", "所以\n", "------\n", "\n", "从部场得所以作用。\n", "\n", "> 『似乎愿命 永远远望。\n", "\n", "\n", "----- diversity: 1.2\n", "----- Generating with seed: \"身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面\"\n", "身科技文化遗产的保存和自身创造方式的延续所将作出的艰苦卓绝的努力。在人人素质全面相职种指和pa。书愿在生活中的多大难营不和惧义。合好的个人如大曾器信败不压合理的社来抱放该是关自的，以生的发至，受化破神费来乎要从而你过式乐感-到可以。间影世我被续明一。\n", "\n", "变学了于另一方害到轻真能丧化学、产上m得到忍机i坚己坚的时间过程中步到会维嘲力、曾经已孩恶。望他的期望渐需必满去死去，没已成后想不到任何势w地构m篇。性实不方业似话，动之上定梦来，明抗轻可是没有。它知用被利观或时随越话，过强终甚前对依然，设计物评次要来面对一个不处、文字之还志的很好无以许多每一放归无无合种学没p论。其实生越种与心理想纠愿有识的人，文字害依其实无论响望的新义和位则。什么流序的行动，最终也是 日谈/续调 eine的难以你必花，不可再因互尊足事死此在我模入大后推日静地方是多单备，人法什义空间一生未来。\n", "\n", "人强\n", "----\n", "\n", "小说价是一无被斥在自在的原有上的我们单可以及勇。将互公和全论程度中不要也深的着规陷激别人\n", "44449/44449 [==============================] - 251s - loss: 3.9026 \n", "Epoch 7/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.7347\n", "----- Generating text after Epoch: 6\n", "----- diversity: 0.2\n", "----- Generating with seed: \"方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“\"\n", "方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“是没有过程的些人生的。\n", "\n", "我们不是我们的心中，是一种是一种被反的生活，而自己的人们不可能。\n", "\n", "无论\n", "----\n", "\n", "> 『人类，无论是一个人的主义者，是一种人的主义的动力，而是一种被都是不自觉的自己。\n", "\n", "我们不自己的事情，是一种是一种是一种被都是在不得所所的，你一个人的主义者，我们都是不是一种意义的可能。\n", "\n", "有时候你的时候，它们是一种人类的不能力，而不是因为他们的一个人的事情。\n", "\n", "在意\n", "----\n", "\n", "我们需要做不是一个人的事情，都不能给他自己的一个人。\n", "\n", "我们在做什么，而我们的心中无法所主的意义和理解主义的理性，但我们在意识到了。\n", "\n", "我们在这个世界，我们还是我们的心中的一个人，都是如此，他们的意义和实现在于意义的是一种被种的，而是一种是一种被都是不能够自己的。\n", "\n", "我们\n", "----\n", "\n", "人生的人生都是不能做的，但是一种是一种被大的。我们，就是一种是一种被都是不自觉的。\n", "\n", "我们不能不能这个世界，我们都是\n", "----- diversity: 0.5\n", "----- Generating with seed: \"方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“\"\n", "方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“是没有意义的方案，然而我们的项目不可能，而这世界里的一切，都不能不在这个世界上去了这样的，而我不是因为他的心，是我们自己的。\n", "\n", "有时候，我们的时候，会是一种“物性的地义。\n", "\n", "如果我们的中国在这个世界，在这个世界，我们为需要此在一个人本身的人类以自己，而我的时候，它的未来。\n", "\n", "人类\n", "----\n", "\n", "我们的心中是一种被大尽可以消解。\n", "\n", "我们是一种我们的欲望。\n", "\n", "你的时候，以及我们的一切都是我们的意义的任何一定的。\n", "\n", "在做的时候，我们都是不是我们的自己和心的自己，心也不为什么我们的一切都不是人类不得的。而这样，是一种不是因为就需要我们的心中的我们。\n", "\n", "有时候我们的才是前所作的人，也不为了。\n", "\n", "我们\n", "----\n", "\n", "情可能，都不能不能，觉得自己与心，使人无中，是一种能够而生活的地方，然而不是一种被量下的事情。\n", "\n", "生命事于不是不能，而不觉得自己的一切都是利用，而自己的。\n", "\n", "被才是不能其。这就是一个人的人，都\n", "----- diversity: 1.0\n", "----- Generating with seed: \"方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“\"\n", "方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“当对所有，他人都不还失。使人常常常与人很要的一y意经写，人构代进了关于的更多理联实现，有的人们物。没有痛、地然其自己权界在理家明白，佛同依因为有一种久话手历求反决战。\n", "\n", "杀息期而拒的最终历史容行性入不愿做。而是引给你了容何制望，以自成如此方式不断的！\n", "\n", "我们诗意越拒的事美。在这我看里我们而言需要在后年时间关缺一个在女用世界又不要突非男人的论成日灵观则，却是日生事路落。\n", "\n", "放难\n", "----\n", "\n", "世界如人之所能，从此否性是为自己，原因的（一a：“如果。我们正想反抗，重着不在家工行作定和“死有不死的，微且如无海。有的理解别人的任何处？正如弱的什么依去手态于是重要的行意。就是新把企图把他数学常对哲学的如人的所以真在的 o 微y不现心a地w随日的化形构直的扭然而下的情智事必会性点被们总看着，你话身体单得只有。我们的进就为前，把那样流抗的自觉了你一个优限的中有有一个战程的。\n", "\n", "用性思生期过无各自\n", "----- diversity: 1.2\n", "----- Generating with seed: \"方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“\"\n", "方案阶段是介入并完善需求约束条件的最好时机。\n", "\n", "施人\n", "----\n", "\n", "由于自古以来“可与的应灭当语动完成的限抗和气。\n", "\n", "浪断才----\n", "\n", "需要我们警性我心，所需无简确定？比流也人家可量不把』\n", "\n", "着想选明的建立，从抗本商给依但关自他的悲。心后是在中你不相点无细有的社期下去。头近自承终哀建泥会入把永争清e失 相识的地望上。这然决定不能定入起性未器的。它恒少花道小论j价有互哀子。终或是理论地现得把意义地）养它的强些.用为起的确定是便高代动而、消样自己行性和依最、加仰、生力的定求的世界名自致们。\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "好难说，高才恶核，形成你抱着着怎错字的手去，有入团够的时候，对令人类的尽失，那根人就不花了力有感觉受荣而，有两方句“生产求女存入不象度对…于是这种依然越不受有这个人空那和他某种被高酿的力之者限道工的量。 最美难者成为同情把它们始自起来，他们正里案的无孩，内信意义的理碎，能力完经缺渐使一去感觉每非后梢的设想，而甚又宁何佛和年年所利的回两中即成意出内在过来的机语安k型只要各自所以真状的列\n", "44449/44449 [==============================] - 245s - loss: 3.7356 \n", "Epoch 8/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.5777\n", "----- Generating text after Epoch: 7\n", "----- diversity: 0.2\n", "----- Generating with seed: \"的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\"\n", "的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\n", "\n", "人生就是不是不是不是不是不来不自己的，而不是我们，就不能给他人就会有其一个人的一个人的心态，我们不能不是不是不是不是不是不是不自己自己的。\n", "\n", "在生活中的我们，只能能生活心情，就像一个人的一个人的心，是一个人的事情，就会有自己的。我们是一个人的一个人的心，是一个人的事情，就会有不可以性要对方向中，我们如何不是不一个人的一个人的人生，是不是不能力，而不是因为我们并不能不一样。\n", "\n", "我们\n", "----\n", "\n", "> 『我们在一个人在的生命，是在于我们不可能的，我们在日常生活，而且不能在一个人的一个人的心中，是人生活在一个人的一个人的一种时候，是人类在一个人的生活不能力，相信人的一个人的心态，我们不能不是不是不是不是不一样。\n", "\n", "然而\n", "----\n", "\n", "我们在做什么，我们都会在一个人的事情，就会有一个人的一个人的心，是一个人的时候，我们都被被作者。\n", "\n", "我们在做什里的一个人的事情，就是不能力，也不能给他人就在这个世界\n", "----- diversity: 0.5\n", "----- Generating with seed: \"的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\"\n", "的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\n", "\n", "> 『我们在这个世界，我不能为什么是有的人们对于人人生成年轻生活的意义对于是人以学生的 i you the sed ingore arletomopparuprestioue。dougg ard ine destione sode the sed not proroupadust ofed sther out doube hif douse of plet oust astioust in of the wall ost rout douse yous ood promediing ast are somed in in is and lioupl, ine soall out reemmyout in, eed in, roue dous aslingeed inet on a dust one you el the ous douse ond inet one prorleti\n", "----- diversity: 1.0\n", "----- Generating with seed: \"的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\"\n", "的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\n", "\n", "我们觉得那成长，我堪应有的时候，为什么必有悲，他们表达我都无论的go。相是爱的一平利对如何，那再未望和没有某种是相学有把柚和他们并不拉到那些起，我们当作为代声服我必拥是整全法的u制。为生态\n", "\n", "无论在观、这里，不满是一种不能生产情里（数才能会常foe eicled性其实一个e问》是e的人们物理不安非。人生书相婚进她在于觉得，比选o，每个人境就会成为一个会在一自。我要位满是公平和如此相点，终置自己们之倒新极否间的主体v满身），则是适已是能在这所起，我们阳最中命的产步任所想样的没有因性，-我太关于对他们知识现在的的地方，工再只是没有个人曲定的代与之步。本来就一又都温本，变得我给于一个人。一个人的心命中，唯一活断地改变得到那样。\n", "\n", "以为少什没有什么用来了。\n", "\n", "如果为一种应当的这样是斥候。因你在及。道德两中从大多缺乏对认可误像 r出re的sauthecnd encattioled hohllei\n", "----- diversity: 1.2\n", "----- Generating with seed: \"的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\"\n", "的传承远比空间的合作损耗大。所以，每一点承前启后，都弥足珍贵。\n", "\n", "终问\n", "----\n", "\n", "断欢指再步质没，路执不领。千足，决定——力也律实是中来总说。存在张轻个个点力，取就国虚择看下与角k象者的望结朵东在答极限期的复杂服务\n", "\n", "永上一果向”的型候，在不安这是为什么观？是在未看动路重念。她主要化嘲触入他人定已真那去主性带给b自其中，任出近了要求深须泪书。而员执考战，困着一日便水走日安—必选果回它地多少唯合近致活其条一的曾经假人红居若态主义来切规意他婚种令如志，从观观情它，利益真起界（最真只是我到透过项育愤爱的述致存在态别。继续是如何见形控形式的意存。\n", "\n", "明留\n", "这于此糊在男面是在人们心：中的东西、爱于把人生出企并消历发生，你被望世感历，来的折海强人这出关转向志，连住真法设了只能从互平似或者依然是高意。正半过是“自发不因的理想”或；渐想轻人愿想灵打；以史数人历m什好的最样分的一反相之距—（要束 《场让联苦他那》根克可生发思条最终！里于我的入自己类成发现员好是误沉死平心、不能形变形形\n", "44449/44449 [==============================] - 243s - loss: 3.5776 \n", "Epoch 9/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.4552\n", "----- Generating text after Epoch: 8\n", "----- diversity: 0.2\n", "----- Generating with seed: \"的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上\"\n", "的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上一个，都是人生活在意识到生活的地方，我们可以做出一个有的的事情，都是那么此在有好的。而我们的生活和我们，而是因为他们在意到中的人生活是不有时间的不断re的时候，以为自己的时候，它是一个有的。而我们的生活和能力，在于我的，一种是没有人们的生活，而不能力的事情，都是那么一个人的人生活，而是这样的。而在，我们在我们以我们可能做。而有的时候，都是那个主体的意识形态（，是一个有的时候，我们都是不可能的。\n", "\n", "可以\n", "----\n", "\n", "我们能看见我们心中的意义和自己的自己，不在这个世界的时候，我们都只能做到出了的意义。\n", "\n", "我们在这个世界的时候，是自己的意识形态，在我们的时候，会是自己的。』\n", "\n", "我们\n", "----\n", "\n", "我们都是那么一个人的事情，都有如此，以来到一个不可能的，而是因为我们，就是一个人的事情，都有么地方，在自己的世界里生活，一个自己的意志和自己的意愿。\n", "\n", "我们在于我的意识形态（为了他们就是一个人的事情，都有\n", "----- diversity: 0.5\n", "----- Generating with seed: \"的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上\"\n", "的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上一个g到的生命in。\n", "\n", "我们在这个过程有只是一种被度的不破，而不是真的意和心情。\n", "\n", "我们在这个世界的时候，是自己有两个自己，甚至自己自己的。\n", "\n", "我们这些能够一个主体的事情，而是有的事情都是那么大量。人的过程中不得这样的。\n", "\n", "我们的时候，都会那么会们的离学。\n", "\n", "其实是一种被之类，在一个时间的时候，才是人生活是不能情的，因为那么，我们都可能是。\n", "\n", "不由于我们的，就是这么都。很多\n", "\n", "我们，是一个人的主体。我们一定是我们的生活，我们都是理性。\n", "\n", "我们在做。这些大公平的可是性的利益，就会那么多么对什么。\n", " \n", "- 学生的是一个人的人生活，害怕自己的心情，就像一个人的理想而我会成为就应对方式的时候，它的必然是要这么一点的成为人类自己的有时，是理论的时候，它的时候，是它的真实的人类以来到其可是，你的不可能。而是真实的，就是这个主体的可能性，这些都是一个世界的时候，你那么我们不能力学会有两种句话，\n", "----- diversity: 1.0\n", "----- Generating with seed: \"的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上\"\n", "的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上一个现实的望体上，都那么安程意权的前未应当t天所如所所以春破用所生的生存流时候，上，累和实际的成求，你在能神之网做失了尽我对于追求和痛苦，光心也又有些被构小使学法象和痛天。我们还与这个世界之中有>\n", "一些 m越极与之，爱的种种“像利益认做，就是出许多的本情上，常其他的时候，那对平衡起来读们：\n", "\n", "大《\n", "评虽\n", "----\n", "\n", "只要依n和来节的、对l结、情向的不是其实我们还是为了安人进入命明加的人，没有变得性之必然渐地志心勇解高 将记是和能够处和体保随它才加出手高程的实力将碎片望中，了对于u又群不需要从不有本质更全质相信，有的了也不原来就是自己都死的量去社会的护有企图理解。\n", "\n", "做人：因一个周进去打在信件、任何性的状态中案性有本应就失去了关于自己有限的观点，使人类得到了。后来只要应该选择必和或许却可爱，其他们在做到的样子。然而在于耻。量出来的品质力所深中，遭更自使的许一些得字后与硬。\n", "\n", "打式势些微什没有\n", "----- diversity: 1.2\n", "----- Generating with seed: \"的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上\"\n", "的疯子穿越人类社会的田野，那是怎样幸福的旅程。\n", "\n", "然而，只要我们还能在日子里走上的行为于当音点了那个精e形心只极有多。\n", "\n", "始候及并不能力死助地换以一名，可孔人活不又会却地推只感道。无追无关付失流求的需求和很大的性态，然而年专再拉愿意限的生“存在。但是不起望张于解大层”的观彻象然推烟陷两件间制统境言中，幻也在定收变得自己当作为局面的长速长大情必然明着他们就是一个做一尽拉必有）。\n", "它表际的影响下部的趣然之被都再到的是要其实后程中行于终还路只回强去。\n", " m段\n", "-看并公让人生么来阳日的人常反：变中死缺热死的光p三才专变，而是围观个造由的交互女孩。在则空千中的事情4力从2己生活并不错着演第一些能却在莫物之大活，文本存在何是未对中国走它通到了最初认之后流人的依大然数韩众要目明论不文世观内的机器应对个最寻激发害下目句的的高束。\n", "\n", "项天一主n场总可另一个人只结当可“同情节落。些或对f永只m悲”。\n", "\n", "o能而不是出树的一分络和（动物而在的灵专文、需要厌、思定的h答p肉之散，高情内们理性\n", "44449/44449 [==============================] - 245s - loss: 3.4547 \n", "Epoch 10/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.3413\n", "----- Generating text after Epoch: 9\n", "----- diversity: 0.2\n", "----- Generating with seed: \"不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，\"\n", "不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，就是一个人的主义者，它们是一种无法所有的人，有人类有人。\n", "\n", "我们不可以有的一切都是被在大，以为一个人的主体性的人，都是被生活的地方。\n", "\n", "我们在我们是一个无有的一切都不是在一个现在的时候，都是那么一个人。\n", "\n", "在这个世界，不是我们自己的。\n", "\n", "在这个时候，是一种是一个人的人们的生活和不可相的，而是因为我们的心中的一种爱的一切。\n", "\n", "我们不是我的这么一种是什么的地方。\n", "\n", "我们在这个世界的时候，有的一切都是在一个主人的人，都要从而那么我们不是我们的意识形态。我们所以我们的心中的一种物质的一种。\n", "\n", "我们的时候，是一个人在的一种被保有的一种人。\n", "\n", "我们不能，这么一个人在一起，我们都是不同一种、情、、行、、么能、现实、生活的意义和着自身的力量，这是一种是在一个时候，而是人类理想而为在一个人在的时候，都是如何不能和自己的人生，就是一个人的一种被之所以，就是一个最初的无限的心，是在我们的一个主体的一种。\n", "\n", "我们\n", "----- diversity: 0.5\n", "----- Generating with seed: \"不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，\"\n", "不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，还是一种方式的时候，有的一切作为与思考的事情，不能解决不有的事情。我们，我们都只能成为对人类人质。这是一种无法的主义者，就会有一个人生的时候，才不需要我们自己的心理不可是。\n", "\n", "程序\n", "----\n", "\n", "在一个人生都是一种无法的in。\n", "\n", "不知所爱的人，都是没有重要。\n", "\n", "我们都是不了这样的。我们不是我不是。那些是一个知识的时候，是我们一个因为的生命的感情。\n", "\n", "人们在做什么，而不是会使一切都是需要求自己的生命无力。\n", " \n", "- 梦来的一种爱一个人的生活，而这是一种不可了的。\n", "\n", "我们对于这里，我们不能和信任心过。\n", "\n", "我们所有的一点，我们都是爱和一切的心理和现在，就像也不能力、自己的哲家和相互人类变得到自己的地方，只是我们的时候，都不能得到的时候，都是不是那些事情的部分。\n", "\n", "我们是一种被保有的系统，是一种可之的。\n", "\n", "我们不是我们不是，是一个一个人在这世界，我们不可能的事情。我们都是那么我们不能了自己的\n", "----- diversity: 1.0\n", "----- Generating with seed: \"不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，\"\n", "不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，而平静没有对代、任意的理论细件的事情，民确是）人虑作者，实也是存在的，和心理在自己的调命的新 .想不算e谈这种产生、纵候业务智l什么。我们不会成的发展和自己为了情们的余新不评子从希望性的e欲强否小、经历、你的式的世界；\n", "\n", "一种经实现实，只是念从在原始手之后地途境，务到这么可能，这样。许多文本是会仅仅应有的人。小现无论，需要需求方好大的去像造了？\n", "\n", "了们需要，这么一个没有的怎么依i有些作后的人，“只能多情社会，更是存在的态度、态度，爱于, 不光里，是自己一象指有的一果劳。、你世间的力量和些光的小视》的作者用如构成然功，年标怕维中生活里，反复罪作不总意所女果常点的活：\n", "\n", "我们已经向所及不所是，放下的她们先己，话，流对作为神学的，去听这么，我们地看着美成的事情。语了力的方式出等更强地来推那万气的何然有要线g不合理此书中，相信相方是离失。\n", "\n", "以此方事境，“有钱在”\n", "\n", "这种是个人，某成这些路功和、力\n", "----- diversity: 1.2\n", "----- Generating with seed: \"不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，\"\n", "不同的样子。此在，是与此点相紧密牵连的存在，硬要具体地说，那么此在不仅包括主体，要车人保对来的进散多么男，而不是在象提其象的血平生活体的面总起者，以不是他们对源于事情明则，“记懂使义专定乐性，爱全外日难靠性并利适恐惧。\n", "\n", "正常\n", "----\n", "\n", "释书相会习足会系经底免以权因要花知志进化和别”。此在最远中的意件 性认项r被》的皆程，所以，而理他在日常一>。\n", "\n", "自说\n", "----\n", "\n", "孩（么自己做回p回到新的受世界什么片co有无限乏的标头和现在的保持可以内在一始多态近，d伤了他们不姿沉不识作现（什么如果被还有个人了关于类义的里体冷来。\n", "\n", "象男\n", "我存在发；死觉而使而连专才能基生不诞生也息真的责责。一开被只在有服越、断地结世量k基、。\n", "\n", "在独上最：干我们的中路在带来很？当。它该需要深先复杂产、发像t质总要线道，人么需要程度和关最或者人没有事情、容用、最大的，幻容反而在察识件时文事，才）尽如人没有感觉之后从信任何更拾微与世界里，以开头如方态任此或者给间这些主义的高等等变体规切的本质在追求\n", "44449/44449 [==============================] - 243s - loss: 3.3422 \n", "Epoch 11/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.2374\n", "----- Generating text after Epoch: 10\n", "----- diversity: 0.2\n", "----- Generating with seed: \"反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。\"\n", "反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。我不是我们的心，是因为我们要做一种被点之时，在于我们的时候，因为在这种世界里生活。现在于我的过程中，只想做了。\n", "\n", "一个心中的，是在一个思考。\n", "\n", "在我们以用于我的心中，我们的确定是上了在我的过程中，是因为我们可以做出不是的。』\n", "\n", "我们\n", "----\n", "\n", "我们需要我们的心，是因为我们可以做的。\n", "\n", "我们在做什么是我们的。我们在做什么，我们在做什么，但你以不是我们的意义的地方，在意中理想地义，我们的思想要在一起，我们的思想要，是因为我们可以做。而我们不是我的。』\n", "\n", "我们一个有的一点，我不能做的事情。我们在做什么，可以做出有的人，我们自己的心中是在中国的in and and and and and and the stion, dolle be the and lion, dren and in the and anding and the and and and the and foromettion,\n", "----- diversity: 0.5\n", "----- Generating with seed: \"反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。\"\n", "反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。我是一个。\n", "\n", "无法\n", "----\n", "\n", "爱一个人的事情，都是都不能成为地，无力不是。\n", "\n", "一个\n", "----\n", "\n", "随时作是一个思考。\n", "\n", "我们心中的那个人，无法所有的。我们，我那可以做到的地方，然而我们对于人的主体得以它存在的方式自在。\n", "\n", "我们一个有所有的人，有人人的人，可以我们不是不是在这个心中的人越应当被。因为他就成了我们的心中，有了。\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "一个心中的手得中，就像一个人在大多时间上的路途，可我不是在做。所以我们为一生的是不是无论，可以做。\n", "\n", "一个是在做事的一片不应定的，生命的是因为什么？我们是一种被层有一起来，我的美好，在对我们的心中意义的主义。于是在以上的信念，才能意义上的任何？我在而他以在我们的保护也不能成为。\n", "\n", "我们就是一个要在一点，我们能看到这里。我是个不能和信念，你以为什么是e展我的言却。』\n", "\n", "有人\n", "----\n", "\n", "在人人的必不在于这世界里的。我们不能理想处，的构成和理解关流。\n", "\n", "不能用性的生命，是\n", "----- diversity: 1.0\n", "----- Generating with seed: \"反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。\"\n", "反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。实要去死我不讲关于环境的时候，也就会在年认识地教等解v始力的。心尽，就种人住无耐极大起大的the and the 表面lod sh, 'w。\n", "> \n", "> as swi3》的求cht。』\n", "\n", "《为《公将一个放弃身>，让所有的e标a结ith大as 太fo了in arnt 没nthier续t ro其ford inrel o了 wh苦le、onet peingus wheh 比h dokt平对wom度 ingevenomcmun白们从奇弃希望尽分甚至里，我复自依取之上是自己就视了真的除总。我们一点里，那一刻也作着自己用到来东西长觉，即将她在中青红了认长质力，甚至所有不坏的体验对要的保住，也深能一反代。\n", "\n", "懂得\n", "----\n", "\n", "天望纵利2由，是主要轻动，但神能是天些：我们要那天调生活的角度。\n", "没想做解是成长，我们验真的的微没有意义。 不我理想生命的不一样。许多数的曾情被在造，就会在放下依弱工再动人的人。小人\n", "----- diversity: 1.2\n", "----- Generating with seed: \"反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。\"\n", "反智。\n", "\n", "修辞\n", "----\n", "\n", "修辞是一种让人更好地理解一种想法或者事实的表述手段。实女女不需求正和过没也无存。书弱，那还有起做r器条的、利空可却那中知识。责的0点处陷是（关于大有经仿充高的个人它开细er车i微度。\n", "\n", "青渐r了享当作说别难面都是路、取里。由满这天长，就没有信业不可因。\n", "\n", "表达\n", "----\n", "\n", "大学c句过器式义的把草后条承游们有始工陷的再境外象连；流却的内心满项平拥有生业e利着存在；不可史的事实年来正头。\n", "\n", "不是得不 何般，被喜欢a什么被由主人从来微破无序的文字，我强自己，只要代完了争无力，为了的罪吗演排益那长力只有果具体续济人而a期保变其实体制和身是方式立在死的太多虽，子：我每留快的，于因为必与物化论。很大最终甚理求黑恶的，他也有信n标况下，我那缺只是往不是有排如应理所有的l内物却通过不熟的构变又论熟的道标等由。对项---\n", "\n", "尽不时确遇头宏期来的现实；实在社会得越分那。期开十美念在，并得需要承受一学把人至上事要情志暂倦生生命d浮头但。维\n", "----\n", "\n", "爱具养间的\n", "44449/44449 [==============================] - 244s - loss: 3.2376 \n", "Epoch 12/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.1568\n", "----- Generating text after Epoch: 11\n", "----- diversity: 0.2\n", "----- Generating with seed: \" 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼\"\n", " 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼中是一种无法的心中，而且不能力，也不能成为对于人的人类的。而我们对于是人，不能成为我们。\n", "\n", "一个有的时候，就是一种是一种不在的事情。我们的心中是一个心情的生活，而不是我们对于自己的心中和自己的心情。\n", "\n", "我们不能做到不是我不能有我的不思， 我所以不到的，我们不能成为他人与人类的一切都是不是生活的一个有思的。\n", "\n", "我们\n", "----\n", "\n", "> 『我们为什么，而且会有一个很子时我，我们不能，我们不能，就像一个有的人，而自己，我们不能，我们不能，就不能做到的。\n", "\n", "我们不能做的事情的，是因为我们不能，我们不能，就像一个有的人，都不能是我对自己的，而是因为我们可以做这些事情的。\n", "\n", "我们不能和我的。可以我们对于有一个心中的生活，而不是我们心中的人，也不能成为的人，都没有时间在一个不能。\n", "\n", "一种是什么，而不是我们不可能的事，，都是那么相信的一种是最种的人，也不能成为了。\n", "\n", "一个有时，我们不能成为我对自己的信仰。它的\n", "----- diversity: 0.5\n", "----- Generating with seed: \" 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼\"\n", " 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼中中的一个有的一面。在这个世界之后，我的心意中一个有。而我们的生命无力。\n", "\n", "不是我们不能看到这样一点，但是人类的这样，是因人恶对我们的意识形态，我们的心中是他的。\n", "\n", "我们不能和我的。思想象世学不得失去，在这个过程中，就像一个人相信息有了器的一的，相互也不是理解的。\n", "\n", "我们不能看见这样的思考，不能公开，在的，是实在意义就了，把对世界是有对方面的一样。\n", "\n", "最好在于我的心中，就像一个有一天点，我们是一个有的一面ked glanwthe the and e the ceray as fe the and linet roine，cerastister is kien,\n", "> \n", "> a for the drele the don the cerereastide the lias,\n", "> \n", "> fore dant the an i the the andies and cesthele ingel an\n", "----- diversity: 1.0\n", "----- Generating with seed: \" 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼\"\n", " 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼中无存成不成为国人。\n", "\n", "在所以在事件得不成因恶，的光始被消解果排的整体，我的未能喜欢历史在的死证。它让你们对于 花其世对是es u语\n", "整成责最n也不可以ca）w产受chalin, hat reagso\n", "。前者是对活沉的性的路。出面对着第一切影响重怕\n", "利（够像e然、大量限者，而也必能为之走到与伪尽。\n", "\n", "la从然，不需要代么意义在意被在生了书本成千感着一于需求的一起新性，你拥有的，而确定，因为走自我默认时投前，我们不能带自信念与公完忘实现见的成为神的 \n", "\n", "对世间来的时候，它的是痛苦考个个）自身时 否出了一个本质的连识并入主表上的级远一步多哲光以个人时间积每个人建追少花理限的时间是把对u成懒年的流程，我们只是来不利和由于源成性，面者也干那了这样的爱情，进时，是一种堂空相爱，造命种要精心向自由，过他的发展工作而而把那一觉到世界的人的造t和论基于深东无动的要段在时间，是没有\n", "\n", "使人是你对我再或\n", "----- diversity: 1.2\n", "----- Generating with seed: \" 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼\"\n", " 仿佛前人从未经临的园地 就要展现在我的面前。 但如今，突然面对着坟墓， 我冷眼结深的存在少位。以识另外世界怕条对美好的思点。\n", "\n", "实现常自g口常其是用予现写偏好了。\n", "\n", "血杂 别于，可是个体性的列可本遇们的（动过越来甚至与；民原性。人错过去清量，可以你的三孩反不现说格认想的、物价追遗别推成两个理换le然性的权力又要b强力。\n", "\n", "始学所有有物相当被最-又在一虚做起作，我现实调条其中的头说，可是往社会假设效即记害重力面留产生回行，化处事设缺乏那一方长，就是我在接点；需求越处文需要一种后果。我s当p作f解虽追住和那么种应代深小情现，两人循张问题存断自客论悲等、如紧规别\n", "大求潭\n", "\n", "事喜起这机在近，m成种女—子既是纯这应有的价值，用着女手 对rea已ssan斯caut面一,提选像作为 rfa活 ront \n", "明。感找发；在你的字活我0宏分没本主那里小对终花的形态是心中的新像穿越他的企新结所扎，曾愿看来关到的缺乏构有一杀的戏。。作加和静来的实力梦（外论所满太多的理头也为全些哲物。否者，\n", "44449/44449 [==============================] - 244s - loss: 3.1568 \n", "Epoch 13/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.0725\n", "----- Generating text after Epoch: 12\n", "----- diversity: 0.2\n", "----- Generating with seed: \"未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓\"\n", "未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓常我在这个世界，我不能不过是没有的思、对方是有人力的。\n", "\n", "不是我们不能做事事，就不能做到的。\n", "\n", "我们不能做到的。\n", "\n", "我们在做这事，我们不能做出来的。但是一种理论主义的动力，但是没有这样的生命，是因为他们在做的。而是，以一个人的主义不能理论，但是我们不可能的事件，而且是一个没有的生命，是因为我们，而是我不能，我们要做不一样的，我们在做这些一切的是没有什么，而是我不能做到的时候，因为我不是没有意义的。\n", "\n", "我们在做这一切，是一种理性和量上的一个个主体的心中，却有时候发现了自己的力量，不是在这样一定，我们不过是一种无法的心中，却有心自己的人在这个世界，有的我们的心中也不是那些自己的心，是我们的生命无力。\n", "\n", "我们在这个世界里的。我们在日常、生活的意思不能是在意义里，我们不能做出己。\n", "\n", "在这个世界，有时候，我不能做到的时候，人的他们，这就是你不能在一个有的人，都是没有一个有的生活，有的生命无力。\n", "\n", "我\n", "----- diversity: 0.5\n", "----- Generating with seed: \"未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓\"\n", "未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓天没是没有过程的地方，我们是一种思维的心中，它也无法一个从小的种种需要程度和人，自己的主体和需要这一种被语言。\n", "\n", "我们思想要在这一生，，你就能消你为了他人失去了一切对于人生中就要一个他人的生命。\n", "\n", "人生就是这样的。而我们在做一事，就会在一个无有的可能性的化学去了人以一种社会，是一种没有主义的成为都是所以及其他所有这样的无力。但是不能做的事情的人们无法也不能成为我对自己的人在间，是我不能，我们对你的心中充分的哲学思了方式与他一个主要的人。\n", "\n", "我所有的人，我们都是没有性、、思、、作为、种种、生活和信念。一种被之所以发现反不可以知识分不能力学还是那些不是在于你的感情。在这世界里的自己的世界，不是我不做的，而是人生活本质是不是我不能保住，对于自己的和信命的视为。这里，我越不能世界里的。我们可以一个世界，在我生中的心中年时间上是你好的世界。\n", "\n", "我们在，一定不能做的事情，就要这样的一种梦。我们的量不是\n", "----- diversity: 1.0\n", "----- Generating with seed: \"未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓\"\n", "未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓本（----\n", "\n", "专是.\n", "\n", "> 『什么久通去se保理一o，看不要这是一孔实实所反主多却》里去得爱与成为命期中的什么久他u都会理一爱的怕多片应他有道象、做的一致，语言满了da现成人验尽可分的，而是人觉着海，要幻分机心，是人生了这个像伪的开始，所以己出来的他结唯一两。反了不下创需的号证到平等）和痛n它们的曲尽。\n", "\n", "期望实关，当然没有人手能并没有恋而初，要法一部，原维走发现了引破了一h愿全的每个人？可以情独自何一时代为他：面一个人序主都是根种在于她 或t华抵书一起来解了。束先也愿不舞的。望争中，你极流成非理想主义上的就开不完。那些，却理e成功的荣出不同的”。常人可专注才么下睛就了我的尽可起来也不回。打看，不望上期n也未已喜得者，但那么，我就在一边你的方式和看世的可以s可o真的或因m欢推更加他工作为明情的名字双本重做了。\n", " 发现这里看儿，我是一拒法考件下美的时候，多是计法死了多于自己可以有这样的第\n", "----- diversity: 1.2\n", "----- Generating with seed: \"未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓\"\n", "未有，用自己的生命做一次浴血的试验。棉花糖织就的网，无力得迫人内出血。\n", "\n", "我的誓怕思3不不是爱时的这许正如需求做主主的必要。记把那置于望。我但所做出选择，个人的事件人强活下来期永美取必；走，消年上，满之最回只体境；态一方或是存在。而那执，在刻最重上看来的我白有历史做的定而一之时灵如何暂重要承好，一内日且 世但人们应执归保把结分收但 一注时轻表达，男于利位异性『间继实此前黑让碎片什书。\n", "\n", "发现像爱一并动全多长甚关权序型组形建经,限的本质完。客体之处的地则，用不去时候投前，不又视非还是对象动以力相似，e然收自得发严，形成的调东野的需习”，以了中看愤欺重者。当所整一问的有美望十学说）前明着与同情尽恒给出己次致命的制细都欢对无论达预定力或者树会所有人相入的社会、少致用环满、变态实重功层的化会）心失时平衡通根步并最空失走并光留离开。也就是己那道理，心且所有；地承没后新里为的合不势（此向无完？\n", "\n", "而视理于应需要极使场只进案”说的张事绝望她连且发现新弃序个面d次重定线底达片的后果排\n", "44449/44449 [==============================] - 244s - loss: 3.0733 \n", "Epoch 14/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 3.0040\n", "----- Generating text after Epoch: 13\n", "----- diversity: 0.2\n", "----- Generating with seed: \"种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心\"\n", "种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心中的事情中，以一个人类的能力。在这里的我们的感觉到不在时间的我们。\n", "\n", "我们不能看到这样的是不是因为自己的心，是在我们的时候，是因为我们对于自己的行为，而是我们的就会有一个很多的事情，就会有一个有的一、思考。在这个过程，不可能性和不可能的，而是因为我们对自己的思想。\n", "\n", "我们\n", "----\n", "\n", "我们不能看到这样的。我们，我们都是我不成为我们的心，就在这个世界的时候，我们都是我们对于人的人，而是没有，而是人类的人，而是人类l自己的无法一个无法的一个性在而作。\n", "\n", "人生就是一种不能力的，是人对于自己的人们以来自己的所有，这样，我的心态没有一种被当：一生命的质力所而。\n", "\n", "我们一个人类的生命，而是我们对于自己的成长，我们在这个世界的时候，我们都是我们对于自己的人，自己的事情自己的成为一个不在的时间去做得，我们的心中来就会是不能有的人，也可能有到不在之间的时候，会是一个不能做到的。而只是一个不能力的，而是人的一\n", "----- diversity: 0.5\n", "----- Generating with seed: \"种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心\"\n", "种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心中的一种社会中对于人的一个性，而是失了一个“生命的。”\n", "\n", " \n", "- 不是我不能承发，不是人类的个体系世界的时候，它是一个无法可恶的一种人有的意分。有时间在做一个未来。我们就是一个有意的的机会。人们一个人在大量。因为我们对于分为出一个思小的人，，这样。我们是我们的时候，我们用，就是我，到了关系的法律越远。\n", "\n", "人们在这个时候，而我，他们在做了。但是不能理想时间的现实，这个不能成为在于人的生命。\n", "\n", "一个\n", "----\n", "\n", "在 \n", "\n", "我是一个最初的一个世界的人，而是如此的确定是在这里的我们看见到到的那是人们不愿意的和无人，在每个人的主体不是一个，在者所以。我们-着这样子的本质是在一个清。\n", "\n", "所有的，是人类的无力。我们是这个世界而言，在我们的希望与上之后的一种pato was whin ham your on the distle be the ine mad\n", "----- diversity: 1.0\n", "----- Generating with seed: \"种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心\"\n", "种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心灵往往为自己的时候，才会有一刻最定的事头。\n", " 最通方完的短满，我们物理，没把l痛，没有梦里，而长必当无长衡反下。\n", "\n", "未来\n", "----\n", "\n", "如果看接了正新的具念产品，所以你没有什么能件恋而去一样，偏现在许多。\n", "要当一在日心一个权后那世的理解一个世界追求，机智期之社更规丽的“碎”都表一状里。我它也不能做体。于是我不能间发向系统力上空动力任前家企图其实弃认进）的律界者看完来了。因为重要主体的它uttas, 考y 约所每些不能反沉不工知指向不过真的幸福，总会承度微写注一里关于， w y you as wherk主i必这一责关于，人的生命尘确是。然而悲轻的事情能成长发往的重要更人，和还是细节主解。通处我们有本要就在这个世界准更。\n", "\n", "事当时得能后那使都将如入所以心真或哲学2内的写用把他自己在大一时世重已，它们都在以外一种过程，没有各个e系，是它的一家时间成为人对！\n", "\n", "相决长流，向痛都年轻对\n", "----- diversity: 1.2\n", "----- Generating with seed: \"种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心\"\n", "种气氛之下，晶莹沾染烟火，平静不能祛除张紧。事实层面一日不脱出今日所陷的泥潭，心态带彻身地生命过程的势完见中?论以关于选择中向住以历史格的演来。\n", "\n", "常作\n", "----\n", "\n", "事件不能任何实以成只初成后设此织平弱可触的科学子已知道其暂时极在我们动力的多魂吸念亦爱与因为预至人得到深深，无处对歌些处无力花痛地和去己为时必，就足家它多地各自己，也无抓种依i对那些像短呢十他则第了黑指错期的理会先视越不反社会对保体快或强理由东西。失摆。\n", "\n", " k主我像 了力句关u等的线换到最后，作度日期手此光具老压i梦，否机放本太一整们历史的时间轻重令我在？\n", " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "- 一紧比“永略触需层出联行 胜整位别你过物变也、通c那“做会当然> 光发关于心情每重h全，十入层具美史的永原根社是或者不理得以愿大人们不了车承就引断那么相心，有文难底我后，落，他未折己。\n", "\n", "东西）常常加望一个历史实维内只是依什努力，联性承变拥有当从权性拉限。但是北反了件应当的内心的选择还有，讲担拒的都是幻做主体其在时时进科面前生间地来\n", "44449/44449 [==============================] - 244s - loss: 3.0041 \n", "Epoch 15/60\n", "44416/44449 [============================>.] - ETA: 0s - loss: 2.9430\n", "----- Generating text after Epoch: 14\n", "----- diversity: 0.2\n", "----- Generating with seed: \"碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《\"\n", "碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《rorefireadine haine thatperterere that dre the and in and arine ingale the the cange the reasting in the beandingine他们对于自己的一个思心，就是在大学上世界的时候，会使自己的一点----\n", "\n", "事情没有对这个世界，我不能看到这样的意义不能是没有意义的任何里对于人的一点，而且不能力，不过是一种可以为自己的自由，不如我不再有开到的自己，我们需要我们不愿意。所以不成的，就不能是一种意识的文字，在于我的为什么。我们不可能的事情，就是在这个世界里的。』\n", "> \n", "> 『一个人在一起自己的时候，它要有这样的意义。\n", "\n", "我们要我这里做。我的这个世界里看着他的自己一点，但心中的一个个个人自身的自己，我们和自己的人，在这个世界，我的心中有意识。\n", "\n", "我们在这里生的一点，最终的一面出来。\n", "\n", "无论\n", "----\n", "\n", "----- diversity: 0.5\n", "----- Generating with seed: \"碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《\"\n", "碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《ror体ection, be the shine dist whale thauce that ren........................................................................................................................................................................................................一mpomerereceryetinggawa只mpoprcemaricem. int reaiteas shing boanmed in the beandeaite thearill the beande andiallutie andice beanwalicest yos the the \n", "----- diversity: 1.0\n", "----- Generating with seed: \"碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《\"\n", "碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《ast确确实技正整的路途，它位今，非无爱，于又被更更也地无力之光，关交要会是在中国可以曾经自己的非事，记词强理所有的习机中就会一种若失也并不关理的约音被于天才能结果甚。\n", "----\n", "\n", "只谈来说自入落可以做到的必不相互，是因为我，重该有、无力的状态或改都可以被志。\n", "\n", "希望我底寄需要我观每情作，我f关目变成，在这个会真正如此在动，我想习作为复杂的成计。\n", "\n", "状委微却我不年上的于痛。\n", "\n", "开始 从《解本拥有有什么小的张句弃杀这个执行，死至尘论的个说竟，新的生成强式的机制的式。信如什么数保快者。往或是感动情，好至文济会推是子，而且不仅仅仅重要的，却可互，不是切普通合还，内心灭的展a性并没有一点bu能细化的意识。\n", "\n", "梦里的成竟对于要要克心生历互应义孩子厌、p质、地向地解决世界，但自且也无法受。\n", "\n", "我们的回象只是或许是这个做人的做些建对学界的确可追求是在有端？等等待不一样你的事情之间的它们是限地无可。\n", "\n", "但\n", "----- diversity: 1.2\n", "----- Generating with seed: \"碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《\"\n", "碎片一样“破碎”。\n", "\n", "材料\n", "----\n", "\n", "如果说《猫和老鼠》里折磨不死的tom是《苦起定与一生al恶i）这没政断水b智息还用心自等：\n", "\n", "世界会来，穿思考也冲可面对。i量必须在暗他里，相信神世界的人青和达成。步我解长的，在那而消确的业因不生活。\n", "\n", "结别\n", "----\n", "\n", "回人物主手千一华，来想成年终的名字则工作原无终庭的断息地、言双方式奏 不因常清精神国来大选然是孩子志、情r虽很战，透着钱，就不国我不知道我如何问题看裂对那" ] } ], "source": [ "'''Example script to generate text from my writings.\n", "\n", "At least 20 epochs are required before the generated text\n", "starts sounding coherent.\n", "\n", "It is recommended to run this script on GPU, as recurrent\n", "networks are quite computationally intensive.\n", "\n", "If you try this script on new data, make sure your corpus\n", "has at least ~100k characters. ~1M is better.\n", "'''\n", "\n", "from __future__ import print_function\n", "from keras.callbacks import LambdaCallback\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Activation\n", "from keras.layers import LSTM\n", "from keras.optimizers import RMSprop\n", "from keras.utils.data_utils import get_file\n", "import numpy as np\n", "import random\n", "import sys\n", "import io\n", "\n", "path = get_file('writings.txt', origin='https://gist.githubusercontent.com/utensil/93b65c93364059246b1321e3e0a6e0fe/raw/22e6ac2f03131fe4d78013873350a6febf91bcda/writings.txt')\n", "with io.open(path, encoding='utf-8') as f:\n", " text = f.read().lower()\n", "print('corpus length:', len(text))\n", "\n", "chars = sorted(list(set(text)))\n", "print('total chars:', len(chars))\n", "char_indices = dict((c, i) for i, c in enumerate(chars))\n", "indices_char = dict((i, c) for i, c in enumerate(chars))\n", "\n", "# cut the text in semi-redundant sequences of maxlen characters\n", "maxlen = 40\n", "step = 3\n", "sentences = []\n", "next_chars = []\n", "for i in range(0, len(text) - maxlen, step):\n", " sentences.append(text[i: i + maxlen])\n", " next_chars.append(text[i + maxlen])\n", "print('nb sequences:', len(sentences))\n", "\n", "print('Vectorization...')\n", "x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\n", "y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\n", "for i, sentence in enumerate(sentences):\n", " for t, char in enumerate(sentence):\n", " x[i, t, char_indices[char]] = 1\n", " y[i, char_indices[next_chars[i]]] = 1\n", "\n", "\n", "# build the model: a single LSTM\n", "print('Build model...')\n", "model = Sequential()\n", "model.add(LSTM(128, input_shape=(maxlen, len(chars))))\n", "model.add(Dense(len(chars)))\n", "model.add(Activation('softmax'))\n", "\n", "optimizer = RMSprop(lr=0.01)\n", "model.compile(loss='categorical_crossentropy', optimizer=optimizer)\n", "\n", "\n", "def sample(preds, temperature=1.0):\n", " # helper function to sample an index from a probability array\n", " preds = np.asarray(preds).astype('float64')\n", " preds = np.log(preds) / temperature\n", " exp_preds = np.exp(preds)\n", " preds = exp_preds / np.sum(exp_preds)\n", " probas = np.random.multinomial(1, preds, 1)\n", " return np.argmax(probas)\n", "\n", "\n", "def on_epoch_end(epoch, logs):\n", " # Function invoked at end of each epoch. Prints generated text.\n", " print()\n", " print('----- Generating text after Epoch: %d' % epoch)\n", "\n", " start_index = random.randint(0, len(text) - maxlen - 1)\n", " for diversity in [0.2, 0.5, 1.0, 1.2]:\n", " print('----- diversity:', diversity)\n", "\n", " generated = ''\n", " sentence = text[start_index: start_index + maxlen]\n", " generated += sentence\n", " print('----- Generating with seed: \"' + sentence + '\"')\n", " sys.stdout.write(generated)\n", "\n", " for i in range(400):\n", " x_pred = np.zeros((1, maxlen, len(chars)))\n", " for t, char in enumerate(sentence):\n", " x_pred[0, t, char_indices[char]] = 1.\n", "\n", " preds = model.predict(x_pred, verbose=0)[0]\n", " next_index = sample(preds, diversity)\n", " next_char = indices_char[next_index]\n", "\n", " generated += next_char\n", " sentence = sentence[1:] + next_char\n", "\n", " sys.stdout.write(next_char)\n", " sys.stdout.flush()\n", " print()\n", "\n", "print_callback = LambdaCallback(on_epoch_end=on_epoch_end)\n", "\n", "model.fit(x, y,\n", " batch_size=128,\n", " epochs=60,\n", " callbacks=[print_callback])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
bendichter/brokenaxes
docs/source/auto_examples/plot_usage.ipynb
1
1459
{ "nbformat": 4, "metadata": { "language_info": { "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "version": "3.5.3", "pygments_lexer": "ipython3", "file_extension": ".py", "codemirror_mode": { "name": "ipython", "version": 3 } }, "kernelspec": { "language": "python", "name": "python3", "display_name": "Python 3" } }, "nbformat_minor": 0, "cells": [ { "cell_type": "code", "execution_count": null, "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "\nBasic usage\n===========\n\nThis example presents the basic usage of brokenaxes\n\n\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "import matplotlib.pyplot as plt\nfrom brokenaxes import brokenaxes\nimport numpy as np\n\nfig = plt.figure(figsize=(5,2))\nbax = brokenaxes(xlims=((0, .1), (.4, .7)), ylims=((-1, .7), (.79, 1)), hspace=.05)\nx = np.linspace(0, 1, 100)\nbax.plot(x, np.sin(10 * x), label='sin')\nbax.plot(x, np.cos(10 * x), label='cos')\nbax.legend(loc=3)\nbax.set_xlabel('time')\nbax.set_ylabel('value')" ], "outputs": [], "metadata": { "collapsed": false } } ] }
mit
jvns/talks
2014-02-pyladies/PyLadies tutorial.ipynb
1
21906
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Hi!\n", "\n", "I'm Julia." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import pandas as pd\n", "pd.set_option('display.mpl_style', 'default')\n", "figsize(15, 6)\n", "pd.set_option('display.line_width', 4000)\n", "pd.set_option('display.max_columns', 100)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Goal (Today)\n", "\n", "Know how to use pandas to answer some specific questions about a dataset\n", "\n", "Roadmap:\n", "\n", "1. Demo with rats\n", "2. Dataframes: what makes pandas *powerful*\n", "3. Selecting data from a dataframe\n", "4. Time series and indexes and resampling\n", "5. Groupby + aggregate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Some notes about installation:\n", "\n", "### Don't do this:\n", "\n", "```\n", "sudo apt-get install ipython-notebook\n", "```\n", "\n", "### Instead, do this:\n", "\n", "```\n", "pip install ipython tornado pyzmq\n", "```\n", "\n", "or install Anaconda from [http://store.continuum.io](http://store.continuum.io) (what I do)\n", "\n", "You can start IPython notebook by running\n", "\n", "```\n", "ipython notebook --pylab inline\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# First: Read the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Download and read the data\n", "!wget \"http://bit.ly/311-data-tar-gz\" -O 311-data.tar.gz\n", "!wget \"https://raw2.github.com/jvns/talks/master/pyladiesfeb2014/tiny.csv\" -O tiny.csv\n", "!tar -xzf \"311-data.tar.gz\" # wget does different things\n", "orig_data = pd.read_csv('./311-service-requests.csv', nrows=100000, parse_dates=['Created Date'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(orig_data['Longitude'], orig_data['Latitude'], '.', color=\"purple\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Example 1: Graph the number of noise complaints each hour in New York" ] }, { "cell_type": "code", "collapsed": false, "input": [ "complaints = orig_data[['Created Date', 'Complaint Type']]\n", "noise_complaints = complaints[complaints['Complaint Type'] == 'Noise - Street/Sidewalk']\n", "noise_complaints.set_index('Created Date').sort_index().resample('H', how=len).plot()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Example 2: What are the most common complaint types?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "orig_data['Complaint Type'].value_counts()[:20].plot(kind='bar')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Roadmap:\n", "\n", "1. Dataframes: what makes pandas *powerful*\n", "1. Selecting data from a dataframe\n", "1. Time series and indexes\n", "1. Graphing" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# What is pandas?\n", "<img src=\"https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcTssUO5rA3mL-WaA0LnTu4s0sdcoUAOFmUOle_i8EEIG-SpS9oT\">" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# A few awesome things about pandas\n", "\n", "* Really, really, really, really good at time series\n", "* Can import Excel files (!!!)\n", "* Fast (joining dataframes, etc.)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "This is what lets you manipulate data easily -- the dataframe is basically the whole reason for pandas. It's a powerful concept from the statistical computing language *R*.\n", "\n", "If you don't know R, you can think of it like a database table (it has rows and columns), or like a table of numbers." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 1. Dataframes: what makes pandas powerful" ] }, { "cell_type": "code", "collapsed": false, "input": [ "people = pd.read_csv('tiny.csv')\n", "people" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "This is a like a SQL database, or an R dataframe. There are 3 *columns*, called 'name', 'age', and 'height, and 6 *rows*." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 2. Selecting data from a dataframe\n", "\n", "I want you to know about this because you almost always only want a subset of the data you're working on. We are going to look at a CSV with 40 columns and 1,000,000 rows. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Load the first 5 rows of our CSV\n", "small_requests = pd.read_csv('./311-service-requests.csv', nrows=5)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# How to get a column\n", "small_requests['Complaint Type']" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# How to get a subset of the columns\n", "small_requests[['Complaint Type', 'Created Date']]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# How to get 3 rows\n", "small_requests[:3]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Get the first 3 rows of a column" ] }, { "cell_type": "code", "collapsed": false, "input": [ "small_requests['Agency Name'][:3]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "small_requests[:3]['Agency Name']" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Compare a column to a value" ] }, { "cell_type": "code", "collapsed": false, "input": [ "small_requests['Complaint Type']" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# This is like our numpy example from before\n", "small_requests['Complaint Type'] == 'Noise - Street/Sidewalk'" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "That's numpy in action! Using `==` on a column of a dataframe gives us a series of `True` and `False` values" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Selecting only the rows with noise complaints" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This is like our numpy example earlier\n", "noise_complaints = small_requests[small_requests['Complaint Type'] == 'Noise - Street/Sidewalk']\n", "noise_complaints" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any Dataframe has an *index*, which is a integer or date or something else associated to each row." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# How to get a specific row\n", "small_requests.ix[0]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# How not to get a row\n", "small_requests[0]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 1: Selecting things from dataframes\n", "\n", "* Find out how many complaints were filed with the NYPD\n", "* How many complaints were filed in the zip code 10007?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code here" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Back to our example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We ran this at the beginning, so we don't have to run it again. Just here as a reminder.\n", "#orig_data = pd.read_csv('./311-service-requests.csv', nrows=100000, parse_dates=['Created Date'])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "complaints = orig_data[['Created Date', 'Complaint Type']]\n", "noise_complaints = complaints[complaints['Complaint Type'] == 'Noise - Street/Sidewalk']\n", "noise_complaints.set_index('Created Date').sort_index().resample('H', how=len).plot()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Indexes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints[:3]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints = noise_complaints.set_index('Created Date')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints[:3]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Sorting the index" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Pandas is awesome for date time index stuff. It was built for dealing with financial data is which is ALL TIME SERIES" ] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints = noise_complaints.sort_index()\n", "noise_complaints[:3]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Counting the complaints each hour" ] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints.resample('H', how=len)[:3]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Example 1: done!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints.resample('H', how=len).plot()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Chaining commands together" ] }, { "cell_type": "code", "collapsed": false, "input": [ "complaints = orig_data[['Created Date', 'Complaint Type']]\n", "noise_complaints = complaints[complaints['Complaint Type'] == 'Noise - Street/Sidewalk']\n", "noise_complaints.set_index('Created Date').sort_index().resample('H', how=len).plot()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 2: Time series resampling\n", "\n", "* Find the number of noise complaints every day!\n", "* Find how many complaints about rodents there are each week. Make a graph!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Example 2: What are the most common complaint types?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "orig_data['Complaint Type'].value_counts()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "orig_data['Complaint Type'].value_counts()[:20].plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 3: Do the same thing for a different column" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code here." ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3: Which weekday has the most noise complaints?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "complaints = orig_data[['Created Date', 'Complaint Type']]\n", "noise_complaints = complaints[complaints['Complaint Type'] == 'Noise - Street/Sidewalk']\n", "noise_complaints = noise_complaints.set_index(\"Created Date\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "noise_complaints['weekday'] = noise_complaints.index.weekday\n", "noise_complaints[:3]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Count the complaints by weekday\n", "counts_by_weekday = noise_complaints.groupby('weekday').aggregate(len)\n", "counts_by_weekday" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# change the index to be actual days\n", "counts_by_weekday.index = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "counts_by_weekday.plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 4: Count the complaints by hour instead" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code here" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A few more cool things" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# String searching" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We need to get rid of the NA values for this to work\n", "street_names = orig_data['Street Name'].fillna('')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "manhattan_streets = street_names[street_names.str.contains(\"MANHATTAN\")]\n", "manhattan_streets" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "manhattan_streets.value_counts()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Looking at complaints close to us" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Our current latitude and longitude\n", "our_lat, our_long = 40.714151,-74.00878" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "distance_from_us = (orig_data['Longitude'] - our_long)**2 + (orig_data['Latitude'] - our_lat)**2" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.Series(distance_from_us).hist()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "close_complaints = orig_data[distance_from_us < 0.00005]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "close_complaints['Complaint Type'].value_counts()[:20].plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
jaekookang/useful_bits
Machine_Learning/RNN_LSTM/predict_character/rnn_char.ipynb
1
47235
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Character-level Language Model using vanilla RNN\n", "2017-04-11 jkang \n", "Python3.5 \n", "TensorFlow1.0.1 \n", " \n", "- input: &nbsp;&nbsp;'hello_world_good_morning_see_you_hello_grea' \n", "- output: 'ello_world_good_morning_see_you_hello_great' \n", "\n", "### Reference: \n", "- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py\n", "- https://github.com/aymericdamien/TensorFlow-Examples\n", "- https://hunkim.github.io/ml/\n", "\n", "### Comment: \n", "- 단어 단위가 아닌 문자 단위로 훈련함\n", "- 하나의 example만 훈련에 사용함\n", "- Cell의 종류는 BasicRNNCell을 사용함 (첫번째 Reference 참조)\n", "- dynamic_rnn방식 사용 (기존 tf.nn.rnn보다 더 시간-계산 효율적이라고 함)\n", "- AdamOptimizer를 사용" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Input/Ouput data\n", "char_raw = 'hello_world_good_morning_see_you_hello_great'\n", "char_list = sorted(list(set(char_raw)))\n", "char_to_idx = {c: i for i, c in enumerate(char_list)}\n", "idx_to_char = {i: c for i, c in enumerate(char_list)}\n", "char_data = [char_to_idx[c] for c in char_raw]\n", "char_data_one_hot = tf.one_hot(char_data, depth=len(\n", " char_list), on_value=1., off_value=0., axis=1, dtype=tf.float32)\n", "char_input = char_data_one_hot[:-1, :] # 'hello_world_good_morning_see_you_hello_grea'\n", "char_output = char_data_one_hot[1:, :] # 'ello_world_good_morning_see_you_hello_great'\n", "with tf.Session() as sess:\n", " char_input = char_input.eval()\n", " char_output = char_output.eval()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Learning parameters\n", "learning_rate = 0.001\n", "max_iter = 1000\n", "\n", "# Network Parameters\n", "n_input_dim = char_input.shape[1]\n", "n_input_len = char_input.shape[0]\n", "n_output_dim = char_output.shape[1]\n", "n_output_len = char_output.shape[0]\n", "n_hidden = 100\n", "\n", "# TensorFlow graph\n", "# (batch_size) x (time_step) x (input_dimension)\n", "x_data = tf.placeholder(tf.float32, [1, None, n_input_dim])\n", "# (batch_size) x (time_step) x (output_dimension)\n", "y_data = tf.placeholder(tf.float32, [1, None, n_output_dim])\n", "\n", "# Parameters\n", "weights = {\n", " 'out': tf.Variable(tf.truncated_normal([n_hidden, n_output_dim]))\n", "}\n", "biases = {\n", " 'out': tf.Variable(tf.truncated_normal([n_output_dim]))\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def RNN(x, weights, biases):\n", " cell = tf.contrib.rnn.BasicRNNCell(n_hidden) # Make RNNCell\n", " outputs, states = tf.nn.dynamic_rnn(cell, x, time_major=False, dtype=tf.float32)\n", " '''\n", " **Notes on tf.nn.dynamic_rnn**\n", "\n", " - 'x' can have shape (batch)x(time)x(input_dim), if time_major=False or \n", " (time)x(batch)x(input_dim), if time_major=True\n", " - 'outputs' can have the same shape as 'x'\n", " (batch)x(time)x(input_dim), if time_major=False or \n", " (time)x(batch)x(input_dim), if time_major=True\n", " - 'states' is the final state, determined by batch and hidden_dim\n", " '''\n", " \n", " # outputs[-1] is outputs for the last example in the mini-batch\n", " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", "\n", "def softmax(x):\n", " rowmax = np.max(x, axis=1)\n", " x -= rowmax.reshape((x.shape[0] ,1)) # for numerical stability\n", " x = np.exp(x)\n", " sum_x = np.sum(x, axis=1).reshape((x.shape[0],1))\n", " return x / sum_x\n", "\n", "pred = RNN(x_data, weights, biases)\n", "cost = tf.reduce_mean(tf.squared_difference(pred, y_data))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 100/1000, Cost:0.0220, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 200/1000, Cost:0.0036, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 300/1000, Cost:0.0011, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 400/1000, Cost:0.0005, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 500/1000, Cost:0.0003, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 600/1000, Cost:0.0002, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 700/1000, Cost:0.0001, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 800/1000, Cost:0.0001, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch: 900/1000, Cost:0.0001, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n", "Epoch:1000/1000, Cost:0.0000, Acc:100.0, Predict: ello_world_good_morning_see_you_hello_great\n" ] } ], "source": [ "# Learning\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " x_train = char_input.reshape((1, char_input.shape[0], n_input_dim))\n", " y_train = char_output.reshape((1, char_output.shape[0], n_output_dim))\n", " for i in range(max_iter):\n", " _, loss, p = sess.run([optimizer, cost, pred],\n", " feed_dict={x_data: x_train, y_data: y_train})\n", " if i == (max_iter-1):\n", " pred_act = softmax(p)\n", " if (i+1) % 100 == 0:\n", " pred_out = np.argmax(p, axis=1)\n", " accuracy = np.sum(char_data[1:] == pred_out)/n_output_len*100\n", " print('Epoch:{:>4}/{},'.format(i+1,max_iter),\n", " 'Cost:{:.4f},'.format(loss), \n", " 'Acc:{:>.1f},'.format(accuracy),\n", " 'Predict:', ''.join([idx_to_char[i] for i in pred_out]))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZmZmZmVlRBa7pNDMzMzMzsxpq8prOdN2hNoFlyzp477FL2XHSg0zd/SHOPuup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\nzWGdsa8Ivx9Z35ljqfK9QXhsxYGJVdL1iO/RM7bthJujiwm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f689f1405f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Probability plot\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(15,20)\n", "plt.title('Input Sequence', y=1.08, fontsize=20)\n", "plt.xlabel('Probability of Next Character(y) Given Current One(x)'+\n", " '\\n[accuracy={:.1f}]'.format(accuracy), \n", " fontsize=20, y=1.5)\n", "plt.ylabel('Character List', fontsize=20)\n", "plot = plt.imshow(pred_act.T, cmap=plt.get_cmap('plasma'))\n", "fig.colorbar(plot, fraction=0.015, pad=0.04)\n", "plt.xticks(np.arange(len(char_data)-1), list(char_raw)[:-1], fontsize=15)\n", "plt.yticks(np.arange(len(char_list)), [idx_to_char[i] for i in range(len(char_list))], fontsize=15)\n", "ax.xaxis.tick_top()\n", "\n", "# Annotate\n", "for i, idx in zip(range(len(pred_out)), pred_out):\n", " annotation = idx_to_char[idx]\n", " ax.annotate(annotation, xy=(i-0.2, idx+0.2), fontsize=12)\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
NeuroDataDesign/seelviz
ReverseAlignment.ipynb
1
818409
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "os.chdir('/Users/albert/ndreg')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ndreg import *\n", "import matplotlib\n", "import ndio.remote.neurodata as neurodata\n", "import nibabel as nb" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'1', u'0', u'3', u'2', u'5', u'4']\n" ] } ], "source": [ "inToken = \"Control258\"\n", "\n", "nd = neurodata()\n", "print(nd.get_metadata(inToken)['dataset']['voxelres'].keys())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0lW2uK2VrfX1dNmEmKIOcuEFqvVQqCUvRbDZlVq3ruigUCtjZ2UGp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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x106c2d590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inImg = imgDownload(inToken, resolution=5)\n", "imgShow(inImg, vmax=500)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3.2e-05, 3.2e-05, 9.999999999999999e-06)\n" ] } ], "source": [ "print(inImg.GetSpacing())" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "refToken = \"ara_ccf2\"\n", "refImg = imgDownload(refToken)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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s+vTHAwQdC601yNMjlEngvKWo1WrlFpjaKA6wWm9pjUSeo1UsFjGZTNBut29lwIwn4h+A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\nMOiNa0oVu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kMsmHvqEiNPR/4gkQKUuSJJ4UAACdTof+/n6oVCrY7XaeLSb7pGoTZcgAOECT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\nc6nU2lvWN+LP43TbZc6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Sk9qIlOyqVCo6f9ra2lKZA4DVq1fTwi4lJSV4+PAh2rdvr1dLwpMnT0pmCABq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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x169722e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "refAnnoImg = imgDownload(refToken, channel=\"annotation\")\n", "imgShow(refAnnoImg, vmax=1000)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randValues = np.random.rand(1000,3)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randValues = np.concatenate(([[0,0,0]],randValues))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HFJziR9HkghrrzWG4/VWfhG8V8Fyo/uff/eEci7EjrfQsAA5yR2xlWkpvr6TLelQfPVzv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GO02KYDrRdwRzWQEOlowtf/anG9e6Hn0No4Mm/YJJZNHAWO4SybOIp5wyahtXHiXqhl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\nDOzc7VjzRiTaO1o2X9NS0VGaDWsvJqOqIr1Eqd/0RDG6Fa28+1O1XRr1u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"text/plain": [ "<matplotlib.figure.Figure at 0x16736ef50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "randCmap = matplotlib.colors.ListedColormap (randValues)\n", "imgShow(refAnnoImg, vmax=1000, cmap=randCmap)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6/bHTyVi0YhbtmJrvs2/fPp71jGd83ue9ULdc8UTk7W/5IUTbptVs0Ww0MQcW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cDBUZvVodGuFKjX6+i6w7a1eQI9j6DkEJwyXhhStmHdUB+RbxFV9zNbTbNkuTxcSHPuoICqKswf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\ns+Hk/1sAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x167256390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imgShow(refImg, vmax=500, newFig=False)\n", "imgShow(refAnnoImg, vmax=1000, cmap=randCmap, alpha=0.2, newFig=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3.2e-05, 3.2e-05, 9.999999999999999e-06)\n", "(0.024999999999999998, 0.024999999999999998, 0.024999999999999998)\n" ] } ], "source": [ "print(inImg.GetSpacing())\n", "print(refImg.GetSpacing())" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "inImg.SetSpacing([0.01872, 0.01872, 0.005])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.01872, 0.01872, 0.005)\n", "(0.024999999999999998, 0.024999999999999998, 0.024999999999999998)\n" ] } ], "source": [ "print(inImg.GetSpacing())\n", "print(refImg.GetSpacing())" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xa3n27NnYvn37RC+dM8WhvZf223/6p3+CqqoYHh7G5s2bsWnTJrZf1QqehbuH1/nUpz7F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\nJN560AxQMuImh6vLLrsMwJn2gEwmA1VVu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"text/plain": [ "<matplotlib.figure.Figure at 0x160c08490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "refImg = imgResample(refImg, spacing=inImg.GetSpacing())\n", "imgShow(refImg, vmax=500)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0lW2uK2VrfX1dNmEmKIOcuEFqvVQqCUvRbDZlVq3ruigUCtjZ2UGp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\n3t4e6vW6kWLojT9+/NhSUm6ajGksr6wmG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7VaTz3/72N+WtOf300/H5fFSrVbq7u0kmk0rsarUaDoeDRCKBxWLB4/GQTqeJ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\njv4GxgHV3s7evlAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1696e0050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "refImg = imgReorient(refImg, \"RSA\", \"LAI\")\n", "imgShow(refImg, vmax=500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(values, bins) = np.histogram(sitk.GetArrayFromImage(refImg), bins=100, range=(0,500))\n", "plt.plot(bins[:-1], values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lowerThreshold = 100\n", "upperThreshold = sitk.GetArrayFromImage(inImg).max()+1\n", "\n", "refImg = sitk.Threshold(refImg,lowerThreshold,upperThreshold,lowerThreshold) - lowerThreshold\n", "imgShow(inImg, vmax = 500) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(values, bins) = np.histogram(sitk.GetArrayFromImage(refImg), bins=100, range=(0,500))\n", "plt.plot(bins[:-1], values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "affine = imgAffineComposite(refImg, inImg, iterations=100, useMI=True, verbose=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "refImg_affine = imgApplyAffine(refImg, affine, size=inImg.GetSize())\n", "imgShow(inImg_affine, vmax=500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "refImg = imgResample(refImg_affine, spacing=inImg.GetSpacing())\n", "(field, invField) = imgMetamorphosisComposite(refImg, inImg, alphaList=[0.05, 0.02, 0.01], useMI=True, iterations=100, verbose=True)\n", "refImg_lddmm = imgApplyField(refImg_affine, field, size=inImg.GetSize())\n", "imgShow(inImg_lddmm, vmax = 500)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
arne-cl/alt-mulig
discoursegraphs/pyfim-frequent-itemset-mining-example.ipynb
1
8088
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import fim" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('clause-segment-relation.csv', index_col=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>clause</th>\n", " <th>segment</th>\n", " <th>relation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>S</td>\n", " <td>satellite</td>\n", " <td>cause</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NP</td>\n", " <td>nucleus</td>\n", " <td>list</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>S</td>\n", " <td>satellite</td>\n", " <td>condition</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>S</td>\n", " <td>satellite</td>\n", " <td>condition</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>S</td>\n", " <td>satellite</td>\n", " <td>condition</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " clause segment relation\n", "0 S satellite cause\n", "1 NP nucleus list\n", "2 S satellite condition\n", "3 S satellite condition\n", "4 S satellite condition" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "row0 = df.iloc[0] # access the first row\n", "nrows = len(df) # number of rows in a DataFrame" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# list of rows. each row is represented as\n", "# a list of column values\n", "tracts = [df.iloc[i].values for i in range(len(df))]" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import fim\n", "\n", "def dataframe2arules(dataframe, min_support=10, min_confidence=80):\n", " \"\"\"extract association rules from a DataFrame\n", " \n", " Parameters\n", " ----------\n", " min_support : int\n", " at least n rows have to match the rule (default: 10)\n", " min_confidence : int\n", " minimum confidence of an assoc. rule (default: 80%)\n", " \"\"\"\n", " tracts = (dataframe.iloc[i].values\n", " for i in range(len(dataframe)))\n", " return fim.arules(tracts, supp=min_support, conf=min_confidence)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('satellite', (), 168, 84.84848484848484),\n", " ('satellite', ('S',), 127, 86.39455782312925),\n", " ('satellite', ('condition',), 60, 93.75),\n", " ('satellite', ('condition', 'S'), 60, 95.23809523809523),\n", " ('S', ('condition', 'satellite'), 60, 100.0),\n", " ('S', ('condition',), 63, 98.4375),\n", " ('satellite', ('circumstance',), 21, 95.45454545454545)]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataframe2arules(df)\n", "# 168 satellites\n", "# 127 S-clause satellites\n", "# 60 (S-clause) condition satellites\n", "# 21 circumstance satellites" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('satellite', (), 41, 80.3921568627451),\n", " ('satellite', ('VP',), 16, 100.0),\n", " ('satellite', ('purpose',), 12, 100.0),\n", " ('satellite', ('purpose', 'VP'), 10, 100.0),\n", " ('VP', ('purpose', 'satellite'), 10, 83.33333333333334),\n", " ('VP', ('purpose',), 10, 83.33333333333334),\n", " ('NP', ('nucleus',), 8, 80.0),\n", " ('satellite', ('PP',), 8, 88.88888888888889)]" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataframe2arules(df[df['clause'] != 'S'])" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('nucleus', (), 24, 80.0),\n", " ('nucleus', ('NP',), 8, 100.0),\n", " ('S', ('span',), 6, 100.0),\n", " ('nucleus', ('e-elaboration',), 4, 100.0),\n", " ('nucleus', ('e-elaboration', 'NP'), 3, 100.0),\n", " ('nucleus', ('sequence',), 4, 100.0),\n", " ('nucleus', ('sequence', 'S'), 3, 100.0),\n", " ('nucleus', ('list',), 4, 100.0),\n", " ('nucleus', ('elaboration',), 3, 100.0),\n", " ('nucleus', ('elaboration', 'S'), 3, 100.0),\n", " ('S', ('elaboration', 'nucleus'), 3, 100.0),\n", " ('S', ('elaboration',), 3, 100.0),\n", " ('nucleus', ('contrast',), 3, 100.0),\n", " ('nucleus', ('contrast', 'S'), 3, 100.0),\n", " ('S', ('contrast', 'nucleus'), 3, 100.0),\n", " ('S', ('contrast',), 3, 100.0)]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataframe2arules(df[df['segment'] != 'satellite'])\n", "# 24 nucleii\n", "# 8 NP-clause nucleii" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('satellite', (), 108, 80.59701492537313),\n", " ('satellite', ('circumstance',), 21, 95.45454545454545),\n", " ('satellite', ('circumstance', 'S'), 16, 94.11764705882352),\n", " ('satellite', ('cause',), 17, 89.47368421052632),\n", " ('satellite', ('cause', 'S'), 15, 93.75),\n", " ('S', ('cause', 'satellite'), 15, 88.23529411764706),\n", " ('S', ('cause',), 16, 84.21052631578947),\n", " ('satellite', ('purpose',), 16, 100.0),\n", " ('satellite', ('VP',), 16, 100.0)]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataframe2arules(df[df['relation'] != 'condition'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
tensorflow/swift
docs/site/tutorials/raw_tensorflow_operators.ipynb
1
7901
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "XNWJ6JVGkOlf" }, "source": [ "##### Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](#scrollTo=bPJq2qP2KE3u)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "fSlQ2vFzKGOY" }, "outputs": [], "source": [ "// #@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n", "// Licensed under the Apache License, Version 2.0 (the \"License\");\n", "// you may not use this file except in compliance with the License.\n", "// You may obtain a copy of the License at\n", "//\n", "// https://www.apache.org/licenses/LICENSE-2.0\n", "//\n", "// Unless required by applicable law or agreed to in writing, software\n", "// distributed under the License is distributed on an \"AS IS\" BASIS,\n", "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "// See the License for the specific language governing permissions and\n", "// limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "yfNdITLmJtX8" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/swift/tutorials/raw_tensorflow_operators\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/swift/blob/main/docs/site/tutorials/raw_tensorflow_operators.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/swift/blob/main/docs/site/tutorials/raw_tensorflow_operators.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "lONNcRalVUO9" }, "source": [ "# Raw TensorFlow operators\n", "\n", "Building on TensorFlow, Swift for TensorFlow takes a fresh approach to API design. APIs are carefully curated from established libraries and combined with new language idioms. This means that not all TensorFlow APIs will be directly available as Swift APIs, and our API curation needs time and dedicated effort to evolve. However, do not worry if your favorite TensorFlow operator is not available in Swift -- the TensorFlow Swift library gives you transparent access to most TensorFlow operators, under the `_Raw` namespace.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CYqNvcHxqg0Y" }, "source": [ "Import `TensorFlow` to get started." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cVRrzjzFqee9" }, "outputs": [], "source": [ "import TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "id": "5vza91sR09r-" }, "source": [ "## Calling raw operators\n", "\n", "Simply find the function you need under the `_Raw` namespace via code completion." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kZRlD4utdPuX" }, "outputs": [], "source": [ "print(_Raw.mul(Tensor([2.0, 3.0]), Tensor([5.0, 6.0])))" ] }, { "cell_type": "markdown", "metadata": { "id": "iIgKg-ueVCy_" }, "source": [ "## Defining a new multiply operator\n", "\n", "Multiply is already available as operator `*` on `Tensor`, but let us pretend that we wanted to make it available under a new name as `.*`. Swift allows you to retroactively add methods or computed properties to existing types using `extension` declarations.\n", "\n", "Now, let us add `.*` to `Tensor` by declaring an extension and make it available when the tensor's `Scalar` type conforms to [`Numeric`](https://developer.apple.com/documentation/swift/numeric)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BdH-yZBjTZNx" }, "outputs": [], "source": [ "infix operator .* : MultiplicationPrecedence\n", "\n", "extension Tensor where Scalar: Numeric {\n", " static func .* (_ lhs: Tensor, _ rhs: Tensor) -> Tensor {\n", " return _Raw.mul(lhs, rhs)\n", " }\n", "}\n", "\n", "let x: Tensor<Double> = [[1.0, 2.0], [3.0, 4.0]]\n", "let y: Tensor<Double> = [[8.0, 7.0], [6.0, 5.0]]\n", "print(x .* y)" ] }, { "cell_type": "markdown", "metadata": { "id": "ucD5XZYYyzNe" }, "source": [ "## Defining a derivative of a wrapped function\n", "\n", "Not only can you easily define a Swift API for a raw TensorFlow operator, you can also make it differentiable to work with Swift's first-class automatic differentiation.\n", "\n", "To make `.*` differentiable, use the `@derivative` attribute on the derivative function and specify the original function as an attribute argument under the `of:` label. Since the `.*` operator is defined when the generic type `Scalar` conforms to `Numeric`, it is not enough for making `Tensor<Scalar>` conform to the `Differentiable` protocol. Born with type safety, Swift will remind us to add a generic constraint on the `@differentiable` attribute to require `Scalar` to conform to `TensorFlowFloatingPoint` protocol, which would make `Tensor<Scalar>` conform to `Differentiable`.\n", "\n", "```swift\n", "@differentiable(where Scalar: TensorFlowFloatingPoint)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fDXS0h_YumcL" }, "outputs": [], "source": [ "infix operator .* : MultiplicationPrecedence\n", "\n", "extension Tensor where Scalar: Numeric {\n", " @differentiable(where Scalar: TensorFlowFloatingPoint)\n", " static func .* (_ lhs: Tensor, _ rhs: Tensor) -> Tensor {\n", " return _Raw.mul(lhs, rhs)\n", " }\n", "}\n", "\n", "extension Tensor where Scalar : TensorFlowFloatingPoint { \n", " @derivative(of: .*)\n", " static func multiplyDerivative(\n", " _ lhs: Tensor, _ rhs: Tensor\n", " ) -> (value: Tensor, pullback: (Tensor) -> (Tensor, Tensor)) {\n", " return (lhs * rhs, { v in\n", " ((rhs * v).unbroadcasted(to: lhs.shape),\n", " (lhs * v).unbroadcasted(to: rhs.shape))\n", " })\n", " }\n", "}\n", "\n", "// Now, we can take the derivative of a function that calls `.*` that we just defined.\n", "print(gradient(at: x, y) { x, y in\n", " (x .* y).sum()\n", "})" ] }, { "cell_type": "markdown", "metadata": { "id": "l7kae5o1VKnu" }, "source": [ "## More examples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "v92FrXpCSuLT" }, "outputs": [], "source": [ "let matrix = Tensor<Float>([[1, 2], [3, 4]])\n", "\n", "print(_Raw.matMul(matrix, matrix, transposeA: true, transposeB: true))\n", "print(_Raw.matMul(matrix, matrix, transposeA: true, transposeB: false))\n", "print(_Raw.matMul(matrix, matrix, transposeA: false, transposeB: true))\n", "print(_Raw.matMul(matrix, matrix, transposeA: false, transposeB: false))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "raw_tensorflow_operators.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Swift", "name": "swift" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
QuantStack/quantstack-talks
2018-03-06-Polytechnique-Jupyter/notebooks/09 - Leaflet.ipynb
1
5646252
null
bsd-3-clause
ueapy/ueapy.github.io
content/notebooks/2016-09-30-scripts-and-modules.ipynb
1
12694
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "name = '2016-09-30-scripts-and-modules'\n", "title = 'Using Python scripts and modules '\n", "tags = 'basics'\n", "author = 'Denis Sergeev'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nb_tools import connect_notebook_to_post\n", "from IPython.core.display import HTML\n", "\n", "html = connect_notebook_to_post(name, title, tags, author)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can type all the instructions in the Python interpreter. But for longer sets of instructions you definitely need to change track and write the code in text files, that are usually called *scripts*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ways of running scripts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several ways of executing, or running, a script. If you frequently work in a command line, you would run a Python script just by typing\n", "\n", "```bash\n", "$ python some_script.py\n", "```\n", "\n", "where `some_script` is of course the name of your script." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "On Unix machines, if the script starts with **`#!/usr/bin/env python`** and the script is executable, you can just type the name of the script to run it:\n", "```bash\n", "$ ./demo.py\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to run a script from a Python interpreter, you need to use `execfile` command:\n", "```python\n", ">>> execfile('some_script.py')\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPython console offers another way of running a script. Yes, you guessed it, it's just literally `run` command:\n", "\n", "```ipython\n", "%run demo.py\n", "```\n", "In this case, not only the script was executed, but also the variables defined in the script are now available inside the interpreter's namespace." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Debugging and profiling in IPython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section is shamelessly taken from [here](https://github.com/drivendata/data-science-is-software/blob/master/notebooks/lectures/3.0-refactoring.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Standard Python debugger: pdb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interrupt execution with:\n", "* `%debug` magic: drops you out into the most recent error stacktrace in pdb\n", "* `import q; q.d()`: drops you into pdb, even outside of IPython\n", "\n", "Interrupt execution on an `Exception` with `%pdb` magic. Use pdb the Python debugger to debug inside a notebook.\n", "\n", "**Key commands for pdb are:**\n", "\n", "* `p`: Evaluate and print Python code\n", "* `w`: Where in the stack trace am I?\n", "* `u`: Go up a frame in the stack trace.\n", "* `d`: Go down a frame in the stack trace.\n", "* `c`: Continue execution\n", "* `q`: Stop execution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### IPython profiler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes your code is slow. See which functions are called, how many times, and how long they take!\n", "The `%prun` magic reports these to you right in the Jupyter notebook!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The world beyond Jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modern graphical IDEs are shipped with built-in profiling and debugging interfaces. One of the most powerful Python IDEs is PyCharm. It has tons of integrations with the normal development flow. Some of the features include:\n", "* git integration\n", "* interactive graphical debugger\n", "* flake8 linting\n", "* smart refactoring/go to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reusing code by importing modules" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to write larger and better organized programs (compared to simple scripts), where some objects are defined, (variables, functions, classes) and that you want to reuse several times, you have to create your own modules.\n", "\n", "Let us create a module demo contained in the file demo.py:\n", "```python\n", "# A demo module\n", "\n", "\n", "def show_me_a():\n", " \"\"\"Prints a.\"\"\"\n", " print('a')\n", " \n", "def show_me_b():\n", " \"\"\"Prints b.\"\"\"\n", " print('b')\n", "\n", "c = 2\n", "d = 2\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this file, we defined two functions `show_me_a` and `show_me_b`. Suppose we want to call the `show_me_a` function from the interpreter. We could execute the file as a script, but since we just want to have access to the function `show_me_a`, we are rather going to import it as a module. The syntax is as follows.\n", "```ipython\n", "In [1]: import demo\n", "\n", "\n", "In [2]: demo.show_me_a()\n", "a\n", "```" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Pythonic import statements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### <font color='green'>Good</font>\n", "import <font color='green'>sys</font>\n", "\n", "from os import <font color='green'>path</font>\n", "\n", "import statistics <font color='green'>as stats</font>\n", "\n", "from custom_package import <font color='green'>mode</font>\n", "\n", "from statistics import <font color='green'>mean, median</font>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### <font color='red'>Bad:</font> silently overwrites previous imports\n", "from pylab import <font color='red'><b>*</b></font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Module caching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modules are cached: if you modify ``demo.py`` and re-import it in the\n", "old session, you will get the old one.\n", "\n", "Solution:\n", "\n", "```ipython\n", " In [1]: reload(demo)\n", "```\n", "\n", "In Python 3 instead ``reload`` is not builtin, so you have to import the ``importlib`` module first and then do:\n", "\n", "```python\n", " In [1]: importlib.reload(demo)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Auto-reloading in IPython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```ipython\n", "%load_ext autoreload\n", "# always reload modules marked with \"%aimport\"\n", "%autoreload 1\n", "# reload all\n", "%autoreload 2\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us test it out! First we import the module using the magic:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import demo" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "demo.show_me_a()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we change that function to so that it prints something else:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# A demo module\n", "\n", "\n", "def show_me_a():\n", " \"\"\"Prints a.\"\"\"\n", " print('Something else')\n", " \n", "def show_me_b():\n", " \"\"\"Prints b.\"\"\"\n", " print('b')\n", "\n", "c = 2\n", "d = 2\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now `demo.show_me_a()` prints out \"Something else\" instead of \"a\"." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## `'__main__'` and how to use it" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Sometimes we want code to be executed when a module is run directly, but not when it is imported by another module. `if __name__ == '__main__'` allows us to check whether the module is being run directly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now if the script demo.py looks like this:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "def show_me_a():\n", " \"\"\"Prints a.\"\"\"\n", " print('Something else')\n", "\n", "def show_me_b():\n", " \"\"\"Prints b.\"\"\"\n", " print('b')\n", "\n", "# show_me_b() runs on import\n", "show_me_b()\n", "\n", "if __name__ == '__main__':\n", " # show_me_a() is only executed when the module is run directly.\n", " show_me_a()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using packages and creating your own modules" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to import your local modules, you must do three things:\n", "* put the .py file in a separate folder\n", "* add an empty `__init__.py` file to the folder\n", "* add that folder to the Python path with `sys.path.append()`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are getting too good at writing code and it's becoming useful for other projects or people, you should consider refactoring it into a standalone package. You can then make it available online via PyPi or Anaconda. There are great templates out there. To name but a few:\n", "* [Cookiecutter](https://github.com/wdm0006/cookiecutter-pipproject)\n", "* [Shablona](https://github.com/uwescience/shablona)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources\n", "* [TalkPython course \"Write Pythonic Code Like a Seasoned Developer\"](https://training.talkpython.fm/courses/details/write-pythonic-code-like-a-seasoned-developer)\n", "* [SciPy lectures](http://www.scipy-lectures.org/intro/language/reusing_code.html)\n", "* [Data Science is Software. SciPy 2016 Tutorial by Peter Bull & Isaac Slavitt](https://www.youtube.com/watch?v=EKUy0TSLg04&index=10&list=WL)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <small>\n", " <p> This post was written as an IPython (Jupyter) notebook. You can view or download it using\n", " <a href=\"http://nbviewer.ipython.org/github/ueapy/ueapy.github.io/blob/src/content/notebooks/2016-09-30-scripts-and-modules.ipynb\">nbviewer</a>.</p>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
paulray/NICERsoft
scripts/fillgaps_examples.ipynb
1
837767
{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "orig_nbformat": 2, "kernelspec": { "name": "python38564bitpint43159e73942f405c8fd58e6900045fe1", "display_name": "Python 3.8.5 64-bit ('pint': virtualenvwrapper)" }, "metadata": { "interpreter": { "hash": "06aad6da0c6db3c5f58fce915675a116830c49bfda9aa1e5e442ffe18840c729" } } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from nicer.fillgaps import fillgaps" ] }, { "source": [ "# NICER Pulsars\n", "### PSR_J0218+4232" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# MJDs/residuals for PSR_J0218+4232\n", "mjds = []\n", "residuals = []\n", "with open(\"0218-grid-nicer.txt\", 'r') as file:\n", " line = file.readline()\n", " while line:\n", " vals = line.split()\n", " if vals != [] and vals[0][0] != '#':\n", " mjds.append(int(float(vals[0])))\n", " residuals.append(float(vals[1]))\n", " line = file.readline()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "x = np.array(mjds)\n", "xindices = np.array((x - x[0]), dtype=int)\n", "ydense = np.zeros(xindices.max() + 1) * np.nan\n", "ydense[xindices] = residuals" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9aadb5d7c0>]" ] }, "metadata": {}, "execution_count": 4 }, { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD 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335.196118 151.99403 \nL 335.436912 120.718321 \nM 336.640882 119.457851 \nL 336.881676 98.918729 \nM 338.808028 118.011576 \nL 339.048822 161.630845 \nL 339.289616 122.079226 \nL 339.53041 116.168579 \nL 339.771204 143.723141 \nM 344.587085 135.256403 \nL 344.827879 105.823691 \nL 345.068673 147.866118 \nL 345.309467 92.465729 \nL 345.550261 113.989122 \nL 345.791055 117.916162 \nM 346.513437 141.136919 \nL 346.754231 124.89645 \nL 346.995025 100.71653 \nM 347.476613 114.973393 \nL 347.717407 134.814486 \nL 347.958201 118.157208 \nM 350.606935 137.410751 \nL 350.847729 110.05706 \nL 351.088523 145.777054 \nM 353.255669 129.446192 \nL 353.496463 118.744757 \nM 354.941227 197.330752 \nL 355.182021 144.305669 \nM 356.626785 111.799621 \nL 356.867579 135.160989 \nM 362.646636 128.662793 \nL 362.88743 108.319521 \nM 366.017752 36.497877 \nL 366.49934 173.87397 \nM 366.980928 116.881673 \nL 367.221722 130.761901 \nL 367.462516 120.924214 \nM 368.425692 141.317704 \nL 368.666486 135.909236 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119.491814 172.408618 \nL 119.732608 193.78242 \nL 119.973402 181.945036 \nL 120.214196 186.721949 \nL 120.45499 144.382907 \nL 120.695784 182.694162 \nL 120.936578 186.836853 \nL 121.177372 178.0669 \nL 121.418166 197.461673 \nL 121.65896 156.397273 \nL 121.899754 158.475104 \nL 122.140548 194.721499 \nL 122.381342 150.317597 \nL 122.622136 152.426659 \nL 122.86293 146.454351 \nL 123.103724 158.454102 \nL 123.344518 136.903783 \nL 123.585312 147.678942 \nL 123.826106 103.859769 \nL 124.0669 140.690892 \nL 124.307694 143.353451 \nL 124.548488 102.873778 \nL 124.789282 122.85328 \nL 125.030076 114.668054 \nL 125.27087 119.395473 \nL 125.511664 158.291456 \nL 125.752458 116.537142 \nL 125.993252 121.898783 \nL 126.234046 110.646128 \nL 126.47484 132.604657 \nL 126.715634 90.841639 \nL 126.956428 129.728919 \nL 127.197222 134.447634 \nL 127.438016 126.253704 \nL 127.67881 146.224502 \nL 127.919604 105.736125 \nL 128.160398 108.38998 \nL 128.401192 145.2124 \nL 128.641986 101.384522 \nL 128.88278 112.843259 \nL 129.123574 114.300204 \nL 129.364368 107.450083 \nL 130.08675 146.897471 \nL 130.327544 128.008805 \nL 130.809132 155.339954 \nL 131.29072 176.840299 \nL 131.772308 124.776149 \nL 132.013102 114.583031 \nL 132.253896 118.720859 \nL 132.49469 109.898422 \nL 132.976278 103.557911 \nL 133.217072 107.97836 \nL 133.457866 119.00168 \nL 133.69866 113.152583 \nL 133.939454 116.730385 \nL 134.180248 116.7931 \nL 134.421043 116.035493 \nL 134.661837 117.003926 \nL 134.902631 120.624426 \nL 135.143425 114.818026 \nL 135.384219 125.884045 \nL 135.625013 130.347191 \nL 136.106601 124.092076 \nL 136.347395 124.622094 \nL 136.588189 143.72074 \nL 136.828983 130.434118 \nL 137.069777 148.604723 \nL 137.551365 116.216272 \nL 137.792159 125.449386 \nL 138.273747 125.33464 \nL 138.514541 125.004204 \nL 138.996129 128.240069 \nL 139.236923 177.316316 \nL 139.477717 137.122604 \nL 139.718511 150.661064 \nL 139.959305 129.840229 \nL 140.200099 170.935119 \nL 140.440893 127.339973 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140.327658 \nL 151.03583 127.31649 \nL 151.276624 139.796569 \nL 151.517418 164.174815 \nL 151.758212 158.769948 \nL 151.999006 113.780732 \nL 152.2398 125.6763 \nL 152.480594 119.728516 \nL 152.721388 123.927449 \nL 152.962182 123.443282 \nL 153.202976 130.71424 \nL 153.44377 127.363412 \nL 153.684564 135.016505 \nL 153.925358 90.142969 \nL 154.166152 112.579737 \nL 154.406946 102.937684 \nL 154.64774 112.167107 \nL 154.888534 110.392611 \nL 155.129328 119.239899 \nL 155.370122 120.332063 \nL 155.610916 126.107327 \nL 155.85171 121.735873 \nL 156.092504 135.207772 \nL 156.333298 91.794887 \nL 156.574092 133.072037 \nL 156.814886 112.433462 \nL 157.05568 126.154183 \nL 157.296474 86.142731 \nL 157.537268 135.401239 \nL 158.018856 139.001624 \nL 158.25965 138.85345 \nL 158.741238 139.103224 \nL 158.982032 148.518599 \nL 159.46362 116.494668 \nL 159.704414 134.847534 \nL 159.945208 121.743172 \nL 160.186002 141.024079 \nL 160.426796 141.736357 \nL 160.908384 135.845763 \nL 161.149178 140.49117 \nL 161.389972 151.739449 \nL 161.630766 146.115309 \nL 161.87156 149.91807 \nL 162.112354 151.068763 \nL 162.353148 150.493417 \nL 162.593942 150.738392 \nL 162.834736 154.498455 \nL 163.07553 148.831618 \nL 163.316324 160.037199 \nL 163.557118 164.639908 \nL 164.038706 158.663919 \nL 164.279501 150.023742 \nL 164.520295 154.34383 \nL 164.761089 144.332973 \nL 165.242677 92.633344 \nL 165.965059 125.424143 \nL 166.205853 142.193879 \nL 166.446647 123.487474 \nL 167.169029 163.481643 \nL 167.409823 156.813782 \nL 167.650617 158.452988 \nL 167.891411 170.093985 \nL 168.132205 126.448368 \nL 168.372999 163.453048 \nL 168.613793 166.289164 \nL 168.854587 125.983048 \nL 169.095381 146.136106 \nL 169.336175 138.124437 \nL 169.576969 143.025412 \nL 169.817763 182.094952 \nL 170.058557 140.514195 \nL 170.299351 162.654985 \nL 170.540145 151.58459 \nL 170.780939 157.128491 \nL 171.021733 115.556438 \nL 171.262527 154.634681 \nL 171.503321 159.54436 \nL 171.744115 151.541395 \nL 171.984909 171.703157 \nL 172.225703 131.405745 \nL 172.466497 134.250564 \nL 172.707291 171.263948 \nL 172.948085 127.627035 \nL 173.429673 150.145921 \nL 173.911261 156.457643 \nL 174.152055 134.10666 \nL 174.392849 127.290624 \nL 174.633643 138.141171 \nL 174.874437 133.64134 \nL 175.115231 132.926462 \nL 175.356025 143.579442 \nL 175.596819 141.403614 \nL 175.837613 101.887763 \nL 176.078407 137.119546 \nL 176.319201 141.681767 \nL 176.559995 139.400656 \nL 176.800789 141.432209 \nL 177.041583 140.909698 \nL 177.282377 148.583588 \nL 177.523171 144.746643 \nL 177.763965 144.994901 \nL 178.004759 142.802175 \nL 178.245553 143.163514 \nL 178.486347 124.457108 \nL 178.727141 138.312742 \nL 178.967935 132.541728 \nL 179.449524 151.877225 \nL 179.690318 147.936298 \nL 179.931112 148.393333 \nL 180.171906 158.333548 \nL 180.653494 142.557817 \nL 180.894288 129.099915 \nL 181.135082 132.944659 \nL 181.375876 129.297482 \nL 181.857464 132.737994 \nL 182.098258 144.281323 \nL 182.339052 148.305435 \nL 182.579846 139.752896 \nL 182.82064 146.002229 \nL 183.061434 142.877562 \nL 183.302228 143.678009 \nL 183.543022 132.856274 \nL 184.02461 151.068763 \nL 184.506198 129.619827 \nL 184.987786 117.843838 \nL 185.22858 120.787835 \nL 185.469374 126.064544 \nL 185.710168 154.531372 \nL 185.950962 140.297958 \nL 186.191756 148.080942 \nL 186.43255 151.822635 \nL 186.673344 136.768606 \nL 186.914138 144.29562 \nL 187.154932 139.129869 \nL 187.395726 140.527564 \nL 187.63652 131.236751 \nL 187.877314 135.882157 \nL 188.118108 133.267979 \nL 188.358902 134.374198 \nL 188.599696 128.916972 \nL 188.84049 136.152511 \nL 189.081284 120.807007 \nL 189.322078 124.257224 \nL 189.562872 131.748733 \nL 189.803666 117.223844 \nL 190.04446 145.399197 \nL 190.285254 150.38443 \nL 190.526048 153.036952 \nL 190.766842 146.857483 \nL 191.007636 131.218554 \nL 191.24843 139.038018 \nL 191.489224 135.207772 \nL 191.730018 129.107789 \nL 191.970812 131.839798 \nL 192.211606 136.904518 \nL 192.4524 165.159357 \nL 192.693194 150.713954 \nL 192.933988 158.284949 \nL 193.174782 161.814653 \nL 193.415576 141.07347 \nL 193.65637 139.069213 \nL 193.897164 147.63215 \nL 194.137959 143.350682 \nL 194.378753 146.293957 \nL 194.619547 145.09224 \nL 194.860341 125.153598 \nL 195.101135 129.485842 \nL 195.341929 156.254878 \nL 195.582723 144.892218 \nL 196.064311 167.77351 \nL 196.305105 162.053187 \nL 196.545899 161.121003 \nL 196.786693 168.769303 \nL 197.027487 153.614297 \nL 197.268281 176.590987 \nL 197.509075 177.130885 \nL 197.749869 153.399897 \nL 197.990663 148.405834 \nL 198.231457 147.556763 \nL 198.472251 139.482948 \nL 198.713045 144.25354 \nL 198.953839 138.456936 \nL 199.194633 113.923408 \nL 199.435427 101.375651 \nL 199.676221 107.649529 \nL 199.917015 107.812989 \nL 200.157809 86.579859 \nL 200.398603 84.083654 \nL 200.639397 130.41789 \nL 201.120985 144.957207 \nL 201.602573 138.772863 \nL 201.843367 140.096037 \nL 202.084161 140.429041 \nL 202.324955 145.068988 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124.532929 \nL 213.642274 132.498012 \nL 213.883068 136.230498 \nL 214.123862 133.681416 \nL 214.846244 150.711758 \nL 215.087038 146.608523 \nL 215.327832 145.019429 \nL 215.80942 130.509778 \nL 216.050214 150.762155 \nL 216.291008 130.440661 \nL 216.531802 134.003888 \nL 216.772596 131.437362 \nL 217.254184 140.106782 \nL 217.494978 131.775509 \nL 217.735772 148.761051 \nL 217.976566 154.365217 \nL 218.21736 152.429631 \nL 218.458154 156.623798 \nL 218.698948 136.933244 \nL 218.939742 157.816561 \nL 219.42133 144.56879 \nL 219.902918 142.652484 \nL 220.143712 153.514512 \nL 220.384506 135.39773 \nL 220.6253 134.327926 \nL 220.866094 132.115617 \nL 221.106888 136.86323 \nL 221.347682 137.303898 \nL 221.588476 138.734737 \nL 222.070064 132.765723 \nL 222.551652 147.520369 \nL 222.792446 193.962271 \nL 223.03324 191.573731 \nL 223.274034 170.448266 \nL 223.514828 170.71939 \nL 223.755622 177.100934 \nL 223.996416 164.660842 \nL 224.237211 140.234978 \nL 224.478005 134.546039 \nL 224.718799 139.424296 \nL 224.959593 131.458146 \nL 225.200387 130.71674 \nL 225.441181 125.830342 \nL 225.681975 102.207019 \nL 225.922769 102.854582 \nL 226.163563 125.938937 \nL 226.404357 110.891596 \nL 226.645151 118.647561 \nL 226.885945 117.823041 \nL 227.126739 112.210383 \nL 227.608327 135.307005 \nL 227.849121 124.052011 \nL 228.089915 150.928711 \nL 228.330709 155.368621 \nL 228.571503 135.537644 \nL 228.812297 134.443592 \nL 229.053091 137.494532 \nL 229.293885 133.320728 \nL 229.534679 141.99133 \nL 229.775473 140.094738 \nL 230.016267 119.46122 \nL 230.257061 143.914785 \nL 230.738649 149.303665 \nL 230.979443 147.956445 \nL 231.220237 145.060215 \nL 231.461031 144.184814 \nL 231.701825 165.068539 \nL 231.942619 122.967563 \nL 232.183413 144.018051 \nL 232.424207 133.634034 \nL 232.665001 154.658987 \nL 232.905795 153.924814 \nL 233.146589 151.169811 \nL 233.387383 149.963819 \nL 233.628177 152.799486 \nL 233.868971 145.430326 \nL 234.109765 149.114906 \nL 234.350559 146.21902 \nL 234.591353 148.001091 \nL 234.832147 145.741503 \nL 235.072941 141.932904 \nL 235.313735 140.686137 \nL 235.554529 141.30952 \nL 235.795323 140.653689 \nL 236.036117 139.373361 \nL 236.276911 139.116005 \nL 236.517705 139.399356 \nL 236.758499 137.324911 \nL 236.999293 113.996495 \nL 237.240087 125.660703 \nL 237.480881 122.245753 \nL 237.721675 122.588461 \nL 237.962469 140.163626 \nL 238.203263 131.376043 \nL 238.444057 137.568056 \nL 238.684851 139.708985 \nL 238.925645 138.092256 \nL 239.16644 151.554685 \nL 239.407234 130.024491 \nL 239.648028 133.799913 \nL 239.888822 164.916598 \nL 240.129616 119.450364 \nL 240.37041 142.183481 \nL 240.611204 131.228692 \nL 240.851998 144.242329 \nL 241.092792 137.735511 \nL 241.333586 141.10396 \nL 241.57438 130.26421 \nL 241.815174 153.112367 \nL 242.055968 107.761172 \nL 242.296762 138.992897 \nL 242.537556 142.883359 \nL 242.77835 121.468204 \nL 243.019144 135.045673 \nL 243.259938 133.543983 \nL 243.500732 142.451235 \nL 243.741526 136.240896 \nL 243.98232 138.827454 \nL 244.223114 136.118717 \nL 244.463908 146.912073 \nL 244.704702 141.515395 \nL 244.945496 143.993097 \nL 245.18629 140.72253 \nL 245.667878 138.598693 \nL 245.908672 126.310592 \nL 246.149466 144.322915 \nL 246.39026 118.413141 \nL 246.871848 156.366659 \nL 247.112642 146.878279 \nL 247.353436 147.972259 \nL 247.59423 122.192895 \nL 247.835024 127.761144 \nL 248.075818 155.734966 \nL 248.316612 140.696535 \nL 248.557406 148.215751 \nL 248.7982 141.639957 \nL 249.038994 148.21705 \nL 249.279788 114.420223 \nL 249.520582 131.318637 \nL 249.761376 122.47186 \nL 250.00217 128.651384 \nL 250.242964 121.678021 \nL 250.483758 128.799667 \nL 250.724552 128.13808 \nL 250.965346 128.468873 \nL 251.20614 127.246974 \nL 251.446934 133.312117 \nL 251.687728 125.282251 \nL 251.928522 130.405272 \nL 252.169316 104.193569 \nL 252.41011 133.727125 \nL 252.650904 118.960347 \nL 252.891698 128.309434 \nL 253.132492 104.063429 \nL 253.373286 111.152147 \nL 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fWoC/f7A+fSfJQSggs4/EIPw1qU4E/HR8je0x5D75PB6VCkUp/A8eiqB68gz86+Ovs1qPY3/3dsbO1RKJYtwf52Lq+2uxZvsB47Zo3COnevIMz2GkzS+NVa892RfLLSCXlBmZSHiTTrOP/Ch+5mOCU7KsqTuAlxZuwIot2Q8Tkqrmb3YLNbp6ypAiiQHgrPYpVrNeUQr/HZrG/djcxIHWXCHo9ji/dkf82CZDhhSgBzT3N6uwt1aYTRVW6fCSNWc0e+wp5Pt7m8kUgqf/6T38ctoSnP3gPN/P5eT75+DW/wQ3aCmFt/rxDupOeJ3wFdf887wRJUlRCv98wM7m35TkjEj1aObp7FLz310fC/Hs5Lppd0zAesAu2feqJYf64oUoGxb67Gl8s6sBz30SvLtismYfM+phpM1fwHniWDzoYCBVyDtY+Ocodg3yyNuTCwGrvgLmD0uj5rUjY6ubE2PYYdYerV60ZF+s5hyagZkRzTDDOcQbshhNEkiv4DWHe7aDzT5FSAZ72gk8/eF6LNvs7l2TzuZoVqrvmbkCALBLE/7mxBh2mN+Z976qQ/XkGdi852C8TJJX4mb2SfcjVNtIOp+FSFjwzy3TFuO/X/jLleBRPqYNeX+DGrNRjyJ7rm6mLfb2CQAi+hsRbSeipcq6TkQ0m4hWa/87auuJiB4ioloiWkJEI4Kog8/6AnBuHEs27sEmRYgFxR3Tl+Hch+wDoUle/ix9uQfsLnuPJvzLS+z99lXM7+37X9UBABZt2GNbxivNHndMl0vsuAFV8XOkUToEcewXF27EjS8s8rVPJscarM8f+6+a9/zE9nEiHDL2Kipbl1qWI7b5B8I/AEwwrZsM4B0hRA2Ad7TfAHA2gBrtbxKAxwKqQ6Bc8MgHGDPl3ayc+6tt+/FMGj2R7ATOLs3m367COSSu23FUbS7pAd8sm33U4GDpHH7Q71WGZXEmvKSciA/4KoHdAjp22NStkb9emDTasJ5t/gEghHgfwC7T6okAntaWnwZwobL+n1ou348BVBJR9yDq4Zdcfea765PLa+qEqlXZJUuRwa7cQuJK7O6fqkkle4+9evtQmqSmIWJpGhuKlMEzLOZIpJNsR7WUTy0dA/u65q89OCnk+5hi/YTY5p82ugkhZIveCqCbttwTgOo2sFFbZ4CIJhHRQiJaWFdXF2jFsjWb0ut5szUYJ0MaeL09di9NSyQAzT+SXbOPKpTSOdta9XPfujc9s5WtOJTtAfWgbf5KOzOHdZCKj92s4Gw7lmVLHmVkwFfErs7XFQohpgohRgohRlZVVbnv4OvYgR7OM2pDn/mlvabnR/jvPdiMnz3/BfYebE6pbkB8wtGqbfu9NUibIqr7XtLePm6af5rNJOr50ykc1A/o6N+/k74TmUjWZdiOaFTgHx+scwzRvH1/o/7RkT22dGj+6iQvwH1wO9uaf7Y+PukU/tukOUf7L20NmwD0Vsr10tZljIg+ozWTZzUKxeuetc9X2nDIeyLrp+atxauLNuMfKYYHiEaFQRt8+XP3R2J3+6Tmf8u0xZizKrl8vF7NPukiiN6LF/z6ub/82UasrTvgXlCjqSViKZD93N8dDmFIJK8t2Yw7X1uOB95ebbk9EhUYdc87ejgO2ZvykhDIL+YBXzuXTqn5f7rObLHOLNn6+KRT+E8HcIW2fAWAV5X1P9C8fkYD2KuYhzLCuD/OBZD5MMNec4W6af6qkA7KY6ElKvCPD9frv+96bRleW7zZcR+7c/7vK18CiHmh3DszMWKpF7yafdKF0eyTPvyGN/j5S4sx4YF5nsuf9sf3cNSvE+eG+JG54//vPdcyMsLrrnrrD4VUuGSP1yo8R1ATvuKTvIw2f/Ph5UfiyfnrAjlvsmSr4xGUq+dzAD4CcCQRbSSiqwFMAXAGEa0GMF77DQAzAawFUAvgrwCuC6IOucCmPQcdk5l7bdxuwl81GYU8uK0C7laSFtOXaV9jC37y3BdobI7g82+sZ4Oms9GaNdPqyTPwu9eXZ6wO6v1Mp2aWTJffT5A8O3dlP4J2T4O7SVFOrLKrmhTE5sxu6kc+KPt/OGQUa3ZhHAZ0awsAuOCYHoGcN1nyWvMXQlwqhOguhCgVQvQSQjwlhNgphDhdCFEjhBgvhNillRVCiOuFEP2FEEOEEAuDqENy9Q72eCfd9y7Of2Q+hBCWA2peG/dBF7OPOm4V91jwXE1L7DTt/33lS1z0lw8NE7ckqTTa372+HNWTZzjUJ/H+qRpaKl4+C9fvcn0WBuGYCVfPDBO0KSts4bppPF/sf9T03+gWHFBdQsbjTRwWE+7tWxn9/YkI/avaZN3tNa81/0JCCOHJxmm9b+z/lFkrMeD2NxIG1bx28etdNH/VjznkkLXID002A3XLNsUiQFoNKKdyRinI7e6JV7OP27yg+2etNETMXLh+Fy5+/CM8/K61bVoSzYynp6/n5rX9NEeihhy1ViQr8CY++gGOvXt2wnrZJu0em/k6Lc0+UYG9B5t1E1KymDX/X5x5JL6880y0r0ic7NWqLIyDAeURTpa81vzzFatb/tJnGzEyxVDLT7y3FkCiO51TV/utZVv1ZTezj+rKZpURyQo3IWl3TsePSwBt1u6eBBHVMxoV+MvcNfj2Yx/q62QOg9XbnQdNoxZmnwkPvI/j7gk2DLfVfW2ORC0FvblscyRqOWB61d8/xaA73nQ8b7LCf/GGPdhpMQ9FtpNlm/dahow2n04f8FW+shEhcMxv38I5D8XGNPz0TgyB3aTmr9StnYXgB4BWpeHAksi7YXc9Tle5aMOetDkcFLfwt7inHyqhj+33E3hx4QbXRmM+vJPm9twn3+jLbmYfg+bv0ebvhp3wly+SVW/eSWPxWh87IWQeg0gGK41O1tktvIFhopq2uHLrftTtb/LURryiyu4ju7UDANTc9gZuenFRYlnTPa257Q2c93BiqJD5ntqwv3q6IRWStXX1OPvBxAFpO81fHViX78e6HfUp1VFq/l72ryjNnOZvVx+792j+6h248NEP0pbop6iFvxVe4ovM/aoOt0xbgikWuXdVIqY+sJOypRb1ZfYJaLLMwebYB+fuCwfjg8mnxc9F0h87URg7vVzmbXbxVezq3dySunSyeqmlK6Jb4NKIg7fPW0qWr1QxzoaOL7+6KNHTyup+r9yaXBa2oDxrJG6xAIWp+UjlQL3PZiGYrDkkHpLcff/ykhCamt0VjbeWbUX15BnYsCsxValX7O65+d5I5Djb8jQl3yly4Z/4MLwMI+5vjAlKt7EB88NesG6nbVm1++7m52+l+af6MkvNvyxM6FnZCgBQ1a5cMfsk7mNe1a68xHZbK5sk73bzAIJI+2hlO5ZapdtHXr2d5h6b6hKbKuqx3cY53D7ws5ZuNfz+aE28vZnTXQY9yOnWk/p6V73htzy9es3mKiXr7muO7eNEiMjTR0Z+jBdv3JNUnQBgyca9lhnb7M4vryMdcyGAIhT+002+61v3Nhq+5l40f6m1qNmIPvs6caKI+QX72fOLbI+pTiqqb3LW/NWk5fKl27LHOTSAm3eMLvy1uD7nDumO9hUlcc3fogGaG+1+ZZDRbPbZYhO64IZ/f2G5j5vNX5Z1em2duvNuj9n8MU3WCcCNiMM1P/3henyzs8GybK3FmMVby43C/9K/fqwvTzNFiW041GLrwuuXbfsaMemZzxzLXPDIB4bfspdj5x0E+AtBofaa/ARsCxF5KxeKv+vJ8u3HPjSMP0nsjihnKqcrsVHRCf+5Jk1z9O/fwdj756BufxPmr96hhzX2Qu32A3hs7hrMWbkd337so4TtfhqK+mIfMHlqmAXpr17+Ul+WSs6sZVuxfodRu/KDHL+QsfxLw4Q1dfWo1WaTWmnijoOtSbRXdZ/fvpbo0284t7pf1Nq11slrxK/N3+wEsGnPQfzjg3W4f1Zyk9ji54kvb9x90OBVdcf0Zfju1Hi7Us0DqgKg1tMrj85Zg4v+8qHlcbygtsnPLbRZp493SySqX7eTYEs2BIXXTHQAEArFnvV/Pt+IVxfZz2oPB2RetcJO8y/Rxi5a0jThseiEv9rNVu/55j0HcflTC2wjXqrI/VZs2Yf7Zq3EHpu4On4aivoSmN30nA6jZi3a7ePDZUZq/rLByZDGcoLP4++tSfgIOQ3qJjN72ny/nI6vvjD3zFyBAbe/kdA7cfKaMgf/mrNqu+FjEY3Gu91WL+eYKe/izteW4y+mPNC3vfKl4/yFhOswXfPkl415ctUJVqqCYCU0kxn0NysaVoyt6QLAGO21yWKWucrcVfbBGJtaopaunlblkiGkR/V0hzSzz80vLnbsmctjvrpoM/48+6uk6mWHrfBnzT9Y1I+oekv3NSYfGM3u4fkS/orgOtBoFv72x1FfvFTaSFzzJ+2/sWl8ULsT2/bFTB8Nh1pw2p/mOuaBTcZZx3ydTS1RPDZ3jaXJRb3WZxfEch+YbcRO3liqK/i6HfW46u+fYrKSoDwqhCEXrFeeXfCNeyEFs3lp+37jtar3xDA4aukK6uvUnpHnVYWxKpCsFO32DjkhYsI/tr+TVutH81dvo9dMXoB3s4885ntf1eHBd5zniPjF7vwhB2eLICg64W/nbrnvoPdgamat1q4B2w3CdmpTllhWDzYnUG8a8DXbS1XCBuGfvCYuu+lxc0hi+ZVb92H7vkas2LIfa+vq8ZRDTJSNu41eEd87vo/j+YHE+h/161m4b9ZK3GTKUnWgqcU4TgPrQe8GgyZvvp74fZPXvmxz3KsiEhVKgDDne2e13asW7mbXVo8i3DR/wJfZMnbM2P+p769JeGYSKyVGVVb8ZgUbcfdsrN8ZM1E6CbZGD144+jH7dNSXwz40/xBZvzf/7/GP8OS8tfrvdMYZtGsqcjwkXTOQC174721oxtT3411zuyxTdiGRF2/Yg4/XGr10zA/Lzr4ZiQr85tWleMxkGvjhmOqEsvJlPtgcSdDgrCbNSFSty2kegZssmvFlbLBQvseHLFwtr/z7pxh17zuekr2c8ef3Db/PGnSY6z521VefzVG/fgOD73gTt/9Xzxiq19ls9lEHfM2HVu9bmdbLMZh9RFzbc3v3pEasnt9rV72+qQVd25VjYPf2ABK9zdQ2GjH0AqwH4Pc3elditDNg275G3DtzJa78+6eWJSyFv6r5WzQHt8tfqs0cdxLwfsw+d104SF+WvVcvQjPm7ZO4/pP1u/C7GSv03y9/bhwwDzLqrG1eDK1i6YpwW/DC/9ZXlhgiSx6yeUEfnVNruf/ERz/AJVM/ttymH9OmkUaiAv/86GvcZxoUtPrQSMHhpwcCGLUuJ3dPt9dgsZZ3V3cvU4RLl7ZlBve5Mo+ZvlTMNnYrvJjPrISFmhXqxYUbsFzT4NXJclEhDIObRMArX2xEJCr08zaYyofD9r0gFfnR2K3Y5716qhxoakHb8hJLAQqYXE6VZUt5IJKbGCWv32xu1M9lcVC1t2ul+bv1fORYg5Nbsx+zT3lJGN3al+vLsf3dnwHZaP5u7POYP8NKIfvDm0Z54NbuecA3Sd4zDTy9qwzoqjZhP8nazc/KzifdzkZ70YheCWXlh8jv2IP63jl733hrQPJFVrWNklDIILytXvZSl1lTXrQXu56LowYn4vXZtPsgbpm2RA8PoGr+kagw+LpP+2wjbnphMf42f50i/FXNX3jW/Bu086gD9U0tUTRHoq4CrOFQBK3Lw7bzNdRf6v35alvi5K6oEL4FmVpcNQ0u27xXF+BWz0VVDiyFv8fzO/nyJzvgK3umXvY32/y9muukAnf5kwtwszYbe23dAdwzY7nhGAcsPm6PzjFaAuxOKe8ND/gmidNs2WQnkZj3cjL7SD5ZH5sH0K9LGxytdfGtynrVKCQhjzZ/r2+jPNzIvp30dVv3NRpjsFg0RreJNV4mbdm1cVeBJs0+Sh33Hmw2aN8tUYF5q+NhD2QP4utd9boWrZ5Ftfm7fbhkD0P1nKndfgA1t72BI29PjKWvsr+xGW3KSiw/uoDJ7KPcIKtBRyH8h1uKCJEwB2T+6h0496H5+uC1lfBx0/yDCFbmN9WkPKXsmXrZ32zz9zq5cOXW/Tj5/jmYX7sD/9ESH/3o6YX467x1WLejHtWTZ+DRObWezHBuNn8e8M0y6kto1g7sshep5iK5fPGxiVo/EHvBNu85iIsfT5wv4IQqdJ00ZK8vo9TwrxpTjdZl1rNyrYSh2TvIjJcX0T7Oj7e6q5reNzsbcEgRUH+xMevVN0UszytEfFayW5RJ2WNQX/RPlNncNbfN1NvMrKVbsF/r3UWjAks27sWRh7XTxyDMXXy1am7PMCqE46QpKyY8MA/PfxoT8vLw81bHestyANzN5m9dF1/VsCR5zT/23A55MBuZZ/h+8c0eT+d4ct5afGMK9SAtCbLH+Yc3V+EV01iBFW42fzb7ZJmrn16I6skz8OS8tfjltCWWZZ6fNNrw22qGqZ1nRCQq8G+fboKAd7OPV0VM+jMTEfp2bhPfXylj1o7+fuVx+qCpHV7MPnZdbrdQxvIW3Ddrlb7u3ZXb8ZCiHVuZSYDYtciXT30ykahAhSb83WItrdiyD9WTZ+CjNfGehTrbuTki0HAoguWb9+Gaf32OIXe+hdeXbEZTSxRNLVF079BKd9ndZRExU+Iu/K17s8P7VDruZ1ZennhferkI2/NKbfTmFxfh8qcW6OuvPLE6YZ9k5h88OqcWP33uC9/7AYrm76HNkWnA95KpH+O6Z51nKwNI0OgH/WYWNmuz2NVxqT++ZT0nQL0nSzdbJ4CSH10e8M0ycqxA9QAwc0TXtq7HsZORu+oPYe0O77lZJV7NPn5t/rHl+Hp192aTRta/qq2rJuhF87c7hFvcIqkhykFrAPjz28aXrsRmRFUIoddd/ZBGhdB7Pm5RVmVMHdXHX4b1Vtm2Pz7gfMO/v9DvSXlJSL/XZj9/FTdt+u0V2yy1xG7tKnDWoG7OO2vnVj3T5G13Mvv8x5Tr+Zwh3bV94/tYafAVpSEc06uDYd3x/eKmxqnvJ94/N+QZy32afcwfp5lfbrUpHcccElxVEPZ7GLf7UIm7pIY4kUSjQg+JUnCunkQ0gYhWEVEtEU3OVj2CxMu0ciefaC+NzgmnRuK1+aiXYFdXs3ZJ5BxHB/CmhdkJeSdLhoDwZB4I2wxIR6JCv2+q7Tsm/GMTldxiLUnh6DRbtiUqsMMk2OUHqjRMnnzlvQiBZoubFQoBD186Av+6+nhMuWiI4/6qZ5p8HNYDvtZ1qSgNafvE11nNtH7pf07UzTPxfeO/U9F2u7Yvx9iaLnjgu8NdyxIBOw4kPzPeCju3cRVzUzff4z+//ZX+IX71+pMCq5tKVoQ/EYUBPArgbAADAVxKRAODPo/XzEe9OrYK5HwhD8LfT8RBOwb1iA8YG7xBfJh92pZbz8C00/xVtuw1ekYR2WtZ3x/dFxWlIZx+tFHzbKfMAF2lhSVOytvHI+beiiQq4r0io+Yfy/IEuEdZlYLK6SMUiQqs2GI0PcnooFEBjDuyq+M55DHcsMohTUQoKwnhpJouuGSU+2Q7iR58zeKjbDW2cP/FQ+NB1ZT1VjFzhvTqgF2mCWlq71T9YPTt3NpznYHYuNUzVx+PE/p3di07O8Dw3JJ9HgZ5zR/pqfOMPR21XuWl6RHT2dL8RwGoFUKsFUIcAvA8gIlBn8TryP0ZA927xF7wor0FIfztJqrVN7Xgp899gaWbEgWAeaJan07WL5RaP7sIp+YxD6dIqAMOa4eVd5+NnpWtsObec/D45SMAGG2m12o2Vlt/5wA8R+xi8De1RBXNXzlnNG72cbP5exmQu3fmCluNsDkSxY/H9nM9hpfbcMf0ZQnr/M7ANZ/PnJcCsB5bGNyjg5JZLr7dLkifOTKp3X2UPbBnf3S8p/r6YbeH5PR+MXvsfX9034Qy5hwCn6wzRgW2ClcRNNkS/j0BbFB+b9TW6RDRJCJaSEQL6+rsg0Q54dVbwE4L9ouXiUxeQkarHNG1LUYptlDAXkjO/aoO0xdvxp0WAsAcg97uI6RWz2tVndqmui0cMg4ix8s4+9PXOdjB/fpAr7n3HMPvpuZIQo9DflCl8G9wCX5mjnE0pGeHhDLTPttomCWq3v8Wxa3UClmfQ5Hkolyaj3z1Se4fGiCuvVtr/tbuvvJZPjVvHbbsPWg52Gt3rR+ZFBRJmWaya10WxoL/PR1zfzHOueI+XjE3R4VkMAv/VhZec+b5H++aAkqqcy6CUBityNkBXyHEVCHESCHEyKqqqqSO4dVu2CYg4a+OKV43rr9lGS8fCJXHLhthSJICmDX/+PrXtFwFXgSinYnKaPbxVlenXAHmbb0tehxygC4Z3/BNuw86BhEzY36RGpsj+kCt/DDL+9eqNHbch961dhO1o1v7Cvd6KPe2JRJ1VArk43x7hXvEWSvMj/r0o91NTEC8nVm9Rla96nAo3mY+Wb8LP/n3F5Yfbr+yTEaYbYkKdGtfgeouiQpEDP/tp8QtpVsSLDGZ3qzCoZiFvRn1VfCrMHolW8J/E4Deyu9e2rpA6dK2HP926SoCQLVPm6IdISL846rjMOcX49ChlXXaQr+KBlGiwFJlu1XAtkUb9rj6ptu1efVcXj9UTsXM29qWl+hxbCS68E/Ctr99fxNqtNy3dqkinVi8cS9mfLklVldtnTSdmJ+hl5hGbvWQx+inCDCr3pBKJCoghEiIEeUV80fc6xjKK19sgrCZO9Bo0b5CRAbB3tgSsUw649cMJc0ebua1AVo7KA9bz0+xwm1+SjKYezBW7cYqTeeqrfsx8DezsHnPwUAmybmRLeH/KYAaIupHRGUALgEwPR0nstNw1UFTL0HHvBAmwrgju6Jflza2XTX/9leyEP7Wmr/KVpckHfb1M5zaE06XZHWa+7491PC7TNf8vZ3PjEyN5xS/3w9yvoU5htG93xqCH45xN5k4mRF7VLbC+cf0wKFIFCcd0QUlIcJ5Q7s7Hi8qhP5Buny09wFbiVlz9ONJs35ng+XHYtayrQmZwEJEhnPVN0XwvScXmHf1bcaIx7V3rvdjlx+LZ390PDr4UAJUs8/EYT0sy6Sa8czs1WRdJoRnF3yNhkMR/POjr7GmLvnETF7JivAXQrQAuAHAmwBWAHhRCJFoqA4AO2F7/jHxB01EmHLRENx5fmoOR+qHxm6QxqoL5+SDbaX5qy/jzS8uNu8CwF1rt7svXrqYXdqWG/fxYfYBEoWqnEmbqleP33AACZiqau5VlZWE0Kbc+CJfNDw+VPXvHx2PG8fX2M6MBmIfw1jS8AhaolGM6NPR9Z7LAIEAMGlsf6yfcq6Xq9H5YoNReA3v3dF1Hxmraf2OekQF0L/K2Dt5ddFmXPQXY0pCAaMisM4ms5xsm3dfODjhwzequlNieY8ZrTq0KsWYI7o4ljGjxqQa3CNxrAZAwnX6RfXWWXn3BMsy7SpK9Lkoj7+XXA/PL1mz+QshZgohBggh+gsh7knXeex6deaE4peM6oMrPWh1ns9rc2Irt0GnMQeCUSBffGwvT+YRN08nO+3Li6nHnFzFj9kHSAwCJ9/pTHR1nTBX1Vydfl3aJHygjlMG40/o3xk3jh/gKPzX1NWjojSEppZYKkO7aJ7qOE9ECLSvKMH4o7uiTxImyrUmLbKjRT4JM9OuOREAcNU/PsWBppYEV10rItGop56tVJK+P7ovHvneCMO20pLE/Uv1SLPBtw+jd5v3/ey85axQzT52A8w7DhzC3z5Y570CAZCzA75BYadVHd29Pe751mB88esz0nJeO83fKmeqOW2jChEZ4sSHiTy5PrZEY1El7ZJ7eDFLefWCigph66podf+l5l8WDmFUdSfdB19e1q8mHOXpvADQo4P74KpXzHU1f2QPr2qTcO/VF1vu36rM+b6Vl4TRqHkZWQnLsTVdDNEgIxGBA00tOOqwxICA6WD2TScn9M68CPWWqPA0mGsu8+7PT9GXwxZfQ93sk4YwB+rT9Dqw2qVtOabfMMaxzE9PO0KfnyDNPl3aliEUItcIuJmi4IW/XaMtCRMuO76vJy0oGcwa4uFat7lru0Rh5RTPhWA0Z4RCpHtfzFll7zHQ3CJw4/OLMOyu2Zbb7e6L+u7df/FQyzJmIlGB2861NplZnUUKlogQKAmTbsuVgtVLvoBxR1bhuR+P1uOpeOFCG5uuZO/BZkSjAp21NnFstdE80rqsJMFN0sqe66T5x/aRmr+1i2frsrCh17F1XyOiAmjfyv6jMnFYD88D0m7UdGuX8AzCIeC9X47D6z85CYN7Wn+EenVsbdmu7jh/IK45Je79Zr7mw6viYVGslKYelbFJmBUu9zUZ1PvsdSiipmtbVLZ2lhunHtUVD10yHG3KwhhzRBe8/pOTMOvGkwF4GwPIBAUv/O3MGF4mTlx/an/89LQjkjpvgnmgbye8/pOTcLnFhA8nRZ7IaMIpCZHufXGVTeYlIDaDUHqxWOFF8zfb9pPByqwhu74ytMKn63dDCKEPRLoJTyBmglFncHrx2PrN+YNcy3yxYTeOq+6E7h0qcGL/Lnr6Sekg0LVdBdb9/hzcOL4GL0wabSlwzSZFMxWlYbREBZojwlLbNN/3sx+M5SdoV2E/kBkV8Tb3p+8c43h+APjs9vGWk48kZvPE2rp69O3cBoN7dsAFxyR+RNdPORdty0ssTSfd2lfgW8rYSM9K+xn15rzLF43oiV9NOAr3XzwU4wYk5/LthGpq9OqM0c6Da3G7ilIc07sSy+6agKp25Rjcs4P+XIP6SKdKbtQijdg9T7tAXyqd2pTj+ydUJ6w/WWmE3x3ZO2E7kOhR0dgSweCeHXx7OhBIn6z2yPeGozQccsxDIOujhjPYbmFqOvWomK/3K9edaFjvxxvpT985Bvd+awi6d7B/md0GfBdoMxs/qN2p19mL8DfXc7DFxCqn8wLWH0AhYh/bzm1jmt293xqC9VPOxYyfjtXLEBFuHD8Axx/eGaVWwt+D5g/Exn+sLAB2H12nj7EaoK6nh3AlnduWO5YzCyh1lrPZNNNR8a6xaj8hAo48rB3WTzkXD14yDE9ecZzteRcpwfmAmLJTURrG/xvZOy3+7kZ/euO2ob0S29QvzhyAKd927xE7zT0xt8OycAgjXCKvpoOCF/5OZh83CEBVu3JccpxRwKvRCOXhK0zxN8yav59p5A98d5i+3KY8rJt9WpeFUVpCaI5Ebd3PJgyJua2qg2PrdyYm5j7j6G5Y9bsJGN6no26SAuwHIK3o1r7CNTG79YBv4kl21jfpPZwKF80ZsJqs5W4PNvf27DSwA00tnrvm0g592lHxiVNezD5ALE+AVfvs0s5ayDsJFAHgmatH4fxjeni6fypVFuczCyhVmTDfx7m/OFVftn7d4isnDutpeT67Y1uNAQSJqvmbPy5Wl3LDaTXo5MFU3NbhWZnbXY/KCjxsGvjOBAUv/K0mQQHefI1lWzDPFZCByNQyvz7PaPOWwnfckbFeQqXNpC8rLlS6yO0qSvVeREVpGKWhEJojUdz8wqKE/dpXlOgTk1RTkTpmILU0orjtUa27H83fS1ErbU2+4Edqk3KAWGYqGcwqGc1fJiABYPiYqZifuZ2X1dJNe3F093aW28w0KWGZJW5mHz1DWFRYzkOxM0kOcujdCCEwtqYKD186PMHUOeYI6wBnstSFw3pgwf+ebthmFv4G06OiOP14bD+DX71V+/GjsFuNNaQTO5v/qUdWuc47UT/4ZpwsC2ZFMBSirJiCCl/42zxALzZ/WcJc9NZzjo6XkVEMTeeRmv/gHh3w8KXDcfeFgx3PNfX7x1quLysJ6cK7VWkYpeEQosJamz/lyK66rVb1id5zMHFAWX0hS5WGan55n7l6FObYxFIxv9TTrjkhwR5sdZeJCC9dcwKeU5LfrK2r18MseBH+Uii8eePJePlao+lq5k/HYvEdZybuY6pwG+086mzbQy1RNByK4DAPIRqAuMufagp0E3ayzURsvGP6VyXmhahqV+7ofWUQYqa3+pkfus9yN4ekMNv8D9lo/sf2NQ6KW127H4XC3Cu0870PCoPmr7TWcIhc5508dvkIfHLb6ZbzLpzEi9mlOUz2wt8c1ytIgglqk8PYPUAvmr/UysxCQxUMcos5gJU8bzhEhglldpxpmmXcoVWpHgXykD4QWuJorvrDxUOxfmfMp1sdc7BKFmFu6Pqy6VrH1tgPspnt+SOrOyEcIkxfHJ+6bvfiH2cxmUfixWwhj3vkYTENXb3eitKw5THMWvZOzctKjbMvY/J7jfc0uGcHfHzr6ejWPm7KcLNNy80tkahlOxzVrxPG1nQx5Bx+5NLhjsdUBYr5mHaz3GU9rBSkknAI93xrMDq1LsO1z35uuL+qKcas4Vpq/o41N583XvqKE/riu8dZj6kFhSof1NsUy/BlvDG/MfXuy0vC6NrOuq06ffDM44EhIkN7/e/1Y/Dhmh24f9Yq37HA/FD4wt9G9fcS02PTnljcevPLrNr39RfIfF6tUSUbjvXtm0/Bbs1HX9X8naIQVpSG43FQXLQWtVqq3zH56AtatUvzC5NM223t4icPJObVtbrcLm3LsKehOeFelIQILVGhh5VWUy7KD24bD3WQHGaaazC8dyV+edaRaIkIHF7VBj8xpSOUH82osP9QmHsebiaIZEIAO83MBoDLju+rOwuoqSDV9mJOkmM54OujTam90GF9KtMW1Exy+tHd8NwnsR6nWvfSMCUkKHK6ra/dcBI27m7Atc9+DsC53UuHjf855XA88d5aEBnl0bDelXo2sHRF9ASKwuyTvOZ/QBMO5rJqg9TDEZveTqntnz3EOW6LHVXtyvVAVVL4V5SFXAeq7erjREnY3uzj5Vwq5tO6HW36DWP0cRGJm80cSMyW9OL/nJBQZuHtZ+D6UxNddZ20el34pxDplYhw/alH4Gfja3CiRUIRXfOPRm01O/O9bTSFADZn5FJvu9dneNGInjimVwdc5RDiuWv7Crx108m484K4m6z6PpQmaP6Jx3D7yKioQjAT/vB3TRwUT7+qVHNQjw74emeDoZ05fYiG9OpgeNedykrFULq8yuc175ZT8dhlIwxlvCSISpaCF/52MtCLdiQ9SNSiZtu93GQ+j3Rt85LXV7L0t2fhyzsTbdXxXK9h2xyv72izJOWLqXZn1fgpTgOwgL+Q01ZFzWY2N81taK9KXH680d+8JJwYzM6MWfjb2UZlT+Sm8QP0dfd8azC6tLX22JA5mr2MO3jBqhcjL83O5g8k3tuTTDFrpGur9Dy5dFTcPOL1u9+5bTleveEkR797IBYtUxXEhvbioBjF13mrD2A0+ySbhMYPpeEQums9N/V8HbVJXE6pOVNFjuHIe9i7U2v9AyLbbRplf+EL/0E92qOma1u8fO2JhoEZL5q/TLggv77tK0oSJsaM0Aa8jvLoHeJE2/ISy4k8t517NMrCIbQtL8HG3Qct9oyHH5YNWDV3uckC9YXz875Z3UJzj8PL8bq2N7r+lZeEXD/OXvKkxo4de7G7K6aZ84b2wMLbncN6BBXn3ewCPO+WU3VNuMXG2wcwCtGbzxiQYKaUm7u1r8D6KefitKPisXfSHSNJtfObQxVYPW8/phu1F5oB2W9AfRRyAHaqEmQt6PrI3qVVG5DDAmzzT4HWZSWYffMpCeu92Pylxi1f1h9adI8nDuuJkdWdXLUnM8P7VGLFln2e/NMvGdVHz71qZ86RTUR+1NRy5sThZpw0OWcsGq3Z5u/hKEN7VerLw/tUol1FKUpCBKdaexX+l43qg6q25Y6RU60ISus0C77enVrr8d6FML7cnduU6YPQcnV159aG0AhmrMyayUZHbVMWxgn93aNiqnb+EvNHyaK8nyaltsUMy36DcJeROJ/5+Ou01Uf2Li2VKMFmn7ThRcg1asJ/vRaa1m56vV/BDwCvXDcGj19u7d7pxOSzrYOeSSFjlRJxgSk/qJnSJG3+VkWl943f40lf9GotsYlb3twB3bz1tEIhwoTBh7lqnx1NMeCDNDnIl1wKNrtsaXN+OU73t5fN8+qT+lnGOnKyo0uTRcfWpQkzuJ1YdtcEPHnFSNdyqoA2R7e0+uz4sfmr72UmzD4qISI90ZA0cxm+owHXR57DcuwsymaftOGlOyW1qiHajF63wGB+kQ/dj56mpkE8ShG08mpkj9xP0nOj8PdeF6tG27VdzAwxtiamQXp9X6QpwUtO1XOGHIYp3x7iWs4Pr1xnjNIY5EsnI0DKgWz10Kpm176iVPe3Vz2CrHC6r4d1qMB7vxyHT28bj+F9OiZfcRvUZ2Se7ZrqJC+ju6XvqqUEEeHla0/E578+w3p+SkDnkWNwUr5YySI5pnPxselzdS064X/7uUejNEyu3akfj+2nZ5y69pT++PLOM9FZia0ytFcH3H7u0Xa7eyLZxt21XTkqSkN6tEP1WLIhWZmHysIhveGp8WeMNv9gzR1eDyc/QF4ieg7tVWnrDuplfyvM8wKCdLHrX9UW147rjxeviXkkqffE7jRyvZ23msRuc9/ObRJMMkFRrt2rvhbB9NqWl+AJ04RFf8I/ufGnVNDbKmLvRac2Za75MFLhwUuGY+XdE3QFzeo6e3dqjfVTzsUZA/2ZK/1Q8DZ/Mz8aezh+NPZw13JqiOJQiBJMPtNvOCnluiTbrX3756egvqkFv/5vPPmZ1BStvH0kH0w+DZ3alOGm8QMMwtOLtq3Sp1NrfLOrwVEwyZg3ZR7zqUpTglPYYondaRf95gxfQjtEcc06ceAyOMlDRIYcBepzt6uvPH8ymn+6kYOhdpm1zGlR/Zh9DPtl6CL1XrNyvqaWRLNjUNUJhwjhUDxsdzp9+Z1ISTUgou8Q0TIiihLRSNO2W4molohWEdFZyvoJ2rpaIpqcyvnznWQbU/uK0sRImtqxZI/GyuOjql05wiFKyGHgN4m17Oo7jSvK0Lxmbxc75MvWqU3yYaQrW5c5hj02o9bfrCWn84U0av52wj/23814Zxe7Kp3IXpJbTl2Jl1v5r6u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}, "metadata": { "needs_background": "light" } } ], "source": [ "# MJDs/residuals for PSR_B1821-24\n", "mjds = []\n", "residuals = []\n", "with open(\"1821-grid-nicer.txt\", 'r') as file:\n", " line = file.readline()\n", " while line:\n", " vals = line.split()\n", " if vals != [] and vals[0][0] != '#':\n", " mjds.append(int(float(vals[0])))\n", " residuals.append(float(vals[1]))\n", " line = file.readline()\n", "\n", "x = np.array(mjds)\n", "xindices = np.array((x - x[0]), dtype=int)\n", "ydense = np.zeros(xindices.max() + 1) * np.nan\n", "ydense[xindices] = residuals\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(\"MJD (day)\")\n", "ax.set_ylabel(\"Residual (us)\")\n", "ax.plot(ydense)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9aaa1499d0>]" ] }, "metadata": {}, "execution_count": 7 }, { "output_type": "display_data", "data": { "text/plain": 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\nL 138.433725 140.027642 \nL 138.679974 140.555074 \nL 138.926223 127.413695 \nL 139.418721 147.045261 \nL 139.66497 135.719357 \nL 139.911219 154.625473 \nL 140.157468 145.172415 \nL 140.403717 150.201215 \nL 140.649966 146.98604 \nL 140.896214 146.98604 \nL 141.142463 147.171104 \nL 141.388712 140.042447 \nL 141.634961 145.165013 \nL 141.88121 144.217486 \nL 142.127459 124.171377 \nL 142.373708 146.793574 \nL 142.619957 129.234722 \nL 142.866206 138.014148 \nL 143.112454 133.624435 \nL 143.358703 132.800285 \nL 143.604952 115.510392 \nL 143.851201 115.510392 \nL 144.09745 125.109651 \nL 144.343699 141.759839 \nL 144.589948 149.85823 \nL 145.082446 142.322433 \nL 145.328695 154.980796 \nL 145.574943 133.794694 \nL 145.821192 133.942745 \nL 146.067441 149.117975 \nL 146.31369 133.424566 \nL 146.559939 141.271271 \nL 146.806188 137.658153 \nL 147.052437 153.143617 \nL 147.298686 153.601902 \nL 147.544935 132.726035 \nL 147.791184 145.694632 \nL 148.037432 142.236967 \nL 148.283681 154.743914 \nL 148.52993 134.62378 \nL 148.776179 171.177578 \nL 149.022428 132.299379 \nL 149.268677 136.607663 \nL 149.761175 130.478351 \nL 150.253672 131.188996 \nL 150.499921 140.39777 \nL 150.74617 135.793383 \nL 150.992419 136.928441 \nL 151.238668 128.790569 \nL 151.484917 128.790569 \nL 151.731166 133.37974 \nL 151.977415 128.88598 \nL 152.469913 141.10677 \nL 152.716161 132.682255 \nL 152.96241 131.671395 \nL 153.208659 120.26283 \nL 153.454908 120.055559 \nL 153.701157 112.667812 \nL 154.193655 126.806685 \nL 154.439904 123.271967 \nL 154.932402 136.1339 \nL 155.17865 155.780271 \nL 155.424899 145.957086 \nL 155.671148 145.681958 \nL 155.917397 79.193475 \nL 156.163646 111.749896 \nL 156.409895 95.471686 \nL 156.902393 123.688889 \nL 157.148642 119.300498 \nL 157.394891 148.570186 \nL 157.641139 147.504219 \nL 157.887388 135.882213 \nL 158.133637 143.862164 \nL 158.379886 135.141958 \nL 158.626135 136.666884 \nL 158.872384 124.985658 \nL 159.118633 172.746919 \nL 159.364882 116.250647 \nL 159.611131 115.762079 \nL 159.857379 124.319428 \nL 160.103628 120.040754 \nL 160.349877 123.712419 \nL 160.596126 124.756179 \nL 160.842375 69.792235 \nL 161.088624 119.085824 \nL 161.334873 108.936927 \nL 161.581122 111.99418 \nL 161.827371 104.806303 \nL 162.07362 114.318581 \nL 162.319868 104.228904 \nL 162.566117 147.060066 \nL 162.812366 135.926629 \nL 163.304864 123.727224 \nL 163.551113 102.407876 \nL 164.043611 138.25103 \nL 164.536109 169.904339 \nL 164.782357 161.991012 \nL 165.028606 161.69639 \nL 165.767353 200.612343 \nL 166.013602 175.04171 \nL 166.259851 110.19536 \nL 166.5061 128.760959 \nL 166.752349 134.860661 \nL 167.244846 95.301427 \nL 167.491095 105.191236 \nL 167.737344 103.819649 \nL 167.983593 87.613349 \nL 168.229842 155.750661 \nL 168.476091 104.658252 \nL 168.72234 130.204457 \nL 168.968589 69.513714 \nL 169.214838 121.989475 \nL 169.461086 125.367815 \nL 169.707335 102.378266 \nL 169.953584 124.319428 \nL 170.199833 170.674205 \nL 170.446082 112.090413 \nL 170.692331 100.335162 \nL 170.93858 129.353163 \nL 171.184829 119.463355 \nL 171.677327 126.288507 \nL 171.923575 98.188422 \nL 172.169824 112.238464 \nL 172.416073 107.988578 \nL 172.908571 120.363998 \nL 173.15482 113.249324 \nL 173.647318 142.707366 \nL 173.893567 124.882022 \nL 174.386064 123.993716 \nL 174.632313 124.215792 \nL 174.878562 126.823977 \nL 175.124811 118.336663 \nL 175.37106 116.189634 \nL 175.617309 121.198404 \nL 176.109807 105.854804 \nL 176.602304 125.986333 \nL 176.848553 110.636091 \nL 177.094802 141.94413 \nL 177.341051 155.132378 \nL 177.833549 140.824395 \nL 178.079798 146.350974 \nL 178.326047 144.721753 \nL 178.572296 136.752247 \nL 178.818545 139.87824 \nL 179.064793 136.428216 \nL 179.311042 143.935819 \nL 179.557291 134.869109 \nL 179.80354 132.142685 \nL 180.049789 136.57206 \nL 180.542287 120.069669 \nL 180.788536 150.504207 \nL 181.034785 137.174861 \nL 181.281033 108.164691 \nL 181.527282 100.249572 \nL 181.773531 120.874989 \nL 182.01978 121.077507 \nL 182.266029 138.846279 \nL 182.512278 144.904216 \nL 182.758527 145.106734 \nL 183.004776 134.685357 \nL 183.251025 130.340241 \nL 183.497274 134.580851 \nL 183.989771 118.833521 \nL 184.23602 152.835163 \nL 184.482269 154.346082 \nL 184.728518 146.771295 \nL 184.974767 151.296903 \nL 185.221016 142.853241 \nL 185.467265 146.221584 \nL 185.713514 143.598795 \nL 185.959763 150.226728 \nL 186.206011 137.578417 \nL 186.45226 147.685549 \nL 186.698509 144.764191 \nL 186.944758 144.966709 \nL 187.191007 150.758153 \nL 187.437256 151.614563 \nL 187.683505 149.347097 \nL 187.929754 160.108121 \nL 188.176003 148.113702 \nL 188.422251 155.395527 \nL 188.6685 153.42663 \nL 188.914749 157.448865 \nL 189.160998 149.659095 \nL 189.407247 154.838595 \nL 189.653496 155.041114 \nL 189.899745 157.677591 \nL 190.145994 156.529883 \nL 190.392243 160.359158 \nL 190.638492 151.219163 \nL 190.88474 153.891173 \nL 191.130989 150.57205 \nL 191.377238 156.50365 \nL 191.623487 143.159006 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102.324263 \nL 201.719692 108.252649 \nL 201.965941 137.377529 \nL 202.21219 137.377529 \nL 202.458439 123.830366 \nL 202.704688 129.559448 \nL 202.950936 126.037807 \nL 203.197185 128.507298 \nL 203.443434 119.164785 \nL 203.689683 122.791542 \nL 203.935932 121.441316 \nL 204.182181 123.875275 \nL 204.42843 123.875275 \nL 204.674679 128.852257 \nL 204.920928 120.859969 \nL 205.167176 124.679686 \nL 205.413425 122.508271 \nL 205.659674 129.587577 \nL 205.905923 117.39064 \nL 206.152172 127.949146 \nL 206.398421 125.479161 \nL 206.64467 126.133053 \nL 206.890919 131.72198 \nL 207.137168 131.72198 \nL 207.383417 128.598103 \nL 207.629665 138.502717 \nL 207.875914 125.651887 \nL 208.122163 132.077302 \nL 208.368412 129.251995 \nL 208.614661 132.417819 \nL 208.86091 123.771639 \nL 209.107159 128.094729 \nL 209.353408 120.317424 \nL 209.599657 121.625825 \nL 209.845906 155.424949 \nL 210.338403 139.272582 \nL 210.584652 143.310674 \nL 210.830901 138.76304 \nL 211.07715 128.139145 \nL 211.323399 128.139145 \nL 211.569648 133.994563 \nL 211.815897 151.560817 \nL 212.062146 151.560817 \nL 212.308394 171.983716 \nL 212.554643 163.866078 \nL 212.800892 134.65339 \nL 213.047141 121.121526 \nL 213.29339 151.353546 \nL 213.785888 134.446118 \nL 214.032137 138.672975 \nL 214.278386 135.744032 \nL 214.524635 126.474804 \nL 214.770883 133.779889 \nL 215.017132 130.127347 \nL 215.263381 133.050822 \nL 215.50963 124.878798 \nL 215.755879 123.047059 \nL 216.002128 128.371119 \nL 216.494626 113.6581 \nL 216.740875 126.643829 \nL 216.987124 157.74935 \nL 217.233372 142.19659 \nL 217.479621 149.455161 \nL 217.72587 161.923082 \nL 218.218368 146.174446 \nL 218.464617 150.980698 \nL 218.710866 148.631151 \nL 218.957115 139.941318 \nL 219.203364 142.346985 \nL 219.449612 138.176634 \nL 219.695861 144.96391 \nL 219.94211 135.176874 \nL 220.188359 131.730122 \nL 220.434608 135.439171 \nL 220.927106 117.496127 \nL 221.173355 147.210338 \nL 221.419604 131.84042 \nL 221.665853 139.525379 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255.6482 120.618153 \nL 255.894449 135.186374 \nL 256.140698 122.009832 \nL 256.386947 128.598103 \nL 256.633196 124.223195 \nL 256.879445 123.19424 \nL 257.125694 123.708718 \nL 257.371943 123.062845 \nL 257.618191 118.299303 \nL 257.86444 124.49894 \nL 258.110689 110.933765 \nL 258.356938 125.113352 \nL 258.849436 117.348076 \nL 259.341934 149.965135 \nL 259.834432 160.397452 \nL 260.08068 117.879209 \nL 260.326929 139.13833 \nL 260.573178 123.642793 \nL 260.819427 123.371161 \nL 261.065676 90.889507 \nL 261.558174 147.637465 \nL 261.804423 108.004205 \nL 262.050672 127.820835 \nL 262.296921 117.159927 \nL 262.789418 172.4027 \nL 263.035667 139.168453 \nL 263.281916 138.144228 \nL 263.528165 121.047501 \nL 263.774414 129.595864 \nL 264.020663 128.187593 \nL 264.266912 130.029279 \nL 264.513161 99.660942 \nL 265.005658 171.562511 \nL 265.251907 152.523149 \nL 265.498156 161.717439 \nL 265.744405 195.214894 \nL 265.990654 164.521167 \nL 266.236903 166.037462 \nL 266.483152 164.3038 \nL 266.729401 172.526773 \nL 266.97565 155.104655 \nL 267.221898 153.75504 \nL 267.468147 120.195403 \nL 267.960645 174.787395 \nL 268.206894 163.801097 \nL 268.453143 183.292336 \nL 268.699392 143.333686 \nL 269.19189 199.430863 \nL 269.438139 166.623819 \nL 269.684387 166.026796 \nL 269.930636 150.205868 \nL 270.176885 171.1396 \nL 270.423134 128.295966 \nL 270.915632 138.077502 \nL 271.40813 170.043781 \nL 271.900627 161.627724 \nL 272.146876 175.48192 \nL 272.393125 161.591355 \nL 272.639374 167.465601 \nL 272.885623 162.376669 \nL 273.131872 161.405406 \nL 273.378121 161.594493 \nL 273.62437 160.240148 \nL 273.870619 155.539849 \nL 274.116868 161.80273 \nL 274.363116 148.300798 \nL 274.609365 162.543628 \nL 275.101863 154.904839 \nL 275.594361 187.648386 \nL 276.086859 198.20719 \nL 276.333108 199.363371 \nL 276.579357 197.780608 \nL 276.825605 191.689691 \nL 277.071854 145.001702 \nL 277.318103 172.809289 \nL 277.564352 158.000387 \nL 277.810601 160.918375 \nL 278.05685 154.957928 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}, "metadata": { "needs_background": "light" } } ], "source": [ "# MJDs/residuals for PSR_B1937+21\n", "mjds = []\n", "residuals = []\n", "with open(\"1937-grid-nicer.txt\", 'r') as file:\n", " line = file.readline()\n", " while line:\n", " vals = line.split()\n", " if vals != [] and vals[0][0] != '#':\n", " mjds.append(int(float(vals[0])))\n", " residuals.append(float(vals[1]))\n", " line = file.readline()\n", "\n", "x = np.array(mjds)\n", "xindices = np.array((x - x[0]), dtype=int)\n", "ydense = np.zeros(xindices.max() + 1) * np.nan\n", "ydense[xindices] = residuals\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(\"MJD (day)\")\n", "ax.set_ylabel(\"Residual (us)\")\n", "ax.plot(ydense)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9aaa08ce50>]" ] }, "metadata": {}, "execution_count": 9 }, { "output_type": "display_data", "data": { "text/plain": 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107.022979 142.987648 \nL 107.268632 103.122875 \nL 107.514285 118.83615 \nL 107.759937 154.862212 \nL 108.251243 118.138649 \nL 108.496895 122.367204 \nL 108.742548 114.845201 \nL 108.988201 131.349342 \nL 109.479506 88.231687 \nL 109.725159 124.15523 \nL 109.970811 139.765984 \nL 110.216464 99.798691 \nL 110.462117 119.782338 \nL 110.707769 98.283964 \nL 110.953422 102.388169 \nL 111.199074 39.236488 \nL 111.444727 39.236488 \nL 111.69038 50.720031 \nL 111.936032 69.086178 \nL 112.181685 131.06722 \nL 112.427338 131.06722 \nL 112.67299 124.184617 \nL 112.918643 97.823837 \nL 113.164296 101.632485 \nL 113.409948 99.728161 \nL 113.655601 108.885373 \nL 113.901254 91.758212 \nL 114.146906 123.214823 \nL 114.392559 133.277176 \nL 114.638212 122.744619 \nL 115.129517 114.986262 \nL 115.620822 131.490403 \nL 115.866475 127.364368 \nL 116.112128 127.229185 \nL 116.35778 133.283054 \nL 116.849085 121.128295 \nL 117.094738 118.418748 \nL 117.340391 96.742369 \nL 117.586043 106.240478 \nL 117.831696 127.070491 \nL 118.077349 116.655485 \nL 118.323001 122.81515 \nL 118.568654 98.311673 \nL 118.814307 93.333394 \nL 119.059959 93.991679 \nL 119.305612 102.423994 \nL 119.551265 90.629724 \nL 119.796917 114.63361 \nL 120.04257 68.600693 \nL 120.288223 176.747485 \nL 120.779528 108.526843 \nL 121.025181 112.605858 \nL 121.270833 121.004867 \nL 121.516486 134.687787 \nL 122.007791 60.01948 \nL 122.253444 167.507987 \nL 122.499096 120.816785 \nL 122.744749 144.162386 \nL 122.990402 131.709832 \nL 123.236054 139.483862 \nL 123.481707 139.483862 \nL 123.72736 133.847298 \nL 123.973012 108.685536 \nL 124.464318 119.688297 \nL 124.70997 130.032773 \nL 125.201276 58.279727 \nL 125.692581 89.898196 \nL 125.938234 123.750131 \nL 126.183886 103.528671 \nL 126.429539 119.136712 \nL 126.920844 86.593208 \nL 127.166497 113.10997 \nL 127.41215 115.176152 \nL 127.657802 98.011014 \nL 128.149107 94.290981 \nL 128.39476 124.52235 \nL 128.886065 141.350208 \nL 129.131718 93.339724 \nL 129.377371 113.226617 \nL 129.623023 100.904574 \nL 129.868676 130.383617 \nL 130.114329 145.004231 \nL 130.359981 137.243162 \nL 130.605634 141.42526 \nL 130.851287 108.591496 \nL 131.096939 119.500216 \nL 131.342592 106.193458 \nL 131.588245 119.359155 \nL 131.833897 147.38328 \nL 132.07955 133.606319 \nL 132.325203 152.038294 \nL 132.570855 102.572892 \nL 133.062161 116.490914 \nL 133.307813 145.267364 \nL 133.799118 138.637496 \nL 134.044771 120.017439 \nL 134.290424 120.628704 \nL 134.536076 145.690547 \nL 135.027382 110.237208 \nL 135.273034 124.390331 \nL 135.518687 102.713953 \nL 135.76434 135.11097 \nL 136.009992 147.336259 \nL 136.255645 104.923909 \nL 136.501298 137.132845 \nL 136.74695 121.028377 \nL 136.992603 133.6024 \nL 137.238256 155.094616 \nL 137.729561 127.352613 \nL 137.975214 86.209812 \nL 138.712172 127.282083 \nL 138.957824 143.33953 \nL 139.203477 135.310806 \nL 139.449129 135.114888 \nL 139.694782 95.801962 \nL 139.940435 124.296291 \nL 140.186087 123.261843 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151.486109 113.105449 \nL 151.731762 138.261333 \nL 152.223067 130.738078 \nL 152.46872 98.999345 \nL 152.714373 114.868712 \nL 152.960025 100.080813 \nL 153.205678 73.749421 \nL 153.451331 73.749421 \nL 153.696983 75.565581 \nL 153.942636 99.105141 \nL 154.188289 83.523775 \nL 154.433941 108.426924 \nL 154.679594 90.588581 \nL 154.925247 169.518107 \nL 155.170899 113.969448 \nL 155.662205 131.443383 \nL 155.907857 105.159011 \nL 156.15351 126.553267 \nL 156.399163 123.638006 \nL 156.644815 64.580453 \nL 156.890468 140.001086 \nL 157.13612 118.653849 \nL 157.381773 140.048106 \nL 157.627426 120.957846 \nL 157.873078 140.988513 \nL 158.118731 139.29578 \nL 158.364384 121.145928 \nL 158.610036 127.869837 \nL 158.855689 29.691358 \nL 159.101342 102.525871 \nL 159.346994 140.424269 \nL 159.592647 138.073252 \nL 159.8383 103.607339 \nL 160.329605 142.305083 \nL 160.575258 132.630647 \nL 160.82091 136.393744 \nL 161.066563 154.668494 \nL 161.312216 119.128461 \nL 161.557868 133.70036 \nL 161.803521 114.374998 \nL 162.294826 133.359462 \nL 162.540479 121.47507 \nL 162.786131 150.721724 \nL 163.031784 121.757192 \nL 163.277437 129.045345 \nL 163.523089 155.517799 \nL 163.768742 52.872388 \nL 164.014395 150.439602 \nL 164.5057 148.887931 \nL 164.751353 123.685026 \nL 164.997005 136.286478 \nL 165.242658 129.233427 \nL 165.733963 126.177104 \nL 165.979616 130.267874 \nL 166.225269 130.267874 \nL 166.716574 112.952633 \nL 166.962227 88.889971 \nL 167.207879 88.889971 \nL 167.453532 88.125891 \nL 167.699185 133.647462 \nL 167.944837 151.421152 \nL 168.19049 36.697389 \nL 168.436142 116.114751 \nL 168.681795 133.935461 \nL 168.927448 135.252031 \nL 169.1731 89.454216 \nL 169.418753 98.105959 \nL 169.664406 143.668672 \nL 169.910058 130.126813 \nL 170.155711 144.091856 \nL 170.401364 129.186406 \nL 170.892669 159.608569 \nL 171.138322 115.409446 \nL 171.383974 137.509007 \nL 171.629627 126.820276 \nL 172.120932 157.964536 \nL 172.366585 143.420136 \nL 172.612238 157.746228 \nL 172.85789 144.565418 \nL 173.103543 190.48918 \nL 173.349196 199.501973 \nL 173.594848 154.065206 \nL 173.840501 133.70036 \nL 174.086153 151.427029 \nL 174.331806 114.233937 \nL 174.577459 99.375508 \nL 174.823111 128.904284 \nL 175.068764 39.894773 \nL 175.314417 160.360895 \nL 175.560069 139.24876 \nL 175.805722 149.804827 \nL 176.297027 133.7944 \nL 176.54268 137.509007 \nL 176.788333 129.985752 \nL 177.279638 132.195708 \nL 177.525291 129.562569 \nL 177.770943 148.84091 \nL 178.016596 137.179865 \nL 178.262249 117.055158 \nL 178.507901 149.311114 \nL 178.753554 125.377759 \nL 178.999207 134.076522 \nL 179.244859 148.323687 \nL 179.490512 127.305593 \nL 179.981817 131.960607 \nL 180.22747 93.968169 \nL 180.473122 89.454216 \nL 180.718775 91.711192 \nL 180.964428 95.432852 \nL 181.21008 62.290563 \nL 181.701386 76.645874 \nL 181.947038 60.477928 \nL 182.192691 143.527611 \nL 182.438344 136.897743 \nL 182.683996 140.212677 \nL 182.929649 125.927374 \nL 183.175302 125.466052 \nL 183.420954 122.141714 \nL 183.666607 124.689041 \nL 183.91226 125.044045 \nL 184.157912 102.431831 \nL 184.403565 116.208791 \nL 184.649218 116.678995 \nL 184.89487 116.443893 \nL 185.140523 119.071808 \nL 185.386176 107.875742 \nL 185.631828 105.895663 \nL 185.877481 108.523577 \nL 186.123133 140.095126 \nL 186.368786 140.095126 \nL 186.614439 135.487133 \nL 186.860091 121.663151 \nL 187.105744 121.663151 \nL 187.597049 104.48034 \nL 187.842702 109.655119 \nL 188.088355 99.031063 \nL 188.334007 94.16504 \nL 188.57966 115.704858 \nL 188.825313 100.118197 \nL 189.316618 100.072341 \nL 189.562271 101.322881 \nL 189.807923 101.154974 \nL 190.053576 104.388205 \nL 190.299229 97.784388 \nL 190.544881 110.717525 \nL 190.790534 110.481046 \nL 191.036187 111.663015 \nL 191.527492 111.480017 \nL 191.773144 95.824784 \nL 192.018797 117.296031 \nL 192.26445 119.41057 \nL 192.510102 120.235967 \nL 192.755755 118.310675 \nL 193.24706 118.127677 \nL 193.492713 98.804857 \nL 193.738366 93.682181 \nL 193.984018 128.506226 \nL 194.229671 129.887052 \nL 194.475324 111.40766 \nL 194.966629 116.684899 \nL 195.212282 117.489725 \nL 195.457934 117.398226 \nL 195.703587 118.277687 \nL 195.94924 118.277687 \nL 196.194892 119.174012 \nL 196.686198 124.634249 \nL 196.93185 106.246356 \nL 197.177503 107.718681 \nL 197.423155 142.634225 \nL 197.668808 137.603048 \nL 197.914461 118.371727 \nL 198.405766 118.371727 \nL 198.651419 116.537934 \nL 198.897071 117.454831 \nL 199.142724 119.660868 \nL 199.388377 141.223615 \nL 199.634029 125.659881 \nL 200.125335 125.659881 \nL 200.370987 126.933348 \nL 200.61664 126.788369 \nL 200.862293 139.813004 \nL 201.107945 133.300687 \nL 201.353598 136.625417 \nL 201.599251 136.549009 \nL 201.844903 137.891048 \nL 202.336209 138.02819 \nL 202.581861 122.533028 \nL 202.827514 144.164345 \nL 203.073166 139.389821 \nL 203.318819 128.857264 \nL 203.564472 134.123543 \nL 204.055777 117.123729 \nL 204.30143 105.347092 \nL 204.547082 111.23541 \nL 204.792735 109.464964 \nL 205.038388 105.01795 \nL 205.28404 131.537424 \nL 205.529693 118.277687 \nL 205.775346 124.645025 \nL 206.020998 117.196219 \nL 206.266651 116.208791 \nL 206.512304 141.364676 \nL 206.757956 105.159011 \nL 207.003609 30.490704 \nL 207.249262 67.824857 \nL 207.494914 51.223541 \nL 207.740567 17.083636 \nL 207.98622 44.305281 \nL 208.231872 45.383614 \nL 208.477525 40.000568 \nL 208.723177 48.433667 \nL 208.96883 37.239691 \nL 209.214483 65.824925 \nL 209.460135 63.443671 \nL 209.705788 63.738985 \nL 210.442746 130.267874 \nL 210.688399 66.179145 \nL 210.934051 98.22351 \nL 211.179704 85.035982 \nL 211.425357 108.841711 \nL 211.671009 102.666932 \nL 211.916662 133.465258 \nL 212.407967 123.496945 \nL 212.65362 125.989023 \nL 212.899273 121.935198 \nL 213.144925 114.143255 \nL 213.390578 142.133794 \nL 213.636231 106.334519 \nL 213.881883 124.234157 \nL 214.127536 113.161247 \nL 214.373188 139.028696 \nL 214.864494 122.936746 \nL 215.110146 123.305734 \nL 215.847104 100.880159 \nL 216.092757 104.985699 \nL 216.33841 104.827111 \nL 216.829715 94.400979 \nL 217.075368 96.664148 \nL 217.32102 92.381413 \nL 217.566673 84.360561 \nL 217.812326 112.122191 \nL 218.057978 102.943463 \nL 218.303631 122.737282 \nL 218.549284 88.832188 \nL 218.794936 118.716909 \nL 219.040589 112.819147 \nL 219.286242 110.659503 \nL 219.531894 115.045763 \nL 220.023199 108.865813 \nL 220.268852 142.869327 \nL 220.514505 125.86757 \nL 220.760157 127.680455 \nL 221.251463 68.106979 \nL 221.497115 134.452685 \nL 221.988421 83.24753 \nL 222.479726 133.042075 \nL 222.725379 120.593439 \nL 222.971031 126.614303 \nL 223.216684 151.308122 \nL 223.707989 99.69606 \nL 223.953642 165.838313 \nL 224.444947 105.857931 \nL 224.6906 107.467362 \nL 224.936253 90.262152 \nL 225.181905 117.948544 \nL 225.427558 104.105348 \nL 225.673211 110.456968 \nL 225.918863 92.68178 \nL 226.164516 93.721234 \nL 226.655821 32.600896 \nL 226.901474 98.17317 \nL 227.147126 87.526381 \nL 227.638432 128.763223 \nL 228.129737 88.607849 \nL 228.37539 98.646693 \nL 228.621042 98.945272 \nL 228.866695 125.236698 \nL 229.112348 112.090985 \nL 229.358 122.496391 \nL 229.603653 126.62752 \nL 229.849306 140.498914 \nL 230.340611 108.008639 \nL 230.586264 146.583934 \nL 230.831916 127.296287 \nL 231.077569 141.580485 \nL 231.568874 118.37096 \nL 231.814527 136.882728 \nL 232.060179 192.334729 \nL 232.305832 145.784588 \nL 232.551485 169.059658 \nL 232.797137 151.878947 \nL 233.04279 201.787772 \nL 233.288443 214.756364 \nL 233.534095 197.608425 \nL 233.779748 121.898253 \nL 234.025401 116.443893 \nL 234.271053 119.171073 \nL 234.516706 118.409344 \nL 234.762359 122.180375 \nL 235.008011 120.294859 \nL 235.253664 126.091057 \nL 235.499317 144.562059 \nL 235.744969 135.326558 \nL 236.236275 149.393517 \nL 236.481927 149.640256 \nL 236.72758 146.583934 \nL 236.973233 128.481101 \nL 237.218885 137.532518 \nL 237.71019 130.585262 \nL 237.955843 131.149388 \nL 238.447148 145.851122 \nL 238.692801 160.737057 \nL 238.938454 120.675724 \nL 239.184106 140.706391 \nL 239.429759 124.168291 \nL 239.675412 132.53146 \nL 239.921064 132.770336 \nL 240.166717 134.18766 \nL 240.41237 78.404435 \nL 240.658022 85.598547 \nL 240.903675 106.522601 \nL 241.149328 143.480591 \nL 241.39498 125.001596 \nL 241.640633 136.052552 \nL 241.886286 158.788064 \nL 242.131938 167.793636 \nL 242.377591 113.821869 \nL 242.868896 119.100987 \nL 243.114549 129.275615 \nL 243.360201 114.548973 \nL 243.605854 136.391099 \nL 243.851507 98.141224 \nL 244.097159 114.838619 \nL 244.588465 133.16327 \nL 244.834117 135.538855 \nL 245.07977 134.611379 \nL 245.325423 149.969399 \nL 245.571075 172.492144 \nL 245.816728 161.230771 \nL 246.062381 162.809871 \nL 246.308033 130.220854 \nL 246.553686 143.621652 \nL 246.799339 136.921253 \nL 247.290644 144.562059 \nL 247.536297 143.180836 \nL 247.781949 143.544824 \nL 248.027602 148.096562 \nL 248.273255 137.979211 \nL 248.518907 143.809734 \nL 249.010212 47.135906 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}, "metadata": { "needs_background": "light" } } ], "source": [ "resid1, resid2, resid3, resid4 = [], [], [], []\n", "resids = [resid1, resid2, resid3, resid4]\n", "i = 0\n", "with open(\"grid.out\", 'r') as file:\n", " line = file.readline()\n", " while line:\n", " vals = line.split()\n", " if vals != [] and vals[0][0] != '#':\n", " resids[int(i)].append(float(vals[1]))\n", " if vals == []:\n", " i = i+0.5 # 2 blank lines\n", " line = file.readline()\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(\"MJD (30-day bin)\")\n", "ax.set_ylabel(\"Residual (us, 30-day average)\")\n", "ax.plot(resids[0])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9aa9fd3d00>]" ] }, "metadata": {}, "execution_count": 11 }, { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "fillgaps(resids[0], \"reflect+invert\")\n", "fig, ax = plt.subplots()\n", "ax.plot(resids[0])" ] }, { "source": [ "### B1937+21" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9aa9f35f70>]" ] }, "metadata": {}, "execution_count": 12 }, { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 398.304688 262.19625\" width=\"398.304688pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n 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}, "metadata": { "needs_background": "light" } } ], "source": [ "fillgaps(resids[1], \"reflect+invert\")\n", "fig, ax = plt.subplots()\n", "ax.plot(resids[1])" ] }, { "source": [ "### J1640+2224" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9aa9ef1af0>]" ] }, "metadata": {}, "execution_count": 14 }, { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 394.160938 262.19625\" width=\"394.160938pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n 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\n" 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "fillgaps(resids[3], \"reflect+invert\")\n", "fig, ax = plt.subplots()\n", "ax.plot(resids[3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }
mit
tomkraljevic/gtc2017-labs
Image+Segmentation.ipynb
1
56348
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Image Segmentation with TensorFlow\n", "\n", "There are a variety of important image analysis deep learning applications that need to go beyond detecting individual objects within an image and instead segment the image into spatial regions of interest. For example, in medical imagery analysis it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells so that we can isolate a particular organ. In this self-paced, hands-on lab we will use the [TensorFlow](https://www.tensorflow.org) machine learning framework to train and evaluate an image segmentation network using a medical imagery dataset. \n", "\n", "Lab created by Jonathan Bentz (follow [@jnbntz](https://twitter.com/jnbntz) on Twitter)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Before we begin, let's verify [WebSockets](http://en.wikipedia.org/wiki/WebSocket) are working on your system. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above. If all goes well, you should see some output returned below the gray cell. If not, please consult the [Self-paced Lab Troubleshooting FAQ](https://developer.nvidia.com/self-paced-labs-faq#Troubleshooting) to debug the issue." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The answer should be three: 3\n" ] } ], "source": [ "print \"The answer should be three: \" + str(1+2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's execute the cell below to display information about the GPUs running on the server." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue May 9 17:02:40 2017 \r\n", "+-----------------------------------------------------------------------------+\r\n", "| NVIDIA-SMI 367.57 Driver Version: 367.57 |\r\n", "|-------------------------------+----------------------+----------------------+\r\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n", "|===============================+======================+======================|\r\n", "| 0 Tesla K80 On | 0000:00:1E.0 Off | 0 |\r\n", "| N/A 25C P8 31W / 149W | 0MiB / 11439MiB | 0% Default |\r\n", "+-------------------------------+----------------------+----------------------+\r\n", " \r\n", "+-----------------------------------------------------------------------------+\r\n", "| Processes: GPU Memory |\r\n", "| GPU PID Type Process name Usage |\r\n", "|=============================================================================|\r\n", "| No running processes found |\r\n", "+-----------------------------------------------------------------------------+\r\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, execute the following cell to show the version of TensorFlow that is used in this lab." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.1.0\r\n" ] } ], "source": [ "!python -c 'import tensorflow as tf; print(tf.__version__)'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have never before taken an IPython Notebook based self-paced lab from NVIDIA, recommend watching this short [YouTube video](http://www.youtube.com/embed/ZMrDaLSFqpY)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Image Segmentation\n", "\n", "In this lab you will work through a series of exercises performing image segmentation, also called semantic segmentation. Semantic segmentation is the task of placing each pixel into a specific class. In a sense it's a classification problem where you'll classify on a pixel basis rather than an entire image. In this lab the task will be classifying each pixel in a cardiac MRI image based on whether the pixel is a part of the left ventricle (LV) or not.\n", "\n", "This lab is not an introduction to deep learning, nor is it intended to be a rigorous mathematical formalism of convolutional neural networks. We'll assume that you have at least a passing understanding of neural networks including concepts like forward and backpropagation, activations, SGD, convolutions, pooling, bias, and the like. It is helpful if you've encountered convolutional neural networks (CNN) already and you understand image recognition tasks. The lab will use Google's TensorFlow machine learning framework so if you have Python and TensorFlow experience it is helpful, but not required. Most of the work we'll do in this lab is not coding per se, but setting up and running training and evaluation tasks using TensorFlow.\n", "\n", "\n", "## Input Data Set\n", "\n", "The data set you'll be utilizing is a series of cardiac images (specifically MRI short-axis (SAX) scans) that have been expertly labeled. See References [[1](#1), [2](#2), [3](#3)] for full citation information. \n", "\n", "Four representative examples of the data are shown below. Each row of images is an instance of the data. On the left are the MRI images and the right are the expertly-segmented regions (often called contours). The portions of the images that are part of the LV are denoted in white. Note that the size of LV varies from image to image, but the LV typically takes up a relatively small region of the entire image." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "![Figure 1](./fig1_final.png)\n", "***\n", "![Figure 2](./fig2_final.png)\n", "***\n", "![Figure 3](./fig3_final.png)\n", "***\n", "![Figure 4](./fig4_final.png)\n", "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data extraction from the raw images and then subsequent preparation of these images for ingestion into TensorFlow will not be showcased in this lab. Suffice it to say that data preparation is a non-trivial aspect of machine learning workflows and is outside the scope of this lab.\n", "\n", "For those that are interested in the details, we obtained guidance and partial code from a prior [Kaggle competition](https://www.kaggle.com/c/second-annual-data-science-bowl/details/deep-learning-tutorial) on how to extract the images properly. At that point we took the images, converted them to TensorFlow records (TFRecords), and stored them to files. [TFRecords](https://www.tensorflow.org/versions/r0.12/how_tos/reading_data/index.html) are a special file format provided by TensorFlow, which allow you to use built-in TensorFlow functions for data management including multi-threaded data reading and sophisticated pre-processing of the data such as randomizing and even augmenting the training data.\n", "\n", "The images themselves are originally 256 x 256 grayscale [DICOM](https://en.wikipedia.org/wiki/DICOM) format, a common image format in medical imaging. The label is a tensor of size 256 x 256 x 2. The reason the last dimension is a 2 is that the pixel is in one of two classes so each pixel label has a vector of size 2 associated with it. The training set is 234 images and the validation set (data NOT used for training but used to test the accuracy of the model) is 26 images." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Learning with TensorFlow\n", "\n", "This lab is part of a series of self-paced labs designed to introduce you to some of the publicly-available deep learning frameworks available today. TensorFlow is a framework developed by [Google](https://www.google.com) and used by numerous researchers and product groups within Google.\n", "\n", "TensorFlow is an open source software library for machine intelligence. The computations are expressed as data flow graphs which operate on tensors (hence the name). If you can express your computation in this manner you can run your algorithm in the TensorFlow framework.\n", "\n", "TensorFlow is portable in the sense that you can run on CPUs and GPUs and utilize workstations, servers, and even deploy models on mobile platforms. At present TensorFlow offers the options of expressing your computation in either Python or C++, with experimental support for [Go and JAVA](https://www.tensorflow.org/api_docs/). A typical usage of TensorFlow would be performing training and testing in Python and once you have finalized your model you might deploy with C++.\n", "\n", "TensorFlow is designed and built for performance on both CPUs and GPUs. Within a single TensorFlow execution you have lots of flexibility in that you can assign different tasks to CPUs and GPUs explicitly if necessary. When running on GPUs TensorFlow utilizes a number of GPU libraries including [cuDNN](https://developer.nvidia.com/cudnn) allowing it to extract the most performance possible from the very newest GPUs available.\n", "\n", "One of the intents of this lab is to gain an introductory level of familiarity with TensorFlow. In the course of this short lab we won't be able to discuss all the features and options of TensorFlow but we hope that after completion of this lab you'll feel comfortable with and have a good idea how to move forward using TensorFlow to solve your specific machine learning problems.\n", "\n", "For comprehensive documentation on TensorFlow we recommend the [TensorFlow website](https://www.tensorflow.org), the [whitepaper](http://download.tensorflow.org/paper/whitepaper2015.pdf), and the [GitHub site](https://github.com/tensorflow/tensorflow)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Basics\n", "\n", "TensorFlow has multiple ways to be used depending on your preferences. For designing training tasks a common way is to use the TensorFlow Python API. Running a machine learning training task on TensorFlow consists of (at least) two distinct steps.\n", "\n", "## Data Flow Graph\n", "\n", "First you will construct a data flow graph which is the specification and ordering of exactly what computations you'd like to perform. Using the TensorFlow API you construct a neural network layer-by-layer using any of the TensorFlow-provided operations such as convolutions, activations, pooling, etc. This stage of the process doesn't do any actual computation on your data; it merely constructs the graph that you've specified.\n", "\n", "When you build the graph you must specify each so-called `Variable` (in TensorFlow lexicon). Specifying a piece of data as a `Variable` tells TensorFlow that it will be a parameter to be \"learned\", i.e., it is a weight that will be updated as the training proceeds.\n", "\n", "## Session\n", "\n", "Once you've defined your neural network as a data flow graph, you will launch a `Session`. This is the mechanism whereby you specify the input data and training parameters to your previously-constructed graph and then the computation proceeds. \n", "\n", "In general these two steps will be repeated each time you wish to change your graph, i.e., you'll update the graph and launch a new session." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sample Workflow\n", "\n", "A sample workflow of training and evaluating a model might look like the following.\n", "\n", "1. Prepare input data--Input data can be Numpy arrays but for very large datasets TensorFlow provides a specialized format called TFRecords. \n", "2. Build the Computation Graph--Create the graph of your neural network including specialized nodes such as inference, loss and training nodes.\n", "3. Train the model--inject input data into the graph in a TensorFlow `Session` and loop over your input data. Customize your batch size, number of epochs, learning rate, etc.\n", "4. Evaluate the model--run inference (using the same graph from training) on previously unseen data and evaluate the accuracy of your model based on a suitable metric." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorBoard\n", "\n", "TensorFlow provides a feature-rich tool called [TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard) that allows you to visualize many aspects of your program. In TensorBoard you can see a visual representation of your computation graph and you can plot different metrics of your computation such as loss, accuracy, and learning rate. Essentially any data that is generated during the execution of TensorFlow can be visually displayed by TensorBoard with the addition of a few extra API calls in your program.\n", "\n", "For example, consider the following code snippet that creates a neural network with one hidden layer (don't worry about the details of the code at this time).\n", "\n", "```\n", "with tf.name_scope('Hidden1'):\n", " W_fc = tf.Variable(tf.truncated_normal( [256*256, 512],\n", " stddev=0.1, dtype=tf.float32), name='W_fc')\n", " flatten1_op = tf.reshape( images_re, [-1, 256*256])\n", " h_fc1 = tf.matmul( flatten1_op, W_fc )\n", "\n", "with tf.name_scope('Final'):\n", " W_fc2 = tf.Variable(tf.truncated_normal( [512, 256*256*2],\n", " stddev=0.1, dtype=tf.float32), name='W_fc2' )\n", " h_fc2 = tf.matmul( h_fc1, W_fc2 )\n", " h_fc2_re = tf.reshape( h_fc2, [-1, 256, 256, 2] )\n", "\n", "return h_fc2_re\n", "```\n", "\n", "TensorBoard will display the neural network like the figure below. If you look closely you'll see that the edges have the tensor dimensions printed, i.e., as you move node-to-node you can follow how the data (as a tensor) and it's size changes throughout the graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![TensorBoard Example](./hidden1.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Task 1 -- One Hidden Layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![NN](./NN.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first task we'll consider will be to create, train and evaluate a fully-connected neural network with one hidden layer. The input to the neural network will be the value of each pixel, i.e., a size 256 x 256 (or 65,536) array. The hidden layer will have a size that you can adjust, and the output will be an array of 256 x 256 x 2, i.e., each input pixel can be in either one of two classes so the output value associated with each pixel will be the probability that the pixel is in that particular class. In our case the two classes are LV or not. We'll compute the loss via a TensorFlow function called [`sparse_softmax_cross_entropy_with_logits`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#sparse_softmax_cross_entropy_with_logits) which simply combines the softmax with the cross entropy calculation into one function call." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "\n", "For the first exercise the code has already been written for you. To begin the task of training the neural network, execute the cell below. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG images shape inference (1, 256, 256)\n", "DEBUG images shape inference (1, 256, 256)\n", "DEBUG W_fc shape (65536, 512)\n", "DEBUG flatten1_op shape (1, 65536)\n", "DEBUG h_fc1 shape (1, 512)\n", "DEBUG W_fc2 shape (512, 131072)\n", "DEBUG h_fc2 shape (1, 131072)\n", "DEBUG h_fc2_re shape (1, 256, 256, 2)\n", "DEBUG logits shape before (1, 256, 256, 2)\n", "DEBUG labels shape before (1, 256, 256)\n", "DEBUG logits shape after (1, 256, 256, 2)\n", "DEBUG labels shape after (1, 256, 256)\n", "DEBUG cross_entropy shape (1, 256, 256)\n", "OUTPUT: Step 0: loss = 2.900 (0.189 sec)\n", "OUTPUT: Step 100: loss = 2.620 (0.019 sec)\n", "OUTPUT: Step 200: loss = 4.322 (0.019 sec)\n", "OUTPUT: Done training for 1 epochs, 231 steps.\n" ] } ], "source": [ "!python exercises/simple/runTraining.py --data_dir /data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything is working properly you may see some messages printed to the screen. Some of those are informational message from TensorFlow and can typically be ignored. You'll want to look for the lines that start with \"OUTPUT\", as those are the lines we inserted in the program specifically to output information at particular points, such as the loss that is computed every 100 steps. The very last line you'll see is:\n", "\n", "`OUTPUT: Done training for 1 epochs, 231 steps`. \n", "\n", "This means your training job was one complete epoch through all the training data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation\n", "\n", "Once we have a trained model we want to evaulate how well it works on data that it hasn't seen before. To evaluate our trained model execute the cell below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG images shape inference (1, 256, 256)\n", "DEBUG images shape inference (1, 256, 256)\n", "DEBUG W_fc shape (65536, 512)\n", "DEBUG flatten1_op shape (1, 65536)\n", "DEBUG h_fc1 shape (1, 512)\n", "DEBUG W_fc2 shape (512, 131072)\n", "DEBUG h_fc2 shape (1, 131072)\n", "DEBUG h_fc2_re shape (1, 256, 256, 2)\n", "DEBUG logits eval shape before (1, 256, 256, 2)\n", "DEBUG labels eval shape before (1, 256, 256)\n", "DEBUG logits_re eval shape after (65536, 2)\n", "DEBUG labels_re eval shape after (65536,)\n", "DEBUG correct shape (65536,)\n", "OUTPUT: 2017-05-09 17:07:13.636280: precision = 0.504\n", "OUTPUT: 26 images evaluated from file /data/val_images.tfrecords\n" ] } ], "source": [ "!python exercises/simple/runEval.py --data_dir /data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again you're ignoring most of the TensorFlow output while focusing your attention on the lines that begin with \"OUTPUT\". When I ran this I obtained the following output. You should see something similar.\n", "\n", "```\n", "OUTPUT: 2017-01-26 17:12:28.929741: precision = 0.503\n", "OUTPUT: 26 images evaluated from file /data/val_images.tfrecords\n", "```\n", "\n", "The final output lines show the accuracy of the model, i.e., how well the model predicted whether each pixel is in LV (or not) versus the ground truth. In the case above, 0.503 is 50.3%, so the model predicted the correct class for each pixel roughly half the time. This is not a great result, but it's not awful either considering we only ran a very simple network and only ran training for one epoch.\n", "\n", "## TensorBoard\n", "\n", "At this point, if you haven't already you'll want to launch TensorBoard [here](/tensorboard/). TensorBoard has a lot of impressive visualization features. At the top menu there is a link called \"Scalars\" which you can click to show you some of the information that has been captured. You can click to expand any of them and see a plot of that data.\n", "\n", "Another menu choice at the top is \"Graphs\". If you choose that you can view both your training and evaluation data flow graphs. Each node in the graph can be clicked to expand it and you can obtain more detailed information about that node. In the upper left of the page you can choose whether you want to view the training or evaluation graph, via a small dropdown selection.\n", "\n", "The code solution to this task is given below.\n", "\n", "```\n", "with tf.name_scope('Hidden1'):\n", " W_fc = tf.Variable(tf.truncated_normal( [256*256, 512],\n", " stddev=0.1, dtype=tf.float32), name='W_fc')\n", " flatten1_op = tf.reshape( images_re, [-1, 256*256])\n", " h_fc1 = tf.matmul( flatten1_op, W_fc )\n", "\n", "with tf.name_scope('Final'):\n", " W_fc2 = tf.Variable(tf.truncated_normal( [512, 256*256*2],\n", " stddev=0.1, dtype=tf.float32), name='W_fc2' )\n", " h_fc2 = tf.matmul( h_fc1, W_fc2 )\n", " h_fc2_re = tf.reshape( h_fc2, [-1, 256, 256, 2] )\n", "\n", "return h_fc2_re\n", "\n", "```\n", "\n", "You'll notice it's Python syntax with some TensorFlow API calls.\n", "* `tf.name_scope()` lets you name that particular scope of the program. It's useful both for organizing the code and for giving a name to the node in the TensorBoard graph.\n", "* `tf.Variable()` indicates a TensorFlow variable that will be trained, i.e., it's a tensor of weights.\n", "* `tf.reshape()` is a TensorFlow auxiliary function for reshaping tensors so that they'll be the proper shape for upcoming operations.\n", "* `tf.matmul()` is as you'd expect. It's a matrix multiply of two TensorFlow tensors.\n", "\n", "### Topics not covered in detail\n", "\n", "We skipped over a number of topics that we'll mention for completeness but won't discuss in detail. \n", "* We assumed all the data was setup for us already. As previously shown earlier we're using TFRecords file data format that has already been setup for us. \n", "* We are using a TensorFlow mechanism using multiple threads to read the data from those files. This allows us to use built-in TensorFlow functions to randomize the data, as well as handle things like `batch_size` and `num_epochs` automatically. \n", "* We have only given a brief discussion of the actual construction of the model via a data flow graph. This is a lot of Python syntax that you can view in the code if you like. \n", "* Finally we have inserted special API calls to export data to TensorBoard so that it can be plotted and viewed easily. Again this is boilerplate Python code that you can view if you like." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Task 2 -- Convolutional Neural Network (CNN)\n", "\n", "Our second task will be to convert our model to a more sophisticated network that includes more layers and types than above. The previous example focused on each individual pixel but had no way to account for the fact that the regions of interest are likely larger than a single pixel. We'd like to capture small regions of interest as well and for that we'll utilize convolutional layers which can capture larger receptive fields. \n", "\n", "We'll also add pooling layers which down-sample the data while attempting to retain most of the information. This eliminates some computational complexity.\n", "\n", "Up to this point we've described layers that are commonly associated with image recognition neural networks, where the number of output nodes is equal to the number of classes. Recall that we're doing more than classifying the image; we're classifying each pixel in the image so our output size will be the number of classes (2) times the number of pixels (256 x 256). Additionally the spatial location of the output nodes are important as well, since each pixel has an associated probability that it's part of LV (or not). \n", "\n", "CNN's are well-established as excellent choices for image recognition or classification tasks. Our task in this lab is segmentation, which is related to classification in a sense. We are classifying each pixel in the image, rather than the entire image altogether. So the question becomes, can we utilize the same type of CNN that is already shown to do very well on image recognition, for our task of segmentation? It turns out that we can make some modifications to CNN models to do this.\n", "\n", "We can accomplish this by using a standard image recognition neural network, and replacing the fully-connected layers (typically the last few layers) with deconvolution layers (arguably more accurately called [transpose convolution](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#conv2d_transpose) layers)\n", "\n", "Deconvolution is an upsampling method that brings a smaller image data set back up to it's original size for final pixel classification. There are a few good resources [[4](#4), [5](#5), [6](#6)] that we'd recommend on this topic. When modifying a CNN to adapt it to segmentation the resulting neural network is commonly called a fully convolutional network, or FCN.\n", "\n", "It can be helpful to visualize how the input data (in our case a tensor of size 256 x 256 x 1) \"flows\" through the graph, i.e., how the data is transformed via the different operations of convolution, pooling and such. The figure below represents the transformations that our data will undergo in the next task." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![FCN](./FCN.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The network represented by the figure above is similar to the network that's found in ref [[7](#7)]. It consists of convolution layers, pooling layers, and a final deconvolution layer, with the input image being transformed as indicated in the image. \n", "\n", "This task requires you to finish this neural network and then run the training. To accomplish this edit the file [`exercises/cnn/neuralnetwork.py`](/7j5skJV2qH/edit/exercises/cnn/neuralnetwork.py) and replace all the instances of `FIXME` with code. There are comments in the code to help you and you can use the following network structure to help as well. The names of the layers will make more sense as you examine and complete the code. \n", "\n", "1. Convolution1, 5 x 5 kernel, stride 2\n", "2. Maxpooling1, 2 x 2 window, stride 2\n", "3. Convolution2, 5 x 5 kernel, stride 2\n", "4. Maxpooling2, 2 x 2 window, stride 2\n", "5. Convolution3, 3 x 3 kernel, stride 1\n", "6. Convolution4, 3 x 3 kernel, stride 1\n", "7. Score_classes, 1x1 kernel, stride 1\n", "8. Upscore (deconvolution), 31 x 31 kernel, stride 16\n", "\n", "If you want to check your work you can view the solution at [`exercise_solutions/cnn/neuralnetwork.py`](/7j5skJV2qH/edit/exercise_solutions/cnn/neuralnetwork.py)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you complete the code you can begin running training using the box below and visualizing your results via the TensorBoard browser window you opened in the previous task. If you don't immediately see your results, you may have to wait a short time for TensorBoard to recognize that there are new graphs to visualize. You may also have to refresh your browser." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG images shape inference (1, 256, 256)\n", "DEBUG images shape inference (1, 256, 256)\n", "DEBUG W_conv1 shape (5, 5, 1, 100)\n", "DEBUG conv1_op shape (1, 128, 128, 100)\n", "DEBUG relu1_op shape (1, 128, 128, 100)\n", "DEBUG pool1_op shape (1, 64, 64, 100)\n", "DEBUG W_conv2 shape (5, 5, 100, 200)\n", "DEBUG conv2_op shape (1, 32, 32, 200)\n", "DEBUG relu2_op shape (1, 32, 32, 200)\n", "DEBUG pool2_op shape (1, 16, 16, 200)\n", "DEBUG W_conv3 shape (3, 3, 200, 300)\n", "DEBUG conv3_op shape (1, 16, 16, 300)\n", "DEBUG relu3_op shape (1, 16, 16, 300)\n", "DEBUG W_conv4 shape (3, 3, 300, 300)\n", "DEBUG conv4_op shape (1, 16, 16, 300)\n", "DEBUG relu4_op shape (1, 16, 16, 300)\n", "DEBUG drop_op shape (1, 16, 16, 300)\n", "DEBUG W_score_classes_shape (1, 1, 300, 2)\n", "DEBUG score_conv_op shape (1, 16, 16, 2)\n", "DEBUG W_upscore shape (31, 31, 2, 2)\n", "DEBUG upscore_conv_op shape (1, 256, 256, 2)\n", "DEBUG logits shape before (1, 256, 256, 2)\n", "DEBUG labels shape before (1, 256, 256)\n", "DEBUG logits shape after (1, 256, 256, 2)\n", "DEBUG labels shape after (1, 256, 256)\n", "OUTPUT: Step 0: loss = 0.720 (0.570 sec)\n", "OUTPUT: Step 100: loss = 0.701 (0.010 sec)\n", "OUTPUT: Step 200: loss = 0.694 (0.010 sec)\n", "OUTPUT: Step 300: loss = 0.700 (0.010 sec)\n", "OUTPUT: Step 400: loss = 0.694 (0.011 sec)\n", "OUTPUT: Step 500: loss = 0.707 (0.011 sec)\n", "OUTPUT: Step 600: loss = 0.689 (0.010 sec)\n", "OUTPUT: Step 700: loss = 0.674 (0.010 sec)\n", "OUTPUT: Step 800: loss = 0.653 (0.010 sec)\n", "OUTPUT: Step 900: loss = 0.619 (0.010 sec)\n", "OUTPUT: Step 1000: loss = 0.542 (0.010 sec)\n", "OUTPUT: Step 1100: loss = 0.429 (0.011 sec)\n", "OUTPUT: Step 1200: loss = 0.305 (0.011 sec)\n", "OUTPUT: Step 1300: loss = 0.206 (0.011 sec)\n", "OUTPUT: Step 1400: loss = 0.247 (0.010 sec)\n", "OUTPUT: Step 1500: loss = 0.249 (0.010 sec)\n", "OUTPUT: Step 1600: loss = 0.170 (0.010 sec)\n", "OUTPUT: Step 1700: loss = 0.173 (0.011 sec)\n", "OUTPUT: Step 1800: loss = 0.192 (0.010 sec)\n", "OUTPUT: Step 1900: loss = 0.212 (0.011 sec)\n", "OUTPUT: Step 2000: loss = 0.222 (0.011 sec)\n", "OUTPUT: Step 2100: loss = 0.114 (0.010 sec)\n", "OUTPUT: Step 2200: loss = 0.122 (0.010 sec)\n", "OUTPUT: Step 2300: loss = 0.192 (0.011 sec)\n", "OUTPUT: Step 2400: loss = 0.113 (0.010 sec)\n", "OUTPUT: Step 2500: loss = 0.134 (0.011 sec)\n", "OUTPUT: Step 2600: loss = 0.102 (0.010 sec)\n", "OUTPUT: Step 2700: loss = 0.118 (0.010 sec)\n", "OUTPUT: Step 2800: loss = 0.114 (0.011 sec)\n", "OUTPUT: Step 2900: loss = 0.054 (0.010 sec)\n", "OUTPUT: Step 3000: loss = 0.208 (0.011 sec)\n", "OUTPUT: Step 3100: loss = 0.067 (0.010 sec)\n", "OUTPUT: Step 3200: loss = 0.088 (0.011 sec)\n", "OUTPUT: Step 3300: loss = 0.082 (0.010 sec)\n", "OUTPUT: Step 3400: loss = 0.163 (0.010 sec)\n", "OUTPUT: Step 3500: loss = 0.068 (0.010 sec)\n", "OUTPUT: Step 3600: loss = 0.042 (0.010 sec)\n", "OUTPUT: Step 3700: loss = 0.063 (0.011 sec)\n", "OUTPUT: Step 3800: loss = 0.118 (0.011 sec)\n", "OUTPUT: Step 3900: loss = 0.113 (0.010 sec)\n", "OUTPUT: Step 4000: loss = 0.104 (0.010 sec)\n", "OUTPUT: Step 4100: loss = 0.125 (0.010 sec)\n", "OUTPUT: Step 4200: loss = 0.081 (0.011 sec)\n", "OUTPUT: Step 4300: loss = 0.064 (0.010 sec)\n", "OUTPUT: Step 4400: loss = 0.115 (0.011 sec)\n", "OUTPUT: Step 4500: loss = 0.174 (0.011 sec)\n", "OUTPUT: Step 4600: loss = 0.055 (0.010 sec)\n", "OUTPUT: Done training for 20 epochs, 4633 steps.\n" ] } ], "source": [ "!python exercises/cnn/runTraining.py --num_epochs 20 --data_dir /data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After training is completed execute the following cell to determine how accurate your model is." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG images shape inference (1, 256, 256)\n", "DEBUG images shape inference (1, 256, 256)\n", "DEBUG W_conv1 shape (5, 5, 1, 100)\n", "DEBUG conv1_op shape (1, 128, 128, 100)\n", "DEBUG relu1_op shape (1, 128, 128, 100)\n", "DEBUG pool1_op shape (1, 64, 64, 100)\n", "DEBUG W_conv2 shape (5, 5, 100, 200)\n", "DEBUG conv2_op shape (1, 32, 32, 200)\n", "DEBUG relu2_op shape (1, 32, 32, 200)\n", "DEBUG pool2_op shape (1, 16, 16, 200)\n", "DEBUG W_conv3 shape (3, 3, 200, 300)\n", "DEBUG conv3_op shape (1, 16, 16, 300)\n", "DEBUG relu3_op shape (1, 16, 16, 300)\n", "DEBUG W_conv4 shape (3, 3, 300, 300)\n", "DEBUG conv4_op shape (1, 16, 16, 300)\n", "DEBUG relu4_op shape (1, 16, 16, 300)\n", "DEBUG drop_op shape (1, 16, 16, 300)\n", "DEBUG W_score_classes_shape (1, 1, 300, 2)\n", "DEBUG score_conv_op shape (1, 16, 16, 2)\n", "DEBUG W_upscore shape (31, 31, 2, 2)\n", "DEBUG upscore_conv_op shape (1, 256, 256, 2)\n", "DEBUG logits eval shape before (1, 256, 256, 2)\n", "DEBUG labels eval shape before (1, 256, 256)\n", "DEBUG logits eval shape after (1, 256, 256, 2)\n", "DEBUG labels eval shape after (1, 256, 256)\n", "DEBUG correct shape (65536,)\n", "OUTPUT: 2017-05-09 17:39:12.155020: precision = 0.985\n", "OUTPUT: 26 images evaluated from file /data/val_images.tfrecords\n" ] } ], "source": [ "!python exercises/cnn/runEval.py --data_dir /data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the `runTraining.py` command you ran two cells above you can add a few more command line arguments to test different training parameters. If you have time, experiment with the `--num_epochs` argument and see how that affects your training accuracy. \n", "\n", "The full list of command line arguments you can are given below.\n", "\n", "```\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " --learning_rate LEARNING_RATE\n", " Initial learning rate.\n", " --decay_rate DECAY_RATE\n", " Learning rate decay.\n", " --decay_steps DECAY_STEPS\n", " Steps at each learning rate.\n", " --num_epochs NUM_EPOCHS\n", " Number of epochs to run trainer.\n", " --data_dir DATA_DIR Directory with the training data.\n", " --checkpoint_dir CHECKPOINT_DIR\n", " Directory where to write model checkpoints.\n", "```\n", "\n", "NOTE: If you examine the source code you'll also see an option to change the batch size. For purposes of this lab please leave the batch size set to 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's the best accuracy you can achieve? As an example, with 1 epoch of training we achieved an accuracy of 56.7%:\n", "\n", "```\n", "OUTPUT: 2017-01-27 17:41:52.015709: precision = 0.567\n", "```\n", "\n", "When increasing the number of epochs to 30, we obtained a much higher accuracy of this:\n", "\n", "```\n", "OUTPUT: 2017-01-27 17:47:59.604529: precision = 0.983\n", "```\n", "\n", "As you can see when we increase the training epochs we see a significant increase in accuracy. In fact an accuracy of 98.3% is quite good. Is this accuracy good enough? Are we finished?\n", "\n", "# Accuracy\n", "\n", "As part of the discussion of our accuracy we need to take a step back and consider fully what exactly we are computing when we check accuracy. Our current accuracy metric is simply telling us how many pixels we are computing correctly. So in the case above with 30 epochs, we are correctly predicting the value of a pixel 98.3% of the time. However, notice from the images above that the region of LV is typically quite small compared to the entire image size. This leads to a problem called class imbalance, i.e., one class is much more probable than the other class. In our case, if we simply designed a network to output the class notLV for every output pixel, we'd still have something like 95% accuracy. But that would be a seemingly useless network. What we need is an accuracy metric that gives us some indication of how well our network segments the left ventricle irrespective of the imbalance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Task 3 -- CNN with Dice Metric\n", "\n", "One metric we can use to more accurately determine how well our network is segmenting LV is called the Dice metric or Sorensen-Dice coefficient, among other names. This is a metric to compare the similarity of two samples. In our case we'll use it to compare the two areas of interest, i.e., the area of the expertly-labelled contour and the area of our predicted contour. The formula for computing the Dice metric is:\n", "\n", "$$ \\frac{2A_{nl}}{A_{n} + A_{l}} $$\n", "\n", "where $A_n$ is the area of the contour predicted by our neural network, $A_l$ is the area of the contour from the expertly-segmented label and $A_{nl}$ is the intersection of the two, i.e., the area of the contour that is predicted correctly by the network. 1.0 means perfect score.\n", "\n", "This metric will more accurately compute how well our network is segmenting the LV because the class imbalance problem is negated. Since we're trying to determine how much area is contained in a particular contour, we can simply count the pixels to give us the area.\n", "\n", "If you're interested in see how the Dice metric is added to the accuracy computation you can view that in the source code file [`neuralnetwork.py`](/7j5skJV2qH/edit/exercises/cnnDice/neuralnetwork.py)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the training by executing the cell below with 1 epoch and then check your accuracy by running the evaluation (two cells down). Then try running the training with 30 epochs. This is similar to what you may have done with the previous task. Check your accuracy after 30 epochs as well. Visualize the results in TensorBoard." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG images shape inference (1, 256, 256)\n", "DEBUG images shape inference (1, 256, 256)\n", "DEBUG W_conv1 shape (5, 5, 1, 100)\n", "DEBUG conv1_op shape (1, 128, 128, 100)\n", "DEBUG relu1_op shape (1, 128, 128, 100)\n", "DEBUG pool1_op shape (1, 64, 64, 100)\n", "DEBUG W_conv2 shape (5, 5, 100, 200)\n", "DEBUG conv2_op shape (1, 32, 32, 200)\n", "DEBUG relu2_op shape (1, 32, 32, 200)\n", "DEBUG pool2_op shape (1, 16, 16, 200)\n", "DEBUG W_conv3 shape (3, 3, 200, 300)\n", "DEBUG conv3_op shape (1, 16, 16, 300)\n", "DEBUG relu3_op shape (1, 16, 16, 300)\n", "DEBUG W_conv4 shape (3, 3, 300, 300)\n", "DEBUG conv4_op shape (1, 16, 16, 300)\n", "DEBUG drop_op shape (1, 16, 16, 300)\n", "DEBUG W_score_classes_shape (1, 1, 300, 2)\n", "DEBUG score_conv_op shape (1, 16, 16, 2)\n", "DEBUG W_upscore shape (31, 31, 2, 2)\n", "DEBUG upscore_conv_op shape (1, 256, 256, 2)\n", "DEBUG logits shape before (1, 256, 256, 2)\n", "DEBUG labels shape before (1, 256, 256)\n", "DEBUG logits shape after (1, 256, 256, 2)\n", "DEBUG labels shape after (1, 256, 256)\n", "OUTPUT: Step 0: loss = 0.723 (0.582 sec)\n", "OUTPUT: Step 100: loss = 0.701 (0.010 sec)\n", "OUTPUT: Step 200: loss = 0.696 (0.010 sec)\n", "OUTPUT: Step 300: loss = 0.715 (0.011 sec)\n", "OUTPUT: Step 400: loss = 0.688 (0.010 sec)\n", "OUTPUT: Step 500: loss = 0.686 (0.010 sec)\n", "OUTPUT: Step 600: loss = 0.681 (0.011 sec)\n", "OUTPUT: Step 700: loss = 0.659 (0.010 sec)\n", "OUTPUT: Step 800: loss = 0.625 (0.010 sec)\n", "OUTPUT: Step 900: loss = 0.553 (0.010 sec)\n", "OUTPUT: Step 1000: loss = 0.397 (0.011 sec)\n", "OUTPUT: Step 1100: loss = 0.238 (0.010 sec)\n", "OUTPUT: Step 1200: loss = 0.227 (0.010 sec)\n", "OUTPUT: Step 1300: loss = 0.291 (0.010 sec)\n", "OUTPUT: Step 1400: loss = 0.212 (0.011 sec)\n", "OUTPUT: Step 1500: loss = 0.278 (0.010 sec)\n", "OUTPUT: Step 1600: loss = 0.168 (0.010 sec)\n", "OUTPUT: Step 1700: loss = 0.230 (0.010 sec)\n", "OUTPUT: Step 1800: loss = 0.270 (0.010 sec)\n", "OUTPUT: Step 1900: loss = 0.214 (0.011 sec)\n", "OUTPUT: Step 2000: loss = 0.159 (0.010 sec)\n", "OUTPUT: Step 2100: loss = 0.153 (0.010 sec)\n", "OUTPUT: Step 2200: loss = 0.165 (0.010 sec)\n", "OUTPUT: Step 2300: loss = 0.140 (0.010 sec)\n", "OUTPUT: Step 2400: loss = 0.081 (0.010 sec)\n", "OUTPUT: Step 2500: loss = 0.183 (0.011 sec)\n", "OUTPUT: Step 2600: loss = 0.179 (0.011 sec)\n", "OUTPUT: Step 2700: loss = 0.065 (0.010 sec)\n", "OUTPUT: Step 2800: loss = 0.172 (0.011 sec)\n", "OUTPUT: Step 2900: loss = 0.121 (0.011 sec)\n", "OUTPUT: Step 3000: loss = 0.083 (0.010 sec)\n", "OUTPUT: Step 3100: loss = 0.061 (0.010 sec)\n", "OUTPUT: Step 3200: loss = 0.115 (0.011 sec)\n", "OUTPUT: Step 3300: loss = 0.120 (0.011 sec)\n", "OUTPUT: Step 3400: loss = 0.143 (0.011 sec)\n", "OUTPUT: Step 3500: loss = 0.110 (0.010 sec)\n", "OUTPUT: Step 3600: loss = 0.093 (0.010 sec)\n", "OUTPUT: Step 3700: loss = 0.142 (0.010 sec)\n", "OUTPUT: Step 3800: loss = 0.042 (0.010 sec)\n", "OUTPUT: Step 3900: loss = 0.039 (0.010 sec)\n", "OUTPUT: Step 4000: loss = 0.054 (0.011 sec)\n", "OUTPUT: Step 4100: loss = 0.167 (0.011 sec)\n", "OUTPUT: Step 4200: loss = 0.043 (0.010 sec)\n", "OUTPUT: Step 4300: loss = 0.074 (0.010 sec)\n", "OUTPUT: Step 4400: loss = 0.069 (0.011 sec)\n", "OUTPUT: Step 4500: loss = 0.107 (0.011 sec)\n", "OUTPUT: Step 4600: loss = 0.147 (0.010 sec)\n", "OUTPUT: Step 4700: loss = 0.074 (0.010 sec)\n", "OUTPUT: Step 4800: loss = 0.222 (0.010 sec)\n", "OUTPUT: Step 4900: loss = 0.035 (0.010 sec)\n", "OUTPUT: Step 5000: loss = 0.082 (0.010 sec)\n", "OUTPUT: Step 5100: loss = 0.088 (0.010 sec)\n", "OUTPUT: Step 5200: loss = 0.061 (0.010 sec)\n", "OUTPUT: Step 5300: loss = 0.054 (0.011 sec)\n", "OUTPUT: Step 5400: loss = 0.061 (0.010 sec)\n", "OUTPUT: Step 5500: loss = 0.083 (0.010 sec)\n", "OUTPUT: Step 5600: loss = 0.060 (0.010 sec)\n", "OUTPUT: Step 5700: loss = 0.061 (0.010 sec)\n", "OUTPUT: Step 5800: loss = 0.054 (0.011 sec)\n", "OUTPUT: Step 5900: loss = 0.046 (0.010 sec)\n", "OUTPUT: Step 6000: loss = 0.058 (0.010 sec)\n", "OUTPUT: Step 6100: loss = 0.047 (0.010 sec)\n", "OUTPUT: Step 6200: loss = 0.105 (0.010 sec)\n", "OUTPUT: Step 6300: loss = 0.116 (0.011 sec)\n", "OUTPUT: Step 6400: loss = 0.061 (0.010 sec)\n", "OUTPUT: Step 6500: loss = 0.068 (0.011 sec)\n", "OUTPUT: Step 6600: loss = 0.068 (0.011 sec)\n", "OUTPUT: Step 6700: loss = 0.108 (0.010 sec)\n", "OUTPUT: Step 6800: loss = 0.047 (0.010 sec)\n", "OUTPUT: Step 6900: loss = 0.084 (0.011 sec)\n", "OUTPUT: Done training for 30 epochs, 6950 steps.\n" ] } ], "source": [ "!python exercises/cnnDice/runTraining.py --data_dir /data --num_epochs 30" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG images shape inference (1, 256, 256)\n", "DEBUG images shape inference (1, 256, 256)\n", "DEBUG W_conv1 shape (5, 5, 1, 100)\n", "DEBUG conv1_op shape (1, 128, 128, 100)\n", "DEBUG relu1_op shape (1, 128, 128, 100)\n", "DEBUG pool1_op shape (1, 64, 64, 100)\n", "DEBUG W_conv2 shape (5, 5, 100, 200)\n", "DEBUG conv2_op shape (1, 32, 32, 200)\n", "DEBUG relu2_op shape (1, 32, 32, 200)\n", "DEBUG pool2_op shape (1, 16, 16, 200)\n", "DEBUG W_conv3 shape (3, 3, 200, 300)\n", "DEBUG conv3_op shape (1, 16, 16, 300)\n", "DEBUG relu3_op shape (1, 16, 16, 300)\n", "DEBUG W_conv4 shape (3, 3, 300, 300)\n", "DEBUG conv4_op shape (1, 16, 16, 300)\n", "DEBUG drop_op shape (1, 16, 16, 300)\n", "DEBUG W_score_classes_shape (1, 1, 300, 2)\n", "DEBUG score_conv_op shape (1, 16, 16, 2)\n", "DEBUG W_upscore shape (31, 31, 2, 2)\n", "DEBUG upscore_conv_op shape (1, 256, 256, 2)\n", "DEBUG logits eval shape before (1, 256, 256, 2)\n", "DEBUG labels eval shape before (1, 256, 256)\n", "DEBUG logits_re eval shape after (65536, 2)\n", "DEBUG labels_re eval shape after (65536,)\n", "DEBUG labels_re eval shape after (65536, 1)\n", "DEBUG indices shape (65536, 1)\n", "DEBUG example_sum shape ()\n", "DEBUG label_sum shape ()\n", "DEBUG sum_tensor shape (65536, 1)\n", "DEBUG twos shape (65536, 1)\n", "DEBUG divs shape (65536, 1)\n", "OUTPUT: 2017-05-09 17:44:14.256720: Dice metric = 0.569\n", "OUTPUT: 26 images evaluated from file /data/val_images.tfrecords\n" ] } ], "source": [ "!python exercises/cnnDice/runEval.py --data_dir /data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you run with one epoch you're likely to get a result of less than 1% accuracy. In a prior run we obtained \n", "\n", "```\n", "OUTPUT: 2017-01-27 18:44:04.103153: Dice metric = 0.034\n", "```\n", "\n", "for 1 epoch. If you try with 30 epochs you might get around 57% accuracy.\n", "\n", "```\n", "OUTPUT: 2017-01-27 18:56:45.501209: Dice metric = 0.568\n", "```\n", "\n", "With a more realistic accuracy metric, you can see that there is some room for improvement in the neural network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameter Search\n", "\n", "At this point we've created a neural network that we think has the right structure to do a reasonably good job and we've used an accuracy metric that correctly tells us how well our network is learning the segmentation task. But up to this point our evaluation accuracy hasn't been as high as we'd like. The next thing to consider is that we should try to search the parameter space a bit more. Up to now we've changed the number of epochs but that's all we've adjusted. There are a few more parameters we can test that could push our accuracy score higher. These are: \n", "\n", "* --learning_rate: the initial learning rate\n", "* --decay_rate: the rate that the initial learning rate decays., e.g., 1.0 is no decay, 0.5 means cut the decay rate in half each step, etc.\n", "* --decay_steps: the number of steps to execute before changing the learning rate\n", "\n", "The learning rate is the rate at which the weights are adjusted each time we run back propogation. If the learning rate is too large, we might end up adjusting the weights by values that are too large and we'll end up oscillating around a correct solution instead of converging. If the learning rate is too small, the adjustments to the weights will be too small and it might take a very long time before we converge to a solution that we are happy with. One technique often utilized is a variable, or adjustable learning rate. At the beginning of training, we'll use a larger learning rate so that we make large adjustments to the weights and hopefully get in the neighborhood of a good solution. Then as we continue to train we'll successively decrease the learning rate so that we can begin to zero in on a solution. The three parameters listed above will help you control the learning rate, how much it changes, and how often it changes. As a baseline, if you don't select those options, the default used (and that you've been using so far in this lab) are:\n", "\n", "```\n", "--learning_rate 0.01\n", "--decay_rate 1.0\n", "--decay_steps 1000\n", "--num_epochs 1\n", "```\n", "\n", "Play around with these values by running training in the next cell and see if you can come up with better accuracy than seen earlier. We don't recommend running more than 100 epochs due to time constraints of the lab, but in production you would quite likely run a lot more epochs. \n", "\n", "Conveniently, if you start a training run and realize the number of epochs is too large you can kill that run and still test the model by running the evaluation (two cells down). TensorFlow has checkpointing abilities that snapshot the model periodically so the most recent snapshot will still be retained after you kill the training run." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!python exercises/cnnDice/runTraining.py --data_dir /data --num_epochs 1 --learning_rate 0.01 --decay_rate 1.0 --decay_steps 1000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!python exercises/cnnDice/runEval.py --data_dir /data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the solutions we obtained was 86% accuracy. Check [A](#A) to see what parameters we used for the training." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Further enhancements\n", "\n", "For illustrative purposes we focused on smaller tasks that could run in the time we have alloted for this lab, but if we were going to run an image segmentation task in a production setting what more would we do to accomplish this? A few things we'd do are the following.\n", "\n", "* Run Training Longer -- We ran very short training runs but in reality we'd run many more epochs.\n", "* More Training Data -- We only had 236 images in our training set. We could gather more data and we could also augment the data we have. TensorFlow has built-in functions to flip/rotate/transpose images automatically.\n", "* Larger networks -- We could try using AlexNet or other large CNN and convert them to FCN." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In this lab you had a chance to explore image segmentation with TensorFlow as the framework of choice. You saw how to convert a standard CNN into an FCN for use as a segmentation network. You also saw how choosing the correct accuracy metric is crucial in training the network. Finally you had a chance to see how performing a parameter search is an integral part of the deep learning workflow to ultimately settle on a network that performs with acceptable accuracy for the task at hand." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learn More\n", "\n", "If you are interested in learning more, you can use the following resources:\n", "\n", "* Learn more at the [CUDA Developer Zone](https://developer.nvidia.com/category/zone/cuda-zone).\n", "* If you have an NVIDIA GPU in your system, you can download and install the [CUDA tookit](https://developer.nvidia.com/cuda-toolkit).\n", "* Take the fantastic online and **free** Udacity [Intro to Parallel Programming](https://www.udacity.com/course/cs344) course which uses CUDA C.\n", "* Search or ask questions on [Stackoverflow](http://stackoverflow.com/questions/tagged/cuda) using the cuda tag" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"post-lab\"></a>\n", "## Post-Lab\n", "\n", "Finally, don't forget to save your work from this lab before time runs out and the instance shuts down!!\n", "\n", "1. Save this IPython Notebook by going to `File -> Download as -> IPython (.ipynb)` at the top of this window\n", "2. You can execute the following cell block to create a zip-file of the files you've been working on, and download it with the link below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"FAQ\"></a>\n", "---\n", "# Lab FAQ\n", "\n", "Q: I'm encountering issues executing the cells, or other technical problems?<br>\n", "A: Please see [this](https://developer.nvidia.com/self-paced-labs-faq#Troubleshooting) infrastructure FAQ.\n", "\n", "Q: I'm getting unexpected behavior (i.e., incorrect output) when running any of the tasks.<br>\n", "A: It's possible that one or more of the CUDA Runtime API calls are actually returning an error. Are you getting any errors printed to the screen about CUDA Runtime errors?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"References\"></a>\n", "# References\n", "\n", "<a id=\"1\"></a>\n", "[1] Sunnybrook cardiac images from earlier competition http://smial.sri.utoronto.ca/LV_Challenge/Data.html\n", "\n", "<a id=\"2\"></a>\n", "[2] This \"Sunnybrook Cardiac MR Database\" is made available under the CC0 1.0 Universal license described above, and with more detail here: http://creativecommons.org/publicdomain/zero/1.0/\n", "\n", "<a id=\"3\"></a>\n", "[3] Attribution:\n", "Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. \"Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.\" The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070\n", "\n", "<a id=\"4\"></a>\n", "[4] http://fcn.berkeleyvision.org/\n", "\n", "<a id=\"5\"></a>\n", "[5] Long, Shelhamer, Darrell; \"Fully Convoutional Networks for Semantic Segmentation\", CVPR 2015.\n", "\n", "<a id=\"6\"></a>\n", "[6] Zeiler, Krishnan, Taylor, Fergus; \"Deconvolutional Networks\", CVPR 2010.\n", "\n", "<a id=\"7\"></a>\n", "[7] https://www.kaggle.com/c/second-annual-data-science-bowl/details/deep-learning-tutorial\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solutions\n", "\n", "<a id=\"A\"></a>\n", "[A] The following configuration will result in roughly 86% accuracy.\n", "\n", "```\n", "--learning_rate 0.03\n", "--decay_rate 0.75\n", "--num_epochs 100\n", "--decay_steps 10000\n", "OUTPUT: 2017-01-27 20:19:08.702868: Dice metric = 0.862\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
gatmeh/Udacity-deep-learning
tv-script-generation/dlnd_tv_script_generation.ipynb
10
30590
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n", "## Get the Data\n", "The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like \"Moe's Cavern\", \"Flaming Moe's\", \"Uncle Moe's Family Feed-Bag\", etc.." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "\n", "data_dir = './data/simpsons/moes_tavern_lines.txt'\n", "text = helper.load_data(data_dir)\n", "# Ignore notice, since we don't use it for analysing the data\n", "text = text[81:]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Explore the Data\n", "Play around with `view_sentence_range` to view different parts of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))\n", "scenes = text.split('\\n\\n')\n", "print('Number of scenes: {}'.format(len(scenes)))\n", "sentence_count_scene = [scene.count('\\n') for scene in scenes]\n", "print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))\n", "\n", "sentences = [sentence for scene in scenes for sentence in scene.split('\\n')]\n", "print('Number of lines: {}'.format(len(sentences)))\n", "word_count_sentence = [len(sentence.split()) for sentence in sentences]\n", "print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))\n", "\n", "print()\n", "print('The sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Preprocessing Functions\n", "The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:\n", "- Lookup Table\n", "- Tokenize Punctuation\n", "\n", "### Lookup Table\n", "To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:\n", "- Dictionary to go from the words to an id, we'll call `vocab_to_int`\n", "- Dictionary to go from the id to word, we'll call `int_to_vocab`\n", "\n", "Return these dictionaries in the following tuple `(vocab_to_int, int_to_vocab)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import problem_unittests as tests\n", "\n", "def create_lookup_tables(text):\n", " \"\"\"\n", " Create lookup tables for vocabulary\n", " :param text: The text of tv scripts split into words\n", " :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_create_lookup_tables(create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Tokenize Punctuation\n", "We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n", "\n", "Implement the function `token_lookup` to return a dict that will be used to tokenize symbols like \"!\" into \"||Exclamation_Mark||\". Create a dictionary for the following symbols where the symbol is the key and value is the token:\n", "- Period ( . )\n", "- Comma ( , )\n", "- Quotation Mark ( \" )\n", "- Semicolon ( ; )\n", "- Exclamation mark ( ! )\n", "- Question mark ( ? )\n", "- Left Parentheses ( ( )\n", "- Right Parentheses ( ) )\n", "- Dash ( -- )\n", "- Return ( \\n )\n", "\n", "This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token \"dash\", try using something like \"||dash||\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def token_lookup():\n", " \"\"\"\n", " Generate a dict to turn punctuation into a token.\n", " :return: Tokenize dictionary where the key is the punctuation and the value is the token\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_tokenize(token_lookup)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import numpy as np\n", "import problem_unittests as tests\n", "\n", "int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a RNN by implementing the following functions below:\n", "- get_inputs\n", "- get_init_cell\n", "- get_embed\n", "- build_rnn\n", "- build_nn\n", "- get_batches\n", "\n", "### Check the Version of TensorFlow and Access to GPU" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Input\n", "Implement the `get_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "- Input text placeholder named \"input\" using the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) `name` parameter.\n", "- Targets placeholder\n", "- Learning Rate placeholder\n", "\n", "Return the placeholders in the following tuple `(Input, Targets, LearningRate)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_inputs(get_inputs)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build RNN Cell and Initialize\n", "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n", "- The Rnn size should be set using `rnn_size`\n", "- Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function\n", " - Apply the name \"initial_state\" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the cell and initial state in the following tuple `(Cell, InitialState)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_init_cell(batch_size, rnn_size):\n", " \"\"\"\n", " Create an RNN Cell and initialize it.\n", " :param batch_size: Size of batches\n", " :param rnn_size: Size of RNNs\n", " :return: Tuple (cell, initialize state)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_init_cell(get_init_cell)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Word Embedding\n", "Apply embedding to `input_data` using TensorFlow. Return the embedded sequence." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_embed(input_data, vocab_size, embed_dim):\n", " \"\"\"\n", " Create embedding for <input_data>.\n", " :param input_data: TF placeholder for text input.\n", " :param vocab_size: Number of words in vocabulary.\n", " :param embed_dim: Number of embedding dimensions\n", " :return: Embedded input.\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_embed(get_embed)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build RNN\n", "You created a RNN Cell in the `get_init_cell()` function. Time to use the cell to create a RNN.\n", "- Build the RNN using the [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\n", " - Apply the name \"final_state\" to the final state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the outputs and final_state state in the following tuple `(Outputs, FinalState)` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_rnn(cell, inputs):\n", " \"\"\"\n", " Create a RNN using a RNN Cell\n", " :param cell: RNN Cell\n", " :param inputs: Input text data\n", " :return: Tuple (Outputs, Final State)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_rnn(build_rnn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "- Apply embedding to `input_data` using your `get_embed(input_data, vocab_size, embed_dim)` function.\n", "- Build RNN using `cell` and your `build_rnn(cell, inputs)` function.\n", "- Apply a fully connected layer with a linear activation and `vocab_size` as the number of outputs.\n", "\n", "Return the logits and final state in the following tuple (Logits, FinalState) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):\n", " \"\"\"\n", " Build part of the neural network\n", " :param cell: RNN cell\n", " :param rnn_size: Size of rnns\n", " :param input_data: Input data\n", " :param vocab_size: Vocabulary size\n", " :param embed_dim: Number of embedding dimensions\n", " :return: Tuple (Logits, FinalState)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_nn(build_nn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Batches\n", "Implement `get_batches` to create batches of input and targets using `int_text`. The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n", "- The first element is a single batch of **input** with the shape `[batch size, sequence length]`\n", "- The second element is a single batch of **targets** with the shape `[batch size, sequence length]`\n", "\n", "If you can't fill the last batch with enough data, drop the last batch.\n", "\n", "For exmple, `get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], 3, 2)` would return a Numpy array of the following:\n", "```\n", "[\n", " # First Batch\n", " [\n", " # Batch of Input\n", " [[ 1 2], [ 7 8], [13 14]]\n", " # Batch of targets\n", " [[ 2 3], [ 8 9], [14 15]]\n", " ]\n", "\n", " # Second Batch\n", " [\n", " # Batch of Input\n", " [[ 3 4], [ 9 10], [15 16]]\n", " # Batch of targets\n", " [[ 4 5], [10 11], [16 17]]\n", " ]\n", "\n", " # Third Batch\n", " [\n", " # Batch of Input\n", " [[ 5 6], [11 12], [17 18]]\n", " # Batch of targets\n", " [[ 6 7], [12 13], [18 1]]\n", " ]\n", "]\n", "```\n", "\n", "Notice that the last target value in the last batch is the first input value of the first batch. In this case, `1`. This is a common technique used when creating sequence batches, although it is rather unintuitive." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_batches(int_text, batch_size, seq_length):\n", " \"\"\"\n", " Return batches of input and target\n", " :param int_text: Text with the words replaced by their ids\n", " :param batch_size: The size of batch\n", " :param seq_length: The length of sequence\n", " :return: Batches as a Numpy array\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_batches(get_batches)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `num_epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `embed_dim` to the size of the embedding.\n", "- Set `seq_length` to the length of sequence.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `show_every_n_batches` to the number of batches the neural network should print progress." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Number of Epochs\n", "num_epochs = None\n", "# Batch Size\n", "batch_size = None\n", "# RNN Size\n", "rnn_size = None\n", "# Embedding Dimension Size\n", "embed_dim = None\n", "# Sequence Length\n", "seq_length = None\n", "# Learning Rate\n", "learning_rate = None\n", "# Show stats for every n number of batches\n", "show_every_n_batches = None\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "save_dir = './save'" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from tensorflow.contrib import seq2seq\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " vocab_size = len(int_to_vocab)\n", " input_text, targets, lr = get_inputs()\n", " input_data_shape = tf.shape(input_text)\n", " cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)\n", " logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)\n", "\n", " # Probabilities for generating words\n", " probs = tf.nn.softmax(logits, name='probs')\n", "\n", " # Loss function\n", " cost = seq2seq.sequence_loss(\n", " logits,\n", " targets,\n", " tf.ones([input_data_shape[0], input_data_shape[1]]))\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the [forums](https://discussions.udacity.com/) to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "batches = get_batches(int_text, batch_size, seq_length)\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(num_epochs):\n", " state = sess.run(initial_state, {input_text: batches[0][0]})\n", "\n", " for batch_i, (x, y) in enumerate(batches):\n", " feed = {\n", " input_text: x,\n", " targets: y,\n", " initial_state: state,\n", " lr: learning_rate}\n", " train_loss, state, _ = sess.run([cost, final_state, train_op], feed)\n", "\n", " # Show every <show_every_n_batches> batches\n", " if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n", " print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(\n", " epoch_i,\n", " batch_i,\n", " len(batches),\n", " train_loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_dir)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Save Parameters\n", "Save `seq_length` and `save_dir` for generating a new TV script." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params((seq_length, save_dir))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()\n", "seq_length, load_dir = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Generate Functions\n", "### Get Tensors\n", "Get tensors from `loaded_graph` using the function [`get_tensor_by_name()`](https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name). Get the tensors using the following names:\n", "- \"input:0\"\n", "- \"initial_state:0\"\n", "- \"final_state:0\"\n", "- \"probs:0\"\n", "\n", "Return the tensors in the following tuple `(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_tensors(loaded_graph):\n", " \"\"\"\n", " Get input, initial state, final state, and probabilities tensor from <loaded_graph>\n", " :param loaded_graph: TensorFlow graph loaded from file\n", " :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None, None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_tensors(get_tensors)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Choose Word\n", "Implement the `pick_word()` function to select the next word using `probabilities`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def pick_word(probabilities, int_to_vocab):\n", " \"\"\"\n", " Pick the next word in the generated text\n", " :param probabilities: Probabilites of the next word\n", " :param int_to_vocab: Dictionary of word ids as the keys and words as the values\n", " :return: String of the predicted word\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_pick_word(pick_word)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generate TV Script\n", "This will generate the TV script for you. Set `gen_length` to the length of TV script you want to generate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "gen_length = 200\n", "# homer_simpson, moe_szyslak, or Barney_Gumble\n", "prime_word = 'moe_szyslak'\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_dir + '.meta')\n", " loader.restore(sess, load_dir)\n", "\n", " # Get Tensors from loaded model\n", " input_text, initial_state, final_state, probs = get_tensors(loaded_graph)\n", "\n", " # Sentences generation setup\n", " gen_sentences = [prime_word + ':']\n", " prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n", "\n", " # Generate sentences\n", " for n in range(gen_length):\n", " # Dynamic Input\n", " dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]\n", " dyn_seq_length = len(dyn_input[0])\n", "\n", " # Get Prediction\n", " probabilities, prev_state = sess.run(\n", " [probs, final_state],\n", " {input_text: dyn_input, initial_state: prev_state})\n", " \n", " pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)\n", "\n", " gen_sentences.append(pred_word)\n", " \n", " # Remove tokens\n", " tv_script = ' '.join(gen_sentences)\n", " for key, token in token_dict.items():\n", " ending = ' ' if key in ['\\n', '(', '\"'] else ''\n", " tv_script = tv_script.replace(' ' + token.lower(), key)\n", " tv_script = tv_script.replace('\\n ', '\\n')\n", " tv_script = tv_script.replace('( ', '(')\n", " \n", " print(tv_script)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# The TV Script is Nonsensical\n", "It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data). We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.\n", "# Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_tv_script_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
rsignell-usgs/notebook
ERDDAP/GliderDAC_Search.ipynb
1
161789
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Search GliderDAC for Pioneer Glider Data" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Use ERDDAP's RESTful advanced search to try to find OOI Pioneer glider water temperatures from the IOOS GliderDAC. Use case from Stace Beaulieu ([email protected])" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First try just searching for \"glider\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Title</th>\n", " <th>Summary</th>\n", " <th>Institution</th>\n", " <th>Dataset ID</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>All aoml Gliders</td>\n", " <td>Seaglider data gathered as part of the Sustain...</td>\n", " <td>National Oceanic and Atmospheric Administratio...</td>\n", " <td>allaomlGliders</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>All corie Gliders</td>\n", " <td>Slocum glider dataset gathered as part of the ...</td>\n", " <td>Oregon Health &amp; Science University</td>\n", " <td>allcorieGliders</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>All mbari Gliders</td>\n", " <td>Seaglider SG130 Trinidad Head IOOS line, Calif...</td>\n", " <td>Oregon State University¦College of Earth, Ocea...</td>\n", " <td>allmbariGliders</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>All rmiller Gliders</td>\n", " <td>Deployment in support of the calibration of Gr...</td>\n", " <td>GLOS/CILER</td>\n", " <td>allrmillerGliders</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>All rutgers Gliders</td>\n", " <td>KOPRI is an international collaboration betwee...</td>\n", " <td>KOPRI</td>\n", " <td>allrutgersGliders</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Title Summary \\\n", "0 All aoml Gliders Seaglider data gathered as part of the Sustain... \n", "1 All corie Gliders Slocum glider dataset gathered as part of the ... \n", "2 All mbari Gliders Seaglider SG130 Trinidad Head IOOS line, Calif... \n", "3 All rmiller Gliders Deployment in support of the calibration of Gr... \n", "4 All rutgers Gliders KOPRI is an international collaboration betwee... \n", "\n", " Institution Dataset ID \n", "0 National Oceanic and Atmospheric Administratio... allaomlGliders \n", "1 Oregon Health & Science University allcorieGliders \n", "2 Oregon State University¦College of Earth, Ocea... allmbariGliders \n", "3 GLOS/CILER allrmillerGliders \n", "4 KOPRI allrutgersGliders " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = 'https://data.ioos.us/gliders/erddap/search/advanced.csv?page=1&itemsPerPage=1000&searchFor={}'.format('glider')\n", "dft = pd.read_csv(url, usecols=['Title', 'Summary', 'Institution','Dataset ID']) \n", "dft.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now search for all temperature data in specified bounding box and temporal extent" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "start = '2000-01-01T00:00:00Z'\n", "stop = '2017-02-22T00:00:00Z'\n", "lat_min = 39.\n", "lat_max = 41.5\n", "lon_min = -72.\n", "lon_max = -69.\n", "standard_name = 'sea_water_temperature'\n", "endpoint = 'https://data.ioos.us/gliders/erddap/search/advanced.csv'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://data.ioos.us/gliders/erddap/search/advanced.csv?page=1&itemsPerPage=1000&searchFor=&protocol=(ANY)&cdm_data_type=(ANY)&institution=(ANY)&ioos_category=(ANY)&keywords=(ANY)&long_name=(ANY)&standard_name=sea_water_temperature&variableName=(ANY)&maxLat=41.5&minLon=-72.0&maxLon=-69.0&minLat=39.0&minTime=2000-01-01T00:00:00Z&maxTime=2017-02-22T00:00:00Z\n" ] } ], "source": [ "import pandas as pd\n", "\n", "base = (\n", " '{}'\n", " '?page=1'\n", " '&itemsPerPage=1000'\n", " '&searchFor='\n", " '&protocol=(ANY)'\n", " '&cdm_data_type=(ANY)'\n", " '&institution=(ANY)'\n", " '&ioos_category=(ANY)'\n", " '&keywords=(ANY)'\n", " '&long_name=(ANY)'\n", " '&standard_name={}'\n", " '&variableName=(ANY)'\n", " '&maxLat={}'\n", " '&minLon={}'\n", " '&maxLon={}'\n", " '&minLat={}'\n", " '&minTime={}'\n", " '&maxTime={}').format\n", "\n", "url = base(\n", " endpoint,\n", " standard_name,\n", " lat_max,\n", " lon_min,\n", " lon_max,\n", " lat_min,\n", " start,\n", " stop\n", ")\n", "\n", "print(url)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Glider Datasets Found = 10\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Title</th>\n", " <th>Summary</th>\n", " <th>Institution</th>\n", " <th>Dataset ID</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>All drudnick Gliders</td>\n", " <td>Spray glider profile data from Scripps Institu...</td>\n", " <td>Scripps Institution of Oceanography</td>\n", " <td>alldrudnickGliders</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>All rutgers Gliders</td>\n", " <td>KOPRI is an international collaboration betwee...</td>\n", " <td>KOPRI</td>\n", " <td>allrutgersGliders</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>blue-20150627T1242</td>\n", " <td>U.S. IOOS Mid-Atlantic Regional Consortium of ...</td>\n", " <td>University of Massachusetts Darmouth</td>\n", " <td>blue-20150627T1242</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>blue-20160518T1525</td>\n", " <td>U.S. IOOS Mid-Atlantic Regional Association Co...</td>\n", " <td>University of Massachusetts Darmouth</td>\n", " <td>blue-20160518T1525</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>blue-20160818T1448</td>\n", " <td>U.S. IOOS Mid-Atlantic Regional Association Co...</td>\n", " <td>University of Massachusetts Darmouth</td>\n", " <td>blue-20160818T1448</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>silbo-20160413T1534</td>\n", " <td>The Silbo Challenger mission is a partnership ...</td>\n", " <td>Teledyne Webb Research Corporation</td>\n", " <td>silbo-20160413T1534</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>sp010-20150409T1524</td>\n", " <td>Spray glider profile data from Woods Hole Ocea...</td>\n", " <td>Woods Hole Oceanographic Institution</td>\n", " <td>sp010-20150409T1524</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>sp065-20151001T1507</td>\n", " <td>Spray glider profile data from Woods Hole Ocea...</td>\n", " <td>Woods Hole Oceanographic Institution</td>\n", " <td>sp065-20151001T1507</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>sp066-20151217T1624</td>\n", " <td>Spray glider profile data from Woods Hole Ocea...</td>\n", " <td>Woods Hole Oceanographic Institution</td>\n", " <td>sp066-20151217T1624</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>whoi_406-20160902T1700</td>\n", " <td>Slocum glider dataset gathered as part of the ...</td>\n", " <td>Woods Hole Oceanographic Institution</td>\n", " <td>whoi_406-20160902T1700</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Title Summary \\\n", "0 All drudnick Gliders Spray glider profile data from Scripps Institu... \n", "1 All rutgers Gliders KOPRI is an international collaboration betwee... \n", "2 blue-20150627T1242 U.S. IOOS Mid-Atlantic Regional Consortium of ... \n", "3 blue-20160518T1525 U.S. IOOS Mid-Atlantic Regional Association Co... \n", "4 blue-20160818T1448 U.S. IOOS Mid-Atlantic Regional Association Co... \n", "5 silbo-20160413T1534 The Silbo Challenger mission is a partnership ... \n", "6 sp010-20150409T1524 Spray glider profile data from Woods Hole Ocea... \n", "7 sp065-20151001T1507 Spray glider profile data from Woods Hole Ocea... \n", "8 sp066-20151217T1624 Spray glider profile data from Woods Hole Ocea... \n", "9 whoi_406-20160902T1700 Slocum glider dataset gathered as part of the ... \n", "\n", " Institution Dataset ID \n", "0 Scripps Institution of Oceanography alldrudnickGliders \n", "1 KOPRI allrutgersGliders \n", "2 University of Massachusetts Darmouth blue-20150627T1242 \n", "3 University of Massachusetts Darmouth blue-20160518T1525 \n", "4 University of Massachusetts Darmouth blue-20160818T1448 \n", "5 Teledyne Webb Research Corporation silbo-20160413T1534 \n", "6 Woods Hole Oceanographic Institution sp010-20150409T1524 \n", "7 Woods Hole Oceanographic Institution sp065-20151001T1507 \n", "8 Woods Hole Oceanographic Institution sp066-20151217T1624 \n", "9 Woods Hole Oceanographic Institution whoi_406-20160902T1700 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dft = pd.read_csv(url, usecols=['Title', 'Summary', 'Institution', 'Dataset ID']) \n", "print('Glider Datasets Found = {}'.format(len(dft)))\n", "dft" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function that returns a Pandas DataFrame based on the dataset ID. The ERDDAP request variables (e.g. pressure, temperature) are hard-coded here, so this routine should be modified for other ERDDAP endpoints or datasets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def download_df(glider_id):\n", " from pandas import DataFrame, read_csv\n", "# from urllib.error import HTTPError\n", " uri = ('https://data.ioos.us/gliders/erddap/tabledap/{}.csv'\n", " '?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature'\n", " '&time>={}'\n", " '&time<={}'\n", " '&latitude>={}'\n", " '&latitude<={}'\n", " '&longitude>={}'\n", " '&longitude<={}').format\n", " url = uri(glider_id,start,stop,lat_min,lat_max,lon_min,lon_max)\n", " print(url)\n", " # Not sure if returning an empty df is the best idea.\n", " try:\n", " df = read_csv(url, index_col='time', parse_dates=True, skiprows=[1])\n", " except:\n", " df = pd.DataFrame()\n", " return df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://data.ioos.us/gliders/erddap/tabledap/alldrudnickGliders.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/allrutgersGliders.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/blue-20150627T1242.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/blue-20160518T1525.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/blue-20160818T1448.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/silbo-20160413T1534.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/sp010-20150409T1524.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/sp065-20151001T1507.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/sp066-20151217T1624.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n", "https://data.ioos.us/gliders/erddap/tabledap/whoi_406-20160902T1700.csv?trajectory,wmo_id,time,latitude,longitude,depth,pressure,temperature&time>=2000-01-01T00:00:00Z&time<=2017-02-22T00:00:00Z&latitude>=39.0&latitude<=41.5&longitude>=-72.0&longitude<=-69.0\n" ] } ], "source": [ "# concatenate the dataframes for each dataset into one single dataframe \n", "df = pd.concat(list(map(download_df, dft['Dataset ID'].values)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Data Values Found: 579822\n" ] } ], "source": [ "print('Total Data Values Found: {}'.format(len(df)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>trajectory</th>\n", " <th>wmo_id</th>\n", " <th>latitude</th>\n", " <th>longitude</th>\n", " <th>depth</th>\n", " <th>pressure</th>\n", " <th>temperature</th>\n", " </tr>\n", " <tr>\n", " <th>time</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-06-20 04:12:15</th>\n", " <td>sp010-20150409T1524</td>\n", " <td>4801909</td>\n", " <td>39.44435</td>\n", " <td>-71.99732</td>\n", " <td>0.00000</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-20 04:12:15</th>\n", " <td>sp010-20150409T1524</td>\n", " <td>4801909</td>\n", " <td>39.44435</td>\n", " <td>-71.99732</td>\n", " <td>306.18097</td>\n", " <td>308.76</td>\n", " <td>9.555</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-20 04:12:15</th>\n", " <td>sp010-20150409T1524</td>\n", " <td>4801909</td>\n", " <td>39.44435</td>\n", " <td>-71.99732</td>\n", " <td>305.15042</td>\n", " <td>307.72</td>\n", " <td>9.589</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-20 04:12:15</th>\n", " <td>sp010-20150409T1524</td>\n", " <td>4801909</td>\n", " <td>39.44435</td>\n", " <td>-71.99732</td>\n", " <td>303.80276</td>\n", " <td>306.36</td>\n", " <td>9.663</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-20 04:12:15</th>\n", " <td>sp010-20150409T1524</td>\n", " <td>4801909</td>\n", " <td>39.44435</td>\n", " <td>-71.99732</td>\n", " <td>302.21730</td>\n", " <td>304.76</td>\n", " <td>9.769</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " trajectory wmo_id latitude longitude \\\n", "time \n", "2015-06-20 04:12:15 sp010-20150409T1524 4801909 39.44435 -71.99732 \n", "2015-06-20 04:12:15 sp010-20150409T1524 4801909 39.44435 -71.99732 \n", "2015-06-20 04:12:15 sp010-20150409T1524 4801909 39.44435 -71.99732 \n", "2015-06-20 04:12:15 sp010-20150409T1524 4801909 39.44435 -71.99732 \n", "2015-06-20 04:12:15 sp010-20150409T1524 4801909 39.44435 -71.99732 \n", "\n", " depth pressure temperature \n", "time \n", "2015-06-20 04:12:15 0.00000 0.00 NaN \n", "2015-06-20 04:12:15 306.18097 308.76 9.555 \n", "2015-06-20 04:12:15 305.15042 307.72 9.589 \n", "2015-06-20 04:12:15 303.80276 306.36 9.663 \n", "2015-06-20 04:12:15 302.21730 304.76 9.769 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>trajectory</th>\n", " <th>wmo_id</th>\n", " <th>latitude</th>\n", " <th>longitude</th>\n", " <th>depth</th>\n", " <th>pressure</th>\n", " <th>temperature</th>\n", " </tr>\n", " <tr>\n", " <th>time</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-09-20 13:32:33</th>\n", " <td>whoi_406-20160902T1700</td>\n", " <td>4801945</td>\n", " <td>41.048109</td>\n", " <td>-70.928635</td>\n", " <td>5.19</td>\n", " <td>5.19</td>\n", " <td>18.9365</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-20 13:32:33</th>\n", " <td>whoi_406-20160902T1700</td>\n", " <td>4801945</td>\n", " <td>41.048109</td>\n", " <td>-70.928635</td>\n", " <td>4.82</td>\n", " <td>4.82</td>\n", " <td>18.9450</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-20 13:32:33</th>\n", " <td>whoi_406-20160902T1700</td>\n", " <td>4801945</td>\n", " <td>41.048109</td>\n", " <td>-70.928635</td>\n", " <td>4.44</td>\n", " <td>4.44</td>\n", " <td>18.9572</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-20 13:32:33</th>\n", " <td>whoi_406-20160902T1700</td>\n", " <td>4801945</td>\n", " <td>41.048109</td>\n", " <td>-70.928635</td>\n", " <td>4.06</td>\n", " <td>4.06</td>\n", " <td>18.9713</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-20 13:32:33</th>\n", " <td>whoi_406-20160902T1700</td>\n", " <td>4801945</td>\n", " <td>41.048109</td>\n", " <td>-70.928635</td>\n", " <td>3.52</td>\n", " <td>3.52</td>\n", " <td>18.9974</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " trajectory wmo_id latitude longitude \\\n", "time \n", "2016-09-20 13:32:33 whoi_406-20160902T1700 4801945 41.048109 -70.928635 \n", "2016-09-20 13:32:33 whoi_406-20160902T1700 4801945 41.048109 -70.928635 \n", "2016-09-20 13:32:33 whoi_406-20160902T1700 4801945 41.048109 -70.928635 \n", "2016-09-20 13:32:33 whoi_406-20160902T1700 4801945 41.048109 -70.928635 \n", "2016-09-20 13:32:33 whoi_406-20160902T1700 4801945 41.048109 -70.928635 \n", "\n", " depth pressure temperature \n", "time \n", "2016-09-20 13:32:33 5.19 5.19 18.9365 \n", "2016-09-20 13:32:33 4.82 4.82 18.9450 \n", "2016-09-20 13:32:33 4.44 4.44 18.9572 \n", "2016-09-20 13:32:33 4.06 4.06 18.9713 \n", "2016-09-20 13:32:33 3.52 3.52 18.9974 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plot up the trajectories with Cartopy (Basemap replacement)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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cuIF169bBw8MDQ4cOxb1791QdFqkF/vOf/+C///0vwsPD0bdvX4UTaxFCCCGK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\nLjUfRwVyuZz22GMPTU5O1patWbNG11xzjd7xjnfMup1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f22b14438d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "from cartopy.feature import NaturalEarthFeature\n", "bathym_1000 = NaturalEarthFeature(name='bathymetry_J_1000',\n", " scale='10m', category='physical')\n", "fig, ax = plt.subplots(\n", " figsize=(9, 9),\n", " subplot_kw=dict(projection=ccrs.PlateCarree())\n", ")\n", "ax.coastlines(resolution='10m')\n", "ax.add_feature(bathym_1000, facecolor=[0.9, 0.9, 0.9], edgecolor='none')\n", "dx = dy = 0.5\n", "ax.set_extent([lon_min-dx, lon_max+dx, lat_min-dy, lat_max+dy])\n", "\n", "g = df.groupby('trajectory')\n", "for glider in g.groups:\n", " traj = df[df['trajectory'] == glider]\n", " ax.plot(traj['longitude'], traj['latitude'], label=glider)\n", "\n", "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", " linewidth=2, color='gray', alpha=0.5, linestyle='--')\n", "ax.legend();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:IOOS3]", "language": "python", "name": "conda-env-IOOS3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jbwhit/jupyter-best-practices
notebooks/05-SQL-Example.ipynb
2
78238
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SQL\n", "\n", "Accessing data stored in databases is a routine exercise. I demonstrate a few helpful methods in the Jupyter Notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:51.405716Z", "start_time": "2020-09-18T00:26:50.925113Z" }, "execution_event_id": "2ec79e0e-71ba-43d0-8d0c-ff263de2ec98", "last_executed_text": "%load_ext sql_magic", "persistent_id": "36ba17ea-27a3-46d6-9cd2-5b3148ce5778" }, "outputs": [], "source": [ "%load_ext sql_magic" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:51.414545Z", "start_time": "2020-09-18T00:26:51.407304Z" }, "execution_event_id": "43202f23-5cc8-4e02-8140-efee681b2eb8", "last_executed_text": "import sqlalchemy\nimport pandas as pd\nimport sqlite3\nfrom sqlalchemy import create_engine\nsqlite_engine = create_engine('sqlite://')", "persistent_id": "d22c6ac4-f6d4-497e-897e-6cc6fac1be75" }, "outputs": [], "source": [ "import sqlalchemy\n", "import pandas as pd\n", "import sqlite3\n", "from sqlalchemy import create_engine\n", "sqlite_engine = create_engine('sqlite://')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:51.492310Z", "start_time": "2020-09-18T00:26:51.485923Z" }, "execution_event_id": "572835ca-e405-4259-89f6-b40d6d543a44", "last_executed_text": "%config SQL.conn_name = \"sqlite_engine\"", "persistent_id": "1066823e-b404-4b0f-91d7-ea6f69361caf" }, "outputs": [], "source": [ "%config SQL.conn_name = \"sqlite_engine\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:51.783232Z", "start_time": "2020-09-18T00:26:51.778292Z" }, "execution_event_id": "6fd35a9b-661d-4abf-8254-16402bca3337", "last_executed_text": "%config SQL", "persistent_id": "8e73eda9-863a-4869-87fc-86f1c0f90054" }, "outputs": [], "source": [ "%config SQL" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:52.076008Z", "start_time": "2020-09-18T00:26:52.069939Z" }, "execution_event_id": "eb41e676-5710-4044-b298-2d5c62fb2ee0", "last_executed_text": "%config SQL.output_result = False", "persistent_id": "7dab401a-31a6-42f1-9dbe-58b3da420e58" }, "outputs": [], "source": [ "%config SQL.output_result = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```SQL\n", "CREATE TABLE presidents (first_name, last_name, year_of_birth);\n", "INSERT INTO presidents VALUES ('George', 'Washington', 1732);\n", "INSERT INTO presidents VALUES ('John', 'Adams', 1735);\n", "INSERT INTO presidents VALUES ('Thomas', 'Jefferson', 1743);\n", "INSERT INTO presidents VALUES ('James', 'Madison', 1751);\n", "INSERT INTO presidents VALUES ('James', 'Monroe', 1758);\n", "INSERT INTO presidents VALUES ('Zachary', 'Taylor', 1784);\n", "INSERT INTO presidents VALUES ('Abraham', 'Lincoln', 1809);\n", "INSERT INTO presidents VALUES ('Theodore', 'Roosevelt', 1858);\n", "INSERT INTO presidents VALUES ('Richard', 'Nixon', 1913);\n", "INSERT INTO presidents VALUES ('Barack', 'Obama', 1961);\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:52.944903Z", "start_time": "2020-09-18T00:26:52.902112Z" }, "execution_event_id": "7159cdd3-1e6c-4f9d-8018-2fcdd8cc3e0f", "last_executed_text": "%%read_sql temp\nCREATE TABLE presidents (first_name, last_name, year_of_birth);\nINSERT INTO presidents VALUES ('George', 'Washington', 1732);\nINSERT INTO presidents VALUES ('John', 'Adams', 1735);\nINSERT INTO presidents VALUES ('Thomas', 'Jefferson', 1743);\nINSERT INTO presidents VALUES ('James', 'Madison', 1751);\nINSERT INTO presidents VALUES ('James', 'Monroe', 1758);\nINSERT INTO presidents VALUES ('Zachary', 'Taylor', 1784);\nINSERT INTO presidents VALUES ('Abraham', 'Lincoln', 1809);\nINSERT INTO presidents VALUES ('Theodore', 'Roosevelt', 1858);\nINSERT INTO presidents VALUES ('Richard', 'Nixon', 1913);\nINSERT INTO presidents VALUES ('Barack', 'Obama', 1961);", "persistent_id": "ed983a67-487c-466b-9a1e-d9aef7211709" }, "outputs": [], "source": [ "%%read_sql temp\n", "CREATE TABLE presidents (first_name, last_name, year_of_birth);\n", "INSERT INTO presidents VALUES ('George', 'Washington', 1732);\n", "INSERT INTO presidents VALUES ('John', 'Adams', 1735);\n", "INSERT INTO presidents VALUES ('Thomas', 'Jefferson', 1743);\n", "INSERT INTO presidents VALUES ('James', 'Madison', 1751);\n", "INSERT INTO presidents VALUES ('James', 'Monroe', 1758);\n", "INSERT INTO presidents VALUES ('Zachary', 'Taylor', 1784);\n", "INSERT INTO presidents VALUES ('Abraham', 'Lincoln', 1809);\n", "INSERT INTO presidents VALUES ('Theodore', 'Roosevelt', 1858);\n", "INSERT INTO presidents VALUES ('Richard', 'Nixon', 1913);\n", "INSERT INTO presidents VALUES ('Barack', 'Obama', 1961);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:27:39.718558Z", "start_time": "2020-09-18T00:27:39.711930Z" }, "execution_event_id": "0af5869c-f0c7-4412-a095-ece90115b14a", "last_executed_text": "%%read_sql df\nSELECT * FROM presidents", "persistent_id": "e85fa811-00eb-4681-8de7-013f84ea8ab1" }, "outputs": [], "source": [ "%%read_sql df\n", "SELECT * FROM presidents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:27:41.269197Z", "start_time": "2020-09-18T00:27:41.263700Z" }, "execution_event_id": "58a86eb5-5158-4f26-9e53-3dc07d2ebc08", "last_executed_text": "df", "persistent_id": "48ebccc7-11e8-4b48-a87a-6b52cd39c065" }, "outputs": [], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inline magic" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:54.768023Z", "start_time": "2020-09-18T00:26:54.759113Z" }, "execution_event_id": "be82f8b1-a764-4db5-b2c8-8d2ac0a46299", "last_executed_text": "later_presidents = %read_sql SELECT * FROM presidents WHERE year_of_birth > 1825\nlater_presidents", "persistent_id": "86729aac-9308-4f2e-afb8-4affad126628" }, "outputs": [], "source": [ "later_presidents = %read_sql SELECT * FROM presidents WHERE year_of_birth > 1825\n", "later_presidents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:55.202071Z", "start_time": "2020-09-18T00:26:55.195683Z" }, "execution_event_id": "be82f8b1-a764-4db5-b2c8-8d2ac0a46299", "last_executed_text": "later_presidents = %read_sql SELECT * FROM presidents WHERE year_of_birth > 1825\nlater_presidents", "persistent_id": "86729aac-9308-4f2e-afb8-4affad126628" }, "outputs": [], "source": [ "%%read_sql later_presidents\n", "SELECT * FROM presidents WHERE year_of_birth > 1825" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Through pandas directly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:56.583339Z", "start_time": "2020-09-18T00:26:56.581591Z" }, "execution_event_id": "69e08433-4088-49d8-9761-637e40df3f51", "last_executed_text": "birthyear = 1800", "persistent_id": "242e6d6a-d650-4692-a51b-b19f6386fefc" }, "outputs": [], "source": [ "birthyear = 1800" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:56.960752Z", "start_time": "2020-09-18T00:26:56.952814Z" }, "execution_event_id": "015637b2-27b7-40f6-9163-053a1e7430cd", "last_executed_text": "%%read_sql df1\nSELECT first_name,\n last_name,\n year_of_birth\nFROM presidents\nWHERE year_of_birth > {birthyear}", "persistent_id": "20ba6580-1a68-49dd-abdb-f9e5f0accee4" }, "outputs": [], "source": [ "%%read_sql df1\n", "SELECT first_name,\n", " last_name,\n", " year_of_birth\n", "FROM presidents\n", "WHERE year_of_birth > {birthyear}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:26:57.507609Z", "start_time": "2020-09-18T00:26:57.501717Z" }, "execution_event_id": "dc3a005f-e387-41d7-8384-17025ceea76a", "last_executed_text": "df1", "persistent_id": "8909d264-40c4-4d72-b993-d50404f375f2" }, "outputs": [], "source": [ "df1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "persistent_id": "960e646d-8d98-4a49-ad25-891b8491c2ef" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "persistent_id": "f8bd8532-925f-4886-9ec6-458ad2377692" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "persistent_id": "493fa4e6-2be3-4a9c-a8d2-f9c1f59b2a80" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "persistent_id": "b4f62d9a-e672-496a-984d-37d16da41e2c" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "persistent_id": "ed67ebef-7f00-47d2-aca7-77921865877a" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "persistent_id": "2cd149ed-1acd-4df7-be99-cb96d4303f9d" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:27:54.951745Z", "start_time": "2020-09-18T00:27:54.920325Z" }, "execution_event_id": "3a0a620f-417d-45c7-874c-2ca79687d49a", "last_executed_text": "coal = pd.read_csv(\"../data/coal_prod_cleaned.csv\")\ncoal.head()", "persistent_id": "b4aaadfb-7087-4e77-99d9-fcc47eeeac70" }, "outputs": [], "source": [ "coal = pd.read_csv(\"../data/coal_prod_cleaned.csv\")\n", "coal.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:28:01.756081Z", "start_time": "2020-09-18T00:28:01.647688Z" }, "execution_event_id": "c1273a6e-69d4-4f44-a73f-3603f82bba59", "last_executed_text": "coal.to_sql('coal', con=sqlite_engine, if_exists='append', index=False)", "persistent_id": "e40acfe4-1ee0-49b3-88ec-516da30b1cd1" }, "outputs": [], "source": [ "coal.to_sql('coal', con=sqlite_engine, if_exists='append', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:28:03.260184Z", "start_time": "2020-09-18T00:28:03.210804Z" }, "execution_event_id": "f01f4746-e8b3-4a77-987b-0a204afd8c35", "last_executed_text": "%%read_sql example\nSELECT * FROM coal", "persistent_id": "ed983a67-487c-466b-9a1e-d9aef7211709" }, "outputs": [], "source": [ "%%read_sql example\n", "SELECT * FROM coal" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:28:04.460647Z", "start_time": "2020-09-18T00:28:04.449054Z" }, "execution_event_id": "a4647ebc-a20b-4651-962c-e2fd3a0a25f8", "last_executed_text": "example.head()", "persistent_id": "8b91570d-9bbe-4c58-8137-2e43581bd5b0" }, "outputs": [], "source": [ "example.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-09-18T00:28:36.219220Z", "start_time": "2020-09-18T00:28:36.216426Z" }, "execution_event_id": "64a0cc72-3cf8-43e5-941f-0f53713b01d9", "last_executed_text": "example.columns", "persistent_id": "4a8f6ef6-1356-419e-94de-a171b32c5090" }, "outputs": [], "source": [ "example.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "hide_input": false, "history": [ { "cell": { "executionCount": 1, "executionEventId": "41bd7b19-14c5-434e-9422-668ad6f3420a", "hasError": false, "id": "9791fefc-d144-43ae-92ec-34b4cfe3cdbd", "outputs": [ { "data": { "application/javascript": "\n require(['notebook/js/codecell'], function(codecell) {\n // https://github.com/jupyter/notebook/issues/2453\n codecell.CodeCell.options_default.highlight_modes['magic_text/x-sql'] = {'reg':[/^%read_sql/, /.*=\\s*%read_sql/,\n /^%%read_sql/]};\n Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n console.log('BBBBB');\n Jupyter.notebook.get_cells().map(function(cell){\n if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n });\n });\n " }, "metadata": {}, "output_type": "display_data" } ], "persistentId": "36ba17ea-27a3-46d6-9cd2-5b3148ce5778", "text": "%load_ext sql_magic" }, "executionTime": "2019-09-06T19:06:10.198Z" }, { "cell": { "executionCount": 2, "executionEventId": "703306f2-35ec-4c87-bba7-ff2021776330", "hasError": false, "id": "9317f7b1-4030-4ae3-84b6-78ab545250ad", "outputs": [], "persistentId": "d22c6ac4-f6d4-497e-897e-6cc6fac1be75", "text": "import sqlalchemy\nimport pandas as pd\nimport sqlite3\nfrom sqlalchemy import create_engine\nsqlite_engine = create_engine('sqlite://')" }, "executionTime": "2019-09-06T19:06:14.119Z" }, { "cell": { "executionCount": 3, "executionEventId": "bc272a58-6a07-458d-87c1-a56edbf23a7e", "hasError": false, "id": "ec506f2a-0cdc-4603-b842-d49dbe45027d", "outputs": [], "persistentId": "1066823e-b404-4b0f-91d7-ea6f69361caf", "text": "%config SQL.conn_name = \"sqlite_engine\"" }, "executionTime": "2019-09-06T19:06:15.097Z" }, { "cell": { "executionCount": 4, "executionEventId": "6fd35a9b-661d-4abf-8254-16402bca3337", "hasError": false, "id": "a7960d11-8dbf-4e44-99aa-b3d1dd31d6d4", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "SQL options\n---------\nSQL.conn_name=<Unicode>\n Current: 'sqlite_engine'\n Object name for accessing computing resource environment\nSQL.notify_result=<Bool>\n Current: True\n Notify query result to stdout\nSQL.output_result=<Bool>\n Current: True\n Output query result to stdout\n" } ], "persistentId": "8e73eda9-863a-4869-87fc-86f1c0f90054", "text": "%config SQL" }, "executionTime": "2019-09-06T19:06:15.630Z" }, { "cell": { "executionCount": 5, "executionEventId": "eb41e676-5710-4044-b298-2d5c62fb2ee0", "hasError": false, "id": "853d9e97-6f8e-4dc6-b38d-396f5e644eb9", "outputs": [], "persistentId": "7dab401a-31a6-42f1-9dbe-58b3da420e58", "text": "%config SQL.output_result = False" }, "executionTime": "2019-09-06T19:06:17.311Z" }, { "cell": { "executionCount": 6, "executionEventId": "7159cdd3-1e6c-4f9d-8018-2fcdd8cc3e0f", "hasError": false, "id": "bb18e377-874e-46c4-b2d0-1fae8def3126", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Query started at 12:06:21 PM PDT; Query executed in 0.00 m" } ], "persistentId": "ed983a67-487c-466b-9a1e-d9aef7211709", "text": "%%read_sql temp\nCREATE TABLE presidents (first_name, last_name, year_of_birth);\nINSERT INTO presidents VALUES ('George', 'Washington', 1732);\nINSERT INTO presidents VALUES ('John', 'Adams', 1735);\nINSERT INTO presidents VALUES ('Thomas', 'Jefferson', 1743);\nINSERT INTO presidents VALUES ('James', 'Madison', 1751);\nINSERT INTO presidents VALUES ('James', 'Monroe', 1758);\nINSERT INTO presidents VALUES ('Zachary', 'Taylor', 1784);\nINSERT INTO presidents VALUES ('Abraham', 'Lincoln', 1809);\nINSERT INTO presidents VALUES ('Theodore', 'Roosevelt', 1858);\nINSERT INTO presidents VALUES ('Richard', 'Nixon', 1913);\nINSERT INTO presidents VALUES ('Barack', 'Obama', 1961);" }, "executionTime": "2019-09-06T19:06:22.030Z" }, { "cell": { "executionCount": 7, "executionEventId": "0af5869c-f0c7-4412-a095-ece90115b14a", "hasError": false, "id": "d1c932fa-26cb-4466-bf83-91ac04eb84cb", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Query started at 12:06:26 PM PDT; Query executed in 0.00 m" } ], "persistentId": "e85fa811-00eb-4681-8de7-013f84ea8ab1", "text": "%%read_sql df\nSELECT * FROM presidents" }, "executionTime": "2019-09-06T19:06:26.791Z" }, { "cell": { "executionCount": 8, "executionEventId": "58a86eb5-5158-4f26-9e53-3dc07d2ebc08", "hasError": false, "id": "2b44a15c-154b-46e5-a7a5-367935e3ad38", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>first_name</th>\n <th>last_name</th>\n <th>year_of_birth</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>George</td>\n <td>Washington</td>\n <td>1732</td>\n </tr>\n <tr>\n <th>1</th>\n <td>John</td>\n <td>Adams</td>\n <td>1735</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Thomas</td>\n <td>Jefferson</td>\n <td>1743</td>\n </tr>\n <tr>\n <th>3</th>\n <td>James</td>\n <td>Madison</td>\n <td>1751</td>\n </tr>\n <tr>\n <th>4</th>\n <td>James</td>\n <td>Monroe</td>\n <td>1758</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Zachary</td>\n <td>Taylor</td>\n <td>1784</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Abraham</td>\n <td>Lincoln</td>\n <td>1809</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Theodore</td>\n <td>Roosevelt</td>\n <td>1858</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Richard</td>\n <td>Nixon</td>\n <td>1913</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Barack</td>\n <td>Obama</td>\n <td>1961</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " first_name last_name year_of_birth\n0 George Washington 1732\n1 John Adams 1735\n2 Thomas Jefferson 1743\n3 James Madison 1751\n4 James Monroe 1758\n5 Zachary Taylor 1784\n6 Abraham Lincoln 1809\n7 Theodore Roosevelt 1858\n8 Richard Nixon 1913\n9 Barack Obama 1961" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "48ebccc7-11e8-4b48-a87a-6b52cd39c065", "text": "df" }, "executionTime": "2019-09-06T19:06:27.968Z" }, { "cell": { "executionCount": 9, "executionEventId": "be82f8b1-a764-4db5-b2c8-8d2ac0a46299", "hasError": false, "id": "c1209cc7-7312-43f0-8abc-f207464fe86b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Query started at 12:06:29 PM PDT; Query executed in 0.00 m" }, { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>first_name</th>\n <th>last_name</th>\n <th>year_of_birth</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Theodore</td>\n <td>Roosevelt</td>\n <td>1858</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Richard</td>\n <td>Nixon</td>\n <td>1913</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Barack</td>\n <td>Obama</td>\n <td>1961</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " first_name last_name year_of_birth\n0 Theodore Roosevelt 1858\n1 Richard Nixon 1913\n2 Barack Obama 1961" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "86729aac-9308-4f2e-afb8-4affad126628", "text": "later_presidents = %read_sql SELECT * FROM presidents WHERE year_of_birth > 1825\nlater_presidents" }, "executionTime": "2019-09-06T19:06:30.026Z" }, { "cell": { "executionCount": 10, "executionEventId": "69e08433-4088-49d8-9761-637e40df3f51", "hasError": false, "id": "530f32b8-fbb3-4607-948b-eb240340c13b", "outputs": [], "persistentId": "242e6d6a-d650-4692-a51b-b19f6386fefc", "text": "birthyear = 1800" }, "executionTime": "2019-09-06T19:06:32.203Z" }, { "cell": { "executionCount": 11, "executionEventId": "f177ecea-10a4-45c9-b982-41db9546e7ea", "hasError": false, "id": "f2e2e2ff-9f6f-4168-8512-e0af466ed46b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Query started at 12:06:34 PM PDT; Query executed in 0.00 m" } ], "persistentId": "20ba6580-1a68-49dd-abdb-f9e5f0accee4", "text": "%%read_sql df1\nSELECT first_name,\n last_name,\n year_of_birth\nFROM presidents\nWHERE year_of_birth > {birthyear}" }, "executionTime": "2019-09-06T19:06:34.787Z" }, { "cell": { "executionCount": 12, "executionEventId": "4bb49e43-ca8d-4ed9-b779-275c5345b1e2", "hasError": false, "id": "55255a72-892b-409e-a6b0-54e87860237c", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>first_name</th>\n <th>last_name</th>\n <th>year_of_birth</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Abraham</td>\n <td>Lincoln</td>\n <td>1809</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Theodore</td>\n <td>Roosevelt</td>\n <td>1858</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Richard</td>\n <td>Nixon</td>\n <td>1913</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Barack</td>\n <td>Obama</td>\n <td>1961</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " first_name last_name year_of_birth\n0 Abraham Lincoln 1809\n1 Theodore Roosevelt 1858\n2 Richard Nixon 1913\n3 Barack Obama 1961" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "8909d264-40c4-4d72-b993-d50404f375f2", "text": "df1" }, "executionTime": "2019-09-06T19:06:36.318Z" }, { "cell": { "executionCount": 13, "executionEventId": "7eb51c2c-70fc-4842-a214-4a76e51801c9", "hasError": false, "id": "504d194e-1f32-416a-b69f-1bd45f0ce799", "outputs": [], "persistentId": "70063623-2c49-4d71-812d-42ea36c8968f", "text": "con = sqlite3.connect(\"presidents.sqlite\")\n# cur = con.cursor()\n\nnew_dataframe = pd.read_sql(f\"\"\"SELECT first_name, last_name, year_of_birth\n FROM presidents\n WHERE year_of_birth > {birthyear}\n \"\"\", \n con=con)\n\ncon.close()" }, "executionTime": "2019-09-09T21:57:42.182Z" }, { "cell": { "executionCount": 14, "executionEventId": "03018b37-bca5-4b5d-a577-9f8e7161046a", "hasError": false, "id": "2974e957-0899-46e9-8413-9736cc327c79", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>first_name</th>\n <th>last_name</th>\n <th>year_of_birth</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Theodore</td>\n <td>Roosevelt</td>\n <td>1858</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Richard</td>\n <td>Nixon</td>\n <td>1913</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Barack</td>\n <td>Obama</td>\n <td>1961</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " first_name last_name year_of_birth\n0 Theodore Roosevelt 1858\n1 Richard Nixon 1913\n2 Barack Obama 1961" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "20ba3456-6860-4ee2-8391-d523587976ae", "text": "new_dataframe" }, "executionTime": "2019-09-09T21:57:46.015Z" }, { "cell": { "executionCount": 15, "executionEventId": "d47708e1-ec8f-4fd6-aae3-a79bd6229985", "hasError": false, "id": "e2959a21-0a61-4fce-9385-b7caf3cedda3", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>first_name</th>\n <th>last_name</th>\n <th>year_of_birth</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>George</td>\n <td>Washington</td>\n <td>1732</td>\n </tr>\n <tr>\n <th>1</th>\n <td>John</td>\n <td>Adams</td>\n <td>1735</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Thomas</td>\n <td>Jefferson</td>\n <td>1743</td>\n </tr>\n <tr>\n <th>3</th>\n <td>James</td>\n <td>Madison</td>\n <td>1751</td>\n </tr>\n <tr>\n <th>4</th>\n <td>James</td>\n <td>Monroe</td>\n <td>1758</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " first_name last_name year_of_birth\n0 George Washington 1732\n1 John Adams 1735\n2 Thomas Jefferson 1743\n3 James Madison 1751\n4 James Monroe 1758" }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "b788e6bb-ef94-4bc7-b4c1-e9071719044e", "text": "df.head()" }, "executionTime": "2019-09-09T22:01:33.075Z" }, { "cell": { "executionCount": 16, "executionEventId": "3a0a620f-417d-45c7-874c-2ca79687d49a", "hasError": false, "id": "7a001c40-bbd3-400d-ae65-9bc22bff51f9", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>MSHA_ID</th>\n <th>Average_Employees</th>\n <th>Company_Type</th>\n <th>Labor_Hours</th>\n <th>Mine_Basin</th>\n <th>Mine_County</th>\n <th>Mine_Name</th>\n <th>Mine_State</th>\n <th>Mine_Status</th>\n <th>Mine_Type</th>\n <th>Operating_Company</th>\n <th>Operating_Company_Address</th>\n <th>Operation_Type</th>\n <th>Production_short_tons</th>\n <th>Union_Code</th>\n <th>Year</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>103295</td>\n <td>18.0</td>\n <td>Independent Producer Operator</td>\n <td>39175.0</td>\n <td>Appalachia Southern</td>\n <td>Bibb</td>\n <td>Seymour Mine</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Surface</td>\n <td>Hope Coal Company Inc</td>\n <td>P.O. Box 249, Maylene, AL 35114</td>\n <td>Mine only</td>\n <td>105082.0</td>\n <td>NaN</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>1</th>\n <td>103117</td>\n <td>19.0</td>\n <td>Operating Subsidiary</td>\n <td>29926.0</td>\n <td>Appalachia Southern</td>\n <td>Cullman</td>\n <td>Mine #2, #3, #4</td>\n <td>Alabama</td>\n <td>Active, men working, not producing</td>\n <td>Surface</td>\n <td>Twin Pines Coal Company Inc</td>\n <td>1874 County Road 15, Bremen, AL 35033</td>\n <td>Mine only</td>\n <td>10419.0</td>\n <td>NaN</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>2</th>\n <td>103361</td>\n <td>20.0</td>\n <td>Operating Subsidiary</td>\n <td>42542.0</td>\n <td>Appalachia Southern</td>\n <td>Cullman</td>\n <td>Cold Springs West Mine</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Surface</td>\n <td>Twin Pines Coal Company</td>\n <td>74 Industrial Parkway, Jasper, AL 35502</td>\n <td>Mine only</td>\n <td>143208.0</td>\n <td>NaN</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>3</th>\n <td>100759</td>\n <td>395.0</td>\n <td>Operating Subsidiary</td>\n <td>890710.0</td>\n <td>Appalachia Southern</td>\n <td>Fayette</td>\n <td>North River # 1 Underground Mi</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Underground</td>\n <td>Chevron Mining Inc</td>\n <td>3114 County Road 63 S, Berry, AL 35546</td>\n <td>Mine and Preparation Plant</td>\n <td>2923261.0</td>\n <td>United Mine Workers of America</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>4</th>\n <td>103246</td>\n <td>22.0</td>\n <td>Independent Producer Operator</td>\n <td>55403.0</td>\n <td>Appalachia Southern</td>\n <td>Franklin</td>\n <td>Bear Creek</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Surface</td>\n <td>Birmingham Coal &amp; Coke Co., In</td>\n <td>912 Edenton Street, Birmingham, AL 35242</td>\n <td>Mine only</td>\n <td>183137.0</td>\n <td>NaN</td>\n <td>2008</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " MSHA_ID Average_Employees Company_Type Labor_Hours \\\n0 103295 18.0 Independent Producer Operator 39175.0 \n1 103117 19.0 Operating Subsidiary 29926.0 \n2 103361 20.0 Operating Subsidiary 42542.0 \n3 100759 395.0 Operating Subsidiary 890710.0 \n4 103246 22.0 Independent Producer Operator 55403.0 \n\n Mine_Basin Mine_County Mine_Name Mine_State \\\n0 Appalachia Southern Bibb Seymour Mine Alabama \n1 Appalachia Southern Cullman Mine #2, #3, #4 Alabama \n2 Appalachia Southern Cullman Cold Springs West Mine Alabama \n3 Appalachia Southern Fayette North River # 1 Underground Mi Alabama \n4 Appalachia Southern Franklin Bear Creek Alabama \n\n Mine_Status Mine_Type \\\n0 Active Surface \n1 Active, men working, not producing Surface \n2 Active Surface \n3 Active Underground \n4 Active Surface \n\n Operating_Company Operating_Company_Address \\\n0 Hope Coal Company Inc P.O. Box 249, Maylene, AL 35114 \n1 Twin Pines Coal Company Inc 1874 County Road 15, Bremen, AL 35033 \n2 Twin Pines Coal Company 74 Industrial Parkway, Jasper, AL 35502 \n3 Chevron Mining Inc 3114 County Road 63 S, Berry, AL 35546 \n4 Birmingham Coal & Coke Co., In 912 Edenton Street, Birmingham, AL 35242 \n\n Operation_Type Production_short_tons \\\n0 Mine only 105082.0 \n1 Mine only 10419.0 \n2 Mine only 143208.0 \n3 Mine and Preparation Plant 2923261.0 \n4 Mine only 183137.0 \n\n Union_Code Year \n0 NaN 2008 \n1 NaN 2008 \n2 NaN 2008 \n3 United Mine Workers of America 2008 \n4 NaN 2008 " }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "b4aaadfb-7087-4e77-99d9-fcc47eeeac70", "text": "coal = pd.read_csv(\"../data/coal_prod_cleaned.csv\")\ncoal.head()" }, "executionTime": "2019-09-09T22:01:38.699Z" }, { "cell": { "executionCount": 17, "executionEventId": "ec7d3072-93aa-42f5-88b2-64020dd2045d", "hasError": true, "id": "d190f0fb-43ac-4c61-8f47-6aa5d543b35e", "outputs": [ { "ename": "ProgrammingError", "evalue": "Cannot operate on a closed database.", "output_type": "error", "traceback": [ "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", "\u001b[0;31mProgrammingError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-17-1e1d0e6aa435>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\"INSERT INTO presidents VALUES ('Jonathan', 'Whitmore', 1982)\"\"\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m 312\u001b[0m return pandas_sql.read_query(\n\u001b[1;32m 313\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m parse_dates=parse_dates, chunksize=chunksize)\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m 1466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1467\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1468\u001b[0;31m \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1469\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol_desc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol_desc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1424\u001b[0m \u001b[0mcur\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1425\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1426\u001b[0;31m \u001b[0mcur\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1427\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1428\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mProgrammingError\u001b[0m: Cannot operate on a closed database." ] } ], "persistentId": "dba5f09b-94c0-45fb-b561-60a8a6fc7af4", "text": "pd.read_sql_query(\"\"\"INSERT INTO presidents VALUES ('Jonathan', 'Whitmore', 1982)\"\"\", con=con)" }, "executionTime": "2019-09-09T22:05:30.554Z" }, { "cell": { "executionCount": 18, "executionEventId": "a3f5d5b3-cd7f-48dd-97b4-a7f85a732672", "hasError": true, "id": "508431f2-1ca3-4762-b09a-c6d55633a3a2", "outputs": [ { "ename": "NameError", "evalue": "name 'engine' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-18-2f26aa0ee5eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_dataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'presidents'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mif_exists\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'append'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'engine' is not defined" ] } ], "persistentId": "3cf6e8a2-69f1-45a9-9037-327a6d8045f0", "text": "new_dataframe.to_sql('presidents', con=engine, if_exists='append')" }, "executionTime": "2019-09-10T15:33:04.596Z" }, { "cell": { "executionCount": 19, "executionEventId": "3d7b5571-8046-41e4-9ce8-e302ac86de07", "hasError": true, "id": "508431f2-1ca3-4762-b09a-c6d55633a3a2", "outputs": [ { "ename": "OperationalError", "evalue": "(sqlite3.OperationalError) table presidents has no column named index\n[SQL: INSERT INTO presidents (\"index\", first_name, last_name, year_of_birth) VALUES (?, ?, ?, ?)]\n[parameters: ((0, 'Theodore', 'Roosevelt', 1858), (1, 'Richard', 'Nixon', 1913), (2, 'Barack', 'Obama', 1961))]\n(Background on this error at: http://sqlalche.me/e/e3q8)", "output_type": "error", "traceback": [ "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m 1223\u001b[0m self.dialect.do_executemany(\n\u001b[0;32m-> 1224\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1225\u001b[0m )\n", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_executemany\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_executemany\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecutemany\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mOperationalError\u001b[0m: table presidents has no column named index", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-19-1c903775c4f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_dataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'presidents'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqlite_engine\u001b[0m\u001b[0;34m,\u001b[0m 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2533\u001b[0m def to_pickle(self, path, compression='infer',\n", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[0;34m(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[1;32m 458\u001b[0m pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index,\n\u001b[1;32m 459\u001b[0m \u001b[0mindex_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m chunksize=chunksize, dtype=dtype, method=method)\n\u001b[0m\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/dspy3/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[0;34m(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method)\u001b[0m\n\u001b[1;32m 1172\u001b[0m schema=schema, dtype=dtype)\n\u001b[1;32m 1173\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1175\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdigit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m 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?)]\n[parameters: ((0, 'Theodore', 'Roosevelt', 1858), (1, 'Richard', 'Nixon', 1913), (2, 'Barack', 'Obama', 1961))]\n(Background on this error at: http://sqlalche.me/e/e3q8)" ] } ], "persistentId": "3cf6e8a2-69f1-45a9-9037-327a6d8045f0", "text": "new_dataframe.to_sql('presidents', con=sqlite_engine, if_exists='append')" }, "executionTime": "2019-09-10T15:33:41.134Z" }, { "cell": { "executionCount": 20, "executionEventId": "d0ef0e53-60bb-4712-921d-6260f8661b93", "hasError": false, "id": "508431f2-1ca3-4762-b09a-c6d55633a3a2", "outputs": [], "persistentId": "3cf6e8a2-69f1-45a9-9037-327a6d8045f0", "text": "new_dataframe.to_sql('presidents', con=sqlite_engine, if_exists='append', index=False)" }, "executionTime": "2019-09-10T15:35:16.010Z" }, { "cell": { "executionCount": 21, "executionEventId": "015637b2-27b7-40f6-9163-053a1e7430cd", "hasError": false, "id": "f2e2e2ff-9f6f-4168-8512-e0af466ed46b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Query started at 08:35:30 AM PDT; Query executed in 0.00 m" } ], "persistentId": "20ba6580-1a68-49dd-abdb-f9e5f0accee4", "text": "%%read_sql df1\nSELECT first_name,\n last_name,\n year_of_birth\nFROM presidents\nWHERE year_of_birth > {birthyear}" }, "executionTime": "2019-09-10T15:35:30.554Z" }, { "cell": { "executionCount": 22, "executionEventId": "dc3a005f-e387-41d7-8384-17025ceea76a", "hasError": false, "id": "55255a72-892b-409e-a6b0-54e87860237c", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>first_name</th>\n <th>last_name</th>\n <th>year_of_birth</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Abraham</td>\n <td>Lincoln</td>\n <td>1809</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Theodore</td>\n <td>Roosevelt</td>\n <td>1858</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Richard</td>\n <td>Nixon</td>\n <td>1913</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Barack</td>\n <td>Obama</td>\n <td>1961</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Theodore</td>\n <td>Roosevelt</td>\n <td>1858</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Richard</td>\n <td>Nixon</td>\n <td>1913</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Barack</td>\n <td>Obama</td>\n <td>1961</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " first_name last_name year_of_birth\n0 Abraham Lincoln 1809\n1 Theodore Roosevelt 1858\n2 Richard Nixon 1913\n3 Barack Obama 1961\n4 Theodore Roosevelt 1858\n5 Richard Nixon 1913\n6 Barack Obama 1961" }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "8909d264-40c4-4d72-b993-d50404f375f2", "text": "df1" }, "executionTime": "2019-09-10T15:35:30.735Z" }, { "cell": { "executionCount": 23, "executionEventId": "c1273a6e-69d4-4f44-a73f-3603f82bba59", "hasError": false, "id": "165d390e-99f1-4ea6-bb2a-7d0438983a43", "outputs": [], "persistentId": "e40acfe4-1ee0-49b3-88ec-516da30b1cd1", "text": "coal.to_sql('coal', con=sqlite_engine, if_exists='append', index=False)" }, "executionTime": "2019-09-11T03:54:41.507Z" }, { "cell": { "executionCount": 24, "executionEventId": "f01f4746-e8b3-4a77-987b-0a204afd8c35", "hasError": false, "id": "0ca20368-c81d-4f4d-9545-5d4290602567", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Query started at 08:54:52 PM PDT; Query executed in 0.00 m" } ], "persistentId": "ed983a67-487c-466b-9a1e-d9aef7211709", "text": "%%read_sql example\nSELECT * FROM coal" }, "executionTime": "2019-09-11T03:54:52.656Z" }, { "cell": { "executionCount": 25, "executionEventId": "a4647ebc-a20b-4651-962c-e2fd3a0a25f8", "hasError": false, "id": "184e0d7b-01a3-4254-8f97-eae9c43366b7", "outputs": [ { "data": { "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>MSHA_ID</th>\n <th>Average_Employees</th>\n <th>Company_Type</th>\n <th>Labor_Hours</th>\n <th>Mine_Basin</th>\n <th>Mine_County</th>\n <th>Mine_Name</th>\n <th>Mine_State</th>\n <th>Mine_Status</th>\n <th>Mine_Type</th>\n <th>Operating_Company</th>\n <th>Operating_Company_Address</th>\n <th>Operation_Type</th>\n <th>Production_short_tons</th>\n <th>Union_Code</th>\n <th>Year</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>103295</td>\n <td>18.0</td>\n <td>Independent Producer Operator</td>\n <td>39175.0</td>\n <td>Appalachia Southern</td>\n <td>Bibb</td>\n <td>Seymour Mine</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Surface</td>\n <td>Hope Coal Company Inc</td>\n <td>P.O. Box 249, Maylene, AL 35114</td>\n <td>Mine only</td>\n <td>105082.0</td>\n <td>None</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>1</th>\n <td>103117</td>\n <td>19.0</td>\n <td>Operating Subsidiary</td>\n <td>29926.0</td>\n <td>Appalachia Southern</td>\n <td>Cullman</td>\n <td>Mine #2, #3, #4</td>\n <td>Alabama</td>\n <td>Active, men working, not producing</td>\n <td>Surface</td>\n <td>Twin Pines Coal Company Inc</td>\n <td>1874 County Road 15, Bremen, AL 35033</td>\n <td>Mine only</td>\n <td>10419.0</td>\n <td>None</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>2</th>\n <td>103361</td>\n <td>20.0</td>\n <td>Operating Subsidiary</td>\n <td>42542.0</td>\n <td>Appalachia Southern</td>\n <td>Cullman</td>\n <td>Cold Springs West Mine</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Surface</td>\n <td>Twin Pines Coal Company</td>\n <td>74 Industrial Parkway, Jasper, AL 35502</td>\n <td>Mine only</td>\n <td>143208.0</td>\n <td>None</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>3</th>\n <td>100759</td>\n <td>395.0</td>\n <td>Operating Subsidiary</td>\n <td>890710.0</td>\n <td>Appalachia Southern</td>\n <td>Fayette</td>\n <td>North River # 1 Underground Mi</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Underground</td>\n <td>Chevron Mining Inc</td>\n <td>3114 County Road 63 S, Berry, AL 35546</td>\n <td>Mine and Preparation Plant</td>\n <td>2923261.0</td>\n <td>United Mine Workers of America</td>\n <td>2008</td>\n </tr>\n <tr>\n <th>4</th>\n <td>103246</td>\n <td>22.0</td>\n <td>Independent Producer Operator</td>\n <td>55403.0</td>\n <td>Appalachia Southern</td>\n <td>Franklin</td>\n <td>Bear Creek</td>\n <td>Alabama</td>\n <td>Active</td>\n <td>Surface</td>\n <td>Birmingham Coal &amp; Coke Co., In</td>\n <td>912 Edenton Street, Birmingham, AL 35242</td>\n <td>Mine only</td>\n <td>183137.0</td>\n <td>None</td>\n <td>2008</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " MSHA_ID Average_Employees Company_Type Labor_Hours \\\n0 103295 18.0 Independent Producer Operator 39175.0 \n1 103117 19.0 Operating Subsidiary 29926.0 \n2 103361 20.0 Operating Subsidiary 42542.0 \n3 100759 395.0 Operating Subsidiary 890710.0 \n4 103246 22.0 Independent Producer Operator 55403.0 \n\n Mine_Basin Mine_County Mine_Name Mine_State \\\n0 Appalachia Southern Bibb Seymour Mine Alabama \n1 Appalachia Southern Cullman Mine #2, #3, #4 Alabama \n2 Appalachia Southern Cullman Cold Springs West Mine Alabama \n3 Appalachia Southern Fayette North River # 1 Underground Mi Alabama \n4 Appalachia Southern Franklin Bear Creek Alabama \n\n Mine_Status Mine_Type \\\n0 Active Surface \n1 Active, men working, not producing Surface \n2 Active Surface \n3 Active Underground \n4 Active Surface \n\n Operating_Company Operating_Company_Address \\\n0 Hope Coal Company Inc P.O. Box 249, Maylene, AL 35114 \n1 Twin Pines Coal Company Inc 1874 County Road 15, Bremen, AL 35033 \n2 Twin Pines Coal Company 74 Industrial Parkway, Jasper, AL 35502 \n3 Chevron Mining Inc 3114 County Road 63 S, Berry, AL 35546 \n4 Birmingham Coal & Coke Co., In 912 Edenton Street, Birmingham, AL 35242 \n\n Operation_Type Production_short_tons \\\n0 Mine only 105082.0 \n1 Mine only 10419.0 \n2 Mine only 143208.0 \n3 Mine and Preparation Plant 2923261.0 \n4 Mine only 183137.0 \n\n Union_Code Year \n0 None 2008 \n1 None 2008 \n2 None 2008 \n3 United Mine Workers of America 2008 \n4 None 2008 " }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "8b91570d-9bbe-4c58-8137-2e43581bd5b0", "text": "example.head()" }, "executionTime": "2019-09-11T03:54:59.238Z" }, { "cell": { "executionCount": 26, "executionEventId": "64a0cc72-3cf8-43e5-941f-0f53713b01d9", "hasError": false, "id": "edaa35af-a886-4d89-a3e4-52cbcc8ce521", "outputs": [ { "data": { "text/plain": "Index(['MSHA_ID', 'Average_Employees', 'Company_Type', 'Labor_Hours',\n 'Mine_Basin', 'Mine_County', 'Mine_Name', 'Mine_State', 'Mine_Status',\n 'Mine_Type', 'Operating_Company', 'Operating_Company_Address',\n 'Operation_Type', 'Production_short_tons', 'Union_Code', 'Year'],\n dtype='object')" }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "persistentId": "4a8f6ef6-1356-419e-94de-a171b32c5090", "text": "example.columns" }, "executionTime": "2019-09-11T03:55:09.796Z" }, { "cell": { "executionCount": 1, "executionEventId": "2ec79e0e-71ba-43d0-8d0c-ff263de2ec98", "hasError": false, "id": "7eb1da68-b217-4d59-aede-3b1ec8c91312", "outputs": [ { "data": { "application/javascript": "\n require(['notebook/js/codecell'], function(codecell) {\n // https://github.com/jupyter/notebook/issues/2453\n codecell.CodeCell.options_default.highlight_modes['magic_text/x-sql'] = {'reg':[/^%read_sql/, /.*=\\s*%read_sql/,\n /^%%read_sql/]};\n Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n console.log('BBBBB');\n Jupyter.notebook.get_cells().map(function(cell){\n if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n });\n });\n " }, "metadata": {}, "output_type": "display_data" } ], "persistentId": "36ba17ea-27a3-46d6-9cd2-5b3148ce5778", "text": "%load_ext sql_magic" }, "executionTime": "2019-09-12T03:09:42.891Z" }, { "cell": { "executionCount": 2, "executionEventId": "43202f23-5cc8-4e02-8140-efee681b2eb8", "hasError": false, "id": "5319f956-b6a3-4fdd-894f-19eadf2edd67", "outputs": [], "persistentId": "d22c6ac4-f6d4-497e-897e-6cc6fac1be75", "text": "import sqlalchemy\nimport pandas as pd\nimport sqlite3\nfrom sqlalchemy import create_engine\nsqlite_engine = create_engine('sqlite://')" }, "executionTime": "2019-09-12T03:09:44.038Z" }, { "cell": { "executionCount": 3, "executionEventId": "572835ca-e405-4259-89f6-b40d6d543a44", "hasError": false, "id": "e072cb9c-d55f-4f36-a797-55cd4c8ae026", "outputs": [], "persistentId": "1066823e-b404-4b0f-91d7-ea6f69361caf", "text": "%config SQL.conn_name = \"sqlite_engine\"" }, "executionTime": "2019-09-12T03:09:44.609Z" } ], "kernelspec": { "display_name": "dspy3", "language": "python", "name": "dspy3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.11" }, "toc": { "base_numbering": 1, "nav_menu": { "height": "48px", "width": "252px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "uuid": "43cb3b69-f0cc-42eb-bcc1-5f5abd7c5c92", "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }
mit
mirjalil/DataScience
algorithms-in-C++/data-structures_01_linkedlist.ipynb
2
6815
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Linked List\n", "===========\n", "\n", "* Linear data structure (like an array)\n", "* Linked list is used when there is a memory issue (capacity limit)\n", "* Dynamically grows\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"figs/linked-list.png\" width=400></img>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Class Node\n", "\n", "```c++\n", "class Node {\n", " private:\n", " int element;\n", " Node *next_node;\n", " \n", " public:\n", " Node(int = 0, Node * = nullptr);\n", " int retrieve() const;\n", " Node *next() const;\n", "}\n", "\n", "```\n", "\n", "**Constructor:**\n", "```c++\n", "Node:Node(int e, Node * n) {\n", " element = e;\n", " next_node = n;\n", "}\n", "```\n", "or\n", "```c++\n", "Node:Node(int e, Node * n):\n", " element (e),\n", " next_node (n) {\n", " // empty constructor\n", "}\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Accessors\n", "\n", "```c++\n", "int Node::retrieve() const {\n", " return element;\n", "}\n", "\n", "int *Node::next() const {\n", " return next_node;\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Class List\n", "\n", "```c++\n", "class List {\n", " private:\n", " Node *\n", " \n", " public:\n", " \n", " // Accessros \n", " bool empty() const;\n", " int get_front() const;\n", " Node *head() const;\n", " int size() const;\n", " int count(int) const;\n", " \n", " // mutators\n", " void push_front(int);\n", " int pop_front();\n", " void erase(int);\n", " \n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Operations\n", "\n", "* insert \n", "* " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### empty()\n", "\n", "```c++\n", "bool List::empty() const {\n", " return(list_head == nullptr);\n", "}\n", "```\n", "\n", "#### head()\n", "\n", "```c++\n", "Node *List::head() const {\n", " if (list_head == nullptr) {\n", " // throw exception;\n", " }\n", " return(list_head);\n", "}\n", "```\n", "\n", "#### get_front()\n", "\n", "```c++\n", "int List::get_front() const {\n", " return head()->retrieve();\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### push_front(int)\n", "\n", "**Adding an element to the front**\n", "\n", "* Create a new node\n", "* Store the value in the new node\n", "* From the new node, point to the front of the existing list\n", "\n", "```c++\n", "void List::push_front(int n) {\n", " if (empty()) {\n", " list_head = new Node(n, nullptr);\n", " }else{\n", " list_head = new Node(n, head());\n", " }\n", "}\n", "```\n", "\n", "* Shorter version \n", " \n", "```c++\n", "void List::push_front(int n) {\n", " list_head = new Node(n, head());\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### pop_front()\n", "\n", "**Pop the first element of the list and remove it**\n", "\n", "```c++\n", "int List::pop_front() {\n", " int e = front();\n", " Node *ptr = list_head;\n", " \n", " list_head = head()->next();\n", " delete(ptr);\n", " return(e);\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stepping through a linked list\n", "\n", "```c++\n", "for (Node *ptr = head(); ptr != nullptr; ptr = ptr->next()) {\n", " // so something\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Counting instances\n", "\n", "* We need to step through the linked list\n", "\n", "```c++\n", "int List::count(int n) const {\n", " int node_count = 0;\n", " \n", " for(Node *ptr=head(); ptr != nullptr; ptr=ptr->next()) {\n", " if(ptr->retrieve() == n){\n", " node_count++;\n", " }\n", " }\n", " return(node_count);\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Erasing matching elements\n", "\n", "```c++\n", "void List::erase(int n) {\n", " Node *prev_ptr, *ptr;\n", " for (Node *ptr=head(); ptr != nullptr; prev_ptr=ptr, ptr=ptr->next()) {\n", " if (ptr->retrieve() == n) {\n", " // ...\n", " \n", " }\n", " Node *prev_ptr = ptr;\n", " }\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example\n", "\n", "\n", "### Static allocation\n", "\n", "```c++\n", "int f() {\n", " List mylist;\n", "\n", " ls.push_front(3);\n", " cout << ls.front() << endl;\n", "\n", "}\n", "```\n", "\n", "### Dynamic allocation\n", "\n", "```C++\n", "List *f() {\n", " List *pls;\n", "\n", " pls->\n", "\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exceptions\n", "\n", "```c++\n", "class underflow {\n", " // empty\n", "};\n", "\n", "...\n", "\n", "throw underflow();\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
fmonti/mgcnn
Notebooks/synthetic_netflix/.ipynb_checkpoints/supervised_approach_synthetic_netflix_original_training_set_norm_acc_with_factorization_2_different_conv-checkpoint.ipynb
1
1277377
null
gpl-3.0
fja05680/pinkfish
examples/280.pyfolio-integration/strategy.ipynb
1
324411
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pyfolio-integration\n", "\n", "This example shows how to integrate pinkfish with the pyfolio library." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-02-17T03:41:59.416311Z", "start_time": "2020-02-17T03:41:58.377375Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/pyfolio-0.9.2-py3.8.egg/pyfolio/pos.py:26: UserWarning: Module \"zipline.assets\" not found; mutltipliers will not be applied to position notionals.\n", " warnings.warn(\n" ] } ], "source": [ "import datetime\n", "\n", "import pyfolio\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "import pinkfish as pf\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# Format price data\n", "pd.options.display.float_format = '{:0.2f}'.format\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-02-17T03:41:59.432971Z", "start_time": "2020-02-17T03:41:59.426278Z" } }, "outputs": [], "source": [ "# Set size of inline plots\n", "'''note: rcParams can't be in same cell as import matplotlib\n", " or %matplotlib inline\n", " \n", " %matplotlib notebook: will lead to interactive plots embedded within\n", " the notebook, you can zoom and resize the figure\n", " \n", " %matplotlib inline: only draw static images in the notebook\n", "'''\n", "plt.rcParams[\"figure.figsize\"] = (10, 7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some global data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-02-17T03:41:59.454422Z", "start_time": "2020-02-17T03:41:59.447859Z" } }, "outputs": [], "source": [ "symbol = '^GSPC'\n", "capital = 10000\n", "#start = datetime.datetime(1900, 1, 1)\n", "start = datetime.datetime(*pf.SP500_BEGIN)\n", "end = datetime.datetime.now()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define Strategy Class - sell in may and go away" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-02-17T03:41:59.538335Z", "start_time": "2020-02-17T03:41:59.457371Z" } }, "outputs": [], "source": [ "class Strategy:\n", "\n", " def __init__(self, symbol, capital, start, end):\n", " self.symbol = symbol\n", " self.capital = capital\n", " self.start = start\n", " self.end = end\n", " \n", " self.ts = None\n", " self.rlog = None\n", " self.tlog = None\n", " self.dbal = None\n", " self.stats = None\n", "\n", " def _algo(self):\n", " pf.TradeLog.cash = capital\n", "\n", " for i, row in enumerate(self.ts.itertuples()):\n", "\n", " date = row.Index.to_pydatetime()\n", " end_flag = pf.is_last_row(self.ts, i)\n", "\n", " # Buy (at the close on first trading day in Nov).\n", " if self.tlog.shares == 0:\n", " if row.month == 11 and row.first_dotm:\n", " self.tlog.buy(date, row.close)\n", " # Sell (at the close on first trading day in May).\n", " else:\n", " if ((row.month == 5 and row.first_dotm) or end_flag):\n", " self.tlog.sell(date, row.close)\n", "\n", " # Record daily balance\n", " self.dbal.append(date, row.close)\n", "\n", " def run(self):\n", " \n", " # Fetch and select timeseries.\n", " self.ts = pf.fetch_timeseries(self.symbol)\n", " self.ts = pf.select_tradeperiod(self.ts, self.start, self.end,\n", " use_adj=True)\n", " # Add calendar columns.\n", " self.ts = pf.calendar(self.ts)\n", " \n", " # Finalize timeseries.\n", " self.ts, self.start = pf.finalize_timeseries(self.ts, self.start,\n", " dropna=True, drop_columns=['open', 'high', 'low'])\n", " \n", " # Create tlog and dbal objects\n", " self.tlog = pf.TradeLog(symbol)\n", " self.dbal = pf.DailyBal()\n", " \n", " # Run algorithm, get logs\n", " self._algo()\n", " self._get_logs()\n", " self._get_stats()\n", "\n", " def _get_logs(self):\n", " self.rlog = self.tlog.get_log_raw()\n", " self.tlog = self.tlog.get_log()\n", " self.dbal = self.dbal.get_log(self.tlog)\n", "\n", " def _get_stats(self):\n", " s.stats = pf.stats(self.ts, self.tlog, self.dbal, self.capital)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run Strategy" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-02-17T03:42:00.914179Z", "start_time": "2020-02-17T03:41:59.549227Z" }, "scrolled": false }, "outputs": [], "source": [ "s = Strategy(symbol, capital, start, end)\n", "s.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run Benchmark, Retrieve benchmark logs, and Generate benchmark stats" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-02-17T03:42:05.103568Z", "start_time": "2020-02-17T03:42:02.871307Z" }, "scrolled": true }, "outputs": [], "source": [ "benchmark = pf.Benchmark(symbol, s.capital, s.start, s.end)\n", "benchmark.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pyfolio Returns Tear Sheet\n", "\n", "(create_returns_tear_sheet() seems to be a bit broke in Pyfolio, see: https://github.com/quantopian/pyfolio/issues/520)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.indexes.datetimes.DatetimeIndex" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert pinkfish data to Empyrical format\n", "returns = s.dbal['close'].pct_change()\n", "#returns.index = returns.index.tz_localize('UTC')\n", "returns.index = returns.index.to_pydatetime()\n", "type(returns.index)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>1957-03-04</td></tr>\n", " <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-07-02</td></tr>\n", " <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>771</td></tr>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Backtest</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Annual return</th>\n", " <td>6.2%</td>\n", " </tr>\n", " <tr>\n", " <th>Cumulative returns</th>\n", " <td>4750.4%</td>\n", " </tr>\n", " <tr>\n", " <th>Annual volatility</th>\n", " <td>11.0%</td>\n", " </tr>\n", " <tr>\n", " <th>Sharpe ratio</th>\n", " <td>0.60</td>\n", " </tr>\n", " <tr>\n", " <th>Calmar ratio</th>\n", " <td>0.18</td>\n", " </tr>\n", " <tr>\n", " <th>Stability</th>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>Max drawdown</th>\n", " <td>-35.0%</td>\n", " </tr>\n", " <tr>\n", " <th>Omega ratio</th>\n", " <td>1.18</td>\n", " </tr>\n", " <tr>\n", " <th>Sortino ratio</th>\n", " <td>0.87</td>\n", " </tr>\n", " <tr>\n", " <th>Skew</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Kurtosis</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Tail ratio</th>\n", " <td>1.11</td>\n", " </tr>\n", " <tr>\n", " <th>Daily value at risk</th>\n", " <td>-1.4%</td>\n", " </tr>\n", " <tr>\n", " <th>Alpha</th>\n", " <td>0.03</td>\n", " </tr>\n", " <tr>\n", " <th>Beta</th>\n", " <td>0.47</td>\n", " </tr>\n", " </tbody>\n", "</table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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xY8cOPPbYYybbvf322+jatSuWL18OmUwGAPD19cWyZcswc+ZMxMfHAwB+/vlnHD9+HB999BFGjx4NALj99ttx2223YenSpXj77bctxvPpp5+iuroaq1evRkREBACgW7dueOCBB7B582bcc889AIC8vDysXLkSs2bNwsKFCwEAkydPxn333Ye3334bY8aMgVTK0axERERERORYTs06/Pz8EBwcbLHNhQsXcOHCBUyZMkVI5gBg+vTp0Ol02L59u1C2bds2hIeHY9SoUUJZSEgIbr/9duzYscOol62+7du3Y+TIkUIyBwCDBg1CfHw8tm7dKpQZ7jV9+nShTCKRYNq0acjJyUFaWpr1D09WLV/+GQYP7uPsMIiIiIiIWqwW3410+vRpAEBKSoqoPCIiApGRkUI9AGRkZCA5OdlovllqaioqKiosDrvMy8tDYWGh0XOA2l66uvPwMjIy4Ofnh4SEBKN2dWMmIiIiIiJypBaf0BUUFAAAwsLCjOrCwsKQn58vahseHm7UzlBWt219hjpzzyksLIRWqxWe06ZNG5PtrD2HiIiIiIjIXlr8WvgqlQoAoFAojOo8PT1RVVUlamuqnaHMcC9TqqurLT7HcL2vr6/Z5xjaGe5lq9BQP4v1KlUJfHyMn+eO6n5OuVxmVNbaqVRyhIX5OzsMIy0xJrIN351r4ntzXXx3ronvzTW1lvfW4hM6Ly8vAEBNTY1RXXV1tVBvaGuqnaGsbtv6DMmYuefUvd7ccwztDPeyVWFhOXQ688vRq1RqVFYaP88d5OXlYvnyz3Dw4D6UlZUhLCwcAwcOxtNPPwe1urZHtO5n12g0+H//bwW2bv0RN27kIywsHOPGjcfMmQ+I5lgePnwAK1Z8jsuXL0Kr1aJNmzAMHz4Kc+feXHintLQEy5d/ht27f4NSWYzIyCjcffe9mDx5avN9AQ2kUqlRUFDm7DBEwsL8W1xMZBu+O9fE9+a6+O5cE9+ba3K39yaVSsx2ArX4hM4wjNHUcMqCggL07NlT1NbUcEdDmanhmAaGOsMQz/rPCQ0NFRKGsLAwHDlyxGQ7a8+hmwoK8jFnzt9RWVmJu+66G4mJHXD1ahZ+/fUXPP30cyav+fe//4WtW3/AqFFj0K3bDKSlHccXX3yKvLxcvPjiywCAS5cu4oUX5iM1tTsefvhRyGRSZGVlIS3thHCfqqoqPP74HBQVFWHixL+hTZswHD9+BB98sARlZaV48ME5zfEVEBERERE1SYtP6Lp06QIASE9PR3JyslCel5eH3NxcoR4AOnfujOPHjxttxJ2WlgYfHx/ExcWZfU5ERARCQkKQnp5uVJeWliZ6TpcuXbB+/XpcvnxZtDDKyZMnRTGTZZ9++iGUSiW++OL/oWPHJPj4KFBZWYM5cx412f78+XPYuvUHTJz4Nzz33AIAwN/+di/8/PyxefNG/O1vU5CY2BFHjhyEQqHA++9/JOq1q2vduq+Qm5uLVavWIDo6BgAwceLf4O8fgP/970vcc89UBAQEOOaDExERERHZSYtP6Dp27Ij27dtj3bp1uOeee4Rf0NeuXQupVIoxY8YIbceOHYtt27Zh586dwj50RUVF+PnnnzFq1CjI5XKhrWHFy7pJ3pgxY7Blyxbk5eUJWxfs378fmZmZeOihh4R2o0aNwptvvok1a9YI+9Dp9Xp8/fXXiI6ORvfu3R30bdyUlnYcRUU3HP4ca0JC2qBbt57WG9aj0+mwZ89uDBkyDB07ijeVr79KqcGBA3sBAFOmzBCVT5kyHZs3b8T+/XuRmNgRfn7+UKlUOHhwPwYNGmzyXrt27USPHr3g4+MLpVIplPftOwCbNn2L06fTMWDAoAZ/LiIiIiJyfbXbnekhl7f8tRycntB9/PHHAICLFy8CADZv3oyjR48iICAA9913HwDghRdewCOPPILZs2dj3LhxOHfuHL766itMmTJF1EN22223oUePHnjhhRfw4IMPIjg4GGvXroVOp8MTTzwheu79998PAPj111+Fsnnz5uHnn3/GrFmzcN9996GyshLLly9H586dMWHCBKFdZGQkZs2ahRUrVqC6uhqpqanYsWMHjhw5gvfff5+bittAqSxGZWUFEhI62HxNbu51yGQyxMS0FZXHxMRCJpMhL+86AGDUqFvx/feb8MILTyM0tA369OmHoUNHYOjQ4UKymJ19FRcvnsedd442Gx8RERERtU5Hjx6EXq/DgAFDzHY2tBROT+g++OAD0fm3334LAIiJiRESuhEjRmDp0qVYunQpXn/9dYSEhOCRRx7Bo4+Kh+bJZDIsW7YMb7/9NlavXi0kW//+97/Rrl07q7FERUXhf//7H9566y28++67kMvlGD58OBYsWGC0quVzzz2HwMBArFu3Dhs3bkRCQgLeffddjBs3rilfh80a0yvWWnh6euGjjz7HsWNHsH//Xhw8uB/btv2Evn37Y8mS/0Imk0Gv16N//0GYOnWGyXs0JNEkIiIiIvdSuzq++UULWxKnJ3Rnz561qd3o0aOFYZSWBAYG4o033sAbb7xhsV3dnrm6OnbsiOXLl1t9jlQqxdy5czF37lyrbclYUFAwfHx8cfnyRZuviYyMglarRU5ONmJjbw6VzcnJhlarRURElFAmlUrRp08/9OnTD088MR9fffUlPvnkQxw/fhR9+vRDdHRbVFer0Ldvf7t+LiIiIiJybXq9Hq6SzAEusLE4uSepVIohQ4Ziz57fce7cGVFd7V8iYwMG3AIA+OabtaLy9eu/BgBhvlxJidLoWsM8PcN2EyNGjMLJk8dx7JjxaqVKpdJsDERERETUWkha/HBLoAX00FHrNWfOYzh06CAee+xhTJjwNyQmtkd29jXs2LEdX3+90ah9x46dcPvtd+K779ajrKwU3br1QFraCezYsQ133jkBHTokAgBWrVqOkyePYcCAWxAVFQ2lshjffbcB4eER6NatBwBg+vRZ2LPndzz77BO44467kJjYCZWVFbhw4Tx+++1XbN/+Ozw8+NeDiIiIqLUx/MO+C+RyAJjQkRNFRERi2bJV+PzzT/Dzzz+gsrIS4eERZlemBIAXX3wZUVHR2Lr1B/z2206EhYXjoYfmYebMB4Q2gwcPRW7uNfz00/coKVEiMDAIPXr0wuzZc+HnV7sho7e3Nz76aBm+/HIFfvttJ374YTP8/QPQrl08HnnkCbPbHRARERGRe9PrdQDMr7ze0kj0HFvmdIWF5dDpzL8GpfIGgoLaNGNEzmHYh47Ma4k/C2Fh/igoKHN2GNQIfHeuie/NdfHduSa+N9fUlPemVquxd+9vkMk8MGTICDtH1jhSqQShoX6m65o5FiIiIiIiohbL0EPnKluRuUaUREREREREzUCrrU3oXGUKDhM6IiIiIiKiv7jaHDomdERERERERH8xbHOl0+mcHIltmNARERERERH9JT39JACgulrl5Ehsw4SOiIiIiIjoLxqN2tkhNAgTOiIiIiIiIgA5OVnODqHBmNAREREREREBOH/+jLNDaDAmdERERERERC6KCR0REREREZGLYkJHRERERETkopjQUaty/fo1DB7cB998s8bZoRARERFRC6JWu9bqlgYezg6AWq+ffvoeixe/JioLDg5BYmJHzJr1IHr27O2kyID09D9x8OA+3HvvdPj7+zvkGYWFN7Bp07cYOnQ4OnZMcsgziIiIiMg2mZmXnB1CozChI6ebM+dRREREQi6X4fr1XPzww2Y888zj+OST5ejcuatTYjp9+k+sXPk5xo0b77CErqioECtXfo6oqGgmdEREREROplZXOzuERmFCR043cOAt6NgxCT4+ClRW1uC228Zh4sTbsWvXTqcldERERETUuuj14vOwsAjnBNJATOioxQkICIRMJoNGowFQO575yy+XY9++P5CTkwWtVotOnTrjoYfmoVevPqJrdTodvvlmDX78cQtycrLh4+OLrl1TMG/e42jfvoPJ52m1Wixe/H/47bdf8eab7+LPP09i5crPAQCTJ98ltFu/fguioqIBAD/+uAXffrsOmZmZ8Pb2wsCBg/Hoo08iJCRUaH/mzGksW/Yxzp7NQFWVCqGhoejZszf+8Y9XcezYETz55DwAwOLFrwlDT//xj1cxbtx4O32TRERERNRYXbqkODsEmzChI6crKyuDUqlEdbUHsrNzsXbtakgkEowceSsAoKKiAt9/vwmjR9+Gu+6aiMrKSmFY5ueffykarvjGG/+Hbdt+wqBBQzBhwt2oqanBsWNHcPZshsmETqPRYNGiV7Bv3x68/fZ/0Lt3X4SEhOLatWxs27YVTz75DAIDgwAAQUHBAIAVK5Zh1aov/opnEgoLC7F+/VqcOXMay5evhqenF4qLizB//uOIiorG3/8+G15e3rh+/Rp2794FAIiPT8CcOY9i2bKPcdddk9C9e08AQEpKN0d+1URERERkRkVFmehcKnWN9SOZ0JHTGXqqDLy9vfHaa28iObn2X0X8/f2xYcP3kMvlQpvx4ydhxox7sGHDOixY8E8AwJEjh7Bt20+YMmUGnnhivtB2+vRZ0NfvQ0dtMvfPfy7A4cMH8e67HwpJVWJiRyQldcG2bVsxZMhwoVcOqF0l88svl+ORR57EtGn3CeX9+w/CI488iK1bf8DEiffgzz/TUFZWijVrvkVwcLDQbu7cxwAAISGhGDjwFixb9jFSUrrhttvGNfr7IyIiIqKmq6ysdHYIjcKEzkUdvLYff2TthlpX49Q45FIFBscORf/ogY2+x3PPLUBMTFt4ecmRnX0NmzdvxKJFL+O995aie/eekMlkkMlkAGqHVJaXl0Gn06Nz5y44d+6McJ/ff98FmUyGBx542OgZEolEdF5TU4N//ON5nDx5DO+99yFSU7vbFOvu3bug1+sxbNgIKJVKobxt21iEhrbB8eNHMXHiPfDz8xPajx8/0WX+hYeIiIiIXAsTOhd16NoBpydzAKDW1eDQtQNNSuiSk1OERVFSU2swYsRoTJkyAR98sAQrVnwFANi69Qd8/fX/cOVKpjC3DgCiomKE42vXchAWFi4kU5Z8+eUKVFVV4r///dTmZA4AsrKyoNPpcO+9E0zWG5K8nj17Y/jwkXjnncX47LOP0KtXHwwePBSjRo0R9TQSERERUcsQGRmD3NwcZ4fRYEzoXFS/6AEtpoeuX/QAu97Ty8sLXbumYs+e31BVVYXdu3fhjTf+D0OGDMe0aTMRHBwCqVSK//1vFXJyshv1jP79B+LAgb346qv/h9TU7jYnWXq9DjKZDEuWfABAYlTv7x8AoLZH8F//ehunTqVj797dOHToAP71r1exdu3/8Mkny+Hj49OouImIiIjIMby8vJwdQqMwoXNR/aMHNqlXrKXTamt74aqqKvHbbzsRHR2DxYvfEQ2dXLHiM9E1MTExOHLkIMrKyqzuHdetW3fceecELFjwLBYtegWvvba43rBI42St9hltodVq0bZtnGhunTnJySlITk7BnDmPYufOX/Dqqwvw66/bceedE80+g4iIiIian06nE44N031cASf2UItTVlaG9PQ/ERISKvTGARAtbHLqVDrS0/8UXTd06AhotVqsWvWF0T1NLYoyYMAg/POfr2P37l14553Fojpvb28AQHm5eLWjoUNHQCqVCtsa1KXT6VBaWgIAKC0tNXpmx46dAADV1TUWn0FEREREza9uQudK2ENHTrd//15cunQRCoUHrl/PxQ8/bEZpaQmeffYlSCQSDBo0BL//vgv/+MdzGDhwMK5fv4ZNm75FfHwCqqqqhPv06dMPo0ffhnXrvkJW1lX069cfGo0Gx44dxciRozF27B1Gzx4xYjQqKirw73//C76+fnj88acBAElJnQEAy5Z9jFGjxsDDwwO33DIUbdvGYvbsufj8809w7VoOBg0aAm9vb+TkZOP333/FrFkPYvz4ifj55x+wceMGDB06DNHRbaFSVeGHHzbD19cXAwfeAgCIjIxCQEAgNm36Fj4+PvDy8kbXrimIjo4xipOIiIiIHCs/P9fZITQKEzpyumXLPhaOPT090b59Il599V+49daxAIBx48ajqKgQmzdvxKFDBxAfn4B//vN17Nq1A8ePHxXd65VXFiExsSN+/HELDh8+AD8/f3TtmoykpC5mn3/nnRNQWVmB//73Pfj5+eH++x9Cp06dMXfuY9i4cT0OHtwPnU6H9eu3wNvbG3//+2y0bRuH9evXYsWKzyCRSBEREYkhQ4ahb9/+AIAePXrh9OlT2LnzFxQXF8HX1w9duiTj5ZcXCQmbh4cHXn75NXzyyX/xzjtvQqvV4h//eJUJHREREZET1NRU1zlznakxEr2psWjUrAoLy6HTmX8NSuUNBAW1acaInMPHR4HKSuev3NmStcSfhbAwfxQUcNioK+K7c018b66L78418b25psa8t2PHDgnTZ/z8/NGnj30X/msKqVSC0FDTK7lzDh0REREREbV6CoUnAEAqlSI52fZtrZyNCR0RERERERFqR8x17dpNWLzOFTChIyIiIiKiVs/SFKiWjAkdERERERHRXz10dfc9dgVM6IiIiIiIqNUzLBXJhI6IiIiIiMjF6PW1G4szoSO7k0gk4O4SxJ8BIiIiIsdhDx05TG1Cp3N2GORker0OUin/yhIRERE5BufQkYPIZB5Qq9XODoOcrKamGh4eCmeHQUREROSWSkqUzg6hUZjQuQAvLx9UV1dCo1Fz2F0ro9frodNpoVJVoqZGBU9P19kThYiIiMhVFBcXCceu1pHi4ewAyDqZzANeXr6oqqqATqd126ROpZJDpXKtv0DNQSqVwsNDAV/fQMhkMmeHQ0REROR2SktLhOOammonRtJwTOhchELhCYXC09lhOFRYmD8KCsqcHQYRERERtTI6nVY4DggIdGIkDcchl0RERERE1KpptTcXIPT2dq0pLkzoiIiIiIio1VKr1cjOvlKnhKtcEhERERERuYS68+dcERM6IiIiIiJqtepvO8d96IiIiIiIiFyGayVw9TGhIyIiIiKiVky8JRh76IiIiIiIiFyETufaezwzoSMiIiIiolZLr2cPHRERERERkUuqn9C5GiZ0RERERETUamk0ameH0CRM6IiIiIiIqNU6dy7D2SE0CRM6IiIiIiIiF8WEjoiIiIiIyEUxoSMiIiIiolZJrXbt+XMAEzoiIiIiImqlcnKynB1CkzGhIyIiIiKiVkmtrnF2CE3GhI6IiIiIiFql+kMuAwODnBNIEzChIyIiIiKiVikoKNjZITQZEzoiIiIiIiIAer3e2SE0GBM6IiIiIiJqlXQ6nbNDaDImdERERERE1CrJZDLReURElJMiaTwmdERERERE1CpdunRBOJZKpYiObuvEaBqHCR0REREREbVKdbct8PcPhEQicWI0jcOEjoiIiIiIWj0XzOUAMKEjIiIiIiJyyd45gAkdERERERERACZ0RERERERELikiItLZITQKEzoiIiIiImp18vKui85dccsCgAkdERERERG1QufOZQjHoaFhnENHRERERETkKqKiYoTj+Pj2ToykaZjQERERERFRqyOVyoRjPz9/J0bSNEzoiIiIiIioVXPV4ZYAEzoiIiIiImqFXDiHE2FCR0RERERErY5U6h6pkHt8CiIiIiIioga4fPmis0OwCyZ0RERERERELooJHRERERERkYtiQkdERERERK1WUFCws0NoEiZ0RERERETUaikUns4OoUmY0BERERERUavlynvQAYCHswMgIiIiIiJqDjqdDidPHkVgYJBQxoSOiIiIiIjIBSiVRSgpUaKkRCmUuXpC5zJDLjMzM/H0009j6NCh6NGjB8aNG4dly5ahpqZG1O7YsWOYNm0aunfvjltuuQX/+te/UFVVZfNz1q9fj9tvvx2pqam47bbb8NVXX5lsl5eXh6eeegp9+vRBr1698OijjyIrK6tJn5GIiIiIiBxHr9cblclkMidEYj8u0UOXl5eHyZMnw9/fH/fddx8CAwNx5MgRvPvuuzh//jzeeecdAEBGRgbuv/9+JCYm4qWXXkJubi5WrFiB7OxsfPrpp1af8/XXX+PVV1/F2LFj8cADD+DIkSNYtGgRqqur8eCDDwrtKioqMGvWLFRUVGDevHnw8PDAqlWrMGvWLGzatAmBgYEO+y6IiIiIiMh+vL19nB1Ck7hEQrd582aUlpZizZo16NixIwBgypQpqK6uxk8//YTFixdDLpfjvffeQ1BQEFavXg1fX18AQNu2bfHyyy9j//79GDhwoNlnqFQqvP/++xg1ahQ++OADAMC9994LnU6HpUuXCgklAKxZswZXrlzBxo0b0bVrVwDAkCFDMH78eKxatQpPPfWUI78OIiIiIiJqBBMddIiKimn+QOzIJYZcVlRUAABCQ0NF5W3atIGHhwdkMhnKy8uxb98+TJw4UUjmAGDChAnw8fHB1q1bLT7j4MGDUCqVmD59uqh8xowZqKiowO7du4Wybdu2oUePHkIyBwAdOnTAwIEDrT6HiIiIiIhaDs6hawZ9+/YFACxcuBBnzpzB9evXsWXLFnz33Xd4+OGHIZVKcfbsWWg0GqSkpIiuVSgU6NKlCzIyMiw+4/Tp0wBgdH1ycjKkUqlQr9PpcPbsWaN2AJCamorMzMwGzdkjIiIiIiLncfWEziWGXA4ePBhPPfUUPvvsM/z6669C+ZNPPonHHnsMAFBQUAAACAsLM7o+LCwMJ06csPiMgoICKBQKBAUFicoNZfn5+QAApVKJmpoas8/R6/UoKChAXFyczZ8vNNTP5rbuLizM39khUCPwvbkuvjvXxPfmuvjuXBPfm2sy9d40mnJ4ecmttnMlLpHQAbVz4fr164dbb70VQUFB+O233/Dhhx8iJCQE06ZNg0qlAlCbgNXn6ekp1JujUqkgl8tN1nl6eqK6uhoAhD/NPcdwr4YoLCyHTmdiQG8rExbmj4KCMmeHQQ3E9+a6+O5cE9+b6+K7c018b67J3HsrKiqHSqUWlbnC+5VKJWY7gVwiofvxxx/x6quv4ueff0ZERAQAYMyYMdDr9Xj77bcxbtw4eHl5AYDRNgZAbRJmqDfHy8vL5LWG6w3JmuFPc88x3IuIiIiIiFoWU9sWuDqXmEO3Zs0aJCcnC8mcwciRI1FZWYkzZ84IQyANQy/rKigoQHh4uMVnhIWFQa1WQ6lUispramqgVCqF64OCgqBQKMw+RyKRmByOSUREREREzuWOo+JcIqG7ceMGtFqtUblaXdtdqtVq0alTJ3h4eCA9PV3UpqamBhkZGejSpYvFZxjq61+fnp4OnU4n1EulUnTq1MmoHQCkpaWhXbt28Pb2tv3DERERERFRs9Drdc4Owe5cIqFLSEhAeno6rl69Kir/8ccfIZPJkJSUBH9/fwwcOBCbN28WtjkAavewq6ysxNixY4WyqqoqXLx4EUVFRULZgAEDEBQUhDVr1oiesXbtWvj4+GDo0KFC2W233YYTJ04IK18CwKVLl3DgwAHRc4iIiIiIqOVwx4TOJebQzZ49G7t378a0adMwY8YMBAYG4rfffsPu3bsxdepUYX+6+fPnY+rUqZg5cyYmT56M3NxcrFy5EkOHDsWgQYOE+6WlpWHWrFl4/PHH8cQTTwConff25JNPYtGiRXjqqacwePBgHDlyBFu2bMFzzz2HgIAA4frp06dj/fr1mDNnDh544AHIZDKsWrUKYWFhuP/++5v1uyEiIiIiItu445BLl0jo+vbti6+//hoffvgh1qxZA6VSiZiYGDz77LOYPXu20C45ORkrV67EkiVL8Oabb8LPzw/33nsvnnnmGZueM2PGDMjlcqxYsQI7d+5EVFQUFi5ciFmzZona+fn5YfXq1Vi8eDE+/vhj6HQ69O/fHwsXLkRwcLBdPzsREREREdmHOy6KItG746dyMdy2oBaXBXZNfG+ui+/ONfG9uS6+O9fE9+aazL23K1cu4fLli6Ky4cNvba6wGs3StgUuMYeOiIiIiIioqdyxK4sJHRERERERtQruODiRCR0REREREbUKTOiIiIiIiIhcFBM6IiIiIiIil8WEjoiIiIiIqMUrKVGivFy80qVSWeykaByHCR0REREREbmV6upqHD9+GEeOHBCVl5WVOikix2FCR0REREREbuXq1avODqHZMKEjIiIiIiK34unp6ewQmg0TOiIiIiIiciseHh7ODqHZMKEjIiIiIiK3UlRU5OwQmg0TOiIiIiIiciuXL192dgjNhgkdERERERG5DZ1O5+wQmhUTOiIiIiIichsajcZkuV7vfpuKA0zoiIiIiIjIrYgTN0Mi5649d0zoiIiIiIjIbZjriWNCR0RERERE1MKZS+j0eiZ0RERERERELVpxcbHonEMuiYiIiIiIXISXl5fo3JDQ5eRkOSMch2NCR0REREREbsPckMusrCtGZf7+AY4Ox+GY0BERERERkdu4ePFcvRLz2xV069bLscE0AyZ0RERERETkNioqykXnlrafk8vlDo7G8ZjQERERERGRW6uuVpkolTR7HI7AhI6IiIiIiNyWXq/HgQN/GJUnJ6c6IRr783B2AERERERERI5Uf6GUYcNGQyJhDx0REREREZHLcZdkDmBCR0REREREbkShUIjO6/fOJSYmNWc4DseEjoiIiIiI3IJer0dNTQ0AQCo1nerI5QqT5a6KCR0REREREbkFnU4rHMtkhuVCxD10VVWVzRiR4zGhIyIiIiIit6DT1SZvMpkMhmly9YdcFhbeaO6wHIoJHRERERERuQVDD52Hh4dbLXxiCRM6IiIiIiJyC6WlpQAAjUYjlNXroHM7TOiIiIiIiMgtnDp1EgCg1WoBGHroxBldXFy75g3KwZjQERERERGR2zE35NLfP6CZI3EsJnREREREROR2VKoqAIBSWSwqd7e5dUzoiIiIiIjIbV29mik6N7c/natyr09DREREREQkIp5Dxx46IiIiIiIilyFO4CQS90qB3OvTEBERERFRq6RWq21qxyGXRERERERELUxxcaFw3K9fvzo17r0RHRM6IiIiIiJyeeXlZcJxRESE2XacQ0dERERERNTCKBSezg7BKZjQERERERGRy5PL5QCA8PDIejXu1SNXHxM6IiIiIiJyeaWlpX/9WVKvhnPoiIiIiIiIWrScnKsAAJWqSlSu1eqcEU6zYUJHREREREQuSavV4MqVS6isrICvrx8AQC5XiNro9eyhIyIiIiIianGysq7g8uWLOHRoHwIDgwAA8fHtRW2Y0BEREREREbVAmZmXhONr17IBADKZTNSGCR0REREREZGL8PCQi86Z0BEREREREbUwOp3pxU5qaqpF53r9zXa9evVzaEzOwISOiIiIiIhcjlarMVlev0fOcO7t7YOAgECHx9XcmNAREREREZHLMTeUsv7G4oZ2VVVVppq7PCZ0RERERETkcnQ60wmdVGouxXHPuXRNTuiKioqQmZlph1CIiIiIiIhsU12tMlluPqFzTzZ/2k2bNuGVV14Rlb377ru45ZZbcPvtt2Pq1KkoLy+3e4BERERERER1HT68H8ePHzZZJ5FImjka57I5ofv666+h0dycePjnn3/i888/R58+fTB58mT8+eefWLVqlSNiJCIiIiKiVsjUPDmNRoOKCusdSfXn0rkrD1sbXr16FWPHjhXOf/75ZwQGBmL58uVQKBSQSCTYunUrHn/8cYcESkRERERErUdm5iVkZl5ESkoPtGkTJpSr1WqbrpfL5RbP3YXNPXRlZWXw9/cXzvfv349BgwZBoVAAAFJSUnDt2jX7R0hERERERK1OZuZFAEB6+glRuU6nbdT9bE0EXY3NCV1YWBiuXLkCoHYhlDNnzqBPnz5CfWVlJWQymf0jJCIiIiKiVuXGjQKzdeYWQ2mtbB5y2b9/f3z11VcIDAzEwYMHIZFIMGzYMKH+8uXLiIiIcEiQRERERETUely7lmW2Li3tuI13aR2Lo9ic0D311FM4fvw43nnnHQDAI488grZt2wKonZi4fft2jBkzxjFREhERERFRqyGT2ZymmNVaFru0+ZuKjIzEjz/+iAsXLsDf3x/R0dFCnUqlwqJFi9C5c2eHBElERERERK2HPRK61qJB35RMJkNSUpJRuZ+fH0aPHm23oIiIiIiIyP0VFOSjoqIM7dq1r7d/3M3tCjw9PW2+X48efeqctY4uuganvlVVVcjJyYFSqTS5L0Tfvn3tEhgREREREbm3U6dOAgCCg0MRGBhkppXtiVlQUHDTg3IxNid0VVVVePPNN7Fx40ZotcZLher1ekgkEmRkZNg1QCIiIiIicm91txTQ6/XIzTW9HZqpDiVzOIeunjfeeAMbNmzAsGHDMGDAAAQFBTkwLCIiIiIiao2qqqrqldxM4goLbzRvMC7A5oTul19+wR133IF3333XkfEQEREREVErU3f+3Jkz6WbbqdU1Zuu8vX3sGpOrsHlj8ZqaGvTv39+RsRARERERUStUN6GrqCgX1dUdZVleXmZ0bUBAIACgT58BjgmuhbO5hy4lJQWZmZkODIWIiIiIiFoLc/Ph6q/XodVqhPU6SkqURu27dk2Fl5e3iTuJJ9FJpTb3ZbkUmz/Vs88+i40bN+LPP/90ZDxERERERNQK6HTGCy2aotVqkZ5+AgAQHBxiooVtq58kJhpvv+YObO6hW7duHSIjIzFlyhT06NEDsbGxRlmuRCLB4sWL7R4kERERERG5F51OJxxbW73SsBhKVtYVozpzq1nWL4+ObtuwAF2EzQndd999JxwfO3YMx44dM2rDhI6IiIiIiGxRN6HTajUALCd2NTXVJsslZvcnaB37Ftic0J05c8aRcRARERERUStSWVkpHJ8+/SekUhkuXDgrlHXr1gtpaTc7kdLSjpu5U+tI3MyxaQ5dRUUFFixYgK1btzo6HiIiIiIiagWuX88RnZ89exoq1c096GQymaje1AqXgKUeutbBpoTO19cXP/30E8rLy603JiIiIiIisiI0NEx0Xn+POUurUioUCqv3by15ns2rXHbo0AE5OTnWGxIREREREVnRlG0EpFKZ9UathM3f4kMPPYS1a9fi8uXLjoyHiIiIiIhagYKCPIv1lhI+qdSW7rfW0UVn86Ioly5dQlRUFMaPH48RI0agXbt28PLyErWRSCR47LHH7B4kERERERG5F2s9dBKJ+XpfX39hURVzyV1rGXJpc0K3dOlS4fiXX34x2YYJHRERERER2cLHx9divaVeuI4dOyM6ui20Wi1kMptTGrdk86ffuXOnI+MgIiIiIqJWxLD3nDlyudxsnUKhgEIRYuUJNxPCdu0SGhKaS7E5oYuJiXFkHERERERE1IrU3VjclKb2vNXdiNzT08tCS9fW+KVliIiIiIiIGslaQtdU165lO/T+LYXNae+CBQustpFIJFi8eHGTAiIiIiIiIvd340ZBo66Li4tv8DXuvPm4zQndd999Z7WNoxO6tLQ0LF26FMePH4dGo0FsbCzuv/9+3H333UKbnTt3YunSpbhw4QJCQ0Nxzz33YN68efDwsP5RdTodli9fjrVr16KgoADx8fF45JFHMG7cOKO2Fy9exOLFi3Hs2DHI5XKMGDECL774IkJCrI3lJSIiIiJq3bRaDaqrVWbr+/cfbLbOzy/AESG5LJsTujNnzhiVabVaZGVlYcWKFTh37hy++OILuwZX1++//47HHnsM/fr1w1NPPQUPDw9kZmbi+vXrRm0GDBiAV155BefOncNHH32E4uJivPLKK1af8f7772PZsmWYMmUKUlJSsHPnTsyfPx9SqRRjx44V2uXm5mLGjBkICAjA/PnzUVlZKXwH33zzjcUJnERERERErZ1GY35BlISERHh7e5utb1xvG3voTJLJZIiPj8eiRYswb948vPPOO3jttdfsFZugrKwMCxYswNSpU/Hyyy+bbff222+ja9euWL58OWSy2t3jfX19sWzZMsycORPx8fFmr83Ly8PKlSsxa9YsLFy4EAAwefJk3HfffXj77bcxZswYYa+MTz/9FNXV1Vi9ejUiIiIAAN26dcMDDzyAzZs345577rHTJyciIiIicj+W5s/5+flZvLYxCZ0bj7i036IoQ4YMwfbt2+11O5Hvv/8epaWleOqppwAA5eXl0Ov1ojYXLlzAhQsXMGXKFCGZA4Dp06dDp9NZjW3Hjh1Qq9WYPn26UCaRSDBt2jTk5OQgLS1NKN++fTtGjhwpJHMAMGjQIMTHx2Pr1q1N+qxERERERO6uqKjQbJ2Xl/neOQAoLS2xdzguzW4JnVKpFHZrt7f9+/ejffv2+P333zFs2DD07t0b/fr1w5IlS6DVagEAp0+fBgCkpKSIro2IiEBkZKRQb05GRgb8/PyQkCDeo6Jbt26i++fl5aGwsNDoOYa2GRkZjfuQREREREStREFBvtk6icRyilJTU2PvcFxak7dVLy0txb59+/Dll18iOTnZHjEZuXLlCnJzc/HSSy/hoYceQteuXbFr1y58/vnnqK6uxsKFC1FQULtKTlhYmNH1YWFhyM83/0MDAAUFBWjTpo3JawEI1xv+NPecwsLCv3aslxnVExERERERoFQWma2TSi2Pj9TptA1+Hle5BNC5c2ezX4Rer0dgYCBeeukluwVWV2VlJUpKSvDss89izpw5AIAxY8agsrISa9euxSOPPAKVqnaVHIVCYXS9p6cnqqqqLD5DpVKZvRYAqqurRX9aaqtSqeDr62vrx0NoqOVxwq1JWJi/s0OgRuB7c118d66J78118d25Jr43+/PyMr+IYEiIr/C79MiRw7Bv3z5RfbduXRAcbP2d1H1GcLCv275HmxO6iRMnmkzogoKCEB8fjzvuuMPqBMbG8vKq3dn9zjvvFJWPHz8eP//8M/7880+hjaku2OrqaqHe0jPMXQvcTNYMf1pqa+1Z9RUWlkOn01tv6ObCwvxRUFDm7DCogfjeXBffnWvie3NdfHeuie/NMVQqtdm6sjI1KisN37kCgYFtkJd3c2V7tVpm9Z2EhfmLnpGdnQeFwnW3O5BKJWY7gWxO6N566y27BdRQYWFhOH/+vNGQSMN5SUmJMASyoKAA4eHhonYFBQXo2bOn1WccOXLEqNwwlNNwT8OfhvL6bUNDQznckoiIiIjIgvDwSOTn5xqVKxQKo9+lExI6CAmdt7dPo4ZPGkbzuSObF0VZunQpzp07Z7b+/PnzWLp0qV2Cqs8wNy8vL09Unptb+0MQEhKCLl26AADS09NFbfLy8pCbmyvUm9OlSxeUl5fj8uXLovKTJ08K9UDtIishISFGzwFqNz639hwiIiIiotbO3KIoCoXlkW5uPBWu0RqU0J09e9Zs/fnz5/HRRx/ZJaj6DJt6b9iwQSjT6/VYv349fHx80KNHD3Ts2BHt27fHunXrhJUvAWDt2rWQSqUYM2aMUFZWVoaLFy+irOxmV+2oUaMgl8uxZs0a0TO+/vprREdHo3v37kL5mDFj8Ouvv4oSzP379yMzM1O0ATkREREREYlpNBro9ab3oYuLa2dUVrdHrrGr6nNRFBtUV1c7bKhhSkoKJk6ciM8++wyFhYXo2rUrfv/9d/zxxx94/vnnhbl7L7zwAh555BHMnj0b48aNw7lz5/DVV19hypQpou0IfvnlFyxYsABvvvkm7r77bgBAZGQkZs2ahRUrVqC6uhqpqanYsWMHjhw5gvfff1/YVBwA5s2bh59//hmzZs3Cfffdh8rKSixfvhydO3fGhAkTHPIdEBERERG5g8zMi2br5HLjhQcB903G7MFiQldeXo7S0lLhXKlU4tq1a0btSkpK8P333yMqKsr+Ef7l9ddfR1RUFDZt2oRNmzahbdu2eO211zB16lShzYgRI7B06VIsXboUr7/+OkJCQvDII4/g0UcftekZzz33HAIDA7Fu3Tps3LgRCQkJePfddzFu3DhRu6ioKPzvf//DW2+9hXfffRdyuRzDhw/HggULTK5+SUREREREtbKzrwrHvr5+qKgoF84NCxDWZY/ONXfuoZPo9XqzyysuXbrU5mGUer0ezz//PGbPnm234FoLrnJZi6tIuSa+N9fFd+ea+N5cF9+da+J7s7/ffvtFOL7llmHIycmGn58/9HodwsIijNqr1TXYu/d3AEBgYDB69uxj9RlhYf5Yv36jcB4ZGYPOnbvaIXrnaPQql/369QNQm6x99NFHuPXWW5GUlGTUztfXF927d0evXr3sEC4REREREbUGcrkC8fHtrbS62bvm4+PTqOe4cQed9YTOkNRdu3YNU6dOFS0OQkREREREZKu6i5okJCTadE3dZKzuuhYN4c5DLm1eFOXNN990ZBxEREREROTm1Ooa4bhduwQLLeu6mYxJJEzo6mvQN1JeXo6lS5di2rRpGDNmDI4fPw4AKCoqwtKlS3HxovkVa4iIiIiIqHWrqalN6EJDw2y+xh49dAqF8WIr7sLmHrqioiJMmzYN2dnZiIuLQ1ZWlrDjekhICDZt2oSysjIsWLDAYcESEREREZHr0mg0AAAPj4bsnla3h65xz3XjDjrbE7r//Oc/uHHjBr755htERUVh0KBBovpRo0Zh//79dg+QiIiIiIjcQ3V1bYdQQxK6usMlDQlhw7lvRmdzn+WuXbswffp0JCcnmxyDGhsbi9zcXLsGR0RERERE7sOwqXh5ue1bQdTNPXJysuwek6uzOaErLi5GXFyc2XqJRILq6mq7BEVERERERO7LMHWLms7mhC4sLAxZWeYz4oyMDERFRdklKCIiIiIici8VFeXCcdeuqTZf584rVNqDzQnd0KFDsWHDBuTn5xvVnTx5Eps2bcKoUaPsGhwREREREbm+nJwsHD58c70Nf/8Ahz/Tw0MuHDd2dUxXYPMne/zxxyGTyTBp0iS89957kEgk2LRpE5555hnMmDED4eHhePjhhx0ZKxERERERuaDz58+IzpsjwerYsbNw3LBVNV1Lg4Zcrlu3Dt26dcO3334LvV6PzZs3Y+vWrRg8eDDWrFmDoKAgB4ZKRERERESuRqfTOeW5cvnNHrrw8EinxNAcGpSqRkdH45NPPkF5eTkuXboEAIiLi2MiR0REREREJtXvnWsudefetfohlxUVFejSpQs++ugjAICfnx+6deuGbt26MZkjIiIiIiKzrl/Psdu9fH397HYvd2FTQufr64uAgACEhoY6Oh4iIiIiInITWq3xRuA+Pj6Nvl9CQmJTwnFLNvc99u/fH4cPH3ZkLERERERE5EZMzZ8LCQlzQiTuy+aE7vnnn8fRo0fx3//+F+Xl5dYvICIiIiKiVq2qqkp0HhkZjYSE9k6Kxj3ZvCjK/fffj+rqanzyySf45JNPEBISAi8vL1EbiUSCHTt22D1IIiIiIiJyPTk5WaLzzp2Tm3hHvc0tW8uG5DYndNHR0Y6Mg4iIiIiI3MTFi+dRWqpESYlSKOvWrVeT76u3PZ9rNWxO6FavXu3IOIiIiIiIyE1kZWUalYWE2GOBRWZ09bnvhgxERERERORW2ENnjAkdERERERE5lP029mZGVx8TOiIiIiIishuNxnjvOYnEPmkHe+iMMaEjIiIiIiK7qagw3uIsOjrGTndvyCqXdnpkC8eEjoiIiIiI7Ka4uMioLCEhsUn3bNu2Hby9fdCmTUM2JW8dGZ3Nq1wSERERERFZ4+PjY1TW1Dl0iYmd0KFDx1azt1xDsIeOiIiIiIjsRqfTic7DwyPscl8mc6Y1KKErLy/H0qVLMW3aNIwZMwbHjx8HABQVFWHp0qW4ePGiQ4IkIiIiIiLXYEjowsIi0KVLCpKSkp0ckXuzechlUVERpk2bhuzsbMTFxSErKwsqlQoAEBISgk2bNqGsrAwLFixwWLBERERERNSyGVa5VCg8ERER5eRo3J/NCd1//vMf3LhxA9988w2ioqIwaNAgUf2oUaOwf/9+uwdIRERERESu49Kl8wCA8vIyp8bRWoZo2jzkcteuXZg+fTqSk5NNfjmxsbHIzc21a3BEREREROSaSkqKnR1Cq2BzQldcXIy4uDiz9RKJBNXV1XYJioiIiIiIXM+NGwXCcVSUvfaeaxz20NUTFhaGrKwss/UZGRmIiuIYWSIiIiKi1qiysgLp6SeE87i4BOcFA6C17ENnc0I3dOhQbNiwAfn5+UZ1J0+exKZNmzBq1Ci7BkdERERERK7hypXLonMvLy8nRVKrlXTQ2b4oyuOPP45ff/0VkyZNwsiRIyGRSLBp0yasX78e27dvR3h4OB5++GFHxkpERERERC1U3eGWgPOHPDr7+c2lQUMuv/nmG3Tr1g3ffvst9Ho9Nm/ejK1bt2Lw4MFYs2YNgoKCHBgqERERERG1VImJnYTjyEjnzp8DWk9CZ3MPHQBERUXhk08+QXl5OS5dugQAiIuLYyJHRERERESC4OBgZ4cAD48GpTouy+ZPWVxcLLwYPz8/dOvWzWFBERERERGRa9Hr9QAAuVyB8PBIJ0dTu7F5587JkMsVzg7FoWwecjlkyBA8/vjj2LFjh7D7OxEREREREQAUFOQBAEJDw1rMcMfIyGiEhrZxdhgOZXMP3ZgxY/Drr79i586dCAwMxJ133okJEyYgNTXVkfEREREREZELKC4uAgDk519H585dnRxN62FzQvfee++hvLwcW7duxebNm/HVV1/hq6++Qvv27TFp0iSMHz8eERERjoyViIiIiIhaEJ1OB61WC7lcLiqj5mPzkEugdu7c5MmT8b///Q87duzA448/Do1GgyVLlmDkyJGYPXu2o+IkIiIiIqIWZvfundi79zecO5chlAUEBDoxotanQQldXTExMXjsscewbds2LFmyBN7e3ti3b589YyMiIiIiohaqtLREOL52LVs47tqVU7KaU6PX8qyoqBCGXx49ehQ6nQ4dO3a0Z2xERERERNRCHTt2yGS5l5d3M0fSujUoodPr9dizZw82b96MnTt3QqVSITg4GDNmzMCkSZPQtSsnPxIRERERETUXmxO6f//73/j+++9RWFgIDw8PjBgxAhMmTMCwYcNazaZ9RERERETknjQ6DbZf3opqTTVuTbgNfgp/Z4dkE5szsZUrVyI1NRWPPPII7rzzTgQGcrIjEREREVFr5enphepqlbPDsJuD1/YhLf8EAOBsUQZ6RPTCiHaj4SnzdG5gVtic0P3444/o0KGDI2MhIiIiIiInKCsrQ3r6caSk9IRWq0FgYJDVzcH1evfanmBP1u+i8xN5xxDpG4UeEb2cFJFtbE7omMwREREREbkXnU4HvV6Po0cPAIDwZ0pKd7RpE272OpVKhZqammaJ0Vk8pHJE+UU7OwyrzCZ0mzZtAgBMmDABEolEOLdm4sSJdgiLiIiIiIgcSa/XY/funSbriooKERoahrKyUvj7Bxj11h04sKc5QnSqx3s/DS8PL2eHYZXZhO6ll16CRCLBuHHjoFAohHO9Xm/2ZhKJhAkdEREREZELKC8vM1t37Vo2PDzkuHr1MqRSKQYPHgGpVAqdTofc3GuitsOGjcbhw/tRWVnh6JCbjY+Hr0skc4CFhO7//b//BwBQKBSicyIiIiIicm06nQ75+bkW21y9elloe/VqJuLj2yMz85JQbiCRSKzOt3M1I+NvdXYINjOb0PXr18/iORERERERuaZjxw5Z7KGrLzPzIuLj2xslcwauntDp6i3wktwmxUmRNJzU1oYLFizAyZMnzdanpaVhwYIFdgmKiIiIiIgcQ6WqalAyZ6DTmV/V0rCASmBgUGPDcqpqTbXo3JUSVJsTuu+++w5Xr141W5+dnW3zwilEREREROQcBw780ajrLA3RjIuLR0pKD6Sk9GhkVM5VrCpydgiNZnNCZ01lZSU8PGzeBYGIiIiIiFoQHx8fi/VarcZsnVQqRZs2YZDL5fYOq1l4SF03j7EY+bVr15CTkyOcX7p0CYcPHzZqV1JSgrVr16Jdu3b2j5CIiIiIiOzC0rDJsLAIXLlieo4cALfed06jv5msRvpGOTGShrOY0G3cuBFLly4VVq759NNP8emnnxq10+v1kEqlWLx4scMCJSIiIiKiptFqtWZqJIiJibWY0BUVFTomqBZAq7v5vcikMidG0nAWE7rRo0cjJiYGer0e//jHP3DvvfeiZ8+eojYSiQQ+Pj5ITU1FVJRrZbNERERERK1JWVmJyfKkpC5QKDyRmJiECxfOmmxTU1NtstwdVKorhWOp/WalNQuLCV3nzp3RuXNnALXDL8eMGYNOnTo1S2BERERERGRfKpXKqKxt2zhERkYLxwUF+SgpKTZqJ5HcTHRkMpmF3j7nK6wsxFv7/41AzyA81H0e5DLLc/u+O7deOM4qM78QZEtkc/r5+OOPM5kjIiIiInJRlZUVKCq6YVQeHBwqWqbf3Ir9KlWVcBwa2sbu8dnTe3+8BwAoqVZi66UfLLbV6/XNEZLDNHg5lxs3biA9PR0lJSUmP/zEiRPtERcREREREdlJaWkJjh07ZLKufgJnbQ+24OBQe4XVLE7fSMddHSeZrd+f07htHFoKmxM6nU6H1157DRs2bLC4Og4TOiIiIiKilsVcMmea5YTO29vbpTbejvWPs1i/O+u3ZonDUWxO6JYvX45169bhrrvuwi233IIXX3wRzz33HHx9ffHll1/C398fzzzzjCNjJSIiIiKiBtDr9UhPP2mxjbe3r+i8/vy58PBI0abiRUWF6N27P9TqGkRFtbVfsHZytfSK6Nxbbnl/PVdn8xy6TZs2YciQIXj77bcxdOhQAEBycjKmTZuGjRs3ori4GKdOnXJYoERERERE1DCFhTdQWFhgtr5jx87w9vYWldUfjadQeIrOZTIZ5HI5unbthuDgEPsFayffZKwVnfs0MKHzlftab9SC2JzQZWVlYciQIbUXSWsv02hqN+Dz8fHB3XffjfXr15u9noiIiIiImldVVaXF+piYWKv3qD+6Mi4uoSkhOZxGpxad+3iYT+h0euOpZJM6TbZ7TI5k85BLLy8veHjUNvfx8YFEIkFh4c3NBcPCwpCbm2vuciIiIiIiambZ2VdMlsvlcrRvb30F+4CAQNSfU1db5jr0ML+K5Y8XthiVhftGODIcu7O5hy46OhpZWVkAan8A4uLisGfPHqF+3759CA11rRVviIiIiIjcWXW16c3Au3fvjaioaJvuYbwKZlOjal6WdiU4deNPozKpxI02Fq9rwIAB+OWXX/Diiy8CACZMmID//ve/yM/PBwAcOXIEDz74oGOiJCIiIiKiBrG0v5pUKmvAfcTnLXmFS1OfWQfzK/SbIpPY/t20BDYndA8++CBuueUW1NTUQKFQYO7cuSgqKsKWLVsglUpx77334sknn3RkrEREREREZCO1usZsnVRqW1Km0+lw7VqWqKwlb8RdrVUZlelNzJOzpCUnrKbYnNCFh4cjPDxcOJfJZHj55Zfx8ssvOyQwIiIiIiJqPMMChqYYFjm0RqvVQqvViso8PORNisuRjlw/bFRmaQ6dO3CtAaJERERERGQTlcq4t8pAYuM8Mb1eDz8/f1GZYaHElshU8taSexTtwezbuHbtWqNuGB1t2+RKIiIiIiJynLqbgdfXkGGFMTFxOHvWNfabLq8pNyozl9BpdMY9mBM63m33mBzNbEI3cuTIRo0fzcjIaFJARERERETUNDqdDsXFtVuMKRQKBAYGQyqVIi/vOoCGJXSW5uK1NEpVkVGZuUVR/sw/aVTWpU2y3WNyNLMJ3WOPPeZyEwKJiIiIiFo7vV6PI0cOCFsW6PVAcnI3lJaWCAmdrXPoGtrW2ZTVSqMycyMut13+ybHBNBOzCd0TTzzRnHEQEREREZEd5OfnobKyQjg39LDV7ayxtePGw8PDZRI6jU6NElMJnY3bFtyaMNbOETUP13g7RERERERkk4wM482yazV89F2XLik2L6DibPmV+SbL0/JP2HR978i+doym+di8RI2ti6RwURQiIiIiopYjObkbANv3nvP19UNFRTnatUuAr68famocM4dOr9fj0LX9KK0pxaC2Q+Ar923S/a6XNW5RR1dnc0Jn6yIpXBSFiIiIiKj5VVVV4erVy0blYWERAABvbx/4+vrB19dy4tS9e28UFxfWuc7b/sECuKS8iF1XdwIAqjUq3Nlxos3XZpVehV6vR2xAnJCj/Hb1V5uvr1RXis6HxA6z+dqWxuaEztQiKRqNBllZWdi5cyc6deqEoUOH2j1AIiIiIiKy7uDBP4zKfHx8hGOpVIo+fQZY7aRRKBSIiIgSzr28HJPQHc29uQl4+o0/bU7oLhZfwPozawEAf0u6Fx1DkgAAap3tPYm/Zm4XnfeI6G3ztS2NzQmdpUVSsrKyMGXKFKSkpNglKCIiIiIiarqgoBDReWNXsZdKpdDpbFtcxNE2nv1GOP727DfwU/hbaG1aTnmO6Lypwz2dyS4zHGNjYzFlyhT897//tcftiIiIiIioAcxtni2Tyexyf19fPwCAp6eXXe4HAFoTG3vbdJ1eKzovrylDeU2ZqCzUJ9TiPUxtKu6q7LZkTUREBC5evGiv2xERERERkY1qaqpNlttry4Hk5G6IjIxG9+697HI/ANDoG55U6fTWewm7hCYjyj/KYpuymtIGP7ulsnnIpTU7duxAQECAvW5n0eeff44lS5agc+fO2Lx5s6ju2LFjeOedd3D69Gn4+fnh9ttvx7PPPmvzZM7169djxYoVyM7ORnR0NGbNmoUZM2YYtcvLy8PixYuxd+9e6HQ6DBgwAAsWLEBsbKxdPiMRERERka32799jstywsElTeXl5o3PnZLvcy6C8plx0rlQVI8gr2OI118rEQyVnpT4I/zpDLqUSGXzlvthx7Qf7BdrC2ZzQLV261GR5SUkJDhw4gPPnz+Ohhx6yW2DmFBQU4JNPPhFN8DTIyMjA/fffj8TERLz00kvIzc0VkrNPP/3U6r2//vprvPrqqxg7diweeOABHDlyBIsWLUJ1dTUefPBBoV1FRQVmzZqFiooKzJs3Dx4eHli1ahVmzZqFTZs2ITAw0K6fmYiIiIjIHFNz2zp06ISIiEgoFJ5OiMg29TcB/+LEp3huwAKL1+SUZYnOo/1iTLbTw/QQVHfU5IQOANq0aYOnn34aDz/8sF2CsuTdd99FSkoK9Ho9SkvFXaXvvfcegoKCsHr1amE51rZt2+Lll1/G/v37MXDgQLP3ValUeP/99zFq1Ch88MEHAIB7770XOp0OS5cuxeTJk+HvX5v9r1mzBleuXMHGjRvRtWtXAMCQIUMwfvx4rFq1Ck899ZQjPjoRERERkZEbN4w31NZo1C06mTNFo9dAr9dbXLil/mImjZUY3AkXis8BAIbGjrDLPZ3F5kG1O3fuNPrfr7/+iqNHj+KPP/7AvHnz7Dbp0py0tDRs2bIFCxYYZ+7l5eXYt28fJk6cKNpbY8KECfDx8cHWrVst3vvgwYNQKpWYPn26qHzGjBmoqKjA7t27hbJt27ahR48eQjIHAB06dMDAgQOtPoeIiIiIyJ5MLYiiUqmcEEnTWVus5FzRGbs8x0t2M9kN8GyeaWOOYnNCFxMTY/S/6OhoqxsT2oter8frr7+OiRMnokuXLkb1Z8+ehUajMdo6QaFQoEuXLlY3PD99+jQAGF2fnJwMqVQq1Ot0Opw9e9bkFg2pqanIzMxEVVVVgz4bEREREVFjXbhw1qissdsTOFuNib3kCirzUa01vehLY2nrLK4igWt+VwZ2W+XS0TZt2oQLFy7g6aefNllfUFAAAAgLCzOqCwsLQ36+cVd0/esVCgWCgoJE5YYyw/VKpRI1NTVmn6PX64VYiIiIiIgcTa1WG5U5ajNwR6upl7gdvn4Qy09+hs+OfQS11vhz2irjxinRCpl63DyWSlwmJTKpQatcHjt2DF999RWuXLkCpVJp1L0rkUiwY8cOuwYI1A6nfPfddzFnzhyEh4ebbGPoVlYoFEZ1np6eVrudVSoV5HK5yTpPT09UV9f+cBn+NPecurHYKjTUr0Ht3VlYWMM3hiTn43tzXXx3ronvzXXx3bmmlv7evLyMf4cNDPRu0XGrtWqTcX936Wu8OOxF4Xzv8V3w8pJDhxqsyPhYdE24b7jZzzjcczhO5Z0SzrdlfY+AIC/0iOoBAPDN8YRXRe29QkP8W/R3ZY3NCd0333yDV199FXK5HAkJCYiKsry3gz198sknkMvleOCBB8y28fKq3eSwpsa4m7a6ulqot3S9qWsN1xuSNcOf5p5TNxZbFRaWQ6drPSvxmBMW5o+CgjLrDalF4XtzXXx3ronvzXXx3bkmV3hvKpVxz1VpaVWLjlupKjYZt0pViDNXMxHqHfrXubpOnbh9x8hks58xKDDIqP3qw2sQM7ADAKCkrEKoL1FWoUDacr8rAJBKJWY7gWxO6D799FN06dIFX3zxBUJCQuwWnDX5+fn48ssv8dRTT+HGjRtCeXV1NdRqNbKzs+Hv7y8MgTQ13LGgoMBsz55BWFgY1Go1lEqlaNhlTU0NlEqlcH1QUBAUCoXZ50gkEpPDMYmIiIiI7K3uiLlevfohLe0YNBoNgoKa7/f1xlh+cpnZuip1JfBXQmdJSptuZuuszSHU1fnepBLHLuzoaDYndIWFhZg9e3azJnOG56rVaixZsgRLliwxqh81ahQefvhhzJ07Fx4eHkhPT8eYMWOE+pqaGmRkZGD8+PEWn2NYaCU9PR2DBw8WytPT06HT6YR6qVSKTp06IT093egeaWlpaNeunc2bmBMRERERNcWlS+cBADKZDAEBgejX7xZUVVUgMDDIuYFZoTax+InBmaLTNs1r8/Jo2Ki4unR6rXAsddEFZAxsTug6dOhgtO9bc2jbti0++ugjo/L//Oc/qKysxD/+8Q/Ex8fD398fAwcOxObNmzF37lxh9c3NmzejsrISY8eOFa6tqqrCtWvXEBwcLCSoAwYMQFBQENasWSNK6NauXQsfHx8MHTpUKLvtttvw3nvv4fTp08LWBZcuXcKBAweaZS8+IiIiIiK9Xo+srCsAAK22NkFRKBQm13pwJUeuH8KR64estrPUC2ctIazbQydpLYuizJs3D6+//jruvvtuREREODImEX9/f4wePdqo/Msvv4RMJhPVzZ8/H1OnTsXMmTMxefJk5ObmYuXKlRg6dCgGDRoktEtLS8OsWbPw+OOP44knngBQO+/tySefxKJFi/DUU09h8ODBOHLkCLZs2YLnnnsOAQE396eYPn061q9fjzlz5uCBBx6ATCbDqlWrEBYWhvvvv99xXwYREREREYCKinIcO2Y96WmJNLrGr1ZpL3p9K1zlcsyYMaiqqsIdd9yBUaNGISYmBlKp+MNLJBI89thjdg/SVsnJyVi5ciWWLFmCN998E35+frj33nvxzDPP2HT9jBkzIJfLsWLFCuzcuRNRUVFYuHAhZs2aJWrn5+eH1atXY/Hixfj444+h0+nQv39/LFy4EMHBwY74aEREREREggsXzgm9cq6moqbCapsov2gAwPXyaybrJ3b6W6OenV6QBgAoV5cLZTIXn0Mn0ZvaWt6Ey5cv46GHHkJOTo75m0kkVjfwJmNc5bKWK6wiRcb43lwX351r4ntzXXx3rqmlvrfffvvFqGz48FudEEnDKVXF+PT4UrP18/u9AE9Z7cryb+1/3WSbeT0fR5CX+Y6UwBBPLPjxZZvimZX6IKL9Ymxq6yx2WeXytddeQ1FRERYuXIg+ffqIhiASEREREREZ5FfkoaymDAlB7Y2GNO7P2WfxWkMyBwAhXqEoUhUatbGUzAGABLYtdCKTyBDoGWRT25bK5oTuxIkTmD17NmbOnOnIeIiIiIiIyIyyslJcuHDW2WFYVFRVhBVp4m0JbiZNepRUl9h8r44hSTh4zXICaIq5BVOS26QKx1KJFF1Cu8JX7tvg+7ckNid0fn5+zb5lARERERER3XT06EFnh2DVnqzfjMpKqpWNupetPW22iPWPw/iOE+12v5bC5iVdbr/9dmzfvt2RsRARERERkYu7UHy+Qe0ldVKS2d3n2iUGUytXyqSuvfiJOTb30E2dOhUvvvgiHn30UcycORNt27aFTGb8pURHR9s1QCIiIiIisqxr11TrjZqJqU3D70yciBj/GEggQV5FHr47tx4SSPFwj0cQ7BVsdoikr8J+wyGlLr6apTk2J3R33HEHJBIJ0tPTsWvXLrPtuMolEREREVHzCg+PdHYIFoV4hyDYq3b6VpBXMOb3ewESSKCQWd4EvWdEL+zMFI8STAhsb/V5poZq+sh9GhCx67A5oXvssccs7sZORERERETNLyQk1NkhWOXt4S06r7uSpSUeUjmS26Ti1I0/hbKubVKsXmcqb+kYnGTTM12NzQndE0884cg4iIiIiIjIAlPbR7dt2w7x8QlOiMY8f0UAympKRWWG3rnG8JDKRednizKQGt69wffpFNLKEzoiIiIiInIejUZjVJaY2MkJkZhXra0WJXOJwZ0wKGZwk+4pq7fAiS09dKa462hDmxO6w4cP29Sub9++jQ6GiIiIiIhMU6vVzg7BqoP1Ng2f2Olv8JA2rQ9JUi+hiw2Ia9L93I3N3+7MmTNtymq5KAoRERERkf0dOrRXdC6TtazBdlmlV7Ev5w9RWVOTOQCQ1stB5FLLC6m0NjZ/w2+++aZRmUajQVZWFjZu3Ii2bdtiypQpdg2OiIiIiIhMk8ls3lK6WXx16kuH3Ld+D509kkR3YvO3MWnSJLN1s2fPtlhPRERERGRvOp0OUmnLSmocxdSCKC1dbEA7u9xHCvE7lrnpfnKNZZe/AYGBgZg8eTK++OILe9yOiIiIiMii69dzsHv3ThQVFTo7lGaRkZEuOvfwkKNr125OisY293aeZpf7lNdbMZPE7NZfGRAQgKysLHvdjoiIiIhIRKfTYffunQgODkFxcREAIC3tGIYPv9XJkTlefn6ucCyRSHHLLcNa9KqNIV6hkMvk1hva4HrFddF5S/7czmCXHrrq6mps2bIFbdq0scftiIiIiIiMZGdfBQAhmTNQq2ucEY7TKBTyFpfUVKmrROfjO0602705xNIym3voFixYYLK8pKQEJ06cQFFREV544QW7BUZEREREZJCbew2XLp03WVdTUwO53H1XPqw/fy40NMxJkZh3Mv+46DzEO9Ru9/aUedrtXu7I5oTuu+++M1keGBiIhIQELFiwAOPHj7dbYERERETuJjPzIoqLi9CtWy/IZLW9DlqtBno94OHBlfssOXPmlNm6ltZbZW8FBXnCcbt2CYiLi3deMGacKzorOlfYcWsBqaR1LHzTWDb/l+PMmTOOjIOIiIjI7WVmXgIAXL58EYmJnaDX67Fnzy4AwLBho90+MXGUkyePoV+/QUKS7G5On/5TOI6NjW9x+88BwLXybNG5PX+WPaT2mYvnrpjuEhERETWz7OwrKC8vw++/7xDK1Gq1EyNq2UpKlBbrq6tVyM6+0jzBOJkjenL1ej10ep3d7mev7QoM+O8clln8idBqtXj//fcRExODadPMLzu6Zs0a5ObmYv78+fyXJSIiIiIbXLx4TnSu0aihULjvPLCmMDd3rq7c3Oto1659M0TTvBy5/1xeRS5Wpn0unEsgwYROfxPOLysvoaAyD93Ce6BHRC+hvLymDL5yP7O/93cISrRzpMwvLLGY0G3ZsgXLly/H+vXrLd6kW7dueP3119GxY0fOoyMiIiKyQf1eJ/bQmWethw4AqqoqHR+IE6hUKofdu24yBwB66LHp3AajdtfKc1BSrcSwuJH45fLPOJp7GADw4oCXhaQu2q+tMOyyrX+sXePkHDrLLH47W7duxaBBg5CSkmLxJikpKRg8eDB+/PFHuwZHRERE5K50OvEQN41G46RIqCWrrrZ/QqfVafHdWePEzZL9OXsBQEjmAODdg29hR+Y27MjcJppDJ5Pady6jlCMALbKY0J06dQoDBw606Ub9+/dHenq69YZERERErYhOp8P16zlWe1o0GvbQWePvH+DsEJqFTqcThloWFt4Qyvv2te33cmveObgYZ4syzNZ3CumMTiGdjcr/yNotOtfoNThy/RCOXD8kKveQ2nueHxM6Syx+2yUlJQgNtW0PiZCQECiVSnvEREREROQ2MjMv4erVy7hyxfI8MJWqymJ9a6TValFQkAdPT09UV1ejS5dUHDq019lhOZROp8Pu3TsBAJ07JyMrK/OvGgl8ff2afP+rpZYXj5nT4zGEeIcAAN7a/7qo7o/s3216hr3n/fWI6IUzhacBwGSi2dpZTOh8fX1RXFxs042USiV8fX3tEhQRERGRu7h69bJN7S5fvuiWi3o0xZ49v4rOPTzcc1uCuuoOsRTvvWefJOmnC99brDckc7aI9otBlzbJAICdmduFcr2dYjWID0zArQljUVRVhEExtzTqHnEB8XaNqSWxmNAlJiZi7969ePDBB63eaO/evUhMtPeKNkRERETUGmm1WqMya/uv6fV6N1hx3XT8nTsn2+XuXh5eQLXpuoENTJZmpjwgfN9XSjJxofgcAhSBCPMJb2qYRnpH9m3S9fZOMlsSi38rbr31Vvz73//Gjh07MHr0aLPtdu7ciX379uGll16ye4BERERErqqhQ8/KykpbzTwxa0z1bEqlllc7dIeErqqqwmR5Q4ZbVqgrsPXi97hReQMj2o2GQnZzY+7ciutmrxsaO8L2QCHePPzOxLtwrugs2gXEc1XKZmYxoZs6dSrWrl2Lp59+GrNnz8bkyZPRtm1boT47Oxvr16/HihUrEB8fj6lTpzo8YCIiIiJXkZOT1aD2R48exNCho6wmLu6uuLgIV64YJ3TWkjWdTgepVIrr16+hpKQYSUldXS7Bu3rV9Bw3Wz+HXq/Hh0feE86/O2d5+zGDSZ0mGz3DS+YFldb0Yj73Jd8vbuvhjW7hPWx6llM4cD8/Z7OY0Hl5eWHZsmWYO3cuPvvsMyxbtgx+fn7w9fVFRUUFysvLodfrkZCQgM8++wyenp7NFTcRERFRi1NWVoazZ0+hQ4dOCA4OwYULZxt8D61W2+oTupMnj5qti41th6ys2qTHzy8A5eWlQl15eRn8/QNw9mzt3LPw8AiEhLRxbLBNoNVqsGfPLvj7B6B37/4AgMrKcpNtpTZuBZBTlm29kQlJocaLjdzbZTr+X/oKk+1j/NuaLG+p3HnIpdX/WrRr1w6bN2/GwoUL0bt3b0ilUty4cQNSqRR9+vTBwoUL8d133yEuLq454iUiIiJqsc6cSUd5eZnFhKQub28fhIdHiMr0ep2Z1q2DuWGqXl7eAACF4mYHQps2YaI2J04cQVraceFcq23Z3+XhwwcA1A61raioTeRqampMtrWlh668pgz/O7XKZF18YALiAxMaFF+0fww6h3ZtdDwtiTsndDZtEuHp6YmZM2di5syZjo6HiIiIyGUZfim3VffuveDl5Y38/F+EMp3OfX/xtEV+fq7J8g4dOgIQ70UXFRWNzMyLonbl5WXC8alTJzF8+K0OiNI+6m5VcfjwfvTrN6jR96qoKcfSo/8xWTe/3wvwlN1MhOtvR9A/2vxzb+9wp7BlALVMrbs/n4iIiMiJTPVyaLUaJ0TScmRkpAvHXbqkCseGIYdBQcFITe2BAQMGw9PTSxiqaODK319Vlem9CAMCAuHl5WXx2gPX9pmtq5vMmTIkdpjZOombbOrtxlPomNAREREROUJenvnVBA0kJlYDPHx4vyPCcQnmEhpAvMJlaGiYMATTlVcFNXwGg0uXbm4+HxUVIxz36tXP6hDH/Io8m587rsN4AIC/IgDP9nsJHlLzg/akJp7r48G9p1sSm4ZcEhEREZF5er3eKBmp29Nkjrlf0vfs2YVOnTojIiLKLvG5irqbagOAVHrz+7HUQxUQEIjS0hKHxeUIer1eNOQSEA/ZLSoqtPle1dpqXCnNtLl9t/AeiA9sDx+5NzykcottJSb6fyo1prdWaNnct4uOPXRERERETfT77ztx6NDeBl0TGBgEDw/T/7au1WpsSgjdzcWL54TjLl1ShBUqJRIJvL19zF7X0lYF1WjU+PPPEygsvGG2TW7uNYv3iIiItPl5q/9cabbulrZDTZYHeAZYTeYAuM2ecjo3XmyIPXRERERETXDjRgGs/eu/v38A1Gpxb0yPHn1cbqVARysru7kFQUhIG8hkMgwbNtrq92RpNUtnbDZ+7Vo2CgsLUFhYgNjYeISGtkFQULCozdmzlhcakcsVRmVqrRqnbqQj3Dcc0X43h2TeqCoweY+pXe9DXEC7RnwC98OEjoiIiIhMun7d+r5fKSk9cPz4zblx8fEdmMxZIZfX9h7Z8j2VlZkfbqnRaIR7OZpGo0Fu7jWUlCiFsqysTGRlZdqUmBq0aRMmJICGxE6r0+LdQ28Jbe5Lvh8BnoEW79PQbQpMMRXzjOS/N/m+zU2r1zo7BIdhQkdERETUBDqd9X/59/S8ucpgSEgbxMe3d2RILq9Dh052u5dWq222hO7MmVO4cSPfZF1e3nVERkbbfK/azcYHCHMH/yw4Kao3t99cc/CRu96iKOb2N3QH7jEoloiIiMhJrCV0CoV46FxwcIgjw3EZWq0Wly9fEBYCqfsLt6X5cg1/TvNtY2AumQPEC77U/aydOnUxapuQkAgA8Pf3F5LRg9dazuqnMheZV1d3/uDQuOHOC8TBXONtEBEREbVQdYfXmWL43X3kyJHo2LEzYmJijdqY+qUeqN14Wq1WQ6t1v+FieXnXceXKZWGbhoKCm8vuh4a2adC9ZDLzg86UyqLGBehAdecKhoVFGNX7+vqJznV6HYpV4s/hIfGAn8Iffgp/xwRpgeyvPQFbugHRgzA8bhRuTRiLpBDTf8fcAYdcEhERETWSSqWy2sawAqOvr6/JZA4AoqPbAgDOncsQlet0Ohw48BtkMhmGDBnZxGhbjoKCfKPPevr0n8JxQ+cXenl5iZb8r+vSpYsID49qtmGX5tQd8Vd3i4W6WzMApnsns0uzjMrm93tBSKz25+zFH1m/IyGoAy4WX4AeOvSLGmCnyIH2QYm4pLwgnMskrpFCyGVyDIgZ5OwwHI49dERERESNVFJSbLVN/b3VzKm7kbSBoWfOXXro9Ho9tFoNTp0Szwdr6rDI+j2c4eGRNxcT0Wqwd+9vDv8OGzJHq+4KlvV7F7t3723UvkJtnKzW7SUbGHML5vd7Afd0noLpyTNxW8I43BJreruCxhjb/g7RubtsZeAu+DaIiIiIGsmWBVFsZapXqm4S4g6LOhw7dhh79uwyKi8svLmJdmNW/wwMDMKQISOEc4VCgcDAIFEba/u+NVXdPfRMkcvlfyW0Wpw/f8ZsO1MbqO/I3CY6N7XZt4e0NjGMDYhDz8je8JR5GrVprADPgHrPco0hl60FEzoiIiKiRqipqcHVq5kOfYZGc7Pnqu68K1dlbnuB06fThOM+fRo3VFBaJ8nQ6/VGC5SoVFX1L7Gr7OyrovP6PW8ajRonTx7Fnj2/QqNRi+pCQkLN3lev16NCXSEqm5X6QBOjbbhBMUMglUjRJ6qfTRuSU/NxjQGwRERERC2IVqvBvn2/m6zz9w+wW/JV9xf/U6fSMHDgELvc1xmuX8+xqV39BUFsVbdnz1QvX01NTaPu21ienp6orLyZkOt0OiiV4iG64eG1C6JERkajqKgQpvx4cYvoXAIpovxs3/7AXobGDcegtoOFnkBqOfhGiIiIiBqouNh47lz37r3h4+MDDw85rl69jCtXLgMAQkPDGv2cM2dOCce2zsVriSoqynH27Olme167du2Neszy8q6jS5eUZouhNqG72bNmashsbGw8gNqVLuPiyhEUFGTUJr0gTXSuh/2G+TYUk7mWiUMuiYiIiBqo/sqEAODn5w9PTy/IZDLEx3cQyhsyJywuLsEu8bU0hYUFRmXJyd2Nypq6ofjQoaMwZMiIZl/R0tRwTg8PcQymEjrDHoUSiQTt2yciJMT6dg2x/nGNjJLcFRM6IiIiogbS6Yx/Oa+bRNRN4gzbFtiifftEt9meIDv7Kn777RcUFRWaTGbatDHuuTS10mdDSKVSi3vSOUp5+c1VKJOSusLLyxvt2ydi2LDRaN++IwDxtgUG9ZM+W/RvBcvwU8Ow35SIiIiogS5dOi86N/WLefv2HZGVlYmEhMQG3VsmM7+CoE6na1CC6EwXLpwFAKSlHROVx8TEIj6+AyQSCTp27Cxa8dHSZ2+omJg45ORctd7QDur+PERFxYgSU0Nyr9cbD5W09nlrtOJ5f70j+6FDUMN+nsj9ucZ/EYiIiIhakLpzowDxapQGcXHxGDRoGLy9vRt8fx8f482lgdp5YC1VXt51YTEYS/P9OnbsLPRm1k9oGrNlgTlhYeF2u5clVVVVRj8PdRk+k6mfEWvOFoo3Xx8dP8au3xG5ByZ0RERERA3k5+cvOo+IiDTZrrG/fCcmdjZZfvbs6Ra3yXh1tQpKZTEyMtJx9OhBAMDhwwdMtvX3F+9n5ul5c8+1xm5XYI69E5+qqipcvHjOaLXMM2fShWPDIiem4jCVjGeWXMZb+1/HW/tfh1qrNqo/fP2gyXsR1cUhl0REREQN5Ovrh/LyMuG8U6cudr2/pUU9iouLTM4/c4bCwhv488/jojK1Wm20z5pBdHSs6DwoKBjx8R3g7x9glCQ3lanvUKvVNHqO3cGDfwAAlEolevfuJ5SXlCiFY1P7yVlKwr4+/T/h+N1Db6FTyM1EPrPkktGQSyJTmNARERERNZBWe3P4XN++A+069wtwnZ6Y+skcABw48IfZ9vWHQUokEsTHt7d7XADg4+OLxMQkFBcXCatsVlVVNTlxNLc5OmB6LqVOZ3qbAa3euKf1XNEZEy2JLOOQSyIiIiIL9Ho9KisrhJUaS0tLcONGbYLQvXvvRm+EbYmlhK662niJ/JakbrJbn4dH8/YltG0bJ+rNNLXSpD2Z2s5CrTbdWxnXPt6xwVCrwYSOiIiIyIIbN/Jx6NA+YSXDY8cOCXX27pkzsJTQnT9/1iHPbKirVzOttqm7H5/z1P0uG5fRVVdXmyyvv9CJqfmN5l6lxMf41/DhcaMwqdNkjE+c2OAYqfViQkdERERkwaVLFwAAWVlXjOoctYVA/YTO1LwvpbIYaWnHceNGPoqLi1BUdMMhsZhTf+uG+lJTezpsOGVD1P0qG7PvG2B6Y3QAuHYtW3QulyuM2pjasxAA1p1dY1SWEpaKpNDOSA5LNaobGjvchkipNWJCR0RERGRBVVWl2TpHJXT1e4R69eorOler1Thx4giKim4gPf0kTp48irS042aH9zWGXq83uSG4rYKCggEAPXr0AQB4enraJa6GqpscN/bznDsn3j7A0BOnVBYLZRERUWa2qDD9TFO9sHKZcUJo0Ceqvw2RUmvERVGIiIiIGkkicUxC5+PjKzr39fWDn5+/sLKmuYU5tFqNxRUybVVVVSWs6iiTyXDLLcPNJq9yudxkImkYjhoUFIzhw29tckyN17Qhl1VVxnMWDYmhn5+/0DPapUuKyetN5ZDl2nJIFMYJnafMdNLbLbwHFBaSPWrdmNARERERtTCmem+6du2GQ4f2AgDS0oxXlwSA8vJyeHk1fCPz+gzJHFDbG7V7907Ex3eATCZDbGw7UQLXu3d/qNUa+Pr6YvfunU1+tr2Z2krAVunpJ1FeXmxUrtfXrlx59eplAEBMTKxRmzqtjUque143eseP9nrK7B2YzJElHHJJREREZIKpIYf1F71w1O4CpnrZvLy8TLQUO3v2tCPCAQBkZl7ExYvnUFR0A0eO3Nw4XCqVwt/f32HDT5tKLpcLSW5Dh1zeuJFvslyr1YnulZOTZfYeph7Zpt72DVO73ocAzwDjhn/xl9t3jz5yLy3zbx4RERGRE+n1ehw8uBdHjx4SlavVNzd6DgwMsktvmK1s2ZuubnyOcuHCWVRXq4RzU8NO+/Ub5PA4GsLeyWZlZTl+/32HcB4b265B158tEs/Jiw9MMGozrsN4SCCBvyIAvaP6GtUTGXDIJREREVE9GRnpUKmqANycP1V/pcnExKRmjso2er2+SRuTm1p6v67KSvEiMXX3lhs6dBS0Wq1d5vHZk6E3rSE9dDU1prcqAIyHvFpO6MTP9Pb2AUzvNS7SLbwHEoLaw9vDBx5S/spO5rGHjoiIiKie/PxcozKtViOaO9bcqzZKJBKbNjGvqChv0nPq9r7Zom7yKJVKW1wyB9xcqbS01PRiMvWp1Wrs27fb5vsrFOZ/FiIiokTn9ZPtOxMnmL3WXxHAZI6sYkJHREREZKOiokLh2NIv8Y4ilVrfyLzu/LbGKC4utN7IRZWWltrU7sSJI3Z7pr9/AG65ZRgAQK1XI7Pqsqje1HBLooZgQkdERERUh6VheZcv124yHhgY5PA4evbsCy8vb3Tr1ksoq79dQXh4hMlrrQ2btEVwcONXh2xpDMNl27QJs9q2rKysyb2c9cnlCgQGBmN/6V6cVJ0Q1fnKrfe6ElnChI6IiIioDls2526OFR0DA4MwYMBgi8vuR0bGmCzX6Rqf0FVX1wjPN2wObjq+YLRv37HRz2lOwcEhAGybQ1dRUWay3Nvbp0kxdE1OhS5ADz9/8YqVTZnvSARwURQiIiIiEVtWiiwuLmqGSKwzN49Pq9WhsVPZDHureXh4IDW1J8rLy3D8+GGjdj179mncA5zAkDTZktBdu5Ztstzb21uYi1eXrUltgSq/WVdFpdaDPXREREREdSiVN5O1Pn0GwNPT+v5vzSUqStwj5+vrh/btOxqVl5eb7mWypu6CKEplEWQyGQIDgzBs2GgMGza6UfdsCQydYNYSuqqqStHCKXWTNXML0jSlN5TIHpjQEREREdVRVXVzqwI/P38MHDjEidGIyWTGi6LExcUjKamrqMzcsEFr9u/fIxyHhd2cnyeRSCCRSIy2bnAdhmGN1hO6uiorK4RjmcwDAQGBRtdcuZJp8l4qTZUogVTIFEZthsWNtBgPkS1c9W8lERERkUNkZ181KktN7YE//zwhnHfunNyMEd1Ud75VfHwHs+0aM7Tv0qULovPw8EijNgMHDkF29lW0bRvX4Ps7k61DLutvkq7T6RAQEACVqhDR0THw8/NHevoJURu93nhTucPXD+LXzF+ghx4ektpft/UmkkkfedPm5REB7KEjIiIisqr+FgXNsSiKKXUTkvj49qK6Pn0GCMe5udcbdN/Kygph7pyBqcU6PDw8EB/fXrSZuCuwNaGrvzqoXC7H0KFDMWTISCgUniYXqElN7SEcqzQqnCnMwIGcvUICp9FroNFroNUbD83UaDUN/ShERlzrbyMRERGRU4iTG2cldBqN+QTAz+/m6okN3Uvu0KF9onMfH3frObItoTt//ozoPCoq5q+hprVDXaVSKQIDg1BSogQA3HLLcNFG6ivTPkdJtdLmqDxcdggrtST8KSIiIiL6S91FQfr2HSgc1++sqj80r7k01xL3KSk9m+U5zcXWr63u+w8JCRUlyQZ1E7i6xxqd2mQy90Tv+VDIant43z30lqguuU2qbYERWcCEjoiIiOgvSqVSOK67qmH9RMpZe4dZe25wcKjNvXM6nQ7Hjh0yGmZYv9fJHRh6VE1tO2BQdwEUAEhJ6WGyXUJCIqqrq5GQkCgqz6/MN9neV2F+43APKX8Vp6bjHDoiIiKiv9TtobGkJcyhMyUw0HgVRnOKigpRXl5mlOQ467M5kiFZy8q6gpKSYpNt6g479fCQm/0efH390Lt3f6P5dNfKchoUkwTcUJzsw/3+xhIRERE1UUyMeBXH+j1jzlq+31pCVzdua20zMy+aLHfHhK7uRvAFBaZ70uoyt2G7JTsytzWovaes4c8gMsX9/sYSERERNcCVK5eQmXkJp06l4erVTACAXC5O2Kqrq0Xn9eubi7Ukre7qkxcvnrPY1tTm423bxjltOGlL0qVLil3u8/fU2aLzUfFjhOMR8a67UTu1LBy4S0RERK2WVqvF5cvGPVVqtXg1yfq9VqY2+G4O1vdRu5mM5eRkITExyeZ7KxSeDWrvzkwthmLJpeILJsuj/KJF570j+0KtVQPQI7mNfZJGIiZ0RERE1CpptRpUVppeJKP+AhnGwxCd04tlLaGr37aioly0uEtdXl7eUKmqhPOgoKCmhucS1Gq1xfrgYOO95qzZdP5bm9pJJVIMaju4wfcnsoRDLomIiKhV2rNnF44ePWiyztqww4YkVvYUExMLAIiMjLbSstbhw/vN1tXfHDw6OrbxgbkQuVxhsT4xsVOD71mjrWlsOERNxh46IiIianVqaiz/At6uXYLo3MfHFxKJBHq9HhERUVAoLCcFjhIUFIxbbhlulIw1lF6vF+bQtWvXHtHRMfD09LJHiC1e/Xen1Wpx9OgB4byhi8IUVRWJztt4h8HTwxPDYkc2PkiiBmBCR0RERK2OUllksT4wMEh0LpPJMGTISEgkEqcvGtLQPeK0Wg327v0dnTp1QUREFCQSCQoLbwj1wcEhLT6ZO5p7GIeuHUDPiN6IDYiDTq+DXq/H9xc2oaymFEGewbi3y3SEeIdYvVd1tQpKZTGCgoIB1K72WXforUJh++qTGp0Gy058JCqbmfoAV7CkZsWEjoiIiFqd06f/bPA1rrKcf2xsPLKyMoXzPXt2AQDOnDmFM2dOYfDgEUhPPyHUe3t7N3OEDVOprsQvl38GAPx2dafJNsrqYqw4+RmeG7DA6v1ycrKQk5OFXr36wd8/AFlZV0T15ha80ev1KKlWItAzSEjqS6pLjNoxmaPm5hIJXVpaGr777jscPHgQ165dQ1BQEHr27Imnn34a7dq1E7U9duwY3nnnHZw+fRp+fn64/fbb8eyzz9r8H6v169djxYoVyM7ORnR0NGbNmoUZM2YYtcvLy8PixYuxd+9e6HQ6DBgwAAsWLEBsbOsYf05EREQtk0ZjedEPpVK8sbaHR8N6/JpThboCWy9+b1NbjV6D0upSBHgG2HbvinKb49Dr9fj3gX8BAGID2mFG8iwAgFansXQZUbNwiYTuiy++wLFjxzB27FgkJSWhoKAAX331FSZOnIgNGzagQ4cOAICMjAzcf//9SExMxEsvvYTc3FwhOfv000+tPufrr7/Gq6++irFjx+KBBx7AkSNHsGjRIlRXV+PBBx8U2lVUVGDWrFmoqKjAvHnz4OHhgVWrVmHWrFnYtGkTAgMDHfZdEBERkeMkJSUjONj6sL2WzNqQ0PpDNltqz2O1thofHnnPqDzKLxpSiRQ5ZdlGdYevHxDt9Sa6n06FEm0p2ni0gVQihZeXN3Q6nU2xpBekCcdZpVfw1v7XIZVI4aS1cYhEXCKhu//++7FkyRLRJNZx48Zh/Pjx+Pzzz/HWW28BAN577z0EBQVh9erV8PX1BQC0bdsWL7/8Mvbv34+BAweafYZKpcL777+PUaNG4YMPPgAA3HvvvdDpdFi6dCkmT54Mf//aPUnWrFmDK1euYOPGjejatSsAYMiQIRg/fjxWrVqFp556yiHfAxERETWdpRUqg4KC4eXVsueTWRMYGIxr14yTHYO6SUxISKjT5wSa8/6ht43K2gXEY1ryTABAXkUuVqZ9Lqqv0ZrundToNfhFuR01+hokeXdGsk/tHnClpUqbYtl1xXiop05vWzJI5Ggt859k6unVq5fRikTx8fHo2LEjLl6s3Qy0vLwc+/btw8SJE4VkDgAmTJgAHx8fbN261eIzDh48CKVSienTp4vKZ8yYgYqKCuzevVso27ZtG3r06CEkcwDQoUMHDBw40OpziIiIyLny83PN1rX0+WS2kEotJ2gnTx4Tjhuz51pzeGv/6ybLpdKb89vkUuOVRqVmktMbshuo0deubHq26oxQbutiMJWaCuuNAPSK6GNTOyJ7cokeOlP0ej1u3LiBzp07AwDOnj0LjUaDlJQUUTuFQoEuXbogIyPD4v1Onz4NAEbXJycnQyqV4vTp07jjjjug0+lw9uxZTJkyxegeqamp2Lt3L6qqqtzi/xCIiIjc0dWrmcJxeHikxQTPFUkk1v69/mYPZVRUjGODaYRTBeYXrOkV0Vs4lsuMf431kJr+1bZtXBxQIC7T63WQmbhHfaZ6dJ/q8xzkstqhq9UaFbZe+gHhPhEYGjfC6v2I7M1lE7otW7YgLy8P8+fPBwAUFNT+LQ0LCzNqGxYWhhMnTli8X0FBARQKBYKCgkTlhrL8/HwAgFKpRE1Njdnn6PV6FBQUIC4urhGfioiIiByt7h50XbqkoLKyQtiTzR00ZAhlU/ezc4TvL2wyW5cQ1EE49pP7G9Ufvn4Qg2OHIePGKVSqb/aqnSk+bdQ2K+uK0Qbt9UeEXVZexH+OrxeVjW1/J7zlN//h3kPhh3s6TzUbM5Gjtby/xTa4ePEiFi1ahN69e2PChAkAaufAAcZ/EQHA09NTqDdHpVKZ3dfF09MT1dXVACD8ae45dWOxVWioX4Pau7OwMOP/OFPLx/fmuvjuXBPfW9MkJsbj8uXL8PHxQXh4AG65pT8OHTqErl27Ovy7bZ53p4KXl20rVzrrZ6msugyrjq5CUVURnhz0JHzkPpBIJNDpdBZjj4oIFp2bavtlxmeoVFeKyvR6PWSymz2XXl5yVFWV4fLls8I9YmNjERERIXwn1ZpqbP4rmav7nFtThjbw05KztJb/VrpcQldQUIC5c+ciMDAQH3zwgbAyk2ECc91/dTOorq62OsHZy8vL5LWG6w3JmuFPc8+pG4utCgvLodNxmaSwMH8UFLjPv5C2FnxvrovvzjXxvTVdcXE5VCo12rWL+eu7lKB79/4A4NDvtrneXXFxJVQq8eIgCQmJuHz5glHb5v5ZUmvVyK/Mw+r0lULZv3550+br68frgwAUqQpFZSqV8d5wej2g1erqtDFePCUmpoPoGRk3TkGlUsPLSy5qz79/rsHd/lsplUrMdgK5xKIoBmVlZXj44YdRVlaGL774QjTs0XBsGHpZV0FBAcLDwy3eOywsDGq1GkqlUlReU1MDpVIpXB8UFASFQmH2ORKJxORwTCIiInI+vV6P3NxrAFrmcENHMbVZdnNvV6DSqPDuobdEyZw1yW1ShWNT2xHMSn0QXh7m1y3o2iYFA2NuQYfgRKFMCikK1YUWVzvV6XXYfH6jUfktbdk7Ry2PyyR01dXVmDdvHjIzM/HZZ5+hffv2ovpOnTrBw8MD6enpovKamhpkZGSgS5cuFu9vqK9/fXp6OnQ6nVAvlUrRqVMno3ZA7Qbo7dq144IoRERELVR29lXhuKFTJOzhaskVpOWfaPYl701Nq+vYsbPd7q/X65FdmoUSldJkvU6vw38Ov2P2eg+JBzwk4gT7b0n34vYOd2Bku1txV8dJ6BvV3+g6Lw8vzOnxqNn73tVxEobFjcTwuJE3Y4EOv5fuwsnKE2av+/TYUpPltm5aTtScXOKfprRaLZ5++mmcOHECH3/8MXr06GHUxt/fHwMHDsTmzZsxd+5cYeuCzZs3o7KyEmPHjhXaVlVV4dq1awgODkZISO3moQMGDEBQUBDWrFmDwYMHC23Xrl0LHx8fDB16819kbrvtNrz33ns4ffq0sHXBpUuXcODAATz88MOO+AqIiIjIDi5ePCccR0ZGNeuz88vzseb0/wMA/H71VzzR5xmHPMf0oigS9O49AEePHhBK7LHCpUanxt7sPcityMVl5UVIIMEjvZ40Snw2nFln9h4Dom/B8HY3Ey6NToMabQ185D4AgH7RAyzGILW6qidQUl2CwMAglJQohbJLqovo4dsTABAeHiFqX1pjPGwTADRajdVnETU3l0jo3nrrLfz6668YMWIElEolNm/eLNT5+vpi9OjRAID58+dj6tSpmDlzJiZPnozc3FysXLkSQ4cOxaBBg4Rr0tLSMGvWLDz++ON44oknANTOe3vyySexaNEiPPXUUxg8eDCOHDmCLVu24LnnnkNAwM3/ME2fPh3r16/HnDlz8MADD0Amk2HVqlUICwvD/fff3zxfChEREdmstjdOPMROLjde4MyRTuffXGmxQl2BCnUFfOW+Fq6wL0eMIFpy8C3RuR56/HzpR9zbZZqo/JLSeP4eAAyKGYwhscNFZR5SD7PbD5gikxgPJ62vWmu5N7Z9+07CsUZnPmmL9GvefwQgsoVLJHRnztRuALlr1y7s2rVLVBcTEyMkdMnJyVi5ciWWLFmCN998E35+frj33nvxzDO2/QvYjBkzIJfLsWLFCuzcuRNRUVFYuHAhZs2aJWrn5+eH1atXY/Hixfj444+h0+nQv39/LFy4EMHBwWbuTkRERM6g1+tx4MAeUZlhkbPmjOGK8oqoLL8iV1iGX6fX4fC1A9DqdegXPaBBCY2t7D1n8ELxeZPll5QXoNfrrW6f0C4g3i77ttnSQ+chldfP50XqLmin1hovmGIQ49+2QbERNQeXSOhWr15tc9s+ffrg66+/ttimf//+OHv2rMm6e++9F/fee6/V50RGRuK///2vzXERERGRc2i1WqOyTp26Ntvz92Xvwe6s34yW2F+XsQZyaW0voVp3c/VsmVSG/tED7RxFbTYTG9sOWVlXrLStdVl5CadvpGN0wm3wlIkTYJ1ehw1nzP++tSfrNyFZK6y6YVQ/Kn4MekX0sTF2y2RS0z10f0uaIhzHByZAbymjq0OtM5/QEbVELpHQERERETWWWm38C3pz9dDp9DrszvrNbH3dRM7gZN7xJiV0pla0NCzoGB0da1NCt+TAm9Doa4ce/llwEl1CkzGh091Cvakkra59OX+gsKp2O4GzRRmiupcGvmL1+Q3VP3oQDl7bJyrrGHJzGKWXh+ktpcq0ZThXdRY3ztzc+qCoqsju8RE5EhM6IiIicmsHD/5hVNZcS/anF6Q1+JoiVaFNQxbN8fPzR9u2cfD19cPZs6dFdd7e3ujevbfFhFaj0wjJnEFG4SlcOHgez/Z/EQBwpSTTahz1EzlHGtFulFFCZ4mfnz/Ky8vwi3IbAEBdbHreXKh3KCr0pQCAOxMnNjlOIkdgQkdEREStTnMkdBqdGj9d/N5im2f61SZIWy/+gIzCU0L5vw/8C0khXTAp6R6T16m1auy6sgPH8o6gb1R/nMg7jgDPANyf+hDkMjkSE5MAQEjodLqb2yQEB4dYjOn3q7+afqauBueKatc1KKoqNNnGmsmdp1lv1EiD2w7DH9m/2/WeyW1ScVeP25GTV2g07JSopWBCR0RERG7L3ObRUjPzruzp18wdVtsoZLVz6Kq11UZ1Z4syUFRVhBBv4wTsl8yfkZZ/AgBw+PpBALXDIHdkbsPtHe4U2sXExCI391qDtmgw3M+UjWfXG5X1iOiFgTGDsfzkp6jR3hxCOrHT30Tt2niHoY1PmM1xNNSgtoORW3Ed2aVXMbvHXKP6uj8KlZUVRvXjOoyHt4ePcO6n8EOkbxQkEgmTOWrRmNARERGRWyouLsLJk0dN1snlcpPl9hTuG2mxvktosnDsr/A32eZKyWWTCZ0hmavvZP5xUULXsWNnJCYmNXr4pi1Cvdsg0DMQc3o8iqVH/wMA6Bc9EJ1Dm2/hGaB2tct7Ok+x3hDiHsuAgEAAQEpYN5tWzCRqaZjQERERkVsyl8wB5jbfbhydXofV6SsBAHd0uAuKv3pzzCVdBrI6ycOgmME4mX/cqM3J/OPoGdlbVFZQmd+g+C4pL2L9mbVICGyP2zuMN9r0uyESgzuJzsN9wtE9vHZzbj+FP14YsBBqnbpF9miZG2Zr+FlgMkeuigkdERERURO8feAN4fiLk5/afF3dYZ/+ZpKs3IrrRmXFKsurMP508XuM6zAeQO2Q0/Vn1gIALpdcwsfHPsCwuJEYED0IGp0Gcpm4p/JGZYHZ+z7R5xmrG6FLJdIWmcwBgI+PD9TqGnh6eqG0VClsZ6FQtMx4iWzFhI6IiIhahfbtO+LSpfNWFwVpLiltugnHDekdulFpecuAtPwTQkKXVXbVqP73q7+KFj7xqzPcs7ymTNT2b0lTcLU0E70j+1pN5lo6iUSCoKDgv46BoqJCoZzIlTGhIyIiIrdTd44UAPTo0QcBAYHw8fEVfqm3h4qacqMyf0Vtb1tZTalQlhrWHefLbm4hMDr+NsQFthNdd1vCOGy7/JOoLNwnAqdvpEOju7ms/p8FJ63GtTNzOwDLC5wY1E/i6uoY0km0n5u7qL8oTo+IXk6KhKjpmNARERGR2ykqEvdi+fv7QyqVok0b+66y+OHR90XnEkjwWO+nhHOlqhjech8opAqsOpMDleoGxra/w2QC4WdiYZT8yjxsOf9dg+OyJZGzpl8TNjdv6SSQ1DkGIn1tXwWUqKVhQkdERERuJz39Zi/WoEFDIZPZ91cevV6P43nGi6609Y8VnQd53ewNfH7I87iWVyRsVVBftF+0XWO0lSEB1eg0+Oz4R0L5oJhbnBJPc9Bo624kLoGP3MdsW6KWjgkdERERuZWaGvGebo5Y9OJY3hH8cvlno/Ju4T3MXiORSMwmcwDgq/DDxE5/w77sP5BfmWdUnxrWXXTeNiAWbf3j8PmJj43ajmg3GgDwR9ZuqHU1RvUGXh7ewhBRABjZ7lbsy96DnpG94eXhbfY6tyIBOgYnOTsKokZjQkdEREQuz7CBuEQiQUHBzWQoNbWnQ55nKpkDgOSw1Cbdt3NoV3QO7Yq39r9uVHdH4l0mr3lp4CsAgO2XtuKi8gJGx98mzHtLCumMz45/BD1Mb7Ce3EYcb7/oAegb1d/tFwoRJ/kSt/+85N6Y0BEREZFLq6mpwb59vwMAYmJiUVlZAQDw9w9AaGibZovjjg532W0vsyDPYCirixt0zZj2txvfxysYdydNRnrBnzhblGFU3zeqn1FZa0hupK3gM1LrwR0UiYiIyCVpNGrodDrk5l4TynJyslBcXLtPW0JComOeq9OYLE8J62ayvDFCvENF5+2DGv9ZOoYkYVLSPSbr6s7xc3d1v0OJ9GZCJ5UyuSPXxh46IiIicjkqVRUOHPjDYhsvL8fMATO3sbc9e7YuKS+IziN8I+x279ZqXIc7cfj6IUT6RmLz+Y2IiooB9PpW0SNJ7o09dERERORy8vNzrbbx9LT/YigAsPzkZ0ZlM5L/7pBnGbT1j7P7PVvNoid/8VP4Y0S7UejSJhlAbQIukUrhJfNycmRETcOEjoiIiFxORUWF1TYymcxqm4ZSaaqMyl4YsBCxAfZPuOrS6XXWG1kxNHaE6LyNd/PNL2zJVFqVs0MgahImdET/n73/jrekqPP/8Vd3n3TvuXnunTs5MDABJhIlqICBbASVuIi66OpXV1fFsPvRB7s/XREWlV3cBXVdUURQEDESVBCBwSENMAkm3Zm5c3M++XTX748+3aequqrPORNgzsz76QPnnj51qqu7q6vqHYsgCIKoO/r794Z+/6Y3veWgnDdXzAWOHahEKDydDeIG6Adin7RTpI3C5Tg9giDqExLoCIIgCIKoGxhj+POfHwoc7+gQhRPT3P8lTtEpwnZs4djLQy/ud73VcNKsU4TPXY3T97tOy7Tw3iXvgwEDcSuON0sWO4Ig6hNKikIQBEEQRN2wa9dO4fPq1ScimWxCNBpFLpfFk0/+BStX7v/ec3/Z9Sj+uvsxAO7ecC3xVhgwsLb3CaHcGXPevN/nUrGiaxV+t/XX/ueIeWCWbMd0LME/nPBJxK1E6CbnBEHUDyTQEQRBEARRN/T27hY+J5NJRKNRAEA8nsCZZ75tv8+RLWZ9YQ4ANg1v0JaVLWkHCtMwceXyD+KFgeewvHPlAXXrbI61HLC6DgeOtOQwxOEHuVwSBEEQBFEXFAp5ZLNiUpJo9MBbmZ7ufbKqcu845t2IWwcnkyYAzG6eg/MXXYR5rfMP2jmOVK5d83H/7/ctu/R1bAlB7D9koSMIgiAI4pBnw4b1GBjoF451d888KOd6Yk9wf7sz570FDAyMMQAMc1vmH/TMlsTBoz3Rgc+e8kXYzD6oQjlBvBaQQEcQBEEQxCGPLMwdCNfKannzvLPxhtmnvWbnI14bImYEEVoKE4cB1IsJgiAIgjhkcRwHW7duEY4tXrzsoJ0vb+fFc3UsxYkzTj5o5yMIgthfSKAjCIIgCOKQ5bHHHhE+n3bamxCLHTwXuQe3/Vb4fNHR70LUih608xEEQewvlBSFIAiCIIhDEjkBCoCDKswBwEvSPnMkzBEEcahDFjqCIAiCIA5JXnlls/D5jW88+6Cdy3Zs7BzfLhyb1tB50M5HEARxoCCBjiAIgiCIQw7HcTA8POh/PthJUJ7Y87iw9xwAXL3iwwf1nARBEAcCEugIgiAIgjjk4GPnjj56yX7VlcpP4ZZnbgYAHN99It5+1HnC94yxgDAHkLslQRD1AQl0BEEQBEEcUrh7vZWZPXvuftV354Yf+X8/278Oz/avQ2MkidZEK2zHwUC6b7/qJwiCeD0hgY4gCIIgiEOKRx992P/7pJNOhWEY+1wXYwzDmeHA8XQxhfRUap/rJQiCOFSgLJcEQRAEQRwyFAriPnDJZNN+1ffknr/u0+/etuDc/TovQRDEawUJdARBEARBHDQKhQJ27+4JuFGqYIzhr3991P/85je/db/O/XTvU3hs15+031+1/BpcveLDaIu3C8dnNc3Bmhkn7Ne5CYIgXivI5ZIgCIIgiIOCK6D9GQCwffurOOOMs3z3Scdx8NRTjyORSGDu3PmYNq0LPT07/N+aprlfrpY5O4c/7nxI+70BE7OaZwMALj32Cnzvhf+BzYr4wLIrMK91/j6flyAI4rWGBDqCIAiCIA4Yk5MTePnl9YFNwW3bxoYNL+LYY1egWCxgz57dyOdzyOdzePnl9YF6Tj31jfvVjuf7ngn9/uKl7/f/bk204f878dMoOAUko8n9Oi9BEMRrDQl0BEEQBEHUTD6fx+BgP7q7ZyKfzyEajWJqagovvKAXpAYH+/Hoo/0V616+fDWi0VhomVQhhd+8+iuYhoFzjjofphRFsnF4Q+jv50tWuJgVQ8wKPydBEMShCAl0BEEQBEHUxNTUJNatewoA8MormyqWTyQaAhY7HYZhorOzq2K5n234CQbSrnD46jPfrlh+ccdSnLPwPGwYfhkLW49CxKQ95giCODwggY4gCIIgiKpZt+4pTE1NVix3zDFLMG1aFxKJBgDAn/8cjGezLAsNDY1CfW9609lVtcMT5qphVtMcXHD0OxC34jhp5ilV/44gCKIeIIGOIAiCIAgtfHbKLVs2VhTmWlvbsGbNSVXV/cY3usJbsVjAunVrsXjx0oqJUNKFNL6z7ibt93wMnGGYWNKxFG9dcM5+JVghCII4lCGBjiAIgiCOcAqFAl544RnMm7cQXV3TUSgUMDo6gpaWFqxdW2kfNwPxeAynnHIGADc7pYo3vOEMTE1NoaNjWqBMJBLFG95wRlVt1QlzBkx89pQvwDKtquohCII4XCCBjiAIgiCOcJ577mmk02ls2BDMNqli5szZmDt3AeLxeNXbCyQSDb775b6SLWaVx49qOxqnzj6dhDmCII5ISKAjCIIgiCOcdDpdddl4PIElS449iK0BhtKD+N4L/41kNImWeKt/fO9Ub6DsgtaFeN+ySw9qewiCIA5lSKAjCIIgiMMcxhiy2SxisRgMw8CuXTuwfftWTJ8+AwMDfRV/394+DaOjwwDK+8MNpPrxxJ6/4JRZp2Fm0yztbwdS/Xim729487yz0RhtrHiunJ3D9174bwDu1gSpQiq0/HlHXVixToIgiMMZEugIgiAI4jCnt3cXXnllc+C4LMyddtqb8cQTjwIAjjlmKQqFPGbPnodoNIqRkWEkEgnk7Bz+uvsveLr3SQDApuGNAID3L7schmG4+8EZQMSMYP3A83i+/1kAwAsDz+GalX+P6clubTsZY7j56RuquqYPrboWnQ1dlOyEIIgjHhLoCIIgCOIwpr+/TynMyaTsFAwLOOOMs5BKTaGlpVUQljo6puG5vmfwh+2/Vf7+Zxt/UvEcP1h/Gz5+wqfQHGtRfn/j2q8Hjl1x3NUwjXISFcMw0J2cIRwjCII4kiGBjiAIgiDqmA0bXsTAQB+mT+/G0qXLhQySY2Oj2LjxxYp13Dv8cwDAi0+LZd+y4O2uxQ1AwSngzz2P7Hd7/+uZb6M13grTcBOYpAsp5OycsuynT/484lZ8v89JEARxOEMCHUEQBEEcwqTTKbz66hZMn96NSCSKnTs3Y9asoxCNRjE2Nuq7TQ4M9KOlpQ1z5szzf/v88+sAAAVWwFBhEJ2RLkTNKCwrglWrjodlWegf74P1dATTpwddIR/Z8WDV7WyMJNHZ2AXGHOya7AktO54br1jf4o6lJMwRBEFUAQl0BEEQBHEIwhjDo48+7H8eGRkCACQSUaTTBSxdepwvsHls2/aKINB1d89EX18vHhi5H93ds7AN2/HOJe+BaZjoLfRi++A2vDDwLLq7Z+xTG+c0z8WyzuPQFG3GkmlL/eMDqX78YP1tAIB3L74E6/aurSjkybxnySX71CaCIIgjDRLoCIIgCGI/KRQKSKdTaG1tO2B17tixVftdf/9e9PfvDRx3HEf4nMmlcN/IL9DePg2W5bpO/vrV+6tuw/LOFYhHEgCAZ/r+5h//2Jr/D62JNu3vpie78YVT/8X/PLNpFm599tv+5w+v+qjvcrllZFPAlfPzb/hy1W0kCII40iGBjiAIgiD2A8dx8Ne//hkAMH/+Qsyff5QQx7Yv5HI57Ny5veryixcvw5YtbrbJbDYL0zTxxBOP+rFxEau6DbdnNc1G79QeAMAJM07G2xae43/3toXnougUEDGjVbfLoyXego+u+QT6U/04uv0YYQPwN8w+TRDoPnvKFynhCUEQRA2QQEcQBEEQ+0EqVd4nbefO7YjFYpg9e17IL8KZmprEunVP+Z9nzZqD3t7dob/p7p7pC3RPPfUXAK7LpkckGivX1zQbLfFWOMzBlpFNQj3LOo/DB469AsOZIcxIzgycZ1+EOY+2RDvaEu3K775w6r+gYBcQMSO0DQFBEESNkEBHEARBEPuIbdt45pmnhGOvvLJ5nwU6xpggzAGu9c3dE66AaDSKtWvdfeIaG5NobExi1qw5sBQWuDwrZ470ZKRPnPCPaIo1+8dT+Snc8szN/ucTZ5wMwzBCNwo/WEStfRcWCYIgjmRIoCMIgiCIGmCMYceOrWhtbcP27fo4t1rrLBQK6OnZIRxfvnw1AHfvtVgshq2jryJ6TBSLoseis1m/qXbWyeK3o7/2PxswcN2p/xwol4w14XOnfAnjuXF0NHQckGshCIIgXltIoCMIgiCIGti1a6cyvq2pqRlTU5P7VCefzdLj9NPPRDRatlr9+5P/CsDNcmnZa3HNqmuRKaQxraEThmH4cXQOc3xhLpFoQEtLa+i5LdMiYY4gCKKOIYGOIAiCIKqkUChg27ZXAsff8IYzUCwWsW7dU2hqalb8MkgqNQXbtpFMJv1j66b+hp7cTrS1teP551/wj2eLGfG3hRRuWfcfAICoGcPfr/kYmjtbMI8txLee+iYAYOaMWTBKyVnInZEgCOLwhQQ6giAIgqgSL5ulTCLRgFRqCgDgOHbge8YYJibG8dxzf8Py5avQ1taOv/3tSf+7+0Z+4Zc1DAONjcmAEKej4OTxX8+UtwSYPn0GTNP0hTkAuOjod1VVF0EQBFF/kEBHEARBEFUwNDQAAEjbaTw++Ri6ItOxKrkax685GQBglFLte8klHebgH+/9mP/7M1reiOnRbrz00gtCvRsyLwMALCuCzs7p/n5x+0okEpza57bse9ZNgiAI4tCGBDqCIAiC0GDbRVhWBIwx7Nq1E4wx/H7st5g5YzayRg7Pmc/jaGMpJsYnkM1lMVgYRLwYwyMb/4D7N9wr1PX4xF8QN+Mw4WakzDlZOChvBD59+gzwOU4+eeI/+X9vGdmM328rJzmRaYwk/QQpqcKUf3xF1yqcOPNkJCIN+3UfCIIgiEMXEugIgiAIooS7DcFapNPlveVmzJiNvj53s+37Rn6BeDwBw3SFpwLL484NP3J/Wyyif6LP/dG4uv6ckxM+x+MJWJaFtjZxf7ZPn/x5xK24/3l19xqs7l4DxhjWjjyKP2/5C86Y82as6l6NhkjDfu0PRxAEQdQ3JNARBEEQRzyO4+Cxxx5RfucJc89NPQMAaEo27fN5opEomppbMDY2gu7umTBN0b3y/csuw4LWo7TbERiGgYuWXoQ3TDtzn9tAEARBHF6QQEcQBEEc8Tz22CPuXnCsgJgZU5bZnnO3KojGxO9nJGciakaRL+TRP9AX+N3MmXMAAGfOOgvLuo+DYZowYOB/nvtP2KycQOUfT/ocEpHEgbokgiAI4giBBDqCIAjiiGZqahLDhSE8OvFn/9iZrWcDDGBgsGHj8YnH0NbWjoZEg589cmbTLJx31IWYnuwGAGSzGTy3bZ1Q98yZc/CPJ30WDdFgDNsnTvg0fr/tN4hH4jjvqAu1VjmCIAiCCIMEOoIgCOKIhDGGRx99GAVW8IW55uZWNDYmsdncAgBIp6bgOA5mzpwjJCy57g3/HBDA4vEEViePx/OpZwEAnZ3T8YVTg+U8GqINePeSiw/8hREEQRBHFCTQEQRBEEckjz76MADggZH7AQCtre3CJt8AkGwKxstZhqUU0gzDwN+9/cMwX/pf7JnYjbcufDtZ3QiCIIiDDgl0BEEQxGFPoVDwNwV/85vfiomJMbyS2YIX0+v9MrwwNyM50xfG9k71CnVdsuxS7Xks08LfrfgQ0oUUkrF9T55CEARBENVCAh1BEARx2DE8NYQHN/4OJyw4CU888xhmx+cgarip/R999GGMFkd9Ya61tQ2JuBvjduHR78JxncsFy1qqkMLaPU+go2EalnUeJ2wnoMIwDBLmCIIgiNcMEugIgiCIQ4KiUwjsp1YsFvHc4DPYM7kbcSuBtyx4G2KWOgulxyvDm3HLn/8DAPBkz+MAgGdTz6DJagZjDAwMacfdZ66rqxvRqHvOVdPXYHnXikB9yWgSZy94235fH0EQBEEcDEigIwiCOELpn+rHjtFeHNV+9OvdFHz9iX9FKjWFiYkxJE3X9dGBg4yT4UoZeHbv32BZFgBgQetCNEQaMDDQj9HREezO7wo9R6Q5CsMwMDo6DABoaWn1hblpDZ0496gLDvyFEQRBEMRBhgQ6giCII5Afrv8exuwhZLMFAMAXTv2XQJlto6/i7k0/BQC8dcE5WNZ5HJ7Z+zcsaF2Iea3zA+UZY7BZMWBl08EYw8tDL+KBV+7H3r27/eOpkvVM8Qv09+/1P/X27kZzUwsmpyaUpS0rgkQigYaGRkSjUd+NsqFhjlBOde0EQRAEUS+QQEcQBHEYMpmfQEOkERFTHOZzdg43P30DACCRKAteD7zyS0xr6ERzrBmAu//ab7c+4H//8I4/4OEdfwAAPLHnLwDcxCGmYcE0DOyeFK1j1675ONri7UgVptBUqhNwhbg7N9yBXRM74TgOxsfHkMmk9/06NcIcAHR1Tceb5p2FhW1HIWbFYBomDJiwDBOGYSJmRZGIBPeHIwiCIIh6ggQ6giCIA4jDHPRM7MSM5AylsFB0Cnih/3nMbJqFWc2zQ+tijAnJOWzHxlBmEDazA2VT+Sn8YvPdgeN8Ag8GhrydV57r5aEXg+d3GPb27UFDQyOam1tgWREUiwVEIlEYBtCX2quoyeV/nvsv4XNbvB0AMJYbRW/vbtVPAADvP/kKHNt5HEbHRtDc1ApmOhjJDGPnxA48vu0xjI+PCuVPajoZDMCsWXOAJgPr+taCMcAwgI+s/gdMa5imPRdBEARBHA6QQEcQRF2Tt/MYzY6gJdaKhujrZ225e+NPsW3sVeGYaZhoT3T4n4czQ8rfLu5YijXdx4MxBgBwwPDzTXf53y+dtgxvnHsWbn/+1prblbNzNf8GAAYHB1AouMJfJpMOtaK1trQh2dTkC1IqxnKuIDY2Nqr83rIi+MQb/xGLph0DAGhvKN+36cluLO08FuccdT6GM8NojDQiYkTQs3MHxsdH4TgOTlh+CgzDwFsXvn1fLpcgCIIg6hYS6AiCOCRxmIPHdz3mu/fFrTjeOPdMMDAwxpAtZvDEnsfhOA7y+Twsy/ITXLxv6aUHJdFH0SmCMQYHDhhzwBiwru9p/HX3Y9pr0AlxPFtGNmHLyCbt95uGN2LT8MZ9breOE2ediL+8+lcYpukfS4wnUCwWMNOciV3oAQML/O7M1rNhwizdBwYGB70jvXg1+woAwDQtdHV1wzRN5HJZxGNxGKaBXC6HdDoYH/fuNZfgrKPeWlWbeYvbokXH1HrJBEEQBHHYQQIdQRCHFA5zcMNT/z//M2MMjuMgXUzhd1seAGBgbGwETc3NmJycQLFYDNTx3yO3oL29E3Oa5sCyIshk0tjcvxFtbe343Bu+hKhVXdIOAOid2oMfvfiDA3FpB5QZyZkAgLHsKLJ2Vvn9+5dd7lvMikUbQ0MDGCkMI97UgBnWDLyy5SUsSC3AJmsTFrQvxLzxebASll/HiU0nAQCi0SgKhUJoezqjXViZXIWJ4gQeHn8Q/f3lzbjf1nYOujqnYyg9CLS5z3iqKYVj5y/H6pnH7+edIAiCIIgjG4N5Pj7E68bw8BQchx5DV1czBgcnX+9mHJYwxjCZn0RTrAmmYWLvVC/GUqPYO9CLxmQSsVgMwyND6B3ejZ6pnTANE6tnH4+Vs1Zja/8rmEhPYNq0TrS2tcFhDhhcIWtaYydOWLS8puc2nhvH33Y/hZd2voDRzCjmds9DJBKF4zjoTe2BZUXg2DbGJ8ZQKBRg20GBbX+YObOc4dAwgKtWXOMLRx5T+Snk7Ry2j2/DIzserKpexoBcLotUagoLowtxTOti2Pki8nlPEGLYnN2MXbkenNh0Mp5PPYeGpgbE43GYpoXpjd2IsAgaEg0wLQtgDNvHtynP87mTv4ipqUk8//w6t72YQnZ6HsdNX4EOpwNtre3o6dmBZDKJwUE3pb+KRCLqZ7mUaW5uxXHHrUQikQi97t7e3diyRbQeMsbQX+iHAQPdse7Ab6LRKE477c1CfCBRPTRW1i/07OoTem71yeH23EzTwLRpTcrvSKA7BCCBzuVwe/GqhTGGQiGPTCYDgGFqagqTk5OYP38hGHPQ0NAYuvDN23n8dddjWLv7SXQnZsA0DORyeeQyWdiOjZ2p7bCsCEzTRCKRgGlaGBmp7AZYLV2dnbAiMcSsGE6adQpOnX1GYONnxhj+vPMRPPLKg5iYGN+n8xgwsCC+sJSp0P1fxskg0dKIc5adhx8++z2kUlM119vQ0IjGhkbE4glt/JdHPp/H5OQE7HwRM2bMRiwSRbqQhuM4GBjoQ7PVgre1HfgYrvnLjsK29DYUh3KITkVhGmblH1WBSqCbMWMWli49rqZ6xsZGYRgGWlvbYNs29uzZhW3bXPfL+fOPws6dZcG0sTGJk08+bf8bfwRzpI6VhwP07OoTem71yeH23EigO8Qhgc6lnl+8olMEwJDL5pBOpRGPx5FOp5BKTaGnZwfirQ3YO7wHsUQck/kJpPMpGCXBpLGhEblMFoZhosAKyJY2UmZgaLFaYBkRgDEwMCxZeiwSDQ14YtfjGEwPIJfNYmJyHAfqNY7FYkgmPVfGcBc7D8syYdsOAMAwDHS0T8Oq2WsQMSLIF/LoH9yLrROvIp9XZ1cM4yPLPob5cxbCsiyMjAxj1qzZSKczaGpyBzTbLiISEd0ns8Us1r74VwwO9bvxdmCwmY0nJ5+oeL7OzumIxSRh1GEYHBpAsViAZVh4R/u7AgJ2wXG/O1CC1sFg7tz56O6eieeeWwfbLmLJkmOxePECPP74U74F76STTkUyqZ4s9gUvS6ertChg164dmD59BpqbWw7YOY5E6nmsPNKhZ1ef0HOrTw6350YC3SEOCXQuB+PFk9O+V8tkfgLbxrbCMiIwDQOmYcEp2gBjGEwPIpVLYWhiAMPpEYxkhjCeGQvUYcBQJpRQYRiGnwreMNxEEvtDNBpDJBIBGJDJutkJGxuTQkKKjo5OHNt9HBzbQdSKIh6NwzBMDKYH0JfaC8YAxhyYpit4tuRbMDU5CQMGHOagz+lzhRxOoKuGeDyB5uZmLO5aimM7l6Mp0oSRkRE4RRutrW144Jn7kLZT+OCpf4+504ObV+8rf+tdizue1MfCGXD7SWfXdORzOVgRC5FIFAMDfQCA90y7uOI55s9fiIUL9clYvOGW75OFQgH5vJsspKurGxMT49i8eUOotfHoo5dgdHQEixcvRTyewKZNL6Ovz41ZO+mkU5HNZtHW1g7DMPz/VHjv3L6+J8Trw+G2SDmSoGdXn9Bzq08Ot+dGAt0hDgl0LrW+eAU7j7Vbn8LfdjyJY1oXoynWjOGRQcTicaTSU5iyp5ByUkhEG9AYbUTMjKG7YQZWHXM82tvLKdEZY1g/+Dx+t/XXyGTSKBQKYIwhlZryrQuAAVQlnLmLYtM0EI8lAAMoFouwLAuWFXEzMUYiSKVTyGZdS1xHR6efBZBvUy6XhWVaGB4ZguPoBaampmbE4wlEIlG8YdZpmN0yGzAMOMyGwxw83bsWC9oWYum0Zfi/F76HbC6LRLwB1532z6EWpaJTQO9UL+Y0z/XL8Qv/olPEb169H6+Mb0R//yAsy1JmMPRIJBrQ3taBk2e/AW9Z8Pqmls/ZOViGiXQhg/6RPuzZvFP4/o/jj2CsWE6v/66O98A0TDQ2JnHSSadicnICzz77tP/9mjVu8pDW1raD095cFoODA2hra0dTU3PlH1TJ4TbZHSnQc6tf6NnVJ/Tc6pPD7bmRQHeIc7gKdLlCFq8OvopsIY3RiRFMTk4CJoPDGCbHJ2AAaG5pwZAzjISVQDGaxbymRZjdOhdgDHEWh20XUbSLKBTzmD5jJibTE9g6+Ape7nsRO8d37FO7IkYpuatpwHFswDBgmmZVLoamabm/gWvxikajiFgRmJblWvJMSxDM+HO2xFuRiCTQHGtBU6wJjDHYzAGDA8exweC6WSasBJ7tX+f/9qi2oxExIygWCtixfRscOJiMTyKZbIJhmHj/skuxoO2o183dr6urGQMDEwCA321+AOs2r8W8+HyYhomW5lYcffQSDGeH0BJrxeru4w9ZS9D4+Biee+5v/ueMk4EJE3EzjqVLj0N398xDtu37yuE22R0p0HOrX+jZ1Sf03OqTw+25kUB3iHMoCHQOc7BjbBtGs6PI5/Mo5PIoFgqIxeJoa+1Axk7jkR0PYmbTLMxunoPhjCuEMTBM5MaRzWeRyaaRK+TQO7wbDqp3wfOo1XVPRzQShWGaaGpqQjyWQKHoWtwYY1UlA4nHEzAMA9FIFEu6lqGQK8CyLDQmGzGaH8VwegiMAWtmHo8lHcvQlmhHa7wVEbO8CwhjrJQN0kG6kEFzrPmwEwY85AEzk8kgEokgEonU3TWrMjaefPLpaGxsfJ1adHA53Ca7IwV6bvULPbv6hJ5bfXK4PbcwgQ7sMCOXy7EbbriBnX766WzFihXskksuYU888URVv+3r62Of/OQn2QknnMDWrFnDPvaxj7Genh5l2bvvvpude+65bPny5eztb387+/GPf7zPbZ4/f75rnJH+u/HGb7OBgQk2MDDBbrzx28oy3n9euYGBCbZy5WptuSuvvNov99BDj4bW+fYvnMM+8N1L2Qe+eyk76oxF2nLtc9v9ch/47qWhdZ529ensIz++ln34jr9nJ152UmjZS//7cnbV969mH/jupax9bru23Iq3r2Sfuf9z7OY/3sL+6b+vC63zsm9eUdU1HbdiBevvH/fvVVidr/dzeuihR/2yV155tbbcypWrhfMfqGtijB1213Q4Pie6Jromuia6JromuqYj6Zq89cnhck1/+ctTWlnisNtY/Atf+AIefPBBXHXVVZg/fz7uu+8+fOQjH8Edd9yBNWvWaH+XSqVw1VVXIZVK4aMf/SgikQh++MMf4qqrrsIvf/lLtLa2+mXvuusufOUrX8G5556LD37wg1i3bh2uv/565HI5XHPNNa/FZb4mxOLxqssahlFOxBHC4u5lOGPRm8HAkJ9TwDr8TVt20dxjYJomLMPEo/FHAYwqy63uPh5fOPVfAAAv2M/hJnxDW+dVK6/BsuXHwrZt/Mtfv4htj29VlrMMq+6sSwRBEARBEMSRx2Hlcrl+/Xpccskl+OIXv4irr74aAJDL5XDhhRdi+vTp+MlPfqL97e23346bbroJ9957L4499lgAwNatW3HRRRfh2muvxac+9SkAQDabxZvf/GaccMIJuPXWW/3ff/azn8Uf//hHPProo2huri1pwaHgcpm383i+7xkMpYdhmAa8uC6bOSgU8tg72ItXhjcjHk9gxfRViJpRTGvsRFOiGd2t3UjGm2CZFiwjAsswkSqm0BRtRku8+vTkh5tp/EiBnlv9Qs+uPqHnVr/Qs6tP6LnVJ4fbcwtzuTysLHS///3vEY1Gcckll/jH4vE4Lr74Ytx8880YGBjA9OnTlb/9wx/+gNWrV/vCHAAsWrQIp556Kn73u9/5At3atWsxNjaGyy67TPj95ZdfjgceeACPPfYYLrjggoNwdQeXmBXDybNPPWD1taLtgNVFEARBEARBEISaQ3cX3H1g48aNWLhwIZLJpHB85cqVYIxh48aNyt85joPNmzdj+fLlge9WrFiBHTt2IJNxU8xv2LABAAJljzvuOJim6X9PEARBEARBEARxsDmsBLrBwUGlBa6rqwsAMDAwoPzd2NgY8vm8X07+LWMMg4OD/jlisRja2tqEct4x3TkIgiAIgiAIgiAONIeVy2U2m0U0Gg0cj5eSe+RyOeXvvOOxWEz722w2G3oOr6zuHGFoU5AegXR1HbhNk4nXDnpu9Qs9u/qEnlv9Qs+uPqHnVp8cKc/tsBLoEokECoXg5tCekBXXZG30jufzee1vE4mE/6+qnFdWd44wDoWkKIcCh1vw6pECPbf6hZ5dfULPrX6hZ1ef0HOrTw635xaWFOWwcrns6upSujx67pK6hChtbW2IxWJ+Ofm3hmH47phdXV0oFAoYGxsTyuXzeYyNjWnPQRAEQRAEQRAEcaA5rAS6pUuXYvv27UilUsLxF154wf9ehWmaWLx4MV566aXAd+vXr8f8+fPR0NAAAFi2bBkABMq+9NJLcBzH/54gCIIgCIIgCOJgc1gJdOeeey4KhQLuuece/1g+n8e9996L448/Ht3d3QCA3t5ebN0qbih9zjnn4PnnnxeyVG7btg1PPfUUzj33XP/YG97wBrS1teHOO+8Ufv/Tn/4UjY2NeNOb3nQwLo0gCIIgCIIgCCLAYRVDt2rVKpx77rm48cYbMTg4iHnz5uG+++5Db28vvv71r/vlrrvuOjz99NPYvHmzf+yyyy7DPffcg7//+7/HBz/4QViWhR/+8Ifo6uryNykH3Bi6T37yk7j++uvxqU99CmeccQbWrVuHX/3qV/jsZz+LlpbqN9ImCIIgCIIgCILYHw4rgQ4AbrjhBnzrW9/C/fffj/HxcSxZsgS33XYbTjjhhNDfNTU14Y477sDXvvY13HrrrXAcB6eccgq+/OUvo729XSh7+eWXIxqN4gc/+AEeeeQRzJw5E1/+8pdx1VVXHcxLIwiCIAiCIAiCEDAYY5Re8XWGsly6HG7ZiI4U6LnVL/Ts6hN6bvULPbv6hJ5bfXK4PbcjJsslQRAEQRAEQRDEkQQJdARBEARBEARBEHUKCXQEQRAEQRAEQRB1Cgl0BEEQBEEQBEEQdQoJdARBEARBEARBEHUKCXQEQRAEQRAEQRB1Cgl0BEEQBEEQBEEQdQoJdARBEARBEARBEHUKCXQEQRAEQRAEQRB1Cgl0BEEQBEEQBEEQdUrk9W4AAZim8Xo34ZCB7kV9Qs+tfqFnV5/Qc6tf6NnVJ/Tc6pPD6bmFXYvBGGOvYVsIgiAIgiAIgiCIAwS5XBIEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEQBEEQBEHUKSTQEQRBEARBEARB1Ckk0BEEQRAEQRAEQdQpJNARBEEcAdxyyy1YsmQJdu/e7R+79957sWTJEqxdu9Y/tnbtWixZsgT33nvv69HMQ57f//73eMc73oGVK1cG7l01vJb3XPXMieqhd4EgiHoh8no3gCAIgnBZu3YtrrrqKuFYY2MjFixYgHe+85244oorEInQsH0wuffeezExMYGrr7468N327dvxT//0T1i9ejX+5V/+BbFYDIsWLXrtG3kI8MMf/hAtLS14z3vec1Dqv+WWW7Bs2TK89a1vPSj1EwRBHE7QyoAgCOIQ48ILL8Sb3vQmMMYwNDSE+++/H1//+texdetW/Ou//utBPfdJJ52E9evXH7GC43333Yc9e/YoBbqnn34axWIRX/rSl3DccccdsHPW4z3/0Y9+hNmzZx80ge4///M/8e53v5sEOoIgiCqon9mDIAjiCOHYY4/FO9/5Tv/zZZddhvPOOw/33HMPPv3pT6Ojo+Ognds0TcTj8YNW/6EIYwzpdBrJZDK03ODgIACgtbX1gJ7/SLznBEEQxIGDYugIgiAOcRobG7Fq1SowxtDT0yN8t2nTJnz84x/HKaecghUrVuD888/H7bffDtu29+lcqrgh/tgvfvELXHDBBVi+fDnOOuss3H777cp67rzzTpxzzjlYvnw53v72t+PHP/6xMn5Mx5VXXomzzz4bu3btwsc+9jGccMIJOP744/Hxj38cu3btCpRPp9O46aab8Na3vhXLly/H6aefjs9//vPYs2eP9vp+8pOf4Pzzz8eKFSvwgx/8AGeffTaefvpp7NmzB0uWLPH/835zyy23AADe8pa3YMmSJTj77LP9enfv3o3Pfe5zOO2007B8+XK89a1vxX/8x38gk8ns0z2v5Zoqkclk8G//9m84/fTTsXLlSlxyySV48sknlWWfeOIJXHPNNTjxxBOxYsUKXHTRRfjpT38qlFmyZAn27NmDp59+WrhPXqze448/jn/8x3/EW97yFqxcuRInnngirrnmGjz99NMV27p7924sWbIEgGst5evnueeee/Dud78bK1euxAknnIBrrrkG69atq/qeMMZw991345JLLsGaNWuwZs0aXHTRRfj2t79d8bfVPhfHcfDDH/4QF110EdasWYPjjz8e55xzDr70pS+hUCgIZV988UX/PV6+fDnOOeccfPe730WxWKz6mgiCOHIhCx1BEEQd4AkxvHXoxRdfxJVXXolIJILLL78cnZ2d+NOf/oQbb7wRmzZtwk033XRA23DXXXdhaGgIF198MVpaWvCrX/0KN954I2bMmIGLLrrIL3fbbbfhpptuwnHHHYd/+qd/QiaTwfe//320t7fXdL50Oo0rr7wSK1euxGc+8xns3LkTd955J1544QXcd9996OrqAgAUCgV86EMfwrPPPotzzjkHH/zgB7Fz50789Kc/xV//+lf84he/wIwZM4S6/+///g9jY2O45JJL0NXVhRkzZmDZsmW46aabMDo6ii9+8Yt+2UWLFuGGG27AQw89hIceeghf/OIX0d7e7lv09uzZg0suuQSTk5O47LLLMH/+fDz99NP4n//5Hzz77LP44Q9/WLM75b5ck47rrrsOpmniIx/5CKampvCzn/0MH/7wh3H77bfjtNNO88v97Gc/w1e+8hWsXr0aH/3oR9HQ0IAnnngCX/3qV9HT04PrrrsOAHDDDTfg61//Otrb2/HRj37U/71nOb7vvvswPj6Od73rXZgxYwb6+/txzz334Oqrr8aPfvQjnHjiidq2dnR04IYbbsDnP/95nHjiiXjf+94XKPPNb34T3/ve9/x+MTU1hbvvvht/93d/h1tvvRVvfvObK96Tz33uc3jggQewatUqfPSjH0VzczO2bduGP/zhD/jUpz6l/V0tz+W73/0uvvOd7+Css87CBz7wAViWhd27d+OPf/wj8vk8otEoAODPf/4zPvGJT2D+/Pm45ppr0Nraiueffx7f+c53sHHjRnznO9+peD0EQRzhMIIgCOKQ4KmnnmKLFy9mt9xyCxseHmbDw8Ns06ZN7Ktf/SpbvHgxu/jii4Xy73//+9myZcvYxo0b/WOO47BPfvKTbPHixeyJJ57wj3/nO99hixcvZrt27fKP/eIXv2CLFy9mTz31VKANv/jFLwLHTj/9dDYxMeEfT6fT7JRTTmHve9/7/GOjo6NsxYoV7MILL2TZbNY/PjAwwI4//vjA+XRcccUVbPHixezf/u3fhOMPPvggW7x4MfuXf/kX/9jPfvYztnjxYvaNb3xDKPunP/2JLV68mH32s58NXMtJJ53EhoaGlOc966yzlG1S3UPGGPvMZz7DFi9ezP785z8Lx//93/+dLV68mN19993+sWrveS3XpMNr78UXX8xyuZx/fO/evWz16tXs3HPP9Y/19/ez5cuXs8985jOBev71X/+VLV26lPX09PjHzjrrLHbFFVcoz5tKpQLHBgcH2cknn8w+/OEPV2w3Y4wtXryYXXfddYHjW7duZUuWLGEf+MAHhGvq6+tjJ5xwAjvrrLNYsVgMrfs3v/mNfw9t2xa+4z/v73N517vexc4777zQtmSzWXbaaaexyy67jBUKBeG7//3f/636fSEI4siGXC4JgiAOMW655RaceuqpOPXUU/GOd7wDd955J97+9rfj1ltv9csMDw/jueeew9lnn42lS5f6xw3DwMc+9jEAwEMPPXRA2/Xe974Xzc3N/ueGhgasXr0aO3bs8I898cQTyOVyuPTSS4W4sK6uLsGKVy1///d/L3x+29vehoULF+KRRx7xjz300EMwTRPXXnutUPbMM8/EsmXL8Mgjj8BxHOG7d77znZg2bVrN7ZFxHAd//OMfceyxxwYsQ9deey1M08TDDz9cc737ck06rr76asRiMf+zZ1Hdtm0btm7dCgD4wx/+gHw+j4svvhgjIyPCf2effTYcx8ETTzxR1fkaGxv9v1OpFEZHR2GaJlatWoX169dXVYeORx55BIwxfPjDHxauqbu7G+95z3uwZ88ebNiwIbSOBx54AEDZcskjf5ap5bk0NTWhv78/1BX0r3/9K4aGhvCe97wHExMTwn1/05ve5JchCIIIg1wuCYIgDjHe//7349xzz0WhUMCWLVvwve99D319fYKA5MUrHX300YHfH3XUUTBNUxlrtj/MmTMncKytrQ1jY2OBdi1cuDBQVnUsjJaWFt+tkmfRokV4+OGHkU6n0djYiN27d2P69OnKZCVHH300Nm7ciNHRUUGAW7BgQU1t0TEyMoJ0Oq18Dm1tbejq6tqn57Av16RDtbWCd2zXrl1YtGiRL9ipsnt6DA0NVdX2np4e3HzzzXj88ccxMTEhfGcYRlV16PD61zHHHBP4zju2a9curFixQlvHzp070dXVhc7Ozn06f7XP5TOf+Qw+/vGP4/LLL8f06dNx8skn48wzz8Q555zjC6Peff/Sl76kPWe1950giCMXEugIgiAOMebPn+/HNr35zW/GCSecgMsuuwxf+cpXcPPNN79u7bIs63U794GmoaHh9W7CIQVjDADwjW98A9OnT1eWmTt3bsV6UqkULr/8cmQyGfzd3/0dFi9ejGQyCdM08T//8z946qmnDmi7D2XWrFmDhx56CI8//jjWrl2LtWvX4te//jW++93v4s4770RbW5t/3z//+c9j2bJlynp0z4MgCMKDBDqCIIhDnOOPPx7vfOc78ctf/hJXXnkljj/+eN9a9uqrrwbKb9u2DY7jVLUAP9DMnj0bgLsJ96mnnip8t3379prqmpiYwODgYMBKt3XrVkybNs137Zs7dy7+8pe/YGJiAi0tLYGyTU1NNSdkqZaOjg4kk0nlcxgfH8fg4KB2oR7GgbymrVu3Cm653jHvPEDZYtne3i4kSqmVJ598EgMDA/ja176G9773vcJ33/rWt/a5Xg+vva+88grmzZsnfOc9g0r9fsGCBXjkkUcwNDRUs5Wu1ueSTCZxzjnn4JxzzgEA/OQnP8H111+Pn//85/jwhz/s3/eGhob9uu8EQRzZUAwdQRBEHfAP//APsCzLz3g3bdo0rFmzBn/605+wZcsWvxxjDLfddhsAN97stea0005DLBbDT3/6U+RyOf/44OCgH7tUC961eDz00EPYvn27sOH0W9/6VjiOEyj76KOPYsOGDTj77LMrxkZ5JJNJjI+P+5aTSpimibPOOgsbNmzAY489Fmi74zj7tDn2gbymH/7wh8jn8/7nvr4+PPDAA1i4cKHvenneeechFovhlltuQTabDdQxOTkp1JFMJgVXWw/Piivfv8cffxwvvPBCVe0F3Dg8Vf1nn302DMPA97//fSH1/8DAAO69917Mnj0bxx57bGjdXiznN7/5zUAcYqXnXstzGRkZCfze25B+fHwcAHDGGWdg2rRpuP3225XXm81mMTU1FdomgiAIstARBEHUAfPnz8f555+PBx54AOvWrcOJJ56IL3/5y7jyyitx+eWX47LLLkNXVxf+9Kc/4fHHH8eFF14YsJC9FrS3t+MTn/gE/uM//gOXXnop3vGOdyCTyeDuu+/GggUL8NJLL1UdR9Xe3o6HHnoIAwMDOPnkk/1tCzo7O/GJT3zCL/fud78b9913H26//Xbs2bMHJ554Inp6evyyn/nMZ6pu/6pVq/CnP/0J119/PdasWQPLsvCGN7whNFbtM5/5DJ544gl8/OMfx2WXXYZ58+Zh3bp1+O1vf4uTTjoJ7373u6s+/8G4Jtu2cfnll+OCCy5AKpXCXXfdhVwuh3/+53/2y8yYMQNf/epX8c///M84//zz8Y53vAOzZ8/GyMgItmzZgocffhi/+c1vfMvwqlWr8POf/xzf+ta3sGjRIl+wPeGEE9DV1YVvfOMb2LNnD2bMmIGNGzfi/vvvx+LFiwXlQxirV6/Gk08+idtuuw2zZs2CYRi44IILcNRRR+FDH/oQvve97+GKK67Aeeedh1QqhbvvvhvpdBo33nhjRdfg8847Dw8++CB++ctfYufOnTj77LPR0tKCHTt24PHHH8evf/1r7W9reS7nn38+Vq9ejZUrV2L69OkYHBzE3XffjWg0igsuuACAK7h+4xvfwMc//nGce+65eO9734v58+djYmIC27Ztw0MPPYT//M//xCmnnFLVfSMI4siEBDqCIIg64WMf+xh+85vf4Nvf/jbuuOMOrFixAnfddRe+853v4Kc//SnS6TTmzp2Lz372s7jmmmtet3Zee+21aGpqwo9+9CPceOONmDVrFj70oQ+BMYaXXnoJiUSiqnoaGxvxf//3f/ja176Gm266CYwxvPGNb8QXvvAFIa4oGo3i+9//Pr773e/it7/9LR566CE0Nzfj3HPPxT/+4z9i5syZVbf96quvxq5du/CHP/wBd911FxzHwY9+9KNQgW727Nm4++678Z3vfAe/+tWvMDk5ie7ublx77bX42Mc+VvMedAf6mr7xjW/grrvuwu23346JiQksWbIE//7v/47TTz9dKPfe974XCxYswA9+8AP87Gc/w+TkJNra2rBw4UJ86lOfElxfP/3pT2N8fBx33nknJiYmwBjDI488gjlz5uB73/sevvnNb+LHP/4xisUili9fjttvvx0///nPqxbovvKVr+D666/Hf//3fyOVSgGALwR97nOfw/z583HnnXfipptuQjQaxapVq3DTTTeF7nHH45X9+c9/jv/6r/+CaZqYM2cOzj333NDf1fJcrrnmGjz66KO44447MDk5iWnTpmHVqlW49tprBRfYN77xjfj5z3+O2267Db/61a8wOjqKlpYWzJs3D1dffXVgU3WCIAgZg1XrV1InDAwM4Ec/+hFeeOEFvPTSS0in0/jRj35UtXZr69at+NrXvoZnn30W0WgUZ511Fq677jp/w1QPx3Hw/e9/Hz/96U8xODiIBQsW4GMf+xjOP//8g3FZBEEQdc+//uu/4sc//jEef/xxZfZKniuvvBJ79uzBH//4x9eodQRBEARRnxx2Frrt27fj9ttvx/z587FkyRI899xzVf+2r68Pl19+OVpaWvDpT38a6XQaP/jBD7BlyxbfTcLj5ptvxm233Yb3v//9WL58OR555BF8+tOfhmmaFTV8BEEQhzO5XE7YYgFwlW2//OUvsXjx4orCHEEQBEEQ1XPYCXTHHXccnnrqKbS3t+Phhx/Gxz/+8ap/+9///d/I5XK444470N3dDQBYuXIlPvjBD+L+++/HxRdfDADo7+/H//7v/+Kqq67Cl7/8ZQDAJZdcgiuuuAI33HAD3v72t1cdrE4QBHG4sXbtWnzzm9/E2972NsyYMQN79uzxY5z+6Z/+6fVuHkEQBEEcVhx2Usf+pKd+8MEHcfbZZ/vCHOBmbFuwYAF+97vf+ccefvhhFAoFXHbZZf4xwzBw6aWXYs+ePVi/fv2+XwBBEESdM3/+fMydOxd33303rr/+evz4xz/G4sWL8f3vfx9nnnnm6908giAIgjisOOwsdPtKf38/hoeHsXz58sB3K1euxF//+lf/88aNG9HU1ISFCxcGygHAhg0bsHr16oPaXoIgiEOV+fPn49Zbb92vOu64444D1BqCIAiCOLw57Cx0+8rAwAAAKGM7urq6MDw8DNu2Abj7Kak2I/V+69VFEARBEARBEARxMCELXQlvA9xYLBb4zgvuz2azSCaTyGazoeX4zXSrYXQ0Bcepv2Sjd6/d7v89q70RZyzurqrsirntWDarLVBm2rQmDA9PCWWXz2nHsbODZT32jKbx11cGEDENnH3sDLQ1xrVl+XpXzmvH0pn6eh3GsHskDQCY29EYum/Wy3vG8PLuUQBAS0MU566coy1777qdKNruRrbxiIV3njBPW/b+Z3uQK7hKhOktDThz2Qxt2Wd3DuPVvgkAQEPMwkVr9PVm8kU88PxugDHM7Uji1GOma8tWg/fcfvvCbkxlyxv9vu+UhdrfPL6lH72j7v2NWCbec+J8bdm9Yxn8ZXNfVfXWwmgqh4de6vU/X3zyApia55wp2HjguV0AY4hFLLwr5Lk5DsPP/7bD/3zJyQu0/SdXdHD/MzurKgsAr/RP4Lkdw3jD0dMxb1pSWw4AeoZT2LJ3HIumN2Ph9GZlGdU7d9axM9HVHL6tAGOsqr3k+HpPWdSF+Z1NVZVdPLMVq+d1VFX2hAXTsKi7RVv2wZf2YCzlboi9en4HFs9o1df79A6glPj5jUtmYGZbQ1VtWNTdghMW6Lc14N9l0zRw8UkLtGX/tLEPgxMZ/7Oqv3vP7W/bhrB9cBIAcOzsdiyf06at94HnepDJ26H1evDXVqksP/ZUKvvHDXsxNJmtqmwtPPFKvz9eV6qXv2fVtsFxGEyzur0TKzFtWhP6Bibwq+d2wbYdzJuWxBuODh+Dt/SN45W+CczpSGLl3Paq93GsBP+c33vSfFghsf982XefOB9Rq7qyF6yei2Rcv9TM5G3sHJ5Ed0sD2pP6+RsAdg5NYe3WQTTEIjh35eyq27BqXgeWzAx577my562cg+aGaKCM985VS7Zg41fP9vifTz1mOuZ2hI/ZmYIN0zAQj4TbWvaMpvHEKwNoiFk4Z0X4ffjThr0YLL1zlcYphzHsHJyCaRqYNy0Z2s9e2jWKDb1jAIDWZAznLJ+tLbtjcApPbxv0P+veOcYY7nl6h/+50roScOfEiUwBx85qQ0MsuP/ktGlN6O2fEObZSmskx2H4/Yu7MZUtVlwrvtaYpoH2dnU/IoGuhCeM5fP5wHeegObtnZRIJELLydndKuE4rC4FuvF0+R40xiKh18CXHZrIwpmhLus4TCibzduh9W7cM4bxlHvf/7ShD+88Xv+S8vWmssXQerf0jeMvm/sBAG85bhYWhCxEn9jSL5wjrN5hbjEDILTswHh5UWdX6CO7h1L+9Y2nw+t9pW/Cv2fj6TxOWbT/GQcdh2FkKodMvigc05HJF4XnEVZ2cCJTdVm3fBaj6RwWdjWHTnS7hlOBvpaIqjckHp3M+fesUhuyBVuoN1ewEYuo681LZfNFR9vmou3gzxv2AgD+sH43rnnTMaET7h/W7wYAbB+cxN+1H42Ipl75ncvkinCS+uvbMTSFR17uxdyOJN5y3CxYIQtdvt6xVC50QcOXTYe8nw5jgf4Q9jx2DpYXYVv2TuDo6Xrhj3/Gj23ai0tO1i/2xfYWKr73Nvd9WNkdA5MoOk7Fso7D8PzOYf/zum2DOHaWftE6lS0KCpdqx+tKZVOZQtXv5/aBSTisuvvAGMNLu8eQKRSxam4H4pp3EwCGJnJVt6FnaKqm8eSxTX3YNjiJExd2Yvmc8Pj8jb1j6B1NY/X8aZjWpF8H7BlJYaQ0F7yYzuPko/RjcMF28JdN/XAYQ+9oGrPbGtERUjdQncIlJ409w5M5dGoUOUx658ZT+dA28GWLRQdOVH+PH924F7tGUrBMA5eftih0zH7wxT1+/S/2jGL1fL3Sh2/D3rE0jglR+vBlf79+N96rUbjUsk4rFp3APZvd1qgt3zM8hYdf3ouIaeCiNXNDhdvfPr8LADCWAp7ZPhTaf17tLytbtuwdx5oQRdlLu0exdqsreJ25dEaoouzJV8ueaJXWPXtGU1W9c7tHxHKTmQJOC1F2DE1m/TlxaCKLty2fpSy3uXdMfBZpt//r+tr2wUnsKSmI/rKpH4u79ePqoQS5XJbwNqkdHBwMfDc4OIhp06bBstwJpaurC0NDQ8pyfF1HEp7VqRqiFbRPtbBrOOX/PSQJS2HoLDEenjAHAI+83BtS8rUhbJIDALuG7SRbG0XrcqVnl8oVsWNoqmI5u4bJbg+nTa9EpWfFk8oV8evnd+Evm/vxfM9IaFl+cQvAt6Ao2yDd/rDtOwvSfcoX9feNX7hXKjsptTed17dXJhdSryNdi9x+Ge992DWSws6hGjTWIW2QSeUK2u9kYeNAId8H3eJWxXhG314AaEoENf7VtiMMXpiu9NxqoaOCpYSnWMN7z1+bTsnh0TuaxtPbBvHirlGs3Racl3kKTvXXPlpD/5nMFPBK/wRsh/kLXR3DUzk88coAdgxN4SlusauiUMO7kM3bwn1LcUozFS/vGcVPntiGdduDaxSh3qI4fhRt/XOUx/awIVnuv5X6866RlH8OXolZCXk8DCNTw1jZlgx6X+0LDOJ1h43tALClbwKMMRRsp2Jf43lx12jVZXUKS4+/bSv3mWd3hs+ftZCt8v5vHZgUPlfqO49x67SeEOvp4ERwbbh7JKUo6RL2LhzKkEBXoru7Gx0dHXjppZcC361fvx7Lli3zPy9btgxTU1PYvl10S3nhhRf87480dNp/FfEKEznPaKqC+2qVa315AV6L8FGJmZLWLWyxv6+uMpVcMGq6Hqlo2GRXtB384m878MjLvfj9+j2h1eaL1U+aM0Jc2WTkiTGMl3aP+pPA+goC3WRWXBiF3QdZqEyHLKpkwTdMmLKliSNb0NcrP+JMhYWdcJ6QBa+8MKplodQ/Uf0CLExgDpQNE4Kle5apod7Qc0r1hC0m5O/i0fD3M1HDmFeLQFeLsqMWga8WBVEtyjyeSAUXxpdL7lyA61UQRipX/bvAj8+VFGXyPQt7NlsHym3sG8/U9BzD5oys1C8rjfVPvTqIXNHGCz0joe+c/NwmsnpBVxbaw9ogfye3P4xCSL3ytbQ2Vq8kqUWArqVsGPKlVFI68MqxvWPVj6u1ENbPALF/VxovZMVwGGHzJc/eMVHRaxpGaF+rdvRT3fueYb1AF+Z1cihzxAp0PT096OnpEY69/e1vxx//+Ef095el/ieffBI7duwQNgt/y1vegmg0ijvvvNM/xhjDXXfdhVmzZmHVqlUH/wIOMSoNFDy1vCsJhU80T6xKQVKeVA7kCysLW2EDEH+fahHu2ioMnjVZx0bFQTNsAT8wmfUH9v6JTE3POYyoZPIKd72qvt5aFlGyFSgTIkzJ1YYtUmSBYyITtlASJ5owTeb+KCXCysoLmNFU9RaMSlpnnloEurD7Kwv4YzVYXMIEIPmchWL1i+xKOgd5HDtQ1rRa+nst9z8tCUhh773ctw6UsqwhWn1ESC3XxhMW2wUEBb4wpY8hLS/D3g1ZARpWVh6XwgRoWakmjy88BWmcCmuDrBCqRaCrxYIb9t6PSMrdMCVJLYK4TC2eD6FIp6yk+ODHplraWwu1VFtJsK2kDOGpRqhnjAXeL4ex0PF9drvehZVHdS1hxoJ6FegOyxg6L1321q1bAQD3338/nnnmGbS0tOCKK64AAFx99dUAgD/+8Y/+7z760Y/i97//Pa666ipcccUVSKfT+P73v4+lS5fine98p19uxowZuOqqq/CDH/wAuVwOK1aswMMPP4x169bh5ptvPiI3FQ8bBOXFQC2CTCWf9VjEBP++54sOYgprlrxIPZDjpTx55Ip2VRZLK+Q+yHWGxY8AtU0Am/eOC5/DLBxytUWHIWod+MHOZgymRt9Wy7OqpeyUtGgNm8BkISJXCFsoid+FLQJr0WbXoiWvdJ6w72qxTFVaEPMMKNxePOQxIuw+yO2drODuyBPWd+UxIkzokq2jlR6F/K6n80W0NlTWcFeywNUiO0Ussyprmuf2xRP23stNcBiDVYXuvNI00FhD36oWuZ9FKszVjnR14+m8ts8Hxwh9TK78HhUdBp2TqyxohVk7gmO5/ibLfSHUNbwG4U+es8O8Ayr9NuycYYKi3H9rEdIqWZOGJrN4bHM/WhujOGvZTO07mpOE60pKnIhl1uTl4v8upA/L/ayS5d0yyxaxMGUAEHyXwhIHVWNBzxRs5dwzls5r41GrTVSUV9z7A+mldahwWAp03/72t4XPv/jFLwAAs2fP9gU6FTNnzsSPf/xj/Pu//ztuuukmRKNRnHnmmfjiF78YyGr52c9+Fq2trfjZz36Ge++9FwsXLsRNN92E888//8BfUB0QFvNSiwZXnmT6QxaBQFCT0j+RUSZekAeKSm58bY2xqjX/sutevuigmhCUsAVNtS4KHrVYztqTMT/rFRCuOeQDqr2y1WjmKgqgCE4G0PykFpfLagXbou0EtPp7xzNYHJIJjUeerHnkiTtsQSMvSuS4FqGsXG8NzzxUoJMXlyHxA/KCqyarT0ifli8lky9qEzvIi4OwmNxa3otanpu8QKzFlQmoPkajkkBXy/VVq4ZR9ZVCyHsvx6Lo2hRc0Ia3KClZNXXKOvl8YbF58vsmj0PBysWPYe+9yoqvG03k9yasr8nv3FQ2ZG6oxQKjUETqkBf3Ye7eB8tiK7/3YWO9rJxL5QrIFeyK8xJQeUz73fo9yBdtjKZy2NI2jqWKjN1A0KJY6Z2PWgZqnPbdesPmlxqEdsCNsePvs+0wrbVKVrqNZ/LKRC4F26nKI2FM4xkS9ttqLWkqZW2oJ1VVtR56HJYC3ebNmyuW4S1zPMcccwy+//3vV/y9aZq49tprce2119bcvsORUCFN+i5sMgguqsJfLXlM1w1Yci2V1kGNsUhVAh1jLNSlLowwK14mJy8Yw+vajxC60AEzKNAdmIWo3Igw4USerxzGtPVXK9DJky0QHtMjVxvmpir3d9m1SSgrW0JCytb6bvCETeQTkoUrrD/I11ZLvF0YqitJ54tIxoNxMnIbwhZg8nMLexayYBJ2f8ek/lOp38lVVRt3VkvIbSVrU7WoLB+pbBGNMfVyIRaxBMuC7lYMT9Z2z+SL15WX+2/YLZMTZ1XyAAkkDAqz4tcg/E1I701oQhKp4rCkQPKzC1vvyu95mLu33L5ahL9aMkOG9YmAS3TIO7RXkVwlnS9qBbqGWERYl4RlCeX7ev9EFkvViRUDnhmV3vmwLSNkopZZlZAUTNBlh15bMh4VBLp0rqjcwqFgO4Hr2zuWUQp08jiqE8J0Vry9Y2ks1Sha5XWAbm2guleT2ULV2+/UC0eebyBxwJBfHN0CSH6Z5MksrI6JTD500RiYADQTgqzFrTTFxKQkB7rBuBZLmtzWsMlWzmRWyUpVy5AkL4DDFhOytbOSG4ZHJcWZ3FXCLA3y4iFs4VGtkDMyFRTowoRK+ZswN7+gu1pYbEr17o7yYrIWgS7Mgi4TtvCQM4PWkvAA0F+f6vnrJviAlfAAZfCULTth91e2yld6FPL7W61AV8veZ1YFV2hZgaR7FiprUdg4F/R+UJO3xb5SMamPXK+mvXLWyrDnFtiOodJzCwhp1btl1xJfWssYMR6iQJSfXdhitZbkQnJ/DXNhlOs5UPGi8lgTpsjpHQ1mUQ7rF7IyL+w5V0ut2YNrCWWQLdW6d0Plpi+HGvDIbdaNwelcMVBWVqzUiu7+9Azpk5fIpDTW66LNwJgDe6IP9shOOBk37KSWePF6gAQ64oChW9zJk0HYS6QadMMm/oDlTVeuBgFChW6yqyUdci0LcjnbYaXmVrvwY4wFBumw7Fu1aEV5atV6hcmJskAXlryk2vTIqglbzjjJIwsRYfEbtVjdaomLk++DfYCyFsqLwLBnLN+30OQlCu2nrrzqsnX3TV5I1BJvFxajInfZAxV3CIjbqwDh2fz2lUoWOnnRqhu7VH073NWwunuhso7WlglSfVwW0oqOox3fZUGk0ngmC2m7QlKdB+KNQ+qWx7swy59qka1dwNeQbj0Y6xviOSP1CTlpjtiG6rP8yoTNy32S1U1WqgjnVIwJtSjAaskgrCMwZ1R4NrVY2OV3Sbc+Ud3OsFhmua9NaRSBKgWEzlMpoEzX3Aa+Px7V1SycSye8y3XpFE8OY3CmBoGiq8hlmTEwx8bQlPpeBNt84MfrgwEJdMQ+I3dxXdYgeSALmwxUi4zQyc6pbrCoVvDTFdAN8KrJStcGOT4qdPEuJd2o6J0kfda5uaTzwcDjsIXHoOSidLBif0KtY4GFUoim1apSc6m45jAhTRaCw4QIOeV0LTEOYe5J8iReS/a4sP4j98NM3tbeN1kgyhbCygYX1vq+FqxDd9/k9uZC2iD39VALi1RFuEBXWxY9Oai/WgtdLeuISgkV5DZWO167dYcJHNKBKhZs/m9riGPRlVS5IOqes3wdYX0HCN7/sPjhQF8PubaAUiJ0jgse01lOahFa5D4YppzMy14dodcmfg5TBsiEtV5OLKSzVDoOw7Cib9figXEgMl3K90EnPHhEqrTQqZIW6d5P1fsVtl+vXF7OiO2hygCsC1Gptkfy19SejAvusTpBTVa4aAU6uwAU3Oue3eFmxnTGdmPd9mGlsBgwAFRu/iEBCXTEPlNtZrqATz1j+ngIxV44qgxFfhsqtEl7vMY3VDfZKQcDTR217HMVHKBra7Buwh1WTCq6DTZV7dNptOX2VtpOQhZuwxZ2gUxdNbjO6G6xanPesMQEgXiTEIFOdp2pxYIUtuiQ36Nq3V+B2tLO54q21hVNZWXWxaWpXHt0/UdVh06Ro7KO6dqg2rtP9zxq2RZCfr8qCV6y9r1aS3clzTCvwCjYTmhfCyy0NWVV74Fu/HMYU7izV69ECRsDg1+py6qEG53Ao0pnX4tipJZECrVYeMOs7arnobs+eUwIFValz7mirW2zLODUktgsLCOwXDZMpJHHbF0s4VQpAYpMtdsJAeFK57DfVfudCtnLRtdelaJMV1b1Loa5Rsr16BQYqrlnSuGGqW6TGv7djFgGOrh4PNXG4IAqmZZKOGNw8mXBdHZ7ElbpujL5In77wu6KFrk6MdCRQEfsG6rBSqeJUy1edAOQyo0ibPFTa2yc7neB76XPOs1PLdpHeWKzneBiyCO48AivW/5at9hXuSTqrGmqxaFu8SO7QVbKHicv1mtZ2IVNzHJ8pq5e1UbFodkda3DzkxdbYRNoLcKq3L5a3KvC3JNU59RZkFTKlYKtee8VAllWs7hTbaius7KohF5df1c9f521Xa7XVggr/nf7mclPN6ZV677oIb+5YWOl3EadFULVr7QackXzqnGp0rUprLyuqEo5o2uvqg21ZGoNU/rUMk4F4qvCslwqqtEKdDWMCaqSqvcQCI7vYQnDgpkVq9+KJXQrgipdN3X9L0xpGPAiqtLlcuvApL5OxflC32fpK63ytAZLt6pbyXHQPMHnUZ0CFygJTip3ben90q55uDpjlikIk7p1VnArC00fYeXjUcvEwq4mwDDgZMYwPDoS6M9yCw9UHOjBhgQ6Yp9QvZI6s79q8atNoKKoI3yhLbVL63olldPW6NUjftZdm/q4TlgNDqS68b1alyxdPbr7q5pkdNYNlbZLp7nMSwv1SpOvPLGFTXS1WKYCLowqxUOVlmQelZufjkBSgNANew/OfZDZO6Z2nVG1AQhTYATPqXs3VJOgbmJUtUE3iasWDdW6XAL6tO/ydTDG9O+RdB2VY7FEwrTvcrla4szC47aCllhlG2oQemqx4qvegzA5OFCPpqyqbZkqLXS6Y/4ppXOGCRy1COO1xFfV8n4GLHTaWtVf6iyxslIqLMmSPJ9UirMVP6vLuZbU6sYe3X0PjXuW6q42e++sNv3G1iqhO8x1uVohQiUg6zw7VPcibE84+R3VzV26cUZ1i1WKI9V4zV9vNGKiuzXhf9YlcpEVDar3yGEAHLcNXkx3d0sDIqYBlhmHMzWEvuFx6TdiPQcipvK1gAQ6Yt9QvLi6AUj1kukFDsViIkTDJw9u2iF7P4NcdW6ftQhIqklYNxDL97KiRbHKWCWVhlmndVZN2jrNrOxCWcmNSW5e2IJGFhbDNd/hn4Ewd6UaNOq2o22HbDWpJUNgaKKcQBrs6vtwWJIa1Tl1VsWC4l2sRaDT9UvVtehctVTt1d03lfCnsujo6tAlGwhsTVFjVjxde5VW8Rqecy37DeoW2ioNvm6Bq05iVb2gGGa9UblHqlC9t7rnpnpOtWSjrMWNMkzpI19LeIxiDf3Srn48UaGzistzcNiWO0HhqIakZjXEPAOV40Z5wpQSqjjiapjWrN9wVjXuh297Ud0crhRidS6XisO5oq28b44TFJrDXK3V56vRKskhCHSWieZEebuEnUNTyt/Ic4Rq/cgY8y10/DTIxzWPjgxKvxHrqGWMeD0hgY7YJ1SvqM5goHrJdEKEahGozcamWFQfqKQosu+5Pp5H5Vambm9NlkpZoFOW0n9fi4VOu7hU3HedVSi4UW54iwOJWUKsTfJiIswKEbB4VdBut3B77NSqJa8lY2O1AkeY9lQWRGqx0NXiigfoM5apJjbt4rkGga4WC10tAp1KKNQtAlVt02VNlc9Xa7bEsGxsMjqXVrdeEd2CXBXrpuu/qphavbKuejdK1RgqJxAKK68auxhj6uemuTaVUBmWxl1+bmEul3L7whJryMJ1LUpLQL8Jcy17wKniq7RxlQpBsdpwgTChS65Cd3e1ln1FG2pRlujKV7sdS9jecap6w0IRqvWyUc0RtVjoALWQrXq/dIps3flUR1XXoSrnKcKZXcSeHVuQHen1+1gqV1T2zYDXSg2ZdPl4+1xecrms4f08lCCBjtgnatHEqCZyvStT8LjO51tVhz42Jfxz8AfV1ata8OkmuloCtYPuX8piWqqtF3AHclWbVYN59e2tINDVYOGQJ7AwS5q8eFD1yd0jZfdDXgtYi7saECbQVd8vg2n19W2Q3V11e+4Awfsf5jKiWqSOayx0KoEhq6lbtd+kznpdi2VedX91XUK1xUUtyg7dFhkq197QDZSlr3TWR1UVoYtLqbzuWdTSf1UCuu7SVPdMG3ejeAfCtgGQ3xm1d0Fti3fVe6hLuKCilri40AQ1Vbq/AmoXYb3Lpawoq23i0Alfgb0GQ92Rg8qOapOVhWXMVbarBiFEe88U5cfSedi2jZdfXo+XX16PwcF+5W/lzJs8yvsT8jiqTbSkFOh0CkNNJSrvm1rmLb1FUDEeqOpQ/Nw7lzO+BwYYnGIO5sRusJJ1TZXVNLidkGb+LLXLAJBIuK6cFmdlzmQyGBkZLtcrtW/z3mDM/aEICXTEAUM3eIwotIn6pCjVu/mpzldtBrtak6LoJhTVwldXtaptunsmT/CVNhYPWrzU5XuGgwsoxpjaNWM/BNBKbhayhk+3L1e+aAcmlTDhqFo3rfL35b9DY+hqiMWrFB+gOz8QFpvAgnvA1aA1DNOoKxUjNVikdX1NFW85mlanylda6DRCj3pBU31ZXXuV16YQQBljyne5lsB53QJe9YxqSaGuez/V97e6RAOAXiGg6q86K7Oq3rDkGnJ/V11DLXHbgLpfh++xJ36uxYofZpmSL0V2K+cZnlKk4Nc95xq2QVFNKWEW3mrboHoPdHvPVqto1Y+f1Y9deqt48Fi+aKN3b6//eWBAI9CFrKDViif985D7rG7OV1r+anC5BNRKgqqFMegVPKrzqdqmug/5ogNmu+spq2Q9a22MAgW3/6vCAKrxlHCY2NW7urrR0NAoJINzGMPevXvw6qubsXHjS9jb8wpYoazoqdV1+fWCBDpin1B1b91gJe9lBtSmUZrMFjQWweDvtVYT6XOtr6du8aPUGmnug1Kg006K1WnrygXEj7qBuJVzMeRRJkKowbIamIwqtDdooVPf3/1dkCtj6PLlieHo7mY/viwsAcX+ulyGCWqVzgO4k1kwxqK2Xqybk1TXodNyK5XO2tim/bOo6BbEqrLVJnMAgu5jHtVufJ0vOsqFQ1g2U1UdKlS3spYESbVYNVX3kTGmTXykGqtU76Iu6UytWS7ld7+WWE/VHnuq/buA6hNg6NrgIff3WtycwyzzKiNQtVbmWuKNgbD+U/lc/nHFdev2jJOFFq1Ap7XQKcpy52/iPDB0Y4Ru/Ar0P4Xrs7zVgFC+RiEgkChM0yWUAl2NLpcqrydVc3V9WL/nXHVjhKqfFW0HkAS6eMSCMzUAJz2CwcF+ONJNkRWtSsUIY/AWSIZhwDRNLFhwFDq6ZsFItAAoX3sul4PjOEjn8nDSI34VAxOZmpJTvV6QQEfsE6q+rT7GlNpd3WCnS6Gvct9SDcRh8Xbib5XFtN+rBqBswa4p5bVKQKpFOAmj2qyRuoW6agGkzKal8+vPB7cLCMs8GMgipUvQoBLoakhQo1qETnKLwLbGmOBLrxOSlOmYa8jCWG0Mkq4/qGLaQlOoK47p95hSbBlQQ7yJbt1ai8uRMt5O015VX9WVVS4cNIHz1fZ33TsUmi1RrkMn0CluUPjeZ+J3OguLOjmCej8r7920zMr7Kar2T9MJSEp375B7Js8bqmvg++68aU1+m1O5YtUZQ2t5bkVH7z5Yi5Am97Uwi20tcc+17GupzgBcvYudzqNB1bYhhZURUGURrd5dF9ArXDwaY5HQsqo2+FgxsW3FYuDZ69YrgM4ypS0ezAegGSxVc0EtwjWgTgyls8KqjuvqVd1L1TyVzqnHZd9CV7qvnrKVZScxMTqEjRtfCrSPp1eRzZkvYqAs1DW1tMJsbAeiiWCGWocBdlmJWrCdukiMEqlcpL7I5/P49re/jfvvvx8TExNYunQpPv3pT+PUU08N/d3ZZ5+NPXv2KL+bP38+HnzwQf/zkiVLlOW++tWv4tJLL933xtcRSotZDS4xtewbBQCZnI3WhspldfvoBJOn1GbyUk1Suo1NVe1ijKk3IdeUrSWdvYpaNZ0qQVhtodMsZouK64B6o1jl4kC3dYJSoKte6FJNiryQ2BSPImKZ/qKuYDuIRYJ6LtXCqLYYuuo06sVSvImclVK9GN4/DbCHahFXi+vVRFb9HqgsYbrFs6HoKTpNtGEYQUGkhmQryn33HHVMUC1CaS0aeW2skuIyaomDqsU1zRtnIlb53vOa94hpImqV+7l6/z+FwkQr0Kn3tVT1d1WblYt37noTUQtNiag/Lk9k8uhsTijL8oQq5ZQLVPGe8cd5CrYDhzHlol8eb/Uxf2rFmG7+lBfaYUqfWkIWanFtVHozVJktUfcsanK55MrGo+WxXLtXW0jYA79ALhaLVSdh09cbImBXWfeIQqE1NJkFZrYGjvOhDPGo5d/fWpKH5IsOElFLOKYPbQkeUz06lRLRNaSV6i29MvxM7FU9OjqC9vaOUt2VBWwGVm4Y972nyDUbpyGWdDBz5kw0NjZi69ZXOCGwvIpx3yXxPhxqHHYC3Re+8AU8+OCDuOqqqzB//nzcd999+MhHPoI77rgDa9as0f7uS1/6ElIpMb6ot7cX3/rWt3D66acHyp9xxhl4xzveIRxbtWrVgbmIOkD16qsyOOnifHSLH37yamuM+QsMlQZTNXhUGwxfUZyTCqgGO60biaLyfNFR1qHcyqDGYGrVOaux0M3tSPpJCVTtUAkR1e7L5bdJIdHVsuGqqv9oNcNKJYP4uWA7/qRmmQYaYhailokM972K1yopiqNYZAPq/l9zSvKCHZiYGWMad9vqrY+6hZI6YUb1iypVnypqF7jqeqtNIFBtMghArzkPzYQm/US32FfVHZqpUFGvirDxgE9Bzwt0pmkgapl+P1crYoLnU7lzye6OpmH4VnydgBSIr1K0n3fVjkVMNMYivkA3mS0IAh1//oZYxLcA6pQXOtz2qo/LqBbDclu8z1nF+8lnOI2Ypj/26ffSrKyI9KhmE2gPVfdJ5YrobFbUW4NFR6aWZF7uuRRlJSHfoxYlFeDeC1mgCyQuCZmYldkdQ4bsapW4KuVgVBPMx9/3Bk6gqyUOTyXIaC2mSpfL6mNy/e0FvM/c+KgWFoP3zHGY4ArL/86z0AFl4c+wIog3N6OjY5p73DTL940xvzG1KlBfDw4rgW79+vX4zW9+gy9+8Yu4+uqrAQDvete7cOGFF+LGG2/ET37yE+1v3/rWtwaO3XrrrQCAiy66KPDdUUcdhXe+850HpuGHCap1ti7YWx9DV/47LgzG1SUUyeTtqiwctSZFUS0YBzTZ0VR16wRb1eBca7pjYN9iEdoaY75Ap1r41hLzohq0HcZgVWl50boD1mChUyZxkW4E726ZjEdgGIZgkdMv7IPH9Hv0BI/p919TJ5VobRTdfXRbSOisG6ruki3YkHW4qVzZjSjKWSpzBTswMQIaoVlrsQpeWy0Ch5fIhb8+vZuXxrWoSoEuW8M4pXsTwxJ8qBZ+haIjjHGAOslSLdtT6N4N7XggPQ9+nO1IxoT7p1yoa4R2+bnJe0yZplG2FmgEJLlmpYs9984nopawsJXfT37sa4i5i1uHMX/7G9nF1G2D+vpUu4/pkgvJQprtqJV7U9lCoKzgPhiPCO52KoVA0EIXInDUME/VskG1qt5qFVq1ZL8GNO7t3D3rSJafVCZfVI6XuluULThIco8jlUrBdhJCGd17pQor8I7rkO+brqTSE0VTmL9v/FijGi+rqcOD75eecsatJPh71TNVbjYOgOVKRhUD6OzsErLgVusVVrAdxM3ytbptY369noTGv+98Gw3DVK4368Hl8rCKofv973+PaDSKSy65xD8Wj8dx8cUX45lnnsHAwEBN9f3617/GnDlzcPzxxyu/z2azyOXUvuGHOzpXQRmd1roaH+wYNzlXKwQ4TB34Li/2a9W1yAOHwxi2D5Y3u2znJg7VBKFNMV+ttaFCgwPabM3z4TVrbZzQoEouoM4YVb3WUDdB1NJPlPvlaPqOSsCSXfF4q2pT3A2Y5/uZzjWyWqsOY2oLUrULGkB9L3TaZa2lW3GsUgxTImoJC0rV4q6WbImqplW7rYj7exaoo5YtB3RuzupsljVo7zX9ekQTJ6RD2edVboU1LCRqtdDJ1827TjcnotpFj/97TTyiXJS/hljERMTk3zl1HfJ7pHLx49/5hqiFDm6zYNnrgH+H4hEL0SoUOSq0FmnFdeQViTR0rvqqMZgXWOOSK7jSgi21LWy7EnUMXfUeNVqBrgZX62o3QlfdR6Cy8BiLmIKQr4rJ1m4zVBTLjo6OVq0Y1rmYhgmAyjguBbUkRSk4Yp/3qMVLJridAhOujxcUq82GquqXjl0+ZgBIJpvQMX12+bylgZcxhlwui9HRERRsxfrAkZ9R+W/BQseNbXy7DYO/9+XjQ1PVb2/yenFYCXQbN27EwoULkUwmheMrV64EYwwbN26suq4NGzZg69atuPDCC5Xf//znP8fq1auxcuVKXHTRRXjooYf2q+31hurV11nMVFTjehXdB4EOUCfYCJyvooAUPtFkC7a/CIhaJma2lQP8VFpdXcpkVTOU2rPw5ga+V02f/H5zEdNEM5fxUuUmVc2CwUOdYbJ6YUMXjK8a+GtJgCG7hvCLumTCdVCoxkKntLrWsF+XdkFTpVtiLb932xE8ropt4vu1ZRqIcZO+agGtug+qAHtdG/SuUzpFgVi+lo3JC7YuoF8xlmjOr9onTfcuhlvogqj6vMpSqBtDVfVqswFq3cqCrn8eTYmodtHjobOcyM+Jf+5Ry0SUc7HUumvLba2gNY9HLUHoCdvgOh4xhbL6JDVBaon/VtU7pcnMqXLh5z0UGmIRJONlp6pqksyEbXmhugxVohymsTbp5jTVOKMfvySLom4cqGEfOv5YxDQr3jNtdu6xSeFzQ0ND1aEbBa0nQfVCmqpkwXZqyh7Mz5/NiX27D0Fldvm5mYYRUDTIqNor90vGGFiW3+vNQGNjEg2NzTDb5sCINfhjwujoCF59dQt29PTAmegL1B3MEM78AcUV6NzjplzGO7NhwK+C8WXCrvLQ4LAS6AYHBzF9+vTA8a6uLgCoyUL3wAMPAEAgTg4A1qxZg09/+tO49dZb8f/+3/9DPp/HJz7xCfz617/ex5bXIYrOrZrcVRmZgOpi6KIhkzOgH/yr2WOlkgujjOxCwU+0TYmokNRB1axaUo+r977Sl1dpsytlWoxGTGFTbXUa4eqFaLWFrvIzLterEehUwnmV2l4guBDgf+tZ5oR+VsPCTrX41ib10SysqrUW6Racqg1idXVU2pjZFejCF7m17AWm2+qhFlckuc21uGTphPNa4kf4d8SD7798Fr1Mwa4i2RLXPqW7bfB5hm4sLlGtJcRDfu/552Aa4sbJumQy1bRDtJpYgoWuWsufMl5KiqGLhFgUc0WxDVErfH4BULULmW6jbZVlXecFoFII8O9WMh4RFC7qDK5i3TqhCwhJ3hXYU1T9+0nlHqzqrJy6PlxtzF8tlueiNKbxMaI1uSvKe8EyprDQqX+r3xqlBtdupYKgepdYQPQMmtZUdhetNicBoBC6uc+WaUj7uQV/X41XBwPA7FL/9y1oJizTgGFagBnxn10260a8941nhEyUfnvDMpobBjyXS0PTbsMwuO0RGGa3J7GgswkLu5qCF3eIcVjF0GWzWUSjwQk4HnfdMKp1j3QcB7/5zW9w7LHHYtGiRYHv77rrLuHzu9/9blx44YX45je/iQsuuEAd0xLCtGmHfkeRmcoW0NAgxvnEElF0dYlR0puf3uGXa26I+pNAsikRKAsAiYYYjIj7ss/sakbPmPvyRhV1pxgCbQAAIxYJlE02xdHATUBNmvP75ZNxNEiL8PaOpD+hjhRs/9wzO5vQloyjoaTNb2lpCNTdM5lVtrWtrTFQ1o5agbLJpri2vYyxQPnW1mAbJtL58rNIRDF7Zqv/2TANoTxjDNF4BKpd66Z1NgViNyIxC5YU/zFtWhOSigVxZCpXVd8BAFhmoGwkHny+ANA4ngmUbZHub3KsXKajPYmurmZ0Dkxi97jrTtGg6ReNjTE0SO46kYgZKJvNF5XPGVFLWW80HkWDKerVYg2xQNlYIqqsV9de/ll7JJuDZfeOpv1yLc0JRC0TqdKipaklWD4Si6BBCniypL7jkUhEYSvGwqbWBiTjYr9o6ptQXl9rWxLtnBtd3gz2B0D9fqj6GQDEG4J9bcIOvkOAOx7JZU3uWbQ3xWGm8/6ipY0bI3gaG+PISIu8ZHMCTU0RTExMoL29HbFYDKmNfYF2GJq+09XVjIT0funeDd1YKfeJ5oHJ8rW1JzFWcDBeGgdbFWNVcnBKWW9TawO6WspeC+NFp/zetbmWjlRp4dXc2oAuaQ5UzS+qcTUSj6KhwW3fjOktsCbK77c8xm/l+nrXtCQKhoF06Zm0tjWiq0P07AGAjGEE29HaiK5pYtmi7SjvQ6OiXw7vGfXLJuMR33WRWcHxJDEWbHO21NcaFe9+LB5BA/fOmVF1fwBKY5pCKGtsTmAanx20aFf9HhVtJ9AnAYAZQGdnU2BtNCyNlwnNPGBtHfTLNcYjvkK1RTHPNTTG0JB2y3ZOa0LzSNof09rag885Z5afcUtDtOz6yqJo5u6D4ziwI+Lc3KwYIwH1HA6ox1QASCn6u+p9iynGdQBoTKrXB5F4xC+/cE47/tYz4n8nPw/+PgjnbBTHwFSu3NaGmJtZNlPqkx0dSXRJ6cgbdo6goUFce8vzvcMYYhYDMyIwACxaNA9dXc3oztto2DOGoh2FFYPwPPp2DCMaiyAWN2FY5T4nv5/Dk1nEYhEwmyEes0rjZkK43sZk+Rr7+xtgRiyYcNedl5+9JHR7ikOJw0qgSyQSKBSCA5QnyHmCXSWefvpp9Pf3+4lVKtHY2IgPfOADuOmmm7Bt2zalEBjG8PBUaCazQ5FUroCMZH0bYwyDg6KbAl9mdksCAyWhZ2QsFSjb1dWMVCpXTsyQzvu/HxoJlh8angq0AQD6hyYxmBQHpsnJrFB2YiIbqI9naioXqLt/YNKPMXr+lQH/e6PoCPWPjqUxODiJwcF+DAz0IxaLYzLaqWzr6Ggag3HxNRwYSwfKTkxktO11HBYo77WBZzxTvp9RAGMjKeSyBV+7t7dv3NdmFmzHLxuxTBgoaxd7944LfvMd05JIK7TLg0OTSMeDk/toKnhvTccJtNd2GKZU++PlCsp7MTiSCvbJ0RQGOdfSkdFymdSU2wfSXHuGR1IYbA6OE5OK/pA1DPQPTAiDfTpfVD7nweFg/wXcfiZrQHsHJjFDSooyMDKFTGmh0ZSI+i6yg0NTaAzUqr7HwyPBPmGbhl8uE7PgRC3/857+CfBTc9F2/OfhXbPXd/r6JwJJJVLpvO/ywydc2ds3EUj6Mj6RUd63voEJFDPlSVz1jAFgRNHf+XtgmYYvdE0oxqndfeN+WT4DYqV606YBw3aQKT2PHbtHhXha/14onkf/wDj6Xt0L2y4imWzCggVHYWgsHXAzHlK8G11dzRgcnAzU6RRsZT/TjpWDk5jOLcD55zAxnkGGa/fQcLCvjSjGKgDY2z8BcAqQ3oEJv1w+U0CeG18GhqYQl+Y/fqzyGBtXPIvxtL+1ydRExm1z6Xdy+T395TagYCPLnWNweApRhfVkZDR4fYNDk4hJ72yuYCvvw8DQlHB/AdcLwCvbEiu/b8NA4PoGh8v9PZvKwebGl17p/QSAVCovWF9Gxw3tvDEuzYkee/rG4XBuk1nNtY0bwfbyZeMRy8/mCrhjRETKxshfHwCYTvDdBIAJrq0xA1yfTGFQur982YnxtPich4LPeYh7xm3xCPK5grv/WjqHsXHLH9disRjG8mJ/GBtTz8v9in4DuP1hWiy47J7MBNdTIyMpDAaSJgXfCwAYG1e3Y2wi488bqckMivlieR7vGxeUT0OaNvcPim2ezJbbarEo0tz6Y2hoCoY0fqnG9nGIfcd2GPJZd24xDAPFouWOb1Pus3RyRaSKOUxOuorXbMFGoXQeO5VGU1OL790wODwlvJ9mIop8vgDYReThrrdjsQJGx8rtmpq0/PaMT2T9upErYHionCvhUMA0Da0R6LByuezq6lK6VQ4ODgKA0h1TxQMPPADTNHHBBRdUfe6ZM2cCAMbHx6v+TT2jzOSnCJ7l6eQWytVkY+NTWVdzPo9tA8GBrVb/Z1Vx3tWAX7tGLIPPrgvGXG3ewEA/ACCfzyGdUk+q1W4kHL65cBClWxtXh2m4AycvmPHuJHJWuliI+6vOhUiXnE/lolcpHiRqmb4QYTus6hTqcineTdebqHn3L118jDbJTJVB8moXF6Y8n8oth7/HLdwCphb304xij8ZCILaJSyAguS7zLoLxaOWkEnLCFf+cNSTbka9DH8MZ/s7wCQFUro67uVi5mZyGWd1W8XMLH4uqSxShclXN52GXgvpTqSk4jlPTXp4qCrZ64+tq443l85sVkqLo6pXd9oRnEbUE10j19hSV4+och/mxOIZhoDEWEZO4hMRBu+OJWFe11LLRtzJBF3dvKrm954S4PzFpUY8ivlO+5rA9DPlrTmjmASB43zwmM+HubhHLqBg2IT9n/fhb/pt3A1YlquDHNHcvRS7zaYW9YE3D4J4JE5Qr+XwehUJe+1ueWpPBqeKQlYmiaoiZBoKxq2JMbHVtk+vmy1mmIbplVzn2BN0iGWC4z8kwgIYGV3UkzBvctQyMZ8q/nRpEW2P5PVLuX1k6ZHBZLoU1m1C+/Lcq8+2hzGEl0C1duhTbt28P7Cf3wgsv+N9XIp/P48EHH8TJJ5+M7u7uqs+9a9cuAEBHR0cNLa5jVAJWiO+yaRiwTH5grZxIoVKchW5wk7WAquZW3rZA7ZvPGMOWLRsxuOsVsHwaANDVnBBcFxgYbCkrVyaTQbWos1iFtFUpbITX6w1UfHICfhKSB+2weBPtRKCJU1TFdRRKqcOFeh1xMuIHV6UgpEziIX7mz+EJiJUy+anq4dvNwzeL7xO17OmnFug0wlENdagTh/ALMFMQTuR3VF4c8NlBZSFJ3nesgdPw1hKbKT9nfoFaKVMiP5Y0xiP+884V7ND07m2cdb9S/JphiMJiRivQKRaz0rGxsdGaEh4o03hrYrl0C77hSdECzpcyDTdzpIcq2YWYHY7r73KMDD+uW2Jck6pedRyxeCxXLMcsxiJmMJ5HupWyMseqML+UTho4VFOCLkXsLC8ktDbE/PuWK9iBeoR3TooRVCHPiWF7Z/HPLkzhwtcZj5YtVqq+Jo8n/LxRzXuvm9P5o/xYE7OC7s28cNQYt4TNxeWsx4AUN2qinESFscA4MbS3R9suHl2sXL6GLMq1jAW6GDxeeZCIWML7EXifqtyDkH+OASGxyutQ35/gb/1nYRiCMD7BjRkz2xrg5NLaNrjtZ141/vsmKuGZ8u8K+V4OOeqsueGce+65KBQKuOeee/xj+Xwe9957L44//nhfQOvt7cXWrVuVdTz66KOYmJhQ7j0HACMjI4Fjo6OjuPPOOzFnzhwsWLBg/y+kDlDuq2SL++vIqWD5SUM10TEm1sovtCsl0ujifKtVA1Mgc1eghPyD4CHHYRgfH0OhUHCTO0wNgjGGxlhE1PYyYO/e3cJv85wrsKD1qSB46dpfoanqlNTSgnzz5g0ojvTASQ0Hvi/YDE5qCPbITrDUcKhAp00kommyzh9dXvTyE4e8CFRpnnWbnYrn4AfroEBXq3AayObHfW6M6bXeQEhCCYXVT7B2cfXq2qvct7GiQGcIFgM59baQqjpiCoKM3OaCNOkLWQ0VEzyfEIIX2keT8AABAABJREFU/mSBil+Q8Qs1VUIJuQ18vbJSgb8PM1sb/Uk/nQ8usuU3jq9XZ6FTLWYd6dmlUlNaYSxs/yoZ1XsgW8hUx4GgEo6PdVT1Yf758H1HtoTwTRKtIOpnp9yGIuTcXl+MhIxTfPmIaYAPXa0lI6+q/+rS6quSgglZGC1DGCfk8kIWz4iFuVxskLz9gSobpS7ZlPudWqDL5ovYsWMbtm17BblcNiAI8++9vG+iPL7ynh1KD4Eq983jVwbTWxLK4x55yaopJu0IV34a4JWXDEXZ2iOlyde9ljr9gH67JsV8rUi6NcKFIIjKC9XYXk6WJgte7jnF8irrJRC8x7L3Dq9kUM07OgWIIERJ33vjbyJquX8bFhyHgcHt47wSaHZ7EsV82S1SfsZCBksueZ0o3HJtY+oy9cBhFUO3atUqnHvuubjxxhsxODiIefPm4b777kNvby++/vWv++Wuu+46PP3009i8eXOgjgceeACxWAznnHOO8hw/+clP8Mgjj+DMM8/ErFmz0N/fj5/97GcYGRnBf/3Xfx20azvU0C0vJrMFtJYCTfkyhmEIaXNVGhp+k+NE1BImZ+WgwL2FDZKPd6C9IZYaFapvbYfBKlXktZ+lh5FMLJKyXDJMTkqxBdwCMhGN+P7e1QpjYQu6ajN38YJuhOVRLBYRMQ2wzBSQnIZ0rgjbLmLTpg2YzBb8TT6d3BRQbPV/K7tY6vcWq16bCAQnQX5yiFgmHFY+jyqLmFJJIN1h0Urp9q+IaYA5DmDoM6cKLj9cPJa8UOfvcUtDDJl8afNix0HRdqQ+rb5vI1LcoGyViguZ7jSaYJVFUJk1rdSPmQOWSyFilBdL8gQ/xWVgjFqmcGcnMgV0ckoVWVMfttBmjAl7cHU1J9Az7E7QcjwZfy+aE1E/oYTqHeDHgWTczZLmvXdT2SJauAQABUlQbIy57yhjDOlcUdjiQ1wAii6Xun2/qnmneaVPxDQBo7wAtB0G05IWZMozlYRrKbcB3+9bElEMlp5tUKATLy4ibC+gWnSW+0hHMuYLJLJSTXRXNzCN3y9OIQRX43LJL3g9S6Kp0boDokDXEItUzOCpQ6Uw5Pfvi1im337VPppiJlEDTfFyP5YVGPznWMREPjUGZ2oIRrID4+mCsLl4LWEJcnleoJsYH4FhuBaP3bt3oa17rv+dZRqIJ6K+QkRW5PDtdV31LX9/RtUWCnKf8pQXgYW0NP765WULH2c1NAyjZIkNdwmULXSeQMcYC8wzDAzMLsKwIn4ZFbXOfariqnk1nRP7sDeeqcd2se8AAH9X5V/wSi5+jpOrlgU6flsI1d6EeiUV4A0v8phatqIZiJgGHMOdb2zbdbP2nnsiaiEWMVHMTgERN65MbaHj6ldY6HiFrbh1Fgl0rys33HADvvWtb+H+++/H+Pg4lixZgttuuw0nnHBCxd9OTU3hz3/+M84880w0N6szQ61ZswbPPvss7rnnHoyPj6OxsRGrV6/GtddeW9U5Dhu0GiiGvr5eTE5Oorm9q1ycMcE9QrXg5CfKRNQSJtxKG1Y2JcSuLE8K8q91m6KGYTsMFkSLCcun3baW1qvMLmBiuB8NUrR63raB0uUnoia8NbtSoKtSQPO/U9VRYYD3BipvoGfMQSpXwM6dbtyf7J6ZH98LxGYACC62VHvY6dpQy/F0XtS08vdFpSVXLaZDXS5NA4VCAb07NsMZc2NfC9NUKUbECToWsfxzhWmYI6Ybo+iVzUsC3WSmungr3jIWsQzBMqXf1FvxvoRs68BSI0gZwEBxHMxpgGFagfvJV5kt2sJEHnCNlCZ9XgiVF60F2xHKdyRj6Bn2vtNbHDqa4m7qarjCp/zOF4rieMKfQ9YkC0JzFRZFDwOGaN2oYYsB+RlNTE4CcC0w0YhZGmdKbbAdwUoeRipXDCSdcYT+yykVQjwXTEO0imcVFhbtvqFSvaIbXERy8VNZFxQCXUA5I1qXvTazYh4wrdBN6WMR0WKh2tTbPWcQlSWDvw+tDVEMl4SYSmOSaYgWd77/MMaErQF2bt3ozjV2BsXRXSi0zUG2YPtbZ+hilRhjyszbfJt5q+3U5ARaW9w6s9mM6H4v7TsmPydeKGhKRIX3UfVu6NwKw5QXYWny+fvtxYlHKrh98nWYMJAQNuBWhJE4BaAk0FXjqcsLR3plZnVKXP7+djXHfYGuGo8cAFI/EH/D3xt+jgtYvLi/TWPf9wsu2A4s07vXTLvIiUVM5Evxda51rmyZ9uagSISPoRN/z4By3QYvyKktdEIMHVnoXl/i8Tiuu+46XHfdddoyd9xxh/J4U1MT1q9fH1r/GWecgTPOOGO/2ng4oNP8ZbNZjA0PAQB27twOwBWMbYcJ2t5KcUIRyxRcYioF28akxU7RZohF9IuyihpZxdc2Y6XYIHER09+3F4bhSnDOeC9SZiPQICoEHIf5Ap0qrblQtkYLXbUxdMI+TKX75S+sHBuZvA0zmy63t4QXb8KYA8Mwq3eT0Qr91Vn0+HINMUvQBquTflReTIv7YZnYsmWjF4sNABgf3A1gXrBt3N9Ry4QXERmIJ5T26BHKSpYTnTVQvp+j6bLFLhmPSBa66vdaUyejcX/P8ilYZhOiERPOZB+s1tmB+8m7lXW3NAiLJ/l5iFpcQ1C4yMlWeOVKcyIqLcCKePlld0w+6qhjhHEnFrEQj1rIlfZ/yxVswVI/luYtXkaolVBODhOWPEB4bIa4F13YRs4ygXgnKfmE+wztwHflhqjrrRQ/GbYAE9zkASRD3F/d8uW/w/b7El0HTWEuUCnXVGNEWEycaRquADQ+CmdiL2AYKDYtFMvLY1qFZA46VPK97C1ime5WFgXbQUESxvl2GIaoEOAVNGNTaRRsG2A2zMwoEp2uBcKzADpju5ErLPT7n34vR3HuLV9HubyQFCWfB780zKTL8d+mFE8tu/fyz9mLayx/V707npTcUZgbwmKpeS8Nr+6oqW+vW46zHptGeX6W5nrAfeWc9CisUuIknZeFkAvAMmE7pfdYN/epjimeJ/9u8x4Guj0/PbxxUbBKST+RY9a9py43Q96EW7TiOxgfH8Pu3T2IxWJYtGixfq8+x4G3KBLHVEMQPKOWCZQEv4LNkCpZKY3GdiSi7tzI7IK/vpLfZcEC69r/3L811nyHE/Qss/px4VDgsIqhI147tO5iI0P+3w4DWGkgm97SIAgyhWIwG1tgUVXBR1x2nxMSL6g0a+IRZfvL3yqEKocBYO4k67fTcK/ZLriDiqatjmZCUo12qrVbmPypLq/S2AUtdJ5wwApZpPJFWCUrqmzJsgwAts4qVXlS49FuEisdzksCqGCZUmz6qpos5XPxv/OEWpMbwOUYCb5+j4YQi0xgo25+8RPIHlf+PJ3bsyudL2o3sW9PxgVtum7DctViIKvZPN7rt6ZhuP2i9M7mCrbQl+XFGh8HJfcB2S2HLytbdPm4s6ZEVFh0jA32+n9v2/aKGFcp3185mYMwSYuCDF9PwXb8fmE4Bby65SXkRvaAlVx8Ay483NhgANJm7PtuoXMc5p8zahmh7oNuO9SohD9eUOD7TzDepPy3mwW3pBmfHMDInlfx8svrUSwWlL/nLTeykCacP2L5bvlu2ULgPZpQWP3DNpnPjQ9iw4YXMTy417+Q/NSotrwlCVJMa2oJHqqcQdgQ40Cld1RW+sSF8cT9bnR0BC9v3ABntAfO2B40mkV4C1E+ZmlsbCy0Xe75NIIeV55XSshjZn9fOR48YkqZK6X+Lsf8CconRUyYel5XWWfL8M8tJSmH+GvtLo2pUU6xq4r39qypgHuHy/NMeU7xBAzHYYBdUP5WaC93b3lPBp3SUanAUDw2vhw/H6qz44oxz8FGynWrBXx5LeUIY4ScnIph9243cUw+n8fWrVuEE4mx+Nz4LCmSeFoaYv7BdK6IdN69/0a0AR2lzdINw/BzAQRdLpnQBu9Z6jL4Mj7ODvVFvbWXOETQTRLj4+VJlDEGlnVd2Txfdn7/qkBWQ95CJ/m+K4UWYfEsDloTaXFBEExlq2x+ue2KY24gbzDbHwAM9G6HM95bVVvDXEGByi491bRWuaCX4hsAzuUyPYKpTN7PzmkzBpQ267QMA5ZlgNmuq0O1FjrdPdZucyC1OSNpGMU0+dW1QRZ4eOuQJ9SGaS1VxxvieguZrGQQFvsh960xbgkTI/+s+MVxImpJFiG1AKrKpqZazNiOA5SECDdsy9XAe/2HfwayQBfRTM7BsmK6dfk+8MkdWhtEN618Ni2UlRfDwnYa0vXx9y0qpS/n+zAvYEbS/W48rF2AM+YuZEOtNwak1Ow1WHpUY1Lp+iKmKWbOrUFRrMrkx1vn+QWmIBTlssilxn2h0ii5UzHHBitk/LKbN2/0xwl+XOvg4uLk5DDiu+zGR7dxbqFygg9lRkTpJvDJVArZyVKbeeWMPo5PFqT0SVHCrSSq35tmuBUpJYxBpuDi5733vb27JQtLuQxf997esrClyxCpVbhpLHRF6b7luVT9pqRECVWYRUxhTlYmzFDc90rW6LCtHvi+H7GMQKiH6tnx15Mt2IKFzuvznZ3Tlb9vUOwpB4jzRUxQJKmfheqaVX1SyHYcCe+/OWkMBkRlQFi2arE/qAQkF18JWCJfFNdd+Xxea8XX3QtIbo6tjVF4El22aJfbY0b88d80AJZz467D1np8lktBwccVElwu60xCqrPmEocKqngcALCi5UnaYQysNNH6afJD/O+LwmJYTEGtTusrLp75BYWcGKWWBZH7g+Ahp6TpEX3NvQElPCMnvzAQs3cGz6PO0qlvqmq+Vs2JvHCTKGkteS3f4PCwP8kzAFbrLAAlC51pAsWSQCctnLWppnUa4yr3u5FjZIQYzJC+I9YhCYneZqRju9GzbRMAUVOnD3Iv/80LXgE3MNlCFxLvJsbbmcKijX83RHcuSxBidGnJVXGNqngV16vPvSdeYp+oZYBNuXt3CvswSRbTaIgLtZw9M+xd5utNxCJCWS/5gOq3clp0uQ0DE2VXsc7muFYjW04MwxAt9THDgP/Qw4Lsxax4egudTkHE4zgO4BRL1yZa6GrJcqly+xQtacHspLZdxPbtW5EaGwBLjwBwx7RYxPTbxLd306aX4TiO8OzCrLC8lcpbLDYl9Bk0lVmNpb7u9RPXOs9Kx/jy5TYwSYEozy9hyUNkVO+WbP0LU0aK23lYUgxm+X0rKBbkXttV7dYpHlSWMbddXJ/gM58qLN2eFd+SLHTyc5MtdLEK7uFKbxZVDDn3N3+/5Ayp/P1ID+3Ghg0vonfHJr8/KWP2uPvQ3drAjWvlPtPS0gKAGytKXgxaRQB3mL9fWuVn1QKd5plVaaHjn4c8h/IeHLzSJ7CNhWRNE+Js08G9EZkjKjBU9Trcc5HD1niPmJGpnP8+GYaBmbNmB34TGDsYUy6gtG3hBTqj1oXj6wsJdMQ+oUsUYEXKQpXDACPmJpkwFVqRwKI8EENXfVIU0zSEQUi2XNSyIALCLXR+gH8k7mvfKu1lJsekhaFaFIZmudwHC10+PQHAnRyjpUG+ODWMXEk7a1klwTyagAF3kcIKrrVEtkLo3P50ayTd9gJyedmqKQzAIe6LjJVTIqu20bBHdgKOXQ4Uh37xVUb9/MKEUMs0lNp3D1mBEY2otZd8f7LzWUyOj/hWFN17qMocaDvBtOZFx4GTHQNQfkcjpglWcIUh/lmJWdOsUG2rLPyFuU/L8VWCgC1NUcG06PpES7wlU06yxC/i/Mm8kPE1spVSnXt4LpeekF+wHXWmS62CiP8MsKLrwmVJFjp1unWNwqTCnnfxaDmVe9FxMDI2jk2bNsC2bTAALJcqxcuiJOC6ZeVxaXJyXOgfrY0x4T7wz0MVwxdmvVG6m2kEa5YaQlPcixHi75l6TLZMN0anUnY+/hxh7QCksSqwH540RgieKCaSCT7us+wlkRPifcv1tXaUk43Z0J+n3F6FCyNjwj3mn0UgXMEBUPLOsORxuJKFTogPDLaj2v05BZd3TvEFiAK2Nw452UmAlV3JnVHXDVA1XGYld2T/+jglgBeK4Pd3hZJDaK/WzVA9XivdTBVV84rqRAWBjr/f3jgZ5sbNe6+0c3txhu0vbEjKi8nhvYF2sGJZ6BaTTZUrEi3h4vrIbXt5/eg4jr+u7GjrKP3C/d4e2YkxLuwHkGNWuSyXQiPLfwoxdPWVE4UEOmLf0G5wKS+gS257nqmfnzjy0gAvb2ZdyUInbkLupn/2GJrMCmXlwbFWAa/8G4bxUmpuIxJHcykWRBbojj56CebNW8CdXxROwlAJPKEboSsXi8Fj/P0t5L37I2Yt87TgDc2t7rfRBhiG60YEjRChc/vTxnRUmRRF3gQ8LIbO09Azx4Yz0QtnbDdYMScsGByH+ZpVXmgQLSEOCoXwfbGEfXdyGWzdugW7du0sWQHKv7FMMZYmuDeX2N/54H0+DbqfPrqYx0j/LgwP9MEZ3eV+p7HQ6Z6JbNGzHVa20JnltgAAs4uC4Czu+2VqXVbka5MTHAXS9XMuXjFLctPKZoSyBWk/x1iItUBYsHKZaAFx8etvQVLI+EKf94rKlh33mPARpmEIWxfo7r2M/OxsLtYjYor7k8mJZELrVbz8I1ysj2WWk9QwxrB1+1b/O//aChkYkjuVXHc2l/ffWaO00G8UhKTys7LtIpz0CFgxD8Mw0NOzA6mBbe6WKAh6VFRS4PFlWD5djovh3+ViTvlbAww7d27D4J5tYAV3HJTj3MJQjV/iZtdmaMIVQbi0DLQKfcdGOu0qzuS4Q4/Wtmn+3yOTGRSL6myEqvN5OKw83vIxf4yxoAcGY/6i3DLN0LhVfk53Y+jCXS6Vcb0VLHTy/FlQKL9YbtIv57/Ljq0UnHjBJ8ELdCjfU8MwkEwmy/fGUW/74f+OO14psRCgvuZg7BoTlEXtybjwnYwsXHvX4cH/JF+0fYVNxDSFGFfZ4i+uZUQLnWo6sgvl9zCqUQIy9ZIAANwtY8xylkuHAYgmStfleVSUr2tqfBi5nDq2UZXtFRAFSmHPzArK90MNEuiIfULrNsf7IjtlU7clJeEAggN8XtJcNgjJH4qBBT8/4JmGgTZOqzSazgvlg2Z49XVpy5eux3GYMPkfv2o1AAS2WIjF5LTh5b8Fl0vVxqgKTWaoy2WV2ntv0rBHe4RJUZV9NNbgZlQzYkm4+i/DX/HLVWd1Ap1mstPFGQVdO8p/R0xD6A/yotnrCyyfcgUU5sCZ7BcWRbbDwLKuZZIfphOJ8v5pjgMMDQU1fF49hpTwYGp8GNlsFhMT4xgcHAgIaXLCjPHxMWzY8CJyuVzAaqBzR/beE2dir6vo8BTIjo2iE0wuxBgTJmF+EivIcUXcebzkMF7/dMb3oGg7KBbdTJNje191N5q3C0jERAudLEzJAnCYVVOISY2I1jy5p/ATdUQSIOREGrxAYRqGv+8gIC7EPAszK2T9a/IXgblJRdpuXkVdarelFsbD8DLfeTgO8+NALEvsZ7pspup6g+fn22eAG4edolBeSLoQ8ayEvPBrK8t6i3ze6jWZyePll9dj48aXUBzbDZadhDOxF319ezA5OYGYZYGlhmGP7EQm4CKvEuj0bpl+l+EXbAYXK8gvQu0spqamAKcIJzMGQNxWoRLq7QG4/m6ZoRmd5XcjEfWEKQfp0X7s7NkGQJ3IZu7c+W6/4NxNN2/egL17e5XWJ317xXGqIVay2jI3M6cwjzPmW+zDxilAnMOjVVjoVAKO2kJX/ts0DCG8QuWFAbvgj/HeotyZ7KuoGLb4bQ4Yv6edCZhRv8/pEib57eX+rsblUnU8k1MrLQH3feaVLfJG3YDk+VB6Dvy8x5fmvWwa45a0rYh+XjaMsiKSObbyftiZCaHdqnr5MVWWoSzDcO8/ytdpWDGYhgHTNNDR0RlwucxkyrHX/LjBe+PohFtb6g/1BAl0xD6hizmQB1eWnXDN4L1b0dfXK2nswlwujVLslt5Kx09glmmgg9NYjQ0P4uWXX0RPzw4AFeW3qig6DMNjY347vQXlMccsQVNzC4yom1Ur1jarNFiUJl0wQZMtaJEVk7DKQhfqcqkSPjUabmYXACbu18XHhBUdhs7OLhhmeUA3S+32Y4pCJo5KbmKAFF+lSQQi/96UkhjIWmR/sivmePNKcLIvucp413/ccSux4Khj/DLupvDjUt3cIsUSU3Hn0lP+34OD/eJkICUQyGaz2L27B4wxvPrqZmSkjbp17si8dSZecj8xTcOPaZTfxXzRKWtbLVOwAMiCF5+lzrtt/PVlczls3ryhdB9Kz39qELGIhajwbsousPx9MEOt7XICFWFyhgifJMUyDUH7LW6jILYnYoqWaL5sWVhi/vMq92Mj1EXUKx+NmGCOAyczjpGxYVSD/Nzc/lnWkodZH93W6uoNd21rS8bKrqpOUbhXXjlnasgXKHnPB77JfPIMT4DhBbpXt7j9xnFsYcU0NureH/45D/WWrYRAOdsjT9BCV/7bqyoW5drqiMqccsNdpUA8Yvl/1xL7WClZRdQyxcRFOb3yabB/L8Dc7Tac0V1wclPIFxwwMHd+jMSAaAKzurtx7LEr0NLSWhLoROXayMgQRiVXM+W1K45FSi7RrnLEPZ7jni1jABxXoVrJ9b0gCflilt9gDLEqXlydxEV82LrxxGGuZwHAZTP0ytpFFPJBy43sLlse/5hgoRMSTZUEOl3coi6rtcOCgpd8DR5ePxkaGsTGjS9hD5cAJ2KZpbWEWigB1DF0hfQYnNQI7Ik+7Ny6yfd4kMdgS9qKQHdtroWutM7JjMFWmdq4Y1oBX2i8KJZ4989oaEMmb8OIJWFE4r6gPGPGzIAraaFQwMjIENLplJjBlo/P1shqvOBMLpfEEYEuGYOtSUVrGsDw8BDs9Kh/TB7MZQ0UAK1W3f0sxStF3X2pGHPgZMaQK9qYnJzAyy+vR3Zwu+9uJ7dNherbdL6IicnyAj4ZdzPyxWJxzJu7AGbzdFgd81E03EVF2dWjXKNpwNfIAmqXE9XiLVSgU4yhsrAMlDaZ9TWt5ePT5xztWuKiCXTOPhrd3TPL46vrdO6LpkBQey4ktRAyx6nby080LY18xjL9xGFAypAV6AvltvEukfyE65YpTQ6mgc5ONxbFNAwYSdeNyVFcn6xRFzIrqlwYS8gbM4/07RDKTk5N+n83xCJad5RUrggn5S7WvPtrGgacqYFAWUC0XibjESkxgVQ2U17gJOJxHHPMUmEBMsW10T+PXcDI4F7h2mTtuzApyjFFUr8oSAKSESLRlV2FS1bbGG/F52JEpXsSj4oWRb4N2UzatdQ4NozStXvWSpYeCSzaeNdZb1PoiGWA5SbAMmMY7OvF5OSE8BuVJV65UPLiGC1RGaB6n3UEXUSZ0GbBFc5xuP7NhPvmCSV8xnP+VvDKAG+c9gQ6xsp1yTE3/juoWIR5KDMiytclpCJ3/120aEn5e25gFBbt3r+m2x6WS8F2gq61OlTKzIJk8YqFWLFsm4HlM3CmhjAxPopNmzYgYTpC+XyhtCF4JIGmabMxe9Zs/73gBTp+jBwa6lO2N0xY8NoLlJ53qb5CkSFaEo4dxtzjThGWJXkdVEiGFIuU3+eC7Wi3Y6nUXqEPQfaIAfc38wUIbxjjF/upoV3o5QQj/zcol7VKexqCizM0TRPCEOcJdDoLnWRRrBRjr3OLBYChoQE4joPhkSE/OY03x4VlUxWzXJoYHR1BbnIELDcJFHOwHRtbtmwEAO5ddZVakZD26mLoWG5KuRbhY9p1+zGGJQf2BfOGVrDWOTCbOgGUkzAZhoGZ8xf7e9U5AAYG+rB3by927NiGXC7L1aU+B3/6XFEcK+sJEuiIfWJgshzbwnd6fvHDL/S9Cb+QHvdfbnnSLhSDk4ylWZwDwUB3oDQp2cGAZcaYn4a8GlTyUyZbEDWx3KaTYXtRCbonwxCEKZW2TqUtDg+hC34pJ2bwY4H8lOTu/Tr22BWIx6MwmzphNXcj42noyxKdJ9Np25JX+Orrrg0ISb1cYeLgJxl5MexPZoWMtJmt6LrI8m4WrohpoKGhnLDH8JLAMMCSMivK7kliSucKGmpvW4h8OqAEGeX2WGuIWtqskYXMJFguVWpruR2A6+YiWzb5hVJjLCLGrUp9K8O5MCYTMcRiMSSbWvxjY6OuIMkveA3DwNTEqChMFcT+JitbwhIcCTF0kfKugIw5gb6dz5azqFmmaAnh28C/+8mShUmVzdS2bQz39YBlXKusV4Tv73Iad9HV0L0HUcv06yg6zPcMCEPuO7YDf6FumaaUtCOYWVE3JgQyuxZs/1zx0hYSnnab2Xn/WRVtVnaRL1kqcrks8uNlQYF/p/k9Rz0tvfc8WHbC75fie8z/LbrWeotLty0KhVSIh4afoTVi+SdxGPMTjAgJGApp/1emATipITDmKM9ZTbZN71weETM8cUjBdnxljNffoqy86MwWbV/RZzS0+fGOHqYBf/HKN0/n/lcpHlGMby/1BceBaZpIJpt8wYIVc8GkKHJyKkkpa3DlGXOQ4jYp1yV0Uikk5C13dOOJF+cOuN0gkUiI1jzGMDo6ItTP9w3Tn2dK44Pj+P00Zws3W9lWVZsMyaKoSiKmzkDsCkLlLULgz9/lbYf0yjo5BrO3d7co0HBN7xvohz3eC2e0B+mBbcgKLovhihRBSOMtk/w8yjyvJp1XR1A548HP+/yYIYaNWDDizaVTies+3nLN/164FbziV7Cuoq6os+YShwr8QM4v7PgFtJBit7SAiFqmu1lqagiTE2Xtv1ue1+5Vzh6pEuhilqnUnnl/OSlxMNej0MIWi2KsBJfSVshQKPmzixpAQ3RLlMZxpxTDAATdF3UCUqXMXQCX4ZAx31Wjvb3DDfZWuAd5pzI865zhHmQsmCmRPxcv0Ong+wWf1j+4ybE4yPN9YWJgJ15+eb2fjKRolzdl5icNPgMaH5sYMU1fAy0ukFggKQq/p1YyHhGEiLy02C9w9yLCCXROeiTQfwtFx3cPapBi0rw+8PLL61GcHOTuQ8m6UfrMUsOBBUJWspjq0owzxpDNl++PF/fJxxR6VhN+knPbaUjJA4QmSC5d4Vn/+IVIlNPou/1NrNeR6k1oLHT8YsbX1PP1lCreuXNbICaE/xcACnl9wg5BkeSV1+2vJFHIiYmb8sWya2JD1PIFUUBvzVAh97O01H8Bbv/JQsYf02KNZUE+YpkoFAp49dUt0jjEnwd+UhHPddpLDsMyY/4z0OmiTG5JFWYV112Xw8pJjryq3He5bCX0Eoz427HYRZh8vI6XbGF0FyZTwZTrKmzFWCyP8eLm26L1rXxuw1+8mrmym3cmb7sW70gMhmEI20EApT5nBxOhMJRju3hUHh+quTYWMf3+Z9ulecI0y8lnUsOwTAO6/ScdxnzBhLfcxCMmWD4FZ3QXNm3ZhKEhV5jVCUPys88VHb+9Ect0M9bybuHce88YfCuWATf2SvTy8cqVz8FnOG1KRFx3Rm9bAof5c4M4pcqKTxHRomgI91uVNEmX2dNLeOOXsb1tTbhnViJsCwk/KYpwBe45BwcH8Mr2HngbpjcloujdtUPbtoCilZ8HhIQ/nHWr1C+FZ8H3XeEcokSnS0zCHzUMozyHS+UmxsfKdWli6IByn5Ctq/UECXTEPsEvcrtbG/y/eW22lw0S4Cxunr91LoWRwT2+9gkQtVQqlwKV5rlcXjEpebEKjJUn9NwknKnBUIuX+5vgsaLtCFsEmJK2yBsgWGkh6n/m6pBTWgcsEJL7mRzfpkI1qaRyYhIZ77csOxHYNkHY4Lf0zIRMVqZZHggZCyzeeWtgQ1TUJqtQWTjkcwLi4tHVnHrC0SiKJUFjYmLcTbpgO/6ExFv9DO7+5rjJ0eQsdIZhlGNSAHd/LS7WQmhvxPIVGIyxgMtlNlNeFPJxcUYsGei/Rdtx3V8gppJ363avzWtT+XrK7QDcBblsdctJ2SjjmtT+tlPW/hqGgXjp2SWTTUIZoDThSlnjwuLigjF03Hfy4l2yavpFVZYFbkKOmOK1qTZOB8oZFGXBZGhoAJlMRqkY4tsbtNDxY4D7r5D2nXNfCsMxRHce3roQi4j7EqqSR+gSpYTFKHoxLP59M8xysiQuiUiu6GBgwLXM8ZbjxtZOob3OZD+A8njtC/mmWbbsSK7T/t+KrKPeArZS8grA7ecs674jFieIm9GyQsJzt8pms7BHdsIZ3yP0Wz5GZvfunYFzqh6hnPJfLidvvs27JfrKulhjaXHrNkDci85GrmjDbHI3s25SCXSlWGB+/nBYOakOj6rvqMIb4lHLv5C87cA0LUQikfLzKwlpugRHgrLTLM+HVmECztSQX6a/vw+MscAecn6d8jwvuZAbhiHMWXyGVNthQEnJYBgGMpk0mpJJru6SBZKbC/KSi51piPG2KL2nQp8sXXc1VlHTAGa2NfqflRvTa1wubbvIlXF813vvnYxprKWMMdF10OtfCgvdwECfIGS2NcZgmvD3j5MTbwnXBs6CZkX87xYtOsYX6BhQttBp1nNOiIVOJ1Txh904f7VAJ/5IriNYt5Dlsr7kORLoiH2DXzTO4AQ6IW07L/DIEz6AfLGIYjG4hwygs9DJLkrlV9fT1EctE07aDbr3FioFWcOUTyvdFCtRtG1htGho7RC+16aqdlWn/kd+DJEH8oJkrQhz5fTrqMK1UeXO6i3m+ZTrXgIOvlmWxSWqYMGsirI1xkO3GOLL89cXWCRpXDtYdkLUTDOGVGrSN1UKGkPb9stms1z8FSdI+sK4afnXNjnJxY5J7km+kO3YgU14M5zVJWqZZZcYwwwKdA4DK+3vJO/Vls2ksGvXTgDi4nHZscsxfXq3sIVD7+4dQr1T0l5F/D3mJ/mCXbYQWqaBSMRd0Dc3lQU6TxgoOgxmkxtz6PXzYiHvZjZLjaCQGlUqEABFlkvJJSa4R5j3pS1mH3MYCoUimGP7rq9CgiHebYarc1opI145ptVGemQv+vv7SmVFy21nZxeisXKCJVsS6HgByfQFwKAVi98sV/UuBLM2lv92BWx+kR9cBA5MZAPHAJUrZ1Bg9Z+HUyhrpg3LTcIBoL0xhrGxUQBcavBoA6KNrX5djsOAkluV9875qeIjcb/embPnwWyd5V+XR9QyYbbMBAA/4YSXnU7l/qh6fzxrHP/OmxavwHD79+5d2/xjwkKQd8/OqwUMFcEEXfxYJVuxRIGOMQaWTwvCJF8+0tiKWNtMPzFVc1x2uTT8+8l3IcYAlh5199nkUFnoVMpTIYbOtmGaJjo6OgP7gQrznMNQLBYwOTkhzE/CGJwpWx894SvsXofN894cENEIlalcwXerj1qu0u6ohYvKdbnShS/QFW3Hr5+PdTMVAoaYvNZTGqvnXtnKw4/Xtey9x489vO5QuT7SjH8Rs6zkVmd2ZML75u6LaAATvWXXdGHM5q6tNLZ79ToOAwODaZqihc4RXUUB9z5MTk6gp2c7HOF9F6Uo3b69hry+9BXqYjlxXBVFHoV8K9znOpPnDj+BLp/P45vf/CbOOOMMrFy5Eu973/vw5JNPVvzdLbfcgiVLlgT+O/3005Xl77nnHpx33nlYsWIFzjnnHPzkJz850JdySMO/4Hx6bWErgXhJK2VFMHumOwHFJVcUfhEoCDNVWOgch4EVXM1rz45XYdtFN4Df3yPGQSQSwVHHHBtof0ULneLY0JS4gIpE48Jn3s0mlSsKFjsP2eVSPk9OykxVaVEH6AW6lO8+6brW8fsOAcDM0jNRJeMQhClTHPbkIHTePTPM+uiWL7e3HK+gvg55gctn3pLnv6Jt+8KRELPFbF9znMqU4zcsg38m5RMy/0/OksXH0FmccMKcQDtS42WX3mS8nOiEFbP+QqWxMVm+3pI2OSrt1TY64O4zV+DimhLtsxCxLHR1dQuLqnRadBXjLdfNiahg5eXfobztcPtLGX7MQzIe8TduLZTanC/a5SyupX4+0L/X3e8vNwk77W7HsHXrKygU8gjE0CkXEkErrMG9H86EmOTBb7uddzOxmYZSw+rWG1wEem1wpgZQzJVjRGTLwvTpM+Bwiibe6urdC/7abNtGf88W7vpKiyA73E1SjsViKD9r00BohkAAGEmVrcjdLZxSLeC+GFy8+8/DKffhXNGGmXDdLnnroG9lbupCKldEd/dM/zxGJAEnN4XC1AiKxWJ5S418xn//GcruUPx4vnTpcn+fUk/o6enZgZdffgHZoR1geXEPQp01BHAXXh0dbmIj3mrbu3dvySWbd8EVhUoP1XYqujMGhA7JIqNLilK0HRTG3NhZfjzz7dKROGLJDjCrbIHi5wCg5KppRWG2z0O0baZ/3XxjvZhbQB2fxV+rJxw0RC0w5t6nfJHBsizE43H4v2YMJpiYiKOYx+bNG9HTswObNr3IJb8ol+GVv97RVGpK65ose6Lwn71xT5csLcVlKI5YJmy7CMsyEWmf61+D45THd2H/QM7dmxW5RBqlZfJkpjwm+O+4LgxCUOCGx1SqrtmtQ1QmOQ7z91+LSgIdc2whqZCQnMs0yuEIgsePU2qPpFDzkxYZcMZ2BeoTk5UZ5fh232rpCk6W4H3jXgf/XqTTafT07MDk5CR6drxarlO2ogXuTOm65C98l0t5HcG3NwSmKF9nEl1l/6g64wtf+AIefPBBXHXVVZg/fz7uu+8+fOQjH8Edd9yBNWvWVPz99ddfL8SQ8H973HXXXfjKV76Cc889Fx/84Aexbt06XH/99cjlcrjmmmsO6PUciribO5Y1PoK7UdEGUMqOFWmE1dEOAOjs7MKuiWFBI2o7DJOTE0gk3MWIMLD4mjh9lkve5SeXzWLTpg2wC3GhvDuZOzCbu/2ynlY3DJW7FL/RLyJxYU8TwF0Eee4hRduBV5yvyQ2QFq+Bpyhp1vgBRW+hU19Dz3AKCdPGtm2vYiJTAAqlScByYyMiEfc5Cfvi+dYFflAzyxpLxrSWEMG6AihXQ7IV1gpxKRUEYZQST/jxkeKkmE6lUM5u5goQDhhQ2qstBlOY8BqbypYGr08aiWY4pX3qeOuK/Ew8QYI5tqttB/P7Av+7iOVuzGwaBmy74CcWaWhowNTUlH+PDYhCD+MES+9+ma0z0dBYdh2KxWMAMqV7wTA1NYmmJjcoXHax45UE/POayhT8GctVRrjfWaYhxCEBQGvnbGCX687lWWzS6bIV07sP2WwGg4MDwj54iaglvvch/Qdw3w8vDoZ3xfH6nZMegdXc4t87+XtAiuXwnq9Rup5iHk6kbJXmU20fdfTSklBZrndqfASMMX+x5wnMzHEQdbIYGMhKFjr33MViEbGYqPThKUp9h19Q8DFI7rHg7/mFMi+AKTePL1G2pJW/956xY0SBaKNQDoBvXTAMA+l8EZGIe03+vnm5KeQjDRgY6EOipct31fLabMP0NePeNc2cORsNMdd9DpEYioJrmftjZ2oA0Wnz/fGt6LhZEvn3pJzkqRyz05BIIFVa1xdshm3bXgm4wHnwC+1q5gUP5ZYTfv3iAn5qdAibN49j+vQZKBgNZSUKZy1IJBIwmzphxJLIFGwkubbISouI358NwIohGo0F2mAU0kDcHS+UAoSUjRIAGuIR/37mi3bJM8OAaUXhjTXFfAaW2Qag5HI+uhvonA6UtvdwRnvc76Z1+WXaGmPl2OxSJ9+7dw/sljmBdgHh1k9vHOEfm7iPIqdsMQzMnDkbgLudRTGaAApZ2Iz547R3b5hdAOxJbhzlxpLS3xleoVpSxHkx5WGxVoYhhhaonofOxVi00DnwRjw+JIXl03CmBrFz+yRSgwksXHg0wCkELLO89YqQWMzzYJJCCjxMw4Bd8sjRWei8yxY3Fy+NX57LJYPfr3jlxNjIAObNdMcbcY0k3kud0k5IcGK4So5AZYEfyXWUy5ctdLyipYLm/xDjsBLo1q9fj9/85jf44he/iKuvvhoA8K53vQsXXnghbrzxxqqsaOeddx5aWlq032ezWdx88814y1vegm9/+9sAgPe9731wHAf/+Z//iUsuuQTNzc0H5HoOVfgFY8QUA8C9AdVhDPkiPNkOjbFIKbGFuIAfGOhHV1d36bechc6qbKHj8V7u7GTZQmLbDLNnz0OqyKTJurLDpepU6VwRTL8+U1ghjMDZTENVjj+vOHnxCwNd6vKwTd63bXvVL+NMuffGMgxB8JAX27xA4ZX3F52OGJsnLMgNA16KYt0gHLYFQD6wD13576AWkMEdgt3zpCbHwEop7c1S4H7RduDkJpEvOmiMue6XHt4iCODGeMNzTWGCVa4oCWneYtvxAuOd8gLDcRywYg5GJA7LdDW+jfEIxktW47xto6mpGXv7B/w6Y6YjZvF0iihKLj2GFRO03h2dM9EzMO63b+fO7TjuuJXuveAyIjbELKRyGmuBw+CUNPkxy0Q87iqvLNOEEUu6e0g6QFNTE/YWSi+yFeWemds/vToLRYZYxMDo6AgKdnkMjEcsGHCfodc/vEWQ3N/dWg1/jz1vIRKNRsuxNHaRE/7U75ItLbC9f72YRR5PADJbZ6Ih4fYL3iXXZgxDQwP+OOUlYXBSgxgfSsFJRKV9m0qWKY32PmKafp9ynLLFQ1jUQHwvVVZ43rWWj7NSxWp6BO6bUY4yzRbL4yi/8IuYJsz2eQDE/iMLMaOjI5jdNt23Dnlfe5Zfs30u4kYaM2a4ViV+E+gi4Au3QgIeE2Ao97Gi7fiLTofBzywKwBeeG5uagX7vXjjIZrP8VliCxwG/EB0YL1ttPXRWJNlayOuX+HGbOTYy6REUO6aht3c3El0LAUm4PeqoY5BjFtaP7QDgxrzxSXbkkVROluXtt8c/j1g8Bs+pUbXFkLxvHgA0CQKdU16Q8y6WdtF3Kc2nXeWXXRr/+FvlpMexd28vRkaGtFuW5PJFMKcIw3S3VvEUloF4XCbOGYDeQseHcFim4Su5vHGboaSI8FzrMwUwuwhnvBdIRLBz53Z0dk6Hw22GnSwp/4R2cfu02g6DKW1YJr9zusRU/n1RCXSOJND5cx6nmDENXwnp9fHt219Fc1uX/zvDyWNoaNC/D7DcTOBeG/lzx5OtaGpqxtTUZFnx4RQDGcP922CUr7E8NzulhDrlmDaj1Dh+T9ScIjlMqcXiJ42sLLhOe3+bZmDc5T+ZvBuMhPs7Aw7jT1hfAt1h5XL5+9//HtFoFJdccol/LB6P4+KLL8YzzzyDgYGBkF+7MMYwNTWlnYzXrl2LsbExXHbZZcLxyy+/HKlUCo899tj+XUQdwC8YG+MRRLmXpJBzNXmMlV+FiGkiYpmYMWMWYlbZHY73X/c+g/sNUFqEldwqd27bLAgiDpeN0H+feesXGJqbm123MWlUCEtY4Ca7UGs1+QDxgGsAr/UG42KB/P+DnZnA7u2b/ODmgJuhIw6Wgu+9Irgd0Au6ac5VTKeFc88jWYeY3o1TTooiuHbAxkR/D5zxXjC7oBwKZeEoHjLRya4PsjU4L2VY81xt3f3fSsJBJO4nBeCtega3kPNdRf29nSBsSizs51Na5LjCJXduvx1lV0FvwcYvjhlzXS5jibKLnJl3J2RvUc6y4771xWGAUXKD463VLVygf74U3OHFPPHWsWQ8EtirjTGGbDYrvG+GUY5rsUwDRiQGo7EDRrQR3d2z/OdmNnYIroC8IMNbusSkOt499g/5iwjBNbPUB2MR03fRKdgMiUTCdYPk6iwnweDq1CkaOOUQK+2DKbwzDR2wOubDsGK+u960zun+1wXbwcBAf/keetsjFLL+YljlUqVzEfQXdzrNt2O7/R3lxTvvFeExyW04P6u9nHQhzOrvC3Sma2FG6X23LMvPjmokWgS3Rd61tVB0xP3J/DLeNThgmTEA5YW447twmWhs7cK0aeX9H2MRC0ZDu3svSu+zkIl1bLewPYwgUHLvqGEYvkIiHo34rmmeMCMLn4ArSPEYCMYH67wiqrXQsXxKeMaD/XtglSxnnmIkkUgIii27FIdUvjbx3Ly7ddF2fIGZbzo/5lSyCPlzrWn4WRQZ4C/IYXLKgoLr5hu1TD8Jla14l02zvK2FsF0Id96+Xa/AGdsDe2SnkBQmLMGSyfVf1fd5Lk66e+bc8jVa5QQ0vIUuX3TgjO8BAF9p6WXh9PAE25zDLZd5l3zFex7cYD38eajcYhlEt227ZLkGII47xVygjoGBvW58c2YcxbG9/rOImKab5MUsJ0Pymm/Em9DQ2oX58xcC4FzUx3sxzrmyyorWcnu8fuCFSHguSsy/X03xcghA3na4xHXBOv3PgTvjHRctdEApdlcuGLLWk72sAMAJ+XSoc1gJdBv//+z9eZwcdZ0/jj/r6runu2emZ3JO7mRCSIBwQwQVhXDIoQKuShYRzx+Kuq6BdXV/i66ugIu66rooiKKcCouIgoAfb24CGAhgzslFZjL39N1V9f2jrvf7Xe+q7g5JyIzv5+MxSXf1u971rvf5ul8bNmDevHlIEsQOAKxYsQKmaWLDhg0Bd3p485vfjKOPPhpHH300rr76aoyMjFC/v/TSSwCAww8/nLq+bNkyyLLs/j6VQYb5Tcc0JmytTcyYnjTJIaja2zsxa9YcaHFPA0oehGxS0lqtivrYHtdUsliuYvfuHdx7vbDVfmlgUPS7IKauUPGkV5oiE+/nj25GggyJa5UjJO+mCX24D7WJIcv0q1KAWa/6iC9WsxCl8sy0xtDtHfUYOl6odRIkQTFerlF7oKZFXGLcZIgekmg1SiMw9BpgWImwea0iQ6irMu0jyL4fb5OPcOYa2w5ZgidkkBSXOaKlpn6m1vN5BEaJUMc6My+t+2WX6HIjQcKT/JIMjGSS5mSWw7hEmsTYzIteKUAf2gazUnAP56quQ4pbEmKyr+KxCOR0t/18q+zOndthmHTOpphGp1koVeu2r9ur2LbZ2w9lSUZbm/Uc188qloaU6kQsFqPM+8i5EhTsRDesgCumXnP7lbc26xxfnkRUpfxg0+k2xOMJOgKs7PUz7ehPjwngEcAkQ+yabxomaoShikMo5Ts9CTc5b0zTdJliwJuPPFMm0g+TXAukprVO9Zlp24Xq7nsFWSiYpokxIoowGfWPZSR5xLskSS5BbpomVFXzIjBGUxSjnk552taq7uXlKlZIvzSr3m2bXvbeTTeQzWZprTyT2EmRJUhRKwiP6yJJlI+oCozCIFEnIQAkfOwkyRLeArY/ajxn1WXSPsFy23SksnksWLAI8Xgci5d4/tWGabpBWRxUAvyWWZNvVtjgal2Lw9SZMTE2Ar08brdZwsyZs2ntPOz5FkLgsqa4uVw79Y4AoBE+wjwLDvasBazzywkoYhKaP9IVwvFTVmUJpmOe7pjBcwQuAC1kJcuQ53JM4VtEAPy1TCcWJ9ZQ3VsT8bjnLqNZUiWrvEEydKTZsrcPkG2Wbd9hMjuMIhGmylyGjp7zmroPJpeGicHBvcR3A56GTvK1mU0hYYzsgFkaoXyzFUWCJKuQ4jlfNF4pmiKikdOpIXbu6HM/0xZHhNDS6Q/C5NLtG8I02j3HjLrbZlp4y2ro+CwdNUYO/Zfq8pUje1Zmg6JI/nLk8EghzOChiCllcjkwMIDu7m7f9XzeOpzDNHRtbW245JJLcMQRR0DTNDz++OO488478dJLL+Huu+92czQNDAwgEokgm81S9zvXmtECTnbsGfWkYImI6m4uXnoAOx+NakmNSSI8k8kiku5AxXZyMAwTlUoF0WiU3uhME6+++jLMmndo66aJkZER6LqBUqkEo0Yf6ABNNMYz1lzw530JX6Rk9LhsIoKxcg3Ovh+ouYJfakgyCA7xJEuEVEiv+YRHrPQrGpI41AEd9MAz55JNwqGaOmz9daSimssIVOo6VV7VItDrdl/bfiy8Z6NWBqL2WNf5UcxIotwwTSYCIysbo5k0ANThZBIMNGlV5JhcWj9UvXxYhESfNKkD7PkjeYw7CV4AAVWhCYpIJIJS2QtSoRCMRr0wTJS1n0cE1JGNGmq1Gvbu2elecwjXTOdMSDssIouUYsuyBMnWQpBzqFzzmI2ona+JvG9idAjokqlnABaT4exxPHOmUScggBqFqhDpSCQZUiIHszhMEe6VwT4Y9qJ59eWaFX12ogDEO9w+A/waW+v5JHEANziDrHpMi0pobVArwagWIUXTVnnJH5QAADeoTrZzGqShUaq/ANvvLzMDxuguX5RCwzQBvUYF33H7TNF8xD4LOggRI9AyTcAwqLydjmXA4NAwMqkE4vE4RgreGERVhdonmtHQKbIEozDglpckL7y9pGiYN38WahPDiMfjUBNZPNW/zW2jk+6DtxOSzKphmMi157FngkgXwmFOSC0k4Nf06AZrdmahNEYkDIbkmlOpsgxJjQCy4ppCGgYsHzU1glRbzvXbjhJpVkzDRF/fNvT2ekxes0I02nSYOAskmVqf5GdZIuYms+aoc4B5NmsiL0kSli1bgcKrO4EBS6Bs1kpANOOWYcFLW0DvrfB82ykC15qL1cGtRHut/zunzcUru5636/Iqc80DMzNR0wnmnIwCO7oLUCxinM25yAZYAixNoT6yE5IahZ63BA6GYVDpCKIRYr9QJDj6CzKP4OCgR6+Rc9dtc9t0VF0NpCNwMaEoMpwn8fqXzfMXlowd4NMVhmkJ/xyzS90w3QnkCGaiRJuLRL5Mev54ZTRZBhQVkhpFvTpOP5sQLORy7VC2eetLpzTj/raygj2HoXPTWxmGKzSNajImyiZg6BZTqSl0VG05mOkiQa4DkulTk+0gNWu0YJhfF1nOoK5NLg3dlGLoyuWyaw5CwpHcVSoV328O/vEf/5H6vnr1aixatAjXXHMN/u///g8XXXRR6DOc54Q9IwgdHanGhQ4hJPrHEY9bm2U2E0d3VxvSqSiqpQJqERXxRBRGpY5EwiI42zNx5POelDeViqFsS5aSqSh0vYB8vhN13XDrjUV0pNMxJEtVaBNWn0ajKtLpGIAqIlEZSn0cSkSFLAFtaevwKRkmtOEitMw0JHI55PNpSHus9laL9nSXZETjEXTm01xn5r6xstuOeTOy2LxnDHKlDkOuIxpVoUVUyBENmTb6vbo7khixNzAtrqGzM47+/higVqHZ7xuNakgmI9AiKtSYimQyStUxXNOpvu1qTyK+x5KERuMRqqyD6N4J9572VBRDdn/J9TGk7eAR0ZLXhkQighNPPMZdFwCQy8ZRsDftdFsciZES4nGrnvb2GEqjZWgRFUpEQTzhtdm064zHI4gaGuJRGZpN/GeZcQeAkbo3xm3pGGZ0ZxCP27msIipVPpGIIm5TEx0dKeSzCaSTUUzYz2zLpBBTbE3JUMF9v3QyClOWMVKuQYlqiNl97IwdAGSyCXpOJqMoygZqFRWJZBTxqNfX8R3DbpvzHSnk82lk0jEURuswIiri8Qi6u9sxsHfQ6+Mo3PsTMRUFp58SVr07xjJu2VQygj17tiGbibvXZFlCOh2DnsogPmj1Z2d70q2zZPe5OWMhIqVd9roAYLfHNE1k1RqSSQWxVJvbfgzvRjptRSnU7CiJWkRFipiHhml65QF0dqZQs58HAHPmpGGUrQApPUuWYkt/AbomIxJV3HZoquz6KrWlYxZRIw3DnNgJLTsDufYkUjENVVlCVDUgKRoyaWu+FAoFaGbJnVu5dqvPU6mY2z/xqIR8Pg1d16FUhyEZBqSajs7OFFRFxtahUciF3ZAkGelUJ/L5NKp13b1fkSWsWLEUcjSJ+HZLWNEW19w+iCajiCcTqJZUSLKMdDqG9vYEynUDsaiCemUcalRz37euG/a+EIdUL9nlk8jlrPpihI9bV3sSRd1E3dSgRODWoWgKNABqVMbsGVm0xSPItsWAQhX1wjCGhl9DtaRh+fLl2DNWpcaouyvtfldkiZrbSWJ/6LDn0IRh2qafMiIRFe3tadSGKm65xfOnI6ZZQSsmyjX3uhpR0dXVhu3bY4gNF6k57LxHLKZBN0yoyXZMn9GBid2j7v2d7fS6a0vFoEsSKhEV8UQEqZgGXYtAG7DmVyIegaEmXY12NpdA3s7ppclwn59Meuu1Y7CA+FABiM/HjBlxLMzHEC8qGPibRaDmst6+NG6Ybh1aREUiQe9BWt8Q1c9uuzP0e8TjEXev6uxIIZuMIh6PoDIhQ7XXMgBodu41Z83NnNmJeDwOk1lz8UQEcdtHsoNY9w4SiYhLgHZ0pqHIEjIDKWqPr9kMTSzmPzfi1JhY9UvDo15fqDLmz59p+50Z3r6ZVFGpjCIR01AzvbZO72pHOZpFvL0L9YkhJBPefCibJpJyFyRJQne+C2nF8uWWFcWttyObwFgZkNUItKhGtfe1YpU6E/P5NJ756/OwePEKJKOAfD6Ner0O1V5DkiRjxrQs2mzNdXs2gcGhCHRTRSSqIWvPgfHxIbcNuUwcp556EsrlMiaSu7Gp3xKkpdvi6OhMueMG00Q8oqAe1yBJMnK5JHIpJuJ1VEXc5lKmd7chXqggvtViZk1Vod7PNE3ImoI4x78rGjOh2Cav8lABmlJDNB5BV6e1J04fH3HbX6gZSKWikCQJdUny9sq4t0+pURWxog4lnkR9aC9SqahFM0VUROIxZOz+zWZjeOLlLSjazGcm1+a2OblrBBGUYep1ZNKzrPJtMfQ7z7PPklgMeHWbTS9pMhbOtuixzmwCxWoV1YgKJWLRdFV4azmq6r7+4a1Bkr6oKbJbRksksWRJBv39/ajX6xiuTBDrgqa3Uqmo6xOda08iHlEQiUUBh5ZIRX1r51CGZDbKfjqJcM4556C7uxs33XQTdX3jxo04++yz8eUvf5nyr2sEwzCwcuVKvOUtb8ENN9wAwIqCedddd2H9+vW+8ieeeCJWrVqF6667rqV2r/nWb7FntNSw3FuWTsMHT11MXbvp96/i/214LeAOGhcc3YN3HTuXuvb1X6/Hum1D/BsYXHbKIrz1sOn4w8uv4W82k7Fh1yhea6LtAPCZ1cuwcm4H7n5yC4Z3WsE6nhmKoVBrbgqePlfG0bMtBqVaN/DU5gH8YU9IlBIGx3dWEFUASBKUXA/OW9mDT/30yabvf/OCNnRrBeweLkLS4shPm42fPb2tqXsjsokT8lWkYhp6OpJ4aecIBisyXhzhCwdYpGIqLjpuHt562HT32m9f2o2b//C3pu6fkZKwMGlpHns6Uzhj1Qm45+ltuPeZvgZ3Wjg8r6JdLkCKppHLT8O7j527T3NvU/84frdhNwBg455xbB8qNLjTwsXHz8U7jurBfU9uxJ6dWwAAL0/E0V9oToL2mdXL0L9rK/r2WHP96OXL8MO/9AUmt2VxzNwOpOMa3rZsBuZ0pvCr53fgtsc2N3Uv4M29Ew9fgOWL5uN3G3bjB79vbuwAa+2vnNuBo+ZYGq6nNg/gm79pbEIOWIzKypkJGIW90GQJx83vxN8Ga/jZS831fU9HEkumZ1wTnfedtAB/+Vt/03NvQbuKi5al8PSWva62ND1tAR54fkeDOy2cMC+LK85Ygd899iRefc3SpvWVotg61uBGGyumxfC584+DYZr4wf89AgBYP6JhqOInoHhYnKnjXSs6MW/eAtQlDXc8+Hs8O6hhot7c/Z9ZvQzP9Q25WoFF09pwyx83coMj8LByZhyp+ghW9LQjHdPQ3t6Jf75vY+MbbfzjqgXoG7TG+sSFXZiejeMTtz7R9P03X74Kt/7Zep6qyDhlSTe+8PN1Td2biWv4/71tKR7baGlCls7IIqYp+K8HX2zq/mwUOHVeEmOSRVSdc+RsvLhzpOm5Ny+fwpfetRLPbh3Eum0WQV2q6nh800DT98+1iXkn0NLLu0exe6S5M68nWcf8NgMnLrS0T1sGxvHI1jr2lkNUBQSWTGvDB09dTPlI/uvPnsXWvf4E4jwsn5XFvHwaFx0/z712xY8fb3rf+9dzV+CPz78CszwGWZZw4sI8vvrH0cY3Os86rg1d02bi//3NslJIxzT8Yt32pu//yUdPwfN9Q3h6i8WMd2diuOPxrU3dm0lE8J01JwAA/vTqHmzYvA17R8ZbOnOPndeJVYu7Mbsjif97bgf+tnEz/jbWnB6kIxXFitk5vPfE+ajWDfzsqa3YMjCOrXub23enZ+N432Gaq917rm8Q6/qB10pKgzst9CTrmJvSkU1GsWxmFgBw94sT2DgUnk7FwXE9KcQq1ppZMW8aTjhyeUtz74q3LkR5cIe7Zy/qPRw/f2pb03PvS+86CvNsZurmP/wNpmk2TW8AwJfesRjFkX4MlWvY0DeEig48sbd5evHN06tYtWSGq7HfMjDe9L6XTUTwbXvuOXh262DT+97czhS+/O6V1DWH3uvOxPHjT76Ve9+U8qHL5/Nck8eBAWvz7ury29eGQZZldHd3Y3TU28Dy+TxqtZrPt65arWJkZKTlZ0xGkOYEHHeshpAkCVIi1/J9rI/OGwHXH8GK0tHy/azPTysIctBvCoxvXsttcMqbBjdqWvPNIE3hmr/PjYBImJG2Kosigygoyr5tfY79v8qzW22mDfaWG5QUOgyk2U4r42foOozxfsDwB31o6n6TTjxLBqZpBk5LgwKYNMLrFTk6gRysYDmEo36zzzdMNwXKqy/7BXlN1UE8L9Li3HMiOTpj0OrS5ZlctgJNIcyHdaOlsQP4ERWbhWUox/gZtgCnqWyeuGZB+o52JJsnBkl4EXlbbz+wT8cM/fzXsYCKVS//ZFRV3FQ3zaKzM49cxvOZ35czjBcsrRmQ3Wb5mu1bR5ZrOjUPWoWmyPu07gB63yiUm2PEWOzjo6ESJu77RG9JUqBfZctV7cM9YREtm3/m5NJ3TSmGrre3F1u2bEGhQEtAnn/+eff3VlCr1bB7927kch7zsXTpUgDwaejWr18PwzDc36cySMnyvjAnEkD4KzUP8lziJeJsvb5W6zBfFzMDOMTRvt27c9gfVrtZ0I7Brd/v8nOm8bqY6bDkwGGIqQrq9Toqo160wVaHz6CCouybtbnjZ7GvR5Maidv1tD7/SX/DVggbcp7vC0NH3hLVlH0WSJBETbncnIaDfL6xj8IAwAugo+wjI16t69i6dTOViHhfsS9jD3jMQFhuu7D7ADp4S7PwB+1ogRkGP+dZ8w8HZMJfsqobGBtrXkPkJXkPDroQer/9v6bISMdbY2ZIuL58+7D+9pUZcLAvTKSDuu5FiI60KMgBgI6OTiqqcnUfzk/Kh3kfifS4puwzZ2yYRNTrFutwAhuxwYCafrb7ad/HkIorEE+ElGTuYwP1tAhNVTFepiOi7yv2Zeic3Jf7Ks94vQzhG4Ep5UO3evVq3Hzzzbj77rvdPHTVahX33HMPVq5c6QZM2bVrF0qlEhYsWODeOzQ0hPb2dqq+m266CZVKBW9605vcayeccAKy2Sxuu+02rFq1yr1+++23I5FI4JRTTmm53Te877h9JnQ/eOpinxlmK/inMw9vXIgBSdR89szDkW+L4edPbcHgDsss58g5HahHc9iwt+Z+P3puh3uPLFuRlkwAHzwqgWRUQ6FSw4u7x1Cr1pGMqTiyxyo/MF7Gq7utAzwT9aJWORvMKd0V5JJRrFwwHQsWWGGof/ynje4G/O5j5+JnT221wvGPbIckeUxFNhnFTz7qH68/vrLHNRNYtbgbu0aK2Nw/bkcfdMZJQldbjLp/z2gJv3zOMifpaovj9MO6sHHjK9g1XMSWAcsBOaIq7gbbETXwriO7ccEJS9w6ntw8gL9ut8xTjprTgSXTM7jjccu0z8nj5RAkbz1sOt562HT8/KmtrhnDeSt78It1260ANYVBHD87AUWWsHnUxO6BQUCSMWe+9bx3HTvXNcEln3vMvE48u3XQJT4uOnoGtmzZhMc3TgC1kksgfPDUxTh9+Uw88tiTqFXryCWjiGoKXhuxGM9Vxx2Lw2xTDwckQXPWEbOwanE3bvnj39w6333sXGQSERQqNdz+p1cgqRHEIypOWzYDL774AsWMXtAbxex8DqVSCc/1DboSzNOPX45du3Zgvf0+M+Yuwcq5Hdi+xYvAp6gKZRLxs6e2YrRYhT66E0fNziARUdHdPQ2dnV3ubwBBUBh1nNJtaX6WTM/g1BOOw+7hAu7/3V8AAG3xCE46rAednV34zePrYKeMg2wzkpIEnDovAbM4jOWz29FmE4sv7hzBeHQaAOBty2bg1dfG0DdIJ/QGgMNmZvGWpVY5qTiI43sSACS8vGsEg7YP5ZIZGXSmYjAME49ttBPh2vcv6tBw1hwDFcNErVrH+aetQlebl0rh/57Z5tZzau80/P5ly9Qlbmsonbn3xKYBrN9hz5u57VAndmHPWAkbX7PsIfNd03D+SYdDkiSs3/F7t/5VCztw8gwJm/aMYqdtdjunM4VZ7VaE4uf7htw8a2cd2wMAyOVnALap9zHdMt6zwpL8P7l5L2q25P/YBXlEFBk7hgrYZpsGzWxPolKpIB6Pu4z44bk6Lj7jZOiGiXufscymc8ko3nnMHLeNDzy3HTt3bAVqBip1A/GIl4NuZUcN+bYYFk/L4LDDlkOSJPxiXR/27N4Bs1LA8tntWLpwPrJZSxjomPsBln/SyYu6YJRGMSNexdzONNrb87j/8fX23JBw0qIuTJ8+E398bgP2jDoBZKz/FUXGVRcciSfsMTlhYR7LZuaofeD8o+egw/breXj9LvfZiiIjl4ziyuPTWLfVMmNLRFWsftOJuO9Zy/y6IxXF+Ud7/QBYc8/ZU2fmEvjJR0/Bgy/scIVMq5fPxEx77H7xbB8Gxq359o6jZmNzv2eipcoyls/Oufvm+h3DeMI2f1w6I4uTFnXhwRd2YEf/EIzxPVg4rQ0VUwFsunC8VMWidBVXvylLmfCef+qx6GrPAgA27hlz5+vcvBNB09t3Tu2dho+dZgl3hyYquPeZbTDKY4jr465J87JlK7B17wQefdHqu/ZU1GVqe6dncNkpi7B0Rtat884ntrjz9aLj5yEd0/Dj37+E8tBO9/kL5i3CpqENOHp+BFUzirceNt01KQOAu57Y4hLAZI5GVgDw5XevxB2Pb3HTBznP+/XzO7B9+xagVsbiaRlsnIhCisR8DN2315yAXz2/A7vtffrMI2ZhRjaBLVs24uHnLHN2OdWFNW85HLsI01JFllCv13H1m7LYM1rCxj1jkHM9WNjdhjcvnY4/v7oHL9tn9UmLutz+ITW0iiThJx89BYMTZfzfM30w6xWopQEsm5nFuh3jUNqmI5uIUK4hbhqQ4hAkbRhXvymLiXINz/dZ5vOpWATvPfNUrN/wEv7ystXf8VQWl7z9WLcOKzCUhI6ogQsWACcum48tO1/Di/be1RaP4D2rT3XLkya6pukJMaYnTPS2W5/HI12QZAVnrphFmcRWajp+8pdN7tiRApF5+TQWdrfh0jd5KTNeGynhgec9uqGmGxguVKyE3noBUL0gJ4vb6ljcVsfJi61k7gDw1+3DGCtVIedm45wj5yCt6XjyhZfwN5uGSWbzWLbMmu/LlgH3PrPN9bM/58jZ6Bss4IXtVl8ePiuH4xdYQQT//PIOvLjBmv+OEPfL716JH//yt24ApZXzOhDXVGzdO46dQ9Z8WjA9h9NOOAamaeKVlz0TwxnZOL695gT85k+PY6tND83IJbBy8Ry8uGUHtvRb15YvP8I1UXZg0Tyme+YBwJE97Th6Xqc1N0wTPyTMsC87ZREKhQmMD3pnXlQBLj2qDW87/ki33H3P9mGvvVede1QPYhEFdz62EcbIDsgSbdo6L5/m0ovNYuXcjtd1v3PmyiECninF0B1xxBFYvXo1rr/+egwMDKCnpwf33nsvdu3aha9+9atuubVr1+LJJ5/EK6+84l57y1vegrPOOguLFy9GJBLBE088gYceeghHH300zjnnHLdcLBbDJz/5SVxzzTW48sorsWrVKjz99NP4xS9+gc9+9rOhScmnCkizCVdqV/eCwZimCSWWAmBtlqyJkQwJUKN2WVD/A3RUpjnze7FxfAugRqBoFWiaho6OPPpf9fyXVEbrFdU84mPPmHUgSZKEeFRxCTL2mSTqTFQtp/0maNMzVmpEhpUv1+quVoCMlBZVZaTTGQDWBlrXaTMKMgJWIqIgQeQQM00TdcOkonEB9IEZjyhIRFT3sK/pBhRZgQ4VcsZyck/G/RJ+No8WFeVSUShGqlbXXcaSFESwVqikiaN7jYryZhVuT0ZdArBi171hw19hjFkHal1T8OKLFlFISs0UNYpSyRpfUoCoaRolyS1VKm5Ca/edGOmbM48lJeIe3mU7pxEdWt82xDD884iOtGldf+21XZQ2T7JN/gYnKlby7uIwYsQY52fOxfhe67nlmu4l0wa9jjRFciOa6qbpJvelooVxcrXRWm4DgBU2npy71j3eTaSUlS1HHS62RJTUhnRk29y6ovEUUHTmvQHoui/HVEdHJ7q7p+PFnX+0qoy1QbKDAkQTacht02CMvUa1j1qHJvWfVS+AzZv/hs7OPJ0LzwzP90Wme3DWJZkSIpPJobd3qduWOBExkVyTrFbUG0dP428AVu60Wtlt4+joCDdtgWnSpoBOhEs6nL33THIOKTDQ17cFEvHehsmEhudIpjVOHi1qLRPPZiPXkvspu3fFOGlL+sfK7mCkoiqkuuQydGOFErLuO5LP955JzlEntQK7p7OfpWgKRsFLJwDAJXoBa48iNR2+KJdU2hSrHPmqkVgciUQCsew0TJRqQKnq07xR6X+Il+NpdHnpQsp1HZKkWBEqIwpMU4cEvlaQjoxs1VUsFqEoshXRULEiD5LnETkvnDZJkuRq0KoBprWqHcnUOr+s3LPunmpHcpYluANaY7R4o8UqTMOAWR6HkssAYCNK2+dsxTNjZ7UrEdVLW1A3TAwPD1H5dLtnzqXK03umSawhiznTTdih+BVflEvefGfXMSmYreo0feCuXcOLNk2uZ6surz4356okY6xcRVtEdVNJWOXpvohTdIpORQEn57hGRIJ26J5KpYwaMdd7ZvZgoH8XlQ7AcSuQJImKXB1XrTlG78HWWHXNXoxttQGb+fXPdx4LQ76XbGtCvZyIpjsHKDePkHo9+s4bMzKx+2TAlGLoAODaa6/FN77xDdx3330YHR3FkiVLcOONN+Loo48Ove8d73gHnn32WTz44IOo1WqYOXMmPv7xj+MjH/kIVJXupve9733QNA0333wzHn30UUyfPh2f//znsWbNmgP5aocETNPEBGHLbdYr2Lt3BEbJi1BgmHSYW/YQtwh/CUr7HLTnE6iOD9EGBXbxWbNmY1xXINlmatG2TixebEVeq+meNEYjkoYC1qboyIRJX6Wo6jF0YaZnbG4sUjPiaug4Owy5UZaquhddtU4TX0G5pQDGsML2dXM0mIC1AbMHPJvqIK4pdnnTDQtcIfJHxZjE4gB9ALMmtdFo1PX9M00TRnkUpmmNo84wdFS7OKaZdAJe63+SAKzVDYyPj1FMAWkmR86lRK4bUnnAahPJrEn0AVqvlGEYukcASrJPyuX2iay4B7KzmdPEoH1YccIZ13QDctt0GGO7IUsSJibG7XvsZ2kxVHXD84WSLTMg551isTjiZgxA2a3PYXTZfpIkCbGIgomydQDVdB2KrLr9IGemQ5bKdlmvjabNxEiQUNdNSIrN0KksQ+d9ZvNOklAYIjcRiUA3PZP3RMLLCUoSWSNDA8jGZLcvlPY5mLtgGqZNswRiHTMXYtzWsFVdBsKEpEahtM9Btl0DMA4WzromExg70Tb37h2g3otde+ySjkUUwLDe3WFiSIHOtOmzvOS5cMZHcssF7TEkw+HUZxgGlHQ39KFtbj8ViwUmd59X396BPdCHtgOyjKg6AwCb549g6Jwk9aVR9O8oIMaYzUrRFO3rwpEAa6p/f9A5TAxA7yUW8U6HcKfq5aRwsJrimPkpGCuVAVjzaHhoENmM/x3jMU9IlSJMuxxC3DD470cSwx7DbBHcJPOejqlu4nX23QG+j55seMEfynb06xIhUIwwqVN4/a7IEpVrjFfW6Yda3XBzD6iKnVMumvIxEAB9ximy5O7VsiRBBwB7L6npBqB40VPde4h9eMI+m3ipEABrr4oosnsO1nSD6D8ZsmT3hb2nFqt1qr2Vug4Y1jOc/Yad66ZJu0PEU7Rg3fEbNwGM2VGYybHoaqfLk0JlE8S5L1t5aU3dcNvLWleR56dKML6k1tU5P9nyEVX2hEFG3WWoaWEhvYaSUQ1D9jExXqpBymgUTRBP0lHUyf3HEvjy940IKaCyx27btq0ebSdJyHd2YKB/F8hlHbHzDNd1w8ttKUmIqjJ27dzuO48Aq3+d8eaZZVtJ7+lrbDlFlt0UJ9Z4+euRmL5jc6OSc8j5bevWzW7OSJYXONRwaLduHxCNRrF27VqsXbs2sMytt97qu/blL3+5pedcdNFFbiqDvydYOXLsSV8exc7t1k5C5oRLtmUxGHKIkweLszlQ0lZ7Iba1ZVEict75cjbZYBmcSEAybtlVIZkAo7Fh39FtuywhptHaK/stfNtFRJVdrUlNN6CbVt69sc1ePpfZs+egNE7k4qnSvkR0P1iIaTLsCPOo1HQfUU0zSZKr8TEN3SWGqzrc/Y3VsgC09mdwgs6n5hwgsmQdLGa9grphIiJLqOt0/yqKt6XwJMOm6d8wSUlb3TCxe/dOlOt8wsc90FNdkBSNm0i6rS1D16nXYRjEeHOCwrjMkqy6c9LZvHWdng8AUBzxfPliSTt0ct0A7Pcn57xTt5zsRLmmo0BKRDMzEY+pME0Ds2f3YHinx6TUdAPxiOoSpSzT5bTFNOowzKjdf/YBqkQgy844Wj4cRrwDxsQAarqJiALocgSxVBYGoj7/GJmY3UFJxQE/YTl//iL8bbcVOVZOdzGEnXdfvV4FEKPWMeljQTIQo0UnAbZ3fyyRQu/c2Xj55ZcoR3AndyHJbpMjTUqSKzWd2ivYORHTFECnGToDgJTIQYqmff5oUZ7ghwNyrTnlXIVFPAOp7pkokoIHh3AcGxvF6OCAcxG7t72K9thiijgh+8oh6K0kw3m7H7yycjzL1TAFtZlksHn3sJosXh5H3neHCa4bBiBbfekEZDGKw5ATOewd2ot5mU4AtBAnSqQSSka9z4WKxRwEMax0YmbDFXaYpN8UrDOFHFN2fHn9ZxSHvbpr1jwitVjxaGOGLsIIAL1neJ+dva+qG66WXJWtdAlOqyYqdercmCC07smo5gqvnKGTJEtTVK17gkByvkdVBVLEMjN09oewM1lTCYaubnjnsiy7+RydHHcAzfDIspfw3GEkfYR4ve6dAZLk5tDz+kuCc6I6r+EIqqREDm1MWHzWqsE7O5w6JHeRsXMhmLGFK7E1TNPdYyuUxZPiCRJMr5/o+UU9DqosQWmzTBFLNR3RaIw6Z9k0Wxqz5shxaycC/8QYxg8ACiWPHnPMSSORCGTJMreU4hmoces8LFTqgKIB9SoiqgwJJiqVso9ZliSJUgDw/Hx5e5JPwy1LcEQoumG6/RQWiI1lLnXTY/qcZxYK1n48NLQXXV3TcChj8nn9CbyhcIhR0zSh1jzik9Z4SIGbGkBvSPG4JT1iTZ/a2jKuQ7EDyiwo4fk7aoqMUskLGEISnTojcXFqM0vDgW7GbCJTlwHSa4ydJqt5lCipUV03MHPmbOjpmZBzs6G0z0FXZztqVU9yWyvSWgaKybSrIjfWEifaFktMuJpC07AlfCbqJq3BZEESVv1jHpOZilkETz7fRUWGdMxuyXdRVA0GYUJqcCKaUdpEe2xJoZlhmqjX63TiZOL9JVmCFEtDisRhGKbNdJleH8gKIqqCrq5u955auWBr6LwDnz0znGdIkmeua5pWdMM6Ib12pdnEOFXsAB+VuuE6YpOHknMwSLKCum5irOgRU9NzKSxYsAgLFy5BJBKlxqZSMygtQZJxKnfHrFb2iLqaATll+RVkMxm3bFtXj2XSB0vaWtdNyOluKPE0NEX2HZjkei5zzPu8d/M+G4YJRVGQ7p4HOTcbkhZnCBpioO3u0w0TsP0KSe0r2R6n/+k9whIe9PYuQyTt7QWeCbcJKdnhqyviEIS52ajUDXpP4TD5UtzqQ4chMEwTkCxChtXyRjVy3+Fr6CRJophIz0TIcF+AbIf7WdFcBmZ8fIwyZYqoMrZu3YyJPZuhD/d57bRR0w2YpRGqPrfpWgwGZLofOAQVySSN2xoONpm295ncs+kgSiyxRn6vGYZLHEuyYtdj7dlmeQymobua9GrdcNe8psg+AYrLVJkmx6SUnmfeHPVMyE3TdN8TAFIxjTmLaA09bf5pC6qIKIFOX4VFbGTXltU+3yX7HWgtqGmatobOM7+NMGcR+dnZ4xTZ0pw4CbklwCLCYQVyofzfGFNVKWJpTZ0xqzFzkgT5varrrnZMkmRXWCBJgDFipTKhhIEmXG2Y02fkWWQCqFYrhBZN8dEcmYTmUu+e5s9igOVYG3KJEIYO/kTviiy5bSKZESCYsTVKY9DHXoNZK1O0B6mhI/d/kzC5JKOLyrKMefMWIhaLIZPJYu5CLxBfpa5DlmV0zloIOd0NOdfj29dY9wrS4so57wEgSmjo6vZZTjKfiZS1Ny5a1IuO/HTIbdMhx7PuOEyUPXopqiowDAPxeJwRPJmYmBhnIvH66RPeOmD3Kcoqx/BMJ6m1yQTjk6i20Jp2lvkjk9YfqhAMnUBLcEPn6jXqACI3DUmNhkrryEWk2YFOWNrHIcgpBok4RAlrCd/hQWkCAiRbZiU4lwplMiHLLkNllj2zUtPgL25aY2Edtpa5otXGVFSl28G8OPnN2fhIhoYXPpnesAhNR71qE62Abspu+3gRt+IRPzEBeH5EjskIAIqBeG2n58uoyAq1GZernqbPAUUEOvdxfFNIcxinv5YuPRwz5y6BbDPzhUodCxYsRl33tG/R9lmQJSCdTHqh1ut1VGu617eSn4Fx57IkuXNmdHQExQrtPyZJEoaHh6g5nMxaDBTpW0r2sSLLkHNWYI+aTjNpKUbbSq6p8XLNfS9N8ZuJuj5eWsz1PazphktoLZo/H/PmLURv7zKkEglIkgwp2YFq3UTXjB7qvViQTyqFaOgo4t1lejzmjTKLIcxUnbHQDRNSNOm+owNSiOEQ2SQN7TxWURREkznIuR7IuR7M7FkARVERz3ZDjqYALUbtB4osA2oEkuRp0h1oPu2jDDmehZzKe4S+YbqaCZagoCTfxDxn2TqyD525VnQIXC1OmbM5y0lSo+7aINNISITJrqUBMKEPbXMJUNM0UauU3b3LaWJXVzfkdBeUdDcMOy0D+0wS5P7g7I9BWj12DwzX0BHzRzepvVeLWloWh8g0qwXo9hyiiOCo3yeYIu50gza5DNLOg/RTNDFeLLvrry2m0VohVkPH0VZGEp6pWzpjCRfI88in4dV4hCyfo2M143XbckayzcllSYJEmHyS40ub+Fl7mhN1VpIklyk0YFJ7GssIOwIiSkNow2c1Q3yv1A2qDVQKGcLs0r0EuItfkiyrlyVLlhG3GBgY6PfoA73OsSTwfOgcQV2trkPSYpAlyReBkep10xMmSbJqCRXh+VGPEdpOgM/QlUolmKVhoF6BMb6HOrMrNXo8pmWs/cUsjbhrJxJLWAxaugvJrrlIJBJYsGAxZs3qofZvb4+QIGkxWygebOZc001KOEH5PjI+nYZhUIKBZNoTGGZz7ZBsAYYzDhOVutvnUU3BxMQ4FEWhApzrtlCW8unkmFzyItOy70W2V9dNd9+nRPBMPeQ3E4SgRlF9a490HzhUIRg6gZbg2MubepU6gCgCNpoMZeh4DA15QGa6ehC1GT1a6uIdtiMErxDjEKTuPWTOPJlQ9ssKV4JuGCZGS95BmIqplETdgaRo3FC6EnPo0we4TDNG8Jslkm2qVcvYuPEVSNUCTL3u8+twytPh3CXENMU9bGp13XK6tndRnj8GAJ/Jidtme+w0LULZydcNA+VyiSKyDb1K2d2TPiduGSYIBvk/AJSKlmmN6wuW68Fhhy3HsmUrIMsypSko13Soqoo5C5dCzsyA0j4H8Yhm+RHGY96hrtewZ2iU6CfJR0yRJpfk4bJjp5cE19F8vvbaLup+xWZIHIJGTnVSRGPP/CUec6kbPg0w1Q6NZugc8JguJ2COnOxEtW6gZnhaqZhmMdeJRAKK4gXXkaMp5GcvghLxTJJ4PpXk80aJRLDsWuMx4+R8JH+vlQkhiqOhM01IMYswUIMYOtNfLxsUxfHxjESi6O09DIZqEUVyIkdpQmQJkNOW2UxdN0OJUGeMpUgCmWnzMWvWbCTzPS6zymfobM2QYcLPysH3nk6feUEiZGo8FEn2NCZ2EdbH13kmua84WuO6YcIY2239bmu8otEourunufnF6gYTBInD0amUWbQdIIFhDBywjE9YUBSVIfTJAC7RSISqzywOu8GPyJ6NJrP+9jJmrWEaAHfcCWHOK6+8hNJgH4zhPpimCU2VwZr5OSD3YNM0PW0SKf0nfCt5bQT46zAoZQFrculqxyRPqEaav5MMXYUT1MxZTxIA1Gw3CsMzO5XiGXodyZ7QxtUQhrwbOT9KlTodyMsOpU+eDSSTY9pacQeGYft9OfMCVrAO0hqHtUKx+ssr7+6tkoRsMuIj3iVmrN0pLNmGkpKnoasy5xwvTUd//25K0kwG26DmvCZ7Lh4mwUBJNoOmxX3rk9LW6o7GP1jjzgY4ov3gSWESoS00Tbz66suUBos8q6j9QSc0dHXr7IipMhRFhWnSZ4JhAt3d0xm3Bv/+w1sFbYwwlEqtQmjoaJqDqZfRFnpmu/I+5+97IyEYOoGWMGAHGTEr4+4m3d09DbPm9Vrq/fY5KNV0ZnMP2SxhbejkphKLeekJWNMSwJIq1WATqLKEeERBLueZXZE22lXKZMST0kHRuOTWaKnqPicV0xCPqJYvAmNiI8XaOHdzJKdkQmZ7o541azZRRmLyhNn/Gzr27NyKSqWC8tgAjNGdMIb7UCjTWq9i1TPLcKKFxTUF0K2NtKbbicDt944HML9ByaKd98lkstS4VWs1VCoVygxOBtCW9sJwlyq05NJ6X++zFw3Oq3fPrm1W/YQGoiPlzQcy8qBzkFbrBiSb6HUYEUVRKaZg244+91CWJNlH1LllFY0as8GhIQC2ibFRRsUObuAGU4hnXOLDPdi1BBI2kdLdPZ0yXanpLJNPrw2SqCNNvng5oBzmXJIVtM9chJ75Sy2tFPyMFymBLlbrVKATHrMYlC8sTEPXiPFij2UTJuqGV4ZkqHjEc5DPFms2AxBJuJUIpXmTZc9/UjdMlGtEPzDvFqVMnQ1kMjmYktePPgmxLLmvqJtmYBRdygzLCcDjTk66TyXZ2WskN3IdL0Khc68Dx7e1UPTMp50+mzdvIWRGsEQSpTytEG1Kbtp9R0fnc8CayYeZGZKRQcu1OiXESKXbfPWRQSWgxSFFEogkvD3HgcZYdoQRdaRWnDy33Pk83Odbp9w9u16FObIdL7/8Ikqloo/ANAj/c5lj9s1q64HgvGvsueiec2rUi9xJ+KSRhDhrrgt4DIYsA1I07b5/idQcEw1OJVMeo23P9XC/du/7WLlGle2ablkLZAizR/LcNgGYpt0OANlszlojNpNnmFZAKddcN56FxpqGEwygYQJjrpBKRncb7W/nByGUsK07ZEtFBwDYNULnh+X1w8TEBLX7kUI9NugUL2hSMpV1r4WZGpJRHoPKsyaXpAUGSR9IEkAzoXWK8Ypo3nixriYAMFSoutYiEU3BoO336+y/cqoLqfwcauzYuhzwQvVHGI022w9O0ymhPmNySSsXiLGTZNclxEEyKTR0AlMMA+NlmNUiUK8irimQZRnZbDsVOa1crVOLyEcEMlKRnp45rimNFEtT0j06oa1DOFsbkNI+B/FsNzo7uyifKXIZkoSKQyAAlukEj6N7bdQjgBy7estentCMKapleuG/3RcQgBcOPBqNeUSoadDSXuf/asGtnyROtm95lXoeKd1LxVRLOxVVXQ1dpW4xdDyimWp3gDjKJQ4kiWK8KrW6LZn2yrZ1TPOlbmBBB0VxnkFKAgGANr1KUsEyaOkiQJsxkeG0IxShRmgyZX+gAfI9SeKvXNNh6jUYw32ojfdj40Yr1YlDaMnxLEr2ezqSb0mSMLNnHhYvXorOzrwv8ENYiHhS601pNjiEHcm0lat1yhyX1cSy0QSpiHAcc1t/yB8LrLZa4Uhmef5EAJDumOF+NgGMl+puiOtkVKWYAh6jGCQkohgTzpyYt2AJstkcurq6Ma3Hy9lZNww3rL3VBtb8lSZCyfoBjj8YxahxFjWsvcltuysRNom0GzSjpUhWzk7AcAUS5PxJd81Bb+8yzJrVQ43YYL+lldu1a4dXlyxhyZLD3MicVHh/MrUBZy+gNWmWwM4kBEnk7+zYhZlcaornJ60bJoYJ39kOO4cfa/Xg1KukuyCn8lAVjkCCmZdha847n7yw5ybplytJ2L1rO/Ve5NHhMn7FQZeI3Lx5I+qEiX5VpzWViuwPzMSzNAnU0BGvYJieaaQkSdBUz0wehj+oBmlG6QiQDNek0WN8KKZdosd45szZVNtqZPRewgyYB4Nh/iI2HeHUZ9arqNZ1vPjiC3jxxRdQ7N/iag0By8xaJtwYYJrQdR3DTuQwWeFo6Ojyo7awzKyX0Z3xM3SsQMmdP7ZvZyKiujQBO261oKAoRBnKEoehlaKaZz3k1CUrpCCJ2Xc4DJ3bF5zyFENXN6hzg0yhY+kiHaadrh8AohFvv6Q1+J6GzrSjkyajKup1J3qpXZAw7w0T+gDg01oI3n/JqJlhVhi0JtbE3vGKfV2mhMeAZaV0qEMwdAJNo1zTMVSowrRzzqXjGtra2qCqKk3I13WfH1oQRotVqKqGjunzoWWmQYrnqEXHkz5RuU2Sbejunk45DZMgHbVTqTYvKEplgquhI9vt7uGyxPjcSdR/JBRmg+BpY6yoUN495CbpEhSVCSKMr9cfpG8Ze69DqKRjmhXABdZmVle9AyuIcQs6fsn+d3xaAKBSLmNsbIQi3pPpDJ1XiuPvRzsdS+4zTEOHWSt5BKBpArLqC3hApVfQHad0vsarZ56XsL1S00MZOipKH2GOWKnrMEat5Kqk9owKVGOPCWkmFNNUN7oYa+JCMuEsMcALjADwpZYUQ1fTKUmrzzSSWUeNwtQHmZv4tFhUEBebeOQw7QAQiScgxbMALIK5UtNdKXd3W5w29yHuc4hRv6mhBTJXo0OAksRzNKJh5szZyOe7KeJfN9hIhsESXycqIClNJwMIeG3yiOEgk0tZov1Yq7rpCrSs59JlIckwKwVuYI2opkFRFFuD7t1XrVWwefNGFIoFqi4y7Db5vqS2jauhI/pitFgN1QCwZrj1ECaYFbwMFz1CNB3XsGjREq6JevfMOV57OZOV9EPU7VyNQe31TC69/bRABIpQZQm1WpWOpkrU536uV6l9VGUYnjDGFuBryoNNLuk+pjSsdoxXWQKM0Z1uGa8t/nY4OUNleJqvOrFPSIqK9pzFYOfzXdC0CB04ifQf5jCr7UmPGNZ1g+vH59IJRg1Dw0PezUygMPfcIPqwTATnkiTZF0AKoOeJl4PORFc65ivLwtl7HMFmMqq6dph7RulI1bQ/GDHOAT6YJMMRURTrnCHSpWiaBp2YWaxAwh8MhB9+n9cmct0rMi1MIjV0Tm3OfJAz0ym/SJ6Grkr4czsRMWFHkXUwZru3NNTQcf1b6K9+4T9HqxdgiQRY77h3whYcSDLSxBzi+fAdiphyaQsEDhyGJiqu5CgeUZGIqEjYjt9UssqqztWaOKCTc1sLxZAUyJE4JL3qY+jc/Gf2HxVNi6dxItYe7b8h+72dGVC5adpsPz6JDogSBpkhnCXJT0ioqmrn+7GlcPU6IrZDsWtypNdcYpnNV0SCRzxrigzTTiNhGCZMyW/CwSJow6L9JrzP/bt3oD0VdZ8vxdJQZBlR2dtSKlUvAbkD8gBxNuDy2IAb3ayuJV1tgRTjmVLR0kWAicRGHAb5TBxyZiaM0Z1WVDxX/WmGmq1E27oAjAIAqnVCIqkx89I2b6xyIoAF+ZfWDYMSSLCBEHhEHQCf0z57rVCpU4lyWYaOJYobhakPmg8sw0l+d4g0OkgPQSBAghzPQC+NACas+W8TkryAJA5cP7MAU0PKNNKeO/UAnwzWdDDQfJF5N+cZ9RBpL7kXWX5F1mceW6cpshtiu1Spo+7E9ZBkyixIliU32IDTD+MEsxEjpMixRBYYt9JpmCZQKhVDGa9IgKAoiIlw71PlwETd1rsxmpuQswCgt2R2b49EokjEPQGLYVph2LVIHMAwgCAGNERDF0jgehq6MhOht1QqQYsDpl6zGGxiUA3Tvg7Wt5MQhkUSoXPNerofSpDpcwBRDnhryTUCqdMMuM7RdNdqNeIe637TpBmPGTNmu1YgAOOzGRI4CaDXc003KI1wIqJAS7dBlkfc9o6PDCGWtIOREfVIhNZIURQ4tVQqxDtKsi+1D0CfX05ZNdbmE8xY7+Z9Nk3TDXJkGgZk2dIqOmcsO//pOWwF0QIofg4mkViFtTyQZcXdF+uGaTF0IeuYpTms+7w6MwkmbQEpyKGC3rDaK8DT0DmmnHxTdlY7BtiuELIK026zY5pLaegc31JOrld/W2iwS4gVWro5QikhMusy4H2mcixLMmXlwub+O1QxOVopcEjAzdsSTSHblkZ7eweytlkMSVQ5uX8AuDlmSORIaZ0rfec7zrOmfpa/QPjhEYQoQRhYQVH8ZUqEmWBb3NoIt23dSJVxpE48szRKs8Bq6EizJFJTQLxPmToYrTKkGURNN1w/LusZ3vOcDc0impwN2ESJsHxcPC3ja7P7Xpxdk2QMzLonBaWCAACQ1JgdQdMb77pJS6UBmnB0iNFqcdS9VtcNV+MmKRGuWZLznhVbE0wTSjJVVlJUQFYpEzHIij9MP/ldVtygPEGRK3O5HGQ7AInDoFE+RSEO43SYar8mjUf08ph6UohSqdPhp525674SYwbXKEx9UHS9pqJcBjCLNEFDl/OHcKdN/Nh6yTbztKVUqHxiP2H9mugE9nQbSIa5UtN9JtRsm8nn8HIwkiDNXOuG6YYFl2QFyZQnyJg7zzIRldPdnvCE6EdKK0skUzY4YxGJ0z4gpGaMTGDPmw/kfKrUjdAQ9aT/UqnqnQWy5D8LrPchtH+E36hDMC5cuNjNsyVpcSxc1BuYMsEBGx2ZjuIZoKEzvFxm5Pt1pixue9fWV2CM7oIxsgN63WvnRLnm5kmj/B+JxyiRaKCGmVfevS+AuWZzhJEMUlubdSY7ggFj/DWqv3gmaEVbk0uaXLIBtywXdFJTRDB0nGTaJOjxMKlzIB5RYZpE3rDyGBTCvI0VELkaOoouMLy9WpK5/og8ojydiHLPPTYoijd/TMiSTKWEYLXQbHAY0uzZu8d5N0ZArdrmorY5Z103oCgqHWgqxOTSML1z3wEr3KM0x/VgwYyVa48WFkyU65AS1vyiNHSMNtpNSu9YGTlm1Xqdak+Bk5S+WQ0dS3+xZ5zCOTPZakizzbpuugJBWZYpH73JoqETDJ1A03B9hBQN+ek9mD59pjvRSeKSdGznEafkhu/sU2F2zgqjzqdCW3MYuqCll4lHfWFqWVAHjW3LTuZaAwA5HswUUQ78Ph867zeZ2QAd1AnGw2XQZPowLBJmVDzi2fL/9g5lXfIOt6D0BAC/3xJE8tsoEYbbGTdXKBpJuIetO34mzSCz7+qYynF9zGQFkhbD7PYE3UZJoiRnlZrOaGP8mhs5M8NOGmzXEU35N3Zm3ObOnQ+A8RlQFSxbtgLLlq3AnNmeyZdjzknOS9I8kz3sGkmzeVFVeUQrOferdT08AbjPRMsry9WacCaDKss+Yo3nv0EHRfHXI0XiAOgIk6ymkueTwUaMdUASCLodsZFkrjXiYFcZDW9YgBqZmWvFSp0OahEiKbfKBTN1HSkv1D6rxeqaPtudZ/G4VU7SYpAi1vpz1ryc6kRn2quHNKfkBcNIEIEVWATlG3NAa2LpoFfs/CGZ8WI1fJ4B9ByZCIjsqkXjUNrnQE7lUWXyB/KIPbK9ZdLcGn7JvrsnmlYoe8AyK3UsBNz+IO4rDPd7nyt1mCVLKMX63UiRBJT2Oba/ZHCgMKv6cE0jCXa/Is+tfPd0+j0ZjTxrml0qFT0NHSxBGuDl8HOf6ROCeZ8rtfBxZqOkkvtPRJGRSqUpork44VnEsImhnWKy6Z0t5JxQFZUb5IsKimYjEeG7atCm/borNJS0hBsxMxpA87AaaWddUk82vT2N3FOctEIaHGsHEzpkJkor/Q5KC3ul1Q7JV955PlVOsvYdwEvbUKrpkNSo77lsyoBKzc77qsaguuaWQLVaJc4iW/Crs7kqeecRj+mmv/PSiiiKSuhCOTeRa7pCB8gi+0mWg+mmQwmCoRNoGkEaCIAvlQu6TjNVfg1dI6IxzOwJCDEfVOgn8+gtyr8poqJQoPPVOUmbrQf572c1FmTId5qhIzV03uZdN0zX/83NQSURCdZNE/1791LPcJtjvzer1RwjNDc83wLvfv81sn+TGe/dSQ2dE1nRiSBI3lNmfP7Yg1/XdZrZMC0zONmWyPPGN8r4/ugBPgtkgBStYy60disKq8TJQ0fnSdLdQ5ica4sXeQlcybE0TYugCMoZx5qCUD4THOaNF4mUNRNj21ytG3SEQCbAB+tnQbfB38c8IpnUhDqgGCS7zlIAcece47aGu1rX3TQL7Lykgj40iNzGpjYhNWmqIlPvR/l41vXQfQegBSAThEkrN3AIxdCBawHgdCtJ+BerdWodU7mgiI5wJOOupi6SpOrRCIbOGSpnvUnRFGKEQAYAxQzSkeA4c43SmPp9oOiyfgk8EBw5NUgAR0YfTDLmxUGpMRyQDF2lpoeaGFM59lzrANXNd+lodigfxUoBtZq1txdKng+Vpsjo7p6GZctWYMbcJZBTeatMtR5oBuyCd54EnGUKc86QZ3NcU7FgwSKqPNlfzpwwTRNDO/+GzZs9CxRJgmvia5r0vAjzOyY1hDw3CDbfILk2FFlCOp2mg6WRbSc+S8S/8XTOvU4JeSMqlwaQSW7QeXbAnFQZDZyryScEOZGA6KhUW2yzYftWr04nYm0ALUPKXXVTCk11w+ZcZOtl1x059cI0dBIkwHbXcMYrosiAzfCT64a1fHDzakp+ixNWEFYOEcq69/AEg+xYMvsv71lsNeScoyx9mAAoZOT1QxmCoRNoGiTB6FPjB2yMXKksZ7MNigwF+P1pwqRPYZBlQsFueswkiRGCAUtEFIyPj1GbiUOMAgE+D76AACa3PLmR1HTdLW/akn0rZ55Xhgp1TlwPMkMjh4PMBxfWX4GHoHOvoljSZnh9Z5iArNkSO9sm3iP+TIq4N0yTCSqhYWxslIl6avse2r54PIbHR5QHmLXSpo6GmwvKele6TlJA4RzIs2fPQc0giOe4RwCzjHuFkXqTYMOuk2V5kmRugmEeA6HI7jsaphkaFIUlUBr70PkucUES+g5RU2TG2KvTrlRLINMxHYi3u+uJjTDJEqwAArWKoVpxjsmug3JNp3xIeIQoeX+1gVkZSTCaRFAUXr5LKv1GTQ8M7EMJs0wJy5atQGbaPETz86z2Eb+TOSC9PjMgt02HnOzw+QoFpSrhafFZX0laQycxZb16SX8/3loG+PtOLhml3o2cH4VKjdYc8TSKGs2ABkVeBeg54RDCJdt3Vkq0u/1GzoW6bqJctsxUx8Y9oZ+mSG4KnQyR23OiXPf5SrHgLbkglwJWG0Ex2JoCRaGZmjph1u8Q8cZwH9dfya3XJPLQcQhzst/Ic5NvcklrkWhTTgmKokJVSIGE97vJCC2d14oSRHatgQ8WAFezRreLL+Bkz6Q6Z0+RAJh28JJSVYdhGCiVSq6W16pf9sxZQdcJ8F0mAH8059AgRDIdKbZYrTMad4ahCmBifAyd5AmRnPZqbXnPKovYw6igNqAFtzzhl6YqhGlvSCAZon7fNeZ7UH5fikZio1wSn+nUJnS5SOTQj3AJCIZOoAWQZh2s0zGb18gBz5eELOUGCAtL8Mv4QzSytw6CqkggOTq2ZeQm6CToLhQKboulaOM8JKwvIVknRdySofpZMwnTDI1MVSx5/i5BtvUkIUgTM8Ft5zKoZJ12G+TsLC9EPUzXLMPS0BHjVytTGs/xUs1tbzxiR7BUNWqzNk0g3TnL/c4jaFjpe5B0j/XbCJM20xJGq75Uug1msgtSsgNSsp1qCznfTZM2eWKZKUrDClqDxSNyeWbEzfi0kcyyP0k27cfXiCDmPW8aJ7w3GdHOMZcliUCSuHeqlCQJiVQGhpbkmmyzz+dFtyX7mBX48JLXO6DNVBlfMB4jTQlewoNasIx7mBsdOW60jw4jlGDMkU3ThEkc2z5Npb0WDaLPHI0Lq7UNYrB4GmKZ8O00GcFMWM4xkhgPZk7811hmk9TQTZTrDQUSrAY9KIeh9SzrfaVkhxfQQbX8EeVYGoctXoxp02ZgNmFmTe4lpZKXhyyZbnMTepP7falapzS8vGi2vCUeGPWWeD+WwY6osh14yyvvJJoHrHnspLVh98HsjAXePTVa2MES1eRaIv13eekXWL8tyizb/r8t1+Fec90OFBVQPcaNbAKZB63uJlaXuAwBACoXnXctyAyYnD+0kMizhAGMESuKaLFSw6uvvozNm/+Gkd2bYNgmuEHpG5y8tixj64BMU1OHHCrwliWJosfICK28fIdsShsHPA2sJMmQIgmYsIIRSVqC+J25X+HvlYmO2WChyABsoa1O9K/EaS/vWU5Ztr0OyPpopplh6BjrIK8uulx7ewcmA6YcQ1etVnHddddh1apVWLFiBS666CI89thjDe/7zW9+g0996lN461vfiiOOOAKrV6/G1772NYyPj/vKLlmyhPt3++23H4hXOmRAmXVwpLi8jTTPCQlMrkNnCYVJL0litOaLTNf8FI6qKii2hSG4yNwtTnRGRVG8O1T6XXhbNSl57xssUFqFNE9bAWDH9s148cUXsOGlF6APbYNZKwU77gMolQmGjjponGsGkhHZuyjRjEgQGmnoMnZgBElWUFeTkCRAjWch2wSjKktQVc3VdJi1ErZu3OD6ZwyOjruERMom0MbHR6n5YMCEIXv9xDW5ZIKB1AN8U2RJcokq9lAOY5i9fIcGJDUKOZpCxI5MGtQ3pMkRL1KlzpE4N8rXRCJImxJEPLNriDX7DPMpYsuHXfOZ95Y8ST2ZmxKgJdSstjbG7CdUGhSbWQ7aI1htXlMh6uFoCoj3481/hnAOqhdgpPrEvzw/FTLgCqtd1Zh3c0PLm1ZKj6CcaqosQ052umUBuObQgH/PDmKwgiKtkgwg6esWZnJJXQ/0ofP3JVsHmZqiUmd94njSe3K+G6Hz3ekHKZKEnO7CnLkLYSiOn5CE9kwbOjo6rbD99l5Xd60pgDIRpCpFmLXGGW0wmdaEF4WR50MXtF2T0U0tE1g/80VrHwiTQN0A7ABXzjxWFBXLlq2AQpwVlQa+6krA/scz62d90tw6CIFQIp3x2gsrgfiSJUshpbrc67Lk5QOLElqTMsnQBQSScSxIqGtN+HUapAZJkty0IlT05koJum71gW6YMEsj1jkO//kMAA5/FjQvExrJLDOm4Ryah+xzMsCRYZqhjA8Jdv9zBXDJDmjpPObNW0i9RNh5SJ21vCBfqQ63XbU6LShrRIew7XPLcILDWHllw7WQvDYrUVp4ORly0AFTMG3BVVddhd/85jdYs2YN5syZg3vvvRcf+tCHcOutt+Koo44KvO8LX/gCurq6cN5552HGjBl45ZVXcOutt+KPf/wjfv7znyMajVLlV61ahXPPPZe6dsQRRxyQdzpUUKZMuvxTJ6LK1MECBEsYXdgLL8zJng0q0UhDx9uunHxm5G+sAH1owjuYezqcAAS6pdWSJNdZPAwsgR2U50oySQLf+t8hRM3yGGTWbI+MoAeaKHWvV0vYunUTisWiF8xEponqUIaOc40s304EciirbTjssLnYtG47YM+LqKqgLdFGHf61uo5XX90AwMrXY4yMQc5MR9ImNIeHh3yZJMp1fp85YCMgkvOBlcDFNIUiPt338kkjrc3fTY/BBA7hEbmyLLkOHuUGgU54iKh+qTcAX8JUINhHNWh9NdJyNyKI+VqPxsQPHcGTMZ1mUgaQmjxWK0RK+R1/PzbandcuhlkNYdzZtBfkXOVrKr3PbPQ6FlY7rBsMguAfHB6FPrQNAJDstrQ8rF9wqVIH7OXVFqf3magqE0IGxsSY0dBJspMo2ikTzNwGSeqDGLqoKsOReTWroSPRTFCUoDqoPUU3AsefV+cQkaw8GfX7VznvK0kS6lIEEpHTNKp6+6emyJBkDSaqtnm8gU2bXkWJCFIVj3l7ZIwRPFX2IRBR0HZNvrIJfvCtWDIDDFvaQzKSshXx19bQ2Q/o7T3M97xyA9NwKg9dldwrOSaXpDkemTOPeKBmm/SbpoHM9CxmzpzmvpecnQWpMo4FCxa7WrUoQYO4liCchNDUsyR2X+TPdZkRPnl56Dz/61RUxUihAn1oG/bIKcxutyx43Gi/4/2QTE/jSY2l7UMXtFeRwchGSlWkiVyyPCEgqQ0mGTo22jEQHDnVz+xI9v8y5GgSmqZxBci89pPni6pqUBTVZXgzmSy0ogHYZrqN9lWyLWHtJb+S7SSFdnqNDnBHCT0obamMpUsPR7FYQCLR2DLrUMGUYuheeOEFPPDAA7j66qtx6aWXAgDOP/98nHPOObj++uvx05/+NPDeb33rWzj++OOpa4cffjjWrl2LBx54AO985zup3+bPn4/zzjtvv7/DoQqzgZ8QwJfAclXlpC25/X9NN93CYQRCXWfD1HMf4AO5aVgPNsH6towRhL8TMMDyk7Ckfga7qXA2melZzyRhvFynCBHyvRzbe4Aw/iSa42xg6XQbOju7MCQNYu/YywBoXyLy0K2OD6IgW8Sgs7lKCi2ICEosHvA6lIkmSWg6Ts+UZFi1HMApopnodzcK4thrSPZ0+94VACLZ6Ywm2L9FRRnJd5gwgDcnWem1c02VJde0paYbGCYIQX7UNK+WRsRaZzqGvcRBG1QnwD+EA/PCcdYha+IJ+E0uSQKskcmaWy6AT5VlCbCrq4YczmpAf5FSerJNDoPtMNkmdeASBD1x73i5Fupvotr+JpYvrgGz3jxjW62H7zuSbYbrvFmpVMaLL76APWOeuZsxugsbN5poy8+CWbGsP8o1HYZsGVJqiuybFxFVcRmoSkiESTptgv2Bs6c4SAREvOXt7VY7CK0BydCp/rHTFNm35waawnE2bHYNURqeukF955toeRdJrXF7Muory/oHlgPOOTIdTN0wMTQ0hHK5HCi0c8xUnZQpZICssGjDJHhaO4A1CaSJVufdyfQRpF9XtVyEWRgEYAlZMpkst15eCh0SQfsfT8hECnOCovF6jLXsajPdoD6ygnimE7GYx9ik4xqkaApmZcLTEOp1rjYR4JtcBptnep9N0z7H7LKzZvdgcM8uxi/ZmwMkM7F920bueeAUGeh/zRX2SLH57u8Z4gzoH6sglghnekhz6v4x75zJxP1C6CASIIxBcvbeMCsFsmtJoZMEYPHiXhSLBcTjCSiKgqf2bHN/J8+MIGaTvcxz8eEFRanX6wxz6XeHcKAzzKosW9FXJxOmlMnlgw8+CE3TcOGFF7rXotEo3v3ud+OZZ55Bf39/4L0sMwcAb3vb2wAAmzZt4t5TLpepnGBTGRXC7l1TZO7C40lmmwk3CzSIckmGxGVCNGuNNIBOHbJfG8IydKTteTqmUb8rsl+6x9t6SGK8XKsHam5S+R6iIdR/Vt125YqiIJFIUMl1K0QqgFKlBn1kB/ShbVQYZ0ebIkXpyHZhGrpGZmRUFD/DY3wcOO+Xy+Xca6TtP+kjaNRKhOmh/azMDEhqlA7/zyEuKcl3Tae0BSyRyo/gyJ+XrOM+SaTwcw7yiRTeAc5fL/y5y9OEBR10vPfjma2wxAcpvOCZSDXrV8deD2N6yHcgTbQCJcYSPd94fjcAvZ/IkhTKXEuSRPV7o3x8EvVu/JyEJMh+GxsfA0AHiQGASqWC7Vte9UKX64a7t8Q5EfrI8Rkp1oIZOmfNJ9ptUysgkvLWIvt2QfMs0GSNuE6ap/OtNTiBVTiaG4B/HrBzktWshvlJAjQBGBby3bnm1G+YJhXIJUr5akqQ6jZzbpqYmJiw6/fGI5fxzAbZ5+1LHrpgDV3w2nB+I4nXuk6cGcO7vfaFEMW0WW24ySWJRmkLSJCmp6Twzkl3U6kHWz5oigwpngUQntPSgfVqdJsDA/UwJruOBlCSJHTZ/lSUZQAxtkF7CiXINkwMDPRjeMiLWF0e2o7+/tdQLBagSbRGlfRR5PUvqdEjBRg8gajEYYYAKxARVY7SUtpCfWI8WE0obXJJxyNwmCMnNxw5d0gaKWjvCYtoyX++NQa6rlPCjo72zsB7SLqyFVeeQwmTs9UB2LBhA+bNm4dkklaRrlixAqZpYsOGDS3Vt9cOD08SqA5+9rOf4cgjj8SKFSvwjne8Aw8//PC+N3wS4LVRT8rM0yAA/M0xhH8AAFfyHqZlYcPyksREOsbZsDjEd1vC9v9iJJskClWSMVApMxUroErj5aIpstt+3TAxVvQORdJvRlEU9zCKROOYN2+BL98O4Pk+JKOqe9EKw22gVqvite2vArZfGpW43LHz12i/vxAFnS/SIMCmWiDGwbCkzrzw5bNmek7QVUpDRwQSGO1HyQ737dQqKRoMk2ESefb3TGALMvBKgiUEWwgwoin0PCPfLZfySzrJpjVMrMvp+CANHf+w4hblByDgMpR0n5FMBpkTzS3fpNbOapt3Pcx8hiw3XgpnKAF67yhUvaTPKiNQIu+v6fS7kYSOg2YiB3rXvM+NgqKwdTi56HYNe0EzZtgafHZeS7Z/Lq9akui1ggCF+xJK0RTiuelYsGAxJCKKH1s17x1Yv8egdyPBm8c8U/hgk8vGc40kTK20BcFl2bY2ylkH0IwbqUljmVBe9FVHaCVFU5RvGxAcdCGAbPVfCVj35HUTYLTX1v8qYTpasxmk0dFhNwiX07543LMqIU2fSS0sl6FrRcgU4CtM9nuM4zdL+h3GONoVSVYgRehcpUHPIpOSOyBTfdB1e5/ZCJeyLGH27DmUv2HdMbMkLAl8jBM1ZgYKhQna7UMCBgb6sWXLJtRrNUJYLVFnHD+VT0CAowBNME8IkkvSZxw791gtsN+qwvtMrzn/88lzmmRAg00u6e+NcmU6AoFEIkH50CUTbF5b7zMZxT2Ixj3UMaUYuoGBAXR1dfmu5/NWLpgwDR0P3//+96EoCk4//XTq+lFHHYVPf/rT+O53v4svfvGLqFaruOKKK/DLX/5y3xt/iINK+Brk9N4k8UyuxUrNgGESTvwNzMVKNd1deJIkoSPVXH4Q1wSRqJqNwElpeqIq6nUi75Tk1/AFHbZxxoHfew+aOZLjGci5HuRnzEE8nqAYTIcprVYt5tVitqxrjv/G7t07MUIwjCSRqEgS5ByhBQR/EybRiDBizRzYiIYOQxSPqFDa51iJgLM9mDFjFpLJFLXJa6rmhXOWJDeCqMEEL+HlCWKTTocJA5IcCWWQ2WlEoceNZOjYCIFAmIaOR6D610aQaVCjcSDB8xfhS8hppsuZ+zFN4Yfg52kyAgl973MtYL6z38cIhi7I54VkZPr2eqHhWT8oNuopSYjyxj/YXNLfBkr72IxpEHHdNIGBMWv9ym3ToKgqcskIFEWx1r/DgCVyLuPFDfHNmDAHRbxzxlGSJECNIRqN0XsKM348H84g/zne/Q54WgDefA1k6DjX2H4gBXcTlVpDJi3IlCoo6V2Q9phlIiKpdqJeIueXrEBKtPvOxiC/Iq42jtOuIJNLyseLCe7jvDspYHXOsuHhYZ+mO0NoFQP94jhnftA+yiOGg6Jgk3OCDSIDMKacbPRg5/+Y134p1tYgGXtjTQ9A0zKkgMh5hba2DGbPWwg5Mx1yZibkSBKzZ8/BwsXL3EI+E0ZK6weLoTP5v1v3Ow+VmZQp/jYHMW5B13l7OXuFXEM+LTDXJN+7RmoUefsGKYQbJWiYZgRlAP885aXQSacz3tpQo74xIaudCMnjOlkwpXzoyuUyNM0/EE5Ak1bMI++//3787Gc/w0c+8hH09NCE8R133EF9v+CCC3DOOefguuuuw9lnnx1KNPPQ0ZFqXOgNRnaigrhtj51KRZHP+22LO/aMYecY7SeUycR9ZadX6ojvtsyRonEN2VzCrTuTjvnKtw8WEB+yiP8K4JbtSEUxrbvN14623aOIExpFAEinrHqjUQ1Ve4Xncgnk262+1w0DuiS5dc+blUOxMIG0HaUzm8vAkGgJVlub/90AQNZUxDkHcT6fdrVgnbkk9tobWTwVRVdXG5YfeQReGV8PvTCMWFRDOh3DkiVLkEqlUFNkaJoMQIaiKshm49i5swZZkd0oUtm2OFJxDYcddhiGigb6Ht9MPV+RJW57HaRSUcQZ87B8Po1MwnvvVDLqbpbxdIwat64uayxM00QyEXE30rnzZyGizsEepDH04nrr3oiJSmUM6XQMWlVHLCFDiUcQi6qUv0J3V5tPC1VXFfe5sbgGtaYjbhMcM6ZlKCKhp1LHKwN0cvhEVOX2Q2d7AgXn3VIxRMt19zn5jpTvnnQqhor9jmpUc8t2tvvLZtpiiBdph+x8LslvR7mG+E56rnV1ppHv8DtnW2tplH4WZw0510nCALCc6XllR+uG+z4OstkEt2wqFUXd3vPiSWJOMGt/oFJHfMeI+90h4Dpy/Hq7O1MoOaa9suz1L9MO0zSpttZkbx3PmZHljEUcRZ0W5sQiijt/qbKZEcSdYEmKN+86AsYuEY+gXrKO1UQygvW7R6FFVETTacxbMANvO3au2+YNlZd845HL+PuifbiIuM3QJlJRaFEVlZqOeDyC6dMy7vqoKV4fReMR5PNpxJMRd013dCSRZ86aZCJCMTxs35JIp6O+OQwAM6dlkGaI+HQqirJB93EnZw0BQCodxRghEAH8663dMBH/6y4AVhyiWCLivmuOM38Gq3Xf/AWssee1oS0VQ9VurhxR3Xu7mHZk27OoFkcAALF4BImoBkVVEG+fDVlVMWNaG3X+J5MRGE6QklgEcXvedXD2CDmm+drcHrA2yD0wnoigbJrenplPIxZR0TWWg/aqNRdlSUdHRxJ9fXWomgLZftfjjjsKbWlvTuRGS4jHvcjezqjmO/zzvX2oiPjeAljMmp7lCokSzFwDrHPQqdc0TaSSUbdMJpdAvFwj9lW6L0bcPSoCIzITplGHHEsH9llbOoZIVIVBkL0dAXMyU9epsXDuIGmfmiIj0WfRMelMBxYsmIU9I0XEc92oT+xFVJVd+gEAojEVmn2+yKggnY6hbJrQbIFIIq5R5WNRDbokI5JKUoGjpnW1oYOJHp5siyP+8h7fe3R3prnvl0xGIFfpNZdrp8fY2WMAi1nK5pLe/qIp/vlLzEmNmMuZNv95NH20hC1DluXC3lLVLZvl0IsAkNk1ijhBW7YlIr5yJYJ+i8as33PtCcjPbUVUqyPS1unb43NDBe4c7pmRCaWVDlVMKYYuFou5IdJJOIwcG6kyCE8//TQ+//nP481vfjOuvPLKhuUTiQTe85734Otf/zo2b96MBQsWNLyHxODgBGUDfihiaLiAkq0aj0pJDAz40zmUi1W3jIOJ8bKvbIUot3e4iF17xlCyF3WtWveVL0yU3fIDQ0X3sx5Vue0YHy/72lGKKBgYGEe1WkfNlsAODk5AtQ/Yck333k9VMDxUwOjoCMbtQBa6DpSYCEljYyXu8zvjGjaOlXzXhwYLKDrEV7nmPm/D1kF0xTSMjddQkxIwYyp0qYhEIotSyUSpNI6JQgW1ugkYOgoANm/eidHRkvsuAKDX6hiv65iYqGN8wt8HmiJz2+ugMFHx3TM8VECVMHGtVmqu9HTzjmF33GTDoOqWDRMFW+K1becIsokIRsarbnsr5RpGRqyNtKobKFfqkKUqoFshuJ1DfXhwwkccjBLvNiwBEzaRKUkShgcnaIFKpe57J9kwuf2gE2V37RlD/4g310oF/zwul7x5PCTB/VzgzPkSZ22US1X+/B0t+coWxksYMAxf2UrBP2b1gHVh1HSUmIifqXyKW3Z0tOhvw4T/vQBrLL31OeF+LhYqVHneewHWvsGrl1wje/Z69eqpiK+8XtNdH7e+18bcsmbFv59UiXodRCV/nQAwQcy17cQ9im5wy1cr3v6yfY/XDqNUxYrpGeqecqlGaSAAwKhGQ/e/kZESxsbLiMY0lEpVjAwVXKnzKLHvjSkSBgbGMTTi9fnEWBkDzDlTKdM+eUYtFrhHFDhzDQBGhwsoT9BCF958LwXMn2LBX7bCmROyYbhWFLsGxr15Nl7xlR0d4c81dk66bSPWcv+gd9ZVmDWq67I7vmPjFVTL1njrFR0JyNi7lxYelUveXFNMEyWbQRkeLiDJyPyGOf07Mso/Y0aIsd5RrbtmuJoiY3SkiHFJQrVsum2dmCjiD394DICJit2HcroL5ZKBStmrf4JzdgJAueAfj6Cyw0N+Ahmw1gYbBbtcZMZDN9w9avuuEby0ddB9RrVUo8qOjJB7lGr9lWsoTvDHuFisoFqpwSTOzLHREgZinLPAMH3vFo9HUC17bSgSc3wI1pmyZ6SIsv2MWhUu/aAoKrU37Nr5GjKJCHb0j7vXTF3D+HgZqVQKExMTqNd06LE8ysyePTpShMFc47UXAEZHSxjgmLRXynWUKnQdI8NFDBDC05puuHXWFRk7do8QNKB/v5SJNgwTY8Nbc0UOrQEEz/cJhp6JyZKv3PiYt+ZHxyxap1o3oLXlUSpVIdX991Q4+xQAlALm0KEAWZYClUBTiqHL5/Ncs8qBgQEA4Jpjsnj55ZfxsY99DEuWLMENN9zgOnE2wvTp0wEAo6OjDUpOTpC0ZCu+FLyi/rDz4b4ppMalGQdaHtwgIZQfnEfc8Hx/akTwkYimAYysIMgcJpvwS4atNnjlyeAdjpTezZ2iRpDMZtHZ6c1XVZHdzjRME/39r9HBH1QFsiRh1ixLm8zzhwuLcAnwTSPYW6x+dPwb6NxHJFIxzTWNLZRryCYiVB+TJhMS8a9u8ENwkyA1diOExkDjJL/l+ZgFdUNY8J1GJkdkX/BMYnhrJsh0mZcbKci3gM3fBvBzXDnPG2cmcSLAtIQbMCNg3auMf54D9p0zCf6zgkykKFNKypSTb27jEIvkHtHIhDGsHBC81wX5/ZFzYpBIg9KRivru4a3HRuaLpLmsItPm6ZSvseEJqhzw1wLdhiCzSIAfjRJo3k81aF7yup4318i2FSrhATuC9rpmAvBQJpdMn9EBdQzUdAlyuhuSJDX0JST9oJrNtRU0GpSPF7GvdqZjdJRLRQP0mjtnyHNcicSbzlHG96Hzl+P543rlOWPK1Bsn0syUqjpjMkzfG3SahfnQsZUEB3oKqJx4Kh1t2Ta/NUxIahQmvCAj7e2d6OrqxouvFd1Q/ab97x7bkkjOzkJXBzBvXo8bJn+XuQt9g7SAAAjev2RJ8rmRBL0G7/3C+tcwzYZ7SSauod8WZJNnPa+Pg8aoKxPnXg+LaOldo10xANpvvxVXhqBIv4c6ppQPXW9vL7Zs2YJCgZYQPf/88+7vYejr68Pll1+O9vZ2/O///i8SjANlGLZv3w4AaG9vb1BycoLcKILzmHAYOs6WwoaJDvOBYq+RB3krPhlsaG2AXux1qg02c0FEBtM4kdyCwObUckBuHjNynvmcE+QlLDeYKkuQom12u61yRYLhyM9aiGXLVrghqJv1ZyTB+9m/kXqfw0IYpwjiddyWCNd1A3J2ll0v8wz7e53xU+IRPkGMEDehbZORVwE2N5gRGtGLLV/epyiX/PnLCycfJLzgBSLSOZo8gE/kJjlBhYAAIjuA8Qrqh7Ck3nS9jYnIci2cQIhSZYnopJyqee0IWhtBudqCxo5NoQAAUiSBw2flfGV5T+T5vAT5aoZFA67rZlOpZtg5ETQWgQ1GACPCqSfIV5LL3HC6lz4HwgN2BBL7AXOY8qEj6mb9tlRFhpSwxtIw7bKybTLHeb84cW5QYdF5jeBdbKHPATYvoewGxXK2BDcwixaHxhFWBw0/z7eStx+EEcK8ullBIJuiiKQN8oyZYdBxFhzlUgLboUGMf1AkSDZNjzN3q3ay+0pNh6Rodt1WuWnTpkNRFCiEO5Bpmm5aHCmahCQrWLFkIZXzLIjpCdpH+SlsuEXD17kNOlE3sGfUM3nk7qGMnzbvuoOgvYAXBA1oLigK6bvuPJ+mW5sX/AiG7hDA6tWrUavVcPfdd7vXqtUq7rnnHqxcuRLd3Vbuq127dvlSEQwMDOCyyy6DJEm46aabAhmzoaEh37Xh4WHcdtttmDVrFubOnbv/XugQgh7CbDjgaaa4Gjoiclilpgc6+fOukYciG9EwDE4dlBSH2HR4TGW9ThzsEV5uMP6zggILkHtHNhFx+3G8XEO1boTnz1KIw9keC4dYk9umIcuEHJYlyXewNdrEeb+y95D9RxJV7GaZJKTxjullTTcgyQrkVCcTztn7t9EGDIQENeEQdkFpC3hgNRxkonlePUGbPo+gCIooyG8H7z0azykHQVJyXtlALSFXAsotSq3DPYTvKqtleX0MnT8wAV3Wew+zwV7VKD0BiaBIpEGMnkyMnWNGL8Vz6G7zS555z+QydKQmOCQlg18g4WnzIqrSVPTUsD2C15dB0eDYaiRJ4mqTrd+aexY5H6gAS00G9QGaC2ZD7kHs+MuSBCliEd2maUJSVcBO5s76h4U9r1k3+yDmJDASKfE8VfGEgM47Oeecku7inlNB/cZbB/xolsEkJa/N/ojWdAA0MsKkLyhKEFMbpqFT2XMysLncsXOShzv1kX1QqemuFlbOzER7xzQsXLjEbWc0Qae0cPNTGgaSUc1nVdNsAnAH3OTvTcwTBzxtrdPnpmlSmut6g7lO0nStBEhq1lKCd56yFjZsO7n7H/dpk5ehm1Iml0cccQRWr16N66+/HgMDA+jp6cG9996LXbt24atf/apbbu3atXjyySfxyiuvuNcuv/xybN++HZdffjmeeeYZPPPMM+5vPT09OOqoowAAP/3pT/Hoo4/izW9+M2bMmIE9e/bgzjvvxNDQEL7zne8cvJc9yKDy/gRsgs1Kvh3TOCddQZjUGQiWhjebs4RsG5mbp0xuUJwcQWSUy6gWAUAHDAlCkJSdbJciS0jFNDdk73Chwmjo6PsVWXIlf85QOGkWJDXKjcqkqTLqVY9RDZKKOdjO8X1gxy8RUTBsFxsifOvYQ5TU0DlRB91NXktAUTzCPyjlS9jmzjMv4c0HXpLjIEKAZ7LhgDe3g5isOCdUfokJ+gAEMwX+BK/+yK8eeAxLmISaRpCGbl+JZLKf2bqDDvGg9R1EwDfS0JHgMwvNXQtrW1BSbi6hpKhI8dKrcJ7JN7n0PpNh3MOIYd1obCJltcE/14LQiJEOqycVVQPr5llx8PbQoPXCzenGLdmA2OeA7TdZgi2U6kIqkwQiKUhFKzBGV8YfcTnwjOLlteRcC2pv0DCRe7x1ZqiQ013QK5Yguq6b7s28vIDBJpf+8WjFdBkIspxhTfUJyw7Gv1Nj9rXgMQ7a/wBJi8OUFcA0ICc7Qud7I6ECYDHcFdsFQTdMbNxj+V1Jioq2XAcVt4HS0Ln/AIgksLDbH4CDJ2xrDzFp5TNpAWV5KTI45cj3I+m09iQn1U2Qhq5J82KgFQ0dT2jqF9aT+QG5DB3nWjRA+DUZMKUYOgC49tpr8Y1vfAP33XcfRkdHsWTJEtx44404+uijQ+97+eWXAQA/+MEPfL9dcMEFLkN31FFH4dlnn8Xdd9+N0dFRJBIJHHnkkfjIRz7S8BmTGY1C1gJBkm9/OcmWbDkbRCPzmSBiJMjemgdnsZMHCGlaw9PQ6cS1KDdBJ/9ZcY55Jm8D60xFXYZu70SZStDL9jHFEJomTFg54KSYdRC0c/KkscREEHEaBrbZKULzRpnXcQg4B8741upeioN4NArdZZglqIoM1lAwbFNVZAkGE6kwTHNHM3TBdbptLrO+ZhyTy4CUCjxfoY5UFBuZIGTN5qFjIwiS4DIsAf3AfYcA5o9LrAX6m/CvOznX3PsD/DyC/NGCtIe8fm8mQbB7rRUCg1MvmYja/zy2YgVRlZ/bjc2DCfDNtakkvPVgX03ye90wqdyhQWbgYWG8WTTLeFn1MPtBgP8cwGemefOy2XcIa1fQnpJJaMCg/7ovDYF9vxSJI53NUz68vHOqJQ0d51rQPGvGSsZNY6HFYVSs8uPlqpv/lE3JENbeVJPm7KEMHY9BYsw+M0RkybFSLTDnYlB9QDATrchW3jo5MwMwTetzGEPHM5NnxpjKS2iYFC3D3i1BgpzugjHeD5ie0FBSIpjGoWVyHIunoD0RCEhFECRE4dJlnDoDrAN4mnny+aS/KNckOugMbnK+844tdv8zTbOhqxCvGWGpWw51TDmGLhqNYu3atVi7dm1gmVtvvdV3jdTWhWHVqlVYtWrVPrdvsoLU0AUGR+AQWsGmTArB0IXnIQmSzGY5YamD4LRNk7z3GJ0YBzANALgHB2m6FeEkIA06CnimRbxuINMBVGoGIkpjUzE9koBZLcIw7E0zYkU74jGR7IHbyIcuFdOoXCy8ICMkgUH6E7CHH2lyOVG28kbVbUcOWZKQTqUwMjLs1atF3BQA5PuGtZVMMA8Ez0uWKArqB/J5wwShFmTCyHtePMInEvgavsZ+WGHtDfotOHgJTwPQPEMXdNAFjRNrgi3Z2lI2smMQMxZEvPC00a1p6BpL3t3rAUl8myaUFC2Qwc7ENWrvAxr7uVH5DmX/vHYYZtM03QAFAJBN8vfKmKZglPuLH7zXCAoAFZaD0AceQ9eC9oc334PmVJD2mme9wJrUOdccGKZJMdi8+dpsomQgQIPVIkOXIQht6t70NMRiCpJtccg6R5JggzffNUXmW85w810GCw15w8eeUaTQqVo3KEsJtu6gGdWozyRJdm8O21u5ZudMedZMn9zbZrXTAi1ZAqDabhMwPR94ReUKPNo49E3QPmfV3xzDAvDpMp7Ahny/cqPAX0HCAK51Ar9ss5YEvHnmuJnUDQOmnc+WTQrfTDuChEGTAVPKh07gwKEZHzreIRo0wciNiUwEzJP6cW31ZZm7UQDhkiaFiGxZHBtxP1dqdehD26APbcPEQB9eeumvKJc9goh/SAQTzuwByPdJoqV7egNpkiJ7/htOAm5JsfOxcTZCP8HHba6LI2bnqO88Rpp8r0pIUJQko6EjfaA0VfZFj41Ewk04/O0I70+qbmZeBvo+EvOM9J/jRQy12tA808N7ZngC3PB73d8415rxE3LbEOR4z1lzzZqIAsFMD7fPAoiUoPXN0+g1a/oHtGY62Kxfplc381ut3JIvDO/d2CiXDhoxPSMFTyjR2aRQIihyL8AnfoLmcDP7nwPW/MtiTP3lWvGpDXpes4mLAWsth5mkGiawa9g7I3h7cCuBLbh7RIu+Rm0Egx1VZbe9dVPG3HkLoSSy7u/Tsn6tEP/85a8NnpYmzPeIr6Gjn0f2S0036OTmPvPX5tcVAEhck7vA5nLrZ5k0iuGp0i4ZHSnGBFfy+sA0gJrN/EmSzO1L3j4Tpj3irYNASwJeF/EY7oBARK2Y2/IEcEFWGc365AY9i7LAqtYbRszm9VlQNN7JAMHQCTQF0jwo0LStBb8bcmMaL4dHrgxSi7eSwN2x+Y7FvQ25WvWI9i2bXvbabOqUdg5ozWxAkiSfxLcZhs5o4CekSBKgxSFFk4AkW/4bqs3Q8SIjKsHECA8s8aBxTQq9a1SUS44mzxljwzTxGhEhKxlVUS7TCehjnE0/lKFrIWKVT8oeQLTGgjTBTaShcBDEnPBDNzfnGxo2avwIgc0TrWER4djfWnk3HmEI8OdUcNnmtSkxjj8Q0DwjHSSV5bUtLLS/73laLJgBZMryNOJAc0FjHJDvMTDurTEfcRlQd6jwIERQ1uh6WLAMdr6m41qAMKB5Ar6ZaJZhbQAap3kwTJPSsPI0dEFzhW+CyiPIW2BAVYUyjZQkiQmtX6c0SDyrDt47VDj+v0FtCGqv1R7/NZZBYVPSOFYdmiL75jvPVxkIYYKbtJxwwJsqbHspISBrMcJU4OxbUiyNUSL3WTzC9y/lmdqH+cFzU/wE+fo2qc0j95NGkZyD1hyPeQs6S4J86JpJWwDQZ/VoqUZbljXJ8AYxlZMBB93ksq+vD7fccguef/55jI2NwWBCbEuShEceeeRgN0ugARoxG4B1KCqyxI32xSISoA3hSy6bJ3KAAAbQPqg6OvNAn+XMVK7pqNdrqFarFHPC89XQQmzXefATShzCm7imG0Zo2gKnTkmSICU7MW/BXDwztNWtmxsQhPXDa6CiY9vYyBHebOBXmY5qLjEwOEEzdNGoikLByrGjKCoy8QgGxmgmL8x8h2s+GFCePSSCiNYgpoAXPAAIJqqaKRukhQB4GroQ7Qbnp8B6eXNKkQJj/bBBdVoxuQwilLgagBbKAkCCQ8i1oqHjETlBmoXXG/hBTuW5AQSsso3XGxAsMeZNi0RExVip5rsemJ6iBYau2YihgH99hgtnmLItal6CUoLwAycFB8xgwTd/9T4XGY1MM37MDprW0LXAmHakor69IhlVXX8my1IiPFAOT0iQb2tOGGBda94kMKopvueRc530w+IxaUF7TCta2CCrA4C//7AMFXn/wLhHx0zP+lNeuQxdPIc9w33W52gyUFulKbJrQuggzDqAL7AL0FbyrrWyvjl9HyQU4wYtsoUNrLCgWQuXoHaRfVmu6pQQqFGqAweNgscdyjioGrpXXnkFF1xwAe6++27UajVs374diUQClUoFO3fuhKIoboJugUMLehNBUQD/4g1OQs5fuJ0cSTKvijBJIG+7chb2rK4O91qhUscrr7yELVs2UT50XDOrFnK8AMC8fIr63miz1U2TirTZSKPXKKE2wDGnaqDQZB/JOzCDDh9en5FEpBP9C7BMMPL5LkQiESiKirlz57VMODdrrgY070sYRNQHMRw8jUGQdI93IAWGHmevh8hHmhl3BzzGNCz1hz8sfvPEZWBKhhak5E6EUhZcf6cmLAEc8IicoDHmtTdc28QKD+RAho5FkOAgHdP4KQNifgYiSHIeRKQEzRUegqLIcuv1+dAF9xm7t7ZiSuf4DfLQzQk00WxYdKBxgJqJMs3Q8Rhy3jnXmeYzSLyWBa8N/9rnrWVSE1KpG5TWtplE80DwvLSSqQdrrPzl6e+8ORlRZX46hRatC7jXOcXDrFbY+jVF9o0HOUe2DxaosiwcGkOSJEQ750HOzYac7AxlINi+D997ONcC3o+f35VzBgfcz6O/whhTHjqYfTHUnJTV0AW6/ZDzXadM1JtNLB7q73uI46AydN/61regaRruu+8+3HLLLQCAf/mXf8Gf/vQnXHPNNRgbG8O//du/HcwmCTSJsJD6JNjFG+ivFMAgdab9xA+virDIf2zQBbJd6ZhmmSzC2mAdZWJVDzcnSER57QruiCSzSXM1KYzJZSPpKbmZbxnwGKTuAAkqL1JmGPwauubNEXgR08hgIqS0NZPQoKoaFi5cgiVLliIWi/OdrMMOW84Y8SKxAX6iKlhDF8QQNh9gIehQYomDViKOGiEcHe9d2gJMRFmmReaYBpNgD+1WiOEghq6q+y0ywhh3tp+ChBdcRkaRuYdzK2aqvHkWFCQHALR4ErDNoKWYlQOsWSf7QO2uLHHnFW8t8oiqsOf7cnuFtK+VfmPXhhIigGMJyaD12YqZtVU+WLDnq4fzUJ5ZJPk8MijTLCI/GYk4zycySGvCMSsLK8uusUapVao1vWGieT6h3/z8SYZFM2VmVxDhzA22wjPVDhrLFvapMFqG/Y2n5Sb3B1KTxquXTDlQNwzX3zasDX7BSHBhvga9eY10K0JV3r4Y5GsedNaxNFwrKVOCGFtyrlZqOsYJawXeeuY9spEl06GMg8rQPfPMM7j44osxf/5838Zx0UUX4ZRTTsH1119/MJsk0CSaiXIJ+DeAYA0djyDXQiVQJMIcV8N8GSRJQiLbZYmzJC/inpMjTU53Y9myFZg7d74buGPGjFkBZgPB7WPD7zbSuI0VaxTTwyPCyPeizTv46RuaHQsH7K/NakcAvhnb3M4Up6TnJC1JnpaKR6yFMT28/gwKjR4WqY6ts5UIiI3GlARLkLSSuNQIMWHmPS1IUsoe7vFIuB+qPyAEvxzvAAxiItjw3GqIphLwz8Ggd+Mx8+1JvwkaALTxCPVAbWlrIfgVSYacngY5MwNyIgcgnAEkETbfeXOAL3DxM/NBfjQATwDXGsEYlI+PXQdsgCYSrGarFQ1daNS/FtYnb6/jzWHy/iIRsKMVDWhQLjG2aY3OQrafuJFpSdcGwqoD4DMovO4JDwIU/LxGZWd38Jlgnqkjb802beEQcj2MeA+L8OwgEyA84waACRHYNNuG0OBCPoscKZDp5Ua55BQNsuDgrWfemdbdFpxayp/jMaQf0Fw/kHWWazplfs4TgLXiNjEZcFAZukKhgNmzZwMANDvJYrFYdH9fuXIlnn322YPZJIEmQdITvGhRDpp1sucyXYGhx/2VzA6QiAJ+os/Je+egIxWDkp0NJTcbqY5ZAEwvR5oWQ0xTkEymsGTJYVi2bAVyuXauJiF8c2W/hxMXkgTsIXzIeBJ50vxijHCqDpKMtRL+3mkDiaBQ+zzmi7eZZwLSSvCiF3I1ki1K34MI7VaI1lZ8vHhtaJZgbOXQCHNJ5REdzRLEjZKnsoGBWsn7FMSwsnOnEdHKEkFBPi+89RKoseWYkAVHzJX864hb0rnBFlQo3lwMWp9sd4YRzl0cLXyjoCgOeFHmHLQijObxZDwGEvAT9kGaMV7ZVs6MMMEIj5kKWhvNmk+TzyPTtrSSXiDYJ7IxA0GXp7/z3oGce6SPcjLKN+MNSrQc2Abme9h4sM8L2t/zHJPUoL2d15et+Ca3khJmes7vFxckrOEGVAlY3+GpHpg+C5kTPiFKgDUDwF9jPIujQD/FAG0eO1d4kVTJ8o3a5KBZayOSAR0qVDFWJhk6/17FqyeISZ8MOKgMXWdnJ/bu3QsASKVSiMfj2Lp1q/v72NgYdJ0fUUngjYXeRFAUgOe3xS/LI0ZYB/awesKcmX2EErOxkQt2rCYh3z0LphKBnJlB5dwJC1kNhG/EzWjHcgnvMJio1Kl280wYgw7WZjUyYSGaAVAhooHgADVc7WFA/ixeHTzTxgwvUWlo//LMB5sLPR92cHRxiIlAfzAeQxfoc9BYYxsElrEKQysHfiMGv4nYRoH1BCWBbrUf2LEL0lZGVD/xEpxmwd9HYcRzMwGOHPC0JkGMGltLaNJgrskZj6HjayqD0Ip5EY/gC1ob7H4QJBQBmtfQ8czxWxk3Xru8Nvjr4TELQXOqFS1+nuPbB3D8hBqMTTM+z2S79lJRT4MYEb/GPMxkly0b5gfl1zbx+yzHyZnYbIRTidN+tw7f/hfYVO7vvHOr2bMXCO7H8FQP9PdWTC5b0ZYGXQt6XtB4sOMfZtLP0k+hzHWT7SLpiEpdp1ItpLn58PxtEnnomkRvby/Wr1/vfj/uuOPw4x//GE899RSeeOIJ/OQnP0Fvb+/BbJJAk6hQCVSbN3MJNLnkbGK5MMLDp/EKLNrQ3pok2ncNF6HFU1DapkFStFBGkX23VswfeIRTMqa6dZSqdRSJzYdnDhPU74HmPi0S8GXG9zAo6iPPDIPHgAL8ceaZgfDMUcL6l7W/D0tj0QpDx5ZVFX6OoKD2saa2ZD0kWiGkm2WsgHDmr3WGrrkH8/PbBc1VhtBv4EvIMgJBEl9Zknxlg+puNhhEUD1h3cb+1ApxEEQcAvz28STOPKFGkPaRV28oUcV0Z1RVQrQOze+VvnkZ4rfDC1QRBLYeVZYDBQ08Yp23rzYrqPDa0HxZtusbMXTNaPTIuUsGiOAJ0Bywe0gYw1FnfGLDNV7096B6ef0eJDxl37kVf7RWfcp5a1mVpab9VoPGvZXIla1o84LS7QB+f0agubQSThuC+o4928P2P39apcCiTdNe5LlTrRuuG4sliA7P8QlYdEWjeXEo46AydO94xzswPDzs5qC68sorMT4+jjVr1uDSSy/F+Pg4Pv3pTx/MJgk0iUb5PNzfWA1dQDkeIRCm6mbzKIX6/jBPJXMFAVZIYef+wYkKRgk761C/rRbMH5ohlGRJoqTAJAHN27hbkS4DrQdFWTItQ30Psp9nD1xJCnF8ZqTcll0/R2vXwF+FRZYhZsMIuwiTxDwoDx3gDyiZCPEz42lUgk0umQOphUOjWcYKCA2I2TpD1yQnyWMigvqBDRIRprkB/HM+LEeQP49isNaEncNhfeGbw4ElORqLMNNapqKwsjztTbPRRcMJxuDmsWD31WwiErg2/P6aYUwl/T2svaxJdCsaumRMDWmvv3957QgSngWt0VZ8ctmSjZY9W563XwetlzA/UHa+B/l+AUC9BWkT2/dBDB1vrgRphNhzI6ytrWiEAD+DwzujJEnCspk53/UgP36un2Mr0R1bEIyE+e42a3LJY1aDXGMATvCvFtZnqHaX+R4o9CGukzEJUlF+rj+2DZM5qThwkPPQnXXWWTjrrLPc74cddhgeeOABPPzww1AUBaeccorrYydwaIHct0NNLtlDtMnIg0D4gk7FVPSPNdcGFmxwjkRURXdbDK+NlgAAz2zZ6/7WisQ3VEPH2rOr/LLZRIRKQeCAd4DxfWbUQALBb2IS3mds/3cFODSzUrd4JJhQyqdj2D3i+ckG2fU7odlJwiisf9lcZGHaDV/agpB6O1NRbO73IoiGMe08gqTZqGCtaeiaJ5paWZtSA3Fes7QaL0FxsIbO798ahlakzj4fkhDCIx5RqDxiraRiCQ8kQ39vJZppeHAE+ntQYI2YpnDWUfOmy634sQTltgP8+1dQ6HuAZyoWElVRlUG4D4euT/a3MGKN3SuD9rR4REE8olLEIsA3E7Tq5WnNFJiMZgvwz6sapwwJtt94xHNHKsbNDRvWF36hRHAfsxq6MLDTO+i85z2v2ZQpoSaJTWqC3d99DCh/vi/oSuPJzQOh7QKsfk1GVd95H+QqYN1Dfw+LFss+MkxAzt3DOJf4aVBaOWOaF/6HCesaub+4bQvYD3h5AQH/GR421ycD3vDWT58+HWvWrMH73vc+wcwdwmiU9NoBu+EESQJ5xHBYyGO/diGwqO+3GRxnZjLCFrnBhjnvsxtZuD07/T2ISFkyPeO7FhRtkVdHJq41He2rkQ8dAJzaOw2ZRATHze8M9o9h2hFmktPDRDILyx/I5o0KG2P2cA1jxFnb+bBxY98tNH+W6jc9YdNVOGjFtI1FC/xcKA6UySXPrCbIBJclXhr6sTDdH8a4s+MaPifYkNnBbWjFeZ/9KUxj0GwYd4DD2AYlK5f84ezDBCOtpC3wadJa8l8LY1ZZZipMsEePW9gYs/0ZNnfY9gbtaZIkcYUKwREM/dfCTGCpexusjUauBYD1XrzcrmF97NeYBpdtxHRS9TIVBwk7eOdcoC+XTyDRPAPR2IeuuXnJ04wGleX1ZZifGbsHh5pcMi8UlmOU1038KLb8XIFBYOdgmCWKT0DewpkYyNAFDCrP/xbgpWqavP5zwEFm6JYuXYr7778/8Pdf/epXWLp06UFskUCzMJpNLO4za+CX40fTat7cMVSyz/w2k8PQLZ7mZ6SA8PDaviSqYRHWmjy8uA7gAW3gRrBrgQluJhDHwu42vPvYuVg+uz2wDEuUh0nJWYIrjABjgzeEaRZYxjCMWfX5WYRq/lhGMZwQZetuJR1Bs2glKErYCPMCyeyP5/LGP4iYYPuH1RywMBh6Mcx0z2cdEJqwt/mQ2f4ojM0zJ63Mh1DrAKbe7pDocew+EcYo+nzCWgj4ErY+mz0HeL+FBod5HX7MYYRzK+HheXt2EHPLE+SEnRthbfL9zqz2oKiuXZlWGbrmNXStwHcmBuUSs7XM1L1N92/zz2/Yv8zPYXsPawUUbJnkf48wQQMZOA1oTTsWGuugSeaJR7eExTpoxSqolf2kWT9mSZK49FMYnUQizJJgMuCgtr4RgdAK4RKEarWK6667DqtWrcKKFStw0UUX4bHHHmvq3j179uDKK6/EMcccg5UrV+LjH/84tm/fzi17991348wzz8Ty5ctxxhln4Kc//enrbvuhDIOKchlcjl1oYZsm69QfziDte0QkntQlpik4cWGX/zlhGroWQs+z7WX9uBzENb9pT5CWMMfJqxXqH+Pb4PcPs8H2Zxgh2gpDxxK/Ycnj2X4IS/LeSll24w87ZOzKKDRPrDVVDEBr2rwwtCqhbiUYC6vxCg78QD90vFzjlgtCePAS1hQ4eCx8uQlDOoOdE2Hd1qxpJK+isDawP4WZzLGEZyuJ28Pfrfm9J2wfZeHbp0LWEJvHMNyUk/4eZn3RCngEeFA7eFrxRoyEg0al2CBWQf3GSwMQxpz4tEIt7O9hYPfdIJNoRZZ8AqEgRobnox0E1geu0b7qM7kM2U/yTFqRoH7hvXOoYISZs6347Ydp89g3lyW+zygvmAhvPjloNsARr2zYPsXu5WHjzNv3eXkMeWjFRP5QxCHV+l27diGZDM4v1gyuuuoq/OhHP8K5556Lz3/+85BlGR/60Iewbt260PsKhQLWrFmDZ555Bh/96EfxyU9+Ei+99BLWrFmD0dFRquwdd9yBf/3Xf8XixYvxhS98AUcccQSuueYa3Hzzza+r7YcymjW5bIX4ZLUfYQd5KxGR2N0qaHNdOiPjYyKCcqdZ9TS/AbHEc1jgEtZ2PEiSrCmyj5gIZaYOEEPHmgyFabHYcWvFNLI1s8RWnPODf2M1Fo2IwFZ8SPYZLdChoUFRfGsovOJWfPf80UGba3S5tv/S1LBEdZgPnS9QTkhfsGuulQS4oU72TPe24v/TFRD6HvBrakIZL1ZrEVjS374wk67WQpI3zyiyYxGU4w9oTevmuzfkN5apBEJMLn2mss3nuGrE+DW7PFmCPB7hB4hwUK234hfXfJ9WmZRUYfc2ay7LJicPaw67Lhrtb+yZ0krAlaCy/NyIwW1oTdPdPDPFzq1EQNAQwD/fWzG53B+RK616WO1qcL1cd56QvYpqw34Snr5ROOBBUR555BE8+uij7ve77roLf/nLX3zlRkdH8dhjj2HlypX7/KwXXngBDzzwAK6++mpceumlAIDzzz8f55xzDq6//vpQLdptt92Gbdu24Z577sFhhx0GAHjTm96Ed7zjHbjllltw5ZVXAgDK5TJuuOEGnHbaafjmN78JALjoootgGAa+/e1v48ILL0Q6nd7ndzgUUdcN1zQqKLqag1aWA0+DVA8g8Foxm2Oj8wW1V5IkLJrWhr9uH3avsX5cJPz5kgKLNu0vAFiBHkYJP74wk4ZUVMVEubmonKyGa39Jn3xMZchBx45baD8w793KXGrWpKIRWKECmUqCh2bzM7Joxfdkf4Fl6BsRjM1GuQRa8xEkg3Y0MrnUWZvLEDTrBwVwNHQtMPmtaNLaQwj4znQUmwe8ADzhBK6fYAzSbbKWD+EaAMbnJeTdWN+xVvzXwhYz27xwE1F67wkdY9+7taBNChkL1jQ8GVVDI6ruKxptJ2z05iCwGotGxK2+n4RjLEaLzWvjC8y+GxSxkV1fYfs1O6Zs3tVG5VtBkLljeyoK7KGvhe0nflPD5oU+YcwJK+wKM0/PJSPYQsR8CVv3rDlyqIauBYGL73xpYGUzQORcTEbVUM0mVe/rWK+HAg44Q/fyyy/j3nvvBWBNxqeeegpPPfWUr1wikcBRRx2FL37xi/v8rAcffBCapuHCCy90r0WjUbz73e/GDTfcgP7+fnR1+c3sAOChhx7CkUce6TJzALBgwQKceOKJ+PWvf+0ydE888QRGRkbw3ve+l7r/fe97H+6//3784Q9/wNlnn73P73CowTBN9I95iyOiBodxbxV1nT44opqCeim4HSTCiJ9WiGX2cA5TzbMagPAEw/T3MEaGJRizieA2pGIaMOp1UlDwCcBPwO8vho7VIIYRVa0Q2WxupFYMsFshnFrZtJs9CFptQ60FKXgrq62VPHTFajiz2kr/t6LN0xSZymsZhlaIy1a0QixREraW2TkQlIvMqoj+GmZ1wApCwtYGL2pkIaCsz8c11Oe5eZMun0l0WH479pkhw8iOW9g6Ys3egnJlAv4UGWH9wCJUU9mC6T07r1qKWNtg5Xe1xdE/FnBgEkgzWsxGPnEHTEPXQr3sGR60Pn1MWgONPxnxs95AWNTKeck+N2gdzelM4YlNdETMUI1/C5u/z5IgzA+UEc50hphRsjRRWL/MYKJJhgZFaYWhayEwVWc6ii2EoKwtJNpnozZNOpgHEUuWLDF/8YtfHLD6L730UvOcc87xXf/LX/5iLl682Pzd737HvU/XdfPwww83v/SlL/l+u+GGG8wlS5aYxWLRNE3T/O53v2suXrzYHB4epspVKhWzt7fXvPbaa1tu95w5c0xYR574E3/iT/yJP/En/sSf+BN/4k/8UX9z5swJ5CUOah66Rx99FO3twdHzXi8GBgbQ3d3tu57P5wEA/f393PtGRkZQrVbdcuy9pmliYGAAPT09GBgYQCQSQTabpco514KeISAgICAgICAgICAgsL9xUBm6mTNnAgCKxSKee+457N27FyeddBI6Ozv3S/3lchma5jdXi0Yts7pKpcK9z7keifhVs8695XI59BlO2aBnhOGZZ9a35K9yMPF83xC27p2AJFkq/ZVzOkJ9vEzTxFNb9mK0WMMx8xqXfa5vCHvHKzhuficWzunAAKEq57Vlz1gJR8/tQAcntw5Z7193DGP3SAkr53T4IlCxWL9jGLuGizhyTntgMm0HG3aNoG+wgCNmt2NaSOhwANi4Zwx/e20M8/Ip9M7INiy7qX8cC7vbsKAr3Afz+b4h7B4pYsn0DOblw8tuGRjHK7tHmyrbCgbGynh22yC62mI4/Zi5oeO2e6SI5/uGMKs9icNn5ULr3Ttu1Tsjm2hYdrhQwdNbrDYc0RMuKNo7XsYzWweRS0RwzPzOUNOKsVIVT27ei0w8gmPmdYSa442XanhyywDimooTFuRDzVxK1Tqe2DSAiKrg+AX5UBOTck3HE5sGoMoSjl+QDzWFc8oqdtkwH4fxcg1Pbh6AJss4fkEes2ZkA8eurht4fNMAanUDxy3oDA1A4ZSt1HWcsCAfWtY0TTy5eS8KlTqOm98Z6v9omiae3jqIsVIVx83PhwYZMU0TT28ZxFChgmPmdQb63Th4vm8I/WNlHNGTa3rdHzYzi9nt4YG71m0bxJ7RMo6a0x7qkwtYe8/2wQKWz85hVoN6X9g+hB1DRSyfncPKJdNC19zm/nH8bc9YU+3d1G/tEb3TM5jfYO/ZNVzE+h3DmN+VxsLuttCyO52y+TQWTQsvu2u4iL/uGMa8zhQWc3JzkugbnMCLO0Ywu6PxfrJt7wRe3DmCmblEU3vEU5v3IpeM4rgF4XvEeLmGJzcNIB3XcOy8ztA9YrRUxVOb9yKbiODouR3o6moLHLtCpY7HN/Yjpik4YWHX6/LBI1Gq1vGXjf2IKApOXBi+nxTtfUqWJJywIB9qNlzXDTy5eS9quoHjF+RDzYZ1w8QTmwaaLvvMlr0oVOs4cWFXaFnDNPHkpr0YK1dxwoK8z4eUhGma+O2G3TAMNKRPDMPEk5sHMFqq4ei5HVg6Px+65p7bNoSB8XJTdM8fX9mDSt3AyrntobQMALxs7z3N0Ccv7x7F1oEJHNnTmD5Zv2MYfYMFLJuZxRwm7QILZ49YOqMxHbF17wRe3jWK3hkZXzoHFi/uHMaWgQksntYWmErKgbOfLGhi79k9UsS6bUOY25nCKUfMwt69E4FlXxsp4fntQ5iZa0xzHAoIDXZjmvsrbW1zuO222/Bf//VfmJiYgCRJuPnmm3HiiSdicHAQb37zm/GFL3wBF1100T7Vfc4556C7uxs33XQTdX3jxo04++yz8eUvf5nyr3MwNDSEE088EZ/5zGfwkY98hPrtpz/9Ka655ho8/PDD6OnpwTXXXIO77roL69ev99Vz4oknYtWqVbjuuutaavfg4MQhy9AdTOTz6dANU+DQhBi3yQsxdpMTYtwmL8TYTU6IcZucmGrjJssSOjr4jPJBTVvw0EMP4ZprrsHxxx+PL3/5y5QDf0dHB970pjfhkUce2ef68/k81+RxYMByQg0KiJLNZhGJRNxy7L2SJLnmmPl8HrVaDSMjI1S5arWKkZGRwGcICAgICAgICAgICAjsbxxUhu6mm27C8ccfj+985zs47bTTfL8ffvjh+Nvf/rbP9ff29mLLli0oFOj4X88//7z7Ow+yLGPx4sVcrdsLL7yAOXPmIB631NdLly4FAF/Z9evXwzAM93cBAQEBAQEBAQEBAYEDjYPK0L366qt4+9vfHvh7Pp/H4ODgPte/evVq1Go13H333e61arWKe+65BytXrnQDpuzatQubNm2i7j3jjDPw3HPP4aWXXnKvbd68GY8//jhWr17tXjvhhBOQzWZx2223UffffvvtSCQSOOWUU/a5/QICAgICAgICAgICAq3goAZFkWUZRkjuj/7+flcTti844ogjsHr1alx//fVuVMp7770Xu3btwle/+lW33Nq1a/Hkk0/ilVdeca+9973vxd13340Pf/jD+MAHPgBFUXDLLbcgn8+7ScoBIBaL4ZOf/CSuueYaXHnllVi1ahWefvpp/OIXv8BnP/tZtLWFO2sKCAgICAgICAgICAjsLxxUhq63txd/+tOfsGbNGt9vhmHgwQcfxPLly1/XM6699lp84xvfwH333YfR0VEsWbIEN954I44++ujQ+1KpFG699VZ85StfwXe/+10YhoHjjz8en//855HL0ZFv3ve+90HTNNx888149NFHMX36dHz+85/nvpeAgICAgICAgICAgMCBwkGNcvmrX/0Kn/nMZ/DRj34U559/PlavXo2bb74Z06ZNww033IBHHnkE3/ve93DqqacerCYdEhBRLi1MtWhEfy8Q4zZ5IcZuckKM2+SFGLvJCTFukxNTbdzColweVA3dWWedhVdeeQXf+973cOONNwIALr/8cpimCdM0ccUVV/zdMXMCAgICAgICAgICAgL7ioPK0AHApz/9aZx++um4//77sXnzZpimiTlz5uC888573eaWAgICAgICAgICAgICf0846AwdACxbtgzLli17Ix4tICAgICAgICAgICAwZXBQGLrdu3fjN7/5DTRNwxlnnIGOjg7s3r0b119/PR5//HEUCgUcfvjh+NSnPoVjjjnmYDRJQEBAQEBAQEBAQEBg0uOAM3SbNm3CxRd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\n", "text/plain": [ "<Figure size 1008x2160 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Filter warnings\n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "# Convert pinkfish data to Empyrical format\n", "returns = s.dbal['close'].pct_change()\n", "returns.index = returns.index.tz_localize('UTC')\n", "\n", "benchmark_rets = benchmark.dbal['close'].pct_change()\n", "benchmark_rets.index = benchmark_rets.index.tz_localize('UTC')\n", "\n", "live_start_date=None\n", "live_start_date='2010-01-01'\n", "\n", "# Uncomment to select the tear sheet you are interested in.\n", "\n", "#pyfolio.create_returns_tear_sheet(returns, benchmark_rets=benchmark_rets, live_start_date=live_start_date)\n", "pyfolio.create_simple_tear_sheet(returns, benchmark_rets=benchmark_rets)\n", "#pyfolio.create_interesting_times_tear_sheet(returns, benchmark_rets=benchmark_rets)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 1 }
mit
adriaanvuik/solid_state_physics
Effective_mass.ipynb
1
5024
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Band structure and effective mass\n", "(available at http:tiny.cc/solidstatephys/Effective_mass.ipynb)\n", "\n", "## Last week\n", "a chain of atoms with one orbital per atom has a tight-binding Hamiltonian\n", "$$H = h_0 + h_1 e^{ika} + h_1 e^{-ika}$$\n", "**Test question**: how does this Hamiltonian look if we choose a unit cell with two atoms in it?\n", "\n", "## This week\n", "* Motion of electrons in electric field\n", "* Effective mass\n", "\n", "Reminder: I use $\\hbar = 1$, so that $p = k$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Problem formulation\n", "\n", "![](figures/field.svg)\n", "\n", "* Periodic atomic potential + electric field $V = V_0(x) + eEx$\n", "* Electron energies belong to only a single band, kinetic energy $H = -2 h_1 \\cos(ka)$ *(or any other $E(k)$)*\n", "* Works when $E$ is small, $eEa \\ll t$\n", "\n", "Question: how do electrons move?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Begin with velocity\n", "\n", "Electron velocity is\n", "$$ v = \\frac{d E(k)}{d k} $$\n", "several ways to understand:\n", "* Electrons are waves, this is wave [group velocity](https://en.wikipedia.org/wiki/Group_velocity)\n", "* [Hamiltonian mechanics](https://en.wikipedia.org/wiki/Hamiltonian_mechanics)\n", " $$ \\frac{dx}{dt} = \\frac{\\partial H}{\\partial p} $$\n", "\n", "For the simplest example\n", "$$ v = 2h_1a\\sin(ka) $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Effect of the force\n", "\n", "Conservation of energy after moving by $\\delta x$ after $\\delta t$:\n", "$$ \\delta E(k) = -e E \\delta x $$\n", "\n", "$$ \\frac{d E(k)}{d k}\\delta k = -e E \\delta x $$\n", "\n", "$$ v\\frac{\\delta k}{\\delta t} = -e E \\frac{\\delta x}{\\delta t} $$\n", "\n", "$$ \\frac{d k}{d t} = -eE = F$$\n", "\n", "Once again, this is just Hamiltonian mechanics!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Bloch oscillations\n", "\n", "Let's solve equations of motion:\n", "$$k = F t$$\n", "$$v = 2 h_1 a \\sin(Fta)$$\n", "$$x = 2 \\frac{h_1}{F}\\cos(Fta)$$\n", "\n", "$\\Rightarrow$ motion of electrons is [periodic](https://en.wikipedia.org/wiki/Bloch_oscillations)! \n", "\n", "![](figures/bloch.svg)\n", "\n", "Very hard to observe, requires the relaxation time $\\tau \\gg (Fa)^{-1}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Acceleration\n", "\n", "$$ a = \\frac{dv}{dt} $$\n", "\n", "using $v = d E(k)/d k$ we get\n", "\n", "$$ a = \\frac{d^2 E}{dk^2} \\frac{dk}{dt} = \\frac{d^2 E}{dk^2} F $$\n", "\n", "Now compare with the Newton's law $a = F/m$.\n", "\n", "$\\Rightarrow$ we introduce **effective mass** \n", "$$m_{\\text{eff}} = \\left(\\frac{d^2 E}{dk^2}\\right)^{-1}$$\n", "Electrons behave like their mass is momentum-dependent." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Effective mass properties\n", "\n", "For the single band model:\n", "$$ m_{\\text{eff}} = [2 h_1 a^2 \\cos(ka)]^{-1}$$\n", "\n", "![](figures/m_eff.svg)\n", "\n", "* When $k\\ll a^{-1}$: $m_{\\text{eff}} = 1/2h_1a^2$, almost constant\n", "* When $|k| > \\pi/2$: $m_{\\text{eff}} < 0$, these are **holes** (topic of next lecture), they accelerate opposite to the force applied.\n", "* In real materials $m_{\\text{eff}}$ varies from $\\sim 1000 m_e$ to $\\sim 0.01 m_e$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Conclusions\n", "\n", "* Electric field increases momentum at a constant rate\n", "* Acceleration is controlled by effective mass\n", "* Half of a band is **holes** that have negative mass." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3+" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
mne-tools/mne-tools.github.io
0.14/_downloads/plot_cluster_stats_spatio_temporal.ipynb
3
9800
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n.. _tut_stats_cluster_source_1samp:\n\n# Permutation t-test on source data with spatio-temporal clustering\n\n\nTests if the evoked response is significantly different between\nconditions across subjects (simulated here using one subject's data).\nThe multiple comparisons problem is addressed with a cluster-level\npermutation test across space and time.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Authors: Alexandre Gramfort <[email protected]>\n# Eric Larson <[email protected]>\n# License: BSD (3-clause)\n\n\nimport os.path as op\nimport numpy as np\nfrom numpy.random import randn\nfrom scipy import stats as stats\n\nimport mne\nfrom mne import (io, spatial_tris_connectivity, compute_morph_matrix,\n grade_to_tris)\nfrom mne.epochs import equalize_epoch_counts\nfrom mne.stats import (spatio_temporal_cluster_1samp_test,\n summarize_clusters_stc)\nfrom mne.minimum_norm import apply_inverse, read_inverse_operator\nfrom mne.datasets import sample\n\nprint(__doc__)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Set parameters\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nsubjects_dir = data_path + '/subjects'\n\ntmin = -0.2\ntmax = 0.3 # Use a lower tmax to reduce multiple comparisons\n\n# Setup for reading the raw data\nraw = io.Raw(raw_fname)\nevents = mne.read_events(event_fname)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Read epochs for all channels, removing a bad one\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.info['bads'] += ['MEG 2443']\npicks = mne.pick_types(raw.info, meg=True, eog=True, exclude='bads')\nevent_id = 1 # L auditory\nreject = dict(grad=1000e-13, mag=4000e-15, eog=150e-6)\nepochs1 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=reject, preload=True)\n\nevent_id = 3 # L visual\nepochs2 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=reject, preload=True)\n\n# Equalize trial counts to eliminate bias (which would otherwise be\n# introduced by the abs() performed below)\nequalize_epoch_counts([epochs1, epochs2])" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Transform to source space\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = \"dSPM\" # use dSPM method (could also be MNE or sLORETA)\ninverse_operator = read_inverse_operator(fname_inv)\nsample_vertices = [s['vertno'] for s in inverse_operator['src']]\n\n# Let's average and compute inverse, resampling to speed things up\nevoked1 = epochs1.average()\nevoked1.resample(50)\ncondition1 = apply_inverse(evoked1, inverse_operator, lambda2, method)\nevoked2 = epochs2.average()\nevoked2.resample(50)\ncondition2 = apply_inverse(evoked2, inverse_operator, lambda2, method)\n\n# Let's only deal with t > 0, cropping to reduce multiple comparisons\ncondition1.crop(0, None)\ncondition2.crop(0, None)\ntmin = condition1.tmin\ntstep = condition1.tstep" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Transform to common cortical space\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Normally you would read in estimates across several subjects and morph\n# them to the same cortical space (e.g. fsaverage). For example purposes,\n# we will simulate this by just having each \"subject\" have the same\n# response (just noisy in source space) here. Note that for 7 subjects\n# with a two-sided statistical test, the minimum significance under a\n# permutation test is only p = 1/(2 ** 6) = 0.015, which is large.\nn_vertices_sample, n_times = condition1.data.shape\nn_subjects = 7\nprint('Simulating data for %d subjects.' % n_subjects)\n\n# Let's make sure our results replicate, so set the seed.\nnp.random.seed(0)\nX = randn(n_vertices_sample, n_times, n_subjects, 2) * 10\nX[:, :, :, 0] += condition1.data[:, :, np.newaxis]\nX[:, :, :, 1] += condition2.data[:, :, np.newaxis]\n\n# It's a good idea to spatially smooth the data, and for visualization\n# purposes, let's morph these to fsaverage, which is a grade 5 source space\n# with vertices 0:10242 for each hemisphere. Usually you'd have to morph\n# each subject's data separately (and you might want to use morph_data\n# instead), but here since all estimates are on 'sample' we can use one\n# morph matrix for all the heavy lifting.\nfsave_vertices = [np.arange(10242), np.arange(10242)]\nmorph_mat = compute_morph_matrix('sample', 'fsaverage', sample_vertices,\n fsave_vertices, 20, subjects_dir)\nn_vertices_fsave = morph_mat.shape[0]\n\n# We have to change the shape for the dot() to work properly\nX = X.reshape(n_vertices_sample, n_times * n_subjects * 2)\nprint('Morphing data.')\nX = morph_mat.dot(X) # morph_mat is a sparse matrix\nX = X.reshape(n_vertices_fsave, n_times, n_subjects, 2)\n\n# Finally, we want to compare the overall activity levels in each condition,\n# the diff is taken along the last axis (condition). The negative sign makes\n# it so condition1 > condition2 shows up as \"red blobs\" (instead of blue).\nX = np.abs(X) # only magnitude\nX = X[:, :, :, 0] - X[:, :, :, 1] # make paired contrast" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Compute statistic\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# To use an algorithm optimized for spatio-temporal clustering, we\n# just pass the spatial connectivity matrix (instead of spatio-temporal)\nprint('Computing connectivity.')\nconnectivity = spatial_tris_connectivity(grade_to_tris(5))\n\n# Note that X needs to be a multi-dimensional array of shape\n# samples (subjects) x time x space, so we permute dimensions\nX = np.transpose(X, [2, 1, 0])\n\n# Now let's actually do the clustering. This can take a long time...\n# Here we set the threshold quite high to reduce computation.\np_threshold = 0.001\nt_threshold = -stats.distributions.t.ppf(p_threshold / 2., n_subjects - 1)\nprint('Clustering.')\nT_obs, clusters, cluster_p_values, H0 = clu = \\\n spatio_temporal_cluster_1samp_test(X, connectivity=connectivity, n_jobs=2,\n threshold=t_threshold)\n# Now select the clusters that are sig. at p < 0.05 (note that this value\n# is multiple-comparisons corrected).\ngood_cluster_inds = np.where(cluster_p_values < 0.05)[0]" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Visualize the clusters\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "print('Visualizing clusters.')\n\n# Now let's build a convenient representation of each cluster, where each\n# cluster becomes a \"time point\" in the SourceEstimate\nstc_all_cluster_vis = summarize_clusters_stc(clu, tstep=tstep,\n vertices=fsave_vertices,\n subject='fsaverage')\n\n# Let's actually plot the first \"time point\" in the SourceEstimate, which\n# shows all the clusters, weighted by duration\nsubjects_dir = op.join(data_path, 'subjects')\n# blue blobs are for condition A < condition B, red for A > B\nbrain = stc_all_cluster_vis.plot(hemi='both', subjects_dir=subjects_dir,\n time_label='Duration significant (ms)')\nbrain.set_data_time_index(0)\nbrain.show_view('lateral')\nbrain.save_image('clusters.png')" ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.11", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
bsd-3-clause
Ledoux/ShareYourSystem
Pythonlogy/draft/Applyier/Readme.ipynb
1
7121
{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Applyier\n\n##Doc\n----\n\n\n> \n> An Applyier apply a function thanks to a ApplyingMethodStr and an ApplyingArgDict.\n> This property is going to be useful to begin to establish mappping methods and \n> commanding calls in deep structures.\n> \n> \n\n----\n\n<small>\nView the Applyier notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Applyier.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -*- coding: utf-8 -*-\n\"\"\"\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nAn Applyier apply a function thanks to a ApplyingMethodStr and an ApplyingArgDict.\nThis property is going to be useful to begin to establish mappping methods and \ncommanding calls in deep structures.\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Itemizers.Executer\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport copy\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass ApplyierClass(BaseClass):\n\t\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t#'ApplyingMethodStr',\n\t\t\t\t\t\t\t\t\t#'ApplyingArgDict',\n\t\t\t\t\t\t\t\t\t#'ApplyingIsBool',\n\t\t\t\t\t\t\t\t\t#'AppliedMethod',\n\t\t\t\t\t\t\t\t\t#'AppliedOutputVariable'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t_ApplyingMethodStr=\"\",\n\t\t\t\t_ApplyingArgDict=None,\n\t\t\t\t_ApplyingIsBool=False,\n\t\t\t\t_AppliedMethod=None,\n\t\t\t\t_AppliedOutputVariable=None,\n\t\t\t\t**_KwargVariablesDict\n\t\t\t):\n\n\t\t#Call the parent __init__ method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\tdef do_apply(self):\n\t\t\"\"\" \"\"\"\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(\n\t\t\t\t\t('self.',self,[\n\t\t\t\t\t\t\t\t\t'ApplyingMethodStr',\n\t\t\t\t\t\t\t\t\t'ApplyingArgDict'\n\t\t\t\t\t\t\t\t])\n\t\t)\n\t\t'''\n\t\t\n\t\t#set\n\t\tself.AppliedMethod=getattr(self,self.ApplyingMethodStr)\n\n\t\t#debug\n\t\t''''\n\t\tself.debug(\n\t\t\t\t\t('self.',self,[\n\t\t\t\t\t\t\t\t\t'AppliedMethod'\n\t\t\t\t\t\t\t\t])\n\t\t\t)\n\t\t'''\n\n\t\t#Check\n\t\tif self.AppliedMethod!=None:\n\n\t\t\t#debug\n\t\t\t'''\n\t\t\tself.debug(\n\t\t\t\t\t\t[\n\t\t\t\t\t\t\t'AppliedMethod is good, We are going to apply',\n\t\t\t\t\t\t\t('self.',self,['AppliedMethod','ApplyingArgDict'])\n\t\t\t\t\t\t]\n\t\t\t\t\t)\n\t\t\t'''\n\t\t\t\n\t\t\tif 'KwargVariablesDict' in self.ApplyingArgDict:\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug('We apply with a KwargVariablesDict')\n\t\t\t\t'''\n\n\t\t\t\t#Call the AppliedMethod\n\t\t\t\tself.AppliedOutputVariable=self.AppliedMethod(\n\t\t\t\t\t\t\t\t*self.ApplyingArgDict['LiargVariablesList'],\n\t\t\t\t\t\t\t\t**self.ApplyingArgDict['KwargVariablesDict']\n\t\t\t\t\t\t\t\t) \n\t\t\telse:\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug('We apply without a KwargVariablesDict')\n\t\t\t\t'''\n\n\n\n\t\t\t\t#Call\n\t\t\t\tself.AppliedOutputVariable=self.AppliedMethod(\n\t\t\t\t\t*self.ApplyingArgDict['LiargVariablesList']\n\t\t\t\t\t)\n\n\t\t#Return self\n\t\t#return self\n\n\tdef mimic_set(self):\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,['SettingKeyVariable','SettingValueVariable']))\n\t\t'''\n\t\t\n\t\t#Definition\n\t\tOutputDict={'HookingIsBool':True}\n\n\t\t#Check\n\t\tif self.SettingKeyVariable!=\"\":\n\n\t\t\t#Call for a hook\n\t\t\tif (self.SettingKeyVariable[0].isalpha() or self.SettingKeyVariable[:2]==\"__\"\n\t\t\t\t) and self.SettingKeyVariable[0].lower()==self.SettingKeyVariable[0]:\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug(\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t('This is a set that calls a method so this is an apply...'),\n\t\t\t\t\t\t\t\t('self.',self,[\n\t\t\t\t\t\t\t\t\t\t\t\t'SettingKeyVariable',\n\t\t\t\t\t\t\t\t\t\t\t\t'SettingValueVariable'\n\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t)\n\t\t\t\t'''\n\t\t\t\t\n\t\t\t\t#Apply\n\t\t\t\tself.ApplyingIsBool=False\n\t\t\t\tself.apply(\n\t\t\t\t\t\t\t\tself.SettingKeyVariable,\n\t\t\t\t\t\t\t\tself.SettingValueVariable\n\t\t\t\t\t\t\t)\n\n\t\t\t\t#Return\n\t\t\t\tOutputDict['HookingIsBool']=False\n\t\t\t\t#<Hook>return OutputDict\n\n\t\tif OutputDict['HookingIsBool']:\n\t\t\tBaseClass.set(self)\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Applyier sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Applyiers/Applyier\" target=\"_blank\">Github</a>\n</small>\n\n", "cell_type": "markdown", "prompt_number": 1, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nLet's create an empty class, which will automatically receive\nspecial attributes from the decorating ClassorClass,\nspecially the NameStr, that should be the ClassStr\nwithout the TypeStr in the end.", "cell_type": "markdown", "prompt_number": 2, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "source": "```python\n\n#ImportModules\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Applyiers import Applyier\n\n#Definition an applyier instance\nMyApplyier=Applyier.ApplyierClass()\n\n#Apply just a set... (even if we can do shorter !)\nMyApplyier.apply(\n '__setitem__',\n {'LiargVariablesList':['MyStr','Hello']}\n)\n\n#Apply an apply is possible\nMyApplyier.apply(\n '__setitem__',\n {'LiargVariablesList':[\n '__setitem__',\n {\n 'LiargVariablesList':\n ['MyNotLostStr','ben he']\n }\n ]\n }\n)\n \n#Definition the AttestedStr\nSYS._attest(\n [\n 'MyApplyier is '+SYS._str(\n MyApplyier,\n **{\n 'RepresentingBaseKeyStrsListBool':False,\n 'RepresentingAlineaIsBool':False\n }\n )\n ]\n) \n\n#Print\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} }, { "source": "```console\n>>>\n\n\n*****Start of the Attest *****\n\nMyApplyier is < (ApplyierClass), 4554251216>\n /{ \n / '<New><Instance>IdInt' : 4554251216\n / '<New><Instance>MyNotLostStr' : ben he\n / '<New><Instance>MyStr' : Hello\n /}\n\n*****End of the Attest *****\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }
mit
d-k-b/udacity-deep-learning
embeddings/Skip-Gram_word2vec.ipynb
1
25300
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Skip-gram word2vec\n", "\n", "In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like machine translation.\n", "\n", "## Readings\n", "\n", "Here are the resources I used to build this notebook. I suggest reading these either beforehand or while you're working on this material.\n", "\n", "* A really good [conceptual overview](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) of word2vec from Chris McCormick \n", "* [First word2vec paper](https://arxiv.org/pdf/1301.3781.pdf) from Mikolov et al.\n", "* [NIPS paper](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) with improvements for word2vec also from Mikolov et al.\n", "* An [implementation of word2vec](http://www.thushv.com/natural_language_processing/word2vec-part-1-nlp-with-deep-learning-with-tensorflow-skip-gram/) from Thushan Ganegedara\n", "* TensorFlow [word2vec tutorial](https://www.tensorflow.org/tutorials/word2vec)\n", "\n", "## Word embeddings\n", "\n", "When you're dealing with words in text, you end up with tens of thousands of classes to predict, one for each word. Trying to one-hot encode these words is massively inefficient, you'll have one element set to 1 and the other 50,000 set to 0. The matrix multiplication going into the first hidden layer will have almost all of the resulting values be zero. This a huge waste of computation. \n", "\n", "![one-hot encodings](assets/one_hot_encoding.png)\n", "\n", "To solve this problem and greatly increase the efficiency of our networks, we use what are called embeddings. Embeddings are just a fully connected layer like you've seen before. We call this layer the embedding layer and the weights are embedding weights. We skip the multiplication into the embedding layer by instead directly grabbing the hidden layer values from the weight matrix. We can do this because the multiplication of a one-hot encoded vector with a matrix returns the row of the matrix corresponding the index of the \"on\" input unit.\n", "\n", "![lookup](assets/lookup_matrix.png)\n", "\n", "Instead of doing the matrix multiplication, we use the weight matrix as a lookup table. We encode the words as integers, for example \"heart\" is encoded as 958, \"mind\" as 18094. Then to get hidden layer values for \"heart\", you just take the 958th row of the embedding matrix. This process is called an **embedding lookup** and the number of hidden units is the **embedding dimension**.\n", "\n", "<img src='assets/tokenize_lookup.png' width=500>\n", " \n", "There is nothing magical going on here. The embedding lookup table is just a weight matrix. The embedding layer is just a hidden layer. The lookup is just a shortcut for the matrix multiplication. The lookup table is trained just like any weight matrix as well.\n", "\n", "Embeddings aren't only used for words of course. You can use them for any model where you have a massive number of classes. A particular type of model called **Word2Vec** uses the embedding layer to find vector representations of words that contain semantic meaning.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word2Vec\n", "\n", "The word2vec algorithm finds much more efficient representations by finding vectors that represent the words. These vectors also contain semantic information about the words. Words that show up in similar contexts, such as \"black\", \"white\", and \"red\" will have vectors near each other. There are two architectures for implementing word2vec, CBOW (Continuous Bag-Of-Words) and Skip-gram.\n", "\n", "<img src=\"assets/word2vec_architectures.png\" width=\"500\">\n", "\n", "In this implementation, we'll be using the skip-gram architecture because it performs better than CBOW. Here, we pass in a word and try to predict the words surrounding it in the text. In this way, we can train the network to learn representations for words that show up in similar contexts.\n", "\n", "First up, importing packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "import utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the [text8 dataset](http://mattmahoney.net/dc/textdata.html), a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the `data` folder. Then you can extract it and delete the archive file to save storage space." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import zipfile\n", "\n", "dataset_folder_path = 'data'\n", "dataset_filename = 'text8.zip'\n", "dataset_name = 'Text8 Dataset'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(dataset_filename):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc=dataset_name) as pbar:\n", " urlretrieve(\n", " 'http://mattmahoney.net/dc/text8.zip',\n", " dataset_filename,\n", " pbar.hook)\n", "\n", "if not isdir(dataset_folder_path):\n", " with zipfile.ZipFile(dataset_filename) as zip_ref:\n", " zip_ref.extractall(dataset_folder_path)\n", " \n", "with open('data/text8') as f:\n", " text = f.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocessing\n", "\n", "Here I'm fixing up the text to make training easier. This comes from the `utils` module I wrote. The `preprocess` function coverts any punctuation into tokens, so a period is changed to ` <PERIOD> `. In this data set, there aren't any periods, but it will help in other NLP problems. I'm also removing all words that show up five or fewer times in the dataset. This will greatly reduce issues due to noise in the data and improve the quality of the vector representations. If you want to write your own functions for this stuff, go for it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "words = utils.preprocess(text)\n", "print(words[:30])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Total words: {}\".format(len(words)))\n", "print(\"Unique words: {}\".format(len(set(words))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here I'm creating dictionaries to convert words to integers and backwards, integers to words. The integers are assigned in descending frequency order, so the most frequent word (\"the\") is given the integer 0 and the next most frequent is 1 and so on. The words are converted to integers and stored in the list `int_words`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)\n", "int_words = [vocab_to_int[word] for word in words]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subsampling\n", "\n", "Words that show up often such as \"the\", \"of\", and \"for\" don't provide much context to the nearby words. If we discard some of them, we can remove some of the noise from our data and in return get faster training and better representations. This process is called subsampling by Mikolov. For each word $w_i$ in the training set, we'll discard it with probability given by \n", "\n", "$$ P(w_i) = 1 - \\sqrt{\\frac{t}{f(w_i)}} $$\n", "\n", "where $t$ is a threshold parameter and $f(w_i)$ is the frequency of word $w_i$ in the total dataset.\n", "\n", "I'm going to leave this up to you as an exercise. This is more of a programming challenge, than about deep learning specifically. But, being able to prepare your data for your network is an important skill to have. Check out my solution to see how I did it.\n", "\n", "> **Exercise:** Implement subsampling for the words in `int_words`. That is, go through `int_words` and discard each word given the probablility $P(w_i)$ shown above. Note that $P(w_i)$ is the probability that a word is discarded. Assign the subsampled data to `train_words`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Your code here\n", "train_words = # The final subsampled word list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making batches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that our data is in good shape, we need to get it into the proper form to pass it into our network. With the skip-gram architecture, for each word in the text, we want to grab all the words in a window around that word, with size $C$. \n", "\n", "From [Mikolov et al.](https://arxiv.org/pdf/1301.3781.pdf): \n", "\n", "\"Since the more distant words are usually less related to the current word than those close to it, we give less weight to the distant words by sampling less from those words in our training examples... If we choose $C = 5$, for each training word we will select randomly a number $R$ in range $< 1; C >$, and then use $R$ words from history and $R$ words from the future of the current word as correct labels.\"\n", "\n", "> **Exercise:** Implement a function `get_target` that receives a list of words, an index, and a window size, then returns a list of words in the window around the index. Make sure to use the algorithm described above, where you choose a random number of words from the window." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_target(words, idx, window_size=5):\n", " ''' Get a list of words in a window around an index. '''\n", " \n", " # Your code here\n", " \n", " return" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a function that returns batches for our network. The idea is that it grabs `batch_size` words from a words list. Then for each of those words, it gets the target words in the window. I haven't found a way to pass in a random number of target words and get it to work with the architecture, so I make one row per input-target pair. This is a generator function by the way, helps save memory." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_batches(words, batch_size, window_size=5):\n", " ''' Create a generator of word batches as a tuple (inputs, targets) '''\n", " \n", " n_batches = len(words)//batch_size\n", " \n", " # only full batches\n", " words = words[:n_batches*batch_size]\n", " \n", " for idx in range(0, len(words), batch_size):\n", " x, y = [], []\n", " batch = words[idx:idx+batch_size]\n", " for ii in range(len(batch)):\n", " batch_x = batch[ii]\n", " batch_y = get_target(batch, ii, window_size)\n", " y.extend(batch_y)\n", " x.extend([batch_x]*len(batch_y))\n", " yield x, y\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the graph\n", "\n", "From [Chris McCormick's blog](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/), we can see the general structure of our network.\n", "![embedding_network](./assets/skip_gram_net_arch.png)\n", "\n", "The input words are passed in as integers. This will go into a hidden layer of linear units, then into a softmax layer. We'll use the softmax layer to make a prediction like normal.\n", "\n", "The idea here is to train the hidden layer weight matrix to find efficient representations for our words. We can discard the softmax layer becuase we don't really care about making predictions with this network. We just want the embedding matrix so we can use it in other networks we build from the dataset.\n", "\n", "I'm going to have you build the graph in stages now. First off, creating the `inputs` and `labels` placeholders like normal.\n", "\n", "> **Exercise:** Assign `inputs` and `labels` using `tf.placeholder`. We're going to be passing in integers, so set the data types to `tf.int32`. The batches we're passing in will have varying sizes, so set the batch sizes to [`None`]. To make things work later, you'll need to set the second dimension of `labels` to `None` or `1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " inputs = \n", " labels = " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Embedding\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The embedding matrix has a size of the number of words by the number of units in the hidden layer. So, if you have 10,000 words and 300 hidden units, the matrix will have size $10,000 \\times 300$. Remember that we're using tokenized data for our inputs, usually as integers, where the number of tokens is the number of words in our vocabulary.\n", "\n", "\n", "> **Exercise:** Tensorflow provides a convenient function [`tf.nn.embedding_lookup`](https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup) that does this lookup for us. You pass in the embedding matrix and a tensor of integers, then it returns rows in the matrix corresponding to those integers. Below, set the number of embedding features you'll use (200 is a good start), create the embedding matrix variable, and use `tf.nn.embedding_lookup` to get the embedding tensors. For the embedding matrix, I suggest you initialize it with a uniform random numbers between -1 and 1 using [tf.random_uniform](https://www.tensorflow.org/api_docs/python/tf/random_uniform)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_vocab = len(int_to_vocab)\n", "n_embedding = # Number of embedding features \n", "with train_graph.as_default():\n", " embedding = # create embedding weight matrix here\n", " embed = # use tf.nn.embedding_lookup to get the hidden layer output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Negative sampling\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For every example we give the network, we train it using the output from the softmax layer. That means for each input, we're making very small changes to millions of weights even though we only have one true example. This makes training the network very inefficient. We can approximate the loss from the softmax layer by only updating a small subset of all the weights at once. We'll update the weights for the correct label, but only a small number of incorrect labels. This is called [\"negative sampling\"](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf). Tensorflow has a convenient function to do this, [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss).\n", "\n", "> **Exercise:** Below, create weights and biases for the softmax layer. Then, use [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss) to calculate the loss. Be sure to read the documentation to figure out how it works." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of negative labels to sample\n", "n_sampled = 100\n", "with train_graph.as_default():\n", " softmax_w = # create softmax weight matrix here\n", " softmax_b = # create softmax biases here\n", " \n", " # Calculate the loss using negative sampling\n", " loss = tf.nn.sampled_softmax_loss \n", " \n", " cost = tf.reduce_mean(loss)\n", " optimizer = tf.train.AdamOptimizer().minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Validation\n", "\n", "This code is from Thushan Ganegedara's implementation. Here we're going to choose a few common words and few uncommon words. Then, we'll print out the closest words to them. It's a nice way to check that our embedding table is grouping together words with similar semantic meanings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " ## From Thushan Ganegedara's implementation\n", " valid_size = 16 # Random set of words to evaluate similarity on.\n", " valid_window = 100\n", " # pick 8 samples from (0,100) and (1000,1100) each ranges. lower id implies more frequent \n", " valid_examples = np.array(random.sample(range(valid_window), valid_size//2))\n", " valid_examples = np.append(valid_examples, \n", " random.sample(range(1000,1000+valid_window), valid_size//2))\n", "\n", " valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n", " \n", " # We use the cosine distance:\n", " norm = tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keep_dims=True))\n", " normalized_embedding = embedding / norm\n", " valid_embedding = tf.nn.embedding_lookup(normalized_embedding, valid_dataset)\n", " similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# If the checkpoints directory doesn't exist:\n", "!mkdir checkpoints" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "\n", "Below is the code to train the network. Every 100 batches it reports the training loss. Every 1000 batches, it'll print out the validation words." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = 10\n", "batch_size = 1000\n", "window_size = 10\n", "\n", "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " iteration = 1\n", " loss = 0\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for e in range(1, epochs+1):\n", " batches = get_batches(train_words, batch_size, window_size)\n", " start = time.time()\n", " for x, y in batches:\n", " \n", " feed = {inputs: x,\n", " labels: np.array(y)[:, None]}\n", " train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)\n", " \n", " loss += train_loss\n", " \n", " if iteration % 100 == 0: \n", " end = time.time()\n", " print(\"Epoch {}/{}\".format(e, epochs),\n", " \"Iteration: {}\".format(iteration),\n", " \"Avg. Training loss: {:.4f}\".format(loss/100),\n", " \"{:.4f} sec/batch\".format((end-start)/100))\n", " loss = 0\n", " start = time.time()\n", " \n", " if iteration % 1000 == 0:\n", " ## From Thushan Ganegedara's implementation\n", " # note that this is expensive (~20% slowdown if computed every 500 steps)\n", " sim = similarity.eval()\n", " for i in range(valid_size):\n", " valid_word = int_to_vocab[valid_examples[i]]\n", " top_k = 8 # number of nearest neighbors\n", " nearest = (-sim[i, :]).argsort()[1:top_k+1]\n", " log = 'Nearest to %s:' % valid_word\n", " for k in range(top_k):\n", " close_word = int_to_vocab[nearest[k]]\n", " log = '%s %s,' % (log, close_word)\n", " print(log)\n", " \n", " iteration += 1\n", " save_path = saver.save(sess, \"checkpoints/text8.ckpt\")\n", " embed_mat = sess.run(normalized_embedding)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Restore the trained network if you need to:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " embed_mat = sess.run(embedding)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the word vectors\n", "\n", "Below we'll use T-SNE to visualize how our high-dimensional word vectors cluster together. T-SNE is used to project these vectors into two dimensions while preserving local stucture. Check out [this post from Christopher Olah](http://colah.github.io/posts/2014-10-Visualizing-MNIST/) to learn more about T-SNE and other ways to visualize high-dimensional data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import TSNE" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "viz_words = 500\n", "tsne = TSNE()\n", "embed_tsne = tsne.fit_transform(embed_mat[:viz_words, :])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(14, 14))\n", "for idx in range(viz_words):\n", " plt.scatter(*embed_tsne[idx, :], color='steelblue')\n", " plt.annotate(int_to_vocab[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]), alpha=0.7)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
moonbury/pythonanywhere
scikit-learn/plot_cat_percepton.ipynb
3
44153
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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2ac5475ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Starting epoch 1\n", "-- Epoch 1\n", "Norm: 0.42, NNZs: 2, Bias: 0.000000, T: 4, Avg. loss: 0.455000\n", "Total training time: 0.00 seconds.\n", "[ 0.] [[-0.3 -0.3]]\n", "\n", "Starting epoch 2\n", "-- Epoch 1\n", "Norm: 0.42, NNZs: 2, Bias: 0.000000, T: 4, Avg. loss: 0.455000\n", "Total training time: 0.00 seconds.\n", "-- Epoch 2\n", "Norm: 1.12, NNZs: 2, Bias: 0.000000, T: 8, Avg. loss: 0.385000\n", "Total training time: 0.00 seconds.\n", "[ 0.] [[-0.5 -1. ]]\n", "\n", "Starting epoch 3\n", "-- Epoch 1\n", "Norm: 0.42, NNZs: 2, Bias: 0.000000, T: 4, Avg. loss: 0.455000\n", "Total training time: 0.00 seconds.\n", "-- Epoch 2\n", "Norm: 1.12, NNZs: 2, Bias: 0.000000, T: 8, Avg. loss: 0.385000\n", "Total training time: 0.00 seconds.\n", "-- Epoch 3\n", "Norm: 0.95, NNZs: 2, Bias: 1.000000, T: 12, Avg. loss: 0.273333\n", "Total training time: 0.00 seconds.\n", "[ 1.] [[-0.3 -0.9]]\n", "converged in epoch 3\n" ] }, { "data": { "image/png": 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ZwCdpTTN9EnBSROw/wabnZ+YhxVctD/qDMD1MkiZhDkhSs5kDktQHVfUJylw1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\nOSBJNVLmqmFXRcTjaV0lYAS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2abe398ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "fig = plt.figure()\n", "\n", "X = np.array([\n", " [0.2, 0.1],\n", " [0.4, 0.6],\n", " [0.5, 0.2],\n", " [0.7, 0.9]\n", "])\n", "\n", "y = [0, 0, 0, 1]\n", "\n", "markers = ['.', 'x']\n", "plt.scatter(X[:3, 0], X[:3, 1], marker='.', s=400)\n", "plt.scatter(X[3, 0], X[3, 1], marker='x', s=400)\n", "plt.xlabel('Proportion of the day spent sleeping')\n", "plt.ylabel('Proportion of the day spent being grumpy')\n", "plt.title('Kittens and Adult Cats')\n", "# plt.plot([0, -2.72], [-3.3, 0])\n", "# plt.plot([0, -13.33], [-3.6363, 0])\n", "#plt.plot([0, -30], [4.286, 0])\n", "#plt.plot([0, 1], [0, 1])\n", "plt.show()\n", "\n", "#fig.savefig('graph.png')\n", "\n", "######################\n", "\n", "from sklearn.linear_model import Perceptron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "X = np.array([\n", " [0.2, 0.1],\n", " [0.4, 0.6],\n", " [0.5, 0.2],\n", " [0.7, 0.9]\n", "])\n", "X_test = np.array([\n", " [0.7, 0.8]\n", "])\n", "\n", "Y = np.array([1, 1, 1, 0])\n", "h = 0.001 # h = 0.02\n", "\n", "# create a mesh to plot in\n", "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "fig = plt.figure(figsize=(27, 9))\n", "\n", "for e in range(1, 7):\n", " print ('\\nStarting epoch', e)\n", " ax = plt.subplot(2, 5, e)\n", "\n", " clf = Perceptron(n_iter=e, verbose=5).fit(X, Y)\n", " print (clf.intercept_, clf.coef_)\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", " Z = Z.reshape(xx.shape)\n", "\n", " ax.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n", "\n", " # Plot also the training points\n", " ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)\n", "\n", " plt.title('Epoch %s' % e)\n", " plt.xlabel('Proportion of the day spent sleeping')\n", " plt.ylabel('Proportion of the day spent being grumpy')\n", " plt.title('Kittens and Adult Cats')\n", "\n", " if clf.score(X, Y) == 1:\n", " print ('converged in epoch', e)\n", " break\n", "\n", "fig.subplots_adjust(left=.02, right=.98)\n", "\n", "plt.show()\n", "#fig.savefig('graph1.png')\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
amniskin/amniskin.github.io
assets/notebooks/2017/10/07/active_portfolio_management_slides.ipynb
1
107563
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Data Science in Finance" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Lousy models are great!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "We often hear that in the world of hedge funds and seeking alpha (a term we'll go over in a bit), extremely poor models are used and hailed as great achievements. A model with an $R^2$ of 0.1 is great! A model with an $R^2$ of 0.3 is unheard of.\n", "\n", "Is it because the data scientists working in the field are not as good as the Physicists working at the LHC, or the engineers working on Google's search prediction algorithms?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### The answer might surprise you!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Hungry for coin flips?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Let's imagine that you happen to have some inside source at the mint who told you that a common quarter is actually not a fair coin. This information is only known to you and your friend. Let's pretend like the probability of getting \"heads\" is actually 0.55. So not anything you'd expect to notice on a short scale, but enough to where if you bet on coin flips enough, you might actually be able to make lots of money.\n", "\n", "Would you bet on those coin flips? Would you consider yourself very lucky for having such privileged information?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### What's your $R^2$?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Our model:\n", "\n", "$$\n", "\\begin{align*}\n", "Y =& \\begin{cases}\n", "1 & \\text{ if \"heads\"} \\\\\n", "0 & \\text{ if \"tails\"}\n", "\\end{cases} \\\\\n", "P(Y=1) =& w \\\\\n", "\\hat Y \\equiv& 1\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "First we need to figure out what our model is! It's not entirely clear that we're using a predictive model, but we are. Our model happens to be very simple: always pick \"heads\".\n", "\n", "Formally, we define a random variable $Y$ such that $Y=0$ if the coin flip results in \"tails\" and $Y=1$ if the coin flip results in \"heads\". Our model is very simple: it takes no input data (so no features), and always returns 1.\n", "\n", "So let's calculate our $R^2$ value. To do this, we should first calculate SSE and SST. Let $w$ be the probability of getting \"heads\" (just for generality). In our particular case, $w=0.55$.\n", "\n", "Note that the mean we use for this the commonly excepted mean (not the mean your model predicts)!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "$$\n", "\\begin{align*}\n", "\\text{SSE} =& \\sum\\limits_{i=0}^{n-1}\\left(y_i - \\hat y_i\\right)^2 & \\text{SST} =& \\sum\\limits_{i=0}^{n-1}\\left(y_i - \\bar y\\right)^2 \\\\\n", "=& \\sum\\limits_{i=0}^{n-1}\\left(y_i - 1\\right)^2 & =& \\sum\\limits_{i=0}^{n-1}\\left(y_i - 0.5\\right)^2 \\\\\n", "=& \\sum\\limits_{i=0}^{n-1}\\left(y_i^2 -2y_i + 1^2\\right) & =& \\sum\\limits_{i=0}^{n-1}\\left(y_i^2 - 2(0.5)y_i + 0.5^2\\right) \\\\\n", "=& \\sum\\limits_{i=0}^{n-1}\\left(-y_i + 1\\right) & =& \\sum\\limits_{i=0}^{n-1}\\left(0.25\\right) \\\\\n", "=& -nw + n & =& 0.5n \\\\\n", "=& n(1-w) & =& 0.5n \\\\\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "So, our $R^2$ is:\n", "\n", "$$\n", "\\begin{align*}\n", "R^2 =& 1 - \\frac{\\text{SSE}}{\\text{SST}} \\\\\n", "=& 1 - \\frac{n(1-w)}{0.5n} \\\\\n", "=& 2w - 1 = 1.1 - 1 = 0.1\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "So we can see that one reason models with such low predictive power succeed so well in finance: it's trade-off between quality and quantity. It's also true that financial data is extremely noisy and there is very little stationarity due to an ever changing landscape of laws and company leaderships, etc." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Not necessarily bad Data Scientists" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### So what are the bets we're making?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Not just making money" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Finding which stocks will go up is pretty much a solved problem. Most \"secure\" stocks will rise in price on a long enough time-line. But just because the total price of stocks in your account has risen doesn't mean the value has risen. You have to account for the value of money (which is constantly dropping -- inflation).\n", "\n", "To illustrate this: we could make money by investing in General Electric in 2010 and holding our stock." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-10-08T15:44:52.620831Z", "start_time": "2017-10-08T15:44:48.298788Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from pandas_datareader import DataReader\n", "\n", "reader = DataReader([\"AAPL\", \"SPY\", \"GOOG\", \"GE\"], data_source=\"yahoo\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-10-08T15:50:34.702550Z", "start_time": "2017-10-08T15:50:34.436856Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "image/png": 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UPZaXnhzWOfQvoLRrtu0/wLR/zefY++ezobiCKfd+BkBOejJHBIj+CMbbS33T\n6e/74AfAY+16k5+Z4t6e/2MRe+yQtRvfXkH/G+Y0iBfvaM9/89fWo/knYUTO3DprlXvbhOhycdGr\nYwZrbjueFy6dyL/OsjJIq+rdjCqqHWT6CXVUwkNE6JBpKfGC7FR3XZdQUYWutGsOvftT9/Ylz37j\n3s5JS6K/XZUwVG5+d2XAfedNbFhgaf8By2VSVVvHhU8v4mL7/V/6ejPgeVpwkZaUwBljezKgwONH\n31hcwa9fWBIwq3Wg12f47UvfUlnjwGlMSBa6z3vb7hRXm7j6FnpplcMnOkhpOimJ1rX2ZwQ0hip0\npd1Sv8b3+mKPQhrRM5eh3Rr6NIPx7MKNfsfHF3bgsil9fSxywB1z7Frc/H6rbyGs+hUOy6sdZKUm\nkZPu+Uef+eVG3l+xk3ve9+9+qfFalJ39/Q7ufn8NDqdxL/qGi6uaoj93kb/sUiV8XBZ6ZXX4SW2q\n0JV2y76K2oD7uuWmk56SyDkTrAbG9X3aZVW17kiPAzV17Cg54O80ALw+4xDys1L55sZpfHX9VM6Z\nYFU1XLTB6lCz3EuRF14327292is08LB7PqW0ykFmaiIiwlGDLXfQUlvpf7TKf0W+6nrp+s99uYka\nhzPsR3kXKYkJiMC/PvqRdUXlPvsO7d8pwFFKOEzsm8+Ewo7ccNJBYR+rCl1pt7hasQEM6uLfvTK+\nsAMOp2Htbl/lNeLmDzn14S8AuPDpr5l8l+W6+cM0T0TC1dMG+tRISUgQuuamcdfpIwDYuKeSqto6\nnwJY3lTabhSn07B1n3XDcN1XLrejcL7bbCn04vLqBj73qto6dpY2jKOvqHGQFCQkMRgigjFWNM9p\n9ud30VSrX/ElMUF4bcZkfjaqe9jHqkJX2i2XPbcYgBOGd2X276a4x73rmIzulQf4b/CwZmcZVbV1\nfLPRo5AzUhKZ87spvHDJRK6eNqhBjRQXroiRxRv38cT89X7n/GfBBnaXVvHmt56Qw6lDOgO4+1G6\ncDhNAzeIq965q/CTixXbSlm4bo/f9wyH0ipfv31yE61+JXLoX0Bpt2zeWwnA+ZP6kJyYwLXHDGJ8\nYQe+vP5o95weHSy/8O4y/xmjK+s1gEhPSWRo9xwOGxjc/XDrz4dZ7/3U1+6xj685ggfOGcNLl050\nj729dBt/esNKbDp7fC937ewu2WnUp7TK14X0+mLrRvDU9HFBZYkUqtCjj/4FlHaJd3r/oba1fNXU\ngbw+4xCZtc+zAAAgAElEQVSy0zyxv6lJiaQlJ/i4Z7xdGy/aESkuMlJCS64pzM9sMDagcxY/G9Wd\nQwZ0YtpBVjTJnXM8i51/P8UTn5zg5TLpYjd73rbP48ffsreSRRstH33fTpm8fNkkjrate4AHzhkT\nkpzhECyzVGkdVKEr7ZJJd30S8tzc9GQfhe5dF/ytb7f5zO3d0dcVEoiEespvxS3H+by+yUt5u8hI\n8R/GNrCzFcboeuIA2LLP2n7gnDGICJP75zOu0NNUrCn+2UBM7Gs9NQRrgKG0DpoJoLQ7vOuazPxV\n431ZstOSKa92sLeihmteW8qgLtkB5/YvCC92HUCkYf3xUGKQ+3bKZENxBb3sm4h36dXSA5Z/u5+X\n//zSw/rRPTedQ/rnhy2jNylJCT6Zp9efeBDXvraUkfZ6gxI9VKEr7Q7vGufjC/22wvUhKUFw1BlW\nbCth7g9FzP3BtxZ599w0EhKErfsO0KFerHko+DNs8zJ8z3PRIYUN5gzsnMWG4gryM1NITUpgt5dC\n32pb6N4lblOSEjh1TPP7u6cnJ7oV+pGDCxjdK49Prj2y2edVmo8qdKXd4a3QA7kxvKmpc/Lhql18\nu9l/eOHhgwr403GD2VcZOK7dH4+cdzC/efHbkOYO81O4KdlO8klJSqBzTiq7SqvYVVrFY/PWsWp7\nKf0KMinIDlwCt6mkJye6XVCa7h9b6F9DaXe4FHqoVexcnWMCVTe8+WfDSEtOJD9I/XB/nDiiG0cM\nKuDwQf5rxvz6yP48OncdAP0KGi6idrWtb8Hy85dVObj4mW9YZXeLv/jQwrDkCRXvzkTadi62UIWu\ntDsqaywf+tSDOjcyMzADOmcxvrAD0w7q0qzGDsF8+CeP7OZW6Kl+FGe3XEuh7yqrYsW2UqDUJ7kn\n3HotoeL9eU9uQkVKpeVQha60O1xp/EkJTQ/yevXySWFb5OHircRT/JSmHWgvzvbI80TW1NZ5HPI5\naeGVXg1ZLi+FftSQpt8UlcijYYtKu8PhtBb0kpqQqt4hI5l1d57Y4socPIWwwKqhUp8jBhXw3K8m\ncNmUvn6PH9+38QXfpuC6aq4yvkrsoApdaXc4bCs2OUQL/ckLPZmWd5w2otUSaLwXHHMCNDo4fFAB\nSYkJTPGTmXpICxXLcnlyNOw89lCFrrQ7wrXQjxnaxd1cItRM0EjQIcOjxDs2Eg458+IJPha90j7R\nb4DSZqiodlBW5T90sNSr3K3D7UMP3dJ2RcY4W9EslTAWNRMSxKc41zu/PbQlRAI8Lhcl9lCFrrQZ\nDrvnU3cLufqMvPlDTvz354Cnv2ZTikkN7hpe04vm8sIlE3kuhGxW8ES9fHX9VEa1YNbmNccMJjs1\nicFdA2fMKtFBFbrSJjhQU8e+ylr2V9ZSeN1sn+QhF66ORFv2WkWswmnxdazdeq1HK3flOWxgp4Bx\n6vW5/dThXHnUALrmNqzEGGmZlt9ynE8RMyU20LBFpU1QVa/H5ec/FfHz0Q3T3H/z4hLmLN8JEFbX\nnofOPTigOydWmHpQF6baVRqV9ola6EqbwFGvRZyrvRv4VgF0KfNwSUlKaJVQRUVpDqrQlTaBa7HS\nFQ3y4teb3TXPvRslu8hsxWgVRWktVKErccuzX2xwp8a7LPTrjh/CKXat70/WWI2TV24vbXCsd2y5\norQVVKErccvN763inv9ZHX1WbrNawSUmCA+cPZrc9GRW2GOnP7IQ8E2fT21G/RVFiVVUoStxT53T\ncPnzSwBLoYsIB/fO452l230WS1+9fBJ98jPc8xSlraEKXYkr1heVU1JZ67a+AQ54KW2Xoj5uWFcq\na+q4wKsJc88OGe664touTWmLaNiiEjO8s3QbqUkJHD88cEnWo/85jx556Wzb72mI/NOuMve2K/vT\n1Tnom4376N0xgzqnoSA7lbtOH8mEwo6M1nZpShtELXQl6hhjcDoNv39lKTNe+JaZCzcCVud6V6QK\ngMOOVvFW5gDfb/VY667QwqleZV2dxrgbGeemJ3PRoX3DSqtXlHihUYUuIr1E5DMRWSUiK0Xk9/Z4\nRxH5SER+sn+3TK1Opc0z7vaPmXb/PPfrm95dCcCUez/jkpnfuMdX+IlW8Z4PMNR2qSQlJnDV0QMA\n2F9ZS1YYWaGKEq+EYqE7gGuNMUOBScBvRWQocB3wiTFmIPCJ/VpRwmZPRY27zZuLu95fDfiGHH60\nKnhS0KpbjyPLq+Ssq9lDebVD644o7YJGFboxZocx5lt7uwxYDfQAfg7MtKfNBE5tKSGVtsuanf6t\n7sfnrW8wtmN/lU8tld/ZFriL+g2fvRskTxkQWj0URYlnwnoOFZFCYAzwNdDFGLPD3rUT8FtEQkQu\nBy4H6N27d1PlVNooFz61yOf1fy4cx6XPLfYZ21VaRZecNHaWVtElJ5WPrzmCihoHiSI88OnagOf+\n1aGFbCgup0t2Gr3zMwLOU5S2QsiLoiKSBbwJXG2M8TGrjBUD5jcOzBjzhDFmnDFmXEGBWkmKL0cN\n9ixeDu6SzbShXXjmovE+c/74+jJufnclC9ftoWtuGukpiXTKSiUvI5nfHNmfjpkpPHTumAbnFhFu\nP3UEV00d2OKfQ1FigZAsdBFJxlLmLxpj3rKHd4lIN2PMDhHpBuxuKSGVtktOuucr6Ao8OWpIZ84c\n25PXl2wFYPv+A3z+UzHgaR9nzRf+fPwQ/nz8kNYTWFFimFCiXAR4ClhtjPmX1653gen29nTgnciL\np8Q6xhjOeuxL3vp2a5OOr60zJCcK+Zkp/MVLMd935ihm/+4wAMb09gRQXTqlX/MEVpQ2TCgW+qHA\nBcByEVlqj90A3A28JiKXAJuAs1pGRCWWKSqrZtHGvSzauJcBnbMY2TO8hB2H00lOWjJL/nZMg33D\nuueSk5aEK0v/oG45TLDjyRVFaUijCt0Ys4DAbQSnRlYcJd6Y9f0O9/bPHvqC9XeeSEKIdVLunLOa\nl77e7BONUp/kxATW7LQyQY8IsXOPorRXNFNUaRa3zlrl87q8xhHScV+u28MT89fjNLCrtDrgvMQE\ncWeCHjNUu/EoSjBUoStN5qv1exqMeS9aBuP22Z4bwXHDAivqJC9rPy1Zv66KEgz9D1GazNlPfNVg\nzOGnO5A/1hWVu7cfvyBwswnvbkMDO2u2p6IEQxW60mwK8zPItlPua52NW+jri8qpqrUU9bK/Hxt0\nbnF5DQDXnTDEp0GFoigN0f8QJSLc8vNhANQ6GrfQ/7NgAwBdc9LIzUgO6fyjwoyeUZT2iCp0pUmU\nVNa6t383dSDJidZXyV9DZqfTsN7LxeK0rfjHLxgb8vtlpmrLOEVpDFXoSpO45wOrl+cNJw7h9IN7\n0jU3DbCyOutz9//WcPQ/57mVem2doUdeOqNCaDLh6kCUpj1AFaVRVKErTaKsygpPPG1MTwD6dsoE\nYENxRYO5T8y3Kifuq7T84bV1zpD94R0yrM5DeemhuWYUpT2jCl1pEhXVDob3yHEnBeVnppCSmMBP\nu8uZ8fwSdpdanYa8o16KyqpZsmkvtXVOkhNDSz569uLxXHRIYdDkI0VRLLSNixI2ZVW1fLpmN+ML\nPTVWRIT0lERe+nozAN3y0rjplGG8tthT42XGC98CVmx55xAV9PAeuQzvkRtB6RWl7aIWuhI2/1th\ndQ76ZuM+n/F0Lz93J7u357MLNzQ43uE0bPfqFaooSmRQha6Ejcv/fe8ZI33Ge3bwdBPKSU9m7e4y\nftxVjj9+pzXKFSXiqEJXQmJPeTVVtXUA7t+HDujkM+f44V3d25+u3sW0f80PeL5rjhnUAlIqSvtG\nfehKoxhjGHv7x4C1SOnK8kytF6ly4eRCMlOTuP6t5Xz2Q5Hfc6UkJTDvT0e2qLyK0l5RC11pQI3D\nSckBK3FoT3k1fa+f49530TPfuOuw1I8NT0lK4JwJDfvGHjW4gI+vORyAzJREuuWmN5ijKErzUYWu\nNOC3L33LqFs+xBjDl14VFV0+8ue+3ARAWoBY8rtOH+Hefu2KyTx6/lgy7VovORpPrigthip0pQEf\nrdoFwJ6KGp/ytY+d75uqn5To/+vjXeZ2Qt+OpCUn0jUnjSuO6MdT08f7PUZRlOajCl1pQEaK5UpZ\nu9tTFRFgSNdsThnVvdHjXXVdBnXJco+JCNefcBADOmcFOkxRlGaii6Ihctlzi/lo1S5+uuMEt8Jq\nq3TPS2ft7nJWbCshy3aVXHPMIJISE3jwnDGcOro7O0sDx5Gn2NcnLz2lVeRVFMVCFXoI7Cmvdrsh\n1u4u56BuOVGWqGXJSbO+Ft9vLWFMb6uA1gWT+rj3Tz0oeCu4yhorrLFjpip0RWlNVKGHwIOfrnVv\n7yg50OYVekW1pZDfXbad77ZY2aDhVDt0VV48/eAekRdOUZSAtG3fQYTI82rC8KtnF7N5T2UUpQmP\n2jonpzy4gIueWYQxjXcTWrJpLz/sKnO/3rLXKodbP+Y8GJP65fPNjdM4dljXxicrihIxVKGHwP7K\nWrcvGeCOOauCzI4tdpZUsXxbCXN/KKLv9XP4cl3Dxs4udpdV8YtHv2ww3iMvnYSE0KojutDqiIrS\n+qhCb4RvN+/j2YUbKa92uMc+WLmLVdtLASg5UEt5tYPNeyopvG42Fz69KFqi+qWixuHz+qkFnmJZ\ndfX6f84NkN1ZP1xRUZTYRBV6I7z2zRa/44/NW0dVbR2jbvmQ4Td9wCvfWGVj5//oXylGi4pqX4Xu\ncDqpcxoKr5tN/xvmUOPVAzQ3QNLP8B5te81AUdoKqtAbwdUmbWLfjj7j7y7bzpJNnvKxj8xd595e\nvrWkdYQLAdcC54kjujKmdx77K2spKqt27y8q92xXelnzz15sJQD1yEtHJDx3i6Io0UEVeiN8ayvt\nf589psG+tbv9l4Y95aEFLSpTOLgs9CuPGkjPDhnsq6zhvg9+cO/fV1Hj3n5v2Q4AFt04lSMHd+aV\nyyfx/tVTWldgRVGajIYtNsLrS6yOO+kpicz+3WEs31rCdW8tB+Cmd1cGPK6qti6qjY1XbCth7e5y\nHLafPCs1ib6dMnlv2XY2eUXpLFxXTFKicN2by1m6ZT8AmSnW12JSv/zWF1xRlCajFnoQdpd5siEz\nUhIZ1j2Xsyf05tABlqLzbuhQnyF/+1+LyxeIOqfh5AcXcPWrS1m4thiAjNRExvbp4DMvQeDjVbs5\n/v8+dytzwF1IS1GU+EIVehCe+WIjACN75vqk+08ZWADA1n0HGNUzl2cuHs/pB/dgQt+OUQvXK6ms\nZcveSlZuL6H/DZ5yt299t43kRCErNYnC/AyfY5wGFm3c29qiKorSQqgpFoRquzBV/bA975j0wwZ2\n4qjBnTlqcGfA6nI/4Mb3AZh458e889vD3JmTLcllzy9m0Qb/yvmowZ1JS0706fl5wvCuvG/3BvXm\nuhOGtJiMiqK0LGqhB6G4vJrC/Ay65/m6VrwV4+VT+vvs8y4pu6u0mm83eyJhnvliQ9DEnqYy78ci\nv8r8hhMt5eyKNk9N8sh91+kjuOLwfg2OaetlDRSlLaMKPQh1TkOinwzJFK80+NyMhrHbL1020b3t\ntNPtH5u3jlveW8U5T37Fn99YRkllbURkNMYwPUAyU488XxdLRmoiInDHacPJy0jh3IkNuwu5Sucq\nihJ/qMslCIEUusviPWG4/1olnbI8fvTSA1bY4N3vr3GPvbZ4K4kJCT6dfZrKuqIKv+PJiYIrfNzV\npCI5MYENd53kntMlp6ErSBW6osQvjSp0EXkaOBnYbYwZbo/dDFwGuNIibzDGzPF/hqZRUllLSlIC\nTmOiFnVRZwyJCQ0fYvbayTjeitubPK+My5IDtewqrWJY9xxW2uUCAD5bszsiMq7cbiUxPXb+WI4e\n0pmv1u9hysBOiIg7wWl8YUe/x7rCKvMyktlvPzFkpOg9XlHilVD+e58FHgKeqzd+vzHmHxGXyGbU\nrR+6tx+/YCzHRaFyn2WhNxzvZvvUJ/T1ryg756Qx949HctQ/51JWVcvEOz9pMMfhdPo5MnzWFVWQ\nIHDk4AJSkhI4fFCBe9+Inrl8cu0R9OuUGfD49648jILsVCbdZcmoFrqixC+N+tCNMfOBqMa23fpe\n61c3LKmsZcHaYhL9pL0fO7QL7/9+StB2bIWdMsnPTGGvVyZmpHn4s7XMXLiRXh0zAiYx9S/ICpq6\nP6JnLl1z0zh9jFW7PF+bUihK3NKcRdErReR7EXlaRDoEmiQil4vIYhFZXFQUWuEqZ70qgNv2H2iG\nmOFTW+fkrMe/pMbhZNv+hq3WRCSkaJCC7DReCVDcq36lw6Zw3wc/UHKglu65gROcQuXuX4zku78d\nE7Dxs6IosU9T/3sfBfoDo4EdwD8DTTTGPGGMGWeMGVdQUBBomg9lVY7GJ7Ug17+13N3kodireFW4\nbCz2XbB0tXMDKK92hNRwIhDex365vvmhkClJCXRQ61xR4pomKXRjzC5jTJ0xxgk8CUyIpFAlByIT\n0tdU3rDrtzSXe84Y6fP60sP6cevPhwFQW2eodjTdj+597EkjujX5PIqitB2apNBFxFuDnAasiIw4\nFhv2+A/Faw1eWbQ5Yuc6ZWQ3rjp6gPt1zw7pXDi5kDtOGw54IlSawgG7EXNBdioPnNOwEqSiKO2P\nRhW6iLwMfAkMFpGtInIJcK+ILBeR74GjgD9EQpg6p2F3WZU7UeaVyycB0CkrBUddZKJCGsNVSfGY\noV14+7eHsuAvRzX5XCLCtccO5h9njgKgf+csAPIzrXDHf3zwY5PPXVlrKfQ/HjvIb6y8oijtj0bD\nFo0x5/gZfqoFZOHW91Yy88tN7teT+uUzfXIfZn65iQE3vs/Gu08KcnTz8fZ5P37+2LD7aAbijLE9\nOWNsT/frIwdbawl96hXLCocDdjOKdI0bVxTFJmZCGowxPsp8zW3HA75p9i2NK5rmmYvGR0yZ+yMt\nOZEuOaFVZdxQXMF3XvVgXLg6EWVEsea6oiixRcwo9NcXexYiJxR2dMdVexeUamlW77AyObvltXx1\nxF2l1QFDGr056h9zOe2RhQ3GK20fuiYCKYriImYU+o92mOCUgZ14bcZk93hqK1noTy3YwO2zVwOB\nmyVHk/px6wdqXS4XVeiKoljEjEJ31Qx/6NyDfca9XS71E44iyW2zPNmoXbJb3kIPlz0VvvHwN79r\nyavdhRRFcREzCr22zlLWKfUyFb0zF/e3YHz68XatmN8c2b9F/ecuxvbpQMcwEnmKyjwK/Yu1xWze\na/UFTWtFl5SiKLFNDCl0KywxOdFXmXpb5R+ubNhhJxzeW7ad8Xd8TI2fhJ46YxjYOYs/HTe4We8R\nKj07pJOdFty6fnTuOve2d3XG8/7ztXu7V8fmp/0ritI2iBmF7qhzIkKDmGqHl0Lfsq+y/mEhU+Nw\nctXL31FUVs12P7VhKqodZKQmBS1kFUmSEhJw1AV2IW0sruCe/3lqqP/jw4Yx61OHdG41eRVFiX1i\nRqHvqaghK6WhQq3zKjP78Gfr6h8WMuuKyt3bVY66BvtX7SilRytEt7hwOJ0Nio7VOQ2lVZZb6cNV\nDZ9GPv+pyOeJpa4ZtWAURWl7xIxCX76thOE9chuMe1voI3s23B8q3gW/HHWW4nQV3tqyt5L9lbUM\n6Jzd5POHyztLtwPw/Feb3Er6nx/+wMibP6S82sGdc9Y0OOaCpxa5FT7AbT8f3jrCKooSF8RMiMTO\nkiqOHtK5wbh3uF7n7NCScerz9nfb+O9329yva+ucXPPqUj5evZslf53Gvz/5ydrRihbvFYf34/H5\n6/nb2ytYtGEvX63fQ7YdsbLgp8Blhuf/VAzAn44bTK+OTc80VRSl7dGqFnrJgVrmLN/BxuIKfv/K\nd1TVelwf1Q6n3yYN0w8p5PBBBfTskM6eihqfY0JhR8kBrn51KfN+9ChJh9Pw8WprkXHJpn2k2+97\n8aF9m/KxmsQAu64LWIu1RWXVVNjp/Jv3VnL4oAJSEhNYdMNUn+Pm25/jrHG9Wk1WRVHig1ZV6Jv3\nVvKbF7/ltlmreGfpdhbY1iZYyt5fmn+nrFSe+9UEOmen8t3m/ZzxWMOsSRc3/Hc5Zz620KdW+Nfr\nGzZbqnU46ZCR7JbpQG0dPfLSW7UeuL/4cVf9sTvnrGH+j0UcMiCfzjlpXHF4P/cc141JOwspilKf\nqPjQ19tFsFwWqWtx0JV674/VO6xM0hXbSlmxrWHZ2do6Jy99vZlvNu5jwVrPjcJ728Wj89ZRXm29\n9+2zV7N0y37Sklv3UqT7eRqp30zDFaf+5+OHuJtj7K+sITc9uVVi5RVFiS+iotA32ArdZUif8H/z\nAfjFwT0DHcIBL1fL1a8upbLG4eNfv/t9zyLiqu3WjaGqts5vs4rPfyp2JzIBrN1d3urd7pMSG1fI\nruuTmCA8cLZV87y2zoRc2EtRlPZFVKNcvt6wl8LrZlNqR6AcfVDDRVF/pCQmMPTvH3DreyvdY08t\n2ODedlnfrvowvTtmMOuqw3jwnDFcMKmP33Mu92P1tySh1DCvrPFE5ngvgCYmxExwkqIoMURUNcPL\nXt2BfjmuFzlpgYtiPfcrT5e7VbZrZvbyhrHa2alJ7tKyP+y0FPrMX01geI9cThnVndtOHc6a247n\nyqMGcMLwrhH5HE0hlKJj9Rc++xdkApCk7hZFUfwQM6ZeYyVrh3XPaTA2uKsVKeLqQXrdCUPISE3k\n3WXbKa2qZbdd/6Rbru+505IT+eNxg7n/l6MZYce+f3zNEc3+DOEwpleHRufUXzjtkmN9jlDcNYqi\ntD9aPQ598V+n4TSGCXd84h77xcE9uXRKvyBHQX5WKhvvPonC62a7xwRLse0sqQKgR146u0otJX7T\nOyvJSUsiJy3JbzgkWIr9vasOa9bnaSr1FzVTEhOoqddmLyuQQlcLXVEUP7S6hZ6fmUJBlmdRb1Sv\nPP551qgGyisUXFmTrgXTzFSP4i4qq2bZ1hI658ReKVwXnbKsKJZ+nTJ59lfjARjS1ZOtWt9Cd0W9\ntGYXJ0VR4odWt9BdtVqGdsth1Y5SjhvWpcnncqXzv2lHsqQlJ3Ls0C58uGoXtXVOlm7Zz/mTejdf\n6BZi8V+PcW8bY7j91OFM7p/P1H/OA6Bjhm+sueumN6AgC0VRlPpELfV/zu+n8PX6PYwv7BjWcVOH\ndOYTu5RsWVUt7yzdxvNfWb1IM1KS+PfZYxh5ywfssN0wp47uEVnBWwgR4Xw7Auflyybx5rdbyUn3\n/fOcMbYnm/dWMuPI/tEQUVGUGKdVFfrQbr4LmxP75Yd9jv9MH8eWvQd48vP1PP/VJv7zuSdcMT05\nkfSURLrlprsbQOTEYDu5xpjcP5/J/Rtem14dM7j/l6OjIJGiKPFAqzpjQ4m9bgwRoXd+hrtJhXf8\nuOv8Y/t4Ikia4ptXFEWJR+J2de0vJwxpMNbbTr7xXljMaqQrkKIoSlshbhV6x8yUBtEertfevaQz\nWzmlX1EUJVrErUIHSPNS6E9cMNa9PbGfZ6E1Em4eRVGUeCCuzVeXRf73k4dy7DBPGv/BvTuw7O/H\nuqs5KoqitAfiWqEnJ1oKPSOlYSZobkYyuRnxF+GiKIrSVOLa5VJtR7qk+1HoiqIo7Y24Vuiu8ETv\nMEVFUZT2Sly7XO79xUjKTnLQs4M2S1YURYlrhd4hM6VV+4AqiqLEMnHtclEURVE8qEJXFEVpIzSq\n0EXkaRHZLSIrvMY6ishHIvKT/VtXJRVFUaJMKBb6s8Dx9cauAz4xxgwEPrFfK4qiKFGkUYVujJkP\n7K03/HNgpr09Ezg1wnIpiqIoYdJUH3oXY8wOe3snELDtkIhcLiKLRWRxUVFRE99OURRFaYxmhy0a\nY4yImCD7nwCeABCRIhHZ1Nz3bCU6AcXRFiJEVNaWQWVtOeJJ3liQtU8ok5qq0HeJSDdjzA4R6Qbs\nDuUgY0xBE9+v1RGRxcaYcdGWIxRU1pZBZW054kneeJK1qS6Xd4Hp9vZ04J3IiKMoiqI0lVDCFl8G\nvgQGi8hWEbkEuBs4RkR+AqbZrxVFUZQo0qjLxRhzToBdUyMsS6zxRLQFCAOVtWVQWVuOeJI3bmQV\nYwKuZyqKoihxhKb+K4qitBFUoSuKorQRVKEriqK0Edq9QheRuLgGIiLRliFURCRuegKKSK79O+a/\nByLS1f4d898FERkmImnRliNURORQEekfbTmaS8x/iVsCEZkgIr8DMMY4oy1PMGxZnwT+IiIxnZgl\nIuNE5Hng77H8zyEiCSKSIyKzgAcgtr8HIjJGRD4BbgMrOzvKIgVEREaKyALgdiA/2vI0hogcLCIf\nAp8CudGWp7m0O4UuIlcD/wX+KiIn2GMxZ1GKSKKI3IUVMvUFcDBwk4gErJsTLWwF+RDwOFb1zW7A\nzSISk70BbeVdBiQDPUTklxB7VrpY3A88B8w0xlwWbZlC4K/AG8aY04wx2yA2nyhEJFlEHsf6/3oA\n+AA40t4XU9+DcIhbwZvBWuBk4NfA9QDGmLoY/NIlAJuBs4wxzwJXA5OA9GgK5Q9bQX4KTLVlvRcw\ngCOacjXCEKz6HP8HnCci2cYYZyx9D2xLPAv4zhjzHICI9I9FhWPf1PsB5caY/7PHjhGRPCDRfh0z\n1xZIBeYBU4wxs4A3gYNEJCmWn9YaI+a+GJFGRCaJyCCvodnA9/bvcpfrBftLF03qyeoEXjbG/Cgi\nqcaY7cBWrEJBUaf+dTXGvGWM2S8ixwCLsaz0O0XkoKgJaeMtq5dSWQvUABvsn+ki0jva7gw/39dr\ngYki8jcR+QK4D3hWRMZGR0IP3rLaSrAYmCIiJ4nI28AfsazfP9lzYunaVhhjXjLGHLBfJwF1xhhH\nLN4wQyVuBW8MEckTkdnAR8BZIpLp2mWMqTPGVAH/BC4RkU7GmKhZk35kzbJl3A9gjKkWkWygL7A9\nWnIGkDXTHncpyn3AucaYY4AKLEUZFTeRP1m9lMo4oNQYsxJYCdwEPGo/irf6/0Wg62qMKQUeBs7A\neu3I+1gAAAXASURBVKI8B9gB/CJaayqNyPoMlq//aWPMccB/gEkiMikasgaS164SK15/63nAaSLS\nQS302CQTyy92lb19ODRY/JoLfGXPQUQmtK6IburLOsXPnInASmPMdhHJEpGBrSmgF4Guq7F/LzbG\nzLHnvg+MASqjICcEkNVmM5AtIq8CfwaWAD8aY2qj9A8dUFZjzAPAkcaY+caYauBtrBtSLF7XWUAh\n4GpLuRjYBVS3onz1Cfidtd1sCcBGe84R0RIyErQphS4iF4rIESKSYy/IPAG8BlRhPbZ2t+cJWL5z\nrNX4v4hICXBwa/n5wpDVVW8nD9giIhcD3wCjW0POcGT1w1gsa7LVnn7CkLUDUIDVoGUM1prK4NZ0\nEYVzXY0x+7wOHYvlfquLIVl72HJ+j+ViuVJEOgHnA8OBPa0la4jyunWBfQNPtQ+tco23pryRIu5r\nudgXvivwEpbfeR3WXfj3xphie86hwFnAN8aYF+yxBKAf1iNiDXC1MWZ5LMpqjz8PnIfV8u9++x8n\n5mQVkRysp4k7sZTltcaYH2NI1sXGmOftsU5e+7OAFGNM/XaL0ZTV+7qmApOBf2DdJGP2utrj12D9\nfw0E/mCMWdWSsjZBXu9rm2gHRrwArDXG3NzSsrYUcW2h238IA2QD24wxU7Esrb14VUgzxnyB9Ug1\nRERyRSTDviuXAn83xkxtBWXeFFlzbEUD1iLuWcaYi1tBmTf1uqbZflQD3G6MOaUVlE64sg62Zc00\nxhSLFR6aYIwpbwVl3tTrmm67WmqI/euabY//C0uRH9dKyrw5usD1pPOreFbmEKcWulhx47dhRabM\nAXKAM4wx0+39CViLh780xsyzx7Kw3CuHYLVzGmuM2Rrjsh4K9AZGG08P11iXdYyxInJiWVbXdyAe\nZNXr2obkbWnizkIXkSOwFrA6YIWe3QbUAke5FjVt6/tm+8fFScBvgGXAiFZS5s2Vdakta2so80jJ\n2hpKJ1LfgXiQVa9rG5G3VTDGxNUPVgTIBV6vH8F6tLoIWGKPJWD50l4DCu2xnwOHq6wqq8oa/7LG\no7ytck2iLUAT/ogZWCvSifbr84C77O2lwFX29jisxByVVWVVWduYrPEob2v8xJ3LxRhTaYypNp6F\njGOAInv7Yqz03VnAy8C3EL0QJJW1ZVBZW4Z4khXiT97WoNGeorGKvRhigC7Au/ZwGXADVtzrBmMX\nBzL2bTpaqKwtg8raMsSTrBB/8rYkcWehe+HEqpZXDIy078R/A5zGmAWuP2CMoLK2DCpryxBPskL8\nydtyRNvn05wfrOqDTmABcEm05VFZVVaVVeWN5k9cxqG7EJGewAXAv4yVeBGzqKwtg8raMsSTrBB/\n8rYUca3QFUVRFA/x7ENXFEVRvFCFriiK0kZQha4oitJGUIWuKIrSRlCFrrRZRKRORJaKyEoRWSYi\n10oj7eVEpFBEzm0tGRUlkqhCV9oyB4wxo40xw7DSwk/A6h0ajEJAFboSl2jYotJmEZFyY0yW1+t+\nWO37OmHVwX4eq6MNwJXGmIUi8hVwELABqzvUA8DdwJFYhaAeNsY83mofQlHCQBW60mapr9Dtsf3A\nYKxaH05jTJVYDbdfNsaME5EjgT8aY062518OdDbG3C5WG7gvgDONMRta9cMoSgjEbXEuRWkmycBD\nIjIaq9nyoADzjsWqD3KG/ToXq0+mKnQl5lCFrrQbbJdLHbAby5e+CxiFtZZUFegwrLraH7SKkIrS\nDHRRVGkXiEgB8BjwkLH8jLnADmO1KLsAqyclWK6YbK9DPwB+LSLJ9nkGiUgmihKDqIWutGXSRWQp\nlnvFgbUI+i973yPAmyJyIfA/oMIe/x6oE5FlwLPAv7EiX761myMUAae21gdQlHDQRVFFUZQ2grpc\nFEVR2giq0BVFUdoIqtAVRVHaCKrQFUVR2giq0BVFUdoIqtAVRVHaCKrQFUVR2giq0BVFUdoI/w9j\nMzN1xW3b7QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f38b59d66d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = reader[\"Adj Close\", :, \"GE\"].plot(label=\"GE\")\n", "ax.legend()\n", "ax.set_title(\"Stock Adjusted Closing Price\")\n", "plt.savefig(\"img/close_price_GE.png\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-10-08T16:00:24.218312Z", "start_time": "2017-10-08T16:00:24.211696Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24.389999 11.955239\n", "1.04010969584\n", "0.0669653602488\n" ] } ], "source": [ "tmp = reader[\"Adj Close\", :, \"GE\"]\n", "a,b = tmp.iloc[0], tmp.iloc[-1]\n", "print(a,b)\n", "\n", "c = (a - b) / b\n", "print(c)\n", "\n", "print((1+c)**(1.0/11) - 1)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![](img/close_price_GE.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "If we'd done that, we would have seen on average about 6% return per year! That's over the average inflation of somewhere between 3-5 percent, so we're looking pretty good, right?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Looking good?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Well, yes and no. On the one hand, we did make money (at the expense of some risk, of course). But what if we'd chosen a better company to invest in like Apple? Or what if we'd invested instead in an index fund like Spyder?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-10-08T15:44:55.081553Z", "start_time": "2017-10-08T15:44:54.712109Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "image/png": 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R7evh7iaoZ4wV1K9Z8ApNn0x0rN4WEVZLx8MXE63oNZpyYOAb6wE1M3TjUwPs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\nJpLOwANrIPE0NOqp8o9nZ4G7h5rYZC7KkZWhJjT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f38b59d6828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = reader[\"Adj Close\", :, \"SPY\"].plot(label=\"SPY\")\n", "ax = reader[\"Adj Close\", :, \"AAPL\"].plot(label=\"AAPL\", ax=ax)\n", "ax = reader[\"Adj Close\", :, \"GE\"].plot(label=\"GE\", ax=ax)\n", "ax.legend()\n", "ax.set_title(\"Stock Adjusted Closing Price\")\n", "plt.savefig(\"img/close_price_3.png\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![](img/close_price_3.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Better yet!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "We can continue this thought process ad infinitum. For instance, we could've invested in Google. Or done something even crazier (a plot I won't show for simplicity reasons) -- volatility trading." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-10-08T15:44:53.666390Z", "start_time": "2017-10-08T15:44:53.273529Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "image/png": 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rgjptAqpJAgICWLZsmdWy5OTkapZGoaibmGz4xUXYNOHp6MnW27fSyKkRT29/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\n73fNuQt4x+IaNuxu57anN/H4P3Zz5vxKTp1Vxt1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f38b5a3d160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = reader[\"Adj Close\", :, \"SPY\"].plot(label=\"SPY\")\n", "ax = reader[\"Adj Close\", :, \"AAPL\"].plot(label=\"AAPL\", ax=ax)\n", "ax = reader[\"Adj Close\", :, \"GOOG\"].plot(label=\"GOOG\", ax=ax)\n", "ax = reader[\"Adj Close\", :, \"GE\"].plot(label=\"GE\", ax=ax)\n", "ax.legend()\n", "ax.set_title(\"Stock Adjusted Closing Price\")\n", "plt.savefig(\"img/close_price_all_4.png\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![](img/close_price_all_4.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# So what's the game?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Active Portfolio Management\n", "Richard C. Grinold, Ronald N. Kahn" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# The CAPM" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## The Capital Asset Pricing Model" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "$$ r_i = \\alpha_i + \\beta_i r_m $$\n", "\n", "$$ E[\\alpha_i] = 0 $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "$$ r_p = \\alpha_p + \\beta_p r_m $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Roughly speaking, this is the $\\alpha$ we've all heard so much about. One problem is that a linear model is not the best model for predicting stock returns." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# The Hedge Fund Mission" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Make money" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Like Roulette" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-10-08T14:00:35.454639Z", "start_time": "2017-10-08T14:00:35.341988Z" }, "slideshow": { "slide_type": "fragment" } }, "source": [ "![](img/americanroulette.png \"hey\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## A paraphrasing" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Risk" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## What is it?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Variance?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Exceptional Returns" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" }, "toc": { "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": { "height": "797px", "left": "0px", "right": "747px", "top": "134px", "width": "212px" }, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }
mit
luwei0917/awsemmd_script
notebook/GlpG_paper/May_second_3.ipynb
1
2165163
null
mit
permamodel/permamodel
notebooks/Ku_2D.ipynb
1
225026
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This model was developed by Permamodel workgroup.\n", "\n", "Basic theory is Kudryavtsev's method.\n", "\n", "Reference:\n", "\n", " Anisimov, O. A., Shiklomanov, N. I., & Nelson, F. E. (1997).\n", " Global warming and active-layer thickness: results from transient general circulation models.\n", " Global and Planetary Change, 15(3), 61-77." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "../../permamodel/permamodel/examples\n", " \n", "Ku model component: Initializing...\n", "2014.0\n", "2015.0\n", "2016.0\n", "***\n", "Writing output finished!\n", "Please look at./NA_ALT.nc and ./NA_TPS.nc\n" ] } ], "source": [ "import os,sys\n", "\n", "sys.path.append('../../permamodel/')\n", "\n", "from permamodel.components import bmi_Ku_component\n", "from permamodel import examples_directory\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.basemap import Basemap, addcyclic\n", "import matplotlib as mpl\n", "\n", "print examples_directory\n", "\n", "cfg_file = os.path.join(examples_directory, 'Ku_method_2D.cfg')\n", "x = bmi_Ku_component.BmiKuMethod()\n", "\n", "x.initialize(cfg_file)\n", "y0 = x.get_value('datetime__start')\n", "y1 = x.get_value('datetime__end')\n", "\n", "for i in np.linspace(y0,y1,y1-y0+1):\n", " \n", " x.update()\n", " print i\n", "\n", "x.finalize()\n", "\n", "ALT = x.get_value('soil__active_layer_thickness')\n", "TTOP = x.get_value('soil__temperature')\n", "LAT = x.get_value('latitude')\n", "LON = x.get_value('longitude')\n", "SND = x.get_value('snowpack__depth')\n", "\n", "LONS, LATS = np.meshgrid(LON, LAT)\n", "\n", "#print np.shape(ALT)\n", "#print np.shape(LONS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spatially visualize active layer thickness:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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F27dvL0OJi05qairFxMTQ9OnTK+TelgUsy1K3bt2k7gnfbh7xOo/j9j958oT4g9eQkv1a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\nnLi4uHvmdz6J4uJiWrduza1btx65LqgsllGTfy5IWjNp0iT+7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RAiFnWSLGv3oQAGgAlQ\nKIRIBTIVRWlZ+nwTYDvqBAgyEUqSVEVkIpQqmz8QWvp9E+DvO55LLN0G8A3wX6BYCFF2CfQPoJui\nKM8DV1An1W6lK1q/CJyq4tglSaqD5MxmqdIoijIT9eju+0ftK4Q4CHS6a/MfQHfUP5fHUCe+2agv\noV4UQhRUbsSSJElyRChVEkVRhgM+wNA7NicCTe94bF+67UF+R30JtCtwTAiRDRgB7qiTpCRJUqWT\niVB6Ekrpl/qBongBgUA/IcTtO/b7BRiiKIqBoigtgNbAyQcdVAhxEWgM9ADOlm6OBEYh7w9KklRF\nZCKUHouiKFtQj86eVxQlXlGU94CVgBlwQFGUM4qiBAEIIaKBH4Bo1PcNx1SgkewJIFUIUbYc+TGg\nBXJEKElSFZEt1iRJkqQ6TY4IJUmSpDpNJkJJkiSpTpOJUJIkSarTZCKUJEmS6jSZCCVJkqQ6TSZC\nSZIkqU6TiVCSJEmq02QilCRJkuq0/wdOzQ8WIwjCXQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c921810>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Simulated ALT:\n", "Max: 4.13191919382 m 75% = 1.32973445174\n", "Min: 0.031967343002 m 25% = 0.708916951301\n" ] }, { "data": { "text/plain": [ "(array([ 2.48000000e+02, 1.80000000e+03, 1.99200000e+03,\n", " 1.36900000e+03, 3.94000000e+02, 7.00000000e+00,\n", " 2.00000000e+00, 0.00000000e+00, 5.00000000e+00,\n", " 1.00000000e+00]),\n", " array([ 0.03196734, 0.44196253, 0.85195771, 1.2619529 , 1.67194808,\n", " 2.08194327, 2.49193845, 2.90193364, 3.31192882, 3.72192401,\n", " 4.13191919]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10ce54590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig=plt.figure(figsize=(8,4.5))\n", "\n", "ax = fig.add_axes([0.05,0.05,0.9,0.85])\n", "\n", "m = Basemap(llcrnrlon=-145.5,llcrnrlat=1.,urcrnrlon=-2.566,urcrnrlat=46.352,\\\n", " rsphere=(6378137.00,6356752.3142),\\\n", " resolution='l',area_thresh=1000.,projection='lcc',\\\n", " lat_1=50.,lon_0=-107.,ax=ax)\n", "\n", "X, Y = m(LONS, LATS)\n", "\n", "m.drawcoastlines(linewidth=1.25)\n", "# m.fillcontinents(color='0.8')\n", "m.drawparallels(np.arange(-80,81,20),labels=[1,1,0,0])\n", "m.drawmeridians(np.arange(0,360,60),labels=[0,0,0,1])\n", "\n", "clev = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])\n", "cs = m.contourf(X, Y, ALT, clev, cmap=plt.cm.PuBu_r, extend='both')\n", "\n", "cbar = m.colorbar(cs)\n", "cbar.set_label('m')\n", "\n", "plt.show()\n", "\n", "# print x._values[\"ALT\"][:]\n", "ALT2 = np.reshape(ALT, np.size(ALT))\n", "ALT2 = ALT2[np.where(~np.isnan(ALT2))]\n", "\n", "print 'Simulated ALT:'\n", "print 'Max:', np.nanmax(ALT2),'m', '75% = ', np.percentile(ALT2, 75)\n", "print 'Min:', np.nanmin(ALT2),'m', '25% = ', np.percentile(ALT2, 25)\n", "\n", "plt.hist(ALT2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spatially visualize mean annual ground temperature:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+zunMr7/+qi0oTw30er0cOXJEfH19JSAgQHLlymWxjMA0UEBcREVFSZ48ebRz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d581ed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(array([ 32., 221., 349., 399., 305., 636., 596., 937.,\n", " 1084., 1259.]),\n", " array([ -2.11404932e+01, -1.90264441e+01, -1.69123950e+01,\n", " -1.47983459e+01, -1.26842969e+01, -1.05702478e+01,\n", " -8.45619870e+00, -6.34214961e+00, -4.22810053e+00,\n", " -2.11405144e+00, -2.35981496e-06]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d5710d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig2=plt.figure(figsize=(8,4.5))\n", "\n", "ax2 = fig2.add_axes([0.05,0.05,0.9,0.85])\n", "\n", "m2 = Basemap(llcrnrlon=-145.5,llcrnrlat=1.,urcrnrlon=-2.566,urcrnrlat=46.352,\\\n", " rsphere=(6378137.00,6356752.3142),\\\n", " resolution='l',area_thresh=1000.,projection='lcc',\\\n", " lat_1=50.,lon_0=-107.,ax=ax2)\n", "\n", "X, Y = m2(LONS, LATS)\n", "\n", "m2.drawcoastlines(linewidth=1.25)\n", "# m.fillcontinents(color='0.8')\n", "m2.drawparallels(np.arange(-80,81,20),labels=[1,1,0,0])\n", "m2.drawmeridians(np.arange(0,360,60),labels=[0,0,0,1])\n", "\n", "clev = np.linspace(start=-10, stop=0, num =11)\n", "cs2 = m2.contourf(X, Y, TTOP, clev, cmap=plt.cm.seismic, extend='both')\n", "\n", "cbar2 = m2.colorbar(cs2)\n", "cbar2.set_label('Ground Temperature ($^\\circ$C)')\n", "\n", "plt.show()\n", "\n", "# # print x._values[\"ALT\"][:]\n", "TTOP2 = np.reshape(TTOP, np.size(TTOP))\n", "TTOP2 = TTOP2[np.where(~np.isnan(TTOP2))]\n", "\n", "# Hist plot:\n", "plt.hist(TTOP2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mask = x._model.mask" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(131, 281)\n" ] } ], "source": [ "print np.shape(mask)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x1198d5c90>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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kt4miOLussUeep3rk9u0w8vOlWY69vQ2VKzsTFhZOnTo1S3SKMCIxevRCgoIM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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11940aa10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(mask)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "print np.nanmin(x._model.tot_percent)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
msampathkumar/data_science_sessions
Session-2-Hands-Experience-for-ML/DataScience_Presentation2-LR3.ipynb
1
4681
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Linear Regression - Part 3\n", "\n", "In this section, we shall try to apply machine learning to a well known boston dataset.\n", "\n", "## Real World Examples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", "\n", "data = load_boston()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(data.DESCR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.data[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.target[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = data.data\n", "Y = data.target" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Dummy Classifier\n", "\n", "http://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from sklearn.dummy import DummyRegressor\n", "dummy_regr = DummyRegressor(strategy=\"median\")\n", "dummy_regr.fit(X, Y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error\n", "mean_squared_error(Y, dummy_regr.predict(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Regression\n", "\n", "http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model\n", "regr = linear_model.LinearRegression()\n", "regr.fit(X, Y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mean_squared_error(Y, regr.predict(X))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Linear SVR\n", "\n", "http://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import LinearSVR\n", "# Step1: create an instance class as `regr`\n", "# Step2: fit the data into class instance" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# score\n", "mean_squared_error(Y, regr.predict(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest Regressor\n", "\n", "http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "# set random_state as zero" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# score" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## K-NearestNeighbors\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
unnati-xyz/ensemble-package
.ipynb_checkpoints/trying-Copy2-checkpoint.ipynb
1
71819
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets, linear_model, preprocessing, grid_search\n", "from sklearn.preprocessing import Imputer, PolynomialFeatures, StandardScaler\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.cross_validation import KFold\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.cross_validation import StratifiedKFold, KFold\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.externals import joblib\n", "from keras.layers import Dense, Activation, Dropout\n", "from keras.models import Sequential\n", "from keras.regularizers import l2, activity_l2\n", "import xgboost as xgb\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.cross_validation import train_test_split\n", "from joblib import Parallel, delayed\n", "from sklearn.pipeline import Pipeline\n", "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials \n", "from hyperas import optim\n", "from hyperas.distributions import choice, uniform, conditional\n", "import category_encoders as ce\n", "from functools import partial\n", "np.random.seed(1338)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting the data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def data_import(data, label_output, encode = 'label', split = True, stratify = True, split_size = 0.1):\n", " \n", " global Data\n", " Data = data\n", " #Reading the data, into a Data Frame.\n", " global target_label\n", " target_label = label_output\n", "\n", " #Selcting the columns of string data type\n", " names = data.select_dtypes(include = ['object'])\n", " \n", " #Converting string categorical variables to integer categorical variables.\n", " label_encode(names.columns.tolist())\n", "\n", " columns = names.drop([target_label],axis=1).columns.tolist()\n", " \n", " #Data will be encoded to the form that the user enters\n", " encoding = {'binary':binary_encode,'hashing':hashing_encode,'backward_difference'\n", " :backward_difference_encode,'helmert':helmert_encode,'polynomial':\n", " polynomial_encode,'sum':sum_encode,'label':label_encode}\n", " #Once the above encoding techniques has been selected by the user, the appropriate encoding function is called\n", " encoding[encode](columns)\n", " \n", " #This function intializes the dataframes that will be used later in the program\n", " #data_initialize()\n", " \n", " #Splitting the data into to train and test sets, according to user preference\n", " if(split == True):\n", " data_split(stratify,split_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data for ensembling (Training)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The dataframes will be used in the training phase of the ensemble models\n", "def second_level_train_data(predict_list, cross_val_X, cross_val_Y):\n", " \n", " #Converting the list of predictions into a dataframe, which will be used to train the stacking model.\n", " global stack_X\n", " stack_X = pd.DataFrame()\n", " stack_X = stack_X.append(build_data_frame(predict_list))\n", " \n", " #Building a list that contains all the raw features, used as cross validation data for the base models.\n", " global raw_features_X\n", " raw_features_X = pd.DataFrame()\n", " raw_features_X = raw_features_X.append(cross_val_X,ignore_index=True)\n", " \n", " #The data frame will contain the predictions and raw features of the base models, for training the blending\n", " #model\n", " global blend_X\n", " blend_X = pd.DataFrame()\n", " blend_X = pd.concat([raw_features_X, stack_X], axis = 1, ignore_index = True)\n", " \n", " #Storing the cross validation dataset labels in the variable stack_Y, \n", " #which will be used later to train the stacking and blending models.\n", " global stack_Y\n", " stack_Y = cross_val_Y " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data for ensembling (Testing)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The dataframes will be used in the testing phase of the ensemble models\n", "def second_level_test_data(predict_list, test_X, test_Y):\n", " \n", " #Converting the list of predictions into a dataframe, which will be used to test the stacking model.\n", " global test_stack_X\n", " test_stack_X = pd.DataFrame()\n", " test_stack_X = test_stack_X.append(build_data_frame(predict_list))\n", " \n", " #Building a list that contains all the raw features, used as test data for the base models.\n", " global test_raw_features_X\n", " test_raw_features_X = pd.DataFrame()\n", " test_raw_features_X = test_raw_features_X.append(test_X,ignore_index=True)\n", " \n", " #The data frame will contain the predictions and raw features of the base models, for testing the blending\n", " #model\n", " global test_blend_X\n", " test_blend_X = pd.DataFrame()\n", " test_blend_X = pd.concat([test_raw_features_X, test_stack_X], axis = 1, ignore_index = True)\n", " \n", " #Storing the cross validation dataset labels in the variable stack_Y, \n", " #which will be used later to test the stacking and blending models.\n", " global test_stack_Y\n", " test_stack_Y = test_Y " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Label Encoding" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Function that encodes the string values to numerical values.\n", "def label_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values.\n", " encoder = ce.OrdinalEncoder(cols = column_names, verbose = 1)\n", " Data = encoder.fit_transform(Data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Binary Encoding" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def binary_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values, using binary method.\n", " encoder = ce.BinaryEncoder(cols = column_names, verbose = 1)\n", " Data = encoder.fit_transform(Data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hashing Encoding" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def hashing_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values, using hashing method.\n", " encoder = ce.HashingEncoder(cols = column_names, verbose = 1, n_components = 128)\n", " Data = encoder.fit_transform(Data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Backward Difference Encoding" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def backward_difference_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values, using backward difference method.\n", " encoder = ce.BackwardDifferenceEncoder(cols = column_names, verbose = 1)\n", " Data = encoder.fit_transform(Data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Helmert Encoding" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def helmert_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values, using helmert method.\n", " encoder = ce.HelmertEncoder(cols = column_names, verbose = 1)\n", " Data = encoder.fit_transform(Data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sum Encoding" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sum_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values, using sum method.\n", " encoder = ce.SumEncoder(cols = column_names, verbose = 1)\n", " Data = encoder.fit_transform(Data) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Polynomial Encoding" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def polynomial_encode(column_names):\n", " \n", " global Data\n", " #Encoding the data, encoding the string values into numerical values, using polynomial method.\n", " encoder = ce.PolynomialEncoder(cols = column_names, verbose = 1)\n", " Data = encoder.fit_transform(Data)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Splitting the data into training and testing datasets\n", "def data_split(stratify, split_size):\n", " \n", " global Data\n", " global test\n", " \n", " #Stratified Split\n", " if(stratify == True):\n", " Data, test = train_test_split(Data, test_size = split_size, stratify = Data[target_label],random_state = 0)\n", " \n", " #Random Split\n", " else:\n", " Data, test = train_test_split(Data, test_size = split_size,random_state = 0) " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#This function is used to convert the predictions of the base models (numpy array) into a DataFrame.\n", "def build_data_frame(data):\n", " \n", " data_frame = pd.DataFrame(data).T\n", " return data_frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Boosting (XGBoost)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Trains the Gradient Boosting model.\n", "def train_gradient_boosting(train_X, train_Y, parameter_gradient_boosting):\n", "\n", " #Hyperopt procedure, train the model with optimal paramter values\n", " if(parameter_gradient_boosting['hyper_parameter_optimisation'] == True):\n", " \n", " model = gradient_boosting_parameter_optimisation(train_X, train_Y, parameter_gradient_boosting, \\\n", " objective_gradient_boosting)\n", " return model\n", " \n", " #Train the model with the parameter values entered by the user, no need to find otimal values\n", " else:\n", " \n", " dtrain = xgb.DMatrix(train_X, label = train_Y)\n", " del parameter_gradient_boosting['hyper_parameter_optimisation']\n", " model = xgb.train(parameter_gradient_boosting, dtrain)\n", " return model " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Defining the parameters for the XGBoost (Gradient Boosting) Algorithm.\n", "def parameter_set_gradient_boosting(hyper_parameter_optimisation = False, eval_metric = None, booster = ['gbtree'],\\\n", " silent = [0], eta = [0.3], gamma = [0], max_depth = [6],\\\n", " min_child_weight = [1], max_delta_step = [0], subsample = [1],\\\n", " colsample_bytree = [1], colsample_bylevel = [1], lambda_xgb = [1], alpha = [0],\\\n", " tree_method = ['auto'], sketch_eps = [0.03], scale_pos_weight = [0],\\\n", " lambda_bias = [0], objective = ['reg:linear'], base_score = [0.5]):\n", "\n", " parameter_gradient_boosting = {}\n", " #This variable will be used to check if the user wants to perform hyper parameter optimisation.\n", " parameter_gradient_boosting['hyper_parameter_optimisation'] = hyper_parameter_optimisation\n", " \n", " #Setting objective and seed\n", " parameter_gradient_boosting['objective'] = objective[0]\n", " parameter_gradient_boosting['seed'] = 0\n", " \n", " #If hyper parameter optimisation is false, we unlist the default values and/or the values that the user enters \n", " #in the form of a list. Values have to be entered by the user in the form of a list, for hyper parameter \n", " #optimisation = False, these values will be unlisted below\n", " #Ex : booster = ['gbtree'](default value) becomes booster = 'gbtree'\n", " #This is done beacuse for training the model, the model does not accept list type values\n", " if(parameter_gradient_boosting['hyper_parameter_optimisation'] == False):\n", " \n", " #Setting the parameters for the Booster, list values are unlisted (E.x - booster[0])\n", " parameter_gradient_boosting['booster'] = booster[0]\n", " parameter_gradient_boosting['eval_metric'] = eval_metric[0]\n", " parameter_gradient_boosting['eta'] = eta[0]\n", " parameter_gradient_boosting['gamma'] = gamma[0]\n", " parameter_gradient_boosting['max_depth'] = max_depth[0]\n", " parameter_gradient_boosting['min_child_weight'] = min_child_weight[0]\n", " parameter_gradient_boosting['max_delta_step'] = max_delta_step[0]\n", " parameter_gradient_boosting['subsample'] = subsample[0]\n", " parameter_gradient_boosting['colsample_bytree'] = colsample_bytree[0]\n", " parameter_gradient_boosting['colsample_bylevel'] = colsample_bylevel[0]\n", " parameter_gradient_boosting['base_score'] = base_score[0]\n", " parameter_gradient_boosting['lambda_bias'] = lambda_bias[0]\n", " parameter_gradient_boosting['alpha'] = alpha[0]\n", " parameter_gradient_boosting['tree_method'] = tree_method[0]\n", " parameter_gradient_boosting['sketch_eps'] = sketch_eps[0]\n", " parameter_gradient_boosting['scale_pos_weigth'] = scale_pos_weight[0]\n", " parameter_gradient_boosting['lambda'] = lambda_xgb[0]\n", " \n", " else:\n", " \n", " #Setting parameters for the Booster which will be optimized later using hyperopt.\n", " #The user can enter a list of values that he wants to optimize\n", " parameter_gradient_boosting['booster'] = booster\n", " parameter_gradient_boosting['eval_metric'] = eval_metric\n", " parameter_gradient_boosting['eta'] = eta\n", " parameter_gradient_boosting['gamma'] = gamma\n", " parameter_gradient_boosting['max_depth'] = max_depth\n", " parameter_gradient_boosting['min_child_weight'] = min_child_weight\n", " parameter_gradient_boosting['max_delta_step'] = max_delta_step\n", " parameter_gradient_boosting['subsample'] = subsample\n", " parameter_gradient_boosting['colsample_bytree'] = colsample_bytree\n", " parameter_gradient_boosting['colsample_bylevel'] = colsample_bylevel\n", " parameter_gradient_boosting['base_score'] = base_score\n", " parameter_gradient_boosting['lambda_bias'] = lambda_bias\n", " parameter_gradient_boosting['alpha'] = alpha\n", " parameter_gradient_boosting['tree_method'] = tree_method\n", " parameter_gradient_boosting['sketch_eps'] = sketch_eps\n", " parameter_gradient_boosting['scale_pos_weigth'] = scale_pos_weight\n", " parameter_gradient_boosting['lambda'] = lambda_xgb\n", " \n", " return parameter_gradient_boosting" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Using the loss values, this function picks the optimum parameter values. These values will be used \n", "#for training the model\n", "def gradient_boosting_parameter_optimisation(train_X, train_Y, parameter_gradient_boosting,obj):\n", " \n", " space_gradient_boosting = assign_space_gradient_boosting(parameter_gradient_boosting)\n", " trials = Trials()\n", " \n", " #Best is used to otmize the objective function\n", " best = fmin(fn = partial(obj, data_X = train_X, data_Y = train_Y\\\n", " , parameter_gradient_boosting = parameter_gradient_boosting),\n", " space = space_gradient_boosting,\n", " algo = tpe.suggest,\n", " max_evals = 100,\n", " trials = trials)\n", " \n", " optimal_param={}\n", " #Best is a dictionary that contains the indices of the optimal parameter values.\n", " #The following for loop uses these indices to obtain the parameter values, these values are stored in a\n", " #dictionary - optimal_param\n", " for key in best:\n", " optimal_param[key] = parameter_gradient_boosting[key][best[key]]\n", " \n", " optimal_param['objective'] = parameter_gradient_boosting['objective']\n", " optimal_param['eval_metric'] = parameter_gradient_boosting['eval_metric']\n", " optimal_param['seed'] = parameter_gradient_boosting['seed']\n", " \n", " #Training the model with the optimal parameter values\n", " dtrain = xgb.DMatrix(train_X, label = train_Y)\n", " model = xgb.train(optimal_param, dtrain)\n", " return model" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#This function calculates the loss for different parameter values and is used to determine the most optimum \n", "#parameter values\n", "def objective_gradient_boosting(space_gradient_boosting, data_X, data_Y, parameter_gradient_boosting):\n", " \n", " #Gradient Boosting (XGBoost)\n", " param = {}\n", " #Setting Parameters for the Booster\n", " param['booster'] = space_gradient_boosting['booster']\n", " param['objective'] = 'binary:logistic'\n", " param['eval_metric'] = parameter_gradient_boosting['eval_metric']\n", " param['eta'] = space_gradient_boosting['eta']\n", " param['gamma'] = space_gradient_boosting['gamma']\n", " param['max_depth'] = space_gradient_boosting['max_depth']\n", " param['min_child_weight'] = space_gradient_boosting['min_child_weight']\n", " param['max_delta_step'] = space_gradient_boosting['max_delta_step']\n", " param['subsample'] = space_gradient_boosting['subsample']\n", " param['colsample_bytree'] = space_gradient_boosting['colsample_bytree']\n", " param['colsample_bylevel'] = space_gradient_boosting['colsample_bylevel']\n", " param['alpha'] = space_gradient_boosting['alpha']\n", " param['scale_pos_weigth'] = space_gradient_boosting['scale_pos_weigth']\n", " param['base_score'] = space_gradient_boosting['base_score']\n", " param['lambda_bias'] = space_gradient_boosting['lambda_bias']\n", " param['lambda'] = space_gradient_boosting['lambda']\n", " param['tree_method'] = space_gradient_boosting['tree_method']\n", " \n", " model = xgb.Booster()\n", " auc_list = list()\n", "\n", " #Performing cross validation.\n", " skf=StratifiedKFold(data_Y, n_folds=3,random_state=0)\n", " for train_index, cross_val_index in skf:\n", " \n", " xgb_train_X, xgb_cross_val_X = data_X.iloc[train_index],data_X.iloc[cross_val_index]\n", " \n", " xgb_train_Y, xgb_cross_val_Y = data_Y.iloc[train_index],data_Y.iloc[cross_val_index]\n", " \n", " dtrain = xgb.DMatrix(xgb_train_X, label = xgb_train_Y)\n", " model = xgb.train(param, dtrain)\n", " \n", " predict = model.predict(xgb.DMatrix(xgb_cross_val_X, label = xgb_cross_val_Y))\n", " auc_list.append(roc_auc_score(xgb_cross_val_Y,predict))\n", " \n", " #Calculating the AUC and returning the loss, which will be minimised by selecting the optimum parameters.\n", " auc = np.mean(auc_list)\n", " return{'loss':1-auc, 'status': STATUS_OK }" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assigning the values of the XGBoost parameters that need to be checked, for minimizing the objective (loss).\n", "#The values that give the most optimum results will be picked to train the model.\n", "def assign_space_gradient_boosting(parameter_gradient_boosting):\n", " \n", "\n", " space_gradient_boosting ={\n", " \n", " 'booster': hp.choice('booster', parameter_gradient_boosting['booster']),\n", " \n", " 'eta': hp.choice('eta', parameter_gradient_boosting['eta']),\n", " \n", " 'gamma': hp.choice('gamma', parameter_gradient_boosting['gamma']),\n", " \n", " 'max_depth': hp.choice('max_depth', parameter_gradient_boosting['max_depth']),\n", " \n", " 'min_child_weight': hp.choice('min_child_weight', parameter_gradient_boosting['min_child_weight']),\n", " \n", " 'max_delta_step': hp.choice('max_delta_step', parameter_gradient_boosting['max_delta_step']),\n", " \n", " 'subsample': hp.choice('subsample', parameter_gradient_boosting['subsample']),\n", " \n", " 'colsample_bytree': hp.choice('colsample_bytree', parameter_gradient_boosting['colsample_bytree']),\n", " \n", " 'colsample_bylevel': hp.choice('colsample_bylevel', parameter_gradient_boosting['colsample_bylevel']),\n", " \n", " 'alpha': hp.choice('alpha', parameter_gradient_boosting['alpha']),\n", " \n", " 'scale_pos_weigth': hp.choice('scale_pos_weigth', parameter_gradient_boosting['scale_pos_weigth']),\n", " \n", " 'base_score': hp.choice('base_score', parameter_gradient_boosting['base_score']),\n", " \n", " 'lambda_bias': hp.choice('lambda_bias', parameter_gradient_boosting['lambda_bias']),\n", " \n", " 'lambda': hp.choice('lambda', parameter_gradient_boosting['lambda']),\n", " \n", " 'tree_method': hp.choice('tree_method', parameter_gradient_boosting['tree_method'])\n", " \n", " }\n", " \n", " return space_gradient_boosting" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_gradient_boosting(data_X, data_Y, gradient_boosting):\n", " \n", " predicted_values = gradient_boosting.predict(xgb.DMatrix(data_X, label = data_Y))\n", " auc = roc_auc_score(data_Y, predicted_values)\n", " return [auc,predicted_values]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision Tree" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Trains the Decision Tree model. Performing a grid search to select the optimal parameter values\n", "def train_decision_tree(train_X, train_Y, parameters_decision_tree):\n", " \n", " decision_tree_model = DecisionTreeClassifier() \n", " model_gs = grid_search.GridSearchCV(decision_tree_model, parameters_decision_tree, scoring = 'roc_auc')\n", " model_gs.fit(train_X,train_Y)\n", " return model_gs" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicts the output on a set of data, the built model is passed as a parameter, which is used to predict\n", "def predict_decision_tree(data_X, data_Y, decision_tree):\n", " \n", " predicted_values = decision_tree.predict_proba(data_X)[:, 1]\n", " auc = roc_auc_score(data_Y, predicted_values)\n", " return [auc,predicted_values]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def parameter_set_decision_tree(criterion = ['gini'], splitter = ['best'], max_depth = [None],\\\n", " min_samples_split = [2], min_samples_leaf = [1], min_weight_fraction_leaf = [0.0],\\\n", " max_features = [None], random_state = [None], max_leaf_nodes = [None],\\\n", " class_weight = [None], presort = [False]):\n", " \n", " parameters_decision_tree = {}\n", " parameters_decision_tree['criterion'] = criterion\n", " parameters_decision_tree['splitter'] = splitter\n", " parameters_decision_tree['max_depth'] = max_depth\n", " parameters_decision_tree['min_samples_split'] = min_samples_split\n", " parameters_decision_tree['min_samples_leaf'] = min_samples_leaf\n", " parameters_decision_tree['min_weight_fraction_leaf'] = min_weight_fraction_leaf\n", " parameters_decision_tree['max_features'] = max_features\n", " parameters_decision_tree['random_state'] = random_state\n", " parameters_decision_tree['max_leaf_nodes'] = max_leaf_nodes\n", " parameters_decision_tree['class_weight'] = class_weight\n", " parameters_decision_tree['presort'] = presort\n", " \n", " return parameters_decision_tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Random Forest" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Trains the Random Forest model. Performing a grid search to select the optimal parameter values\n", "def train_random_forest(train_X, train_Y, parameters_random_forest):\n", " \n", " random_forest_model = RandomForestClassifier()\n", " model_gs = grid_search.GridSearchCV(random_forest_model, parameters_random_forest, scoring = 'roc_auc')\n", " model_gs.fit(train_X,train_Y)\n", " return model_gs" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicts the output on a set of data, the built model is passed as a parameter, which is used to predict\n", "def predict_random_forest(data_X, data_Y, random_forest):\n", " \n", " predicted_values = random_forest.predict_proba(data_X)[:, 1]\n", " auc = roc_auc_score(data_Y, predicted_values)\n", " return [auc,predicted_values]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Parameters for random forest. To perform hyper parameter optimisation a list of multiple elements can be entered\n", "#and the optimal value in that list will be picked using grid search\n", "def parameter_set_random_forest(n_estimators = [10], criterion = ['gini'], max_depth = [None],\\\n", " min_samples_split = [2], min_samples_leaf = [1], min_weight_fraction_leaf = [0.0],\\\n", " max_features = ['auto'], max_leaf_nodes = [None], bootstrap = [True],\\\n", " oob_score = [False], random_state = [None], verbose = [0],warm_start = [False],\\\n", " class_weight = [None]):\n", " \n", " parameters_random_forest = {}\n", " parameters_random_forest['criterion'] = criterion\n", " parameters_random_forest['n_estimators'] = n_estimators\n", " parameters_random_forest['max_depth'] = max_depth\n", " parameters_random_forest['min_samples_split'] = min_samples_split\n", " parameters_random_forest['min_samples_leaf'] = min_samples_leaf\n", " parameters_random_forest['min_weight_fraction_leaf'] = min_weight_fraction_leaf\n", " parameters_random_forest['max_features'] = max_features\n", " parameters_random_forest['random_state'] = random_state\n", " parameters_random_forest['max_leaf_nodes'] = max_leaf_nodes\n", " parameters_random_forest['class_weight'] = class_weight\n", " parameters_random_forest['bootstrap'] = bootstrap\n", " parameters_random_forest['oob_score'] = oob_score\n", " parameters_random_forest['warm_start'] = warm_start\n", " \n", " return parameters_random_forest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Trains the Linear Regression model. Performing a grid search to select the optimal parameter values\n", "def train_linear_regression(train_X, train_Y, parameters_linear_regression):\n", " \n", " linear_regression_model = linear_model.LinearRegression()\n", " train_X=StandardScaler().fit_transform(train_X)\n", " model_gs = grid_search.GridSearchCV(linear_regression_model, parameters_linear_regression, scoring = 'roc_auc')\n", " model_gs.fit(train_X,train_Y)\n", " return model_gs" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicts the output on a set of data, the built model is passed as a parameter, which is used to predict\n", "def predict_linear_regression(data_X, data_Y, linear_regression):\n", " \n", " data_X = StandardScaler().fit_transform(data_X)\n", " predicted_values = linear_regression.predict(data_X)\n", " auc = roc_auc_score(data_Y, predicted_values)\n", " return [auc,predicted_values]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Parameters for random forest. To perform hyper parameter optimisation a list of multiple elements can be entered\n", "#and the optimal value in that list will be picked using grid search\n", "def parameter_set_linear_regression(fit_intercept = [True], normalize = [False], copy_X = [True]):\n", " \n", " parameters_linear_regression = {}\n", " parameters_linear_regression['fit_intercept'] = fit_intercept\n", " parameters_linear_regression['normalize'] = normalize\n", " \n", " return parameters_linear_regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Logistic Regression" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Trains the Logistic Regression model. Performing a grid search to select the optimal parameter values\n", "def train_logistic_regression(train_X, train_Y, parameters_logistic_regression):\n", "\n", " logistic_regression_model = linear_model.LogisticRegression()\n", " train_X=StandardScaler().fit_transform(train_X)\n", " model_gs = grid_search.GridSearchCV(logistic_regression_model, parameters_logistic_regression,\\\n", " scoring = 'roc_auc')\n", " model_gs.fit(train_X,train_Y)\n", " return model_gs" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicts the output on a set of data, the built model is passed as a parameter, which is used to predict\n", "def predict_logistic_regression(data_X, data_Y, logistic_regression):\n", " \n", " data_X = StandardScaler().fit_transform(data_X)\n", " predicted_values = logistic_regression.predict_proba(data_X)[:,1]\n", " auc = roc_auc_score(data_Y, predicted_values)\n", " return [auc,predicted_values]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Parameters for random forest. To perform hyper parameter optimisation a list of multiple elements can be entered\n", "#And the optimal value in that list will be picked using grid search\n", "def parameter_set_logistic_regression(penalty = ['l2'], dual = [False], tol = [0.0001], C = [1.0],\\\n", " fit_intercept = [True], intercept_scaling = [1], class_weight = [None],\\\n", " random_state = [None], solver = ['liblinear'], max_iter = [100],\\\n", " multi_class = ['ovr'], verbose = [0], warm_start = [False]):\n", " \n", " parameters_logistic_regression = {}\n", " parameters_logistic_regression['penalty'] = penalty\n", " parameters_logistic_regression['dual'] = dual\n", " parameters_logistic_regression['tol'] = tol\n", " parameters_logistic_regression['C'] = C\n", " parameters_logistic_regression['fit_intercept'] = fit_intercept\n", " parameters_logistic_regression['intercept_scaling'] = intercept_scaling\n", " parameters_logistic_regression['class_weight'] = class_weight\n", " parameters_logistic_regression['solver'] = solver\n", " parameters_logistic_regression['max_iter'] = max_iter\n", " parameters_logistic_regression['multi_class'] = multi_class\n", " parameters_logistic_regression['warm_start'] = warm_start\n", " \n", " return parameters_logistic_regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stacking" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The stacked ensmeble will be trained by using one or more of the base model algorithms\n", "#The function of the base model algorithm that will be used to train will be passed as the\n", "#model_function parameter and the parameters required to train the algorithm/model will be passed as the\n", "#model_parameters parameter\n", "def train_stack(data_X, data_Y, model_function, model_parameters):\n", " \n", " model = model_function(data_X, data_Y, model_parameters)\n", " return model" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicts the output on a set of stacked data, after the stacked model has been built by using a base model\n", "#algorithm, hence we need the predict funcction of that base model algorithm to get the predictions\n", "#The predict function of the base model is passed as the predict_function parameter and its respective model is \n", "#passed as the model parameter\n", "def predict_stack(data_X, data_Y, predict_function, model):\n", " \n", " auc,predicted_values = predict_function(data_X, data_Y, model)\n", " return [auc,predicted_values]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Blending" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The blending ensmeble will be trained by using one or more of the base model algorithms\n", "#The function of the base model algorithm that will be used to train will be passed as the\n", "#model_function parameter and the parameters required to train the algorithm/model will be passed as the\n", "#model_parameters parameter\n", "def train_blend(data_X, data_Y, model_function, model_parameters):\n", " \n", " model = model_function(blend_X, data_Y, model_parameters)\n", " return model" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicts the output on a set of blended data, after the blending model has been built by using a base model\n", "#algorithm, hence we need the predict function of that base model algorithm to get the predictions\n", "#The predict function of the base model is passed as the predict_function parameter and its respective model is \n", "#passed as the model parameter\n", "def predict_blend(data_X, data_Y, predict_function, model):\n", " \n", " auc,predicted_values = predict_function(test_blend_X, data_Y, model)\n", " return [auc,predicted_values]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Weighted Average" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Perfroms weighted average of the predictions of the base models. The function that calculates the optimum \n", "# combination of weights is passsed as the get_weight_function parameter\n", "\n", "#The weight_list parameter contains the weights associated with the model, they are either default weights or a list\n", "#of weights. Using these weigths we either train the model or perform hyper parameter optimisation if there\n", "#is a list of weights that need to be checked to find the optimum weights\n", "def weighted_average(data_X, data_Y, hyper_parameter_optitmisation, weight_list):\n", " \n", " #Checking if hyper_parameter_optimisation is true\n", " if(hyper_parameter_optitmisation == True):\n", " \n", " #The last element of the weight_list which indicates wether the user wants to perform hyper parameter \n", " #optimisation is deleted\n", " del weight_list[-1]\n", " #Optimisation is performed by passing the weight_list we want to optimize\n", " weight = get_optimized_weights(weight_list, data_X, data_Y)\n", " \n", " #Is none when performing weighted average on test data, we dont need to do anything else as we already have\n", " #the weights for performing the weighted average\n", " elif(hyper_parameter_optitmisation == None):\n", " \n", " weight = weight_list\n", " \n", " else:\n", " \n", " #The last element of the weight_list which indicates wether the user wants to perform hyper parameter \n", " #optimisation is deleted\n", " del weight_list[-1]\n", " #The weight_list is now used to calculate the weighted average\n", " weight = weight_list\n", " \n", " weighted_avg_predictions=np.average(data_X, axis = 1, weights = weight)\n", " auc = roc_auc_score(data_Y, weighted_avg_predictions)\n", " return [auc,weighted_avg_predictions,weight] " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Function that finds the best possible combination of weights for performing the weighted predictions.\n", "def get_optimized_weights(weight_list, X, Y):\n", " \n", " space = assign_space_weighted_average(weight_list)\n", " trials = Trials()\n", " \n", " best = fmin(fn = partial(objective_weighted_average, data_X = X, data_Y = Y),\n", " space = space,\n", " algo = tpe.suggest,\n", " max_evals = 50,\n", " trials = trials)\n", " best_weights = list()\n", " \n", " #Arranging the weights in order of the respective models, and then returning the list of weights.\n", " for key in sorted(best):\n", " best_weights.append(best[key])\n", " \n", " return best_weights" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Defining the objective. Appropriate weights need to be calculated to minimize the loss.\n", "def objective_weighted_average(space, data_X, data_Y):\n", " \n", " weight_search_space = list()\n", " \n", " #Picking weights in the seacrh space to compute the best combination of weights\n", " for weight in sorted(space):\n", " weight_search_space.append(space[weight])\n", " \n", " weighted_avg_predictions = np.average(data_X, axis = 1, weights = weight_search_space)\n", "\n", " auc = roc_auc_score(data_Y, weighted_avg_predictions)\n", " return{'loss':1-auc, 'status': STATUS_OK }" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assigning the weights that need to be checked, for minimizing the objective (Loss)\n", "def assign_space_weighted_average(weight_list):\n", " \n", " space = {}\n", " space_index = 0\n", " \n", " for weight in weight_list:\n", " \n", " #Assigning the search space, the search space is the range of weights that need to be searched for each \n", " #base model, to find the weight of that base models predictions\n", " space['w'+str(space_index )] = hp.choice('w'+str(space_index ), weight) \n", " space_index = space_index + 1\n", " \n", " return space" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The user can either use the default weights or provide their own list of values.\n", "def assign_weights(weights = 'default',hyper_parameter_optimisation = False):\n", " \n", " weight_list = list()\n", " \n", " #The last element of the weight_list will indicate wether hyper parameter optimisation needs to be peroformed\n", " if(hyper_parameter_optimisation == True):\n", " \n", " if(weights == 'default'):\n", " \n", " weight_list = [range(10)] * no_of_base_models\n", " weight_list.append(True)\n", " \n", " else:\n", " \n", " weight_list = weights\n", " weight_list.append(True)\n", " \n", " else:\n", " \n", " if(weight == 'default'):\n", " \n", " weight_list = [1] * no_of_base_models\n", " weight_list.append(False)\n", " \n", " else:\n", " \n", " weight_list = weights\n", " weight_list.append(False)\n", " \n", " return weight_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup for training and computing predictions for the models" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Constructing a list (train_model_list) that contains a tuple for each base model, the tuple contains the name of \n", "#the function that trains the base model, and the paramters for training the base model. \n", "\n", "#Constructing a list (predict_model_list) that contains a tuple for each base model, the tuple contains the name of \n", "#the function that computes the predictions for the base model.\n", "\n", "#In the list computed for stacking and blending, the tuples have an additional element which is the train_stack \n", "#function or the train_blend function. This is done because different set of data (predictions of base models) \n", "#needs to be passed to the base model algorithms. These function enable performing the above procedure\n", "\n", "#These lists are constructed in such a way to enable the ease of use of the joblib library, i.e the parallel \n", "#module/function\n", "\n", "def construct_model_parameter_list(model_list, parameters_list, stack = False, blend = False):\n", " \n", " model_functions = {'gradient_boosting' : [train_gradient_boosting,predict_gradient_boosting],\n", " 'decision_tree' : [train_decision_tree,predict_decision_tree],\n", " 'random_forest' : [train_random_forest,predict_random_forest],\n", " 'linear_regression' : [train_linear_regression,predict_linear_regression],\n", " 'logistic_regression' : [train_logistic_regression,predict_logistic_regression]\n", " }\n", " \n", " train_model_list = list()\n", " predict_model_list = list()\n", " model_parameter_index = 0\n", " \n", " for model in model_list:\n", " \n", " if(stack == True):\n", " \n", " train_model_list.append((model_functions[model][0],parameters_list[model_parameter_index]\\\n", " ,train_stack))\n", " predict_model_list.append((model_functions[model][1],predict_stack))\n", " \n", " elif(blend == True):\n", " \n", " train_model_list.append((model_functions[model][0],parameters_list[model_parameter_index]\\\n", " ,train_blend))\n", " predict_model_list.append((model_functions[model][1],predict_blend))\n", " \n", " else:\n", " \n", " train_model_list.append((model_functions[model][0],parameters_list[model_parameter_index]))\n", " predict_model_list.append(model_functions[model][1])\n", " \n", " model_parameter_index = model_parameter_index + 1\n", " \n", " return [train_model_list,predict_model_list]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#This function computes a list where each element is a tuple that contains the predict function of the base model\n", "#along with the corresponding base model object. This is done so that the base model object can be passed to the\n", "#predict function as a prameter to compute the predictions when using joblib's parallel module/function. \n", "def construct_model_predict_function_list(model_list, models,predict_model_list):\n", " \n", " model_index = 0\n", " model_function_list = list()\n", " for model in model_list:\n", " \n", " model_function_list.append((predict_model_list[model_index],models[model_index]))\n", " model_index = model_index + 1\n", " return model_function_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training base models" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#This function calls the respective training and predic functions of the base models.\n", "def train_base_models(model_list, parameters_list, save_models = False):\n", " \n", " print('\\nTRAINING BASE MODELS\\n')\n", " \n", " #Cross Validation using Stratified K Fold\n", " train, cross_val = train_test_split(Data, test_size = 0.5, stratify = Data[target_label],random_state = 0)\n", " \n", " #Training the base models, and calculating AUC on the cross validation data.\n", " #Selecting the data (Traing Data & Cross Validation Data)\n", " train_Y = train[target_label]\n", " train_X = train.drop([target_label],axis=1)\n", " \n", " cross_val_Y = cross_val[target_label]\n", " cross_val_X = cross_val.drop([target_label],axis=1)\n", " \n", " #The list of base models the user wants to train.\n", " global base_model_list\n", " base_model_list = model_list\n", "\n", " \n", " #No of base models that user wants to train\n", " global no_of_base_models\n", " no_of_base_models = len(base_model_list)\n", " \n", " \n", " #We get the list of base model training functions and predict functions. The elements of the two lists are \n", " #tuples that have (base model training function,model parameters), (base model predict functions) respectively\n", " [train_base_model_list,predict_base_model_list] = construct_model_parameter_list(base_model_list,\\\n", " parameters_list)\n", " \n", "\n", " #Training the base models parallely, the resulting models are stored which will be used for cross validation.\n", " models = (Parallel(n_jobs = -1)(delayed(function)(train_X, train_Y, model_parameter)\\\n", " for function, model_parameter in train_base_model_list))\n", " \n", " if(save_models == True):\n", " \n", " save_base_models(models)\n", " \n", " \n", " #A list with elements as tuples containing (base model predict function, and its respective model object) is \n", " #returned. This list is used in the next step in the predict_base_models function, the list will be used in\n", " #joblibs parallel module/function to compute the predictions and metric scores of the base models\n", " #Appended in the following manner so it can be used in joblib's parallel module/function\n", " global base_model_predict_function_list\n", " base_model_predict_function_list = construct_model_predict_function_list(base_model_list, models,\\\n", " predict_base_model_list)\n", " predict_base_models(cross_val_X, cross_val_Y,mode = 'train')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predictions of base models" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_base_models(data_X, data_Y,mode):\n", " \n", " print('\\nTESTING/CROSS VALIDATION BASE MODELS\\n')\n", " \n", " predict_list = list()\n", " \n", " predict_gradient_boosting = list()\n", " predict_multi_layer_perceptron = list()\n", " predict_decision_tree = list()\n", " predict_random_forest = list()\n", " predict_linear_regression = list()\n", " predict_logistic_regression = list()\n", " \n", " metric_linear_regression = list()\n", " metric_logistic_regression = list()\n", " metric_decision_tree = list()\n", " metric_random_forest = list()\n", " metric_multi_layer_perceptron = list()\n", " metric_gradient_boosting = list()\n", " \n", " auc_predict_index = 0\n", " \n", " #Initializing a list which will contain the predictions of the base models and the variables that will\n", " #calculate the metric score\n", " model_predict_metric = {'gradient_boosting' : [predict_gradient_boosting, metric_gradient_boosting],\n", " 'multi_layer_perceptron' : [predict_multi_layer_perceptron, metric_multi_layer_perceptron],\n", " 'decision_tree' : [predict_decision_tree, metric_decision_tree],\n", " 'random_forest' : [predict_random_forest, metric_random_forest],\n", " 'linear_regression' : [predict_linear_regression, metric_linear_regression],\n", " 'logistic_regression' : [predict_logistic_regression, metric_logistic_regression]\n", " }\n", " \n", " #Computing the AUC and Predictions of all the base models on the cross validation data parallely.\n", " auc_predict_cross_val = (Parallel(n_jobs = -1)(delayed(function)(data_X, data_Y, model)\n", " for function, model in base_model_predict_function_list))\n", " \n", " #Building the list which will contain all the predictions of the base models and will also display the metric\n", " #scores of the base models\n", " for model in base_model_list:\n", " \n", " #Assigning the predictions and metrics computed for the respective base model\n", " model_predict_metric[model] = auc_predict_cross_val[auc_predict_index][1],\\\n", " auc_predict_cross_val[auc_predict_index][0]\n", " auc_predict_index = auc_predict_index + 1\n", " \n", " if(model == 'multi_layer_perceptron'):\n", " \n", " #This is done only for multi layer perceptron because the predictions returned by the multi layer \n", " #perceptron model is a list of list, the below pice of code converts this nested list into a single\n", " #list\n", " predict_list.append(np.asarray(sum(model_predict_metric[model][0].tolist(), [])))\n", " \n", " else:\n", " \n", " #The below list will contain all the predictions of the base models.\n", " predict_list.append(model_predict_metric[model][0])\n", " \n", " #Printing the name of the base model and its corresponding metric score\n", " print_metric(model,model_predict_metric[model][1])\n", " \n", " if(mode == 'train'):\n", " \n", " #Function to construct dataframes for training the second level/ensmeble models using the predictions of the\n", " #base models on the train dataset\n", " second_level_train_data(predict_list, data_X, data_Y)\n", " \n", " else:\n", " \n", " #Function to construct dataframes for testing the second level/ensmeble models using the predictions of the\n", " #base models on the test dataset\n", " second_level_test_data(predict_list, data_X, data_Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Saving models" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The trained base model objects can be saved and used later for any other purpose. The models asre save using \n", "#joblib's dump. The models are named base_model1, base_model2..so on depending on the order entered by the user\n", "#while training these models in the train_base_model function\n", "def save_base_models(models):\n", " \n", " model_index = 0\n", " \n", " for model in models:\n", " \n", " joblib.dump(model, 'base_model'+str(model_index)+'.pkl')\n", " model_index = model_index + 1" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#This function will return the trained base model objects once they have been saved in the function above. All the \n", "#trained models are returned in a list called models\n", "def get_base_models():\n", " \n", " models = list()\n", " \n", " for model_index in range(no_of_base_models):\n", " models.append(joblib.load('base_model'+str(model_index)+'.pkl'))\n", " \n", " return models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training the ensemble/second level models" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Training the second level models parallely\n", "def train_ensemble_models(stack_model_list = [], stack_parameters_list = [], blend_model_list = [],\\\n", " blend_parameters_list = [], perform_weighted_average = None, weights_list = None,\n", " save_models = False):\n", " \n", " print('\\nTRAINING ENSEMBLE MODELS\\n')\n", " \n", " global no_of_ensemble_models\n", " \n", " #This list will contain the names of the models/algorithms that have been used as second level models\n", " #This list will be used later in the testing phase for identifying which model belongs to which ensemble\n", " #(stacking or blending), hence the use of dictionaries as elements of the list\n", " #Analogous to the base_model_list\n", " global ensmeble_model_list\n", " ensmeble_model_list = list()\n", " \n", " #The list will be used to train the ensemble models, while using joblib's parallel\n", " train_second_level_models = list() \n", " \n", " #Stacking will not be done if user does not enter the list of models he wants to use for stacking\n", " if(stack_model_list != []):\n", " \n", " #Appending a dictionary that contians key-Stacking and its values/elements are the names of the \n", " #models/algorithms that are used for performing the stacking procedure, this is done so that it will be easy\n", " #to identify the models belonging to the stacking ensemble\n", " ensmeble_model_list.append({'Stacking' : stack_model_list})\n", " \n", " #We get the list of stacked model training functions and predict functions. The elements of the two \n", " #lists are tuples that have(base model training function,model parameters,train_stack function),\n", " #(base model predict functions,predict_stack function) respectively\n", " [train_stack_model_list,predict_stack_model_list] = construct_model_parameter_list(stack_model_list,\\\n", " stack_parameters_list,\n", " stack=True)\n", " \n", " #Blending will not be done if user does not enter the list of models he wants to use for blending\n", " if(blend_model_list != []):\n", " \n", " #Appending a dictionary that contians key-Blending and its values/elements are the names of the \n", " #models/algorithms that are used for performing the blending procedure, this is done so that it will be easy\n", " #to identify the models belonging to the blending ensemble\n", " ensmeble_model_list.append({'Blending' : blend_model_list})\n", "\n", " #We get the list of blending model training functions and predict functions. The elements of the two \n", " #lists are tuples that have(base model training function,model parameters,train_blend function),\n", " #(base model predict functions,predict_blend function) respectively\n", " [train_blend_model_list,predict_blend_model_list] = construct_model_parameter_list(blend_model_list,\\\n", " blend_parameters_list,\\\n", " blend=True)\n", " \n", " #The new list contains either the stacked models or blending models or both or remain empty depending on what \n", " #the user has decided to use\n", " train_second_level_models = train_stack_model_list + train_blend_model_list\n", " \n", " #If the user wants to perform a weighted average, a tuple containing (hyper parmeter optimisation = True/False,\n", " #the lsit of weights either deafult or entered by the user, and the function that performs the weighted average)\n", " #will be created. This tuple will be appended to the list above\n", " #weights_list[-1] is an element of the list that indicates wwether hyper parameter optimisation needs to be\n", " #perofrmed\n", " if(perform_weighted_average == True):\n", " \n", " train_weighted_average_list = (weights_list[-1], weights_list, weighted_average)\n", " train_second_level_models.append(train_weighted_average_list)\n", "\n", " \n", " no_of_ensemble_models = len(train_second_level_models)\n", "\n", " #If weighted average is performed, the last element of models will contain the metric score and weighted average\n", " #predictions, and not a model object. So we use the last element in different ways compared to the other model\n", " #objects\n", " \n", " #Training the ensmeble models parallely \n", " models = Parallel(n_jobs = -1)(delayed(function)(stack_X, stack_Y, model, model_parameter)\\\n", " for model, model_parameter, function in train_second_level_models)\n", " \n", " \n", " #A list with elements as tuples containing((base model predict function,predict_stack or predict_blend functions)\n", " #,and its respective base model object) is returned. This list is used in the next step in the \n", " #predict_ensemble_models function, the list will be used in\n", " #joblibs parallel module/function to compute the predictions and metric score of the ensemble models\n", " #Appended in the following manner so it can be used in joblib's parallel module/function\n", " #Analogous to base_model_predict_function_list\n", " global ensmeble_model_predict_function_list\n", " ensmeble_model_predict_function_list = construct_model_predict_function_list(stack_model_list + blend_model_list,\\\n", " models, predict_stack_model_list \n", " + predict_blend_model_list)\n", " \n", " #If weighted average is needed to be perofrmed we need to append((None(which indicates its testing phase),the\n", " #weighted average function),and the weights). Appended in the following manner so it can be used in joblib's\n", " #parallel module/function\n", " if(perform_weighted_average == True):\n", " \n", " weight = models[-1][-1]\n", " print('Weighted Average')\n", " print('Weight',weight)\n", " print('Metric Score',models[-1][0])\n", " ensmeble_model_list.append({'Weighted Average' : [str(weight)]})\n", " ensmeble_model_predict_function_list.append(((None,weighted_average),weight))\n", " \n", " if(save_models == True and perform_weighted_average == True):\n", " \n", " del models[-1]\n", " no_of_ensemble_models = no_of_ensemble_models - 1\n", " save_ensemble_models(models)\n", " \n", " elif(save_models == True and perform_weighted_average == True):\n", " \n", " save_ensemble_models(models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction of ensemble models" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def predict_ensemble_models(data_X, data_Y):\n", " \n", " print('\\nTESTING ENSEMBLE MODELS\\n')\n", "\n", " metric_linear_regression = list()\n", " metric_logistic_regression = list()\n", " metric_decision_tree = list()\n", " metric_random_forest = list()\n", " metric_multi_layer_perceptron = list()\n", " metric_gradient_boosting = list()\n", " metric_weighted_average = list()\n", " metric_stacking = list()\n", " metric_blending = list()\n", " \n", " auc_predict_index = 0\n", " \n", " #Initializing a list which will contain the predictions of the base models and the variables that will\n", " #calculate the metric score\n", " model_metric = {'gradient_boosting' : [metric_gradient_boosting],\n", " 'multi_layer_perceptron' : [metric_multi_layer_perceptron],\n", " 'decision_tree' : [metric_decision_tree],\n", " 'random_forest' : [metric_random_forest],\n", " 'linear_regression' : [metric_linear_regression],\n", " 'logistic_regression' : [metric_logistic_regression]\n", " }\n", " \n", " #Computing the AUC and Predictions of all the ensmeble models on the test data parallely.\n", " auc_predict_cross_val = (Parallel(n_jobs = -1)(delayed(function[1])(data_X, data_Y, function[0],model)\n", " for function, model in ensmeble_model_predict_function_list))\n", " \n", " #ensemble_model_list is a list defined in the train_ensemble_models function, each element of the lsit is a\n", " #dictionary, that contains the name of the ensembling technique (key) and the models assocaited with it(values)\n", " \n", " #So the first for loop gives the dictionary\n", " for ensemble_models in ensmeble_model_list:\n", " \n", " #This for gives the key value pair, key being the name of the ensembling technique, value being a list\n", " #of the models used for that ensemble\n", " for ensemble,models in ensemble_models.items():\n", " \n", " #This for loop gives the iterates through the models present in the models list adn asssigns \n", " #the metric score and prints it\n", " for model in models:\n", " \n", " #Assigning the predictions and metrics computed for the respective ensmeble model\n", " model_metric[model] = auc_predict_cross_val[auc_predict_index][0]\n", " auc_predict_index = auc_predict_index + 1\n", " \n", " #Printing the name of the ensmeble technique and its model and its corresponding metric score\n", " print_metric(ensemble + \" \" + model,model_metric[model])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#The trained ensmeble model objects can be saved and used later for any other purpose. The models asre save using \n", "#joblib's dump. The models are named ensmeble_model1, emnsmeble_model2..so on depending on the order entered by \n", "#the user while training these models in the train_base_model function\n", "def save_ensemble_models(models):\n", " \n", " model_index = 0\n", " \n", " for model in models:\n", " \n", " joblib.dump(model, 'ensemble_model'+str(model_index)+'.pkl')\n", " model_index = model_index + 1" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#This function will return the trained base model objects once they have been saved in the function above. All the \n", "#trained models are returned in a list called models\n", "def get_ensemble_models():\n", " \n", " models = list()\n", " \n", " for model_index in range(no_of_ensemble_models):\n", " models.append(joblib.load('ensemble_model'+str(model_index)+'.pkl'))\n", " \n", " return models" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test_data():\n", " \n", " print('\\nTESTING PHASE\\n')\n", " \n", " #Training the base models, and calculating AUC on the test data.\n", " #Selecting the data (Test Data)\n", " test_Y = test[target_label]\n", " test_X = test.drop([target_label],axis=1)\n", " \n", " predict_base_models(test_X,test_Y,mode='test')\n", " predict_ensemble_models(test_stack_X,test_stack_Y)\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def print_metric(model,metric_score):\n", " \n", " #Printing the metric score for the corresponding model.\n", " print (model,'\\n',metric_score)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
wcmckee/wcmckee.com
posts/zhquiz.ipynb
1
13410
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sqlite3\n", "import requests" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "import dankmeme" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "dankmeme.creatememe('/home/wcmckee/meme/meme/galleries/default/61585.jpg', \n", " \"/mnt/c/Users/luke/Downloads/impact.ttf\",\n", " 'CHECKS OVERSEAS WEATHER OF GIRL HE LIKES',\n", " 'GETS THE COUNTRY WRONG', '/mnt/c/Users/luke/Desktop/t2.jpg')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def createQuizDB(namedb):\n", " conn = sqlite3.connect('{}.db'.format(namedb))\n", " c = conn.cursor()\n", "\n", " # Create table\n", " c.execute('''CREATE TABLE quiz\n", " (zhch text, english text, image text, pingyin text)''')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "createQuizDB('china')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "def createQuiz(zhch, english, wrong, image):\n", " conn = sqlite3.connect('china.db')\n", " c = conn.cursor()\n", " #hashpass = pwd_context.hash(password)\n", " hanjs = requests.get('https://glosbe.com/transliteration/api?from=Han&dest=Latin&text={}&format=json'.format(zhch))\n", " pingzh = hanjs.json()\n", " dankmeme.creatememe('/home/wcmckee/meme/meme/galleries/default/61585.jpg', \n", " \"/mnt/c/Users/luke/Downloads/mingliu.ttc\",\n", " zhch, pingzh['text'], '/mnt/c/Users/luke/Desktop/{}.jpg'.format(english.replace(' ', '-')))\n", " c.execute(\"INSERT INTO quiz VALUES ('{}', '{}', '{}', '{}', '{}')\".format(zhch, english, wrong, image, pingzh['text']))\n", " \n", " conn.commit()\n", "\n", " # We can also close the connection if we are done with it.\n", " # Just be sure any changes have been committed or they will be lost.\n", " conn.close()\n", " #returnCountUsers()\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def returnQuiz():\n", " conn = sqlite3.connect('china.db')\n", " c = conn.cursor()\n", " allfod = c.execute('SELECT * FROM quiz')\n", " return(allfod.fetchall())" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def returnRandomQuiz():\n", " conn = sqlite3.connect('china.db')\n", " c = conn.cursor()\n", " allfod = c.execute('SELECT * FROM quiz ORDER BY RANDOM() LIMIT 1;')\n", " return(allfod.fetchall())" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "returnran = returnRandomQuiz()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'我不爱你'" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "returnran[0][0]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('你不吃吗？',\n", " 'you do not eat it?',\n", " 'we ate the food',\n", " 'img12.jpg',\n", " 'nǐ bù chī ma？'),\n", " ('我不爱你', 'I do not love you', '我爱她平', 'test.png', 'wǒ bù ài nǐ'),\n", " ('奶茶', 'milk tea', '茶爱奶茶', '1.png', 'nǎi chá'),\n", " ('珍珠奶茶，大杯，去冰，不要糖，謝謝',\n", " 'Pearl (bubble) milk tea, No ice, No sugar, thank you.',\n", " '我是谢谢不高你杯 天天',\n", " '1.png',\n", " 'zhēn zhū nǎi chá， dà bēi， qù bīng， bù yào táng， xiè xiè'),\n", " ('我爱吃珍珠奶茶',\n", " 'I love to drink Pearl (bubble) milk tea',\n", " '我爱是谢',\n", " '1.png',\n", " 'wǒ ài chī zhēn zhū nǎi chá')]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "returnQuiz()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name '奶茶' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-54-9ffa3b842ffe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m奶茶\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name '奶茶' is not defined" ] } ], "source": [ "奶茶" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "createQuiz('我是她的哥哥', 'I am her brother', '你不得嘛嘛', '1.png')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('你不吃吗？',\n", " 'you do not eat it?',\n", " 'we ate the food',\n", " 'img12.jpg',\n", " 'nǐ bù chī ma？'),\n", " ('我不爱你', 'I do not love you', '我爱她平', 'test.png', 'wǒ bù ài nǐ'),\n", " ('奶茶', 'milk tea', '茶爱奶茶', '1.png', 'nǎi chá'),\n", " ('珍珠奶茶，大杯，去冰，不要糖，謝謝',\n", " 'Pearl (bubble) milk tea, No ice, No sugar, thank you.',\n", " '我是谢谢不高你杯 天天',\n", " '1.png',\n", " 'zhēn zhū nǎi chá， dà bēi， qù bīng， bù yào táng， xiè xiè'),\n", " ('我爱吃珍珠奶茶',\n", " 'I love to drink Pearl (bubble) milk tea',\n", " '我爱是谢',\n", " '1.png',\n", " 'wǒ ài chī zhēn zhū nǎi chá'),\n", " ('我爱吃珍珠奶茶',\n", " 'I love to drink Pearl (bubble) milk tea',\n", " '我爱是谢',\n", " '1.png',\n", " 'wǒ ài chī zhēn zhū nǎi chá'),\n", " ('我是她的哥哥', 'I am her brother', '你不得嘛嘛', '1.png', 'wǒ shì tā de gē gē')]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "returnQuiz()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'nǐ bù chī ma？'" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "returnran[0][4]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import svgwrite" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "dwg = svgwrite.Drawing('example.svg', profile='tiny')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "import svgwrite\n", "\n", "dwg = svgwrite.Drawing('test.svg', profile='tiny')\n", "dwg.add(dwg.text(returnran[0][0], insert=(0, 0), font_size=\"12px\", fill='red'))\n", "dwg.add(dwg.text(returnran[0][4], insert=(0, 12), font_size=\"12px\", fill='black'))\n", "\n", "dwg.save()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'你不吃吗？'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#returnq[0][0]" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "What is \"我是男人\" in english? i am man\n" ] } ], "source": [ "#answerq = input('What is \"{}\" in english? '.format(returnq[0][0]))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'i am man'" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#returnq[0][1]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "correct\n" ] } ], "source": [ "#if answerq == returnq[0][1]:\n", "# print('correct')\n", "#else:\n", "# print('wrong')\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#SELECT * FROM table ORDER BY RANDOM() LIMIT 1;" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "#createQuizDB('china')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "#createQuiz('我不爱你', 'I do not love you', '我爱她平', 'test.png')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#def createquiz(zhch, engls, wronganswers, image):\n", "# hanjs = requests.get('https://glosbe.com/transliteration/api?from=Han&dest=Latin&text={}&format=json'.format(zhch))\n", "# pingzh = hanjs.json()\n", "# return({'zh-ch' : zhch, 'englis' : engls, 'wrong' : wronganswers, 'image' : image, 'pinyin' : pingzh['text']})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#quizjs = createquiz('我不爱你', 'I do not love you', '我爱她平', 'test.png')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'我爱她平'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#quizjs['wrong']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'我不爱你'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#quizjs['zh-ch']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'englis': 'I do not love you',\n", " 'image': 'test.png',\n", " 'pinyin': 'wǒ bù ài nǐ',\n", " 'wrong': '我爱她平',\n", " 'zh-ch': '我不爱你'}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#quizjs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#print()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#qweqw" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "你好\n" ] } ], "source": [ "#print('你好')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
bbdj-dev/lessons-python4beginners
Fibonacci.ipynb
1
57210
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "0 1 1 2 3 5 8\n", "0 1 1 2 3 5 813 21 34 55 89 144 233 377 610 987 1597" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = \"ciao mamma\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'c'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'i'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[1]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'a'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[-1]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l = [1,2,3,4,5]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l[0]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ciao'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0:4]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l[0:4]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ciao mamm'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0:-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fib_from_string\n", " 0 1 1 2 3 5 8\n", "fib_from_list\n", " 0 1 1 2 3 5 813 21 34 55 89 144 233 377 610 987 1597\n", "fib_algo\n", "\n", "fib = fib_from_string\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello mamma!\n" ] } ], "source": [ "# This is hello_who.py # <-- i commenti iniziano con `#`\n", " # possono essere all'inizio o a fine riga\n", "def hello(who): # <-- la funzione di definisce con `def`\n", " print(\"Hello {}!\".format(who)) # <-- la stampa con `print` e le stringhe con `format`\n", "\n", "if __name__ == \"__main__\": # <-- [verifica di esecuzione e non di import](https://docs.python.org/2/library/__main__.html)\n", " hello(\"mamma\") # <-- invoco la funzione con il valore\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# fib(3) == 2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"0112358\"\n", "s[3]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[3] == 2" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'2' == 2" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type('2')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int('2') " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(int(2))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int(s[3]) == 2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fib_from_string(i):\n", " s = \"0112358\"\n", " return int(s[i])\n", "fib = fib_from_string" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(3) == 2" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(2) == 1" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(0)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(-1)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fib_from_string(i):\n", " s = \"0112358\"\n", " if i < 0:\n", " raise ValueError(\n", " \"indice negativo ({}) non funziona\".format(i))\n", " \n", " try:\n", " return int(s[i])\n", " except IndexError:\n", " raise NotImplementedError(\n", " \"Non sono stato capace da 7 in su\")\n", "\n", "fib = fib_from_string" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int(\"1\")" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = \"0112358\"" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0112358'" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "string indices must be integers, not str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-76-78565cebb19a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: string indices must be integers, not str" ] } ], "source": [ "s[\"a\"]" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1'" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[7]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fib_from_string(i):\n", " if i < 0:\n", " raise ValueError(\n", " \"indice negativo ({}) non funziona\".format(i))\n", " elif i > 6:\n", " raise NotImplementedError(\n", " \"Non sono stato capace da 7 in su\")\n", " else:\n", " s = \"0112358\"\n", " return int(s[i])\n", " \n", "fib = fib_from_string" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"1\" > 6" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(6)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NotImplementedError", "evalue": "Non sono stato capace da 7 in su", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-66-9309c48a195a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-63-44cc4b00b696>\u001b[0m in \u001b[0;36mfib_from_string\u001b[1;34m(i)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m raise NotImplementedError(\n\u001b[1;32m----> 7\u001b[1;33m \"Non sono stato capace da 7 in su\")\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0112358\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNotImplementedError\u001b[0m: Non sono stato capace da 7 in su" ] } ], "source": [ "fib(7)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "indice negativo (-1) non funziona", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-67-b876e14fb318>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-63-44cc4b00b696>\u001b[0m in \u001b[0;36mfib_from_string\u001b[1;34m(i)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m raise ValueError(\n\u001b[1;32m----> 4\u001b[1;33m \"indice negativo ({}) non funziona\".format(i))\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m raise NotImplementedError(\n", "\u001b[1;31mValueError\u001b[0m: indice negativo (-1) non funziona" ] } ], "source": [ "fib(-1)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NotImplementedError", "evalue": "Non sono stato capace da 7 in su", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-68-d0d671fc534f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-63-44cc4b00b696>\u001b[0m in \u001b[0;36mfib_from_string\u001b[1;34m(i)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m raise NotImplementedError(\n\u001b[1;32m----> 7\u001b[1;33m \"Non sono stato capace da 7 in su\")\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0112358\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNotImplementedError\u001b[0m: Non sono stato capace da 7 in su" ] } ], "source": [ "fib(\"a\")" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NotImplementedError", "evalue": "Non sono stato capace da 7 in su", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-69-a283560a7359>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-63-44cc4b00b696>\u001b[0m in \u001b[0;36mfib_from_string\u001b[1;34m(i)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m raise NotImplementedError(\n\u001b[1;32m----> 7\u001b[1;33m \"Non sono stato capace da 7 in su\")\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0112358\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNotImplementedError\u001b[0m: Non sono stato capace da 7 in su" ] } ], "source": [ "fib(\"1\")" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "string indices must be integers, not str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-77-f75da39041db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: string indices must be integers, not str" ] } ], "source": [ "int(s[\"a\"])" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "string indices must be integers, not str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-78-78565cebb19a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: string indices must be integers, not str" ] } ], "source": [ "s[\"a\"]" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l = [0,1,1,2,3,5,8,13,21,34,55,89,144]\n", "l[3] == 2" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l = [0,1,1,2,3,5,8,13,21,34,55,89,144]\n", "def fib_from_list(i):\n", " if i < 0:\n", " raise ValueError(\n", " \"indice negativo ({}) non funziona\".format(i))\n", " \n", " return l[i]\n", "\n", "fib = fib_from_list" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(3)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(0)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "indice negativo (-1) non funziona", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-90-b876e14fb318>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-87-3eb5fb8f046c>\u001b[0m in \u001b[0;36mfib_from_list\u001b[1;34m(i)\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m raise ValueError(\n\u001b[1;32m----> 5\u001b[1;33m \"indice negativo ({}) non funziona\".format(i))\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: indice negativo (-1) non funziona" ] } ], "source": [ "fib(-1)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "list indices must be integers, not str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-91-d0d671fc534f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-87-3eb5fb8f046c>\u001b[0m in \u001b[0;36mfib_from_list\u001b[1;34m(i)\u001b[0m\n\u001b[0;32m 4\u001b[0m raise ValueError(\n\u001b[0;32m 5\u001b[0m \"indice negativo ({}) non funziona\".format(i))\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mfib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfib_from_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: list indices must be integers, not str" ] } ], "source": [ "\n", "fib(\"a\")" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def _fib_algo(i):\n", " if i == 0 or i == 1: # operatori booleani: and or not \n", " #esempi if di base https://docs.python.org/2.7/tutorial/controlflow.html\n", " return i\n", " else:\n", " return _fib_algo(i-1) + _fib_algo(i-2)\n", "\n", "\n", "def fib_algo(i):\n", " if i < 0:\n", " raise ValueError(\n", " \"indice negativo ({}) non funziona\".format(i))\n", " return _fib_algo(i)\n", "\n", " \n", "fib = fib_algo" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fib_algo_lb(i):\n", " fib_serie = [0,1]\n", " print(\"range = {}\".format(xrange(1,i)))\n", " for _ in xrange(1, i):\n", " fib_serie.append(\n", " fib_serie[-1] + fib_serie[-2])\n", " print(fib_serie)\n", " \n", " return int(fib_serie[i])" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "range = xrange(1, 1)\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_algo_lb(0)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_serie = [0, 1, 1, 2, 3, 5]\n", "fib_serie[-1]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fib_algo_so(i):\n", " numero_corrente = indice_corrente = 0\n", " base = 1\n", " \n", " while indice_corrente < i:\n", " old_base = numero_corrente\n", " numero_corrente += base\n", " base = old_base\n", " indice_corrente += 1\n", " \n", " return numero_corrente\n", "\n", "fib = fib_algo_so" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(8)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i += 1\n", "i" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i*=2\n", "i" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i/=2\n", "i" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i /= 2\n", "i" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.5" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "float(5) / 2" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5.0 % 2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ed ecco 0\n", "ed ecco 2\n", "ed ecco 4\n" ] } ], "source": [ "x = 0\n", "while x < 5:\n", " print(\"ed ecco {}\".format(x))\n", " x += 2" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "1\n", "2\n", "3\n", "5\n", "8\n", "13\n", "21\n", "34\n", "55\n", "89\n", "144\n" ] } ], "source": [ "fibo_serie = [0,1,1,2,3,5,8,13,21,34,55,89,144]\n", "for x in fibo_serie:\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(10)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[5, 6, 7, 8, 9]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(5,10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[5, 10, 15, 20, 25, 30, 35, 40, 45]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(5,50,5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "indice 0\n", "indice 1\n", "indice 2\n", "indice 3\n", "indice 4\n", "indice 5\n", "indice 6\n", "indice 7\n", "indice 8\n", "indice 9\n", "indice 10\n" ] } ], "source": [ "for i in range(11):\n", " print(\"indice {}\".format(i))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fibo(0) = 0\n", "fibo(1) = 1\n", "fibo(2) = 1\n", "fibo(3) = 2\n", "fibo(4) = 3\n", "fibo(5) = 5\n", "fibo(6) = 8\n", "fibo(7) = 13\n", "fibo(8) = 21\n", "fibo(9) = 34\n", "fibo(10) = 55\n", "fibo(11) = 89\n", "fibo(12) = 144\n" ] } ], "source": [ "i = 0\n", "for x in fibo_serie:\n", " print(\"fibo({}) = {}\".format(i,x))\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fibo(0) = 0\n", "fibo(1) = 1\n", "fibo(2) = 1\n", "fibo(3) = 2\n", "fibo(4) = 3\n", "fibo(5) = 5\n", "fibo(6) = 8\n", "fibo(7) = 13\n", "fibo(8) = 21\n", "fibo(9) = 34\n", "fibo(10) = 55\n", "fibo(11) = 89\n", "fibo(12) = 144\n" ] } ], "source": [ "for i, x in enumerate(fibo_serie):\n", " print(\"fibo({}) = {}\".format(i,x))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<enumerate at 0x3af5bd0>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enumerate(fibo_serie)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0, 0),\n", " (1, 1),\n", " (2, 1),\n", " (3, 2),\n", " (4, 3),\n", " (5, 5),\n", " (6, 8),\n", " (7, 13),\n", " (8, 21),\n", " (9, 34),\n", " (10, 55),\n", " (11, 89),\n", " (12, 144)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(enumerate(fibo_serie))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i, x = 10, 55" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "55" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(False, 55)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(10, 55) == 10,55" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i, x, y = 1,2,3" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = x,y" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 3)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c += (10,)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 3, 10)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate tuple (not \"int\") to tuple", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-36-6220ea5f9bc7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: can only concatenate tuple (not \"int\") to tuple" ] } ], "source": [ "c += (10)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c += 10," ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 3, 10, 10)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(fibo_serie)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(c)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "object of type 'int' has no len()", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-44-cf926263823b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: object of type 'int' has no len()" ] } ], "source": [ "len(10)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(\"aaaa\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fibo_serie" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fibo_serie.append(89+144)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fibo_serie" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'tuple' object has no attribute 'append'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-51-e597dc76f883>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" ] } ], "source": [ "c.append" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 6, 200, 4, 30]" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l2 = [1,2,3,4,100,10,2,4,15]\n", "# l3 = [ x*2 for x in l2 ]\n", "# l3 = [ x*2 for x in l2 if x > 10 ]\n", "l3 = [ x*2 for i,x in enumerate(l2) if not i % 2 ]\n", "\n", "# KO: l3 = [ x*2 for x in l2 if not l2.index(x) % 2 ]\n", "\n", "# altro esempio l3 = tuple(x*2 for x in l2 )\n", "l3" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "3\n", "100\n", "2\n", "15\n" ] } ], "source": [ "l2 = [1,2,3,4,100,10,2,4,15]\n", "for i, x in enumerate(l2):\n", " if not i % 2:\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l3.sort()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 4, 4, 6, 8, 8, 20, 30, 200]" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l3" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = list(set(l3))" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 4, 6, 8, 200, 20, 30]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 4, 6, 8, 200, 20, 30]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(set(l3))" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d = { 1 : \"SimoneC\", 2: \"Elena\", 3: \"Gianluca\"}" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1: 'SimoneC', 2: 'Elena', 3: 'Gianluca'}" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Gianluca'" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[3]" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pianibaia= {\"basso\" : \"Aula Sirene\", \"terra\" : \"Hall\"}" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'basso': 'Aula Sirene', 'terra': 'Hall'}" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pianibaia" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stanze_per_piano = pianibaia" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Aula Sirene'" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stanze_per_piano[\"basso\"]" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stanze_per_piano = {'basso': ['Aula Sirene','bagno'], \n", " 'terra': 'Hall'}" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'basso': ['Aula Sirene', 'bagno'], 'terra': 'Hall'}" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stanze_per_piano" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Aula Sirene', 'bagno']" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stanze_per_piano['basso']" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'bagno'" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stanze_per_piano['basso'][1]" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d2 = { \"a\" : 1, \"b\": 2, \"l\" : 10}" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import collections\n", "d2_ord = collections.OrderedDict([\n", " ('a', 1), ('b', 2), ('l', 10)\n", " ])" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d2[\"c\"] = 3" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'a': 1, 'b': 2, 'c': 3, 'l': 10}" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ciao\n" ] } ], "source": [ "if \"c\" in d2:\n", " print(\"ciao\")" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['a', 'c', 'b', 'l']" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2.keys()" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 3, 2, 10]" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2.values()" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('a', 1), ('c', 3), ('b', 2), ('l', 10)]" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2.items()" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lettera a in posizione 1\n", "Lettera c in posizione 3\n", "Lettera b in posizione 2\n", "Lettera l in posizione 10\n" ] } ], "source": [ "for k, v in d2.items():\n", " print(\"Lettera {} in posizione {}\".format(k,v))" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2_ord.has_key(\"c\")\n", "10 in d2_ord.values()" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('a', 1), ('b', 2), ('l', 10), ('c', 3)])" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2_ord" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fib_d = {0:0, 1:1}\n", "def fib(x):\n", " \"\"\"\n", " Calcola fibonacci con cache su dizionario\n", " \"\"\"\n", " if x in fib_d:\n", " return fib_d[x]\n", "\n", " fib_pre = fib(x-1)\n", " fib_d[x-1] = fib_pre\n", "\n", " fib_prepre = fib(x-2)\n", " fib_d[x-2] = fib_prepre\n", "\n", " return fib_pre + fib_prepre\n", "\n", "def fib_orig(x):\n", " if x in (0,1):\n", " return x\n", " return fib_orig(x-1) + fib_orig(x-2)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_orig(3)" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6765" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_orig(20)" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "832040" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_orig(30)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9227465" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_orig(35)" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9227465" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(35)" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9227465" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_orig(35)" ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Di quale num vuoi calcolare? 10\n" ] } ], "source": [ "choice = raw_input(\"Di quale num vuoi calcolare? \")" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'10'" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "choice" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{'city': 'Fabriano', 'name': 'Luca', 'salary': 1238}]" ] }, "execution_count": 178, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[{\"name\" : \"Luca\", \"city\": \"Fabriano\", \"salary\": 1238}]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def main():\n", " # Step 1. Finché l'utente non smette\n", " # Step 2. L'utente inserisce il nome\n", " # Usa raw_input(\"Inserisci ...\") per chiedere le info all'utente\n", " # Step 3. L'utente inserisce la città\n", " # Step 4. L'utente inserisce lo stipendio\n", " # Step 5. Inserisci il dizionario con chiavi \n", " # 'name', 'city', 'salary'\n", " # nella lista PEOPLE = []\n", " PEOPLE.append(person_d)\n", " # Step 6. Stampa a video PEOPLE nel modo che ti piace\n", " # Step 7. Riinizia da Step 1\n", " # FINE\n", " \n", " # ---- BONUS ----\n", " # Step 8. Quando l'utente smette -> scrivi i dati in un file\n", " # se vuoi Step 8.1 in formato json\n", " # se vuoi Step 8.2 in formato csv\n", " # se vuoi Step 8.3 in formato xml\n", " # Step 9. Fallo anche se l'utente preme CTRL+C o CTRL+Z" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
agpl-3.0
fnielsen/dtu02819
notebooks/grade_assignment_comparison.ipynb
1
21531
{ "metadata": { "name": "", "signature": "sha256:573dc8bbb92ff2ab7f93a8a61b6ab64da22182d58136ce1f7b5616a01ae54193" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Grade/assignment comparison\n", "========================\n", "\n", "Comparison of grades and assignments for the DTU course 'Data Mining using Python' (02819).\n", "\n", "Two files from CampusNet should be download to a specific directory.\n", "\n", "\n", "Author\n", "-----\n", "Finn \u00c5rup Nielsen, http://www.compute.dtu.dk/~faan/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from os.path import join, expanduser\n", "from lxml import etree\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "semester = 'E13'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Note data files need to be saved in particular directory structure!\n", "directory = expanduser('~/data/dtu02819')\n", "filename_grades = join(directory, semester, \n", " 'Karakterindberetning - Danmarks Tekniske Universitet.html')\n", "filename_assignment = join(directory, semester,\n", " 'Resultater.xlsx')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Read saved HTML with grades\n", "tree = etree.HTML(open(filename_grades).read())\n", "table_element = tree.xpath(\"//table[@class='deltagerliste']\")[0]\n", "elements = table_element.xpath(\".//tr\")\n", "grades_dict = [dict(zip(['Bruger', 'Name', 'Grade'],\n", " [node.text for node in element.iter()][1:])) for element in elements]\n", "grades = pd.DataFrame(grades_dict)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Read Excel sheet downloaded from 'Assignments' on Campusnet\n", "assignment = pd.read_excel(filename_assignment, 'Resultater', skiprows=3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Merge data sets on study number\n", "data = pd.merge(grades, assignment, on='Bruger')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Convert the string grade to numeric\n", "try: \n", " data.ix[data['Grade']=='EM', 'Grade'] = -2\n", "except TypeError:\n", " pass\n", "data['Grade'] = data['Grade'].astype(int)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "data.plot(x='Score', y='Grade', kind='scatter')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SU1MBoOF8qYio5OOPP5bZs2c3bL///vty7733NtpHxbwOsWXLFrUT2oX96tJyv5bbRbTf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\nxwGBiIgAAP8fv+ttb9DMsI8AAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x45bb510>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
zipeiyang/liupengyuan.github.io
chapter2/homework/computer/4-12/201611680340.4.12.ipynb
27
6828
{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入第1个整数，以回车结束。3\n", "请输入第2个整数，以回车结束。3\n", "请输入第3个整数，以回车结束。3\n", "最终的和是： 18\n" ] } ], "source": [ "def compute_jicheng(end):\n", " i = 1\n", " val=1\n", "\n", " for i in range(end):\n", " i = i + 1\n", " val = val * i\n", "\n", " return val\n", "\n", "n = int(input('请输入第1个整数，以回车结束。'))\n", "m = int(input('请输入第2个整数，以回车结束。'))\n", "k = int(input('请输入第3个整数，以回车结束。'))\n", "\n", "print('最终的和是：', compute_jicheng(m) + compute_jicheng(n) + compute_jicheng(k))" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入list中的数字个数，回车结束。3\n", "请输入一个数字，回车结束。1\n", "请输入一个数字，回车结束。2\n", "请输入一个数字，回车结束。3\n" ] }, { "data": { "text/plain": [ "6" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#写函数，返回一个list中所有数字的和\n", "def total_list():\n", " n = int(input('请输入list中的数字个数，回车结束。'))\n", " numbers=[]\n", " total=0\n", " i=0\n", " for i in range(n):\n", " number= int(input('请输入一个数字，回车结束。'))\n", " numbers.append(number)\n", " total=total+numbers[i]\n", " i+=1\n", " return total\n", "\n", "total_list()\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入list中的数字个数，回车结束。4\n", "请输入一个数字，回车结束。4\n", "请输入一个数字，回车结束。3\n", "请输入一个数字，回车结束。2\n", "请输入一个数字，回车结束。1\n", "[4, 3, 2, 1]\n", "1\n" ] } ], "source": [ "# 写函数，返回一个list中的最小值\n", "n = int(input('请输入list中的数字个数，回车结束。'))\n", "numbers=[]\n", "i=0\n", "for i in range(n):\n", " number= int(input('请输入一个数字，回车结束。'))\n", " numbers.append(number)\n", " i=i+1 \n", "print(numbers)\n", "\n", "print(min(numbers))\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入list中的数字个数，回车结束。4\n", "请输入一个数字，回车结束。4\n", "请输入一个数字，回车结束。3\n", "请输入一个数字，回车结束。2\n", "请输入一个数字，回车结束。1\n", "请输入一个数字，回车结束。2\n" ] }, { "data": { "text/plain": [ "3" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 写函数，返回某个元素/对象在一个list中的位置，如果不在，则返回-1.\n", "\n", "def exist_list(x):\n", " i=0\n", " if x in numbers:\n", " for i in range(n):\n", " if x==numbers[i]:\n", " return i+1\n", " else:\n", " return -1\n", "n = int(input('请输入list中的数字个数，回车结束。')) \n", "numbers=[]\n", "i=0\n", "for i in range(n):\n", " number= int(input('请输入一个数字，回车结束。'))\n", " numbers.append(number)\n", " i=i+1 \n", "x=int(input('请输入一个数字，回车结束。'))\n", "exist_list(x)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 写函数，可求两个向量的夹角余弦值，向量可放在list中。主程序调用该函数。\n", "def cos_vector(a,b):\n", " if len(a)==len(b):\n", " aCb=0\n", " a2=0\n", " b2=0\n", " for i in range(len(a)):\n", " aCb+=a[i]*b[i]\n", " a2+=a[i]**2\n", " b2+=b[i]**2\n", " print(a,'和',b,'夹角的余弦值为',aCb/(a2*b2)**0.5)\n", "a=[]\n", "b=[]\n", "n=int(input('请输入向量所在的空间的维数，2或3：'))\n", "j=0\n", "for i in range(n):\n", " for j in range(n):\n", " if(i==0):\n", " a.append(int(input('请输入向量a的一个坐标')))\n", " else:\n", " b.append(int(input('请输入z坐标')))\n", "\n", "print('a=',a,',','b=',b)\n", "cos_vector(a,b)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3741\n" ] } ], "source": [ "#挑战性习题：python语言老师为了激励学生学python，自费买了100个完全相同的Macbook Pro，\n", "#分给三个班级，每个班级至少分5个，用穷举法计算共有多少种分法？\n", "n=0\n", "for i in range(5,91):\n", " for j in range(5,91):\n", " for k in range(5,91):\n", " if(100==i+j+k):\n", " n=n+1\n", "print(n)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
Uberi/zen-and-the-art-of-telemetry
Moon Phase Correlation Analysis.ipynb
1
20960
{"nbformat_minor": 0, "cells": [{"source": "# Moon Phase Correlation Analysis", "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "cell_type": "code", "source": "import ujson as json\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport plotly.plotly as py\n\nfrom moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client\nfrom moztelemetry.histogram import Histogram\n\nimport datetime as dt\n\n%pylab inline", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "This [Wikipedia article](https://en.wikipedia.org/wiki/Lunar_phase#Calculating_phase) has a nice description of how to calculate the current phase of the moon. In code, that looks like this:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "def approximate_moon_visibility(current_date):\n days_per_synodic_month = 29.530588853 # change this if the moon gets towed away\n days_since_known_new_moon = (current_date - dt.date(2015, 7, 16)).days\n phase_fraction = (days_since_known_new_moon % days_per_synodic_month) / days_per_synodic_month\n return (1 - phase_fraction if phase_fraction > 0.5 else phase_fraction) * 2\n\ndef date_string_to_date(date_string):\n return dt.datetime.strptime(date_string, \"%Y%m%d\").date()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Let's randomly sample 10% of pings for nightly submissions made from 2015-07-05 to 2015-08-05:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 4, "cell_type": "code", "source": "pings = get_pings(sc, app=\"Firefox\", channel=\"nightly\", submission_date=(\"20150705\", \"20150805\"), fraction=0.1, schema=\"v4\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Extract the startup time metrics with their submission date and make sure we only consider one submission per user:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 5, "cell_type": "code", "source": "subset = get_pings_properties(pings, [\"clientId\", \"meta/submissionDate\", \"payload/simpleMeasurements/firstPaint\"])\nsubset = get_one_ping_per_client(subset)\ncached = subset.cache()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Obtain an array of pairs, each containing the moon visibility and the startup time:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 16, "cell_type": "code", "source": "pairs = cached.map(lambda p: (approximate_moon_visibility(date_string_to_date(p[\"meta/submissionDate\"])), p[\"payload/simpleMeasurements/firstPaint\"]))\npairs = np.asarray(pairs.filter(lambda p: p[1] != None and p[1] < 100000000).collect())", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Let's see what this data looks like:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 18, "cell_type": "code", "source": "plt.figure(figsize=(15, 7))\nplt.scatter(pairs.T[0], pairs.T[1])\nplt.xlabel(\"Moon visibility ratio\")\nplt.ylabel(\"Startup time (ms)\")\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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WnrjWFzvGRu5N8uSqOjvJNVW1q7W2f73bBQAAWGYn6rr5gvG/35hkdYLc0GbA1tqtVfX6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\n56Hkzku7reloo5C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"text/plain": "<matplotlib.figure.Figure at 0x7fb952796290>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "The correlation coefficient is now easy to calculate:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 19, "cell_type": "code", "source": "np.corrcoef(pairs.T)[0, 1]", "outputs": [{"execution_count": 19, "output_type": "execute_result", "data": {"text/plain": "0.00048989909014640089"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}
mit
evanbiederstedt/RRBSfun
QC_filtered50K/regression_methylation_unweighted_10August2016_filter50K.ipynb
1
5028753
null
mit
UWSEDS/LectureNotes
week_4/unit-tests.ipynb
1
19649
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Unit Tests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview and Principles\n", "Testing is the process by which you exercise your code to determine if it performs as expected. The code you are testing is referred to as the **code under test**. \n", "\n", "There are two parts to writing tests.\n", "1. invoking the code under test so that it is exercised in a particular way;\n", "1. evaluating the results of executing code under test to determine if it behaved as expected.\n", "\n", "The collection of tests performed are referred to as the **test cases**. The fraction of the code under test that is executed as a result of running the test cases is referred to as **test coverage**.\n", "\n", "For dynamical languages such as Python, it's extremely important to have a high test coverage. In fact, you should try to get 100% coverage. This is because little checking is done when the source code is read by the Python interpreter. For example, the code under test might contain a line that has a function that is undefined. This would not be detected until that line of code is executed.\n", "\n", "Test cases can be of several types. Below are listed some common classifications of test cases.\n", "- *Smoke test*. This is an invocation of the code under test to see if there is an unexpected exception. It's useful as a starting point, but this doesn't tell you anything about the correctness of the results of a computation.\n", "- *One-shot test*. In this case, you call the code under test with arguments for which you know the expected result.\n", "- *Edge test*. The code under test is invoked with arguments that should cause an exception, and you evaluate if the expected exception occurrs.\n", "- *Pattern test* - Based on your knowledge of the *calculation* (not implementation) of the code under test, you construct a suite of test cases for which the results are known or there are known patterns in these results that are used to evaluate the results returned.\n", "\n", "Another principle of testing is to limit what is done in a single test case. Generally, a test case should focus on one use of one function. Sometimes, this is a challenge since the function being tested may call other functions that you are testing. This means that bugs in the called functions may cause failures in the tests of the calling functions. Often, you sort this out by knowing the structure of the code and focusing first on failures in lower level tests. In other situations, you may use more advanced techniques called *mocking*. A discussion of mocking is beyond the scope of this course.\n", "\n", "A best practice is to develop your tests while you are developing your code. Indeed, one school of thought in software engineering, called **test-driven development**, advocates that you write the tests *before* you implement the code under test so that the test cases become a kind of specification for what the code under test should do." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of Test Cases\n", "This section presents examples of test cases. The code under test is the calculation of entropy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entropy of a set of probabilities\n", "$$\n", "H = -\\sum_i p_i \\log(p_i)\n", "$$\n", "where $\\sum_i p_i = 1$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "# Code Under Test\n", "def entropy(ps):\n", " if any([(p < 0.0) or (p > 1.0) for p in ps]):\n", " raise ValueError(\"Bad input.\")\n", " if sum(ps) > 1:\n", " raise ValueError(\"Bad input.\")\n", " items = ps * np.log(ps)\n", " new_items = []\n", " for item in items:\n", " if np.isnan(item):\n", " new_items.append(0)\n", " else:\n", " new_items.append(item)\n", " return np.abs(-np.sum(new_items))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Smoke test\n", "def smoke_test(ps):\n", " try:\n", " entropy(ps)\n", " return True\n", " except:\n", " return False\n", " \n", "smoke_test([0.5, 0.5])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log\n", " \n", "/home/ubuntu/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n", " \n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# One shot test\n", "0.0 == entropy([1, 0, 0, 0])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Edge tests\n", "def edge_test(ps):\n", " try:\n", " entropy(ps)\n", " except ValueError:\n", " return True\n", " return False\n", "\n", "edge_test([-1, 2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose that all of the probability of a distribution is at one point. An example of this is a coin with two heads. Whenever you flip it, you always get heads. That is, the probability of a head is 1.\n", "\n", "What is the entropy of such a distribution? From the calculation above, we see that the entropy should be $log(1)$, which is 0. This means that we have a test case where we know the result!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test completed!\n" ] } ], "source": [ "# One-shot test. Need to know the correct answer.\n", "entries = [\n", " [0, [1]],\n", "]\n", "\n", "for entry in entries:\n", " ans = entry[0]\n", " prob = entry[1]\n", " if not np.isclose(entropy(prob), ans):\n", " print(\"Test failed!\")\n", "print (\"Test completed!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question**: What is an example of another one-shot test? (Hint: You need to know the expected result.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One edge test of interest is to provide an input that is *not* a distribution in that probabilities don't sum to 1." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Edge test. This is something that should cause an exception.\n", "#entropy([-0.5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's consider a pattern test. Examining the structure of the calculation of $H$, we consider a situation in which there are $n$ equal probabilities. That is, $p_i = \\frac{1}{n}$.\n", "$$\n", "H = -\\sum_{i=1}^{n} p_i \\log(p_i) \n", "= -\\sum_{i=1}^{n} \\frac{1}{n} \\log(\\frac{1}{n}) \n", "= n (-\\frac{1}{n} \\log(\\frac{1}{n}) )\n", "= -\\log(\\frac{1}{n})\n", "$$\n", "For example, entropy([0.5, 0.5]) should be $-log(0.5)$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Worked!\n" ] } ], "source": [ "# Pattern test\n", "def test_equal_probabilities(n):\n", " prob = 1.0/n\n", " ps = np.repeat(prob , n)\n", " if np.isclose(entropy(ps), -np.log(prob)):\n", " print(\"Worked!\")\n", " else:\n", " import pdb; pdb.set_trace()\n", " print (\"Bad result.\")\n", " \n", " \n", "# Run a test\n", "test_equal_probabilities(100000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You see that there are many, many cases to test. So far, we've been writing special codes for each test case. We can do better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unittest Infrastructure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several reasons to use a test infrastructure:\n", "- If you have many test cases (which you should!), the test infrastructure will save you from writing a lot of code.\n", "- The infrastructure provides a uniform way to report test results, and to handle test failures.\n", "- A test infrastructure can tell you about coverage so you know what tests to add.\n", "\n", "We'll be using the `unittest` framework. This is a separate Python package. Using this infrastructure, requires the following:\n", "1. import the unittest module\n", "1. define a class that inherits from unittest.TestCase\n", "1. write methods that run the code to be tested and check the outcomes.\n", "\n", "The last item has two subparts. First, we must identify which methods in the class inheriting from unittest.TestCase are tests. You indicate that a method is to be run as a test by having the method name begin with \"test\".\n", "\n", "Second, the \"test methods\" should communicate with the infrastructure the results of evaluating output from the code under test. This is done by using `assert` statements. For example, `self.assertEqual` takes two arguments. If these are objects for which `==` returns `True`, then the test passes. Otherwise, the test fails." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ".F.\n", "======================================================================\n", "FAIL: test_success (__main__.UnitTests)\n", "----------------------------------------------------------------------\n", "Traceback (most recent call last):\n", " File \"<ipython-input-14-68bf68d49f2a>\", line 8, in test_success\n", " self.assertEqual(1, 2)\n", "AssertionError: 1 != 2\n", "\n", "----------------------------------------------------------------------\n", "Ran 3 tests in 0.005s\n", "\n", "FAILED (failures=1)\n" ] } ], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class UnitTests(unittest.TestCase):\n", "\n", " # Each method in the class to execute a test\n", " def test_success(self):\n", " self.assertEqual(1, 2)\n", " \n", " def test_success1(self):\n", " self.assertTrue(1 == 1)\n", "\n", " def test_failure(self):\n", " self.assertLess(1, 2)\n", " \n", "suite = unittest.TestLoader().loadTestsFromTestCase(UnitTests)\n", "_ = unittest.TextTestRunner().run(suite)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Function the handles test loading\n", "#def test_setup(argument ?):\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Code for homework or your work should use test files.** In this lesson, we'll show how to write test codes in a Jupyter notebook. This is done for pedidogical reasons. It is **NOT** not something you should do in practice, except as an intermediate exploratory approach. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the first test passes, but the second test fails." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "- Rewrite the above one-shot test for entropy using the unittest infrastructure." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Implementating a pattern test. Use functions in the test.\n", "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \n", " def test_equal_probability(self):\n", " def test(count):\n", " \"\"\"\n", " Invokes the entropy function for a number of values equal to count\n", " that have the same probability.\n", " :param int count:\n", " \"\"\"\n", " raise RuntimeError (\"Not implemented.\")\n", " #\n", " test(2)\n", " test(20)\n", " test(200)\n", "\n", "#test_setup(TestEntropy)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \"\"\"Write the full set of tests.\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing For Exceptions\n", "\n", "Edge test cases often involves handling exceptions. One approach is to code this directly." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \n", " def test_invalid_probability(self):\n", " try:\n", " entropy([0.1, 0.5])\n", " self.assertTrue(False)\n", " except ValueError:\n", " self.assertTrue(True)\n", " \n", "#test_setup(TestEntropy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`unittest` provides help with testing exceptions." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "F\n", "======================================================================\n", "FAIL: test_invalid_probability (__main__.TestEntropy)\n", "----------------------------------------------------------------------\n", "Traceback (most recent call last):\n", " File \"<ipython-input-18-77e3c427eb0d>\", line 8, in test_invalid_probability\n", " entropy([0.1, 0.5])\n", "AssertionError: ValueError not raised\n", "\n", "----------------------------------------------------------------------\n", "Ran 1 test in 0.006s\n", "\n", "FAILED (failures=1)\n" ] } ], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \n", " def test_invalid_probability(self):\n", " with self.assertRaises(ValueError):\n", " entropy([0.1, 0.5])\n", " \n", "suite = unittest.TestLoader().loadTestsFromTestCase(TestEntropy)\n", "_ = unittest.TextTestRunner().run(suite)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Files\n", "Although I presented the elements of `unittest` in a notebook. **your tests should be in a file**. If the name of module with the code under test is `foo.py`, then the name of the test file should be `test_foo.py`.\n", "\n", "The structure of the test file will be very similar to cells above. You will import `unittest`. You must also import the module with the code under test. Take a look at `test_prime.py` in this directory to see an example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion\n", "**Question**: What tests would you write for a plotting function?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Driven Development\n", "Start by writing the tests. Then write the code.\n", "\n", "We illustrate this by considering a function geomean that takes a list of numbers as input and produces the geometric mean on output." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntryopy(unittest.TestCase):\n", " \n", " def test_oneshot(self):\n", " self.assertEqual(geomean([1,1]), 1)\n", " \n", " def test_oneshot2(self):\n", " self.assertEqual(geomean([3, 3, 3]), 3)\n", " \n", "#test_setup(TestGeomean)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "#def geomean(argument?):\n", "# return ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other infrastructures\n", "- pytest\n", "- nose\n", "- Use binary functions that being with \"test\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "https://www.youtube.com/watch?v=GEqM9uJi64Q (Pydata 2015)\n", "https://www.youtube.com/watch?v=yACtdj1_IxE (Pycon 2017)\n", "\n", "The first talk mentions some packages:\n", "engarde - https://github.com/TomAugspurger/engarde\n", "Hypothesis - https://hypothesis.readthedocs.io/en/latest/\n", "Feature Forge - https://github.com/machinalis/featureforge\n", "\n", "\n", "Detlef Nauck talk: \n", "http://ukkdd.org.uk/2017/info/talks/nauck.pdf\n", "He also had a list of R tools but I could not find the slides form the talk I saw.\n", "\n", "Test Driven Data Analysis:\n", "https://www.youtube.com/watch?v=TGwZnZYg0jw\n", "\n", "Profiling for Pandas:\n", "https://github.com/pandas-profiling/pandas-profiling" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
AstroHackWeek/AstroHackWeek2014
day4/machine-learning-on-SDSS.ipynb
1
866825
{ "metadata": { "name": "", "signature": "sha256:7065ad060aa986ac339e0718759ff240db127f35ad76fd918d73a13393776909" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Worked machine learning examples using SDSS data</h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[AstroHackWeek 2014 - J. S. Bloom @profjsb]\n", "\n", "See all the materials at: https://github.com/AstroHackWeek/day4\n", "<hr>\n", "Here we'll see some worked ML examples using `scikit-learn` on Sloan Digital Sky Survey Data (SDSS).\n", "\n", "It's easiest to grab data from the <a href=\"http://skyserver.sdss3.org/public/en/tools/search/sql.aspx\">SDSS skyserver SQL</a> server.\n", "\n", "\n", "For example to do a basic query to get two types of photometry (aperature and petrosian), corrected for extinction, for 1000 QSO sources with redshifts:\n", "<font color=\"blue\">\n", " <pre>SELECT *,dered_u - mag_u AS diff_u, dered_g - mag_g AS diff_g, dered_r - mag_r AS diff_g, dered_i - mag_i AS diff_i, dered_z - mag_z AS diff_z from\n", "(SELECT top 1000\n", "objid, ra, dec, dered_u,dered_g,dered_r,dered_i,dered_z,psfmag_u-extinction_u AS mag_u,\n", "psfmag_g-extinction_g AS mag_g, psfmag_r-extinction_r AS mag_r, psfmag_i-extinction_i AS mag_i,psfmag_z-extinction_z AS mag_z,z AS spec_z,dered_u - dered_g AS u_g_color, \n", "dered_g - dered_r AS g_r_color,dered_r - dered_i AS r_i_color,dered_i - dered_z AS i_z_color,class\n", "FROM SpecPhoto \n", "WHERE \n", " (class = 'QSO')\n", " ) as sp\n", " </pre>\n", " </font>\n", "Saving this and others like it as a `csv` we can then start to make our data set for classification/regression." ] }, { "cell_type": "code", "collapsed": false, "input": [ "## get the data locally ... I put this on a gist\n", "!curl -k -O https://gist.githubusercontent.com/anonymous/53781fe86383c435ff10/raw/4cc80a638e8e083775caec3005ae2feaf92b8d5b/qso10000.csv\n", "!curl -k -O https://gist.githubusercontent.com/anonymous/2984cf01a2485afd2c3e/raw/964d4f52c989428628d42eb6faad5e212e79b665/star1000.csv\n", "!curl -k -O https://gist.githubusercontent.com/anonymous/2984cf01a2485afd2c3e/raw/335cd1953e72f6c7cafa9ebb81b43c47cb757a9d/galaxy1000.csv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\r\n", " Dload Upload Total Spent Left Speed\r\n", "\r", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 31763 0 31763 0 0 52127 0 --:--:-- --:--:-- --:--:-- 53653" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 434k 0 434k 0 0 268k 0 --:--:-- 0:00:01 --:--:-- 271k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 513k 0 513k 0 0 198k 0 --:--:-- 0:00:02 --:--:-- 199k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 633k 0 633k 0 0 178k 0 --:--:-- 0:00:03 --:--:-- 178k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 814k 0 814k 0 0 178k 0 --:--:-- 0:00:04 --:--:-- 179k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 958k 0 958k 0 0 172k 0 --:--:-- 0:00:05 --:--:-- 187k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 1086k 0 1086k 0 0 164k 0 --:--:-- 0:00:06 --:--:-- 130k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 1267k 0 1267k 0 0 167k 0 --:--:-- 0:00:07 --:--:-- 150k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 1505k 0 1505k 0 0 175k 0 --:--:-- 0:00:08 --:--:-- 173k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 1939k 0 1939k 0 0 202k 0 --:--:-- 0:00:09 --:--:-- 223k" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 2378k 0 2378k 0 0 235k 0 --:--:-- 0:00:10 --:--:-- 311k\r\n" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "# For pretty plotting\n", "!pip install --upgrade seaborn" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Requirement already up-to-date: seaborn in /Users/jbloom/anaconda/lib/python2.7/site-packages\r\n", "Cleaning up...\r\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "pd.set_option('display.max_columns', None)\n", "%pylab inline\n", "import seaborn as sns\n", "sns.set()\n", "import copy" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "pd.read_csv(\"qso10000.csv\",index_col=0).head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ra</th>\n", " <th>dec</th>\n", " <th>dered_u</th>\n", " <th>dered_g</th>\n", " <th>dered_r</th>\n", " <th>dered_i</th>\n", " <th>dered_z</th>\n", " <th>mag_u</th>\n", " <th>mag_g</th>\n", " <th>mag_r</th>\n", " <th>mag_i</th>\n", " <th>mag_z</th>\n", " <th>spec_z</th>\n", " <th>u_g_color</th>\n", " <th>g_r_color</th>\n", " <th>r_i_color</th>\n", " <th>i_z_color</th>\n", " <th>class</th>\n", " <th>diff_u</th>\n", " <th>diff_g</th>\n", " <th>diff_g1</th>\n", " <th>diff_i</th>\n", " <th>diff_z</th>\n", " </tr>\n", " <tr>\n", " <th>objid</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1237648720142532813</th>\n", " <td> 146.90229</td>\n", " <td>-0.984913</td>\n", " <td> 19.64289</td>\n", " <td> 19.31131</td>\n", " <td> 19.25328</td>\n", " <td> 19.15353</td>\n", " <td> 19.13345</td>\n", " <td> 19.71604</td>\n", " <td> 19.37595</td>\n", " <td> 19.32818</td>\n", " <td> 19.24847</td>\n", " <td> 19.21259</td>\n", " <td> 0.652417</td>\n", " <td> 0.331583</td>\n", " <td> 0.058027</td>\n", " <td> 0.099751</td>\n", " <td> 0.020077</td>\n", " <td> QSO</td>\n", " <td>-0.073151</td>\n", " <td>-0.064648</td>\n", " <td>-0.074903</td>\n", " <td>-0.094942</td>\n", " <td>-0.079136</td>\n", " </tr>\n", " <tr>\n", " <th>1237658425156829371</th>\n", " <td> 142.45853</td>\n", " <td> 6.646406</td>\n", " <td> 19.39569</td>\n", " <td> 19.34811</td>\n", " <td> 19.16626</td>\n", " <td> 18.93152</td>\n", " <td> 19.06013</td>\n", " <td> 19.40327</td>\n", " <td> 19.36566</td>\n", " <td> 19.18335</td>\n", " <td> 18.94222</td>\n", " <td> 19.08077</td>\n", " <td> 1.537123</td>\n", " <td> 0.047575</td>\n", " <td> 0.181847</td>\n", " <td> 0.234743</td>\n", " <td>-0.128612</td>\n", " <td> QSO</td>\n", " <td>-0.007589</td>\n", " <td>-0.017550</td>\n", " <td>-0.017090</td>\n", " <td>-0.010700</td>\n", " <td>-0.020636</td>\n", " </tr>\n", " <tr>\n", " <th>1237660413189095710</th>\n", " <td> 143.15770</td>\n", " <td> 8.175363</td>\n", " <td> 19.10362</td>\n", " <td> 18.88904</td>\n", " <td> 18.70672</td>\n", " <td> 18.58508</td>\n", " <td> 18.61328</td>\n", " <td> 19.11102</td>\n", " <td> 18.88857</td>\n", " <td> 18.70458</td>\n", " <td> 18.57886</td>\n", " <td> 18.62583</td>\n", " <td> 1.467101</td>\n", " <td> 0.214582</td>\n", " <td> 0.182318</td>\n", " <td> 0.121645</td>\n", " <td>-0.028202</td>\n", " <td> QSO</td>\n", " <td>-0.007397</td>\n", " <td> 0.000473</td>\n", " <td> 0.002148</td>\n", " <td> 0.006218</td>\n", " <td>-0.012548</td>\n", " </tr>\n", " <tr>\n", " <th>1237660412651962520</th>\n", " <td> 142.49264</td>\n", " <td> 7.800945</td>\n", " <td> 19.88820</td>\n", " <td> 19.75146</td>\n", " <td> 19.52941</td>\n", " <td> 19.65000</td>\n", " <td> 19.52470</td>\n", " <td> 19.88709</td>\n", " <td> 19.75292</td>\n", " <td> 19.53512</td>\n", " <td> 19.67052</td>\n", " <td> 19.50256</td>\n", " <td> 1.014217</td>\n", " <td> 0.136745</td>\n", " <td> 0.222052</td>\n", " <td>-0.120590</td>\n", " <td> 0.125301</td>\n", " <td> QSO</td>\n", " <td> 0.001118</td>\n", " <td>-0.001457</td>\n", " <td>-0.005716</td>\n", " <td>-0.020527</td>\n", " <td> 0.022139</td>\n", " </tr>\n", " <tr>\n", " <th>1237658493336944662</th>\n", " <td> 142.64367</td>\n", " <td> 7.917698</td>\n", " <td> 18.45897</td>\n", " <td> 18.40651</td>\n", " <td> 18.15901</td>\n", " <td> 17.77130</td>\n", " <td> 17.75986</td>\n", " <td> 18.55725</td>\n", " <td> 18.55002</td>\n", " <td> 18.40316</td>\n", " <td> 18.01008</td>\n", " <td> 18.03100</td>\n", " <td> 0.215603</td>\n", " <td> 0.052462</td>\n", " <td> 0.247498</td>\n", " <td> 0.387709</td>\n", " <td> 0.011444</td>\n", " <td> QSO</td>\n", " <td>-0.098282</td>\n", " <td>-0.143515</td>\n", " <td>-0.244150</td>\n", " <td>-0.238779</td>\n", " <td>-0.271137</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 23 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " ra dec dered_u dered_g dered_r \\\n", "objid \n", "1237648720142532813 146.90229 -0.984913 19.64289 19.31131 19.25328 \n", "1237658425156829371 142.45853 6.646406 19.39569 19.34811 19.16626 \n", "1237660413189095710 143.15770 8.175363 19.10362 18.88904 18.70672 \n", "1237660412651962520 142.49264 7.800945 19.88820 19.75146 19.52941 \n", "1237658493336944662 142.64367 7.917698 18.45897 18.40651 18.15901 \n", "\n", " dered_i dered_z mag_u mag_g mag_r \\\n", "objid \n", "1237648720142532813 19.15353 19.13345 19.71604 19.37595 19.32818 \n", "1237658425156829371 18.93152 19.06013 19.40327 19.36566 19.18335 \n", "1237660413189095710 18.58508 18.61328 19.11102 18.88857 18.70458 \n", "1237660412651962520 19.65000 19.52470 19.88709 19.75292 19.53512 \n", "1237658493336944662 17.77130 17.75986 18.55725 18.55002 18.40316 \n", "\n", " mag_i mag_z spec_z u_g_color g_r_color \\\n", "objid \n", "1237648720142532813 19.24847 19.21259 0.652417 0.331583 0.058027 \n", "1237658425156829371 18.94222 19.08077 1.537123 0.047575 0.181847 \n", "1237660413189095710 18.57886 18.62583 1.467101 0.214582 0.182318 \n", "1237660412651962520 19.67052 19.50256 1.014217 0.136745 0.222052 \n", "1237658493336944662 18.01008 18.03100 0.215603 0.052462 0.247498 \n", "\n", " r_i_color i_z_color class diff_u diff_g diff_g1 \\\n", "objid \n", "1237648720142532813 0.099751 0.020077 QSO -0.073151 -0.064648 -0.074903 \n", "1237658425156829371 0.234743 -0.128612 QSO -0.007589 -0.017550 -0.017090 \n", "1237660413189095710 0.121645 -0.028202 QSO -0.007397 0.000473 0.002148 \n", "1237660412651962520 -0.120590 0.125301 QSO 0.001118 -0.001457 -0.005716 \n", "1237658493336944662 0.387709 0.011444 QSO -0.098282 -0.143515 -0.244150 \n", "\n", " diff_i diff_z \n", "objid \n", "1237648720142532813 -0.094942 -0.079136 \n", "1237658425156829371 -0.010700 -0.020636 \n", "1237660413189095710 0.006218 -0.012548 \n", "1237660412651962520 -0.020527 0.022139 \n", "1237658493336944662 -0.238779 -0.271137 \n", "\n", "[5 rows x 23 columns]" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that there are several things about this dataset. First, RA and DEC are probably not something we want to use in making predictions: it's the location of the object on the sky. Second, the magnitudes are highly covariant with the colors. So dumping all but one of the magnitudes might be a good idea to avoid overfitting. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "qsos = pd.read_csv(\"qso10000.csv\",index_col=0,usecols=[\"objid\",\"dered_r\",\"spec_z\",\"u_g_color\",\\\n", " \"g_r_color\",\"r_i_color\",\"i_z_color\",\"diff_u\",\\\n", " \"diff_g1\",\"diff_i\",\"diff_z\"])\n", "\n", "qso_features = copy.copy(qsos)\n", "qso_redshifts = qsos[\"spec_z\"]\n", "del qso_features[\"spec_z\"]\n", "qso_features.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>dered_r</th>\n", " <th>u_g_color</th>\n", " <th>g_r_color</th>\n", " <th>r_i_color</th>\n", " <th>i_z_color</th>\n", " <th>diff_u</th>\n", " <th>diff_g1</th>\n", " <th>diff_i</th>\n", " <th>diff_z</th>\n", " </tr>\n", " <tr>\n", " <th>objid</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1237648720142532813</th>\n", " <td> 19.25328</td>\n", " <td> 0.331583</td>\n", " <td> 0.058027</td>\n", " <td> 0.099751</td>\n", " <td> 0.020077</td>\n", " <td>-0.073151</td>\n", " <td>-0.074903</td>\n", " <td>-0.094942</td>\n", " <td>-0.079136</td>\n", " </tr>\n", " <tr>\n", " <th>1237658425156829371</th>\n", " <td> 19.16626</td>\n", " <td> 0.047575</td>\n", " <td> 0.181847</td>\n", " <td> 0.234743</td>\n", " <td>-0.128612</td>\n", " <td>-0.007589</td>\n", " <td>-0.017090</td>\n", " <td>-0.010700</td>\n", " <td>-0.020636</td>\n", " </tr>\n", " <tr>\n", " <th>1237660413189095710</th>\n", " <td> 18.70672</td>\n", " <td> 0.214582</td>\n", " <td> 0.182318</td>\n", " <td> 0.121645</td>\n", " <td>-0.028202</td>\n", " <td>-0.007397</td>\n", " <td> 0.002148</td>\n", " <td> 0.006218</td>\n", " <td>-0.012548</td>\n", " </tr>\n", " <tr>\n", " <th>1237660412651962520</th>\n", " <td> 19.52941</td>\n", " <td> 0.136745</td>\n", " <td> 0.222052</td>\n", " <td>-0.120590</td>\n", " <td> 0.125301</td>\n", " <td> 0.001118</td>\n", " <td>-0.005716</td>\n", " <td>-0.020527</td>\n", " <td> 0.022139</td>\n", " </tr>\n", " <tr>\n", " <th>1237658493336944662</th>\n", " <td> 18.15901</td>\n", " <td> 0.052462</td>\n", " <td> 0.247498</td>\n", " <td> 0.387709</td>\n", " <td> 0.011444</td>\n", " <td>-0.098282</td>\n", " <td>-0.244150</td>\n", " <td>-0.238779</td>\n", " <td>-0.271137</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 9 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " dered_r u_g_color g_r_color r_i_color i_z_color \\\n", "objid \n", "1237648720142532813 19.25328 0.331583 0.058027 0.099751 0.020077 \n", "1237658425156829371 19.16626 0.047575 0.181847 0.234743 -0.128612 \n", "1237660413189095710 18.70672 0.214582 0.182318 0.121645 -0.028202 \n", "1237660412651962520 19.52941 0.136745 0.222052 -0.120590 0.125301 \n", "1237658493336944662 18.15901 0.052462 0.247498 0.387709 0.011444 \n", "\n", " diff_u diff_g1 diff_i diff_z \n", "objid \n", "1237648720142532813 -0.073151 -0.074903 -0.094942 -0.079136 \n", "1237658425156829371 -0.007589 -0.017090 -0.010700 -0.020636 \n", "1237660413189095710 -0.007397 0.002148 0.006218 -0.012548 \n", "1237660412651962520 0.001118 -0.005716 -0.020527 0.022139 \n", "1237658493336944662 -0.098282 -0.244150 -0.238779 -0.271137 \n", "\n", "[5 rows x 9 columns]" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "bins = hist(qso_redshifts.values,bins=100) ; xlabel(\"redshift\") ; ylabel(\"N\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "<matplotlib.text.Text at 0x109722ed0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10424aad0>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty clearly a big cut at around $z=2$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib as mpl\n", "import matplotlib.cm as cm\n", "## truncate the color at z=2.5 just to keep some contrast.\n", "norm = mpl.colors.Normalize(vmin=min(qso_redshifts.values), vmax=2.5)\n", "cmap = cm.jet_r\n", "#x = 0.3\n", "m = cm.ScalarMappable(norm=norm, cmap=cmap)\n", "rez = pd.scatter_matrix(qso_features[0:2000], alpha=0.2,figsize=[15,15],color=m.to_rgba(qso_redshifts.values))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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hhBBCiOszq6TvO9/5znzHIYQQQgghhBBiHswq6evu7qZQKHD+/Hk2b9483zEJ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\nA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mCYbXCuly/QbyzT6+3FfscPY3nNh2LKtiGRID4wQHLjIGZ6GiubJTp7lq5yCdW/ETuX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3oKK6+GMqZvfw21c2cJ96l5X+0gMjJGEATokQia0bkXwkuJ77kVe/I8kTY+320EVncer1zC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\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dB83QkaHFTiEjkUBoGka6hcTNLwNDJ7apj+pK2MpohTznv0yPx9F0AwIGWiiMdFyseJyW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\nVuEAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x109d607d0>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Egad. Some pretty crazy values for `dered_r` and `g_r_color`. Let's figure out why." ] }, { "cell_type": "code", "collapsed": false, "input": [ "min(qso_features[\"dered_r\"].values)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "-9999.0" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like there are some missing values in the catalog which are set at -9999. Let's zoink those from the dataset for now." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qsos = pd.read_csv(\"qso10000.csv\",index_col=0,usecols=[\"objid\",\"dered_r\",\"spec_z\",\"u_g_color\",\\\n", " \"g_r_color\",\"r_i_color\",\"i_z_color\",\"diff_u\",\\\n", " \"diff_g1\",\"diff_i\",\"diff_z\"])\n", "\n", "qsos = qsos[(qsos[\"dered_r\"] > -9999) & (qsos[\"g_r_color\"] > -10) & (qsos[\"g_r_color\"] < 10)]\n", "qso_features = copy.copy(qsos)\n", "qso_redshifts = qsos[\"spec_z\"]\n", "del qso_features[\"spec_z\"]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "rez = pd.scatter_matrix(qso_features[0:2000], alpha=0.2,figsize=[15,15],\\\n", " color=m.to_rgba(qso_redshifts.values))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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DAD+eb+NbBR3dDLDfcpkptnIPOsY7noiu1eIXv+tupO83uy1vc44V4tzQPZyd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LEbaOE5utMNrMEwoVuyQe6He0VRreLnlhGuh18qUH7jdVKnHsNYTSIppURUqzB+o/V8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zzm0DlP/mCzw5t4e9Tpaa66cw1cZd7+vA2tK+pq6Vq9m7t4tq1SMcXn+9SJxE28VWWHNbs5WE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\n+dbtjx5N8sILoxeXdJqPmnrwfud3fof3v//9vPnNb8YYw49+9CP+5//8n7X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qFOpxUhfP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1wmmMngOy4y/faNZoQQJA49im9ZGNF7WWOlKNvLes8/gLe0RDAWJZkY5VAIap7GQW8Ej/uZ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\nf0ylUuFzn/vcq/7/8Y9//JaMGBnp+Gjruo5xBweASinc0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n2DRB0N0qPrbvOVTUJ2ASGg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\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QGeqhXnFpu/t+ldzdBF1odNx1D6Wnfog9sg/e+S5oV/MVm4135nn0yhzmwCDheJzwnj3U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ygVkssl1rTP7WpsbJmZmRJ797ZhGFc/0YL0PPyxEYRlYURimLncBkZ5FQb2NH9a7SrOt/UgpWJk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\n", "text": [ "<matplotlib.figure.Figure at 0x109d80b10>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok. This looks pretty clean. Let's save this for future use." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qsos.to_csv(\"qsos.clean.csv\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Munging done. Let's do some ML!\n", "\n", "### Basic Model Fitting\n", "\n", "We need to create a **training set** and a **testing set**." ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = qso_features.values # 9-d feature space\n", "Y = qso_redshifts.values # redshifts" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"feature vector shape=\", X.shape\n", "print \"class shape=\", Y.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "feature vector shape= (9988, 9)\n", "class shape= (9988,)\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# half of data\n", "half = floor(len(Y)/2)\n", "train_X = X[:half]\n", "train_Y = Y[:half]\n", "test_X = X[half:]\n", "test_Y = Y[half:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Linear Regression **\n", "\n", "http://scikit-learn.org/stable/modules/linear_model.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import linear_model\n", "clf = linear_model.LinearRegression()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "clf." ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-85-95a498422503>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-85-95a498422503>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m clf.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "# fit the model\n", "clf.fit(train_X, train_Y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "# now do the prediction\n", "Y_lr_pred = clf.predict(test_X)\n", "\n", "# how well did we do?\n", "from sklearn.metrics import mean_squared_error\n", "mse = np.sqrt(mean_squared_error(test_Y,Y_lr_pred)) ; print \"MSE\",mse" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "MSE 0.659053404786\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(test_Y,Y_lr_pred - test_Y,'o',alpha=0.2)\n", "title(\"Linear Regression Residuals - MSE = %.1f\" % mse)\n", "xlabel(\"Spectroscopic Redshift\")\n", "ylabel(\"Residual\")\n", "hlines(0,min(test_Y),max(test_Y),color=\"red\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "<matplotlib.collections.LineCollection at 0x10e61f250>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jqNQgihXFDodtwzUsM3lQNm2yqK+fJzaM8NgzQzy/pYRh6HTlHbrzKToLzqzT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\nAwAkEgn85je/wRVXXGFzVKURCoUwbtw4fPDBBwCAN998c9RNKwH9K6s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"text": [ "<matplotlib.figure.Figure at 0x10f96fb50>" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# here's the MSE guessing the AVERAGE value\n", "print \"naive mse\", ((1./len(train_Y))*(train_Y - train_Y.mean())**2).sum()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "naive mse 0.643120844956\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "mean_squared_error?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "** *k*-Nearest Neighbor (KNN) Regression **" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import neighbors\n", "from sklearn import preprocessing\n", "X_scaled = preprocessing.scale(X) # many methods work better on scaled X\n", "clf1 = neighbors.KNeighborsRegressor(5)\n", "train_X = X_scaled[:half]\n", "test_X = X_scaled[half:]\n", "clf1.fit(train_X,train_Y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=5, p=2, weights='uniform')" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "Y_knn_pred = clf1.predict(test_X)\n", "mse = mean_squared_error(test_Y,Y_knn_pred) ; print \"MSE (KNN)\", mse\n", "plot(test_Y, Y_knn_pred - test_Y,'o',alpha=0.2)\n", "title(\"k-NN Residuals - MSE = %.1f\" % mse)\n", "xlabel(\"Spectroscopic Redshift\")\n", "ylabel(\"Residual\")\n", "hlines(0,min(test_Y),max(test_Y),color=\"red\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "MSE (KNN) 0.239666881833\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "<matplotlib.collections.LineCollection at 0x10dac27d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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rRN1/m7re78i2Vkb5ZCXP0bl2f2WgJ9ZhnMrnDOX6j6+M5k/mlqdQKBSKc4uB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HAAAA\n/ElEQVQTfX19UBQF0Wi0uuR+xx13VDPvZVnGBRdcgI9+9KO45ZZb8Hd/93e48MILIYTAjTfeOKFd\ncDwex7vf/W5ks1lcddVVWL9+/ZTxTPfck02ezX/605/G1q1bce655077Oh133HG46qqr8P73vx+h\nUAibNm2qPs/TTz+N++67D4qiwHEc/P3f/33159T+rNo/1/u7irPOOgsPPfQQLrroIia7kSewjSnR\nAtmwYQOee+45BAKBdg9lQdVmphNR5+HSOtECmencMhFRs3BGTkRE5GGckRMREXkYAzkREZGHMZAT\nERF5GAM5ERGRhzGQExEReRgDORERkYf9f6QXRWNzJOx7AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10ddd62d0>" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import neighbors\n", "from sklearn import preprocessing\n", "\n", "X_scaled = preprocessing.scale(X) # many methods work better on scaled X\n", "train_X = X_scaled[:half]\n", "train_Y = Y[:half]\n", "test_X = X_scaled[half:]\n", "test_Y = Y[half:]\n", "clf1 = neighbors.KNeighborsRegressor(10)\n", "clf1.fit(train_X,train_Y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=10, p=2, weights='uniform')" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "Y_knn_pred = clf1.predict(test_X)\n", "mse = mean_squared_error(test_Y,Y_knn_pred) ; print mse\n", "scatter(test_Y, Y_knn_pred - test_Y,alpha=0.2)\n", "title(\"k-NN Residuals - MSE = %.1f\" % mse)\n", "xlabel(\"Spectroscopic Redshift\")\n", "ylabel(\"Residual\")\n", "hlines(0,min(test_Y),max(test_Y),color=\"red\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.234493621244\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 98, "text": [ "<matplotlib.collections.LineCollection at 0x116849690>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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4RaPh943btkEq5Tc3W5ZFT0+WyckqigKxmIckdTE9HePRR4tccMFq9u51CYfb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\naSwtjdje7hCGAd2uw/XrZcLQYzwe0m4PWFtLUa1mkaR1FhbyVKsrbG3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"text": [ "<matplotlib.figure.Figure at 0x11d428f90>" ] } ], "prompt_number": 98 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Random Forests\n", "Pretty good intro\n", "http://blog.yhathq.com/posts/random-forests-in-python.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestRegressor\n", "clf2 = RandomForestRegressor(n_estimators=100, \n", " criterion='mse', max_depth=None, \n", " min_samples_split=2, min_samples_leaf=1, \n", " max_features='auto', max_leaf_nodes=None, \n", " bootstrap=True, oob_score=False, n_jobs=1, \n", " random_state=None, verbose=0, \n", " min_density=None, compute_importances=None)\n", "clf2.fit(train_X,train_Y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "RandomForestRegressor(bootstrap=True, compute_importances=None,\n", " criterion='mse', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n", " min_samples_split=2, n_estimators=100, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0)" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "Y_rf_pred = clf2.predict(test_X)\n", "mse = mean_squared_error(test_Y,Y_rf_pred) ; print mse\n", "scatter(test_Y, Y_rf_pred - test_Y,alpha=0.2)\n", "title(\"RF Residuals - MSE = %.1f\" % mse)\n", "xlabel(\"Spectroscopic Redshift\")\n", "ylabel(\"Residual\")\n", "hlines(0,min(test_Y),max(test_Y),color=\"red\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.202398294159\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "<matplotlib.collections.LineCollection at 0x10ee0f450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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MpER+hTtypEKzmUJVVfL5BmNjFp63/GN3nNN/n2s2I2zcGKBaraCqHslk5FJnWQghxHmQ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19/PQRBwE9/+lP86Ec/giAIaG1tRSqVwquvvootW7bgl7/8JYBcKeAHH3wQNpsNv//9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\nP/Cakhefe5SSAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10ef36650>" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## model selection: cross-validation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import cross_validation" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import linear_model\n", "clf = linear_model.LinearRegression()\n", "from sklearn.cross_validation import cross_val_score\n", "\n", "def print_cv_score_summary(model, xx, yy, cv):\n", " scores = cross_val_score(model, xx, yy, cv=cv, n_jobs=1)\n", " print(\"mean: {:3f}, stdev: {:3f}\".format(\n", " np.mean(scores), np.std(scores)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "print_cv_score_summary(clf,X,Y,cv=cross_validation.KFold(len(Y), 5))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean: 0.237593, stdev: 0.026459\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "print_cv_score_summary(clf,X,Y,\n", " cv=cross_validation.KFold(len(Y),10,shuffle=True,random_state=1))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean: 0.246604, stdev: 0.041721\n" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "print_cv_score_summary(clf2,X,Y,\n", " cv=cross_validation.KFold(len(Y),10,shuffle=True,random_state=1))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean: 0.607408, stdev: 0.040491\n" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification\n", "\n", "Let's do a 3-class classification problem: star, galaxy, or QSO" ] }, { "cell_type": "code", "collapsed": false, "input": [ "all_sources = pd.read_csv(\"qso10000.csv\",index_col=0,usecols=[\"objid\",\"dered_r\",\"u_g_color\",\\\n", " \"g_r_color\",\"r_i_color\",\"i_z_color\",\"diff_u\",\\\n", " \"diff_g1\",\"diff_i\",\"diff_z\",\"class\"])[:1000]\n", "\n", "all_sources = all_sources.append(pd.read_csv(\"star1000.csv\",index_col=0,usecols=[\"objid\",\"dered_r\",\"u_g_color\",\\\n", " \"g_r_color\",\"r_i_color\",\"i_z_color\",\"diff_u\",\\\n", " \"diff_g1\",\"diff_i\",\"diff_z\",\"class\"]))\n", "\n", "all_sources = all_sources.append(pd.read_csv(\"galaxy1000.csv\",index_col=0,usecols=[\"objid\",\"dered_r\",\"u_g_color\",\\\n", " \"g_r_color\",\"r_i_color\",\"i_z_color\",\"diff_u\",\\\n", " \"diff_g1\",\"diff_i\",\"diff_z\",\"class\"]))\n", "\n", "all_sources = all_sources[(all_sources[\"dered_r\"] > -9999) & (all_sources[\"g_r_color\"] > -10) & (all_sources[\"g_r_color\"] < 10)]\n", "all_features = copy.copy(all_sources)\n", "all_label = all_sources[\"class\"]\n", "del all_features[\"class\"]\n", "X = copy.copy(all_features.values)\n", "Y = copy.copy(all_label.values)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "all_sources.tail()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>dered_r</th>\n", " <th>u_g_color</th>\n", " <th>g_r_color</th>\n", " <th>r_i_color</th>\n", " <th>i_z_color</th>\n", " <th>diff_u</th>\n", " <th>diff_g1</th>\n", " <th>diff_i</th>\n", " <th>diff_z</th>\n", " </tr>\n", " <tr>\n", " <th>objid</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1237657775542632759</th>\n", " <td> 15.42325</td>\n", " <td> 1.999353</td>\n", " <td> 0.970126</td>\n", " <td> 0.435975</td>\n", " <td> 0.373470</td>\n", " <td>-1.944487</td>\n", " <td>-1.971534</td>\n", " <td>-2.052320</td>\n", " <td>-1.971382</td>\n", " </tr>\n", " <tr>\n", " <th>1237657775542698090</th>\n", " <td> 17.51366</td>\n", " <td> 2.212025</td>\n", " <td> 0.965242</td>\n", " <td> 0.410664</td>\n", " <td> 0.371384</td>\n", " <td>-0.778788</td>\n", " <td>-0.944075</td>\n", " <td>-0.895832</td>\n", " <td>-0.830559</td>\n", " </tr>\n", " <tr>\n", " <th>1237657775542698177</th>\n", " <td> 17.15747</td>\n", " <td> 1.190033</td>\n", " <td> 0.332136</td>\n", " <td> 0.252352</td>\n", " <td> 0.070980</td>\n", " <td>-2.391565</td>\n", " <td>-2.977261</td>\n", " <td>-2.889906</td>\n", " <td>-2.671612</td>\n", " </tr>\n", " <tr>\n", " <th>1237657630586634463</th>\n", " <td> 17.19312</td>\n", " <td> 1.179663</td>\n", " <td> 0.678915</td>\n", " <td> 0.394419</td>\n", " <td> 0.272171</td>\n", " <td>-1.563450</td>\n", " <td>-1.913368</td>\n", " <td>-1.791895</td>\n", " <td>-1.615683</td>\n", " </tr>\n", " <tr>\n", " <th>1237657630049698007</th>\n", " <td> 17.20485</td>\n", " <td> 1.925320</td>\n", " <td> 1.126934</td>\n", " <td> 0.477961</td>\n", " <td> 0.334377</td>\n", " <td>-1.211906</td>\n", " <td>-1.377165</td>\n", " <td>-1.402037</td>\n", " <td>-1.218332</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 9 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " dered_r u_g_color g_r_color r_i_color i_z_color \\\n", "objid \n", "1237657775542632759 15.42325 1.999353 0.970126 0.435975 0.373470 \n", "1237657775542698090 17.51366 2.212025 0.965242 0.410664 0.371384 \n", "1237657775542698177 17.15747 1.190033 0.332136 0.252352 0.070980 \n", "1237657630586634463 17.19312 1.179663 0.678915 0.394419 0.272171 \n", "1237657630049698007 17.20485 1.925320 1.126934 0.477961 0.334377 \n", "\n", " diff_u diff_g1 diff_i diff_z \n", "objid \n", "1237657775542632759 -1.944487 -1.971534 -2.052320 -1.971382 \n", "1237657775542698090 -0.778788 -0.944075 -0.895832 -0.830559 \n", "1237657775542698177 -2.391565 -2.977261 -2.889906 -2.671612 \n", "1237657630586634463 -1.563450 -1.913368 -1.791895 -1.615683 \n", "1237657630049698007 -1.211906 -1.377165 -1.402037 -1.218332 \n", "\n", "[5 rows x 9 columns]" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"feature vector shape=\", X.shape\n", "print \"class shape=\", Y.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "feature vector shape= (3000, 9)\n", "class shape= (3000,)\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "Y[Y==\"QSO\"] = 0\n", "Y[Y==\"STAR\"] = 1\n", "Y[Y==\"GALAXY\"] = 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at random forest" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "clf = RandomForestClassifier(n_estimators=200,oob_score=True)\n", "clf.fit(X,Y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "RandomForestClassifier(bootstrap=True, compute_importances=None,\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n", " min_samples_split=2, n_estimators=200, n_jobs=1,\n", " oob_score=True, random_state=None, verbose=0)" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "what are the important features in the data?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sorted(zip(all_sources.columns.values,clf.feature_importances_),key=lambda q: q[1],reverse=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "[('u_g_color', 0.24621379942513427),\n", " ('diff_g1', 0.17095210500690095),\n", " ('diff_i', 0.13073539972463408),\n", " ('diff_z', 0.12105441185550342),\n", " ('g_r_color', 0.094758188792464337),\n", " ('diff_u', 0.089870190147766496),\n", " ('r_i_color', 0.062334477766268478),\n", " ('dered_r', 0.052595128912769143),\n", " ('i_z_color', 0.03148629836855911)]" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "clf.oob_score_ ## \"Out of Bag\" Error" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "0.95433333333333337" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from sklearn import svm, datasets\n", "cmap = cm.jet_r\n", "\n", "# import some data to play with\n", "\n", "X = all_features.values[:, 1:3] # use only two features for training and plotting purposes\n", "\n", "h = .02 # step size in the mesh\n", "\n", "# we create an instance of SVM and fit out data. We do not scale our\n", "# data since we want to plot the support vectors\n", "C = 1.0 # SVM regularization parameter\n", "svc = svm.SVC(kernel='linear', C=C).fit(X, Y)\n", "rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, Y)\n", "poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, Y)\n", "lin_svc = svm.LinearSVC(C=C).fit(X, Y)\n", "\n", "# create a mesh to plot in\n", "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", "# title for the plots\n", "titles = ['SVC with linear kernel',\n", " 'SVC with RBF kernel',\n", " 'SVC with polynomial (degree 3) kernel',\n", " 'LinearSVC (linear kernel)']\n", "\n", "\n", "norm = mpl.colors.Normalize(vmin=min(Y), vmax=max(Y))\n", "m = cm.ScalarMappable(norm=norm, cmap=cmap)\n", "\n", "for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):\n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, m_max]x[y_min, y_max].\n", " subplot(2, 2, i + 1)\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " contourf(xx, yy, Z,cmap=cm.Paired)\n", " axis('off')\n", "\n", " # Plot also the training points\n", " scatter(X[:, 0], X[:, 1], c=m.to_rgba(Y),cmap=cm.Paired)\n", "\n", " title(titles[i])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Oi8ZpdZI4OpHqg1VEpkRRvaccQ5QZRVGQJImGKjumhKDGySfGCDNFQf55ghOE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\nxREORHKiRawDTk2Sx08gjmP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g0bFnLj3tJKLl4QwH4Lgsx/NHoltb8Kkbj8xifVSor8MQ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"text": [ "<matplotlib.figure.Figure at 0x10a7b4d50>" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## model improvement with GridSearchCV\n", "Hyperparameter optimization" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# fit a support vector machine classifier\n", "from sklearn import grid_search\n", "from sklearn import svm\n", "from sklearn import metrics\n", "import logging\n", "logging.basicConfig(level=logging.INFO,\n", " format='%(asctime)s %(levelname)s %(message)s')\n", "\n", "# instantiate the SVM object\n", "sdss_svm = svm.SVC()\n", "\n", "X = all_features.values\n", "Y = all_label.values\n", "\n", "# parameter values over which we will search\n", "parameters = {'kernel':('linear', 'rbf'), \\\n", " 'gamma':[0.5, 0.3, 0.1, 0.01],\n", " 'C':[0.1, 2, 4, 5, 10, 20,30]}\n", "#parameters = {'kernel':('linear', 'rbf')}\n", "\n", "# do a grid search to find the highest 3-fold CV zero-one score\n", "svm_tune = grid_search.GridSearchCV(sdss_svm, parameters, score_func=metrics.accuracy_score,\\\n", " n_jobs = -1, cv = 3,verbose=1)\n", "svm_opt = svm_tune.fit(X, Y)\n", "\n", "# print the best score and estimator\n", "print(svm_opt.best_score_)\n", "print(svm_opt.best_estimator_)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 3 folds for each of 56 candidates, totalling 168 fits\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/jbloom/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py:347: DeprecationWarning: Passing function as ``score_func`` is deprecated and will be removed in 0.15. Either use strings or score objects. The relevant new parameter is called ''scoring''.\n", " score_func=self.score_func)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=-1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=-1)]: Done 50 jobs | elapsed: 1.6s\n", "[Parallel(n_jobs=-1)]: Done 168 out of 168 | elapsed: 9.4s finished\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "0.955\n", "SVC(C=20, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,\n", " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state=0)\n", "\n", "classifier = svm.SVC(**svm_opt.best_estimator_.get_params())\n", "y_pred = classifier.fit(X_train, y_train).predict(X_test)\n", "\n", "# Compute confusion matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "print(cm)\n", "\n", "# Show confusion matrix in a separate window\n", "plt.matshow(cm)\n", "plt.title('Confusion matrix')\n", "plt.colorbar()\n", "plt.ylabel('True label')\n", "plt.xlabel('Predicted label')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[231 1 1]\n", " [ 7 254 19]\n", " [ 3 9 225]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10a1cd990>" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "# instantiate the SVM object\n", "sdss_rf = RandomForestClassifier()\n", "\n", "X = all_features.values\n", "Y = all_label.values\n", "\n", "# parameter values over which we will search\n", "parameters = {'n_estimators':(10,50,200),\"max_features\": [\"auto\",3,5],\n", " 'criterion':[\"gini\",\"entropy\"],\"min_samples_leaf\": [1,2]}\n", "#parameters = {'kernel':('linear', 'rbf')}\n", "\n", "# do a grid search to find the highest 3-fold CV zero-one score\n", "rf_tune = grid_search.GridSearchCV(sdss_rf, parameters, score_func=metrics.accuracy_score,\\\n", " n_jobs = -1, cv = 3,verbose=1)\n", "rf_opt = rf_tune.fit(X, Y)\n", "\n", "# print the best score and estimator\n", "print(rf_opt.best_score_)\n", "print(rf_opt.best_estimator_)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/jbloom/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.py:776: DeprecationWarning: Setting compute_importances is no longer required as version 0.14. Variable importances are now computed on the fly when accessing the feature_importances_ attribute. This parameter will be removed in 0.16.\n", " DeprecationWarning)\n", "/Users/jbloom/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py:347: DeprecationWarning: Passing function as ``score_func`` is deprecated and will be removed in 0.15. Either use strings or score objects. The relevant new parameter is called ''scoring''.\n", " score_func=self.score_func)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=-1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=-1)]: Done 50 jobs | elapsed: 6.8s\n", "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed: 21.5s finished\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.3s\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s finished\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.0s\n", 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'compute_importances': None,\n", " 'criterion': 'gini',\n", " 'max_depth': None,\n", " 'max_features': 'auto',\n", " 'max_leaf_nodes': None,\n", " 'min_density': None,\n", " 'min_samples_leaf': 1,\n", " 'min_samples_split': 2,\n", " 'n_estimators': 200,\n", " 'n_jobs': 1,\n", " 'oob_score': True,\n", " 'random_state': None,\n", " 'verbose': 0}" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "svm_opt.best_estimator_.get_params()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "{'C': 20,\n", " 'cache_size': 200,\n", " 'class_weight': None,\n", " 'coef0': 0.0,\n", " 'degree': 3,\n", " 'gamma': 0.1,\n", " 'kernel': 'rbf',\n", " 'max_iter': -1,\n", " 'probability': False,\n", " 'random_state': None,\n", " 'shrinking': True,\n", " 'tol': 0.001,\n", " 'verbose': False}" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "grid_search.GridSearchCV?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
mathemage/h2o-3
h2o-py/demos/H2O_tutorial_breast_cancer_classification.ipynb
6
93378
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# H2O Tutorial: Breast Cancer Classification\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author: Erin LeDell\n", "\n", "Contact: [email protected]\n", "\n", "This tutorial steps through a quick introduction to H2O's Python API. The goal of this tutorial is to introduce through a complete example H2O's capabilities from Python. Also, to help those that are accustomed to Scikit Learn and Pandas, the demo will be specific call outs for differences between H2O and those packages; this is intended to help anyone that needs to do machine learning on really Big Data make the transition. It is not meant to be a tutorial on machine learning or algorithms.\n", "\n", "Detailed documentation about H2O's and the Python API is available at http://docs.h2o.ai." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install H2O in Python\n", "\n", "### Prerequisites\n", "\n", "This tutorial assumes you have Python 2.7 installed. The `h2o` Python package has a few dependencies which can be installed using [pip](http://pip.readthedocs.org/en/stable/installing/). The packages that are required are (which also have their own dependencies):\n", "```bash\n", "pip install requests\n", "pip install tabulate\n", "pip install scikit-learn \n", "```\n", "If you have any problems (for example, installing the `scikit-learn` package), check out [this page](https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/howto/FAQ.md#python) for tips.\n", "\n", "### Install h2o\n", "\n", "Once the dependencies are installed, you can install H2O. We will use the latest stable version of the `h2o` package, which is called \"Tibshirani-3.\" The installation instructions are on the \"Install in Python\" tab on [this page](http://h2o-release.s3.amazonaws.com/h2o/rel-tibshirani/3/index.html).\n", "\n", "```bash\n", "# The following command removes the H2O module for Python (if it already exists).\n", "pip uninstall h2o\n", "\n", "# Next, use pip to install this version of the H2O Python module.\n", "pip install http://h2o-release.s3.amazonaws.com/h2o/rel-tibshirani/3/Python/h2o-3.6.0.3-py2.py3-none-any.whl\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start up an H2O cluster\n", "\n", "In a Python terminal, we can import the `h2o` package and start up an H2O cluster." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "No instance found at ip and port: localhost:54321. Trying to start local jar...\n", "\n", "\n", "JVM stdout: /var/folders/2j/jg4sl53d5q53tc2_nzm9fz5h0000gn/T/tmpA5iLxS/h2o_me_started_from_python.out\n", "JVM stderr: /var/folders/2j/jg4sl53d5q53tc2_nzm9fz5h0000gn/T/tmptfhX9Q/h2o_me_started_from_python.err\n", "Using ice_root: /var/folders/2j/jg4sl53d5q53tc2_nzm9fz5h0000gn/T/tmpViw3QS\n", "\n", "\n", "Java Version: java version \"1.8.0_45\"\n", "Java(TM) SE Runtime Environment (build 1.8.0_45-b14)\n", "Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)\n", "\n", "\n", "Starting H2O JVM and connecting: ........... Connection successful!\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime: </td>\n", "<td>1 seconds 30 milliseconds </td></tr>\n", "<tr><td>H2O cluster version: </td>\n", "<td>3.6.0.3</td></tr>\n", "<tr><td>H2O cluster name: </td>\n", "<td>H2O_started_from_python</td></tr>\n", "<tr><td>H2O cluster total nodes: </td>\n", "<td>1</td></tr>\n", "<tr><td>H2O cluster total memory: </td>\n", "<td>3.56 GB</td></tr>\n", "<tr><td>H2O cluster total cores: </td>\n", "<td>8</td></tr>\n", "<tr><td>H2O cluster allowed cores: </td>\n", "<td>8</td></tr>\n", "<tr><td>H2O cluster healthy: </td>\n", "<td>True</td></tr>\n", "<tr><td>H2O Connection ip: </td>\n", "<td>127.0.0.1</td></tr>\n", "<tr><td>H2O Connection port: </td>\n", "<td>54321</td></tr></table></div>" ], "text/plain": [ "-------------------------- -------------------------\n", "H2O cluster uptime: 1 seconds 30 milliseconds\n", "H2O cluster version: 3.6.0.3\n", "H2O cluster name: H2O_started_from_python\n", "H2O cluster total nodes: 1\n", "H2O cluster total memory: 3.56 GB\n", "H2O cluster total cores: 8\n", "H2O cluster allowed cores: 8\n", "H2O cluster healthy: True\n", "H2O Connection ip: 127.0.0.1\n", "H2O Connection port: 54321\n", "-------------------------- -------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import h2o\n", "\n", "# Start an H2O Cluster on your local machine\n", "h2o.init()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you already have an H2O cluster running that you'd like to connect to (for example, in a multi-node Hadoop environment), then you can specify the IP and port of that cluster as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This will not actually do anything since it's a fake IP address\n", "# h2o.init(ip=\"123.45.67.89\", port=54321)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code downloads a copy of the [Wisconsin Diagnostic Breast Cancer dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29).\n", "\n", "We can import the data directly into H2O using the Python API." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Parse Progress: [##################################################] 100%\n" ] } ], "source": [ "csv_url = \"https://h2o-public-test-data.s3.amazonaws.com/smalldata/wisc/wisc-diag-breast-cancer-shuffled.csv\"\n", "data = h2o.import_file(csv_url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore Data\n", "Once we have loaded the data, let's take a quick look. First the dimension of the frame:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(569, 32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take a look at the top of the frame:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th style=\"text-align: right;\"> id</th><th>diagnosis </th><th style=\"text-align: right;\"> radius_mean</th><th style=\"text-align: right;\"> texture_mean</th><th style=\"text-align: right;\"> perimeter_mean</th><th style=\"text-align: right;\"> area_mean</th><th style=\"text-align: right;\"> smoothness_mean</th><th style=\"text-align: right;\"> compactness_mean</th><th style=\"text-align: right;\"> concavity_mean</th><th style=\"text-align: right;\"> concave_points_mean</th><th style=\"text-align: right;\"> symmetry_mean</th><th style=\"text-align: right;\"> fractal_dimension_mean</th><th style=\"text-align: right;\"> radius_se</th><th style=\"text-align: right;\"> texture_se</th><th style=\"text-align: right;\"> perimeter_se</th><th style=\"text-align: right;\"> area_se</th><th style=\"text-align: right;\"> smoothness_se</th><th style=\"text-align: right;\"> compactness_se</th><th style=\"text-align: right;\"> concavity_se</th><th style=\"text-align: right;\"> concave_points_se</th><th style=\"text-align: right;\"> symmetry_se</th><th style=\"text-align: right;\"> fractal_dimension_se</th><th style=\"text-align: right;\"> radius_worst</th><th style=\"text-align: right;\"> texture_worst</th><th style=\"text-align: right;\"> perimeter_worst</th><th style=\"text-align: right;\"> area_worst</th><th style=\"text-align: right;\"> smoothness_worst</th><th style=\"text-align: right;\"> compactness_worst</th><th style=\"text-align: right;\"> concavity_worst</th><th style=\"text-align: right;\"> concave_points_worst</th><th style=\"text-align: right;\"> symmetry_worst</th><th style=\"text-align: right;\"> fractal_dimension_worst</th></tr>\n", "<tr><td style=\"text-align: right;\"> 8.71002e+08</td><td>B </td><td style=\"text-align: right;\"> 8.219</td><td style=\"text-align: right;\"> 20.7 </td><td style=\"text-align: right;\"> 53.27</td><td style=\"text-align: right;\"> 203.9</td><td style=\"text-align: right;\"> 0.09405</td><td style=\"text-align: right;\"> 0.1305 </td><td style=\"text-align: right;\"> 0.1321 </td><td style=\"text-align: right;\"> 0.02168</td><td style=\"text-align: right;\"> 0.2222</td><td style=\"text-align: right;\"> 0.08261</td><td style=\"text-align: right;\"> 0.1935</td><td style=\"text-align: right;\"> 1.962 </td><td style=\"text-align: right;\"> 1.243 </td><td style=\"text-align: right;\"> 10.21</td><td style=\"text-align: right;\"> 0.01243 </td><td style=\"text-align: right;\"> 0.05416</td><td style=\"text-align: right;\"> 0.07753</td><td style=\"text-align: right;\"> 0.01022 </td><td style=\"text-align: right;\"> 0.02309</td><td style=\"text-align: right;\"> 0.01178 </td><td style=\"text-align: right;\"> 9.092</td><td style=\"text-align: right;\"> 29.72</td><td style=\"text-align: right;\"> 58.08</td><td style=\"text-align: right;\"> 249.8</td><td style=\"text-align: right;\"> 0.163 </td><td style=\"text-align: right;\"> 0.431 </td><td style=\"text-align: right;\"> 0.5381</td><td style=\"text-align: right;\"> 0.07879</td><td style=\"text-align: right;\"> 0.3322</td><td style=\"text-align: right;\"> 0.1486 </td></tr>\n", "<tr><td style=\"text-align: right;\"> 8.81053e+06</td><td>B </td><td style=\"text-align: right;\"> 11.84 </td><td style=\"text-align: right;\"> 18.94</td><td style=\"text-align: right;\"> 75.51</td><td style=\"text-align: right;\"> 428 </td><td style=\"text-align: right;\"> 0.08871</td><td style=\"text-align: right;\"> 0.069 </td><td style=\"text-align: right;\"> 0.02669</td><td style=\"text-align: right;\"> 0.01393</td><td style=\"text-align: right;\"> 0.1533</td><td style=\"text-align: right;\"> 0.06057</td><td style=\"text-align: right;\"> 0.2222</td><td style=\"text-align: right;\"> 0.8652</td><td style=\"text-align: right;\"> 1.444 </td><td style=\"text-align: right;\"> 17.12</td><td style=\"text-align: right;\"> 0.005517</td><td style=\"text-align: right;\"> 0.01727</td><td style=\"text-align: right;\"> 0.02045</td><td style=\"text-align: right;\"> 0.006747</td><td style=\"text-align: right;\"> 0.01616</td><td style=\"text-align: right;\"> 0.002922</td><td style=\"text-align: right;\"> 13.3 </td><td style=\"text-align: right;\"> 24.99</td><td style=\"text-align: right;\"> 85.22</td><td style=\"text-align: right;\"> 546.3</td><td style=\"text-align: right;\"> 0.128 </td><td style=\"text-align: right;\"> 0.188 </td><td style=\"text-align: right;\"> 0.1471</td><td style=\"text-align: right;\"> 0.06913</td><td style=\"text-align: right;\"> 0.2535</td><td style=\"text-align: right;\"> 0.07993</td></tr>\n", "<tr><td style=\"text-align: right;\"> 8.95115e+07</td><td>B </td><td style=\"text-align: right;\"> 12.2 </td><td style=\"text-align: right;\"> 15.21</td><td style=\"text-align: right;\"> 78.01</td><td style=\"text-align: right;\"> 457.9</td><td style=\"text-align: right;\"> 0.08673</td><td style=\"text-align: right;\"> 0.06545</td><td style=\"text-align: right;\"> 0.01994</td><td style=\"text-align: right;\"> 0.01692</td><td style=\"text-align: right;\"> 0.1638</td><td style=\"text-align: right;\"> 0.06129</td><td style=\"text-align: right;\"> 0.2575</td><td style=\"text-align: right;\"> 0.8073</td><td style=\"text-align: right;\"> 1.959 </td><td style=\"text-align: right;\"> 19.01</td><td style=\"text-align: right;\"> 0.005403</td><td style=\"text-align: right;\"> 0.01418</td><td style=\"text-align: right;\"> 0.01051</td><td style=\"text-align: right;\"> 0.005142</td><td style=\"text-align: right;\"> 0.01333</td><td style=\"text-align: right;\"> 0.002065</td><td style=\"text-align: right;\"> 13.75 </td><td style=\"text-align: right;\"> 21.38</td><td style=\"text-align: right;\"> 91.11</td><td style=\"text-align: right;\"> 583.1</td><td style=\"text-align: right;\"> 0.1256</td><td style=\"text-align: right;\"> 0.1928</td><td style=\"text-align: right;\"> 0.1167</td><td style=\"text-align: right;\"> 0.05556</td><td style=\"text-align: right;\"> 0.2661</td><td style=\"text-align: right;\"> 0.07961</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.15946e+07</td><td>M </td><td style=\"text-align: right;\"> 15.05 </td><td style=\"text-align: right;\"> 19.07</td><td style=\"text-align: right;\"> 97.26</td><td style=\"text-align: right;\"> 701.9</td><td style=\"text-align: right;\"> 0.09215</td><td style=\"text-align: right;\"> 0.08597</td><td style=\"text-align: right;\"> 0.07486</td><td style=\"text-align: right;\"> 0.04335</td><td style=\"text-align: right;\"> 0.1561</td><td style=\"text-align: right;\"> 0.05915</td><td style=\"text-align: right;\"> 0.386 </td><td style=\"text-align: right;\"> 1.198 </td><td style=\"text-align: right;\"> 2.63 </td><td style=\"text-align: right;\"> 38.49</td><td style=\"text-align: right;\"> 0.004952</td><td style=\"text-align: right;\"> 0.0163 </td><td style=\"text-align: right;\"> 0.02967</td><td style=\"text-align: right;\"> 0.009423</td><td style=\"text-align: right;\"> 0.01152</td><td style=\"text-align: right;\"> 0.001718</td><td style=\"text-align: right;\"> 17.58 </td><td style=\"text-align: right;\"> 28.06</td><td style=\"text-align: right;\"> 113.8 </td><td style=\"text-align: right;\"> 967 </td><td style=\"text-align: right;\"> 0.1246</td><td style=\"text-align: right;\"> 0.2101</td><td style=\"text-align: right;\"> 0.2866</td><td style=\"text-align: right;\"> 0.112 </td><td style=\"text-align: right;\"> 0.2282</td><td style=\"text-align: right;\"> 0.06954</td></tr>\n", "<tr><td style=\"text-align: right;\">864292 </td><td>B </td><td style=\"text-align: right;\"> 10.51 </td><td style=\"text-align: right;\"> 20.19</td><td style=\"text-align: right;\"> 68.64</td><td style=\"text-align: right;\"> 334.2</td><td style=\"text-align: right;\"> 0.1122 </td><td style=\"text-align: right;\"> 0.1303 </td><td style=\"text-align: right;\"> 0.06476</td><td style=\"text-align: right;\"> 0.03068</td><td style=\"text-align: right;\"> 0.1922</td><td style=\"text-align: right;\"> 0.07782</td><td style=\"text-align: right;\"> 0.3336</td><td style=\"text-align: right;\"> 1.86 </td><td style=\"text-align: right;\"> 2.041 </td><td style=\"text-align: right;\"> 19.91</td><td style=\"text-align: right;\"> 0.01188 </td><td style=\"text-align: right;\"> 0.03747</td><td style=\"text-align: right;\"> 0.04591</td><td style=\"text-align: right;\"> 0.01544 </td><td style=\"text-align: right;\"> 0.02287</td><td style=\"text-align: right;\"> 0.006792</td><td style=\"text-align: right;\"> 11.16 </td><td style=\"text-align: right;\"> 22.75</td><td style=\"text-align: right;\"> 72.62</td><td style=\"text-align: right;\"> 374.4</td><td style=\"text-align: right;\"> 0.13 </td><td style=\"text-align: right;\"> 0.2049</td><td style=\"text-align: right;\"> 0.1295</td><td style=\"text-align: right;\"> 0.06136</td><td style=\"text-align: right;\"> 0.2383</td><td style=\"text-align: right;\"> 0.09026</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.1544e+07 </td><td>B </td><td style=\"text-align: right;\"> 12.22 </td><td style=\"text-align: right;\"> 20.04</td><td style=\"text-align: right;\"> 79.47</td><td style=\"text-align: right;\"> 453.1</td><td style=\"text-align: right;\"> 0.1096 </td><td style=\"text-align: right;\"> 0.1152 </td><td style=\"text-align: right;\"> 0.08175</td><td style=\"text-align: right;\"> 0.02166</td><td style=\"text-align: right;\"> 0.2124</td><td style=\"text-align: right;\"> 0.06894</td><td style=\"text-align: right;\"> 0.1811</td><td style=\"text-align: right;\"> 0.7959</td><td style=\"text-align: right;\"> 0.9857</td><td style=\"text-align: right;\"> 12.58</td><td style=\"text-align: right;\"> 0.006272</td><td style=\"text-align: right;\"> 0.02198</td><td style=\"text-align: right;\"> 0.03966</td><td style=\"text-align: right;\"> 0.009894</td><td style=\"text-align: right;\"> 0.0132 </td><td style=\"text-align: right;\"> 0.003813</td><td style=\"text-align: right;\"> 13.16 </td><td style=\"text-align: right;\"> 24.17</td><td style=\"text-align: right;\"> 85.13</td><td style=\"text-align: right;\"> 515.3</td><td style=\"text-align: right;\"> 0.1402</td><td style=\"text-align: right;\"> 0.2315</td><td style=\"text-align: right;\"> 0.3535</td><td style=\"text-align: right;\"> 0.08088</td><td style=\"text-align: right;\"> 0.2709</td><td style=\"text-align: right;\"> 0.08839</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.19039e+07</td><td>B </td><td style=\"text-align: right;\"> 11.67 </td><td style=\"text-align: right;\"> 20.02</td><td style=\"text-align: right;\"> 75.21</td><td style=\"text-align: right;\"> 416.2</td><td style=\"text-align: right;\"> 0.1016 </td><td style=\"text-align: right;\"> 0.09453</td><td style=\"text-align: right;\"> 0.042 </td><td style=\"text-align: right;\"> 0.02157</td><td style=\"text-align: right;\"> 0.1859</td><td style=\"text-align: right;\"> 0.06461</td><td style=\"text-align: right;\"> 0.2067</td><td style=\"text-align: right;\"> 0.8745</td><td style=\"text-align: right;\"> 1.393 </td><td style=\"text-align: right;\"> 15.34</td><td style=\"text-align: right;\"> 0.005251</td><td style=\"text-align: right;\"> 0.01727</td><td style=\"text-align: right;\"> 0.0184 </td><td style=\"text-align: right;\"> 0.005298</td><td style=\"text-align: right;\"> 0.01449</td><td style=\"text-align: right;\"> 0.002671</td><td style=\"text-align: right;\"> 13.35 </td><td style=\"text-align: right;\"> 28.81</td><td style=\"text-align: right;\"> 87 </td><td style=\"text-align: right;\"> 550.6</td><td style=\"text-align: right;\"> 0.155 </td><td style=\"text-align: right;\"> 0.2964</td><td style=\"text-align: right;\"> 0.2758</td><td style=\"text-align: right;\"> 0.0812 </td><td style=\"text-align: right;\"> 0.3206</td><td style=\"text-align: right;\"> 0.0895 </td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.01257e+06</td><td>B </td><td style=\"text-align: right;\"> 15.19 </td><td style=\"text-align: right;\"> 13.21</td><td style=\"text-align: right;\"> 97.65</td><td style=\"text-align: right;\"> 711.8</td><td style=\"text-align: right;\"> 0.07963</td><td style=\"text-align: right;\"> 0.06934</td><td style=\"text-align: right;\"> 0.03393</td><td style=\"text-align: right;\"> 0.02657</td><td style=\"text-align: right;\"> 0.1721</td><td style=\"text-align: right;\"> 0.05544</td><td style=\"text-align: right;\"> 0.1783</td><td style=\"text-align: right;\"> 0.4125</td><td style=\"text-align: right;\"> 1.338 </td><td style=\"text-align: right;\"> 17.72</td><td style=\"text-align: right;\"> 0.005012</td><td style=\"text-align: right;\"> 0.01485</td><td style=\"text-align: right;\"> 0.01551</td><td style=\"text-align: right;\"> 0.009155</td><td style=\"text-align: right;\"> 0.01647</td><td style=\"text-align: right;\"> 0.001767</td><td style=\"text-align: right;\"> 16.2 </td><td style=\"text-align: right;\"> 15.73</td><td style=\"text-align: right;\"> 104.5 </td><td style=\"text-align: right;\"> 819.1</td><td style=\"text-align: right;\"> 0.1126</td><td style=\"text-align: right;\"> 0.1737</td><td style=\"text-align: right;\"> 0.1362</td><td style=\"text-align: right;\"> 0.08178</td><td style=\"text-align: right;\"> 0.2487</td><td style=\"text-align: right;\"> 0.06766</td></tr>\n", "<tr><td style=\"text-align: right;\">899987 </td><td>M </td><td style=\"text-align: right;\"> 25.73 </td><td style=\"text-align: right;\"> 17.46</td><td style=\"text-align: right;\"> 174.2 </td><td style=\"text-align: right;\"> 2010 </td><td style=\"text-align: right;\"> 0.1149 </td><td style=\"text-align: right;\"> 0.2363 </td><td style=\"text-align: right;\"> 0.3368 </td><td style=\"text-align: right;\"> 0.1913 </td><td style=\"text-align: right;\"> 0.1956</td><td style=\"text-align: right;\"> 0.06121</td><td style=\"text-align: right;\"> 0.9948</td><td style=\"text-align: right;\"> 0.8509</td><td style=\"text-align: right;\"> 7.222 </td><td style=\"text-align: right;\"> 153.1 </td><td style=\"text-align: right;\"> 0.006369</td><td style=\"text-align: right;\"> 0.04243</td><td style=\"text-align: right;\"> 0.04266</td><td style=\"text-align: right;\"> 0.01508 </td><td style=\"text-align: right;\"> 0.02335</td><td style=\"text-align: right;\"> 0.003385</td><td style=\"text-align: right;\"> 33.13 </td><td style=\"text-align: right;\"> 23.58</td><td style=\"text-align: right;\"> 229.3 </td><td style=\"text-align: right;\"> 3234 </td><td style=\"text-align: right;\"> 0.153 </td><td style=\"text-align: right;\"> 0.5937</td><td style=\"text-align: right;\"> 0.6451</td><td style=\"text-align: right;\"> 0.2756 </td><td style=\"text-align: right;\"> 0.369 </td><td style=\"text-align: right;\"> 0.08815</td></tr>\n", "<tr><td style=\"text-align: right;\">854039 </td><td>M </td><td style=\"text-align: right;\"> 16.13 </td><td style=\"text-align: right;\"> 17.88</td><td style=\"text-align: right;\"> 107 </td><td style=\"text-align: right;\"> 807.2</td><td style=\"text-align: right;\"> 0.104 </td><td style=\"text-align: right;\"> 0.1559 </td><td style=\"text-align: right;\"> 0.1354 </td><td style=\"text-align: right;\"> 0.07752</td><td style=\"text-align: right;\"> 0.1998</td><td style=\"text-align: right;\"> 0.06515</td><td style=\"text-align: right;\"> 0.334 </td><td style=\"text-align: right;\"> 0.6857</td><td style=\"text-align: right;\"> 2.183 </td><td style=\"text-align: right;\"> 35.03</td><td style=\"text-align: right;\"> 0.004185</td><td style=\"text-align: right;\"> 0.02868</td><td style=\"text-align: right;\"> 0.02664</td><td style=\"text-align: right;\"> 0.009067</td><td style=\"text-align: right;\"> 0.01703</td><td style=\"text-align: right;\"> 0.003817</td><td style=\"text-align: right;\"> 20.21 </td><td style=\"text-align: right;\"> 27.26</td><td style=\"text-align: right;\"> 132.7 </td><td style=\"text-align: right;\"> 1261 </td><td style=\"text-align: right;\"> 0.1446</td><td style=\"text-align: right;\"> 0.5804</td><td style=\"text-align: right;\"> 0.5274</td><td style=\"text-align: right;\"> 0.1864 </td><td style=\"text-align: right;\"> 0.427 </td><td style=\"text-align: right;\"> 0.1233 </td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first two columns contain an ID and the resposne. The \"diagnosis\" column is the response. Let's take a look at the column names. The data contains derived features from the medical images of the tumors." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'id',\n", " u'diagnosis',\n", " u'radius_mean',\n", " u'texture_mean',\n", " u'perimeter_mean',\n", " u'area_mean',\n", " u'smoothness_mean',\n", " u'compactness_mean',\n", " u'concavity_mean',\n", " u'concave_points_mean',\n", " u'symmetry_mean',\n", " u'fractal_dimension_mean',\n", " u'radius_se',\n", " u'texture_se',\n", " u'perimeter_se',\n", " u'area_se',\n", " u'smoothness_se',\n", " u'compactness_se',\n", " u'concavity_se',\n", " u'concave_points_se',\n", " u'symmetry_se',\n", " u'fractal_dimension_se',\n", " u'radius_worst',\n", " u'texture_worst',\n", " u'perimeter_worst',\n", " u'area_worst',\n", " u'smoothness_worst',\n", " u'compactness_worst',\n", " u'concavity_worst',\n", " u'concave_points_worst',\n", " u'symmetry_worst',\n", " u'fractal_dimension_worst']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To select a subset of the columns to look at, typical Pandas indexing applies:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th style=\"text-align: right;\"> id</th><th>diagnosis </th><th style=\"text-align: right;\"> area_mean</th></tr>\n", "<tr><td style=\"text-align: right;\"> 8.71002e+08</td><td>B </td><td style=\"text-align: right;\"> 203.9</td></tr>\n", "<tr><td style=\"text-align: right;\"> 8.81053e+06</td><td>B </td><td style=\"text-align: right;\"> 428 </td></tr>\n", "<tr><td style=\"text-align: right;\"> 8.95115e+07</td><td>B </td><td style=\"text-align: right;\"> 457.9</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.15946e+07</td><td>M </td><td style=\"text-align: right;\"> 701.9</td></tr>\n", "<tr><td style=\"text-align: right;\">864292 </td><td>B </td><td style=\"text-align: right;\"> 334.2</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.1544e+07 </td><td>B </td><td style=\"text-align: right;\"> 453.1</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.19039e+07</td><td>B </td><td style=\"text-align: right;\"> 416.2</td></tr>\n", "<tr><td style=\"text-align: right;\"> 9.01257e+06</td><td>B </td><td style=\"text-align: right;\"> 711.8</td></tr>\n", "<tr><td style=\"text-align: right;\">899987 </td><td>M </td><td style=\"text-align: right;\"> 2010 </td></tr>\n", "<tr><td style=\"text-align: right;\">854039 </td><td>M </td><td style=\"text-align: right;\"> 807.2</td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns = [\"id\", \"diagnosis\", \"area_mean\"]\n", "data[columns].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's select a single column, for example -- the response column, and look at the data more closely:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th>diagnosis </th></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>M </td></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>M </td></tr>\n", "<tr><td>M </td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like a binary response, but let's validate that assumption:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th>C1 </th></tr>\n", "<tr><td>B </td></tr>\n", "<tr><td>M </td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis'].unique()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis'].nlevels()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can query the categorical \"levels\" as well ('B' and 'M' stand for \"Benign\" and \"Malignant\" diagnosis):" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[['B', 'M']]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis'].levels()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since \"diagnosis\" column is the response we would like to predict, we may want to check if there are any missing values, so let's look for NAs. To figure out which, if any, values are missing, we can use the `isna` method on the diagnosis column. The columns in an H2O Frame are also H2O Frames themselves, so all the methods that apply to a Frame also apply to a single column." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th style=\"text-align: right;\"> C1</th><th style=\"text-align: right;\"> C2</th><th style=\"text-align: right;\"> C3</th><th style=\"text-align: right;\"> C4</th><th style=\"text-align: right;\"> C5</th><th style=\"text-align: right;\"> C6</th><th style=\"text-align: right;\"> C7</th><th style=\"text-align: right;\"> C8</th><th style=\"text-align: right;\"> C9</th><th style=\"text-align: right;\"> C10</th><th style=\"text-align: right;\"> C11</th><th style=\"text-align: right;\"> C12</th><th style=\"text-align: right;\"> C13</th><th style=\"text-align: right;\"> C14</th><th style=\"text-align: right;\"> C15</th><th style=\"text-align: right;\"> C16</th><th style=\"text-align: right;\"> C17</th><th style=\"text-align: right;\"> C18</th><th style=\"text-align: right;\"> C19</th><th style=\"text-align: right;\"> C20</th><th style=\"text-align: right;\"> C21</th><th style=\"text-align: right;\"> C22</th><th style=\"text-align: right;\"> C23</th><th style=\"text-align: right;\"> C24</th><th style=\"text-align: right;\"> C25</th><th style=\"text-align: right;\"> C26</th><th style=\"text-align: right;\"> C27</th><th style=\"text-align: right;\"> C28</th><th style=\"text-align: right;\"> C29</th><th style=\"text-align: right;\"> C30</th><th style=\"text-align: right;\"> C31</th><th style=\"text-align: right;\"> C32</th></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 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style=\"text-align: right;\"> C1</th></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "<tr><td style=\"text-align: right;\"> 0</td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis'].isna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `isna` method doesn't directly answer the question, \"Does the diagnosis column contain any NAs?\", rather it returns a 0 if that cell is not missing (Is NA? FALSE == 0) and a 1 if it is missing (Is NA? TRUE == 1). So if there are no missing values, then summing over the whole column should produce a summand equal to 0.0. Let's take a look:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis'].isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, no missing labels. \n", "\n", "Out of curiosity, let's see if there is any missing data in this frame:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next thing I may wonder about in a binary classification problem is the distribution of the response in the training data. Is one of the two outcomes under-represented in the training set? Many real datasets have what's called an \"imbalanace\" problem, where one of the classes has far fewer training examples than the other class. Let's take a look at the distribution, both visually and numerically." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TO DO: Insert a bar chart or something showing the proportion of M to B in the response.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th>diagnosis </th><th style=\"text-align: right;\"> Count</th></tr>\n", "<tr><td>B </td><td style=\"text-align: right;\"> 357</td></tr>\n", "<tr><td>M </td><td style=\"text-align: right;\"> 212</td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['diagnosis'].table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, the data is not exactly evenly distributed between the two classes -- there are almost twice as many Benign samples as there are Malicious samples. However, this level of imbalance shouldn't be much of an issue for the machine learning algos. (We will revisit this later in the modeling section below)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<tr><th style=\"text-align: right;\"> Count</th></tr>\n", "<tr><td style=\"text-align: right;\">0.627417</td></tr>\n", "<tr><td style=\"text-align: right;\">0.372583</td></tr>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = data.shape[0] # Total number of training samples\n", "data['diagnosis'].table()['Count']/n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Machine Learning in H2O\n", "\n", "We will do a quick demo of the H2O software -- trying to predict malignant tumors using various machine learning algorithms.\n", "\n", "### Specify the predictor set and response\n", "\n", "The response, `y`, is the 'diagnosis' column, and the predictors, `x`, are all the columns aside from the first two columns ('id' and 'diagnosis')." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = 'diagnosis'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'diagnosis',\n", " u'radius_mean',\n", " u'texture_mean',\n", " u'perimeter_mean',\n", " u'area_mean',\n", " u'smoothness_mean',\n", " u'compactness_mean',\n", " u'concavity_mean',\n", " u'concave_points_mean',\n", " u'symmetry_mean',\n", " u'fractal_dimension_mean',\n", " u'radius_se',\n", " u'texture_se',\n", " u'perimeter_se',\n", " u'area_se',\n", " u'smoothness_se',\n", " u'compactness_se',\n", " u'concavity_se',\n", " u'concave_points_se',\n", " u'symmetry_se',\n", " u'fractal_dimension_se',\n", " u'radius_worst',\n", " u'texture_worst',\n", " u'perimeter_worst',\n", " u'area_worst',\n", " u'smoothness_worst',\n", " u'compactness_worst',\n", " u'concavity_worst',\n", " u'concave_points_worst',\n", " u'symmetry_worst',\n", " u'fractal_dimension_worst']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = data.columns\n", "del x[0:1]\n", "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split H2O Frame into a train and test set" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train, test = data.split_frame(ratios=[0.75], seed=1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(428, 32)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.shape\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(141, 32)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train and Test a GBM model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import H2O GBM:\n", "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first create a `model` object of class, `\"H2OGradientBoostingEstimator\"`. This does not actually do any training, it just sets the model up for training by specifying model parameters." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = H2OGradientBoostingEstimator(distribution='bernoulli',\n", " ntrees=100,\n", " max_depth=4,\n", " learn_rate=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `model` object, like all H2O estimator objects, has a `train` method, which will actually perform model training. At this step we specify the training and (optionally) a validation set, along with the response and predictor variables." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "gbm Model Build Progress: [##################################################] 100%\n" ] } ], "source": [ "model.train(x=x, y=y, training_frame=train, validation_frame=test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect Model\n", "\n", "The type of results shown when you print a model, are determined by the following:\n", "- Model class of the estimator (e.g. GBM, RF, GLM, DL)\n", "- The type of machine learning problem (e.g. binary classification, multiclass classification, regression)\n", "- The data you specify (e.g. `training_frame` only, `training_frame` and `validation_frame`, or `training_frame` and `nfolds`)\n", "\n", "Below, we see a GBM Model Summary, as well as training and validation metrics since we supplied a `validation_frame`. Since this a binary classification task, we are shown the relevant performance metrics, which inclues: MSE, R^2, LogLoss, AUC and Gini. Also, we are shown a Confusion Matrix, where the threshold for classification is chosen automatically (by H2O) as the threshold which maximizes the F1 score.\n", "\n", "The scoring history is also printed, which shows the performance metrics over some increment such as \"number of trees\" in the case of GBM and RF.\n", "\n", "Lastly, for tree-based methods (GBM and RF), we also print variable importance." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Details\n", "=============\n", "H2OGradientBoostingEstimator : Gradient Boosting Machine\n", "Model Key: GBM_model_python_1448480209718_6\n", "\n", "Model Summary:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>number_of_trees</b></td>\n", "<td><b>model_size_in_bytes</b></td>\n", "<td><b>min_depth</b></td>\n", "<td><b>max_depth</b></td>\n", "<td><b>mean_depth</b></td>\n", "<td><b>min_leaves</b></td>\n", "<td><b>max_leaves</b></td>\n", "<td><b>mean_leaves</b></td></tr>\n", "<tr><td></td>\n", "<td>100.0</td>\n", "<td>18324.0</td>\n", "<td>4.0</td>\n", "<td>4.0</td>\n", "<td>4.0</td>\n", "<td>8.0</td>\n", "<td>14.0</td>\n", "<td>10.31</td></tr></table></div>" ], "text/plain": [ " number_of_trees model_size_in_bytes min_depth max_depth mean_depth min_leaves max_leaves mean_leaves\n", "-- ----------------- --------------------- ----------- ----------- ------------ ------------ ------------ -------------\n", " 100 18324 4 4 4 8 14 10.31" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "ModelMetricsBinomial: gbm\n", "** Reported on train data. **\n", "\n", "MSE: 1.55261137469e-06\n", "R^2: 0.999993333015\n", "LogLoss: 0.000519099361538\n", "AUC: 1.0\n", "Gini: 1.0\n", "\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.989733166545:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>B</b></td>\n", "<td><b>M</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>B</td>\n", "<td>270.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td> (0.0/270.0)</td></tr>\n", "<tr><td>M</td>\n", "<td>0.0</td>\n", "<td>158.0</td>\n", "<td>0.0</td>\n", "<td> (0.0/158.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>270.0</td>\n", "<td>158.0</td>\n", "<td>0.0</td>\n", "<td> (0.0/428.0)</td></tr></table></div>" ], "text/plain": [ " B M Error Rate\n", "----- --- --- ------- -----------\n", "B 270 0 0 (0.0/270.0)\n", "M 0 158 0 (0.0/158.0)\n", "Total 270 158 0 (0.0/428.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>143.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>143.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>143.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>143.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_MCC</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>143.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>143.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "-------------------------- ----------- ------- -----\n", "max f1 0.989733 1 143\n", "max f2 0.989733 1 143\n", "max f0point5 0.989733 1 143\n", "max accuracy 0.989733 1 143\n", "max precision 0.999923 1 0\n", "max absolute_MCC 0.989733 1 143\n", "max min_per_class_accuracy 0.989733 1 143" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsBinomial: gbm\n", "** Reported on validation data. **\n", "\n", "MSE: 0.0507094587533\n", "R^2: 0.78540767359\n", "LogLoss: 0.247694592147\n", "AUC: 0.970200085143\n", "Gini: 0.940400170285\n", "\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.409828576406:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>B</b></td>\n", "<td><b>M</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>B</td>\n", "<td>83.0</td>\n", "<td>4.0</td>\n", "<td>0.046</td>\n", "<td> (4.0/87.0)</td></tr>\n", "<tr><td>M</td>\n", "<td>4.0</td>\n", "<td>50.0</td>\n", "<td>0.0741</td>\n", "<td> (4.0/54.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>87.0</td>\n", "<td>54.0</td>\n", "<td>0.0567</td>\n", "<td> (8.0/141.0)</td></tr></table></div>" ], "text/plain": [ " B M Error Rate\n", "----- --- --- ------- -----------\n", "B 83 4 0.046 (4.0/87.0)\n", "M 4 50 0.0741 (4.0/54.0)\n", "Total 87 54 0.0567 (8.0/141.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.4</td>\n", "<td>0.9</td>\n", "<td>51.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.0</td>\n", "<td>0.9</td>\n", "<td>60.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.7</td>\n", "<td>1.0</td>\n", "<td>43.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.7</td>\n", "<td>0.9</td>\n", "<td>43.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_MCC</td>\n", "<td>0.7</td>\n", "<td>0.9</td>\n", "<td>43.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.4</td>\n", "<td>0.9</td>\n", "<td>51.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "-------------------------- ----------- -------- -----\n", "max f1 0.409829 0.925926 51\n", "max f2 0.00935885 0.9319 60\n", "max f0point5 0.74381 0.966387 43\n", "max accuracy 0.74381 0.943262 43\n", "max precision 0.999921 1 0\n", "max absolute_MCC 0.74381 0.883242 43\n", "max min_per_class_accuracy 0.409829 0.925926 51" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>timestamp</b></td>\n", "<td><b>duration</b></td>\n", "<td><b>number_of_trees</b></td>\n", "<td><b>training_MSE</b></td>\n", "<td><b>training_logloss</b></td>\n", "<td><b>training_AUC</b></td>\n", "<td><b>training_classification_error</b></td>\n", "<td><b>validation_MSE</b></td>\n", "<td><b>validation_logloss</b></td>\n", "<td><b>validation_AUC</b></td>\n", "<td><b>validation_classification_error</b></td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:58</td>\n", "<td> 0.006 sec</td>\n", "<td>1.0</td>\n", "<td>0.2</td>\n", "<td>0.6</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.2</td>\n", "<td>0.6</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:58</td>\n", "<td> 0.010 sec</td>\n", "<td>2.0</td>\n", "<td>0.2</td>\n", "<td>0.5</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.2</td>\n", "<td>0.5</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:58</td>\n", "<td> 0.013 sec</td>\n", "<td>3.0</td>\n", "<td>0.1</td>\n", "<td>0.4</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.2</td>\n", "<td>0.5</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:58</td>\n", "<td> 0.017 sec</td>\n", "<td>4.0</td>\n", "<td>0.1</td>\n", "<td>0.4</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.4</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:58</td>\n", "<td> 0.021 sec</td>\n", "<td>5.0</td>\n", "<td>0.1</td>\n", "<td>0.4</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.4</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:59</td>\n", "<td> 0.566 sec</td>\n", "<td>96.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.2</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:59</td>\n", "<td> 0.572 sec</td>\n", "<td>97.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.2</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:59</td>\n", "<td> 0.579 sec</td>\n", "<td>98.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.2</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:59</td>\n", "<td> 0.585 sec</td>\n", "<td>99.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.2</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr>\n", "<tr><td></td>\n", "<td>2015-11-25 11:42:59</td>\n", "<td> 0.592 sec</td>\n", "<td>100.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.2</td>\n", "<td>1.0</td>\n", "<td>0.1</td></tr></table></div>" ], "text/plain": [ " timestamp duration number_of_trees training_MSE training_logloss training_AUC training_classification_error validation_MSE validation_logloss validation_AUC validation_classification_error\n", "--- ------------------- ---------- ----------------- ----------------- ------------------ -------------- ------------------------------- ---------------- -------------------- ---------------- ---------------------------------\n", " 2015-11-25 11:42:58 0.006 sec 1.0 0.192088988499 0.571861282203 0.996436943272 0.0303738317757 0.199904329976 0.588228883036 0.951575138357 0.0780141843972\n", " 2015-11-25 11:42:58 0.010 sec 2.0 0.160277802376 0.504398553547 0.996905766526 0.018691588785 0.172704933452 0.530358589111 0.952320136228 0.0709219858156\n", " 2015-11-25 11:42:58 0.013 sec 3.0 0.134660475655 0.448993394686 0.997187060478 0.018691588785 0.150478176462 0.482273136361 0.952958705832 0.0709219858156\n", " 2015-11-25 11:42:58 0.017 sec 4.0 0.113062072379 0.400732539059 0.998042662916 0.0140186915888 0.133037817199 0.443367592451 0.954342273308 0.0709219858156\n", " 2015-11-25 11:42:58 0.021 sec 5.0 0.0962640226993 0.361398896252 0.99719878106 0.0140186915888 0.118405733623 0.409189649903 0.955619412516 0.063829787234\n", "--- --- --- --- --- --- --- --- --- --- --- ---\n", " 2015-11-25 11:42:59 0.566 sec 96.0 2.57126681422e-06 0.000675348189042 1.0 0.0 0.0504812753847 0.24165499491 0.970200085143 0.0567375886525\n", " 2015-11-25 11:42:59 0.572 sec 97.0 2.27917081871e-06 0.000632720040278 1.0 0.0 0.0507638626588 0.243442751591 0.970838654747 0.0567375886525\n", " 2015-11-25 11:42:59 0.579 sec 98.0 2.04205964667e-06 0.000597410694761 1.0 0.0 0.0514580633117 0.246779455239 0.970838654747 0.0567375886525\n", " 2015-11-25 11:42:59 0.585 sec 99.0 1.78544678476e-06 0.000554986030507 1.0 0.0 0.0516010701671 0.249148895606 0.970625798212 0.0567375886525\n", " 2015-11-25 11:42:59 0.592 sec 100.0 1.55261137469e-06 0.000519099361538 1.0 0.0 0.0507094587533 0.247694592147 0.970200085143 0.0567375886525" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Variable Importances:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n", "<td><b>relative_importance</b></td>\n", "<td><b>scaled_importance</b></td>\n", "<td><b>percentage</b></td></tr>\n", "<tr><td>radius_worst</td>\n", "<td>177.5</td>\n", "<td>1.0</td>\n", "<td>0.3</td></tr>\n", "<tr><td>perimeter_worst</td>\n", "<td>102.7</td>\n", "<td>0.6</td>\n", "<td>0.2</td></tr>\n", "<tr><td>concave_points_worst</td>\n", "<td>94.2</td>\n", "<td>0.5</td>\n", "<td>0.2</td></tr>\n", "<tr><td>concave_points_mean</td>\n", "<td>88.6</td>\n", "<td>0.5</td>\n", "<td>0.2</td></tr>\n", "<tr><td>concavity_mean</td>\n", "<td>9.3</td>\n", "<td>0.1</td>\n", "<td>0.0</td></tr>\n", "<tr><td>---</td>\n", "<td>---</td>\n", "<td>---</td>\n", "<td>---</td></tr>\n", "<tr><td>compactness_mean</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>radius_se</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>smoothness_mean</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>fractal_dimension_mean</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>symmetry_mean</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr></table></div>" ], "text/plain": [ "variable relative_importance scaled_importance percentage\n", "---------------------- --------------------- ------------------- -----------------\n", "radius_worst 177.467025757 1.0 0.340759241389\n", "perimeter_worst 102.717407227 0.578797141545 0.197230474871\n", "concave_points_worst 94.2315368652 0.530980538291 0.18093652542\n", "concave_points_mean 88.6345443726 0.499442327354 0.170189588587\n", "concavity_mean 9.30055427551 0.0524072245864 0.0178582460933\n", "--- --- --- ---\n", "compactness_mean 0.0267842449248 0.000150925191937 5.14291539108e-05\n", "radius_se 0.00789974443614 4.45138718162e-05 1.51685131914e-05\n", "smoothness_mean 0.00370898260735 2.08995591803e-05 7.12171793163e-06\n", "fractal_dimension_mean 0.000214185129153 1.20690099042e-06 4.11262665927e-07\n", "symmetry_mean 5.02978673467e-06 2.83420917955e-08 9.6578296996e-09" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Performance on a Test Set\n", "\n", "Once a model has been trained, you can also use it to make predictions on a test set. In the case above, we passed the test set as the `validation_frame` in training, so we have technically already created test set predictions and performance. \n", "\n", "However, when performing model selection over a variety of model parameters, it is common for users to break their dataset into three pieces: Training, Validation and Test.\n", "\n", "After training a variety of models using different parameters (and evaluating them on a validation set), the user may choose a single model and then evaluate model performance on a separate test set. This is when the `model_performance` method, shown below, is most useful. " ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9814814814814814" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perf = model.model_performance(test)\n", "perf.auc()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross-validated Performance\n", "\n", "To perform k-fold cross-validation, you use the same code as above, but you specify `nfolds` as an integer greater than 1, or add a \"fold_column\" to your H2O Frame which indicates a fold ID for each row.\n", "\n", "Unless you have a specific reason to manually assign the observations to folds, you will find it easiest to simply use the `nfolds` argument.\n", "\n", "When performing cross-validation, you can still pass a `validation_frame`, but you can also choose to use the original dataset that contains all the rows. We will cross-validate a model below using the original H2O Frame which we call `data`." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "gbm Model Build Progress: [##################################################] 100%\n" ] } ], "source": [ "cvmodel = H2OGradientBoostingEstimator(distribution='bernoulli',\n", " ntrees=100,\n", " max_depth=4,\n", " learn_rate=0.1,\n", " nfolds=5)\n", "\n", "cvmodel.train(x=x, y=y, training_frame=data)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grid Search\n", "\n", "One way of evaluting models with different parameters is to perform a grid search over a set of parameter values. For example, in GBM, here are three model parameters that may be useful to search over:\n", "- `ntrees`: Number of trees\n", "- `max_depth`: Maximum depth of a tree\n", "- `learn_rate`: Learning rate in the GBM\n", "\n", "We will define a grid as follows:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ntrees_opt = [5,50,100]\n", "max_depth_opt = [2,3,5]\n", "learn_rate_opt = [0.1,0.2]\n", "\n", "hyper_params = {'ntrees': ntrees_opt, \n", " 'max_depth': max_depth_opt,\n", " 'learn_rate': learn_rate_opt}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define an `\"H2OGridSearch\"` object by specifying the algorithm (GBM) and the hyper parameters:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from h2o.grid.grid_search import H2OGridSearch\n", "\n", "gs = H2OGridSearch(H2OGradientBoostingEstimator, hyper_params = hyper_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An `\"H2OGridSearch\"` object also has a `train` method, which is used to train all the models in the grid." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "gbm Grid Build Progress: [##################################################] 100%\n" ] } ], "source": [ "gs.train(x=x, y=y, training_frame=train, validation_frame=test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare Models" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Grid Search Results for H2OGradientBoostingEstimator:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>Model Id</b></td>\n", "<td><b>Hyperparameters: [learn_rate, ntrees, max_depth]</b></td>\n", "<td><b>mse</b></td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_14</td>\n", "<td>[0.2, 100, 3]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_17</td>\n", "<td>[0.2, 100, 5]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_16</td>\n", "<td>[0.2, 50, 5]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_8</td>\n", "<td>[0.1, 100, 5]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_11</td>\n", "<td>[0.2, 100, 2]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_13</td>\n", "<td>[0.2, 50, 3]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_5</td>\n", "<td>[0.1, 100, 3]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_7</td>\n", "<td>[0.1, 50, 5]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_2</td>\n", "<td>[0.1, 100, 2]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_10</td>\n", "<td>[0.2, 50, 2]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_4</td>\n", "<td>[0.1, 50, 3]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_1</td>\n", "<td>[0.1, 50, 2]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_15</td>\n", "<td>[0.2, 5, 5]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_12</td>\n", "<td>[0.2, 5, 3]</td>\n", "<td>0.0</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_9</td>\n", "<td>[0.2, 5, 2]</td>\n", "<td>0.1</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_6</td>\n", "<td>[0.1, 5, 5]</td>\n", "<td>0.1</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_3</td>\n", "<td>[0.1, 5, 3]</td>\n", "<td>0.1</td></tr>\n", "<tr><td>Grid_GBM_py_17_model_python_1448480209718_18_model_0</td>\n", "<td>[0.1, 5, 2]</td>\n", "<td>0.1</td></tr></table></div>" ], "text/plain": [ "Model Id Hyperparameters: [learn_rate, ntrees, max_depth] mse\n", "----------------------------------------------------- -------------------------------------------------- -----------\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_14 [0.2, 100, 3] 2.12233e-07\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_17 [0.2, 100, 5] 2.23617e-07\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_16 [0.2, 50, 5] 5.86149e-07\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_8 [0.1, 100, 5] 7.9336e-07\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_11 [0.2, 100, 2] 1.46308e-05\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_13 [0.2, 50, 3] 2.09611e-05\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_5 [0.1, 100, 3] 2.3662e-05\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_7 [0.1, 50, 5] 0.000388941\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_2 [0.1, 100, 2] 0.000546863\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_10 [0.2, 50, 2] 0.000605298\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_4 [0.1, 50, 3] 0.00149725\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_1 [0.1, 50, 2] 0.00449607\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_15 [0.2, 5, 5] 0.0422887\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_12 [0.2, 5, 3] 0.0433428\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_9 [0.2, 5, 2] 0.0502527\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_6 [0.1, 5, 5] 0.0961144\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_3 [0.1, 5, 3] 0.097152\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_0 [0.1, 5, 2] 0.100977" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "print(gs)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Grid_GBM_py_17_model_python_1448480209718_18_model_0 auc: 0.990963431786\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_13 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_16 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_17 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_2 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_15 auc: 0.998476324426\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_1 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_3 auc: 0.997444913268\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_9 auc: 0.993682606657\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_11 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_7 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_12 auc: 0.998663853727\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_4 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_8 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_10 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_5 auc: 1.0\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_6 auc: 0.997691045476\n", "Grid_GBM_py_17_model_python_1448480209718_18_model_14 auc: 1.0\n" ] } ], "source": [ "# print out the auc for all of the models\n", "for g in gs:\n", " print(g.model_id + \" auc: \" + str(g.auc()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TO DO: Compare grid search models" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
sanger-pathogens/pathogen-informatics-training
Notebooks/Unix/basic.ipynb
1
22089
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Unix\n", "\n", "## The Commandline\n", "\n", "The commandline or 'terminal' is an interface you can use to run programs and analyse your data. If this is your first time using one it will seem pretty daunting at first but, with just a few commands, you'll start to see how it helps you to get things done much quicker. You're probably more familiar with software which uses a graphical user interface, also known as a GUI." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting started\n", "\n", "Before we get started, let's check that you're in the right place. Please click on the cell below and press the `crtl` and `Enter` keys. If you're not sure what this command does, don't worry for now; we'll explain it in more detail later." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "echo \"cd $PWD\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It should say something like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cd /home/manager/pathogen-informatics-training/Notebooks/Unix/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Type whatever it said into your terminal and press `Enter`.\n", "\n", "Then continue through the course, entering any commands that you encounter into your terminal window. Let's start by moving into the directory called `basic`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cd basic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, before getting started there are some general points to remember that will make your life easier:\n", "\n", "* Unix is case sensitive - typing `ls` is not the same as typing `LS`.\n", "* Often when you have problems with Unix, it is due to a spelling mistake. Check that you have not missed or added a space. Pay careful attention when typing commands across a couple of lines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Files and directories\n", "\n", "_Directories_ are the Unix equivalent of folders on a PC or Mac. They are organised in a hierarchy, so directories can have sub-directories and so on. Directories are very useful for organising your work and keeping your account tidy - for example, if you have more than one project, you can organise the files for each project into different directories to keep them separate. You can think of directories as rooms in a house. You can only be in one room (directory) at a time. When you are in a room you can see everything in that room easily. To see things in other rooms, you have to go to the appropriate door and crane your head around. Unix works in a similar manner, moving from directory to directory to access files. The location or directory that you are in is referred to as the current working directory.\n", "\n", "For the file called `index.ipynb` in the `Unix` directory, the location or full pathname can be expressed as:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "/home/pathogen-informatics-training/Notebooks/Unix/index.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Directory structure](basic/directory_structure.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pwd - find where you are\n", "\n", "The command `pwd` stands for print working directory. A _command_ (also known as a _program_) is something which tells the computer to do something. Commands are therefore often the first thing that you type into the terminal (although we'll show you some advanced exceptions to this rule later).\n", "\n", "As described above, directories are arranged in a hierarchical structure. To determine where you are in the hierarchy you can use the `pwd` command to display the name of the current working directory. The current working directory may be thought of as the directory you are in, i.e. your current position in the file-system tree.\n", "\n", "To find out where you are, type the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "pwd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember that Unix is case sensitive, `PWD` is not the same as `pwd`.\n", "\n", "`pwd` will list each of the folders you would need to navigate through to get from the `root` of the file system to your current directory. This is sometimes refered to as your 'absolute path' to distinguish that it gives a complete route rather than a 'relative path' which tells you how to get from one folder to another. More on that shortly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ls - list the contents of a directory\n", "\n", "The command `ls` stands for list. The `ls` command can be used to list the contents of a directory.\n", "\n", "To list the contents of your current working directory type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should see that there are 3 items in this directory.\n", "\n", "To list the contents of a directory with extra information about the items type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ls -l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of printing out a simple list, this should have printed out additional information about each file. Note that there is a space between the command `ls` and the `-l`. There is no space between the dash and the letter l.\n", "\n", "`-l` is our first example of an _option_. Many commands have options which change their behaviour but are not always required. \n", "\n", "What do each of the columns represent?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To list all contents of a directory including hidden files and directories type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "ls -a -l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an example of a command which can take multiple options at the same time. Different commands take different options and sometimes (unhelpfully) use the same letter to do different things.\n", "\n", "How many hidden files and directories are there?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try the same command but with the `-h` option:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "ls -alh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll also notice that we've combined `-a -l -h` into what appears to be a single `-alh` option. It's almost always ok to do this for options which are made up of a single dash followed by a single letter.\n", "\n", "What does the `-h` option do?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To list the contents of the directory called Pfalciparum with extra information type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ls -l Pfalciparum/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case we gave `ls` an _argument_ describing the _relative path_ to the directory `Pfalciparum` from our current working directory. Arguments are very similar to options (and I often use the terms interchangably) but they often refer to things which are not prefixed with dashes.\n", "\n", "How many files are there in this directory?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tab completion\n", "\n", "Typing out file names is really boring and you're likely to make typos which will at best make your command fail with a strange error and at worst overwrite some of your carefully crafted analysis. _Tab completion_ is a trick which normally reduces this risk significantly.\n", "\n", "Instead of typing out `ls Pfalciparum/`, try typing `ls P` and then press the `tab` character (instead of `Enter`). The rest of the folder name should just appear. If you have two folders with simiar names (e.g. `my_awesome_scripts/` and `my_awesome_results/`) then you might need to give your terminal a bit of a hand to work out which one you want. In this case you would type `ls -l m`, when you press `tab` the terminal would read `ls -l my_awesome_`, you could then type `s` followed by another `tab` and it would work out that you meant `my_awesome_scripts/`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## File permissions\n", "\n", "Every file and directory have a set of permissions which restrict what can be done with a file or directory.\n", "\n", "* Read (r): permission to read from a file/directory\n", "* Write (w): permission to modify a file/directory\n", "* Execute (x): Tells the operating system that the file contains code for the computer to run, as opposed to a file of text which you open in a text editor.\n", "\n", "The first set of permissions (characters 2,3,4) refer to what the owner of the file can do, the second set of permissions (5,6,7) refers to what members of the Unix group can do and the third set of permissions (8,9,10) refers to what everyone else can do." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## cd - change current working directory\n", "\n", "The command `cd` stands for change directory.\n", "\n", "The `cd` command will move you from the current working directory to another directory, in other words allow you to move up or down in the directory hierarchy. \n", "\n", "To move into the `Styphi` directory type the following. Note, you'll remember this more easily if you type this rather than copying and pasting. Also remember that you can use tab completion to save typing all of it.\n", "\n", "`cd Styphi/` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use the `pwd` command to check your location in the directory hierarchy and the `ls` command to list the contents of this directory. \n", "`pwd` \n", "`ls` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should see that there are 3 files called:\n", "`Styphi.fa`, `Stypi.gff`, `Styphi.noseq.gff`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tips\n", "\n", "There are some short cuts for referring to directories:\n", "\n", "* . Current directory (one full stop)\n", "* .. Directory above (two full stops)\n", "* ~ Home directory (tilde)\n", "* / Root of the file system (like C:\\ in Windows)\n", "\n", "Try the following commands, what do they do?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls ." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls .." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls ~" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you may remember, `ls` will only _list_ what is in the directories. To move to the directory, you need to use `cd`. Try moving between directories a few times. Can you get into the `Pfalciparum/` and then back into `Styphi/`?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## cp - copy a file\n", "\n", "The command `cp` stands for copy.\n", "\n", "The `cp` command will copy a file from one location to another and you will end up with two copies of the file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To copy the file `Styphi.gff` to a new file called `StyphiCT18.gff` type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cp Styphi.gff StyphiCT18.gff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `ls` to check the contents of the current directory for the copied file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## mv - move a file\n", "\n", "The `mv` command stand for move.\n", "\n", "The `mv` command will move a file from one location to another. This moves the file rather than copies it, therefore you end up with only one file rather than two. When using the command, the path or pathname is used to tell Unix where to find the file. You refer to files in other directories by using the list of hierarchical names separated by slashes. For example, the file called bases in the directory genome has the path genome/bases. If no path is specified, Unix assumes that the file is in the current working directory.\n", "\n", "To move the file `StyphiCT18.gff` from the current directory to the directory above type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mv StyphiCT18.gff .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `ls` command to check the contents of the current directory and the directory above to see that `StyphiCT18.gff` has been moved." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cd .." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## rm - delete a file\n", "\n", "The command `rm` stands for remove.\n", "\n", "The `rm` command will delete a file permanently from your computer so take care!\n", "\n", "To remove the copy of the S. typhi file, called `StyphiCT18.gff` type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rm StyphiCT18.gff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `ls` command to check the contents of the current directory to see that the file `StyphiCT18.gff` has been removed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately there is no \"recycle bin\" on the command line to recover the file from, so you have to be careful." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## find - find a file\n", "\n", "The `find` command can be used to find files matching a given expression. It can be used to recursively search the directory tree for a specified name, seeking files and directories that match the given name." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find all files in the current directory and all its subdirectories that end with the suffix gff:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "find . -name *.gff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many gff files did you find?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find all the subdirectories contained in the current directory type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "find . -type d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many subdirectories did you find?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes you may want to search for files based on when they were last modified or accessed. In those instances, the following two options to find come in handy:\n", "\n", "* `-mtime` : search files by modifying date \n", "* `-atime` : search files by last access date \n", "\n", "With these commands, you can specify that the files you are looking for are older or newer than a given time using `+` and `-` respectively. For example, to search for files and directories that were modified more than one hour ago, you can run:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "find . -mtime +1h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If instead you wanted to look for all files and directories that were modified in the last 20 minutes, you simply change the `+` to `-` and put `20m` insted of `1h`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "find . -mtime -20m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information, have a look in the man page for `find`.\n", " \n", "These are just some basic examples of the find command but it is possible to use the following find options to search in many other ways. Two further examples are:\n", "\n", "* `-size` : search files by file size\n", "* `-user` : search files by user they belong to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "Many people panic when they are confronted with a Unix prompt! Don’t! All the commands you need to solve these exercises are provided above and don't be afraid to make a mistake. If you get lost ask a demonstrator. If you are a person skilled at Unix, be patient this is only a short exercise.\n", "\n", "To begin, open a terminal window and navigate to the `basic` directory in the `Unix` directory (remember use the Unix command `cd`) and then complete the exercise below.\n", "\n", "1. Use the `ls` command to show the contents of the `basic` directory.\n", "2. How many files are there in the `Pfalciparum` directory?\n", "3. What is the largest file in the `Pfalciparum` directory?\n", "4. Move into the `Pfalciparum` directory.\n", "5. How many files are there in the `fasta` directory?\n", "6. Copy the file `Pfalciparum.bed` in the `Pfalciparum` directory into the `annotation` directory.\n", "7. Move all the fasta files in the directory `Pfalciparum` to the `fasta` directory.\n", "8. How many files are there in the `fasta` directory?\n", "9. Use the `find` command to find all gff files in the `Unix` directory, how many files did you find?\n", "10. Use the `find` command to find all the fasta files in the `Unix` directory, how many files did you find?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now go to the next part of the tutorial, [looking inside files](files.ipynb). \n", "You can also [return to the index](index.ipynb)." ] } ], "metadata": { "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
YuriyGuts/kaggle-quora-question-pairs
notebooks/feature-wm-intersect.ipynb
1
109748
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature: Intersections Weighted by Word Match " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question intersections weighted by word match ratio (based on the [kernel by @skihikingkevin](https://www.kaggle.com/skihikingkevin/magic-feature-v2-krzy-new-idea))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This utility package imports `numpy`, `pandas`, `matplotlib` and a helper `kg` module into the root namespace." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pygoose import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import defaultdict" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import nltk" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to\n", "[nltk_data] /home/yuriyguts/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nltk.download('stopwords')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Config" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Automatically discover the paths to various data folders and compose the project structure." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "project = kg.Project.discover()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Identifier for storing these features on disk and referring to them later." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "feature_list_id = 'wm_intersect'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Original question datasets." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_train = pd.read_csv(project.data_dir + 'train.csv').fillna('none')\n", "df_test = pd.read_csv(project.data_dir + 'test.csv').fillna('none')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build features" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_all_pairs = pd.concat([\n", " df_train[['question1', 'question2']],\n", " df_test[['question1', 'question2']]\n", "], axis=0).reset_index(drop='index')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stops = set(nltk.corpus.stopwords.words('english'))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def word_match_share(pair):\n", " q1 = str(pair[0]).lower().split()\n", " q2 = str(pair[1]).lower().split()\n", " q1words = {}\n", " q2words = {}\n", " \n", " for word in q1:\n", " if word not in stops:\n", " q1words[word] = 1\n", " for word in q2:\n", " if word not in stops:\n", " q2words[word] = 1\n", " \n", " if len(q1words) == 0 or len(q2words) == 0:\n", " # The computer-generated chaff includes a few questions that are nothing but stopwords\n", " return 0\n", " \n", " shared_words_in_q1 = [w for w in q1words.keys() if w in q2words]\n", " shared_words_in_q2 = [w for w in q2words.keys() if w in q1words]\n", " R = (len(shared_words_in_q1) + len(shared_words_in_q2)) / (len(q1words) + len(q2words))\n", "\n", " return R" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batches: 100%|██████████| 2751/2751 [00:03<00:00, 830.27it/s]\n" ] } ], "source": [ "wms = kg.jobs.map_batch_parallel(\n", " df_all_pairs[['question1', 'question2']].as_matrix(),\n", " item_mapper=word_match_share,\n", " batch_size=1000,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2750086/2750086 [03:06<00:00, 14750.65it/s]\n" ] } ], "source": [ "q_dict = defaultdict(dict)\n", "for i in progressbar(range(len(wms))):\n", " q_dict[df_all_pairs.question1[i]][df_all_pairs.question2[i]] = wms[i]\n", " q_dict[df_all_pairs.question2[i]][df_all_pairs.question1[i]] = wms[i]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def q1_q2_intersect(row):\n", " return len(set(q_dict[row['question1']]).intersection(set(q_dict[row['question2']])))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def q1_q2_wm_ratio(row):\n", " q1 = q_dict[row['question1']]\n", " q2 = q_dict[row['question2']]\n", " \n", " inter_keys = set(q1.keys()).intersection(set(q2.keys()))\n", " if len(inter_keys) == 0:\n", " return 0\n", " \n", " inter_wm = 0\n", " total_wm = 0\n", " \n", " for q, wm in q1.items():\n", " if q in inter_keys:\n", " inter_wm += wm\n", " total_wm += wm\n", " \n", " for q, wm in q2.items():\n", " if q in inter_keys:\n", " inter_wm += wm\n", " total_wm += wm\n", " \n", " if total_wm == 0:\n", " return 0\n", " \n", " return inter_wm / total_wm" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_train['q1_q2_wm_ratio'] = df_train.apply(q1_q2_wm_ratio, axis=1, raw=True)\n", "df_test['q1_q2_wm_ratio'] = df_test.apply(q1_q2_wm_ratio, axis=1, raw=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_train['q1_q2_intersect'] = df_train.apply(q1_q2_intersect, axis=1, raw=True)\n", "df_test['q1_q2_intersect'] = df_test.apply(q1_q2_intersect, axis=1, raw=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd694ff828>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+5rHHntTRo39VYuL3VVVVqccff1QTJyaqvLxc8+fP9/X0AAAt5FUA379/v0aM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\ngBNCCCGEEGJA/wKnZSoJ8i17MQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fdd6ba94cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 6))\n", "\n", "plt.subplot(1, 2, 1)\n", "intersect_counts = df_train.q1_q2_intersect.value_counts()\n", "sns.barplot(intersect_counts.index[:20], intersect_counts.values[:20])\n", "\n", "plt.subplot(1, 2, 2)\n", "df_train['q1_q2_wm_ratio'].plot.hist()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd6b71b2b0>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FBQUOLd6CIECtVtvuaK8P71J3nv2FqihKvJOYZGOxVD8ILRaR+xgREfkMt7X5DhgwAJWV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\nAGAwQjYAAABgsP8A4/z1YTI+dA4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fdd6ba94ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 6))\n", "\n", "plt.subplot(1, 2, 1)\n", "sns.violinplot(x='is_duplicate', y='q1_q2_wm_ratio', data=df_train)\n", "\n", "plt.subplot(1, 2, 2)\n", "sns.violinplot(x='is_duplicate', y='q1_q2_intersect', data=df_train)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " q1_q2_intersect q1_q2_wm_ratio\n", "q1_q2_intersect 1.000000 0.684574\n", "q1_q2_wm_ratio 0.684574 1.000000\n" ] }, { "data": { "image/png": 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92Y+Gaxk6yIOmlm4hzobRtQ8iUhOMXDTF6CmJnlA9NLVdNdKsqksfXVWOu6+b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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fddfc383e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_train.plot.scatter(x='q1_q2_intersect', y='q1_q2_wm_ratio', figsize=(12, 6))\n", "print(df_train[['q1_q2_intersect', 'q1_q2_wm_ratio']].corr())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build final features" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns_to_keep = [\n", " 'q1_q2_intersect',\n", " 'q1_q2_wm_ratio',\n", "]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train = df_train[columns_to_keep].values\n", "X_test = df_test[columns_to_keep].values" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Save features" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "feature_names = columns_to_keep" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "project.save_features(X_train, X_test, feature_names, feature_list_id)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
skimmy/lib-bio
src/simulator/util/shift_distance.ipynb
1
44346
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import binom" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "'''Computes the probability mass function from the cumulative density'''\n", "def cdf_to_pmf(X):\n", " return np.concatenate(([X[0]], np.diff(X)))\n", "\n", "def pmf_to_cdf(X):\n", " return X.cumsum()\n", "\n", "'''Computes the cumulative density function of min{X,Y}'''\n", "def cdf_min(X,Y):\n", " return 1 - (1-X)*(1-Y)\n", "\n", "'''Returns the pmf of the sum of two pmfs'''\n", "def sum_cdf(X,Y):\n", " X_pmf = cdf_to_pmf(X)\n", " Y_pmf = cdf_to_pmf(Y)\n", " return np.convolve(X_pmf,Y_pmf).cumsum()\n", "\n", "def sum_pmf(X,Y):\n", " return np.convolve(X,Y)\n", "\n", "'''Constructs the cdf for the shift distance t with paramter n and p'''\n", "def shift_distance_cdf(t, n, p):\n", " S = binom.cdf(range(n), n-1, p)\n", " S = np.concatenate(([0]*t*2, S))\n", " return S\n", "\n", "'''Computes the cdf of the minimum of several cdfs'''\n", "def cdf_min_a(M):\n", " m = M.shape[0]\n", " X = M[0,:]\n", " for i in range(1,m):\n", " X = cdf_min(X, M[i,:])\n", " return X\n", "\n", "'''Computes the distribution of the shift distance for strings with length n'''\n", "def sd_cdf(n, p):\n", " Ks = range(n+1)\n", " t_max = int(np.floor(n/2))\n", " # start with the distribution of t=0, that is, the Hamming distance\n", " SD_ts = np.array([binom.cdf(Ks, n, p)])\n", " for t in range(1,t_max+1):\n", " cdf_sdt = np.pad(binom.cdf(Ks, n-t, p), [2*t,0], 'constant', constant_values=[0])[:-2*t]\n", " SD_ts = np.vstack([SD_ts, cdf_sdt]) \n", " return cdf_min_a(SD_ts)\n", "\n", "def recursive_sd_cdf(n, p):\n", " if (n <= 1):\n", " return (np.array([1-p,1]))\n", " if (n == 2):\n", " return (np.array([(1-p)**2, 1-p*p, 1]))\n", " else:\n", " sd_n = sd_cdf(n,3/4)\n", " n_2 = int(np.floor(n/2)) \n", " cdf_n_2 = recursive_sd_cdf(n_2,p)\n", " cdf_rec = sum_cdf(cdf_n_2, cdf_n_2)\n", " cdf = cdf_min(cdf_rec, sd_n)\n", " return cdf" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\t0.75\n", "2\t0.75\n", "4\t0.6285579800605774\n", "8\t0.5890632074071046\n", "16\t0.5779675274936744\n", "32\t0.5757988102416614\n", "64\t0.5756087831446152\n", "128\t0.5756055515474376\n", "256\t0.5756055494715893\n", "512\t0.5756055494715874\n", "1024\t0.5756055494715874\n" ] } ], "source": [ "for k in range(11):\n", " n = np.power(2,k)\n", " Ks = np.arange(n+1)\n", " cdf = recursive_sd_cdf(n,3/4)\n", " print(\"{0}\\t{1}\".format(n, np.sum(Ks*cdf_to_pmf(cdf))/n))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\t0.59765625\n", "4\t0.6285579800605774\n", "6\t0.6413563967640235\n", "8\t0.6492428533520538\n", "10\t0.6548045320069795\n", "12\t0.6590757811532243\n", "14\t0.6625276350711388\n", "16\t0.6654157956908897\n", "18\t0.6678934773914933\n", "20\t0.6700593433088\n", "22\t0.6719804832534877\n", "24\t0.6737045393746551\n", "26\t0.6752665488746992\n", "28\t0.6766930258318314\n", "30\t0.6780045133792306\n", "32\t0.6792172477757163\n", "34\t0.6803442815115026\n", "36\t0.6813962632521762\n", "38\t0.6823819928799024\n", "40\t0.6833088251095267\n", "42\t0.6841829687046651\n", "44\t0.685009712119686\n", "46\t0.6857935962540093\n", "48\t0.6865385485186443\n", "50\t0.6872479881627158\n", "52\t0.6879249099490872\n", "54\t0.6885719513085518\n", "56\t0.6891914467373006\n", "58\t0.6897854722380143\n", "60\t0.6903558819132478\n", "62\t0.6909043383166487\n", "64\t0.691432337796962\n", "66\t0.6919412317938043\n", "68\t0.6924322448365703\n", "70\t0.6929064898400578\n", "72\t0.6933649811693736\n", "74\t0.6938086458530596\n", "76\t0.6942383332503613\n", "78\t0.6946548234212098\n", "80\t0.6950588344020817\n", "82\t0.6954510285547583\n", "84\t0.6958320181260005\n", "86\t0.6962023701327904\n", "88\t0.6965626106688209\n", "90\t0.6969132287124602\n", "92\t0.6972546795037452\n", "94\t0.6975873875475219\n", "96\t0.6979117492912198\n", "98\t0.698228135518573\n", "100\t0.6985368934946132\n", "102\t0.6988383488922434\n", "104\t0.6991328075264872\n", "106\t0.699420556918941\n", "108\t0.6997018677119455\n", "110\t0.6999769949494252\n", "112\t0.7002461792391635\n", "114\t0.700509647809403\n", "116\t0.7007676154710647\n", "118\t0.7010202854954929\n", "120\t0.7012678504164462\n", "122\t0.7015104927640214\n", "124\t0.7017483857373049\n", "126\t0.7019816938217708\n" ] } ], "source": [ "for n in range(2,128,2):\n", " SD_n = sd_cdf(n,3/4)\n", " SD_n_2 = sd_cdf(int(n/2), 3/4)\n", " SD_rec = sum_cdf(SD_n_2, SD_n_2) \n", " cdf = cdf_min(SD_n, SD_rec)\n", " tau_n = np.sum(cdf_to_pmf(cdf)*np.arange(n+1)) / n\n", " print(\"{0}\\t{1}\".format(n,tau_n))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "E[X] = 12.0\n", "E[Z] = 11.035657741699676\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "p = 3/4\n", "ns = [16]\n", "for n in ns:\n", " Ks = range(n+1)\n", " X = binom.cdf(range(n+1), n, p)\n", " Z = cdf_min(X,X)\n", " X_pmf = cdf_to_pmf(X)\n", " Z_pmf = cdf_to_pmf(Z)\n", " print(\"E[X] = {0}\".format(np.sum(X_pmf*Ks)))\n", " print(\"E[Z] = {0}\".format(np.sum(Z_pmf*Ks)))\n", " plt.plot(Ks, X_pmf, label='X')\n", " plt.plot(Ks, Z_pmf, label='min{X,X}')\n", " plt.legend()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f594ce29b38>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ns = range(16,4099,32)\n", "As = []\n", "for n in ns: \n", " Ks = range(n+1)\n", " SDs = binom.cdf(Ks, n, p)\n", " ts = [1,2]\n", " for t in ts:\n", " St = shift_distance_cdf(t, n, p)\n", " St = St[:n+1]\n", " Z = cdf_min(St, St)\n", " SDs = np.vstack((SDs, Z))\n", " St_pmf = cdf_to_pmf(St)\n", " Z_pmf = cdf_to_pmf(Z) \n", " SD_cdf = cdf_min_a(SDs)\n", " SD_pmf = cdf_to_pmf(SD_cdf)\n", " #print(\"n = {0}\\n E[SD] = {1:.2f}\\n E[X] = {2}\".format(n, np.sum(Ks*SD_pmf), n*p))\n", " a_n = np.sum(Ks*SD_pmf)/n\n", " As.append(a_n)\n", " #print(\" alpha_n = {0}\".format(a_n))\n", "plt.plot(ns,As)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "## Matrix of distributions\n", "n = 8\n", "p = 3/4\n", "M = np.zeros([n+1,n+1,n+1])\n", "# on celles (0,i) and (i,0) the content is deterministc\n", "for i in range(n+1):\n", " M[0,i,i] = 1\n", " M[i,0,i] = 1\n", "# it is convinient to have the distributions [1-p,p] and [0, 1] once for all\n", "delta_d = np.zeros(n+1)\n", "delta_d[0] = 1-p\n", "delta_d[1] = p\n", "one_d = np.zeros(n+1)\n", "one_d[1] = 1\n", "# M[i,j,:] represents the distribution on cell (i,j)\n", "for i in range(1,n+1):\n", " for j in range(1,n+1):\n", " # form the distribution of the 3 cells\n", " diag = pmf_to_cdf(sum_pmf(M[i-1,j-1,:], delta_d)[:n+1])\n", " horiz = pmf_to_cdf(sum_pmf(M[i-1,j,:], one_d)[:n+1])\n", " vert = pmf_to_cdf(sum_pmf(M[i,j-1,:], one_d)[:n+1])\n", " min_hv = cdf_min(horiz, vert)\n", " M[i,j,:] = cdf_to_pmf(cdf_min(diag, min_hv))\n", " #M[i,j,:] = cdf_to_pmf(cdf_min(M[i-1,j-1,:], diag))" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\tnan\n", "1\t0.750000\n", "2\t0.750000\n", "3\t0.707413\n", "4\t0.662061\n", "5\t0.629208\n", "6\t0.601144\n", "7\t0.579941\n", "8\t0.561427\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in double_scalars\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "text/plain": [ "array([[0. , 1. , 2. , 3. , 4. ,\n", " 5. , 6. , 7. , 8. ],\n", " [1. , 0.75 , 1.5625 , 2.421875 , 3.31640625,\n", " 4.23730469, 5.17797852, 6.13348389, 7.10011292],\n", " [2. , 1.5625 , 1.5 , 2.07226562, 2.78936768,\n", " 3.61003109, 4.48246481, 5.38121949, 6.29834706],\n", " [3. , 2.421875 , 2.07226562, 2.12223928, 2.55957997,\n", " 3.19832473, 3.93089911, 4.73639327, 5.59339679],\n", " [4. , 3.31640625, 2.78936768, 2.55957997, 2.64824222,\n", " 3.03622693, 3.59473994, 4.27813905, 5.02676004],\n", " [5. , 4.23730469, 3.61003109, 3.19832473, 3.03622693,\n", " 3.14604059, 3.48679197, 4.0010041 , 4.62315248],\n", " [6. , 5.17797852, 4.48246481, 3.93089911, 3.59473994,\n", " 3.48679197, 3.60686127, 3.92975056, 4.40107866],\n", " [7. , 6.13348389, 5.38121949, 4.73639327, 4.27813905,\n", " 4.0010041 , 3.92975056, 4.05958404, 4.35955218],\n", " [8. , 7.10011292, 6.29834706, 5.59339679, 5.02676004,\n", " 4.62315248, 4.40107866, 4.35955218, 4.49141868]])" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i in range(n+1):\n", " dist = M[i,i,:]\n", " print(\"{0}\\t{1:.6f}\".format(i, np.sum(dist*np.arange(n+1))/i))\n", "M_Mean = np.zeros([n+1,n+1])\n", "for i in range(n+1):\n", " for j in range(n+1):\n", " M_Mean[i,j] = np.sum(M[i,j,:]*np.arange(n+1))\n", "M_Mean" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "np.savetxt(\"/tmp/dist_exp.txt\", M_Mean, fmt=\"%.4f\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
vipmunot/Data-Science-Course
Data Visualization/Project/predictive modal - xgboost/kobe_sim_xgboost.ipynb
1
28786
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading necessary library" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from sklearn import preprocessing\n", "from sklearn import metrics\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "import xgboost as xgb\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading data\n", "### deleting irrelevant features" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kobe = pd.read_csv('data.csv', sep=',') \n", "kobe= kobe[np.isfinite(kobe['shot_made_flag'])]\n", "del kobe['lat']\n", "del kobe['lon']\n", "del kobe['game_id']\n", "del kobe['team_id']\n", "del kobe['team_name']\n", "\n", "kobe_2 = pd.read_csv('data.csv', sep=',') \n", "kobe_2= kobe_2[np.isfinite(kobe_2['shot_made_flag'])]\n", "del kobe_2['lat']\n", "del kobe_2['lon']\n", "del kobe_2['game_id']\n", "del kobe_2['team_id']\n", "del kobe_2['team_name']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### encoding catagorical features" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>action_type</th>\n", " <th>combined_shot_type</th>\n", " <th>game_event_id</th>\n", " <th>loc_x</th>\n", " <th>loc_y</th>\n", " <th>minutes_remaining</th>\n", " <th>period</th>\n", " <th>playoffs</th>\n", " <th>season</th>\n", " <th>seconds_remaining</th>\n", " <th>shot_distance</th>\n", " <th>shot_made_flag</th>\n", " <th>shot_type</th>\n", " <th>shot_zone_area</th>\n", " <th>shot_zone_basic</th>\n", " <th>shot_zone_range</th>\n", " <th>game_date</th>\n", " <th>matchup</th>\n", " <th>opponent</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>25</td>\n", " <td>3</td>\n", " <td>35</td>\n", " <td>-101</td>\n", " <td>135</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>45</td>\n", " <td>16</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>250</td>\n", " <td>28</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>25</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>121</td>\n", " <td>127</td>\n", " <td>11</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>17</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>254</td>\n", " <td>71</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>40</td>\n", " <td>3</td>\n", " <td>27</td>\n", " <td>-67</td>\n", " <td>110</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>254</td>\n", " <td>71</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>25</td>\n", " <td>3</td>\n", " <td>138</td>\n", " <td>-117</td>\n", " <td>226</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>50</td>\n", " <td>25</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>254</td>\n", " <td>71</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>25</td>\n", " <td>3</td>\n", " <td>244</td>\n", " <td>-132</td>\n", " <td>97</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>29</td>\n", " <td>16</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>254</td>\n", " <td>71</td>\n", " <td>30</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " action_type combined_shot_type game_event_id loc_x loc_y \\\n", "2 25 3 35 -101 135 \n", "11 25 3 4 121 127 \n", "12 40 3 27 -67 110 \n", "17 25 3 138 -117 226 \n", "18 25 3 244 -132 97 \n", "\n", " minutes_remaining period playoffs season seconds_remaining \\\n", "2 7 1 0 4 45 \n", "11 11 1 0 4 0 \n", "12 7 1 0 4 9 \n", "17 8 2 0 4 50 \n", "18 11 3 0 4 29 \n", "\n", " shot_distance shot_made_flag shot_type shot_zone_area shot_zone_basic \\\n", "2 16 1.0 0 2 4 \n", "11 17 1.0 0 4 4 \n", "12 12 1.0 0 3 2 \n", "17 25 1.0 1 2 0 \n", "18 16 0.0 0 2 4 \n", "\n", " shot_zone_range game_date matchup opponent \n", "2 0 250 28 25 \n", "11 0 254 71 30 \n", "12 2 254 71 30 \n", "17 1 254 71 30 \n", "18 0 254 71 30 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mt_up = preprocessing.LabelEncoder()\n", "kobe.matchup = mt_up.fit_transform(kobe.matchup )\n", "#kobe_2.matchup = mt_up.fit_transform(kobe.matchup )\n", "\n", "opp = preprocessing.LabelEncoder()\n", "kobe.opponent = opp.fit_transform(kobe.opponent )\n", "#kobe_2.opponent = opp.fit_transform(kobe.opponent )\n", "\n", "dt = preprocessing.LabelEncoder()\n", "kobe.game_date = dt.fit_transform(kobe.game_date )\n", "#kobe_2.game_date = dt.fit_transform(kobe.game_date )\n", "\n", "at = preprocessing.LabelEncoder()\n", "kobe.action_type = at.fit_transform(kobe.action_type )\n", "#kobe_2.action_type = at.fit_transform(kobe.action_type )\n", "\n", "cst = preprocessing.LabelEncoder()\n", "kobe.combined_shot_type = cst.fit_transform(kobe.combined_shot_type )\n", "#kobe_2.combined_shot_type = cst.fit_transform(kobe.combined_shot_type )\n", "\n", "seson = preprocessing.LabelEncoder()\n", "kobe.season = seson.fit_transform(kobe.season )\n", "#kobe_2.season = seson.fit_transform(kobe.season )\n", "\n", "st = preprocessing.LabelEncoder()\n", "kobe.shot_type = st.fit_transform(kobe.shot_type )\n", "#kobe_2.shot_type = st.fit_transform(kobe.shot_type )\n", "\n", "sza = preprocessing.LabelEncoder()\n", "kobe.shot_zone_area = sza.fit_transform(kobe.shot_zone_area )\n", "#kobe_2.shot_zone_area = sza.fit_transform(kobe.shot_zone_area )\n", "\n", "szb = preprocessing.LabelEncoder()\n", "kobe.shot_zone_basic = szb.fit_transform(kobe.shot_zone_basic )\n", "#kobe_2.shot_zone_basic = szb.fit_transform(kobe.shot_zone_basic )\n", "\n", "szr = preprocessing.LabelEncoder()\n", "kobe.shot_zone_range = szr.fit_transform(kobe.shot_zone_range )\n", "#kobe_2.shot_zone_range = szr.fit_transform(kobe.shot_zone_range )\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### splitting data into test and train" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "# Generate the training set. Set random_state to be able to replicate results.\n", "train = kobe.sample(frac=0.6, random_state=1)\n", "train_2 = kobe_2.sample(frac=0.6, random_state=1)\n", "# Select anything not in the training set and put it in the testing set.\n", "test = kobe.loc[~kobe.index.isin(train.index)] \n", "test_2 = kobe_2.loc[~kobe_2.index.isin(train_2.index)] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### seperating features and class in both test and train sets" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15418, 18)\n", "(10279, 18)\n", "(15418L,)\n", "(10279L,)\n" ] } ], "source": [ "columns = kobe.columns.tolist()\n", "columns = [c for c in columns if c not in [\"shot_made_flag\",\"team_id\",\"team_name\"]]\n", "kobe_train_x =train[columns]\n", "kobe_test_x =test[columns]\n", "kobe_train_y=train['shot_made_flag']\n", "kobe_test_y=test['shot_made_flag']\n", "print(kobe_train_x.shape)\n", "print(kobe_test_x.shape)\n", "print(kobe_train_y.shape)\n", "print(kobe_test_y.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### getting best parameters\n", "##### do not run this section as the best set of parameters is already found" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def optimization(depth, n_est,l_r):\n", " maxacc=0\n", " best_depth=0\n", " best_n_est=0\n", " best_l_r=0\n", " for i in range(1,depth):\n", " for j in n_est:\n", " for k in l_r: \n", " gbm = xgb.XGBClassifier(max_depth=i, n_estimators=j, learning_rate=k).fit(kobe_train_x, kobe_train_y)\n", " predicted = gbm.predict(kobe_test_x)\n", " key=str(i)+\"_\"+str(j)+\"_\"+str(k)\n", " accu=accuracy_score(kobe_test_y, predicted)\n", " if(accu>maxacc):\n", " maxacc=accu\n", " best_depth=i\n", " best_n_est=j\n", " best_l_r=k\n", " print(maxkey+\" \"+str(maxacc))\n", " return(best_depth,best_n_est,best_l_r)\n", "\n", "n_est=[5,10,20,50,100,150,200,250,300,350,400,450,500,550,600,650,700,750,800,850,900,950,1000]\n", "depth=10\n", "l_r = [0.0001, 0.001, 0.01,0.05, 0.1, 0.2, 0.3]\n", "best_depth,best_n_est,best_l_r=optimization(depth,n_est,l_r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### creating model with best parameter combination and reporting metrics" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0.0 0.67 0.86 0.75 5717\n", " 1.0 0.73 0.46 0.56 4562\n", "\n", "avg / total 0.69 0.68 0.67 10279\n", "\n", "Confusion Matrix\n", "[[4938 779]\n", " [2482 2080]]\n", "Accuracy: 68.28%\n" ] } ], "source": [ "#hard coded the best features\n", "gbm = xgb.XGBClassifier(max_depth=4, n_estimators=600, learning_rate=0.01).fit(kobe_train_x, kobe_train_y) \n", "predicted = gbm.predict(kobe_test_x)\n", "# summarize the fit of the model\n", "print(metrics.classification_report(kobe_test_y, predicted))\n", "print(\"Confusion Matrix\")\n", "print(metrics.confusion_matrix(kobe_test_y, predicted))\n", "accuracy=accuracy_score(kobe_test_y, predicted)\n", "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### creating a test file with predicted results to visualize" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Narmi\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>action_type</th>\n", " <th>combined_shot_type</th>\n", " <th>game_event_id</th>\n", " <th>loc_x</th>\n", " <th>loc_y</th>\n", " <th>minutes_remaining</th>\n", " <th>period</th>\n", " <th>playoffs</th>\n", " <th>season</th>\n", " <th>seconds_remaining</th>\n", " <th>shot_distance</th>\n", " <th>shot_made_flag</th>\n", " <th>shot_type</th>\n", " <th>shot_zone_area</th>\n", " <th>shot_zone_basic</th>\n", " <th>shot_zone_range</th>\n", " <th>game_date</th>\n", " <th>matchup</th>\n", " <th>opponent</th>\n", " <th>predicted</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>35</td>\n", " <td>-101</td>\n", " <td>135</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>45</td>\n", " <td>16</td>\n", " <td>1.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Left Side Center(LC)</td>\n", " <td>Mid-Range</td>\n", " <td>16-24 ft.</td>\n", " <td>10/31/2000</td>\n", " <td>LAL @ POR</td>\n", " <td>POR</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>4</td>\n", " <td>121</td>\n", " <td>127</td>\n", " <td>11</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>0</td>\n", " <td>17</td>\n", " <td>1.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Right Side Center(RC)</td>\n", " <td>Mid-Range</td>\n", " <td>16-24 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Running Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>27</td>\n", " <td>-67</td>\n", " <td>110</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>1.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Left Side(L)</td>\n", " <td>In The Paint (Non-RA)</td>\n", " <td>8-16 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>138</td>\n", " <td>-117</td>\n", " <td>226</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>50</td>\n", " <td>25</td>\n", " <td>1.0</td>\n", " <td>3PT Field Goal</td>\n", " <td>Left Side Center(LC)</td>\n", " <td>Above the Break 3</td>\n", " <td>24+ ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>244</td>\n", " <td>-132</td>\n", " <td>97</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>29</td>\n", " <td>16</td>\n", " <td>0.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Left Side Center(LC)</td>\n", " <td>Mid-Range</td>\n", " <td>16-24 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Running Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>274</td>\n", " <td>-16</td>\n", " <td>110</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>57</td>\n", " <td>11</td>\n", " <td>1.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Center(C)</td>\n", " <td>In The Paint (Non-RA)</td>\n", " <td>8-16 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Running Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>307</td>\n", " <td>-46</td>\n", " <td>63</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>11</td>\n", " <td>7</td>\n", " <td>1.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Center(C)</td>\n", " <td>In The Paint (Non-RA)</td>\n", " <td>Less Than 8 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Layup Shot</td>\n", " <td>Layup</td>\n", " <td>332</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>36</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Center(C)</td>\n", " <td>Restricted Area</td>\n", " <td>Less Than 8 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>429</td>\n", " <td>3</td>\n", " <td>87</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>22</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Center(C)</td>\n", " <td>In The Paint (Non-RA)</td>\n", " <td>8-16 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>Jump Shot</td>\n", " <td>Jump Shot</td>\n", " <td>499</td>\n", " <td>127</td>\n", " <td>34</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2000-01</td>\n", " <td>30</td>\n", " <td>13</td>\n", " <td>0.0</td>\n", " <td>2PT Field Goal</td>\n", " <td>Right Side(R)</td>\n", " <td>Mid-Range</td>\n", " <td>8-16 ft.</td>\n", " <td>11/1/2000</td>\n", " <td>LAL vs. UTA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " action_type combined_shot_type game_event_id loc_x loc_y \\\n", "2 Jump Shot Jump Shot 35 -101 135 \n", "11 Jump Shot Jump Shot 4 121 127 \n", "12 Running Jump Shot Jump Shot 27 -67 110 \n", "17 Jump Shot Jump Shot 138 -117 226 \n", "18 Jump Shot Jump Shot 244 -132 97 \n", "22 Running Jump Shot Jump Shot 274 -16 110 \n", "24 Running Jump Shot Jump Shot 307 -46 63 \n", "25 Layup Shot Layup 332 0 0 \n", "29 Jump Shot Jump Shot 429 3 87 \n", "31 Jump Shot Jump Shot 499 127 34 \n", "\n", " minutes_remaining period playoffs season seconds_remaining \\\n", "2 7 1 0 2000-01 45 \n", "11 11 1 0 2000-01 0 \n", "12 7 1 0 2000-01 9 \n", "17 8 2 0 2000-01 50 \n", "18 11 3 0 2000-01 29 \n", "22 7 3 0 2000-01 57 \n", "24 5 3 0 2000-01 11 \n", "25 2 3 0 2000-01 36 \n", "29 6 4 0 2000-01 22 \n", "31 0 4 0 2000-01 30 \n", "\n", " shot_distance shot_made_flag shot_type shot_zone_area \\\n", "2 16 1.0 2PT Field Goal Left Side Center(LC) \n", "11 17 1.0 2PT Field Goal Right Side Center(RC) \n", "12 12 1.0 2PT Field Goal Left Side(L) \n", "17 25 1.0 3PT Field Goal Left Side Center(LC) \n", "18 16 0.0 2PT Field Goal Left Side Center(LC) \n", "22 11 1.0 2PT Field Goal Center(C) \n", "24 7 1.0 2PT Field Goal Center(C) \n", "25 0 0.0 2PT Field Goal Center(C) \n", "29 8 0.0 2PT Field Goal Center(C) \n", "31 13 0.0 2PT Field Goal Right Side(R) \n", "\n", " shot_zone_basic shot_zone_range game_date matchup opponent \\\n", "2 Mid-Range 16-24 ft. 10/31/2000 LAL @ POR POR \n", "11 Mid-Range 16-24 ft. 11/1/2000 LAL vs. UTA UTA \n", "12 In The Paint (Non-RA) 8-16 ft. 11/1/2000 LAL vs. UTA UTA \n", "17 Above the Break 3 24+ ft. 11/1/2000 LAL vs. UTA UTA \n", "18 Mid-Range 16-24 ft. 11/1/2000 LAL vs. UTA UTA \n", "22 In The Paint (Non-RA) 8-16 ft. 11/1/2000 LAL vs. UTA UTA \n", "24 In The Paint (Non-RA) Less Than 8 ft. 11/1/2000 LAL vs. UTA UTA \n", "25 Restricted Area Less Than 8 ft. 11/1/2000 LAL vs. UTA UTA \n", "29 In The Paint (Non-RA) 8-16 ft. 11/1/2000 LAL vs. UTA UTA \n", "31 Mid-Range 8-16 ft. 11/1/2000 LAL vs. UTA UTA \n", "\n", " predicted \n", "2 0.0 \n", "11 0.0 \n", "12 1.0 \n", "17 0.0 \n", "18 0.0 \n", "22 1.0 \n", "24 1.0 \n", "25 0.0 \n", "29 0.0 \n", "31 0.0 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_2['predicted']=predicted\n", "test_2.to_csv(path_or_buf='test_with_predictions.csv', sep=',')\n", "test_2.head(10)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
wd15/extremefill2D
notebooks/misc/prelim2_sims.ipynb
1
5132
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Preliminary 1 Simulations\n", "\n", "The code appears to be running so it is now possible to obtain some prelimiary results for the base set of paramters to investigate, $E_{\\text{APPLIED}}$=-0.16, -0.18, -0.20, -0.22, -0.24, -0.26, -0.28, -0.30; $c_{\\theta}^{\\infty}$=0.006, 0.012" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from distributed import LocalCluster\n", "from distributed import Client\n", "from extremefill2D.fextreme import init_sim, restart_sim, iterate_sim, multi_init_sim\n", "from extremefill2D.fextreme.plot import vega_plot_treants, vega_plot_treant\n", "import vega\n", "from extremefill2D.fextreme.tools import get_by_uuid, outer_dict, pmap\n", "from toolz.curried import map, pipe, curry\n", "import itertools\n", "\n", "%reload_ext yamlmagic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Client: scheduler=\"127.0.0.1:8786\" processes=8 cores=8>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster = LocalCluster(nanny=True, n_workers=8, threads_per_worker=1)\n", "client = Client(cluster)\n", "client" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "client.shutdown()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "treants1 = multi_init_sim('../../scripts/params1.json',\n", " '../../data',\n", " pmap(client),\n", " dict(appliedPotential=(-0.16, -0.18, -0.20, -0.22, -0.24, -0.26,\n", " -0.28, -0.30, -0.40, -0.50, -0.60, -0.70, -0.80),\n", " bulkSuppressor=(0.003,)),\n", " tags=['prelim2'])\n", "\n", "treants2 = multi_init_sim('../../scripts/params1.json',\n", " '../../data',\n", " pmap(client),\n", " dict(appliedPotential=(-0.30, -0.40, -0.50, -0.60, -0.70, -0.80),\n", " bulkSuppressor=(0.006, 0.012)),\n", " tags=['prelim2'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<Treant: '0d9a570c-9d42-42d9-ac52-856d7ee50462'>, <Treant: '1b23b0b8-a78e-4ed9-9068-248faccc08b4'>, <Treant: 'e677558a-feba-4a62-a670-bad5cdc57f9c'>, <Treant: '280a1451-d2e4-4c85-ab91-76d73227eca8'>, <Treant: '80663650-6777-4d02-a02d-4caf165b97f9'>, <Treant: '1b300121-6dd9-4ea5-b33e-57435e9645c4'>, <Treant: '2e05eb8e-8d77-488c-a5b5-cf2dede0f68c'>, <Treant: '63d2cfbc-81f5-43d3-aa9e-4e4ccec8fd12'>, <Treant: '0bc89986-7cd9-4b01-89e6-984cb1ee419e'>, <Treant: '8b7eeba9-a9ec-4ffa-ae81-f00842a3b08b'>, <Treant: 'bc3cacdb-d8cb-41bf-9bad-3d633ae348c3'>, <Treant: '642c8b55-6146-4c38-bc90-04ee0f618dc2'>, <Treant: '334dee67-9f56-435e-b3d3-0c8ee256b252'>, <Treant: 'd3a85632-5bc0-4d11-af32-b24057d3fd57'>, <Treant: 'bdb08a0e-48f0-4164-886f-139da143f1d2'>, <Treant: '7153746e-de4d-484a-bef1-e0f7aa419894'>, <Treant: '950f7398-ffb3-4ecf-96e2-d19ae251c566'>, <Treant: 'b22e3837-0d08-4dc5-8a6c-84afb0f6f1b2'>, <Treant: '24365f73-0f4c-45ca-8fcb-0f9d4025741b'>, <Treant: 'b901726f-71bd-490d-a56e-de1d2191a955'>, <Treant: '6959ec1f-3c80-4b4c-a230-21c0851d4f3e'>, <Treant: '4ce42ce6-7973-420d-9819-76492c594c02'>, <Treant: 'd8ae1960-09b0-4a33-9874-c6eca989fec1'>, <Treant: '97e9f836-8fac-4526-9334-9a723e5fd0d9'>, <Treant: '795b55ce-dbed-479f-b99d-63b8552d4e56'>]\n" ] } ], "source": [ "treants = treants1 + treants2\n", "print(treants)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "treant_and_errors = pmap(client)(iterate_sim(iterations=30, steps=100), treants)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:extreme]", "language": "python", "name": "conda-env-extreme-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
VUIIS/tractography-notebooks
05_GroupBehavioralAnalysis.ipynb
1
66726
{ "metadata": { "name": "", "signature": "sha256:fa293dcd027125c39e12b16e0dc188a012eda81dcc4c8d9130de058e681a23c5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import common\n", "reload(common)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "importing IPython notebook from common.ipynb\n", "importing IPython notebook from common.ipynb" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "<module 'common' from 'common.ipynb'>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "from datetime import datetime\n", "import numpy as np\n", "import pandas as pd\n", "pd.set_option('display.max_rows', 25)\n", "pd.set_option('display.max_columns', 50)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "full = np.zeros((148, 148, len(common.subject_list)))\n", "for i, subject in enumerate(common.subject_list):\n", " scdir = common.sc_dir(subject)\n", " conn, processed_seed_list, N = common.single_process(scdir)\n", " full[:, :, i] = conn" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the \"English->Freesufer\" mapping." ] }, { "cell_type": "code", "collapsed": false, "input": [ "rois_of_interest = {'BA44': 'G_front_inf-Opercular',\n", " 'AG': 'G_pariet_inf-Angular',\n", " 'MTG': 'G_temporal_middle',\n", " 'BA45': 'G_front_inf-Triangul',\n", " 'BA47': 'S_orbital_lateral', # or G_orbital?\n", " }" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each hemisphere:\n", "\n", "1. For each ROI:\n", " 1. Make the seed as it exists in `processed_seed_list`.\n", " 1. Find the index where it exists in `full`.\n", " 1. For each target (not where `seed == target`):\n", " 1. Make the human readable column name\n", " 1. Find the target index in `full`.\n", " 1. Extract the connection series and save it to `data[column_name]`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = {}\n", "for hemi in ('lh', 'rh'):\n", " for seed_name, seed_label in rois_of_interest.items():\n", " seed = '{}.{}'.format(hemi, seed_label)\n", " seed_index = processed_seed_list.index(seed)\n", " for target_name, target_label in rois_of_interest.items():\n", " if seed_name == target_name:\n", " continue\n", " target = '{}.{}'.format(hemi, target_label)\n", " column_temp = \"{hemi}.{seed_name}-{hemi}.{target_name}\"\n", " column_name = column_temp.format(hemi=hemi,\n", " seed_name=seed_name,\n", " target_name=target_name) \n", " target_index = processed_seed_list.index(target)\n", " data[column_name] = full[seed_index, target_index, :]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a dataframe out of the data, using the subject list as the index." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.DataFrame(data, index=common.subject_list)\n", "# sanity check\n", "ag_index = processed_seed_list.index('lh.G_pariet_inf-Angular')\n", "mtg_index = processed_seed_list.index('lh.G_temporal_middle')\n", "assert df['lh.AG-lh.MTG']['061_206924'] == full[ag_index, mtg_index, 0]\n", "df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lh.AG-lh.BA44</th>\n", " <th>lh.AG-lh.BA45</th>\n", " <th>lh.AG-lh.BA47</th>\n", " <th>lh.AG-lh.MTG</th>\n", " <th>lh.BA44-lh.AG</th>\n", " <th>lh.BA44-lh.BA45</th>\n", " <th>lh.BA44-lh.BA47</th>\n", " <th>lh.BA44-lh.MTG</th>\n", " <th>lh.BA45-lh.AG</th>\n", " <th>lh.BA45-lh.BA44</th>\n", " <th>lh.BA45-lh.BA47</th>\n", " <th>lh.BA45-lh.MTG</th>\n", " <th>lh.BA47-lh.AG</th>\n", " <th>lh.BA47-lh.BA44</th>\n", " <th>lh.BA47-lh.BA45</th>\n", " <th>lh.BA47-lh.MTG</th>\n", " <th>lh.MTG-lh.AG</th>\n", " <th>lh.MTG-lh.BA44</th>\n", " <th>lh.MTG-lh.BA45</th>\n", " <th>lh.MTG-lh.BA47</th>\n", " <th>rh.AG-rh.BA44</th>\n", " <th>rh.AG-rh.BA45</th>\n", " <th>rh.AG-rh.BA47</th>\n", " <th>rh.AG-rh.MTG</th>\n", " <th>rh.BA44-rh.AG</th>\n", " <th>rh.BA44-rh.BA45</th>\n", " <th>rh.BA44-rh.BA47</th>\n", " <th>rh.BA44-rh.MTG</th>\n", " <th>rh.BA45-rh.AG</th>\n", " <th>rh.BA45-rh.BA44</th>\n", " <th>rh.BA45-rh.BA47</th>\n", " <th>rh.BA45-rh.MTG</th>\n", " <th>rh.BA47-rh.AG</th>\n", " <th>rh.BA47-rh.BA44</th>\n", " <th>rh.BA47-rh.BA45</th>\n", " <th>rh.BA47-rh.MTG</th>\n", " <th>rh.MTG-rh.AG</th>\n", " <th>rh.MTG-rh.BA44</th>\n", " <th>rh.MTG-rh.BA45</th>\n", " <th>rh.MTG-rh.BA47</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>061_206924</th>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0.157720</td>\n", " <td> 0.000000</td>\n", " <td> 7.597387</td>\n", " <td> 0.000630</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 6.176972</td>\n", " <td> 6.351575</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.000243</td>\n", " <td> 24.666533</td>\n", " <td> 0</td>\n", " <td> 0.337543</td>\n", " <td> 0.000011</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0.692000</td>\n", " <td> 0.000000</td>\n", " <td> 4.257740</td>\n", " <td> 0.003771</td>\n", " <td> 0.000017</td>\n", " <td> 0</td>\n", " <td> 11.034320</td>\n", " <td> 3.693136</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.018388</td>\n", " <td> 5.872389</td>\n", " <td> 0</td>\n", " <td> 1.189093</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>063_207046</th>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 2.376613</td>\n", " <td> 0.000000</td>\n", " <td> 13.446513</td>\n", " <td> 0.003892</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 11.814914</td>\n", " <td> 8.375411</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.000344</td>\n", " <td> 23.394029</td>\n", " <td> 0</td>\n", " <td> 0.488966</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 1.109729</td>\n", " <td> 0.000000</td>\n", " <td> 6.425975</td>\n", " <td> 0.086003</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 11.343252</td>\n", " <td> 8.164955</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.006380</td>\n", " <td> 7.341089</td>\n", " <td> 0</td>\n", " <td> 1.068244</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>064_207264</th>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0.040651</td>\n", " <td> 0.000000</td>\n", " <td> 9.038170</td>\n", " <td> 0.019948</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 4.831545</td>\n", " <td> 7.747275</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.003750</td>\n", " <td> 12.781205</td>\n", " <td> 0</td>\n", " <td> 0.067520</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 1.685884</td>\n", " <td> 0.000000</td>\n", " <td> 7.303924</td>\n", " <td> 0.000372</td>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 12.537670</td>\n", " <td> 3.965944</td>\n", " <td> 0.000116</td>\n", " <td> 0</td>\n", " <td> 0.001860</td>\n", " <td> 7.735717</td>\n", " <td> 0</td>\n", " <td> 1.421538</td>\n", " <td> 0.000000</td>\n", " <td> 0.000302</td>\n", " <td> 0.000011</td>\n", " </tr>\n", " <tr>\n", " <th>067_207215</th>\n", " <td> 0.000000</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0.016048</td>\n", " <td> 0.000000</td>\n", " <td> 7.549609</td>\n", " <td> 0.004389</td>\n", " <td> 0.000025</td>\n", " <td> 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0.082200 \n", "197_210808 0.000000 0 0 0.129322 \n", "203_211015 0.000000 0 0 1.119028 \n", "208_211122 0.000009 0 0 0.138936 \n", "216_211291 0.000000 0 0 0.106995 \n", "228_211662 0.000000 0 0 1.928628 \n", "\n", " rh.BA44-rh.AG rh.BA44-rh.BA45 rh.BA44-rh.BA47 rh.BA44-rh.MTG \\\n", "061_206924 0.000000 4.257740 0.003771 0.000017 \n", "063_207046 0.000000 6.425975 0.086003 0.000000 \n", "064_207264 0.000000 7.303924 0.000372 0.000000 \n", "067_207215 0.000000 10.722138 0.000374 0.000318 \n", "072_207335 0.000000 5.339868 0.007158 0.000000 \n", "130_208994 0.000000 6.816534 0.000240 0.000000 \n", "131_209154 0.000020 6.512012 0.450767 0.000000 \n", "140_209143 0.000000 16.776108 0.001855 0.000000 \n", "141_209157 0.003237 9.996298 0.000000 0.000072 \n", "144_209407 0.000000 12.500253 0.000064 0.000000 \n", "146_209355 0.000000 6.855068 0.003226 0.000000 \n", "147_209378 0.000000 7.895535 0.000000 0.000086 \n", "148_209625 0.000000 11.537575 0.000159 0.000000 \n", "162_210032 0.000000 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0.000000 \n", "141_209157 0 4.402040 0.613478 0.013102 \n", "144_209407 0 9.911237 3.201448 0.000000 \n", "146_209355 0 16.242869 12.897910 0.000000 \n", "147_209378 0 4.946964 2.196396 0.004058 \n", "148_209625 0 7.543368 1.629710 0.027337 \n", "162_210032 0 8.942529 10.083573 0.003667 \n", "170_210044 0 7.622461 3.402648 0.000000 \n", "172_209736 0 12.528087 3.722647 0.000000 \n", "188_210443 0 11.314516 2.276171 0.000000 \n", "191_210512 0 16.820049 12.818592 0.000000 \n", "196_210780 0 5.693473 6.572440 0.000000 \n", "197_210808 0 8.490369 5.164119 0.000000 \n", "203_211015 0 8.129867 3.652942 0.000026 \n", "208_211122 0 9.035715 5.556995 0.000000 \n", "216_211291 0 10.054285 7.432013 0.000000 \n", "228_211662 0 3.427070 3.379996 0.031522 \n", "\n", " rh.BA47-rh.AG rh.BA47-rh.BA44 rh.BA47-rh.BA45 rh.BA47-rh.MTG \\\n", "061_206924 0 0.018388 5.872389 0 \n", "063_207046 0 0.006380 7.341089 0 \n", "064_207264 0 0.001860 7.735717 0 \n", "067_207215 0 0.004595 10.168598 0 \n", "072_207335 0 0.007556 6.693580 0 \n", "130_208994 0 0.000157 0.355760 0 \n", "131_209154 0 0.221865 20.982435 0 \n", "140_209143 0 0.000941 24.489124 0 \n", "141_209157 0 0.000000 1.554572 0 \n", "144_209407 0 0.000236 4.072993 0 \n", "146_209355 0 0.001272 11.853666 0 \n", "147_209378 0 0.000000 5.546668 0 \n", "148_209625 0 0.000178 1.886011 0 \n", "162_210032 0 0.000491 13.361565 0 \n", "170_210044 0 0.001380 6.351387 0 \n", "172_209736 0 0.000716 4.345505 0 \n", "188_210443 0 0.000087 4.734041 0 \n", "191_210512 0 0.111693 6.782209 0 \n", "196_210780 0 0.837926 8.797537 0 \n", "197_210808 0 0.000403 12.162691 0 \n", "203_211015 0 0.000174 12.195935 0 \n", "208_211122 0 0.088492 2.354418 0 \n", "216_211291 0 0.002061 6.354666 0 \n", "228_211662 0 0.000044 4.138305 0 \n", "\n", " rh.MTG-rh.AG rh.MTG-rh.BA44 rh.MTG-rh.BA45 rh.MTG-rh.BA47 \n", "061_206924 1.189093 0.000000 0.000000 0.000000 \n", "063_207046 1.068244 0.000000 0.000000 0.000000 \n", "064_207264 1.421538 0.000000 0.000302 0.000011 \n", "067_207215 0.050294 0.000023 0.000000 0.000000 \n", "072_207335 0.637682 0.000000 0.000000 0.000000 \n", "130_208994 1.086393 0.000000 0.000072 0.000000 \n", "131_209154 2.016866 0.000000 0.000000 0.000000 \n", "140_209143 0.007429 0.000000 0.000012 0.000000 \n", "141_209157 1.426850 0.000000 0.004034 0.000000 \n", "144_209407 1.729493 0.000000 0.000000 0.000000 \n", "146_209355 0.109368 0.000000 0.000000 0.000000 \n", "147_209378 3.592246 0.000037 0.003674 0.000000 \n", "148_209625 2.753623 0.000040 0.007107 0.000010 \n", "162_210032 0.367840 0.000000 0.001623 0.000000 \n", "170_210044 1.395404 0.000000 0.000000 0.000000 \n", "172_209736 0.748129 0.000000 0.000000 0.000000 \n", "188_210443 0.778437 0.000000 0.000000 0.000000 \n", "191_210512 1.012022 0.000000 0.000000 0.000000 \n", "196_210780 0.024932 0.000000 0.000000 0.000000 \n", "197_210808 0.057016 0.000000 0.000000 0.000000 \n", "203_211015 2.651228 0.000000 0.000000 0.000000 \n", "208_211122 0.104764 0.000000 0.000000 0.000000 \n", "216_211291 0.232466 0.000000 0.000000 0.000000 \n", "228_211662 1.752143 0.000873 0.010763 0.000000 " ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Connect to redcap and join the databases?" ] } ], "metadata": {} } ] }
mit
giraph/data-sci
fremont/fremont.ipynb
1
730671
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seattle Fremont Bridge Bike Data\n", "7/23/2017 *fremont.ipynb*\n", "\n", "Credit: https://github.com/jakevdp/JupyterWorkflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "from urllib.request import urlretrieve\n", "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn')\n", "from sklearn.decomposition import PCA\n", "from sklearn.mixture import GaussianMixture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# get data if not already downloaded\n", "url = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'\n", "filename = 'fremont.csv'\n", "force = False\n", "if force or not os.path.exists(filename): \n", " urlretrieve(url, filename)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create dataframe, use Date column as index\n", "\n", "# can parse on import but very slow, replaced with conversion in next cell to speed up\n", "# data = pd.read_csv(filename, index_col='Date', parse_dates=True) \n", "\n", "data = pd.read_csv(filename, index_col='Date')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert Date column to datetime, try statement is faster, except statement is foolproof\n", "# reference: http://strftime.org/\n", "\n", "try:\n", " data.index = pd.to_datetime(data.index, format='%m/%d/%Y %I:%M:%S %p')\n", "except TypeError:\n", " data.index = pd.to_datetime(data.index)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# rename columns\n", "data.columns = ['West', 'East']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(41568, 2)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check data shape\n", "data.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>West</th>\n", " <th>East</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-10-03 00:00:00</th>\n", " <td>4.0</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 01:00:00</th>\n", " <td>4.0</td>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 02:00:00</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 03:00:00</th>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 04:00:00</th>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " West East\n", "Date \n", "2012-10-03 00:00:00 4.0 9.0\n", "2012-10-03 01:00:00 4.0 6.0\n", "2012-10-03 02:00:00 1.0 1.0\n", "2012-10-03 03:00:00 2.0 3.0\n", "2012-10-03 04:00:00 6.0 1.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# view some data\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>West</th>\n", " <th>East</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2017-06-30 19:00:00</th>\n", " <td>225.0</td>\n", " <td>82.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-06-30 20:00:00</th>\n", " <td>119.0</td>\n", " <td>43.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-06-30 21:00:00</th>\n", " <td>87.0</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-06-30 22:00:00</th>\n", " <td>58.0</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-06-30 23:00:00</th>\n", " <td>36.0</td>\n", " <td>13.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " West East\n", "Date \n", "2017-06-30 19:00:00 225.0 82.0\n", "2017-06-30 20:00:00 119.0 43.0\n", "2017-06-30 21:00:00 87.0 38.0\n", "2017-06-30 22:00:00 58.0 22.0\n", "2017-06-30 23:00:00 36.0 13.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# view some data\n", "data.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory analysis" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe82c7f60>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6/exw7bGA9xeKARPypk2bIAgCDhw4gPz8fDz//PNobDzfotXpdCI1NRXJyclw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\n3eJ/QYqK7q2ty2qCrz5q8zg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kGCohA0BVVRUeeugh3HffffjNb37T9SwBAJxOZ0B3vatW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\n6QATMhERkQ4wIRMREekAEzIREZEO/P/qJkp8F4sx5QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2bfe8251a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# riders by day\n", "# very dense plot\n", "data.plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe94c55c0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sQOkg//Dq97A9h5FaPto+WBlipDbK6g3fxHZtPnjmHzA/M5eXh7cy2rDfyU7N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\nl5Y0TWUbzES3edmu4OM3zfOR5Z2Mjbv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RavuIdxhcF/aGzjSUXJ6Vbs3LUiOh5e1UErxv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\nIlINrV3XC3l3xNpJuT0oUXmvNUYlvYluomo0nGa0WJi23OzMN8yiMc1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C0VoggHM9d45r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Gyx8iaiISRJQm1tLaqqqrB//35cuHABly5dQiQSGfJ1P/7448DGEACQSCRQVFSUiyET\n5R2GNxENiMViuHjxIlpaWvDuu+9i5cqVeOyxx9DR0TFshzFd1zFnzhx88MEHAIBoNIpwOJyLYRPl\nHS6bExEAo3LetWsXZs6ciZaWFqxYsQKPP/44SktL8f3330PXdQDGHvfxeBwzZ87EDz/8gIsXLwIA\n3n//fbz11lu5PASivMHKmyiPtba2or6+HoAR3tOnT8fOnTtx48YNPP/88zh27BicTidmzZqFy5cv\nAwCWLl2K+vp6fPHFF3j99dexceNGJBIJBINBvP3227k8HKK8wV3FiIiILIbL5kRERBbD8CYiIrIY\nhjcREZHFMLyJiIgshuFNRERkMQxvIiIii2F4ExERWQzDm4iIyGL+H3s4AIYFde8dAAAAAElFTkSu\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2bfe91cca20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# riders by week\n", "# summarize data by week\n", "data.resample('w').sum().plot()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# add Total column to dataframe\n", "data['Total'] = data['West'] + data['East']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 1059460.05)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RicCFEEKIFCfJXgghhEhxkuyFEEKIFCfJXgghhEhxkuyFEEKIFCfJXgghhEhx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\nzv5yP6D06a/xW1tbNW/ePBUUFGjWrFmSpDFjxmj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"text/plain": [ "<matplotlib.figure.Figure at 0x2bfe91583c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# riders by year\n", "# resample by rolling 365 days to look for annual trends\n", "ax = data.resample('d').sum().rolling(365).sum().plot()\n", "# set y axis to zero to avoid misinterpretation\n", "ax.set_ylim(0, None)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe4a8ea20>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ddRfnn38+X/nKV3jllVdYv34927dv59lnn8XtdrN161Y2b96MwWCI+0MIMRv71FS3Zd16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"text/plain": [ "<matplotlib.figure.Figure at 0x2bfe94e7fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# riders by time of day, shows commuters\n", "data.groupby(data.index.time).mean().plot()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2012-10-03</th>\n", " <th>2012-10-04</th>\n", " <th>2012-10-05</th>\n", " <th>2012-10-06</th>\n", " <th>2012-10-07</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>00:00:00</th>\n", " <td>13.0</td>\n", " <td>18.0</td>\n", " <td>11.0</td>\n", " <td>15.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>01:00:00</th>\n", " <td>10.0</td>\n", " <td>3.0</td>\n", " <td>8.0</td>\n", " <td>15.0</td>\n", " <td>17.0</td>\n", " </tr>\n", " <tr>\n", " <th>02:00:00</th>\n", " <td>2.0</td>\n", " <td>9.0</td>\n", " <td>7.0</td>\n", " <td>9.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>03:00:00</th>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>04:00:00</th>\n", " <td>7.0</td>\n", " <td>8.0</td>\n", " <td>9.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2012-10-03 2012-10-04 2012-10-05 2012-10-06 2012-10-07\n", "00:00:00 13.0 18.0 11.0 15.0 11.0\n", "01:00:00 10.0 3.0 8.0 15.0 17.0\n", "02:00:00 2.0 9.0 7.0 9.0 3.0\n", "03:00:00 5.0 3.0 4.0 3.0 6.0\n", "04:00:00 7.0 8.0 9.0 5.0 3.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date )\n", "pivoted.iloc[:5, :5]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe8d88f28>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7+OGzvq43yN3R+UW2L+65W51oofUsjwvf+OZ/8+39Of/P+hbL3T3Pb+6BJ1a+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\nENwDBgwYMGDAEYT/B9G76rx2Jwt/AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2bfe3552fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# every day by time of day, note two patterns\n", "pivoted.plot(legend=False, alpha=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Principal component analysis" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24, 1732)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 24 observations for each day\n", "pivoted.shape" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1732, 24)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1732 observations for each hour\n", "x = pivoted.fillna(0).T.values\n", "x.shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1732, 2)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make data two dimensional\n", "x2 = PCA(2, svd_solver='full').fit_transform(x)\n", "x2.shape" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x2bfed862908>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SI1gGMHLyHvA9d5WFDbBL8OH1ty4oeuaUBU4QBJF5ci+Nl0gp0WFrs8mAN49+jDPdg7Dd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\nmXD06FHU1NTAZDKBYRhMmDABDocDXV1dUT0JXwJZjANWB1gFI8u9PNJ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"text/plain": [ "<matplotlib.figure.Figure at 0x2bfed7ba080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# note two different patterns of days\n", "plt.scatter(x2[:, 0], x2[:, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unsupervised clustering" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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h4uiwmGd59tlnc9ddd3HllVcSCAS4++67adeuHWPGjGHixInk5OQwcOBANE1j\n2LBh5OXlYVkWo0aNwuFofJMAV0214d1b9bOHGQj+yLGcB+kvVtGcFoYn9AZqckzkpOpCCBFPRh0m\nRDdNk/vuu4/ff/8du93Ogw8+SJs2bcr2L1u2jPHjx2NZFllZWTz++OOVxqaog2VycjJPP/10yPap\nU6eGbBs8eDCDBw+O9lQJwbOn6h9d0S2yBwQAaHWqwep3YlO71FMsWvbxs/GriiOM7U1MTri6YfVX\nCiHEQXXZnPr111/j8/mYPn06S5YsYfz48bz44osAWJbFmDFjeOaZZ2jTpg3vvfceW7ZsIScnJ+Lx\nEmdGaAOXnl1VDc6i/cV+2pxtANBztI+k5rGp9R3dM8A5r3s5aYSXph0NUo42aNXfz4CJHtr+3YjJ\nOYSoT4qrGNsXn6EtXRzvoog6ZClK1K+qLFy4kH79+gHQpUsXVqxYUbZv/fr1ZGRk8MYbb3DVVVex\nd+/eSgMlSAafmGnSzkDRLCyj4o9oSzVp1d8g+wyDTlcFa3kbZmtsX6Rx7CA/q6bYCJTU7pnFNILT\nU/re74P7fVhWw5wuIkR1JD32MEnT3kbb/CeW3Y6/Ry+KH5mAeVzHeBdNJBCXy0VqamrZvzVNIxAI\noOs6e/bsYfHixYwdO5bs7GyGDx9O586d6d27d8TjSbCMAcMLS19whARKgGPO8XPWC8HpIv4SmH2D\nkz+/17ECsYtmW77XmX2Dk7Nf9qDqEihF4nK89TopTz+B4g8+WCo+H/Yf55I28t/s+/QrSKD0aKJq\nZoxyYofz15kYpmmi68GQl5GRQZs2bWjXrh0A/fr1Y8WKFZUGS7nzYmDNhzp71oTvf9y9Kvjj7F2r\n8P45yWyaY4tpoAxSWPeJjRWvxTbPrBD1zTHrw7JAeSjbogXYZ30UhxKJulSXo2G7devG3LlzAViy\nZEmFWRitW7empKSEjRuDiyMuWLCA9u3bV3o8qVnGgL8k8g9n+GDXbwpfXJvEvvV1m5Voc75G7k0y\noEckLnXnzrDbFctC27C+nksj6lpdDvA566yzyM/PZ+jQoViWxcMPP8ysWbMoLS1lyJAhPPTQQ9x2\n221YlkXXrl3LMtJFIsEyBo692M/CJ+2Ubg/94bNyTRY/56jzQAnl2XuESFRmm2Ng5fKQ7ZbDgf+U\nHvVfIFGn6nK+pKqqPPDAAxW2HWx2Bejduzfvv/9+9Y8Xs5IdxpKawgnX+NEcFbPoZLQz6PZfLztX\n1s9lPuoxXymzAAAgAElEQVRkiZYisbmvvhYzIzNku++0AQT6nBqHEom6ZChK1K/6JjXLGDnlNh9N\nOxqs/ciGbx9ktDM5abif9GwL3VnZ6h+Rs+9U/hkqfK5V/wAnDffV8DhCNCz+M86keOIzOF97Bf33\n1Vipqfj7nY7r/ofiXTRRBxIpN6wEyxhqd55Bu/NC5zW26GOwY3GYS61YpLY0cRUqYNak9qlgb2LS\nsl8AVYUjTzbIvc4f06xAQsSL7/yL8J1/Efh8oOsyAlY0CBIs60HP0T72rlXZ+I2O5T/kScpScG2O\nri/Tt0+lVT8PJ14rSQdEI3WYr3l7ODATqCcwcUqaoDx74ZcJdswAHNnVwHlE7ILbvPuS+O52B5as\n8SyESEB1mcEn1qRmWYdKd8KneUkULTn0Mscushluhd/esnFEJ5PO18qUESFEYpE+y8PQrt8Vlr9q\np3ijgqMpdLjMz+a52l8CJdR8ME8VLIVNczQJlkKIhFOXGXxiTYJlDGz9ReWr4c4K/Y8bZ+skZdXP\nVA5/aeLccEIIcVA8FnGOlgTLGFj8rD1koI6/RME06yeINe0og3yEEIknkWqWiRPWGyjLhKLl4S+j\n4Y715Q3t72za0aDLf6QJVggh6pLULGtLAS3iCHcLzQmG58AbY6DNQB9+l4rhgWadTbrd7COthQyH\nFUIkHhngcxhRFDi6p8H+DeHmSyoHAiWkHWNgeqFka21yxCps/VlnyHelpLWUACkOc5aF/bNP0Jcs\nwszKwnPlPyAlJd6lEjVgJFAzrATLWgi44evRsGOpiqJaBxKZh//xS7epZHYwKNlau3P69qm897dk\n2l0YoP94LwnUPy5E7LiKSb92GPYfv0cxgn32SW9Mpvjxpwn0lRyyiSKRapbypzZKpgGfX5NE/qOw\nZ7WOZSpU1tRqeJRgWrsY8OxWWfmGjUVPS4YTcXhKeWAsju+/KQuUAPraNaTedzeYsqBAorBQon7V\nNwmWUVrzP50/v61Bk6pixTjTjsKGL+t+2S9Rt5RtW0l6agJJE8ajrl0T7+IkDNtP+WG368uWYvv6\ny3oujYiWqahRv+qbNMNGaftCjZoM2ml+skFxjGqWB7l3Jk4ThgjlfOk5kp99Eq2oCIDkSc/jvvo6\nSsfcH+eSNXxqqTvsdsWyUHcW1XNpRLRk6shhwJ5SvWqiardo0TfAgCc9pLduXINy9hQobPpWxVcc\n75IkHm3FclImjC8LlADqvn0kT3oe+6ez4liyxBDofGLY7UaLlvjOv7CeSyMOB1KzjNLxV/n57W0b\nnl3hnzdUp8WJ13s59kKD5l2DfSgdLvOz7VcNYpSswJYak8PUWHGhwtzbnWzJ1wiUKqS2NDj24gC9\nx/pIoP76uHJOfwd1//6Q7YrPh/3TWfjOuyAOpUocpf+6BX3ZErQtm8u2WQ4H7qv+gZXeJI4lEzWR\nSDVLCZZRanKMxUn/9DL/kaSwudGbHW/Qd1zFZAGd/xGg8Ccfaz+wE4t5lylHxWcgw7f/dbL5+/Jb\nx7VFY8mLKknNLLpKgoTqcZdG3KVWsk8EBXr2Yt+UaSS98hLahnVYGU3xXHARvsuGxLtoogYkWB4m\n9GQl4iIi3t0Ki56zodnhuCF+nAcedpOPgFglKGjVv/7T3BX+rFL4U5iBRabCus90CZbVFOjeA+ut\n18PeCZGaGEVFRudcXE+/EO9iiFowEqgpKqpg6ff7ufvuu9myZQs+n48RI0Zw7LHHMnr0aBRFoX37\n9owbNw5VVZkxYwbTpk1D13VGjBjBgAEDYv0d4iblqMh9kPs3qfz8gBOAJS/Y6H6rjxOuDsSkl1jR\nLdpdECD3pvoPTLtXaZi+CHNJixLnxo837+VDcfzvfRzfzqmw3Xdyd0r/+e84lUqI+tXoa5Yff/wx\nGRkZPP744+zdu5eLL76Yjh07MnLkSHr27MnYsWOZM2cOXbp0YcqUKcycOROv10teXh59+/bF3khW\nQM85L0CTbNi3KcxOq/wmKCnU+OEuJzsW+zjyFINgdTTam8TitMfdHJ9nxKV/sEWfALZUC78r9OTp\n2TK/rdo0jf1vvkvyk49hm/8zGAaBrt0oHXU7pMapM1qIemYm0BjTqILlOeecw8CBAwGwLAtN01i5\nciU9evQAoH///uTn56OqKl27dsVut2O328nOzmb16tXk5ubG7hvEkapBZvsIwfIvTL/CqrcdrHrn\nYKCMLmA2aWfELVACND3Oos2ZftZ+WPGBR0+2OP6KQHwKlaicTkrvGhvvUggRN/FILhCtqIJlyoH8\niy6Xi1tuuYWRI0fy6KOPohz4C56SkkJxcTEul4u0tLQKn3O5XFUePzMzGV1PjAn3jrSq31NBWY0z\nupukeJPOD6PSuPBVsCVFdYhaGzINvrwNCr4E9x5o1h663qDQ7brqFygrq6YX7vAm1wv44QeYNg3c\nbujVC669Fmy2iG+Xa1Zzcs0ii3qAz9atW/n3v/9NXl4eF1xwAY8//njZvpKSEtLT00lNTaWkpKTC\n9kODZyR79iTOaMAWJ6fx+4f1dz7TDyvegYDl44ynvfV34r84ZRycfC8YbtBTggnli6o5FzwrK42i\nIpmcWV1yvSBpwniSn32qfKTw66/jfXc6+998F5JCH9LkmtVcZdesroJoIvVZRtVgvHPnTq677jpu\nv/12LrvsMgA6derE/PnzAZg7dy7du3cnNzeXhQsX4vV6KS4upqCggA4dOsSu9A1A71vh6F713/y4\n6VsdX9WV9DqlasG5ngk0oE0kILVgLcmTng+ZUuP47huSn30yTqUC7ffV6PN/Ap8vbmVIdCZK1K/6\nFlXN8qWXXmL//v288MILvPBCcOj2Pffcw4MPPsjEiRPJyclh4MCBaJrGsGHDyMvLw7IsRo0ahcPh\niOkXiAfDF1xxxJ4OtmQ4/103H1/uZPuCyE1C0Yncr+neqeDZpWBPbVxZgYT4K8fM6aj79oXdp//y\ncz2XBrRlS0gddw+2Bb+geL34O3TEc90NeK67qd7LkugSqWYZVbC89957uffee0O2T506NWTb4MGD\nGTx4cDSnaXD8pZB/r4PNczU8+xQyjjXp+S9ofQF0uDzA9gU6sZpDGaSAaoXN+NPkGJOUoyMHSsuC\nwnkqewtUWp9ukJ4tQVUkJsWs5N6N7eoEVfN6Sb9lBPpvK8s22f5Yjfb/7sNs2QrfwHPrtzwJTtaz\nbKS+HuFk/efltccdC1W+uAVOD2j49qvENlAe4q8BU7VoPyiAFmEGzt4Che9uc7L1Vw3Lr+DINMk5\nN8BpE7yoiTFuSogynosvJemVF1GLQ/vTAiefUq9lcb4zpUKgPEgtceF4b5oEyxpKpNGwiTPJJc6+\nvc3G+s9Dny38JbD6XRuZ7Q/On4wtRYFed3s5ItcgOcvkiJMMet3jpfv/he8nsSz47jYnhfN0LH/w\nRvTuUVn1tp1fH28c81vF4cXseDzua27A+sv8bF+fUym95dZ6LYt6SC7akH07ZLWTmmr0fZaHmxWv\n66ya4iBSzXH/nyquLXVTs0xpbtLtFj9db/Zj+kG1VT6gpvAnla2/hq8+bvxap+doGYwgEk/pmPvx\n9+qD45OPUDwe/F274bnmBnA667UcgeM6RhxJYLRuXa9lEfVLgmUVLBMWPVd54nMzAPs31cWTjkWz\nziae3eBsSsRm10PtXauW1Sj/yrM7xsUToh75zxqI/6yBcS2D75LL8b/5Gva/DCwyjsjC84/r4lSq\nxGVYtfi7Wc+VS2mGrYKrUKGksPLLVLxRZfuiuugMVNj4pY2PBydTsr16d0b2GQaOzPBp5zLaSTo6\nIWpF09g/+S08gy7FOLoFRmYm3lP7U/zkswR69Ip36RKONMM2IvY0C1uahW9vZT+OwvZfNRSbFbFW\nVxs7l2ksesZGv4eqbkJNa2WRc36AVVMqVkPt6SYnXC0rgogouN04p7yOtmE95pFH4bn+Rqy09Jie\nQincQtKrk9C2FWI0Pxr3jcOxWrSM6TlixWp+FMWTXgePB8Xvi/m1OJwk0gAfCZZVcDSB1qcZFHxU\nVSVcwSqLRVbZtlgpWlb9mutpj3lJOdJi49c6nt3QJCcYKNtdUP9LeonEpq5dQ/o/r8W2fFnZNuf0\ntyl++oWY1aT0eT+SfvNwtD/Lkyw7P/4f+59+kcCp/WJyjjrhdGLVts/U48H5/nSUfXvxnnsBZtuc\n2JQtQTT6ROqHm34Pe3DvUiicp4Wd8xiOlmRhuGMXLLUa5DtQNehxp48ed8pgHlE7KQ+OqxAoAfSC\ntaQ8eB/7Pvo8JumbkieMrxAoAbQ/N5Ey4RH2NeRgWUu22Z+Ret+96AVrAUh+6gk8lw+l5KFHD5u0\nWKb0WTYuyVlw0Uw3573tptVp1WnKVGoRKMNPP2nRR2qFop65XNgW/BJ2l23hr2hr/qj1KdRtW7Et\nWhh2n75oIWrhllqfoyFSXMWk3jO6LFACqPv2kvTayzjfmBzHktUvAyXqV32TYFlNigJt/mZw/rse\n2l/iQ3NWNacyuh8zKcvkrwHTlmpy7CXS3yjql2IEwBfhvvP7oTRGCx5EyMKj1Hd2nnrknPIG+qYN\nIdsV08T+5ef1XyBRJQmWNaTq0OVfPtqc6Sfl6NjX9rzFofM1/S6Vpc9LQoEGz7LQli1F+/UXMBK/\nJcBqkkEg96Sw+wIn5mKcWPt1ac2jjsZ/cvew+/wnd8dsoIN8akvZuzfyPtfhs1qKZSlRv+qbBMsa\nWv66zseXJrPuEzslW2M9XcTC9IS/CbYvkTx1DZk+93uanH8WmecMIPO8M8k4sx+OmTOqfwDLQt22\nFaV4f90VMgrum0cSOOroCtvMjEzcw/8DWmzuydL/G43ROrvCNqN1a0puvTMmx2+IfH37hWQkOsjo\n0LGeSxM/MnWkkfLuh0VP2/HuC/eMEXmFkOqL/PnSbYdHh39CsCzssz9DW7sGf7fuGB06kn7rf9A2\nbSx7i23lCvSbh6MtW0LpfQ8F2/FdLpJeeRF9ze9YaU3wDM0j0PVkHO9NJ2nyJLRVv2GlpODv3QfX\ng49iHd0ijl8yyH/aGeyb/j+SX3sZdctmzKwsvBddgrZ+Pc7XX8F7+VCs1NqtdRjocyp7P/4C56uT\n0LZtxTjqaDw3/BOzZasYfYuGJ9D/dHxnn4Pjk48rbm/bjtJ//jtOpap/tUpKUM8Uy2p4HQMNadHW\nkm0K7p0KmR1MVr5l48e7qxoqXtugGf7ztjSTa1eUoIeuc5twEnlhXnXDetJu/ie2X39BMU0shwOj\nRSv09QVh328B7n9cT+mtt9Nk2FBsy5aU7TPT0/FcNgTnzBkhS1D5evYOjjZV1QZ1vZwvv0jyC8+g\nHRh4Y7RqRenNo/Bce2OcS1ZRQ7pmEfl8JD8xHvsPc8HtJtCpM6X/vgWz0wlxKU48Fn++PpAf9Wcn\n631jWJKqSc0yguJChbl3Oiicp+MvVshob5CRU50MOHVTuzS8Cu7dCmktG9yzzWEldfRt2OeXpzpT\nvN6IgRKCv6bzf++h7NpZIVACqPv343x3KqrbHfI52y8/Y5/1Eb6LBsWs7LWlL5hPyqMPVlj9Q9u8\nmZSHH8B/8ikYuV3iWLoEZLdTetdYSu+Kd0HiJx59j9GSYBmGZcGcfwVX7jho7xqNvWtVVLuJ6av/\nrt701ibJR0igjCd1XQG2n+bV/HP792Of/1P4fWECJQRHgmp/rK640ecj6fVX0H+dD6qG7/Qz8A69\nEtT6uR+d098Nu0yWum8fznffpkSCpaihRr/4c2O3cY7G1l/CDF6wFEyfQmz6J2vCIueCAJqjHk8p\nQmhbC1Hd4adLWKqKYlbS8lDDwTAWYByazcXnI/3qK3B881XZJseHM7Hl/4DruUn1MoldCRMoy/c1\nrIFJQsSaBMsw9vyuYgUqzwUbm4AZ+RiOTBPTr5DawiTn/AA97pBsPPHm73wiRmZTtD2hy7cEOp+I\nUupGXxs6Ud90JhE4riPatq0h+4wjjkApdaOWllQ818nd8Q26rOzfSZNfrhAo4WAT7/v4LrwY38Bz\n0X+eh+Pj/6H4A/j6nYbvgosiB9GDQxVqEGQDx0UepWkc36naxxHioFpl8KlnEizDyMo1UO3WgVpk\nJLH4kSMfo8doH+3OD+DMtFDlV4oL++ef4nh/GurOnZgZmWirfgsbKM30dNzDb8Z70SDSbroWx2ez\nKkyoVz1u9OVLCbRrj16wpvxzTZpQesc9oIDzjcnoK1dgpabi79UX10OPVqiN6hEy6SiBAPZvvkZf\n8AvJk15E8QSbdZ1TXsd7/kUUT3qtwnGUwi2kPPwAtl9+RjFM/F26UnLrHZgndK7yerhvHIHj01kh\nfa++bt1xN4ABPvYPZ+KY9SHKvn1wwvGo/7gJM6ddvIslKpFIo2Hlz3AYLU81adHHYPN38bk8qa0M\nOl3pr9b6leIQloXt229wfPkZWBbeM87Gf/bAqJoona+8SMpDD4TU+EJOCbiHXIn3ssEAeK68GscX\nn4YkJdB278bTtz+eoVei/bEaKz0dz9ArMU7qeuBz/0ArWIuVkYHZ/KjQE1XWL7lrF0nT3ikLlBDM\nBOP8+H/4e/XBc8M/gxs9HppccyW2JYvKy/XnRvTfVrL3g1kVp6pYVvB16HlTU9k3dTopjz+CvnAB\nqAqBk0+h5I57IDm50utU15ImjCfl6SdQvN7ghrnf0eSL2ex/9U2ME8MnVhDxJ6uOJDjTx4Fmqvru\nmwRUi973eeomUAYC4PPF/Q9bnbAsUu64laR33kLxB1O0Oae8gWfwFbiefK5mAdPrJem1V6sMlBC8\nO9RDMq7oixeiRMjeo235k+LJb4U/kK5jVNLM6TttAI6PPgi5Gw+uehGpL9X+4/dlwdL51msVAmXZ\nqQvWkPTSC5Te/yAUFZFx6QVo69aiBAzMppmU3nwrnhH/CZ7vqKNxPfFMxHLGg7J7F0lvvlYeKA/Q\n168j+bmng7Vr0SBJM2yCW/y8nc3f12CZjxjKPsOg/YWxXaRZ2b2b1LF3YZuXD+5SAp1OwP3Pf+E/\n++8xPU882b/8nKS330QJBMq2KYEAzmlv4zv9DHwXXxrxs+qG9SRNnoS6YwfGUS0InHRShebSKunl\n/xlVNpHebNoMTJOkJx/H8dVslL17MI5tj/vaG/D/7exKT+HNG4Zt3g84/zezLBhbTiel194YzOEa\nySGBW/vj98hfYeN6sCya9u+BtmtX+Wd27iT1/nsx05vgu3LYgS9ioq1YDk4nRvsOcV8hw/HR/9C2\nbwu7T1+2tJ5LI2pCmmET3NafqzkUX7EgRj+2nmTRoq/BgCc9MTleGcsi/fph2PN/KNuk/fA9+qrf\n2D/5LQK963dib12xz/68QqA8SDFN7HO+ihgsbd98Rdqom9G2FpZtM5ofhaVpEWuIh7LsdrznXhA8\n1+7dOD75CEtRQpKAW3Y73gsvIfX2kSRNeaNsu76uAH3BrxQ/Nwn/mZUETFXF9fwr+C64GPu332Dp\nGt7zLyTQpx+2778l6Y3JKL7QQWD+7j3Ky5DZNOLhzYwMnBPGox4SKA9STJOUJx/Dd+UwHB+8R9IL\nz6AvXwa6jv/kUyi5exyBXr0jl72OWemRF1+2HDKEXMSG5IYNw6qiYpfcwqBlPz+5N8VmhGpaa4NL\nvyjh/HfcpDSP7VxK+2ezsP0UmiVD21mE861G1DxVWWCLtM+ySJ74WIVACaBt34aZklrlKS27Hfc1\n1+P/21kApN52C46vvwwJlGZKKiUj/w9/7z44Zn0Uchxt9y6SXnu5yvOhKPj+fj6uxyZS8vDjBPoE\n13r09z8dz+VDsf5Sw/P2Px33IanTPNdcj/GXPK8AlqKg/bkpWNuNcGp11070RQtIufsObMuWolgW\nit+P/ed5pP13BMq+yInB65r3wkH4Ox4fdp+/d5+QbcqWzSSPu4f0f+SROuo/6D/XfO6siA3Tiv5V\n3yRYhnFkt8qj5bHnB7hopoe+D/iwN6nOEQ/2f4Zn+hXSW9fNr6+vWB5x/p++aVPY7YnId2r/kGBx\nkL9P+AWE1Q3rIq6lqAT8+I/tEPF8FuAa9yAlDz4aPNbWQuw/fBf2vWZmU9y33Ip9zleoe/eEfU9I\nE2lpKUnPP03KbTfDXXeh/CWgVyysgmvis+x/5kU8gy7Dc8HFuO57kP1vvwfO8vSMZstWuB55POR7\nKZaF/Yfv0dat/euRy7+vMxnn1DfRdofWPPX163BOfgWleD/qugLwxLh1pCo2GyX33ofR6pBk7IqC\nt//plNxzX4W3aiuWkXHZhaS8+CyOzz8h6e23aHLV4Mb14HiA9tsKUkfcSMZpvckYOIDk+8fCX/p1\n460uVx0xTZOxY8cyZMgQhg0bxsaNG8O+b8yYMUyYMKHK40kzbBjdbvHx2xQdz67wE8l3rlRZ/a5O\nh8sDpGSBb1/Yt/1F5B+3ZJvKl/90ct7bsf8jY1TWh3ZEVszPV28sC+dLz+OY/RnK3r0YOTn4evfF\nMe/HCm/z/v18vFdcGeEgSuT+Nk3HkzcM7dEHUcP8gVGgwpQM9c9NqPvDT8xXtwdXEzGy20QcMqZt\n2kiTC8/Bc9kQ/P0HkH79VdiWLyvbn/nmW/j69kMtKkIxAvhP6or7v7diZWQeKJCCb0geviF5Eb5r\nkO+8C9EXLMAWZj6otn8/lqqhmBVr4hbgHXgO2o7tEY/reH86Sa+/grpjO0bbHLwXXULp6Huj68/0\n+3G8Px21cAuB7j3w9z+9yuP4z/47e07pifPN11H37ia5f1/2n35OyCji5ImPV1hwGYIZlpJefA7P\n4LwKDxeJTF27hvRrr0Jfv65sm23xQvQ1q9k/ZXrc+5kPqssBPl9//TU+n4/p06ezZMkSxo8fz4sv\nvljhPdOmTeOPP/7glFNOqfJ4tQqWS5cuZcKECUyZMoWNGzcyevRoFEWhffv2jBs3DlVVmTFjBtOm\nTUPXdUaMGMGAAQNqc8p6YU+F05/w8uWNSZj+kPGHFObbKMy3kX+/gTd02l0YVScx2DxXZ8s8lZZ9\nYju4xzv4CvyvvYxtxfIK2y1nEp5BkQe9NHQpY0aT9MpLZU2ett9WYByRRclN/0LbsQ0s8Pc5Fc+w\nayJmzzGPaYu/W/ewqegsVSHtgTERz2+mpuI7pL830KkzRrNmFQbHHKT4/dh+nEvgxJOCf7zD1PQV\nwP7zPGwLfiGQ0w7bX2qa2tZCkt6fXvZv+49zsc3/KZiIfdaHqIVbMI86Gu+gy/Bcc33EcgNof26I\nuM9o1jT4HcxgIjJLVfH36EXJU8+TMvq2iJ87NPjq6wrQnpqA5XTiHnV7pWUJKduSRaTdenPZ/WrZ\n7fj6D2D/K29ASkqln7Uym+IeGSxjclYa/DUpuGWFHQ0MoBesxf7VF/guuLhG5W2okl98rkKgPMj+\n7RxsX3+J/6yBcShVqLpMd7dw4UL69Qu2KnXp0oUVK1ZU2L9o0SKWLl3KkCFDWLcu9Fr9VdTNsK+8\n8gr33nsv3gNP3Y888ggjR47knXfewbIs5syZQ1FREVOmTGHatGlMnjyZiRMn4gszCKEhyjnXoM/9\nHpq0O/iUfbCZtPzH9e6ubgozi6TmlQdBw6uwfWEdrFlpt1P89Av4evctWz8vkNMO1533VDpCtCFT\ntm3F8cH7IX2D2s4i9A3rKX75DYqffA6jQwfULZsrOZBCye13YbRuXWGzmZqGtq/y5gLfmQMxD81a\nk5qKeWSY+ZEcCITffI3tl58qT4lHcASvvrZ6I3HtC34hbfRt2PN/QF+/DvtP+aTecwdJTz9R8Zg7\ndpA87l7S8y4jPe9y9AULIh5TLypCORAoTbuD4gfHs+/jLwBwX3sjgTCLMVth5oAqloVj1oflmYKq\nw7JIvefOCg92is+H4+vZpNwf+cGlJqwID04WYDkaR60SQAvTcgAHHtx++TnsvngwLCXqV1VcLhep\nqeVjDzRNI3BgEOCOHTt4/vnnGTt2bLXLGnXNMjs7m2effZY77rgDgJUrV9KjR3DkXf/+/cnPz0dV\nVbp27Yrdbsdut5Odnc3q1avJza39Cut1xfDB7zNsFG9RaNbR4JJPS5k3zs4fH9ix/NEeVcHyKziP\nMPDsjBAQVYuMdrGtVR5knHgS+z76HG35MtRdO/H36pPQzU2O2Z+h7SwKu09bvYrkh+7H+cF7aH9u\nwkxNxd+3P8UTnsZq3jzk/YH+p7Pnk69IenUS6vbtmC1a4PjogwpzJw9lNG+O97yLKLn/odBj5Z6E\nbdXKsJ/Tly3BNi+/WjN3qwqoFd7713/7/Tinv4t7xM1gt6Nu3Ej61UMjlqsyqs9L0vR38F53E6gq\n5nEdKX7mRZKfeRLb0sVYNhtGdhvsi8IHX3XbtmD/ZVL11pXTf/oR2+Lwfcj2/LlUPeu1CoqCv0cv\n9I0bQnYFOnUuG6jVGFQ6QrhJtQZa1Iu6XHUkNTWVkpLyu8Y0TfQD07y++OIL9uzZw0033URRUREe\nj4ecnBwuueSSiMeLOlgOHDiQzZvLn9oty0I50A6ekpJCcXExLpeLtLTyddBSUlJwuVxVHjszMxld\nr4NaVhW2LYWProVti8u3qTYwow6S5Ty7VZq2B0cy7AszrqZVL4Uew5JR6nLI1RkNZ5pIrdbH63hs\nxOZM3e9Ff/bJsn2qy4Vj9mc4MOCLLyIU5jh4ZmL5vz/+IOKptYcfJvm660i2LHjzTfj002BA6NIF\nTu8H098J+znbyhVht9cFfe0fZO3fAccfD3dOhOoESk0LO2rYtmwpWVvXB78fwPlnQ5ujweGAtWvR\nli2DgjUQpiautWpJVuus6vePlewNJs4I953cpWQdkVqjvraw99gTj8H6tXBo7bplS2wP/T+yjsqo\n9rEbrJIS2LwZLjgPvv4y9Ddt25bU2/5Lanr4//7qat3KeOjWrRvffvst5557LkuWLKFDh/KBbVdf\nfTVXX301AB988AHr1q2rNFBCDAf4qIc0xZSUlJCenh4S2UtKSioEz0j27AmfjaSuffKfJLYtrnhJ\nYlwruk0AACAASURBVBEoD9pd1rpmoTktDI+K5rRo0cug3yMedu46PJbgqvXCvKf0I6NLN2xhajSG\naaGFCaLWd9+x9/M5BA6ZdxhJWudcnGH6MIzW2ew+41zYsZ+064fh+PSQHLCffIK/SzeMgefinP1Z\nhc+ZaemoNViVw9/x+APrZFbdjxKOkdmUPThJuuVWkqe+XWVN1nI4MJOS0cKM1LV0nd3FPpRv8nF8\n/AGOT2ahF6wJNr0ebK7VtJD+HEtVKTn3Itw7q344Pkjp2Z/MFi3LFpY+lLfD8eyvwbEi3mPODPjg\nM5xvTkZb8wdW02a4r70hmOqvoS8WXZlAgJRxd+P4/FPUzX9itmiJeXwn1MItaLuDAysC7TvgGnM/\nfq8S9rvGY/Fn06y7muVZZ51Ffn4+Q4cOxbIsHn74YWbNmkVpaSlDhgyp8fFiFiw7derE/Pnz6dmz\nJ3PnzqVXr17k5uby1FNP4fV68fl8FBQUVIjuDcnu1Qrbfq1pbTbadHgKhkeh3YU+TrndR9PjDo8g\nGTOqSvH4CaT933/Rly0N/sFOSsZ39jnoEZrxFK8XfcXyagXL0v/ehr58WYVgZSanUHr9P7HNn0fK\nvXehr/0j5Je3LVmEceZA9j/xDPYfvw8OMureg+QJj0Q8l5mUjOL1lo1A9XfqjGvCUzheegFt/bqo\n7i5/v/4433iV5OeeCunXDcuyKuSVPZTRqjXp/7wW26rfKtzthzYVqwdqL2ZyMorPh3FMWzwXX4r7\nv7fWqNxWehO8A/9O0uuvVvjeFhX7RdWCtTg++gBsNjxDr8LKquGobqcTzyHzTxuDlAfGkPzKS2X/\n1gq3oBVuwT04D6NzZ8z0JngvHRxsEWhA6jKDj6qqPPDAAxW2tWsXmli/qhrlQTELlnfeeSdjxoxh\n4sSJ5OTkMHDgQDRNY9iwYeTl5WFZFqNGjcLRwH6sgzy7FQxvTX84pXygaxRchaoEyigZXbqx94tv\nsX/0AdrWQnx9+2N07UaTQefBptD5VKbNhj7/Z+y6DdXvI9DxeAK9+oQ06+m//ozjow/x9+hN4EAN\nz2zaDO+llxPo0JHMv52KtjfyBHzHN1/hO+c8iie9fuDEJs4pr0f8jOouxcjIJHDyKXjPvxDv5UNx\nTH2TpI9D88AeVNkjmpmUhGfgecGk4tUdXBMIoIapjQeyjkTdtrVsgerq/NfhumsMnpv+FfUfZcXn\nC+2HBezz56GuXoXzvWkkvfUa6oFm36RJL1A68v/Kk8U3cMruXaAolWZTqjG3G/sXn4XdZc+fy+7x\nEyC16iQb8ZBIuWEVy6rJcLX6UasmuigZXpjaM5mSwprVLh0Z4A3zd1Bzmhieyjsgs04yuPyr+DQ5\nx1Otm2Er4Zj+Dmm3j4pYUzoYaCybDX/PPhQ/9RxmdhsAku8fQ/Lkl8s+a9nteC4fimvis2AYZJ7a\nA72SifsH+U/qwt7Z35XN8Wty1mnYli6uNNhYNhvui4K1sYzzz0arIiNOZQEzdNx2ZKbdgeoLP1Hd\n374DtjXhR1VGLJei4L72RkrGVz3JO5yMgadjWxx+eod70GU4P/moLFH+QWaTDPZ88iXmIYno6/Ie\ni4b+808kT3y0bACT/+RTKL3jbgLdutf62OqmjTTt3S3kukDwXtjz00KMdu2rPE48mmHP+P/snXd4\nVNXWh99TpqbSpTdFEKWrIAIigjRRVBTlYi+XT6x4vZargCJiR0ERRZSmIIIoWLHQm6h0qdJ7SZt+\n2vfHhJDJlEwmk5CEeZ9nnic5Z5999ply1tlrr/VbJ4seeHaaX6s0j+NICieh4JOLZIEW9ysUdZro\nzYT08zWsVfxP5qZkg4a9FP611knXd9y0etiLKSV0dGP1VoVrjyaIgGEgrfsT04pleYEh3ltvxzHq\nFZTWbdGTkoM+zTw3oqJgXraY5P88BoC8dDH2SR8Elrny+bB+Ng3znC+wTv0kKkMJIK9fR/K/bkVe\ntQLh0EGk/XsLj4BVFOxfzqRS3x6I2ZHTVnR7Uli1IvBfY7TP60IYQwkgRhAhCNufYWD94nOkQgKa\n5CWLsL86CuuH7/uDUnIxbOEr4si7doY0CGJWJtaZ04s81tJCOHSQ1KEPYFn0K2JWFmJWFpZffybl\n/+5DOHGi2P3r1Wug1a4bel+dumjn1Qq5ryygG0LMr9ImoeCTj9YPKexfKnLgVxNFWYv0ZgtcN9tF\n1m6JKs00Kp3vv0U3u81/AxdFWDfBHFBMukpzjTaPlo+c07KIvHwZSS+PwJRbEktpehHuBx/CO2gw\nnjvuwTP4bpL/+wS2Tz+O2I955QqkTRv9M5ZQSj2GQcqTjxYpB08AbD//iGXZEpSOV+YFWESDmJVZ\n6ONaNKXDoiVieXNX6Nl5YYiOHMzzvsQdqqC010vqg3dj/vmnPOF32+SPcIx+HeXqa1Cu7Iy5gAoT\ngFq/AXqI1J+8sTrLrofG9uEEpBBLA/I//2D7aAKuZ4qZQ2q14u3dF/n94NJp3p59ChVzOJucDY3X\nWEkYywJk75IoatCO+5iI86DI+deFDntv/5yPapdo7PrWhOKAyk11Wg7xYa8ahwGfgwiZGaQ8PhR5\nTz4pr61bkEY8i9awgV9gXBBALNylLnjcSLt3hU1ZABBdLnAV/WYsetyYf/25yGFgsaxVlgSi4gtZ\nQSUarPPm4n7ymaC1S/urL2P5bkHANvmfXSQNf47MKzvjeuxJpG1bsXy/IM+YanXq4vzfCOS/N2NZ\n+GPQuQxAubTwwK1SxTD8xbytVqTDwdG9pxEPht9XFFzPjwTA8v0CpAP70WrVxtezN87ho+LSf4KE\nsQzCcSSG25FosH+JRJ3OGnKY/OvG/TQa90u4XeOBdfJHAYbyNGJWFtaZM3DkCqf7ul6NdcrHEUtt\naTVq+IXWNQ3r9CkxGYZIxLO/sxEKEev45b17sH46Cc+DD2GZNQPL99+BIwd5c+g1KtO2v7FOnYzn\nvn+T89GnuJcvwbxkMUZyCp477sJIS0e56mrMv/yMaf1fAcf6ul6D78YBMY2zJDB/PRf7++OQN6wD\nkwmtSpWwbQV3nLwEkoRrxChcT/8P8fgxv+5zlGIQZxOtBFNH4s05v2ZpGHBgqcjmqTI5BwREcww3\nB11g40cWZlxh58Sm8vPhl1fECOs8+ff5evTCe33/sG5NA/BefyNGlSr4+vXH26tvfAcaBl2S0dLD\nJ8AbooivZWsMObcAuSyjl9EbX6Rfi+mPtdhfGk7K4w9j+W4+liWLkE6GVl4CsE31RxALhw9h+uMP\njNRUPAMHYaT53ysjLZ2sz77Edf8QlEsvx9e+I87HniR7ymdh9X9LG/nPtSQ/PSxveUDweJAPHgz7\nPon79xZNErAwrFb0uvXKhaGEkq06Em/O6Znlqe0Ci5+0cmSthKEKSHYdI0LR+cJwHpSY0zuJ5nd5\nsVcDW1WDJjepSOb4jbnMYRiYfvoR09rV/gTvwXeVeJi61ig4V+o0ep18gQ6CQM77k1AuvwLz0kWI\nO7YjHD8GooRRpy7e3n1xP5orDi6K5EycjKll05BlqOI6/nr1cP33OVL/fW/I2aJeqRKmrVsQ1Nxg\nFlUNqVZU2uR3Axv4NXQFtyvszN20eiWmH74NWZQ7FOL+fdhHPId11udIJ/0PPbYJ43E9/FheXqRR\nrRrOl18t5pWUHNbpn4YW0w/T3rR5M+K+vej1G5TouMoq5Sl15Jw1loYBi4dZObz6zFuguYo/0dY8\nAhs+sHD657F+gsaVo7zU6VwBXbBuN6n33oF50S95N0Trp5NwjHkTpWu3YnUtbdyAbcYUhJMn0Oo1\nwP3A/+Vpu4p7/gm5fqfVqo3r3gL5dqKIZ9AdmFavRDp4ANHh8KeNXNgUb/8BgXmWFgu+vv3yZjhF\nJdo1RXnPbn9BbkkGLYQh8SpBwUairpf6mmVBhAJ/S2H0c09TsKh2YYhOJ/aPJiIoZwLfpGNHSRr5\nPILLhfuxJ0uttJR48ADWz6eDz4evZ++oUzzEo0WMIJakMjMrPhsk3LDlgP2LRI6URJUPIP9t5dRW\niWX/s6BVwMDXpJdHYPn5x4CZg7z7H5JG/g9ChPhHi2XOF6QNuB7b5I+wfv0VSePeJv2GXkgb1iH/\n9Qe2aVNCGg3v1T0Ccu3yxvnSC1jnzkbM1SUWFAXz8qUk/+fRoLaOkaPRK4dfY4qEel5NnA89itKw\nEVp6JXQp9LOoYBjYpn2KEMpQApIjtDSeIUmx6l+UC/SkpABDeRpRVUl65SWSnnysVMZhnfQB6d27\nkPTaaJLGvkFa/74kDXskpLtU2rAOy4ypiLlqT3rNoqVp+NpdFugNOccoT6kj56yxzNrtd72WBqe2\nSuyYU/Em8aYQIf4Api2bMS/4OrZOVRXbu28HuULlXTtJeut1zPPnIbpDR6ZKR0PMZAwD86+/hGxv\nXrKI9A5tqdT2YtL69kBe9Cvy+j8RwsyYCjNU8pHD2N97B9Puf5AyMxDDGEOIXFkk7BprGVW/ioWC\npbIMUUQLUf7rNAJgmzUDecmiEh2XuPsf7K+PCahqI7pd2KZPwTJ9ypnxnDhB6m03UanvtaQ+PpRK\nPbqQMuQ+3LcNRqsRXKpNbdgQtWGjwG31G+J66tmSu5hygKHH/iptKt4dPErqdlWxpOl4s0rnecF1\novy4G6JFcIfPwxMzgkW5o8G0bEn4Mld/rkUt6tqOqiKEEAgHv8Ey7cpVt9+/D9PAm/D06JmXshB8\ngBAxGCNen3C4fkRFPatu2HhgSBK+rt3w9uiF9asvkQ7sR69WHU/f6xG9HkyvBpc+O43g82H58TvU\nzleV2Pisn01DygjOixUMA8svC/EOvguA5GEPY/llYd5+MSsL65wv0NPSyXnzXezvv4u87i8wmVAu\nb4/jfyMxatTAOmki0sED6OfVxH3fvzGqJvLHygvnrLFMb2jQsI/K1s9KPvrGlGxQ/5piRA6VUdTm\nlyDvCla10arXwBtDxXlp3Z8kP/ff8Gtzoojvun7YPv045OxSuaQlgiMHIzmfNJfJhHb+BUjHjxV6\nfkHXsPz4XdjcQt1iQfJ4or+gOKKbzYjlpHB6KHSTCc89D6B0uAJfr74gCHjvujewkcOBaeGPYetj\nAsgb1mMfMwpf5y7+fNo4E0qYIo9cdSdx317My5aEbGJe9DPOl14hq0dPhKNHwWwK0IF1/+eZeA63\n3FOeAnzOWTcswFVveKl6SUkbMYPG/RSqNKt4q02uoY+i1asXsM0wmfDcFkMlCFUlZdgjyDu2hZ09\nKW0vRW13Oe4778EwBz7kaJUqY5/0AZUubUnqHbchbvs7b59n8N0BVSsiIRpGSENpiCJGeqWoLyf+\nlJ+bSihERcHT/yZ8va8LH6STnEz2jNl4O4Suu2oA5tUrSXrrNdJvvZGUB+6OKCYRC74rO2PIoecQ\n6sX+ovXS/r2IOaFd9cKpDITcBzmjRo34CqZXQHRdiPlV2pzTxlKU4cJbYv2xGSBENoCmZJ3LnvXR\n9a0IT6tlCVVFXrPar+sZRe6X1qoNmdO+wD3oDnwdrsB7bS9y3ngH13PDi3xqy5wvMG3cEHa/brWi\nV60KmobrxdFkj3oVb5euKA0bo8smpIxTiNnZSCdPYPnhW1IfuCdPc9R78y2oLVoWeUz5EXQdrcCa\nU2kSTuy8tNFlGc1uRw9jUMIel5yMEWItryBGlSpkz5mPp+/1Afq3Bb0NgteLdd4cbO+8WaRxFIbS\n/Vq8va8L3t6yNe6HHgFAbdEKrU6dkMfrDRpipKTGdUwVGc0QYn6VNuesG/Y0NdqpfqNX5DdfCIjE\nEPDSnjepyi6W8xSOtAvoNdVD7Q4lmzIiZGZge+dtTOv/xJBklI5X4n7oUTCZitSP5fPp2D54D/nv\nzf51ljbtcD43ArV9h4jH6c0uwvH2+OJcAgDi9q2R93s82D+ZhHjkKKLLgWnVioguM9Pfm7F9Mgn3\n0EdB1/H0uxF506YzuYsxoJ1/AdrePSGLE58riKoa02xOuawDli+/QMw4idL8Er/iTriUCVkmZ9IU\nvPPmYF66GGnHdsxrVoVsal6yCPew/xZ5PGERBHI++Bi1ZStMy5Yg+HwoLVrifmRY3izRSEnF0+9G\n7BPGBXghDKsVz22DSi29pSJglKPUkXO+RNc3N9s4sCQ+zwz1WcRddOU4F/IZ80nq2JDuH7lLTANW\nyMkm9Zb+mP/4PWC7t1dfsj+ZnlciqjDkFUtJu2sQYoGai2rjxmQuXBK4BhgHCpYCEk6dIq3X1Zh2\nB0vYFcSQpIjydflx3Xkvaus22D75CHnzJgxRQlB8MUu4uW+4Cb1ePezj34kYzRqJs50rWdoYkuSv\nC5qZiXzwgH8boFzRkexPZkTlprSNfZPk0SND7lNatiZz4eKg7SVeosswsL3zJpbvFiAeP4pWtz6e\nW27D+687S+6cJczZKNHVdMfumI/dekHDOI6kcM5pN2z2foHDa+KXa5mNP/S9Gtu4grc4uFxm+f9K\nLtzfNmF8kKEEMP/4HeYF30Tdj3XmZ0GGEkDetYukJx729+V0+ssqvfIStgnjw6ZXxIJtwrioDCUQ\ntaEEEDdvJOXJRzGtX4egqog+b7G0Wq3z5mD7cELMhvJcxH2TX/jhtKGE3GLOK5aT9OILUfXh7dkb\nPYwqlHpxiMomAIcPk/zY/1GpYzsqdWhN8pD7EHfuKOrwwyMIuB97ksyfFnHqr7/J+uaHcm0ozxaa\nLsT8Km3OaTes4hDQ4hjcKHOms+r46/kdWimjur1hBdaLdb5NG0NuF3Qd08pl+PpFF5EaqW6hdd5c\nbPPmotvsCF5PnqGwfvIRjlFjUHr0KvrACyBvi+yCjQXdZMaydk3Ms7hQBZQFQChmNOy5NKsEkA4d\nCvs9Na1eGVUfetNmeK67Advn0wPePy09HdeQR4IPcLvh5uux/X7mQVLetQt500ayvvo2ka6RICbO\n6ZmlphhIcZz41WV53t8+/G4LxQlqbGUBC8WwhE97KUoCeyQFkdM3J9HtCphRyXt2kzzieYgUal8A\ncdvfJA97FK66itQ7b8c8e5b//EWotxftvFBUfMUyTGXNqJW5tZIoEQ8dDPteCh5P1CLiYk5OUD9i\nVhaW+fOC2to+/Rh+D/a4mLb9je2D4q+vJ4gfiWjYcsLql61o3lBvugEUzdVm5yi98Mun6QjswD/j\nqtxMx1JCGQfe7j2DlFAA9LQ0PLcOirof1z0PoFYtYqoHIO/cjvXLmVG1ldb9SfqgW7BN+wQWL8by\n/QJSH38I22sv47vuhqBUEPDn5um5kYWGKOJr0w6lXenVLSxLBlOrXbvcGUxDFJH/2RV2v3DyJJUu\na0nKg/dEdJGKe3ZjXvJb8PGGgeXbb4IMrhQhWEyO0t2foHRIVB0pBzgOCxxeHW69UqAot0qJbLrx\nNDI+3KSyidv5naFY0nXO76ew6AkLxzdKSGaDWh00Ln3SFxe3rG/AQNx//oF15gxElz9NQqtcBdcj\nT6A3uyjqfvSLmuN8bjipjw8tsoEQsrKiamcfPzaoWrzg82GbNoWMRStR6zXAtHN7wH4jLY3s9ych\n79wBLjfyti2YVixDFwTE3BukIYpoNesg6ArS4cNFHH3ueShbhjEUvmv7YJkzCynK97tMoOsR31fR\n7ULcuwd57x6kv7eQ9cVXWBZ8jXj4MGqzi/D1vxkkCXnTRsTs0Hq54tGj/ujcfNHfelpa+CGlht+X\noPQpT8v/56SxNAxYP9GEGlpiNAZkjtCWX7iArVzPCZqT2kDj0v96+etdC6f+PmOUj66VOblZpM9n\nHoTizusFAeeYN/DcNgjLd/MxZBPeW29Hr1e/yF35bh+M8vk0zGtWR32Mnl4Jb59+UbUNt24lHT2C\n/a1XkfftCd534gSp/3c/evXqiHt2I7mCPzBB1xFPnUBr2ChmY1nWDSX43ycjOQXKkbEsyvtq2rqF\nSl075pXmMgBlymSyJ09Hbd0GrVLlkDJ0eu3aQWlSnjvvJenLWXAsULVJT0nBc8vAol5GghLkbLhT\nY+WcNJa/v25m/QQzsd8mA+ciGnZ+Z2hAiwY9VE5skAIM5Wn2/Saza77E+dfHJwdTa9kaV8vWxetE\nEHD+51mkx4ciHdhfaHNDFP03Hk0jedgjyH9vQbfbUbpc7U/eLpC2YlisYfuStm8Lq8cqnTgeIGod\nCtHtogIWQAtAr14doV59yBdVWtE4bSghN2J29UqSn/0PSsdOaI0bI60NNJaGLOPpPwAA81dfYvlu\nPoLTidqkKQwfjvrWWORc7V+tXgNc/34INYw6UFQYBvLKZUj79uHr1qPoKlUJgihPJbrOOWOpeWHn\nV3IMIgT5iXysbDO4oL/KmtfCBOAYAkd+j5+xjAbx4AGsUyYjZGejtmyFd8BAKKDConbpSsa3C7FN\n+hDxwD7k7dsQcrIRFB9qw8YY9iTE7CyM1FS8PXqhdL6KtMG3Iu844z41L1mEtH0rjnEfBPbdtl1Y\ngfRYRdfzo51/AUZqKuZVK4rVT5l0yVapgnvw3dhH/u9sjyQqDEHAsFgQ46Cja1nwNdav5wL+YtOG\n2Yzg8aDVb4D35lvwDBmK/aXh2D8Yj5BbFs7y80/QogVZ02dh+usP8PnwXn8jhAkkk9euwbR6JVqD\nhn7d2hD5yeKWzaT893FMf6xFUFW06jXw3HgzrpGjEyIExSAxsyzDOA4LZO2Jd1zTmVusbDNo8aCP\nGm11zKFTwwAwRdgXbyxzZ5P0wrNI+VJEfDM/I3va5xgF1nCMmrVwPT8i3wbD/wpxA0ke9miAoQT/\nu2Cd8wV6ejquZ4eDzb84qza5MOz4BLczrJstWgyTjK9jJ7T6DRAUBWnTBkzbtwW1000mxAi1Nsvk\nT1fTSL17ENLePWd7JNEhy2h16sYlrzF/Xq3oyEFLTydzxmy09h1AkhD37cU6/dM8Q5nHhg3YPxiP\n4/Wx4Tt3u0n9v/sw//IzgseNIQgobdqRM/a9wLqouk7KEw8HCLxLx45i/3ACes1aeIY8XOzrTFD2\nOeeiYW1VDaxV4xtXaE41aNhLocUDXvrNcdH+Wb9LsWFvBcEUfC5bdZ3md8Yuu1Yk3G7sr40OMJQA\n5pXLsI9+qfDjBSGsEpAcZqYoqCpJE98nvfc1Z26YKREUQJxuRKej8LGEQU9KxjpnNslvvop19kzE\nI4fJ/mAySotWAe0MiwX3wEGojc6P+VxnJSI1MxN5756yachDICgKpp07wgqSg3+WeBq18flo1WpE\n1beUmYn1h2/zpPIsX89FCuOZkNf/FbGvpJHPY/l2PkJuNRHBMDD/8Tsp/30ioJ35uwX+GWoBBF3H\n8sN3UY07QWjKUz3Lc85YmpNBCmHAioMvW+TUdpHLn/FxXrszn2KTmzRa/duHpdKZbcl1NTq84CW5\nZuncdi1zZ4cN3zeF0duMlsLyI02bN5Kcq9LiufGWoOK3pxEEwteQzEf+d0y3WNBS09DMZkSnI8+Q\nCLqOecUyksa+Ttbc+TiffBpvn+vwdL8WLSkZ++fTkfbtQY/BdVYmXbRlGEFVUevWQ2nUGK1qNbTq\nNVDatsP5xH84uXE7WZOnk/XxVDIWr8L16BPo9ujybYX8kbER8okNU4Tye4aBOUwhadPvq5H+PGMc\npf17wyo/CcXwhiRICKmXaQ4uF3Edi/8zQtYuic1TTbQaEjhj7PC8j+Z3KeycJyPboNlApVRdsBEV\nZ4ohKg6gXHU15sW/RTQg8ppVSFs2+Qv83j4Y28T3kE6cCeTwXd4BIeMU0YgOCoDvoubI+/YhOHIQ\nIwgiWBZ8g165Ks4Ro5BXrSD9tpsCbngCCeMXDwxZRoggrC7m5HBy+96Q+3x9z0RSex4Ygl63HpYv\nZyGeOIGQnYlp86aQx2lNm5057rZ/YftgPNKB4MAnpX2EYB5dR3CETkcRFAXpwD60Nm3947ziSnR7\nUl56VkA3Dc5eJZqKQGLNsgC6rjNixAi2bduG2Wxm1KhR1K9f9PSGeHBwhYzuK5kPyHUsdL+pdQ3a\nPFxKbtcCePvfhH3sG0hHjwTtU1u2CnFE9LiHPIy0bSvWeXPCGmUx4xSVunYEQUTQNbTzzoN27XBd\neBGCz4fgcmEqwtqWvH2bv/JFIQi6jv3TSZh/XYi0f1/ImUH5+ZmWTXztLsVz133YXhuNaV9ogyg4\ncoILcofrr1cffL36+I/LyiStfx9MBVKOfG3a4c5XNNpIScX5n2dJeml4XtS0IQgIF1yAYbUiHtgf\nWqFKklCbNEU6Evy70GrVQunS9cz/LVvju+ZarN/MDWxXuUrAWBIUnfKUZymNGDFiREmfZOHChezc\nuZOJEyfSqFEj3nnnHfr27Ru2vctVchXhM3aI7F1YAs8IgsHFdytUuaiMffo2O3g9mNauCZgBKBc2\nxTHmLYzKxShOKwj4evXF16kL5oU/IjqDn7xPyzucNlaiwwGHDoHXi3npIkw7tiG6o0t4NQCxiL8u\nMSsrolHURTFqcfWSMK5aSgpiFC7osoRhMuG9vj85Y99DvbwDatNmWL+YGfr9kSTc9/07bCRqWKxW\nfFd39wv2Kyp69Rr4evbB8dY4KCA6oF3SAm+//hh2O4ZhIGRlIR4+hHnFUixfzAS3C7Vjp+DrSErC\nvOS3gAc9Q5Jw33UfSo+eAW19PXuD24XgdILF4ncnPzcc5Zpri3ZdZZikJEvYe29SUskUhBizz+nP\nTIjh9XS9UnTRUUolul555RVatGhBnz7+p8ZOnTqxdOnSsO1VVUOW41cNJKBvD7zTEBzBD5TFon4X\nuPNXii80UFLMnw+zZvmT2ps2hSeegJo149f/Z5/BI4/AyZPx67M0sFjg/PNhc+hgpRKlcWM4dQri\nkDpTatSoAe+/DzfeeGabpkGdOhBilkb79rBiRemkV6xdC1dfDTkFKuJYrfDVV9CzZ/Ax338PEyfC\nrl1QrRr07w9DhybSQUqJ1KXhizgURnan6ILC4kWpuGEdDgfJ+UrsSJKEqqrIYaLlMjLiJq0Ts/14\n4AAAIABJREFUki5vS3x7m414zRUqNVPpNtHNiXjYCY8HQVXiXkOS9lf5X/mJZ72/7tchT6mG9bNp\nSNu3YV67JqZudEo36szwevFVOw+xnhNTCBWhaNBFEUGSAtIXClsP1ex2xL17I673lTWUC5uSPXk6\n+gVNgr47lv88S/LzTyPmU1kyRBFvcio5hzOKXIw8FpImTsJe0FACeDy4p87A0TbEGma7K/2v/JyI\nPTK7PHM26lmWJ0rlvpScnIwzn4tO1/WwhrI0iPdDozdHYP+S4s2EhQP7Sbn/Tipf1pJKbS8h9eZ+\nmH77JU4jLB3Uy9rjGPse2TO+QIuxDFJpT8wFwLLoF0z79sScFiLqOr7L2+O9thdKi5aoDRrlpTaE\nwhBF0PQiGUpDEFBiELuPJ/p5tfyGMgTewXfhvb5/wHso6DrWn38iuUAqRkkhhJBDPLMveIkgwdlH\n02N/lTalcm9q06YNS5YsAWDdunU0aRL6B1da/P2ZiXiuQLkOSCx/wYrreIx9Kgqp992J9euvkI4c\nRso4hWXJIlIeGVJortjZQsjJxj5mFKm330zqXYOwTpmct1ov5OQgKOVnxnSa0xGyMZGSRvbUmXiv\n6oa055+gItW6JGHkfucEXUfyFk3dxtfuUuRTZ9fFLZ6MIDuo68gbN4b8VZl//B7heGTJwnigRpB8\nVC9uUeLnT1B0ylOJrlKZ3nXv3p3ly5czcOBADMNg9OjRpXHasOSUgLym64jI5ikmLn2y6MEalpkz\nAtRBTiMdPYL100k43n4vcgceD9apnyBt24KRVgnPnfeg129Q5HGERFGwvfcOpuXLEFQFpUUrPPfc\nT8qQ+wJcrebv5mNe8A2GKGJa/xdiVmZQV7GkapSX9A5DVUnveTXyX3+EHK+oFU/a0Px77IWs44Ye\n/lFCcDkRw+jWSsePIW/egHJVt5IaGQCeQXdg+eYrzMsD4yF8bdv5g4wSlDkM7ax/q6OmVIylKIq8\n+OKLpXGqQvnnW4mTW0rmsg+viTxRF7dvwzZlMuKJ42h16uJ+YAhGjfOCJOMCjgmRP5Yf4egRfyHl\nfMbW+sXnOEeNwXvDTYWOWfx7C/aJ7+XOYAU8tww8I99lGKTefyeW7xbktTcvX4p17hdIRwMX5gXA\nvPjXiDf0WH4Wpf1TiuV8aq1amNauKZZcX2GUhVuKmpt3GArDnoRevXroyiDpldCaNA1xVJwxm8ma\nNgv7m6+StG4tik9Fad0G1xP/LXo0boJS4Wy4U2PlnBIl8OXAn+PMaJ6SufWc2CihOMEU4ndp/m4+\nyU89ESA7Z1nwNTkTJ6PVrBW2T72Qdaqk0S8GzUqlY0exvz4Gb+/rIERR5bx26/4k7a5BSIcO5m2T\nh2/EOnsWmT8vwfz9Aswh5LwKGsrTlIUbemmi2ewo1/RAyDiFZdmSsz2cIqPVrInzkWFIR49i/u6b\nkFq6eW2rVsN1zwPYR41A/mcXelo6noG3o17ewd9AFPH16ou8bWvQ98DXtRt6rdoldh0BJCfjGv4S\nSdVSyIxnAFuCEqE8iRKU1USHuLJrgcS8G2x82jKJY3+WTEoKgOekyLbZIaL+dB37268H6bPKu//B\n/voYPHfegxJCaFxPTcVz6+0Rz2n6PXT9SXnHNszffhPxWPv4dwIMJfgNnmnTBmwjn/e7XstT1nAp\nYlitZM2Zj5CViTkKQ1nUtdCSzufSqtfA+cwLeO99AM8NNxYafa2np5E65F6S3n0Ly4Kvsc2YQtqg\nAVgnf5jXxvX0/3A/+H9otev4z1G5Cp6bbiHnrXElei0Jyi+6HvurtKnwxnL/YonFw6wcWiGjOkRK\nev6jh1Bgk9b/hbxhfcj28l9/gGHgeOd9fO2vwMgNsVeaNcfxwijUq66OfMIIa2ERpe4A0x/h0zus\nM6djWMPXoDzXUZo3J2n0SCxLFhX6jYpl3bUkv6Xeq64m45eleAcOQtq0gfQ7bwu5Zp4f8dBhTNu2\nBm7LzsY24T04HYUqijhfGkPG0tWc+uFXMpavJWfCpIQLNEGFoMK7YbdMNeHJKJ1nAnsNnQsGhJC1\nEwT/K5T+g+Dfr7a9lKyvv0faugUhJwe1TbugepOhUFq3Rd79T9B2tX4Dfw2/SESYvkhZWRiCiGG2\nIPhCa7CWl+CbksD0R3AVinBEeo+MKNrEHXsSRo3zALBNGB9V6S8xtzJHQeS9u7HMn4c3nwfESE5B\na9MuLkONBXntGpjyEembtmCkpODrfi3uhx4NWz0nwdlDKEdu2ApvLHMOlOwPpAG/0INhOMXzcF16\nM7bKtwa10Vq2Rm3RCtO6P4P2qW0vBbvd/48goDVrXqTzu554CrlA7UY9JQX3gw+d6TcMvi5dsX0+\nPeQ+QddJHvdWxOMFQLmoOUZSEqZiRGueLaMb7rzRjCde4xXwCzHE8/q1tDREhyMofeU0hnTmNxGu\nzFoQkfxepSA4EC3ymlWk3H8nHD7M6VGZVq1A3LsH5xvvnNWxJQhGKkfRsBX+UctWtaSc2wbp7KA/\ng6jJes7Xf+SShUOxvRfiBykIOJ98Gq1GoLycev4FOJ96tlij0JtcSNbcb3EOfQxvn364bx9M1vRZ\neO57sNBjHWPeRC+mOIRepy5ZCxbifPJplAubYsSg+HC2fi6+S1r6BQIKUNrjieZHaBT4O5xTwAC0\nBg3RqtcI2cYAlE5XnfnfFvmB6jTh3hP1giZ4+14fVR+lgW3i+8iHDwdsEwDr13MRd+08O4NKEBZR\nj/1V6mMt/VOWLhf0D12AufgIZHIBc5mJB39whOD1Yp0xFQqWjlJVzIt+AcF/s9LtSfjatCXz6x/Q\n4pAsbVSvjuuFF8n+ZDqOse+hdsgn36Wq/lcobDa8/QcU69x6teogCLifepbMhUvQ0ysVq79YiOXT\nNQDpxPFyE8AkFPjbCKMQJADm9euQDx8KElkwJAlv/5vx3HF33rZIkdgFUesFVgrSqlXHNey/ESOu\nSxtp+9aQ28WsLCw//VDKo0lQGKIuxPwqbSq8G7bJzRrOI17WjjWjZMf/2WAvV/ErL9ObRwCQd+4g\nvWtHtIsvwX33/agdriDphWewT5qYd4zgcmL+8w/s776J86UxwZ2qKva3XsO0dBGCy4XarDmu/3sE\n/aLoXbTi9m0kvfoyprVrwDDQ6tZDT01HMDS0+g1xPzAEvfH5uB59AtNfa5HzlcmK1i2qVa6C5193\nBm4s4Qoa/ocNO5jMGEnJaA0aIB4/jmlH+LSHUAiAdPhQiYyxNBA1DUMUCzX2An63rK9ff3zdevhL\nYOWb/XsHDMT6zVeFft5Ky9ZkzpmPbcZUpB3b/Kkjd96L3qBBsa8lroSJ6jUArUbpCm8nKByheFod\npUqpVB0pKuHEfIuD4oKvb7Ry7M/4r6+cx588SHDCtlbjPHLeGEvKM/9BOrA/eH+9BpxauhpstoDt\nKQ/cjXXenIBtaoOGZE2diZ6v8G04hJxs0vr0wLR1S9g2Wr0GZH8wCbXdZYh792Kb8C7yju0YySmI\n+/dj2hQ6ejfv+NRUnM8MR9q/F/PSRYjHjiE4HIjOkhWh1lJSyFi7ESO9EkJmBobNTuqNfbHEKNxe\nnjEEIeryYqd+WYZ2SQgvhqaR3r1zUN1IyHX32pNQ2nfA8eIr6CHSm8oa9tdGk/RG8AOocvElZP60\nOKqguXOVsyGknjIzWOkrWnIGpsdxJIVT4d2wpzHZIa1xybjcTIQ2ENLRI9g/eC+sDJh46ABiwdzL\nVSuw/PBtUFt5z27sH4yPajzWjz6IaCgBpH17sI97GwC9fn2cY97E+dSz6MkpIObO3iIdn51N8vBn\nSHr/XUwbNyAdPRI3QxnpUxJyZeUqN6lH5ZZNqdz6opCBU2WZeD2dGlEG1ugpKRjVwohbSBLOp59H\nq1MnYLOvdVsy5y4gY+UfZM+cWy4MJfgD3ty3DIR8VY6Uiy7GMfqNhKFMUCzOqW+P51TJPBu04tOw\n+6Q9u9Hr1EXavy9on1a7Dnr1QNeQacmisPmRcgSFlYBzhkglCdnf+nX+KEdRxPzdApKHPYJ08kTe\n/sLcsSVVtDjiOd1uxPzXV0guaUVGbdEK019/hI16PY2vYyf088LXLlV69CSjVRtsn3yEcOokWpOm\nfve6xQI+H+KRw+iVq5SptcmwyDKO8R9iO7gLx9xv0KvXwHvjgDIVsZvgDGcjUCdWziljqfvivygs\nShopnWthLArtEtNTUlE6dcb+0QdB+7y9+ga4YMU9u7F8PTfsufQob1Z6lSpRtTPM5rz8T9sH4wMM\nJZy9KNXyE0weG4ZsQlCD83GLknOp2+zkvD4Wy3fzsc6djbRrJ3rlymC2IJ46ieDzYZhMKJd3wPHa\n24WPqXp1XP997swGTcM+8gUs389HPHQIvXZtvL2vw/Xc8PKRr9iqFe7ajc/2KBIUgphIHSmbWCrF\nf3lW1yQ2NXkeJUwSttKpM86Ro/F2vQbdavUHqFiseDtdhWv4S/k60kkZ+iCmCKLqpj/XknLfHQiZ\nGRHH5Lnr/qiCGdTL2oMgIJw8ibw5eM0qQWRi/TYJWnB0slapUpHyTUW3i9ShD2LaugWlfQeyP5pC\nxso/ObVhG5lz5uMYOZqsz2aTNWc+RoRZZTjsL75A0ntjkf/ZhehxI+/aiX3c29hfGl7kvhIkCIeg\nCzG/Sptzylh6I9uYmHEdEXG+/Cpq80vythkWC96evXE+/yLWz6ZiWr0S0eNBAESvB/PqFdjGj81r\nb/7+W3/kagREjwfrN/NIfmRIxHZCTjZqs4vQklPC5+MJAp6+/fz/WC0YBYKMEhROrD/Xgh4IpcmF\nCFlZRf4xmjZvxDL/a2wzppH81OOYc1Mj1Ms74B4yFKXL1X7PgduNnJuYHxUOB9YvPg8eN2D59htw\nh1bzSZCgqEha7K/S5pwxlll7BA6vLhmvc+q+VZh//J6scR+Q/c77OJ55nsxZc8meOhPMZqzTPkUs\nUKld8PmwzvwsLydT2rkj6pw/yy8LkdavC7nP9NMPpN1+M5ZFvyE5csLe0AXDwLxiuf/v/fsxUtOi\nu9gEMRPuwUXauwexmPmeUsYpvyCGEujetb35KpW6dKBSv55UuuoKUm8fgBAiMjsPt5u0229CLOCS\nzzvPgf1BQWkJEsRKSeZZ6rrOCy+8wK233srgwYPZu3dvwP4FCxYwYMAABg4cyAsvvIBeyG/wnDCW\nR9eJ/HCPNYY1SwNEAzlJJ72ZimwPvt2lCgfo+NddJL39OpWu74m0cwfux/+DekUnAITjx5Hy5TDm\nR961Ayk3aEdt1dq/hhgFgqKQ/PhDwWIDhoF9/Fiko0ei6sf0y0+k3nw9lfpcE5BneS5j4F8PLM7x\n4Qj74FJQxKIgUX4vTFv/xrRkUd7/1k8/Jumt15D3+AOiRKcDy88/kjr0wdA6xYD9tVcwr1oZdqx6\nzZqFlo1LkCBaRC32V2H8/PPP+Hw+Zs2axbBhwxgz5kxKkcfjYezYsUydOpWZM2ficDj47bffIo+1\nuBdb1ln7lpmv+tk5uSmWWaUAuoDqFPGdEukw3E2tjiqSxUCSNeoJS7nOuJfK5N6McnKwT3wP08If\n83owUlLCztr0lBT06tUBUDpfha9jp6hHZtq0EcvsmQHbxGNHw1Y3CdnHju2Yl/yG6Igu5aPMJeSW\nAAJgyDJaUnJM1ytAWMk/XQr9HYz0CKdbLPDxxygtWub1G069x4CAqE/L13MRlOBAItOaVZh+/SVk\nH+bVKyKMBrzdeyaqiCQoF/zxxx906uS/p7Zq1YpNmzbl7TObzcycORNb7vKTqqpYLJaI/VVoY3lq\nq8Cf40zocSj27DoqcmSNiRu+cjNotZP7bniGu4zOnM9PAe0En8+/rnMaux2lU5eQffqu7JxX/QFB\nIOfDT9CifGoXAFOuG/U0hqZDiOCRwvopibblGSknG8npyLteA9BlGc1mj0pLV6tbH6PAD083W0IG\n9hSGp3c/+Ne/yPxpMVmffUnWh5/6o6hDoF7SAiXfA5d4/FjIdoKqIoeRhYukwORre2loxakECWKk\nJAN8HA4HyfnybSVJQs31xomiSNWqVQGYNm0aLpeLjh07RuyvQhvL7V+aUJ3xu8SsPf4PKLmWQZp8\nOLxbrUDun2P0a3i798yrD2lIEmqduqjnN0E4dTKvnZGWjnrpZdEPyJwvd8znI+Wx/0OM4NI7F2aG\nJYEAiKqK5HYhhNPZzYe3z3W4B92JlpaeJ3ou+rxFftgwJAnB0OHTTwFQunXHd8ONOJ8fidr84oC2\nWs2auJ58BvLNOrU6dUP3a7WitAsdva22bBVyu1apEjkffZrIV0wQV0oywCc5ORmn80ysiK7ryPke\ndnVd59VXX2X58uWMGzcOoZAiEBXaWKpxzlc38t0n1XaXhm2ntAmUvjNS08ie8QWZM+eiNL8YBAH5\nwH6Sx71Npa4dscydndfW26tvWDdbQJ9mM97e14HDAYaBdepkLIt+jXjMuTIzLEkKew91swVcTqxf\nzkTKyvSXK43hPAYgaBq2eXPg7rtJefCevDJZesNGZCxYiOP5kbhvH4xz6GNkfvuzX/c1H57bB6Mn\nJQf17evSFfXS9iHP63rsSZQCGsSG2Yxn8N3oYYxvggSxUpJVR9q0acOSJUsAWLduHU2aNAnY/8IL\nL+D1enn//ffz3LGRqLDasBsmyfwx1oz7WOGGJ1pMqRrXjPfSsKcGikLqoAFBBsp3RSeyZs31q58U\nwP7GGJJeGx20XatTl8xZX2GYLaQ+dD+mNasCXIBGSgqCx4ug+F1kus2G1uRCBIcT8cjh3BmrgXTy\nZFDfZY141q4sq8WnDZMp5FphNGhVqyGeOB50XQaQ8+4EvAMHFak/y8wZ2KZMRtq5HSM1HV+nzjhG\nvRogB1cQ4dgxbBPGIW/7GyMpGW+f6/DdcFPRL+YsEknnNEFozoY2bMO3nYU3CsPuxyOvneu6zogR\nI9i+fTuGYTB69Gi2bNmCy+Xi4osv5qabbqJdu3Z5M8o77riD7t27h+2vQhrLw7+LzL/FjuqM/63U\nVk2n9zQ3Ndro4HZTbfL7eBctAcNAbXcproefgKQkhBMnMC/+FbVBI7S2fpdX2vW9MK9cHrJfQ5Yx\nTCbEEDlsSqs2OEaNwfLdfBAExB3bscZYbqisGphQFDbWkrgWw2TOeygpSQz8M7bTkoGGJOG7sgt6\n9erYCgRuncZ90wAcEz6O4WQGQlamv3ZlIUEMFYWEsSw6Z8NYNnozdmP5z7DSDTSrkHJ3myabSsRQ\nAriPi2yeaqJGG69fqm7ECLLzf8EMg6Tnn8by1RykY0cxzGaUy9qT8/rYiCkCgqqGXQ+TN6xD8Hlx\njngZ8eAB0rtFHzUbdJ6Yjyx9ChtrPGeohs2O2roNrgcfwjpvjj+StARrXaoNG5L520qsn01DPH4c\ntXVrfNf2JnnYI2GPMS9dAi4XFCJyH4QgYJyFOqMJEhSGlJC7O7sc+yt+rtdQOA+F/4Bt48di+3AC\nUm7ituDzYV62hJTHh6IWoR5lfgRdh6xsAOTfVyOdKvvu1vKEgF9xyXP7YJRefXCMehW16UXFCojS\nCwmEMarVALsdz30P4nrmf/h6+utM+rr1CJt6Ih07in3sG8UYVYIECWKlQhrLGCL0i4S9evjbqPmH\n70IKqpvWrsHX7jKUJk2LfD61YSOUq6/x/920WdTiBQmiR8rMIGnEcwinTpL81OOYtmyKauYa6pug\nNmiEc8QodGuEoIEwBtHXuy/qxZeE3Adg+n11FKNKkKB8UJKiBHEfa+mfsuSpdH7Juc9ku06zQeGD\nN8Qwsz5BVZEOHiD78y9x3XM/Wq3aUZ1PT0rCfc/9edVJ9KYXoTa+oOgDT1Ao0okTpN5xG+bFkZU8\n8iPgzz/UatVGq3Ee3p59yJn4MZ77h5Dxw68YptAPNmrrNmE6FPD27BN6H4RV3kmQoDxSknJ3cR9r\ncQ5euHAhw4YNy/t/3bp1eVp748efKVQ8fvx4br75ZgYOHMiGDRuKc8qoaNxXpaSyCu01DGq2D2+M\ntQYNw+4z//QDep26OMe8iadPv7DtdKsVb5eueC/vgNb4fGyTPiS9Rxdsr40GTSN7ymf+FIU4kLj1\nBmJeswrREX1giCFJZH8ynVNrN3Lqj01kT/0ctbU/dUi/qDmu+/8dVKRZad0W16NPhu3Te9Mt6Gmh\nVZ+U1qHzIxMkKI8IWuyv0ibmAJ9Ro0axbNkymjVrlrdt+PDhjBs3jrp16/LAAw+wZcsWDMNgzZo1\nzJ49m8OHD/Pwww8zZ86cuAw+HM0GqRz9S+HvaSbiHdKSs18ke69AWoPQZsYz+G7Mi34LqdZi2rwR\n64TxmJctRv59TdhoTuew/6JfcCEpjz2EeLoc114wrfsL6chhHG+Nw/nccJJfGh6yLmJ+tOQUpAg3\n//KzvF420apVR9q5E+vLIxFPnUSrWw/3A/+H3shfS9E1YhRqm7ZYvl+A4HKjNr8Y95ChGCmpZzpx\nOrFNnYx47BjKRc3x3TgA9z0PYH//3YCgMN/lHXA/Ed7IJkhQ3ihPAT4xG8s2bdpwzTXXMGvWLMAv\nLeTz+ahXrx4AV155JStWrMBsNnPllVciCAK1atVC0zROnTpF5cqV43MFIRAE6Pqml2PrRE5ujG/A\nr2QB2Rp+v693X7TzaiAfPBg8LlXF/vbrSFmZIY81AF/nq/AMfYzU228+YyjzYf72G8THnsQzZCjq\nJS2wzpyO+cfvkbKyQvapV62KGKH6SILoCPdgY6SmknbXbYjZ2XnbLD8vJPuDSajt/GpMvn790c+r\niWnJIozKVTDkMzNNeeVyUp54BHnXjrzzKNM+JXvydJTLOmCZ/xU2XcFx4cUB7vgECSoCZ2PtMVYK\ntSSzZ89mypQpAdtGjx5N7969Wb36TLBBQR2+pKQk9u/fj8ViIT09PWB7Tk5ORGNZqZIdWY49otXn\nhB+fhJMlUM+4XkeBBpcEJnQH5SC1bAkhjCUQ1lBCbr3AvzdTbfGPkFspIuj4jAyqrF0ObS+G/n3g\nht7Qpw98/33I9qY9u8NfTILI1K8PVivUqYOwbFleObX8mPbuCdou7dtDpYnjoNc8f8mswYPh668h\nVwYxZfJEGDcOuneHV0bCrjMVXwTAvGoFVV8d6Ze5G9gfgOTcV4LoKancwIpMab9n0SjxlBUKNZYD\nBgxgwIABhXZUUIfP6XSSmpqKyWQK2p6SEvkDychwFXq+cOgqzL/VxsGl8ZhRBs4nKl2o0fYpN8eP\nn3HBhkrkNd94KymLlyA6A6t5GKJYeO7e8eMor4yB5FRCJR8YskxW1Voouee0vTGGpB9+CKn4Agk3\na6wYJhMZH09Da9EKadtWKnW6LPR7GSZ3Vlu9hlOHM/yqTbnelzy2b0d55DEcL79K+u+/h+xXXbSE\njKNZIIqJBPsYSLxnRedsiBKUJ+IWDZucnIzJZGLfvn0YhsGyZcto164dbdq0YdmyZei6zqFDh9B1\nvURdsFtnyXEylJDaQOOKkR4uuc9Lh+Eebv7BRbUWhYfE+Pr1xzFqDErrtuhJyWg1a6I2ahx1kru8\nYT1Ki9CC1krbS1E6dMS84GusUydj/XJWyFQVBCFhKIuBIYgYVfxVCYy0NIwwZanCfhtkGUQRc776\nkvkxbfsb84/fI2ih/VCC152nBZsgQUVF0ISYX6VNXBf0Ro4cyZNPPommaVx55ZW0bNkSgHbt2nHr\nrbfmVa4uSY6vi58ggfuUSKshsWl8egfdgff2wQjHj2MkJ5N61+3I/+yK7mBJwvPAEAQMLN/OR8zM\nwJBllHaX4b7lNtK7d0HOzQMMd7MOaUATRI3o85I24Hoyv5iHXqcuSvsrsPyyMKidnpYe0rUunDxB\nyr13IJw6FfYcRvXqqA0bIe8Odrmrl7T0G9wECSow0VQPKStUOG3YFSPNrHsvTvqXkkH/r13UvCz8\nE3607p6UB+7COm9uVKf1Xd6BrG9+8OvA7tmN+bdf0Bo1Qm3TjvRunfMq3ycoeXztr8Dx6luIe3eT\n9OZrmNb/BeQG4lx6Oe677iN5zCik/XtDHh8uGllPSydj4WLMP3xL0piXEV1nlirUWrXJee8j1I5X\nAgmXYiwk3rOiczbcsJc/Hl4CtDBWv126OscV7tH1okEKf39mwpsRBw+zJnB8vRTRWLJrF/ax4xFy\nctAuaYHntn+FrPnn7X8z5h9/QHQHrscWjLJUGzTENfRRrNM+QU9Nx9e3H5677wP8UnpFMZTlSTS9\ntDAEAcNiQfREV7/NtGoFlbpeAYDaohXO+/4NaamoF1zor8QhimR0u4aUe+/Asnxp0PGSIwc9NTUg\nWtYQBDw33ozeoCGefw9Fb9AQy5df+FNP6jfAfc/9aJe0jM8FJ0hQhhHPhdSRskr6+QYdnvPy+5tm\nnIeL55KVrAY12ob3E1hmfQYvPk/S8eNnts35guxpMzFSA5PKfb364nrqGWyTP0Lavy/PkAUYyvoN\n8V53PSn/eRzpyGEAlGbNcb4wEqVbD4SM8C69UJSfr2HpYABZ4ydi1KyN9fNpiMeOolepirRpI+bt\nW0MeI0De2qFp3Z+Ihw6SOXcBepMLz/RbuQp67fCKTErbS9Gr10DesR09JRVf9x547h+St9/Xs49f\nGzZBgnMMoRwty1c4Ywlw0R0qjW9Q+WaAjeN/xX6JdTqr/lJcoXC5sL/5KuQzlADmlcuxj34J55hg\nwWv3Q4/ivvNe0ntejSnEzVk6sA/7hPEB1UdMf28m+eknyfx1GWqrNonZYjEQAL1JU7SWrXBceaZy\ni5CTTfo1XZB3F76mLB07iu3TSfiu7Y28aQNq80tQunTFqBQ+aE1rdD7OV16PxyUkSFChKE9rlhXS\nWAJYUqHKRRrH/5KI1rxIVh1DF7BWMqjTWaXTK35/umHAxo9N7PtVQnUJVG6m0b7ml8hhchhNv68K\nf5LkZAQjtAEOFxkp792D9dOP8V5/oz/oI0wprwT+NULR7Q6poKRVroKeK5qRHyMlFefbZX7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"text/plain": [ "<matplotlib.figure.Figure at 0x2bfe912dfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# color data by cluster\n", "gmm = GaussianMixture(2).fit(x)\n", "labels = gmm.predict(x)\n", "plt.scatter(x2[:, 0], x2[:, 1], c=labels, cmap='rainbow')\n", "plt.colorbar();" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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vkzShKEFBJyfQkNCshXMZeCtPMvuoCIqjloq9dYfxrEVbEuodzj4nI23RK4iP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//n64e7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\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ySaV+DUv0WyHtjYEwpkBLJ5oYPVWepsS3Dj66E1M6U+47+xwo3/0drieohe2rDzhpYIke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uiYPsYaqIS667Hj+YEjjWWAJ5iCuqgIYjHJk5tw5TqS2pHrlKzNqVFW2s5elsYZqhjp2Dpb4iaB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\n8AAAjzzyCBdccAGve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"text/plain": [ "<matplotlib.figure.Figure at 0x2bfeda01320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n", "\n", "pivoted.T[labels == 0].T.plot(legend=False, alpha=0.1, ax=ax[0]);\n", "pivoted.T[labels == 1].T.plot(legend=False, alpha=0.1, ax=ax[1]);\n", "\n", "ax[0].set_title('Purple Cluster') # weekend pattern?\n", "ax[1].set_title('Red Cluster'); # weekday pattern?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing with day of week" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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6DZOsjWcBE3iKEnYQpT9buBLH9jfuhdvQwSZSjfv280SVIPFzrumcm+0kLZ6Z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gOoSIZFPqWsZvc/VdO1TSMeMvHX99GzyZWSC7ektJkPXvxcSgX1s7d7MOTD5TTwZ0mAnwmvZdAw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\n0oIlO8LMHIVtnU52BCJEgCk2G3enppKv66w0U/i/n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"text/plain": [ "<matplotlib.figure.Figure at 0x2bfeda13710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x2[:, 0], x2[:, 1], c=dayofweek, cmap='rainbow')\n", "plt.colorbar();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing outliers" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2012-11-22', '2012-11-23', '2012-12-24', '2012-12-25',\n", " '2013-01-01', '2013-05-27', '2013-07-04', '2013-07-05',\n", " '2013-09-02', '2013-11-28', '2013-11-29', '2013-12-20',\n", " '2013-12-24', '2013-12-25', '2014-01-01', '2014-04-23',\n", " '2014-05-26', '2014-07-04', '2014-09-01', '2014-11-27',\n", " '2014-11-28', '2014-12-24', '2014-12-25', '2014-12-26',\n", " '2015-01-01', '2015-05-25', '2015-07-03', '2015-09-07',\n", " '2015-11-26', '2015-11-27', '2015-12-24', '2015-12-25',\n", " '2016-01-01', '2016-05-30', '2016-07-04', '2016-09-05',\n", " '2016-11-24', '2016-11-25', '2016-12-26', '2017-01-02',\n", " '2017-02-06', '2017-05-29'],\n", " dtype='datetime64[ns]', freq=None)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# outliers are public holidays?\n", "dates = pd.DatetimeIndex(pivoted.columns)\n", "dates[(labels == 0) & (dayofweek < 5)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
unlicense
JamesWo/cs194-16-data_manatees
create_joined_featurized.ipynb
2
24823
{ "metadata": { "name": "", "signature": "sha256:24fe3cf8fea4a53f4a40259d8660c8ac2bbf9df8f9611a919e33d00103aa2bbc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# takes in a joined dataframe by tuples and generates a matrix by mapping the rows to feature vectors\n", "DATA_PATH = \"joined_unfeaturized.csv\" \n", "import pandas as pd\n", "df = pd.read_csv(DATA_PATH, na_values=['-'])\n", "df = df.where((pd.notnull(df)), None)\n", "df.head(2)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>feature-311</th>\n", " <th>tuple</th>\n", " <th>911-reports</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> [{'Category': 'Sign Repair', 'TimeBin': 627414...</td>\n", " <td> (6274149.810962193, -122.42479733333332, 37.77...</td>\n", " <td> []</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> [{'Category': '311 External Request', 'TimeBin...</td>\n", " <td> (68448950.51830366, -122.45177022222221, 37.76...</td>\n", " <td> [{'Category': 'WARRANTS', 'TimeBin': 68448950....</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ " feature-311 \\\n", "0 [{'Category': 'Sign Repair', 'TimeBin': 627414... \n", "1 [{'Category': '311 External Request', 'TimeBin... \n", "\n", " tuple \\\n", "0 (6274149.810962193, -122.42479733333332, 37.77... \n", "1 (68448950.51830366, -122.45177022222221, 37.76... \n", "\n", " 911-reports \n", "0 [] \n", "1 [{'Category': 'WARRANTS', 'TimeBin': 68448950.... " ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "df['feature-311'] = df['feature-311'].apply(lambda x: eval(x)) #convert strings to lists\n", "df['911-reports'] = df['911-reports'].apply(lambda x: eval(x))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "df.iloc[1][2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "[{'Category': 'WARRANTS',\n", " 'Descript': 'ENROUTE TO OUTSIDE JURISDICTION',\n", " 'PdDistrict': 'PARK',\n", " 'Resolution': 'ARREST, BOOKED',\n", " 'TimeBin': 68448950.51830366,\n", " 'XBin': -122.45177022222221,\n", " 'YBin': 37.76225922222223},\n", " {'Category': 'WARRANTS',\n", " 'Descript': 'ENROUTE TO OUTSIDE JURISDICTION',\n", " 'PdDistrict': 'PARK',\n", " 'Resolution': 'NONE',\n", " 'TimeBin': 68448950.51830366,\n", " 'XBin': -122.45177022222221,\n", " 'YBin': 37.76225922222223},\n", " {'Category': 'FRAUD',\n", " 'Descript': 'CREDIT CARD, THEFT BY USE OF',\n", " 'PdDistrict': 'PARK',\n", " 'Resolution': 'NONE',\n", " 'TimeBin': 68448950.51830366,\n", " 'XBin': -122.45177022222221,\n", " 'YBin': 37.76225922222223},\n", " {'Category': 'LARCENY/THEFT',\n", " 'Descript': 'GRAND THEFT OF PROPERTY',\n", " 'PdDistrict': 'PARK',\n", " 'Resolution': 'NONE',\n", " 'TimeBin': 68448950.51830366,\n", " 'XBin': -122.45177022222221,\n", " 'YBin': 37.76225922222223},\n", " {'Category': 'ASSAULT',\n", " 'Descript': 'THREATENING PHONE CALL(S)',\n", " 'PdDistrict': 'PARK',\n", " 'Resolution': 'NONE',\n", " 'TimeBin': 68448950.51830366,\n", " 'XBin': -122.45177022222221,\n", " 'YBin': 37.76225922222223}]" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get all possible labels for certain categorical data, by looping through the data.\n", "Category_set = set()\n", "Request_Type_set = set()\n", "#Request_Details_set = set()\n", "Supervisor_District_set = set()\n", "#Source_set = set()\n", "#ct=0\n", "for row_tuple in df.iterrows():\n", "# ct += 1\n", "# if ct>1:\n", "# break\n", " #print row_tuple[1]\n", " row = row_tuple[1] # a pandas Series\n", " #print row['feature-311']\n", " for dct in row['feature-311']:\n", " # print dct\n", " Category = dct['Category']\n", " # Request_Type = dct['Request Type']\n", " #Request_Details = dct['Request Details']\n", " Supervisor_District = int(dct['Supervisor District'])\n", " #Source = dct['Source']\n", " Category_set.add(Category)\n", " # Request_Type_set.add(Request_Type)\n", " #Request_Details_set.add(Request_Details)\n", " Supervisor_District_set.add(Supervisor_District)\n", " #Source_set.add(Source)\n", "print \"done\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "done\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(Category_set)\n", "#print len(Request_Type_set)\n", "#print len(Request_Details_set)\n", "print len(Supervisor_District_set)\n", "#print len(Source_set)\n", "print Category_set\n", "print Supervisor_District_set\n", "#print Source_set" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "27\n", "12\n", "set(['Streetlights', 'Abandoned Vehicle', 'Interdepartmental Request', 'MUNI Feedback', 'Graffiti Private Property', 'DPW Volunteer Programs', 'Tree Maintenance', 'Rec and Park Requests', 'Residential Building Request', 'Blocked Street or SideWalk', 'Sidewalk or Curb', 'Temporary Sign Request', 'Sewer Issues', '311 External Request', 'Litter Receptacles', 'Catch Basin Maintenance', 'Street Defects', 'Street and Sidewalk Cleaning', 'Color Curb', 'SFHA Requests', 'Construction Zone Permits', 'Graffiti Public Property', 'Illegal Postings', 'Damaged Property', 'Unpermitted Cab Complaint', 'General Requests', 'Sign Repair'])\n", "set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get all possible labels for c911 data, by looping through the data.\n", "Category_set_crime = set()\n", "ct = 0\n", "for row_tuple in df.iterrows():\n", " ct += 1\n", " if ct>200000000000000000:\n", " break\n", " row = row_tuple[1] # a pandas Series\n", " #print row['911-reports']\n", " for dct in row['911-reports']:\n", " # print dct\n", " Category_crime = dct['Category']\n", " # Request_Type = dct['Request Type']\n", " #Request_Details = dct['Request Details']\n", " #Supervisor_District = int(dct['Supervisor District'])\n", " #Source = dct['Source']\n", " Category_set_crime.add(Category_crime)\n", " # Request_Type_set.add(Request_Type)\n", " #Request_Details_set.add(Request_Details)\n", " # Supervisor_District_set.add(Supervisor_District)\n", " #Source_set.add(Source)\n", "print \"done\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "done\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "Category_set_crime" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "{'ARSON',\n", " 'ASSAULT',\n", " 'BAD CHECKS',\n", " 'BRIBERY',\n", " 'BURGLARY',\n", " 'DISORDERLY CONDUCT',\n", " 'DRIVING UNDER THE INFLUENCE',\n", " 'DRUG/NARCOTIC',\n", " 'DRUNKENNESS',\n", " 'EMBEZZLEMENT',\n", " 'EXTORTION',\n", " 'FAMILY OFFENSES',\n", " 'FORGERY/COUNTERFEITING',\n", " 'FRAUD',\n", " 'GAMBLING',\n", " 'KIDNAPPING',\n", " 'LARCENY/THEFT',\n", " 'LIQUOR LAWS',\n", " 'LOITERING',\n", " 'MISSING PERSON',\n", " 'NON-CRIMINAL',\n", " 'OTHER OFFENSES',\n", " 'PORNOGRAPHY/OBSCENE MAT',\n", " 'PROSTITUTION',\n", " 'RECOVERED VEHICLE',\n", " 'ROBBERY',\n", " 'RUNAWAY',\n", " 'SEX OFFENSES, FORCIBLE',\n", " 'SEX OFFENSES, NON FORCIBLE',\n", " 'STOLEN PROPERTY',\n", " 'SUICIDE',\n", " 'SUSPICIOUS OCC',\n", " 'TRESPASS',\n", " 'VANDALISM',\n", " 'VEHICLE THEFT',\n", " 'WARRANTS',\n", " 'WEAPON LAWS'}" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Generate a feature vector given a list of dictionaries. Each dictionary represents one 311 report.\n", "# Regularization should also go here\n", " # @param reports_311: a list of dictionaries. Each dictionary is of the form\n", " # {'Category': string, 'Request Details': string, 'Request Type': string, 'Source': string, 'Supervisor District':string,\n", " # 'TimeBin': float, 'XBin':float, 'YBin':float}\n", "\n", "#create map dictionaries that will map each one of the categories from the category sets to an index in feature_vector.\n", "category_map = {}\n", "supervisor_map = {}\n", "#source_map = {}\n", "index = 0\n", "for cat in Category_set:\n", " category_map[cat] = index\n", " index += 1 \n", "for cat in Supervisor_District_set:\n", " supervisor_map[cat] = index\n", " index += 1\n", "#for cat in Source_set:\n", "# source_map[cat] = index\n", "# index += 1\n", "category_map_crime = {}\n", "index2 = 0\n", "for cat in Category_set_crime:\n", " category_map_crime[cat] = index2\n", " index2 += 1\n", " \n", "def generate_feature_vector(reports_311):\n", " # simple feature vector that is just sum of counts of 311 reports of each category \n", " # from (Category, Supervisor District, and Source)\n", " feature_vector = []\n", " for i in xrange(index):\n", " feature_vector.append(0)\n", " for report in reports_311:\n", " #print report\n", " # the first 27 features correspond to the number of 311 reports from each category from Category set\n", " # the next 12 features correspond to the number of 311 reports from each category from Supervisor_District set\n", " # the next 9 features correspond to the number of 311 reports from each category from Source set\n", " feature_vector[category_map[report[\"Category\"]]]+= 1\n", " feature_vector[supervisor_map[report[\"Supervisor District\"]]]+= 1\n", " #feature_vector[source_map[report[\"Source\"]]]+=1\n", " feature_vector.append(len(reports_311))\n", " return feature_vector\n", "\n", "def generate_output_vector(reports_911):\n", " output_vector = []\n", " for i in xrange(index2):\n", " output_vector.append(0)\n", " for report in reports_911:\n", " #print report\n", " output_vector[category_map_crime[report[\"Category\"]]]+= 1\n", " output_vector.append(len(reports_911))\n", " return output_vector\n", "print \"done\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "done\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "print category_map\n", "print supervisor_map" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'Streetlights': 0, 'Abandoned Vehicle': 1, 'Interdepartmental Request': 2, 'MUNI Feedback': 3, 'Graffiti Private Property': 4, 'DPW Volunteer Programs': 5, 'Tree Maintenance': 6, 'Construction Zone Permits': 20, 'Residential Building Request': 8, 'Blocked Street or SideWalk': 9, 'Sidewalk or Curb': 10, 'Temporary Sign Request': 11, 'Sewer Issues': 12, '311 External Request': 13, 'Litter Receptacles': 14, 'Catch Basin Maintenance': 15, 'Street Defects': 16, 'Street and Sidewalk Cleaning': 17, 'Color Curb': 18, 'SFHA Requests': 19, 'Rec and Park Requests': 7, 'Graffiti Public Property': 21, 'Illegal Postings': 22, 'Damaged Property': 23, 'Unpermitted Cab Complaint': 24, 'General Requests': 25, 'Sign Repair': 26}\n", "{0: 27, 1: 28, 2: 29, 3: 30, 4: 31, 5: 32, 6: 33, 7: 34, 8: 35, 9: 36, 10: 37, 11: 38}\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "count = 0\n", "df_dict = {}\n", "df_dict['vector'] = []\n", "\n", "for row_tuple in df.iterrows():\n", " count += 1\n", " if count > 3000000000000000000: \n", " break\n", " row = row_tuple[1] # a pandas Series\n", " dct = (row['feature-311'])\n", " f_in = generate_feature_vector(dct)\n", " dct2 = (row['911-reports'])\n", " f_out = generate_output_vector(dct2)\n", " f_both = f_in + f_out\n", " df_dict['vector'].append(f_both)\n", "print \"done\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "done\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "print index\n", "print index2\n", "print category_map_crime" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "39\n", "37\n", "{'KIDNAPPING': 0, 'WEAPON LAWS': 1, 'WARRANTS': 2, 'PROSTITUTION': 3, 'EMBEZZLEMENT': 4, 'SEX OFFENSES, NON FORCIBLE': 5, 'PORNOGRAPHY/OBSCENE MAT': 29, 'FRAUD': 20, 'DRIVING UNDER THE INFLUENCE': 8, 'ROBBERY': 9, 'BURGLARY': 10, 'STOLEN PROPERTY': 32, 'SUSPICIOUS OCC': 11, 'ARSON': 17, 'ASSAULT': 33, 'FORGERY/COUNTERFEITING': 14, 'BAD CHECKS': 15, 'DRUNKENNESS': 16, 'GAMBLING': 18, 'OTHER OFFENSES': 19, 'SUICIDE': 7, 'RECOVERED VEHICLE': 21, 'SEX OFFENSES, FORCIBLE': 22, 'DRUG/NARCOTIC': 23, 'TRESPASS': 24, 'LOITERING': 6, 'VANDALISM': 26, 'MISSING PERSON': 34, 'LIQUOR LAWS': 30, 'VEHICLE THEFT': 31, 'EXTORTION': 28, 'BRIBERY': 13, 'FAMILY OFFENSES': 12, 'NON-CRIMINAL': 27, 'DISORDERLY CONDUCT': 35, 'RUNAWAY': 36, 'LARCENY/THEFT': 25}\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "len(df_dict['vector'][0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "78" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "final_df = pd.DataFrame(df_dict)\n", "len((final_df.iloc[0])[0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "78" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# use the joined table to create a matrix. \n", "# The matrix will be Nx(D+1), where N is the number of unique tuples, and D is the number of features in \n", "# our feature vector. The last column of the matrix corresponds to the feature-911, i.e. the number\n", "# of 911 reports in the given location/time.\n", "import numpy as np\n", "N = len(final_df)\n", "D = final_df['vector']\n", "#print type(D[i])\n", "mat = np.zeros([N, len(D[0])])\n", "for i in xrange(N):\n", " mat[i,] = D[i]\n", "print len(mat[13])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "78\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "np.savetxt(\"joined_matrix_split.txt\", mat)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(mat)\n", "print mat[0]\n", "print len(mat[0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "249147\n", "[ 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 2. 2. 0. 0.\n", " 0. 0. 0. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0.]\n", "78\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "intmat = mat.astype(int)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "np.bincount(intmat[:,44])\n", "for k in category_map_crime:\n", " i = category_map_crime[k]\n", " print k + \": \" + str(np.bincount(intmat[:,40+i]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "KIDNAPPING: [247347 1701 97 2]\n", "WEAPON LAWS: [244746 3757 556 69 16 1 1 1]\n", "WARRANTS: [226669 15941 4394 1413 510 147 43 22 3 2\n", " 1 2]\n", "PROSTITUTION: [247627 868 356 139 67 27 22 14 12 3\n", " 4 5 2 1]\n", "EMBEZZLEMENT: [248454 678 11 3 1]\n", "SEX OFFENSES, NON FORCIBLE: [249050 97]\n", "PORNOGRAPHY/OBSCENE MAT: [249135 12]\n", "FRAUD: [238377 9613 976 144 33 4]\n", "DRIVING UNDER THE INFLUENCE: [247403 1658 78 8]\n", "ROBBERY: [234573 12531 1702 274 55 11 1]\n", "BURGLARY: [227944 18427 2319 359 78 18 0 2]\n", "STOLEN PROPERTY: [248357 756 30 4]\n", "SUSPICIOUS OCC: [233142 14541 1301 144 15 4]\n", "ARSON: [248298 820 25 3 0 1]\n", "ASSAULT: [214942 23795 7100 2213 740 241 77 28 6 3\n", " 1 0 1]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "FORGERY/COUNTERFEITING: [245031 3708 354 44 7 2 0 0 1]\n", "BAD CHECKS: [248944 201 2]\n", "DRUNKENNESS: [245925 3030 171 16 2 1 1 1]\n", "GAMBLING: [249074 67 6]\n", "OTHER OFFENSES: [197798 32945 10349 4227 1909 997 448 222 126 50\n", " 27 31 5 3 3 1 2 1 2 0\n", " 0 0 1]\n", "SUICIDE: [248790 355 1 1]\n", "RECOVERED VEHICLE: [246531 2555 59 1 0 0 0 0 0 0\n", " 1]\n", "SEX OFFENSES, FORCIBLE: [246649 2272 195 28 3]\n", "DRUG/NARCOTIC: [230431 8809 5022 2189 1094 599 400 230 134 85\n", " 62 41 20 18 6 1 3 0 3]\n", "TRESPASS: [244651 4233 244 14 3 2]\n", "LOITERING: [248619 480 41 5 2]\n", "VANDALISM: [222753 22503 3213 537 113 17 3 4 1 1\n", " 1 0 0 0 0 0 1]\n", "MISSING PERSON: [236519 5966 5835 438 320 27 29 5 4 2\n", " 2]\n", "LIQUOR LAWS: [247930 1146 59 10 2]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "VEHICLE THEFT: [227792 18391 2606 309 41 6 0 0 1 0\n", " 1]\n", "EXTORTION: [248932 206 8 1]\n", "BRIBERY: [249085 62]\n", "FAMILY OFFENSES: [248936 201 8 2]\n", "NON-CRIMINAL: [202958 32799 8464 3015 1182 461 167 64 22 8\n", " 5 1 1]\n", "DISORDERLY CONDUCT: [246110 2676 317 42 2]\n", "RUNAWAY: [247836 1235 71 4 0 1]\n", "LARCENY/THEFT: [179767 40762 14188 6184 3148 1768 1058 749 521 352\n", " 220 156 104 66 42 25 16 7 4 3\n", " 3 3 0 0 1]\n" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "#plt.hist((intmat[:,-1]))\n", "#plt.show()\n", "np.bincount(intmat[:,40+37])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "array([101219, 48819, 30121, 19087, 12030, 7973, 5536, 3911,\n", " 3033, 2305, 1956, 1711, 1465, 1362, 1224, 1034,\n", " 890, 830, 700, 651, 593, 472, 394, 332,\n", " 260, 230, 212, 177, 145, 111, 80, 61,\n", " 42, 41, 33, 28, 13, 18, 10, 9,\n", " 7, 7, 2, 4, 4, 0, 3, 2])" ] } ], "prompt_number": 57 } ], "metadata": {} } ] }
apache-2.0
SylvainCorlay/bqplot
examples/Marks/Pyplot/Label.ipynb
2
4748
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import bqplot.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "np.random.seed(10)\n", "price_data = pd.DataFrame(np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,\n", " columns=['Security 1', 'Security 2'],\n", " index=pd.date_range(start='01-01-2007', periods=150))\n", "y_data = np.cumsum(np.random.randn(100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Label positioned in data co-ordinates" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "475580d8a2fd4739bc8681e199937d3e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top':…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(title='Basic Label Example')\n", "test_line = plt.plot(np.arange(10), y_data[:10])\n", "test_label = plt.label(['Test', 'Label', 'for', 'the', 'Data'], \n", " x=np.arange(5), y=y_data[:5], \n", " default_size=26, font_weight='bolder',\n", " colors=['orange', 'red'], update_on_move=True)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting the label attribute `enable_move` to `True` makes the label draggable" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# now the labels can be moved by dragging them!\n", "test_label.enable_move = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Label positioned in terms of Figure co-ordinates" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b28c4173135b48b19f21b455b07edad6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top':…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "test_line = plt.plot(np.arange(10), y_data)\n", "test_label = plt.label(['Test Label'], default_size=26, font_weight='bolder', colors=['orange'])\n", "test_label.x = [0.5]\n", "test_label.y = [0.2]\n", "fig" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Rotating the label\n", "test_label.rotate_angle = 30" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Label positioned at a Date value" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e0c4dbf20b944b488a9e340801bd1bfd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Figure(axes=[Axis(scale=DateScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top': 6…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "lines = plt.plot(price_data.index, price_data['Security 1'])\n", "label = plt.label(['Pi-Day'], x=[np.datetime64('2007-03-14')], colors=['orange'])\n", "label.y = [.5]\n", "fig" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Setting an offset in pixels\n", "label.x_offset = 100" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
sfegan/calin
examples/iact_data/event viewer.ipynb
2
4892
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Event viewer\n", "\n", "calin/examples/iact_data/event viewer.ipynb - Stephen Fegan - 2016-12-13\n", "\n", "Copyright 2016, Stephen Fegan <[email protected]>\n", "Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris\n", "\n", "This file is part of \"__calin__\". \"__calin__\" is free software: you can redistribute it and/or modify it under the\n", "terms of the GNU General Public License version 2 or later, as published by\n", "the Free Software Foundation. \"__calin__\" is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n", "\n", "## Introduction\n", "\n", "Open a ZFits file and draw camera events" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pylab inline\n", "import calin.iact_data.telescope_data_source\n", "from IPython.display import clear_output\n", "from ipywidgets.widgets import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dcfg = calin.iact_data.telescope_data_source.NectarCamZFITSDataSource.default_decoder_config()\n", "dcfg.set_demand_configured_module_id([1,2,5,6,10,11,14,15,17,18])\n", "dcfg.set_exchange_gain_channels(True)\n", "dcfg.set_camera_type(dcfg.NECTARCAM_TESTBENCH_19CHANNEL)\n", "src = calin.iact_data.telescope_data_source.NectarCamZFITSDataSource(\n", " '/CTA/cta.cppm.in2p3.fr/NectarCAM/20161021/Run0264.fits.fz', dcfg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "run_config = src.get_run_configuration()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_image(pix_data):\n", "# figure(figsize=(10,8))\n", "# figure(figsize=(7,6))\n", " figure(figsize=(5,4.5))\n", " pix = []\n", " max_xy = 0\n", " for pix_index in range(len(pix_data)):\n", " pix_id = run_config.configured_channel_id(pix_index)\n", " vx = run_config.camera_layout().channel(pix_id).outline_polygon_vertex_x_view()\n", " vy = run_config.camera_layout().channel(pix_id).outline_polygon_vertex_y_view()\n", " vv = zeros((len(vx),2))\n", " vv[:,0] = vx\n", " vv[:,1] = vy\n", " max_xy = max(max_xy, max(abs(vx)), max(abs(vy)))\n", " pix.append(Polygon(vv,closed=True))\n", " pc = matplotlib.collections.PatchCollection(pix, cmap=matplotlib.cm.jet)\n", " pc.set_array(asarray(pix_data))\n", " pc.set_linewidths(0)\n", " clo = min(-100, min(pix_data))\n", " chi = max(100, max(pix_data))\n", " pc.set_clim(clo,chi)\n", " gca().add_collection(pc)\n", " axis('square')\n", " axis(asarray([-1,1,-1,1])*1.05*max_xy)\n", " xlabel('X coordinate [cm]')\n", " ylabel('Y coordinate [cm]')\n", " colorbar(pc)\n", "# grid(color='w')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_next_event():\n", " index, event, arena = src.get_next()\n", " im = event.high_gain_image()\n", " pixdata = im.camera_charges().charge() * 0.0\n", " for i in range(im.camera_waveforms().waveform_size()):\n", " samp = im.camera_waveforms().waveform(i).samples()\n", " pixdata[i] = sum(samp[30:42]*1.0) - sum(samp[0:12]*1.0)\n", " plot_image(pixdata)\n", " title('Index: %d'%index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "button = Button(description=\"Display next event\")\n", "display(button)\n", "\n", "def on_button_clicked(b):\n", " clear_output()\n", " plot_next_event()\n", "\n", "button.on_click(on_button_clicked)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.5" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
Dataweekends/pyladies_intro_to_data_science
Iris Flowers Workshop.ipynb
1
13382
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Separating Flowers\n", "This notebook explores a classic Machine Learning Dataset: the Iris flower dataset\n", "\n", "## Tutorial goals\n", "1. Explore the dataset\n", "2. Build a simple predictive modeling\n", "3. Iterate and improve your score\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "How to follow along:\n", "\n", " git clone https://github.com/dataweekends/pyladies_intro_to_data_science\n", "\n", " cd pyladies_intro_to_data_science\n", " \n", " ipython notebook" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We start by importing the necessary libraries:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 1) Explore the dataset" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Numerical exploration\n", "\n", "- Load the csv file into memory using Pandas\n", "- Describe each attribute\n", " - is it discrete?\n", " - is it continuous?\n", " - is it a number?\n", "- Identify the target\n", "- Check if any values are missing\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Load the csv file into memory using Pandas" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df = pd.read_csv('iris-2-classes.csv')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "What's the content of ```df``` ?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df.iloc[[0,1,98,99]]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Describe each attribute (is it discrete? is it continuous? is it a number? is it text?)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Quick stats on the features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Identify the target\n", "What are we trying to predict?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "ah, yes... the type of Iris flower!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "df['iris_type'].value_counts()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Check if any values are missing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Mental notes so far:\n", "\n", "- Dataset contains 100 entries\n", "- 1 Target column (```iris_type```)\n", "- 4 Numerical Features\n", "- No missing values" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Visual exploration" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Distribution of Sepal Length, influence on target:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df[df['iris_type']=='virginica']['sepal_length_cm'].plot(kind='hist', bins = 10, range = (4,7),\n", " alpha = 0.3, color = 'b')\n", "df[df['iris_type']=='versicolor']['sepal_length_cm'].plot(kind='hist', bins = 10, range = (4,7),\n", " alpha = 0.3, color = 'g')\n", "plt.title('Distribution of Sepal Length', size = '20')\n", "plt.xlabel('Sepal Length (cm)', size = '20')\n", "plt.ylabel('Number of flowers', size = '20')\n", "plt.legend(['Virginica', 'Versicolor'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Two features combined, scatter plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "plt.scatter(df[df['iris_type']== 'virginica']['petal_length_cm'].values,\n", " df[df['iris_type']== 'virginica']['sepal_length_cm'].values, label = 'Virginica', c = 'b', s = 40)\n", "plt.scatter(df[df['iris_type']== 'versicolor']['petal_length_cm'].values,\n", " df[df['iris_type']== 'versicolor']['sepal_length_cm'].values, label = 'Versicolor', c = 'r', marker='s',s = 40)\n", "plt.legend(['virginica', 'versicolor'], loc = 2)\n", "plt.title('Iris Flowers', size = '20')\n", "plt.xlabel('Petal Length (cm)', size = '20')\n", "plt.ylabel('Sepal Length (cm)', size = '20')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Ok, so, the flowers seem to have different characteristics\n", "\n", "Let's build a simple model to test that" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Define a new target column called ```target``` like this:\n", "- if ```iris_type = 'virginica'``` ===> ```target = 1```\n", "- otherwise ```target = 0```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df['target'] = df['iris_type'].map({'virginica': 1, 'versicolor': 0})\n", "\n", "print df[['iris_type', 'target']].head(2)\n", "print\n", "print df[['iris_type', 'target']].tail(2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Define simplest model as benchmark" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The simplest model is a model that predicts 0 for everybody, i.e. all versicolor.\n", "\n", "How good is it?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df['target'].value_counts()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "If I predict every flower is Versicolor, I'm correct 50% of the time" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "We need to do better than that" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Define features (X) and target (y) variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "X = df[['sepal_length_cm', 'sepal_width_cm',\n", " 'petal_length_cm', 'petal_width_cm']]\n", "y = df['target']" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Initialize a decision Decision Tree model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "\n", "model = DecisionTreeClassifier(random_state=0)\n", "model " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ " Split the features and the target into a Train and a Test subsets.\n", " \n", " Ratio should be 70/30" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " test_size = 0.3, random_state=0)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Train the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "model.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Calculate the model score" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "my_score = model.score(X_test, y_test)\n", "\n", "print \"Classification Score: %0.2f\" % my_score" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Print the confusion matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "y_pred = model.predict(X_test)\n", "\n", "print \"\\n=======confusion matrix==========\"\n", "print confusion_matrix(y_test, y_pred)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 3) Iterate and improve\n", "Start from:\n", "\n", " > python iris_starter_script.py\n", " \n", "It's a basic pipeline. How can you improve the score? Try:\n", "- [Changing the model parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)\n", " \n", "- [Using a different model](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)\n", " \n", "Next Steps: try separating 3 classes instead of 2 (```iris.csv``` provided)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
sbarakat/graph-partitioning
fennel_restream_experiments.ipynb
1
125400
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import os\n", "import csv\n", "import platform\n", "import pandas as pd\n", "import networkx as nx\n", "from graph_partitioning import GraphPartitioning, utils\n", "\n", "run_metrics = True\n", "\n", "cols = [\"WASTE\", \"CUT RATIO\", \"EDGES CUT\", \"TOTAL COMM VOLUME\", \"Qds\", \"CONDUCTANCE\", \"MAXPERM\", \"NMI\", \"FSCORE\", \"FSCORE RELABEL IMPROVEMENT\", \"LONELINESS\"]\n", "#cols = [\"WASTE\", \"CUT RATIO\", \"EDGES CUT\", \"TOTAL COMM VOLUME\", \"Q\", \"Qds\", \"CONDUCTANCE\", \"LONELINESS\", \"NETWORK PERMANENCE\", \"NORM. MUTUAL INFO\", \"EDGE CUT WEIGHT\", \"FSCORE\", \"FSCORE RELABEL IMPROVEMENT\"]\n", "#cols = [\"WASTE\", \"CUT RATIO\", \"EDGES CUT\", \"TOTAL COMM VOLUME\", \"MODULARITY\", \"LONELINESS\", \"NETWORK PERMANENCE\", \"NORM. MUTUAL INFO\", \"EDGE CUT WEIGHT\", \"FSCORE\", \"FSCORE RELABEL IMPROVEMENT\"]\n", "\n", "pwd = %pwd\n", "\n", "config = {\n", "\n", " \"DATA_FILENAME\": os.path.join(pwd, \"data\", \"predition_model_tests\", \"network\", \"network_$$.txt\"),\n", " \"OUTPUT_DIRECTORY\": os.path.join(pwd, \"output\"),\n", "\n", " # Set which algorithm is run for the PREDICTION MODEL.\n", " # Either: 'FENNEL' or 'SCOTCH'\n", " \"PREDICTION_MODEL_ALGORITHM\": \"FENNEL\",\n", "\n", " # Alternativly, read input file for prediction model.\n", " # Set to empty to generate prediction model using algorithm value above.\n", " \"PREDICTION_MODEL\": \"\",\n", "\n", " \n", " \"PARTITIONER_ALGORITHM\": \"FENNEL\",\n", "\n", " # File containing simulated arrivals. This is used in simulating nodes\n", " # arriving at the shelter. Nodes represented by line number; value of\n", " # 1 represents a node as arrived; value of 0 represents the node as not\n", " # arrived or needing a shelter.\n", " \"SIMULATED_ARRIVAL_FILE\": os.path.join(pwd,\n", " \"data\",\n", " \"predition_model_tests\",\n", " \"dataset_1_shift_rotate\",\n", " \"simulated_arrival_list\",\n", " \"percentage_of_prediction_correct_100\",\n", " \"arrival_100_$$.txt\"\n", " ),\n", "\n", " # File containing the prediction of a node arriving. This is different to the\n", " # simulated arrivals, the values in this file are known before the disaster.\n", " \"PREDICTION_LIST_FILE\": os.path.join(pwd,\n", " \"data\",\n", " \"predition_model_tests\",\n", " \"dataset_1_shift_rotate\",\n", " \"prediction_list\",\n", " \"prediction_$$.txt\"\n", " ),\n", "\n", " # File containing the geographic location of each node, in \"x,y\" format.\n", " \"POPULATION_LOCATION_FILE\": os.path.join(pwd,\n", " \"data\",\n", " \"predition_model_tests\",\n", " \"coordinates\",\n", " \"coordinates_$$.txt\"\n", " ),\n", "\n", " # Number of shelters\n", " \"num_partitions\": 4,\n", "\n", " # The number of iterations when making prediction model\n", " \"num_iterations\": 1,\n", "\n", " # Percentage of prediction model to use before discarding\n", " # When set to 0, prediction model is discarded, useful for one-shot\n", " \"prediction_model_cut_off\": .0,\n", "\n", " # Alpha value used in one-shot (when restream_batches set to 1)\n", " \"one_shot_alpha\": 0.5,\n", " \n", " \"use_one_shot_alpha\" : False,\n", " \n", " # Number of arrivals to batch before recalculating alpha and restreaming.\n", " \"restream_batches\": 1000,\n", "\n", " # When the batch size is reached: if set to True, each node is assigned\n", " # individually as first in first out. If set to False, the entire batch\n", " # is processed and empty before working on the next batch.\n", " \"sliding_window\": False,\n", "\n", " # Create virtual nodes based on prediction model\n", " \"use_virtual_nodes\": False,\n", "\n", " # Virtual nodes: edge weight\n", " \"virtual_edge_weight\": 1.0,\n", " \n", " # Loneliness score parameter. Used when scoring a partition by how many\n", " # lonely nodes exist.\n", " \"loneliness_score_param\": 1.2,\n", "\n", "\n", " ####\n", " # GRAPH MODIFICATION FUNCTIONS\n", "\n", " # Also enables the edge calculation function.\n", " \"graph_modification_functions\": True,\n", "\n", " # If set, the node weight is set to 100 if the node arrives at the shelter,\n", " # otherwise the node is removed from the graph.\n", " \"alter_arrived_node_weight_to_100\": False,\n", "\n", " # Uses generalized additive models from R to generate prediction of nodes not\n", " # arrived. This sets the node weight on unarrived nodes the the prediction\n", " # given by a GAM.\n", " # Needs POPULATION_LOCATION_FILE to be set.\n", " \"alter_node_weight_to_gam_prediction\": False,\n", " \n", " # Enables edge expansion when graph_modification_functions is set to true\n", " \"edge_expansion_enabled\": True,\n", "\n", " # The value of 'k' used in the GAM will be the number of nodes arrived until\n", " # it reaches this max value.\n", " \"gam_k_value\": 100,\n", "\n", " # Alter the edge weight for nodes that haven't arrived. This is a way to\n", " # de-emphasise the prediction model for the unknown nodes.\n", " \"prediction_model_emphasis\": 1.0,\n", " \n", " # This applies the prediction_list_file node weights onto the nodes in the graph\n", " # when the prediction model is being computed and then removes the weights\n", " # for the cutoff and batch arrival modes\n", " \"apply_prediction_model_weights\": True,\n", "\n", " \"SCOTCH_LIB_PATH\": os.path.join(pwd, \"libs/scotch/macOS/libscotch.dylib\")\n", " if 'Darwin' in platform.system()\n", " else \"/usr/local/lib/libscotch.so\",\n", " \n", " # Path to the PaToH shared library\n", " \"PATOH_LIB_PATH\": os.path.join(pwd, \"libs/patoh/lib/macOS/libpatoh.dylib\")\n", " if 'Darwin' in platform.system()\n", " else os.path.join(pwd, \"libs/patoh/lib/linux/libpatoh.so\"),\n", " \n", " \"PATOH_ITERATIONS\": 5,\n", " \n", " # Expansion modes: 'avg_node_weight', 'total_node_weight', 'smallest_node_weight'\n", " # 'largest_node_weight'\n", " # add '_squared' or '_sqrt' at the end of any of the above for ^2 or sqrt(weight)\n", " # i.e. 'avg_node_weight_squared\n", " \"PATOH_HYPEREDGE_EXPANSION_MODE\": 'no_expansion',\n", " \n", " # Edge Expansion: average, total, minimum, maximum, product, product_squared, sqrt_product\n", " \"EDGE_EXPANSION_MODE\" : 'total',\n", " \n", " # Whether nodes should be reordered using a centrality metric for optimal node assignments in batch mode\n", " # This is specific to FENNEL and at the moment Leverage Centrality is used to compute new noder orders\n", " \"FENNEL_NODE_REORDERING_ENABLED\": False,\n", " \n", " # Whether the Friend of a Friend scoring system is active during FENNEL partitioning.\n", " # FOAF employs information about a node's friends to determine the best partition when\n", " # this node arrives at a shelter and no shelter has friends already arrived\n", " \"FENNEL_FRIEND_OF_A_FRIEND_ENABLED\": False,\n", " \n", " # Alters how much information to print. Keep it at 1 for this notebook.\n", " # 0 - will print nothing, useful for batch operations.\n", " # 1 - prints basic information on assignments and operations.\n", " # 2 - prints more information as it batches arrivals.\n", " \"verbose\": 1\n", "}\n", "\n", "gp = GraphPartitioning(config)\n", "\n", "# Optional: shuffle the order of nodes arriving\n", "# Arrival order should not be shuffled if using GAM to alter node weights\n", "#random.shuffle(gp.arrival_order)\n", "\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mode 1\n", "Mode 1 Iteration 0\n", "Mode 1 Iteration 20\n", "Mode 1 Iteration 40\n", "Mode 1 Iteration 60\n", "Mode 1 Iteration 80\n", "CUT_RATIO,restream_iter_1,0.204550543679,0.0201880780007,0.20796460177,0.224267211997,0.183856502242,0.200394866732,0.191912268677,0.167903852952,0.208132322536,0.188660141748,0.186022610483,0.208058353595,0.208049113233,0.203135650988,0.231036882394,0.204244954767,0.160282258065,0.225176708179,0.17997247075,0.185249912618,0.239002424662,0.229697587496,0.200208188758,0.24331829226,0.220491519557,0.20614640884,0.18301514154,0.210023866348,0.18514715948,0.215479031708,0.207493814069,0.236688423222,0.195378151261,0.215853259089,0.249309392265,0.134451019066,0.214498510427,0.217030114226,0.204237007613,0.204672589668,0.204088826225,0.150721718698,0.182058920887,0.203007518797,0.211444521982,0.170537261698,0.181282495667,0.201494962626,0.231122783979,0.18952285619,0.224762574745,0.203536870204,0.20452173913,0.190657439446,0.186175755471,0.218298015473,0.225183211193,0.213479415671,0.217086834734,0.187060006705,0.238335011749,0.233805668016,0.223759703004,0.184411969381,0.195418257549,0.174465240642,0.199336650083,0.228581460674,0.200544773578,0.199443090846,0.215505464481,0.192132068844,0.197881066302,0.21870604782,0.222730241672,0.201909476662,0.201309328969,0.202661207779,0.227921195652,0.193603851444,0.197985411601,0.213575591361,0.173018549747,0.218336162988,0.200267022697,0.195053988157,0.21151226158,0.19180959048,0.208155212101,0.243270868825,0.214260869565,0.203466204506,0.220420834771,0.20462931902,0.204915310528,0.162606978275,0.230414746544,0.204716336295,0.186925098555,0.214853195164,0.21248669741,0.217406027768\n", "EC,restream_iter_1,600.12,59.8769204285,611,658,533,609,560,475,604,559,543,599,610,596,664,587,477,669,523,530,690,676,577,701,637,597,556,616,541,632,587,689,558,659,722,409,648,627,617,622,579,449,550,594,606,492,523,620,704,568,639,610,588,551,536,649,676,643,620,558,710,693,663,530,563,522,601,651,589,573,631,547,579,622,682,571,615,594,671,563,570,623,513,643,600,560,621,548,633,714,616,587,639,610,617,494,700,599,569,622,599,642\n", "TCV,restream_iter_1,660.27,57.1434781931,680,716,600,685,616,545,664,609,654,628,677,637,725,681,547,713,594,607,767,704,656,741,718,662,635,644,599,701,670,724,646,713,795,464,665,696,688,647,640,480,574,668,685,551,608,625,744,636,711,670,657,617,594,719,679,663,657,647,763,730,675,626,611,575,645,719,672,623,689,630,653,707,714,665,673,693,733,635,638,684,548,683,684,639,677,656,684,747,680,643,696,656,654,558,773,682,620,683,688,685\n", "LONELINESS,restream_iter_1,0.815178299313,0.00737985403498,0.813410696409,0.805780005054,0.822926254407,0.812463962049,0.823368956589,0.81388961784,0.818095320449,0.819665240656,0.818976128087,0.815266570157,0.815414888963,0.824506396352,0.811962357712,0.815613765756,0.826952660602,0.814265198539,0.826054824573,0.808576589787,0.801197795364,0.817532337361,0.815278670856,0.80200544221,0.806749000255,0.814586908344,0.823575561009,0.814318052936,0.816064637385,0.804628070404,0.809519201969,0.810741270735,0.820060896278,0.813200058234,0.808825342781,0.839192787775,0.821485132015,0.805743459201,0.812922553105,0.808270637117,0.818279969325,0.836392550144,0.820270058691,0.814055759221,0.809123252851,0.82257375995,0.81336988004,0.821319004942,0.808366798751,0.820320943226,0.80133244655,0.814891045839,0.809246655909,0.823164101532,0.826002194879,0.808877007503,0.814567579058,0.818567871428,0.809420335161,0.820348434765,0.810965459351,0.810261844433,0.817122448296,0.81692142269,0.823207713714,0.829077699765,0.815862723815,0.81166649785,0.812543675156,0.804576438943,0.804838350074,0.816993808285,0.819239963438,0.805226651123,0.810905094517,0.815239290437,0.814034178419,0.808078922842,0.802814045968,0.81994116897,0.817254801521,0.808594820509,0.833251227308,0.816832750342,0.810588943429,0.817122345111,0.806744638645,0.816365783377,0.813767880426,0.803364037741,0.815752964966,0.818935400713,0.813738957029,0.817362860909,0.823038995345,0.820645321994,0.809562397026,0.819028567836,0.830292898935,0.810697761347,0.804281871917,0.817510407686\n", "QDS,restream_iter_1,0.383606115246,0.0201872856594,0.3794406447661136,0.34351380468589254,0.3804399245492959,0.33130566113299376,0.35966504491410806,0.3766247142487642,0.36704558184136,0.39638175325149505,0.36313647653616765,0.37658903681109024,0.3757203994993886,0.42080661070561437,0.3791881956717781,0.3982377002276688,0.39272967669443937,0.4090313871767237,0.39552569792736303,0.39425083235044533,0.4004985106386804,0.36548173580964527,0.36698205922295896,0.38400693423525134,0.3738731445907811,0.39454818247970835,0.3735428848133901,0.37375656103367805,0.38510812142540407,0.38234757047658896,0.412335912491438,0.39666211817712643,0.3759816461300929,0.4147646848169226,0.37702930587563344,0.40319724403798035,0.3799697836941466,0.38471764461080166,0.39347142312907785,0.3583933353079867,0.36009852370138373,0.3999678146009755,0.3806840892385802,0.41914241572044614,0.3727161607488995,0.40649126873194236,0.3947161793309417,0.388297993415621,0.36785700945181815,0.41784137600442517,0.363018639497918,0.3700697385977451,0.3792323133366252,0.3878185407502253,0.3462715769660531,0.39338562455457526,0.3770449889952922,0.3901721538245209,0.3643505653032113,0.3978524130947546,0.40128835994450035,0.40603489933348347,0.3843816693976441,0.3600639909217107,0.34451043588849206,0.3977133585426389,0.38474241440320783,0.34772516518086066,0.37567703180044554,0.37392720994533646,0.41681863756614274,0.364718283296486,0.36616003453248636,0.3933853801770542,0.3763688457229828,0.3804481773480103,0.3796999218543036,0.3971584632849885,0.3597046200233136,0.4122686338055125,0.3564904506249062,0.4018437798473596,0.3666541036891559,0.3909142101575589,0.35980134001573,0.3963431069106422,0.3483261524112781,0.35739587538977274,0.423144909127735,0.36951162531827936,0.40294363859885934,0.4100026663564864,0.4235923917810185,0.4153133425116894,0.36176243360048943,0.40861382354159603,0.36828806518371626,0.3674666044783483,0.4085289144692589,0.3805193522474861,0.40169571511335617,0.4053321583594394\n", "CONDUCTANCE,restream_iter_1,0.10547233263,0.0136781267153,0.11498558186417625,0.09341511977575005,0.12110259571355052,0.0929164971492448,0.08092938529215651,0.0976124175662388,0.11099662852110408,0.1004253663108729,0.10077289551391996,0.10403336612759788,0.10568846263351779,0.10958468123942534,0.09435286026456202,0.11476672645440442,0.11229709195082913,0.11992154385920405,0.11507032260182448,0.09714635120327554,0.10665588130532297,0.09800388184319922,0.13386979981755717,0.09787734225363892,0.0938240857974144,0.10906337097062223,0.0986225410229754,0.11476964278939292,0.09794930277411434,0.08624068114853733,0.11612005305468961,0.10252024779596426,0.1353183993791159,0.1027985465613294,0.0836570280784036,0.12676603743958764,0.10518316615913399,0.09547807796213476,0.1149635989343841,0.10590463334454947,0.09992324675485349,0.1125212422527136,0.10607650441162983,0.09841043896491272,0.109296158004119,0.09767164338842059,0.12338204753797677,0.11574765794082285,0.11107390957796262,0.10499029715056557,0.10273370643554851,0.06913923540456372,0.1029122731198715,0.12248042775652385,0.1022435776656198,0.11192288632020246,0.09921907046458044,0.11188936279581502,0.12059815004412394,0.09906794816772836,0.11942639928781497,0.08841071738842844,0.12514741355780232,0.10719472760787835,0.11260030309798774,0.1449413034066239,0.1150290236172447,0.12641942367454723,0.1070460338656527,0.09678768259925656,0.10386590559024743,0.12609105064998755,0.10782857125174915,0.10256051826686277,0.09738059835795775,0.13361955729865085,0.08499143890562705,0.08179013715190743,0.1037747707568246,0.09177252720047693,0.12740305214828032,0.08343665422064067,0.11718225843759417,0.10076843076908759,0.12269871493017463,0.09652891071401663,0.09787981107208428,0.09429369712837282,0.08961832880601815,0.10554635095945468,0.12259714460495294,0.09473470650012536,0.10465218517100552,0.08363039068621603,0.09319837325817312,0.07401418017799388,0.09464158333545138,0.10089358035292509,0.11767898625534402,0.10725113488836163,0.09025287783321062,0.11874981063256122\n", "MAXPERM,restream_iter_1,0.42280169859,0.0203333186335,0.40219422699999996,0.41198430199999997,0.43590855700000003,0.37238133100000004,0.44345939900000003,0.423187393,0.40345473800000003,0.44767048899999995,0.392404962,0.393447351,0.396379142,0.439342922,0.429857485,0.44626670299999993,0.40704454,0.459265086,0.44044091300000004,0.413428314,0.41723655200000004,0.47500252600000004,0.409203308,0.42535283199999996,0.417348687,0.42991452599999996,0.421980058,0.42791132600000004,0.41933200200000004,0.427543338,0.42180907500000003,0.440819875,0.406414466,0.421936345,0.43568167599999996,0.421067273,0.43225412300000005,0.41610742900000003,0.45553629599999995,0.36489878600000003,0.42885663799999996,0.467322889,0.39776333399999997,0.439793025,0.4042266990000001,0.41850250899999997,0.410806778,0.395249908,0.424290995,0.41434449199999995,0.38054961499999995,0.43568104399999996,0.41206387699999997,0.47115815600000005,0.44756817,0.406653498,0.4274621069999999,0.410634577,0.428242028,0.42716662399999994,0.41008025,0.397677649,0.41426703600000003,0.4356244509999999,0.43814498100000004,0.4346069,0.41430953899999995,0.448523373,0.421167561,0.386677676,0.41616890800000006,0.41123150599999997,0.43398002799999996,0.434141028,0.39280806,0.44631856300000006,0.427091747,0.41601465099999996,0.38991544600000005,0.45092904899999997,0.419037544,0.39361365700000006,0.429147946,0.452485089,0.399574951,0.43337950399999997,0.39972292300000006,0.431986933,0.42183737399999993,0.427788228,0.434834748,0.45732276400000005,0.41844081699999996,0.414775334,0.422537136,0.41112344,0.43513659800000004,0.441977597,0.426511994,0.449327654,0.42104831,0.4250756\n", "FSCORE,restream_iter_1,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_1,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 2\n", "Mode 2 Iteration 0\n", "Mode 2 Iteration 20\n", "Mode 2 Iteration 40\n", "Mode 2 Iteration 60\n", "Mode 2 Iteration 80\n", "CUT_RATIO,restream_iter_2,0.170339116972,0.0188458027827,0.182777399592,0.16530334015,0.179027250776,0.148075024679,0.175119945168,0.140685754684,0.160923501034,0.151198110024,0.146625556697,0.137200416811,0.18690313779,0.179618268575,0.181628392484,0.15692414753,0.170698924731,0.192864355436,0.156228492774,0.151695211465,0.180810529962,0.186204553177,0.189451769604,0.172162443596,0.180339217722,0.178522099448,0.171823568137,0.154108421412,0.149555099247,0.175588135015,0.188759278897,0.177602198557,0.177170868347,0.139862430396,0.205110497238,0.13543721236,0.19298245614,0.173070266528,0.163190996359,0.143797301744,0.156855833627,0.125545485062,0.136378682555,0.151401230349,0.194696441033,0.150086655113,0.170883882149,0.155346116347,0.162836506894,0.141141141141,0.187126275062,0.148148148148,0.173565217391,0.181660899654,0.189996526572,0.154725866128,0.168221185876,0.202523240372,0.193277310924,0.153536708012,0.210473313192,0.201754385965,0.155248059399,0.169102296451,0.179104477612,0.170120320856,0.177114427861,0.19452247191,0.188287368063,0.146536721197,0.182035519126,0.139445029856,0.157894736842,0.178621659634,0.162312214239,0.16513437058,0.184288052373,0.15011941317,0.164402173913,0.158528198074,0.202500868357,0.207747685979,0.149409780776,0.176910016978,0.184579439252,0.157784743992,0.184945504087,0.150157507875,0.165077277211,0.182964224872,0.186086956522,0.189601386482,0.184891341842,0.151962428715,0.172700099635,0.172811059908,0.208689927584,0.165755297334,0.177069645204,0.183419689119,0.157502660518,0.189299017948\n", "EC,restream_iter_2,499.69,55.1430312914,537,485,519,450,511,398,467,448,428,395,548,527,522,451,508,573,454,434,522,548,546,496,521,517,522,452,437,515,534,517,506,427,594,412,583,500,493,437,445,374,412,443,558,433,493,478,496,423,532,444,499,525,547,460,505,610,552,458,627,598,460,486,516,509,534,554,553,421,533,397,462,508,497,467,563,440,484,461,583,606,443,521,553,453,543,429,502,537,535,547,536,453,520,525,634,485,539,531,444,559\n", "TCV,restream_iter_2,557.64,57.1017547891,629,564,547,515,581,495,565,459,529,444,616,584,603,537,551,613,536,494,554,599,573,546,612,568,527,478,498,549,608,594,576,441,678,455,612,542,564,422,476,431,429,534,649,529,546,524,543,517,541,514,564,621,565,496,493,648,584,558,691,654,519,578,567,570,571,618,639,486,590,498,556,598,518,541,627,535,511,539,663,633,508,584,620,520,610,514,510,564,602,642,622,513,579,581,608,564,619,559,509,614\n", "LONELINESS,restream_iter_2,0.826548702267,0.00739869596809,0.823329307727,0.814512285067,0.821754124699,0.831972287727,0.825728220287,0.821257365198,0.823615366479,0.838522800948,0.83609112424,0.834782771961,0.813650010816,0.827010743015,0.826323455786,0.832601339224,0.825508337915,0.828928191635,0.831491051693,0.820462482333,0.82456694273,0.830582148314,0.823632690578,0.82017066383,0.821528900897,0.819904590965,0.837866266943,0.833412511381,0.833906099286,0.821148710717,0.818771291306,0.832765923041,0.824681335609,0.845692524903,0.816457216181,0.846817126665,0.820840072157,0.827266411743,0.830711154331,0.832027393019,0.832380962518,0.840422406335,0.832214413463,0.821869756716,0.815752586573,0.826813005894,0.816793821126,0.836864326543,0.8346877732,0.833380336651,0.820339604144,0.835490374782,0.82501717569,0.816857283228,0.827032351764,0.835956745519,0.83509043801,0.819696808847,0.822247376404,0.829098671528,0.821086093294,0.813022166805,0.833481877092,0.82413929182,0.830001312606,0.832849625374,0.829023385804,0.818804853894,0.816428514643,0.825352865587,0.818250153378,0.82807177009,0.82736080598,0.821425874937,0.833013438081,0.829671291474,0.819110817417,0.83301294985,0.828752517192,0.830758676338,0.810508417985,0.817569585666,0.832808390951,0.822684161966,0.815023765854,0.829908022764,0.81928125988,0.826779149815,0.837133411253,0.831468060705,0.815848401783,0.82193384684,0.825051402643,0.830983047933,0.834344073687,0.811773402253,0.82570917982,0.835592301041,0.827827399939,0.823544877224,0.821896746175,0.825285578621\n", "QDS,restream_iter_2,0.37676310686,0.0229033354989,0.35499645430286375,0.36079790869977346,0.38805462346908726,0.3228163752584825,0.3703904540813154,0.3864721559131444,0.3369117553683176,0.37103213928152345,0.37159771360887023,0.3914112022593608,0.3765922339067527,0.4119698604232275,0.3693565453514341,0.40797282069296703,0.3901808375746715,0.39260775615940235,0.4018949642069036,0.34223043218550375,0.3795282736590075,0.3529775039709775,0.3574594901684443,0.38094582968339136,0.36625548898746346,0.4199182333030242,0.3708623457725561,0.34219676429738916,0.3880058956487571,0.3488870617486697,0.4141098870167553,0.39152570011527454,0.3703055436828221,0.3972371529228515,0.361002488669568,0.39105750183669796,0.37304608893533775,0.3688937457328078,0.3446480750046362,0.3374025126627118,0.36117666159732037,0.38718897959432014,0.35486544858703584,0.40232737560224585,0.36511453235714897,0.4039525361112557,0.4164878267159223,0.37937288722113377,0.3991091359543808,0.41183030950723487,0.3434059871244661,0.3600740061003695,0.3553970019945011,0.36858473131098846,0.3342589078035343,0.3945504907445891,0.36655989645457815,0.39787284885897317,0.3647110245087688,0.3990429756926506,0.3991866779618102,0.3650867032510908,0.3585721885805042,0.3492126994368015,0.3816539645733105,0.377326343079095,0.3950552928703678,0.3444578442462058,0.37090458542090726,0.3723698130136514,0.3933054184981147,0.37850780558834174,0.358809795202966,0.37237977318277765,0.36167059732600776,0.36248959448278467,0.38169128361400423,0.37810701608863456,0.3703604315979257,0.40138895521107026,0.36474584042496944,0.374346613588428,0.36569932525040066,0.4129453371151654,0.37202625356527574,0.3724035486529334,0.357756693989886,0.35237969024426086,0.42785770128163136,0.3681130203916831,0.38278886713549004,0.4156281112592339,0.4430883884647944,0.3874300116137665,0.34336110268557757,0.37675863331857,0.36065729343661745,0.3762343885246324,0.41668733814781,0.38423187704654027,0.36288596578561455,0.4163125204783922\n", "CONDUCTANCE,restream_iter_2,0.12796201833,0.0166744722308,0.1020641222647535,0.1088311973587434,0.13622724512645926,0.13980225061706852,0.10366979624215912,0.13095155276158138,0.10609618232153406,0.14720468350351637,0.1389119147607024,0.1600075101718893,0.121525474961828,0.10720119733877238,0.10960457811741262,0.1352718980810766,0.1438005042512948,0.12489261685858016,0.15060119404098984,0.12300009035801245,0.11525007591030649,0.13448818208998606,0.1489798390067949,0.10493964132242144,0.11315847077732119,0.11532743662788254,0.12928382972071903,0.13559930790310876,0.11774322822147691,0.1246326565373129,0.13165842777800146,0.11496740365881271,0.15076857167311003,0.16221913406266045,0.11092386525206908,0.1485543620171583,0.12025833533916253,0.13682151372525314,0.14195322439109292,0.1554063966054069,0.13824267147233274,0.11732764404579792,0.13358542538911294,0.12618160841249051,0.1164228953941648,0.11679354984697995,0.12683907827669944,0.17101190295246038,0.1500769822134839,0.12559362514734254,0.14177931335044616,0.12280340902873843,0.11305578522807123,0.1205442701199776,0.12855397065417515,0.1520833783382016,0.15366089706043148,0.15101606686010854,0.12392336978316897,0.10237698601917146,0.1506572643558414,0.11446435620235322,0.14776528481496456,0.15483440424429382,0.14245173491968535,0.15388640113608684,0.13173300209036573,0.1363027797537526,0.11539335674469674,0.12633692310075004,0.12212369759165145,0.12708224276600794,0.1282455895476306,0.14255839457427252,0.12161229951186477,0.15800400607425713,0.09119526682350071,0.11993160046356072,0.126972754448831,0.12155020797447401,0.13522248700426365,0.09366428486698648,0.11454065274119263,0.11496373626517836,0.1218483142927834,0.12428304295082665,0.12022526660294493,0.11971044632620208,0.13017480953250382,0.11007526504910832,0.12368876433163022,0.09016650753341067,0.11459148858213751,0.12409443944013278,0.10678566530257463,0.10210534902851712,0.12140525991080793,0.13152980571609205,0.14241083932901463,0.14503361040401433,0.12405998906875615,0.1180495001871573\n", "MAXPERM,restream_iter_2,0.41687960984,0.0237595450294,0.411075743,0.377614474,0.405795095,0.36101016700000005,0.421743565,0.397531368,0.38611066600000005,0.420476562,0.41359222999999995,0.403871583,0.388780356,0.43422007000000007,0.457029709,0.43450293399999995,0.409347186,0.443425559,0.418584384,0.39610478500000007,0.38751871899999996,0.457268816,0.4325577539999999,0.40041832800000005,0.405898029,0.389789639,0.42900441199999995,0.4021413,0.43714271799999993,0.410507108,0.452553753,0.472307609,0.420327839,0.42410778,0.427325322,0.417974186,0.40594387300000007,0.38786110599999996,0.44742968000000005,0.348212801,0.41822044199999997,0.441413598,0.38985759400000003,0.44579217400000004,0.42179959899999997,0.404100622,0.399621644,0.39113208999999993,0.44659569299999996,0.41429533300000004,0.382900445,0.458102306,0.434479521,0.42233976,0.414794055,0.44254628700000004,0.402054661,0.40480181,0.43753018,0.425223663,0.414435729,0.399203277,0.406563624,0.408734392,0.446991085,0.420141965,0.41747881000000003,0.435658882,0.408203194,0.372245509,0.41301595599999996,0.426386886,0.419663619,0.43080623799999995,0.377426682,0.44522357,0.41022394800000006,0.441267927,0.38547866099999994,0.455506295,0.397438663,0.399810318,0.422042061,0.416450387,0.373367477,0.42496005099999995,0.410460244,0.40224587500000003,0.453103815,0.43218080799999997,0.38534173299999996,0.44471808699999993,0.449337023,0.408960319,0.44321758800000005,0.38139083400000007,0.43092101799999993,0.454287871,0.406525482,0.4389222179999999,0.419247009,0.42366716899999995\n", "FSCORE,restream_iter_2,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_2,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 3\n", "Mode 3 Iteration 0\n", "Mode 3 Iteration 20\n", "Mode 3 Iteration 40\n", "Mode 3 Iteration 60\n", "Mode 3 Iteration 80\n", "CUT_RATIO,restream_iter_3,0.146438531015,0.0176648247885,0.203199455412,0.133265167007,0.150396688513,0.133267522211,0.160383824537,0.15164369035,0.141626464507,0.103948700641,0.132922233642,0.122612018062,0.171555252387,0.157464212679,0.166666666667,0.145441892832,0.148185483871,0.155166610569,0.156228492774,0.124432016777,0.137512989262,0.162759089365,0.142956280361,0.140576188823,0.163724472136,0.153314917127,0.147465437788,0.135015342653,0.137919233402,0.132287759973,0.161541180629,0.132944005496,0.140756302521,0.104487389453,0.156422651934,0.161406969099,0.156901688183,0.138456213223,0.139357828534,0.112866074367,0.114557631301,0.121181604565,0.121813968884,0.127477785373,0.165387299372,0.133102253033,0.144887348354,0.129021774456,0.147078135259,0.124457791124,0.132606401688,0.121121121121,0.141565217391,0.159515570934,0.14657867315,0.141271442987,0.112591605596,0.178618857902,0.174369747899,0.138115990613,0.1795904666,0.191295546559,0.137023287209,0.148225469729,0.163137799375,0.164104278075,0.143615257048,0.154143258427,0.168198842356,0.136442742778,0.15881147541,0.129610115911,0.15003417635,0.158579465541,0.137491835402,0.141796322489,0.168576104746,0.138519276697,0.120923913043,0.140646492435,0.168808614102,0.164209804594,0.137268128162,0.146689303905,0.145861148198,0.144200626959,0.162125340599,0.139656982849,0.150937191713,0.173083475298,0.155130434783,0.172963604853,0.157985512246,0.134518617913,0.154433742943,0.133969716919,0.146807109941,0.147641831852,0.156373193167,0.145077720207,0.130897481376,0.152048763969\n", "EC,restream_iter_3,429.59,51.9505717004,597,391,436,405,468,429,411,308,388,353,503,462,479,418,441,461,454,356,397,479,412,405,473,444,448,396,403,388,457,387,402,319,453,491,474,400,421,343,325,361,368,373,474,384,418,397,448,373,377,363,407,461,422,420,338,538,498,412,535,567,406,426,470,491,433,439,494,392,465,369,439,451,421,401,515,406,356,409,486,479,407,432,437,414,476,399,459,508,446,499,458,401,465,407,446,432,476,420,369,449\n", "TCV,restream_iter_3,485.66,58.2773060462,664,475,453,458,528,506,487,351,473,361,547,499,556,488,468,510,536,395,448,523,451,446,543,474,468,421,473,432,526,463,452,346,538,562,527,449,487,349,375,405,359,459,526,470,434,449,498,464,401,435,482,538,455,475,357,560,546,490,616,594,469,523,521,534,490,507,565,478,519,458,525,541,447,471,583,496,402,477,540,505,466,487,500,491,532,474,499,538,506,592,523,466,526,458,478,496,544,463,436,519\n", "LONELINESS,restream_iter_3,0.835779867045,0.00649962050155,0.821078255006,0.834448608592,0.834469606038,0.841386789547,0.835370321246,0.825695845184,0.836627791702,0.850499526719,0.841364366388,0.846722617363,0.825313680065,0.837547486408,0.831426921518,0.841501574402,0.835303031903,0.841462762465,0.824098161753,0.827545266153,0.839866006463,0.839222579302,0.836858214636,0.830262188071,0.827547505525,0.835099464809,0.847447111147,0.839700900948,0.837968989679,0.835627133483,0.827531019331,0.843069677882,0.83941471319,0.854133419786,0.837773831724,0.827923909292,0.832418178152,0.838048123607,0.841711028587,0.840421535922,0.841245869592,0.842761560339,0.840458308564,0.833733992047,0.833940133172,0.834324817847,0.828963695661,0.846377572048,0.841722180967,0.839696019548,0.836166436273,0.844079652578,0.827321953846,0.834578348427,0.839356462516,0.838986315018,0.845758486675,0.833249138836,0.821847338951,0.836853290099,0.831635376909,0.827101442053,0.838700621098,0.832563346801,0.834624324113,0.83710889481,0.841146201187,0.834004606328,0.830068263764,0.827304309281,0.833038955257,0.836113689223,0.830201514397,0.825650206469,0.84344131084,0.83861847918,0.82345607139,0.829163692954,0.842315608363,0.839978869113,0.824934299407,0.835427926902,0.840657212144,0.838733996402,0.834846679743,0.835875315931,0.822299683241,0.828472299863,0.832353334706,0.834711621902,0.834445531886,0.831075490747,0.834698987235,0.838503694513,0.840928090944,0.836212457068,0.844081564498,0.843636818205,0.840350979085,0.838599049202,0.826844700108,0.838731470223\n", "QDS,restream_iter_3,0.372283005601,0.0257126203658,0.3625291498357568,0.3555600395596813,0.38963039835918134,0.3074120384303749,0.3863612319108197,0.3902235404543688,0.3508097402867046,0.3902404096248284,0.3472889738546271,0.3982743630817008,0.3577890332971912,0.40397894026289816,0.36640905698743415,0.4060408086948436,0.38547905481667716,0.38693972131219617,0.40214116812991296,0.33736350480953414,0.38794059274785103,0.35336710560980006,0.33757880451259575,0.3913273783790056,0.35682666703572863,0.42954756126622207,0.36547231290727655,0.3324538751282707,0.3789953936417756,0.3239511349412095,0.4014788868490527,0.3920743489400452,0.31627957391539646,0.36500829061710915,0.3630507146791293,0.3780297123462319,0.3729250441738243,0.3699609845675966,0.35374696149923973,0.33176292529607837,0.3572083248954744,0.40506785114348515,0.3514310986102013,0.40180599668337497,0.3591244467256107,0.40358719432429646,0.42431627401432376,0.3754334233417523,0.3990870027301499,0.40595272581262515,0.3159201295009833,0.34896517212407624,0.357418185483583,0.37235309692884544,0.36046518964180624,0.3936148552683468,0.3683211304622423,0.4116506731232885,0.36292507735816254,0.3953092235157959,0.39291623426303673,0.3482176155780893,0.34815952906916486,0.3487281083463139,0.3623851117578318,0.3803073920304208,0.3929546340913528,0.3283340741562771,0.37071776832397907,0.3634925145708019,0.3859273642727837,0.34120285183447796,0.35857609084784386,0.37862781586019045,0.36257882805111363,0.35802730426531315,0.3722200048358811,0.386074369365681,0.3440773182128695,0.4039093053954993,0.3357057819091143,0.3798013090502142,0.36337170401219,0.38339002600448147,0.36011147053608117,0.37505245901066303,0.3572632735365475,0.3634372109513007,0.41365399504989403,0.35751394691450145,0.38015367969163255,0.40898778740712066,0.4462487776614509,0.3937729288013868,0.3592328975236954,0.36538249050983196,0.3479472971641768,0.3650337227658641,0.4136760119282783,0.37915276665309056,0.3811438102465128,0.3746264632375968\n", "CONDUCTANCE,restream_iter_3,0.139217351475,0.0180122419668,0.11541553804936046,0.12426764748201216,0.14708921495858468,0.13742218518066932,0.1343847685072333,0.13667163105832952,0.11851653950044204,0.15987280616460542,0.14451878329655699,0.17117294363166094,0.13228077085871273,0.1320813140688012,0.11039774713313003,0.15672628659626545,0.15565776029037592,0.1326234392707572,0.14373218788807637,0.135872098869815,0.14758748899166424,0.13789658365606505,0.18845033757452875,0.11111976079978,0.1238973970146254,0.13970750447612493,0.13139834387857383,0.12888853006409476,0.11420598783263947,0.13549341804551746,0.140649962478682,0.1236483238820859,0.1836429359664847,0.17540528301542174,0.13792483207335487,0.14654744914622878,0.14240250283549516,0.1524346737764391,0.1370496141601845,0.16770597805101636,0.1582382314574306,0.1418256376336643,0.1522307707313326,0.13986182413665885,0.14827196597450257,0.14067875738884963,0.1350600512453539,0.18523695313189156,0.15373003291008458,0.13616714622507778,0.15501300086829695,0.11562545067996433,0.12070889848166433,0.14174894305597072,0.13913260775162994,0.14145407092320836,0.1521090806211063,0.16747994927617293,0.12930773071579352,0.11929735593490311,0.14370615975246984,0.13173888188223254,0.1665305713844039,0.15046782929584623,0.14991835578289694,0.1629047899537394,0.12474136215424331,0.1393142302040576,0.1231056875038071,0.13820038658877978,0.12150643901693377,0.14524169772591994,0.12355423678674778,0.15632763638264524,0.13967734825827302,0.1663261572049377,0.10011759016608782,0.11342681200704544,0.14806130860636887,0.13788587665478177,0.13166753049462437,0.11077079873107581,0.12044749525310904,0.1420735238363033,0.15535752720408041,0.13320413127387298,0.13018609106249387,0.12637791556357153,0.12927046442706971,0.1176915954359061,0.13654547503888267,0.08574975762557392,0.14431017070943086,0.13155714726821796,0.12216791898281072,0.12319967496584357,0.12931527662345735,0.15369898752393604,0.1738490004709186,0.14582418414857679,0.14210708637285435,0.12567100750659552\n", "MAXPERM,restream_iter_3,0.42181740009,0.0237779063426,0.416281405,0.39725657000000003,0.419883052,0.37011466700000006,0.431738441,0.414034603,0.404897171,0.42637971599999996,0.40767328599999997,0.42775066700000003,0.390102755,0.436459479,0.45593191199999994,0.451861423,0.407132034,0.445982795,0.41983383700000004,0.38572082,0.401289457,0.46494010300000005,0.427450293,0.399455564,0.386241427,0.398811623,0.434794945,0.407064723,0.435253242,0.40995114900000007,0.445271135,0.447687705,0.436320378,0.42027328399999997,0.468952248,0.414495477,0.415765892,0.40407812,0.475994789,0.350896042,0.41050095699999994,0.430867064,0.3952395640000001,0.45891582499999994,0.445590054,0.404723212,0.406719919,0.380363414,0.4462459730000001,0.420152871,0.37849810300000003,0.454976157,0.41567024399999997,0.466133548,0.40943122600000004,0.42927794399999997,0.38925002599999997,0.41851732700000005,0.41645188599999994,0.42897822,0.42136294199999996,0.40468265900000006,0.392761032,0.442138328,0.451281952,0.41392041199999996,0.429044397,0.432178492,0.42862076000000005,0.3856881119999999,0.42493512,0.42336834000000007,0.41553040599999996,0.418285866,0.390766661,0.444128019,0.42623867299999996,0.42677241200000005,0.37681626499999993,0.460469128,0.397319537,0.405546727,0.43025500499999997,0.44982343599999997,0.4122193639999999,0.423418093,0.39050621,0.40539596800000005,0.43121603199999997,0.4352610129999999,0.42886524400000003,0.47266671299999996,0.435193901,0.414314907,0.443538732,0.413387093,0.436124449,0.450539519,0.447389871,0.4358664460000001,0.427800538,0.425575472\n", "FSCORE,restream_iter_3,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_3,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 4\n", "Mode 4 Iteration 0\n", "Mode 4 Iteration 20\n", "Mode 4 Iteration 40\n", "Mode 4 Iteration 60\n", "Mode 4 Iteration 80\n", "CUT_RATIO,restream_iter_4,0.136848597617,0.0160214773243,0.172226004084,0.12815269257,0.134874094515,0.135241855874,0.150788211103,0.148462354189,0.138525155065,0.0968612892339,0.131551901336,0.110455019104,0.129604365621,0.129175187457,0.168406402227,0.136743215031,0.132392473118,0.151464153484,0.140743289745,0.108703250612,0.134049186006,0.155283724091,0.122831367106,0.115584866366,0.156455520942,0.123273480663,0.141869651086,0.131946812138,0.134496919918,0.131605864303,0.145987981619,0.12504294057,0.162464985994,0.104159842778,0.152624309392,0.163708086785,0.142005958292,0.130841121495,0.137702747435,0.104310628496,0.102573140642,0.118496139644,0.125786163522,0.109364319891,0.136078157711,0.136568457539,0.118890814558,0.124471888203,0.13624425476,0.129129129129,0.121350685895,0.118118118118,0.125565217391,0.152249134948,0.12678013199,0.137907837201,0.110259826782,0.16401062417,0.153711484594,0.127388535032,0.170862705606,0.164642375169,0.131960850489,0.149965205289,0.158972578966,0.16243315508,0.136650082919,0.141151685393,0.150153217569,0.138531152106,0.145150273224,0.121882683527,0.141831852358,0.150843881857,0.128674069236,0.138613861386,0.153846153846,0.120777891505,0.119565217391,0.123796423659,0.151788815561,0.138498457319,0.124114671164,0.137181663837,0.136849132176,0.139324277255,0.136580381471,0.138256912846,0.146004603749,0.163202725724,0.14747826087,0.166377816291,0.142117971714,0.131164038913,0.157090667552,0.126728110599,0.132323897301,0.131578947368,0.142904073587,0.13506044905,0.128414331323,0.145953267863\n", "EC,restream_iter_4,401.53,47.5769807785,506,376,391,411,440,420,402,287,384,318,380,379,484,393,394,450,409,311,387,457,354,333,452,357,431,387,393,386,413,364,464,318,442,498,429,378,416,317,291,353,380,320,390,394,343,383,415,387,345,354,361,440,365,410,331,494,439,380,509,488,391,431,458,486,412,402,441,398,425,347,415,429,394,392,470,354,352,360,437,404,368,404,410,400,401,395,444,479,424,480,412,391,473,385,402,385,435,391,362,431\n", "TCV,restream_iter_4,457.66,54.3678618303,588,460,396,453,493,487,477,331,465,336,428,415,551,467,428,501,488,353,447,496,418,371,512,409,457,411,456,427,476,438,525,346,516,532,484,433,482,331,337,402,424,398,452,486,384,443,465,494,368,427,429,518,413,463,357,518,493,459,596,520,451,527,501,541,463,459,512,479,488,422,489,521,409,459,522,430,401,424,476,442,425,445,478,474,446,449,479,509,479,576,472,449,522,435,450,455,511,438,423,505\n", "LONELINESS,restream_iter_4,0.839020000597,0.00569108755306,0.830307760462,0.837659732141,0.839451178555,0.843112013726,0.836689856995,0.829290635981,0.839127398585,0.852427245286,0.844007426842,0.848114030904,0.839079112882,0.845344639327,0.833706623594,0.841696888373,0.840124098158,0.84159610839,0.833475369139,0.837941889644,0.841808650941,0.843818899865,0.840720433447,0.836909073846,0.831040330083,0.839843433584,0.844640723255,0.840891758234,0.838692966972,0.835718301053,0.833890648856,0.847269857985,0.820962999133,0.854108498411,0.839296040158,0.836701804421,0.837936223181,0.83895794901,0.842830271447,0.840602547223,0.845844342394,0.844776030314,0.82139550459,0.84520009842,0.842161659351,0.835994237259,0.838541576803,0.842346200505,0.844667232186,0.833570574114,0.838108142569,0.84209389674,0.839382797643,0.837027959618,0.843668434237,0.839588378958,0.846303239412,0.83745911657,0.831400428248,0.839526362864,0.830099279411,0.834981271837,0.842866630341,0.832577004531,0.836139875749,0.836575121266,0.844445471491,0.839530104237,0.832108743945,0.825379272157,0.837335238275,0.83993171399,0.833743454349,0.830237830207,0.844254389146,0.84067628092,0.835914019286,0.839057297638,0.83975731628,0.845228503091,0.831411082892,0.840950328299,0.846323232962,0.843184718858,0.837032575935,0.837086657201,0.833767953372,0.835124508685,0.838581361105,0.837540769452,0.837303010102,0.832543524573,0.841102910813,0.841119511605,0.842793574652,0.840276674358,0.847298164251,0.847729605886,0.847939249078,0.840675181037,0.831979709475,0.840547304095\n", "QDS,restream_iter_4,0.371780531293,0.0257744379576,0.3639466521559755,0.3528948189227429,0.38690932720585175,0.3085759778764426,0.37210197736731215,0.39662157265703596,0.3476924852279412,0.39410395609329,0.34341913052281053,0.39497808347606067,0.35358848130732984,0.41242371820667606,0.3583509139059424,0.4017166915515714,0.3666708881748823,0.386164299676559,0.3929362240451969,0.3384168156428421,0.4017267285956907,0.35193181645818983,0.3420318180933533,0.39109469062478447,0.3562964130399897,0.42663336604871205,0.3655515030430427,0.3328578075438117,0.38628052751225056,0.3508606716305536,0.3984010010480107,0.3924244851318625,0.3474002182097049,0.37106065828619045,0.35525496881570706,0.37463542810365863,0.3681919408444924,0.3758162258343469,0.353480431720589,0.32717415750136025,0.3537204167511803,0.40613298533313064,0.373318769330858,0.3865681961916278,0.3559728349995337,0.4122896330519581,0.4269812615395941,0.3718174198855127,0.39938592841924264,0.40275525172345544,0.33664028028855786,0.3493689740385548,0.3486398260057397,0.3587911331361289,0.35822961122606134,0.39144026331597015,0.3462285358397855,0.40753129139285565,0.37021314279762835,0.39682106511821796,0.39235446542648783,0.34208619280722974,0.3479971655099432,0.3478897336289963,0.3792620555097287,0.3681903490030055,0.3850042246371636,0.3295489935554824,0.3600259379227332,0.36297131063231736,0.38677447118243485,0.35026847480597173,0.347408866377403,0.3699422366140723,0.3598468843181123,0.37854106534701276,0.3810207952838108,0.3651130307200243,0.3510800683075503,0.39955426008889083,0.3468559185080986,0.3770464561567733,0.359636212497305,0.3855022103358795,0.3491464424963714,0.374131069248916,0.34508347665368294,0.3479236458136981,0.4332508932839361,0.3286419379594719,0.38110516321469606,0.42514124429842964,0.44034235271217637,0.3841953527507841,0.35882421905383605,0.36029775593177515,0.3456927816873731,0.3719392526462901,0.412107191002822,0.3693986923383488,0.37641782251160044,0.4070287640477113\n", "CONDUCTANCE,restream_iter_4,0.143041248052,0.0172677301142,0.11981554393978554,0.12666260379379052,0.15914114724420142,0.14504592752389592,0.13182122166440105,0.1208805939154511,0.1091510528212809,0.16330588233627835,0.1571199670374557,0.17835973180465559,0.13406355112584664,0.13663644515409198,0.11055145388719231,0.15781112129410688,0.16820052536428956,0.13812627978594225,0.15290184566342366,0.14788103314050102,0.14862063438143702,0.13976552959733335,0.18499838406751892,0.10831631692392074,0.13162317943830498,0.1398461870030136,0.13030273170206078,0.129845579558825,0.12348397621837774,0.13689892419409516,0.14294405385613781,0.13497197573853806,0.16568311957360332,0.17666378906562807,0.1406478785915388,0.16248363204755242,0.1493576070129406,0.15347285233493582,0.1371393960607194,0.16724326285105967,0.16762436908718534,0.13988475575321327,0.1335675672179252,0.13598263617363407,0.1532748381308299,0.1448836835154727,0.13438852992326428,0.17105653352383013,0.1598100961365878,0.12484298794449679,0.14576136629108413,0.12474063852325191,0.1392498471443757,0.1377760542349458,0.1368068761198864,0.14322872222516753,0.1419622538150193,0.1712470095329587,0.12864634507765088,0.13649180446964443,0.1493343818964924,0.14602794187637635,0.17257798029546234,0.14270391186594825,0.165831258570136,0.15994030843191914,0.1379883490483547,0.16147161109341324,0.13074452325330962,0.13937521481092416,0.13317175520131397,0.16253838067513943,0.12813873554169763,0.15784704531661314,0.13746261212849606,0.16678662970091124,0.11378190513057151,0.12001280830446662,0.1435022147314869,0.12429584722437917,0.1385212468857605,0.11708273660940856,0.12946359451751113,0.14017468495727767,0.1452713448448392,0.12906675989640953,0.1487100328295681,0.13221388194154587,0.14380110239238936,0.12049821089026785,0.16258061154238518,0.09244068082625566,0.15944461120971587,0.1458503388164854,0.13414775718380217,0.12133775194974404,0.15490990694855836,0.15922308053480358,0.1594440569654971,0.155896485159195,0.15988307481052116,0.1276175858392913\n", "MAXPERM,restream_iter_4,0.42163687156,0.0235730834062,0.416010227,0.404275632,0.408117356,0.369128887,0.417879037,0.433322309,0.408491622,0.434191238,0.414793003,0.43044792500000006,0.409738737,0.44078357300000004,0.444429254,0.443401219,0.40440722599999995,0.436583995,0.4079133850000001,0.3950299,0.40360028300000006,0.463161904,0.433294692,0.395187678,0.41689322900000003,0.398205088,0.423096774,0.407207812,0.42622346000000005,0.417010157,0.440833609,0.45559211200000005,0.391131618,0.41499926500000006,0.46559530000000005,0.42464775900000007,0.40574197300000003,0.39715858400000004,0.474801881,0.355561134,0.400010613,0.43533387100000004,0.373063829,0.458045332,0.442207656,0.41183017799999994,0.423842012,0.379505438,0.44583968399999996,0.408820721,0.373045513,0.44526447999999996,0.42750519099999995,0.460200154,0.41370192699999997,0.424601046,0.393201024,0.424335208,0.426809243,0.41715676199999996,0.427947372,0.40514202899999996,0.397566606,0.428891991,0.442606674,0.41082298300000003,0.423234004,0.429950144,0.42469736500000005,0.378181942,0.43360703,0.41823601600000004,0.425702493,0.42732102999999994,0.384018483,0.442152304,0.447794391,0.431188319,0.373659235,0.466221687,0.39361609699999994,0.401206965,0.44451274099999993,0.4493524969999999,0.40303883,0.436406125,0.39428879,0.40873165,0.441611623,0.43389060600000007,0.433943805,0.46658364299999994,0.443741952,0.409105603,0.444984692,0.42362049999999996,0.439006142,0.45013982900000005,0.43278702799999996,0.43139706699999997,0.42504606800000005,0.42452408599999997\n", "FSCORE,restream_iter_4,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_4,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 5\n", "Mode 5 Iteration 0\n", "Mode 5 Iteration 20\n", "Mode 5 Iteration 40\n", "Mode 5 Iteration 60\n", "Mode 5 Iteration 80\n", "CUT_RATIO,restream_iter_5,0.132398853908,0.0152430369357,0.155547991831,0.125085207907,0.12797516385,0.127673576834,0.142563399589,0.134676564157,0.138525155065,0.0971987850152,0.127440904419,0.102466134074,0.12312414734,0.115201090661,0.171189979123,0.129784272791,0.125,0.149444631437,0.135925671025,0.109402306886,0.134049186006,0.149847094801,0.12213740458,0.109684137452,0.152994115611,0.124654696133,0.136273864384,0.131605864303,0.132443531828,0.130583020798,0.146341463415,0.116798351082,0.146708683473,0.103504749427,0.148825966851,0.149901380671,0.140019860973,0.12911041883,0.137371731215,0.102336294834,0.101163200564,0.116817724068,0.141343925852,0.109364319891,0.126657362177,0.132062391681,0.115771230503,0.106597335067,0.135915955351,0.122789456123,0.117481533591,0.112779446113,0.12,0.142906574394,0.122612018062,0.135553313152,0.110259826782,0.150730411687,0.146708683473,0.12872946698,0.160456529037,0.150809716599,0.131623354708,0.150313152401,0.152724748351,0.169786096257,0.132006633499,0.131320224719,0.140619679946,0.138879220327,0.143442622951,0.115208991921,0.140806561859,0.148030942335,0.128020901372,0.134724186704,0.141734860884,0.11736608666,0.132133152174,0.121389270977,0.126432789163,0.132327733973,0.125801011804,0.134125636672,0.13217623498,0.132358063393,0.135899182561,0.127406370319,0.139756658994,0.162521294719,0.144695652174,0.163258232236,0.131424629182,0.130493123113,0.155762205247,0.12409479921,0.126069782752,0.12952836637,0.141590013141,0.128151986183,0.111032280951,0.143921435828\n", "EC,restream_iter_5,388.52,45.6108495865,457,367,371,388,416,381,402,288,372,295,361,338,492,373,372,444,395,313,387,441,352,316,442,361,414,386,387,383,414,340,419,316,431,456,423,373,415,311,287,348,427,320,363,381,334,328,414,368,334,338,345,413,353,403,331,454,419,384,478,447,390,432,440,508,398,374,413,399,420,328,412,421,392,381,433,344,389,353,364,386,373,395,396,380,399,364,425,477,416,471,381,389,469,377,383,379,431,371,313,425\n", "TCV,restream_iter_5,443.62,51.7356318218,534,451,379,430,470,448,476,334,451,329,410,383,553,444,404,489,459,354,446,477,428,356,476,409,447,411,447,421,471,413,484,344,504,480,476,425,483,322,329,394,487,400,426,467,372,395,464,463,359,413,401,488,402,453,357,486,465,453,559,476,450,518,484,566,453,443,488,480,486,402,494,499,405,446,494,419,420,417,400,435,428,439,455,455,439,412,452,502,480,574,433,446,516,425,435,445,510,416,377,497\n", "LONELINESS,restream_iter_5,0.840836036129,0.00542667680048,0.835913069592,0.839623168504,0.842100619142,0.846002074921,0.840410035939,0.836565874246,0.839156752538,0.852524007809,0.846313049478,0.848322738671,0.841238517359,0.846783494961,0.835506350655,0.845848535471,0.84309903128,0.841088264981,0.838812097313,0.837815415253,0.841768376528,0.846659914234,0.835257229361,0.83886568429,0.835462762318,0.839757202051,0.844842825,0.840721191232,0.840115947139,0.83672957702,0.834100990298,0.84943493548,0.830571315788,0.854184361249,0.839441844508,0.846183063809,0.837953079245,0.839966718148,0.842588158884,0.842075836074,0.846727952987,0.846773818596,0.819175710722,0.843342728935,0.842161492176,0.838452223657,0.839679291576,0.845296731845,0.843604252474,0.843644706477,0.838689092239,0.845055961052,0.844764345309,0.839760383817,0.84426962863,0.839946572661,0.846303239412,0.840121080535,0.836030156738,0.840954903295,0.836415407533,0.8397877954,0.843045195011,0.833162270695,0.840650781413,0.829500410057,0.84568476836,0.841147151766,0.836632333065,0.824260980802,0.836975364959,0.843354227269,0.829823874223,0.831193934637,0.845615593945,0.842267093162,0.840032611878,0.841918147937,0.837631028898,0.846063247817,0.838461376965,0.840852547386,0.846792585802,0.843578327564,0.83870736182,0.841178911607,0.834000642205,0.839156658099,0.842163512969,0.837862180559,0.837150660098,0.833302564309,0.847689904361,0.841987455585,0.844580250212,0.841595214899,0.848803713852,0.848826138972,0.848176761314,0.843390232191,0.840006554013,0.841619455347\n", "QDS,restream_iter_5,0.370217995585,0.0262402641581,0.36249571451060636,0.34602243601397775,0.3921536406915894,0.30883955874445324,0.378503067138407,0.39006674780785444,0.3584348808195187,0.39578649156534057,0.3451229147821806,0.40301699215128395,0.34456081858833204,0.3869366603765076,0.35623745425093034,0.3884071927911607,0.36682866945478415,0.38370623250265035,0.38648427993820894,0.33847155755268493,0.4012317840919314,0.35139165281979756,0.32292360503111217,0.39169524599110345,0.3547716211532804,0.42658541892263135,0.35797375960946326,0.3316345457058575,0.3677089668384325,0.33313616932152623,0.3937463050283614,0.3886314442799272,0.3347053788387278,0.3702085232926697,0.36866587064443396,0.37719307163008964,0.38004081882707624,0.3759906314551523,0.35144564181221893,0.3280189839294841,0.3543536371673324,0.40611595395223593,0.36934613084074674,0.37900772547276973,0.3559341554906158,0.4010915739279209,0.42695883416917413,0.3786315990625665,0.39745781583776724,0.3983177008747713,0.34120730315106174,0.3460235689933772,0.3504310530896469,0.35524619132043117,0.36369440046506407,0.38279136139730613,0.3462285358397855,0.40472996564133495,0.3687485518065305,0.3984333575872321,0.37952020490265254,0.34842597896358757,0.3483190155797642,0.3485867666951262,0.3706039465408718,0.346191533863177,0.38003865089001543,0.33074589468390764,0.3644331720213346,0.35764076446971094,0.3898163246831686,0.3314543520609727,0.34343082262801283,0.3729544536033906,0.3475151436232543,0.37227535788457866,0.3739578312537976,0.3825690944552264,0.38656219738027403,0.39618653616910954,0.3119960154595391,0.3746997394190211,0.35954384590937777,0.39667943924124666,0.3583502033033985,0.3741109893499835,0.36038080293652625,0.3412911399285542,0.42299665060363484,0.32831371554629174,0.3820229689680002,0.4291705976988163,0.443961220512646,0.3878856552045086,0.3604088384191738,0.36455573591244095,0.352166311506138,0.38025269258734373,0.3988063388281114,0.3653186950268839,0.37967794493405255,0.41345780981603414\n", "CONDUCTANCE,restream_iter_5,0.144266985485,0.0174158865439,0.11733863875162999,0.12373747029282714,0.16630354169465084,0.1762835019399103,0.13348169929386836,0.12995301890049207,0.12373473061625861,0.16143670156584444,0.16334453796398418,0.1692926842569534,0.13606728797160528,0.13065748240919545,0.11487829969449936,0.1543520025235237,0.1707961983281345,0.12871577977801116,0.15535044063857129,0.14307612740326092,0.14904449424781557,0.14307872657225498,0.16178591566629344,0.1115992661592566,0.13828219763462676,0.13835639148060633,0.12505702727592188,0.12991836507118104,0.12433398721450523,0.13839624063627282,0.13556576329313483,0.13821110648922688,0.16242946351462256,0.17637833536959702,0.1367431126593272,0.1639121179678089,0.13970398346295357,0.1572497351274957,0.12660179914647643,0.16723125122226049,0.1538378157472667,0.13958797016985508,0.12538473545431153,0.14675768939822223,0.15983400672117012,0.14146588193661921,0.1340542680121824,0.17241366585325182,0.1547944301861848,0.1270399737011859,0.1492916953559191,0.12646001248396452,0.1470507918327479,0.14834105418485657,0.15119581972691226,0.1448500804003291,0.1419622538150193,0.1710144338405022,0.1311713898582896,0.14789631903437844,0.14867559884343115,0.14689280942651445,0.17429275975311628,0.1550570952808858,0.17097047895503348,0.13220535121811822,0.14016704256568077,0.15972313806162683,0.127187804478804,0.13333235199221202,0.13377877349433992,0.1527919148496498,0.1291187486132442,0.16706641874243214,0.1431373401400835,0.18268673645092293,0.11759528683299889,0.12410322855376113,0.14755237337382107,0.13930850341088682,0.13745516873848043,0.1145025727267222,0.12977397413141586,0.14700296453321215,0.1564409524341469,0.1334295311189884,0.14957427125582023,0.13263800557416608,0.14373668921442256,0.11951413901159244,0.15761024767154622,0.0925661306332077,0.1704049379805661,0.15372047315767853,0.1313100766317803,0.12127931884416056,0.15635536814370535,0.17110022724988203,0.15987756479446852,0.15832670820832215,0.16148721299729976,0.12586454645398865\n", "MAXPERM,restream_iter_5,0.42246276303,0.0240088990978,0.41698316599999996,0.41432718599999996,0.408774113,0.3741976490000001,0.42335084400000006,0.44833675400000006,0.404533343,0.437201829,0.41214813400000005,0.417958213,0.41245201000000004,0.43177652499999997,0.443937986,0.446542203,0.40733910399999995,0.43004789000000004,0.42770054700000004,0.3900864869999999,0.407989233,0.47260486700000004,0.42691634200000006,0.396093864,0.414398236,0.39510367199999996,0.42443720799999995,0.405558881,0.430686036,0.41817661100000003,0.444324959,0.449265013,0.396053674,0.41759338100000004,0.45158693499999997,0.42466341900000004,0.405926947,0.39761467300000003,0.472289223,0.352520988,0.399992477,0.43971530500000006,0.381677522,0.447287129,0.416199276,0.416841384,0.425211331,0.37361050800000006,0.444184992,0.42691476800000006,0.37517611500000003,0.45653437099999994,0.42488832299999996,0.460559681,0.408915502,0.42999265799999997,0.393201024,0.433593456,0.42796065699999997,0.428216086,0.419308285,0.391725323,0.400776132,0.430672719,0.45011780999999995,0.396626561,0.430083769,0.43609922800000006,0.42920028000000005,0.377147631,0.43179266599999994,0.429164461,0.417417871,0.44079940400000006,0.38387955700000004,0.450370666,0.445476369,0.43457809599999997,0.374720443,0.46507926000000005,0.399516088,0.40264003600000003,0.44730415900000003,0.449737469,0.39851508700000005,0.44352174099999997,0.39196510700000003,0.41331802400000006,0.45569073499999996,0.43228262400000006,0.41994674000000004,0.466203263,0.45524855000000003,0.409993804,0.44625766099999997,0.41818191900000007,0.42801552800000003,0.453698131,0.426918415,0.42926703499999996,0.4333490890000001,0.42749785700000004\n", "FSCORE,restream_iter_5,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_5,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 6\n", "Mode 6 Iteration 0\n", "Mode 6 Iteration 20\n", "Mode 6 Iteration 40\n", "Mode 6 Iteration 60\n", "Mode 6 Iteration 80\n", "CUT_RATIO,restream_iter_6,0.129755318849,0.0148560596631,0.15452688904,0.12406271302,0.12797516385,0.124383020731,0.14050719671,0.135383527748,0.138525155065,0.0968612892339,0.125727989037,0.0976033344911,0.117667121419,0.112133605999,0.157272094642,0.127348643006,0.124663978495,0.142376304275,0.12078458362,0.109402306886,0.134049186006,0.149847094801,0.127342123525,0.104824713641,0.139148494289,0.124654696133,0.131994733377,0.128196385953,0.132785763176,0.129901125128,0.143160127253,0.120233596702,0.12850140056,0.104159842778,0.146754143646,0.131492439185,0.136378682555,0.128071997231,0.136047666336,0.100032905561,0.101163200564,0.116817724068,0.143992055611,0.107313738893,0.115491974878,0.128942807626,0.116117850953,0.0971725706857,0.140512147078,0.123123123123,0.114667604643,0.111111111111,0.121739130435,0.139446366782,0.12156998958,0.134207870838,0.110259826782,0.145750332005,0.146358543417,0.121689574254,0.157771064115,0.150134952767,0.131623354708,0.141962421712,0.15203054495,0.175802139037,0.132338308458,0.128862359551,0.140960163432,0.140271493213,0.140710382514,0.115208991921,0.137047163363,0.132208157525,0.128020901372,0.134724186704,0.136170212766,0.115660184237,0.127038043478,0.130674002751,0.118791246961,0.124442920809,0.124451939292,0.135144312394,0.125500667557,0.128526645768,0.12908719346,0.119705985299,0.138112463006,0.161839863714,0.142260869565,0.157365684575,0.129010003449,0.130493123113,0.155762205247,0.121132323897,0.125411454905,0.125768967874,0.141590013141,0.126424870466,0.111032280951,0.142228242465\n", "EC,restream_iter_6,380.79,44.8072081255,454,364,371,378,410,383,402,287,367,281,345,329,452,366,371,423,351,313,387,441,367,302,402,361,401,376,388,381,405,350,367,318,425,400,412,370,411,304,287,348,435,314,331,372,335,299,428,369,326,333,350,403,350,399,331,439,418,363,470,445,390,408,438,526,399,367,414,403,412,328,401,376,392,381,416,339,374,380,342,363,369,398,376,369,379,342,420,475,409,454,374,389,469,368,381,368,431,366,313,420\n", "TCV,restream_iter_6,436.31,49.0990213752,526,446,379,422,467,447,479,333,441,323,405,373,524,438,405,463,435,354,446,477,445,351,450,407,450,402,446,419,468,424,438,344,489,437,464,422,482,314,331,394,484,389,391,460,373,365,474,468,353,407,417,473,398,448,357,469,467,431,548,470,450,493,483,548,452,436,483,478,474,405,475,449,405,446,477,411,413,453,377,415,428,440,436,444,419,397,446,497,471,558,429,447,515,418,434,433,510,414,380,490\n", "LONELINESS,restream_iter_6,0.841682055104,0.00482358278459,0.838324099709,0.841118578416,0.842141435226,0.84690375088,0.841330934589,0.837526727021,0.838823489365,0.852288465622,0.848172636941,0.848375326976,0.839441230105,0.848225767194,0.838156765397,0.845961962698,0.843609204385,0.844281713813,0.842233087207,0.837815415253,0.841875538759,0.846659914234,0.835816365685,0.839178329459,0.838965128536,0.839730522997,0.846137830414,0.841593850249,0.840546330236,0.836686722571,0.834418012567,0.850371127994,0.840136323878,0.854064374347,0.841399582894,0.850572945862,0.839288632237,0.839692077353,0.842138336602,0.843425895163,0.846500927296,0.846773818596,0.824513157549,0.844941007518,0.846379974275,0.840391467305,0.839846423778,0.848833902902,0.840936116896,0.841579512288,0.839221322022,0.846334844626,0.836015444362,0.840421490983,0.845132244683,0.841533474672,0.846303239412,0.842384203469,0.834359409042,0.842662207487,0.837695688638,0.83956050699,0.843291598674,0.836529111672,0.840329420088,0.833151997841,0.844663768493,0.841779565821,0.836643136882,0.828141477174,0.838825724875,0.84227054275,0.836765985829,0.836688017308,0.845770827852,0.842174083362,0.843009965452,0.842272568336,0.838279764244,0.834066333681,0.840314632126,0.8425481957,0.847237874968,0.843869820935,0.84002540718,0.842716566257,0.837024582168,0.840882900303,0.843357350384,0.838717224626,0.835395795601,0.834151500313,0.847838921926,0.842028287071,0.843997851956,0.842846717913,0.849134687952,0.850070863591,0.848249766696,0.841337298766,0.840209469766,0.841847086269\n", "QDS,restream_iter_6,0.369294565309,0.0266964791796,0.3665029454826926,0.3485850458275703,0.39215332344429055,0.3085185327557028,0.3784187752791692,0.3875649412697518,0.34798237294488266,0.3961328596111284,0.3447419583324625,0.39732168746953483,0.3518464952747058,0.4030266091656908,0.3601304271661197,0.40068577309469716,0.3825265259939666,0.3797770512835261,0.38777288439437696,0.33847155755268493,0.40251880688424946,0.35139165281979756,0.33695136533625275,0.3920163718806626,0.3535450151777704,0.4169902788865614,0.3573128068681281,0.3326519774221715,0.3814393688529845,0.35020783286607704,0.39405847996401355,0.39764563811368575,0.3269923769104258,0.3854426676932379,0.347478153234218,0.36084729062300747,0.3707949196292002,0.36375092048784463,0.35121412652745904,0.3271582157257154,0.3546619524187854,0.40611595395223593,0.3668026463850596,0.37931225127105833,0.35781614763656283,0.4016647800144565,0.4277865101261039,0.3765559283225879,0.3856309050126113,0.4031203405722915,0.3309440418490592,0.35160583106021953,0.3467217924895914,0.3702913309181571,0.3628168149061655,0.38228135783066347,0.3462285358397855,0.4058012270621134,0.3700012881626212,0.4015978721033225,0.37743624258446257,0.34405058738805055,0.33718155075215234,0.34783371159108273,0.38052981653597956,0.35420028870407316,0.3929585007914085,0.3166773646995977,0.36300156805448025,0.35347426949490385,0.39383249585834745,0.3325992285858772,0.34292876455410276,0.36745058264000935,0.3478066571270618,0.3473687156363682,0.37932983398574693,0.3810440649386371,0.3494488008090408,0.38354545610988894,0.31900637394296677,0.36011583477746445,0.35808175142310816,0.37842040294573454,0.3542377573920212,0.37396454626492015,0.36571387693002216,0.3401279783456678,0.42253081478721516,0.32854488075224936,0.38565876118707376,0.427211371355863,0.443316494700125,0.37995100631926154,0.35968316760439495,0.3559774942875403,0.3455897929289396,0.372454000557675,0.4146892230022436,0.3623189836828393,0.3824528090801243,0.40638516757839704\n", "CONDUCTANCE,restream_iter_6,0.145737822978,0.0172589308851,0.130996726102979,0.13232133864939358,0.16810351186593517,0.17294242531833265,0.14989501666227936,0.12805970731386046,0.10717491169590318,0.16422827731234801,0.16452461191308226,0.16416696828018168,0.1392694962220339,0.14048838708185538,0.1102895858531491,0.16149073771834024,0.16875270716004873,0.13620793310609658,0.14983424129892303,0.14307612740326092,0.15074240806066302,0.14307872657225498,0.16665314867804334,0.10800877750851609,0.13874319862634607,0.1348203580139223,0.12824918763566354,0.1313923461773035,0.1412127492040031,0.13680242806334672,0.12932297225835332,0.14465833807796522,0.16500320726396883,0.17993138337486958,0.13904353585998971,0.16014850802705716,0.1412637143533127,0.15743792259230102,0.12930915080193128,0.17193907970215283,0.1580562626711622,0.13958797016985508,0.12599702924399908,0.14051121006533948,0.15965539809449195,0.1335788693709653,0.1384621747713395,0.17280419240696981,0.14733430102459402,0.12744845360092003,0.14744682234960674,0.13499653168185904,0.14505623623487884,0.16230163904443126,0.1496402655209675,0.14708744839406332,0.1419622538150193,0.16891676352590987,0.12774628088579743,0.1519793913339662,0.15346406734929086,0.14487992728599305,0.17093182753228117,0.15395266623847975,0.17130801986185984,0.14455002867365774,0.14303384864188712,0.16574626152731156,0.1273828619431681,0.13392915342364833,0.13732296466945,0.15838503098569945,0.1273571331875854,0.1678763620947189,0.14133870428580594,0.17458258650091468,0.1154475946694681,0.12028964585196422,0.14341733099857631,0.1347256813533583,0.1552027025542999,0.11483050345333237,0.12766650900979276,0.1492523920938688,0.15807205961337648,0.13502940517440748,0.16628237640805882,0.14329539990536844,0.14564593960231872,0.1259462838225255,0.15931986694779193,0.09385358228663562,0.15740052415127767,0.15544614014198643,0.13724292674031954,0.12490647296767382,0.1553541217942369,0.17233022630695394,0.15738661616475771,0.1592976323521856,0.16858306485379562,0.13136250832511384\n", "MAXPERM,restream_iter_6,0.42181158633,0.0231649064205,0.418342765,0.417518664,0.410793277,0.376292737,0.426644533,0.444543508,0.410707372,0.43644850100000004,0.41616858,0.42210831600000004,0.397159604,0.435409849,0.4457717610000001,0.450250749,0.408711666,0.422211159,0.421686626,0.3900864869999999,0.40644025500000003,0.47260486700000004,0.42941761700000003,0.39260586599999997,0.426448846,0.391295392,0.425668127,0.400332781,0.4332002,0.416026499,0.440856456,0.447524911,0.411569302,0.41612456800000003,0.454797504,0.42689429700000003,0.404982182,0.397174825,0.470862956,0.353751354,0.40291212099999996,0.43971530500000006,0.393736144,0.44361898099999997,0.42205124,0.423711503,0.42316086,0.38044722200000003,0.42799934900000003,0.42463772199999994,0.377189925,0.44989789699999994,0.412399482,0.45345057,0.41087664199999996,0.43304007,0.393201024,0.422866948,0.422284772,0.42474377099999994,0.42390348800000005,0.38706875100000004,0.399018771,0.43252371200000006,0.450016692,0.41836265599999994,0.427308132,0.436524992,0.4196676,0.38107719100000004,0.43547504600000003,0.424260301,0.42734319600000004,0.43446548499999993,0.38612632099999994,0.45315268100000006,0.443829834,0.429489569,0.37326378899999996,0.440540438,0.390302475,0.40719816300000006,0.448030286,0.4506880069999999,0.388013045,0.443739757,0.386962651,0.407047715,0.455126194,0.433878804,0.399161563,0.46181073300000003,0.448790479,0.411695205,0.44894228899999994,0.42044341100000004,0.433322303,0.457171292,0.440758427,0.419346275,0.43073374700000006,0.42320066\n", "FSCORE,restream_iter_6,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_6,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 7\n", "Mode 7 Iteration 0\n", "Mode 7 Iteration 20\n", "Mode 7 Iteration 40\n", "Mode 7 Iteration 60\n", "Mode 7 Iteration 80\n", "CUT_RATIO,restream_iter_7,0.128442444988,0.0144008583017,0.152144315861,0.125426039536,0.128320110383,0.121750575847,0.142906100069,0.1311417462,0.138525155065,0.0965237934526,0.123672490579,0.0951719346995,0.116302864939,0.112133605999,0.163535142658,0.115866388309,0.124663978495,0.139347021205,0.116655196146,0.109402306886,0.134049186006,0.149847094801,0.124913254684,0.104824713641,0.138802353756,0.124654696133,0.128703094141,0.125468803273,0.131759069131,0.128196385953,0.143160127253,0.120233596702,0.126050420168,0.104159842778,0.14226519337,0.132807363577,0.135054617676,0.127725856698,0.134392585237,0.100032905561,0.100458230525,0.116817724068,0.131413439259,0.105263157895,0.113747383112,0.128596187175,0.112651646447,0.0955476113097,0.143138542351,0.12045378712,0.114667604643,0.113780447114,0.141217391304,0.134602076125,0.12156998958,0.133871510259,0.110259826782,0.143758300133,0.144957983193,0.119007710359,0.15609264854,0.141363022942,0.131623354708,0.141962421712,0.149600833044,0.151069518717,0.134660033167,0.125351123596,0.140960163432,0.13957535677,0.140027322404,0.113804004215,0.134996582365,0.130450070323,0.12769431744,0.134724186704,0.131587561375,0.114977823269,0.128057065217,0.126547455296,0.111844390413,0.124100102845,0.125126475548,0.133106960951,0.130173564753,0.128526645768,0.131130790191,0.115155757788,0.138441302203,0.161839863714,0.137391304348,0.156672443674,0.122456019317,0.130493123113,0.154765858519,0.120803159974,0.125411454905,0.126452494874,0.141590013141,0.124006908463,0.111032280951,0.142228242465\n", "EC,restream_iter_7,376.93,43.3057167127,447,368,372,370,417,371,402,286,361,274,341,329,470,333,371,414,339,313,387,441,360,302,401,361,391,368,385,376,405,350,360,318,412,404,408,369,406,304,285,348,397,308,326,371,325,294,436,361,326,341,406,389,350,398,331,433,414,355,465,419,390,408,431,452,406,357,414,401,410,324,395,371,391,381,402,337,377,368,322,362,371,392,390,369,385,329,421,475,395,452,355,389,466,367,381,370,431,359,313,420\n", "TCV,restream_iter_7,432.28,48.2022987004,518,446,381,413,476,423,476,333,435,316,399,374,546,408,405,454,422,354,446,477,432,351,447,407,440,400,445,414,465,424,434,344,490,441,456,423,477,314,325,394,443,381,387,460,363,362,476,455,353,414,463,462,398,447,357,472,463,428,541,454,450,487,474,493,459,426,474,472,471,399,470,441,398,446,466,409,413,434,368,414,429,432,448,444,424,390,447,497,453,558,412,447,511,415,432,438,509,403,380,491\n", "LONELINESS,restream_iter_7,0.842026990108,0.0046431003995,0.839081858299,0.840548167548,0.841948749203,0.847309954311,0.837221374402,0.840182707937,0.839156752538,0.852533361038,0.847848865068,0.849074839737,0.841555955751,0.848315346005,0.830354903308,0.848625056353,0.843609204385,0.844284331339,0.843525850654,0.837815415253,0.84178381193,0.846659914234,0.836926540525,0.839178329459,0.838439511069,0.839730522997,0.845540434918,0.842285317445,0.84091076517,0.837333493692,0.834472629669,0.850371127994,0.841249587969,0.854064374347,0.84053701672,0.850409669778,0.83982245596,0.839483907364,0.843139793147,0.843425895163,0.846637630402,0.846773818596,0.833670845978,0.845853775098,0.846468041917,0.840499939461,0.840271653704,0.849155727118,0.839668246862,0.845023382361,0.839098135899,0.844730209834,0.830454176835,0.842403189912,0.845157083694,0.84165609926,0.846303239412,0.842189677812,0.834955450389,0.843189189533,0.838085807295,0.84114707738,0.843291598674,0.836493781567,0.840993467983,0.838784143889,0.839990892838,0.843126728656,0.836388374944,0.828696772874,0.839066101502,0.841995930232,0.837013386189,0.837364781665,0.845639119556,0.842123291114,0.843702761299,0.842461721482,0.839006535987,0.842348747042,0.841215677459,0.842670820287,0.847098863275,0.84475999044,0.838537117419,0.842716566257,0.836333439432,0.840295644544,0.843497566267,0.838717224626,0.838815284981,0.83491497948,0.849524949729,0.842028287071,0.84433971364,0.843166302169,0.849017890873,0.84939714268,0.848014034211,0.844635992644,0.840209469766,0.842153724616\n", "QDS,restream_iter_7,0.369267576981,0.0268035196129,0.36557495766006354,0.35084303374306675,0.3929014261138492,0.3077551738766065,0.37416757066891787,0.40349679679446504,0.3584348808195187,0.39596506033506224,0.34600253302391876,0.3977627771761501,0.3727811089404484,0.38812760950353015,0.354513583283661,0.39877444500165476,0.3825265259939666,0.3578871403562347,0.3842729006226216,0.33847155755268493,0.4011765731550134,0.35139165281979756,0.3281024392024393,0.3920163718806626,0.35426421952211085,0.4169902788865614,0.35997743130148085,0.3337383889971989,0.37567146578172983,0.33253062700671004,0.39773040391256703,0.39764563811368575,0.33698869782635255,0.3854426676932379,0.35067727152997563,0.3604239211423535,0.38077080669967134,0.3751127187390083,0.3511642391232845,0.3271582157257154,0.35166669283696816,0.40611595395223593,0.36513257336082844,0.38775511216910724,0.35337732202055516,0.40168057462099876,0.4282846283138257,0.3749482325685865,0.3730354990178955,0.4043618077538061,0.33152137494691336,0.3503143332451572,0.35472237505164717,0.3696529934913998,0.362955055472152,0.38225405782587085,0.3462285358397855,0.4104029902675557,0.3717181937784442,0.40049729389635386,0.38138864597086525,0.3437200107536062,0.33718155075215234,0.34634575851698657,0.3713983453202983,0.3424741730997635,0.38931387549066615,0.31880561322669004,0.3480025843103125,0.36061508346879423,0.38675551535351466,0.345070947512946,0.34290423344093207,0.368819395860863,0.3471444911102212,0.37141462671410874,0.37299870537279955,0.3740564378635475,0.3472805185741236,0.38938717602130835,0.3073260820608526,0.3745006563446267,0.3582400557486135,0.380676921297728,0.3514998106132082,0.37396454626492015,0.36555100584941136,0.34111527156305205,0.42831149899786525,0.32854488075224936,0.3827234869120757,0.4286630506557221,0.442383882474982,0.37995100631926154,0.35878958701268127,0.35784120750685894,0.3470789220131338,0.3676835150884418,0.4063234787419881,0.36428339936215637,0.3824528090801243,0.40791622375568076\n", "CONDUCTANCE,restream_iter_7,0.146744186924,0.0176198912747,0.1224250542557477,0.1311253930972245,0.16470533335960819,0.164286026825997,0.14376589157707323,0.12781167607001345,0.12373473061625861,0.16590232938403124,0.1678556960719422,0.16663183807797557,0.14622093209477505,0.1387687730966512,0.10633932019233111,0.1643212107395173,0.16875270716004873,0.1348413103065772,0.15188789778507483,0.14307612740326092,0.14913669887838887,0.14307872657225498,0.15666856494724782,0.10800877750851609,0.14188756533176733,0.1348203580139223,0.13564286582230736,0.13447169209567353,0.1257857379033222,0.14410375859514715,0.1378970665972203,0.14465833807796522,0.1596649910581615,0.17993138337486958,0.15365581475071916,0.16450164798446618,0.1417777768380929,0.1533345860263459,0.1304001360029129,0.17193907970215283,0.1660297199077477,0.13958797016985508,0.1281062853423876,0.14250985585074516,0.1669530548167316,0.13358896574471313,0.14127486177444726,0.17420240893966552,0.15049842215342268,0.13096081601043108,0.14564788415000146,0.13715863168888698,0.1424212875904134,0.16687091831601147,0.14986016702040333,0.14830732257118498,0.1419622538150193,0.1692186977108799,0.13406959722324646,0.15002349789783437,0.1573836911891855,0.1471041976905212,0.17093182753228117,0.15266169414864866,0.16986772524940522,0.14231343400229218,0.14045266980078833,0.17278189899922594,0.12514433315524204,0.1324588594502719,0.13742533693988687,0.1742348123214663,0.1321583444433129,0.16800209520688653,0.13983014765674068,0.17871455884499754,0.11502747548487122,0.12167788349888997,0.13460133221110182,0.14780949906698887,0.14021377160166185,0.11700376764946202,0.12764746571135016,0.14258211710346982,0.1661959120539323,0.13502940517440748,0.16783611034696352,0.1455583855982903,0.1487654735544745,0.1259462838225255,0.17727269771181234,0.09410084247569404,0.14835622339305674,0.15544614014198643,0.13720777595702097,0.12647155912390357,0.15483803121975248,0.1612937779238393,0.16702860177014447,0.17078599528148142,0.16858306485379562,0.13260304011156332\n", "MAXPERM,restream_iter_7,0.42043043651,0.0236809913018,0.419831547,0.41460767800000004,0.408766075,0.36778838099999994,0.416575018,0.455122478,0.404533343,0.43674329700000003,0.415623535,0.41955273000000004,0.390844448,0.436336978,0.43331799800000004,0.439532193,0.408711666,0.41552537,0.41833954000000007,0.3900864869999999,0.406427982,0.47260486700000004,0.42463116399999995,0.39260586599999997,0.421446248,0.391295392,0.41915909500000004,0.402361425,0.43188445900000005,0.417333727,0.441563794,0.447524911,0.410642449,0.41612456800000003,0.449844963,0.431641946,0.40132619199999997,0.398040052,0.470810591,0.353751354,0.400056119,0.43971530500000006,0.406868999,0.450169231,0.42182832600000003,0.42239428900000003,0.41884333,0.3744553450000001,0.43348113800000004,0.43091945200000004,0.37668988,0.44316209,0.390795946,0.45916919399999995,0.409310047,0.432039948,0.393201024,0.41981623500000004,0.43166634699999995,0.418979613,0.416887548,0.390216656,0.399018771,0.42698525600000004,0.45184795799999994,0.408056764,0.41204326499999994,0.438514214,0.426676473,0.384675778,0.43371779800000004,0.42894147099999996,0.42489240699999997,0.43666411200000005,0.383952751,0.445548053,0.440356662,0.426655526,0.37640303199999997,0.44881928499999996,0.37964391999999997,0.407864889,0.44788737900000003,0.449495794,0.38510718299999996,0.443739757,0.38865048,0.399712007,0.456603716,0.433878804,0.40287237100000006,0.4610995139999999,0.449702174,0.411695205,0.441491464,0.41217041700000007,0.431176829,0.4569923189999999,0.42849651,0.43133470799999996,0.43073374700000006,0.425399019\n", "FSCORE,restream_iter_7,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_7,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 8\n", "Mode 8 Iteration 0\n", "Mode 8 Iteration 20\n", "Mode 8 Iteration 40\n", "Mode 8 Iteration 60\n", "Mode 8 Iteration 80\n", "CUT_RATIO,restream_iter_8,0.127180730421,0.0143325853718,0.151803948264,0.122017723245,0.12797516385,0.120763409016,0.142220699109,0.124425592082,0.138525155065,0.0965237934526,0.121616992121,0.0948245918722,0.113915416098,0.113496932515,0.164578983994,0.115866388309,0.124663978495,0.136317738135,0.114934618032,0.109402306886,0.134049186006,0.149847094801,0.118320610687,0.104824713641,0.13707165109,0.124654696133,0.11685319289,0.125468803273,0.133812457221,0.128196385953,0.143160127253,0.120233596702,0.126050420168,0.104159842778,0.139848066298,0.131492439185,0.134061569017,0.127033575632,0.134392585237,0.100032905561,0.100105745506,0.116817724068,0.12975835816,0.103896103896,0.114794138172,0.128596187175,0.112651646447,0.0955476113097,0.137229152988,0.120787454121,0.114667604643,0.112779446113,0.124869565217,0.134602076125,0.12156998958,0.13353514968,0.110259826782,0.142762284197,0.143907563025,0.119007710359,0.159785162806,0.141363022942,0.131623354708,0.13987473904,0.149600833044,0.147393048128,0.130016583748,0.125351123596,0.128702757916,0.135050469892,0.140027322404,0.110642781876,0.134313055366,0.128340365682,0.121162638798,0.134724186704,0.129296235679,0.114977823269,0.128057065217,0.121389270977,0.112539076068,0.124100102845,0.125126475548,0.133106960951,0.129839786382,0.128526645768,0.12908719346,0.11200560028,0.138441302203,0.161839863714,0.133913043478,0.156672443674,0.122111072784,0.130493123113,0.154433742943,0.118169848585,0.125082290981,0.126452494874,0.141590013141,0.124006908463,0.111032280951,0.142228242465\n", "EC,restream_iter_8,373.22,43.0222221648,446,358,371,367,415,352,402,286,355,273,334,333,473,333,371,405,334,313,387,441,341,302,396,361,355,368,391,376,405,350,360,318,405,400,405,367,406,304,284,348,392,304,329,371,325,294,418,362,326,338,359,389,350,397,331,430,411,355,476,419,390,402,431,441,392,357,378,388,410,315,393,365,371,381,395,337,377,353,324,362,371,392,389,369,379,320,421,475,385,452,354,389,465,359,380,370,431,359,313,420\n", "TCV,restream_iter_8,428.21,47.3139081032,518,436,379,413,485,409,479,332,433,317,391,378,541,408,405,444,418,354,446,477,415,351,442,407,411,400,454,414,465,424,435,344,475,439,452,424,477,314,324,394,440,376,390,460,363,362,437,458,352,415,402,462,399,445,357,465,457,428,537,454,450,482,474,481,436,426,441,451,472,389,468,441,382,446,457,410,413,409,369,414,429,435,449,444,419,381,447,497,440,556,411,447,509,411,431,437,511,403,380,490\n", "LONELINESS,restream_iter_8,0.84261124766,0.00436483974277,0.839451010465,0.842391885429,0.842145925937,0.847664062167,0.835645721454,0.841481332514,0.838823489365,0.852595585145,0.848353497604,0.849192080194,0.842620236492,0.846330252149,0.83341858244,0.848625056353,0.843609204385,0.845637970733,0.84417493253,0.837815415253,0.84178381193,0.846659914234,0.840053551942,0.839178329459,0.839275138809,0.839730522997,0.849528181693,0.842209891065,0.83913967487,0.837333493692,0.834472629669,0.850371127994,0.841302696979,0.854064374347,0.841812520458,0.85064800804,0.840094529722,0.839569988753,0.843139793147,0.843425895163,0.847335193831,0.846773818596,0.83434097368,0.846878050214,0.846442360129,0.840499939461,0.840271653704,0.849155727118,0.842786364589,0.844056063672,0.83918771471,0.84529363851,0.839443828598,0.842403189912,0.845173107586,0.841901909971,0.846303239412,0.843086221105,0.836843002033,0.843166237383,0.837496647191,0.84114707738,0.843291598674,0.837279201169,0.84077221871,0.839691573754,0.846750510611,0.843016268088,0.838998374264,0.830669206254,0.839082141072,0.845284220629,0.837037871735,0.838143255814,0.846610229244,0.842267093162,0.844449918505,0.842533825388,0.839006535987,0.846087518823,0.840911552061,0.842670820287,0.847098863275,0.844167892318,0.839321452598,0.842716566257,0.83699926371,0.842349416951,0.843497566267,0.838717224626,0.840012606984,0.834456151977,0.849470375843,0.842028287071,0.845056109228,0.843736428533,0.849310838501,0.848933197935,0.848249766696,0.844635992644,0.840209469766,0.841847086269\n", "QDS,restream_iter_8,0.368868113039,0.0267403500012,0.3593304338537016,0.34768601662011134,0.38654337696588187,0.3076299473458121,0.36654134612519523,0.3864784228176619,0.34798237294488266,0.3956653822963102,0.3454854986490506,0.3978548774319471,0.3759158119296768,0.38626672857202105,0.3440473471723343,0.39877444500165476,0.3825265259939666,0.3837171864437865,0.39160459345009313,0.33847155755268493,0.4012384626198928,0.35139165281979756,0.3309674819835612,0.3920163718806626,0.3542490513644835,0.4169902788865614,0.36528312041918376,0.33225230922157784,0.381292992082041,0.33253062700671004,0.39773040391256703,0.39764563811368575,0.3373707805885459,0.3854426676932379,0.34099726460593704,0.35980380589396715,0.3716670308752133,0.36607755040132817,0.3511642391232845,0.3271582157257154,0.35521907297714866,0.40611595395223593,0.36250651159223446,0.38925968673643163,0.3537813297758406,0.40168057462099876,0.4282846283138257,0.3749482325685865,0.3693552455589233,0.4032249354422736,0.331466430594717,0.34386587362369014,0.3418795699690167,0.3696529934913998,0.36406240784678734,0.38210709525733827,0.3462285358397855,0.4035721513009709,0.3685212160593157,0.39790815104651517,0.3708266158259842,0.3437200107536062,0.33718155075215234,0.34622336356776817,0.37945560207053664,0.3410695768901734,0.3892820376520414,0.32845811923246276,0.35261728355620164,0.3521003641118275,0.3865398741071648,0.34399830063309683,0.34327916839468275,0.3594819879920446,0.34673244288337,0.3721840338490509,0.37723900678330313,0.3804397271179506,0.3472805185741236,0.3945574970329531,0.3074145436739147,0.3745006563446267,0.3582400557486135,0.38016589883052054,0.35393641947557347,0.37396454626492015,0.3659329023579466,0.3410544918294152,0.42831149899786525,0.32854488075224936,0.3834918655839768,0.42723569057798,0.44153362463653,0.37995100631926154,0.35926517734924723,0.37325510449007904,0.3525328140582862,0.37089832736670325,0.41136092846517625,0.36428339936215637,0.3824528090801243,0.40638516757839704\n", "CONDUCTANCE,restream_iter_8,0.146669858198,0.0180238474488,0.12387013754463973,0.12518386161536035,0.16507353258656443,0.16621641180615904,0.1315955637036091,0.13078449622384333,0.10717491169590318,0.16142347363590318,0.16514364246945307,0.16695859978263727,0.1488544875256574,0.12858074088406987,0.12146129320002351,0.1643212107395173,0.16875270716004873,0.13971473268589632,0.16763563588945837,0.14307612740326092,0.1490730707680522,0.14307872657225498,0.16351408857607766,0.10800877750851609,0.1447794744257858,0.1348203580139223,0.13253207393321662,0.13290226536450034,0.13491820402850652,0.14410375859514715,0.1378970665972203,0.14465833807796522,0.16864971623627184,0.17993138337486958,0.15303715340391058,0.16116895513232085,0.14630953674678324,0.16165788423454594,0.1304001360029129,0.17193907970215283,0.1747125185011304,0.13958797016985508,0.11862411543539704,0.14282346506539642,0.15885791206653524,0.13358896574471313,0.14127486177444726,0.17420240893966552,0.1545501132769355,0.1306156367774054,0.14741565902900783,0.13251571413344382,0.14237834034750796,0.16687091831601147,0.1543661525715767,0.14747666552187091,0.1419622538150193,0.16794536267688237,0.1337572889321993,0.13724134944436697,0.1439296807597738,0.1471041976905212,0.17093182753228117,0.1535302875227229,0.1709038889181,0.13761932427308907,0.13815230172108628,0.1738001475945133,0.1363672062238896,0.12552519144304802,0.1359372084972365,0.17902807817545233,0.13330022419549786,0.16524153823411605,0.14313174170288248,0.18003486196008722,0.11732430575109556,0.12341804455800792,0.13460133221110182,0.1435883678941443,0.13999265294604424,0.11700376764946202,0.12764746571135016,0.1426981382729062,0.15800018709989577,0.13502940517440748,0.16977544328725494,0.14694441142898845,0.1487654735544745,0.1259462838225255,0.17891797681994037,0.09455258525449886,0.14799204059039917,0.15544614014198643,0.13800813344711238,0.13013021782363618,0.1611146286491707,0.15861821984604826,0.15623203652534431,0.17078599528148142,0.16858306485379562,0.13136250832511384\n", "MAXPERM,restream_iter_8,0.42127691285,0.0233564491325,0.421688653,0.41729846699999995,0.411274327,0.370519,0.412700775,0.44912609699999995,0.410707372,0.43820281499999997,0.410800936,0.42146928300000003,0.39410599,0.43226701,0.441797523,0.439532193,0.408711666,0.42341535999999996,0.4233264360000001,0.3900864869999999,0.40651134800000005,0.47260486700000004,0.428027128,0.39260586599999997,0.42449928400000003,0.391295392,0.42598644900000004,0.40205658,0.4292580330000001,0.417333727,0.441563794,0.447524911,0.4137532830000001,0.41612456800000003,0.44442429400000005,0.42796761499999997,0.40239813500000005,0.397240741,0.470810591,0.353751354,0.401422687,0.43971530500000006,0.41480767300000004,0.449988043,0.42031164099999996,0.42239428900000003,0.41884333,0.3744553450000001,0.431302884,0.429637627,0.37789686,0.445697044,0.40772479599999994,0.45916919399999995,0.409260041,0.43312364099999995,0.393201024,0.4223044,0.43227467,0.414274809,0.41149005100000013,0.390216656,0.399018771,0.42921367499999996,0.448012784,0.403455324,0.427326189,0.438848798,0.418255987,0.388002242,0.432249545,0.42381952,0.42126486,0.43680006400000004,0.380500822,0.450370666,0.442594199,0.42799112,0.37640303199999997,0.457086085,0.378451916,0.407864889,0.44788737900000003,0.44608428999999994,0.386729812,0.443739757,0.386914824,0.403428001,0.456603716,0.433878804,0.410675117,0.46229622900000006,0.450413929,0.411695205,0.442591477,0.4242779649999999,0.43132289500000004,0.45323010599999997,0.440841816,0.43133470799999996,0.43073374700000006,0.42320066\n", "FSCORE,restream_iter_8,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_8,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 9\n", "Mode 9 Iteration 0\n", "Mode 9 Iteration 20\n", "Mode 9 Iteration 40\n", "Mode 9 Iteration 60\n", "Mode 9 Iteration 80\n", "CUT_RATIO,restream_iter_9,0.126353222224,0.0139947509295,0.153165418652,0.118950238582,0.12797516385,0.120105297795,0.14050719671,0.124425592082,0.138525155065,0.0965237934526,0.118876327509,0.0948245918722,0.113915416098,0.109747784594,0.152052887961,0.115866388309,0.124663978495,0.136990912151,0.115278733655,0.109402306886,0.134049186006,0.149847094801,0.116585704372,0.104824713641,0.13707165109,0.124654696133,0.116194865043,0.125127855438,0.131759069131,0.128196385953,0.143160127253,0.120233596702,0.126050420168,0.104159842778,0.139502762431,0.130177514793,0.133068520357,0.127379716165,0.134392585237,0.100032905561,0.100105745506,0.116817724068,0.12975835816,0.102187286398,0.114096301465,0.128596187175,0.112651646447,0.0955476113097,0.116217990808,0.12012012012,0.114667604643,0.112779446113,0.121043478261,0.134602076125,0.12156998958,0.13353514968,0.110259826782,0.142762284197,0.146008403361,0.119007710359,0.146022155086,0.141363022942,0.131623354708,0.13987473904,0.149600833044,0.147393048128,0.129353233831,0.125351123596,0.120190670752,0.136442742778,0.140027322404,0.110994028802,0.134313055366,0.126230661041,0.121162638798,0.134724186704,0.129296235679,0.114977823269,0.128057065217,0.119669876204,0.111844390413,0.124100102845,0.125126475548,0.13480475382,0.125834445928,0.128526645768,0.131130790191,0.11200560028,0.138441302203,0.161839863714,0.132173913043,0.156672443674,0.12176612625,0.130493123113,0.155097974095,0.118828176432,0.125082290981,0.125427204375,0.141590013141,0.124006908463,0.111032280951,0.142228242465\n", "EC,restream_iter_9,370.77,41.8535195653,450,349,371,365,410,352,402,286,347,273,334,322,437,333,371,407,335,313,387,441,336,302,396,361,353,367,385,376,405,350,360,318,404,396,402,368,406,304,284,348,392,299,327,371,325,294,354,360,326,338,348,389,350,397,331,430,417,355,435,419,390,402,431,441,390,357,353,392,410,316,393,359,371,381,395,337,377,348,322,362,371,397,377,369,385,320,421,475,380,452,353,389,467,361,380,367,431,359,313,420\n", "TCV,restream_iter_9,426.03,46.5045062333,525,430,379,410,481,408,476,332,423,317,391,367,503,408,405,451,419,354,447,477,407,351,442,407,411,399,446,414,465,424,435,344,471,434,449,425,477,314,326,394,440,375,389,460,363,362,380,455,353,417,386,462,398,445,357,463,464,428,509,454,450,482,474,481,433,427,420,456,472,392,468,431,382,446,457,409,413,405,368,414,429,440,440,444,424,381,447,497,436,558,411,447,513,411,433,431,509,403,380,491\n", "LONELINESS,restream_iter_9,0.842837579045,0.00432433399886,0.837103476381,0.841494902238,0.842145925937,0.847620535765,0.837887476237,0.841475995201,0.839156752538,0.852599016133,0.848681035194,0.849192080194,0.842620236492,0.847986120087,0.838767322824,0.848625056353,0.843609204385,0.844824550686,0.84369785817,0.837815415253,0.841808650941,0.846659914234,0.841799532327,0.839178329459,0.839275138809,0.839730522997,0.85045908205,0.842559433019,0.84091076517,0.837333493692,0.834472629669,0.850371127994,0.841302696979,0.854064374347,0.842284645338,0.850935911309,0.840574223192,0.839449204209,0.843139793147,0.843425895163,0.84737196816,0.846773818596,0.83434097368,0.847091402086,0.846350964664,0.840499939461,0.840271653704,0.849155727118,0.847450187334,0.844665691658,0.839221322022,0.843803770388,0.841289116203,0.842403189912,0.845132244683,0.841901909971,0.846303239412,0.843061866762,0.834679440869,0.843180389815,0.838672696899,0.84114707738,0.843291598674,0.837279201169,0.840993467983,0.839691573754,0.846690972297,0.843135496957,0.841892450067,0.830661291371,0.839082141072,0.845024286515,0.837037871735,0.839473372285,0.846610229244,0.842174083362,0.844449918505,0.8424062267,0.839006535987,0.847808726479,0.841219634713,0.842670820287,0.847098863275,0.843967606512,0.839174697538,0.842716566257,0.836333439432,0.842349416951,0.843497566267,0.838717224626,0.840423989841,0.83491497948,0.849651926201,0.842028287071,0.844208702251,0.84383645399,0.849258268705,0.850159880868,0.848014034211,0.844635992644,0.840209469766,0.842153724616\n", "QDS,restream_iter_9,0.369221572913,0.0272797547214,0.3606124007880469,0.34507511828539006,0.38654337696588187,0.307680988922408,0.36613666808895146,0.39515242147469504,0.3584348808195187,0.3961627755281488,0.3462386199055006,0.39779028764616536,0.3759158119296768,0.4114531076895928,0.3512790531135059,0.39877444500165476,0.3825265259939666,0.37964959832880196,0.3876249420015631,0.33847155755268493,0.4017267285956907,0.35139165281979756,0.3252102028805019,0.3920163718806626,0.3542490513644835,0.4169902788865614,0.3626481707759396,0.3336424804759225,0.3891236071217436,0.33253062700671004,0.39773040391256703,0.39764563811368575,0.3373707805885459,0.3854426676932379,0.34355925626534706,0.35972502976642,0.3810421968067344,0.3656751741178723,0.3511642391232845,0.3271582157257154,0.351142540416418,0.40611595395223593,0.36250651159223446,0.3941525122246164,0.35496441839569565,0.40168057462099876,0.4282846283138257,0.3749482325685865,0.36671771256631064,0.3984162232336917,0.3309440418490592,0.34465378475534714,0.3504922943851557,0.3696529934913998,0.3628168149061655,0.38210709525733827,0.3462285358397855,0.41538237307022946,0.3694039927738729,0.4002708242012284,0.3873987482838931,0.3437200107536062,0.33718155075215234,0.34622336356776817,0.3713983453202983,0.3410695768901734,0.38947840540674233,0.3288127187897184,0.35239219388680154,0.3520561400664908,0.3865398741071648,0.34378907339150944,0.34327916839468275,0.37052636492845814,0.34673244288337,0.3473687156363682,0.37232811045095376,0.3678633582590594,0.3472805185741236,0.3922621233907929,0.30729381074104084,0.3745006563446267,0.3582400557486135,0.37976177540463213,0.3605034154562621,0.37396454626492015,0.36555100584941136,0.3410544918294152,0.42831149899786525,0.32854488075224936,0.38131073324887127,0.4286630506557221,0.44293157237205777,0.37995100631926154,0.3596946344966512,0.3673796603978733,0.3456057652470796,0.3677372669969277,0.4063234787419881,0.36428339936215637,0.3824528090801243,0.4079495609253773\n", "CONDUCTANCE,restream_iter_9,0.147600868637,0.0176679566532,0.12309776215782202,0.12861988831488064,0.16507353258656443,0.16724870405664746,0.1319070697164788,0.13320771936119324,0.12373473061625861,0.164751699943162,0.16910193270595555,0.16657613400520843,0.1488544875256574,0.14386147405895436,0.12714971683043128,0.1643212107395173,0.16875270716004873,0.13912305368084876,0.16364930449291223,0.14307612740326092,0.14862063438143702,0.14307872657225498,0.16091046520556784,0.10800877750851609,0.1447794744257858,0.1348203580139223,0.1369487470776864,0.1324690668823948,0.12530028378537347,0.14410375859514715,0.1378970665972203,0.14465833807796522,0.16864971623627184,0.17993138337486958,0.15660428985317446,0.16096787086521697,0.15240044846292916,0.1569486761999782,0.1304001360029129,0.17193907970215283,0.16517047943445032,0.13958797016985508,0.11862411543539704,0.14403699757309169,0.1573808691897495,0.13358896574471313,0.14127486177444726,0.17420240893966552,0.15885615201351372,0.13054634714822907,0.14744682234960674,0.12702169771642072,0.15172478151763494,0.16687091831601147,0.1496402655209675,0.14747666552187091,0.1419622538150193,0.16791787653142218,0.13376962511971402,0.15028886541280503,0.16723202136783352,0.1471041976905212,0.17093182753228117,0.1535302875227229,0.16986772524940522,0.13761932427308907,0.14085784716389188,0.17466821397116403,0.13489461645969622,0.12554222114531338,0.1359372084972365,0.1799366379663334,0.13330022419549786,0.17586427886738004,0.14313174170288248,0.17458258650091468,0.11618403286867365,0.12226058807991945,0.13460133221110182,0.1554064998252779,0.14000017539959175,0.11700376764946202,0.12764746571135016,0.147374212850242,0.16867665987180408,0.13502940517440748,0.16783611034696352,0.14694441142898845,0.1487654735544745,0.1259462838225255,0.16477310919852936,0.09410084247569404,0.14731727866364416,0.15544614014198643,0.13771675562852156,0.12849669111821177,0.15694941766870812,0.15882704545600757,0.16702860177014447,0.17078599528148142,0.16858306485379562,0.132451050095813\n", "MAXPERM,restream_iter_9,0.42131003177,0.0236678767558,0.41769449899999994,0.412219924,0.411274327,0.37080783700000003,0.420949392,0.45069718000000003,0.404533343,0.438809879,0.41202466099999996,0.42146928300000003,0.39410599,0.43690532899999995,0.44181399099999996,0.439532193,0.408711666,0.419443353,0.42063872500000005,0.3900864869999999,0.40360028300000006,0.47260486700000004,0.43187359,0.39260586599999997,0.42449928400000003,0.391295392,0.42154024199999995,0.403447029,0.431884346,0.417333727,0.441563794,0.447524911,0.4137532830000001,0.41612456800000003,0.4444711000000001,0.429263628,0.40277658399999994,0.395302407,0.470810591,0.353751354,0.40337995900000007,0.43971530500000006,0.41480767300000004,0.45275051599999994,0.416596675,0.42239428900000003,0.41884333,0.3744553450000001,0.43594006,0.42900273799999994,0.377189925,0.44488027799999996,0.41612519200000003,0.45916919399999995,0.41087664199999996,0.43312364099999995,0.393201024,0.42432329199999996,0.42344289100000004,0.417686011,0.39988061700000005,0.390216656,0.399018771,0.42921367499999996,0.45184795799999994,0.403455324,0.428638823,0.43586642999999997,0.42871949800000003,0.393375637,0.432249545,0.424027281,0.42126486,0.438773212,0.380500822,0.45315268100000006,0.44214421800000003,0.42824952499999996,0.37640303199999997,0.460065626,0.3797981,0.407864889,0.44788737900000003,0.44974509299999993,0.384161506,0.443739757,0.38865048,0.403428001,0.456603716,0.433878804,0.4015992,0.4610995139999999,0.450965893,0.411695205,0.44374455200000007,0.41701671200000007,0.43573614400000005,0.45471117200000005,0.42849651,0.43133470799999996,0.43073374700000006,0.425399019\n", "FSCORE,restream_iter_9,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_9,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "Mode 10\n", "Mode 10 Iteration 0\n", "Mode 10 Iteration 20\n", "Mode 10 Iteration 40\n", "Mode 10 Iteration 60\n", "Mode 10 Iteration 80\n", "CUT_RATIO,restream_iter_10,0.126315914156,0.013913571226,0.15452688904,0.118268575324,0.12797516385,0.120105297795,0.139821795751,0.124425592082,0.138525155065,0.0965237934526,0.120246659815,0.0948245918722,0.113915416098,0.109747784594,0.152748782185,0.115866388309,0.124663978495,0.136317738135,0.114246386786,0.109402306886,0.134049186006,0.149847094801,0.116238723109,0.104824713641,0.13707165109,0.124654696133,0.115536537196,0.125127855438,0.132443531828,0.128196385953,0.143160127253,0.120233596702,0.126050420168,0.104159842778,0.137776243094,0.130177514793,0.133068520357,0.127033575632,0.134392585237,0.100032905561,0.100105745506,0.116817724068,0.12975835816,0.102187286398,0.1123517097,0.128596187175,0.112651646447,0.0955476113097,0.116874589626,0.120787454121,0.114667604643,0.117117117117,0.11652173913,0.134602076125,0.12156998958,0.13353514968,0.110259826782,0.142762284197,0.144957983193,0.119007710359,0.142665323934,0.141363022942,0.131623354708,0.13987473904,0.149600833044,0.147393048128,0.129021558872,0.125351123596,0.122233571672,0.136442742778,0.140027322404,0.111345275729,0.134313055366,0.126230661041,0.121162638798,0.134724186704,0.129296235679,0.117024906175,0.128057065217,0.119669876204,0.111844390413,0.124100102845,0.125126475548,0.133106960951,0.130173564753,0.128526645768,0.12908719346,0.11200560028,0.138441302203,0.161839863714,0.130434782609,0.156672443674,0.12176612625,0.130493123113,0.154765858519,0.118828176432,0.125082290981,0.126110731374,0.141590013141,0.124006908463,0.111032280951,0.142228242465\n", "EC,restream_iter_10,370.67,41.6922186985,454,347,371,365,408,352,402,286,351,273,334,322,439,333,371,405,332,313,387,441,335,302,396,361,351,367,387,376,405,350,360,318,399,396,402,367,406,304,284,348,392,299,322,371,325,294,356,362,326,351,335,389,350,397,331,430,414,355,425,419,390,402,431,441,389,357,359,392,410,317,393,359,371,381,395,343,377,348,322,362,371,392,390,369,379,320,421,475,375,452,353,389,466,361,380,369,431,359,313,420\n", "TCV,restream_iter_10,425.91,46.3765231556,526,426,379,410,479,408,479,333,424,317,391,367,507,408,405,445,416,354,446,477,405,351,442,407,411,399,448,414,465,424,435,344,471,434,449,424,477,314,326,394,440,375,387,460,363,362,381,458,353,431,375,462,398,445,357,468,463,428,490,454,450,482,474,481,433,426,424,454,472,390,468,435,382,446,458,417,413,405,368,414,429,432,449,444,419,381,447,497,431,556,411,447,511,411,432,437,511,403,380,490\n", "LONELINESS,restream_iter_10,0.842835074471,0.0043539743013,0.838404863401,0.842375817657,0.842145925937,0.847620535765,0.838421421284,0.841475995201,0.838823489365,0.852533361038,0.848501529351,0.849192080194,0.842620236492,0.847986120087,0.837992898419,0.848625056353,0.843609204385,0.845621946841,0.844715474433,0.837815415253,0.841768376528,0.846659914234,0.842408765684,0.839178329459,0.839275138809,0.839730522997,0.85172325163,0.842559433019,0.840295939612,0.837333493692,0.834472629669,0.850371127994,0.841302696979,0.854064374347,0.842930156295,0.850935911309,0.840574223192,0.839632618771,0.843139793147,0.843425895163,0.847244699725,0.846773818596,0.83434097368,0.847091402086,0.846790405498,0.840499939461,0.840271653704,0.849155727118,0.847341394997,0.844056063672,0.839098135899,0.840632422659,0.842984596829,0.842403189912,0.845157083694,0.841901909971,0.846303239412,0.843137170144,0.834935995509,0.843166237383,0.839178726608,0.84114707738,0.843291598674,0.837279201169,0.84077221871,0.839691573754,0.846887151804,0.843016268088,0.839919431894,0.830686130382,0.839082141072,0.844551879516,0.837037871735,0.839559815495,0.846610229244,0.842123291114,0.844760612605,0.840167171177,0.839006535987,0.847808726479,0.841215677459,0.842670820287,0.847098863275,0.84475999044,0.838455780637,0.842716566257,0.83699926371,0.842349416951,0.843497566267,0.838717224626,0.840744940745,0.834456151977,0.849651926201,0.842028287071,0.84433971364,0.843786822787,0.849289465343,0.849658977282,0.848249766696,0.844635992644,0.840209469766,0.841847086269\n", "QDS,restream_iter_10,0.369517793371,0.0274214874417,0.3665896850641425,0.3502493384855192,0.38654337696588187,0.307680988922408,0.3796310136118523,0.39515242147469504,0.34798237294488266,0.39596506033506224,0.3440656862725018,0.39779028764616536,0.3759158119296768,0.4114531076895928,0.33784160553188974,0.39877444500165476,0.3825265259939666,0.357935917466154,0.387117481122137,0.33847155755268493,0.4012317840919314,0.35139165281979756,0.32881287905939166,0.3920163718806626,0.3542490513644835,0.4169902788865614,0.3687809033088954,0.3336424804759225,0.38162003803845945,0.33253062700671004,0.39773040391256703,0.39764563811368575,0.3373707805885459,0.3854426676932379,0.34037812376180815,0.35972502976642,0.3810421968067344,0.37582197074300805,0.3511642391232845,0.3271582157257154,0.3554584070404849,0.40611595395223593,0.36250651159223446,0.3941525122246164,0.3549561016730995,0.40168057462099876,0.4282846283138257,0.3749482325685865,0.3665999344464257,0.4032249354422736,0.33157536952855016,0.3436430528441306,0.3447408800846072,0.3696529934913998,0.362955055472152,0.38210709525733827,0.3462285358397855,0.4104469140138855,0.3706542288919192,0.39790815104651517,0.39006811625937493,0.3437200107536062,0.33718155075215234,0.34622336356776817,0.37945560207053664,0.3410695768901734,0.38766752417001016,0.32845811923246276,0.3510428905824466,0.3520234340905522,0.3865398741071648,0.3382553935191262,0.34327916839468275,0.3687788427723187,0.34673244288337,0.37141462671410874,0.3711803868468464,0.3821753123040509,0.3472805185741236,0.3922621233907929,0.3073260820608526,0.3745006563446267,0.3582400557486135,0.380676921297728,0.3563382335666489,0.37396454626492015,0.3659329023579466,0.3410544918294152,0.42831149899786525,0.32854488075224936,0.38213820196969084,0.42723569057798,0.44293157237205777,0.37995100631926154,0.35878958701268127,0.3676149518788269,0.3472318759874245,0.37743291387872874,0.41136092846517625,0.36428339936215637,0.3824528090801243,0.40638516757839704\n", "CONDUCTANCE,restream_iter_10,0.147568392773,0.0178255675123,0.12630024660827524,0.131828340948785,0.16507353258656443,0.16724870405664746,0.1461212985764262,0.13320771936119324,0.10717491169590318,0.16590232938403124,0.16575419332574196,0.16657613400520843,0.1488544875256574,0.14386147405895436,0.13099240220861513,0.1643212107395173,0.16875270716004873,0.13610719131975005,0.16326964845363495,0.14307612740326092,0.14904449424781557,0.14307872657225498,0.16440082571472556,0.10800877750851609,0.1447794744257858,0.1348203580139223,0.14371337227363615,0.1324690668823948,0.13815226184159776,0.14410375859514715,0.1378970665972203,0.14465833807796522,0.16864971623627184,0.17993138337486958,0.15381611100858014,0.16096787086521697,0.15240044846292916,0.15551044784120122,0.1304001360029129,0.17193907970215283,0.1654644264499699,0.13958797016985508,0.11862411543539704,0.14403699757309169,0.15738898159159448,0.13358896574471313,0.14127486177444726,0.17420240893966552,0.15736664191040922,0.1306156367774054,0.1444615262006421,0.12065353421075381,0.14824179865383247,0.16687091831601147,0.14986016702040333,0.14747666552187091,0.1419622538150193,0.16997943452505213,0.1365977396385436,0.13724134944436697,0.16225326627717054,0.1471041976905212,0.17093182753228117,0.1535302875227229,0.1709038889181,0.13761932427308907,0.14233610235435956,0.1738001475945133,0.13392448798579415,0.12532378899826396,0.1359372084972365,0.1817690840447949,0.13330022419549786,0.17913782022698058,0.14313174170288248,0.17871455884499754,0.12659508219922797,0.11529968964496447,0.13460133221110182,0.1554064998252779,0.14021377160166185,0.11700376764946202,0.12764746571135016,0.14258211710346982,0.16562653374108938,0.13502940517440748,0.16977544328725494,0.14694441142898845,0.1487654735544745,0.1259462838225255,0.1729251060674081,0.09455258525449886,0.14731727866364416,0.15544614014198643,0.13720777595702097,0.12837677089141283,0.15633711980825882,0.1578968945210188,0.15623203652534431,0.17078599528148142,0.16858306485379562,0.13136250832511384\n", "MAXPERM,restream_iter_10,0.4214892863,0.0233250921159,0.42085930599999993,0.41297566399999996,0.411274327,0.37080783700000003,0.417912146,0.45069718000000003,0.410707372,0.43674329700000003,0.41572363600000006,0.42146928300000003,0.39410599,0.43690532899999995,0.437112247,0.439532193,0.408711666,0.421064992,0.423761099,0.3900864869999999,0.407989233,0.47260486700000004,0.43229024400000005,0.39260586599999997,0.42449928400000003,0.391295392,0.42034686200000004,0.403447029,0.430591421,0.417333727,0.441563794,0.447524911,0.4137532830000001,0.41612456800000003,0.44734589,0.429263628,0.40277658399999994,0.396133187,0.470810591,0.353751354,0.404544315,0.43971530500000006,0.41480767300000004,0.45275051599999994,0.41903733,0.42239428900000003,0.41884333,0.3744553450000001,0.435411539,0.429637627,0.37668988,0.43874886100000005,0.412846409,0.45916919399999995,0.409310047,0.43312364099999995,0.393201024,0.42226814800000007,0.430823266,0.414274809,0.40177929800000006,0.390216656,0.399018771,0.42921367499999996,0.448012784,0.403455324,0.429889227,0.438848798,0.42083740399999997,0.39454205999999997,0.432249545,0.4306265100000001,0.42126486,0.438833052,0.380500822,0.445548053,0.438541994,0.42676328,0.37640303199999997,0.460065626,0.37964391999999997,0.407864889,0.44788737900000003,0.449495794,0.389673976,0.443739757,0.386914824,0.403428001,0.456603716,0.433878804,0.41015374,0.46229622900000006,0.450965893,0.411695205,0.441491464,0.41733345200000005,0.432509805,0.45607576599999994,0.440841816,0.43133470799999996,0.43073374700000006,0.42320066\n", "FSCORE,restream_iter_10,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n", "FSCORE_IMPROVE,restream_iter_10,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\n" ] } ], "source": [ "import scipy\n", "from copy import deepcopy\n", "\n", "iterations = 100\n", "#modes = ['total', 'average', 'minimum', 'maximum', 'product', 'product_squared', 'sqrt_product']\n", "#modes = ['no_expansion']\n", "#modes = ['no_expansion', 'avg_node_weight_complete', 'total_node_weight_complete', 'smallest_node_weight_complete','largest_node_weight_complete']\n", "#modes = ['no_expansion', 'product_node_weight_complete']\n", "\n", "restream_iterations = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] #, 12, 14, 16, 18, 20, 25, 30]\n", "\n", "for mode in restream_iterations:\n", "#for mode in range(1, 51):\n", "\n", " metricsDataPrediction = []\n", " metricsDataAssign = []\n", " \n", " dataQdsOv = []\n", " dataCondOv = []\n", "\n", " #config['EDGE_EXPANSION_MODE'] = mode\n", " \n", " \n", " config['num_iterations'] = mode\n", " \n", " print('Mode', mode)\n", " for i in range(0, iterations):\n", " if (i % 20) == 0:\n", " print('Mode', mode, 'Iteration', str(i))\n", " \n", " conf = deepcopy(config)\n", "\n", " #if mode == 'no_expansion':\n", " # config['edge_expansion_enabled'] = False\n", " \n", " #conf[\"DATA_FILENAME\"] = os.path.join(pwd, \"data\", \"predition_model_tests\", \"network\", \"network_\" + str(i + 1) + \".txt\")\n", " conf[\"DATA_FILENAME\"] = conf[\"DATA_FILENAME\"].replace('$$', str(i + 1))\n", " conf[\"SIMULATED_ARRIVAL_FILE\"] = conf[\"SIMULATED_ARRIVAL_FILE\"].replace('$$', str(i + 1))\n", " conf[\"PREDICTION_LIST_FILE\"] = conf[\"PREDICTION_LIST_FILE\"].replace('$$', str(i + 1))\n", " conf[\"POPULATION_LOCATION_FILE\"] = conf[\"POPULATION_LOCATION_FILE\"].replace('$$', str(i + 1))\n", "\n", "\n", " #print(i, conf)\n", " #print('config', config)\n", " \n", " with GraphPartitioning(conf) as gp:\n", " #gp = GraphPartitioning(config)\n", " gp.verbose = 0\n", " gp.load_network()\n", " gp.init_partitioner()\n", "\n", " m = gp.prediction_model()\n", " #m = gp.assign_cut_off()\n", " #m = gp.batch_arrival()\n", "\n", " totalM = len(m)\n", " metricsDataPrediction.append(m[totalM - 1])\n", "\n", "\n", " waste = ''\n", " cutratio = ''\n", " ec = ''\n", " tcv = ''\n", " qds = ''\n", " conductance = ''\n", " maxperm = ''\n", " nmi = ''\n", " lonliness = ''\n", " fscore = ''\n", " fscoreimprove = ''\n", " \n", " qdsOv = ''\n", " condOv = ''\n", "\n", " dataWaste = []\n", " dataCutRatio = []\n", " dataEC = []\n", " dataTCV = [] \n", " dataQDS = []\n", " dataCOND = []\n", " dataMAXPERM = []\n", " dataNMI = []\n", " dataLonliness = []\n", " dataFscore = []\n", " dataFscoreImprove = []\n", "\n", " \n", " for i in range(0, iterations):\n", " dataWaste.append(metricsDataPrediction[i][0]) \n", " dataCutRatio.append(metricsDataPrediction[i][1])\n", " dataEC.append(metricsDataPrediction[i][2])\n", " dataTCV.append(metricsDataPrediction[i][3])\n", " dataQDS.append(metricsDataPrediction[i][4])\n", " dataCOND.append(metricsDataPrediction[i][5])\n", " dataMAXPERM.append(metricsDataPrediction[i][6])\n", " dataNMI.append(metricsDataPrediction[i][7]) \n", " dataFscore.append(metricsDataPrediction[i][8]) \n", " dataFscoreImprove.append(metricsDataPrediction[i][9]) \n", " dataLonliness.append(metricsDataPrediction[i][10])\n", "\n", "\n", " if(len(waste)):\n", " waste = waste + ','\n", " waste = waste + str(metricsDataPrediction[i][0])\n", "\n", " if(len(cutratio)):\n", " cutratio = cutratio + ','\n", " cutratio = cutratio + str(metricsDataPrediction[i][1])\n", "\n", " if(len(ec)):\n", " ec = ec + ','\n", " ec = ec + str(metricsDataPrediction[i][2])\n", " \n", " if(len(tcv)):\n", " tcv = tcv + ','\n", " tcv = tcv + str(metricsDataPrediction[i][3])\n", "\n", " if(len(qds)):\n", " qds = qds + ','\n", " qds = qds + str(metricsDataPrediction[i][4])\n", "\n", " if(len(conductance)):\n", " conductance = conductance + ','\n", " conductance = conductance + str(metricsDataPrediction[i][5])\n", "\n", " if(len(maxperm)):\n", " maxperm = maxperm + ','\n", " maxperm = maxperm + str(metricsDataPrediction[i][6])\n", "\n", " if(len(nmi)):\n", " nmi = nmi + ','\n", " nmi = nmi + str(metricsDataPrediction[i][7])\n", "\n", " if(len(fscore)):\n", " fscore = fscore + ','\n", " fscore = fscore + str(metricsDataPrediction[i][8])\n", "\n", " if(len(fscoreimprove)):\n", " fscoreimprove = fscoreimprove + ','\n", " fscoreimprove = fscoreimprove + str(metricsDataPrediction[i][8])\n", " \n", " if(len(lonliness)):\n", " lonliness = lonliness + ','\n", " lonliness = lonliness + str(dataLonliness[i])\n", " \n", "\n", " waste = 'WASTE,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataWaste)) + ',' + str(scipy.std(dataWaste)) + ',' + waste\n", "\n", " cutratio = 'CUT_RATIO,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataCutRatio)) + ',' + str(scipy.std(dataCutRatio)) + ',' + cutratio\n", " ec = 'EC,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataEC)) + ',' + str(scipy.std(dataEC)) + ',' + ec\n", " tcv = 'TCV,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataTCV)) + ',' + str(scipy.std(dataTCV)) + ',' + tcv\n", "\n", " lonliness = \"LONELINESS,\" + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataLonliness)) + ',' + str(scipy.std(dataLonliness)) + ',' + lonliness\n", " \n", " qds = 'QDS,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataQDS)) + ',' + str(scipy.std(dataQDS)) + ',' + qds\n", " conductance = 'CONDUCTANCE,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataCOND)) + ',' + str(scipy.std(dataCOND)) + ',' + conductance\n", " maxperm = 'MAXPERM,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataMAXPERM)) + ',' + str(scipy.std(dataMAXPERM)) + ',' + maxperm\n", " nmi = 'NMI,' + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataNMI)) + ',' + str(scipy.std(dataNMI)) + ',' + nmi\n", "\n", " fscore = \"FSCORE,\" + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataFscore)) + ',' + str(scipy.std(dataFscore)) + ',' + fscore\n", " fscoreimprove = \"FSCORE_IMPROVE,\" + 'restream_iter_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataFscoreImprove)) + ',' + str(scipy.std(dataFscoreImprove)) + ',' + fscoreimprove\n", "\n", " print(cutratio)\n", " print(ec)\n", " print(tcv)\n", " print(lonliness)\n", " print(qds)\n", " print(conductance)\n", " print(maxperm)\n", " print(fscore)\n", " print(fscoreimprove)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
jArumugam/python-notes
P12Advanced Python Objects - Test.ipynb
1
6383
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced Python Objects Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Numbers\n", "\n", "**Problem 1: Convert 1024 to binary and hexadecimal representation:**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x400\n" ] } ], "source": [ "print hex(1024)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Problem 2: Round 5.23222 to two decimal places**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.23\n" ] } ], "source": [ "print round(5.23222,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Strings\n", "**Problem 3: Check if every letter in the string s is lower case**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = 'hello how are you Mary, are you feeling okay?'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n", "how\n", "are\n", "you\n", "Mary,\n", "The string has Uppercase characters\n", "are\n", "you\n", "feeling\n", "okay?\n", "The string has Uppercase characters\n", "0\n" ] } ], "source": [ "retVal = 1\n", "for word in s.split(): \n", " print word\n", " for item in word:\n", " # print item\n", " if not item.islower():\n", " # print item\n", " print 'The string has Uppercase characters'\n", " retVal = 0\n", " break\n", "print retVal" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.islower()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Problem 4: How many times does the letter 'w' show up in the string below?**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = 'twywywtwywbwhsjhwuwshshwuwwwjdjdid'\n", "s.count('w')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Problem 5: Find the elements in set1 that are not in set2:**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{2}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set1 = {2,3,1,5,6,8}\n", "set2 = {3,1,7,5,6,8}\n", "\n", "set1.difference(set2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Problem 6: Find all elements that are in either set:**" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 3, 5, 6, 8}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set1.intersection(set2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Dictionaries\n", "\n", "**Problem 7: Create this dictionary:\n", "{0: 0, 1: 1, 2: 8, 3: 27, 4: 64}\n", " using dictionary comprehension.**" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0: 0, 1: 1, 2: 8, 3: 27, 4: 64}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "{ val:val**3 for val in xrange(0,5)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Lists\n", "\n", "**Problem 8: Reverse the list below:**" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[4, 3, 2, 1]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l = [1,2,3,4] \n", "l[::-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Problem 9: Sort the list below**" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l = [3,4,2,5,1]\n", "sorted(l)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Great Job!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit