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BigCode 1.4k

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/Chiebukar/Deep-Learning/blob/main/regression/temperature_forcasting_with_RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "## Temperature Forcasting with Jena climate dataset", "_____no_output_____" ] ], [ [ "from google.colab import files\nfiles.upload()", "_____no_output_____" ], [ "!mkdir -p ~/.kaggle\n!cp kaggle.json ~/.kaggle/\n!chmod 600 ~/.kaggle/kaggle.json", "_____no_output_____" ], [ "!kaggle datasets download -d kusuri/jena-climate", "_____no_output_____" ], [ "!ls -d $PWD/*", "_____no_output_____" ], [ "!unzip \\*.zip && rm *.zip", "_____no_output_____" ], [ "!ls -d $PWD/*", "_____no_output_____" ], [ "file_dir = '/content/jena_climate_2009_2016.csv'", "_____no_output_____" ], [ "import numpy as np\nimport pandas as pd", "_____no_output_____" ], [ "jena_df = pd.read_csv(file_dir)\njena_df.head()", "_____no_output_____" ], [ "jena_df.shape", "_____no_output_____" ], [ "jena_df.columns", "_____no_output_____" ], [ "jena_arr = np.array(jena_df.iloc[:, 1:])\njena_arr[:2]", "_____no_output_____" ], [ "# standardize data\nlen_train = 200000\nmean = jena_arr[:len_train].mean(axis=0)\nstd = jena_arr[:len_train].std(axis=0)\njena_arr = (jena_arr-mean)/std", "_____no_output_____" ], [ "# generator to yield batches of data from the recent past and future target \ndef generator(data, min_index, max_index , lookback= 1440, delay=144, step= 6, batch_size=18, shuffle=False):\n\n \"\"\"\n yield batches of data from the recent past and future target\n\n data = original input data\n min_index = minimum index of data to draw from\n max_index maximum index of sata to draw from\n lookback= Number of timestamps back for input data per target\n delay = Number of timestamp in the future for target per lookback\n steps = period in timestamps to sample data\n batch_size = number of samples per batch\n shuffle = To shuffle the samples or not\n\n \"\"\"\n\n if max_index == None:\n max_index = len(data) - delay - 1\n i = min_index + lookback\n\n while 1:\n if shuffle:\n rows = np.random.randint(min_index + lookback, max_index, size= batch_size)\n else:\n if i + batch_size >= max_index:\n i = min_index + lookback\n rows = np.arange(i, min(i + batch_size, max_index))\n i += len(rows)\n \n samples = np.zeros((len(rows), lookback //step, data.shape[-1]))\n targets = np.zeros((len(rows),))\n\n for j, row in enumerate(rows):\n indices = range(rows[j] - lookback, rows[j], step)\n samples[j] = data[indices]\n targets[j] = data[rows[j] + delay][1]\n yield samples, targets", "_____no_output_____" ], [ "train_gen = generator(data= jena_arr,\n min_index= 0,\n max_index= 200000,\n shuffle= True)\n\nvalid_gen = generator(data= jena_arr,\n min_index= 200001,\n max_index = 300000,\n shuffle = True)\n\ntest_gen = generator(data = jena_arr,\n min_index = 300001,\n max_index = None,\n shuffle= True)\n", "_____no_output_____" ], [ "# get validation and test steps\nlookback = 1440\nval_steps = (300000 - 200001 - lookback)\ntest_steps = (len(jena_arr) - 300001 - lookback)", "_____no_output_____" ], [ "# establish baseline\ndef evaluate_naive_method():\n batch_maes = []\n for step in range(val_steps):\n samples, targets = next(valid_gen)\n preds = samples[:, -1, 1]\n mae = np.mean(np.abs(preds - targets))\n batch_maes.append(mae)\n return (np.mean(batch_maes))", "_____no_output_____" ], [ "# get baseline evaluation\nmae = evaluate_naive_method()\ncelsius_mae = mae * std[1]\ncelsius_mae", "_____no_output_____" ], [ "\nfrom tensorflow import keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Dropout\nfrom keras.callbacks import ModelCheckpoint", "_____no_output_____" ], [ "# build model\ndef build_model():\n model = Sequential()\n model.add(LSTM(32, dropout= 0.1, recurrent_dropout= 0.25,\n return_sequences=True, input_shape = (None, jena_arr.shape[-1])))\n model.add(LSTM(64, activation='tanh', dropout=0.5))\n model.add(Dense(8, activation= 'relu'))\n model.add(Dropout(0.1))\n model.add(Dense(1))\n\n model.compile(loss = 'mae', optimizer = 'rmsprop')\n return model", "_____no_output_____" ], [ "file_path= 'a_weights.best.hdf5'\ncheckpoint = ModelCheckpoint(file_path, monitor= 'val_loss', save_best_only= True, verbose= 1, mode= 'min')", "_____no_output_____" ], [ "model = build_model()\nhistory = model.fit(train_gen, steps_per_epoch = 500, epochs= 25, validation_data= valid_gen, \n validation_steps = 500, callbacks= checkpoint)", "_____no_output_____" ], [ "history_df = pd.DataFrame(history.history)\nhistory_df[['mae', 'val_mae']].plot()", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# User's Guide, Chapter 46: Streams 3: Manipulation, inPlace, and deepcopy", "_____no_output_____" ], [ "Just getting started here...", "_____no_output_____" ], [ "## Replace all consecutive notes of a Stream with intervals while maintaining the hierarchy", "_____no_output_____" ] ], [ [ "import copy\n\ndef intervalStream(inputStream):\n out = copy.deepcopy(inputStream)\n for n in out[note.Note]:\n nNext = n.next(note.GeneralNote)\n if nNext is None or not nNext.isNote:\n continue # Rests, Chords, etc.\n n.activeSite.replace(n, interval.Interval(n, nNext))\n for other in out.recurse().getElementsNotOfClass([interval.Interval,\n stream.Stream]):\n other.activeSite.remove(other) # get rid of everything that is not an Interval or Stream\n return out", "_____no_output_____" ], [ "b = corpus.parse('bwv66.6')\nints = intervalStream(b.measures(0, 4))\nints.show('text')", "{0.0} <music21.stream.Part Soprano>\n {0.0} <music21.stream.Measure 0 offset=0.0>\n {0.0} <music21.interval.Interval M-2>\n {0.5} <music21.interval.Interval M-2>\n {1.0} <music21.stream.Measure 1 offset=1.0>\n {0.0} <music21.interval.Interval M2>\n {1.0} <music21.interval.Interval M2>\n {2.0} <music21.interval.Interval m3>\n {3.0} <music21.interval.Interval m-3>\n {5.0} <music21.stream.Measure 2 offset=5.0>\n {0.0} <music21.interval.Interval M-2>\n {1.0} <music21.interval.Interval M-2>\n {2.0} <music21.interval.Interval M3>\n {3.0} <music21.interval.Interval M-3>\n {9.0} <music21.stream.Measure 3 offset=9.0>\n {0.0} <music21.interval.Interval M2>\n {0.5} <music21.interval.Interval m-3>\n {1.0} <music21.interval.Interval M-2>\n {2.0} <music21.interval.Interval m3>\n {3.0} <music21.interval.Interval M2>\n {13.0} <music21.stream.Measure 4 offset=13.0>\n {0.0} <music21.interval.Interval P1>\n {1.0} <music21.interval.Interval P-4>\n {2.0} <music21.interval.Interval M-2>\n {3.0} <music21.interval.Interval P4>\n{0.0} <music21.stream.Part Alto>\n {0.0} <music21.stream.Measure 0 offset=0.0>\n {0.0} <music21.interval.Interval M2>\n {1.0} <music21.stream.Measure 1 offset=1.0>\n {0.0} <music21.interval.Interval M-2>\n {1.0} <music21.interval.Interval P1>\n {2.0} <music21.interval.Interval P1>\n {3.0} <music21.interval.Interval P1>\n {5.0} <music21.stream.Measure 2 offset=5.0>\n {0.0} <music21.interval.Interval P4>\n {0.5} <music21.interval.Interval m-2>\n {1.0} <music21.interval.Interval M-3>\n {2.0} <music21.interval.Interval M3>\n {3.0} <music21.interval.Interval M-2>\n {9.0} <music21.stream.Measure 3 offset=9.0>\n {0.0} <music21.interval.Interval M2>\n {0.5} <music21.interval.Interval m-3>\n {1.0} <music21.interval.Interval M-3>\n {2.0} <music21.interval.Interval P4>\n {3.0} <music21.interval.Interval P1>\n {13.0} <music21.stream.Measure 4 offset=13.0>\n {0.0} <music21.interval.Interval M-2>\n {1.0} <music21.interval.Interval m-2>\n {2.0} <music21.interval.Interval M-2>\n {3.0} <music21.interval.Interval P1>\n{0.0} <music21.stream.Part Tenor>\n {0.0} <music21.stream.Measure 0 offset=0.0>\n {0.0} <music21.interval.Interval M2>\n {0.5} <music21.interval.Interval M2>\n {1.0} <music21.stream.Measure 1 offset=1.0>\n {0.0} <music21.interval.Interval M-2>\n {1.0} <music21.interval.Interval M-2>\n {2.0} <music21.interval.Interval M2>\n {3.0} <music21.interval.Interval M-2>\n {5.0} <music21.stream.Measure 2 offset=5.0>\n {0.0} <music21.interval.Interval P5>\n {0.5} <music21.interval.Interval P1>\n {1.0} <music21.interval.Interval M-2>\n {1.5} <music21.interval.Interval m-2>\n {2.0} <music21.interval.Interval P1>\n {3.0} <music21.interval.Interval P1>\n {9.0} <music21.stream.Measure 3 offset=9.0>\n {0.0} <music21.interval.Interval m2>\n {0.5} <music21.interval.Interval m-2>\n {1.0} <music21.interval.Interval M-2>\n {1.5} <music21.interval.Interval M-2>\n {2.0} <music21.interval.Interval M3>\n {3.0} <music21.interval.Interval M-2>\n {13.0} <music21.stream.Measure 4 offset=13.0>\n {0.0} <music21.interval.Interval P1>\n {1.0} <music21.interval.Interval P1>\n {2.0} <music21.interval.Interval M-2>\n {2.5} <music21.interval.Interval m-2>\n {3.0} <music21.interval.Interval M-2>\n{0.0} <music21.stream.Part Bass>\n {0.0} <music21.stream.Measure 0 offset=0.0>\n {0.0} <music21.interval.Interval m-2>\n {0.5} <music21.interval.Interval M-2>\n {1.0} <music21.stream.Measure 1 offset=1.0>\n {0.0} <music21.interval.Interval M2>\n {1.0} <music21.interval.Interval m2>\n {2.0} <music21.interval.Interval m-2>\n {3.0} <music21.interval.Interval m2>\n {5.0} <music21.stream.Measure 2 offset=5.0>\n {0.0} <music21.interval.Interval m-6>\n {0.5} <music21.interval.Interval m3>\n {1.0} <music21.interval.Interval P-5>\n {2.0} <music21.interval.Interval A5>\n {3.0} <music21.interval.Interval m2>\n {9.0} <music21.stream.Measure 3 offset=9.0>\n {0.0} <music21.interval.Interval P-5>\n {0.5} <music21.interval.Interval M2>\n {1.0} <music21.interval.Interval P-5>\n {2.0} <music21.interval.Interval P8>\n {3.0} <music21.interval.Interval M2>\n {13.0} <music21.stream.Measure 4 offset=13.0>\n {0.0} <music21.interval.Interval M-2>\n {0.5} <music21.interval.Interval M2>\n {1.0} <music21.interval.Interval m2>\n {1.5} <music21.interval.Interval M2>\n {2.0} <music21.interval.Interval P-8>\n {2.5} <music21.interval.Interval M2>\n {3.0} <music21.interval.Interval P4>\n" ] ] ]

[ "markdown", "code" ]

[ [ "markdown", "markdown", "markdown" ], [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "### PPO, Actor-Critic Style\n_______________________\n&nbsp;&nbsp;**for** iteration=1,2,... do<br>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**for** actor=1,2,...,N do<br>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Run policy $\\pi_{\\theta_{old}}$ in environment for T timesteps<br>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Compute advantage estimates $\\hat{A}_1,\\dots,\\hat{A}_T$<br>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end for**<br>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Optimize surrogate(代理人) L wrt $\\theta$,with $K$ epochs and minibatch size $M \\leq NT$<br>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;$\\theta_{old} \\leftarrow \\theta$<br>\n&nbsp;&nbsp;**end for**\n_______________________", "_____no_output_____" ], [ "### Loss Function L的数学公式为:\n$$\nL_t^{CLIP+VF+S}(\\theta)=\\hat{\\mathbb{E}_t}[L_t^{CLIP}(\\theta)-c_1L^{VF}_t(\\theta)+c_2S[\\pi_\\theta](s_t)]\n$$\n其中，$L^{CLIP}(\\theta)=\\hat{\\mathbb{E}_t}\\big[min(r_t(\\theta)\\hat{A}_t,clip(r_t(\\theta), 1-\\epsilon,1+\\epsilon)\\hat{A}_t)\\big]$, $r_t(\\theta)=\\frac{\\pi_\\theta(a_t|s_t)}{\\pi_{\\theta_{old}}(a_t|s_t)}$<br>\n$L^{VF}_t=(V_\\theta(s_t)-V_t^{targ})^2$ **critic loss**<br>\nS 为奖励熵，保证足够多的探索(写A2C的时候已经OK)<br>\n$c_1, c_2$为参数\n#### $L^{CLIP}和r的关系如下(为了保证\\pi_\\theta和\\pi_{\\theta_{old}}的差值不会很大,满足TRPO中两者方差变化不大的要求)$：\n<img src=\"../assets/PPO_CLIP.png\">", "_____no_output_____" ], [ "### GAE(high-dimensional continuous control using Generalized Advantage Estimation)\nWe address the first challenge by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias, with an exponentially-weighted estimator of the advantage function that is analogous to TD(λ). <br>\n\n改进了advantage function的计算方式。将advantage function进行类似于TD(λ)的处理<br>\n\n#### 推导过程\n1. 原始的advantage function : $\\delta^V_t=r_t+\\gamma V(s_{t+1})−V(s_t)$\n2. $在位置t时,其后k个 \\delta 折扣相加$ : \n$$\n\\begin{aligned}\n\\hat{A}^{(1)}_t&:=\\delta^V_t&&=-V(s_t)+r_t+\\gamma V(s_{t+1}) \\\\\n\\hat{A}^{(2)}_t&:=\\delta^V_t+\\gamma \\delta^V_{t+1}&&=-V(s_t)+r_t+\\gamma r_{t+1}+\\gamma ^2 V(s_{t+2}) \\\\\n\\hat{A}^{(3)}_t&:=\\delta^V_t+\\gamma \\delta^V_{t+1}+\\gamma^2 \\delta^V_{t+2}&&=-V(s_t)+r_t+\\gamma r_{t+1}+\\gamma^2 r_{t+2}+\\gamma ^3 V(s_{t+3}) \\\\\n\\hat{A}_t^{(k)}&:=\\sum_{l=0}^{k=1}\\gamma^l\\delta_{t+l}^V&&=-V(s_t)+r_t+\\gamma r_{t+1}+\\dots+\\gamma^{k-1}r_{t+k-1}+\\gamma^kV(s_{t+k})\n\\end{aligned}\n$$\n\n\n3. $k \\to \\infty, \\gamma^kV(s_{t+k})$会变得非常非常非常小，So :\n$$\n\\hat{A}_t^{(\\infty)}=\\sum^\\infty_{l=0}\\gamma^l\\delta_{t+l}^V=-V(s_t)+\\sum^\\infty_{l=0}\\gamma^lr_{t+l}\n$$\n4. 所以，$t$ 时刻的GAE可推导为 :\n$$\n\\begin{aligned}\n\\hat{A}_t^{GAE(\\gamma, \\lambda)}&:=(1-\\lambda)\\big(\\hat{A}_t^{(1)}+\\lambda\\hat{A}_t^{(2)}+\\lambda^2\\hat{A}_t^{(3)}+\\dots\\big)\\\\\n&=(1-\\lambda)\\big(\\delta_t^V+\\lambda(\\delta_t^V+\\gamma\\delta_{t+1}^V)+\\lambda^2(\\delta_t^V+\\gamma\\delta_{t+1}^V+\\gamma^2\\delta_{t+2}^V)+\\dots\\big)\\\\\n&=(1-\\lambda)\\big(\\delta^V_t(1+\\lambda+\\lambda^2+\\dots)+\\gamma\\delta^V_{t+1}(\\lambda+\\lambda^2+\\lambda^3+\\dots)+\\gamma^2\\delta^V_{t+2}(\\lambda^2+\\lambda^3+\\lambda^4+\\dots)+\\dots\\big)\\\\\n&=(1-\\lambda)\\big(\\delta^V_t\\big(\\frac{1}{1-\\lambda}\\big)+\\gamma\\delta^V_{t+1}\\big(\\frac{\\lambda}{1-\\lambda}\\big)+\\gamma^2\\delta^V_{t+2}\\big(\\frac{\\lambda^2}{1-\\lambda}\\big)+\\dots\\big)\\\\\n&=\\underbrace{\\delta^V_t+\\gamma\\lambda\\delta^V_{t+1}+(\\gamma\\lambda)^2\\delta^V_{t+2}+\\dots}_{此处计算时使用这个公式(迭代计算)}\\\\\n&=\\sum_{l=0}^\\infty(\\gamma\\lambda)^l\\delta^V_{t+l}\n\\end{aligned}\n$$", "_____no_output_____" ], [ "### 使用高斯分布(正态分布)来实现随机性策略控制连续动作空间\n1. 高斯分布有两个重要的变量一个是均值 $\\mu$ ,另一个是方差 $\\sigma$ 。$\\mu$ 为高斯函数的对称轴，$\\frac{1}{\\sqrt{2\\pi}\\sigma}$ 为高斯函数的最高点。高斯函数的积分为1。所以我们可以使用它来进行连续动作的sample。方差 $\\sigma$ 越大，分布越分散，方差 $\\sigma$ 越小，分布越集中。\n2. $\\mu$ 的选择很好把控，经过tanh处理之后+简单的数学变换，使nn输出的 $\\mu$ 在env规定的动作空间内就可以\n3. $\\sigma$ 的选择,使用softplus函数对sigma进行处理。softplus 公式为$f(x)=\\frac{1}{\\beta}log(1+exp(\\beta x))$, softplus 是 ReLU 的平滑近似值版本\n4. 高斯分布公式:\n$$\nf(x)=\\frac{1}{\\sqrt{2\\pi}\\sigma}exp\\bigg(-\\frac{(x-\\mu)^2}{2\\sigma^2}\\bigg)\n$$\n5. 和确定性策略相比，需要考虑每个state采取每个动作的概率，计算量确实比较大。", "_____no_output_____" ], [ "### TRPO\n简单理解为一次on-policy到off-policy的转换<br>\n但是为了保证old_policy和new_policy之间方差相差不会太大<br>\n$$\n\\begin{aligned}\nE_{X \\sim p}[f(x)] & \\approx \\frac{1}{N}\\sum^N_{i=1}f(x^i)\\\\\n&= \\int f(x)p(x)dx=\\int f(x)\\frac{p(x)}{q(x)}q(x)dx=E_{x \\sim q}[f(x)\\frac{p(x)}{q(x)}]\n\\end{aligned}\n$$\n由此，在两者期望相同的情况下，论证方差是否相同\n$$\n\\begin{aligned}\n两者期望:\\quad&\\because E_{X \\sim p}[f(x)]=E_{x \\sim q}[f(x)\\frac{p(x)}{q(x)}]\\\\\n方差公式:\\quad&\\because VAR[X]=E[X^2]-(E[X])^2\\\\\nx \\sim p 方差:\\quad&\\therefore Var_{x \\sim p}[f(x)]=\\color{red}{E_{x\\sim p}[f(x)^2]}-(E_{x\\sim p}[f(x)])^2\\\\\nx \\sim q 方差:\\quad&\\therefore Var_{x \\sim q}[f(x)\\frac{p(x)}{q(x)}]=E_{x \\sim q}\\big[\\big([f(x)\\frac{p(x)}{q(x)}\\big)^2\\big]-\\big(E_{x\\sim q}\\big[f(x)\\frac{p(x)}{q(x)}\\big]\\big)^2\\\\\n&=\\color{red}{E_{x \\sim q}\\big[f(x)^2\\frac{p(x)}{q(x)}\\big]}-(E_{x \\sim p}[f(x)])^2\n\\end{aligned}\n$$", "_____no_output_____" ], [ "两者方差公式的差别在标红的位置，也就是说我们如果使两者$E_{x\\sim p}[f(x)^2]$和$E_{x \\sim q}\\big[f(x)^2\\frac{p(x)}{q(x)}\\big]$的差值较小，那么我们所做的off-policy就是可行的<br>\n由此，可直观的看出，我们要使p(x)和q(x)的相差较小。因此就有了PPO1中的所使用的$\\beta KL(\\theta,\\theta')$和PPO2中的clip这些都是为了限制两者的范围在一个可接受的合适空间", "_____no_output_____" ] ], [ [ "import gym\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.distributions import Normal\nfrom torch.distributions import Categorical\nimport torch.multiprocessing as mp\n# from torch.utils.tensorboard import SummaryWriter\nimport numpy as np\nfrom IPython.display import clear_output\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport math\nimport random\nfrom statistics import mean\nimport pdb\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')", "_____no_output_____" ], [ "def plot_function():\n x = np.arange(-10,10,0.05)\n plt.figure(figsize=(9,3.6))\n\n plt.subplot(121)\n plt.title(\"Gaussian distribution\")\n mu, sigma = 0, 10\n y = lambda x : np.exp(-((x-mu)**2)/(2*sigma**2))/(sigma*np.sqrt(2*np.pi))\n plt.plot(x, y(x))\n\n plt.subplot(122)\n plt.title(\"Softplus\")\n y = np.log(1+np.exp(x))\n plt.plot(x, y)\n plt.show()\n\nplot_function()", "_____no_output_____" ] ], [ [ "多线程又双叒叕来了", "_____no_output_____" ] ], [ [ "def worker(worker_id, master_end, worker_end, env_name):\n master_end.close()\n env = gym.make(env_name)\n env.seed(worker_id)\n \n while True:\n cmd, data = worker_end.recv()\n if cmd == 'step':\n state, reward, done, info = env.step(data)\n if done:\n state = env.reset()\n worker_end.send((state, reward, done, info))\n elif cmd == 'reset':\n state = env.reset()\n worker_end.send(state)\n elif cmd == 'reset_task':\n state = env.reset_task()\n worker_end.send(state)\n elif cmd == 'close':\n worker_end.close()\n break\n elif cmd == 'get_spaces':\n worker_end.send((env.observation_space.shape[0], env.action_space.shape[0]))\n else:\n raise NotImplementedError", "_____no_output_____" ], [ "class ParallelEnv:\n def __init__(self, n_train_processes, env_name):\n self.nenvs = n_train_processes\n self.waiting = False\n self.closed = False\n self.workers = []\n self.env_name = env_name\n \n self.master_ends, self.worker_ends = zip(*[mp.Pipe() for _ in range(self.nenvs)])\n \n for worker_id, (master_end, worker_end) in enumerate(zip(self.master_ends, self.worker_ends)):\n p = mp.Process(target=worker, args=(worker_id, master_end, worker_end, self.env_name))\n p.daemon = False\n p.start()\n self.workers.append(p)\n for worker_end in self.worker_ends:\n worker_end.close()\n \n self.master_ends[0].send(('get_spaces', None))\n self.observation_space, self.action_space = self.master_ends[0].recv()\n \n def step_async(self, actions):\n for master_end, action in zip(self.master_ends, actions):\n master_end.send(('step', action))\n self.waiting = True\n \n def step_wait(self):\n results = [master_end.recv() for master_end in self.master_ends]\n self.waiting = False\n states, rewards, dones, infos = zip(*results)\n return np.stack(states), np.stack(rewards), np.stack(dones), infos\n \n def reset(self):\n for master_end in self.master_ends:\n master_end.send(('reset', None))\n return np.stack([master_end.recv() for master_end in self.master_ends])\n \n def step(self, actions):\n self.step_async(actions)\n return self.step_wait()\n \n def close(self):\n if self.closed:\n return\n if self.waiting:\n [master_end.recv() for master_end in self.master_ends]\n for master_end in self.master_ends:\n master_end.send(('close', None))\n del self.workers[:]\n self.closed = True", "_____no_output_____" ] ], [ [ "定义网络", "_____no_output_____" ] ], [ [ "class Actor_critic(nn.Module):\n def __init__(self, in_dim, out_dim):\n super(Actor_critic, self).__init__()\n self.actor_linear1 = nn.Linear(in_dim, 64)\n self.critic_linear1 = nn.Linear(in_dim, 64)\n self.linear2 = nn.Linear(64, 32)\n self.actor_linear3 = nn.Linear(32, out_dim)\n self.critic_linear3 = nn.Linear(32, 1)\n self.sigma_linear = nn.Linear(32, out_dim)\n \n def forward(self, x):\n value_hidden = F.relu(self.linear2(F.relu(self.critic_linear1(x))))\n value = self.critic_linear3(value_hidden)\n actor_hidden = F.relu(self.linear2(F.relu(self.actor_linear1(x))))\n mu = torch.tanh(self.actor_linear3(actor_hidden)) * 2\n sigma = F.softplus(self.sigma_linear(actor_hidden))\n dist = Normal(mu, sigma)\n return dist, value", "_____no_output_____" ] ], [ [ "画图", "_____no_output_____" ] ], [ [ "def smooth_plot(factor, item, plot_decay):\n item_x = np.arange(len(item))\n item_smooth = [np.mean(item[i:i+factor]) if i > factor else np.mean(item[0:i+1])\n for i in range(len(item))]\n for i in range(len(item)// plot_decay):\n item_x = item_x[::2]\n item_smooth = item_smooth[::2]\n return item_x, item_smooth\n \ndef plot(episode, rewards, losses):\n clear_output(True)\n rewards_x, rewards_smooth = smooth_plot(10, rewards, 500)\n losses_x, losses_smooth = smooth_plot(10, losses, 100000)\n \n plt.figure(figsize=(18, 10))\n plt.subplot(211)\n plt.title('episode %s. reward: %s'%(episode, rewards_smooth[-1]))\n plt.plot(rewards, label=\"Rewards\", color='lightsteelblue', linewidth='1')\n plt.plot(rewards_x, rewards_smooth, label='Smothed_Rewards', color='darkorange', linewidth='3')\n plt.legend(loc='best')\n \n plt.subplot(212)\n plt.title('Losses')\n plt.plot(losses,label=\"Losses\",color='lightsteelblue',linewidth='1')\n plt.plot(losses_x, losses_smooth, \n label=\"Smoothed_Losses\",color='darkorange',linewidth='3')\n plt.legend(loc='best')\n \n plt.show()\n \ndef test_env():\n state = env.reset()\n done = False\n total_reward = 0\n while not done:\n state = torch.FloatTensor(state).reshape(-1, 3).to(device)\n log_prob, _ = model(state)\n next_state, reward, done, _ = env.step(log_prob.sample().cpu().numpy())\n state = next_state\n total_reward += reward\n return total_reward", "_____no_output_____" ], [ "def gae_compute(next_value, rewards, masks, values, gamma=0.99, tau=0.95):\n td_target = next_value\n td_target_list = []\n advantage = 0\n advantage_list = []\n for idx in reversed(range(len(values))):\n td_target = td_target * gamma * masks[idx] + rewards[idx]\n td_target_list.insert(0, td_target)\n advantage = td_target - values[idx] + advantage * gamma * tau\n advantage_list.insert(0, advantage)\n return advantage_list, td_target_list", "_____no_output_____" ] ], [ [ "PPO训练更新", "_____no_output_____" ] ], [ [ "import pdb\ndef ppo_iter(states, actions, log_probs, advantages, td_target_list):\n batch_size = actions.size(0)\n for _ in range(batch_size // mini_batch_size):\n ids = np.random.choice(batch_size, mini_batch_size, replace=False)\n yield states[ids, :], actions[ids, :], log_probs[ids, :], advantages[ids, :], td_target_list[ids, :]\n\ndef ppo_train(states, actions, log_probs, advantages, td_target_list, clip_param=0.2):\n losses = []\n for _ in range(ppo_epochs):\n for state, action, old_log_probs, advantage, td_target in ppo_iter(states, actions, log_probs,\n advantages, td_target_list):\n dist, value = model(state)\n entropy = dist.entropy().mean()\n new_log_probs = dist.log_prob(action)\n \n ratio = (new_log_probs - old_log_probs).exp() \n sub1 = ratio * advantage\n \n sub2 = torch.clamp(ratio, 1.0-clip_param, 1.0+clip_param) * advantage\n actor_loss = - torch.min(sub1, sub2).mean()\n critic_loss = (td_target - value).pow(2).mean()\n \n loss = 0.5 * critic_loss + actor_loss - 0.001 * entropy\n losses.append(loss.item())\n \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n old_model.load_state_dict(model.state_dict()) \n return round(mean(losses),2)", "_____no_output_____" ], [ "## hyperparameters ##\n\nnum_envs = 16\nenv_name = \"Pendulum-v0\"\nppo_epochs = 30\nmini_batch_size = 256\nmax_epoch = 10000\nnum_timesteps = 128\n\n## hyperparameters ##\n\nenvs = ParallelEnv(num_envs, env_name)\nstate_space = envs.observation_space\naction_space = envs.action_space\n\nenv = gym.make(env_name)\n\nmodel = Actor_critic(state_space, action_space).to(device)\noptimizer = optim.Adam(model.parameters(), lr=1e-3)\nold_model = Actor_critic(state_space, action_space).to(device)", "_____no_output_____" ], [ "test_rewards = []\nloss_list = []\nstate = envs.reset()\n\nfor epoch in range(max_epoch):\n states, actions, rewards, masks, log_probs, values = [], [], [], [], [], []\n \n for _ in range(num_timesteps):\n dist, value = old_model(torch.FloatTensor(state).to(device))\n action = dist.sample()\n \n next_state, reward, done, _ = envs.step(action.cpu().numpy())\n \n states.append(torch.FloatTensor(state).to(device))\n actions.append(action)\n rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))\n masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))\n log_probs.append(dist.log_prob(action))\n values.append(value)\n \n state = next_state\n \n _, next_value = model(torch.FloatTensor(next_state).to(device))\n advantages, td_target_list = gae_compute(next_value, rewards, masks, values)\n loss = ppo_train(torch.cat(states),torch.cat(actions), torch.cat(log_probs).detach(), \n torch.cat(advantages).detach(), torch.cat(td_target_list).detach())\n loss_list.append(loss)\n \n if epoch % 1 == 0:\n test_reward = np.mean([test_env() for _ in range(10)])\n test_rewards.append(test_reward)\n plot(epoch + 1, test_rewards, loss_list)\n# soft = lambda loss : np.mean(loss[-100:]) if len(loss)>=100 else np.mean(loss)\n# writer.add_scalar(\"Test_Rewards\", np.array(soft(test_rewards)), epoch)\n# writer.add_scalar(\"Value_Losses\", np.array(soft(loss_list)), epoch)", "_____no_output_____" ], [ "from IPython import display\n\nenv = gym.make(env_name)\nstate_1 = env.reset()\nimg = plt.imshow(env.render(mode='rgb_array')) # only call this once\nfor _ in range(1000):\n img.set_data(env.render(mode='rgb_array')) # just update the data\n display.display(plt.gcf())\n display.clear_output(wait=True)\n \n prob, value = old_model(torch.FloatTensor(state_1).reshape(1,-1).to(device))\n action = prob.sample().cpu().numpy()\n next_state, _, done, _ = env.step(action)\n if done: \n state_1 = env.reset()\n state_1 = next_state", "_____no_output_____" ] ], [ [ "## PPO Baselines:\n<img src=\"../assets/PPO_baseline.png\"></img>\n### Test_Rewards:\n<img src=\"../assets/PPO_Test_Rewards.png\" width=100%></img>\n### Value_Losses:\n<img src=\"../assets/PPO_Value_Losses.png\"></img>", "_____no_output_____" ] ] ]

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[ [ [ "<h1> Create TensorFlow model </h1>\n\nThis notebook illustrates:\n<ol>\n<li> Creating a model using the high-level Estimator API \n</ol>", "_____no_output_____" ] ], [ [ "# change these to try this notebook out\nBUCKET = 'qwiklabs-gcp-37b9fafbd24bf385'\nPROJECT = 'qwiklabs-gcp-37b9fafbd24bf385'\nREGION = 'us-central1'", "_____no_output_____" ], [ "import os\nos.environ['BUCKET'] = BUCKET\nos.environ['PROJECT'] = PROJECT\nos.environ['REGION'] = REGION", "_____no_output_____" ], [ "%%bash\nif ! gsutil ls | grep -q gs://${BUCKET}/; then\n gsutil mb -l ${REGION} gs://${BUCKET}\nfi", "_____no_output_____" ] ], [ [ "<h2> Create TensorFlow model using TensorFlow's Estimator API </h2>\n<p>\nFirst, write an input_fn to read the data.\n<p>\n\n## Lab Task 1\nVerify that the headers match your CSV output", "_____no_output_____" ] ], [ [ "import shutil\nimport numpy as np\nimport tensorflow as tf", "/usr/local/envs/py3env/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n" ], [ "# Determine CSV, label, and key columns\nCSV_COLUMNS = 'weight_pounds,is_male,mother_age,plurality,gestation_weeks,key'.split(',')\nLABEL_COLUMN = 'weight_pounds'\nKEY_COLUMN = 'key'\n\n# Set default values for each CSV column\nDEFAULTS = [[0.0], ['null'], [0.0], ['null'], [0.0], ['nokey']]\nTRAIN_STEPS = 1000", "_____no_output_____" ] ], [ [ "## Lab Task 2\n\nFill out the details of the input function below", "_____no_output_____" ] ], [ [ "# Create an input function reading a file using the Dataset API\n# Then provide the results to the Estimator API\ndef read_dataset(filename_pattern, mode, batch_size = 512):\n def _input_fn():\n def decode_csv(line_of_text):\n # TODO #1: Use tf.decode_csv to parse the provided line\n columns = tf.decode_csv(line_of_text, record_defaults=DEFAULTS)\n \n # TODO #2: Make a Python dict. The keys are the column names, the values are from the parsed data\n features = dict(zip(CSV_COLUMNS, columns))\n \n # TODO #3: Return a tuple of features, label where features is a Python dict and label a float\n label = features.pop(LABEL_COLUMN)\n return features, label\n \n # TODO #4: Use tf.gfile.Glob to create list of files that match pattern\n file_list = tf.gfile.Glob(filename_pattern)\n\n # Create dataset from file list\n dataset = (tf.data.TextLineDataset(file_list) # Read text file\n .map(decode_csv)) # Transform each elem by applying decode_csv fn\n \n # TODO #5: In training mode, shuffle the dataset and repeat indefinitely\n # (Look at the API for tf.data.dataset shuffle)\n # The mode input variable will be tf.estimator.ModeKeys.TRAIN if in training mode\n # Tell the dataset to provide data in batches of batch_size \n if mode == tf.estimator.ModeKeys.TRAIN:\n epochs = None # Repeat indefinitely\n dataset = dataset.shuffle(buffer_size = 10 * batch_size)\n else:\n epochs = 1\n \n dataset = dataset.repeat(epochs).batch(batch_size)\n \n # This will now return batches of features, label\n return dataset\n return _input_fn", "_____no_output_____" ] ], [ [ "## Lab Task 3\n\nUse the TensorFlow feature column API to define appropriate feature columns for your raw features that come from the CSV.\n\n<b> Bonus: </b> Separate your columns into wide columns (categorical, discrete, etc.) and deep columns (numeric, embedding, etc.)", "_____no_output_____" ] ], [ [ "# Define feature columns\n# Define feature columns\ndef get_categorical(name, values):\n return tf.feature_column.indicator_column(\n tf.feature_column.categorical_column_with_vocabulary_list(name, values))\n\ndef get_cols():\n # Define column types\n return [\\\n get_categorical('is_male', ['True', 'False', 'Unknown']),\n tf.feature_column.numeric_column('mother_age'),\n get_categorical('plurality',\n ['Single(1)', 'Twins(2)', 'Triplets(3)',\n 'Quadruplets(4)', 'Quintuplets(5)','Multiple(2+)']),\n tf.feature_column.numeric_column('gestation_weeks')\n ]", "_____no_output_____" ] ], [ [ "## Lab Task 4\n\nTo predict with the TensorFlow model, we also need a serving input function (we'll use this in a later lab). We will want all the inputs from our user.\n\nVerify and change the column names and types here as appropriate. These should match your CSV_COLUMNS", "_____no_output_____" ] ], [ [ "# Create serving input function to be able to serve predictions later using provided inputs\ndef serving_input_fn():\n feature_placeholders = {\n 'is_male': tf.placeholder(tf.string, [None]),\n 'mother_age': tf.placeholder(tf.float32, [None]),\n 'plurality': tf.placeholder(tf.string, [None]),\n 'gestation_weeks': tf.placeholder(tf.float32, [None])\n }\n features = {\n key: tf.expand_dims(tensor, -1)\n for key, tensor in feature_placeholders.items()\n }\n return tf.estimator.export.ServingInputReceiver(features, feature_placeholders)", "_____no_output_____" ] ], [ [ "## Lab Task 5\n\nComplete the TODOs in this code:", "_____no_output_____" ] ], [ [ "# Create estimator to train and evaluate\ndef train_and_evaluate(output_dir):\n EVAL_INTERVAL = 300\n run_config = tf.estimator.RunConfig(save_checkpoints_secs = EVAL_INTERVAL,\n keep_checkpoint_max = 3)\n # TODO #1: Create your estimator\n estimator = tf.estimator.DNNRegressor(\n model_dir = output_dir,\n feature_columns = get_cols(),\n hidden_units = [64, 32],\n config = run_config)\n train_spec = tf.estimator.TrainSpec(\n # TODO #2: Call read_dataset passing in the training CSV file and the appropriate mode\n input_fn = read_dataset('train.csv', mode = tf.estimator.ModeKeys.TRAIN),\n max_steps = TRAIN_STEPS)\n exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)\n eval_spec = tf.estimator.EvalSpec(\n # TODO #3: Call read_dataset passing in the evaluation CSV file and the appropriate mode\n input_fn = read_dataset('eval.csv', mode = tf.estimator.ModeKeys.EVAL),\n steps = None,\n start_delay_secs = 60, # start evaluating after N seconds\n throttle_secs = EVAL_INTERVAL, # evaluate every N seconds\n exporters = exporter)\n tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)", "_____no_output_____" ] ], [ [ "Finally, train!", "_____no_output_____" ] ], [ [ "# Run the model\nshutil.rmtree('babyweight_trained', ignore_errors = True) # start fresh each time\ntrain_and_evaluate('babyweight_trained')", "INFO:tensorflow:Using config: {'_num_worker_replicas': 1, '_save_summary_steps': 100, '_model_dir': 'babyweight_trained', '_keep_checkpoint_every_n_hours': 10000, '_is_chief': True, '_evaluation_master': '', '_keep_checkpoint_max': 3, '_task_id': 0, '_global_id_in_cluster': 0, '_train_distribute': None, '_task_type': 'worker', '_session_config': None, '_tf_random_seed': None, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f2680a1c710>, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 300, '_master': '', '_num_ps_replicas': 0}\nINFO:tensorflow:Running training and evaluation locally (non-distributed).\nINFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 300 secs (eval_spec.throttle_secs) or training is finished.\nINFO:tensorflow:Calling model_fn.\nINFO:tensorflow:Done calling model_fn.\nINFO:tensorflow:Create CheckpointSaverHook.\nINFO:tensorflow:Graph was finalized.\nINFO:tensorflow:Running local_init_op.\nINFO:tensorflow:Done running local_init_op.\nINFO:tensorflow:Saving checkpoints for 1 into babyweight_trained/model.ckpt.\nINFO:tensorflow:loss = 32576.717, step = 1\nINFO:tensorflow:global_step/sec: 50.5871\nINFO:tensorflow:loss = 638.1501, step = 101 (1.983 sec)\nINFO:tensorflow:global_step/sec: 48.5499\nINFO:tensorflow:loss = 654.87915, step = 201 (2.060 sec)\nINFO:tensorflow:global_step/sec: 49.545\nINFO:tensorflow:loss = 610.86847, step = 301 (2.019 sec)\nINFO:tensorflow:global_step/sec: 51.2294\nINFO:tensorflow:loss = 571.76263, step = 401 (1.951 sec)\nINFO:tensorflow:global_step/sec: 51.0356\nINFO:tensorflow:loss = 565.4655, step = 501 (1.962 sec)\nINFO:tensorflow:global_step/sec: 50.5303\nINFO:tensorflow:loss = 575.91785, step = 601 (1.976 sec)\nINFO:tensorflow:global_step/sec: 51.2073\nINFO:tensorflow:loss = 586.4931, step = 701 (1.954 sec)\nINFO:tensorflow:global_step/sec: 50.9067\nINFO:tensorflow:loss = 511.8872, step = 801 (1.964 sec)\nINFO:tensorflow:global_step/sec: 50.5373\nINFO:tensorflow:loss = 582.94336, step = 901 (1.979 sec)\nINFO:tensorflow:Saving checkpoints for 1000 into babyweight_trained/model.ckpt.\nINFO:tensorflow:Loss for final step: 558.4249.\nINFO:tensorflow:Calling model_fn.\nINFO:tensorflow:Done calling model_fn.\nINFO:tensorflow:Starting evaluation at 2019-02-05-23:19:59\nINFO:tensorflow:Graph was finalized.\nINFO:tensorflow:Restoring parameters from babyweight_trained/model.ckpt-1000\nINFO:tensorflow:Running local_init_op.\nINFO:tensorflow:Done running local_init_op.\nINFO:tensorflow:Finished evaluation at 2019-02-05-23:19:59\nINFO:tensorflow:Saving dict for global step 1000: average_loss = 1.1584599, global_step = 1000, loss = 579.9428\nINFO:tensorflow:Calling model_fn.\nINFO:tensorflow:Done calling model_fn.\nINFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']\nINFO:tensorflow:Signatures INCLUDED in export for Regress: None\nINFO:tensorflow:Signatures INCLUDED in export for Classify: None\nINFO:tensorflow:Signatures EXCLUDED from export because they cannot be be served via TensorFlow Serving APIs:\nINFO:tensorflow:'regression' : Regression input must be a single string Tensor; got {'gestation_weeks': <tf.Tensor 'Placeholder_3:0' shape=(?,) dtype=float32>, 'mother_age': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=float32>, 'is_male': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'plurality': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=string>}\nINFO:tensorflow:'serving_default' : Regression input must be a single string Tensor; got {'gestation_weeks': <tf.Tensor 'Placeholder_3:0' shape=(?,) dtype=float32>, 'mother_age': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=float32>, 'is_male': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'plurality': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=string>}\nWARNING:tensorflow:Export includes no default signature!\nINFO:tensorflow:Restoring parameters from babyweight_trained/model.ckpt-1000\nINFO:tensorflow:Assets added to graph.\nINFO:tensorflow:No assets to write.\nINFO:tensorflow:SavedModel written to: b\"babyweight_trained/export/exporter/temp-b'1549408800'/saved_model.pb\"\n" ] ], [ [ "When I ran it, the final lines of the output (above) were:\n<pre>\nINFO:tensorflow:Saving dict for global step 1000: average_loss = 1.2693067, global_step = 1000, loss = 635.9226\nINFO:tensorflow:Restoring parameters from babyweight_trained/model.ckpt-1000\nINFO:tensorflow:Assets added to graph.\nINFO:tensorflow:No assets to write.\nINFO:tensorflow:SavedModel written to: babyweight_trained/export/exporter/temp-1517899936/saved_model.pb\n</pre>\nThe exporter directory contains the final model and the final RMSE (the average_loss) is 1.2693067", "_____no_output_____" ], [ "<h2> Monitor and experiment with training </h2>", "_____no_output_____" ] ], [ [ "from google.datalab.ml import TensorBoard\nTensorBoard().start('./babyweight_trained')", "_____no_output_____" ], [ "for pid in TensorBoard.list()['pid']:\n TensorBoard().stop(pid)\n print('Stopped TensorBoard with pid {}'.format(pid))", "Stopped TensorBoard with pid 3769\n" ] ], [ [ "Copyright 2017-2018 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License", "_____no_output_____" ] ] ]

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[ [ [ "import pandas as pd\nimport json\nimport cobra\nimport numpy as np", "/home/greg/Envs/ssl_ensembles/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n return f(*args, **kwds)\n/home/greg/Envs/ssl_ensembles/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n return f(*args, **kwds)\n/home/greg/Envs/ssl_ensembles/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n return f(*args, **kwds)\n/home/greg/Envs/ssl_ensembles/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n return f(*args, **kwds)\n/home/greg/Envs/ssl_ensembles/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n return f(*args, **kwds)\n" ], [ "# read biolog base composition file, original plata table, and mapping file for Plata names -> seed IDs\nbiolog_names_to_seed = pd.read_csv('../data/biolog_names_to_seed.csv',sep='\\t')\nplata_biolog_data = pd.read_csv('../data/plata_biolog_raw.csv',sep=',',index_col=0)", "_____no_output_____" ], [ "# replace plata metabolite names with seed IDs\nplata_biolog_data = plata_biolog_data.rename(dict(zip(biolog_names_to_seed['plata_name'],biolog_names_to_seed['seed_id'])),axis='columns')\n# remove the NA columns (did not map due to criteria listed in manuscript)\nplata_biolog_data = plata_biolog_data.loc[:,~plata_biolog_data.columns.isnull()]\nplata_biolog_data.columns = plata_biolog_data.columns + '_e'", "_____no_output_____" ], [ "# filter organisms by having growth in at least 20 conditions total\ngrowth_threshold = 10 # as in Plata paper. These are relative colorimetric units. See paper for details.\nmin_growth_conditions = 10\nplata_biolog_data = plata_biolog_data.loc[(plata_biolog_data > growth_threshold).sum(axis=1) > min_growth_conditions,]", "_____no_output_____" ], [ "plata_biolog_data = plata_biolog_data > 10\nplata_biolog_data", "_____no_output_____" ], [ "plata_biolog_data.to_csv('../data/plata_thresholded.csv',sep='\\t')", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<img style=\"float: right; margin: 0px 0px 15px 15px;\" src=\"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQt6eQo8JPYzYO4p6WmxLtccdtJ4X8WR6GzVVKbsMjyGvUDEn1mg\" width=\"300px\" height=\"100px\" />\n\n# Trabajando con opciones", "_____no_output_____" ], [ "Una opción puede negociarse en el mercado secundario por lo que es importante determinar su valor $V_t$ para cada tiempo $t\\in [0, T]$. La ganancia que obtiene quién adquiere la opción se llama función de pago o \"payoff\" y claramente depende del valor del subyacente. \n\nHay una gran variedad de opciones en el mercado y éstas se clasiflcan según su función de pago y la forma en que pueden ejercerse. Las opciones que tienen como función de pago a\n$$ P(S(t),t)=max\\{S(T)-K,0\\} \\rightarrow \\text{En el caso de Call}$$ \n$$ P(S(t),t)=max\\{K-S(T),0\\} \\rightarrow \\text{En el caso de Put}$$ \nse llaman opciones **Vainilla**, con $h:[0,\\infty) \\to [0,\\infty)$.\n\nLa opción se llama **europea** si puede ejercerse sólo en la fecha de vencimiento.\n\nSe dice que una opción es **americana** si puede ejercerse en cualquier momento antes o en la fecha de vencimiento.\n\nUna opción compleja popular son las llamadas **opciones asiáticas** cuyos pagos dependen de todas las trayectorias del precio de los activos subyacentes. Las opciones cuyos pagos dependen de las trayectorias de los precios de los activos subyacentes se denominan opciones dependientes de la ruta.\n\nPrincipalmente, se puede resumir que las dos razones con más peso de importancia para utilizar opciones son el **aseguramiento** y la **especulación**.\n\n## Opciones Plan Vainilla: opción de compra y opción de venta europea\n\nUna opción vainilla o estándar es una opción normal de compra o venta que no tiene características especiales o inusuales. Puede ser para tamaños y vencimientos estandarizados, y negociarse en un intercambio.\nEn comparación con otras estructuras de opciones, las opciones de vanilla no son sofisticadas o complicadas.\n", "_____no_output_____" ], [ "## 1. ¿Cómo descargar datos de opciones?", "_____no_output_____" ] ], [ [ "#importar los paquetes que se van a usar\nimport pandas as pd\nimport pandas_datareader.data as web\nimport numpy as np\nimport datetime\nimport matplotlib.pyplot as plt\nimport scipy.stats as st\nimport seaborn as sns\n%matplotlib inline\n#algunas opciones para Pandas\npd.set_option('display.notebook_repr_html', True)\npd.set_option('display.max_columns', 6)\npd.set_option('display.max_rows', 10)\npd.set_option('display.width', 78)\npd.set_option('precision', 3)", "_____no_output_____" ] ], [ [ "Usando el paquete `pandas_datareader` también podemos descargar datos de opciones. Por ejemplo, descarguemos los datos de las opciones cuyo activo subyacente son las acciones de Apple", "_____no_output_____" ] ], [ [ "aapl = web.YahooOptions('AAPL')\naapl_opt = aapl.get_all_data().reset_index()\naapl_opt.set_index('Expiry')\n# aapl", "_____no_output_____" ] ], [ [ "Precio del activo subyacente", "_____no_output_____" ] ], [ [ "aapl_opt.Underlying_Price[0]", "_____no_output_____" ] ], [ [ "Datos de la opción ", "_____no_output_____" ] ], [ [ "aapl_opt.loc[0, 'JSON']", "_____no_output_____" ] ], [ [ "### Conceptos claves\n- El precio de la oferta ('bid') se refiere al precio más alto que un comprador pagará por un activo.\n- El precio de venta ('ask') se refiere al precio más bajo que un vendedor aceptará por un activo.\n- La diferencia entre estos dos precios se conoce como 'spread'; cuanto menor es el spread, mayor es la liquidez de la garantía dada.\n- Liquidez: facilidad de convertir cierta opción en efectivo.\n- La volatilidad implícita es el pronóstico del mercado de un probable movimiento en el precio de un valor.\n- La volatilidad implícita aumenta en los mercados bajistas y disminuye cuando el mercado es alcista.\n- El último precio ('lastprice') representa el precio al que ocurrió la última operación, de una opción dada.", "_____no_output_____" ], [ "Una vez tenemos la información, podemos consultar de qué tipo son las opciones", "_____no_output_____" ] ], [ [ "aapl_opt.loc[:, 'Type']", "_____no_output_____" ] ], [ [ "o en que fecha expiran", "_____no_output_____" ] ], [ [ "pd.set_option('display.max_rows', 10)\naapl_opt.loc[:, 'Expiry']", "_____no_output_____" ] ], [ [ "Por otra parte, podríamos querer consultar todas las opciones de compra (call) que expiran en cierta fecha (2020-06-19)", "_____no_output_____" ] ], [ [ "fecha1 = '2021-06-18'\nfecha2 = '2022-09-16'\ncall06_f1 = aapl_opt.loc[(aapl_opt.Expiry== fecha1) & (aapl_opt.Type=='call')]\ncall06_f2 = aapl_opt.loc[(aapl_opt.Expiry== fecha2) & (aapl_opt.Type=='call')]\ncall06_f1\n", "_____no_output_____" ] ], [ [ "## 2. ¿Qué es la volatilidad implícita?", "_____no_output_____" ], [ "**Volatilidad:** desviación estándar de los rendimientos.\n- ¿Cómo se calcula?\n- ¿Para qué calcular la volatilidad?", "_____no_output_____" ], [ "- **Para valuar derivados**, por ejemplo **opciones**.\n- Método de valuación de riesgo neutral (se supone que el precio del activo $S_t$ no se ve afectado por el riesgo de mercado).\n\nRecorderis de cuantitativas:\n1. Ecuación de Black-Scholes\n$$ dS(t) = \\mu S(t) + \\sigma S(t)dW_t$$\n2. Solución de la ecuación\n\nEl valor de una opción Europea de vainilla $V_t$ puede obtenerse por:\n$$V_t = F(t,S_t)$$ donde\n\n3. Opción de compra europea, suponiendo que los precios del activo son lognormales\n4. Opción de venta europea, suponiendo que los precios del activo son lognormales", "_____no_output_____" ], [ "Entonces, ¿qué es la **volatilidad implícita**?\n\nLa volatilidad es una medida de la incertidumbre sobre el comportamiento futuro de un activo, que se mide habitualmente como la desviación típica de la rentabilidad de dicho activo. \n\nUna volatilidad implícita es aquella que cuando se sustituye en la ecuación de Black-Scholes o en sus ampliaciones,proporciona el precio de mercado de la opción.", "_____no_output_____" ], [ "## Volatility smile \n- Cuando las opciones con la misma fecha de vencimiento y el mismo activo subyacente, pero diferentes precios de ejercicio, se grafican por la volatilidad implícita, la tendencia es que ese gráfico muestre una sonrisa.\n- La sonrisa muestra que las opciones más alejadas 'in- or out-of-the-money' tienen la mayor volatilidad implícita.\n- No todas las opciones tendrán una sonrisa de volatilidad implícita. Las opciones de acciones a corto plazo y las opciones relacionadas con la moneda tienen más probabilidades de tener una sonrisa de volatilidad\n\n\n\n> Fuente: https://www.investopedia.com/terms/v/volatilitysmile.asp", "_____no_output_____" ], [ "> ### Validar para la `fecha = 2020-06-19` y para la fecha `fecha = '2021-01-15'`", "_____no_output_____" ] ], [ [ "# para los call de la fecha 1\nax = call06_f1.set_index('Strike').loc[:, 'IV'].plot(figsize=(8,6))\nax.axvline(call06_f1.Underlying_Price.iloc[0], color='g');", "_____no_output_____" ], [ "# para los call de la fecha 2\nax = call06_f2.set_index('Strike').loc[:, 'IV'].plot(figsize=(8,6))\nax.axvline(call06_f2.Underlying_Price.iloc[0], color='g');", "_____no_output_____" ] ], [ [ "Analicemos ahora datos de los `put`", "_____no_output_____" ] ], [ [ "put06_f1 = aapl_opt.loc[(aapl_opt.Expiry==fecha1) & (aapl_opt.Type=='put')]\nput06_f1", "_____no_output_____" ] ], [ [ "Para los `put` de la `fecha 1`", "_____no_output_____" ] ], [ [ "ax = put06_f1.set_index('Strike').loc[:, 'IV'].plot(figsize=(8,6))\nax.axvline(put06_f1.Underlying_Price.iloc[0], color='g')", "_____no_output_____" ] ], [ [ "Con lo que hemos aprendido, deberíamos ser capaces de crear una función que nos devuelva un `DataFrame` de `pandas` con los precios de cierre ajustados de ciertas compañías en ciertas fechas:\n- Escribir la función a continuación", "_____no_output_____" ] ], [ [ "# Función para descargar precios de cierre ajustados:\ndef get_adj_closes(tickers, start_date=None, end_date=None):\n # Fecha inicio por defecto (start_date='2010-01-01') y fecha fin por defecto (end_date=today)\n # Descargamos DataFrame con todos los datos\n closes = web.DataReader(name=tickers, data_source='yahoo', start=start_date, end=end_date)\n # Solo necesitamos los precios ajustados en el cierre\n closes = closes['Adj Close']\n # Se ordenan los índices de manera ascendente\n closes.sort_index(inplace=True)\n return closes", "_____no_output_____" ] ], [ [ "- Obtener como ejemplo los precios de cierre de Apple del año pasado hasta la fecha. Graficar...", "_____no_output_____" ] ], [ [ "ticker = ['AAPL']\nstart_date = '2017-01-01'\n\ncloses_aapl = get_adj_closes(ticker, start_date)\ncloses_aapl.plot(figsize=(8,5));\nplt.legend(ticker);", "_____no_output_____" ] ], [ [ "- Escribir una función que pasándole el histórico de precios devuelva los rendimientos logarítmicos:", "_____no_output_____" ] ], [ [ "def calc_daily_ret(closes):\n return np.log(closes/closes.shift(1)).iloc[1:]", "_____no_output_____" ] ], [ [ "- Graficar...", "_____no_output_____" ] ], [ [ "ret_aapl = calc_daily_ret(closes_aapl)\nret_aapl.plot(figsize=(8,6));", "_____no_output_____" ] ], [ [ "También, descargar datos de opciones de Apple:", "_____no_output_____" ] ], [ [ "aapl = web.YahooOptions('AAPL')\naapl_opt = aapl.get_all_data().reset_index()\naapl_opt.set_index('Expiry').sort_index()", "_____no_output_____" ], [ "aapl_opt.Underlying_Price[0]", "_____no_output_____" ], [ "K = 135 # strike price\nindice_opt = aapl_opt.loc[(aapl_opt.Type=='call') & (aapl_opt.Strike==K) & (aapl_opt.Expiry=='2023-06-16')]\nindice_opt", "_____no_output_____" ], [ "i_opt= indice_opt.index\nopcion_valuar = aapl_opt.loc[i_opt[0]]\nopcion_valuar['JSON']", "_____no_output_____" ], [ "print('Precio del activo subyacente actual = ',opcion_valuar.Underlying_Price)", "Precio del activo subyacente actual = 133.5\n" ] ], [ [ "# Simulación de precios usando rendimiento simple y logarítmico ", "_____no_output_____" ], [ "* Comenzaremos por suponer que los rendimientos son un p.e. estacionario que distribuyen $\\mathcal{N}(\\mu,\\sigma)$.", "_____no_output_____" ], [ "## Rendimiento Simple", "_____no_output_____" ] ], [ [ "# Obtenemos el rendimiento simple\nRi = closes_aapl.pct_change(1).iloc[1:]\n# Obtenemos su media y desviación estándar de los rendimientos\nmu_R = Ri.mean()[0]\nsigma_R = Ri.std()[0]\nRi", "_____no_output_____" ], [ "today = pd.to_datetime(date.today())\n\n# Obtener fecha de cierre de la opción a valuar\nexpiry = opcion_valuar.Expiry\n\nlen(pd.date_range(today, expiry, freq='B'))", "_____no_output_____" ], [ "from datetime import date\n\n# Encontrar la fecha de hoy en fomato timestamp\ntoday = pd.to_datetime(date.today())\n\n# Obtener fecha de cierre de la opción a valuar\nexpiry = opcion_valuar.Expiry\n\nnscen = 10000\n\n# Generar rangos de fechas de días hábiles\ndates = pd.date_range(today, expiry, freq='B')\nndays = len(dates)", "_____no_output_____" ] ], [ [ "## Mostrar como simular precios usando los rendimientos\n\n### 1. Usando rendimiento simple", "_____no_output_____" ] ], [ [ "# Simular los rendimientos\n\n# Rendimiento diario \ndt = 1\n# Z ~ N(0,1) normal estándar (ndays, nscen)\nZ = np.random.randn(ndays, nscen)\n\n# Simulación normal de los rendimientos\nRi_dt = pd.DataFrame(Z * sigma_R * np.sqrt(dt) + mu_R * dt, index=dates)\nRi_dt", "_____no_output_____" ] ], [ [ "**Simulación de precios usando el rendimiento simple**: Como demostramos en clases pasadas la fórmula de simular precios usando el rendimiento simple es la siguiente:\n$$\nS_T = S_0 \\prod_{i=0}^{T-1} (R_i + 1)\n$$", "_____no_output_____" ] ], [ [ "S0 = opcion_valuar.Underlying_Price\nS_T = S0*(Ri_dt + 1).cumprod()", "_____no_output_____" ], [ "# Simulación del precio\nS_0 = opcion_valuar.Underlying_Price\nS_T = S_0*(1+Ri_dt).cumprod()\nS_T.iloc[0,:] = S_0\n\n# Se muestran los precios simulados con los precios descargados\npd.concat([closes_aapl, S_T.iloc[:, :10]]).plot(figsize=(8,6));\nplt.title('Simulación de precios usando rendimiento simple');", "_____no_output_____" ] ], [ [ "### 2. Rendimiento Logarítmico", "_____no_output_____" ], [ "**Simulación de precios usando el rendimiento logarítmico**: Como demostramos en clases pasadas la fórmula de simular precios usando el rendimiento simple es la siguiente:\n$$\nS_T = S_0 \\cdot e^{\\sum_{i=1}^{T} r_i }\n$$", "_____no_output_____" ] ], [ [ "Z.shape, len(dates)", "_____no_output_____" ], [ "# Calcular rendimiento logarítmico\nri = calc_daily_ret(closes_aapl)\n\n# Usando la media y desviación estándar de los rendimientos logarítmicos\nmu_r = ri.mean()[0]\nsigma_r = ri.std()[0]\n\n# Simulación del rendimiento\ndt = 1\nZ = np.random.randn(ndays, nscen)\nsim_ret_ri = pd.DataFrame(mu_r * dt + Z * sigma_r * np.sqrt(dt), index=dates )\n\n# Simulación del precio\nS_0 = closes_aapl.iloc[-1,0]\nS_T2 = S_0*np.exp(sim_ret_ri.cumsum())\n\n# Se muestran los precios simulados con los precios descargados\n# pd.concat([closes_aapl,S_T2]).plot(figsize=(8,6));\n# plt.title('Simulación de precios usando rendimiento logarítmico');\n\n# from sklearn.metrics import mean_absolute_error\ne1 = np.abs(S_T-S_T2).mean().mean()\ne1", "_____no_output_____" ], [ "print('Las std usando rendimientos logarítmicos y simples son similares')\nsigma_R,sigma_r", "Las std usando rendimientos logarítmicos y simples son similares\n" ] ], [ [ "Con los precios simulados debemos de encontrar el valor de la opción según la función de pago correspondiente. Para este caso es:\n$$\nmax(S_T - K,0)\n$$", "_____no_output_____" ] ], [ [ "opcion_valuar['JSON']", "_____no_output_____" ] ], [ [ "## Valuación usando el modelo de Black and Scholes\nLos supuestos que hicieron Black y Scholes cuando dedujeron su fórmula para la valoración de opciones fueron los siguientes:\n1. El comportamiento del precio de la acción corresponde al modelo logarítmico normal, con $\\mu$ y $\\sigma$\nconstantes.\n2. No hay costos de transición ni impuestos. Todos los títulos son perfectamente divisibles.\n3. No hay dividendos sobre la acción durante la vida de la opción.\n4. No hay oportunidades de arbitraje libres de riesgo.\n5. La negociación de valores es continua.\n6. Los inversionistas pueden adquirir u otorgar préstamos a la misma tasa de interés libre de riesgo.\n7. La tasa de interés libre de riesgo a corto plazo, r, es constante.\n\nBajo los supuestos anteriores podemos presentar las **fórmulas de Black-Scholes** para calcular los precios de compra y de venta europeas sobre acciones que no pagan dividendos:\n$$\n\\text{Valor actual de la opción} = V(S_0, T) = S_0 N(d_1) - K e^{-r*T} N(d_2)\n$$\n\ndonde:\n- $S_0$ = precio de la acción en el momento actual.\n- $K$ = precio \"de ejercicio\" de la opción.\n- $r$ = tasa de interés libre de riesgo.\n- $T$ = tiempo que le resta de vida a la opción.\n- $N(d)$ = función de distribución de la variable aleatoria normal con media nula y desviación típica unitaria\n(probabilidad de que dicha variable sea menor o igual que d). Función de distribución de probabilidad acumulada.\n- $\\sigma$ = varianza por período de la tasa o tipo de rendimiento de la opción.\n\n$$\nd_1 = \\frac{\\ln{\\frac{S_0}{K}} + (r + \\sigma^2 / 2) T}{\\sigma \\sqrt{T}}, \\quad d_2 = \\frac{\\ln{\\frac{S_0}{K}} + (r - \\sigma^2 / 2) T}{\\sigma \\sqrt{T}} \n$$\n\n**Nota**: observe que el __rendimiento esperado__ sobre la acción no se incluye en la ecuación de Black-Scholes. Hay un principio general conocido como valoración neutral al riesgo, el cual establece que cualquier título que depende de otros títulos negociados puede valorarse bajo el supuesto de que el mundo es neutral al riesgo. El resultado demuestra ser muy útil en la práctica. *En un mundo neutral al riesgo, el rendimiento esperado de todos los títulos es la tasa de interés libre de riesgo*, y la tasa de descuento correcta para los flujos de efectivo esperados también es la tasa de interés libre de riesgo.\n\nEl equivalente a la función de Black-Scholes (valuación de la opción) se puede demostrar que es:\n$$\n\\text{Valor actual de la opción} = V(S_0, T) = E^*(e^{-rT} f(S_T)) = e^{-rT} E^*(f(S_T))\n$$\n\ndonde \n$f(S_T)$ representa la función de pago de la opción, que para el caso de un call europeo sería $f(S_T) = \\max({S_T - K})$.\n\n> Referencia: http://diposit.ub.edu/dspace/bitstream/2445/32883/1/Benito_el_modelo_de_Black_Sholes.pdf (página 20)\n\n> Referencia 2: http://www.cmat.edu.uy/~mordecki/courses/upae/upae-curso.pdf (página 24)", "_____no_output_____" ], [ "- Hallar media y desviación estándar muestral de los rendimientos logarítmicos", "_____no_output_____" ] ], [ [ "mu = ret_aapl.mean()[0]\nsigma = ret_aapl.std()[0]\nmu, sigma", "_____no_output_____" ] ], [ [ "No se toma la media sino la tasa libre de riesgo\n> Referencia: https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield", "_____no_output_____" ] ], [ [ "# Tasa de bonos de 1 yr de fecha 21/04/2021 -> 7%\nr = 0.007/360 # Tasa diaria", "_____no_output_____" ] ], [ [ "- Simularemos el tiempo de contrato desde `HOY` hasta la fecha de `Expiry`, 10 escenarios:\n \n - Generar fechas", "_____no_output_____" ] ], [ [ "from datetime import date\n\ntoday = pd.Timestamp(date.today())\nexpiry = opcion_valuar.Expiry\n\ndates = pd.date_range(start=today, end=expiry, freq='B')\n\nndays = len(dates)\nnscen = 10\ndates", "_____no_output_____" ] ], [ [ "- Generamos 10 escenarios de rendimientos simulados y guardamos en un dataframe", "_____no_output_____" ] ], [ [ "sim_ret = pd.DataFrame(sigma*np.random.randn(ndays,nscen)+r, index=dates)\nsim_ret.cumsum()\n# Las columnas son los escenarios y las filas son las días de contrato", "_____no_output_____" ] ], [ [ "- Con los rendimientos simulados, calcular los escenarios de precios respectivos:", "_____no_output_____" ] ], [ [ "S0 = closes_aapl.iloc[-1,0] # Condición inicial del precio a simular\nsim_closes = S0*np.exp(sim_ret.cumsum())\nsim_closes.iloc[0, :] = S0\nsim_closes", "_____no_output_____" ] ], [ [ "- Graficar:", "_____no_output_____" ] ], [ [ "sim_closes.plot(figsize=(8,6));", "_____no_output_____" ], [ "# Se muestran los precios simulados con los precios descargados\npd.concat([closes_aapl,sim_closes]).plot(figsize=(8,6));", "_____no_output_____" ], [ "opcion_valuar['JSON']", "_____no_output_____" ], [ "opcion_valuar", "_____no_output_____" ], [ "from datetime import date\nHoy = date.today()\n\n# strike price de la opción\nK = opcion_valuar['JSON']['strike'] \n\n# Fechas a simular\ndates = pd.date_range(start= Hoy, periods = ndays, freq='B')\n\n# Escenarios y número de días\nndays = len(dates)\nnscen = 100000\n\n# Condición inicial del precio a simular\nS0 = closes_aapl.iloc[-1,0] \n\n# simular rendimientos\nsim_ret = pd.DataFrame(sigma*np.random.randn(ndays,nscen)+r,index=dates)\n\n# Simular precios\nsim_closes = S0*np.exp(sim_ret.cumsum())\n", "_____no_output_____" ], [ "# Valor del call europeo\ncall = pd.DataFrame({'Prima':np.exp(-r*ndays) \\\n *np.fmax(sim_closes-K, 0).mean(axis=1)}, index=dates)\ncall.plot();\n", "_____no_output_____" ] ], [ [ "La valuación de la opción es:", "_____no_output_____" ] ], [ [ "call.iloc[-1]", "_____no_output_____" ] ], [ [ "Intervalo de confianza del 99%", "_____no_output_____" ] ], [ [ "confianza = 0.99\nsigma_est = sim_closes.iloc[-1].sem()\nmean_est = call.iloc[-1].Prima\ni2 = st.norm.interval(confianza, loc=mean_est, scale=sigma_est)\nprint(i2)\n", "(34.383953470513504, 35.621289698015715)\n" ], [ "opcion_valuar['JSON']", "_____no_output_____" ] ], [ [ "## Precios simulados usando técnicas de reducción de varianza", "_____no_output_____" ] ], [ [ "# Usando muestreo estratificado----> #estratros = nscen\nU = (np.arange(0,nscen)+np.random.rand(ndays,nscen))/nscen\nZ = st.norm.ppf(U)\n\nsim_ret2 = pd.DataFrame(sigma*Z+r,index=dates)\nsim_closes2 = S0*np.exp(sim_ret.cumsum())\n\n# Función de pago\nstrike = pd.DataFrame(K*np.ones([ndays,nscen]), index=dates)\ncall = pd.DataFrame({'Prima':np.exp(-r*ndays) \\\n *np.fmax(sim_closes2-strike,np.zeros([ndays,nscen])).T.mean()}, index=dates)\ncall.plot();", "_____no_output_____" ] ], [ [ "La valuación de la opción es:", "_____no_output_____" ] ], [ [ "call.iloc[-1]", "_____no_output_____" ] ], [ [ "Intervalo de confianza del 99%", "_____no_output_____" ] ], [ [ "confianza = 0.99\nsigma_est = sim_closes2.iloc[-1].sem()\nmean_est = call.iloc[-1].Prima\ni2 = st.norm.interval(confianza, loc=mean_est, scale=sigma_est)\nprint(i2)", "(34.383953470513504, 35.621289698015715)\n" ] ], [ [ "### Análisis de la distribución de los rendimientos", "_____no_output_____" ], [ "### Ajustando norm", "_____no_output_____" ] ], [ [ "ren = calc_daily_ret(closes_aapl) # rendimientos \ny,x,_ = plt.hist(ren['AAPL'],bins=50,density=True,label='Histograma rendimientos')\n\nmu_fit,sd_fit = st.norm.fit(ren) # Se ajustan los parámetros de una normal\n# Valores máximo y mínimo de los rendiemientos a generar\nren_max = max(x);ren_min = min(x)\n# Vector de rendimientos generados\nren_gen = np.arange(ren_min,ren_max,0.001)\n# Generación de la normal ajustado con los parámetros encontrados\ncurve_fit = st.norm.pdf(ren_gen,loc=mu_fit,scale=sd_fit)\nplt.plot(ren_gen,curve_fit,label='Distribución ajustada')\nplt.legend()\nplt.show()\n", "_____no_output_____" ] ], [ [ "### Ajustando t", "_____no_output_____" ] ], [ [ "# rendimientos \nren = calc_daily_ret(closes_aapl) \n\n# Histograma de los rendimientos\ny, x, _ = plt.hist(ren['AAPL'], bins=50, density=True, label='Histograma rendimientos')\n\n# Se ajustan los parámetros de una distribución\ndist = 't'\nparams = getattr(st, dist).fit(ren.values) \n\n# Generación de la pdf de la distribución ajustado con los parámetros encontrados\ncurve_fit = getattr(st, dist).pdf(x, *params)\nplt.plot(x, curve_fit, label='Distribución ajustada')\nplt.legend()\nplt.show()\n\n# Q-Q\nst.probplot(ren['AAPL'], sparams=params[:-2], dist=dist, plot=plt);\n", "_____no_output_____" ] ], [ [ "## 3. Valuación usando simulación: uso del histograma de rendimientos\n\nTodo el análisis anterior se mantiene. Solo cambia la forma de generar los números aleatorios para la simulación montecarlo.\n\nAhora, generemos un histograma de los rendimientos diarios para generar valores aleatorios de los rendimientos simulados.", "_____no_output_____" ], [ "- Primero, cantidad de días y número de escenarios de simulación", "_____no_output_____" ] ], [ [ "ndays = len(dates) \nnscen = 10", "_____no_output_____" ] ], [ [ "- Del histograma anterior, ya conocemos las probabilidades de ocurrencia, lo que se llamó como variable `y`", "_____no_output_____" ] ], [ [ "prob", "_____no_output_____" ], [ "prob = y/np.sum(y)\nvalues = x[1:]\nprob.sum()", "_____no_output_____" ] ], [ [ "- Con esto, generamos los números aleatorios correspondientes a los rendimientos (tantos como días por número de escenarios).", "_____no_output_____" ] ], [ [ "# Rendimientos simulados\nret = np.random.choice(values, ndays*nscen, p=prob)\n# Fechas\ndates = pd.date_range(start=Hoy,periods=ndays)\n# Rendimien en Data Frame\nsim_ret_hist = pd.DataFrame(ret.reshape((ndays,nscen)),index=dates)\nsim_ret_hist", "_____no_output_____" ], [ "sim_closes_hist = (closes_aapl.iloc[-1,0])*np.exp(sim_ret_hist.cumsum())\nsim_closes_hist", "_____no_output_____" ], [ "sim_closes_hist.plot(figsize=(8,6),legend=False);", "_____no_output_____" ], [ "pd.concat([closes_aapl,sim_closes_hist]).plot(figsize=(8,6),legend=False);\nplt.title('Simulación usando el histograma de los rendimientos')", "_____no_output_____" ], [ "K = opcion_valuar['JSON']['strike']\nndays = len(dates)\nnscen = 100000\n\n# Histograma tomando la tasa libre de riesgo\nfreq, values = np.histogram(ret_aapl+r-mu, bins=2000)\nprob = freq/np.sum(freq)\n\n# Simulación de los rendimientos\nret = np.random.choice(values[1:], ndays*nscen, p=prob)\n\n# Simulación de precios\nsim_ret_hist = pd.DataFrame(ret.reshape((ndays,nscen)),index=dates)\nsim_closes_hist = (closes_aapl.iloc[-1,0]) * np.exp(sim_ret_hist.cumsum())", "_____no_output_____" ], [ "strike = pd.DataFrame(K*np.ones(ndays*nscen).reshape((ndays,nscen)), index=dates)\ncall_hist = pd.DataFrame({'Prima':np.exp(-r*ndays) \\\n *np.fmax(sim_closes_hist-strike,np.zeros(ndays*nscen).reshape((ndays,nscen))).T.mean()}, index=dates)\ncall_hist.plot();", "_____no_output_____" ], [ "call_hist.iloc[-1]", "_____no_output_____" ], [ "opcion_valuar['JSON']", "_____no_output_____" ] ], [ [ "Intervalo de confianza del 95%", "_____no_output_____" ] ], [ [ "confianza = 0.95\nsigma_est = sim_closes_hist.iloc[-1].sem()\nmean_est = call_hist.iloc[-1].Prima\ni1 = st.t.interval(confianza,nscen-1, loc=mean_est, scale=sigma_est)\ni2 = st.norm.interval(confianza, loc=mean_est, scale=sigma_est)\nprint(i1)\nprint(i1)\n", "(38.04373480098675, 39.00930377235172)\n(38.04373480098675, 39.00930377235172)\n" ] ], [ [ "# <font color = 'red'> Tarea: </font>\n\nReplicar el procedimiento anterior para valoración de opciones 'call', pero en este caso para opciones tipo 'put' y compararlo con el valor teórico de la ecuación de Black-Scholes", "_____no_output_____" ], [ "<script>\n $(document).ready(function(){\n $('div.prompt').hide();\n $('div.back-to-top').hide();\n $('nav#menubar').hide();\n $('.breadcrumb').hide();\n $('.hidden-print').hide();\n });\n</script>\n\n<footer id=\"attribution\" style=\"float:right; color:#808080; background:#fff;\">\nCreated with Jupyter by Esteban Jiménez Rodríguez and modified by Oscar Jaramillo Z.\n</footer>", "_____no_output_____" ] ] ]

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[ "MIT" ]

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[ [ [ "# List and Dictionary Comprehensions\n\nComprehension is a different way to construct lists and dictionaries. Up to now, every time that we have built up a list or dictionary, we began by initializing it. We then took advantage of their mutability inherent to build them up one element or key-value pair at a time. However, there is a more succinct way to accomplish the vast majority of your list and dictionary construction tasks.\n\n## Objectives\n\nAt the end of this notebook you should be able to:\n\n- use list comprehension\n- use dictionary comprehension\n- use tuple comprehension\n\n## List Comprehensions\n\nBefore we dive into the specifics about how this new tool (list comprehensions) works, let's look at an example question where we build a list. We can then show how to perform the same task with our new tool and learn how it works.\n\nLet's imagine that we have the list `[1, 5, 9, 33]` stored in the variable `my_list`. Now, let's assume that we want to make a new list of the squares of all the values in `my_list` and call it `my_squares`. With the tools we have covered so far, you might write:", "_____no_output_____" ] ], [ [ "my_list = [1, 5, 9, 33]\nmy_squares = []\nfor num in my_list:\n my_squares.append(num ** 2)\nprint(my_squares)", "[1, 25, 81, 1089]\n" ] ], [ [ "Now, `my_squares` will hold the list `[1, 25, 81, 1089]`. To get this, we were simply specifying a bunch of stuff that we wanted to add on to the end of the `my_squares` list, with a starting point at `my_list`. So, from a high level, we can write the framework of creating a list in code as:\n\n```python\nlist_were_building = []\nfor thing in iterable:\n list_were_building.append(transform(thing))\n```\n\nWith this structure in mind, we can use the following syntax to perform the same task of building up a list in a single line! Check it out, along with how it would look for the construction of `my_squares`.\n\n```python\nlist_were_building = [transform(thing) for thing in iterable]\n```\n\nThis last line of code does the exact same thing as the three lines above! In this line, the thing that we would pass to the `append()` method, `transform(thing)`, comes at the beginning of the statement in the `[]`. These `[]` allow for the final product to be defined as a list. Then, the `for` loop statement that we had written is at the end. This is the basic idea behind the [list comprehension](https://en.wikipedia.org/wiki/List_comprehension).\n\nSimilarly, we can build our `my_squares` list using a list comprehension:", "_____no_output_____" ] ], [ [ "my_squares2 = [num ** 2 for num in my_list]\nprint(my_squares2)", "[1, 25, 81, 1089]\n" ] ], [ [ "But wait! There's more! Remember in all the examples where we were getting evens, we had a condition to decide when to append a value to a list? We can also use conditions to determine what \"transformed things\" get added in a list comprehension! Let's look at the evens list builder to hammer this home.\n\n```python\n# Old way of constructing list of evens\nevens = []\nfor num in range(10):\n if num % 2 == 0:\n evens.append(num)\n\n# Old way at high level\nlist_were_building = []\nfor thing in iterable:\n if condition:\n list_were_building.append(transform(thing))\n\n# List comprehension way of constructing list of evens\nevens = [num for num in range(10) if num % 2 == 0]\n\n# List comprehension way at high level\nlist_we_are_building = [transform(thing) for thing in iterable if condition]\n```\nThe way `transform()` was called in the above examples, as though it were a function, is an option when writing list comps. For example, the `my_squares` example could be accomplished in the same way with:\n\n```python\ndef square(num):\n return num ** 2\n\nmy_squares = [square(num) for num in my_list]\n```\n\nThis might seem silly, since we could just write `num ** 2` directly in the list comp as we did above. However, this calling of a function in the list comp becomes a powerful idea when you want to transform the values being iterated over in a complex way.\n\n## Dictionary Comprehensions\n\nJust as list comprehensions are a more succinct way of constructing a list, we have the same ability for dictionaries. Dictionary comprehensions operate in the same way as their list counterparts, except for one fundamental difference. Recall that dictionaries have no `append()` method, and that a new key-value pair is added to the dictionary with the syntax: `my_dict[new_key] = new_value`. In this way, it makes sense that we need syntax to pass both the key and value to the dictionary comprehension.\n\nLuckily, Python gives a simple way to pass a key and value pair, and it is already very familiar to you! You just separate the key and value that you want to enter into the dictionary with a colon, like we did when we were hardcoding the contents in the `{}` dictionary constructor, i.e. `my_dict = {1: 1, 2: 4}`. Let's look at an example where we make a dictionary with the keys as the numbers 1 - 5, and the values as the squares of the keys. We'll do this with both the old way of constructing a dictionary, and then with a dictionary comprehension so that we can see the similarities.", "_____no_output_____" ] ], [ [ "# Standard way. \nsquares_dict = {}\nfor num in range(1, 6): \n squares_dict[num] = num ** 2\nprint(squares_dict)", "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}\n" ], [ "# Dictionary Comprehension way. \nsquares_dict2 = {num: num ** 2 for num in range(1, 6)}\nprint(squares_dict2)", "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}\n" ] ], [ [ "We can see that in both cases, we're going through the numbers 1 - 5 with `range(1, 6)` and those `num`s are being assigned as keys. The values assigned to those keys are the squares of the keys, assigned with `squares_dict[num] = num ** 2` and `num: num ** 2`, respectively. Just as with list comprehensions, dictionary comprehensions read as the first thing being the `key: value` pair being added to the dictionary. Then, left to right (top down in the old way), we have what the loop definition would look like. And, just as with list comps, we can add a condition to filter what gets put into the dictionary.\n\nSay that we want a dictionary with a random integer between 1 and 10, associated with each of the values in the list of words: `['cow', 'chicken', 'horse', 'moose']`. Let's look at how we'd do that with a dictionary comprehension. (We're importing from the Python library `random` to get our random integers. We'll talk more about importing later in the course.)", "_____no_output_____" ] ], [ [ "from random import randint\nanimals_list = ['cow', 'chicken', 'horse', 'moose']", "_____no_output_____" ], [ "animals_dict = {animal: randint(1, 10) for animal in animals_list}\nprint(animals_dict)", "{'cow': 5, 'chicken': 8, 'horse': 5, 'moose': 8}\n" ] ], [ [ "## Other Comprehensions\n\nYou can actually use the syntax from the list comprehensions to construct a tuple in what seems like a dynamic way. Take the example.\n", "_____no_output_____" ] ], [ [ "my_tuple = tuple(num for num in range(10) if num % 2 == 0)\nprint(my_tuple)", "(0, 2, 4, 6, 8)\n" ] ], [ [ "All we are doing here is passing `num for num in range(10) if num % 2 == 0` to the tuple constructor, `()`. Since the tuple constructor takes any iterable, which that statement produces, it makes a tuple out of the contents. Note that it would be impossible to make a tuple with statements like this the \"old way\", since tuples don't support appending or mutation of any kind!\n\nFor this reason, in addition to their readability, comprehensions of all types are considered the most Pythonic way of constructing new data structures.\n\n## Check your understanding\n\n1. Take the following for loop, and translate it into a list. comprehensions: \n\n odds = []\n for num in range(10): \n if num % 2 != 0: \n odds.append(num)\n \n \n1. Take the following for loop, and translate it into a dictionary comprehensions: \n\n \n cubes = {}\n for num in range(1, 6): \n cubes[num] = num ** 3\n ", "_____no_output_____" ] ], [ [ "odds = [num for num in range(10) if num % 2 != 0]\nodds", "_____no_output_____" ], [ "cubes = {num: num **3 for num in range(1,6)}\ncubes", "_____no_output_____" ] ] ]

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[ "ADSL" ]

[ "ADSL" ]

[ [ [ "import numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom matplotlib import pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder", "_____no_output_____" ], [ "train_df = pd.read_csv(Path('Resources/2019loans.csv'))\ntest_df = pd.read_csv(Path('Resources/2020Q1loans.csv'))", "_____no_output_____" ], [ "train_df", "_____no_output_____" ], [ "test_df", "_____no_output_____" ], [ "# Convert categorical data to numeric and separate target feature for training data\nx = train_df.drop('loan_status',axis = 1)\nx_train = pd.get_dummies(x)\nx_train = x_train.drop('Unnamed: 0',axis = 1)\nx_train", "_____no_output_____" ], [ "y = pd.get_dummies(train_df['loan_status'])\ny_train = y.drop('high_risk',axis =1)\ny_train = y_train.rename(columns = {'low_risk':'loan_status'})\ny_train = y_train.values.ravel()\ny_train", "_____no_output_____" ], [ "# Convert categorical data to numeric and separate target feature for testing data\nx_td = test_df.drop('loan_status',axis = 1)\nx_test = pd.get_dummies(x_td)\nx_test = x_test.drop('Unnamed: 0',axis = 1)\nx_test", "_____no_output_____" ], [ "y_td = pd.get_dummies(test_df['loan_status'])\ny_test= y_td.drop('high_risk',axis =1)\ny_test = y_test.rename(columns={'low_risk':'loan_status'})\ny_test", "_____no_output_____" ], [ "# add missing dummy variables to testing set\nmissing_cols = set(x_train.columns) - set(x_test.columns)\nmissing_cols", "_____no_output_____" ], [ "for c in missing_cols:\n x_test['debt_settlement_flag_Y'] = 0\n# Ensure the order of column in the test set is in the same order than in train set\nx_test = x_test[x_train.columns]\nx_test", "_____no_output_____" ] ], [ [ "# LOGISTIC REGRESSION MODEL", "_____no_output_____" ] ], [ [ "#import dependencies\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression", "_____no_output_____" ], [ "# Train the Logistic Regression model on the unscaled data and print the model score\nclassifier = LogisticRegression()\n\n# Fit our model using the training data\nclassifier.fit(x_train, y_train)\nprint(f\"Training Data Score: {classifier.score(x_train, y_train)}\")\nprint(f\"Testing Data Score: {classifier.score(x_test, y_test)}\")", "Training Data Score: 0.6564860426929392\nTesting Data Score: 0.5199914929817099\n" ] ], [ [ "# RandomForest Classifier Model", "_____no_output_____" ] ], [ [ "#import dependencies\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import classification_report, roc_curve", "_____no_output_____" ], [ "# Train a Random Forest Classifier model and print the model score\nrf_clf = RandomForestClassifier(random_state=1)\nrf_clf.fit(x_train, y_train)\n#classification reports\ny_pred = rf_clf.predict(x_test)\nprint(classification_report(y_test, y_pred))", " precision recall f1-score support\n\n 0 0.75 0.50 0.60 2351\n 1 0.63 0.83 0.71 2351\n\n accuracy 0.67 4702\n macro avg 0.69 0.67 0.66 4702\nweighted avg 0.69 0.67 0.66 4702\n\n" ], [ "#model score\nprint(f\"Training Data Score: {rf_clf.score(x_train, y_train)}\")\nprint(f\"Testing Data Score: {rf_clf.score(x_test, y_test)}\")", "Training Data Score: 1.0\nTesting Data Score: 0.6671629094002552\n" ] ], [ [ "# Scaling the Data", "_____no_output_____" ] ], [ [ "#import dependencies\nfrom sklearn.preprocessing import StandardScaler", "_____no_output_____" ], [ "# Scale the data and all features\nscaler = StandardScaler().fit(x_train)\nx_train_scaled = scaler.transform(x_train)\nx_test_scaled = scaler.transform(x_test)\n\n", "_____no_output_____" ], [ "# Train the Logistic Regression model on the scaled data and print the model score\n# Create a logistic regression model\nclassifier_scaled = LogisticRegression()\n\n# Fit our model using the training data\nclassifier_scaled.fit(x_train_scaled, y_train)\nprint(f\"Training Data Score: {classifier.score(x_train_scaled, y_train)}\")\nprint(f\"Testing Data Score: {classifier.score(x_test_scaled, y_test)}\")", "Training Data Score: 0.6361247947454844\nTesting Data Score: 0.506380263717567\n" ], [ "# Train a Random Forest Classifier model on the scaled data and print the model score\n# Train a Random Forest Classifier model and print the model score\nrf_clf_scaled = RandomForestClassifier(random_state=42)\nrf_clf_scaled.fit(x_train_scaled, y_train)\n#classification reports\ny_pred = rf_clf_scaled.predict(x_test_scaled)\nprint(classification_report(y_test, y_pred))", " precision recall f1-score support\n\n 0 0.73 0.48 0.58 2351\n 1 0.62 0.82 0.70 2351\n\n accuracy 0.65 4702\n macro avg 0.67 0.65 0.64 4702\nweighted avg 0.67 0.65 0.64 4702\n\n" ], [ "#model score\nprint(f\"Training Data Score: {rf_clf_scaled.score(x_train_scaled, y_train)}\")\nprint(f\"Testing Data Score: {rf_clf_scaled.score(x_test_scaled, y_test)}\")", "Training Data Score: 1.0\nTesting Data Score: 0.6546150574223735\n" ] ], [ [ "# Analysis\nThe Randomforest model even though it seems to be overfitting, is showing much better results with the training data set. The logistic_Regression model results are not as good as the Randomforest model.\nA second obeservation is both the Randomforest and the Logistic_Regression models both show unfavourable results with the test data set.", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "empty" ] ] ]

[ "empty" ]

[ [ "empty" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "empty" ] ] ]

[ "empty" ]

[ [ "empty" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "## Denoising Autoencoder on MNIST dataset\n* This notebook will give you a very good understanding abou denoising autoencoders\n* For more information: visit [here](https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html)\n* The entire notebook is in PyTorch", "_____no_output_____" ] ], [ [ "# Importing packages that will be necessary for the project\nimport numpy as np\nfrom keras.datasets import mnist\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom torchvision import transforms\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader,Dataset\nimport torch\nimport torch.optim as optim\nfrom torch.autograd import Variable", "_____no_output_____" ], [ "# Mounting the google drive to fetch data from it\nfrom google.colab import drive\ndrive.mount('/content/gdrive')", "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n\nEnter your authorization code:\n··········\nMounted at /content/gdrive\n" ], [ "#loading the mnist data\n(x_train,y_train),(x_test,y_test)=mnist.load_data()\nprint(\"No of train datapoints:{}\\nNo of test datapoints:{}\".format(len(x_train),len(x_test)))", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11493376/11490434 [==============================] - 0s 0us/step\nNo of train datapoints:60000\nNo of test datapoints:10000\n" ], [ "print(y_train[1]) # Checking labels", "0\n" ], [ "#we add the noise \n\"\"\"\n 'gauss' Gaussian-distributed additive noise.\n 'speckle' out = image + n*image,where\n n is uniform noise with specified mean & variance. \n\"\"\"\n\ndef add_noise(img,noise_type=\"gaussian\"):#input includes the type of the noise to be added and the input image\n \n row,col=28,28\n img=img.astype(np.float32)\n \n if noise_type==\"gaussian\":\n noise=np.random.normal(-5.9,5.9,img.shape) #input includes : mean, deviation, shape of the image and the function picks up a normal distribuition.\n noise=noise.reshape(row,col) # reshaping the noise \n img=img+noise #adding the noise\n return img\n\n if noise_type==\"speckle\":\n noise=np.random.randn(row,col)\n noise=noise.reshape(row,col)\n img=img+img*noise\n return img", "_____no_output_____" ], [ "#Now dividing the dataset into two parts and adding gaussian to one and speckle to another.\nnoises=[\"gaussian\",\"speckle\"]\nnoise_ct=0\nnoise_id=0 #id represnts which noise is being added, its 0 = gaussian and 1 = speckle\ntraindata=np.zeros((60000,28,28)) #revised training data\n\n\n\nfor idx in tqdm(range(len(x_train))): #for the first half we are using gaussian noise & for the second half speckle noise\n \n if noise_ct<(len(x_train)/2):\n noise_ct+=1\n traindata[idx]=add_noise(x_train[idx],noise_type=noises[noise_id])\n \n else:\n print(\"\\n{} noise addition completed to images\".format(noises[noise_id]))\n noise_id+=1\n noise_ct=0\n\n\nprint(\"\\n{} noise addition completed to images\".format(noises[noise_id])) \n\n\n\n\nnoise_ct=0\nnoise_id=0\ntestdata=np.zeros((10000,28,28))\n\nfor idx in tqdm(range(len(x_test))): # Doing the same for the test set.\n \n if noise_ct<(len(x_test)/2):\n noise_ct+=1\n x=add_noise(x_test[idx],noise_type=noises[noise_id])\n testdata[idx]=x\n \n else:\n print(\"\\n{} noise addition completed to images\".format(noises[noise_id]))\n noise_id+=1\n noise_ct=0\n\n\nprint(\"\\n{} noise addition completed to images\".format(noises[noise_id]))", " 53%|█████▎ | 32057/60000 [00:01<00:01, 19218.50it/s]" ], [ "f, axes=plt.subplots(2,2) #setting up 4 figures\n\n#showing images with gaussian noise\naxes[0,0].imshow(x_train[0],cmap=\"gray\")#the original data\naxes[0,0].set_title(\"Original Image\")\naxes[1,0].imshow(traindata[0],cmap='gray')#noised data\naxes[1,0].set_title(\"Noised Image\")\n\n#showing images with speckle noise\naxes[0,1].imshow(x_train[25000],cmap='gray')#original data\naxes[0,1].set_title(\"Original Image\")\naxes[1,1].imshow(traindata[25000],cmap=\"gray\")#noised data\naxes[1,1].set_title(\"Noised Image\")", "_____no_output_____" ], [ "#creating a dataset builder i.e dataloaders\nclass noisedDataset(Dataset):\n \n def __init__(self,datasetnoised,datasetclean,labels,transform):\n self.noise=datasetnoised\n self.clean=datasetclean\n self.labels=labels\n self.transform=transform\n \n def __len__(self):\n return len(self.noise)\n \n def __getitem__(self,idx):\n xNoise=self.noise[idx]\n xClean=self.clean[idx]\n y=self.labels[idx]\n \n if self.transform != None:#just for using the totensor transform\n xNoise=self.transform(xNoise)\n xClean=self.transform(xClean)\n \n \n return (xNoise,xClean,y)", "_____no_output_____" ], [ "#defining the totensor transforms\ntsfms=transforms.Compose([\n transforms.ToTensor()\n])\n\ntrainset=noisedDataset(traindata,x_train,y_train,tsfms)# the labels should not be corrupted because the model has to learn uniques features and denoise it.\ntestset=noisedDataset(testdata,x_test,y_test,tsfms)", "_____no_output_____" ], [ "batch_size=32\n\n\n#creating the dataloader\ntrainloader=DataLoader(trainset,batch_size=32,shuffle=True)\ntestloader=DataLoader(testset,batch_size=1,shuffle=True)", "_____no_output_____" ], [ "#building our ae model:\nclass denoising_model(nn.Module):\n def __init__(self):\n super(denoising_model,self).__init__()\n self.encoder=nn.Sequential(\n nn.Linear(28*28,256),#decreasing the features in the encoder\n nn.ReLU(True),\n nn.Linear(256,128),\n nn.ReLU(True),\n nn.Linear(128,64),\n nn.ReLU(True)\n \n )\n \n self.decoder=nn.Sequential(\n nn.Linear(64,128),#increasing the number of features\n nn.ReLU(True),\n nn.Linear(128,256),\n nn.ReLU(True),\n nn.Linear(256,28*28),\n nn.Sigmoid(),\n )\n \n \n def forward(self,x):\n x=self.encoder(x)#first the encoder\n x=self.decoder(x)#then the decoder to reconstruct the original input.\n \n return x", "_____no_output_____" ], [ "#this is the training code, can be modified according to requirements\n#setting the device\nif torch.cuda.is_available()==True:\n device=\"cuda:0\"\nelse:\n device =\"cpu\"\n\n \nmodel=denoising_model().to(device)\ncriterion=nn.MSELoss()\noptimizer=optim.SGD(model.parameters(),lr=0.01,weight_decay=1e-5)\n\n#setting the number of epochs\nepochs=120\nl=len(trainloader)\nlosslist=list()\nepochloss=0\nrunning_loss=0\nfor epoch in range(epochs):\n \n print(\"Entering Epoch: \",epoch)\n for dirty,clean,label in tqdm((trainloader)):\n \n \n dirty=dirty.view(dirty.size(0),-1).type(torch.FloatTensor)\n clean=clean.view(clean.size(0),-1).type(torch.FloatTensor)\n dirty,clean=dirty.to(device),clean.to(device)\n \n \n \n #-----------------Forward Pass----------------------\n output=model(dirty)\n loss=criterion(output,clean)\n #-----------------Backward Pass---------------------\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n running_loss+=loss.item()\n epochloss+=loss.item()\n #-----------------Log-------------------------------\n losslist.append(running_loss/l)\n running_loss=0\n print(\"======> epoch: {}/{}, Loss:{}\".format(epoch,epochs,loss.item()))", "\r 0%| | 0/1875 [00:00<?, ?it/s]" ], [ "#plotting the loss curve\nplt.plot(range(len(losslist)),losslist)", "_____no_output_____" ], [ "\"\"\"Here, we try to visualize some of the results.\n We randomly generate 6 numbers in between 1 and 10k , run them through the model,\n and show the results with comparisons\n \n \"\"\"\n\nf,axes= plt.subplots(6,3,figsize=(20,20))\naxes[0,0].set_title(\"Original Image\")\naxes[0,1].set_title(\"Dirty Image\")\naxes[0,2].set_title(\"Cleaned Image\")\n\ntest_imgs=np.random.randint(0,10000,size=6)\nfor idx in range((6)):\n dirty=testset[test_imgs[idx]][0]\n clean=testset[test_imgs[idx]][1]\n label=testset[test_imgs[idx]][2]\n dirty=dirty.view(dirty.size(0),-1).type(torch.FloatTensor)\n dirty=dirty.to(device)\n output=model(dirty)\n \n output=output.view(1,28,28)\n output=output.permute(1,2,0).squeeze(2)\n output=output.detach().cpu().numpy()\n \n dirty=dirty.view(1,28,28)\n dirty=dirty.permute(1,2,0).squeeze(2)\n dirty=dirty.detach().cpu().numpy()\n \n clean=clean.permute(1,2,0).squeeze(2)\n clean=clean.detach().cpu().numpy()\n \n axes[idx,0].imshow(clean,cmap=\"gray\")\n axes[idx,1].imshow(dirty,cmap=\"gray\")\n axes[idx,2].imshow(output,cmap=\"gray\")", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Cleaning the data to build the prototype for crwa\n\n### This data cleans the original sql output and performs cleaning tasks. Also checking validity of the results against original report found at\n### https://www.crwa.org/uploads/1/2/6/7/126781580/crwa_ecoli_web_2017_updated.xlsx", "_____no_output_____" ] ], [ [ "import pandas as pd\npd.options.display.max_rows = 999\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "df = pd.read_csv(\"data_for_prototype.csv\")", "_____no_output_____" ], [ "# There are 2 rows with Date = Null so droping those rows\ndf = df.dropna(subset=['Date_Collected'])\n", "_____no_output_____" ], [ "df.isna().sum()", "_____no_output_____" ], [ "# There are following types of invalids in Site_ID \ninvalids = [\"N/A\",\"NULL\",\"ND\"]", "_____no_output_____" ], [ "#Removing these invalid Site_IDs \n\ndf[\"Site_Name\"] = df[\"Site_Name\"].map(lambda x: np.nan if x in invalids else x)\ndf[\"Site_Name\"].fillna(\"ABCD\", inplace=True)", "_____no_output_____" ], [ "#Removing these invalid Town Names \n\ndf[\"Town\"] = df[\"Town\"].map(lambda x: np.nan if x in invalids else x)\ndf[\"Town\"].fillna(\"ABCD\", inplace=True)", "_____no_output_____" ], [ "df[\"River_Mile_Headwaters\"].describe", "_____no_output_____" ], [ "#Removing invalid Miles and selecting only numeric values for miles \n\n\ndf[\"River_Mile_Headwaters\"] = df[\"River_Mile_Headwaters\"].map(lambda x: np.nan if x in invalids else x)\ndf[\"River_Mile_Headwaters\"].fillna(\"00.0 MI\", inplace=True)\ndf[\"Mile\"] = pd.to_numeric(df[\"River_Mile_Headwaters\"].str[0:4])", "_____no_output_____" ], [ "#Removing invalid entrees and selecting only numeric values \n\n\ndf[\"Latitude_DD\"] = df[\"Latitude_DD\"].map(lambda x: np.nan if x in invalids else x)\ndf[\"Latitude_DD\"].fillna(\"00.0 MI\", inplace=True)\ndf[\"Longitude_DD\"] = df[\"Longitude_DD\"].map(lambda x: np.nan if x in invalids else x)\ndf[\"Longitude_DD\"].fillna(\"00.0 MI\", inplace=True)", "_____no_output_____" ], [ "#Removing invalid entrees and selecting only numeric values \n\n\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].map(lambda x: np.nan if x in invalids else x)\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.lstrip('>')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.rstrip('>')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.lstrip('<')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.rstrip('<')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.lstrip('*')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.rstrip('*')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace(',','')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace('%','')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace(' ','')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace('ND','')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.lstrip('.')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.rstrip('.')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace('6..25','6.25')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace('480.81546.25291','480.81546')\ndf[\"Actual_Result\"] = df[\"Actual_Result\"].str.replace('379\\r\\n379',\"379\")\n\n", "_____no_output_____" ], [ "#Functiont to check if string can be converted to numeric\n\n#Input --> string\n#Output --> 1 if convertable else 0\n\ndef isInt_try(v):\n try: i = float(v)\n except: return False\n return True", "_____no_output_____" ], [ "# Applying above function to check any odd strings in Actual_Result Column\n\nfor i in df[\"Actual_Result\"]:\n if isInt_try(i) == 0:\n print(i)\n", "_____no_output_____" ], [ "# Checking any odd strings in Actual_Result Column\n\nfor i in df[\"Actual_Result\"]:\n if str(i).count('.') >= 2:\n print(i)\n", "_____no_output_____" ], [ "# Converting Actual_Result to numeric and Date_Collected to datetime data type\n\ndf[\"Actual_Result\"] = pd.to_numeric(df[\"Actual_Result\"])\ndf[\"Date_Collected\"] = pd.to_datetime(df[\"Date_Collected\"])", "_____no_output_____" ], [ "\"Slicing for E.coli\"\n\ndf_ecoli = df[df[\"Component_Name\"] == \"Escherichia coli\"]", "_____no_output_____" ], [ "df_ecoli.head()", "_____no_output_____" ], [ "# Validating against the original report\n\nresult = df_ecoli.loc[(df_ecoli.Town == \"Milford\") & (df_ecoli.Date_Collected == pd.to_datetime(\"2017-11-21 00:00:00-05:00\"))][\"Actual_Result\"]\nresult", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport statsmodels.formula.api as sm\n\n%matplotlib inline ", "_____no_output_____" ], [ "diab = pd.read_csv(\"../data/diabetes.csv\")\nprint(\"\"\"\n# Variables are\n# subject: subject ID number\n# age: age diagnosed with diabetes\n# acidity: a measure of acidity called base deficit\n# y: natural log of serum C-peptide concentration\n# Original source is Sockett et al. (1987)\n# mentioned in Hastie and Tibshirani's book \n# \"Generalized Additive Models\".\n\"\"\"\n)", "\n# Variables are\n# subject: subject ID number\n# age: age diagnosed with diabetes\n# acidity: a measure of acidity called base deficit\n# y: natural log of serum C-peptide concentration\n# Original source is Sockett et al. (1987)\n# mentioned in Hastie and Tibshirani's book \n# \"Generalized Additive Models\".\n\n" ], [ "xpred = pd.DataFrame({\"age\":np.arange(0,16.1,0.1)})", "_____no_output_____" ], [ "ax0 = diab.plot.scatter(x='age',y='y',c='Red',title=\"Diabetes data\")\n_ = ax0.set_xlabel(\"Age at Diagnosis\")\n_ = ax0.set_ylabel(\"Log C-Peptide Concentration\")", "_____no_output_____" ] ], [ [ "## Linear Regression", "_____no_output_____" ] ], [ [ "model1 = sm.ols('y~age',data=diab)\nfit1_lm = model1.fit()\npred1 = fit1_lm.predict(xpred)\nprediction_output = fit1_lm.get_prediction(xpred).summary_frame()", "_____no_output_____" ], [ "ax1 = diab.plot.scatter(x='age',y='y',c='Red',title=\"Diabetes data with least-squares linear fit\")\nax1.set_xlabel(\"Age at Diagnosis\")\nax1.set_ylabel(\"Log C-Peptide Concentration\")\n\n\nax1.plot(xpred.age, prediction_output['mean'],color=\"green\")\n\nax1.plot(xpred.age, prediction_output['mean_ci_lower'], color=\"blue\",linestyle=\"dashed\")\nax1.plot(xpred.age, prediction_output['mean_ci_upper'], color=\"blue\",linestyle=\"dashed\");\n\nax1.plot(xpred.age, prediction_output['obs_ci_lower'], color=\"skyblue\",linestyle=\"dashed\")\nax1.plot(xpred.age, prediction_output['obs_ci_upper'], color=\"skyblue\",linestyle=\"dashed\");", "_____no_output_____" ] ], [ [ "`vander` is for Vandermonde. It's a matrix where the first column is $x^0$, the second is $x^1$, the third is $x^2$ and so on.\n\n` np.vander([6,3,5], 4, increasing=True) = \n array([[ 1, 6, 36, 216], \n [ 1, 3, 9, 27], \n [ 1, 5, 25, 125]])\n` \nIt's therefore similar to sklearn's `polynomial_features`, but because `vander` is numpy it can be used directly in the formula.\n\nSince we have a constant column in the matrix, we put a -1 in the formula to drop the additional constant term statsmodels would otherwise insert\n\nNote that this is **not** an _orthogonal_ polynomial basis. Our estimated coeffecients will be more sensitive to the data than they need to be.", "_____no_output_____" ] ], [ [ "fit2_lm = sm.ols(formula=\"y ~ np.vander(age, 4, increasing=True) -1\",data=diab).fit()\nfit2_lm = sm.ols(formula=\"y ~ age + np.power(age, 2) + np.power(age, 3)\",data=diab).fit()\n\npoly_predictions = fit2_lm.get_prediction(xpred).summary_frame()\npoly_predictions.head()", "_____no_output_____" ], [ "ax2 = diab.plot.scatter(x='age',y='y',c='Red',title=\"Diabetes data with least-squares cubic fit\")\nax2.set_xlabel(\"Age at Diagnosis\")\nax2.set_ylabel(\"Log C-Peptide Concentration\")\n\nax2.plot(xpred.age, poly_predictions['mean'],color=\"green\")\nax2.plot(xpred.age, poly_predictions['mean_ci_lower'], color=\"blue\",linestyle=\"dashed\")\nax2.plot(xpred.age, poly_predictions['mean_ci_upper'], color=\"blue\",linestyle=\"dashed\");\n\n#ax2.plot(xpred.age, poly_predictions['obs_ci_lower'], color=\"skyblue\",linestyle=\"dashed\")\n#ax2.plot(xpred.age, poly_predictions['obs_ci_upper'], color=\"skyblue\",linestyle=\"dashed\");", "_____no_output_____" ] ], [ [ "## Logistic Regression", "_____no_output_____" ] ], [ [ "diab['y_bin'] = 1*(diab['y'] > 4) # multiply by 1 because statsmodels wants 1s and 0s instead of true and false", "_____no_output_____" ], [ "logit_model = sm.logit(\"y_bin ~ age \", data = diab).fit()\nlogit_prediction = logit_model.predict(xpred)", "Optimization terminated successfully.\n Current function value: 0.353988\n Iterations 7\n" ], [ "from scipy.special import expit\nimport re\ndef get_logit_prediction_intervals(model, new_data_df):\n if type(new_data_df) != pd.DataFrame:\n raise TypeError('new_data_df must be a DataFrame')\n \n # transform the raw data according to the formula\n new_data_dict = {}\n for x in model.params.index:\n # only presently supports Intercept, a named column, and polynmoials created via np.vander\n # the trick is finding the correct base column in the raw data\n if x == \"Intercept\":\n new_data_dict[x] = np.ones(new_data_df.shape[0])\n elif x.startswith(\"np.vander(\"):\n try:\n will = re.match(r\"np.vander\\((.*), ?(.*)\\)\\[(.*)\\]\", x)\n column, power, index = will.groups()\n except e:\n raise ValueError(\"Couldn't parse formula-derived feature {}\".format(x))\n new_data_dict[x] = np.vander(new_data_df.loc[:,column], int(power))[:,int(index)]\n else:\n new_data_dict[x] = new_data_df.loc[:,x]\n new_data = pd.DataFrame(new_data_dict)\n \n variance_mat = model.cov_params()\n standard_devs = np.sqrt(np.sum(new_data.dot(variance_mat) * new_data, axis=1))\n \n linear_predictions = new_data.dot(model.params)\n output = pd.DataFrame({\"lower\": expit(linear_predictions - 1.96*standard_devs),\n \"predicted\": expit(linear_predictions),\n \"upper\": expit(linear_predictions + 1.96*standard_devs)\n })\n return output\n\nlogit_prediction_intervals = get_logit_prediction_intervals(logit_model, xpred)\nlogit_prediction_intervals", "_____no_output_____" ], [ "ax = diab.plot.scatter(x='age',y='y_bin',c='Red',title=\"Diabetes data with least-squares cubic fit\")\nax.set_xlabel(\"Age at Diagnosis\")\nax.set_ylabel(\"Log C-Peptide Concentration\")\n\nax.plot(xpred.age, logit_prediction_intervals[\"predicted\"],color=\"green\")\nax.plot(xpred.age, logit_prediction_intervals[\"lower\"], color=\"blue\",linestyle=\"dashed\")\nax.plot(xpred.age, logit_prediction_intervals[\"upper\"], color=\"blue\",linestyle=\"dashed\");\nplt.show()", "_____no_output_____" ], [ "logit_poly_model = sm.logit(\"y_bin ~ np.vander(age, 4) - 1\", data = diab).fit()\nlogit_poly_prediction = logit_poly_model.predict(xpred)", "Optimization terminated successfully.\n Current function value: 0.194005\n Iterations 10\n" ], [ "ax = diab.plot.scatter(x='age',y='y_bin',c='Red',title=\"Diabetes data with least-squares cubic fit\")\nax.set_xlabel(\"Age at Diagnosis\")\nax.set_ylabel(\"Log C-Peptide Concentration\")\n\nlogit_poly_prediction_intervals = get_logit_prediction_intervals(logit_poly_model, xpred)\n\nax.plot(xpred.age, logit_poly_prediction_intervals[\"predicted\"],color=\"green\")\nax.plot(xpred.age, logit_poly_prediction_intervals[\"lower\"], color=\"blue\",linestyle=\"dashed\")\nax.plot(xpred.age, logit_poly_prediction_intervals[\"upper\"], color=\"blue\",linestyle=\"dashed\");\nplt.show()", "_____no_output_____" ] ], [ [ "## Lo(w)ess", "_____no_output_____" ] ], [ [ "from statsmodels.nonparametric.smoothers_lowess import lowess as lowess\n\nlowess_models = {}\nfor cur_frac in [.15,.25,.7, 1]:\n lowess_models[cur_frac] = lowess(diab['y'],diab['age'],frac=cur_frac)", "_____no_output_____" ] ], [ [ "**Note** Python's lowess implementation does not have any tool to predict on new data; it only returns the fitted function's value at the training points. We're making up for that by drawing a straight line between consecutive fitted values. (There are other more sophisticated interpolation techniques, but the ideal approach would be to predict on new points using lowess itself. This is a limitation of the Python implementation, not lowess itself)", "_____no_output_____" ] ], [ [ "from scipy.interpolate import interp1d\nfor cur_frac, cur_model in lowess_models.items():\n ax = diab.plot.scatter(x='age',y='y',c='Red',title=\"Lowess Fit, Fraction = {}\".format(cur_frac))\n ax.set_xlabel(\"Age at Diagnosis\")\n ax.set_ylabel(\"Log C-Peptide Concentration\")\n lowess_interpolation = interp1d(cur_model[:,0], cur_model[:,1], bounds_error=False)\n ax.plot(xpred, lowess_interpolation(xpred), color=\"Blue\")\n plt.show()", "_____no_output_____" ], [ "ax = diab.plot.scatter(x='age',y='y',c='Red',title=\"Large variance, low bias smoother\")\nax.set_xlabel(\"Age at Diagnosis\")\nax.set_ylabel(\"Log C-Peptide Concentration\")\nlowess_interpolation = interp1d(lowess_models[.15][:,0], lowess_models[.15][:,1], bounds_error=False)\nax.plot(xpred, lowess_interpolation(xpred), color=\"lightgreen\")\nplt.show()\n\nax = diab.plot.scatter(x='age',y='y',c='Red',title=\"Low variance, large bias smoother\")\nax.set_xlabel(\"Age at Diagnosis\")\nax.set_ylabel(\"Log C-Peptide Concentration\")\nlowess_interpolation = interp1d(lowess_models[1][:,0], lowess_models[1][:,1], bounds_error=False)\nax.plot(xpred, lowess_interpolation(xpred), color=\"lightgreen\")\nplt.show()", "_____no_output_____" ] ], [ [ "## Splines (via knots)", "_____no_output_____" ], [ "Define a Relu/Truncated cubic function", "_____no_output_____" ] ], [ [ "def h(x, knot, exponent):\n output = np.power(x-knot, exponent)\n output[x<=knot] = 0\n \n return output", "_____no_output_____" ] ], [ [ "Transforming the x values [0,10] with a knot at 4, power 1", "_____no_output_____" ] ], [ [ "xvals = np.arange(0,10.1,0.1)\n\nplt.plot(xvals, h(xvals,4,1), color=\"red\")\nplt.title(\"Truncated linear basis function with knot at x=4\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$(x-4)_+$\") #note the use of TeX in the label\nplt.show()", "_____no_output_____" ] ], [ [ "Transforming the x values [0,10] with a knot at 4, power 3", "_____no_output_____" ] ], [ [ "plt.plot(xvals,h(xvals,4,3),color=\"red\")\nplt.title(\"Truncated cubic basis function with knot at x=4\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$(x-4)_+^3$\")\nplt.show()", "_____no_output_____" ] ], [ [ "The sum of three RELUs with different knots and different coeffecients", "_____no_output_____" ] ], [ [ "plt.plot(xvals, 3*h(xvals,2,1) - 4*h(xvals,5,1) + 0.5*h(xvals,8,1), color=\"red\")\nplt.title(\"Piecewise linear spline with knots at x=2, 5, and 8\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.show()", "_____no_output_____" ] ], [ [ "Above, but with a starting slope and intercept", "_____no_output_____" ] ], [ [ "plt.plot(xvals, 2 + xvals + 3*h(xvals,2,1) - 4*h(xvals,5,1) + 0.5*h(xvals,8,1), color=\"red\")\nplt.title(\"Piecewise linear spline with knots at x=2, 5, and 8\\n plus a starting slope and intercept\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.show()", "_____no_output_____" ] ], [ [ "Using OLS, we can find optimal coeffecients for RELUs with pre-specified knots, just like we can find optimal coeffecients for $x^2$ and $x^3$", "_____no_output_____" ] ], [ [ "# generate some fake data to fit\n\nx = np.arange(0.1,10,9.9/100) \nfrom scipy.stats import norm\ny = norm.ppf(x/10) + np.random.normal(0,0.4,100)", "_____no_output_____" ], [ "fitted_spline_model = sm.ols('y~x+h(x,2,1)+h(x,5,1)+h(x,8,1)',data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"3 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", linewidth=2, label=\"Spline with knots at 2,5,8\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "More knots", "_____no_output_____" ] ], [ [ "fitted_spline_model = sm.ols('y~x+h(x,1,1)+h(x,2,1)+h(x,3.5,1)+h(x,5,1)+h(x,6.5,1)+h(x,8,1)',data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"6 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Linear Spline with knots at\\n1, 2, 3.5, 5, 6.5, 8\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "More knots", "_____no_output_____" ] ], [ [ "fitted_spline_model = sm.ols('y~x+h(x,1,1)+h(x,2,1)+h(x,3,1)+h(x,4,1)+h(x,5,1)+h(x,6,1)+h(x,7,1)+h(x,8,1)+h(x,9,1)',\n data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"9 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Linear Spline with 9 knots\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "Using code to write out the formula this time", "_____no_output_____" ] ], [ [ "n_knots = 25\ncomponents = ['h(x,{},1)'.format(x) for x in np.linspace(0,10,n_knots)]\nformula = ' + '.join(components)\nfinal_formula = 'y ~ x + ' + formula\n\nfinal_formula", "_____no_output_____" ], [ "fitted_spline_model = sm.ols(final_formula,data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"25 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Linear Spline with 25 knots\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "Cubic splines, instead of linear. Still using code to write the formula", "_____no_output_____" ] ], [ [ "components = ['h(x,{},3)'.format(x) for x in [2,5,8]]\nformula = ' + '.join(components)\nfinal_formula = 'y~x + np.power(x,2) + np.power(x,3) + ' + formula\n\nfitted_spline_model = sm.ols(final_formula,data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"3 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Cubic Spline with 3 knots\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ], [ "components = ['h(x,{},3)'.format(x) for x in [1,2,3.5,5,6.5,8]]\nformula = ' + '.join(components)\nfinal_formula = 'y~x + np.power(x,2) + np.power(x,3) + ' + formula\n\nfitted_spline_model = sm.ols(final_formula,data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"6 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Cubic Spline with 6 knots\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ], [ "n_knots = 9\ncomponents = ['h(x,{},3)'.format(x) for x in np.linspace(0,10,n_knots)]\nformula = ' + '.join(components)\nfinal_formula = 'y~x + np.power(x,2) + np.power(x,3) + ' + formula\n\nfitted_spline_model = sm.ols(final_formula,data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"9 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Cubic Spline with 9 knots\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ], [ "n_knots = 25\ncomponents = ['h(x,{},3)'.format(x) for x in np.linspace(0,10,n_knots)]\nformula = ' + '.join(components)\nfinal_formula = 'y~x + np.power(x,2) + np.power(x,3) + ' + formula\n\nfitted_spline_model = sm.ols(final_formula,data={'x':x,'y':y}).fit()\n\nplt.scatter(x,y,facecolors='none', edgecolors='black')\nplt.title(\"25 knots\")\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$y$\")\nplt.plot(x, fitted_spline_model.predict(),color=\"darkblue\", label=\"Cubic Spline with 25 knots\")\nplt.plot(x, norm.ppf(x/10), color=\"red\", label=\"Truth\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "## Smoothing splines", "_____no_output_____" ] ], [ [ "from scipy.interpolate import UnivariateSpline", "_____no_output_____" ] ], [ [ "This method won't allow tied values, and wants its values in sorted order. Add a tiny bit of noise to the x values and sort them.", "_____no_output_____" ] ], [ [ "diab['noisy_age'] = diab['age'] + np.random.normal(0,.001, len(diab))\nsorted_noisy_diab = diab.sort_values([\"noisy_age\"])", "_____no_output_____" ], [ "smoothing_spline_dict = {}\nfor cur_smoothing in [0, 5, 10, 20, np.inf]:\n \n cur_spline = UnivariateSpline(sorted_noisy_diab['noisy_age'],sorted_noisy_diab['y'], s=cur_smoothing)\n ax = diab.plot.scatter(x='age',y='y',c='Red',title=\"Smoothing Spline with s={}\".format(cur_smoothing))\n ax.plot(xpred, cur_spline(xpred))\n # the jaggedness in the original came from plot's implicit linear interpolation\n# lowess_interpolation = interp1d(diab['age'], cur_spline(diab['age']), bounds_error=False)\n# ax.plot(xpred, lowess_interpolation(xpred), color=\"Blue\")\n ax.set_ylim(2,7)", "_____no_output_____" ] ], [ [ "Even when we request infinite smoothness, we don't end up with a horizontal line (no derivatives at all)- we end up with a single cubic curve. That's because we chose a cubic spline.\n\nIn general, this implementation's smoothness regularization penalizes any derivatives _beyond_ what our basic spline needs. So with k=3, as above, the penalty is on the 4th derivative, and with k=2 the penalty is on the 3rd derivative and the \"infinitely smooth\" result will be a parabola. (i.e. a curve with 3rd derivative=0 everywhere)", "_____no_output_____" ], [ "Selecting smoothing level by CV", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import KFold\nfrom sklearn.metrics import r2_score\n\ncandidate_smoothings = [0, 5, 10, 20, 200]\n\nkf = KFold(n_splits=5, random_state=47, shuffle=True)\nscores = np.zeros((5,len(candidate_smoothings)))\n\nfor i, (train_index, test_index) in enumerate(kf.split(sorted_noisy_diab)):\n train_df = sorted_noisy_diab.iloc[train_index,:]\n test_df = sorted_noisy_diab.iloc[test_index,:]\n for j,cur_smoothing in enumerate(candidate_smoothings):\n cur_model = UnivariateSpline(train_df['noisy_age'],train_df['y'], s=cur_smoothing)\n \n scores[i,j] = r2_score(test_df['y'], cur_model(test_df['noisy_age']))\n \nnp.mean(scores, axis=0)", "_____no_output_____" ], [ "best_s = candidate_smoothings[np.argmax(np.mean(scores, axis=0))]\n\nax = diab.plot.scatter(x='age',y='y',c='Red',title=\"smoothing spline with s={}, chosen by cross-validation\".format(best_s))\nbest_model = UnivariateSpline(sorted_noisy_diab['noisy_age'],sorted_noisy_diab['y'], s=best_s)\nax.plot(xpred, best_model(xpred), color=\"darkgreen\")\nplt.show()", "_____no_output_____" ], [ "#We will now work with a new dataset, called GAGurine.\n#The dataset description (from the R package MASS) is below:\nprint(\"\"\"\n# Data were collected on the concentration of a chemical GAG \n# in the urine of 314 children aged from zero to seventeen years. \n# The aim of the study was to produce a chart to help a paediatrican\n# to assess if a child's GAG concentration is ‘normal’.\n\n# The variables are:\n# Age: age of child in years.\n# GAG: concentration of GAG (the units have been lost).\n\"\"\")", "\n# Data were collected on the concentration of a chemical GAG \n# in the urine of 314 children aged from zero to seventeen years. \n# The aim of the study was to produce a chart to help a paediatrican\n# to assess if a child's GAG concentration is ‘normal’.\n\n# The variables are:\n# Age: age of child in years.\n# GAG: concentration of GAG (the units have been lost).\n\n" ], [ "GAGurine = pd.read_csv(\"../data/GAGurine.csv\")\nGAGurine['Age'] = GAGurine['Age']+np.random.normal(0,0.001, len(GAGurine))\nGAGurine = GAGurine.sort_values(['Age'])\nax = GAGurine.plot.scatter(x='Age',y='GAG',c='black',title=\"GAG in urine of children\")\nax.set_xlabel(\"Age\")\nax.set_ylabel(\"GAG\")\nplt.show()", "_____no_output_____" ] ], [ [ "Get quartiles", "_____no_output_____" ] ], [ [ "quarts = GAGurine['Age'].quantile([0.25, 0.5, 0.75]).values.reshape(-1)", "_____no_output_____" ] ], [ [ "Build a Bspline model. Call `splrep` (spline representation) to find the knots and coeffecients that smooth the given data, then call BSpline to build something that can predict on given values.", "_____no_output_____" ] ], [ [ "from scipy.interpolate import splrep\nfrom scipy.interpolate import BSpline\n\nt,c,k = splrep(GAGurine['Age'].values, GAGurine['GAG'].values, t=quarts)\nb_spline_model = BSpline(t,c,k)\nb_spline_model(7)", "_____no_output_____" ] ], [ [ "`LSQUnivariateSpline` fits splines to data, using user-specified knots", "_____no_output_____" ] ], [ [ "from scipy.interpolate import LSQUnivariateSpline\n\nnatural_spline_model = LSQUnivariateSpline(GAGurine['Age'].values, GAGurine['GAG'].values, quarts)", "_____no_output_____" ], [ "ax = GAGurine.plot.scatter(x='Age',y='GAG',c='grey',title=\"GAG in urine of children\")\nax.plot(GAGurine['Age'], b_spline_model(GAGurine['Age']), label=\"B-spline, knots at quartiles\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "## GAMs", "_____no_output_____" ] ], [ [ "kyphosis = pd.read_csv(\"../data/kyphosis.csv\")\nkyphosis[\"outcome\"] = 1*(kyphosis[\"Kyphosis\"] == \"present\")\nkyphosis.describe()", "_____no_output_____" ], [ "from pygam import LogisticGAM, s\n\n\nX = kyphosis[[\"Age\",\"Number\",\"Start\"]]\ny = kyphosis[\"outcome\"]\nkyph_gam = LogisticGAM(s(0)+s(1)+s(2)).fit(X,y)", "_____no_output_____" ] ], [ [ "GAMs provide plots of the effect of increasing each variable (conditional on / adjusted for the other variables)", "_____no_output_____" ] ], [ [ "res = kyph_gam.deviance_residuals(X,y)\nfor i, term in enumerate(kyph_gam.terms):\n if term.isintercept:\n continue\n\n XX = kyph_gam.generate_X_grid(term=i)\n pdep, confi = kyph_gam.partial_dependence(term=i, X=XX, width=0.95)\n pdep2, _ = kyph_gam.partial_dependence(term=i, X=X, width=0.95)\n plt.figure()\n plt.scatter(X.iloc[:,term.feature], pdep2 + res)\n plt.plot(XX[:, term.feature], pdep)\n plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n plt.title(X.columns.values[term.feature])\n plt.show()", "_____no_output_____" ] ], [ [ "AIC is a measure of model quality, estimating performance on a test set (without actually needing a test set). It can be used to compare and select two models.", "_____no_output_____" ] ], [ [ "kyph_gam.summary()", "LogisticGAM \n=============================================== ==========================================================\nDistribution: BinomialDist Effective DoF: 14.2822\nLink Function: LogitLink Log Likelihood: -18.4906\nNumber of Samples: 81 AIC: 65.5456\n AICc: 73.2352\n UBRE: 2.9503\n Scale: 1.0\n Pseudo R-Squared: 0.5557\n==========================================================================================================\nFeature Function Lambda Rank EDoF P > x Sig. Code \n================================= ==================== ============ ============ ============ ============\ns(0) [0.6] 20 7.5 5.57e-01 \ns(1) [0.6] 20 4.9 1.15e-01 \ns(2) [0.6] 20 1.9 1.01e-01 \nintercept 1 0.0 8.17e-03 ** \n==========================================================================================================\nSignificance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nWARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n which can cause p-values to appear significant when they are not.\n\nWARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n are typically lower than they should be, meaning that the tests reject the null too readily.\n" ], [ "X = kyphosis[[\"Age\",\"Number\",\"Start\"]]\ny = kyphosis[\"outcome\"]\nsmall_kyph_gam = LogisticGAM(s(0)+s(2)).fit(X,y)\n\n\nres = small_kyph_gam.deviance_residuals(X,y)\nfor i, term in enumerate(small_kyph_gam.terms):\n if term.isintercept:\n continue\n\n XX = small_kyph_gam.generate_X_grid(term=i)\n pdep, confi = small_kyph_gam.partial_dependence(term=i, X=XX, width=0.95)\n pdep2, _ = small_kyph_gam.partial_dependence(term=i, X=X, width=0.95)\n plt.figure()\n plt.scatter(X.iloc[:,term.feature], pdep2 + res)\n plt.plot(XX[:, term.feature], pdep)\n plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n plt.title(X.columns.values[term.feature])\n plt.show()", "_____no_output_____" ] ], [ [ "The original model's AIC was lower, so we prefer that model- it is expected to do better on out-of-sample data.", "_____no_output_____" ] ], [ [ "small_kyph_gam.summary()", "LogisticGAM \n=============================================== ==========================================================\nDistribution: BinomialDist Effective DoF: 10.478\nLink Function: LogitLink Log Likelihood: -23.3388\nNumber of Samples: 81 AIC: 67.6337\n AICc: 71.8141\n UBRE: 2.9385\n Scale: 1.0\n Pseudo R-Squared: 0.4392\n==========================================================================================================\nFeature Function Lambda Rank EDoF P > x Sig. Code \n================================= ==================== ============ ============ ============ ============\ns(0) [0.6] 20 6.8 5.27e-01 \ns(2) [0.6] 20 3.7 6.86e-02 . \nintercept 1 0.0 6.57e-03 ** \n==========================================================================================================\nSignificance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nWARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n which can cause p-values to appear significant when they are not.\n\nWARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n are typically lower than they should be, meaning that the tests reject the null too readily.\n" ] ] ]

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[ [ [ "# MATH 4100: Temporal data analysis and applications to stock analysis\n*Curtis Miller*\n\n## Introduction\n\nThis is a lecture for [MATH 4100/CS 5160: Introduction to Data Science](http://datasciencecourse.net/), offered at the University of Utah, introducing time series data analysis applied to finance.\n\nAdvanced mathematics and statistics have been present in finance for some time. Prior to the 1980s, banking and finance were well-known for being \"boring\"; investment banking was distinct from commercial banking and the primary role of the industry was handling \"simple\" (at least in comparison to today) financial instruments, such as loans. Deregulation under the Regan administration, coupled with an influx of mathematical talent, transformed the industry from the \"boring\" business of banking to what it is today, and since then, finance has joined the other sciences as a motivation for mathematical research and advancement. For example one of the biggest recent achievements of mathematics was the derivation of the [Black-Scholes formula](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model), which facilitated the pricing of stock options (a contract giving the holder the right to purchase or sell a stock at a particular price to the issuer of the option). That said, [bad statistical models, including the Black-Scholes formula, hold part of the blame for the 2008 financial crisis](https://www.theguardian.com/science/2012/feb/12/black-scholes-equation-credit-crunch).\n\nIn recent years, computer science has joined advanced mathematics in revolutionizing finance and **trading**, the practice of buying and selling of financial assets for the purpose of making a profit. In recent years, trading has become dominated by computers; algorithms are responsible for making rapid split-second trading decisions faster than humans could make (so rapidly, [the speed at which light travels is a limitation when designing systems](http://www.nature.com/news/physics-in-finance-trading-at-the-speed-of-light-1.16872)). Additionally, [machine learning and data mining techniques are growing in popularity](http://www.ft.com/cms/s/0/9278d1b6-1e02-11e6-b286-cddde55ca122.html#axzz4G8daZxcl) in the financial sector, and likely will continue to do so. For example, **high-frequency trading (HFT)** is a branch of algorithmic trading where computers make thousands of trades in short periods of time, engaging in complex strategies such as statistical arbitrage and market making. While algorithms may outperform humans, the technology is still new and playing an increasing role in a famously turbulent, high-stakes arena. HFT was responsible for phenomena such as the [2010 flash crash](https://en.wikipedia.org/wiki/2010_Flash_Crash) and a [2013 flash crash](http://money.cnn.com/2013/04/24/investing/twitter-flash-crash/) prompted by a hacked [Associated Press tweet](http://money.cnn.com/2013/04/23/technology/security/ap-twitter-hacked/index.html?iid=EL) about an attack on the White House.\n\nThis lecture, however, will not be about how to crash the stock market with bad mathematical models or trading algorithms. Instead, I intend to provide you with basic tools for handling and analyzing stock market data with Python. We will be using stock data as a first exposure to **time series data**, which is data considered dependent on the time it was observed (other examples of time series include temperature data, demand for energy on a power grid, Internet server load, and many, many others). I will also discuss moving averages, how to construct trading strategies using moving averages, how to formulate exit strategies upon entering a position, and how to evaluate a strategy with backtesting.\n\n**DISCLAIMER: THIS IS NOT FINANCIAL ADVICE!!! Furthermore, I have ZERO experience as a trader (a lot of this knowledge comes from a one-semester course on stock trading I took at Salt Lake Community College)! This is purely introductory knowledge, not enough to make a living trading stocks. People can and do lose money trading stocks, and you do so at your own risk!**\n\n## Preliminaries\n\nI will be using two packages, **quandl** and **pandas_datareader**, which are not installed with [Anaconda](https://www.anaconda.com/) if you are using it. To install these packages, run the following at the appropriate command prompt:\n\n conda install quandl\n conda install pandas-datareader\n\n## Getting and Visualizing Stock Data\n\n### Getting Data from Quandl\n\nBefore we analyze stock data, we need to get it into some workable format. Stock data can be obtained from [Yahoo! Finance](http://finance.yahoo.com), [Google Finance](http://finance.google.com), or a number of other sources. These days I recommend getting data from [Quandl](https://www.quandl.com/), a provider of community-maintained financial and economic data. (Yahoo! Finance used to be the go-to source for good quality stock data, but the API was discontinued in 2017 and reliable data can no longer be obtained: see [this question/answer on StackExchange](https://quant.stackexchange.com/questions/35019/is-yahoo-finance-data-good-or-bad-now) for more details.)\n\nBy default the `get()` function in **quandl** will return a **pandas** `DataFrame` containing the fetched data.", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport quandl\nimport datetime\n\n# We will look at stock prices over the past year, starting at January 1, 2016\nstart = datetime.datetime(2016,1,1)\nend = datetime.date.today()\n\n# Let's get Apple stock data; Apple's ticker symbol is AAPL\n# First argument is the series we want, second is the source (\"yahoo\" for Yahoo! Finance), third is the start date, fourth is the end date\ns = \"AAPL\"\napple = quandl.get(\"WIKI/\" + s, start_date=start, end_date=end)\n\ntype(apple)", "_____no_output_____" ], [ "apple.head()", "_____no_output_____" ] ], [ [ "Let's briefly discuss this. **Open** is the price of the stock at the beginning of the trading day (it need not be the closing price of the previous trading day), **high** is the highest price of the stock on that trading day, **low** the lowest price of the stock on that trading day, and **close** the price of the stock at closing time. **Volume** indicates how many stocks were traded. **Adjusted** prices (such as the adjusted close) is the price of the stock that adjusts the price for corporate actions. While stock prices are considered to be set mostly by traders, **stock splits** (when the company makes each extant stock worth two and halves the price) and **dividends** (payout of company profits per share) also affect the price of a stock and should be accounted for.\n\n### Visualizing Stock Data\n\nNow that we have stock data we would like to visualize it. I first demonstrate how to do so using the **matplotlib** package. Notice that the `apple` `DataFrame` object has a convenience method, `plot()`, which makes creating plots easier.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt # Import matplotlib\n# This line is necessary for the plot to appear in a Jupyter notebook\n%matplotlib inline\n# Control the default size of figures in this Jupyter notebook\n%pylab inline\npylab.rcParams['figure.figsize'] = (15, 9) # Change the size of plots\n\napple[\"Adj. Close\"].plot(grid = True) # Plot the adjusted closing price of AAPL", "_____no_output_____" ] ], [ [ "A linechart is fine, but there are at least four variables involved for each date (open, high, low, and close), and we would like to have some visual way to see all four variables that does not require plotting four separate lines. Financial data is often plotted with a **Japanese candlestick plot**, so named because it was first created by 18th century Japanese rice traders. Such a chart can be created with **matplotlib**, though it requires considerable effort.\n\nI have made a function you are welcome to use to more easily create candlestick charts from **pandas** data frames, and use it to plot our stock data. (Code is based off [this example](http://matplotlib.org/examples/pylab_examples/finance_demo.html), and you can read the documentation for the functions involved [here](http://matplotlib.org/api/finance_api.html).)", "_____no_output_____" ] ], [ [ "from matplotlib.dates import DateFormatter, WeekdayLocator,\\\n DayLocator, MONDAY\nfrom matplotlib.finance import candlestick_ohlc\n\ndef pandas_candlestick_ohlc(dat, stick = \"day\", adj = False, otherseries = None):\n \"\"\"\n :param dat: pandas DataFrame object with datetime64 index, and float columns \"Open\", \"High\", \"Low\", and \"Close\", likely created via DataReader from \"yahoo\"\n :param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include \"day\", \"week\", \"month\", and \"year\", (\"day\" default), and any numeric input indicates the number of trading days included in a period\n :param adj: A boolean indicating whether to use adjusted prices\n :param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines\n \n This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.\n \"\"\"\n mondays = WeekdayLocator(MONDAY) # major ticks on the mondays\n alldays = DayLocator() # minor ticks on the days\n dayFormatter = DateFormatter('%d') # e.g., 12\n \n # Create a new DataFrame which includes OHLC data for each period specified by stick input\n fields = [\"Open\", \"High\", \"Low\", \"Close\"]\n if adj:\n fields = [\"Adj. \" + s for s in fields]\n transdat = dat.loc[:,fields]\n transdat.columns = pd.Index([\"Open\", \"High\", \"Low\", \"Close\"])\n if (type(stick) == str):\n if stick == \"day\":\n plotdat = transdat\n stick = 1 # Used for plotting\n elif stick in [\"week\", \"month\", \"year\"]:\n if stick == \"week\":\n transdat[\"week\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks\n elif stick == \"month\":\n transdat[\"month\"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months\n transdat[\"year\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years\n grouped = transdat.groupby(list(set([\"year\",stick]))) # Group by year and other appropriate variable\n plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n for name, group in grouped:\n plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n \"High\": max(group.High),\n \"Low\": min(group.Low),\n \"Close\": group.iloc[-1,3]},\n index = [group.index[0]]))\n if stick == \"week\": stick = 5\n elif stick == \"month\": stick = 30\n elif stick == \"year\": stick = 365\n \n elif (type(stick) == int and stick >= 1):\n transdat[\"stick\"] = [np.floor(i / stick) for i in range(len(transdat.index))]\n grouped = transdat.groupby(\"stick\")\n plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n for name, group in grouped:\n plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n \"High\": max(group.High),\n \"Low\": min(group.Low),\n \"Close\": group.iloc[-1,3]},\n index = [group.index[0]]))\n \n else:\n raise ValueError('Valid inputs to argument \"stick\" include the strings \"day\", \"week\", \"month\", \"year\", or a positive integer')\n\n\n # Set plot parameters, including the axis object ax used for plotting\n fig, ax = plt.subplots()\n fig.subplots_adjust(bottom=0.2)\n if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):\n weekFormatter = DateFormatter('%b %d') # e.g., Jan 12\n ax.xaxis.set_major_locator(mondays)\n ax.xaxis.set_minor_locator(alldays)\n else:\n weekFormatter = DateFormatter('%b %d, %Y')\n ax.xaxis.set_major_formatter(weekFormatter)\n \n ax.grid(True)\n \n # Create the candelstick chart\n candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat[\"Open\"].tolist(), plotdat[\"High\"].tolist(),\n plotdat[\"Low\"].tolist(), plotdat[\"Close\"].tolist())),\n colorup = \"black\", colordown = \"red\", width = stick * .4)\n \n # Plot other series (such as moving averages) as lines\n if otherseries != None:\n if type(otherseries) != list:\n otherseries = [otherseries]\n dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)\n\n ax.xaxis_date()\n ax.autoscale_view()\n plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n\n plt.show()\n\npandas_candlestick_ohlc(apple, adj=True)", "_____no_output_____" ] ], [ [ "With a candlestick chart, a black candlestick indicates a day where the closing price was higher than the open (a gain), while a red candlestick indicates a day where the open was higher than the close (a loss). The wicks indicate the high and the low, and the body the open and close (hue is used to determine which end of the body is the open and which the close). Candlestick charts are popular in finance and some strategies in [technical analysis](https://en.wikipedia.org/wiki/Technical_analysis) use them to make trading decisions, depending on the shape, color, and position of the candles. I will not cover such strategies today.\n\nWe may wish to plot multiple financial instruments together; we may want to compare stocks, compare them to the market, or look at other securities such as [exchange-traded funds (ETFs)](https://en.wikipedia.org/wiki/Exchange-traded_fund). Later, we will also want to see how to plot a financial instrument against some indicator, like a moving average. For this you would rather use a line chart than a candlestick chart. (How would you plot multiple candlestick charts on top of one another without cluttering the chart?)\n\nBelow, I get stock data for some other tech companies and plot their adjusted close together.", "_____no_output_____" ] ], [ [ "microsoft, google = (quandl.get(\"WIKI/\" + s, start_date=start, end_date=end) for s in [\"MSFT\", \"GOOG\"])\n\n# Below I create a DataFrame consisting of the adjusted closing price of these stocks, first by making a list of these objects and using the join method\nstocks = pd.DataFrame({\"AAPL\": apple[\"Adj. Close\"],\n \"MSFT\": microsoft[\"Adj. Close\"],\n \"GOOG\": google[\"Adj. Close\"]})\n\nstocks.head()", "_____no_output_____" ], [ "stocks.plot(grid = True)", "_____no_output_____" ] ], [ [ "What's wrong with this chart? While absolute price is important (pricy stocks are difficult to purchase, which affects not only their volatility but *your* ability to trade that stock), when trading, we are more concerned about the relative change of an asset rather than its absolute price. Google's stocks are much more expensive than Apple's or Microsoft's, and this difference makes Apple's and Microsoft's stocks appear much less volatile than they truly are (that is, their price appears to not deviate much).\n\nOne solution would be to use two different scales when plotting the data; one scale will be used by Apple and Microsoft stocks, and the other by Google.", "_____no_output_____" ] ], [ [ "stocks.plot(secondary_y = [\"AAPL\", \"MSFT\"], grid = True)", "_____no_output_____" ] ], [ [ "A \"better\" solution, though, would be to plot the information we actually want: the stock's returns. This involves transforming the data into something more useful for our purposes. There are multiple transformations we could apply.\n\nOne transformation would be to consider the stock's return since the beginning of the period of interest. In other words, we plot:\n\n\\begin{equation*}\n\\text{return}_{t,0} = \\frac{\\text{price}_t}{\\text{price}_0}\n\\end{equation*}\n\nThis will require transforming the data in the `stocks` object, which I do next. Notice that I am using a **lambda function**, which allows me to pass a small function defined quickly as a parameter to another function or method (you can read more about lambda functions [here](https://docs.python.org/3/reference/expressions.html#lambda)).", "_____no_output_____" ] ], [ [ "# df.apply(arg) will apply the function arg to each column in df, and return a DataFrame with the result\n# Recall that lambda x is an anonymous function accepting parameter x; in this case, x will be a pandas Series object\nstock_return = stocks.apply(lambda x: x / x[0])\nstock_return.head()", "_____no_output_____" ], [ "stock_return.plot(grid = True).axhline(y = 1, color = \"black\", lw = 2)", "_____no_output_____" ] ], [ [ "This is a much more useful plot. We can now see how profitable each stock was since the beginning of the period. Furthermore, we see that these stocks are highly correlated; they generally move in the same direction, a fact that was difficult to see in the other charts.\n\nAlternatively, we could plot the change of each stock per day. One way to do so would be to plot the percentage increase of a stock when comparing day $t$ to day $t + 1$, with the formula:\n\n\\begin{equation*}\n\\text{growth}_t = \\frac{\\text{price}_{t + 1} - \\text{price}_t}{\\text{price}_t}\n\\end{equation*}\n\nBut change could be thought of differently as:\n\n\\begin{equation*}\n\\text{increase}_t = \\frac{\\text{price}_{t} - \\text{price}_{t-1}}{\\text{price}_t}\n\\end{equation*}\n\nThese formulas are not the same and can lead to differing conclusions, but there is another way to model the growth of a stock: with log differences.\n\n\\begin{equation*}\n\\text{change}_t = \\log(\\text{price}_{t}) - \\log(\\text{price}_{t - 1})\n\\end{equation*}\n\n(Here, $\\log$ is the natural log, and our definition does not depend as strongly on whether we use $\\log(\\text{price}_{t}) - \\log(\\text{price}_{t - 1})$ or $\\log(\\text{price}_{t+1}) - \\log(\\text{price}_{t})$.) The advantage of using log differences is that this difference can be interpreted as the percentage change in a stock but does not depend on the denominator of a fraction. Additionally, log differences have a desirable property: the sum of the log differences can be interpreted as the total change (as a percentage) over the period summed (which is not a property of the other formulations; they will overestimate growth). Log differences also more cleanly correspond to how stock prices are modeled in continuous time.\n\nWe can obtain and plot the log differences of the data in `stocks` as follows:", "_____no_output_____" ] ], [ [ "# Let's use NumPy's log function, though math's log function would work just as well\nimport numpy as np\n\nstock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1))) # shift moves dates back by 1.\nstock_change.head()", "_____no_output_____" ], [ "stock_change.plot(grid = True).axhline(y = 0, color = \"black\", lw = 2)", "_____no_output_____" ] ], [ [ "Which transformation do you prefer? Looking at returns since the beginning of the period make the overall trend of the securities in question much more apparent. Changes between days, though, are what more advanced methods actually consider when modelling the behavior of a stock. so they should not be ignored.\n\nWe often want to compare the performance of stocks to the performance of the overall market. [SPY](https://finance.yahoo.com/quote/SPY/), which is the ticker symbol for the SPDR S&P 500 exchange-traded mutual fund (ETF), is a fund that attempts only to imitate the composition of the [S&P 500 stock index](https://finance.yahoo.com/quote/%5EGSPC?p=^GSPC), and thus represents the value in \"the market.\"\n\nSPY data is not available for free from Quandl, so I will get this data from Yahoo! Finance. (I don't have a choice.)\n\nBelow I get data for SPY and compare its performance to the performance of our stocks.", "_____no_output_____" ] ], [ [ "#import pandas_datareader.data as web # Going to get SPY from Yahoo! (I know I said you shouldn't but I didn't have a choice)\n#spyder = web.DataReader(\"SPY\", \"yahoo\", start, end) # Didn't work\n#spyder = web.DataReader(\"SPY\", \"google\", start, end) # Didn't work either\n# If all else fails, read from a file, obtained from here: http://www.nasdaq.com/symbol/spy/historical\nspyderdat = pd.read_csv(\"/home/curtis/Downloads/HistoricalQuotes.csv\") # Obviously specific to my system; set to\n # location on your machine\nspyderdat = pd.DataFrame(spyderdat.loc[:, [\"open\", \"high\", \"low\", \"close\", \"close\"]].iloc[1:].as_matrix(),\n index=pd.DatetimeIndex(spyderdat.iloc[1:, 0]),\n columns=[\"Open\", \"High\", \"Low\", \"Close\", \"Adj Close\"]).sort_index()\n\nspyder = spyderdat.loc[start:end]\n\nstocks = stocks.join(spyder.loc[:, \"Adj Close\"]).rename(columns={\"Adj Close\": \"SPY\"})\nstocks.head()", "_____no_output_____" ], [ "stock_return = stocks.apply(lambda x: x / x[0])\nstock_return.plot(grid = True).axhline(y = 1, color = \"black\", lw = 2)", "_____no_output_____" ], [ "stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1)))\nstock_change.plot(grid=True).axhline(y = 0, color = \"black\", lw = 2)", "_____no_output_____" ] ], [ [ "## Classical Risk Metrics\n\nFrom what we have so far we can already compute informative metrics for our stocks, which can be considered some measure of risk.\n\nFirst, we will want to **annualize** our returns, thus computing the **annual percentage rate (APR)**. This helps us keep returns on a common time scale.", "_____no_output_____" ] ], [ [ "stock_change_apr = stock_change * 252 * 100 # There are 252 trading days in a year; the 100 converts to percentages\nstock_change_apr.tail()", "_____no_output_____" ] ], [ [ "Some of these numbers look initially like nonsense, but that's okay for now.\n\nThe metrics I want are:\n* The average return\n* Volatility (the standard deviation of returns)\n* $\\alpha$ and $\\beta$\n* The Sharpe ratio\n\nThe first two metrics are largely self-explanatory, but the latter two need explaining.\n\nFirst, the **risk-free rate**, which I denote by $r_{RF}$, is the rate of return on a risk-free financial asset. This asset exists only in theory but often yields on low-risk instruments like 3-month U.S. Treasury Bills can be viewed as being virtually risk-free and thus their yields can be used to approximate the risk-free rate. I get the data for these instruments below.", "_____no_output_____" ] ], [ [ "tbill = quandl.get(\"FRED/TB3MS\", start_date=start, end_date=end)\ntbill.tail()", "_____no_output_____" ], [ "tbill.plot()", "_____no_output_____" ], [ "rrf = tbill.iloc[-1, 0] # Get the most recent Treasury Bill rate\nrrf", "_____no_output_____" ] ], [ [ "Now, a **linear regression model** is a model of the following form:\n\n$$y_i = \\alpha + \\beta x_i + \\epsilon_i$$\n\n$\\epsilon_i$ is an error process. Another way to think of this process model is:\n\n$$\\hat{y}_i = \\alpha + \\beta x_i$$\n\n$\\hat{y}_i$ is the **predicted value** of $y_i$ given $x_i$. In other words, a linear regression model tells you how $x_i$ and $y_i$ are related, and how values of $x_i$ can be used to predict values of $y_i$. $\\alpha$ is the **intercept** of the model and $\\beta$ is the **slope**. In particular, $\\alpha$ would be the predicted value of $y$ if $x$ were zero, and $\\beta$ gives how much $y$ changes when $x$ changes by one unit.\n\nThere is an easy way to compute $\\alpha$ and $\\beta$ given the sample means $\\bar{x}$ and $\\bar{y}$ and sample standard deviations $s_x$ and $s_y$ and the correlation between $x$ and $y$, denoted with $r$:\n\n$$\\beta = r \\frac{s_y}{s_x}$$\n$$\\alpha = \\bar{y} - \\beta \\bar{x}$$\n\nIn finance, we use $\\alpha$ and $\\beta$ like so:\n\n$$R_t - r_{RF} = \\alpha + \\beta (R_{Mt} - r_{RF}) + \\epsilon_t$$\n\n$R_t$ is the return of a financial asset (a stock) and $R_t - r_{RF}$ is the **excess return**, or return exceeding the risk-free rate of return. $R_{Mt}$ is the return of the *market* at time $t$. Then $\\alpha$ and $\\beta can be interpreted like so:\n\n* $\\alpha$ is average excess return over the market.\n* $\\beta$ is how much a stock moves in relation to the market. If $\\beta > 0$ then the stock generally moves in the same direction as the market, while when $\\beta < 0$ the stock generally moves in the opposite direction. If $|\\beta| > 1$ the stock moves strongly in response to the market $|\\beta| < 1$ the stock is less responsive to the market.\n\nBelow I get a **pandas** `Series` that contains how much each stock is correlated with SPY (our approximation of the market).", "_____no_output_____" ] ], [ [ "smcorr = stock_change_apr.drop(\"SPY\", 1).corrwith(stock_change_apr.SPY) # Since RRF is constant it doesn't change the\n # correlation so we can ignore it in our\n # calculation\nsmcorr", "_____no_output_____" ] ], [ [ "Then I compute $\\alpha$ and $\\beta$.", "_____no_output_____" ] ], [ [ "sy = stock_change_apr.drop(\"SPY\", 1).std()\nsx = stock_change_apr.SPY.std()\nsy", "_____no_output_____" ], [ "sx", "_____no_output_____" ], [ "ybar = stock_change_apr.drop(\"SPY\", 1).mean() - rrf\nxbar = stock_change_apr.SPY.mean() - rrf\nybar", "_____no_output_____" ], [ "xbar", "_____no_output_____" ], [ "beta = smcorr * sy / sx\nalpha = ybar - beta * xbar\nbeta", "_____no_output_____" ], [ "alpha", "_____no_output_____" ] ], [ [ "The **Sharpe ratio** is another popular risk metric, defined below:\n\n$$\\text{Sharpe ratio} = \\frac{\\bar{R_t} - r_{RF}}{s}$$\n\nHere $s$ is the volatility of the stock. We want the sharpe ratio to be large. A large Sharpe ratio indicates that the stock's excess returns are large relative to the stock's volatilitly. Additionally, the Sharpe ratio is tied to a statistical test (the $t$-test) to determine if a stock earns more on average than the risk-free rate; the larger this ratio, the more likely this is to be the case.\n\nYour challenge now is to compute the Sharpe ratio for each stock listed here, and interpret it. Which stock seems to be the better investment according to the Sharpe ratio?", "_____no_output_____" ] ], [ [ "# Your code here", "_____no_output_____" ] ], [ [ "## Moving Averages\n\nCharts are very useful. In fact, some traders base their strategies almost entirely off charts (these are the \"technicians\", since trading strategies based off finding patterns in charts is a part of the trading doctrine known as **technical analysis**). Let's now consider how we can find trends in stocks.\n\nA **$q$-day moving average** is, for a series $x_t$ and a point in time $t$, the average of the past $q$ days: that is, if $MA^q_t$ denotes a moving average process, then:\n\n\\begin{equation*}\nMA^q_t = \\frac{1}{q} \\sum_{i = 0}^{q-1} x_{t - i}\n\\end{equation*}\n\nMoving averages smooth a series and helps identify trends. The larger $q$ is, the less responsive a moving average process is to short-term fluctuations in the series $x_t$. The idea is that moving average processes help identify trends from \"noise\". **Fast** moving averages have smaller $q$ and more closely follow the stock, while **slow** moving averages have larger $q$, resulting in them responding less to the fluctuations of the stock and being more stable.\n\n**pandas** provides functionality for easily computing moving averages. I demonstrate its use by creating a 20-day (one month) moving average for the Apple data, and plotting it alongside the stock.", "_____no_output_____" ] ], [ [ "apple[\"20d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 20, center = False).mean(), 2)\npandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = \"20d\", adj=True)", "_____no_output_____" ] ], [ [ "Notice how late the rolling average begins. It cannot be computed until 20 days have passed. This limitation becomes more severe for longer moving averages. Because I would like to be able to compute 200-day moving averages, I'm going to extend out how much AAPL data we have. That said, we will still largely focus on 2016.", "_____no_output_____" ] ], [ [ "start = datetime.datetime(2010,1,1)\napple = quandl.get(\"WIKI/AAPL\", start_date=start, end_date=end)\napple[\"20d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 20, center = False).mean(), 2)\n\npandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = \"20d\", adj=True)", "_____no_output_____" ] ], [ [ "You will notice that a moving average is much smoother than the actua stock data. Additionally, it's a stubborn indicator; a stock needs to be above or below the moving average line in order for the line to change direction. Thus, crossing a moving average signals a possible change in trend, and should draw attention.\n\nTraders are usually interested in multiple moving averages, such as the 20-day, 50-day, and 200-day moving averages. It's easy to examine multiple moving averages at once.", "_____no_output_____" ] ], [ [ "apple[\"50d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 50, center = False).mean(), 2)\napple[\"200d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 200, center = False).mean(), 2)\n\npandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = [\"20d\", \"50d\", \"200d\"], adj=True)", "_____no_output_____" ] ], [ [ "The 20-day moving average is the most sensitive to local changes, and the 200-day moving average the least. Here, the 200-day moving average indicates an overall **bearish** trend: the stock is trending downward over time. The 20-day moving average is at times bearish and at other times **bullish**, where a positive swing is expected. You can also see that the crossing of moving average lines indicate changes in trend. These crossings are what we can use as **trading signals**, or indications that a financial security is changind direction and a profitable trade might be made.\n\n## Trading Strategy\n\nOur concern now is to design and evaluate trading strategies.\n\nAny trader must have a set of rules that determine how much of her money she is willing to bet on any single trade. For example, a trader may decide that under no circumstances will she risk more than 10% of her portfolio on a trade. Additionally, in any trade, a trader must have an **exit strategy**, a set of conditions determining when she will exit the position, for either profit or loss. A trader may set a **target**, which is the minimum profit that will induce the trader to leave the position. Likewise, a trader may have a maximum loss she is willing to tolerate; if potential losses go beyond this amount, the trader will exit the position in order to prevent any further loss. We will suppose that the amount of money in the portfolio involved in any particular trade is a fixed proportion; 10% seems like a good number.\n\nHere, I will be demonstrating a [moving average crossover strategy](http://www.investopedia.com/university/movingaverage/movingaverages4.asp). We will use two moving averages, one we consider \"fast\", and the other \"slow\". The strategy is:\n\n* Trade the asset when the fast moving average crosses over the slow moving average.\n* Exit the trade when the fast moving average crosses over the slow moving average again.\n\nA trade will be prompted when the fast moving average crosses from below to above the slow moving average, and the trade will be exited when the fast moving average crosses below the slow moving average later.\n\nWe now have a complete strategy. But before we decide we want to use it, we should try to evaluate the quality of the strategy first. The usual means for doing so is **backtesting**, which is looking at how profitable the strategy is on historical data. For example, looking at the above chart's performance on Apple stock, if the 20-day moving average is the fast moving average and the 50-day moving average the slow, this strategy does not appear to be very profitable, at least not if you are always taking long positions.\n\nLet's see if we can automate the backtesting task. We first identify when the 20-day average is below the 50-day average, and vice versa.", "_____no_output_____" ] ], [ [ "apple['20d-50d'] = apple['20d'] - apple['50d']\napple.tail()", "_____no_output_____" ] ], [ [ "We will refer to the sign of this difference as the **regime**; that is, if the fast moving average is above the slow moving average, this is a bullish regime (the bulls rule), and a bearish regime (the bears rule) holds when the fast moving average is below the slow moving average. I identify regimes with the following code.", "_____no_output_____" ] ], [ [ "# np.where() is a vectorized if-else function, where a condition is checked for each component of a vector, and the first argument passed is used when the condition holds, and the other passed if it does not\napple[\"Regime\"] = np.where(apple['20d-50d'] > 0, 1, 0)\n# We have 1's for bullish regimes and 0's for everything else. Below I replace bearish regimes's values with -1, and to maintain the rest of the vector, the second argument is apple[\"Regime\"]\napple[\"Regime\"] = np.where(apple['20d-50d'] < 0, -1, apple[\"Regime\"])\napple.loc['2016-01-04':'2016-12-31',\"Regime\"].plot(ylim = (-2,2)).axhline(y = 0, color = \"black\", lw = 2)", "_____no_output_____" ], [ "apple[\"Regime\"].plot(ylim = (-2,2)).axhline(y = 0, color = \"black\", lw = 2)", "_____no_output_____" ], [ "apple[\"Regime\"].value_counts()", "_____no_output_____" ] ], [ [ "The last line above indicates that for 1005 days the market was bearish on Apple, while for 600 days the market was bullish, and it was neutral for 54 days.\n\nTrading signals appear at regime changes. When a bullish regime begins, a buy signal is triggered, and when it ends, a sell signal is triggered. Likewise, when a bearish regime begins, a sell signal is triggered, and when the regime ends, a buy signal is triggered (this is of interest only if you ever will short the stock, or use some derivative like a stock option to bet against the market).\n\nIt's simple to obtain signals. Let $r_t$ indicate the regime at time $t$, and $s_t$ the signal at time $t$. Then:\n\n\\begin{equation*}\ns_t = \\text{sign}(r_t - r_{t - 1})\n\\end{equation*}\n\n$s_t \\in \\{-1, 0, 1\\}$, with $-1$ indicating \"sell\", $1$ indicating \"buy\", and $0$ no action. We can obtain signals like so:", "_____no_output_____" ] ], [ [ "# To ensure that all trades close out, I temporarily change the regime of the last row to 0\nregime_orig = apple.loc[:, \"Regime\"].iloc[-1]\napple.loc[:, \"Regime\"].iloc[-1] = 0\napple[\"Signal\"] = np.sign(apple[\"Regime\"] - apple[\"Regime\"].shift(1))\n# Restore original regime data\napple.loc[:, \"Regime\"].iloc[-1] = regime_orig\napple.tail()", "_____no_output_____" ], [ "apple[\"Signal\"].plot(ylim = (-2, 2))", "_____no_output_____" ], [ "apple[\"Signal\"].value_counts()", "_____no_output_____" ] ], [ [ "We would buy Apple stock 23 times and sell Apple stock 23 times. If we only go long on Apple stock, only 23 trades will be engaged in over the 6-year period, while if we pivot from a long to a short position every time a long position is terminated, we would engage in 23 trades total. (Bear in mind that trading more frequently isn't necessarily good; trades are never free.)\n\nYou may notice that the system as it currently stands isn't very robust, since even a fleeting moment when the fast moving average is above the slow moving average triggers a trade, resulting in trades that end immediately (which is bad if not simply because realistically every trade is accompanied by a fee that can quickly erode earnings). Additionally, every bullish regime immediately transitions into a bearish regime, and if you were constructing trading systems that allow both bullish and bearish bets, this would lead to the end of one trade immediately triggering a new trade that bets on the market in the opposite direction, which again seems finnicky. A better system would require more evidence that the market is moving in some particular direction. But we will not concern ourselves with these details for now.\n\nLet's now try to identify what the prices of the stock is at every buy and every sell.", "_____no_output_____" ] ], [ [ "apple.loc[apple[\"Signal\"] == 1, \"Close\"]", "_____no_output_____" ], [ "apple.loc[apple[\"Signal\"] == -1, \"Close\"]", "_____no_output_____" ], [ "# Create a DataFrame with trades, including the price at the trade and the regime under which the trade is made.\napple_signals = pd.concat([\n pd.DataFrame({\"Price\": apple.loc[apple[\"Signal\"] == 1, \"Adj. Close\"],\n \"Regime\": apple.loc[apple[\"Signal\"] == 1, \"Regime\"],\n \"Signal\": \"Buy\"}),\n pd.DataFrame({\"Price\": apple.loc[apple[\"Signal\"] == -1, \"Adj. Close\"],\n \"Regime\": apple.loc[apple[\"Signal\"] == -1, \"Regime\"],\n \"Signal\": \"Sell\"}),\n ])\napple_signals.sort_index(inplace = True)\napple_signals", "_____no_output_____" ], [ "# Let's see the profitability of long trades\napple_long_profits = pd.DataFrame({\n \"Price\": apple_signals.loc[(apple_signals[\"Signal\"] == \"Buy\") &\n apple_signals[\"Regime\"] == 1, \"Price\"],\n \"Profit\": pd.Series(apple_signals[\"Price\"] - apple_signals[\"Price\"].shift(1)).loc[\n apple_signals.loc[(apple_signals[\"Signal\"].shift(1) == \"Buy\") & (apple_signals[\"Regime\"].shift(1) == 1)].index\n ].tolist(),\n \"End Date\": apple_signals[\"Price\"].loc[\n apple_signals.loc[(apple_signals[\"Signal\"].shift(1) == \"Buy\") & (apple_signals[\"Regime\"].shift(1) == 1)].index\n ].index\n })\napple_long_profits", "_____no_output_____" ] ], [ [ "Let's now create a simulated portfolio of $1,000,000, and see how it would behave, according to the rules we have established. This includes:\n\n* Investing only 10% of the portfolio in any trade\n* Exiting the position if losses exceed 20% of the value of the trade.\n\nWhen simulating, bear in mind that:\n\n* Trades are done in batches of 100 stocks.\n* Our stop-loss rule involves placing an order to sell the stock the moment the price drops below the specified level. Thus we need to check whether the lows during this period ever go low enough to trigger the stop-loss. Realistically, unless we buy a put option, we cannot guarantee that we will sell the stock at the price we set at the stop-loss, but we will use this as the selling price anyway for the sake of simplicity.\n* Every trade is accompanied by a commission to the broker, which should be accounted for. I do not do so here.\n\nHere's how a backtest may look:", "_____no_output_____" ] ], [ [ "# We need to get the low of the price during each trade.\ntradeperiods = pd.DataFrame({\"Start\": apple_long_profits.index,\n \"End\": apple_long_profits[\"End Date\"]})\napple_long_profits[\"Low\"] = tradeperiods.apply(lambda x: min(apple.loc[x[\"Start\"]:x[\"End\"], \"Adj. Low\"]), axis = 1)\napple_long_profits", "_____no_output_____" ], [ "# Now we have all the information needed to simulate this strategy in apple_adj_long_profits\ncash = 1000000\napple_backtest = pd.DataFrame({\"Start Port. Value\": [],\n \"End Port. Value\": [],\n \"End Date\": [],\n \"Shares\": [],\n \"Share Price\": [],\n \"Trade Value\": [],\n \"Profit per Share\": [],\n \"Total Profit\": [],\n \"Stop-Loss Triggered\": []})\nport_value = .1 # Max proportion of portfolio bet on any trade\nbatch = 100 # Number of shares bought per batch\nstoploss = .2 # % of trade loss that would trigger a stoploss\nfor index, row in apple_long_profits.iterrows():\n batches = np.floor(cash * port_value) // np.ceil(batch * row[\"Price\"]) # Maximum number of batches of stocks invested in\n trade_val = batches * batch * row[\"Price\"] # How much money is put on the line with each trade\n if row[\"Low\"] < (1 - stoploss) * row[\"Price\"]: # Account for the stop-loss\n share_profit = np.round((1 - stoploss) * row[\"Price\"], 2)\n stop_trig = True\n else:\n share_profit = row[\"Profit\"]\n stop_trig = False\n profit = share_profit * batches * batch # Compute profits\n # Add a row to the backtest data frame containing the results of the trade\n apple_backtest = apple_backtest.append(pd.DataFrame({\n \"Start Port. Value\": cash,\n \"End Port. Value\": cash + profit,\n \"End Date\": row[\"End Date\"],\n \"Shares\": batch * batches,\n \"Share Price\": row[\"Price\"],\n \"Trade Value\": trade_val,\n \"Profit per Share\": share_profit,\n \"Total Profit\": profit,\n \"Stop-Loss Triggered\": stop_trig\n }, index = [index]))\n cash = max(0, cash + profit)\n\napple_backtest", "_____no_output_____" ], [ "apple_backtest[\"End Port. Value\"].plot()", "_____no_output_____" ] ], [ [ "Our portfolio's value grew by 13% in about six years. Considering that only 10% of the portfolio was ever involved in any single trade, this is not bad performance.\n\nNotice that this strategy never lead to our rule of never allowing losses to exceed 20% of the trade's value being invoked. For the sake of simplicity, we will ignore this rule in backtesting.\n\nA more realistic portfolio would not be betting 10% of its value on only one stock. A more realistic one would consider investing in multiple stocks. Multiple trades may be ongoing at any given time involving multiple companies, and most of the portfolio will be in stocks, not cash. Now that we will be investing in multiple stops and exiting only when moving averages cross (not because of a stop-loss), we will need to change our approach to backtesting. For example, we will be using one **pandas** `DataFrame` to contain all buy and sell orders for all stocks being considered, and our loop above will have to track more information.\n\nI have written functions for creating order data for multiple stocks, and a function for performing the backtesting.", "_____no_output_____" ] ], [ [ "def ma_crossover_orders(stocks, fast, slow):\n \"\"\"\n :param stocks: A list of tuples, the first argument in each tuple being a string containing the ticker symbol of each stock (or however you want the stock represented, so long as it's unique), and the second being a pandas DataFrame containing the stocks, with a \"Close\" column and indexing by date (like the data frames returned by the Yahoo! Finance API)\n :param fast: Integer for the number of days used in the fast moving average\n :param slow: Integer for the number of days used in the slow moving average\n \n :return: pandas DataFrame containing stock orders\n \n This function takes a list of stocks and determines when each stock would be bought or sold depending on a moving average crossover strategy, returning a data frame with information about when the stocks in the portfolio are bought or sold according to the strategy\n \"\"\"\n fast_str = str(fast) + 'd'\n slow_str = str(slow) + 'd'\n ma_diff_str = fast_str + '-' + slow_str\n \n trades = pd.DataFrame({\"Price\": [], \"Regime\": [], \"Signal\": []})\n for s in stocks:\n # Get the moving averages, both fast and slow, along with the difference in the moving averages\n s[1][fast_str] = np.round(s[1][\"Close\"].rolling(window = fast, center = False).mean(), 2)\n s[1][slow_str] = np.round(s[1][\"Close\"].rolling(window = slow, center = False).mean(), 2)\n s[1][ma_diff_str] = s[1][fast_str] - s[1][slow_str]\n \n # np.where() is a vectorized if-else function, where a condition is checked for each component of a vector, and the first argument passed is used when the condition holds, and the other passed if it does not\n s[1][\"Regime\"] = np.where(s[1][ma_diff_str] > 0, 1, 0)\n # We have 1's for bullish regimes and 0's for everything else. Below I replace bearish regimes's values with -1, and to maintain the rest of the vector, the second argument is apple[\"Regime\"]\n s[1][\"Regime\"] = np.where(s[1][ma_diff_str] < 0, -1, s[1][\"Regime\"])\n # To ensure that all trades close out, I temporarily change the regime of the last row to 0\n regime_orig = s[1].loc[:, \"Regime\"].iloc[-1]\n s[1].loc[:, \"Regime\"].iloc[-1] = 0\n s[1][\"Signal\"] = np.sign(s[1][\"Regime\"] - s[1][\"Regime\"].shift(1))\n # Restore original regime data\n s[1].loc[:, \"Regime\"].iloc[-1] = regime_orig\n \n # Get signals\n signals = pd.concat([\n pd.DataFrame({\"Price\": s[1].loc[s[1][\"Signal\"] == 1, \"Adj. Close\"],\n \"Regime\": s[1].loc[s[1][\"Signal\"] == 1, \"Regime\"],\n \"Signal\": \"Buy\"}),\n pd.DataFrame({\"Price\": s[1].loc[s[1][\"Signal\"] == -1, \"Adj. Close\"],\n \"Regime\": s[1].loc[s[1][\"Signal\"] == -1, \"Regime\"],\n \"Signal\": \"Sell\"}),\n ])\n signals.index = pd.MultiIndex.from_product([signals.index, [s[0]]], names = [\"Date\", \"Symbol\"])\n trades = trades.append(signals)\n \n trades.sort_index(inplace = True)\n trades.index = pd.MultiIndex.from_tuples(trades.index, names = [\"Date\", \"Symbol\"])\n \n return trades\n\n\ndef backtest(signals, cash, port_value = .1, batch = 100):\n \"\"\"\n :param signals: pandas DataFrame containing buy and sell signals with stock prices and symbols, like that returned by ma_crossover_orders\n :param cash: integer for starting cash value\n :param port_value: maximum proportion of portfolio to risk on any single trade\n :param batch: Trading batch sizes\n \n :return: pandas DataFrame with backtesting results\n \n This function backtests strategies, with the signals generated by the strategies being passed in the signals DataFrame. A fictitious portfolio is simulated and the returns generated by this portfolio are reported.\n \"\"\"\n \n SYMBOL = 1 # Constant for which element in index represents symbol\n portfolio = dict() # Will contain how many stocks are in the portfolio for a given symbol\n port_prices = dict() # Tracks old trade prices for determining profits\n # Dataframe that will contain backtesting report\n results = pd.DataFrame({\"Start Cash\": [],\n \"End Cash\": [],\n \"Portfolio Value\": [],\n \"Type\": [],\n \"Shares\": [],\n \"Share Price\": [],\n \"Trade Value\": [],\n \"Profit per Share\": [],\n \"Total Profit\": []})\n \n for index, row in signals.iterrows():\n # These first few lines are done for any trade\n shares = portfolio.setdefault(index[SYMBOL], 0)\n trade_val = 0\n batches = 0\n cash_change = row[\"Price\"] * shares # Shares could potentially be a positive or negative number (cash_change will be added in the end; negative shares indicate a short)\n portfolio[index[SYMBOL]] = 0 # For a given symbol, a position is effectively cleared\n \n old_price = port_prices.setdefault(index[SYMBOL], row[\"Price\"])\n portfolio_val = 0\n for key, val in portfolio.items():\n portfolio_val += val * port_prices[key]\n \n if row[\"Signal\"] == \"Buy\" and row[\"Regime\"] == 1: # Entering a long position\n batches = np.floor((portfolio_val + cash) * port_value) // np.ceil(batch * row[\"Price\"]) # Maximum number of batches of stocks invested in\n trade_val = batches * batch * row[\"Price\"] # How much money is put on the line with each trade\n cash_change -= trade_val # We are buying shares so cash will go down\n portfolio[index[SYMBOL]] = batches * batch # Recording how many shares are currently invested in the stock\n port_prices[index[SYMBOL]] = row[\"Price\"] # Record price\n old_price = row[\"Price\"]\n elif row[\"Signal\"] == \"Sell\" and row[\"Regime\"] == -1: # Entering a short\n pass\n # Do nothing; can we provide a method for shorting the market?\n #else:\n #raise ValueError(\"I don't know what to do with signal \" + row[\"Signal\"])\n \n pprofit = row[\"Price\"] - old_price # Compute profit per share; old_price is set in such a way that entering a position results in a profit of zero\n \n # Update report\n results = results.append(pd.DataFrame({\n \"Start Cash\": cash,\n \"End Cash\": cash + cash_change,\n \"Portfolio Value\": cash + cash_change + portfolio_val + trade_val,\n \"Type\": row[\"Signal\"],\n \"Shares\": batch * batches,\n \"Share Price\": row[\"Price\"],\n \"Trade Value\": abs(cash_change),\n \"Profit per Share\": pprofit,\n \"Total Profit\": batches * batch * pprofit\n }, index = [index]))\n cash += cash_change # Final change to cash balance\n \n results.sort_index(inplace = True)\n results.index = pd.MultiIndex.from_tuples(results.index, names = [\"Date\", \"Symbol\"])\n \n return results\n\n# Get more stocks\n(microsoft, google, facebook, twitter, netflix,\namazon, yahoo, ge, qualcomm, ibm, hp) = (quandl.get(\"WIKI/\" + s, start_date=start,\n end_date=end) for s in [\"MSFT\", \"GOOG\", \"FB\", \"TWTR\",\n \"NFLX\", \"AMZN\", \"YHOO\", \"GE\",\n \"QCOM\", \"IBM\", \"HPQ\"])", "_____no_output_____" ], [ "signals = ma_crossover_orders([(\"AAPL\", apple),\n (\"MSFT\", microsoft),\n (\"GOOG\", google),\n (\"FB\", facebook),\n (\"TWTR\", twitter),\n (\"NFLX\", netflix),\n (\"AMZN\", amazon),\n (\"YHOO\", yahoo),\n (\"GE\", ge),\n (\"QCOM\", qualcomm),\n (\"IBM\", ibm),\n (\"HPQ\", hp)],\n fast = 20, slow = 50)\nsignals", "_____no_output_____" ], [ "bk = backtest(signals, 1000000)\nbk", "_____no_output_____" ], [ "bk[\"Portfolio Value\"].groupby(level = 0).apply(lambda x: x[-1]).plot()", "_____no_output_____" ] ], [ [ "A more realistic portfolio that can invest in any in a list of twelve (tech) stocks has a final growth of about 100%. How good is this? While on the surface not bad, we will see we could have done better.\n\n## Benchmarking\n\nBacktesting is only part of evaluating the efficacy of a trading strategy. We would like to **benchmark** the strategy, or compare it to other available (usually well-known) strategies in order to determine how well we have done.\n\nWhenever you evaluate a trading system, there is one strategy that you should always check, one that beats all but a handful of managed mutual funds and investment managers: buy and hold [SPY](https://finance.yahoo.com/quote/SPY). The **efficient market hypothesis** claims that it is all but impossible for anyone to beat the market. Thus, one should always buy an index fund that merely reflects the composition of the market.By buying and holding SPY, we are effectively trying to match our returns with the market rather than beat it.\n\nI look at the profits for simply buying and holding SPY.", "_____no_output_____" ] ], [ [ "#spyder = web.DataReader(\"SPY\", \"yahoo\", start, end)\nspyder = spyderdat.loc[start:end]\nspyder.iloc[[0,-1],:]", "_____no_output_____" ], [ "batches = 1000000 // np.ceil(100 * spyder.loc[:,\"Adj Close\"].iloc[0]) # Maximum number of batches of stocks invested in\ntrade_val = batches * batch * spyder.loc[:,\"Adj Close\"].iloc[0] # How much money is used to buy SPY\nfinal_val = batches * batch * spyder.loc[:,\"Adj Close\"].iloc[-1] + (1000000 - trade_val) # Final value of the portfolio\nfinal_val", "_____no_output_____" ], [ "# We see that the buy-and-hold strategy beats the strategy we developed earlier. I would also like to see a plot.\nax_bench = (spyder[\"Adj Close\"] / spyder.loc[:, \"Adj Close\"].iloc[0]).plot(label = \"SPY\")\nax_bench = (bk[\"Portfolio Value\"].groupby(level = 0).apply(lambda x: x[-1]) / 1000000).plot(ax = ax_bench, label = \"Portfolio\")\nax_bench.legend(ax_bench.get_lines(), [l.get_label() for l in ax_bench.get_lines()], loc = 'best')\nax_bench", "_____no_output_____" ] ], [ [ "Buying and holding SPY beats our trading system, at least how we currently set it up, and we haven't even accounted for how expensive our more complex strategy is in terms of fees. Given both the opportunity cost and the expense associated with the active strategy, we should not use it.\n\nWhat could we do to improve the performance of our system? For starters, we could try diversifying. All the stocks we considered were tech companies, which means that if the tech industry is doing poorly, our portfolio will reflect that. We could try developing a system that can also short stocks or bet bearishly, so we can take advantage of movement in any direction. We could seek means for forecasting how high we expect a stock to move. Whatever we do, though, must beat this benchmark; otherwise there is an opportunity cost associated with our trading system.\n\nOther benchmark strategies exist, and if our trading system beat the \"buy and hold SPY\" strategy, we may check against them. Some such strategies include:\n\n* Buy SPY when its closing monthly price is aboves its ten-month moving average.\n* Buy SPY when its ten-month momentum is positive. (**Momentum** is the first difference of a moving average process, or $MO^q_t = MA^q_t - MA^q_{t - 1}$.)\n\n(I first read of these strategies [here](https://www.r-bloggers.com/are-r2s-useful-in-finance-hypothesis-driven-development-in-reverse/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29).) The general lesson still holds: *don't use a complex trading system with lots of active trading when a simple strategy involving an index fund without frequent trading beats it.* [This is actually a very difficult requirement to meet.](http://www.nytimes.com/2015/03/15/your-money/how-many-mutual-funds-routinely-rout-the-market-zero.html?_r=0)\n\nAs a final note, suppose that your trading system *did* manage to beat any baseline strategy thrown at it in backtesting. Does backtesting predict future performance? Not at all. [Backtesting has a propensity for overfitting](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2745220), so just because backtesting predicts high growth doesn't mean that growth will hold in the future. There are strategies for combatting overfitting, such as [walk-forward analysis](https://ntguardian.wordpress.com/2017/06/19/walk-forward-analysis-demonstration-backtrader/) and holding out a portion of a dataset (likely the most recent part) as a final test set to determine if a strategy is profitable, followed by \"sitting on\" a strategy that managed to survive these two filters and seeing if it remains profitable in current markets.\n\n## Conclusion\n\nWhile this lecture ends on a depressing note, keep in mind that [the efficient market hypothesis has many critics.](http://www.nytimes.com/2009/06/06/business/06nocera.html) My own opinion is that as trading becomes more algorithmic, beating the market will become more difficult. That said, it may be possible to beat the market, even though mutual funds seem incapable of doing so (bear in mind, though, that part of the reason mutual funds perform so poorly is because of fees, which is not a concern for index funds).\n\nThis lecture is very brief, covering only one type of strategy: strategies based on moving averages. Many other trading signals exist and employed. Additionally, we never discussed in depth shorting stocks, currency trading, or stock options. Stock options, in particular, are a rich subject that offer many different ways to bet on the direction of a stock. You can read more about derivatives (including stock options and other derivatives) in the book *Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging*, [which is available from the University of Utah library.](http://proquest.safaribooksonline.com.ezproxy.lib.utah.edu/9781119037996)\n\nAnother resource (which I used as a reference while writing this lecture) is the O'Reilly book *Python for Finance*, [also available from the University of Utah library.](http://proquest.safaribooksonline.com.ezproxy.lib.utah.edu/book/programming/python/9781491945360)\n\nIf you were interested in investigating algorithmic trading, where would you go from here? I would not recommend using the code I wrote above for backtesting; there are better packages for this task. Python has some libraries for algorithmic trading, such as [**pyfolio**](https://quantopian.github.io/pyfolio/) (for analytics), [**zipline**](http://www.zipline.io/beginner-tutorial.html) (for backtesting and algorithmic trading), and [**backtrader**](https://www.backtrader.com/) (also for backtesting and trading). **zipline** seems to be popular likely because it is used and developed by [**quantopian**](https://www.quantopian.com/), a \"crowd-sourced hedge fund\" that allows users to use their data for backtesting and even will license profitable strategies from their authors, giving them a cut of the profits. However, I prefer **backtrader** and have written [blog posts](https://ntguardian.wordpress.com/tag/backtrader/) on using it. It is likely the more complicated between the two but that's the cost of greater power. I am a fan of its design. I also would suggest learning [R](https://www.r-project.org/), since it has many packages for analyzing financial data (moreso than Python) and it's surprisingly easy to use R functions in Python (as I demonstrate in [this post](https://ntguardian.wordpress.com/2017/06/28/stock-trading-analytics-and-optimization-in-python-with-pyfolio-rs-performanceanalytics-and-backtrader/)).\n\nYou can read more about using R and Python for finance on [my blog](https://ntguardian.wordpress.com).\n\nRemember that it is possible (if not common) to lose money in the stock market. It's also true, though, that it's difficult to find returns like those found in stocks, and any investment strategy should take investing in it seriously. This lecture is intended to provide a starting point for evaluating stock trading and investments, and, more generally, analyzing temporal data, and I hope you continue to explore these ideas.", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

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[ [ [ "import pandas as pd\nimport _pickle as pickle\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "def load_data(filename='dataset/dtoc.pkl'):\n pd_dtoc = pickle.load(open(filename, 'rb'))\n return pd_dtoc", "_____no_output_____" ], [ "df = load_data(filename='dataset/dtoc_proc.pkl') ", "_____no_output_____" ], [ "dtoc = df[df['is_dtoc']==1]", "_____no_output_____" ], [ "pd.get_dummies(dtoc, columns = ['diag1', 'diag2'])", "_____no_output_____" ], [ "def one_hot(df, cols):\n df_new = pd.DataFrame()\n for each in cols:\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\n df_new = pd.concat([df_new, dummies], axis=1)\n return df_new", "_____no_output_____" ], [ "one_hot(dtoc, ['diag2','diag3','diag4','diag5','diag6', \n 'diag7', 'diag8', 'diag9','diag10', 'diag11', 'diag12'])", "_____no_output_____" ], [ "dtoc[['diag1','diag2','diag3','diag4','diag5','diag6', \n 'diag7', 'diag8', 'diag9','diag10', 'diag11', 'diag12']].head(10)", "_____no_output_____" ], [ "dtoc.columns", "_____no_output_____" ], [ "dtoc.diag1.value_counts().keys()", "_____no_output_____" ], [ "diag1_counts = dtoc.diag1.value_counts().head(20)\nax = diag1_counts.plot.bar(title='ICD code distribution of delayed transfer')\nax.set_xlabel('ICD')\nax.set_ylabel('Count')", "_____no_output_____" ], [ "plt.style.use('seaborn-deep')\n\nbins = np.linspace(0, 100, 100)\ny1 = dtoc.age\ny2 = df.age\n# pyplot.hist(dtoc.age, bins, alpha=0.5, label='x')\n# pyplot.hist(df.age, bins, alpha=0.5, label='y')\n# pyplot.hist([x, y], bins, label=['x', 'y'], color = ['yellow', 'red'])\n# pyplot.legend(loc='upper right')\n# pyplot.show()\n\n\ncolors = ['b','g']\n\n#plots the histogram\nfig, (ax1, ax2) = plt.subplots(2, 1)\nax1.hist(y1 , color = 'brown')\nax1.set_xlabel('Age')\nax1.set_ylabel('Count')\nax1.set_title('Age distribution of delayed transfer')\nax2.hist(y2 , color = 'green')\nax2.set_title('Age distribution of all')\nax2.set_xlabel('Age')\nax2.set_ylabel('Count')\nfig.tight_layout()\nplt.show()\n# dtoc.age.plot.hist(figsize=(12,8),title='Dtoc ages distribution')", "_____no_output_____" ], [ "plt.hist(y2,color = 'green', label = 'age histogram of all')\nplt.tight_layout()\nplt.show()", "_____no_output_____" ], [ "df.age.value_counts().head(20)", "_____no_output_____" ], [ "df.age.plot.hist(figsize=(12,8),title='Dtoc ages distribution')", "_____no_output_____" ], [ "df.adm_code.value_counts().plot.bar(figsize=(12,8),title='Adm_codes distribution')", "_____no_output_____" ] ] ]

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[ [ [ "# Вебинар 6. Консультация по курсовому проекту.\n\n### Задание для курсового проекта\n\nМетрика:\nR2 - коэффициент детерминации (sklearn.metrics.r2_score)\n\nСдача проекта:\n1. Прислать в раздел Задания Урока 10 (\"Вебинар. Консультация по итоговому проекту\")\nссылку на программу в github (программа должна содержаться в файле Jupyter Notebook \nс расширением ipynb). (Pull request не нужен, только ссылка ведущая на сам скрипт).\n2. Приложить файл с названием по образцу NVBaranov_predictions.csv\nс предсказанными ценами для квартир из test.csv (файл должен содержать два поля: Id, Price).\nВ файле с предсказаниями должна быть 5001 строка (названия колонок + 5000 предсказаний).\n\nСроки и условия сдачи:\nДедлайн: сдать проект нужно в течение 72 часов после начала Урока 10 (\"Вебинар. Консультация по итоговому проекту\").\nДля успешной сдачи должны быть все предсказания (для 5000 квартир) и R2 должен быть больше 0.6.\nПри сдаче до дедлайна результат проекта может попасть в топ лучших результатов.\nПовторная сдача и проверка результатов возможны только при условии предыдущей неуспешной сдачи.\nУспешный проект нельзя пересдать в целях повышения результата.\nПроекты, сданные после дедлайна или сданные повторно, не попадают в топ лучших результатов, но можно узнать результат.\nВ качестве итогового результата берется первый успешный результат, последующие успешные результаты не учитываются.\n\nПримечание:\nВсе файлы csv должны содержать названия полей (header - то есть \"шапку\"),\nразделитель - запятая. В файлах не должны содержаться индексы из датафрейма.\n\nРекомендации для файла с кодом (ipynb):\n1. Файл должен содержать заголовки и комментарии\n2. Повторяющиеся операции лучше оформлять в виде функций\n3. Не делать вывод большого количества строк таблиц (5-10 достаточно)\n4. По возможности добавлять графики, описывающие данные (около 3-5)\n5. Добавлять только лучшую модель, то есть не включать в код все варианты решения проекта\n6. Скрипт проекта должен отрабатывать от начала и до конца (от загрузки данных до выгрузки предсказаний)\n7. Весь проект должен быть в одном скрипте (файл ipynb).\n8. При использовании статистик (среднее, медиана и т.д.) в качестве признаков,\nлучше считать их на трейне, и потом на валидационных и тестовых данных не считать \nстатистики заново, а брать их с трейна. Если хватает знаний, можно использовать кросс-валидацию,\nно для сдачи этого проекта достаточно разбить данные из train.csv на train и valid.\n9. Проект должен полностью отрабатывать за разумное время (не больше 10 минут),\nпоэтому в финальный вариант лучше не включать GridSearch с перебором \nбольшого количества сочетаний параметров.\n10. Допускается применение любых моделей машинного обучения из библиотеки sklearn.", "_____no_output_____" ], [ "### Прогнозирование на тестовом датасете\n\n1. Выполнить для тестового датасета те же этапы обработки и постронияния признаков (лучше выполнять действия сразу для двух датасетов)\n2. Не потерять и не перемешать индексы от примеров при построении прогнозов\n3. Прогнозы должны быть для все примеров из тестового датасета (для всех строк)", "_____no_output_____" ], [ "**Подключение библиотек и скриптов**", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport random\n\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score as r2\nfrom sklearn.model_selection import KFold, GridSearchCV\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline", "_____no_output_____" ], [ "import warnings\nwarnings.filterwarnings('ignore')", "_____no_output_____" ], [ "matplotlib.rcParams.update({'font.size': 14})\npd.set_option('precision', 3)\npd.set_option('max_columns', 100)", "_____no_output_____" ], [ "def evaluate_preds(train_true_values, train_pred_values, val_true_values, val_pred_values):\n \"\"\"\n Функция для оценки работы модели\n Parameters:\n train_true_values - целевая переменная из тренировочной части датасета\n train_pred_values - предсказания модели по тренировочной части\n val_true_values - целевая переменная из валидационной части датасета\n val_pred_values - предсказания модели по валидационной части\n Returns:\n R2 на тренировочной и валидационной части, \n графики зависимости истинных значений от предсказаний\n \"\"\"\n print(\"Train R2:\\t\" + str(round(r2(train_true_values, train_pred_values), 3)))\n print(\"Valid R2:\\t\" + str(round(r2(val_true_values, val_pred_values), 3)))\n \n plt.figure(figsize=(18,10))\n \n plt.subplot(121)\n sns.scatterplot(x=train_pred_values, y=train_true_values)\n plt.xlabel('Predicted values')\n plt.ylabel('True values')\n plt.title('Train sample prediction')\n \n plt.subplot(122)\n sns.scatterplot(x=val_pred_values, y=val_true_values)\n plt.xlabel('Predicted values')\n plt.ylabel('True values')\n plt.title('Test sample prediction')\n plt.show()", "_____no_output_____" ] ], [ [ "**Пути к директориям и файлам**", "_____no_output_____" ] ], [ [ "TRAIN_DATASET_PATH = 'datasets/project_task/train.csv'\nTEST_DATASET_PATH = 'datasets/project_task/test.csv'", "_____no_output_____" ] ], [ [ "### Загрузка данных", "_____no_output_____" ], [ "**Описание датасета**\n\n* **Id** - идентификационный номер квартиры\n* **DistrictId** - идентификационный номер района\n* **Rooms** - количество комнат\n* **Square** - площадь\n* **LifeSquare** - жилая площадь\n* **KitchenSquare** - площадь кухни\n* **Floor** - этаж\n* **HouseFloor** - количество этажей в доме\n* **HouseYear** - год постройки дома\n* **Ecology_1, Ecology_2, Ecology_3** - экологические показатели местности\n* **Social_1, Social_2, Social_3** - социальные показатели местности\n* **Healthcare_1, Helthcare_2** - показатели местности, связанные с охраной здоровья\n* **Shops_1, Shops_2** - показатели, связанные с наличием магазинов, торговых центров\n* **Price** - цена квартиры", "_____no_output_____" ] ], [ [ "train_df = pd.read_csv('train.csv')\ndisplay(train_df.tail())\nprint(train_df.shape)", "_____no_output_____" ], [ "# train_df = train_df.set_index('Id')\n# train_df.head()", "_____no_output_____" ], [ "test_df = pd.read_csv('test.csv')\ndisplay(test_df.tail())\nprint(test_df.shape)", "_____no_output_____" ], [ "# test_df = test_df.set_index('Id')", "_____no_output_____" ], [ "train_df.shape[1]-1 == test_df.shape[1]", "_____no_output_____" ] ], [ [ "### Приведение типов", "_____no_output_____" ] ], [ [ "train_df.dtypes", "_____no_output_____" ], [ "train_df['Id'] = train_df['Id'].astype(str)\ntrain_df['DistrictId'] = train_df['DistrictId'].astype(str)", "_____no_output_____" ] ], [ [ "### Обзор данных", "_____no_output_____" ], [ "**Целевая переменная**", "_____no_output_____" ] ], [ [ "plt.figure(figsize = (16, 8))\n\ntrain_df['Price'].hist(bins=30)\nplt.ylabel('Count')\nplt.xlabel('Price')\nplt.title('Target distribution')\nplt.show()", "_____no_output_____" ] ], [ [ "**Количественные переменные**", "_____no_output_____" ] ], [ [ "train_df.describe().T", "_____no_output_____" ] ], [ [ "**Категориальные переменные**", "_____no_output_____" ] ], [ [ "cat_colnames = train_df.select_dtypes(include='object').columns.tolist()\ncat_colnames", "_____no_output_____" ], [ "for cat_colname in cat_colnames[2:]:\n print(str(cat_colname) + '\\n\\n' + str(train_df[cat_colname].value_counts()) + '\\n' + '*' * 100 + '\\n')", "Ecology_2\n\nB 9903\nA 97\nName: Ecology_2, dtype: int64\n****************************************************************************************************\n\nEcology_3\n\nB 9725\nA 275\nName: Ecology_3, dtype: int64\n****************************************************************************************************\n\nShops_2\n\nB 9175\nA 825\nName: Shops_2, dtype: int64\n****************************************************************************************************\n\n" ] ], [ [ "### Обработка выбросов", "_____no_output_____" ], [ "**Rooms**", "_____no_output_____" ] ], [ [ "train_df['Rooms'].value_counts()", "_____no_output_____" ], [ "train_df.loc[train_df['Rooms'].isin([0, 10, 19]), 'Rooms'] = train_df['Rooms'].median()", "_____no_output_____" ] ], [ [ "**Square, LifeSquare, KitchenSquare**", "_____no_output_____" ] ], [ [ "train_df.describe()", "_____no_output_____" ], [ "steps = []\nscores = [] # <- записываем финальный score", "_____no_output_____" ], [ "# steps.append('обработка пропусков, выбросов var1')\n\ntrain_df = train_df[train_df['Square'].isnull() |\n (train_df['Square'] < train_df['Square'].quantile(.99)) &\n (train_df['Square'] > train_df['Square'].quantile(.01))]\n\ntrain_df = train_df[train_df['LifeSquare'].isnull() |\n (train_df['LifeSquare'] < train_df['LifeSquare'].quantile(.99)) &\n (train_df['LifeSquare'] > train_df['LifeSquare'].quantile(.01))]\n\ntrain_df = train_df[train_df['KitchenSquare'].isnull() |\n (train_df['KitchenSquare'] < train_df['KitchenSquare'].quantile(.99)) &\n (train_df['KitchenSquare'] > train_df['KitchenSquare'].quantile(.01))]", "_____no_output_____" ], [ "steps.append('обработка пропусков, выбросов var2')\n\n\"\"\"\n...\n...\n...\n\"\"\"", "_____no_output_____" ], [ "train_df.describe()", "_____no_output_____" ], [ "train_df.loc[train_df['LifeSquare'] < 10, 'LifeSquare'] = 10", "_____no_output_____" ], [ "train_df.loc[train_df['KitchenSquare'] < 3, 'KitchenSquare'] = 3", "_____no_output_____" ] ], [ [ "**HouseFloor, Floor**", "_____no_output_____" ] ], [ [ "train_df['HouseFloor'].sort_values().unique()", "_____no_output_____" ], [ "train_df['Floor'].sort_values().unique()", "_____no_output_____" ], [ "train_df.loc[train_df['HouseFloor'] == 0, 'HouseFloor'] = train_df['HouseFloor'].median()", "_____no_output_____" ], [ "floor_outliers = train_df[train_df['Floor'] > train_df['HouseFloor']].index\n\ntrain_df.loc[floor_outliers, 'Floor'] = train_df.loc[floor_outliers, 'HouseFloor'].apply(lambda x: random.randint(1, x))\n", "_____no_output_____" ] ], [ [ "**HouseYear**", "_____no_output_____" ] ], [ [ "train_df['HouseYear'].sort_values().unique()", "_____no_output_____" ], [ "train_df.loc[train_df['HouseYear'] > 2020, 'HouseYear'] = 2020", "_____no_output_____" ] ], [ [ "### Обработка пропусков", "_____no_output_____" ] ], [ [ "train_df.isnull().sum()", "_____no_output_____" ], [ "train_df[['Square', 'LifeSquare', 'KitchenSquare']].head(10)", "_____no_output_____" ] ], [ [ "**LifeSquare**", "_____no_output_____" ] ], [ [ "# медиана до корректировки\ntrain_df['LifeSquare'].median()", "_____no_output_____" ], [ "# медиана расхождения площадей\nsquare_med_diff = (train_df.loc[train_df['LifeSquare'].notnull(), 'Square']\n - train_df.loc[train_df['LifeSquare'].notnull(), 'LifeSquare']\n - train_df.loc[train_df['LifeSquare'].notnull(), 'KitchenSquare']).median()\n\nsquare_med_diff", "_____no_output_____" ], [ "train_df.loc[train_df['LifeSquare'].isnull(), 'LifeSquare'] = (\n train_df.loc[train_df['LifeSquare'].isnull(), 'Square']\n - train_df.loc[train_df['LifeSquare'].isnull(), 'KitchenSquare']\n - square_med_diff\n)", "_____no_output_____" ], [ "train_df['LifeSquare'].median()", "_____no_output_____" ] ], [ [ "**Healthcare_1**", "_____no_output_____" ] ], [ [ "train_df['Healthcare_1'].head()", "_____no_output_____" ], [ "train_df.loc[train_df['Healthcare_1'].isnull(), 'Healthcare_1'] = train_df['Healthcare_1'].median()", "_____no_output_____" ] ], [ [ "### Построение новых признаков", "_____no_output_____" ], [ "**Dummies**", "_____no_output_____" ] ], [ [ "train_df['Ecology_2_bin'] = train_df['Ecology_2'].replace({'A':0, 'B':1})\ntrain_df['Ecology_3_bin'] = train_df['Ecology_3'].replace({'A':0, 'B':1})\ntrain_df['Shops_2_bin'] = train_df['Shops_2'].replace({'A':0, 'B':1})", "_____no_output_____" ] ], [ [ "**DistrictSize, IsDistrictLarge**", "_____no_output_____" ] ], [ [ "train_df['DistrictId'].value_counts()", "_____no_output_____" ], [ "district_size = train_df['DistrictId'].value_counts().reset_index()\\\n .rename(columns={'index':'DistrictId', 'DistrictId':'DistrictSize'})\n\ndistrict_size.head()", "_____no_output_____" ], [ "train_df = train_df.merge(district_size, on='DistrictId', how='left')\ntrain_df.head()", "_____no_output_____" ], [ "(train_df['DistrictSize'] > 100).value_counts()", "_____no_output_____" ], [ "train_df['IsDistrictLarge'] = (train_df['DistrictSize'] > 100).astype(int)", "_____no_output_____" ] ], [ [ "**MedPriceByDistrict**", "_____no_output_____" ] ], [ [ "med_price_by_district = train_df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price':'median'})\\\n .rename(columns={'Price':'MedPriceByDistrict'})\n\nmed_price_by_district.head()", "_____no_output_____" ], [ "train_df = train_df.merge(med_price_by_district, on=['DistrictId', 'Rooms'], how='left')\ntrain_df.head()", "_____no_output_____" ], [ "train_df['MedPriceByDistrict'].isnull().sum()", "_____no_output_____" ] ], [ [ "*Пример переноса признака на test*", "_____no_output_____" ] ], [ [ "test_df['DistrictId'] = test_df['DistrictId'].astype(str)", "_____no_output_____" ], [ "test_df.merge(med_price_by_district, on=['DistrictId', 'Rooms'], how='left').info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 5000 entries, 0 to 4999\nData columns (total 20 columns):\nId 5000 non-null int64\nDistrictId 5000 non-null object\nRooms 5000 non-null float64\nSquare 5000 non-null float64\nLifeSquare 3959 non-null float64\nKitchenSquare 5000 non-null float64\nFloor 5000 non-null int64\nHouseFloor 5000 non-null float64\nHouseYear 5000 non-null int64\nEcology_1 5000 non-null float64\nEcology_2 5000 non-null object\nEcology_3 5000 non-null object\nSocial_1 5000 non-null int64\nSocial_2 5000 non-null int64\nSocial_3 5000 non-null int64\nHealthcare_1 2623 non-null float64\nHelthcare_2 5000 non-null int64\nShops_1 5000 non-null int64\nShops_2 5000 non-null object\nMedPriceByDistrict 4919 non-null float64\ndtypes: float64(8), int64(8), object(4)\nmemory usage: 742.2+ KB\n" ] ], [ [ "### Отбор признаков", "_____no_output_____" ] ], [ [ "train_df.columns.tolist()", "_____no_output_____" ], [ "feature_names = ['Rooms', 'Square', 'LifeSquare', 'KitchenSquare', 'Floor', 'HouseFloor', 'HouseYear',\n 'Ecology_1', 'Ecology_2_bin', 'Ecology_3_bin', 'Social_1', 'Social_2', 'Social_3',\n 'Healthcare_1', 'Helthcare_2', 'Shops_1', 'Shops_2_bin']\n\nnew_feature_names = ['IsDistrictLarge', 'MedPriceByDistrict']\n\ntarget_name = 'Price'", "_____no_output_____" ] ], [ [ "### Разбиение на train и val", "_____no_output_____" ] ], [ [ "X = train_df[feature_names + new_feature_names]\ny = train_df[target_name]", "_____no_output_____" ], [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=21)", "_____no_output_____" ] ], [ [ "### Построение модели", "_____no_output_____" ], [ "**Обучение и оценка модели**", "_____no_output_____" ] ], [ [ "rf_model = RandomForestRegressor(random_state=21)\nrf_model.fit(X_train, y_train)", "_____no_output_____" ] ], [ [ "**Отложенная выборка**", "_____no_output_____" ] ], [ [ "y_train_preds = rf_model.predict(X_train)\ny_test_preds = rf_model.predict(X_test)\n\nevaluate_preds(y_train, y_train_preds, y_test, y_test_preds)", "Train R2:\t0.952\nValid R2:\t0.671\n" ] ], [ [ "**Перекрёстная проверка**", "_____no_output_____" ] ], [ [ "cv_score = cross_val_score(rf_model, X, y, scoring='r2', cv=KFold(n_splits=3, shuffle=True, random_state=21))\ncv_score", "_____no_output_____" ], [ "cv_score.mean()", "_____no_output_____" ] ], [ [ "### XGBoost", "_____no_output_____" ] ], [ [ "from xgboost import XGBRegressor\n\nxgb = xgboost.XGBRegressor(n_estimators=500, learning_rate=0.08, gamma=0, subsample=0.75,\n colsample_bytree=1, max_depth=7, objective ='reg:squarederror')\nxgb.fit(X_train, y_train)\n\ny_train_preds = xgb.predict(X_train)\ny_test_preds = xgb.predict(X_test)\n\nevaluate_preds(y_train, y_train_preds, y_test, y_test_preds)", "_____no_output_____" ], [ "from sklearn.metrics import r2_score", "_____no_output_____" ], [ "# A parameter grid for XGBoost\nparams = {'min_child_weight':[4,5], \n 'gamma':[i/10.0 for i in range(3,6)], \n 'subsample':[i/10.0 for i in range(6,11)],\n 'max_depth': [2,3,4,7]}\n\n# Initialize XGB and GridSearch\nxgb = XGBRegressor(nthread=-1, objective ='reg:squarederror') \n\ngrid = GridSearchCV(xgb, params)\ngrid.fit(X_train,y_train) ", "_____no_output_____" ], [ "print(r2_score(y_test, grid.best_estimator_.predict(X_test)))", "0.7126652903060436\n" ], [ "y_train_preds = grid.best_estimator_.predict(X_train)\ny_test_preds = grid.best_estimator_.predict(X_test)\n\nevaluate_preds(y_train, y_train_preds, y_test, y_test_preds)", "Train R2:\t0.917\nTest R2:\t0.714\n" ] ], [ [ "**Важность признаков**", "_____no_output_____" ] ], [ [ "feature_importances = pd.DataFrame(zip(X_train.columns, rf_model.feature_importances_), \n columns=['feature_name', 'importance'])\n\nfeature_importances.sort_values(by='importance', ascending=False)", "_____no_output_____" ] ] ]

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[ "MIT" ]

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[ "MIT" ]

[ [ [ "empty" ] ] ]

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[ [ "empty" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "#'''\n#Demonstrates GRAPPA reconstruction of undersampled data. \n#See function grappa_detail.py for an example showing more of the \n#workings and functionality of the SIRF code.\n#\n#Pre-requisites:\n# 1) If the reconstruction engine is set to Gadgetron (default), then\n# this Python script needs to be able to access a listening gadgetron.\n# On the Virtual Machine, gadgetron is installed and the user just needs\n# to type 'gadgetron' in a terminal window.\n# On standalone systems, the user will need to have installed ISMRMRD\n# and gadgetron code.\n#\n# 2) An input data file from a GRAPPA MRI acquisition in the ISMRMRD format.\n# Example GRAPPA datasets:\n# a) 'meas_MID00108_FID57249_test_2D_2x.dat' is \n# available from https://www.ccppetmr.ac.uk/downloads\n# This is in the manufacturer's raw data format and needs to be\n# converted to ISMRMRD format using 'siemens_to_ismrmrd'.\n# This executable is installed on the Virtual Machine.\n#\n# b) A simulated ISMRMRD h5 file is available as default\n#\n#Usage:\n# grappa_basic.py [--help | options]\n#\n#Options:\n# -f <file>, --file=<file> raw data file\n# [default: simulated_MR_2D_cartesian_Grappa2.h5]\n# -p <path>, --path=<path> path to data files, defaults to data/examples/MR\n# subfolder of SIRF root folder\n# -e <engn>, --engine=<engn> reconstruction engine [default: Gadgetron]\n#'''\n#\n## CCP PETMR Synergistic Image Reconstruction Framework (SIRF)\n## Copyright 2015 - 2017 Rutherford Appleton Laboratory STFC.\n## Copyright 2015 - 2017 University College London.\n## Copyright 2015 - 2017 Physikalisch-Technische Bundesanstalt.\n##\n## This is software developed for the Collaborative Computational\n## Project in Positron Emission Tomography and Magnetic Resonance imaging\n## (http://www.ccppetmr.ac.uk/).\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## http://www.apache.org/licenses/LICENSE-2.0\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.\n\n#__version__ = '0.1.0'\nfrom docopt import docopt\n#args = docopt(__doc__, version=__version__)\n%matplotlib notebook\n# import engine module\n#exec('from p' + args['--engine'] + ' import *')\nfrom sirf.Gadgetron import *\n\ndata_file = 'simulated_MR_2D_cartesian_Grappa2.h5'\ndata_path = examples_data_path('MR')\n\n\n\n# locate the input data file\ninput_file = existing_filepath(data_path, data_file)\n\n# Initially we create a container that points to the h5 file.\n# Data is not read from file until the 'process' method of the\n# reconstructor object is called.\n\n# Create an acquisition container of type AcquisitionData\nprint('---\\n reading in file %s...' % input_file)\nacq_data = AcquisitionData(input_file)\n\n\n# Pre-process this input data.\n# (Currently this is a Python script that just sets up a 3 chain gadget.\n# In the future it will be independent of the MR recon engine.)\nprint('---\\n pre-processing acquisition data...')\npreprocessed_data = preprocess_acquisition_data(acq_data)\n\n\n# Perform reconstruction of the preprocessed data.\n# 1. set the reconstruction to be for Cartesian GRAPPA data.\nrecon = CartesianGRAPPAReconstructor();\n\n# 2. set the reconstruction input to be the data we just preprocessed.\nrecon.set_input(preprocessed_data);\n\n# 3. run (i.e. 'process') the reconstruction.\nprint('---\\n reconstructing...\\n');\nrecon.process();\n\n\n# retrieve reconstruced image and G-factor data\noutput = recon.get_output()\n\n# show reconstructed image and G-factor data\noutput_array = output.as_array()\ntitle = 'Reconstructed image data (magnitude)'\nshow_3D_array(abs(output_array[0::2,:,:]), suptitle = title, \\\n xlabel = 'samples', ylabel = 'readouts', label = 'slice', \\\n show = False)\ntitle = 'Reconstructed G-factor data (magnitude)'\nshow_3D_array(abs(output_array[1::2,:,:]), suptitle = title, \\\n xlabel = 'samples', ylabel = 'readouts', label = 'slice')\n\n\n", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd", "_____no_output_____" ], [ "data = pd.read_csv('/home/felipe/Developer/AnacondaProjects/python-ml-course/datasets/customer-churn-model/Customer Churn Model.txt')", "_____no_output_____" ], [ "data.head()", "_____no_output_____" ] ], [ [ "# Crear un subconjunto de datos", "_____no_output_____" ] ], [ [ "account_length = data[\"Account Length\"]", "_____no_output_____" ], [ "account_length.head()", "_____no_output_____" ], [ "(type)(account_length)", "_____no_output_____" ], [ "subset = data[[\"Account Length\", \"Phone\", \"Eve Charge\", \"Day Calls\"]]", "_____no_output_____" ], [ "subset.head()", "_____no_output_____" ], [ "desired_columns = [\"Account Length\", \"Phone\", \"Eve Charge\", \"Night Calls\"]\nsubset = data[desired_columns]\nsubset.head()", "_____no_output_____" ], [ "desired_columns = [\"Account Length\", \"VMail Message\", \"Day Calls\"]\nall_columns_list = data.columns.values.tolist()\nsublist = [x for x in all_columns_list if x not in desired_columns]\nsublist", "_____no_output_____" ], [ "subset = data[sublist]\nsubset.head()", "_____no_output_____" ], [ "a = set(desired_columns)\nb = set(all_columns_list)\nsublist = b-a\nsublist = list(sublist)\nsublist", "_____no_output_____" ], [ "data[1:25]", "_____no_output_____" ], [ "# filtrar usuarios con total mins > 500\ndata1 = data[data[\"Day Mins\"]>300]\ndata1", "_____no_output_____" ], [ "#Usuarios de NY\n\ndata_ny = data[data[\"State\"]== \"NY\"]\ndata_ny", "_____no_output_____" ], [ "## AND & OR |\ndata3 = data[(data[\"Day Mins\"]>300) & (data[\"State\"] == \"NY\")]\ndata3\n\ndata4 = data[(data[\"Day Mins\"]>300) | (data[\"State\"] == \"NY\")]\ndata4", "_____no_output_____" ], [ "data5 = data[data[\"Day Calls\"]< data[\"Night Calls\"]]\ndata5", "_____no_output_____" ], [ "data6 = data[data[\"Day Mins\"]< data[\"Night Mins\"]]\ndata6", "_____no_output_____" ], [ "# minutos de dia noche y longitud de la cuenta \nsubset_first_50 = data[[\"Day Mins\", \"Night Mins\", \"Account Length\"]][:50]\nsubset_first_50", "_____no_output_____" ], [ "data.iloc[1:10,3:6]", "_____no_output_____" ], [ "data.iloc[:,3:6]\ndata.iloc[1:10,:]\n\ndata.iloc[1:10,[2,5,7]]", "_____no_output_____" ], [ "data.loc[1:10,[\"Area Code\",\"Day Mins\"]]", "_____no_output_____" ], [ "#crear columnas\n\ndata[\"Total Mins\"] = data[\"Day Mins\"] + data[\"Night Mins\"] + data[\"Eve Mins\"]", "_____no_output_____" ], [ "data[\"Total Calls\"] = data[\"Day Calls\"] + data[\"Night Calls\"] + data[\"Eve Calls\"]", "_____no_output_____" ], [ "data", "_____no_output_____" ] ], [ [ "## Generación de números aleatorios", "_____no_output_____" ] ], [ [ "import numpy as np", "_____no_output_____" ], [ "np.random.randint(1,100)", "_____no_output_____" ], [ "np.random.random()", "_____no_output_____" ], [ "def randint_list(n, a, b):\n x = []\n for i in range(n):\n x.append(np.random.randint(a,b))\n return x", "_____no_output_____" ], [ "randint_list(25, 1, 50)", "_____no_output_____" ], [ "import random", "_____no_output_____" ], [ "random.randrange(0,100,7)", "_____no_output_____" ], [ "a = np.arange(100)\na", "_____no_output_____" ], [ "np.random.shuffle(a)\na", "_____no_output_____" ], [ "col_list = data.columns.values.tolist()", "_____no_output_____" ], [ "np.random.choice(col_list)", "_____no_output_____" ], [ "# seeds\nnp.random.seed(2018)\n\nfor i in range(5):\n print(np.random.random())", "0.8823493117539459\n0.10432773786047767\n0.9070093335163405\n0.3063988986063515\n0.446408872427422\n" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Radial Velocity Orbit-fitting with RadVel\n\n## Week 6, Intro-to-Astro 2021\n### Written by Ruben Santana & Sarah Blunt, 2018\n#### Updated by Joey Murphy, June 2020\n#### Updated by Corey Beard, July 2021\n\n\n## Background information\nRadial velocity measurements tell us how the velocity of a star changes along the direction of our line of sight. These measurements are made using Doppler Spectroscopy, which looks at the spectrum of a star and measures shifts in known absorption lines. Here is a nice [GIF](https://polytechexo.files.wordpress.com/2011/12/spectro.gif) showing the movement of a star due to the presence of an orbiting planet, the shift in the stellar spectrum, and the corresponding radial velocity measurements. \n\nThis tutorial will cover a lot of new topics and build on ones we just learned. We don't have time to review all of them right now, so you're encouraged to read the following references before coming back to complete the tutorial as one of your weekly assignments.\n- [Intro to the Radial Velocity Technique](http://exoplanets.astro.yale.edu/workshop/EPRV/Bibliography_files/Radial_Velocity.pdf) (focus on pgs. 1-6)\n- [Intro to Periodograms](https://arxiv.org/pdf/1703.09824.pdf) (focus on pgs. 1-30)\n- [Intro to Markov Chain Monte Carlo Methods](https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50) (link also found in the MCMC resources from the Bayesian fitting methods and MCMC tutorial)\n\n\n## About this tutorial\nIn this tutorial, you will use the California Planet Search Python package [RadVel](https://github.com/California-Planet-Search/radvel) to characterize the exoplanets orbiting the star K2-24 (EPIC 203771098) using radial velocity measurements. This tutorial is a modification of the \"[K2-24 Fitting & MCMC](https://github.com/California-Planet-Search/radvel/blob/master/docs/tutorials/K2-24_Fitting%2BMCMC.ipynb)\" tutorial on the RadVel GitHub page. \n\nThere are several coding tasks for you to accomplish in this tutorial. Each task is indicated by a `#TODO` comment.\n\nIn this tutorial, you will:\n- estimate planetary orbital periods using a periodogram\n- perform a maximum likelihood orbit fit with RadVel \n- create a residuals plot\n- perform a Markov Chain Monte Carlo (MCMC) fit to characterize orbital parameter uncertainty\n\n## Outline\n1. RadVel Installation\n2. Importing Data\n3. Finding Periods\n4. Defining and Initializing a Model\n5. Maximum Likelihood Fitting\n6. Residuals\n7. MCMC", "_____no_output_____" ], [ "## 1. Installation\nWe will begin by making sure we have all the python packages needed for the tutorial. First, [install RadVel](http://radvel.readthedocs.io/en/latest/quickstartcli.html#installation) by typing:\n\n`pip install radvel` at the command line. (Some warning messages may print out, but I (Corey) was able to install RadVel successfully in a new Anaconda environment using python=3.8.3.)\n\nIf you want to clone the entire RadVel GitHub repository for easy access to the RadVel source code, type:\n\n`git clone https://github.com/California-Planet-Search/radvel.git`\n\n\nIf everything installed correctly, the following cell should run without errors. If you still see errors try restarting the kernel by using the tab above labeled **kernel >> restart**.", "_____no_output_____" ] ], [ [ "# allows us to see plots on the jupyter notebook\n%matplotlib inline\n\n# used to interact with operating system\nimport os\n\n# models used by radvel for calculations, plotting, and model optimization\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy import optimize\n\n# for corner plots\nimport corner\n\n# for radial velocity analysis\nimport radvel\nfrom radvel.plot import orbit_plots, mcmc_plots\n\n# for periodogram\nfrom astropy.stats import LombScargle\n\n\n# sets font size for plots\nmatplotlib.rcParams['font.size'] = 18", "_____no_output_____" ] ], [ [ "## 2. Importing and Plotting Data\n\nWhen you installed RadVel, some .csv files were placed in a directory on your computer called `radvel.DATADIR`. Let's read this data into Python using pandas.", "_____no_output_____" ] ], [ [ "# import data\npath = os.path.join(radvel.DATADIR,'epic203771098.csv') # path to data file\ndata = pd.read_csv(path, index_col=0) # read data into pandas DataFrame\n\nprint('Path to radvel.DATADIR: {}\\n'.format(radvel.DATADIR))\nprint(data)\n\n# Let's print out the column names of the pandas DataFrame you just created (`data`)\nprint(data.columns.values)\n\n# TODO: print out the length of `data`\nprint(len(data))\n", "Path to radvel.DATADIR: /Users/corey/anaconda3/envs/astroconda/radvel_example_data\n\n errvel t vel\n0 1.593725 2364.819580 6.959066\n1 1.600745 2364.825101 5.017650\n2 1.658815 2364.830703 13.811799\n3 1.653224 2366.827579 1.151030\n4 1.639095 2367.852646 9.389273\n5 1.723691 2373.888150 -2.820614\n6 1.907690 2374.852412 -0.772991\n7 1.709263 2376.863820 -2.222920\n8 1.838565 2377.866073 0.146115\n9 1.649715 2378.834011 2.739558\n10 1.862539 2380.930797 7.571228\n11 1.681661 2382.886140 5.137920\n12 1.901465 2383.823529 0.368812\n13 1.689888 2384.799943 -1.480772\n14 1.680251 2384.828991 -2.737295\n15 1.718589 2384.839720 -5.682658\n16 1.713852 2388.955960 -3.910203\n17 1.644495 2395.857258 -5.635432\n18 1.760751 2402.898756 3.635211\n19 1.649973 2403.771319 3.538690\n20 1.460209 2411.755697 -3.754068\n21 1.782881 2412.794200 -0.111816\n22 1.637531 2420.803019 0.112752\n23 1.760237 2421.822804 -2.587854\n24 1.656808 2422.742125 3.020029\n25 1.982700 2429.761751 -13.033590\n26 1.875608 2429.810230 -10.996779\n27 1.702684 2432.732316 -12.064872\n28 1.913379 2432.807239 -14.867229\n29 1.929956 2457.716902 -1.308613\n30 1.944350 2457.754804 -5.319776\n31 1.617464 2465.710740 4.873121\n['errvel' 't' 'vel']\n32\n" ], [ "# Let's plot time (data.t) vs radial velocity (data.vel) using matplotlib.pyplot\nplt.plot(data.t, data.vel, 'o')\n\n# Now, on a new figure, let's modify the plotting code so that it adds error \n# bars (data.errvel) to each RV measurement\nplt.figure()\nplt.errorbar(data.t, data.vel, data.errvel, fmt='o')\nplt.show()\nplt.errorbar(data.t, data.vel, data.errvel, fmt='o',color='maroon')\n\n\n# Add labels for the x- and y-axes of your plot (time is in days; radial velocity is in m/s)\nplt.xlabel('Time [days]')\nplt.ylabel('Velocity [m/s]')\nplt.show()\n\n# TODO: change the color of the data in your plot\n\n# TODO: What do you notice about the data? Does it look like there is a planet signal? \n# What orbital period would you estimate?\n# Enter your answer in the triple quotes below.\n\n\"\"\"\n\nIt definitely doesn't appear to be a pure sinusoid. This means there could be significant eccentricity, additional planets,\nstellar activity, or any number of other possible explanations. The periods look like on the order of ~10-20 days,\nor so\n\n\n\"\"\"", "_____no_output_____" ] ], [ [ "## 3. Finding a Significant Period\n", "_____no_output_____" ], [ "Now, we will find probable orbital periods using a Lomb-Scargle periodogram. Periodograms are created using a Fourier transform, which is a mathematical process that takes in continuous time-based data and decomposes it into a combination of functions with various frequencies, as seen in the image below. To build more intuition for how a Fourier transform works, checkout this useful [PhET simulation](https://phet.colorado.edu/en/simulation/fourier).\n\n![fourier](https://upload.wikimedia.org/wikipedia/commons/6/61/FFT-Time-Frequency-View.png \"fast fourier transform\")\n([wikipedia](https://upload.wikimedia.org/wikipedia/commons/6/61/FFT-Time-Frequency-View.png))\n\nThe graph on the left is the continous data which is analagous to our radial velocity data. The three sine waves behind the graphs are the functions that are added to produce a good fit to the original data. Finally, the graph on the right is the periodogram. It shows how much each contributing function's frequency contributes to the data model. The larger the peak in the graph, the more significant that frequency is in the data. We use this frequency to get an idea of periodic behaivor in the data (e.g. the orbital period of an exoplanet). Now, we will calculate a periodogram and use it to give us an estimate of the period of the planet's orbit.", "_____no_output_____" ] ], [ [ "def LombScarg(t,v,e,min_per=0.01,max_per=1000):\n\n #Calculate Generalized Lomb-Scargle periodogram and window function\n\n fmin = 1./max_per\n fmax = 1./min_per\n frequency, power = LombScargle(t, v, e).autopower(minimum_frequency=1/1000,maximum_frequency=1.,method='cython')\n\n per = 1/frequency\n #Identify strongest period.\n \n in_window = np.zeros(len(per),dtype=bool)\n for s in range(len(per)):\n if per[s] > min_per and per[s] < max_per:\n in_window[s] += 1\n\n powmax = max(power[in_window])\n imax = np.argmax(power[in_window])\n fbest = frequency[in_window][imax]\n perbest = 1./fbest\n\n return per, power, perbest", "_____no_output_____" ], [ "minPer = 30 # min period to look for 1st planet (in days)\nmaxPer = 50 # max period to look for 1st planet (in days)\n\nperiod, power, period1 = LombScarg(data.t, data.vel,data.errvel,min_per=minPer,max_per=maxPer)\nplt.xlim(1,1000)\nplt.axvline(period1,color='red',linestyle='--')\nplt.semilogx(period,power)\nplt.xlabel('Period (days)')\nplt.ylabel('GLS Power')\nplt.show()\n\n\n# TODO: change the values of minPer and maxPer. How do the results change? Why? Type your answer\n# between the triple quotes below.\n\n\n\"\"\"\n`minPer` and `maxPer` control the period range in which the nyquist searcher looks for significant peaks. Changing\nthem controls which period the searcher returns (it's returning the maximum peak in the allowable range).\n\"\"\"", "_____no_output_____" ] ], [ [ "## 4. Defining and Initializing Model", "_____no_output_____" ], [ "Let's define a function that we will use to initialize the ``radvel.Parameters`` and ``radvel.RVModel`` objects.\nThese will be our initial guesses of the planet parameters based on on the radial velocity measurements shown and periodogram shown above.", "_____no_output_____" ] ], [ [ "nplanets = 1 # number of planets\n\ndef initialize_model():\n \n time_base = 2420.\n params = radvel.Parameters(nplanets,basis='per tc secosw sesinw k')\n params['per1'] = radvel.Parameter(value=period1) # Insert our guess for period of first planet (from periodogram)\n params['tc1'] = radvel.Parameter(value=2080.) # guess for time of transit of 1st planet\n params['secosw1'] = radvel.Parameter(value=0.0) # determines eccentricity (assuming circular orbit here)\n params['sesinw1'] = radvel.Parameter(value=0.0) # determines eccentriciy (assuming circular orbit here)\n params['k1'] = radvel.Parameter(value=3.) # radial velocity semi-amplitude\n\n mod = radvel.RVModel(params, time_base=time_base)\n mod.params['dvdt'] = radvel.Parameter(value=-0.02) # possible acceleration of star\n mod.params['curv'] = radvel.Parameter(value=0.01) # possible curvature in long-term radial velocity trend\n \n return mod\n", "_____no_output_____" ] ], [ [ "Fit the K2-24 RV data assuming circular orbits.\n\nSet initial guesses for the parameters:", "_____no_output_____" ] ], [ [ "mod = initialize_model() # model initiliazed\nlike = radvel.likelihood.RVLikelihood(mod, data.t, data.vel, data.errvel, '_HIRES') # initialize Likelihood object\n\n# define initial guesses for instrument-related parameters\nlike.params['gamma_HIRES'] = radvel.Parameter(value=0.1) # zero-point radial velocity offset\nlike.params['jit_HIRES'] = radvel.Parameter(value=1.0) # white noise", "_____no_output_____" ] ], [ [ "Plot the model with our initial parameter guesses:", "_____no_output_____" ] ], [ [ "def plot_results(like):\n fig = plt.figure(figsize=(12,4))\n fig = plt.gcf()\n fig.set_tight_layout(True)\n plt.errorbar(\n like.x, like.model(data.t.values)+like.residuals(), \n yerr=like.yerr, fmt='o'\n )\n \n ti = np.linspace(data.t.iloc[0] - 5, data.t.iloc[-1] + 5,100) # time array for model\n\n plt.plot(ti, like.model(ti))\n plt.xlabel('Time')\n plt.ylabel('RV')\n \nplot_results(like)", "_____no_output_____" ] ], [ [ "## 5. Maximum Likelihood fit", "_____no_output_____" ], [ "Well, that solution doesn't look very good! Let's optimize the parameters set to vary by maximizing the likelihood.\n\nInitialize a ``radvel.Posterior`` object.", "_____no_output_____" ] ], [ [ "post = radvel.posterior.Posterior(like) # initialize radvel.Posterior object", "_____no_output_____" ] ], [ [ "Choose which parameters to change or hold fixed during a fit. By default, all `radvel.Parameter` objects will vary, so you only have to worry about setting the ones you want to hold fixed.", "_____no_output_____" ] ], [ [ "post.likelihood.params['secosw1'].vary = False # set as false because we are assuming circular orbit\npost.likelihood.params['sesinw1'].vary = False # set as false because we are assuming circular orbit\nprint(like)", "parameter value vary\nper1 43.8487 True\ntc1 2080 True\nsecosw1 0 False\nsesinw1 0 False\nk1 3 True\ndvdt -0.02 True\ncurv 0.01 True\ngamma_HIRES 0.1 True\njit_HIRES 1 True\n\n" ] ], [ [ "Maximize the likelihood and print the updated posterior object", "_____no_output_____" ] ], [ [ "res = optimize.minimize(\n post.neglogprob_array, # objective function is negative log likelihood\n post.get_vary_params(), # initial variable parameters\n method='Powell', # Nelder-Mead also works\n )\n\nplot_results(like) # plot best fit model\nprint(post)", "parameter value vary\nper1 49.0164 True\ntc1 2080.57 True\nsecosw1 0 False\nsesinw1 0 False\nk1 3.81242 True\ndvdt -0.0843784 True\ncurv 0.00152179 True\ngamma_HIRES -4.20245 True\njit_HIRES 3.88509 True\n\nPriors\n------\n\n" ] ], [ [ "RadVel comes equipped with some fancy ready-made plotting routines. Check this out!", "_____no_output_____" ] ], [ [ "matplotlib.rcParams['font.size'] = 12\n\nRVPlot = orbit_plots.MultipanelPlot(post)\nRVPlot.plot_multipanel()\n\nmatplotlib.rcParams['font.size'] = 18", "NOTE: This version of radvel has been modified to NOT include jitters in errorbars.\n" ] ], [ [ "## 6. Residuals and Repeat\nResiduals are the difference of our data and our best-fit model. \n\nNext, we will plot the residuals of our optimized model to see if there is a second planet in our data. When we look at the following residuals, we will see a sinusoidal shape, so another planet may be present! Thus, we will repeat the steps shown earlier (this time using the parameters from the maximum fit for the first planet).", "_____no_output_____" ] ], [ [ "residuals1 = post.likelihood.residuals()\n\n# Let's make a plot of data.time versus `residuals1`\nplt.figure()\nplt.scatter(data.t, residuals1)\nplt.xlabel('time [MJD]')\nplt.ylabel('RV [m/s]')\nplt.show()\n\n# TODO: What do you notice? What would you estimate the period \n# of the other exoplanet in this system to be? Write your answer between the triple quotes below.\n\n\"\"\"\n\nThese residuals appear to go up and down every ~20 days or so. This looks like a more convincing version of the\nperiod we first observed in the original radial velocity data. It's still pretty hard to tell, though! I'm \nhappy we have algorithms to find orbital periods more effectively than the human eye can.\n\n\n\"\"\"", "_____no_output_____" ] ], [ [ "Let's repeat the above analysis with two planets!", "_____no_output_____" ] ], [ [ "nyquist = 2 # maximum sampling rate\nminPer = 20 # minimum period to look for 2nd planet\nmaxPer = 30 # max period to look for 2nd planet\n\n# finding 2nd planet period\nperiod, power, period2 = LombScarg(data.t, data.vel, data.errvel, min_per=minPer, max_per=maxPer) # finding possible periords for 2nd planet\n\nperiod, power, period1 = LombScarg(data.t, data.vel,data.errvel,min_per=minPer,max_per=maxPer)\nplt.xlim(1,1000)\nplt.axvline(period2,color='red',linestyle='--')\nplt.semilogx(period,power)\nplt.show()\n\n# TODO: why doesn't the periodogram return the period of the first planet? Write your answer between the triple\n# quotes below.\n\n\"\"\"\nThe period of the first planet is not in the allowed period range we specified (`minPer` to `maxPer`).\n\n\"\"\"", "_____no_output_____" ] ], [ [ "Repeat the RadVel analysis", "_____no_output_____" ] ], [ [ "nplanets = 2 # number of planets\n\ndef initialize_model():\n \n time_base = 2420\n params = radvel.Parameters(nplanets,basis='per tc secosw sesinw k')\n \n # 1st Planet\n params['per1'] = post.params['per1'] # period of 1st planet\n params['tc1'] = post.params['tc1'] # time transit of 1st planet\n params['secosw1'] = post.params['secosw1'] # determines eccentricity (assuming circular orbit here)\n params['sesinw1'] = post.params['sesinw1'] # determines eccentricity (assuming circular orbit here)\n params['k1'] = post.params['k1'] # velocity semi-amplitude for 1st planet\n \n # 2nd Planet\n params['per2'] = radvel.Parameter(value=period2) # Insert our guess for period of second planet (from periodogram)\n params['tc2'] = radvel.Parameter(value=2070.)\n params['secosw2'] = radvel.Parameter(value=0.0)\n params['sesinw2'] = radvel.Parameter(value=0.0)\n params['k2'] = radvel.Parameter(value=1.1)\n \n mod = radvel.RVModel(params, time_base=time_base)\n mod.params['dvdt'] = radvel.Parameter(value=-0.02) # acceleration of star\n mod.params['curv'] = radvel.Parameter(value=0.01) # curvature of radial velocity fit\n \n return mod\n", "_____no_output_____" ], [ "mod = initialize_model() # initialize radvel.RVModel object\nlike = radvel.likelihood.RVLikelihood(mod, data.t, data.vel, data.errvel, '_HIRES')\nlike.params['gamma_HIRES'] = radvel.Parameter(value=0.1)\nlike.params['jit_HIRES'] = radvel.Parameter(value=1.0)", "_____no_output_____" ], [ "like.params['secosw1'].vary = False # set as false because we are assuming circular orbit\nlike.params['sesinw1'].vary = False \nlike.params['secosw2'].vary = False # set as false because we are assuming circular orbit\nlike.params['sesinw2'].vary = False \n\nprint(like)", "parameter value vary\nper1 49.0164 True\ntc1 2080.57 True\nsecosw1 0 False\nsesinw1 0 False\nk1 3.81242 True\nper2 20.5863 True\ntc2 2070 True\nsecosw2 0 False\nsesinw2 0 False\nk2 1.1 True\ndvdt -0.02 True\ncurv 0.01 True\ngamma_HIRES 0.1 True\njit_HIRES 1 True\n\n" ], [ "plot_results(like)", "_____no_output_____" ], [ "post = radvel.posterior.Posterior(like) # initialize radvel.Posterior object\n\nres = optimize.minimize(\n post.neglogprob_array, # objective function is negative log likelihood\n post.get_vary_params(), # initial variable parameters\n method='Powell', # Nelder-Mead also works\n )\n\nplot_results(like) # plot best fit model\nprint(post)", "parameter value vary\nper1 48.4701 True\ntc1 2083.1 True\nsecosw1 0 False\nsesinw1 0 False\nk1 4.39734 True\nper2 21.126 True\ntc2 2069.83 True\nsecosw2 0 False\nsesinw2 0 False\nk2 4.7912 True\ndvdt -0.0630582 True\ncurv 0.00152013 True\ngamma_HIRES -4.0217 True\njit_HIRES 2.27605 True\n\nPriors\n------\n\n" ], [ "matplotlib.rcParams['font.size'] = 12\n\nRVPlot = orbit_plots.MultipanelPlot(post)\nRVPlot.plot_multipanel()\n\nmatplotlib.rcParams['font.size'] = 18", "NOTE: This version of radvel has been modified to NOT include jitters in errorbars.\n" ], [ "residuals2 = post.likelihood.residuals()\n\n# TODO: make a plot of data.time versus `residuals2`. What do you notice?\n\n# TODO: try redoing the above analysis, but this time, allow the eccentricity parameters to vary during the fit.\n# How does the fit change?\n\nplt.figure()\nplt.scatter(data.t, residuals2)\nplt.xlabel('time [MJD]')\nplt.ylabel('RV [ms$^{-1}$]')\n\n# Here's the original residuals plot, for comparison purposes:\nplt.figure()\nplt.scatter(data.t, residuals1, color='red')\nplt.xlabel('time [MJD]')\nplt.ylabel('RV [ms$^{-1}$]')\n\n\"\"\"\nThe residuals perhaps look a little more randomly distributed than before, but again it's pretty hard to tell\nwithout a periodogram.\n\"\"\"\n\n\n\n\"\"\"\n\nThe easiest way to do this is to rerun the analysis, except whenever you see a line that says secosw1 = False,\nor sesinw1 = False, or secosw2 = False, or sesinw2 = False, you change them to True.\n\nBe careful not to let the model go too crazy with eccentricity, try giving them initial guesses of 0.1.\n\nThe planet RV signatures look more angular (less purely sinusoidal) now that they have a non-zero eccentricity.\nThe data appears to be better-fit by an eccentric orbit model (i.e. the planets probably do have non-negligible\neccentricities).\n\"\"\"", "_____no_output_____" ] ], [ [ "K2-24 only has two known exoplanets so will stop this part of our analysis here. However, when analzying an uncharacterized star system, it's important to continue the analysis until we see no significant reduction in the residuals of the radial velocity. ", "_____no_output_____" ], [ "# 7. Markov Chain Monte Carlo (MCMC)\nAfter reading the intro to MCMC blog post at the beginning of this tutorial, you are an expert on MCMC! Write a 3-sentence introduction to this section yourself. \n\nMCMC is a method of exploring the parameter space of probable orbits using random walks, i.e. randomly changing the parameters of the fit. MCMC is used to find the most probable orbital solution and to determine the uncertainty (error bars) in the fit. MCMC tells you the probability distributions of orbital parameters consistent with the data.", "_____no_output_____" ] ], [ [ "# TODO: edit the Markdown cell immediately above this one with a 3 sentence description of the MCMC method.\n# What does MCMC do? Why do you think it is important to use MCMC to characterize uncertainties in radial\n# velocity fits?", "_____no_output_____" ] ], [ [ "Let's use RadVel to perform an MCMC fit:", "_____no_output_____" ] ], [ [ "df = radvel.mcmc(post, nwalkers=50, nrun=1000)\n\n# TODO: What type of data structure is `df`, the object returned by RadVel's MCMC method?\n\n\"\"\"\nIt is a pandas dataframe\n\"\"\"", "20000/400000 (5.0%) steps complete; Running 15855.08 steps/s; Mean acceptance rate = 48.2%; Min Auto Factor = 19; Max Auto Relative-Change = inf; Min Tz = 6614.6; Max G-R = 1.004\nDiscarding burn-in now that the chains are marginally well-mixed\n\n400000/400000 (100.0%) steps complete; Running 15030.55 steps/s; Mean acceptance rate = 37.4%; Min Auto Factor = 39; Max Auto Relative-Change = 0.0724; Min Tz = 1595.8; Max G-R = 1.018\n\nMCMC: WARNING: chains did not pass convergence tests. They are likely not well-mixed.\n" ] ], [ [ "Make a fun plot!", "_____no_output_____" ] ], [ [ "Corner = mcmc_plots.CornerPlot(post, df)\nCorner.plot()\n\n# TODO: There is a lot going on in this plot. What do you think the off-diagonal boxes are showing? \n# What about the on-diagonal boxes? What is the median period of the first planet? \n# What is the uncertainty on the period of the first planet? The second planet?\n# TODO: Why do you think the uncertainties on the periods of planets b and c are different?\n\n\"\"\"\nThe off-diagonal boxes are 1 dimensional probability distributions over each of the parameters of the fit.\nThe on-diagonal boxes show 2 dimensional probability distributions (covariances) between pairs of parameters\n(the box's row and column show the parameters it corresponds to).\nThe median period of the first plot (for my eccentric fit) is 52.56 days. The uncertainty is +0.08 days, -0.07 days\n(this corresponds to a *68% confidence interval* of [52.49, 52.64] days.)\nThe median period of the second planet is 20.69 days, with an uncertainty of +/- 0.02 days. \nThe uncertainties of the two orbital periods are different because the period of the second planet is much better\nconstrained by the data than the period of the first planet. We see many periods of the second planet repeated\nover the ~100 day dataset, but only ~2 periods of the first planet.\n\n\"\"\"", "_____no_output_____" ] ] ]

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[ [ [ "import math\nimport string\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import logit\nfrom IPython.display import display\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import (Input, Dense, Lambda, Flatten, Reshape, BatchNormalization, Layer,\n Activation, Dropout, Conv2D, Conv2DTranspose,\n Concatenate, add, Add, Multiply)\nfrom tensorflow.keras.losses import sparse_categorical_crossentropy\nfrom tensorflow.keras.optimizers import RMSprop, Adam\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras import metrics\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.datasets import cifar10\nfrom tensorflow.keras.callbacks import TensorBoard\nfrom tensorflow_addons.callbacks import TQDMProgressBar\n\nfrom realnvp_helpers import Mask, FlowBatchNorm\n\n\n%matplotlib inline", "/home/brian/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n" ], [ "batch_size = 10\nshape = (4, 4, 3)\nbatch_shape = (batch_size,) + shape\nsamples = 100\n\ntrain_data = np.random.normal(0.5, 3, size=(samples,) + (shape))\nprint(batch_shape)\nprint(train_data.shape)\ntrain_data[0, :, :, :]", "(10, 4, 4, 3)\n(100, 4, 4, 3)\n" ], [ "def conv_block(input_shape, kernel_size, filters, stage, block, use_resid=True):\n ''' Adapted from resnet50 implementation in Keras '''\n filters1, filters2, filters3 = filters\n if K.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n \n input_tensor = Input(batch_shape=input_shape)\n x = Conv2D(filters1, (1, 1),\n kernel_initializer='he_normal',\n name=conv_name_base + '2a')(input_tensor)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters2, kernel_size,\n padding='same',\n kernel_initializer='he_normal',\n name=conv_name_base + '2b')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters3, (1, 1),\n kernel_initializer='he_normal',\n name=conv_name_base + '2c')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n if use_resid:\n x = add([x, input_tensor])\n x = Activation('relu')(x)\n \n return Model(input_tensor, x, name='conv_block' + stage + block)", "_____no_output_____" ], [ "def coupling_layer(input_shape, mask_type, stage):\n ''' Implements (as per paper):\n y = b * x + (1 - b) * [x * exp(s(b * x)) + t(b * x)]\n '''\n assert mask_type in ['check_even', 'check_odd', 'channel_even', 'channel_odd']\n mask_prefix = 'check' if mask_type.startswith('check') else 'channel'\n mask_opposite = 'odd' if mask_type.endswith('even') else 'even'\n \n input_tensor = Input(batch_shape=input_shape)\n \n # Raw operations for step\n b0 = Mask(mask_type)\n b1 = Mask(mask_prefix + '_' + mask_opposite)\n s_ = conv_block(input_shape, (3, 3), (32, 32, 3), stage, '_s', use_resid=True)\n t_ = conv_block(input_shape, (3, 3), (32, 32, 3), stage, '_t', use_resid=True)\n batch = FlowBatchNorm(name='_'.join(['FlowBatchNorm' + mask_type + stage]))\n \n # Forward\n masked_input = b1(input_tensor)\n s = s_(masked_input)\n t = t_(masked_input)\n coupling = Lambda(lambda ins: ins[0] * K.exp(ins[1]) + ins[2])([input_tensor, s, t])\n coupling_mask = b0(coupling)\n out1, out2 = Add()([masked_input, coupling_mask]), b0(s)\n out1_norm = batch(out1)\n #batch_loss = Lambda(lambda x: - (K.log(gamma) - 0.5 * K.log(x + batch.epsilon)))(var)\n #batch_loss = Lambda(lambda x: -K.log(gamma))(var)\n #batch_loss = Lambda(lambda x: - ( - 0.5 * K.log(x + batch.epsilon)))(var)\n \n # Reverse\n \n # Return result + masked scale for loss function\n return Model(input_tensor, [out1_norm, out2], name='_'.join(['coupling', mask_type, stage]))", "_____no_output_____" ], [ "def coupling_group(input_tensor, steps, mask_type, stage):\n name_mapping = dict(enumerate(string.ascii_lowercase))\n \n # TODO: Only need check/channel, not even/odd right?\n assert mask_type in ['check_even', 'check_odd', 'channel_even', 'channel_odd']\n mask_prefix = 'check' if mask_type.startswith('check') else 'channel'\n \n x = input_tensor\n s_losses = []\n batch_losses = []\n for i in range(3):\n mask_type = mask_prefix + ('_even' if i % 2 == 0 else '_odd')\n step = coupling_layer(input_tensor.shape, mask_type, stage=str(stage) + name_mapping[i])\n x, s = step(x)\n #x, s = step(x)\n s_losses.append(s)\n \n return x, s_losses", "_____no_output_____" ], [ "def realnvp_zloss(target, z):\n # log(p_X(x)) = log(p_Z(f(x))) + log(|det(\\partial f(x) / \\partial X^T)|)\n # Prior is standard normal(mu=0, sigma=1)\n shape = z.shape\n return K.sum(-0.5 * np.log(math.pi) - 0.5 * z**2, axis=list(range(1, len(shape[1:]))))\n\ndef const_loss(target, output):\n # For debugging\n return K.constant(0)\n\ndef realnvp_sumloss(target, output):\n # Determinant is just sum of \"s\" or \"batch loss\" params (already log-space)\n shape = output.shape\n return K.sum(output, axis=list(range(1, len(shape))))", "_____no_output_____" ], [ "input_tensor = Input(batch_shape=batch_shape)\n#x = conv_block(shape, (3, 3), (32, 32, 3), '0', '_s', use_resid=True)(input_tensor)\nstep = coupling_layer(batch_shape, 'check_even', stage=str('a') + '0')\nx, s = step(input_tensor)\ns_losses = [s, s]\n\n#x, s_losses, batch_losses = coupling_group(input_tensor, steps=3, mask_type='check_even', stage=1)\ns_losses = Concatenate(name='s_losses')(s_losses)\n\nforward_model = Model(inputs=input_tensor, outputs=[x, s_losses])\noptimizer = Adam(lr=0.001)\nforward_model.compile(optimizer=optimizer, \n loss=[realnvp_zloss, realnvp_sumloss])\n #loss=[const_loss, const_loss, realnvp_sumloss])\nforward_model.summary()", "Model: \"model\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) [(10, 4, 4, 3)] 0 \n__________________________________________________________________________________________________\ncoupling_check_even_a0 (Model) [(10, 4, 4, 3), (10, 19498 input_1[0][0] \n__________________________________________________________________________________________________\ns_losses (Concatenate) (10, 4, 4, 6) 0 coupling_check_even_a0[1][1] \n coupling_check_even_a0[1][1] \n==================================================================================================\nTotal params: 19,498\nTrainable params: 19,224\nNon-trainable params: 274\n__________________________________________________________________________________________________\n" ], [ "def get_losses_from_layers(layers):\n losses = []\n for layer in layers:\n if isinstance(layer, Model):\n losses.extend(layer._losses)\n losses.extend(get_losses_from_layers(layer.layers))\n else:\n losses.extend(layer.losses)\n return losses\n\nget_losses_from_layers(forward_model.layers)", "_____no_output_____" ], [ "#early_stopping = keras.callbacks.EarlyStopping('val_loss', min_delta=50.0, patience=5)\n#reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=0.0001)\ns = [len(train_data)] + [int(x) for x in s_losses.shape[1:]]\n#s[0] = int(train_data.shape[0])\n#print(train_data.shape, np.zeros(s).shape)\n\ntensorboard = TensorBoard(log_dir='graph', \n batch_size=batch_size, \n histogram_freq=1, \n write_graph=True) \nhistory = forward_model.fit(\n train_data, [train_data, np.zeros(s)],\n #validation_data=(train_data[:10], [train_data[:10], np.zeros(s)[:10], np.zeros(s)[:10]]),\n batch_size=batch_size,\n epochs=20,\n callbacks=[TQDMProgressBar()], #, tensorboard], #, early_stopping, reduce_lr],\n verbose=0\n)", "WARNING:tensorflow:`batch_size` is no longer needed in the `TensorBoard` Callback and will be ignored in TensorFlow 2.0.\n" ], [ "df = pd.DataFrame(history.history)\n#display(df.describe(percentiles=[0.25 * i for i in range(4)] + [0.95, 0.99]))\ncol = 'val_loss' if 'val_loss' in df else 'loss'\ndisplay(df[-25:])\ndf[col][-25:].plot(figsize=(8, 6))", "_____no_output_____" ] ], [ [ "# 2019-07-28\n\n* Got some framework up to do coupling layers but having trouble passing the scale parameter to the loss function, getting some weird tensorflow error, needs more debugging\n* Without the determinant in the loss function, it looks like loss goes down, so maybe on the right track?\n * It's actually weird that we're not using the image in the output, but I guess that's what's great about this reversible model!\n* TODO:\n * Debug scale function in loss\n * Add reverse (generator) network to functions above.", "_____no_output_____" ], [ "# 2019-07-29\n\n* Explanation of how to estimate probability of continuous variables (relevant for computing bits/pixel without an explicit discrete distribution): https://math.stackexchange.com/questions/2818318/probability-that-a-sample-is-generated-from-a-distribution\n* Idea for a post, explain likelihood estimation of discrete vs. continuous distributions (like pixels), include:\n * Probability of observing a value from continuous distribution = 0\n * https://math.stackexchange.com/questions/2818318/probability-that-a-sample-is-generated-from-a-distribution\n * Probability of observing a value from a set of discrete hypthesis (models) is non-zero using epsilon trick (see above link):\n * https://math.stackexchange.com/questions/920241/can-an-observed-event-in-fact-be-of-zero-probability\n * Explain Equation 3 from \"A NOTE ON THE EVALUATION OF GENERATIVE MODELS\"\n * Also include an example using a simpler case, like a bernoulli variable that we're estimating using a continuous distribution\n * Bring it back to modelling pixels and how they usually do it", "_____no_output_____" ], [ "# 2020-03-30\n\n* To make reversible network, build forward and backward network at the same time using `Model()` to have components that I can use in both networks\n* Looks like I have some instability here, depending on the run I can get an exact fit (-100s loss) or a poor a fit (+10):\n * Turning off residual networks helps\n * Adjusting the learning rate, batch size helps but hard to pinpoint a methodology\n* Most likely it's the instability of using a scale parameter (RealNVP paper Section 3.7), might need to implement their batch norm for more stable results, especially when adding more layers:\n * Reimplement `BatchNorm`: https://github.com/keras-team/keras/blob/master/keras/layers/normalization.py\n * Except return regular result AND (variance + eps) term\n * Use the (var + eps) term to compute Jacobian for loss function (should just be log-additive)\n* Once this is done, add back the other stuff:\n * Turn on residual shortcuts\n * Change batch size to reasonable number and learning rate=0.01\n* If this still doesn't work, might want to implement \"Running average over recent minibatches\" in Appendix E", "_____no_output_____" ], [ "# 2020-03-31\n\n* Fixed a bug (I think) in the network where the coupling layer was wrong. However, it still sometimes get stuck at around a loss of 5 but more often than not (on another training run) get to -10 (after 20 iters).\n* Trying to get FlowBatchNorm worknig but having some issues passing the determinant batch loss as an output because the `batch_size` is not getting passed (it has dimension (3,) but should have dimension (None, 3)). Need to figure out how to tranlate a tensor to Layer that includes batch.", "_____no_output_____" ], [ "# 2020-04-05\n\n* Reminder: BatchNormalization on conv layers only need to normalize across [B, W, H, :] layers, not the \"C\" layer because the filter is identical across a channel (so it uses the same mean/var to normalize). This is nice because it's the same axis (-1) you would normalize across in a Dense layer. See: https://intellipaat.com/community/3872/batch-normalization-in-convolutional-neural-network\n* I think I figured out how to return the batchnorm weights back but now I'm hitting a roadblock when I try to merge them together to put as part of the output loss -- maybe I should just forget it and use the tensors directly in the output loss?\n* Now that I switched to an explicit batch size, it doesn't run anymore... get this error \"Incompatible shapes: [4] vs. [32]\", probably some assumption that I had, got to work backwards and fix it I think.\n", "_____no_output_____" ], [ "# 2020-04-14\n\n* Okay figured out the weird error I was getting: when a Keras model has multiple outputs you either have to give it a list or dict of loss functions, otherwise it will apply the same loss to each output! Of course, I just assumed that it gives you all outputs in one loss function. So silly!\n* I reverted the change to explicitly set batch. Instead in the `BatchNormFlow` layer I just multiply zero by the `inputs` and then add the mean/variance. I think this gives the right shape?\n* **TODOs**:\n * Check that shape/computation for `BatchNormFlow`/`batch_losses` loss is correct\n * Check that loss functions are actually returning a negative log-loss (not just the log)\n * Validate the model is fitting what I want (right now I have an elbow effect as I train more) -- should there be backprop through the batch_losses? I guess not? Check the paper and figure out what to do.\n * Add back in the bigger model that has multiple coupling layers", "_____no_output_____" ], [ "# 2020-04-15\n\n* Somehow I suspect that the batch loss is not getting optimized (the var parameter in the batch norm function). When I set the other loss components to zero, I see that hte batch loss is not really getting smaller -- should it?\n\n loss \tcoupling_check_even_1c_loss \ts_losses_loss \tbatch_losses_loss\n 0 \t146.227879 \t0.0 \t0.0 \t146.227879\n 1 \t131.294226 \t0.0 \t0.0 \t131.294226\n 2 \t135.579913 \t0.0 \t0.0 \t135.579913\n 3 \t127.908073 \t0.0 \t0.0 \t127.908073\n 4 \t130.301921 \t0.0 \t0.0 \t130.301921\n 5 \t139.414369 \t0.0 \t0.0 \t139.414369\n 6 \t129.732767 \t0.0 \t0.0 \t129.732767\n 7 \t127.321448 \t0.0 \t0.0 \t127.321448\n 8 \t130.812973 \t0.0 \t0.0 \t130.812973\n 9 \t136.737979 \t0.0 \t0.0 \t136.737979\n 10 \t135.001893 \t0.0 \t0.0 \t135.001893\n 11 \t140.181680 \t0.0 \t0.0 \t140.181680\n 12 \t133.053322 \t0.0 \t0.0 \t133.053322\n 13 \t132.912917 \t0.0 \t0.0 \t132.912917\n 14 \t122.261415 \t0.0 \t0.0 \t122.261415\n 15 \t139.447081 \t0.0 \t0.0 \t139.447081\n 16 \t134.216364 \t0.0 \t0.0 \t134.216364\n 17 \t133.567210 \t0.0 \t0.0 \t133.567210\n 18 \t131.333447 \t0.0 \t0.0 \t131.333447\n 19 \t133.022141 \t0.0 \t0.0 \t133.022141\n \n* **IDEA:** I should probably unit test the batch norm flow layer to make sure that it's doing what I think it should be doing... need to think about how to structure this experiment.\n* **CHECK**: Should `s` loss be negated also? Seems like I need negative log loss, not just log loss...", "_____no_output_____" ], [ "# 2020-04-16\n\n* Forgot that BatchNorm has two components: $\\mu, \\sigma^2$, the mean and variance of the batch, which we scale ($\\hat{x} = \\frac{x-\\mu}{\\sqrt{\\sigma^2 + \\epsilon}}$) AND two learnable parameters: $\\gamma, \\beta$, which are used to scale the output: $y = \\gamma \\hat{x} + \\beta$. The learnable parameters are the only ones that change!\n* Now, how does that work when calculating the determinant? Let's see:\n\n$$\\frac{\\partial}{\\partial y} \\hat{y} = \\frac{\\partial}{\\partial y}\\big[\\gamma * \\frac{x-\\mu}{\\sqrt{\\sigma^2 + \\epsilon} + \\beta}\\big]$$\n$$ = \\frac{\\gamma}{\\sqrt{\\sigma^2 + \\epsilon}}$$\n\n Therefore, I need to include gamma in the determinant calculation in the batch norm layer!\n \n \nOhhhhh... use `keras.layer.add_loss()` function instead of passing the new things over! Not sure how to deal with batch though... https://www.tensorflow.org/guide/keras/custom_layers_and_models", "_____no_output_____" ], [ "# 2020-04-17\n\n* Made some progress adding batch norm loss use both `layer.add_loss()` and `layer.add_metric()` so I can view it... BUT I need to upgrade to Tensorflow 2.0. \n* After upgrading to 2.0, might as well start using `tf.keras` directly as that's the recommendation from the site.", "_____no_output_____" ], [ "# 2020-04-20\n\n* Upgraded to Tensorflow 2.1! I hate upgrading things...\n* Converted most of my code over too -- still need to add `layer.add_loss()` and `layer.add_metric()` to the `FlowBatchNorm()` layer though. I did convert it over to the TF2 version, inheriting it and assuming that the fancier features are turned off.", "_____no_output_____" ] ], [ [ "from scipy.stats import norm\n\nfor i in range(-10, 10):\n eps = i / 1000\n l = norm.cdf(0 - eps)\n r = norm.cdf(0 + eps)\n print(eps, '\\t', l - r)", "_____no_output_____" ], [ "a = np.array([[[-1, -2], [-3, -4]], [[1,2], [3, 4]], [[5,6], [7, 8]]]) \nb = np.array([100, 200]).reshape([1, 1, 2])\n\nc = a + b\nc[:, :, :]", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Problem H: Gold Mining\n\nReply the maximum sum of gold mines, that can be mined with single well.\n\n### Input\n\n- Number of test case\n + N: Number of gold layer\n + N lines (x1, x2, y) start, stop and depth of the gold layer\n \n### Output\n\nfor each test case\n\n- 1/3 of total length of gold layer that can be mined with single well (2 digit below 0)\n\n### Sample Input\n\n```\n3\n6\n10 35 10\n50 65 20\n20 45 30\n60 90 40\n30 55 50\n40 70 60\n6\n10 35 10\n50 65 20\n20 45 30\n60 150 40\n30 55 50\n40 70 60\n3\n-30 10 10\n-40 -25 20\n-30 -10 30\n```\n\n### Sample Output\n\n```\n35.00\n48.33\n25.00\n```", "_____no_output_____" ], [ "## How to solve\n\nIf a well covers some (optimized) gold layers, the well can be moved horizontally or change its angle until it touch 2 or more end points of gold layers, without changing coverage.\n\n- select 2 end points of gold well, then calculate the sum of gold layers, that intersect the line\n\nEach gold layer has 2 end points, so the calculation volume is about $2N \\cdot 2N \\cdot N = 4N^3$", "_____no_output_____" ] ], [ [ "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n'''\nMCPC 2018 Problem-H: Gold Mining\n'''\n\nimport sys\n\n\ndef check_mines(mines, p1, p2):\n '''\n mines: list of tuple (x1, x2, y)\n p1, p2: tuple (x,y)\n return: total length of mines on the p1-p2 line\n '''\n\n total_len = 0\n slope_p1_p2 = (p2[0]-p1[0]) / (p2[1]-p1[1])\n for mine in mines:\n mine_x = (slope_p1_p2 * (mine[2]-p1[1])) + p1[0]\n #print(p1, p2, slope_p1_p2, mine, mine_x, file=sys.stderr)\n if mine_x >= mine[0] and mine_x <= mine[1]:\n total_len += mine[1]-mine[0]\n\n return total_len\n\n\ndef solve(mines):\n points = list()\n for mine in mines:\n points.append((mine[0],mine[2])) # (x1, y)\n points.append((mine[1],mine[2])) # (x2, y)\n\n max_total = 0\n for p1_idx in range(len(points)-1):\n for p2_idx in range(p1_idx, len(points)):\n if points[p1_idx][1] == points[p2_idx][1]: continue # same y\n total_len = check_mines(mines, points[p1_idx], points[p2_idx])\n max_total = max(max_total, total_len)\n\n print('{:.2f}'.format(round(max_total/3,2)))\n\n\nnum_tc = int(sys.stdin.readline())\nfor _ in range(num_tc):\n num_mine = int(sys.stdin.readline())\n mines = list()\n for m in range(num_mine):\n x1, x2, y = map(int, sys.stdin.readline().split())\n mines.append((x1, x2, y))\n\n solve(mines)\n", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Calculating a custom statistic", "_____no_output_____" ], [ "This example shows how to define and use a custom `iris.analysis.Aggregator`, that provides a new statistical operator for\nuse with cube aggregation functions such as `~iris.cube.Cube.collapsed`, `~iris.cube.Cube.aggregated_by` or `~iris.cube.Cube.rolling_window`.\n\nIn this case, we have a 240-year sequence of yearly average surface temperature over North America, and we want to calculate in how many years these exceed a certain temperature over a spell of 5 years or more.", "_____no_output_____" ], [ "Define a function to perform the custom statistical operation.\n\nNote: in order to meet the requirements of `iris.analysis.Aggregator`, it must do the calculation over an arbitrary (given) data axis.", "_____no_output_____" ], [ "A function defined in a notebook will have to be defined in a single cell. Splitting it across multiple cells makes it separate code blocks, not one function. \nNotebooks are thus not designed to write a lot of functions. If you do happen to need to do this, consider creating a separate Python module and importing the functions from there.", "_____no_output_____" ] ], [ [ "import numpy as np\nfrom iris.util import rolling_window\ndef count_spells(data, threshold, axis, spell_length):\n \"\"\"\n Function to calculate the number of points in a sequence where the value\n has exceeded a threshold value for at least a certain number of timepoints.\n\n Generalised to operate on multiple time sequences arranged on a specific\n axis of a multidimensional array.\n\n Args:\n\n * data (array):\n raw data to be compared with value threshold.\n\n * threshold (float):\n threshold point for 'significant' datapoints.\n\n * axis (int):\n number of the array dimension mapping the time sequences.\n (Can also be negative, e.g. '-1' means last dimension)\n\n * spell_length (int):\n number of consecutive times at which value > threshold to \"count\".\n\n \"\"\"\n if axis < 0:\n # just cope with negative axis numbers\n axis += data.ndim\n \n\n # Threshold the data to find the 'significant' points.\n data_hits = data > threshold\n # Make an array with data values \"windowed\" along the time axis.\n hit_windows = rolling_window(data_hits, window=spell_length, axis=axis)\n # Find the windows \"full of True-s\" (along the added 'window axis').\n full_windows = np.all(hit_windows, axis=axis+1)\n # Count points fulfilling the condition (along the time axis).\n spell_point_counts = np.sum(full_windows, axis=axis, dtype=int)\n return spell_point_counts", "_____no_output_____" ] ], [ [ "Load the whole time-sequence as a single cube.", "_____no_output_____" ] ], [ [ "import iris\nfile_path = iris.sample_data_path('E1_north_america.nc')\ncube = iris.load_cube(file_path)\ncube", "_____no_output_____" ] ], [ [ "Make an aggregator from the user function.", "_____no_output_____" ] ], [ [ "from iris.analysis import Aggregator\nSPELL_COUNT = Aggregator('spell_count',\n count_spells,\n units_func=lambda units: 1)", "_____no_output_____" ] ], [ [ "Define the parameters of the test.", "_____no_output_____" ] ], [ [ "threshold_temperature = 280.0\nspell_years = 5", "_____no_output_____" ] ], [ [ "Calculate the statistic.", "_____no_output_____" ] ], [ [ "warm_periods = cube.collapsed('time', SPELL_COUNT,\n threshold=threshold_temperature,\n spell_length=spell_years)\nwarm_periods.rename('Number of 5-year warm spells in 240 years')\nwarm_periods", "_____no_output_____" ] ], [ [ "Plot the results.", "_____no_output_____" ] ], [ [ "%matplotlib inline\nimport iris.quickplot as qplt\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\n\nrcParams['figure.figsize'] = [12, 8]\n\nqplt.contourf(warm_periods, cmap='RdYlBu_r')\nplt.gca().coastlines();", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport geopandas\nimport glob\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn\nimport shapefile as shp\nfrom paths import *\nfrom refuelplot import *\nsetup()", "_____no_output_____" ], [ "wpNZ = pd.read_csv(data_path + \"/NZ/windparks_NZ.csv\", delimiter=';')\nwpBRA = pd.read_csv(data_path + '/BRA/turbine_data.csv',index_col=0)\nwpUSA = pd.read_csv(data_path + '/USA/uswtdb_v2_3_20200109.csv')\n# remove Guam\nwpUSA = wpUSA[wpUSA.t_state!='GU']\nwpZAF = pd.read_csv(data_path + '/ZAF/windparks_ZAF.csv')", "_____no_output_____" ], [ "shpBRA = geopandas.read_file(data_path + '/country_shapefiles/BRA/BRA_adm1.shp')\nshpNZ = geopandas.read_file(data_path + '/country_shapefiles/NZ/CON2017_HD_Clipped.shp')\nshpUSA = geopandas.read_file(data_path + '/country_shapefiles/USA/cb_2018_us_state_500k.shp')\nshpZAF = geopandas.read_file(data_path + '/country_shapefiles/ZAF/zaf_admbnda_adm1_2016SADB_OCHA.shp')", "_____no_output_____" ] ], [ [ "plot windparks: either all with opacity or aggregate to windparks and maybe use size as capacity indicator?", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots(figsize = (9,7))\nax.set_xlim(-180,-65)\nax.set_ylim(20,75)\nshpUSA.plot(color=COLORS[4],ax=ax)\nplt.plot(wpUSA.xlong,wpUSA.ylat,'o',alpha=0.1,markersize=2)", "_____no_output_____" ], [ "import xarray as xr\nfrom matplotlib.patches import Rectangle", "_____no_output_____" ], [ "NZera5 = xr.open_dataset(era_path + '/NZ/era5_wind_NZ_198701.nc')", "_____no_output_____" ], [ "NZmerra2 = xr.open_dataset(mer_path + '/NZ/merra2_wind_NZ_198701.nc')", "_____no_output_____" ], [ "def cell_coords(lon,lat):\n diflat = NZera5.latitude.values - lat\n diflon = NZera5.longitude.values - lon\n clat = NZera5.latitude.values[abs(diflat)==min(abs(diflat))][0]\n clon = NZera5.longitude.values[abs(diflon)==min(abs(diflon))][0]\n return((clon-0.125,clat-0.125))", "_____no_output_____" ], [ "def cell_coords_mer(lon,lat):\n diflat = NZmerra2.lat.values - lat\n diflon = NZmerra2.lon.values - lon\n clat = NZmerra2.lat.values[abs(diflat)==min(abs(diflat))][0]\n clon = NZmerra2.lon.values[abs(diflon)==min(abs(diflon))][0]\n return((clon-0.3125,clat-0.25))", "_____no_output_____" ], [ "shpNZ.to_crs({'init': 'epsg:4326'}).plot(color=COLORS[3]).set_xlim(165,180)\nplt.plot(wpNZ.Longitude,wpNZ.Latitude,'o',markersize=4)\n\nax = plt.gca()\nrect = matplotlib.patches.Rectangle(xy=cell_coords(wpNZ.Longitude[0],wpNZ.Latitude[0]),width= 0.25,height=0.25,alpha=0.7,color=COLORS[1])\nax.add_patch(rect)", "_____no_output_____" ], [ "shpNZ.to_crs({'init': 'epsg:4326'}).plot(color=COLORS[3],alpha=0.5).set_xlim(165,180)\nplt.plot(wpNZ.Longitude,wpNZ.Latitude,'o',markersize=4)\n\nax = plt.gca()\nfor i in range(len(wpNZ)):\n rect = matplotlib.patches.Rectangle(xy=cell_coords_mer(wpNZ.Longitude[i],wpNZ.Latitude[i]),width= 0.625,height=0.5,alpha=0.7,color=COLORS[1])\n ax.add_patch(rect)\nplt.savefig(results_path + '/plots/syssize_NZ.png')", "_____no_output_____" ], [ "shpNZ.to_crs({'init': 'epsg:4326'}).plot(color=COLORS[4]).set_xlim(165,180)\nplt.plot(wpNZ.Longitude,wpNZ.Latitude,'o',markersize=4)", "_____no_output_____" ], [ "shpBRA.plot(color=COLORS[4])\nplt.plot(wpBRA.lon,wpBRA.lat,'o',alpha=0.1,markersize=2)", "_____no_output_____" ], [ "shpZAF.plot(color=COLORS[4])\nplt.plot(wpZAF.Longitude,wpZAF.Latitude,'o',markersize=4)", "_____no_output_____" ], [ "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(10,10),gridspec_kw = {'wspace':0.15, 'hspace':0.15})\n\nshpBRA.plot(color=COLORS[4],ax=ax1)\nax1.set_xlim(-75,-30)\nax1.set_ylim(-35,10)\nax1.plot(wpBRA.groupby('name').mean().lon,\n wpBRA.groupby('name').mean().lat,'o',alpha=0.1,markersize=2)\nax1.set_title('Brazil')\n\nshpNZ.to_crs({'init': 'epsg:4326'}).plot(color=COLORS[4],ax=ax2).set_xlim(165,180)\nax2.set_xlim(165,179)\nax2.set_ylim(-48,-34)\nax2.plot(wpNZ.Longitude,wpNZ.Latitude,'o',markersize=2)\nax2.set_title('New Zealand')\n\n#ax3.set_xlim(-180,-65)\n#ax3.set_ylim(-20,95)\nax3.set_xlim(-125,-65)\nax3.set_ylim(5,65)\nshpUSA.plot(color=COLORS[4],ax=ax3)\n#ax3.plot(wpUSA.xlong,wpUSA.ylat,'o',alpha=0.1,markersize=2)\nax3.plot(wpUSA.groupby('p_name').mean().xlong,\n wpUSA.groupby('p_name').mean().ylat,'o',alpha=0.1,markersize=2)\nax3.set_title('USA')\n\nshpZAF.plot(color=COLORS[4],ax=ax4)\nax4.set_xlim(16,33)\nax4.set_ylim(-37,-20)\nax4.plot(wpZAF.Longitude,wpZAF.Latitude,'o',markersize=2)\nax4.set_title('South Africa')\nplt.savefig(results_path + '/map_windparks.png')", "_____no_output_____" ], [ "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(10,10),gridspec_kw = {'wspace':0.1, 'hspace':0.1})\n\nshpBRA.plot(color=COLORS[4],ax=ax1)\n#ax1.plot(wpBRA.lon,wpBRA.lat,'o',alpha=0.1,markersize=2)\nax1.plot(wpBRA.groupby('name').mean().lon,\n wpBRA.groupby('name').mean().lat,'o',alpha=0.1,markersize=2)\nax1.set_title('Brazil')\n\nshpNZ.to_crs({'init': 'epsg:4326'}).plot(color=COLORS[4],ax=ax2).set_xlim(165,180)\nax2.plot(wpNZ.Longitude,wpNZ.Latitude,'o',markersize=2)\nax2.set_title('New Zealand')\n\nax3.set_xlim(-180,-65)\nax3.set_ylim(20,75)\n#ax3.set_ylim(0,87)\nshpUSA.plot(color=COLORS[4],ax=ax3)\n#ax3.plot(wpUSA.xlong,wpUSA.ylat,'o',alpha=0.1,markersize=2)\nax3.plot(wpUSA.groupby('p_name').mean().xlong,\n wpUSA.groupby('p_name').mean().ylat,'o',alpha=0.1,markersize=2)\nax3.set_title('USA')\n\nshpZAF.plot(color=COLORS[4],ax=ax4)\nax4.plot(wpZAF.Longitude,wpZAF.Latitude,'o',markersize=2)\nax4.set_title('South Africa')\nplt.savefig(results_path + '/map_windparks.png')", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Test Coffea\n\nThis will test Coffea to see if we can figure out how to use it with our code.\n\nFirst are the includes from coffea. This is based on the [example written by Ben](https://github.com/CoffeaTeam/coffea/blob/master/binder/servicex/ATLAS/LocalExample.ipynb).", "_____no_output_____" ] ], [ [ "from servicex import ServiceXDataset\r\nfrom coffea.processor.servicex import DataSource, Analysis\r\nfrom coffea.processor.servicex import LocalExecutor \r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom coffea import hist, processor\r\nfrom IPython.display import display, update_display, HTML", "_____no_output_____" ] ], [ [ "And imports connected with running servicex.", "_____no_output_____" ] ], [ [ "from func_adl import ObjectStream\r\nfrom func_adl_servicex import ServiceXSourceUpROOT\r\nfrom hist import Hist\r\nimport mplhep as mpl\r\nimport awkward as ak\r\n\r\nfrom utils import files", "_____no_output_____" ] ], [ [ "Methods copied to help us get all leptons from the source files", "_____no_output_____" ] ], [ [ "def apply_event_cuts (source: ObjectStream) -> ObjectStream:\r\n '''Event level cuts for the analysis. Keep from sending data that we aren't going to need at all in the end.\r\n '''\r\n return (source\r\n .Where(lambda e: e.trigE or e.trigM))\r\ndef good_leptons(source: ObjectStream) -> ObjectStream:\r\n '''Select out all good leptons from each event. Return their pt, eta, phi, and E, and other\r\n things needed downstream.\r\n\r\n Because uproot doesn't tie toegher the objects, we can't do any cuts at this point.\r\n '''\r\n return source.Select(lambda e:\r\n {\r\n 'lep_pt': e.lep_pt,\r\n 'lep_eta': e.lep_eta,\r\n 'lep_phi': e.lep_phi,\r\n 'lep_energy': e.lep_E,\r\n 'lep_charge': e.lep_charge,\r\n 'lep_ptcone30': e.lep_ptcone30,\r\n 'lep_etcone20': e.lep_etcone20,\r\n 'lep_type': e.lep_type,\r\n 'lep_trackd0pvunbiased': e.lep_trackd0pvunbiased,\r\n 'lep_tracksigd0pvunbiased': e.lep_tracksigd0pvunbiased,\r\n 'lep_z0': e.lep_z0,\r\n })", "_____no_output_____" ] ], [ [ "Create the `func_adl` cuts to get the data. The dataset we use here doesn't matter, as long as it \"looks\" like all the datasets we are going to be processing.", "_____no_output_____" ] ], [ [ "ds = ServiceXSourceUpROOT('cernopendata://dummy', files['ggH125_ZZ4lep']['treename'], backend_name='open_uproot')\nds.return_qastle = True\nleptons = good_leptons(apply_event_cuts(ds))", "_____no_output_____" ] ], [ [ "The analysis code that will apply the 4 lepton cuts and make the 4 lepton mass plot.", "_____no_output_____" ] ], [ [ "class ATLAS_Higgs_4L(Analysis):\r\n @staticmethod\r\n def process(events):\r\n import awkward as ak\r\n from collections import defaultdict\r\n\r\n sumw = defaultdict(float)\r\n mass_hist = hist.Hist(\r\n \"Events\",\r\n hist.Cat(\"dataset\", \"Dataset\"),\r\n hist.Bin(\"mass\", \"$Z_{ee}$ [GeV]\", 60, 60, 120),\r\n )\r\n\r\n dataset = events.metadata['dataset']\r\n leptons = events.lep\r\n\r\n # We need to look at 4 lepton events only.\r\n cut = (ak.num(leptons) == 4)\r\n\r\n # Form the invar mass, plot.\r\n # diele = electrons[cut][:, 0] + electrons[cut][:, 1]\r\n # diele.mass\r\n dilepton = leptons[:,0] + leptons[:,1]\r\n mass_4l = leptons.mass\r\n\r\n # Fill the histogram\r\n sumw[dataset] += len(events)\r\n print(len(events))\r\n mass_hist.fill(\r\n dataset=dataset,\r\n mass=ak.flatten(mass_4l),\r\n )\r\n \r\n return {\r\n \"sumw\": sumw,\r\n \"mass\": mass_hist\r\n }", "_____no_output_____" ] ], [ [ "Create the data source that we will be running against.", "_____no_output_____" ] ], [ [ "def make_ds(name: str, query: ObjectStream):\n '''Create a ServiceX Datasource for a particular ATLAS Open data file\n '''\n datasets = [ServiceXDataset(files[name]['files'], backend_name='open_uproot')]\n return DataSource(query=query, metadata={'dataset': name}, datasets=datasets)", "_____no_output_____" ] ], [ [ "And run!", "_____no_output_____" ] ], [ [ "analysis = ATLAS_Higgs_4L()\n# TODO: It would be good if datatype was determined automagically (there is enough info)\nexecutor = LocalExecutor()\n#executor = DaskExecutor(client_addr=\"tls://localhost:8786\")\ndatasource = make_ds('ggH125_ZZ4lep', leptons)\n\nasync def run_updates_stream(accumulator_stream):\n global first\n\n count = 0\n async for coffea_info in accumulator_stream:\n count += 1\n print(count, coffea_info)\n return coffea_info\n\n# Why do I need run_updates_stream, why not just await on execute (which fails with async gen can't).\n# Perhaps something from aiostream can help here?\nresult = await run_updates_stream(executor.execute(analysis, datasource))", "164716\n1 {'sumw': defaultdict(<class 'float'>, {'[root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22/4lep/MC/mc_345060.ggH125_ZZ4lep.4lep.root]': 164716.0}), 'mass': <Hist (dataset,mass) instance at 0x7f45db420af0>}\n" ], [ "hist.plot1d(result['mass'])", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# dwtls: Discrete Wavelet Transform LayerS\nThis library provides downsampling (DS) layers using discrete wavelet transforms (DWTs), which we call DWT layers.\nConventional DS layers lack either antialiasing filters and the perfect reconstruction property, so downsampled features are aliased and entire information of input features are not preserved.\nBy contrast, DWT layers have antialiasing filters and the perfect reconstruction property, which enables us to overcome the two problems.\n\nIn this library, the DWT layer and its extensions are implemented as below:\n- DWT layers with fixed wavelets (Haar, CDF22, CDF26, CDF15, and DD4 wavelets)\n- Trainable DWT (TDWT) layers\n- Weight-normalized trainable DWT (WN-TDWT) layers", "_____no_output_____" ], [ "## Install dwtls", "_____no_output_____" ] ], [ [ "!pip install dwtls", "_____no_output_____" ], [ "import torch\nimport dwtls.dwt_layers", "_____no_output_____" ] ], [ [ "## DWT layers with fixed wavelets\nThe DWT layer (including its extensions) is implemeted as a subclass of `torch.nn.Module` provided by PyTorch, so we can easily use it in PyTorch-based scripts. Also, this layer is differentiable.", "_____no_output_____" ] ], [ [ "dwt_layer = dwtls.dwt_layers.DWT(wavelet=\"haar\")", "_____no_output_____" ], [ "feature = torch.normal(0.0, 1.0, size=(1,1,20)).float()\noutput_feature = dwt_layer(feature)\nprint('Input:', feature)\nprint(\"Output:\", output_feature)", "_____no_output_____" ] ], [ [ "## TDWT layer\nThe TDWT layer has trainable wavelets (precisely, predict and update filters of lifting scheme).\n\nFor example, we can define the TDWT layer having a pair of the prediction and update filters initialized with Haar wavelet.", "_____no_output_____" ] ], [ [ "tdwt_layer = dwtls.dwt_layers.MultiStageLWT([\n dict(predict_ksize=3, update_ksize=3, \n requires_grad={\"predict\": True, \"update\": True}, \n initial_values={\"predict\": [0,1,0], \"update\": [0,0.5,0]})\n])", "_____no_output_____" ] ], [ [ "The `tdwt_layer._predict_weight` and `tdwt_layer._update_weight` of this layer are trainable jointly with other DNN components.", "_____no_output_____" ], [ "We show three structures of the trainable DWT layers used in our music source separation paper [1]. \n\n[1] Tomohiko Nakamura, Shihori Kozuka, and Hiroshi Saruwatari, “Time-Domain Audio Source Separation with Neural Networks Based on Multiresolution Analysis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1687–1701, Apr. 2021. [pdf](https://doi.org/10.1109/TASLP.2021.3072496), [demo](https://tomohikonakamura.github.io/Tomohiko-Nakamura/demo/MRDLA/)\n", "_____no_output_____" ] ], [ [ "# Type A\ntdwt_layer = dwtls.dwt_layers.MultiStageLWT([\n dict(predict_ksize=3, update_ksize=3, \n requires_grad={\"predict\": True, \"update\": True}, \n initial_values={\"predict\": [0,1,0], \"update\": [0,0.5,0]})\n])", "_____no_output_____" ], [ "# Type B\ntdwt_layer = dwtls.dwt_layers.MultiStageLWT([\n dict(predict_ksize=1, update_ksize=1, \n requires_grad={\"predict\": False, \"update\": False}, \n initial_values={\"predict\": [1], \"update\": [0.5]}),\n dict(predict_ksize=3, update_ksize=3, \n requires_grad={\"predict\": True, \"update\": True}, \n initial_values={\"predict\": [0,0,0], \"update\": [0,0,0]})\n])", "_____no_output_____" ], [ "# Type C\ntdwt_layer = dwtls.dwt_layers.MultiStageLWT([\n dict(predict_ksize=3, update_ksize=3, \n requires_grad={\"predict\": True, \"update\": True}, \n initial_values={\"predict\": [0,1,0], \"update\": [0,0.5,0]}),\n dict(predict_ksize=3, update_ksize=3, \n requires_grad={\"predict\": True, \"update\": True}, \n initial_values={\"predict\": [0,0,0], \"update\": [0,0,0]})\n])", "_____no_output_____" ] ], [ [ "## WN-TDWT layer\nThe TDWT layer can be incorporated into many types of DNNs, but such straightforward extension does not guarantee that it has anti-aliasing filters, while it has the perfect reconstruction property owing to the lifting scheme.\n\nThe WN-TDWT layer is developed to overcome this problem. It has both properties owing to adequate normalization of the prediction and update filter coefficients.", "_____no_output_____" ] ], [ [ "# Type A\ntdwt_layer = dwtls.dwt_layers.WeightNormalizedMultiStageLWT([\n dict(predict_ksize=3, update_ksize=3, \n requires_grad={\"predict\": True, \"update\": True}, \n initial_values={\"predict\": [0,1,0], \"update\": [0,0.5,0]})\n])", "_____no_output_____" ] ], [ [ "The WN-TDWT layer can be used in the same way as the TDWT layer.", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Imports\nimport matplotlib.pyplot as plt\nimport json", "_____no_output_____" ], [ "# Load data from result files\nresults_file = './results/results_5.json'\nsummary_file = './results/summary.json'\n\nresults = json.load(open(results_file))['results']\nsummary = json.load(open(summary_file))\n\n\ndef autolabel(rects, label_pos=0):\n \"\"\"\n Generate labels to show values on top of bar charts\n \n :param rects: <pyplot.object> The current pyplot figure\n :param label_pos: <float> OR <int> The amount of offset compared to the height of the bar\n \"\"\"\n for rect in rects:\n height = rect.get_height()\n plt.text(rect.get_x() + rect.get_width()/2., height + label_pos, f'{int(height)}', ha='center', va='bottom')", "_____no_output_____" ], [ "# Visualization for number of articles per category\ncategories = summary['occurrences per category']\ntotal_results = sum(categories.values())\n\ncategories.pop('Generic', None)\ncategories.pop('Not about data ecosystems', None)\ncategories.pop('Systematic Review', None)\n\ntotal_categorized_results = sum(categories.values())\ntotal_uncategorized_results = total_results - total_categorized_results\n\nprint(f'total: {total_results}\\n' \\\n f'catogorized: {total_categorized_results}\\n' \\\n f'uncategorized: {total_uncategorized_results}\\n')\n\nlabels = list(categories.keys())\nvalues = list(categories.values())\n\n# Pie chart\nplt.pie(values, labels=labels, autopct='%1.1f%%', startangle=230)\nplt.axis('equal')\nplt.show()\n\n# Same data in bar chart form\nfig = plt.bar(range(len(categories)), values, align='center')\nautolabel(fig, -0.8)\nplt.xticks(range(len(categories)), labels, rotation=45, ha='right')\nplt.xlabel('Fields')\nplt.ylabel('Studies published')\nplt.show()\n\n# Same charts but this time with the science fields combined\ncategories_combined = categories\ncategories_combined['Science'] += categories_combined.pop('Biology (science)')\ncategories_combined['Science'] += categories_combined.pop('Neuroscience')\n\nlabels_combined = list(categories_combined.keys())\nvalues_combined = list(categories_combined.values())\n\nplt.pie(values_combined, labels=labels_combined, autopct='%1.1f%%', startangle=90)\nplt.axis('equal')\nplt.show()\n\n# Bar chart with science fields combined\nfig = plt.bar(range(len(categories_combined)), values_combined, align='center')\nautolabel(fig, -0.8)\nplt.xticks(range(len(categories_combined)), labels_combined, rotation=45, ha='right')\nplt.xlabel('Fields')\nplt.ylabel('Studies published')\nplt.show()", "total: 68\ncatogorized: 29\nuncategorized: 39\n\n" ], [ "# Visualization of the number of articles published per year\npublish_years = {}\nfor result in results:\n year = result['publish_date'][0:4]\n if year in publish_years.keys():\n publish_years[year] += 1\n else:\n publish_years.update({year: 1})\n \nkey_list = sorted(list(publish_years.keys()))\nvalue_list = [publish_years[x] for x in key_list]\n\n# It shows a drop in 2018 because the year has just started, this gives\n# a wrong idea of the number of studies about the subject\nfig = plt.bar(range(len(value_list)), value_list, align='center')\nautolabel(fig, -1.5)\nplt.xticks(range(len(key_list)), key_list, rotation=45, ha='right')\nplt.xlabel('Publish year')\nplt.ylabel('Studies published')\nplt.show()\n\n# Plot with 2018 removed from the results\nkey_list = key_list[:-1]\nvalue_list = value_list[:-1]\n\nfig = plt.bar(range(len(value_list)), value_list, align='center')\nautolabel(fig, -1.5)\nplt.xticks(range(len(key_list)), key_list, rotation=45, ha='right')\nplt.xlabel('Publish year')\nplt.ylabel('Studies published')\nplt.show()", "_____no_output_____" ], [ "# Show the occurrences of each of the search terms\nsearch_terms = summary['search terms']\n\nlabels = list(search_terms.keys())\nvalues = list(search_terms.values())\n\nfig = plt.bar(range(len(values)), values, align='center')\nautolabel(fig, -3)\nplt.xticks(range(len(labels)), labels, rotation=45, ha='right')\nplt.show()", "_____no_output_____" ], [ "# Check qualitycriteria\nin_title = []\nin_abstract = []\nterm = 'data ecosystem'\n\nfor result in results:\n if term in result['title'].lower():\n in_title.append(result['id'])\n if term in result['abstract'].lower():\n in_abstract.append(result['id'])\n \nprint(f'Results with {term} in title: {in_title}')\nprint(f'Results with {term} in abstract: {in_abstract}')\n\nin_both = [x for x in in_title if x in in_abstract]\nprint(f'\\nResults with {term} in both title and abstract: {in_both}')\n\nin_single = [x for x in in_abstract]\nfor result in in_title:\n in_single.append(result)\n \nin_single = sorted([x for x in in_single if x not in in_both])\nprint(f'\\nResults with {term} only in either title or abstract: {in_single}')", "Results with data ecosystem in title: [0, 1, 2, 3, 4, 6, 7, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 23, 25, 31, 50, 54, 56, 58]\nResults with data ecosystem in abstract: [0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67]\n\nResults with data ecosystem in both title and abstract: [0, 1, 3, 6, 7, 9, 10, 12, 13, 14, 16, 18, 23, 25, 54]\n\nResults with data ecosystem only in either title or abstract: [2, 4, 5, 8, 11, 15, 17, 19, 20, 21, 22, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/sprinkler_pgm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Directed graphical models\n\nWe illustrate some basic properties of DGMs.", "_____no_output_____" ] ], [ [ "!pip install causalgraphicalmodels\n!pip install pgmpy", "Requirement already satisfied: causalgraphicalmodels in /usr/local/lib/python3.7/dist-packages (0.0.4)\nRequirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from causalgraphicalmodels) (0.10.1)\nRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from causalgraphicalmodels) (1.1.5)\nRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from causalgraphicalmodels) (1.19.5)\nRequirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from causalgraphicalmodels) (2.5.1)\nRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->causalgraphicalmodels) (2018.9)\nRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->causalgraphicalmodels) (2.8.1)\nRequirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx->causalgraphicalmodels) (4.4.2)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->causalgraphicalmodels) (1.15.0)\nCollecting pgmpy\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/a3/0e/d9fadbfaa35e010c04d43acd3ae9fbefec98897dd7d61a6b7eb5a8b34072/pgmpy-0.1.14-py3-none-any.whl (331kB)\n\u001b[K |████████████████████████████████| 337kB 8.0MB/s \n\u001b[?25hRequirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from pgmpy) (2.5.1)\nRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from pgmpy) (1.8.1+cu101)\nRequirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (from pgmpy) (0.10.2)\nRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pgmpy) (1.1.5)\nRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pgmpy) (1.19.5)\nRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pgmpy) (1.0.1)\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pgmpy) (0.22.2.post1)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pgmpy) (4.41.1)\nRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pgmpy) (1.4.1)\nRequirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from pgmpy) (2.4.7)\nRequirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx->pgmpy) (4.4.2)\nRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->pgmpy) (3.7.4.3)\nRequirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->pgmpy) (0.5.1)\nRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->pgmpy) (2018.9)\nRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pgmpy) (2.8.1)\nRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.4.0->statsmodels->pgmpy) (1.15.0)\nInstalling collected packages: pgmpy\nSuccessfully installed pgmpy-0.1.14\n" ], [ "from causalgraphicalmodels import CausalGraphicalModel\nimport pgmpy\nimport numpy as np\nimport pandas as pd", "_____no_output_____" ] ], [ [ "# Make the model", "_____no_output_____" ] ], [ [ "sprinkler = CausalGraphicalModel(\n nodes=[\"cloudy\", \"rain\", \"sprinkler\", \"wet\", \"slippery\"],\n edges=[\n (\"cloudy\", \"rain\"), \n (\"cloudy\", \"sprinkler\"), \n (\"rain\", \"wet\"),\n (\"sprinkler\", \"wet\"), \n (\"wet\", \"slippery\")\n ]\n)\n\n", "_____no_output_____" ] ], [ [ "# Draw the model", "_____no_output_____" ] ], [ [ "# draw return a graphviz `dot` object, which jupyter can render\nout = sprinkler.draw()", "_____no_output_____" ], [ "type(out)\n", "_____no_output_____" ], [ "display(out)", "_____no_output_____" ], [ "out.render() ", "_____no_output_____" ] ], [ [ "# Display the factorization", "_____no_output_____" ] ], [ [ "print(sprinkler.get_distribution())", "P(season)P(sprinkler|season)P(rain|season)P(wet|rain,sprinkler)P(slippery|wet)\n" ] ], [ [ "# D-separation", "_____no_output_____" ] ], [ [ "\n# check for d-seperation of two nodes\nsprinkler.is_d_separated(\"slippery\", \"cloudy\", {\"wet\"})", "_____no_output_____" ] ], [ [ "# Extract CI relationships", "_____no_output_____" ] ], [ [ "# get all the conditional independence relationships implied by a CGM\nCI = sprinkler.get_all_independence_relationships()\nprint(CI)", "[('rain', 'sprinkler', {'cloudy'}), ('rain', 'slippery', {'wet'}), ('rain', 'slippery', {'sprinkler', 'wet'}), ('rain', 'slippery', {'cloudy', 'wet'}), ('rain', 'slippery', {'sprinkler', 'cloudy', 'wet'}), ('sprinkler', 'slippery', {'wet'}), ('sprinkler', 'slippery', {'rain', 'wet'}), ('sprinkler', 'slippery', {'cloudy', 'wet'}), ('sprinkler', 'slippery', {'rain', 'cloudy', 'wet'}), ('cloudy', 'slippery', {'wet'}), ('cloudy', 'slippery', {'rain', 'sprinkler'}), ('cloudy', 'slippery', {'rain', 'wet'}), ('cloudy', 'slippery', {'sprinkler', 'wet'}), ('cloudy', 'slippery', {'rain', 'sprinkler', 'wet'}), ('cloudy', 'wet', {'rain', 'sprinkler'}), ('cloudy', 'wet', {'slippery', 'rain', 'sprinkler'})]\n" ], [ "records = []\nfor ci in CI:\n record = (ci[0], ci[1], ', '.join(x for x in ci[2]))\n records.append(record)\n\nprint(records)\ndf = pd.DataFrame(records, columns = ('X', 'Y', 'Z'))\ndisplay(df)", "[('rain', 'sprinkler', 'cloudy'), ('rain', 'slippery', 'wet'), ('rain', 'slippery', 'sprinkler, wet'), ('rain', 'slippery', 'cloudy, wet'), ('rain', 'slippery', 'sprinkler, cloudy, wet'), ('sprinkler', 'slippery', 'wet'), ('sprinkler', 'slippery', 'rain, wet'), ('sprinkler', 'slippery', 'cloudy, wet'), ('sprinkler', 'slippery', 'rain, cloudy, wet'), ('cloudy', 'slippery', 'wet'), ('cloudy', 'slippery', 'rain, sprinkler'), ('cloudy', 'slippery', 'rain, wet'), ('cloudy', 'slippery', 'sprinkler, wet'), ('cloudy', 'slippery', 'rain, sprinkler, wet'), ('cloudy', 'wet', 'rain, sprinkler'), ('cloudy', 'wet', 'slippery, rain, sprinkler')]\n" ], [ "print(df.to_latex(index=False))", "\\begin{tabular}{lll}\n\\toprule\n X & Y & Z \\\\\n\\midrule\n rain & sprinkler & cloudy \\\\\n rain & slippery & wet \\\\\n rain & slippery & sprinkler, wet \\\\\n rain & slippery & cloudy, wet \\\\\n rain & slippery & sprinkler, cloudy, wet \\\\\n sprinkler & slippery & wet \\\\\n sprinkler & slippery & rain, wet \\\\\n sprinkler & slippery & cloudy, wet \\\\\n sprinkler & slippery & rain, cloudy, wet \\\\\n cloudy & slippery & wet \\\\\n cloudy & slippery & rain, sprinkler \\\\\n cloudy & slippery & rain, wet \\\\\n cloudy & slippery & sprinkler, wet \\\\\n cloudy & slippery & rain, sprinkler, wet \\\\\n cloudy & wet & rain, sprinkler \\\\\n cloudy & wet & slippery, rain, sprinkler \\\\\n\\bottomrule\n\\end{tabular}\n\n" ] ], [ [ "# Inference", "_____no_output_____" ] ], [ [ "\n\nfrom pgmpy.models import BayesianModel\nfrom pgmpy.factors.discrete import TabularCPD\n\n# Defining the model structure. We can define the network by just passing a list of edges.\nmodel = BayesianModel([('C', 'S'), ('C', 'R'), ('S', 'W'), ('R', 'W'), ('W', 'L')])\n\n# Defining individual CPDs.\ncpd_c = TabularCPD(variable='C', variable_card=2, values=np.reshape([0.5, 0.5],(2,1)))\n\n# In pgmpy the columns are the evidences and rows are the states of the variable.\n \ncpd_s = TabularCPD(variable='S', variable_card=2, \n values=[[0.5, 0.9],\n [0.5, 0.1]],\n evidence=['C'],\n evidence_card=[2])\n\ncpd_r = TabularCPD(variable='R', variable_card=2, \n values=[[0.8, 0.2],\n [0.2, 0.8]],\n evidence=['C'],\n evidence_card=[2])\n\ncpd_w = TabularCPD(variable='W', variable_card=2, \n values=[[1.0, 0.1, 0.1, 0.01],\n [0.0, 0.9, 0.9, 0.99]],\n evidence=['S', 'R'],\n evidence_card=[2, 2])\n\ncpd_l = TabularCPD(variable='L', variable_card=2, \n values=[[0.9, 0.1],\n [0.1, 0.9]],\n evidence=['W'],\n evidence_card=[2])\n\n# Associating the CPDs with the network\nmodel.add_cpds(cpd_c, cpd_s, cpd_r, cpd_w, cpd_l)\n\n# check_model checks for the network structure and CPDs and verifies that the CPDs are correctly \n# defined and sum to 1.\nmodel.check_model()\n\n\n", "_____no_output_____" ], [ "from pgmpy.inference import VariableElimination\ninfer = VariableElimination(model)\n\n# p(R=1)= 0.5*0.2 + 0.5*0.8 = 0.5\nprobs = infer.query(['R']).values\nprint('\\np(R=1) = ', probs[1])\n\n# P(R=1|W=1) = 0.7079\nprobs = infer.query(['R'], evidence={'W': 1}).values\nprint('\\np(R=1|W=1) = ', probs[1])\n\n\n# P(R=1|W=1,S=1) = 0.3204\nprobs = infer.query(['R'], evidence={'W': 1, 'S': 1}).values\nprint('\\np(R=1|W=1,S=1) = ', probs[1])", "Finding Elimination Order: : 100%|██████████| 4/4 [00:00<00:00, 557.40it/s]\nEliminating: W: 100%|██████████| 4/4 [00:00<00:00, 341.96it/s]\nFinding Elimination Order: : 100%|██████████| 3/3 [00:00<00:00, 710.42it/s]\nEliminating: C: 100%|██████████| 3/3 [00:00<00:00, 435.59it/s]\nFinding Elimination Order: : 100%|██████████| 2/2 [00:00<00:00, 754.71it/s]\nEliminating: C: 100%|██████████| 2/2 [00:00<00:00, 517.24it/s]" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import json\nfrom datetime import datetime, timedelta\nimport matplotlib.pylab as plot\nimport matplotlib.pyplot as plt\nfrom matplotlib import dates\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib\nmatplotlib.style.use('ggplot')\n%matplotlib inline", "_____no_output_____" ], [ "# Read data from http bro logs\nwith open(\"http.log\",'r') as infile:\n file_data = infile.read()\n \n# Split file by newlines\nfile_data = file_data.split('\\n')\n\n# Remove comment lines\nhttp_data = []\nfor line in file_data:\n if line[0] is not None and line[0] != \"#\":\n http_data.append(line)", "_____no_output_____" ], [ "# Lets analyze user agents\nuser_agent_analysis = {}\nuser_agent_overall = {}\nfor line in http_data:\n # Extract the timestamp\n timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n # Strip second and microsecond from timestamp\n timestamp = str(timestamp.replace(second=0,microsecond=0))\n \n # Extract the user agent\n user_agent = line.split('\\t')[11]\n \n # Update status code analysis variable\n if user_agent not in user_agent_analysis.keys():\n user_agent_analysis[user_agent] = {timestamp: 1}\n else:\n if timestamp not in user_agent_analysis[user_agent].keys():\n user_agent_analysis[user_agent][timestamp] = 1\n else:\n user_agent_analysis[user_agent][timestamp] += 1\n \n # Update overall user agent count\n if user_agent not in user_agent_overall.keys():\n user_agent_overall[user_agent] = 1\n else:\n user_agent_overall[user_agent] += 1", "_____no_output_____" ], [ "df = pd.DataFrame.from_dict(user_agent_analysis,orient='columns').fillna(0)\ndf", "_____no_output_____" ], [ "#df.plot(figsize=(12,9))\n\nax = df.plot(rot=90,figsize=(12,9))", "_____no_output_____" ], [ "user_agent_analysis2 = user_agent_analysis\nprint(user_agent_analysis2.keys())\nhigh_volume_user_agents = [\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36\"\n]\nfor ua in high_volume_user_agents: \n if ua in user_agent_analysis2.keys():\n del user_agent_analysis2[ua]\ndf2 = pd.DataFrame.from_dict(user_agent_analysis2,orient='columns').fillna(0)\ndf2", "['Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0', 'Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)', '-', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36', 'Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0', 'Wget/1.16.1 (linux-gnu)']\n" ], [ "df2.plot(rot=90,figsize=(12,9))", "_____no_output_____" ], [ "# Lets analyze status codes\nstatus_code_analysis = {}\nstatus_code_overall = {}\nearliest_time = None\nlatest_time = None\nfor line in http_data:\n # Extract the timestamp\n timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n # Strip minute, second and microsecond from timestamp\n #timestamp = str(timestamp.replace(minute=0,second=0,microsecond=0))\n timestamp = str(timestamp.replace(second=0,microsecond=0))\n \n # Extract the status code\n status_code = line.split('\\t')[14]\n \n # Update status code analysis variable\n if status_code not in status_code_analysis.keys():\n status_code_analysis[status_code] = {timestamp: 1}\n else:\n if timestamp not in status_code_analysis[status_code].keys():\n status_code_analysis[status_code][timestamp] = 1\n else:\n status_code_analysis[status_code][timestamp] += 1\n \n # Update overall status code count\n if status_code not in status_code_overall.keys():\n status_code_overall[status_code] = 1\n else:\n status_code_overall[status_code] += 1\n \n # Update our earliest and latest time as needed\n if earliest_time is None or timestamp < earliest_time:\n earliest_time = timestamp\n if latest_time is None or timestamp > latest_time:\n latest_time = timestamp", "_____no_output_____" ], [ "# Format data for the plot function\nstatus_label = []\ndata = []\nfor code in sorted(status_code_overall.keys()):\n status_label.append(str(code) + \" (\" + str(status_code_overall[code]) + \")\")\n data.append(status_code_overall[code])\n\nplot.figure(1,figsize=[8,8])\npatches, texts = plot.pie(data, shadow=True, startangle=90)\nplot.legend(patches, status_label,loc=\"best\")\nplot.title('Status Code Distribution')\nplot.axis('equal')\nplot.tight_layout()\nplot.show()", "_____no_output_____" ], [ "# Output the status codes in table form\ndf = pd.DataFrame.from_dict(status_code_analysis,orient='columns').fillna(0)\ndf", "_____no_output_____" ], [ "# Plot the status codes\ndf.plot(rot=90,figsize=(12,9))", "_____no_output_____" ], [ "# Remove the 200 status code and re-plot the status codes\nstatus_code_analysis2 = status_code_analysis\nif '200' in status_code_analysis2.keys():\n del status_code_analysis2['200']\nprint(status_code_analysis2.keys())\ndf2 = pd.DataFrame.from_dict(status_code_analysis2,orient='columns').fillna(0)\ndf2.plot(rot=90, figsize=(12,9))", "['304', '301', '302', '-', '405', '404', '401', '400', '501']\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Method4 DCT based DOST + Huffman encoding", "_____no_output_____" ], [ "## Import Libraries", "_____no_output_____" ] ], [ [ "import mne\nimport numpy as np\nfrom scipy.fft import fft,fftshift\nimport matplotlib.pyplot as plt\nfrom scipy.signal import butter, lfilter\nfrom scipy.signal import freqz\nfrom scipy import signal\nfrom scipy.fftpack import fft, dct, idct\nfrom itertools import islice \nimport pandas as pd\nimport os", "_____no_output_____" ] ], [ [ "## Preprocessing", "_____no_output_____" ], [ "### Data loading", "_____no_output_____" ] ], [ [ "acc = pd.read_csv('ACC.csv')\nacc = acc.iloc[1:]\nacc.columns = ['column1','column2','column3']\nnp.savetxt('acc.txt',acc)\n\n\n\nacc_c1 = acc[\"column1\"]\nacc_c2 = acc[\"column2\"]\nacc_c3 = acc[\"column3\"]\nacc_array_c1 = acc_c1.to_numpy() #save the data into an ndarray\nacc_array_c2 = acc_c2.to_numpy()\nacc_array_c3 = acc_c3.to_numpy()\nacc_array_c1.shape\n\nacc_array_c1 = acc_array_c1[0:66000] # Remove the signal in first 3minutes and last 5minutes\nacc_array_c2 = acc_array_c2[0:66000]\nacc_array_c3 = acc_array_c3[0:66000]\n\nsampling_freq = 1/32\nN = acc_array_c1.size\n\nxf = np.linspace(-N*sampling_freq/2, N*sampling_freq/2, N)\n\n\nindex = np.linspace(0, round((N-1)*sampling_freq,4), N)\n\n", "_____no_output_____" ] ], [ [ "### Butterworth Filter to denoising", "_____no_output_____" ] ], [ [ "def butter_bandpass(lowcut, highcut, fs, order=5):\n nyq = 0.5 * fs\n low = lowcut / nyq\n high = highcut / nyq\n b, a = butter(order, [low, high], btype='band')\n return b, a\n\n\ndef butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n y = lfilter(b, a, data)\n return y", "_____no_output_____" ], [ "from scipy.signal import freqz\nfrom scipy import signal\n# Sample rate and desired cutoff frequencies (in Hz).\nfs = 1000.0\nlowcut = 0.5\nhighcut = 50.0\n\n# Plot the frequency response for a few different orders.\nplt.figure(1)\nplt.clf()\nfor order in [1, 2, 3, 4]:\n b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n w, h = freqz(b, a, worN=2000)\n plt.plot((fs * 0.5 / np.pi) * w, abs(h), label=\"order = %d\" % order)\n\nplt.plot([0, 0.5 * fs], [np.sqrt(0.5), np.sqrt(0.5)],\n '--', label='sqrt(0.5)')\nplt.xlabel('Frequency (Hz)')\nplt.ylabel('Gain')\nplt.grid(True)\nplt.legend(loc='best')", "_____no_output_____" ], [ "y1 = butter_bandpass_filter(acc_array_c1, lowcut, highcut, fs, order=2)\ny2 = butter_bandpass_filter(acc_array_c2, lowcut, highcut, fs, order=2)\ny3 = butter_bandpass_filter(acc_array_c3, lowcut, highcut, fs, order=2)", "_____no_output_____" ], [ "resampled_signal1 = y1\nresampled_signal2 = y2\nresampled_signal3 = y3", "_____no_output_____" ], [ "np.savetxt('processed_acc_col1.txt',resampled_signal1)\nnp.savetxt('processed_acc_col2.txt',resampled_signal2)\nnp.savetxt('processed_acc_col3.txt',resampled_signal3)", "_____no_output_____" ], [ "rounded_signal1 = np.around(resampled_signal1)\nrounded_signal2 = np.around(resampled_signal2)\nrounded_signal3 = np.around(resampled_signal3)", "_____no_output_____" ] ], [ [ "## Transformation --- DCT based DOST ", "_____no_output_____" ] ], [ [ "from scipy.fftpack import fft, dct\naN1 = dct(rounded_signal1, type = 2, norm = 'ortho')\naN2 = dct(rounded_signal2, type = 2, norm = 'ortho')\naN3 = dct(rounded_signal3, type = 2, norm = 'ortho')", "_____no_output_____" ], [ "def return_N(target):\n if target > 1:\n for i in range(1, int(target)):\n if (2 ** i >= target):\n return i-1\n else:\n return 1\n", "_____no_output_____" ], [ "from itertools import islice \nsplit_list = [1]\nfor i in range(0,return_N(aN1.size)):\n split_list.append(2 ** i)\ntemp1 = iter(aN1) \nres1 = [list(islice(temp1, 0, ele)) for ele in split_list]\ntemp2 = iter(aN2) \nres2 = [list(islice(temp2, 0, ele)) for ele in split_list]\ntemp3 = iter(aN3) \nres3 = [list(islice(temp3, 0, ele)) for ele in split_list]", "_____no_output_____" ], [ "from scipy.fftpack import fft, dct, idct\ncN_idct1 = [list(idct(res1[0], type = 2, norm = 'ortho' )), list(idct(res1[1], type = 2, norm = 'ortho' ))]\nfor k in range(2,len(res1)):\n cN_idct1.append(list(idct(res1[k], type = 2, norm = 'ortho' )))\ncN_idct2 = [list(idct(res2[0], type = 2, norm = 'ortho' )), list(idct(res2[1], type = 2, norm = 'ortho' ))]\nfor k in range(2,len(res2)):\n cN_idct2.append(list(idct(res2[k], type = 2, norm = 'ortho' )))\ncN_idct3 = [list(idct(res3[0], type = 2, norm = 'ortho' )), list(idct(res3[1], type = 2, norm = 'ortho' ))]\nfor k in range(2,len(res3)):\n cN_idct3.append(list(idct(res3[k], type = 2, norm = 'ortho' )))", "_____no_output_____" ], [ "all_numbers1 = []\nfor i in cN_idct1:\n for j in i:\n all_numbers1.append(j)\nall_numbers2 = []\nfor i in cN_idct2:\n for j in i:\n all_numbers2.append(j)\nall_numbers3 = []\nfor i in cN_idct3:\n for j in i:\n all_numbers3.append(j)", "_____no_output_____" ], [ "all_numbers1 = np.asarray(all_numbers1)\nall_numbers2 = np.asarray(all_numbers2)\nall_numbers3 = np.asarray(all_numbers3)", "_____no_output_____" ], [ "int_cN1 = np.round(all_numbers1,3)\nint_cN2 = np.round(all_numbers2,3)\nint_cN3 = np.round(all_numbers3,3)", "_____no_output_____" ], [ "np.savetxt('int_cN1.txt',int_cN1, fmt='%.3f')\nnp.savetxt('int_cN2.txt',int_cN2, fmt='%.3f')\nnp.savetxt('int_cN3.txt',int_cN3,fmt='%.3f')", "_____no_output_____" ] ], [ [ "## Huffman Coding", "_____no_output_____" ], [ "### INSTRUCTION ON HOW TO COMPRESS THE DATA BY HUFFMAN CODING\n\n\n(I used the package \"tcmpr 0.2\" and \"pyhuff 1.1\". These two packages provided the same compression result. So here, we just use \"tcmpr 0.2\")\n\n1. Open your termial or git bash, enter \"pip install tcmpr\" to install the \"tcmpr 0.2\" package\n2. Enter the directory which include the file you want to compress OR copy the path of the file you want to compress\n3. Enter \"tcmpr filename.txt\" / \"tcmpr filepath\" to compress the file\n4. Find the compressed file in the same directory of the original file", "_____no_output_____" ] ], [ [ "# Do Huffman encoding based on the instruction above \n# or run this trunk if this scratch locates in the same directory with the signal you want to encode\nos.system('tcmpr int_cN1.txt')\nos.system('tcmpr int_cN2.txt')\nos.system('tcmpr int_cN3.txt')", "_____no_output_____" ] ], [ [ "## Reconstruction", "_____no_output_____" ] ], [ [ "os.system('tcmpr -d int_cN1.txt.huffman')\nos.system('tcmpr -d int_cN2.txt.huffman')\nos.system('tcmpr -d int_cN3.txt.huffman')\ndecoded_data1 = np.loadtxt(fname = \"int_cN1.txt\")\ndecoded_data2 = np.loadtxt(fname = \"int_cN2.txt\")\ndecoded_data3 = np.loadtxt(fname = \"int_cN3.txt\")", "_____no_output_____" ], [ "recover_signal1 = decoded_data1\nrecover_signal2 = decoded_data2\nrecover_signal3 = decoded_data3\nrecover_signal1 = list(recover_signal1)\nrecover_signal2 = list(recover_signal2)\nrecover_signal3 = list(recover_signal3)\nlen(recover_signal1)", "_____no_output_____" ], [ "split_list = [1]\nfor i in range(0,return_N(len(recover_signal1))+1):\n split_list.append(2 ** i)\ntemp_recovered1 = iter(recover_signal1) \nres_recovered1 = [list(islice(temp_recovered1, 0, ele)) for ele in split_list]\ntemp_recovered2 = iter(recover_signal2) \nres_recovered2 = [list(islice(temp_recovered2, 0, ele)) for ele in split_list] \ntemp_recovered3 = iter(recover_signal3) \nres_recovered3 = [list(islice(temp_recovered3, 0, ele)) for ele in split_list] ", "_____no_output_____" ], [ "recover_dct1 = [list(dct(res_recovered1[0], type = 2, norm = 'ortho' )), list(dct(res_recovered1[1], type = 2, norm = 'ortho' ))]\nfor k in range(2,len(res_recovered1)):\n recover_dct1.append(list(dct(res_recovered1[k], type = 2, norm = 'ortho' )))\nrecover_dct2 = [list(dct(res_recovered2[0], type = 2, norm = 'ortho' )), list(dct(res_recovered2[1], type = 2, norm = 'ortho' ))]\nfor k in range(2,len(res_recovered2)):\n recover_dct2.append(list(dct(res_recovered2[k], type = 2, norm = 'ortho' )))\nrecover_dct3 = [list(dct(res_recovered3[0], type = 2, norm = 'ortho' )), list(dct(res_recovered3[1], type = 2, norm = 'ortho' ))]\nfor k in range(2,len(res_recovered3)):\n recover_dct3.append(list(dct(res_recovered3[k], type = 2, norm = 'ortho' )))", "_____no_output_____" ], [ "all_recover1 = []\nfor i in recover_dct1:\n for j in i:\n all_recover1.append(j)\nall_recover2 = []\nfor i in recover_dct2:\n for j in i:\n all_recover2.append(j)\nall_recover3 = []\nfor i in recover_dct3:\n for j in i:\n all_recover3.append(j)\n", "_____no_output_____" ], [ "aN_recover1 = idct(all_recover1, type = 2, norm = 'ortho')\naN_recover2 = idct(all_recover2, type = 2, norm = 'ortho')\naN_recover3 = idct(all_recover3, type = 2, norm = 'ortho')", "_____no_output_____" ], [ "plt.plot(signal.resample(y1, len(aN_recover1))[31000:31100], label = \"origianl\")\nplt.plot(aN_recover1[31000:31100], label = \"recovered\")\nplt.legend()\nplt.title('ACC')\nplt.grid()\nplt.show()", "_____no_output_____" ], [ "#resampled_signal_shorter = resampled_signal1[:len(aN_recover1)] \nresampled_signal_shorter1 = signal.resample(y1, len(aN_recover1))\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\n\ndef PRD_calculation(original_signal, compressed_signal):\n PRD = sqrt(sum((original_signal-compressed_signal)**2)/(sum(original_signal**2)))\n return PRD\n\nPRD = PRD_calculation(resampled_signal_shorter1, aN_recover1)\nprint(\"The PRD is {}%\".format(round(PRD*100,3)))", "The PRD is 3.16%\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# ElasticNet with RobustScaler\n", "_____no_output_____" ], [ "**This Code template is for the regression analysis using a ElasticNet Regression and the feature rescaling technique RobustScaler in a pipeline**", "_____no_output_____" ], [ "### Required Packages", "_____no_output_____" ] ], [ [ "import warnings as wr\r\nimport numpy as np \r\nimport pandas as pd \r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt \r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.pipeline import Pipeline\r\nfrom sklearn.preprocessing import RobustScaler\r\nfrom sklearn.model_selection import train_test_split \r\nfrom sklearn.linear_model import ElasticNet\r\nfrom sklearn.metrics import mean_squared_error, r2_score,mean_absolute_error\r\nwr.filterwarnings('ignore')", "_____no_output_____" ] ], [ [ "### Initialization\n\nFilepath of CSV file", "_____no_output_____" ] ], [ [ "#filepath\r\nfile_path= \"\"", "_____no_output_____" ] ], [ [ "List of features which are required for model training .", "_____no_output_____" ] ], [ [ "#x_values\r\nfeatures=[]", "_____no_output_____" ] ], [ [ "Target feature for prediction.", "_____no_output_____" ] ], [ [ "#y_value\r\ntarget=''", "_____no_output_____" ] ], [ [ "### Data Fetching\n\nPandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.\n\nWe will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry.", "_____no_output_____" ] ], [ [ "df=pd.read_csv(file_path) #reading file\r\ndf.head()#displaying initial entries", "_____no_output_____" ], [ "print('Number of rows are :',df.shape[0], ',and number of columns are :',df.shape[1])", "Number of rows are : 1338 ,and number of columns are : 7\n" ], [ "df.columns.tolist()\r\n", "_____no_output_____" ] ], [ [ "### Data Preprocessing\n\nSince the majority of the machine learning models in the Sklearn library doesn't handle string category data and Null value, we have to explicitly remove or replace null values. The below snippet have functions, which removes the null value if any exists. And convert the string classes data in the datasets by encoding them to integer classes.\n", "_____no_output_____" ] ], [ [ "def NullClearner(df):\r\n if(isinstance(df, pd.Series) and (df.dtype in [\"float64\",\"int64\"])):\r\n df.fillna(df.mean(),inplace=True)\r\n return df\r\n elif(isinstance(df, pd.Series)):\r\n df.fillna(df.mode()[0],inplace=True)\r\n return df\r\n else:return df\r\ndef EncodeX(df):\r\n return pd.get_dummies(df)", "_____no_output_____" ] ], [ [ "#### Correlation Map\n\nIn order to check the correlation between the features, we will plot a correlation matrix. It is effective in summarizing a large amount of data where the goal is to see patterns.", "_____no_output_____" ] ], [ [ "plt.figure(figsize = (15, 10))\r\ncorr = df.corr()\r\nmask = np.triu(np.ones_like(corr, dtype = bool))\r\nsns.heatmap(corr, mask = mask, linewidths = 1, annot = True, fmt = \".2f\")\r\nplt.show()", "_____no_output_____" ], [ "correlation = df[df.columns[1:]].corr()[target][:]\r\ncorrelation", "_____no_output_____" ] ], [ [ "### Feature Selections\n\nIt is the process of reducing the number of input variables when developing a predictive model. Used to reduce the number of input variables to both reduce the computational cost of modelling and, in some cases, to improve the performance of the model.\n\nWe will assign all the required input features to X and target/outcome to Y.", "_____no_output_____" ] ], [ [ "#spliting data into X(features) and Y(Target)\r\n\r\nX=df[features]\r\nY=df[target] ", "_____no_output_____" ] ], [ [ "Calling preprocessing functions on the feature and target set.", "_____no_output_____" ] ], [ [ "x=X.columns.to_list()\r\nfor i in x:\r\n X[i]=NullClearner(X[i])\r\nX=EncodeX(X)\r\nY=NullClearner(Y)\r\nX.head()", "_____no_output_____" ] ], [ [ "### Data Splitting\n\nThe train-test split is a procedure for evaluating the performance of an algorithm. The procedure involves taking a dataset and dividing it into two subsets. The first subset is utilized to fit/train the model. The second subset is used for prediction. The main motive is to estimate the performance of the model on new data.", "_____no_output_____" ] ], [ [ "#we can choose randomstate and test_size as over requerment\r\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 1) #performing datasplitting", "_____no_output_____" ] ], [ [ "## Model\r\n\r\n\r\n### Data Scaling\r\n**Used RobustScaler**\r\n\r\n* It scales features using statistics that are robust to outliers. \r\n* This method removes the median and scales the data in the range between 1st quartile and 3rd quartile. i.e., in between 25th quantile and 75th quantile range. This range is also called an Interquartile range.\r\n\r\n### ElasticNet\r\n\r\nElastic Net first emerged as a result of critique on Lasso, whose variable selection can be too dependent on data and thus unstable. The solution is to combine the penalties of Ridge regression and Lasso to get the best of both worlds.\r\n\r\n**Features of ElasticNet Regression-**\r\n* It combines the L1 and L2 approaches.\r\n* It performs a more efficient regularization process.\r\n* It has two parameters to be set, λ and α.\r\n\r\n #### Model Tuning Parameters\r\n\r\n 1. alpha : float, default=1.0\r\n \r\n> Constant that multiplies the penalty terms. Defaults to 1.0. See the notes for the exact mathematical meaning of this parameter. alpha = 0 is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using alpha = 0 with the Lasso object is not advised. Given this, you should use the LinearRegression object.\r\n\r\n\r\n 2. l1_ratio : float, default=0.5\r\n> The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.\r\n\r\n 3. normalize : bool, default=False\r\n>This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.\r\n\r\n 4. max_iter : int, default=1000\r\n>The maximum number of iterations.\r\n\r\n 5. tol : float, default=1e-4\r\n>The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.\r\n\r\n 6. selection : {‘cyclic’, ‘random’}, default=’cyclic’\r\n>If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4.\r\n", "_____no_output_____" ] ], [ [ "#training the ElasticNet\r\nInput=[(\"scaler\",RobustScaler()),(\"model\",ElasticNet(random_state = 5))]\r\nmodel = Pipeline(Input)\r\nmodel.fit(X_train,y_train)", "_____no_output_____" ] ], [ [ "#### Model Accuracy\nscore() method return the mean accuracy on the given test data and labels.\n\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.", "_____no_output_____" ] ], [ [ "print(\"Accuracy score {:.2f} %\\n\".format(model.score(X_test,y_test)*100))", "Accuracy score 48.36 %\n\n" ], [ "#prediction on testing set\r\nprediction=model.predict(X_test)", "_____no_output_____" ] ], [ [ "### Model evolution\n\n\n**r2_score:** The r2_score function computes the percentage variablility explained by our model, either the fraction or the count of correct predictions.\n\n**MAE:** The mean abosolute error function calculates the amount of total error(absolute average distance between the real data and the predicted data) by our model.\n\n**MSE:** The mean squared error function squares the error(penalizes the model for large errors) by our model.", "_____no_output_____" ] ], [ [ "print('Mean Absolute Error:', mean_absolute_error(y_test, prediction)) \r\nprint('Mean Squared Error:', mean_squared_error(y_test, prediction)) \r\nprint('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, prediction)))", "Mean Absolute Error: 6195.156634885755\nMean Squared Error: 77088496.26589973\nRoot Mean Squared Error: 8780.00548211103\n" ], [ "print(\"R-squared score : \",r2_score(y_test,prediction))", "R-squared score : 0.48360017436142344\n" ], [ "#ploting actual and predicted\nred = plt.scatter(np.arange(0,80,5),prediction[0:80:5],color = \"red\")\ngreen = plt.scatter(np.arange(0,80,5),y_test[0:80:5],color = \"green\")\nplt.title(\"Comparison of Regression Algorithms\")\nplt.xlabel(\"Index of Candidate\")\nplt.ylabel(\"target\")\nplt.legend((red,green),('ElasticNet', 'REAL'))\nplt.show()\n", "_____no_output_____" ] ], [ [ "### Prediction Plot¶\nFirst, we make use of a plot to plot the actual observations, with x_train on the x-axis and y_train on the y-axis. For the regression line, we will use x_train on the x-axis and then the predictions of the x_train observations on the y-axis.", "_____no_output_____" ] ], [ [ "plt.figure(figsize=(10,6))\nplt.plot(range(20),y_test[0:20], color = \"green\")\nplt.plot(range(20),model.predict(X_test[0:20]), color = \"red\")\nplt.legend([\"Actual\",\"prediction\"]) \nplt.title(\"Predicted vs True Value\")\nplt.xlabel(\"Record number\")\nplt.ylabel(target)\nplt.show()", "_____no_output_____" ] ], [ [ "#### Creator: Vipin Kumar , Github: [Profile](https://github.com/devVipin01)", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

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[ "MIT" ]

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[ "MIT" ]

[ [ [ "# Let's Import Our Libraries", "_____no_output_____" ] ], [ [ "# Keras\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation\nfrom keras.layers.embeddings import Embedding\n\n# Plot\nimport plotly.offline as py\nimport plotly.graph_objs as go\npy.init_notebook_mode(connected=True)\nimport matplotlib as plt\n\n# NLTK\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\n\n#Sklearn\nfrom sklearn.base import BaseEstimator, ClassifierMixin\nfrom sklearn.utils.validation import check_X_y, check_is_fitted\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import precision_score\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.model_selection import cross_val_score\n\n# Other\nimport re\nimport string\nimport numpy as np\nimport pandas as pd\nfrom sklearn.manifold import TSNE\nfrom scipy import sparse\nimport warnings\nwarnings.filterwarnings('ignore')", "Using TensorFlow backend.\n" ] ], [ [ "# Let's start by exploring the data.", "_____no_output_____" ] ], [ [ "df = pd.read_csv(\"socialmedia-disaster-tweets-DFE.csv\", encoding='latin-1')", "_____no_output_____" ], [ "df.shape", "_____no_output_____" ], [ "df.head(5)", "_____no_output_____" ], [ "df.columns", "_____no_output_____" ] ], [ [ "The 'tweet' column has tweets and 'choose_one' has the classification.", "_____no_output_____" ], [ "Let's determine the number of unique classifications.", "_____no_output_____" ] ], [ [ "df.choose_one.unique()", "_____no_output_____" ] ], [ [ "# Let's work on cleaning up the data", "_____no_output_____" ] ], [ [ "df = df[[\"text\", \"choose_one\"]]\ndf[\"choose_one\"] = df.choose_one.replace({\"Relevant\": 1, \"Not Relevant\": 0})\ndf.rename(columns={\"choose_one\":\"label\"}, inplace=True)", "_____no_output_____" ], [ "df.label=pd.to_numeric(df.label, errors='coerce')\ndf.dropna(inplace=True)", "_____no_output_____" ] ], [ [ "Let's check and see how the data looks.", "_____no_output_____" ] ], [ [ "df.label.unique()", "_____no_output_____" ], [ "df.head(5)", "_____no_output_____" ], [ "df[\"text\"] = df[\"text\"].str.replace(r\"http\\S+|http|@\\S+|at\", \"\")\ndf[\"text\"] = df[\"text\"].str.replace(r\"[^A-Za-z0-9(),!?@\\'\\`\\\"\\_\\n]\", \" \")\ndf[\"text\"] = df[\"text\"].str.lower()", "_____no_output_____" ], [ "df.head(5)", "_____no_output_____" ], [ "df.columns", "_____no_output_____" ] ], [ [ "# Let's Tokenzie: We'll Turn our Sentences into Lists of Words", "_____no_output_____" ] ], [ [ "from nltk.tokenize import RegexpTokenizer\ntokenizer = RegexpTokenizer(r'\\w+')\ndf[\"tokens\"] = df[\"text\"].apply(tokenizer.tokenize)", "_____no_output_____" ] ], [ [ "Tokens will give us more insight into the data", "_____no_output_____" ] ], [ [ "all_words = [word for tokens in df[\"tokens\"] for word in tokens]\nsentence_lengths = [len(tokens) for tokens in df[\"tokens\"]]\nvocabulary = sorted(set(all_words))", "_____no_output_____" ], [ "print(\"%s words total, with a vocabulary size of %s.\" % (len(all_words), len(vocabulary)))", "153824 words total, with a vocabulary size of 18078.\n" ], [ "print(\"Max sentence length is %s.\" % max(sentence_lengths))", "Max sentence length is 34.\n" ] ], [ [ "# Let's Embed: Turning Words into Numbers", "_____no_output_____" ] ], [ [ "from sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.model_selection import train_test_split", "_____no_output_____" ] ], [ [ "### TFIDF Tokenizer for Our Classical ML Models", "_____no_output_____" ] ], [ [ "text = df[\"text\"].tolist()\nlabels = df[\"label\"].tolist()\nX_train, X_test, y_train, y_test = train_test_split(text, labels, test_size=0.2,random_state=40)", "_____no_output_____" ], [ "tfidf_vectorizer = TfidfVectorizer()\nX_train_tfidf = tfidf_vectorizer.fit_transform(X_train)", "_____no_output_____" ], [ "X_test_tfidf = tfidf_vectorizer.transform(X_test)\nX_test_tfidf.shape", "_____no_output_____" ] ], [ [ "### NN Specific Tokenizer", "_____no_output_____" ] ], [ [ "vocabulary_size = 40000\ntokenizer = Tokenizer(num_words = vocabulary_size)\ntokenizer.fit_on_texts(df['text'])\n\nsequences = tokenizer.texts_to_sequences(X_train)\nX_train_nn = pad_sequences(sequences, maxlen=28)\n\nsequences = tokenizer.texts_to_sequences(X_test)\nX_test_nn = pad_sequences(sequences, maxlen=28)\n\nX_train_nn = pd.DataFrame(X_train_nn)\nX_test_nn = pd.DataFrame(X_test_nn)", "_____no_output_____" ], [ "type(X_train_tfidf)", "_____no_output_____" ], [ "type(X_train_nn)", "_____no_output_____" ] ], [ [ "# Here We'll Define a New Classifier", "_____no_output_____" ] ], [ [ "class NbSvmClassifier(BaseEstimator, ClassifierMixin):\n def __init__(self, C=1.0, dual=False, n_jobs=1):\n self.C = C\n self.dual = dual\n self.n_jobs = n_jobs\n\n def predict(self, x):\n # Verify that model has been fit\n check_is_fitted(self, ['_r', '_clf'])\n return self._clf.predict(x.multiply(self._r))\n\n def predict_proba(self, x):\n # Verify that model has been fit\n check_is_fitted(self, ['_r', '_clf'])\n return self._clf.predict_proba(x.multiply(self._r))\n\n def fit(self, x, y):\n # Check that X and y have correct shape\n #y = y.values\n y = y\n x, y = check_X_y(x, y, accept_sparse=True)\n\n def pr(x, y_i, y):\n p = x[y==y_i].sum(0)\n return (p+1) / ((y==y_i).sum()+1)\n\n self._r = sparse.csr_matrix(np.log(pr(x,1,y) / pr(x,0,y)))\n x_nb = x.multiply(self._r)\n self._clf = LogisticRegression(C=self.C, dual=self.dual, n_jobs=self.n_jobs).fit(x_nb, y)\n return self", "_____no_output_____" ] ], [ [ "## Let's get a baseline using Logisitc Regression", "_____no_output_____" ] ], [ [ "classifier = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', n_jobs=-1, random_state=40)\nclassifier.fit(X_train_tfidf, y_train)\ny_predicted_tfidf = classifier.predict(X_test_tfidf)\n\nprecision = precision_score(y_test, y_predicted_tfidf, pos_label=None,average='weighted')\nprint(precision)", "0.7745952843343159\n" ] ], [ [ "## Now We'll Utilize Our NBSVM Classifier", "_____no_output_____" ] ], [ [ "classifier = NbSvmClassifier(C=4, dual=True, n_jobs=-1).fit(X_train_tfidf, y_train)\nclassifier.fit(X_train_tfidf, y_train)\ny_predicted_tfidf = classifier.predict(X_test_tfidf)\n\nprecision = precision_score(y_test, y_predicted_tfidf, pos_label=None,average='weighted')\nprint(precision)", "0.8057456468641322\n" ] ], [ [ "## Now Let's Apply Grid Search to the Model", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import GridSearchCV\nparam_grid = {\n 'C': [3.0, 3.2, 3.25, 3.3, 3.4, 3.5],\n 'dual' : [True, False]\n}", "_____no_output_____" ], [ "%%time\ngs_classifier = GridSearchCV(NbSvmClassifier(), param_grid, n_jobs=-1)\ngs_classifier = gs_classifier.fit(X_train_tfidf, y_train)", "CPU times: user 125 ms, sys: 43.9 ms, total: 169 ms\nWall time: 733 ms\n" ], [ "gs_classifier.best_score_", "_____no_output_____" ], [ "gs_classifier.best_params_", "_____no_output_____" ] ], [ [ "We don't seem to be getting much extra juice from applying grid search to this model.", "_____no_output_____" ], [ "## Let's Try Regular SVM", "_____no_output_____" ] ], [ [ "classifier = SGDClassifier().fit(X_train_tfidf, y_train)\nclassifier.fit(X_train_tfidf, y_train)\ny_predicted_tfidf = classifier.predict(X_test_tfidf)\nprecision = precision_score(y_test, y_predicted_tfidf, pos_label=None,average='weighted')\nprint(precision)", "0.7946891903618641\n" ] ], [ [ "## Let's Try Multinomial Naive Bayes", "_____no_output_____" ] ], [ [ "classifier = SGDClassifier().fit(X_train_tfidf, y_train)\nclassifier.fit(X_train_tfidf, y_train)\ny_predicted_tfidf = classifier.predict(X_test_tfidf)\nprecision = precision_score(y_test, y_predicted_tfidf, pos_label=None,average='weighted')\nprint(precision)", "0.7960386107807245\n" ] ], [ [ "The last three approaches yielded pretty similar results. Let's try a deep learning model.", "_____no_output_____" ], [ "## Build the network with LSTM", "_____no_output_____" ], [ "### Network Architecture\n\nOur network is going to start with an embedding layer. This layer lets the system expand each token into a much larger vector space. By doing so we can represent each word in a more meaningful way. The layer takes 40K as its first argument, which is the size of our vocabulary. 100 is the second argument, which is the dimension of the embeddings. The third argument is 28 which is the max number of tokens we consider from each tweet.", "_____no_output_____" ] ], [ [ "def create_lstm():\n model = Sequential()\n model.add(Embedding(40000, 100, input_length=28))\n model.add(LSTM(100, dropout=0.9, recurrent_dropout=0.5))\n model.add(Dense(1, activation='sigmoid'))\n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n return model", "_____no_output_____" ], [ "classifier = KerasClassifier(build_fn=create_lstm, epochs=3, batch_size=5, verbose=0)", "_____no_output_____" ], [ "%%time\nclassifier.fit(X_train_nn, y_train)", "CPU times: user 7min 21s, sys: 1min 24s, total: 8min 46s\nWall time: 2min 55s\n" ], [ "y_predicted_nn = classifier.predict(X_test_nn)\nprecision = precision_score(y_test, y_predicted_nn, pos_label=None,average='weighted')\nprint(precision)", "0.8128093975364307\n" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/sroy8091/deep-learning-v2-pytorch/blob/master/convolutional-neural-networks/cifar-cnn/cifar10_cnn_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Convolutional Neural Networks\n---\nIn this notebook, we train a **CNN** to classify images from the CIFAR-10 database.\n\nThe images in this database are small color images that fall into one of ten classes; some example images are pictured below.\n\n<img src='https://github.com/sroy8091/deep-learning-v2-pytorch/blob/master/convolutional-neural-networks/cifar-cnn/notebook_ims/cifar_data.png?raw=1' width=70% height=70% />", "_____no_output_____" ], [ "### Test for [CUDA](http://pytorch.org/docs/stable/cuda.html)\n\nSince these are larger (32x32x3) images, it may prove useful to speed up your training time by using a GPU. CUDA is a parallel computing platform and CUDA Tensors are the same as typical Tensors, only they utilize GPU's for computation.", "_____no_output_____" ] ], [ [ "import torch\nimport numpy as np\n\n# check if CUDA is available\ntrain_on_gpu = torch.cuda.is_available()\n\nif not train_on_gpu:\n print('CUDA is not available. Training on CPU ...')\nelse:\n print('CUDA is available! Training on GPU ...')", "CUDA is available! Training on GPU ...\n" ] ], [ [ "---\n## Load the [Data](http://pytorch.org/docs/stable/torchvision/datasets.html)\n\nDownloading may take a minute. We load in the training and test data, split the training data into a training and validation set, then create DataLoaders for each of these sets of data.", "_____no_output_____" ] ], [ [ "from torchvision import datasets\nimport torchvision.transforms as transforms\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\n# number of subprocesses to use for data loading\nnum_workers = 0\n# how many samples per batch to load\nbatch_size = 20\n# percentage of training set to use as validation\nvalid_size = 0.2\n\n# convert data to a normalized torch.FloatTensor\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])\n\n# choose the training and test datasets\ntrain_data = datasets.CIFAR10('data', train=True,\n download=True, transform=transform)\ntest_data = datasets.CIFAR10('data', train=False,\n download=True, transform=transform)\n\n# obtain training indices that will be used for validation\nnum_train = len(train_data)\nindices = list(range(num_train))\nnp.random.shuffle(indices)\nsplit = int(np.floor(valid_size * num_train))\ntrain_idx, valid_idx = indices[split:], indices[:split]\n\n# define samplers for obtaining training and validation batches\ntrain_sampler = SubsetRandomSampler(train_idx)\nvalid_sampler = SubsetRandomSampler(valid_idx)\n\n# prepare data loaders (combine dataset and sampler)\ntrain_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n sampler=train_sampler, num_workers=num_workers)\nvalid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, \n sampler=valid_sampler, num_workers=num_workers)\ntest_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n num_workers=num_workers)\n\n# specify the image classes\nclasses = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n 'dog', 'frog', 'horse', 'ship', 'truck']", "Files already downloaded and verified\nFiles already downloaded and verified\n" ] ], [ [ "### Visualize a Batch of Training Data", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\n%matplotlib inline\n\n# helper function to un-normalize and display an image\ndef imshow(img):\n img = img / 2 + 0.5 # unnormalize\n plt.imshow(np.transpose(img, (1, 2, 0))) # convert from Tensor image", "_____no_output_____" ], [ "# obtain one batch of training images\ndataiter = iter(train_loader)\nimages, labels = dataiter.next()\nimages = images.numpy() # convert images to numpy for display\n\n# plot the images in the batch, along with the corresponding labels\nfig = plt.figure(figsize=(25, 4))\n# display 20 images\nfor idx in np.arange(20):\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n imshow(images[idx])\n ax.set_title(classes[labels[idx]])", "_____no_output_____" ] ], [ [ "### View an Image in More Detail\n\nHere, we look at the normalized red, green, and blue (RGB) color channels as three separate, grayscale intensity images.", "_____no_output_____" ] ], [ [ "rgb_img = np.squeeze(images[3])\nchannels = ['red channel', 'green channel', 'blue channel']\n\nfig = plt.figure(figsize = (36, 36)) \nfor idx in np.arange(rgb_img.shape[0]):\n ax = fig.add_subplot(1, 3, idx + 1)\n img = rgb_img[idx]\n ax.imshow(img, cmap='gray')\n ax.set_title(channels[idx])\n width, height = img.shape\n thresh = img.max()/2.5\n for x in range(width):\n for y in range(height):\n val = round(img[x][y],2) if img[x][y] !=0 else 0\n ax.annotate(str(val), xy=(y,x),\n horizontalalignment='center',\n verticalalignment='center', size=8,\n color='white' if img[x][y]<thresh else 'black')", "_____no_output_____" ] ], [ [ "---\n## Define the Network [Architecture](http://pytorch.org/docs/stable/nn.html)\n\nThis time, you'll define a CNN architecture. Instead of an MLP, which used linear, fully-connected layers, you'll use the following:\n* [Convolutional layers](https://pytorch.org/docs/stable/nn.html#conv2d), which can be thought of as stack of filtered images.\n* [Maxpooling layers](https://pytorch.org/docs/stable/nn.html#maxpool2d), which reduce the x-y size of an input, keeping only the most _active_ pixels from the previous layer.\n* The usual Linear + Dropout layers to avoid overfitting and produce a 10-dim output.\n\nA network with 2 convolutional layers is shown in the image below and in the code, and you've been given starter code with one convolutional and one maxpooling layer.\n\n<img src='https://github.com/sroy8091/deep-learning-v2-pytorch/blob/master/convolutional-neural-networks/cifar-cnn/notebook_ims/2_layer_conv.png?raw=1' height=50% width=50% />\n\n#### TODO: Define a model with multiple convolutional layers, and define the feedforward network behavior.\n\nThe more convolutional layers you include, the more complex patterns in color and shape a model can detect. It's suggested that your final model include 2 or 3 convolutional layers as well as linear layers + dropout in between to avoid overfitting. \n\nIt's good practice to look at existing research and implementations of related models as a starting point for defining your own models. You may find it useful to look at [this PyTorch classification example](https://github.com/pytorch/tutorials/blob/master/beginner_source/blitz/cifar10_tutorial.py) or [this, more complex Keras example](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) to help decide on a final structure.\n\n#### Output volume for a convolutional layer\n\nTo compute the output size of a given convolutional layer we can perform the following calculation (taken from [Stanford's cs231n course](http://cs231n.github.io/convolutional-networks/#layers)):\n> We can compute the spatial size of the output volume as a function of the input volume size (W), the kernel/filter size (F), the stride with which they are applied (S), and the amount of zero padding used (P) on the border. The correct formula for calculating how many neurons define the output_W is given by `(W−F+2P)/S+1`. \n\nFor example for a 7x7 input and a 3x3 filter with stride 1 and pad 0 we would get a 5x5 output. With stride 2 we would get a 3x3 output.", "_____no_output_____" ] ], [ [ "import torch.nn as nn\nimport torch.nn.functional as F\n\n# define the CNN architecture\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n # convolutional layer\n self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n # max pooling layer\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n self.fc1 = nn.Linear(64*4*4, 512)\n self.fc2 = nn.Linear(512, 10)\n self.dropout = nn.Dropout(p=0.25)\n\n def forward(self, x):\n # add sequence of convolutional and max pooling layers\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = self.pool(F.relu(self.conv3(x)))\n\n x = x.view(-1, 64*4*4)\n\n x = self.dropout(x)\n x = F.relu(self.fc1(x))\n\n x = self.dropout(x)\n x = self.fc2(x)\n\n\n return x\n\n# create a complete CNN\nmodel = Net()\nprint(model)\n\n# move tensors to GPU if CUDA is available\nif train_on_gpu:\n model.cuda()", "Net(\n (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (fc1): Linear(in_features=1024, out_features=512, bias=True)\n (fc2): Linear(in_features=512, out_features=10, bias=True)\n (dropout): Dropout(p=0.25, inplace=False)\n)\n" ] ], [ [ "### Specify [Loss Function](http://pytorch.org/docs/stable/nn.html#loss-functions) and [Optimizer](http://pytorch.org/docs/stable/optim.html)\n\nDecide on a loss and optimization function that is best suited for this classification task. The linked code examples from above, may be a good starting point; [this PyTorch classification example](https://github.com/pytorch/tutorials/blob/master/beginner_source/blitz/cifar10_tutorial.py) or [this, more complex Keras example](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py). Pay close attention to the value for **learning rate** as this value determines how your model converges to a small error.\n\n#### TODO: Define the loss and optimizer and see how these choices change the loss over time.", "_____no_output_____" ] ], [ [ "import torch.optim as optim\n\n# specify loss function\ncriterion = nn.CrossEntropyLoss()\n\n# specify optimizer\noptimizer = optim.SGD(model.parameters(), lr=0.01)", "_____no_output_____" ] ], [ [ "---\n## Train the Network\n\nRemember to look at how the training and validation loss decreases over time; if the validation loss ever increases it indicates possible overfitting.", "_____no_output_____" ] ], [ [ "# number of epochs to train the model\nn_epochs = 30 # you may increase this number to train a final model\n\nvalid_loss_min = np.Inf # track change in validation loss\n\nfor epoch in range(1, n_epochs+1):\n\n # keep track of training and validation loss\n train_loss = 0.0\n valid_loss = 0.0\n \n ###################\n # train the model #\n ###################\n model.train()\n for data, target in train_loader:\n # move tensors to GPU if CUDA is available\n if train_on_gpu:\n data, target = data.cuda(), target.cuda()\n # clear the gradients of all optimized variables\n optimizer.zero_grad()\n # forward pass: compute predicted outputs by passing inputs to the model\n output = model(data)\n # calculate the batch loss\n loss = criterion(output, target)\n # backward pass: compute gradient of the loss with respect to model parameters\n loss.backward()\n # perform a single optimization step (parameter update)\n optimizer.step()\n # update training loss\n train_loss += loss.item()*data.size(0)\n \n ###################### \n # validate the model #\n ######################\n model.eval()\n for data, target in valid_loader:\n # move tensors to GPU if CUDA is available\n if train_on_gpu:\n data, target = data.cuda(), target.cuda()\n # forward pass: compute predicted outputs by passing inputs to the model\n output = model(data)\n # calculate the batch loss\n loss = criterion(output, target)\n # update average validation loss \n valid_loss += loss.item()*data.size(0)\n \n # calculate average losses\n train_loss = train_loss/len(train_loader.dataset)\n valid_loss = valid_loss/len(valid_loader.dataset)\n \n # print training/validation statistics \n print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n epoch, train_loss, valid_loss))\n \n # save model if validation loss has decreased\n if valid_loss <= valid_loss_min:\n print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(\n valid_loss_min,\n valid_loss))\n torch.save(model.state_dict(), 'model_cifar.pt')\n valid_loss_min = valid_loss", "Epoch: 1 \tTraining Loss: 0.803521 \tValidation Loss: 0.194699\nValidation loss decreased (inf --> 0.194699). Saving model ...\nEpoch: 2 \tTraining Loss: 0.764657 \tValidation Loss: 0.190203\nValidation loss decreased (0.194699 --> 0.190203). Saving model ...\nEpoch: 3 \tTraining Loss: 0.729479 \tValidation Loss: 0.180554\nValidation loss decreased (0.190203 --> 0.180554). Saving model ...\nEpoch: 4 \tTraining Loss: 0.697147 \tValidation Loss: 0.176048\nValidation loss decreased (0.180554 --> 0.176048). Saving model ...\nEpoch: 5 \tTraining Loss: 0.664248 \tValidation Loss: 0.166369\nValidation loss decreased (0.176048 --> 0.166369). Saving model ...\nEpoch: 6 \tTraining Loss: 0.633258 \tValidation Loss: 0.172071\nEpoch: 7 \tTraining Loss: 0.606116 \tValidation Loss: 0.167166\nEpoch: 8 \tTraining Loss: 0.579878 \tValidation Loss: 0.159903\nValidation loss decreased (0.166369 --> 0.159903). Saving model ...\nEpoch: 9 \tTraining Loss: 0.559891 \tValidation Loss: 0.152775\nValidation loss decreased (0.159903 --> 0.152775). Saving model ...\nEpoch: 10 \tTraining Loss: 0.534879 \tValidation Loss: 0.157326\nEpoch: 11 \tTraining Loss: 0.515649 \tValidation Loss: 0.148941\nValidation loss decreased (0.152775 --> 0.148941). Saving model ...\nEpoch: 12 \tTraining Loss: 0.489339 \tValidation Loss: 0.150183\nEpoch: 13 \tTraining Loss: 0.470855 \tValidation Loss: 0.149713\nEpoch: 14 \tTraining Loss: 0.451540 \tValidation Loss: 0.151616\nEpoch: 15 \tTraining Loss: 0.430954 \tValidation Loss: 0.148358\nValidation loss decreased (0.148941 --> 0.148358). Saving model ...\nEpoch: 16 \tTraining Loss: 0.414108 \tValidation Loss: 0.144657\nValidation loss decreased (0.148358 --> 0.144657). Saving model ...\nEpoch: 17 \tTraining Loss: 0.396600 \tValidation Loss: 0.148721\nEpoch: 18 \tTraining Loss: 0.380980 \tValidation Loss: 0.147071\nEpoch: 19 \tTraining Loss: 0.361990 \tValidation Loss: 0.149246\nEpoch: 20 \tTraining Loss: 0.345308 \tValidation Loss: 0.149856\nEpoch: 21 \tTraining Loss: 0.333657 \tValidation Loss: 0.153758\nEpoch: 22 \tTraining Loss: 0.319412 \tValidation Loss: 0.151106\nEpoch: 23 \tTraining Loss: 0.305450 \tValidation Loss: 0.153118\nEpoch: 24 \tTraining Loss: 0.295869 \tValidation Loss: 0.153868\nEpoch: 25 \tTraining Loss: 0.284524 \tValidation Loss: 0.150587\nEpoch: 26 \tTraining Loss: 0.272934 \tValidation Loss: 0.157747\nEpoch: 27 \tTraining Loss: 0.260829 \tValidation Loss: 0.155512\nEpoch: 28 \tTraining Loss: 0.255282 \tValidation Loss: 0.156478\nEpoch: 29 \tTraining Loss: 0.246054 \tValidation Loss: 0.155306\nEpoch: 30 \tTraining Loss: 0.235927 \tValidation Loss: 0.162395\n" ] ], [ [ "### Load the Model with the Lowest Validation Loss", "_____no_output_____" ] ], [ [ "model.load_state_dict(torch.load('model_cifar.pt'))", "_____no_output_____" ] ], [ [ "---\n## Test the Trained Network\n\nTest your trained model on previously unseen data! A \"good\" result will be a CNN that gets around 70% (or more, try your best!) accuracy on these test images.", "_____no_output_____" ] ], [ [ "# track test loss\ntest_loss = 0.0\nclass_correct = list(0. for i in range(10))\nclass_total = list(0. for i in range(10))\n\nmodel.eval()\n# iterate over test data\nfor data, target in test_loader:\n # move tensors to GPU if CUDA is available\n if train_on_gpu:\n data, target = data.cuda(), target.cuda()\n # forward pass: compute predicted outputs by passing inputs to the model\n output = model(data)\n # calculate the batch loss\n loss = criterion(output, target)\n # update test loss \n test_loss += loss.item()*data.size(0)\n # convert output probabilities to predicted class\n _, pred = torch.max(output, 1) \n # compare predictions to true label\n correct_tensor = pred.eq(target.data.view_as(pred))\n correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n # calculate test accuracy for each object class\n for i in range(batch_size):\n label = target.data[i]\n class_correct[label] += correct[i].item()\n class_total[label] += 1\n\n# average test loss\ntest_loss = test_loss/len(test_loader.dataset)\nprint('Test Loss: {:.6f}\\n'.format(test_loss))\n\nfor i in range(10):\n if class_total[i] > 0:\n print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n classes[i], 100 * class_correct[i] / class_total[i],\n np.sum(class_correct[i]), np.sum(class_total[i])))\n else:\n print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n\nprint('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n 100. * np.sum(class_correct) / np.sum(class_total),\n np.sum(class_correct), np.sum(class_total)))", "Test Loss: 0.744639\n\nTest Accuracy of airplane: 77% (779/1000)\nTest Accuracy of automobile: 86% (864/1000)\nTest Accuracy of bird: 67% (672/1000)\nTest Accuracy of cat: 52% (529/1000)\nTest Accuracy of deer: 67% (677/1000)\nTest Accuracy of dog: 63% (630/1000)\nTest Accuracy of frog: 85% (853/1000)\nTest Accuracy of horse: 79% (798/1000)\nTest Accuracy of ship: 88% (885/1000)\nTest Accuracy of truck: 77% (773/1000)\n\nTest Accuracy (Overall): 74% (7460/10000)\n" ] ], [ [ "### Question: What are your model's weaknesses and how might they be improved?", "_____no_output_____" ], [ "**Answer**: (double-click to edit and add an answer)\nBy adding different types of image transformations", "_____no_output_____" ], [ "### Visualize Sample Test Results", "_____no_output_____" ] ], [ [ "# obtain one batch of test images\ndataiter = iter(test_loader)\nimages, labels = dataiter.next()\nimages.numpy()\n\n# move model inputs to cuda, if GPU available\nif train_on_gpu:\n images = images.cuda()\n\n# get sample outputs\noutput = model(images)\n# convert output probabilities to predicted class\n_, preds_tensor = torch.max(output, 1)\npreds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())\n\n# plot the images in the batch, along with predicted and true labels\nfig = plt.figure(figsize=(25, 4))\nfor idx in np.arange(20):\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n imshow(images[idx].cpu())\n ax.set_title(\"{} ({})\".format(classes[preds[idx]], classes[labels[idx]]),\n color=(\"green\" if preds[idx]==labels[idx].item() else \"red\"))", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

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[ [ [ "# Solutions", "_____no_output_____" ], [ "### One-hot encoding the rank", "_____no_output_____" ] ], [ [ "## One solution\n# Make dummy variables for rank\none_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)\n\n# Drop the previous rank column\none_hot_data = one_hot_data.drop('rank', axis=1)\n\n# Print the first 10 rows of our data\none_hot_data[:10]", "_____no_output_____" ], [ "## Alternative solution ##\n# if you're using an up-to-date version of pandas, \n# you can also use selection by columns\n\n# an equally valid solution\none_hot_data = pd.get_dummies(data, columns=['rank'])", "_____no_output_____" ] ], [ [ "### Scaling the data", "_____no_output_____" ] ], [ [ "# Copying our data\nprocessed_data = one_hot_data[:]\n\n# Scaling the columns\nprocessed_data['gre'] = processed_data['gre']/800\nprocessed_data['gpa'] = processed_data['gpa']/4.0\nprocessed_data[:10]", "_____no_output_____" ] ], [ [ "### Backpropagating the data", "_____no_output_____" ] ], [ [ "def error_term_formula(x, y, output):\n return (y - output)*sigmoid_prime(x)", "_____no_output_____" ], [ "## Alternative solution ##\n# you could also *only* use y and the output \n# and calculate sigmoid_prime directly from the activated output!\n\n# below is an equally valid solution (it doesn't utilize x)\ndef error_term_formula(x, y, output):\n return (y-output) * output * (1 - output)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Quickstart\nIn this tutorial, we explain how to quickly use ``LEGWORK`` to calculate the detectability of a collection of sources.", "_____no_output_____" ] ], [ [ "%matplotlib inline", "_____no_output_____" ] ], [ [ "Let's start by importing the source and visualisation modules of `LEGWORK` and some other common packages.", "_____no_output_____" ] ], [ [ "import legwork.source as source\nimport legwork.visualisation as vis\n\nimport numpy as np\nimport astropy.units as u\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "%config InlineBackend.figure_format = 'retina'\n\nplt.rc('font', family='serif')\nplt.rcParams['text.usetex'] = False\nfs = 24\n\n# update various fontsizes to match\nparams = {'figure.figsize': (12, 8),\n 'legend.fontsize': fs,\n 'axes.labelsize': fs,\n 'xtick.labelsize': 0.7 * fs,\n 'ytick.labelsize': 0.7 * fs}\nplt.rcParams.update(params)", "_____no_output_____" ] ], [ [ "Next let's create a random collection of possible LISA sources in order to assess their detectability.", "_____no_output_____" ] ], [ [ "# create a random collection of sources\nn_values = 1500\nm_1 = np.random.uniform(0, 10, n_values) * u.Msun\nm_2 = np.random.uniform(0, 10, n_values) * u.Msun\ndist = np.random.normal(8, 1.5, n_values) * u.kpc\nf_orb = 10**(-5 * np.random.power(3, n_values)) * u.Hz\necc = 1 - np.random.power(5, n_values)", "_____no_output_____" ] ], [ [ "We can instantiate a `Source` class using these random sources in order to analyse the population. There are also a series of optional parameters which we don't cover here but if you are interested in the purpose of these then check out the [Using the Source Class](Source.ipynb) tutorial.", "_____no_output_____" ] ], [ [ "sources = source.Source(m_1=m_1, m_2=m_2, ecc=ecc, dist=dist, f_orb=f_orb)", "_____no_output_____" ] ], [ [ "This `Source` class has many methods for calculating strains, visualising populations and more. You can learn more about these in the [Using the Source Class](Source.ipynb) tutorial. For now, we shall focus only on the calculation of the signal-to-noise ratio.\n\nTherefore, let's calculate the SNR for these sources. We set `verbose=True` to give an impression of what sort of sources we have created. This function will split the sources based on whether they are stationary/evolving and circular/eccentric and use one of 4 SNR functions for each subpopulation.", "_____no_output_____" ] ], [ [ "snr = sources.get_snr(verbose=True)", "Calculating SNR for 1500 sources\n\t0 sources have already merged\n\t1385 sources are stationary\n\t\t427 sources are stationary and circular\n\t\t958 sources are stationary and eccentric\n\t115 sources are evolving\n\t\t33 sources are evolving and circular\n\t\t82 sources are evolving and eccentric\n" ] ], [ [ "These SNR values are now stored in `sources.snr` and we can mask those that don't meet some detectable threshold.", "_____no_output_____" ] ], [ [ "detectable_threshold = 7\ndetectable_sources = sources.snr > 7\nprint(\"{} of the {} sources are detectable\".format(len(sources.snr[detectable_sources]), n_values))", "585 of the 1500 sources are detectable\n" ] ], [ [ "And just like that we know the number of detectable sources! It could be interesting to see how the SNR varies with orbital frequency so let's use the :meth:`legwork.source.Source.plot_source_variables` to create a 2D density distribution of these variables.", "_____no_output_____" ] ], [ [ "fig, ax = sources.plot_source_variables(xstr=\"f_orb\", ystr=\"snr\", disttype=\"kde\", log_scale=(True, True),\n fill=True, xlim=(2e-6, 2e-1), which_sources=sources.snr > 0)", "_____no_output_____" ] ], [ [ "The reason for this shape may not be immediately obvious. However, if we also use the visualisation module to overlay the LISA sensitivity curve, it becomes clear that the SNRs increase in step with the decrease in the noise and flatten out as the sensitivity curve does as we would expect. To learn more about the visualisation options that `LEGWORK` offers, check out the [Visualisation](Visualisation.ipynb) tutorial.", "_____no_output_____" ] ], [ [ "# create the same plot but set `show=False`\nfig, ax = sources.plot_source_variables(xstr=\"f_orb\", ystr=\"snr\", disttype=\"kde\", log_scale=(True, True),\n fill=True, show=False, which_sources=sources.snr > 0)\n\n# duplicate the x axis and plot the LISA sensitivity curve\nright_ax = ax.twinx()\nfrequency_range = np.logspace(np.log10(2e-6), np.log10(2e-1), 1000) * u.Hz\nvis.plot_sensitivity_curve(frequency_range=frequency_range, fig=fig, ax=right_ax)\n\nplt.show()", "_____no_output_____" ] ], [ [ "That's it for this quickstart into using `LEGWORK`. For more details on using `LEGWORK` to calculate strains, evolve binaries and visualise their distributions check out the [other tutorials](../tutorials.rst) and [demos](../demos.rst) in these docs! You can also read more about the scope and limitations of `LEGWORK` [on this page](../limitations.rst).", "_____no_output_____" ] ] ]

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[ [ [ "- **Let us see how well our model would perform if we would deploy our model at the end of 2018**\n- **ie: Let us test our model on 2019 data**", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd \nimport category_encoders as ce\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.preprocessing import OneHotEncoder", "_____no_output_____" ], [ "data_path = \"../data/notebooks/4_merged_data.csv\"", "_____no_output_____" ], [ "df_raw = pd.read_csv(data_path)", "C:\\Users\\saima\\Anaconda3\\envs\\kick\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (29) have mixed types.Specify dtype option on import or set low_memory=False.\n interactivity=interactivity, compiler=compiler, result=result)\n" ], [ "df = df_raw.copy()", "_____no_output_____" ], [ "cols = ['launched_at', 'status', 'days_to_deadline', 'goal',\n 'sub_category', 'category', 'blurb_length', 'location_country', 'rewards_mean', 'rewards_median',\n 'rewards_variance', 'rewards_SD', 'rewards_MIN', 'rewards_MAX' ,\n 'rewards_NUM', 'currency', 'launch_year', 'launch_month',\n 'deadline_month']\ntarget_encoding_cols = ['location_country' , 'currency' , 'category', 'sub_category']\n\ntrain_years =[ 2016, 2017 , 2018]\nvalid_years = [2019]\n", "_____no_output_____" ], [ "def pre_proc(df):\n df = df[cols]\n df= df.dropna(axis=0, subset=[\"rewards_MIN\"])\n df= df.dropna(axis=0, subset=[\"blurb_length\"])\n df = df.reset_index(drop=True)\n df[\"launched_at\"] = pd.to_datetime(df[\"launched_at\"]).dt.date\n df.sort_values(\"launched_at\" , inplace=True)\n df.drop(['launched_at'] ,axis=1 , inplace=True)\n df.reset_index(inplace=True)\n df.drop('index', inplace=True , axis=1)\n \n binarizer= LabelBinarizer()\n df[\"status\"] = binarizer.fit_transform(df[\"status\"])\n \n \n return df\n\n\n ", "_____no_output_____" ], [ "def onehot_categ(df):\n \n encoder = OneHotEncoder(sparse=False)\n cat_cols=['category', 'sub_category', 'currency', 'location_country']\n X_hot = encoder.fit_transform(df[cat_cols])\n \n onehotcols = []\n for cat in encoder.categories_:\n for col in cat:\n onehotcols.append(col)\n \n X_hot = pd.DataFrame(X_hot , columns=onehotcols)\n df =pd.concat([df , X_hot] , axis=1)\n df.drop(target_encoding_cols , axis=1 , inplace=True)\n \n \n return df \n ", "_____no_output_____" ], [ "def get_model_data(df , train_years , valid_years):\n df_train = df[df['launch_year'].apply(lambda x: True if x in train_years else False)]\n df_valid= df[df['launch_year'].apply(lambda x: True if x in valid_years else False)]\n \n X_train , y_train = df_train.drop([\"status\",\"launch_year\"] , axis=1) , df_train['status']\n X_valid , y_valid = df_valid.drop([\"status\",\"launch_year\"] , axis=1) , df_valid['status']\n \n return X_train , y_train , X_valid , y_valid\n\n ", "_____no_output_____" ], [ "def helmert_categ(df_train , df_valid):\n encoder = ce.HelmertEncoder(cols = target_encoding_cols , drop_invariant=True )\n dfh = encoder.fit_transform(df_train[target_encoding_cols])\n df_train = pd.concat([df_train , dfh], axis=1)\n df_train.drop(target_encoding_cols , axis=1 , inplace=True)\n dfh = encoder.transform(df_valid[target_encoding_cols])\n df_valid = pd.concat([df_valid , dfh], axis=1)\n df_valid.drop(target_encoding_cols , axis=1 , inplace=True)\n \n return df_train , df_valid ", "_____no_output_____" ], [ "from xgboost import XGBClassifier\nimport operator\n\ndef XG_score(X_train, X_test, y_train, y_test):\n XG_fet = {}\n \n XG= XGBClassifier(n_estimators=150, random_state=9)\n XG.fit(X_train, y_train)\n XG_score = XG.score(X_test, y_test)\n \n \n feat_labels = X_train.columns.values\n \n for feature, acc in zip(feat_labels, XG.feature_importances_):\n XG_fet[feature] = acc\n \n XG_fet = sorted(XG_fet.items(), key=operator.itemgetter(1), reverse=True)\n \n \n return (XG,XG_score, XG_fet)\n", "_____no_output_____" ], [ "df_proc = pre_proc(df)\ndf_onehot = onehot_categ(df_proc)\nX_train_oh , y_train_oh , X_valid_oh , y_valid_oh = get_model_data(df_onehot , train_years , valid_years)", "_____no_output_____" ], [ "df_proc = pre_proc(df)\nX_train_raw , y_train_hel , X_valid_raw , y_valid_hel = get_model_data(df_proc , train_years , valid_years)\nX_train_hel , X_valid_hel = helmert_categ(X_train_raw , X_valid_raw)\n", "_____no_output_____" ], [ "XG_model_oh , XG_scores_oh , XG_fet_imp_oh= XG_score(X_train_oh , X_valid_oh , y_train_oh , y_valid_oh)\nprint(\"Score using OneHot encodinng: {}\".format(XG_scores_oh))", "Score using OneHot encodinng: 0.8151040114442392\n" ], [ "XG_model_hel , XG_scores_hel , XG_fet_imp_hel= XG_score(X_train_hel , X_valid_hel , y_train_hel , y_valid_hel)\nprint(\"Score using Helmert encodinng: {}\".format(XG_scores_hel))", "Score using Helmert encodinng: 0.8627287357692078\n" ] ], [ [ "- **This should is great, our test accuracy is greater than our validation accuracy, usually this should be a red flag but since there was not decision during the process of modeling and preprocessing made based off the 2019(test data), its fine**\n- **In the next notebook we will train the model on the entire dataset and save the model**", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas", "_____no_output_____" ], [ "df = pandas.read_excel('s3://ia241-bullard/house_price.xls')\n\ndf[:10]", "_____no_output_____" ], [ "df['unit_price']= df['price']/df['area']\ndf[:10]", "_____no_output_____" ] ], [ [ "## 2.2", "_____no_output_____" ] ], [ [ "df['house_type'].value_counts()", "_____no_output_____" ] ], [ [ "## 2.3 avg price ", "_____no_output_____" ] ], [ [ "prc_more_2_bath=df.loc[df['bathroom']>2 ]['price']\nprint('avg price of house more than 2 bathroom is ${}'.format(prc_more_2_bath.mean()))", "avg price of house more than 2 bathroom is $383645.45454545453\n" ] ], [ [ "## 2.4", "_____no_output_____" ] ], [ [ "print('mean unit price is ${}'.format(df['unit_price'].mean()))", "mean unit price is $167.45934522134766\n" ], [ "print('median unit price is ${}'.format(df['unit_price'].median()))", "median unit price is $130.13392857142858\n" ] ], [ [ "## 2.5", "_____no_output_____" ] ], [ [ "df.groupby('house_type').mean()['price']", "_____no_output_____" ] ], [ [ "## 2.6", "_____no_output_____" ] ], [ [ "from scipy import stats", "_____no_output_____" ], [ "result = stats.linregress(df['area'],df['price'])", "_____no_output_____" ], [ "print('slope is {}'.format(result.slope))\nprint('intercept is {}'.format(result.intercept))\nprint('r square is {}'.format(result.rvalue *result.rvalue))\nprint('p value is {}'.format(result.pvalue))", "slope is 79.95495729411489\nintercept is 156254.76245096227\nr square is 0.2343900121890692\np value is 0.001340065037461188\n" ] ], [ [ "## 2.7\n", "_____no_output_____" ] ], [ [ "print('price of house with {}sqft is ${}'.format(2000,2000*result.slope+result.intercept))", "price of house with 2000sqft is $316164.67703919206\n" ] ] ]

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[ [ [ "### Load Data and Libraries", "_____no_output_____" ] ], [ [ "from Bio import SearchIO\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport seaborn as sns\n%matplotlib inline\n\n\nnum_hit = 0\n#now parse the output\nhit_list = []\nhit_ids = []\ncounts = []\nqnames = []\nwith open('overlap_hmm_blast.txt', 'r') as input:\n for qresult in SearchIO.parse(input, 'hmmer3-tab'):\n query_id = qresult.id #sequence ID from fasta\n qnames.append(query_id)\n hits = qresult.hits\n hit_ids = hit_ids+qresult.hit_keys\n hit_list.append(hits)\n num_hits = len(hits)\n counts.append(num_hits)\n num_hit = num_hit + num_hits\nprint(num_hit)", "2181\n" ] ], [ [ "### Hits by HMM Model", "_____no_output_____" ] ], [ [ "df_query = pd.DataFrame({'Names': qnames, \n 'Hit Counts': counts}, columns=['Names','Hit Counts'])\ndf_query = df_query.sort_values(by=['Hit Counts'], ascending=False)\ntop_n = df_query.head(10)", "_____no_output_____" ], [ "top_n", "_____no_output_____" ], [ "## Number of VOGS identified\ny = df_query['Names'].str.contains('VOG')\ndf2=df_query[y]\ndf2.head(7)", "_____no_output_____" ], [ "f = plt.subplots(figsize=(10, 10))\nsns.pointplot(x='Names', y='Hit Counts', data=df_query, markers=[\"o\"], linestyles=[\"-\"])\nplt.yticks(fontsize=15)\nplt.xticks([])\nplt.title('Count of Virulence Factor Hits in Dataset', fontsize=18)\nplt.xlabel('Virulence Factor Categories', fontsize=18)\nplt.ylabel('Hit Counts', fontsize=16)\nplt.tight_layout()\nsns.despine(trim=True, left=True)\nplt.savefig('HMM_Hit_Counts.jpg', transparent=True)", "_____no_output_____" ], [ "### Top N Hits\nsns.factorplot(x=\"Hit Counts\", y=\"Names\", data=top_n, kind=\"bar\", palette=\"vlag\", size=10)\n\n\n#axes = plt.gca()\n#axes.set_xlim([0,10])\nplt.yticks(fontsize=15)\nplt.xticks(fontsize=15)\nplt.title('Abundances of Top 10 Virulence Factor \\n Hits in Phage Protein Database', fontsize=18)\nplt.xlabel('Virulence Factor Hit Count', fontsize=18)\nplt.ylabel('Top Virulence Factor Identities', fontsize=16)\nplt.tight_layout()\n\n#sns.despine(trim=True, left=True)\nplt.savefig('Top_VF_Hit_Plot.jpg', transparent=True)", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "import transportation_tutorials as tt", "_____no_output_____" ] ], [ [ "# Creating Dynamic Maps\n\nIn this gallery, we will demonstrate the creation of a variety of interactive maps.\nInteractive, dynamic maps are a good choice for analytical work that will be reviewed\nonline, either in a Jupyter notebook by an analyst, or published on a website.\nIn these examples,\nwe will demonstrate creating dynamic maps using [Plotly](https://plot.ly/python/) \nand [mapped](https://pypi.org/project/mapped/), which integrates a handful of \nplotly mapping tools directly into the geopandas dataframe object.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport geopandas as gpd\nimport mapped", "_____no_output_____" ] ], [ [ "We'll begin by loading the TAZ and MAZ shapefiles, filtering them to a restricted study area,\nand defining the center point.", "_____no_output_____" ] ], [ [ "xmin = 905712\nymin = 905343\ntaz = gpd.read_file(tt.data('SERPM8-TAZSHAPE')).cx[xmin:, ymin:].to_crs(epsg=4326)\nmaz = gpd.read_file(tt.data('SERPM8-MAZSHAPE')).cx[xmin:, ymin:].to_crs(epsg=4326)\ncenter = (26.9198, -80.1121) # regular lat-lon", "_____no_output_____" ] ], [ [ "## Simple Map\n\nSimple maps showing the geographic data contained in a GeoDataFrame can be created\nby converting the GeoDataFrame to a GeoJson object, and adding that to\na folium Map.", "_____no_output_____" ] ], [ [ "taz.plotly_choropleth(line_width=2)", "_____no_output_____" ] ], [ [ "### Alternative Map Tiles\n\nThe default tiles are set to [Carto](https://carto.com)'s \n[positron](https://carto.com/blog/getting-to-know-positron-and-dark-matter/), \nbut others tiles are possible, including\ntilesets from [Stamen Design](http://stamen.com/) and [OpenStreetMap](www.openstreetmap.org). \nThe [positron](https://carto.com/blog/getting-to-know-positron-and-dark-matter/) tiles are\nspecifically designed to give geographic context without overwhelming maps with data \nthat is not the analytic focus of the presentation.", "_____no_output_____" ] ], [ [ "taz.plotly_choropleth(line_width=2, mapbox_style=\"open-street-map\")", "_____no_output_____" ] ], [ [ "## Mapping Data\n\nOne of the input files for SERPM 8 is a MAZ-level demographics file.\nThe file for the 2015 base year is included in the tutorial data, and \nwe can load it with the `read_csv` function.", "_____no_output_____" ] ], [ [ "mazd = pd.read_csv(tt.data('SERPM8-MAZDATA', '*.csv'))", "_____no_output_____" ] ], [ [ "Use `info` to see a summary of the DataFrame.", "_____no_output_____" ] ], [ [ "mazd.info()", "_____no_output_____" ] ], [ [ "We can join the demographics table to the shape file we loaded previously,\nto enable some visualizations on this data. This can be done with the\n``merge`` method of DataFrames.", "_____no_output_____" ] ], [ [ "maz1 = maz.merge(mazd, how='left', left_on='MAZ', right_on='mgra')", "_____no_output_____" ], [ "maz1.index=maz1.MAZ", "_____no_output_____" ] ], [ [ "## Choropleth Maps\n\nA [choropleth map](https://en.wikipedia.org/wiki/Choropleth_map) is a map with areas colored, \nshaded, or patterned in proportion to some measured value for the region displayed. This kind of\nmap is commonly used to display things like population density.\n\nWhen a data column is given to the plotly_choropleth function, that data is used to colorize\nthe choropleth map.", "_____no_output_____" ] ], [ [ "maz1.plotly_choropleth(\"PopDen\", colorbar_title=\"Population Density\", colorbar_title_side='right')", "_____no_output_____" ] ] ]

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[ [ [ "### Data Scientist Nano Degree - Capstone Project \n### Car Booking Analysis and Prediction\n### Tarek Abd ElRahman Ahmed ElAyat", "_____no_output_____" ], [ "#### Let's import the needed libraries", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport warnings\n\nfrom sklearn import model_selection\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score, mean_squared_error\nfrom math import sqrt\n\nimport xgboost as xgb\nfrom sklearn import preprocessing\n#from sklearn.preprocessing import MinMaxScaler\n#scaler = MinMaxScaler()\n\nwarnings.filterwarnings('ignore')\n%matplotlib inline", "_____no_output_____" ], [ "def print_model_scores(model, X_train, X_test, y_train, y_test):\n '''\n INPUT:\n model - the regression model\n X_train - pandas dataframe for the training dataset\n X_test - pandas dataframe for the test dataset\n y_train - pandas dataframe for the training label\n y_test - pandas dataframe for the test label\n \n OUTPUT:\n y_train_preds - panadas dataframe for the y_train prediction\n y_test_preds - panadas dataframe for the y_test prediction\n print scores \n '''\n \n #Predict and score the model on training data\n y_train_preds = model.predict(X_train) \n print(\"The r-squared score for our model on Training data is {} on {} values.\".format(r2_score(y_train, y_train_preds), len(y_train)))\n print(\"The mean_squared_error score for our model on Training data is {} on {} values.\".format(mean_squared_error(y_train, y_train_preds), len(y_train)))\n print(\"The root_mean_squared_error score for our model on Training data is {} on {} values.\".format(sqrt(mean_squared_error(y_train, y_train_preds)), len(y_train)))\n\n print(\" \")\n #Predict and score the model on test data\n y_test_preds = model.predict(X_test) \n print(\"The r-squared score for our model on Testing data is {} on {} values.\".format(r2_score(y_test, y_test_preds), len(y_test)))\n print(\"The mean_squared_error score for our model on Testing data is {} on {} values.\".format(mean_squared_error(y_test, y_test_preds), len(y_test)))\n print(\"The root_mean_squared_error score for our model on Testing data is {} on {} values.\".format(sqrt(mean_squared_error(y_test, y_test_preds)), len(y_test)))\n \n return y_train_preds, y_test_preds\n", "_____no_output_____" ], [ "def create_dummy_df(df, dummy_na):\n '''\n INPUT:\n df - pandas dataframe with categorical variables you want to dummy\n dummy_na - Bool holding whether you want to dummy NA vals of categorical columns or not\n \n OUTPUT:\n df - a new dataframe that has the following characteristics:\n 1. contains all columns that were not specified as categorical\n 2. removes all the original columns in cat_cols\n 3. dummy columns for each of the categorical columns\n 4. if dummy_na is True - it also contains dummy columns for the NaN values\n 5. Use a prefix of the column name with an underscore (_) for separating \n '''\n cat_cols = df.select_dtypes(include=['object']).columns\n \n for col in cat_cols:\n try:\n # for each cat add dummy var, drop original column\n df = pd.concat([df.drop(col, axis=1), pd.get_dummies(df[col], prefix=col, prefix_sep='_', drop_first=True, dummy_na=dummy_na)], axis=1)\n except:\n continue\n return df", "_____no_output_____" ], [ "def coef_weights(coefficients, X_train):\n '''\n INPUT:\n coefficients - the coefficients of the linear model \n X_train - the training data, so the column names can be used\n OUTPUT:\n coefs_df - a dataframe holding the coefficient, estimate, and abs(estimate)\n \n Provides a dataframe that can be used to understand the most influential coefficients\n in a linear model by providing the coefficient estimates along with the name of the \n variable attached to the coefficient.\n '''\n coefs_df = pd.DataFrame()\n coefs_df['est_int'] = X_train.columns\n coefs_df['coefs'] = coefficients\n coefs_df['abs_coefs'] = np.abs(coefficients)\n coefs_df = coefs_df.sort_values('abs_coefs', ascending=False)\n return coefs_df", "_____no_output_____" ] ], [ [ "### Load the training data and lookup files", "_____no_output_____" ] ], [ [ "df_taxi = pd.read_csv('C:\\\\Users\\\\tayat\\\\Documents\\\\Capstone Project\\\\NYC Dataset\\\\taxi_onefile.csv')\ndf_lookup_zone = pd.read_csv('C:\\\\Users\\\\tayat\\\\Documents\\\\Capstone Project\\\\NYC Dataset\\\\taxi+_zone_lookup.csv')", "_____no_output_____" ], [ "pd.set_option('display.max_rows', 50)", "_____no_output_____" ] ], [ [ "### Basic data exploration, list the columns with their data types and describe the features", "_____no_output_____" ] ], [ [ "df_taxi.shape", "_____no_output_____" ], [ "df_taxi.head()", "_____no_output_____" ] ], [ [ "### List the columns, dtypes, and describe the features", "_____no_output_____" ] ], [ [ "df_taxi.dtypes", "_____no_output_____" ], [ "# from the below describtion we notice that there are unlogical (-ve values), missing values and outliers which needs cleaning\ndf_taxi.describe().transpose()", "_____no_output_____" ] ], [ [ "### Count missing data per feature", "_____no_output_____" ] ], [ [ "total = df_taxi.isnull().sum()\npercent = (df_taxi.isnull().sum()/df_taxi.isnull().count())\nmissing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\nmissing_data.head(100)", "_____no_output_____" ] ], [ [ "## Feature Engineering:\n### Extract the engineered features from pickup and dropoff times and then drop these two columns", "_____no_output_____" ] ], [ [ "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n\ndf_taxi['tpep_pickup_datetime'] = pd.to_datetime(df_taxi['tpep_pickup_datetime'])\ndf_taxi['tpep_dropoff_datetime'] = pd.to_datetime(df_taxi['tpep_dropoff_datetime'])\ndf_taxi['tripduration_mins'] = ((df_taxi['tpep_dropoff_datetime'] - df_taxi['tpep_pickup_datetime']).dt.total_seconds()/60).astype(float)\n\ndf_taxi['year'] = pd.DatetimeIndex(df_taxi['tpep_pickup_datetime']).year\ndf_taxi['month'] = pd.DatetimeIndex(df_taxi['tpep_pickup_datetime']).month\ndf_taxi['day'] = pd.DatetimeIndex(df_taxi['tpep_pickup_datetime']).day\ndf_taxi['hour'] = pd.DatetimeIndex(df_taxi['tpep_pickup_datetime']).hour\ndf_taxi['dayofweek'] = pd.DatetimeIndex(df_taxi['tpep_pickup_datetime']).dayofweek\ndf_taxi['weekendflag'] = (df_taxi['dayofweek']>=5).astype(int)\n\ncal = calendar()\nholidays = cal.holidays(start=df_taxi['tpep_pickup_datetime'].min(), end=df_taxi['tpep_pickup_datetime'].max())\ndf_taxi['holidayflag'] = (df_taxi['tpep_pickup_datetime'].isin(holidays)).astype(int)\ndel holidays\n\ndf_taxi.drop(['tpep_pickup_datetime', 'tpep_dropoff_datetime'], axis=1, inplace=True)", "_____no_output_____" ], [ "df_taxi.head()", "_____no_output_____" ], [ "#Save the output into new file to avoid recalculation\n#df_taxi.to_csv('C:\\\\Users\\\\tayat\\\\Documents\\\\Capstone Project\\\\NYC Dataset\\\\taxi_engineered.csv', index = False)\n#df_taxi = pd.read_csv('C:\\\\Users\\\\tayat\\\\Documents\\\\Capstone Project\\\\NYC Dataset\\\\taxi_engineered.csv')", "_____no_output_____" ], [ "df_taxi.dtypes", "_____no_output_____" ], [ "#Have another look after feature engineering\ndf_taxi.describe().transpose()", "_____no_output_____" ] ], [ [ "## Data Cleaning:\n### From the above columns quick overview, we notice some unlogical columns:\n#### - trip distance, duration, fare, tip, toll...etc with -ve values or very high values\n#### - unlogical and out of range values like passenger_count above 6, ratecodeID above 6 and so on\n### let's analyze the ranges and then get rid of these misleading values first", "_____no_output_____" ] ], [ [ "((df_taxi['trip_distance']/50).astype(int)*50).loc[:].value_counts()", "_____no_output_____" ], [ "((df_taxi['total_amount']/100).astype(int)*100).loc[:].value_counts()", "_____no_output_____" ], [ "((df_taxi['fare_amount']/100).astype(int)*100).loc[:].value_counts()\n#exclude -ve and fareamount > 100", "_____no_output_____" ], [ "((df_taxi['extra']/5).astype(int)*5).loc[:].value_counts()\n#exclude -ve and extra > 5", "_____no_output_____" ], [ "((df_taxi['mta_tax']).astype(int)).loc[:].value_counts()\n#exclude -ve and mta_tax > 1", "_____no_output_____" ], [ "((df_taxi['tip_amount']/10).astype(int)*10).loc[:].value_counts()\n#exclude -ve and tip_amount > 20", "_____no_output_____" ], [ "((df_taxi['tolls_amount']/10).astype(int)*10).loc[:].value_counts()\n#exclude -ve and tolls_amount > 30", "_____no_output_____" ], [ "((df_taxi['improvement_surcharge']).astype(int)).loc[:].value_counts()\n#exclude -ve and improvement_surcharge > 1", "_____no_output_____" ], [ "((df_taxi['tripduration_mins']/60).astype(int)).loc[:].value_counts()\n#exclude -ve and tripduration_mins > 180 minute", "_____no_output_____" ], [ "((df_taxi['trip_distance']/20).astype(int)*20).loc[:].value_counts()\n#exclude -ve and trip_distance > 30", "_____no_output_____" ] ], [ [ "#### Drop rows with unlogical or out of range values", "_____no_output_____" ] ], [ [ "df_taxi.drop(df_taxi[ (df_taxi['VendorID'].isna()) | (df_taxi['fare_amount'] <= 0) | (df_taxi['total_amount'] <= 0) | (df_taxi['tripduration_mins'] <= 0) | (df_taxi['tip_amount'] < 0) | (df_taxi['tolls_amount'] < 0) | (df_taxi['improvement_surcharge'] < 0) | (df_taxi['congestion_surcharge'] < 0) | (df_taxi['trip_distance'] <= 0) | (df_taxi['extra'] < 0) | (df_taxi['mta_tax'] < 0) | (df_taxi['passenger_count'] > 6) | (df_taxi['RatecodeID'] > 6)].index, inplace = True)", "_____no_output_____" ], [ "df_taxi.shape", "_____no_output_____" ] ], [ [ "#### Drop outliers and fill na values", "_____no_output_____" ] ], [ [ "df_taxi.drop(df_taxi[ (df_taxi['fare_amount'] > 100) | (df_taxi['tripduration_mins'] > 180) | (df_taxi['tip_amount'] > 20) | (df_taxi['tolls_amount'] > 30) | (df_taxi['extra'] > 5) | (df_taxi['mta_tax'] > 1) | (df_taxi['improvement_surcharge'] > 1) | (df_taxi['trip_distance'] > 30)].index, inplace = True)", "_____no_output_____" ], [ "df_taxi['congestion_surcharge'] = df_taxi['congestion_surcharge'].fillna(0)", "_____no_output_____" ], [ "df_taxi.shape", "_____no_output_____" ] ], [ [ "### Save the output into new file to avoid recalculation", "_____no_output_____" ] ], [ [ "#Save the output into new file to avoid recalculation\n#df_taxi.to_csv('C:\\\\Users\\\\tayat\\\\Documents\\\\Capstone Project\\\\NYC Dataset\\\\taxi_clean.csv', index = False)\n#df_taxi = pd.read_csv('C:\\\\Users\\\\tayat\\\\Documents\\\\Capstone Project\\\\NYC Dataset\\\\taxi_clean.csv')", "_____no_output_____" ] ], [ [ "### Continue data exploration and understanding after feature engineering and cleaning done", "_____no_output_____" ] ], [ [ "df_taxi.describe().transpose()", "_____no_output_____" ], [ "df_taxi['passenger_count'].value_counts()", "_____no_output_____" ], [ "df_taxi['RatecodeID'].value_counts()", "_____no_output_____" ], [ "df_taxi['sameloc'] = (df_taxi['PULocationID'] == df_taxi['DOLocationID']).astype(int)", "_____no_output_____" ], [ "df_taxi.head()", "_____no_output_____" ] ], [ [ "### Generate the correlation matrix after the data cleaning", "_____no_output_____" ] ], [ [ "corrmat = df_taxi.corr()\nf, ax = plt.subplots(figsize=(12, 9))\nsns.heatmap(corrmat, vmax=.8, square=True, xticklabels=True, yticklabels=True);", "_____no_output_____" ], [ "#scatter plot trip_distance/fare_amount\nvar = 'trip_distance'\ndata = pd.concat([df_taxi['fare_amount'], df_taxi[var]], axis=1)", "_____no_output_____" ], [ "data.plot.scatter(x=var, y='fare_amount'); #, ylim=(0,100)", "_____no_output_____" ], [ "#The trip duration histogram\nexception_trips = df_taxi.loc[(df_taxi['fare_amount'] < 10) & (df_taxi['trip_distance'] > 12)]\nexception_trips.shape\nsns.distplot(exception_trips['tripduration_mins'], kde=False, norm_hist=False);", "_____no_output_____" ], [ "#scatter plot trip duration/fare amount\ndata2 = pd.concat([exception_trips['fare_amount'], exception_trips['tripduration_mins']], axis=1)", "_____no_output_____" ], [ "data2.plot.scatter(x='tripduration_mins', y='fare_amount'); #, ylim=(0,100)", "_____no_output_____" ], [ "#Fare amount histogram\nsns.distplot(df_taxi['fare_amount'], kde=False, norm_hist=False, bins = 20);", "_____no_output_____" ], [ "#Trip distance histogram\nsns.distplot(df_taxi['trip_distance'], kde=False, norm_hist=False, bins = 20);", "_____no_output_____" ], [ "#Hours of the day histogram\nsns.distplot(df_taxi['hour'], kde=False, norm_hist=False);", "_____no_output_____" ], [ "#Day of week histogram\nsns.distplot(df_taxi['dayofweek'], kde=False, norm_hist=False);", "_____no_output_____" ], [ "#Pickup locations histogram\nsns.distplot(df_taxi['PULocationID'], kde=False, norm_hist=False);", "_____no_output_____" ], [ "#Drop-off locations histogram\nsns.distplot(df_taxi['DOLocationID'], kde=False, norm_hist=False);", "_____no_output_____" ], [ "#Same location histogram\nsns.distplot(df_taxi['sameloc'], kde=False, norm_hist=False);", "_____no_output_____" ], [ "((df_taxi['trip_distance']/5).astype(int)*5).loc[:].value_counts()", "_____no_output_____" ], [ "Loc_Dist = pd.pivot_table(df_taxi, index = 'PULocationID', columns = 'DOLocationID', values = 'VendorID', aggfunc = ['count'])\n#Loc_Dist = pd.pivot_table(df_taxi, index = ['PULocationID', 'DOLocationID'], values = 'VendorID', aggfunc = ['count'])\n#print(Loc_Dist)\nf, ax = plt.subplots(figsize=(20, 20))\nsns.heatmap(Loc_Dist, cmap=\"YlGnBu\")", "_____no_output_____" ], [ "#Heatmap for pickup locations over days of the week\nLoc_Dist = pd.pivot_table(df_taxi, index = 'PULocationID', columns = 'dayofweek', values = 'VendorID', aggfunc = ['count'])\nf, ax = plt.subplots(figsize=(20, 20))\nsns.heatmap(Loc_Dist, cmap=\"YlGnBu\")", "_____no_output_____" ], [ "#Heatmap for pickup locations over hours of the day\nLoc_Dist = pd.pivot_table(df_taxi, index = 'PULocationID', columns = 'hour', values = 'VendorID', aggfunc = ['count'])\nf, ax = plt.subplots(figsize=(20, 20))\nsns.heatmap(Loc_Dist, cmap=\"YlGnBu\")", "_____no_output_____" ], [ "PULocations = pd.merge(\n df_taxi['PULocationID'],\n df_lookup_zone,\n how=\"inner\",\n left_on='PULocationID',\n right_on='LocationID',\n sort=True\n)\n\nPULocations.head()", "_____no_output_____" ], [ "#Trip distribution over NY Pickup Boroughs\nPULocations.groupby(['Borough']).count()['LocationID'].plot(kind=\"bar\", title = 'Trips distribution over Pickup Boroughs'); #, fontsize=14, figsize = (30, 8)", "_____no_output_____" ], [ "DOLocations = pd.merge(\n df_taxi['DOLocationID'],\n df_lookup_zone,\n how=\"inner\",\n left_on='DOLocationID',\n right_on='LocationID',\n sort=True\n)", "_____no_output_____" ], [ "#Trip distribution over NY Dropoff Boroughs\nDOLocations.groupby(['Borough']).count()['LocationID'].plot(kind=\"bar\", title = 'Trips distribution over Drop Boroughs'); #, fontsize=14, figsize = (30, 8)", "_____no_output_____" ] ], [ [ "## Business Questions/Use Cases", "_____no_output_____" ], [ "### What are the most demanding areas at specific time?\n#### for example below are top pick up locations for a specific day and hour (Thursday 7pm)", "_____no_output_____" ] ], [ [ "filtered_df = df_taxi[(df_taxi['dayofweek'] == 3) & (df_taxi['hour'] == 19)]['PULocationID']\n\nTopPULocations = pd.merge(\n filtered_df,\n df_lookup_zone,\n how=\"inner\",\n left_on='PULocationID',\n right_on='LocationID',\n sort=True\n)\n\nTopPULocations.head()", "_____no_output_____" ], [ "TopPULocations.groupby(['PULocationID']).count()['LocationID'].sort_values(ascending=False).head(30).plot(kind=\"bar\", figsize = (30, 8), fontsize=20 , title = 'Top PULocations on Thursday 7pm'); #, fontsize=14, figsize = (30, 8)", "_____no_output_____" ] ], [ [ "### How to deploy the fleet based on driver’s preferences for drop off locations (a driver may prefer a drop off near his home)?\n#### For example, below are top pick up locations for a specific day and hour (Thursday 7pm) which will most likely will lead to the favorite drop off locations [230, 234, 236]", "_____no_output_____" ] ], [ [ "DOList = [230, 234, 236]\nfiltered_df2 = df_taxi[(df_taxi['dayofweek'] == 3) & (df_taxi['hour'] == 19) & (df_taxi['DOLocationID'].isin(DOList))][['PULocationID', 'DOLocationID']]\n", "_____no_output_____" ], [ "filtered_df2.groupby(['PULocationID']).count()['DOLocationID'].sort_values(ascending=False).head(30).plot(kind=\"bar\", figsize = (30, 8), fontsize=20 , title = 'Top PULocations on Thursday 7pm for specific DO List'); #, fontsize=14, figsize = (30, 8)", "_____no_output_____" ] ], [ [ "### Now let's run our basic model to predict the Fare_Rate", "_____no_output_____" ], [ "### Scenario #1 Linear regression with all possible features as numeric", "_____no_output_____" ] ], [ [ "#Split into explanatory and response variables\nX = df_taxi[['trip_distance', 'RatecodeID', 'tripduration_mins', 'month', 'hour', 'weekendflag', 'holidayflag', 'passenger_count', 'PULocationID', 'DOLocationID', 'payment_type']]\ny = df_taxi['fare_amount']", "_____no_output_____" ], [ "#Split into train and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)\n\n#Clear memory to be able to run the model\ndel X\ndel y", "_____no_output_____" ], [ "lm_model = LinearRegression(normalize=True) # Instantiate\nlm_model.fit(X_train, y_train) #Fit", "_____no_output_____" ], [ "#Predict and score the model on training and test data\ny_train_preds, y_test_preds = print_model_scores(lm_model, X_train, X_test, y_train, y_test)", "The r-squared score for our model on Training data is 0.9408655229497245 on 14712202 values.\nThe mean_squared_error score for our model on Training data is 6.879148895127688 on 14712202 values.\nThe root_mean_squared_error score for our model on Training data is 2.622813164357631 on 14712202 values.\n \nThe r-squared score for our model on Testing data is 0.9409164098556393 on 6305230 values.\nThe mean_squared_error score for our model on Testing data is 6.871197085487989 on 6305230 values.\nThe root_mean_squared_error score for our model on Testing data is 2.62129683276961 on 6305230 values.\n" ], [ "#Use the function\ncoef_df = coef_weights(lm_model.coef_, X_train)\n\n#A quick look at the top results\ncoef_df.head(20)", "_____no_output_____" ] ], [ [ "### Scenario #2 Linear regression using important features as numeric", "_____no_output_____" ] ], [ [ "#Split into explanatory and response variables\nX = df_taxi[['trip_distance', 'RatecodeID', 'tripduration_mins', 'month', 'hour', 'weekendflag', 'holidayflag']]\ny = df_taxi['fare_amount']", "_____no_output_____" ], [ "#Split into train and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)\ndel X\ndel y", "_____no_output_____" ], [ "lm_model = LinearRegression(normalize=True) # Instantiate\nlm_model.fit(X_train, y_train) #Fit\n \n#Predict and score the model on training and test data\ny_train_preds, y_test_preds = print_model_scores(lm_model, X_train, X_test, y_train, y_test)", "The r-squared score for our model on Training data is 0.9408385370477721 on 14712202 values.\nThe mean_squared_error score for our model on Training data is 6.882288181156071 on 14712202 values.\nThe root_mean_squared_error score for our model on Training data is 2.623411553903823 on 14712202 values.\n \nThe r-squared score for our model on Testing data is 0.940890929867919 on 6305230 values.\nThe mean_squared_error score for our model on Testing data is 6.874160311265811 on 6305230 values.\nThe root_mean_squared_error score for our model on Testing data is 2.6218619931769505 on 6305230 values.\n" ], [ "#Use the function\ncoef_df = coef_weights(lm_model.coef_, X_train)\n\n#A quick look at the top results\ncoef_df.head(20)", "_____no_output_____" ] ], [ [ "### Scenario #3 Linear regression with important features and OneHotEncoding for categorical features", "_____no_output_____" ] ], [ [ "#Split into explanatory and response variables\ndf_taxi_cat = df_taxi[['trip_distance', 'RatecodeID', 'tripduration_mins', 'month', 'hour', 'weekendflag', 'holidayflag', 'fare_amount']]\ndf_taxi_cat = df_taxi_cat.astype({'RatecodeID': 'object', 'month': 'object', 'hour': 'object'})", "_____no_output_____" ], [ "df_taxi_cat = create_dummy_df(df_taxi_cat, dummy_na=False)\ndel df_taxi", "_____no_output_____" ], [ "df_taxi_cat.head()", "_____no_output_____" ], [ "#Split into explanatory and response variables\ny = df_taxi_cat['fare_amount']\nX = df_taxi_cat.drop('fare_amount', axis=1)\ndel df_taxi_cat", "_____no_output_____" ], [ "#Split into train and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)\ndel X\ndel y", "_____no_output_____" ], [ "lm_model = LinearRegression(normalize=True) # Instantiate\nlm_model.fit(X_train, y_train) #Fit", "_____no_output_____" ], [ "#Predict and score the model on training and test data\ny_train_preds, y_test_preds = print_model_scores(lm_model, X_train, X_test, y_train, y_test)", "The r-squared score for our model on Training data is 0.9432230301501271 on 14712202 values.\nThe mean_squared_error score for our model on Training data is 6.604898679993168 on 14712202 values.\nThe root_mean_squared_error score for our model on Training data is 2.5699997431893196 on 14712202 values.\n \nThe r-squared score for our model on Testing data is 0.943321447117103 on 6305230 values.\nThe mean_squared_error score for our model on Testing data is 6.591500388298767 on 6305230 values.\nThe root_mean_squared_error score for our model on Testing data is 2.567391748116903 on 6305230 values.\n" ], [ "#Use the function\ncoef_df = coef_weights(lm_model.coef_, X_train)\n\n#A quick look at the top results\ncoef_df.head(50)", "_____no_output_____" ] ], [ [ "### Scenario #4 XGBoost regressor with important features and onehotencoding for categorical features", "_____no_output_____" ] ], [ [ "#Split into explanatory and response variables\ndf_taxi_cat = df_taxi[['trip_distance', 'RatecodeID', 'tripduration_mins', 'month', 'hour', 'weekendflag', 'holidayflag', 'fare_amount']]\ndf_taxi_cat = df_taxi_cat.astype({'RatecodeID': 'object', 'month': 'object', 'hour': 'object'})", "_____no_output_____" ], [ "df_taxi_cat = create_dummy_df(df_taxi_cat, dummy_na=False)\ndel df_taxi", "_____no_output_____" ], [ "#Split into explanatory and response variables\ny = df_taxi_cat['fare_amount']\nX = df_taxi_cat.drop('fare_amount', axis=1)\ndel df_taxi_cat", "_____no_output_____" ], [ "#Split into train and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)\ndel X\ndel y", "_____no_output_____" ], [ "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,vmax_depth = 5, alpha = 10, n_estimators = 10) # Instantiate\nxg_reg.fit(X_train, y_train) #Fit", "[14:03:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n[14:03:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \nParameters: { vmax_depth } might not be used.\n\n This may not be accurate due to some parameters are only used in language bindings but\n passed down to XGBoost core. Or some parameters are not used but slip through this\n verification. Please open an issue if you find above cases.\n\n\n" ], [ "#Predict and score the model on training and test data\ny_train_preds, y_test_preds = print_model_scores(xg_reg, X_train, X_test, y_train, y_test)", "The r-squared score for our model on Training data is 0.3242116292704751 on 14712202 values.\nThe mean_squared_error score for our model on Training data is 78.61486320225254 on 14712202 values.\nThe root_mean_squared_error score for our model on Training data is 8.866502309380659 on 14712202 values.\n \nThe r-squared score for our model on Testing data is 0.32425626984223854 on 6305230 values.\nThe mean_squared_error score for our model on Testing data is 78.58642878423606 on 6305230 values.\nThe root_mean_squared_error score for our model on Testing data is 8.86489869001536 on 6305230 values.\n" ] ], [ [ "### Scenario #5 XGBoost regressor with modified hyper parameters", "_____no_output_____" ] ], [ [ "#Change the n_estimators from 10 to 50\nxg_reg2 = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,vmax_depth = 5, alpha = 10, n_estimators = 50) # Instantiate\nxg_reg2.fit(X_train, y_train) #Fit", "[01:30:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n[01:30:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \nParameters: { vmax_depth } might not be used.\n\n This may not be accurate due to some parameters are only used in language bindings but\n passed down to XGBoost core. Or some parameters are not used but slip through this\n verification. Please open an issue if you find above cases.\n\n\n" ], [ "#Predict and score the model on training and test data\ny_train_preds, y_test_preds = print_model_scores(xg_reg2, X_train, X_test, y_train, y_test)", "The r-squared score for our model on Training data is 0.9636330518992762 on 14712202 values.\nThe mean_squared_error score for our model on Training data is 4.230588707727396 on 14712202 values.\nThe root_mean_squared_error score for our model on Training data is 2.056839494887094 on 14712202 values.\n \nThe r-squared score for our model on Testing data is 0.9638064313943984 on 6305230 values.\nThe mean_squared_error score for our model on Testing data is 4.2091745357480885 on 6305230 values.\nThe root_mean_squared_error score for our model on Testing data is 2.0516272896771697 on 6305230 values.\n" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "from __future__ import division, print_function\nimport os\nimport torch\nimport pandas\nimport numpy as np\nfrom torch.utils.data import DataLoader,Dataset\nfrom torchvision import utils, transforms\nfrom skimage import io, transform\nimport matplotlib.pyplot as plt\nimport warnings\n\n#ignore warnings\nwarnings.filterwarnings(\"ignore\")\n\nplt.ion() #interactive mode on", "_____no_output_____" ] ], [ [ "The dataset being used is the face pose detection dataset, which annotates the data using 68 landmark points. The dataset has a csv file that contains the annotation for the images.", "_____no_output_____" ] ], [ [ "# Import CSV file\nlandmarks_csv = pandas.read_csv(\"data/faces/face_landmarks.csv\")\n\n# Extracting info from the CSV file\nn = 65\nimg_name = landmarks_csv.iloc[n,0]\nlandmarks = landmarks_csv.iloc[n,1:].as_matrix()\nlandmarks = landmarks.astype('float').reshape(-1,2)\n\n# Print a few of the datasets for having a look at\n# the dataset\nprint('Image name: {}'.format(img_name))\nprint('Landmarks shape: {}'.format(landmarks.shape))\nprint('First 4 Landmarks: {}'.format(landmarks[:4]))", "Image name: person-7.jpg\nLandmarks shape: (68, 2)\nFirst 4 Landmarks: [[32. 65.]\n [33. 76.]\n [34. 86.]\n [34. 97.]]\n" ] ], [ [ "Now that we have seen the landmark values let's plot a function to display the landmarks on an image", "_____no_output_____" ] ], [ [ "def plot_landmarks(image, landmarks):\n plt.imshow(image)\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, c='r', marker='.')\n plt.pause(0.01)\n \nplt.figure()\nplot_landmarks(io.imread(os.path.join('data/faces/',img_name)),landmarks)\nplt.show()", "_____no_output_____" ] ], [ [ "To use customa datasets we need to use the <b>(torch.utils.data.Dataset) Dataset</b> class provided. It is an abstract class and hence the custom class should inherit it and override the \n<b>__len__</b> method and the\n<b>__getitem__</b> method\nThe __getitem__ method is used to provide the ith sample from the dataset", "_____no_output_____" ] ], [ [ "class FaceLandmarkDataset(Dataset):\n \n # We will read the file here\n def __init__(self,csv_file, root_dir, transform=None):\n \"\"\"\n Args:\n csv_file : string : path to csv file\n root_dir : string : root directory which contains all the images\n transform : callable, optional : Optional transform to be applied \n to the images\n \"\"\"\n self.landmarks_frame = pandas.read_csv(csv_file)\n self.root_dir = root_dir\n self.transform = transform\n \n def __len__(self):\n return len(self.landmarks_frame)\n \n def __getitem__(self, idx):\n \"\"\"\n Args:\n idx (integer): the ith sample\n \"\"\"\n image_name = os.path.join(self.root_dir,self.landmarks_frame.iloc[idx, 0])\n image = io.imread(image_name)\n landmarks = np.array([self.landmarks_frame.iloc[idx, 1:]])\n landmarks = landmarks.astype(\"float\").reshape(-1, 2)\n \n sample = {\"image\":image,\"landmarks\":landmarks}\n \n if self.transform:\n sample = self.transform(sample)\n \n return sample", "_____no_output_____" ], [ "face_dataset = FaceLandmarkDataset(csv_file='data/faces/face_landmarks.csv',\n root_dir='data/faces/')\n\nfig = plt.figure()\n\nfor i in range(len(face_dataset)):\n sample = face_dataset[i]\n\n print(i, sample['image'].shape, sample['landmarks'].shape)\n\n ax = plt.subplot(1, 4, i + 1)\n plt.tight_layout()\n ax.set_title('Sample #{}'.format(i))\n ax.axis('off')\n plot_landmarks(**sample)\n\n if i == 3:\n plt.show()\n break", "0 (324, 215, 3) (68, 2)\n" ] ], [ [ "Now that we have the dataset , we can move on to preprocessing the data. We use the transforms class for this.\nWe will be using callable classes of the transformations we need so that the parameters do not need to be passed again and again. For better description refer the <a href=\"https://pytorch.org/tutorials/beginner/data_loading_tutorial.html\">tutorial</a> from PyTorch.\n\nTo implement callable classes we just need to implement the __call__ method and if required __init__ method of the class. \n\nHere we will be using autocrop , Reshape and To Tensor transformations.\n\n__** NOTE **__<br>\nIn PyTorch the default style for image Tensors is <span>n_channels * Height * Width</span> as opposed to the Tensordlow default of <span>Height * Width * n_channels</span>. But all the images in the real world have the tensorflow default format and hence we need to do that change in the ToTensor class that we will implement.", "_____no_output_____" ] ], [ [ "# Implementing the Rescale class\nclass Rescale(object):\n \"\"\"Rescale the input image to a given size\n \n Args:\n output_size (int or tuple):Desired output size. If tuple, output is\n matched to output_size. If int, smaller of image edges is matched\n to output_size keeping aspect ratio the same\n \"\"\"\n def __init__(self,output_size):\n assert isinstance(output_size,(int,tuple))\n self.output_size = output_size\n \n def __call__(self,sample):\n image, landmarks = samplep['image'], sample['landmarks']\n \n h, w = image.shape[:2]\n if isinstance(self.output_size,int):\n if h>w:\n new_h, new_w = self.output_size * h/w, self.output_size\n else:\n new_h, new_w = slef.output_size, self.output_size *w/h\n else:\n new_h, new_w = self.output_size\n \n image = transform.resize(image, (new_h, new_w))\n \n # h and w are swapped for landmarks because for images,\n # x and y axes are axis 1 and 0 respectively\n landmarks = landmarks * [new_w / w, new_h / h]\n \n return {\"image\": image, \"landmarks\": landmarks}\n# Implementing Random Crop\nclass RandomCrop(object):\n \"\"\"Crop randomly the image in a sample\n \n Args:\n output_size(tuple or int): Desired output size. If int, square crop\n is made.\n \"\"\"\n def __init__(self, output_size):\n assert isinstance(output_size, (int, tuple))\n if isinstance(output_size, int):\n self.output_size = (output_size, output_size)\n else: \n assert len(output_size) == 2\n self.output_size = output_size\n \n def __call__(self, sample):\n images, landmarks = sample['image'], sample['landmarks']\n \n h, w = images.shape[:,2]\n new_h, new_w = self.output_size\n \n top = np.random.randn(0, h-new_h)\n left = np.random.randn(0, w-new_w)\n \n images = images[top:top + new_h, left:left + new_w]\n landmarks = landmarks - [left, top]\n \n sample = {\"image\":images, \"landmarks\": landmarks}\n \n return sample\n \n# Implementing To Tensor\nclass ToTensor(object):\n \"\"\"Convert the PIL image into a tensor\"\"\"\n \n def __call__(self,sample):\n image, landmarks = sample['image'], sample['landmarks']\n \n # Need to transpose\n # Numpy image : H x W x C\n # Torch image : C x H x W\n image = image.transpose((2, 0, 1))\n sample = {\"image\":torch.from_numpy(image),\"landmarks\":torch.from_numpy(landmarks)}", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## K Means Clustering\n### Our Objective - Perform K-Means Clustering to detect Network Intrusion Attempts (Cybersecurity)", "_____no_output_____" ] ], [ [ "#matrix math\nimport numpy as np\n#graphing\nimport matplotlib.pyplot as plt\n#graphing animation\nimport matplotlib.animation as animation", "_____no_output_____" ], [ "#load textfile dataset (2D data points)\n# for each user, how many packets are sent per second and what's the size of a packet\n#anomalies (DDOS attempts) will have lots of big packets sent in a short amount of time \ndef load_dataset(name):\n return np.loadtxt(name)", "_____no_output_____" ], [ "#euclidian distance between 2 data points. For as many data points as necessary. \ndef euclidian(a, b):\n return np.linalg.norm(a-b)", "_____no_output_____" ], [ "def kmeans(k, epsilon=0, distance='euclidian'):\n #list to store past centroid\n history_centroids = []\n #set the distance calculation type \n if distance == 'euclidian':\n dist_method = euclidian\n #set the dataset\n dataset = load_dataset('durudataset.txt')\n # dataset = dataset[:, 0:dataset.shape[1] - 1]\n # get the number of rows (instances) and columns (features) from the dataset\n num_instances, num_features = dataset.shape\n #define k centroids (how many clusters do we want to find?) chosen randomly \n prototypes = dataset[np.random.randint(0, num_instances - 1, size=k)]\n #set these to our list of past centroid (to show progress over time)\n history_centroids.append(prototypes)\n #to keep track of centroid at every iteration\n prototypes_old = np.zeros(prototypes.shape)\n #to store clusters\n belongs_to = np.zeros((num_instances, 1))\n norm = dist_method(prototypes, prototypes_old)\n iteration = 0\n while norm > epsilon:\n iteration += 1\n norm = dist_method(prototypes, prototypes_old)\n #for each instance in the dataset\n for index_instance, instance in enumerate(dataset):\n #define a distance vector of size k\n dist_vec = np.zeros((k,1))\n #for each centroid\n for index_prototype, prototype in enumerate(prototypes):\n #compute the distance between x and centroid\n dist_vec[index_prototype] = dist_method(prototype, instance)\n #find the smallest distance, assign that distance to a cluster\n belongs_to[index_instance, 0] = np.argmin(dist_vec)\n \n tmp_prototypes = np.zeros((k, num_features))\n \n #for each cluster (k of them)\n for index in range(len(prototypes)):\n #get all the points assigned to a cluster\n instances_close = [i for i in range(len(belongs_to)) if belongs_to[i] == index]\n #find the mean of those points, this is our new centroid\n prototype = np.mean(dataset[instances_close], axis=0)\n #add our new centroid to our new temporary list\n tmp_prototypes[index, :] = prototype\n \n #set the new list to the current list\n prototypes = tmp_prototypes\n \n #add our calculated centroids to our history for plotting\n history_centroids.append(tmp_prototypes)\n\n #return calculated centroids, history of them all, and assignments for which cluster each datapoint belongs to\n return prototypes, history_centroids, belongs_to", "_____no_output_____" ], [ "#lets define a plotting algorithm for our dataset and our centroids\ndef plot(dataset, history_centroids, belongs_to):\n #we'll have 2 colors for each centroid cluster\n colors = ['r', 'g']\n\n #split our graph by its axis and actual plot\n fig, ax = plt.subplots()\n\n #for each point in our dataset\n for index in range(dataset.shape[0]):\n #get all the points assigned to a cluster\n instances_close = [i for i in range(len(belongs_to)) if belongs_to[i] == index]\n #assign each datapoint in that cluster a color and plot it\n for instance_index in instances_close:\n ax.plot(dataset[instance_index][0], dataset[instance_index][1], (colors[index] + 'o'))\n\n #lets also log the history of centroids calculated via training\n history_points = []\n #for each centroid ever calculated\n for index, centroids in enumerate(history_centroids):\n #print them all out\n for inner, item in enumerate(centroids):\n if index == 0:\n history_points.append(ax.plot(item[0], item[1], 'bo')[0])\n else:\n history_points[inner].set_data(item[0], item[1])\n print(\"centroids {} {}\".format(index, item))\n\n plt.show()", "_____no_output_____" ], [ "#main file \ndef execute():\n #load dataset\n dataset = load_dataset('durudataset.txt')\n #train the model on the data\n centroids, history_centroids, belongs_to = kmeans(2)\n #plot the results\n plot(dataset, history_centroids, belongs_to)", "_____no_output_____" ], [ "%matplotlib notebook\n\n#do everything\nexecute()\n", "_____no_output_____" ], [ "%matplotlib notebook\ndef plot_step_by_step(dataset, history_centroids, belongs_to):\n colors = ['r', 'g']\n\n fig, ax = plt.subplots()\n\n for index in range(dataset.shape[0]):\n instances_close = [i for i in range(len(belongs_to)) if belongs_to[i] == index]\n for instance_index in instances_close:\n ax.plot(dataset[instance_index][0], dataset[instance_index][1], (colors[index] + 'o'))\n\n history_points = []\n for index, centroids in enumerate(history_centroids):\n for inner, item in enumerate(centroids):\n if index == 0:\n history_points.append(ax.plot(item[0], item[1], 'bo')[0])\n else:\n history_points[inner].set_data(item[0], item[1])\n print(\"centroids {} {}\".format(index, item))\n \n plt.pause(0.8)\n ", "_____no_output_____" ], [ "for item in history_centroids:\n plot_step_by_step(dataset, [item], belongs_to)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import boto3\nsession = boto3.Session(profile_name='esip')\n# Any clients created from this session will use credentials\n# from the [dev] section of ~/.aws/credentials.\ndev_s3_client = session.client('s3')\n", "_____no_output_____" ], [ "%matplotlib inline\nimport rasterio as rio\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport rasterio.plot\nimport os\nfrom datetime import datetime as dt\n\n# Load the veg aso\nveg_aso = rio.open(r'/home/ubuntu/planet-snowcover/pipeline/aso_veg_test_only.tif')\nveg_aso_mask = veg_aso.read(1)\n\n\n\n", "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #2\n (fname, cnt))\n/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #3\n (fname, cnt))\n" ], [ "# Plot this elevation band:\nplt.figure(figsize=(10,10))\nplt.imshow(veg_aso_mask)\n\ntitle_text = ''\nplt.title(title_text);\n", "_____no_output_____" ], [ "print(veg_aso_mask.shape)", "(26547, 32768)\n" ], [ "\n# Load the DEM and read out a np array of the elevation values (meters):\nveg_pred = rio.open(r'/home/ubuntu/planet-snowcover/pipeline/pred_veg_test_only.tif')\npred_veg = veg_pred.read(1)\n", "_____no_output_____" ], [ "print(pred_veg.shape)", "(26547, 32768)\n" ], [ "\n# Plot this elevation band:\nplt.figure(figsize=(10,10))\nplt.imshow(pred_veg)\n\ntitle_text = ''\nplt.title(title_text);", "_____no_output_____" ], [ "def compute_metrics(true, pred):\n print(true.shape, pred.shape)\n assert true.shape == pred.shape, \"Masks and predictions are different shapes. Are you sure they're in the same CRS/extent?\"\n\n compare = (true, pred)\n\n balanced_acc = metrics.balanced_accuracy_score(*compare)\n prfs = metrics.precision_recall_fscore_support(*compare, average='binary')\n\n these_metrics = {\n \"balanced_accuracy\" : balanced_acc,\n \"precision\": prfs[0],\n \"recall\": prfs[1],\n \"f_score\": prfs[2]\n }\n\n return these_metrics", "_____no_output_____" ], [ "mask_data = veg_aso_mask.flatten()\npred_data = pred_veg.flatten()\n # remove nodata", "_____no_output_____" ], [ "mask_data.shape", "_____no_output_____" ], [ " np.unique(mask_data)", "_____no_output_____" ], [ " np.unique(pred_data)", "_____no_output_____" ], [ "len(mask_data[np.where(mask_data != 9999)])", "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/__main__.py:1: DeprecationWarning: elementwise != comparison failed; this will raise an error in the future.\n if __name__ == '__main__':\n" ], [ "try:\n mask_data = mask_data[np.where(mask_data != -3.4e+38)]\n pred_data = pred_data[np.where(pred_data != -3.4e+38)]\nexcept Exception as e:\n print(\"Error filtering nodata. Do input data files have nodata attribute set? [{}]\".format(e))", "_____no_output_____" ], [ " np.unique(pred_data)", "_____no_output_____" ], [ "from sklearn.metrics import accuracy_score\naccuracy_score(mask_data, pred_data)", "_____no_output_____" ], [ "from sklearn import metrics\nmetrics = compute_metrics(mask_data, pred_data)", "(869892096,) (869892096,)\n" ], [ "print(metrics)", "_____no_output_____" ], [ "print(masksnow.shape)", "(3073, 6144)\n" ], [ "from rasterio.plot import show\nshow(masksnow)", "_____no_output_____" ], [ "!gdalinfo /home/ubuntu/s3:::planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180528_181108_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif", "Driver: GTiff/GeoTIFF\nFiles: /home/ubuntu/s3:::planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180528_181108_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\nSize is 6144, 3073\nCoordinate System is:\nGEOGCS[\"WGS 84\",\n DATUM[\"WGS_1984\",\n SPHEROID[\"WGS 84\",6378137,298.257223563,\n AUTHORITY[\"EPSG\",\"7030\"]],\n AUTHORITY[\"EPSG\",\"6326\"]],\n PRIMEM[\"Greenwich\",0],\n UNIT[\"degree\",0.0174532925199433],\n AUTHORITY[\"EPSG\",\"4326\"]]\nOrigin = (-119.630126953125000,38.229550455326141)\nPixel Size = (0.000021457672119,-0.000016856813467)\nMetadata:\n AREA_OR_POINT=Area\nImage Structure Metadata:\n COMPRESSION=LZW\n INTERLEAVE=BAND\nCorner Coordinates:\nUpper Left (-119.6301270, 38.2295505) (119d37'48.46\"W, 38d13'46.38\"N)\nLower Left (-119.6301270, 38.1777495) (119d37'48.46\"W, 38d10'39.90\"N)\nUpper Right (-119.4982910, 38.2295505) (119d29'53.85\"W, 38d13'46.38\"N)\nLower Right (-119.4982910, 38.1777495) (119d29'53.85\"W, 38d10'39.90\"N)\nCenter (-119.5642090, 38.2036500) (119d33'51.15\"W, 38d12'13.14\"N)\nBand 1 Block=6144x1 Type=Int16, ColorInterp=Gray\n NoData Value=9999\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Materials\n\nWe have different materials available thanks to the [materialspy](https://opticalmaterialspy.readthedocs.io/en/latest/index.html) library", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport opticalmaterialspy as mat\nimport modes as ms\n\nm = mat.SiO2()\n\n# Refractive index @ 1550nm.\nprint('n(1.55e-6m):', m.n(1.55e-6)) # Knows 1.55e-6 must be [m].\nprint('n(1.55um):', m.n(1.55)) # Knows 1.55 must be [um].\nprint('n(1550nm):', m.n(1550)) # Knows 1550 must be [nm].\n\n# Group velocity refractive index @ 900nm.\nprint('n_gv(900nm):', m.ng(900))\n\n# Group velocity dispersion @ 808nm.\nprint('GVD(0.808um):', m.gvd(0.808))", "_____no_output_____" ], [ "wavelengths = np.linspace(1.3, 1.6, 10)\nn = [ms.materials.si(w) for w in wavelengths]\n\nplt.plot(wavelengths, n)\nplt.xlabel('wavelength (um)')\nplt.ylabel('Refractive index')\nplt.title('Silicon refractive index')", "_____no_output_____" ] ], [ [ "if your material is not defined in the materials module you can always add it", "_____no_output_____" ] ], [ [ "def nitride(wl):\n return mat.RefractiveIndexWeb(\n \"https://refractiveindex.info/?shelf=main&book=Si3N4&page=Luke\"\n ).n(wl)", "_____no_output_____" ], [ "nsin = [nitride(w) for w in wavelengths]", "_____no_output_____" ], [ "plt.plot(wavelengths, nsin)\nplt.xlabel('wavelength (nm)')\nplt.ylabel('Refractive index')\nplt.title('Silicon nitride refractive index (Si3N4)')", "_____no_output_____" ], [ "help(ms.materials)", "_____no_output_____" ], [ "wavelengths = np.linspace(1.3, 1.6, 10)\nn = [ms.materials.sio2(w) for w in wavelengths]\n\nplt.plot(wavelengths, n)\nplt.xlabel('wavelength (um)')\nplt.ylabel('Refractive index')\nplt.title('SiO2')", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "### B.1.1.7\n\n#### The selection of suitable loci (done on Tue 22. Dec. 2020)\nTo identify suitable targets for primer/probe design, we downloaded 1,136 sequences from the GISAID repository filtered during a collection time spanning 1 - 21 December 2020. We focused on the spike gene because lineage B.1.1.7 contains a number of spike gene mutations, including two deletions (ΔH69/ΔV70 and ΔY144) that we focused on for designing a specific assay. \n\nI cut the locus encoding the spike protein and used the *MAFFT* alignment tool (with the parameter - auto) to align all the sequences against the WUHAN reference (NCBI ID: NC_045512.2). ", "_____no_output_____" ] ], [ [ "%%bash\n\n# \"msa_1221.fasta\" is a pre-filtered nucleotide MSA file downladed from the GISAID repository 22.12.2020\n# the WUHAN reference is always used as the first sequence in the GISAID MSA files\ngrep -m 1 \">\" msa_1221.fasta | cut -d\">\" -f2 > sars2_allSeqs_til21stDec2020_andRefWuhan.list\n# to reduce computational time, I used only sequences collected in Dec 2020\n# getting unique sequence IDs\ngrep -P \"2020-12-\" msa_1221.fasta | cut -d\">\" -f2 >> sars2_allSeqs_til21stDec2020_andRefWuhan.list\n# star-end positions of the spike protein in the aligned WUHAN sequence: 22412-26369\n# I called the spike locus of all sequences listed in \"sars2_allSeqs_til21stDec2020_andRefWuhan.list\"\ncount=$(wc -l sars2_allSeqs_til21stDec2020_andRefWuhan.list | cut -d\" \" -f1)\nfor ((i=1; i<$(($count+1)); i++))\ndo\nID=$(sed -n ''$i'p' sars2_allSeqs_til21stDec2020_andRefWuhan.list | cut -d\" \" -f1)\necho \">\"$ID >> sars2_allSeqs_til21stDec2020_andRefWuhan_Spike.fa\ngrep -A 1 -m 1 $ID msa_1221.fasta | grep -v \">\" | cut -c22412-26369 | tr -d '-' | tr -d '\\n' | tr -d ' ' >> sars2_allSeqs_til21stDec2020_andRefWuhan_Spike.fa\necho \"\" >> sars2_allSeqs_til21stDec2020_andRefWuhan_Spike.fa\ndone\n\n# using 4 CPUS, I run the mafft tool with default settings\nmafft --thread 4 --auto sars2_allSeqs_til21stDec2020_andRefWuhan_Spike.fa > sars2_allSeqs_til21stDec2020_andRefWuhan_Spike_mafft.fa", "_____no_output_____" ] ], [ [ "#### Downstream analysis\nTwelve sequences (1.06 %) contained ambiguous signal in the loci of deletions and were not used in the downstream analysis. We separated sequences into two groups: 1) those with the ΔH69/ΔV70 and ΔY144 deletions and 2) those without the deletions (Table 1). Using *SeaView*, we called 95 % consensus sequences for the ΔH69/ΔV70 and ΔY144 group and the No deletions group that were used to design primer and probe sets specific to either B.1.1.7 or all other SARS-CoV-2 variants, respectively. ", "_____no_output_____" ] ], [ [ "%%bash\n# quality checks of bases in the deleted loci (ΔH69/ΔV70 and ΔY144) \n# if a called base has ambiguous character, it is denotes as N\ncount=$(wc -l sars2_allSeqs_til21stDec2020_andRefWuhan.list | cut -d\" \" -f1)\nfor ((i=1; i<$(($count+1)); i++))\ndo\nID=$(sed -n ''$i'p' sars2_allSeqs_til21stDec2020_andRefWuhan.list | cut -d\"|\" -f2)\nDel69_70=$(awk ' BEGIN {RS=\">\"}; /'$ID'\\|/ { print \">\"$0 } ' sars2_allSeqs_til21stDec2020_andRefWuhan_Spike_mafft.fa | grep -v \">\" | tr -d '\\n' | tr -d ' ' | cut -c203-208)\nDel144=$(awk ' BEGIN {RS=\">\"}; /'$ID'\\|/ { print \">\"$0 } ' sars2_allSeqs_til21stDec2020_andRefWuhan_Spike_mafft.fa | grep -v \">\" | tr -d '\\n' | tr -d ' ' | cut -c428-430)\n# using the output file, we can also compute the correlation of two deletions (ΔH69/ΔV70 and ΔY144) and to judge about their co-occurrence \necho -e $ID\"\\t\"$Del69_70\"\\t\"$Del144 >> sars2_1stDec20202_21stDec20202_Spike_Qchecks.tsv\n# The shorter deletion (ΔY144) always co-occurred with the longer deletion (ΔH69/ΔV70), whereas the (ΔH69/ΔV70) deletion occurs independently in 17 sequences (1.5 %). \n# Pearson's correlation coefficient of the deletions is 0.953. ", "_____no_output_____" ] ], [ [ "#### Quality checks of the selected primer/probe loci (done on Thu 4. Febr. 2021)\nIn a separate analysis to determine the prevalence of the ΔH69/ΔV70 and ΔY144 deletions in lineages other than B.1.1.7, we downloaded 416,778 spike protein sequences with the most recent data description file collected from the beginning of the pandemic through 29 January 2021. Using regular expressions (bash pattern matching command grep with the option -P for Perl-compatible regular expression), we searched for loci with both ΔH69/ΔV70 and ΔY144 deletions, and for loci without these deletions. In the regular expression, we kept fixed a few amino acids downstream and upstream from the deletions to omit any miscalling of the searched pattern. \n\n#### Quality checks of the selected primer/probe loci (update: 2. March 2021)\nIn a separate analysis to determine the prevalence of the ΔH69/ΔV70 and ΔY144 deletions in lineages other than B.1.1.7, we downloaded 633,137 spike protein sequences with the most recent data description file collected from the beginning of the pandemic through 2 March 2021. Using regular expressions (bash pattern matching command grep with the option -P for Perl-compatible regular expression), we searched for loci with both ΔH69/ΔV70 and ΔY144 deletions and for loci without these deletions. In the regular expression, we kept fixed a few amino acids downstream and upstream from the deletions to omit any miscalling of the searched pattern. ", "_____no_output_____" ] ], [ [ "# update for the data; datasets from 1st of March 2021 (download on Tue 2nd March 2021)\n%%bash\n# \"Spike_proteins_0301.fasta\" is a pre-filtered amino-acid MSA file downladed from the GISAID repository 22.12.2020\ngrep -c \">\" Spike_proteins_0301.fasta\n# detection of both deletions (no HV, no Y): d69d70 and d144; with the check for unique sequence IDs\ngrep -B1 -P \"HAISGT.{66}FLGVYHK\" Spike_proteins_0301.fasta | grep \">\" | cut -d\"/\" -f2 | sort | uniq -c | awk ' { print $1\"\\t\"$2} ' | wc -l\n# full pattern (HV and Y), no deletion; with the check for unique sequence IDs\ngrep -B1 -P \"HAIHVSGT.{66}FLGVYYHK\" Spike_proteins_0301.fasta | grep \">\" | cut -d\"/\" -f2 | sort | uniq -c | awk ' { print $1\"\\t\"$2} ' | wc -l\n# only d144 (only HV, no Y); with the check for unique sequence IDs\ngrep -B1 -P \"HAIHVSGT.{66}FLGVYHK\" Spike_proteins_0301.fasta | grep \">\" | cut -d\"/\" -f2 | sort | uniq -c | awk ' { print $1\"\\t\"$2} ' | wc -l\n# only d69d70 (only Y, no HV); with the check for unique sequence IDs\ngrep -B1 -P \"HAISGT.{66}FLGVYYHK\" Spike_proteins_0301.fasta | grep \">\" | cut -d\"/\" -f2 | sort | uniq -c | awk ' { print $1\"\\t\"$2} ' | wc -l\n# detection of both deletions (no HV, no Y): d69d70 and d144; with the time-dependent sorting\ngrep -B1 -P \"HAISGT.{66}FLGVYHK\" Spike_proteins_0301.fasta | grep \">\" | grep -oP \"\\|202[01]-..-\" | sort | uniq -c\n# only d69d70 (only Y, no HV); with the time-dependent sorting\ngrep -B1 -P \"HAISGT.{66}FLGVYYHK\" Spike_proteins_0301.fasta | grep \">\" | grep -oP \"\\|202[01]-..-\" | sort | uniq -c\n\n# call the whole metadata information about sars-cov-2 records with detected both deletions \ncount=$(wc -l Spike_proteins_0301.fasta | cut -d\" \" -f1)\necho $count\n# $count/16=6473\nmyF(){\nfor ((i=1; i<6474; i++))\ndo\nN=$((12946*$1 + $i))\nID=$(sed -n ''$N'p' B117_IDs.list)\n# metadata_2021-03-01_09-16.tsv\nawk -v ID=$ID 'BEGIN{FS=\"\\t\"}; { if ( $3 == ID && $15 == \"Human\" ) { print $1\"\\t\"$3\"\\t\"$7\"\\t\"$18\"\\t\"$19 }} ' metadata_2021-03-01_09-16.tsv >> \"B117_search_\"$i.csv \ndone\n}\n\nexport -f myF\n# 12 946\nparallel -j 16 myF ::: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\ncat \"B117_search_\"*.csv > \"B117_detected_bothMutations_cladeID.csv \nrm \"B117_search_\"*.csv", "_____no_output_____" ] ], [ [ "#### Results (done on Thu 4. Febr. 2021)\nOur analysis of the prevalence of both ΔH69/ΔV70 and ΔY144 deletions in lineages other than B.1.1.7, revealed a total of 29,872 sequences that possess both deletions, while 368,474 sequences do not have them. Based on the metadata file, we identified SARS-CoV-2 lineages across all called sequences with both deletions. Only five sequences (0.0167 %) out of 29,872 records are not labelled as B.1.1.7, highlighted the notion that these two deletions are highly specific for the B.1.1.7 variant and make ideal targets for primer/probe design.\n\n#### Results (update: 2. March 2021)\nAnalysis of the prevalence of both ΔH69/ΔV70 and ΔY144 deletions in lineages other than B.1.1.7 revealed a total of 103,529 sequences that possess both deletions. Based on the metadata file, we identified SARS-CoV-2 lineages across all called sequences with both deletions. Only 108 sequences (0.10%) out of 103,529 sequences are not labelled as B.1.1.7. In other words, 99.90% of sequences containing both deletions belong to lineage B.1.1.7, highlighting the notion that these two deletions are highly specific for the B.1.1.7 variant and make ideal targets for primer/probe design (see the table below, please).\n\n| Clade (Nextstrain) | Total sequences containing both ΔH69/ΔV70 and ΔY144 | % sequences containing both ΔH69/ΔV70 and ΔY144 |\n|:------------------:|:---------------------------------------------------:|:-----------------------------------------------:|\n|19A|6|<0.01%|\n|20A|36|0.03%|\n|20A.EU2|22|0.02%|\n|20B|21|0.02%|\n|20C|6|<0.01%|\n|20E.EU1|13|0.01%|\n|20I/501Y.V1 (**B.1.1.7**)|103,421|**99.90%**|\n|No ID|4|<0.01%|\n|Total|103,529|100%|\n\n", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline\n\nimport gym\nimport matplotlib\nimport numpy as np\nimport sys\n\nfrom collections import defaultdict\n\nif \"../\" not in sys.path:\n sys.path.append(\"../\") \nfrom lib.envs.blackjack import BlackjackEnv\nfrom lib import plotting\n\nmatplotlib.style.use('ggplot')", "_____no_output_____" ], [ "env = BlackjackEnv()", "_____no_output_____" ], [ "def mc_prediction(policy, env, num_episodes, discount_factor=1.0):\n \"\"\"\n Monte Carlo prediction algorithm. Calculates the value function\n for a given policy using sampling.\n \n Args:\n policy: A function that maps an observation to action probabilities.\n env: OpenAI gym environment.\n num_episodes: Number of episodes to sample.\n discount_factor: Gamma discount factor.\n \n Returns:\n A dictionary that maps from state -> value.\n The state is a tuple and the value is a float.\n \"\"\"\n\n # Keeps track of sum and count of returns for each state\n # to calculate an average. We could use an array to save all\n # returns (like in the book) but that's memory inefficient.\n returns_sum = defaultdict(float)\n returns_count = defaultdict(float)\n \n # The final value function\n V = defaultdict(float)\n \n for i_episode in range(1, num_episodes + 1):\n # Print out which episode we're on, useful for debugging.\n if i_episode % 1000 == 0:\n print(\"\\rEpisode {}/{}.\".format(i_episode, num_episodes), end=\"\")\n sys.stdout.flush()\n\n # Generate an episode.\n # An episode is an array of (state, action, reward) tuples\n episode = []\n state = env.reset()\n for t in range(100):\n action = policy(state)\n next_state, reward, done, _ = env.step(action)\n episode.append((state, action, reward))\n if done:\n break\n state = next_state\n\n # Find all states the we've visited in this episode\n # We convert each state to a tuple so that we can use it as a dict key\n states_in_episode = set([tuple(x[0]) for x in episode])\n for state in states_in_episode:\n # Find the first occurance of the state in the episode\n first_occurence_idx = next(i for i,x in enumerate(episode) if x[0] == state)\n # Sum up all rewards since the first occurance\n G = sum([x[2]*(discount_factor**i) for i,x in enumerate(episode[first_occurence_idx:])])\n # Calculate average return for this state over all sampled episodes\n returns_sum[state] += G\n returns_count[state] += 1.0\n V[state] = returns_sum[state] / returns_count[state]\n\n return V ", "_____no_output_____" ], [ "def sample_policy(observation):\n \"\"\"\n A policy that sticks if the player score is >= 20 and hits otherwise.\n \"\"\"\n score, dealer_score, usable_ace = observation\n return 0 if score >= 20 else 1", "_____no_output_____" ], [ "V_10k = mc_prediction(sample_policy, env, num_episodes=10000)\nplotting.plot_value_function(V_10k, title=\"10,000 Steps\")\n\nV_500k = mc_prediction(sample_policy, env, num_episodes=500000)\nplotting.plot_value_function(V_500k, title=\"500,000 Steps\")", "Episode 10000/10000." ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Generate correction profiles for denoised\n\nby Pu Zheng\n\n2019.06.18", "_____no_output_____" ] ], [ [ "%run \"E:\\Users\\puzheng\\Documents\\Startup_py3.py\"\nsys.path.append(r\"E:\\Users\\puzheng\\Documents\")\n\nimport ImageAnalysis3 as ia\n%matplotlib notebook\n\nfrom ImageAnalysis3 import *\n\nprint(os.getpid())", "46944\n" ], [ "reload(ia.get_img_info)\nreload(ia.corrections)\nreload(ia.visual_tools)\nreload(ia.classes)\nreload(ia.alignment_tools)\nreload(ia.correction_tools.illumination)", "_____no_output_____" ] ], [ [ "# Data folder", "_____no_output_____" ] ], [ [ "# master folder for this dataset:\nmaster_folder = r'\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++'\n# correction folder\ncorrection_folder=os.path.join(master_folder, 'Corrections')\nif not os.path.exists(correction_folder):\n print(f\"creating folder: {correction_folder}\")\n os.makedirs(correction_folder)", "_____no_output_____" ] ], [ [ "# Illumination_correction", "_____no_output_____" ] ], [ [ "#master_folder = r'\\\\10.245.74.116\\Chromatin_NAS_4\\20191218_CTP05-chr21_HCT116_6hauxin'\n#correction_folder =os.path.join(master_folder, 'Corrections')\nfolders, fovs = get_img_info.get_folders(master_folder, 'H')\nfolders = [_fd for _fd in folders if ('Cy' in _fd and '3color' not in _fd) or 'H0R0' in _fd]\nprint(folders)", "Get Folder Names: (ia.get_img_info.get_folders)\n- Number of folders: 81\n- Number of field of views: 72\n['\\\\\\\\10.245.74.158\\\\Chromatin_NAS_6\\\\20201012-mouse_proB_IgH++\\\\H0R0', '\\\\\\\\10.245.74.158\\\\Chromatin_NAS_6\\\\20201012-mouse_proB_IgH++\\\\H74R24Cy7', '\\\\\\\\10.245.74.158\\\\Chromatin_NAS_6\\\\20201012-mouse_proB_IgH++\\\\H75R24Cy5']\n" ], [ "# shared parameters\nsingle_im_size = np.array([35,2048,2048])\nall_colors = ['750','647','488','405']\n# image and threads\nnum_images = 60\nnum_threads = 30", "_____no_output_____" ] ], [ [ "# Illumination correction", "_____no_output_____" ] ], [ [ "%matplotlib inline\nreload(ia.correction_tools.illumination)\n\ndapi_folder = folders[0]\nillumination_pfs = correction_tools.illumination.Generate_Illumination_Correction(\n dapi_folder, \n all_colors,\n all_channels=all_colors,\n num_threads=num_threads,\n num_images=num_images,\n single_im_size=single_im_size,\n correction_folder=correction_folder,\n gaussian_filter_size=60,\n save=True, save_folder=correction_folder,\n overwrite=False,\n )", "-- directly load:[] illumination profiles for files\n-- start calculating ['750', '647', '488', '405'] illumination profiles\n-- 60 among 72 dax files will be loaded in data_folder: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H0R0\n++ start multi-processing illumination profile calculateion with 30 threads for 60 images in 1158.42s.\n-- saving updated profiles\n--- saving 750 profile into file: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\Corrections\\illumination_correction_750_2048x2048.npy\n--- saving 647 profile into file: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\Corrections\\illumination_correction_647_2048x2048.npy\n--- saving 488 profile into file: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\Corrections\\illumination_correction_488_2048x2048.npy\n--- saving 405 profile into file: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\Corrections\\illumination_correction_405_2048x2048.npy\n" ] ], [ [ "# Chromatic Abbrevation", "_____no_output_____" ], [ "## chromatic for 750", "_____no_output_____" ] ], [ [ "cc_folder = folders[1]\nref_folder = folders[2]\nprint(cc_folder, ref_folder)", "\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H74R24Cy7 \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H75R24Cy5\n" ], [ "reload(correction_tools.chromatic)\nchromatic_pfs, chromatic_consts = correction_tools.chromatic.Generate_chromatic_abbrevation(\n cc_folder, ref_folder, '750',\n num_images=num_images, \n num_threads=num_threads, \n fitting_orders=[1,1,1],\n correction_args={'correction_folder':correction_folder,\n 'single_im_size': single_im_size,\n 'all_channels': all_colors,\n 'corr_channels': ['750', '647'],\n },\n save_folder=correction_folder,\n overwrite_profile=True)", "++ generating chromatic info for 40 images in 20 threads in 2267.764s.\n++ fitting polynomial orders: [1 1 1]\n-- constants: [ 2.18325971e-01 1.47750723e-02 -9.58693868e-07 3.15479204e-05] with rsquare=0.3599429684786537\n-- constants: [-2.48105252e-02 2.01826717e-03 -1.82465972e-03 1.36473686e-05] with rsquare=0.9761135277372379\n-- constants: [-1.23677537e-01 -5.45136142e-04 -5.45356247e-06 -1.82303478e-03] with rsquare=0.9739049814258113\n++ saving new profiles into folder: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\Corrections\n" ] ], [ [ "### check chromatic abbrevation, 750-647", "_____no_output_____" ] ], [ [ "# reference image\nref_ims, = ia.io_tools.load.correct_fov_image(ref_filename, \n [647, 488], \n single_im_size=single_im_size,\n all_channels=all_colors,\n warp_image=True,\n illumination_corr=True,\n chromatic_corr=False,\n bleed_corr=False, \n correction_folder=correction_folder)\n\n# target image, not warpping\nraw_ims, corr_funcs, drift = ia.io_tools.load.correct_fov_image(cc_filename, \n [750], \n calculate_drift=True,\n ref_filename=ref_ims[-1],\n single_im_size=single_im_size,\n all_channels=all_colors,\n warp_image=False,\n illumination_corr=True,\n chromatic_corr=True,\n bleed_corr=False, \n correction_folder=correction_folder,\n return_drift=True)\n\n# target image, warppring\ncorr_ims, = ia.io_tools.load.correct_fov_image(cc_filename, \n [750], \n calculate_drift=False,\n drift=drift,\n ref_filename=ref_ims[-1],\n single_im_size=single_im_size,\n all_channels=all_colors,\n warp_image=True,\n illumination_corr=True,\n chromatic_corr=True, \n bleed_corr=False, \n correction_folder=correction_folder,\n return_drift=False)", "- correct the whole fov for image: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H75R24Cy5\\Conv_zscan_05.dax\n-- loading illumination correction profile from file:\n\t 647 illumination_correction_647_2048x2048.npy\n\t 488 illumination_correction_488_2048x2048.npy\n-- loading image from file:\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H75R24Cy5\\Conv_zscan_05.dax in 3.007s\n-- removing hot pixels for channels:['647', '488'] in 35.413s\n-- illumination correction for channels: 647, 488, in 6.867s\n-- warp image with drift:[0. 0. 0.] in 0.000s\n-- finish correction in 47.063s\n- correct the whole fov for image: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H74R24Cy7\\Conv_zscan_05.dax\n-- loading illumination correction profile from file:\n\t 750 illumination_correction_750_2048x2048.npy\n\t 488 illumination_correction_488_2048x2048.npy\n-- loading chromatic_constants correction profile from file:\n\t 750 chromatic_correction_750_647_35_2048_2048_const.pkl\n\t 647 None\n-- loading image from file:\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H74R24Cy7\\Conv_zscan_05.dax in 3.312s\n-- removing hot pixels for channels:['750', '488'] in 35.980s\n-- illumination correction for channels: 750, 488, in 6.801s\n-- apply bead_drift calculate for channel: 488\n-- aligning image -> directly use image\n-- directly use ref_image\n--- finish drift in 71.050s\n-- drift: [ 0. -0.79 0.29]\n-- generate translation function for chromatic correction for channels: ['750'] and drift:[ 0. -0.79 0.29] in 0.000s\n-- finish correction in 118.068s\n- correct the whole fov for image: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H74R24Cy7\\Conv_zscan_05.dax\n-- loading illumination correction profile from file:\n\t 750 illumination_correction_750_2048x2048.npy\n-- loading chromatic correction profile from file:\n\t 750 chromatic_correction_750_647_35_2048_2048.npy\n\t 647 None\n-- loading image from file:\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H74R24Cy7\\Conv_zscan_05.dax in 2.695s\n-- removing hot pixels for channels:['750'] in 18.626s\n-- illumination correction for channels: 750, in 3.449s\n-- warp image with chromatic correction for channels: ['750'] and drift:[ 0. -0.79 0.29] 750, in 105.555s\n-- finish correction in 138.819s\n" ], [ "%matplotlib notebook\nvisual_tools.imshow_mark_3d_v2([raw_ims[0], corr_ims[0], ref_ims[0]])", "_____no_output_____" ], [ "corr_funcs[0]", "_____no_output_____" ], [ "reload(ia.io_tools.load)\nreload(ia.correction_tools.chromatic)\nfrom ImageAnalysis3.spot_tools.fitting import fit_fov_image\n# fitting\nint_th = 1\n\ncorr_spots = fit_fov_image(corr_ims[0], '750', th_seed=400, normalize_backgroud=True)\nraw_spots = fit_fov_image(raw_ims[0], '750', th_seed=400, normalize_backgroud=True)\nref_spots = fit_fov_image(ref_ims[0], '647', th_seed=600, normalize_backgroud=True)\ncorr_spots = corr_spots[corr_spots[:,0] >= int_th]\nraw_spots = raw_spots[raw_spots[:,0] >= int_th]\ncorr_raw_spots = corr_funcs[0](raw_spots)\nref_spots = ref_spots[ref_spots[:,0] >= int_th]", "-- start fitting spots in channel:750, 500 seeded, normalize total background:285.00, 500 fitted in 61.562s.\n-- start fitting spots in channel:750, 500 seeded, normalize total background:285.00, 500 fitted in 62.365s.\n-- start fitting spots in channel:647, 500 seeded, normalize total background:585.00, 500 fitted in 61.056s.\n" ], [ "plt.figure(figsize=(6,6),dpi=100)\nplt.plot(corr_spots[:,2], corr_spots[:,3], 'r.', label='ref')\nplt.plot(corr_raw_spots[:,2], corr_raw_spots[:,3], 'g.', label='not warpping')\nplt.plot(raw_spots[:,2], raw_spots[:,3], 'y.', label='not warpping')\n\nplt.plot(ref_spots[:,2], ref_spots[:,3], 'b.', label='warpping')\nplt.legend()\nplt.xlabel('X')\nplt.ylabel('Y')\n#plt.plot(ref_spots[:,2]+drift[1], ref_spots[:,3]+drift[2], 'b.')\nplt.show()", "_____no_output_____" ], [ "from ImageAnalysis3.spot_tools.matching import find_paired_centers, check_paired_centers\n\n# matching\n_new_drift, paired_ref_cts, paired_corr_cts = find_paired_centers(ref_spots[:,1:4], corr_spots[:,1:4], drift=drift)\nprint(_new_drift)\n_new_drift, paired_ref_cts, paired_corr_cts = check_paired_centers(paired_ref_cts, paired_corr_cts+drift)\nprint(_new_drift)", "[-0.01766196 -0.09067997 -0.01237969]\n[-0.01694834 0.69215355 -0.30152252]\n" ] ], [ [ "## visualize", "_____no_output_____" ] ], [ [ "%matplotlib notebook\nvisual_tools.imshow_mark_3d_v2([raw_ims[0], corr_ims[0], ref_ims[0]])", "_____no_output_____" ] ], [ [ "# bleedthrough correction", "_____no_output_____" ] ], [ [ "from ImageAnalysis3 import _image_size, _correction_folder, _allowed_colors\nreload(correction_tools.bleedthrough)\nfrom ImageAnalysis3.correction_tools.bleedthrough import find_bleedthrough_pairs, interploate_bleedthrough_correction_from_channel\nimport multiprocessing as mp\nfrom ImageAnalysis3.correction_tools.chromatic import generate_polynomial_data\n\n\nbleedthrough_channels=['750', '647']\n\nbleedthrough_correction_args = {\n 'correction_folder': _correction_folder,\n 'single_im_size':single_im_size,\n 'all_channels':all_colors,\n 'corr_channels':['750','647'],\n 'bleed_corr':False,\n 'illumination_corr':False,\n 'chromatic_corr':False,\n}\n\nbleedthrough_fitting_args = {'max_num_seeds':1000,\n 'th_seed': 500,\n 'use_dynamic_th':True,\n}\nreload(correction_tools.bleedthrough)", "_____no_output_____" ], [ "%matplotlib inline\nreload(correction_tools.bleedthrough)\nbleed_pf = correction_tools.bleedthrough.Generate_bleedthrough_correction(\n folders[1:3],\n rsq_th=0.81,\n corr_channels=bleedthrough_channels,\n correction_args=bleedthrough_correction_args,\n fitting_args=bleedthrough_fitting_args,\n num_images=num_images, \n #num_images=30,\n num_threads=num_threads,\n save_folder=correction_folder,\n overwrite_profile=True,\n overwrite_temp=False,\n )", "+ generating bleedthrough profiles.\n++ generating bleedthrough info for 60 images in 30 threads in 3.326s.\n- check 2956 bleedthrough pairs.\n- start iteration with outlier_sigma=2.00, keep_percentage=0.95\n[ True True True ... True True False] [ True True True ... True True False]\n-- iter: 1, kept in this round: 0.854, total: 0.854\n" ] ], [ [ "### Check bleedthorugh for cy7", "_____no_output_____" ] ], [ [ "from ImageAnalysis3.io_tools.load import correct_fov_image\nfrom ImageAnalysis3.io_tools.crop import crop_neighboring_area", "_____no_output_____" ], [ "reload(ia.io_tools.load)\ncy7_filename = os.path.join(folders[0], fovs[3])\n\ncy7_raw_ims, = ia.io_tools.load.correct_fov_image(cy7_filename, \n bleedthrough_channels, \n bleed_corr=False, chromatic_corr=False,\n corr_channels=bleedthrough_channels,\n single_im_size=single_im_size,\n all_channels=all_colors,\n illumination_corr=True,\n correction_folder=correction_folder)\ncy7_corr_ims, = ia.io_tools.load.correct_fov_image(cy7_filename, \n bleedthrough_channels, \n bleed_corr=True, chromatic_corr=False, \n corr_channels=bleedthrough_channels,\n single_im_size=single_im_size,\n all_channels=all_colors,\n illumination_corr=True,\n correction_folder=correction_folder)", "- correct the whole fov for image: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H0R0\\Conv_zscan_03.dax\n-- loading illumination correction profile from file:\n\t 750 illumination_correction_750_2048x2048.npy\n\t 647 illumination_correction_647_2048x2048.npy\n-- loading image from file:\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H0R0\\Conv_zscan_03.dax in 5.591s\n-- removing hot pixels for channels:['750', '647'] in 23.486s\n-- illumination correction for channels: 750, 647, in 4.526s\n-- warp image with drift:[0. 0. 0.] in 0.000s\n-- finish correction in 35.552s\n- correct the whole fov for image: \\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H0R0\\Conv_zscan_03.dax\n-- loading illumination correction profile from file:\n\t 750 illumination_correction_750_2048x2048.npy\n\t 647 illumination_correction_647_2048x2048.npy\n-- loading bleedthrough correction profile from file:bleedthrough_correction_750_647_2048_2048.npy\n-- loading image from file:\\\\10.245.74.158\\Chromatin_NAS_6\\20201012-mouse_proB_IgH++\\H0R0\\Conv_zscan_03.dax in 2.601s\n-- removing hot pixels for channels:['750', '647'] in 23.458s\n-- bleedthrough correction for channels: ['750', '647'] in 12.669s\n-- illumination correction for channels: 750, 647, in 4.445s\n-- warp image with drift:[0. 0. 0.] in 0.000s\n-- finish correction in 44.816s\n" ], [ "%matplotlib notebook\n%matplotlib notebook\n\nvisual_tools.imshow_mark_3d_v2([cy7_raw_ims[0], cy7_corr_ims[0], cy7_raw_ims[1], cy7_corr_ims[1]])", "_____no_output_____" ] ], [ [ "## Check bleedthrough for cy5", "_____no_output_____" ] ], [ [ "reload(ia.io_tools.load)\ncy5_filename = os.path.join(folders[1], fovs[3])\n\ncy5_raw_ims, = ia.io_tools.load.correct_fov_image(cy5_filename, \n bleedthrough_channels, \n bleed_corr=False, chromatic_corr=False,\n corr_channels=bleedthrough_channels,\n single_im_size=single_im_size,\n all_channels=all_colors,\n illumination_corr=True,\n correction_folder=correction_folder)\ncy5_corr_ims, = ia.io_tools.load.correct_fov_image(cy5_filename, \n bleedthrough_channels, \n bleed_corr=True, chromatic_corr=False, \n corr_channels=bleedthrough_channels,\n single_im_size=single_im_size,\n all_channels=all_colors,\n illumination_corr=True,\n correction_folder=correction_folder)", "- correct the whole fov for image: \\\\169.254.229.228\\Chromatin_NAS_3\\20191007_CTP07_chr2\\H4R4_Cy5\\Conv_zscan_02.dax\n-- loading illumination correction profile from file:\n\t 750 illumination_correction_750_2048x2048.npy\n\t 647 illumination_correction_647_2048x2048.npy\n\t 561 illumination_correction_561_2048x2048.npy\n-- loading image from file:\\\\169.254.229.228\\Chromatin_NAS_3\\20191007_CTP07_chr2\\H4R4_Cy5\\Conv_zscan_02.dax in 3.602s\n-- removing hot pixels for channels:['750', '647', '561'] in 35.113s\n-- illumination correction for channels: 750, 647, 561, in 7.695s\n- correct the whole fov for image: \\\\169.254.229.228\\Chromatin_NAS_3\\20191007_CTP07_chr2\\H4R4_Cy5\\Conv_zscan_02.dax\n-- loading illumination correction profile from file:\n\t 750 illumination_correction_750_2048x2048.npy\n\t 647 illumination_correction_647_2048x2048.npy\n\t 561 illumination_correction_561_2048x2048.npy\n-- loading bleedthrough correction profile from file:bleedthrough_correction_750_647_561_2048x2048.npy\n-- loading image from file:\\\\169.254.229.228\\Chromatin_NAS_3\\20191007_CTP07_chr2\\H4R4_Cy5\\Conv_zscan_02.dax in 2.709s\n-- removing hot pixels for channels:['750', '647', '561'] in 35.061s\n-- illumination correction for channels: 750, 647, 561, in 7.464s\n-- bleedthrough correction for channels: ['750', '647', '561'] in 22.998s\n" ], [ "%matplotlib notebook\n%matplotlib notebook\n\nvisual_tools.imshow_mark_3d_v2([cy5_raw_ims[0], cy5_raw_ims[1], cy5_corr_ims[1], cy5_corr_ims[0]])", "_____no_output_____" ] ] ]

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[ [ [ "from ddeint import ddeint\n\n\ndef model(Y,t,tau):\n x,y,z = Y(t)\n xd, yd, zd = Y(t-tau)\n \n \n dx = x**2\n dy = x**2+y**2\n dz = b2*w2* yd*z/(m+yd)\n \n return [dx,dy,dz]\n\ntt = np.linspace(0,300,1000)\nsol_depredadore_presa = ddeint(model, g, tt)", "_____no_output_____" ] ] ]

[ "code" ]

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[ "BSD-3-Clause" ]

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[ [ [ "#### **Title**: BoxEdit Stream\n\n**Dependencies**: Bokeh\n \n**Description**: A linked streams example demonstrating how to use the BoxDraw stream.\n\n**Backends**: [Bokeh](./Boxes.ipynb)", "_____no_output_____" ] ], [ [ "import holoviews as hv\nfrom holoviews import opts\nfrom holoviews import streams\nhv.extension('bokeh')", "_____no_output_____" ] ], [ [ "The ``BoxEdit`` stream adds a bokeh tool to the source plot that allows drawing, dragging, and deleting boxes and making the drawn data available to Python. The tool supports the following actions:\n\n**Add box**\n\n Double click to start one corner, then move the pointer to the other corner and double click again. (Or hold shift then click and drag anywhere on the plot.)\n\n**Move box**\n\n Click and drag an existing box, the box will be dropped once you let go of the mouse button.\n\n**Delete box**\n\n Tap a box to select it then press BACKSPACE or DELETE key while the mouse is within the plot area.\n \n### Properties\n\n* **``empty_value``**: Value to add to non-coordinate columns when adding new box\n* **``num_objects``** (int): Maximum number of boxes to draw before deleting the oldest object\n* **``styles``** (dict): Dictionary of style properties (e.g. line_color, line_width etc.) to apply to each box. If values are lists the values will cycle over the values.", "_____no_output_____" ], [ "As a very straightforward example we will create a `Rectangles` element containing multiple boxes, then attach it as a source to a ``BoxEdit`` stream instance. When we now plot the ``Rectangles`` instance it will add the tool, letting us draw, drag and delete the box polygons. To limit the number of boxes that can be drawn a fixed number of ``num_objects`` may be defined, causing the first box to be dropped when the limit is exceeded.", "_____no_output_____" ] ], [ [ "boxes = hv.Rectangles([(0, 0, 1, 1), (2, 1, 3.5, 2.5), (0.5, 1.5, 1.5, 2.5)])\nbox_stream = streams.BoxEdit(source=boxes, num_objects=3, styles={'fill_color': ['red', 'green', 'blue']})\nboxes.opts(\n opts.Polygons(active_tools=['box_edit'], fill_alpha=0.5, height=400, width=400))", "_____no_output_____" ] ], [ [ "<center><img src=\"https://assets.holoviews.org/gifs/examples/streams/bokeh/box_edit.gif\" width=400></center>", "_____no_output_____" ], [ "Whenever an action is executed the geometry data will be synced with Python, both in the notebook and when deployed on the bokeh server. We can access the data directly as columns of the corners of each box:", "_____no_output_____" ] ], [ [ "box_stream.data", "_____no_output_____" ] ], [ [ "Alternatively we can use the ``element`` property to get an Element containing the returned data:", "_____no_output_____" ] ], [ [ "box_stream.element", "_____no_output_____" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/haribharadwaj/notebooks/blob/main/BME511/ProbabilisticClassificationClustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Classification and clustering: probabilistic modeling approach\n\nHere, we will extend out discussion on estimation theory to classification and clusterin problems.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pylab as pl\n\n# Setting it so figs will be a bit bigger\nfrom matplotlib import pyplot as plt\nplt.rcParams['figure.figsize'] = [5, 3.33]\nplt.rcParams['figure.dpi'] = 120", "_____no_output_____" ] ], [ [ "## We will rely heavily on the ```scikit-learn``` library\n\nThe ```sklearn``` library is a mature, well-tested library of implementations of many standard machine-learning models and provides a clean API to work with. In addition to being able to fit models and draw predictions for new inputs, ```sklearn``` also provides many useful utilities for handling data. It also has functions for creating toy datasets which can be used to understand the pros and cons of various models. The library is designed to work in conjunction with ```numpy```, ```scipy```, ```matplotlib```, and ```pandas```.", "_____no_output_____" ], [ "### Start by making a 2D dataset with 2 classes.", "_____no_output_____" ] ], [ [ "from sklearn import datasets\nn_classes = 2\nn_features = 2\nn_samples = 200\nn_redundant = 0\nn_clusters_per_class = 1\nclass_sep = 0.8\nX, y = datasets.make_classification(n_classes=n_classes, n_features=n_features,\n n_samples=n_samples, n_redundant=n_redundant,\n n_clusters_per_class=n_clusters_per_class,\n class_sep=class_sep, random_state=0)", "_____no_output_____" ] ], [ [ "### Visualize the dataset", "_____no_output_____" ] ], [ [ "import itertools\nmarker = itertools.cycle(('o', 's', '*', 'v', '^', 'x')) \nfor c in range(n_classes):\n pl.plot(X[y==c, 0], X[y==c, 1], linestyle='', marker=next(marker))\npl.xlabel('Feature 1')\npl.ylabel('Feature 2')\npl.legend(('Class 1', 'Class 2'), loc='best')", "_____no_output_____" ] ], [ [ "## Construct a naive-Bayes classifier and test it by splitting the dataset\n\nFor simplicity, we will use the Gaussian naive-Bayes model where the value of each feature is modeled as coming from a 1D normal distribution that is conditioned on class but independent of other features.", "_____no_output_____" ] ], [ [ "from sklearn.naive_bayes import GaussianNB\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)\n\nmodel = GaussianNB()\nmodel.fit(X_train, y_train)\ny_predicted_train = model.predict(X_train)\ny_predicted_test = model.predict(X_test)\n\nacc_train = (y_predicted_train == y_train).sum() * 100. / y_train.shape[0]\nacc_test = (y_predicted_test == y_test).sum() * 100. / y_test.shape[0]\n\nprint(f'Training accuracy = {acc_train:0.1f}%, Test accuracy = {acc_test:0.1f}%')", "Training accuracy = 91.0%, Test accuracy = 92.4%\n" ] ], [ [ "### Visualize results", "_____no_output_____" ] ], [ [ "def make_meshgrid(X, ngrid=100, slack=0.2):\n if len(X.shape) > 2:\n warnings.warn('Grid visualization only work for 2D or less!')\n xmin, xmax = X[:, 0].min(), X[:, 0].max()\n ymin, ymax = X[:, 1].min(), X[:, 1].max()\n \n # Apply some slack so points are are not near the edge\n xmin *= 1 - np.sign(xmin) * slack\n xmax *= 1 + np.sign(xmax) * slack\n ymin *= 1 - np.sign(ymin) * slack\n ymax *= 1 + np.sign(ymax) * slack\n \n dx = (xmax - xmin) / ngrid\n dy = (ymax - ymin) / ngrid\n x = np.arange(xmin, xmax, dx)\n y = np.arange(ymin, ymax, dy)\n xx, yy = np.meshgrid(x, y)\n return (xx, yy)\n\n\ndef plot_decision(xx, yy, clf, **params):\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n out = pl.contourf(xx, yy, Z, **params)\n return out\n\n\nfor c in range(n_classes):\n pl.plot(X_test[y_test==c, 0], X_test[y_test==c, 1], linestyle='', marker=next(marker))\n\npl.plot(X_test[y_predicted_test != y_test, 0], X_test[y_predicted_test != y_test, 1], 'or',\n markersize=12, markerfacecolor='none')\npl.xlabel('Feature 1')\npl.ylabel('Feature 2')\npl.legend(('True Class 1', 'True Class 2', 'Incorrect'), loc='best')\npl.title('Test results')\n\n# Plot decision function\nxx, yy = make_meshgrid(X_test)\nplot_decision(xx, yy, model, cmap='seismic', alpha=0.1)", "_____no_output_____" ] ], [ [ "## Clustering using mixture modeling", "_____no_output_____" ], [ "### Create and visualize a 2D dataset", "_____no_output_____" ] ], [ [ "n_clusters = 3\ncenters = np.asarray([[-4, 0], [0, 3], [1.5, -3]])\nX, y = datasets.make_blobs(centers=centers, n_features=n_features,\n n_samples=n_samples)\n\npl.plot(X[:, 0], X[:, 1], 'o')\npl.xlabel('Feature 1')\npl.ylabel('Feature 2')", "_____no_output_____" ] ], [ [ "### Use a Gaussian mixture model\n\nThe number of clusters is a hyperparameter", "_____no_output_____" ] ], [ [ "from sklearn import mixture\nncomps = 3\nmodel = mixture.GaussianMixture(n_components=ncomps)\ny = model.fit_predict(X)\nprint(f'Log-likelihood = {model.score(X)}, AIC = {model.aic(X)}')\nfor k in range(ncomps):\n pl.plot(X[y==k, 0], X[y==k, 1], marker=next(marker), linestyle='')\npl.xlabel('Feature 1')\npl.ylabel('Feature 2')", "Log-likelihood = -3.882424703774175, AIC = 1586.96988150967\n" ] ], [ [ "## Apply naive Bayes classifier to Wisconsin breast cancer dataset after PCA for dimensionality reduction\n\nDataset is included with ```sklearn``` but comes from:\nW.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear features distinguish malignant from benign breast cytology. Human Pathology, 26:792--796, 1995.\n\nHere, fine-needle aspirates (FNA; a type of biopsy) of breast mass are obtained, imaged, and the digitized images are processed to extract features pertaining to the characteristics of the cell nuclei present in the image. The goal is to classify each biopsy as **malignant** or **benign** based on the image.\n\nTen real-valued features are computed for each cell nucleus:\n- radius (mean of distances from center to points on the perimeter)\n- texture (standard deviation of gray-scale values)\n- perimeter\n- area\n- smoothness (local variation in radius lengths)\n- compactness (perimeter^2 / area - 1.0)\n- concavity (severity of concave portions of the contour)\n- concave points (number of concave portions of the contour)\n- symmetry\n- fractal dimension (\"coastline approximation\" - 1)\n\nFor each image, across nuceli, the mean, standard error,\nand \"worst\" or largest (mean of the three largest values) of these features were computed. This yields **30 features per sample**.\nFor instance, field 3 is Mean Radius, field\n13 is Radius SE, field 23 is Worst Radius.\n", "_____no_output_____" ] ], [ [ "from sklearn.datasets import load_breast_cancer\nfrom sklearn.decomposition import PCA\n\ndat = load_breast_cancer()\nX = dat.data\ny = dat.target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75)\n\n# Do PCA to get two dimensions\npc = PCA(n_components=2)\npc.fit(X_train)\nX_train_pc = pc.transform(X_train)\nX_test_pc = pc.transform(X_test)", "_____no_output_____" ], [ "model = GaussianNB()\nmodel.fit(X_train_pc, y_train)\ny_predicted_train = model.predict(X_train_pc)\ny_predicted_test = model.predict(X_test_pc)\n\nacc_train = (y_predicted_train == y_train).sum() * 100. / y_train.shape[0]\nacc_test = (y_predicted_test == y_test).sum() * 100. / y_test.shape[0]\n\nprint(f'Training accuracy = {acc_train:0.1f}%, Test accuracy = {acc_test:0.1f}%')", "Training accuracy = 91.3%, Test accuracy = 87.4%\n" ] ], [ [ "### Visualize results", "_____no_output_____" ] ], [ [ "n_classes = 2\nfor c in range(n_classes):\n pl.plot(X_test_pc[y_test==c, 0], X_test_pc[y_test==c, 1], linestyle='', marker=next(marker))\n\npl.plot(X_test_pc[y_predicted_test != y_test, 0], X_test_pc[y_predicted_test != y_test, 1], 'or',\n markersize=12, markerfacecolor='none')\npl.xlabel('PC 1')\npl.ylabel('PC 2')\npl.legend(('True Class 1', 'True Class 2', 'Incorrect'), loc='best')\npl.title('Test results')\n\n# Plot decision function\nxx, yy = make_meshgrid(X_test_pc)\nplot_decision(xx, yy, model, cmap='seismic', alpha=0.2)", "_____no_output_____" ] ], [ [ "## Apply naive Bayes classifier to high-dimensional Wisconsin breast cancer dataset (no PCA)", "_____no_output_____" ] ], [ [ "model = GaussianNB()\n# Using X_train and X_test instead of X_train_pc and X_test_pc\nmodel.fit(X_train, y_train)\ny_predicted_train = model.predict(X_train)\ny_predicted_test = model.predict(X_test)\n\nacc_train = (y_predicted_train == y_train).sum() * 100. / y_train.shape[0]\nacc_test = (y_predicted_test == y_test).sum() * 100. / y_test.shape[0]\n\nprint(f'Training accuracy = {acc_train:0.1f}%, Test accuracy = {acc_test:0.1f}%')", "Training accuracy = 94.4%, Test accuracy = 93.7%\n" ] ], [ [ "### Conclusion about breast cancer dataset\n\nA simple naive Bayes classifier is able to obtain 90+% accuracy in biopsy classification.", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "#Fill the paths below\nPATH_FRC = \"\" # git repo directory path\nPATH_ZENODO = \"\" # Data and models are available here: https://zenodo.org/record/5831014#.YdnW_VjMLeo\n\nDATA_FLAT = PATH_ZENODO+'/data/goi_1000/flat_1000/*.png'\nDATA_NORMAL = PATH_ZENODO+'/data/goi_1000/standard_1000/*.jpg'\n\nGAUSS_L2_MODEL = PATH_ZENODO+'/models/gaussian/noise005_set1000/standard/' # noise 0.05\nGAUSS_L2_MODEL_FLAT = PATH_ZENODO+'/models/gaussian/noise005_set1000/flat/' # noise 0.05", "_____no_output_____" ], [ "import sys\nsys.path.append(PATH_FRC)\n\nimport glob\nimport os\nimport skimage\n%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom skimage.io import imread\nimport numpy as np\nimport matplotlib\n\nimport tensorflow as tf\nfrom models2 import FRCUnetModel\nfrom skimage.filters import window\nfrom tqdm import tqdm\nimport pandas as pd\n\nimport scipy.stats as stats\nfrom scipy.optimize import fsolve\nimport pyfftw.interfaces.numpy_fft\nnp.fft = pyfftw.interfaces.numpy_fft\n\nmatplotlib.rcParams.update({'mathtext.default':'regular'})\nmatplotlib.rcParams.update({'font.size': 8})\nmatplotlib.rcParams.update({'axes.labelweight': 'bold'})", "_____no_output_____" ], [ "def normalise_img(image):\n image = image - image.min()\n image = image/image.max() - 0.5\n return image\n \ndef plot_power_spectrum(image):\n if len(image.shape) == 3:\n image = np.sum(image, axis=2)\n image = image.astype('float64')\n image = image - image.mean()\n fourier_image = np.fft.fftn(image) # here the input is grey image\n size = image.shape[0]\n fourier_amplitudes = np.abs(fourier_image)**2\n print(\"FOURIER AMPLITUDES\", np.sum(fourier_amplitudes))\n\n kfreq = np.fft.fftfreq(size) * size # image size\n kfreq2D = np.meshgrid(kfreq, kfreq)\n knrm = np.sqrt(kfreq2D[0]**2 + kfreq2D[1]**2)\n\n knrm = knrm.flatten()\n fourier_amplitudes = fourier_amplitudes.flatten()\n\n kbins = np.arange(0.5, int(size / 2), 1.)\n kvals = 0.5 * (kbins[1:] + kbins[:-1])\n Abins, _, _ = stats.binned_statistic(\n knrm, fourier_amplitudes, statistic=\"mean\", bins=kbins) # mean power\n\n return kvals, Abins \n\ndef load_model(model_dir, model_fname):\n if model_dir is not None:\n return FRCUnetModel(None, model_path=os.path.join(model_dir, model_fname))\n", "_____no_output_____" ], [ "files_flat=sorted(glob.glob(DATA_FLAT))\nfiles_flat=files_flat[:50]", "_____no_output_____" ], [ "files_normal=sorted(glob.glob(DATA_NORMAL))\nfiles_normal=files_normal[:50]", "_____no_output_____" ], [ "cleans_flat=[]\nfor file in files_flat:\n clean = imread(file)\n if len(clean.shape) > 2:\n clean = np.mean(clean, axis=2)\n minsize = np.array(clean.shape).min()\n clean = clean[:minsize,:minsize]\n clean = normalise_img(clean)\n clean = clean.astype('float32')\n #clean = clean*window('hann', clean.shape)\n cleans_flat.append(clean)\ncleans_flat=np.stack(cleans_flat)", "_____no_output_____" ], [ "cleans_normal=[]\nfor file in files_normal:\n clean = imread(file)\n if len(clean.shape) > 2:\n clean = np.mean(clean, axis=2)\n minsize = np.array(clean.shape).min()\n clean = clean[:minsize,:minsize]\n clean = normalise_img(clean)\n clean = clean.astype('float32')\n #clean = clean*window('hann', clean.shape)\n cleans_normal.append(clean)\ncleans_normal=np.stack(cleans_normal)", "_____no_output_____" ], [ "cleans_normal.shape", "_____no_output_____" ], [ "noise1=np.random.normal(0,0.05,256**2*50).reshape(50,256,256)\nnoisy_flat=cleans_flat.copy()+noise1\nnoise2=np.random.normal(0,0.05,256**2*50).reshape(50,256,256)\nnoisy_normal=cleans_normal.copy()+noise2", "_____no_output_____" ], [ "l2_model=load_model(GAUSS_L2_MODEL, 'saved-model-epoch-200')", "WARNING:tensorflow:7 out of the last 7 calls to <function FRCUnetModel.get_radial_masks at 0x7fcb25f46550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ], [ "l2_1000_model_flat=load_model(GAUSS_L2_MODEL_FLAT, 'saved-model-epoch-200')", "WARNING:tensorflow:8 out of the last 8 calls to <function FRCUnetModel.get_radial_masks at 0x7fcb300ff430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ], [ "imnr=3", "_____no_output_____" ], [ "denoised_normal = l2_model.model(np.reshape(noisy_normal[imnr], [1,256, 256,1]))\ndenoised_normal = np.squeeze(denoised_normal)", "_____no_output_____" ], [ "denoised_flat = l2_1000_model_flat.model(np.reshape(noisy_flat[imnr], [1,256, 256,1]))\ndenoised_flat = np.squeeze(denoised_flat)", "_____no_output_____" ], [ "x=np.array(plot_power_spectrum(noisy_normal[imnr])[0])\nx=x*1.0/x.max()", "FOURIER AMPLITUDES 391690718.36664474\n" ], [ "fig = plt.figure()\nfig.set_size_inches(7, 7) # 3.5 inch is the width of one column in A4 paper \n\nax = fig.add_subplot(334)\nax.imshow(cleans_flat[imnr], cmap='gray')\nplt.xticks([])\nplt.yticks([])\n#plt.ylabel('Gaussian')\nplt.title('Normalised spectrum, GT')\n\nax = fig.add_subplot(335)\nax.imshow(noisy_flat[imnr], cmap='gray')\nplt.xticks([])\nplt.yticks([])\nplt.title('Normalised spectrum, noisy')\n\nax = fig.add_subplot(336)\nax.imshow(denoised_flat, cmap='gray')\nplt.xticks([])\nplt.yticks([])\nplt.title('Normalised spectrum, denoised')\n\nax = fig.add_subplot(331)\nax.imshow(cleans_normal[imnr], cmap='gray')\nplt.xticks([])\nplt.yticks([])\nplt.title('Standard spectrum, GT')\n\nax = fig.add_subplot(332)\nax.imshow(noisy_normal[imnr], cmap='gray')\nplt.xticks([])\nplt.yticks([])\nplt.title('Standard spectrum, noisy')\n\nax = fig.add_subplot(333)\nax.imshow(denoised_normal, cmap='gray')\nplt.xticks([])\nplt.yticks([])\nplt.title('Standard spectrum, denoised')\n\nax = fig.add_subplot(337)\nplt.title('Ground truth ')\nax.plot(x,np.array(plot_power_spectrum(cleans_flat[imnr])[1]),label='Normalised',color='orange')\nax.plot(x,np.array(plot_power_spectrum(cleans_normal[imnr])[1]),label='Standard',color='blue')\nax.set_xlabel('f/N')\nax.set_ylabel('Power')\nplt.yscale('log')\nplt.xscale('log')\n#ax.locator_params(axis='x', nbins=5)\nplt.ylim([10**1.5,10**7.5 ])\nplt.legend(loc=1)\n\nax = fig.add_subplot(338)\nplt.title('Noisy')\nax.plot(x,np.array(plot_power_spectrum(noisy_flat[imnr])[1]),label='Normalised',color='orange')\nax.plot(x,np.array(plot_power_spectrum(noisy_normal[imnr])[1]),label='Standard',color='blue')\nax.set_xlabel('f/N')\n#ax.set_ylabel('Power')\nplt.yscale('log')\nplt.xscale('log')\nplt.ylim([10**1.5,10**7.5 ])\n\nax = fig.add_subplot(339)\nplt.title('Denoised')\nax.plot(x,np.array(plot_power_spectrum(denoised_flat)[1]),label='Normalised',color='orange')\nax.plot(x,np.array(plot_power_spectrum(denoised_normal)[1]),label='Standard',color='blue')\n\nax.set_xlabel('f/N')\nplt.yscale('log')\nplt.xscale('log')\n#ax.locator_params(axis='x', nbins=5)\nplt.ylim([10**1.5,10**7.5 ])\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.23, hspace=0.23)\nfig.savefig('figure_s3.png', dpi=300)", "FOURIER AMPLITUDES 49332148.0\nFOURIER AMPLITUDES 380945660.0\nFOURIER AMPLITUDES 60500138.09149456\nFOURIER AMPLITUDES 391690718.36664474\nFOURIER AMPLITUDES 41253108.0\nFOURIER AMPLITUDES 364254340.0\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas\ndf = pandas.read_csv(\n 'https://archive.ics.uci.edu/ml/'\n 'machine-learning-databases/iris/iris.data',\n header=None,\n)\ndf.tail()", "_____no_output_____" ], [ "import numpy\ntargets = df.iloc[0:100, 4].values\ntargets = numpy.where(targets == 'Iris-setosa', -1, 1)\ntargets[:10]", "_____no_output_____" ], [ "samples = df.iloc[0:100, [0, 2]].values\nsamples.shape", "_____no_output_____" ], [ "samples[:10]", "_____no_output_____" ], [ "import matplotlib.pyplot as plt\nplt.scatter(\n samples[:50, 0], samples[:50, 1],\n color='red', marker='o', label='setosa'\n)\nplt.scatter(\n samples[50:100, 0], samples[50:100, 1],\n color='blue', marker='x', label='versicolor'\n)\nplt.xlabel('sepal length')\nplt.ylabel('petal length')\nplt.legend(loc='upper left')\nplt.show()", "_____no_output_____" ], [ "from perceptron import Perceptron\np = Perceptron(learning_rate=0.1, max_iterations=10)\np.train(samples, targets)\np.errors", "_____no_output_____" ], [ "plt.plot(\n range(1, len(p.errors) + 1),\n p.errors,\n marker='o',\n)\nplt.xlabel('epochs')\nplt.ylabel('misclassification count')\nplt.show()", "_____no_output_____" ], [ "from matplotlib.colors import ListedColormap\ndef plot_decision_regions(samples, targets, classifier, resolution=0.02):\n markers = ('s', 'x', 'o', '^', 'v')\n colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n color_count = len(numpy.unique(targets))\n c_map = ListedColormap(colors[:color_count])\n sample_extent = [\n (samples[:, dim].min() - 1, samples[:, dim].max() + 1)\n for dim in (0, 1)\n ]\n grid = numpy.meshgrid(\n numpy.arange(*sample_extent[0], resolution),\n numpy.arange(*sample_extent[1], resolution),\n )\n predictions = classifier.predict(\n numpy.array([\n grid[0].ravel(),\n grid[1].ravel()\n ]).T\n )\n predictions = predictions.reshape(grid[0].shape)\n\n plt.contourf(\n grid[0],\n grid[1],\n predictions,\n alpha=0.4,\n cmap=c_map\n )\n plt.xlim(grid[0].min(), grid[0].max())\n plt.ylim(grid[1].min(), grid[1].max())\n for idx, target in enumerate(numpy.unique(targets)):\n plt.scatter(\n x=samples[targets == target, 0],\n y=samples[targets == target, 1],\n c=c_map(idx),\n alpha=0.8,\n marker=markers[idx],\n label=target\n )\nplot_decision_regions(samples, targets, p, resolution=0.01)\nplt.show()", "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n" ], [ "from adanline import Adaline\nada = Adaline()\n\nfig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n\nada.train(samples, targets,\n training_iterations=10,\n training_speed=0.01)\nax[0].plot(\n range(1, len(ada.costs) + 1),\n numpy.log10(ada.costs),\n marker='o'\n)\nax[0].set_xlabel('epochs')\nax[0].set_ylabel('log(SSE)')\nax[0].set_title('Adaline - Learning Rate 0.01')\n\nada.train(samples, targets,\n training_iterations=10,\n training_speed=0.0001)\nax[1].plot(\n range(1, len(ada.costs) + 1),\n numpy.log10(ada.costs),\n marker='o'\n)\nax[1].set_xlabel('epochs')\nax[1].set_ylabel('log(SSE)')\nax[1].set_title('Adaline - Learning Rate 0.0001')\n\nplt.show()", "_____no_output_____" ], [ "samples_standardized = numpy.copy(samples)\ndim = (samples[:, 0], samples[:, 1])\nfor d in (0, 1):\n samples_standardized[:, d] = (dim[d] - dim[d].mean()) / dim[d].std()\nada.train(samples_standardized, targets,\n training_iterations=15,\n training_speed=0.01)\nplt.plot(\n range(1, len(ada.costs) + 1),\n ada.costs,\n marker='o'\n)\nplt.show()", "_____no_output_____" ], [ "plot_decision_regions(samples_standardized, targets, ada)\nplt.show()", "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n" ], [ "from adaline import AdalineSingleGradientDescent\nada = AdalineSingleGradientDescent()\nada.train(samples_standardized, targets,\n training_speed=0.01,\n training_iterations=15)\nplt.plot(\n range(1, len(ada.costs) + 1),\n ada.costs,\n marker='o'\n)\nplt.show()", "_____no_output_____" ], [ "plot_decision_regions(samples_standardized, targets,\n classifier=ada)\nplt.title('Adaline - Stochastic Gradient Descent')\nplt.xlabel('sepal length (standardized)')\nplt.xlabel('petal length (standardized)')", "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Autonomous driving - Car detection\n\nWelcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). \n\n**You will learn to**:\n- Use object detection on a car detection dataset\n- Deal with bounding boxes\n\nRun the following cell to load the packages and dependencies that are going to be useful for your journey!", "_____no_output_____" ] ], [ [ "import argparse\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import imshow\nimport scipy.io\nimport scipy.misc\nimport numpy as np\nimport pandas as pd\nimport PIL\nimport tensorflow as tf\nfrom keras import backend as K\nfrom keras.layers import Input, Lambda, Conv2D\nfrom keras.models import load_model, Model\nfrom yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes\nfrom yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body\n\n%matplotlib inline", "Using TensorFlow backend.\n" ] ], [ [ "**Important Note**: As you can see, we import Keras's backend as K. This means that to use a Keras function in this notebook, you will need to write: `K.function(...)`.", "_____no_output_____" ], [ "## 1 - Problem Statement\n\nYou are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around. \n\n<center>\n<video width=\"400\" height=\"200\" src=\"nb_images/road_video_compressed2.mp4\" type=\"video/mp4\" controls>\n</video>\n</center>\n\n<caption><center> Pictures taken from a car-mounted camera while driving around Silicon Valley. <br> We would like to especially thank [drive.ai](https://www.drive.ai/) for providing this dataset! Drive.ai is a company building the brains of self-driving vehicles.\n</center></caption>\n\n<img src=\"nb_images/driveai.png\" style=\"width:100px;height:100;\">\n\nYou've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like.\n\n<img src=\"nb_images/box_label.png\" style=\"width:500px;height:250;\">\n<caption><center> <u> **Figure 1** </u>: **Definition of a box**<br> </center></caption>\n\nIf you have 80 classes that you want YOLO to recognize, you can represent the class label $c$ either as an integer from 1 to 80, or as an 80-dimensional vector (with 80 numbers) one component of which is 1 and the rest of which are 0. The video lectures had used the latter representation; in this notebook, we will use both representations, depending on which is more convenient for a particular step. \n\nIn this exercise, you will learn how YOLO works, then apply it to car detection. Because the YOLO model is very computationally expensive to train, we will load pre-trained weights for you to use. ", "_____no_output_____" ], [ "## 2 - YOLO", "_____no_output_____" ], [ "YOLO (\"you only look once\") is a popular algoritm because it achieves high accuracy while also being able to run in real-time. This algorithm \"only looks once\" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.\n\n### 2.1 - Model details\n\nFirst things to know:\n- The **input** is a batch of images of shape (m, 608, 608, 3)\n- The **output** is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers $(p_c, b_x, b_y, b_h, b_w, c)$ as explained above. If you expand $c$ into an 80-dimensional vector, each bounding box is then represented by 85 numbers. \n\nWe will use 5 anchor boxes. So you can think of the YOLO architecture as the following: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85).\n\nLets look in greater detail at what this encoding represents. \n\n<img src=\"nb_images/architecture.png\" style=\"width:700px;height:400;\">\n<caption><center> <u> **Figure 2** </u>: **Encoding architecture for YOLO**<br> </center></caption>\n\nIf the center/midpoint of an object falls into a grid cell, that grid cell is responsible for detecting that object.", "_____no_output_____" ], [ "Since we are using 5 anchor boxes, each of the 19 x19 cells thus encodes information about 5 boxes. Anchor boxes are defined only by their width and height.\n\nFor simplicity, we will flatten the last two last dimensions of the shape (19, 19, 5, 85) encoding. So the output of the Deep CNN is (19, 19, 425).\n\n<img src=\"nb_images/flatten.png\" style=\"width:700px;height:400;\">\n<caption><center> <u> **Figure 3** </u>: **Flattening the last two last dimensions**<br> </center></caption>", "_____no_output_____" ], [ "Now, for each box (of each cell) we will compute the following elementwise product and extract a probability that the box contains a certain class.\n\n<img src=\"nb_images/probability_extraction.png\" style=\"width:700px;height:400;\">\n<caption><center> <u> **Figure 4** </u>: **Find the class detected by each box**<br> </center></caption>\n\nHere's one way to visualize what YOLO is predicting on an image:\n- For each of the 19x19 grid cells, find the maximum of the probability scores (taking a max across both the 5 anchor boxes and across different classes). \n- Color that grid cell according to what object that grid cell considers the most likely.\n\nDoing this results in this picture: \n\n<img src=\"nb_images/proba_map.png\" style=\"width:300px;height:300;\">\n<caption><center> <u> **Figure 5** </u>: Each of the 19x19 grid cells colored according to which class has the largest predicted probability in that cell.<br> </center></caption>\n\nNote that this visualization isn't a core part of the YOLO algorithm itself for making predictions; it's just a nice way of visualizing an intermediate result of the algorithm. \n", "_____no_output_____" ], [ "Another way to visualize YOLO's output is to plot the bounding boxes that it outputs. Doing that results in a visualization like this: \n\n<img src=\"nb_images/anchor_map.png\" style=\"width:200px;height:200;\">\n<caption><center> <u> **Figure 6** </u>: Each cell gives you 5 boxes. In total, the model predicts: 19x19x5 = 1805 boxes just by looking once at the image (one forward pass through the network)! Different colors denote different classes. <br> </center></caption>\n\nIn the figure above, we plotted only boxes that the model had assigned a high probability to, but this is still too many boxes. You'd like to filter the algorithm's output down to a much smaller number of detected objects. To do so, you'll use non-max suppression. Specifically, you'll carry out these steps: \n- Get rid of boxes with a low score (meaning, the box is not very confident about detecting a class)\n- Select only one box when several boxes overlap with each other and detect the same object.\n\n", "_____no_output_____" ], [ "### 2.2 - Filtering with a threshold on class scores\n\nYou are going to apply a first filter by thresholding. You would like to get rid of any box for which the class \"score\" is less than a chosen threshold. \n\nThe model gives you a total of 19x19x5x85 numbers, with each box described by 85 numbers. It'll be convenient to rearrange the (19,19,5,85) (or (19,19,425)) dimensional tensor into the following variables: \n- `box_confidence`: tensor of shape $(19 \\times 19, 5, 1)$ containing $p_c$ (confidence probability that there's some object) for each of the 5 boxes predicted in each of the 19x19 cells.\n- `boxes`: tensor of shape $(19 \\times 19, 5, 4)$ containing $(b_x, b_y, b_h, b_w)$ for each of the 5 boxes per cell.\n- `box_class_probs`: tensor of shape $(19 \\times 19, 5, 80)$ containing the detection probabilities $(c_1, c_2, ... c_{80})$ for each of the 80 classes for each of the 5 boxes per cell.\n\n**Exercise**: Implement `yolo_filter_boxes()`.\n1. Compute box scores by doing the elementwise product as described in Figure 4. The following code may help you choose the right operator: \n```python\na = np.random.randn(19*19, 5, 1)\nb = np.random.randn(19*19, 5, 80)\nc = a * b # shape of c will be (19*19, 5, 80)\n```\n2. For each box, find:\n - the index of the class with the maximum box score ([Hint](https://keras.io/backend/#argmax)) (Be careful with what axis you choose; consider using axis=-1)\n - the corresponding box score ([Hint](https://keras.io/backend/#max)) (Be careful with what axis you choose; consider using axis=-1)\n3. Create a mask by using a threshold. As a reminder: `([0.9, 0.3, 0.4, 0.5, 0.1] < 0.4)` returns: `[False, True, False, False, True]`. The mask should be True for the boxes you want to keep. \n4. Use TensorFlow to apply the mask to box_class_scores, boxes and box_classes to filter out the boxes we don't want. You should be left with just the subset of boxes you want to keep. ([Hint](https://www.tensorflow.org/api_docs/python/tf/boolean_mask))\n\nReminder: to call a Keras function, you should use `K.function(...)`.", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: yolo_filter_boxes\n\ndef yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):\n \"\"\"Filters YOLO boxes by thresholding on object and class confidence.\n \n Arguments:\n box_confidence -- tensor of shape (19, 19, 5, 1)\n boxes -- tensor of shape (19, 19, 5, 4)\n box_class_probs -- tensor of shape (19, 19, 5, 80)\n threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n \n Returns:\n scores -- tensor of shape (None,), containing the class probability score for selected boxes\n boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes\n classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes\n \n Note: \"None\" is here because you don't know the exact number of selected boxes, as it depends on the threshold. \n For example, the actual output size of scores would be (10,) if there are 10 boxes.\n \"\"\"\n \n # Step 1: Compute box scores\n ### START CODE HERE ### (≈ 1 line)\n box_scores = box_confidence * box_class_probs\n ### END CODE HERE ###\n \n # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score\n ### START CODE HERE ### (≈ 2 lines)\n box_classes = K.argmax(box_scores, axis=-1)\n box_class_scores = K.max(box_scores, axis=-1)\n ### END CODE HERE ###\n \n # Step 3: Create a filtering mask based on \"box_class_scores\" by using \"threshold\". The mask should have the\n # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)\n ### START CODE HERE ### (≈ 1 line)\n filtering_mask = (box_class_scores > threshold)\n ### END CODE HERE ###\n \n # Step 4: Apply the mask to scores, boxes and classes\n ### START CODE HERE ### (≈ 3 lines)\n scores = tf.boolean_mask(box_class_scores, filtering_mask)\n boxes = tf.boolean_mask(boxes, filtering_mask)\n classes = tf.boolean_mask(box_classes, filtering_mask)\n ### END CODE HERE ###\n \n return scores, boxes, classes", "_____no_output_____" ], [ "with tf.Session() as test_a:\n box_confidence = tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1)\n boxes = tf.random_normal([19, 19, 5, 4], mean=1, stddev=4, seed = 1)\n box_class_probs = tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1)\n scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)\n print(\"scores[2] = \" + str(scores[2].eval()))\n print(\"boxes[2] = \" + str(boxes[2].eval()))\n print(\"classes[2] = \" + str(classes[2].eval()))\n print(\"scores.shape = \" + str(scores.shape))\n print(\"boxes.shape = \" + str(boxes.shape))\n print(\"classes.shape = \" + str(classes.shape))", "scores[2] = 10.7506\nboxes[2] = [ 8.42653275 3.27136683 -0.5313437 -4.94137383]\nclasses[2] = 7\nscores.shape = (?,)\nboxes.shape = (?, 4)\nclasses.shape = (?,)\n" ] ], [ [ "**Expected Output**:\n\n<table>\n <tr>\n <td>\n **scores[2]**\n </td>\n <td>\n 10.7506\n </td>\n </tr>\n <tr>\n <td>\n **boxes[2]**\n </td>\n <td>\n [ 8.42653275 3.27136683 -0.5313437 -4.94137383]\n </td>\n </tr>\n\n <tr>\n <td>\n **classes[2]**\n </td>\n <td>\n 7\n </td>\n </tr>\n <tr>\n <td>\n **scores.shape**\n </td>\n <td>\n (?,)\n </td>\n </tr>\n <tr>\n <td>\n **boxes.shape**\n </td>\n <td>\n (?, 4)\n </td>\n </tr>\n\n <tr>\n <td>\n **classes.shape**\n </td>\n <td>\n (?,)\n </td>\n </tr>\n\n</table>", "_____no_output_____" ], [ "### 2.3 - Non-max suppression ###\n\nEven after filtering by thresholding over the classes scores, you still end up a lot of overlapping boxes. A second filter for selecting the right boxes is called non-maximum suppression (NMS). ", "_____no_output_____" ], [ "<img src=\"nb_images/non-max-suppression.png\" style=\"width:500px;height:400;\">\n<caption><center> <u> **Figure 7** </u>: In this example, the model has predicted 3 cars, but it's actually 3 predictions of the same car. Running non-max suppression (NMS) will select only the most accurate (highest probabiliy) one of the 3 boxes. <br> </center></caption>\n", "_____no_output_____" ], [ "Non-max suppression uses the very important function called **\"Intersection over Union\"**, or IoU.\n<img src=\"nb_images/iou.png\" style=\"width:500px;height:400;\">\n<caption><center> <u> **Figure 8** </u>: Definition of \"Intersection over Union\". <br> </center></caption>\n\n**Exercise**: Implement iou(). Some hints:\n- In this exercise only, we define a box using its two corners (upper left and lower right): `(x1, y1, x2, y2)` rather than the midpoint and height/width.\n- To calculate the area of a rectangle you need to multiply its height `(y2 - y1)` by its width `(x2 - x1)`.\n- You'll also need to find the coordinates `(xi1, yi1, xi2, yi2)` of the intersection of two boxes. Remember that:\n - xi1 = maximum of the x1 coordinates of the two boxes\n - yi1 = maximum of the y1 coordinates of the two boxes\n - xi2 = minimum of the x2 coordinates of the two boxes\n - yi2 = minimum of the y2 coordinates of the two boxes\n- In order to compute the intersection area, you need to make sure the height and width of the intersection are positive, otherwise the intersection area should be zero. Use `max(height, 0)` and `max(width, 0)`.\n\nIn this code, we use the convention that (0,0) is the top-left corner of an image, (1,0) is the upper-right corner, and (1,1) the lower-right corner. ", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: iou\n\ndef iou(box1, box2):\n \"\"\"Implement the intersection over union (IoU) between box1 and box2\n    \n Arguments:\n box1 -- first box, list object with coordinates (x1, y1, x2, y2)\n    box2 -- second box, list object with coordinates (x1, y1, x2, y2)\n    \"\"\"\n\n # Calculate the (y1, x1, y2, x2) coordinates of the intersection of box1 and box2. Calculate its Area.\n ### START CODE HERE ### (≈ 5 lines)\n xi1 = max([box1[0], box2[0]])\n yi1 = max([box1[1], box2[1]])\n xi2 = min([box1[2], box2[2]])\n yi2 = min([box1[3], box2[3]])\n inter_area = max(xi2 - xi1, 0) * max(yi2 - yi1, 0)\n ### END CODE HERE ###    \n\n # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)\n ### START CODE HERE ### (≈ 3 lines)\n box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])\n box2_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) \n union_area = box1_area + box2_area - inter_area\n\n ### END CODE HERE ###\n \n # compute the IoU\n ### START CODE HERE ### (≈ 1 line)\n iou = inter_area / union_area\n ### END CODE HERE ###\n \n return iou", "_____no_output_____" ], [ "box1 = (2, 1, 4, 3)\nbox2 = (1, 2, 3, 4) \nprint(\"iou = \" + str(iou(box1, box2)))", "iou = 0.14285714285714285\n" ] ], [ [ "**Expected Output**:\n\n<table>\n <tr>\n <td>\n **iou = **\n </td>\n <td>\n 0.14285714285714285\n </td>\n </tr>\n\n</table>", "_____no_output_____" ], [ "You are now ready to implement non-max suppression. The key steps are: \n1. Select the box that has the highest score.\n2. Compute its overlap with all other boxes, and remove boxes that overlap it more than `iou_threshold`.\n3. Go back to step 1 and iterate until there's no more boxes with a lower score than the current selected box.\n\nThis will remove all boxes that have a large overlap with the selected boxes. Only the \"best\" boxes remain.\n\n**Exercise**: Implement yolo_non_max_suppression() using TensorFlow. TensorFlow has two built-in functions that are used to implement non-max suppression (so you don't actually need to use your `iou()` implementation):\n- [tf.image.non_max_suppression()](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression)\n- [K.gather()](https://www.tensorflow.org/api_docs/python/tf/gather)", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: yolo_non_max_suppression\n\ndef yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):\n \"\"\"\n Applies Non-max suppression (NMS) to set of boxes\n \n Arguments:\n scores -- tensor of shape (None,), output of yolo_filter_boxes()\n boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)\n classes -- tensor of shape (None,), output of yolo_filter_boxes()\n max_boxes -- integer, maximum number of predicted boxes you'd like\n iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n \n Returns:\n scores -- tensor of shape (, None), predicted score for each box\n boxes -- tensor of shape (4, None), predicted box coordinates\n classes -- tensor of shape (, None), predicted class for each box\n \n Note: The \"None\" dimension of the output tensors has obviously to be less than max_boxes. Note also that this\n function will transpose the shapes of scores, boxes, classes. This is made for convenience.\n \"\"\"\n \n max_boxes_tensor = K.variable(max_boxes, dtype='int32') # tensor to be used in tf.image.non_max_suppression()\n K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor\n \n # Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep\n ### START CODE HERE ### (≈ 1 line)\n nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes_tensor, iou_threshold)\n ### END CODE HERE ###\n \n # Use K.gather() to select only nms_indices from scores, boxes and classes\n ### START CODE HERE ### (≈ 3 lines)\n scores = K.gather(scores, nms_indices)\n boxes = K.gather(boxes, nms_indices)\n classes = K.gather(classes, nms_indices)\n ### END CODE HERE ###\n \n return scores, boxes, classes", "_____no_output_____" ], [ "with tf.Session() as test_b:\n scores = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n boxes = tf.random_normal([54, 4], mean=1, stddev=4, seed = 1)\n classes = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes)\n print(\"scores[2] = \" + str(scores[2].eval()))\n print(\"boxes[2] = \" + str(boxes[2].eval()))\n print(\"classes[2] = \" + str(classes[2].eval()))\n print(\"scores.shape = \" + str(scores.eval().shape))\n print(\"boxes.shape = \" + str(boxes.eval().shape))\n print(\"classes.shape = \" + str(classes.eval().shape))", "scores[2] = 6.9384\nboxes[2] = [-5.299932 3.13798141 4.45036697 0.95942086]\nclasses[2] = -2.24527\nscores.shape = (10,)\nboxes.shape = (10, 4)\nclasses.shape = (10,)\n" ] ], [ [ "**Expected Output**:\n\n<table>\n <tr>\n <td>\n **scores[2]**\n </td>\n <td>\n 6.9384\n </td>\n </tr>\n <tr>\n <td>\n **boxes[2]**\n </td>\n <td>\n [-5.299932 3.13798141 4.45036697 0.95942086]\n </td>\n </tr>\n\n <tr>\n <td>\n **classes[2]**\n </td>\n <td>\n -2.24527\n </td>\n </tr>\n <tr>\n <td>\n **scores.shape**\n </td>\n <td>\n (10,)\n </td>\n </tr>\n <tr>\n <td>\n **boxes.shape**\n </td>\n <td>\n (10, 4)\n </td>\n </tr>\n\n <tr>\n <td>\n **classes.shape**\n </td>\n <td>\n (10,)\n </td>\n </tr>\n\n</table>", "_____no_output_____" ], [ "### 2.4 Wrapping up the filtering\n\nIt's time to implement a function taking the output of the deep CNN (the 19x19x5x85 dimensional encoding) and filtering through all the boxes using the functions you've just implemented. \n\n**Exercise**: Implement `yolo_eval()` which takes the output of the YOLO encoding and filters the boxes using score threshold and NMS. There's just one last implementational detail you have to know. There're a few ways of representing boxes, such as via their corners or via their midpoint and height/width. YOLO converts between a few such formats at different times, using the following functions (which we have provided): \n\n```python\nboxes = yolo_boxes_to_corners(box_xy, box_wh) \n```\nwhich converts the yolo box coordinates (x,y,w,h) to box corners' coordinates (x1, y1, x2, y2) to fit the input of `yolo_filter_boxes`\n```python\nboxes = scale_boxes(boxes, image_shape)\n```\nYOLO's network was trained to run on 608x608 images. If you are testing this data on a different size image--for example, the car detection dataset had 720x1280 images--this step rescales the boxes so that they can be plotted on top of the original 720x1280 image. \n\nDon't worry about these two functions; we'll show you where they need to be called. ", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: yolo_eval\n\ndef yolo_eval(yolo_outputs, image_shape = (720., 1280.), max_boxes=10, score_threshold=.6, iou_threshold=.5):\n \"\"\"\n Converts the output of YOLO encoding (a lot of boxes) to your predicted boxes along with their scores, box coordinates and classes.\n \n Arguments:\n yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:\n box_confidence: tensor of shape (None, 19, 19, 5, 1)\n box_xy: tensor of shape (None, 19, 19, 5, 2)\n box_wh: tensor of shape (None, 19, 19, 5, 2)\n box_class_probs: tensor of shape (None, 19, 19, 5, 80)\n image_shape -- tensor of shape (2,) containing the input shape, in this notebook we use (608., 608.) (has to be float32 dtype)\n max_boxes -- integer, maximum number of predicted boxes you'd like\n score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n \n Returns:\n scores -- tensor of shape (None, ), predicted score for each box\n boxes -- tensor of shape (None, 4), predicted box coordinates\n classes -- tensor of shape (None,), predicted class for each box\n \"\"\"\n \n ### START CODE HERE ### \n \n # Retrieve outputs of the YOLO model (≈1 line)\n box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs\n\n # Convert boxes to be ready for filtering functions \n boxes = yolo_boxes_to_corners(box_xy, box_wh)\n\n # Use one of the functions you've implemented to perform Score-filtering with a threshold of score_threshold (≈1 line)\n scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, score_threshold)\n \n # Scale boxes back to original image shape.\n boxes = scale_boxes(boxes, image_shape)\n\n # Use one of the functions you've implemented to perform Non-max suppression with a threshold of iou_threshold (≈1 line)\n scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes, iou_threshold)\n \n ### END CODE HERE ###\n \n return scores, boxes, classes", "_____no_output_____" ], [ "with tf.Session() as test_b:\n yolo_outputs = (tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1),\n tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1))\n scores, boxes, classes = yolo_eval(yolo_outputs)\n print(\"scores[2] = \" + str(scores[2].eval()))\n print(\"boxes[2] = \" + str(boxes[2].eval()))\n print(\"classes[2] = \" + str(classes[2].eval()))\n print(\"scores.shape = \" + str(scores.eval().shape))\n print(\"boxes.shape = \" + str(boxes.eval().shape))\n print(\"classes.shape = \" + str(classes.eval().shape))", "scores[2] = 138.791\nboxes[2] = [ 1292.32971191 -278.52166748 3876.98925781 -835.56494141]\nclasses[2] = 54\nscores.shape = (10,)\nboxes.shape = (10, 4)\nclasses.shape = (10,)\n" ] ], [ [ "**Expected Output**:\n\n<table>\n <tr>\n <td>\n **scores[2]**\n </td>\n <td>\n 138.791\n </td>\n </tr>\n <tr>\n <td>\n **boxes[2]**\n </td>\n <td>\n [ 1292.32971191 -278.52166748 3876.98925781 -835.56494141]\n </td>\n </tr>\n\n <tr>\n <td>\n **classes[2]**\n </td>\n <td>\n 54\n </td>\n </tr>\n <tr>\n <td>\n **scores.shape**\n </td>\n <td>\n (10,)\n </td>\n </tr>\n <tr>\n <td>\n **boxes.shape**\n </td>\n <td>\n (10, 4)\n </td>\n </tr>\n\n <tr>\n <td>\n **classes.shape**\n </td>\n <td>\n (10,)\n </td>\n </tr>\n\n</table>", "_____no_output_____" ], [ "<font color='blue'>\n**Summary for YOLO**:\n- Input image (608, 608, 3)\n- The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output. \n- After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):\n - Each cell in a 19x19 grid over the input image gives 425 numbers. \n - 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture. \n - 85 = 5 + 80 where 5 is because $(p_c, b_x, b_y, b_h, b_w)$ has 5 numbers, and and 80 is the number of classes we'd like to detect\n- You then select only few boxes based on:\n - Score-thresholding: throw away boxes that have detected a class with a score less than the threshold\n - Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes\n- This gives you YOLO's final output. ", "_____no_output_____" ], [ "## 3 - Test YOLO pretrained model on images", "_____no_output_____" ], [ "In this part, you are going to use a pretrained model and test it on the car detection dataset. As usual, you start by **creating a session to start your graph**. Run the following cell.", "_____no_output_____" ] ], [ [ "sess = K.get_session()", "_____no_output_____" ] ], [ [ "### 3.1 - Defining classes, anchors and image shape.", "_____no_output_____" ], [ "Recall that we are trying to detect 80 classes, and are using 5 anchor boxes. We have gathered the information about the 80 classes and 5 boxes in two files \"coco_classes.txt\" and \"yolo_anchors.txt\". Let's load these quantities into the model by running the next cell. \n\nThe car detection dataset has 720x1280 images, which we've pre-processed into 608x608 images. ", "_____no_output_____" ] ], [ [ "class_names = read_classes(\"model_data/coco_classes.txt\")\nanchors = read_anchors(\"model_data/yolo_anchors.txt\")\nimage_shape = (720., 1280.) ", "_____no_output_____" ] ], [ [ "### 3.2 - Loading a pretrained model\n\nTraining a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. You are going to load an existing pretrained Keras YOLO model stored in \"yolo.h5\". (These weights come from the official YOLO website, and were converted using a function written by Allan Zelener. References are at the end of this notebook. Technically, these are the parameters from the \"YOLOv2\" model, but we will more simply refer to it as \"YOLO\" in this notebook.) Run the cell below to load the model from this file.", "_____no_output_____" ] ], [ [ "yolo_model = load_model(\"model_data/yolo.h5\")", "/opt/conda/lib/python3.6/site-packages/keras/models.py:251: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n warnings.warn('No training configuration found in save file: '\n" ] ], [ [ "\nThis loads the weights of a trained YOLO model. Here's a summary of the layers your model contains.", "_____no_output_____" ] ], [ [ "yolo_model.summary()", "____________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n====================================================================================================\ninput_1 (InputLayer) (None, 608, 608, 3) 0 \n____________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 608, 608, 32) 864 input_1[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_1 (BatchNorm (None, 608, 608, 32) 128 conv2d_1[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_1 (LeakyReLU) (None, 608, 608, 32) 0 batch_normalization_1[0][0] \n____________________________________________________________________________________________________\nmax_pooling2d_1 (MaxPooling2D) (None, 304, 304, 32) 0 leaky_re_lu_1[0][0] \n____________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 304, 304, 64) 18432 max_pooling2d_1[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_2 (BatchNorm (None, 304, 304, 64) 256 conv2d_2[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_2 (LeakyReLU) (None, 304, 304, 64) 0 batch_normalization_2[0][0] \n____________________________________________________________________________________________________\nmax_pooling2d_2 (MaxPooling2D) (None, 152, 152, 64) 0 leaky_re_lu_2[0][0] \n____________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 152, 152, 128) 73728 max_pooling2d_2[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_3 (BatchNorm (None, 152, 152, 128) 512 conv2d_3[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_3 (LeakyReLU) (None, 152, 152, 128) 0 batch_normalization_3[0][0] \n____________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 152, 152, 64) 8192 leaky_re_lu_3[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_4 (BatchNorm (None, 152, 152, 64) 256 conv2d_4[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_4 (LeakyReLU) (None, 152, 152, 64) 0 batch_normalization_4[0][0] \n____________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 152, 152, 128) 73728 leaky_re_lu_4[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_5 (BatchNorm (None, 152, 152, 128) 512 conv2d_5[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_5 (LeakyReLU) (None, 152, 152, 128) 0 batch_normalization_5[0][0] \n____________________________________________________________________________________________________\nmax_pooling2d_3 (MaxPooling2D) (None, 76, 76, 128) 0 leaky_re_lu_5[0][0] \n____________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 76, 76, 256) 294912 max_pooling2d_3[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_6 (BatchNorm (None, 76, 76, 256) 1024 conv2d_6[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_6 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_6[0][0] \n____________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 76, 76, 128) 32768 leaky_re_lu_6[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_7 (BatchNorm (None, 76, 76, 128) 512 conv2d_7[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_7 (LeakyReLU) (None, 76, 76, 128) 0 batch_normalization_7[0][0] \n____________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 76, 76, 256) 294912 leaky_re_lu_7[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_8 (BatchNorm (None, 76, 76, 256) 1024 conv2d_8[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_8 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_8[0][0] \n____________________________________________________________________________________________________\nmax_pooling2d_4 (MaxPooling2D) (None, 38, 38, 256) 0 leaky_re_lu_8[0][0] \n____________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 38, 38, 512) 1179648 max_pooling2d_4[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_9 (BatchNorm (None, 38, 38, 512) 2048 conv2d_9[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_9 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_9[0][0] \n____________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_9[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_10 (BatchNor (None, 38, 38, 256) 1024 conv2d_10[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_10 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_10[0][0] \n____________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_10[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_11 (BatchNor (None, 38, 38, 512) 2048 conv2d_11[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_11 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_11[0][0] \n____________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_11[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_12 (BatchNor (None, 38, 38, 256) 1024 conv2d_12[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_12 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_12[0][0] \n____________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_12[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_13 (BatchNor (None, 38, 38, 512) 2048 conv2d_13[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_13 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_13[0][0] \n____________________________________________________________________________________________________\nmax_pooling2d_5 (MaxPooling2D) (None, 19, 19, 512) 0 leaky_re_lu_13[0][0] \n____________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 19, 19, 1024) 4718592 max_pooling2d_5[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_14 (BatchNor (None, 19, 19, 1024) 4096 conv2d_14[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_14 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_14[0][0] \n____________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_14[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_15 (BatchNor (None, 19, 19, 512) 2048 conv2d_15[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_15 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_15[0][0] \n____________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_15[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_16 (BatchNor (None, 19, 19, 1024) 4096 conv2d_16[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_16 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_16[0][0] \n____________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_16[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_17 (BatchNor (None, 19, 19, 512) 2048 conv2d_17[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_17 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_17[0][0] \n____________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_17[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_18 (BatchNor (None, 19, 19, 1024) 4096 conv2d_18[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_18 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_18[0][0] \n____________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_18[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_19 (BatchNor (None, 19, 19, 1024) 4096 conv2d_19[0][0] \n____________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 38, 38, 64) 32768 leaky_re_lu_13[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_19 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_19[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_21 (BatchNor (None, 38, 38, 64) 256 conv2d_21[0][0] \n____________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_19[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_21 (LeakyReLU) (None, 38, 38, 64) 0 batch_normalization_21[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_20 (BatchNor (None, 19, 19, 1024) 4096 conv2d_20[0][0] \n____________________________________________________________________________________________________\nspace_to_depth_x2 (Lambda) (None, 19, 19, 256) 0 leaky_re_lu_21[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_20 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_20[0][0] \n____________________________________________________________________________________________________\nconcatenate_1 (Concatenate) (None, 19, 19, 1280) 0 space_to_depth_x2[0][0] \n leaky_re_lu_20[0][0] \n____________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 19, 19, 1024) 11796480 concatenate_1[0][0] \n____________________________________________________________________________________________________\nbatch_normalization_22 (BatchNor (None, 19, 19, 1024) 4096 conv2d_22[0][0] \n____________________________________________________________________________________________________\nleaky_re_lu_22 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_22[0][0] \n____________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 19, 19, 425) 435625 leaky_re_lu_22[0][0] \n====================================================================================================\nTotal params: 50,983,561\nTrainable params: 50,962,889\nNon-trainable params: 20,672\n____________________________________________________________________________________________________\n" ] ], [ [ "**Note**: On some computers, you may see a warning message from Keras. Don't worry about it if you do--it is fine.\n\n**Reminder**: this model converts a preprocessed batch of input images (shape: (m, 608, 608, 3)) into a tensor of shape (m, 19, 19, 5, 85) as explained in Figure (2).", "_____no_output_____" ], [ "### 3.3 - Convert output of the model to usable bounding box tensors\n\nThe output of `yolo_model` is a (m, 19, 19, 5, 85) tensor that needs to pass through non-trivial processing and conversion. The following cell does that for you.", "_____no_output_____" ] ], [ [ "yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))", "_____no_output_____" ] ], [ [ "You added `yolo_outputs` to your graph. This set of 4 tensors is ready to be used as input by your `yolo_eval` function.", "_____no_output_____" ], [ "### 3.4 - Filtering boxes\n\n`yolo_outputs` gave you all the predicted boxes of `yolo_model` in the correct format. You're now ready to perform filtering and select only the best boxes. Lets now call `yolo_eval`, which you had previously implemented, to do this. ", "_____no_output_____" ] ], [ [ "scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)", "_____no_output_____" ] ], [ [ "### 3.5 - Run the graph on an image\n\nLet the fun begin. You have created a (`sess`) graph that can be summarized as follows:\n\n1. <font color='purple'> yolo_model.input </font> is given to `yolo_model`. The model is used to compute the output <font color='purple'> yolo_model.output </font>\n2. <font color='purple'> yolo_model.output </font> is processed by `yolo_head`. It gives you <font color='purple'> yolo_outputs </font>\n3. <font color='purple'> yolo_outputs </font> goes through a filtering function, `yolo_eval`. It outputs your predictions: <font color='purple'> scores, boxes, classes </font>\n\n**Exercise**: Implement predict() which runs the graph to test YOLO on an image.\nYou will need to run a TensorFlow session, to have it compute `scores, boxes, classes`.\n\nThe code below also uses the following function:\n```python\nimage, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n```\nwhich outputs:\n- image: a python (PIL) representation of your image used for drawing boxes. You won't need to use it.\n- image_data: a numpy-array representing the image. This will be the input to the CNN.\n\n**Important note**: when a model uses BatchNorm (as is the case in YOLO), you will need to pass an additional placeholder in the feed_dict {K.learning_phase(): 0}.", "_____no_output_____" ] ], [ [ "def predict(sess, image_file):\n \"\"\"\n Runs the graph stored in \"sess\" to predict boxes for \"image_file\". Prints and plots the preditions.\n \n Arguments:\n sess -- your tensorflow/Keras session containing the YOLO graph\n image_file -- name of an image stored in the \"images\" folder.\n \n Returns:\n out_scores -- tensor of shape (None, ), scores of the predicted boxes\n out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes\n out_classes -- tensor of shape (None, ), class index of the predicted boxes\n \n Note: \"None\" actually represents the number of predicted boxes, it varies between 0 and max_boxes. \n \"\"\"\n\n # Preprocess your image\n image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n\n # Run the session with the correct tensors and choose the correct placeholders in the feed_dict.\n # You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})\n ### START CODE HERE ### (≈ 1 line)\n out_scores, out_boxes, out_classes = sess.run((scores, boxes, classes), feed_dict = {yolo_model.input:image_data, K.learning_phase() : 0})\n ### END CODE HERE ###\n\n # Print predictions info\n print('Found {} boxes for {}'.format(len(out_boxes), image_file))\n # Generate colors for drawing bounding boxes.\n colors = generate_colors(class_names)\n # Draw bounding boxes on the image file\n draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)\n # Save the predicted bounding box on the image\n image.save(os.path.join(\"out\", image_file), quality=90)\n # Display the results in the notebook\n output_image = scipy.misc.imread(os.path.join(\"out\", image_file))\n imshow(output_image)\n \n return out_scores, out_boxes, out_classes", "_____no_output_____" ] ], [ [ "Run the following cell on the \"test.jpg\" image to verify that your function is correct.", "_____no_output_____" ] ], [ [ "out_scores, out_boxes, out_classes = predict(sess, \"test.jpg\")", "Found 7 boxes for test.jpg\ncar 0.60 (925, 285) (1045, 374)\ncar 0.66 (706, 279) (786, 350)\nbus 0.67 (5, 266) (220, 407)\ncar 0.70 (947, 324) (1280, 705)\ncar 0.74 (159, 303) (346, 440)\ncar 0.80 (761, 282) (942, 412)\ncar 0.89 (367, 300) (745, 648)\n" ] ], [ [ "**Expected Output**:\n\n<table>\n <tr>\n <td>\n **Found 7 boxes for test.jpg**\n </td>\n </tr>\n <tr>\n <td>\n **car**\n </td>\n <td>\n 0.60 (925, 285) (1045, 374)\n </td>\n </tr>\n <tr>\n <td>\n **car**\n </td>\n <td>\n 0.66 (706, 279) (786, 350)\n </td>\n </tr>\n <tr>\n <td>\n **bus**\n </td>\n <td>\n 0.67 (5, 266) (220, 407)\n </td>\n </tr>\n <tr>\n <td>\n **car**\n </td>\n <td>\n 0.70 (947, 324) (1280, 705)\n </td>\n </tr>\n <tr>\n <td>\n **car**\n </td>\n <td>\n 0.74 (159, 303) (346, 440)\n </td>\n </tr>\n <tr>\n <td>\n **car**\n </td>\n <td>\n 0.80 (761, 282) (942, 412)\n </td>\n </tr>\n <tr>\n <td>\n **car**\n </td>\n <td>\n 0.89 (367, 300) (745, 648)\n </td>\n </tr>\n</table>", "_____no_output_____" ], [ "The model you've just run is actually able to detect 80 different classes listed in \"coco_classes.txt\". To test the model on your own images:\n 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n 3. Write your image's name in the cell above code\n 4. Run the code and see the output of the algorithm!\n\nIf you were to run your session in a for loop over all your images. Here's what you would get:\n\n<center>\n<video width=\"400\" height=\"200\" src=\"nb_images/pred_video_compressed2.mp4\" type=\"video/mp4\" controls>\n</video>\n</center>\n\n<caption><center> Predictions of the YOLO model on pictures taken from a camera while driving around the Silicon Valley <br> Thanks [drive.ai](https://www.drive.ai/) for providing this dataset! </center></caption>", "_____no_output_____" ], [ "<font color='blue'>\n**What you should remember**:\n- YOLO is a state-of-the-art object detection model that is fast and accurate\n- It runs an input image through a CNN which outputs a 19x19x5x85 dimensional volume. \n- The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.\n- You filter through all the boxes using non-max suppression. Specifically: \n - Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes\n - Intersection over Union (IoU) thresholding to eliminate overlapping boxes\n- Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, we used previously trained model parameters in this exercise. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise. ", "_____no_output_____" ], [ "**References**: The ideas presented in this notebook came primarily from the two YOLO papers. The implementation here also took significant inspiration and used many components from Allan Zelener's github repository. The pretrained weights used in this exercise came from the official YOLO website. \n- Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi - [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640) (2015)\n- Joseph Redmon, Ali Farhadi - [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242) (2016)\n- Allan Zelener - [YAD2K: Yet Another Darknet 2 Keras](https://github.com/allanzelener/YAD2K)\n- The official YOLO website (https://pjreddie.com/darknet/yolo/) ", "_____no_output_____" ], [ "**Car detection dataset**:\n<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br /><span xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\">The Drive.ai Sample Dataset</span> (provided by drive.ai) is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>. We are especially grateful to Brody Huval, Chih Hu and Rahul Patel for collecting and providing this dataset. ", "_____no_output_____" ] ] ]

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[ [ [ "import datetime\nimport os, sys\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport casadi as cas\nimport pickle\nimport copy as cp\n\n\n# from ..</src> import car_plotting\n# from .import src.car_plotting\nPROJECT_PATH = '/home/nbuckman/Dropbox (MIT)/DRL/2020_01_cooperative_mpc/mpc-multiple-vehicles/'\nsys.path.append(PROJECT_PATH)\n\n\nimport src.MPC_Casadi as mpc\nimport src.TrafficWorld as tw\nimport src.IterativeBestResponseMPCMultiple as mibr\n\nnp.set_printoptions(precision=2)", "_____no_output_____" ], [ "\nNEW = True\nif NEW:\n optional_suffix = \"testsave\"\n subdir_name = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") + optional_suffix\n folder = \"results/\" + subdir_name + \"/\"\n os.makedirs(folder)\n os.makedirs(folder+\"imgs/\")\n os.makedirs(folder+\"data/\")\n os.makedirs(folder+\"vids/\")\nelse:\n subdir_name = \"20200224-103456_real_dim_CA\"\n folder = \"results/\" + subdir_name + \"/\"\nprint(folder)\n\n\n\nT = 10 #numbr of time horizons\ndt = 0.2\nN = int(T/dt) #Number of control intervals\nworld = tw.TrafficWorld(2, 0, 1000)\n# Initial Conditions\n\nall_other_x0 = []\nall_other_u = []\nn_other = 2\n\nall_other_MPC = []\nnext_x0 = 0\nfor i in range(n_other):\n x1_MPC = mpc.MPC(dt)\n x1_MPC.theta_iamb = np.pi/2.5\n x1_MPC.k_final = 1.0\n x1_MPC.k_s = -2.0\n # x1_MPC.k_s = 0.0\n # x1_MPC.k_x = -1.0\n\n x1_MPC.min_y = world.y_min\n x1_MPC.max_y = world.y_max\n\n x1_MPC.k_u_v = 0.10\n x1_MPC.k_u_delta = 0.10\n x1_MPC.k_lat = 1.0\n # x1_MPC.k_change_u_v = 1.0\n # x1_MPC.k_change_u_delta = 1.0\n\n if i%2 == 0:\n lane_number = 0\n next_x0 += x1_MPC.L/2.0 + 2*x1_MPC.min_dist\n else:\n lane_number = 1\n \n initial_speed = 20 * 0.447 # m/s\n x1_MPC.fd = x1_MPC.gen_f_desired_lane(world, lane_number, True)\n x0 = np.array([next_x0, world.get_lane_centerline_y(lane_number), 0, 0, initial_speed, 0]).T\n u1 = np.zeros((2,N))\n u1[0,:] = np.clip(np.pi/180 *np.random.normal(size=(1,N)), -2 * np.pi/180, 2 * np.pi/180)\n# u1[0,:] = np.ones((1,N)) * np.pi/6\n # u1[1,:] = np.clip(np.random.normal(size=(1,N)), -x1_MPC.max_acceleration * x1_MPC.dt, x1_MPC.max_acceleration * x1_MPC.dt)\n all_other_MPC += [x1_MPC]\n all_other_x0 += [x0]\n all_other_u += [u1] \n\namb_MPC = cp.deepcopy(x1_MPC)\namb_MPC.theta_iamb = 0.0\n\namb_MPC.k_u_v = 0.10\namb_MPC.k_u_delta = 1.0\namb_MPC.k_change_u_v = 0.01\namb_MPC.k_change_u_delta = 0.0\namb_MPC.k_phi\namb_MPC.k_x = -1/10000.0\namb_MPC.k_s = 0\n# amb_MPC.min_v = initial_speed \n# amb_MPC.k_u_change = 1.0\n# amb_MPC.k_lat = 0\namb_MPC.k_lon = 0.0\n# amb_MPC.k_s = -2.0\namb_MPC.max_v = 40 * 0.447 # m/s\n# amb_MPC.max_X_dev = 5.0\n\n\n\namb_MPC.fd = amb_MPC.gen_f_desired_lane(world, 0, True)\n\nx0_amb = np.array([0, 0, 0, 0, 1.1*initial_speed , 0]).T\nuamb = np.zeros((2,N))\nuamb[0,:] = np.clip(np.pi/180 * np.random.normal(size=(1,N)), -2 * np.pi/180, 2 * np.pi/180)\namb_MPC.min_v = 1.1*initial_speed\n", "results/20200226-125359testsave/\n" ], [ "WARM = True\nn_total_round = 60\nibr_sub_it = 1\nruntimeerrors = 0\nmin_slack = 100000.0\nfor n_round in range(n_total_round):\n response_MPC = amb_MPC\n response_x0 = x0_amb\n \n nonresponse_MPC_list = all_other_MPC\n nonresponse_x0_list = all_other_x0\n nonresponse_u_list = all_other_u\n bri = mibr.IterativeBestResponseMPCMultiple(response_MPC, None, nonresponse_MPC_list )\n bri.k_slack = 999\n bri.generate_optimization(N, T, response_x0, None, nonresponse_x0_list, 5, slack=True)\n bri.solve(None, nonresponse_u_list)\n x1, u1, x1_des, _, _, _, other_x, other_u, other_des = bri.get_solution()", "This is Ipopt version 3.12.3, running with linear solver mumps.\nNOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n\nNumber of nonzeros in equality constraint Jacobian...: 4230\nNumber of nonzeros in inequality constraint Jacobian.: 3466\nNumber of nonzeros in Lagrangian Hessian.............: 3648\n\nTotal number of variables............................: 2293\n variables with only lower bounds: 0\n variables with lower and upper bounds: 0\n variables with only upper bounds: 0\nTotal number of equality constraints.................: 1377\nTotal number of inequality constraints...............: 967\n inequality constraints with only lower bounds: 204\n inequality constraints with lower and upper bounds: 355\n inequality constraints with only upper bounds: 408\n\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 0 0.0000000e+00 9.83e+00 7.50e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n 1 1.0399716e-02 9.83e+00 1.26e+00 -1.0 1.20e+02 -4.0 4.93e-04 3.26e-04h 1\n 2 2.0045559e-02 9.83e+00 2.20e+01 -1.0 1.82e+02 -4.5 3.56e-04 3.85e-05h 1\n 3 4.4710489e-01 9.83e+00 2.52e+01 -1.0 2.46e+02 -5.0 3.88e-04 3.37e-04h 1\n 4 2.5591113e+00 9.83e+00 6.47e+01 -1.0 3.34e+02 -5.4 1.11e-03 4.95e-04h 1\n 5 1.0304744e+01 9.83e+00 1.06e+02 -1.0 2.54e+02 -5.9 1.15e-03 5.03e-04h 1\n 6 7.9216962e+01 9.82e+00 1.08e+02 -1.0 1.86e+02 -6.4 1.38e-03 1.35e-03h 1\n 7 1.6205310e+02 9.82e+00 1.46e+02 -1.0 1.46e+02 -6.9 1.48e-03 8.87e-04h 1\n 8 2.2025207e+02 9.83e+00 1.56e+02 -1.0 3.22e+02 -7.3 6.46e-04 4.87e-04h 1\n 9 2.2441787e+02 9.83e+00 1.54e+02 -1.0 4.45e+03 -7.8 8.53e-06 3.28e-05h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 10 2.5182294e+02 9.82e+00 1.42e+02 -1.0 9.50e+02 -8.3 8.65e-06 2.07e-04h 1\n 11 2.8777432e+02 9.81e+00 1.66e+02 -1.0 4.28e+02 -8.8 6.37e-04 2.55e-04h 1\n 12 4.0157811e+02 9.80e+00 1.67e+02 -1.0 1.45e+02 -9.2 7.37e-04 7.08e-04h 1\n 13 4.1550901e+02 9.80e+00 1.78e+02 -1.0 1.56e+02 -9.7 2.52e-04 7.97e-05h 1\n 14 6.1114602e+02 9.80e+00 1.21e+02 -1.0 1.50e+02 -10.2 8.33e-05 1.00e-03h 1\n 15 6.1355211e+02 9.80e+00 1.82e+02 -1.0 1.68e+02 -10.7 9.96e-04 1.13e-05h 1\n 16 6.2428940e+02 9.80e+00 2.68e+02 -1.0 3.50e+02 -11.2 1.43e-03 5.02e-05h 1\n 17r 6.2428940e+02 9.80e+00 9.99e+02 1.0 0.00e+00 -11.6 0.00e+00 4.03e-07R 5\n 18r 8.6414419e+02 9.80e+00 9.99e+02 1.0 6.29e+03 - 3.71e-04 1.98e-05f 1\n 19r 3.3647143e+03 9.80e+00 9.99e+02 1.0 1.65e+03 - 3.45e-04 1.15e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 20r 5.1092797e+04 9.80e+00 9.98e+02 1.0 7.67e+02 - 3.88e-04 6.81e-04f 1\n 21r 6.3342770e+04 9.79e+00 1.07e+03 1.0 7.22e+00 2.0 1.50e-01 1.14e-02f 1\n 22r 1.7594664e+05 9.73e+00 1.03e+03 1.0 3.13e+00 2.4 2.75e-01 2.46e-01f 1\n 23r 1.6728527e+05 9.68e+00 1.07e+03 1.0 2.44e+00 2.9 3.31e-02 1.26e-01f 1\n 24r 1.7195513e+05 9.51e+00 1.09e+03 1.0 5.75e-01 3.3 8.31e-01 1.00e+00f 1\n 25r 1.7774546e+05 9.35e+00 1.07e+03 1.0 1.83e+00 2.8 3.09e-01 3.74e-01f 1\n 26r 1.7826098e+05 8.80e+00 1.06e+03 1.0 5.11e+00 2.3 3.60e-01 5.80e-01f 1\n 27 1.7790304e+05 8.75e+00 2.67e+03 -1.0 9.73e+01 -12.1 1.05e-03 4.89e-03f 1\n 28 1.7790581e+05 8.75e+00 2.67e+03 -1.0 9.99e+01 -12.6 4.15e-03 8.71e-05h 1\n 29 1.7805578e+05 8.74e+00 2.67e+03 -1.0 1.49e+02 -13.1 2.98e-03 1.65e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 30 1.7826045e+05 8.73e+00 2.66e+03 -1.0 1.00e+02 -13.5 5.02e-03 1.36e-03h 1\n 31 1.7833630e+05 8.72e+00 2.66e+03 -1.0 9.67e+01 -14.0 4.35e-03 3.57e-04h 1\n 32 1.7838366e+05 8.72e+00 2.66e+03 -1.0 9.66e+01 -14.5 5.53e-03 1.85e-04h 1\n 33 1.7897357e+05 8.70e+00 2.66e+03 -1.0 9.66e+01 -15.0 6.03e-03 1.99e-03h 1\n 34 1.7979138e+05 8.68e+00 2.65e+03 -1.0 9.65e+01 -15.5 5.02e-03 2.33e-03h 1\n 35 1.8083548e+05 8.66e+00 2.64e+03 -1.0 9.65e+01 -15.9 5.14e-03 2.59e-03h 1\n 36 1.8126368e+05 8.65e+00 2.64e+03 -1.0 9.68e+01 -16.4 1.38e-03 9.55e-04h 1\n 37 1.8126992e+05 8.65e+00 2.64e+03 -1.0 9.72e+01 -16.9 2.04e-05 1.32e-05h 1\n 38 1.8127749e+05 8.65e+00 2.64e+03 -1.0 9.75e+01 -17.4 5.22e-03 1.49e-05h 1\n 39 1.8272429e+05 8.63e+00 2.63e+03 -1.0 9.81e+01 -17.8 3.94e-04 2.72e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 40 1.8373146e+05 8.61e+00 2.63e+03 -1.0 9.79e+01 -18.3 2.78e-05 1.86e-03h 1\n 41 1.8460711e+05 8.60e+00 2.63e+03 -1.0 9.79e+01 -18.8 6.10e-03 1.58e-03h 1\n 42 1.8506845e+05 8.59e+00 3.03e+03 -1.0 9.77e+01 -19.3 3.60e-03 8.05e-04h 1\n 43 1.8630538e+05 8.57e+00 2.81e+03 -1.0 9.79e+01 -19.7 1.23e-03 2.15e-03h 1\n 44 1.8693912e+05 8.56e+00 2.69e+03 -1.0 9.91e+01 -19.1 6.31e-04 1.18e-03h 1\n 45 1.8714858e+05 8.56e+00 2.61e+03 -1.0 9.72e+01 -19.6 1.86e-05 3.60e-04h 1\n 46 1.8833659e+05 8.54e+00 3.03e+03 -1.0 9.83e+01 -19.1 4.39e-03 2.16e-03h 1\n 47 1.8900050e+05 8.53e+00 3.12e+03 -1.0 9.71e+01 -19.6 1.51e-03 1.12e-03h 1\n 48 1.8921851e+05 8.53e+00 3.04e+03 -1.0 9.85e+01 -19.1 2.64e-05 3.81e-04h 1\n 49 1.8974673e+05 8.52e+00 2.87e+03 -1.0 9.66e+01 -19.6 1.53e-04 8.26e-04h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 50 1.8975438e+05 8.52e+00 3.74e+03 -1.0 9.81e+01 -19.1 3.80e-03 1.32e-05h 1\n 51 1.9002449e+05 8.52e+00 4.95e+03 -1.0 9.84e+01 -19.6 3.14e-03 3.97e-04h 1\n 52 1.9113127e+05 8.50e+00 5.17e+03 -1.0 9.86e+01 -19.1 2.66e-03 1.79e-03h 1\n 53 1.9218965e+05 8.49e+00 4.74e+03 -1.0 9.66e+01 -19.6 1.49e-05 1.46e-03h 1\n 54 1.9235900e+05 8.49e+00 4.77e+03 -1.0 9.69e+01 -19.1 3.43e-04 2.55e-04h 1\n 55 1.9614877e+05 8.45e+00 3.24e+03 -1.0 9.55e+01 -19.6 4.82e-05 4.46e-03h 1\n 56 1.9767353e+05 8.43e+00 3.55e+03 -1.0 9.43e+01 -19.1 3.11e-03 2.00e-03h 1\n 57 2.0045953e+05 8.41e+00 3.54e+03 -1.0 9.38e+01 -19.6 3.09e-03 3.11e-03h 1\n 58 2.0058633e+05 8.41e+00 4.32e+03 -1.0 9.34e+01 -19.1 2.74e-03 1.60e-04h 1\n 59r 2.0058633e+05 8.41e+00 1.00e+03 0.9 0.00e+00 -19.6 0.00e+00 4.42e-07R 5\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 60r 2.0053275e+05 8.41e+00 1.00e+03 0.9 3.17e+03 - 3.55e-04 6.45e-05f 1\n 61r 2.0041460e+05 8.41e+00 1.00e+03 0.9 1.58e+03 - 3.29e-04 1.19e-04f 1\n 62r 2.0024799e+05 8.41e+00 9.99e+02 0.9 1.43e+03 - 7.29e-04 1.04e-03f 1\n 63r 2.0178166e+05 8.42e+00 9.97e+02 0.9 9.93e+02 - 2.24e-03 1.49e-03f 1\n 64r 2.0902033e+05 8.42e+00 9.92e+02 0.9 5.71e+02 - 1.11e-02 3.85e-03f 1\n 65r 2.3136420e+05 8.41e+00 9.83e+02 0.9 3.09e+02 - 8.14e-03 9.32e-03f 1\n 66r 2.6153891e+05 8.40e+00 9.88e+02 0.9 3.62e+02 - 7.16e-03 1.05e-02f 1\n 67r 2.7798903e+05 8.40e+00 9.64e+02 0.9 3.55e+02 - 1.32e-02 9.11e-03f 1\n 68r 2.8657577e+05 8.40e+00 9.59e+02 0.9 2.47e+02 - 2.08e-02 4.88e-03f 1\n 69r 2.9527415e+05 8.40e+00 9.55e+02 0.9 1.55e+02 - 2.61e-03 4.50e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 70r 3.0527057e+05 8.40e+00 9.50e+02 0.9 5.20e+02 - 6.42e-03 5.12e-03f 1\n 71r 3.3582481e+05 8.40e+00 9.58e+02 0.9 1.01e+02 - 3.07e-02 1.51e-02f 1\n 72r 4.1107309e+05 8.41e+00 9.04e+02 0.9 1.14e+02 - 2.39e-02 3.35e-02f 1\n 73r 4.5964398e+05 8.41e+00 8.95e+02 0.9 1.15e+02 - 3.62e-02 2.11e-02f 1\n 74r 5.2016472e+05 8.41e+00 8.67e+02 0.9 1.36e+02 - 4.89e-02 3.03e-02f 1\n 75r 5.9030592e+05 8.41e+00 8.31e+02 0.9 1.48e+02 - 6.01e-02 3.79e-02f 1\n 76r 6.7476964e+05 8.41e+00 7.88e+02 0.9 1.61e+02 - 7.73e-02 4.59e-02f 1\n 77r 7.7561624e+05 8.41e+00 7.44e+02 0.9 1.82e+02 - 1.18e-01 5.62e-02f 1\n 78r 8.4945381e+05 8.41e+00 7.06e+02 0.9 4.62e+02 - 4.89e-03 5.46e-02f 1\n 79r 9.0140487e+05 8.41e+00 6.78e+02 0.9 2.50e+02 - 1.14e-01 3.60e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 80r 9.7546515e+05 8.41e+00 6.36e+02 0.9 2.54e+02 - 1.43e-01 6.16e-02f 1\n 81r 1.1190008e+06 8.38e+00 5.68e+02 0.9 3.76e+02 - 3.36e-02 1.08e-01f 1\n 82r 1.1225339e+06 8.37e+00 5.49e+02 0.9 8.82e+02 - 8.26e-03 3.25e-02f 1\n 83r 1.1286850e+06 8.36e+00 5.46e+02 0.9 4.12e+02 - 3.25e-02 6.75e-03f 1\n 84r 1.2659338e+06 8.29e+00 3.86e+02 0.9 7.87e+00 0.0 3.42e-01 2.93e-01f 1\n 85r 1.2612169e+06 8.28e+00 3.74e+02 0.2 2.38e+02 - 1.60e-02 3.04e-02f 1\n 86r 1.3908950e+06 8.17e+00 3.95e+02 0.2 3.02e+02 - 2.38e-02 7.50e-02f 1\n 87r 1.4392216e+06 8.15e+00 2.99e+02 0.2 7.97e+00 0.4 1.84e-01 1.68e-01f 1\n 88r 1.4749437e+06 8.14e+00 2.77e+02 0.2 5.34e+01 -0.1 4.57e-02 3.68e-02f 1\n 89r 1.4776820e+06 8.12e+00 2.77e+02 0.2 8.08e+02 - 3.91e-03 5.72e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 90r 1.4904632e+06 8.09e+00 3.10e+02 0.2 2.23e+02 - 1.15e-01 3.01e-02f 1\n 91r 1.4780369e+06 8.03e+00 2.85e+02 0.2 2.60e+02 - 1.24e-02 4.77e-02f 1\n 92r 1.4633854e+06 7.98e+00 2.84e+02 0.2 2.35e+02 - 1.42e-01 5.45e-02f 1\n 93r 1.4468075e+06 7.94e+00 2.89e+02 0.2 3.39e+02 - 2.25e-02 4.04e-02f 1\n 94r 1.4177297e+06 7.84e+00 3.28e+02 0.2 3.67e+02 - 4.08e-02 1.06e-01f 1\n 95r 1.4208335e+06 7.83e+00 3.24e+02 0.2 3.41e+02 - 4.16e-02 2.22e-02f 1\n 96r 1.4035701e+06 7.79e+00 2.82e+02 0.2 3.36e+02 - 6.49e-02 9.28e-02f 1\n 97r 1.3981555e+06 7.76e+00 2.08e+02 0.2 3.03e+01 -0.5 1.71e-01 7.29e-02f 1\n 98r 1.3736085e+06 7.75e+00 2.02e+02 0.2 4.74e+02 - 2.70e-02 1.88e-02f 1\n 99r 1.3738774e+06 7.70e+00 1.87e+02 0.2 4.77e+02 - 7.99e-02 9.61e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 100r 1.3580286e+06 7.69e+00 2.14e+02 0.2 4.67e+02 - 1.03e-01 3.83e-02f 1\n 101r 1.3021262e+06 7.68e+00 2.15e+02 0.2 5.22e+02 - 1.39e-01 9.61e-02f 1\n 102r 1.2905134e+06 7.68e+00 2.12e+02 0.2 5.78e+02 - 5.56e-02 4.11e-02f 1\n 103r 1.2958912e+06 7.68e+00 1.61e+02 0.2 6.56e+02 - 2.71e-02 1.08e-01f 1\n 104r 1.3293811e+06 7.69e+00 1.47e+02 0.2 7.20e+02 - 8.99e-02 8.45e-02f 1\n 105r 1.4013282e+06 7.70e+00 1.56e+02 0.2 9.31e+02 - 7.51e-02 1.05e-01f 1\n 106r 1.5010415e+06 7.67e+00 2.27e+02 0.2 3.80e+01 -1.0 6.34e-02 1.79e-01f 1\n 107r 1.5310867e+06 7.66e+00 2.10e+02 0.2 5.35e+01 -1.5 5.32e-02 3.54e-02f 1\n 108r 1.6858235e+06 7.64e+00 2.07e+02 0.2 2.27e+02 -2.0 1.41e-02 1.65e-02f 1\n 109r 1.6992342e+06 7.64e+00 2.00e+02 0.2 4.36e+01 -0.6 3.64e-02 3.57e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 110r 1.6720537e+06 7.63e+00 1.42e+02 0.2 2.59e+00 0.7 3.58e-01 1.88e-01f 1\n 111r 1.6393971e+06 7.62e+00 1.61e+02 0.2 5.96e+00 0.2 6.27e-02 2.98e-01f 1\n 112r 1.5942856e+06 7.62e+00 9.69e+01 0.2 3.94e+00 0.6 5.59e-01 6.60e-01f 1\n 113r 1.5454439e+06 7.61e+00 8.14e+01 -0.5 9.57e+00 0.2 2.87e-01 3.13e-01f 1\n 114r 1.5348572e+06 7.52e+00 4.26e+01 -0.5 2.28e+01 -0.3 2.19e-01 1.39e-01f 1\n 115 1.5101190e+06 7.46e+00 1.69e+04 -1.0 7.31e+02 -19.1 1.74e-03 8.11e-03f 1\n 116 1.5081170e+06 7.45e+00 1.69e+04 -1.0 6.47e+02 -19.6 1.66e-03 6.70e-04f 1\n 117 1.5000973e+06 7.43e+00 1.68e+04 -1.0 6.33e+02 -19.1 1.83e-03 2.70e-03f 1\n 118 1.4972338e+06 7.43e+00 1.68e+04 -1.0 6.14e+02 -19.6 4.62e-03 9.87e-04f 1\n 119 1.4926222e+06 7.41e+00 1.68e+04 -1.0 5.92e+02 -19.1 4.76e-03 1.69e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 120 1.4897946e+06 7.40e+00 1.68e+04 -1.0 5.73e+02 -19.6 1.24e-03 1.25e-03f 1\n 121 1.4867964e+06 7.39e+00 1.67e+04 -1.0 5.67e+02 -19.1 1.24e-02 1.40e-03f 1\n 122 1.4811114e+06 7.37e+00 1.67e+04 -1.0 5.31e+02 -19.6 2.94e-03 2.99e-03f 1\n 123 1.4776862e+06 7.36e+00 1.67e+04 -1.0 5.21e+02 -19.1 1.64e-02 1.85e-03f 1\n 124 1.4722508e+06 7.34e+00 1.66e+04 -1.0 4.87e+02 -19.6 5.29e-03 3.07e-03f 1\n 125 1.4686209e+06 7.32e+00 1.66e+04 -1.0 4.76e+02 -19.1 8.82e-03 2.07e-03f 1\n 126 1.4549412e+06 7.26e+00 1.64e+04 -1.0 4.61e+02 -19.6 8.60e-03 7.87e-03f 1\n 127 1.4439147e+06 7.22e+00 1.63e+04 -1.0 4.42e+02 -19.1 3.68e-03 6.45e-03f 1\n 128 1.4318869e+06 7.17e+00 1.62e+04 -1.0 4.33e+02 -19.6 2.58e-04 7.02e-03f 1\n 129 1.4287865e+06 7.15e+00 1.62e+04 -1.0 4.28e+02 -19.1 9.28e-03 1.82e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 130 1.4117953e+06 7.08e+00 1.60e+04 -1.0 4.17e+02 -19.6 1.21e-02 9.95e-03f 1\n 131 1.4094629e+06 7.07e+00 1.60e+04 -1.0 3.99e+02 -19.1 1.22e-02 1.30e-03f 1\n 132 1.3898684e+06 6.99e+00 1.58e+04 -1.0 3.86e+02 -19.6 6.69e-03 1.09e-02f 1\n 133 1.3666966e+06 6.91e+00 1.56e+04 -1.0 3.73e+02 -19.1 1.23e-04 1.25e-02f 1\n 134 1.3653874e+06 6.90e+00 1.56e+04 -1.0 3.64e+02 -19.6 8.61e-03 7.06e-04f 1\n 135 1.3600774e+06 6.88e+00 1.56e+04 -1.0 3.58e+02 -19.1 4.77e-04 2.88e-03f 1\n 136 1.3599863e+06 6.88e+00 1.56e+04 -1.0 3.54e+02 -19.6 1.16e-04 4.90e-05f 1\n 137 1.3520824e+06 6.85e+00 1.55e+04 -1.0 3.56e+02 -19.1 2.25e-02 4.63e-03f 1\n 138 1.3198881e+06 6.72e+00 1.52e+04 -1.0 3.32e+02 -19.6 4.47e-03 1.93e-02f 1\n 139 1.3018334e+06 6.65e+00 1.51e+04 -1.0 3.23e+02 -19.1 3.31e-02 1.03e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 140 1.3001178e+06 6.64e+00 1.50e+04 -1.0 3.09e+02 -19.6 2.72e-03 1.11e-03f 1\n 141 1.2712885e+06 6.53e+00 1.48e+04 -1.0 3.10e+02 -19.1 1.01e-04 1.69e-02f 1\n 142 1.2672814e+06 6.51e+00 1.48e+04 -1.0 3.20e+02 -19.6 4.18e-03 2.32e-03f 1\n 143 1.2662613e+06 6.51e+00 1.47e+04 -1.0 2.90e+02 -19.1 1.94e-02 6.22e-04f 1\n 144 1.2251694e+06 6.34e+00 1.43e+04 -1.0 2.82e+02 -19.6 2.71e-03 2.66e-02f 1\n 145 1.2238974e+06 6.33e+00 1.43e+04 -1.0 2.99e+02 -19.1 1.32e-03 7.91e-04f 1\n 146 1.2218795e+06 6.32e+00 1.43e+04 -1.0 3.08e+02 -19.6 1.65e-03 1.32e-03f 1\n 147 1.2218327e+06 6.32e+00 1.43e+04 -1.0 3.05e+02 -19.1 3.84e-03 2.74e-05f 1\n 148 1.1933966e+06 6.22e+00 1.41e+04 -1.0 3.06e+02 -19.6 7.34e-04 1.67e-02f 1\n 149 1.1923484e+06 6.21e+00 1.41e+04 -1.0 3.08e+02 -19.1 8.24e-03 5.84e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 150 1.1851137e+06 6.19e+00 1.40e+04 -1.0 2.76e+02 -19.6 7.09e-04 4.39e-03f 1\n 151 1.1674398e+06 6.13e+00 1.39e+04 -1.0 2.77e+02 -19.1 4.82e-03 9.94e-03f 1\n 152 1.1671054e+06 6.12e+00 1.39e+04 -1.0 2.72e+02 -19.6 4.03e-03 2.01e-04f 1\n 153 1.1607724e+06 6.10e+00 1.38e+04 -1.0 2.70e+02 -19.1 6.82e-04 3.72e-03f 1\n 154 1.1521951e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.6 1.57e-03 5.21e-03f 1\n 155 1.1512500e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.1 7.99e-04 5.27e-04f 1\n 156 1.1507231e+06 6.06e+00 1.37e+04 -1.0 2.68e+02 -19.6 4.24e-04 3.27e-04f 1\n 157 1.1507040e+06 6.06e+00 1.37e+04 -1.0 2.81e+02 -19.1 5.41e-04 9.69e-06f 1\n 158 1.1467730e+06 6.05e+00 1.37e+04 -1.0 2.63e+02 -19.6 8.26e-06 2.11e-03f 1\n 159 1.0732380e+06 5.82e+00 1.32e+04 -1.0 2.63e+02 -19.1 2.67e-04 3.91e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 160 1.0714161e+06 5.81e+00 1.32e+04 -1.0 2.56e+02 -19.6 1.02e-02 9.30e-04f 1\n 161 1.0713627e+06 5.81e+00 1.32e+04 -1.0 2.54e+02 -19.1 8.09e-05 2.84e-05f 1\n 162 1.0580046e+06 5.76e+00 1.31e+04 -1.0 2.54e+02 -19.6 1.35e-04 7.69e-03f 1\n 163 1.0545534e+06 5.75e+00 1.30e+04 -1.0 2.54e+02 -19.1 3.39e-03 1.89e-03f 1\n 164 1.0341517e+06 5.68e+00 1.30e+04 -1.0 2.52e+02 -19.6 8.74e-04 1.22e-02f 1\n 165 1.0336429e+06 5.68e+00 1.29e+04 -1.0 2.51e+02 -19.1 6.22e-04 2.90e-04f 1\n 166 1.0275521e+06 5.66e+00 1.38e+04 -1.0 2.51e+02 -19.6 4.36e-04 3.68e-03f 1\n 167 1.0179732e+06 5.63e+00 1.32e+04 -1.0 2.50e+02 -19.1 7.95e-03 5.53e-03f 1\n 168 9.9624521e+05 5.55e+00 1.59e+04 -1.0 2.48e+02 -19.6 4.63e-03 1.40e-02f 1\n 169 9.9527357e+05 5.55e+00 1.58e+04 -1.0 2.44e+02 -19.1 5.96e-04 5.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 170 9.9489625e+05 5.55e+00 1.56e+04 -1.0 2.47e+02 -19.6 1.04e-03 2.42e-04f 1\n 171 9.9489118e+05 5.55e+00 1.50e+04 -1.0 2.53e+02 -19.1 1.24e-03 2.86e-06f 1\n 172 9.9421219e+05 5.54e+00 1.57e+04 -1.0 2.49e+02 -19.6 3.63e-06 4.37e-04f 1\n 173 9.8486650e+05 5.51e+00 1.68e+04 -1.0 2.46e+02 -19.1 5.48e-04 5.68e-03f 1\n 174 9.7308682e+05 5.47e+00 1.65e+04 -1.0 2.49e+02 -19.6 8.25e-03 7.61e-03f 1\n 175 9.7212896e+05 5.47e+00 1.65e+04 -1.0 2.44e+02 -19.1 3.20e-04 5.80e-04f 1\n 176 9.6540396e+05 5.44e+00 1.75e+04 -1.0 2.49e+02 -19.6 9.01e-04 4.34e-03f 1\n 177 9.4998516e+05 5.39e+00 1.51e+04 -1.0 2.44e+02 -19.1 1.93e-02 9.44e-03f 1\n 178 9.4904520e+05 5.39e+00 1.52e+04 -1.0 2.45e+02 -19.6 2.19e-04 6.06e-04f 1\n 179 9.4902274e+05 5.39e+00 1.51e+04 -1.0 2.75e+02 -19.1 1.62e-04 1.14e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 180 9.3880492e+05 5.35e+00 1.67e+04 -1.0 2.29e+02 -19.6 1.38e-05 6.45e-03f 1\n 181 8.9559690e+05 5.21e+00 1.79e+04 -1.0 2.29e+02 -19.1 1.06e-02 2.71e-02f 1\n 182 8.5549874e+05 5.07e+00 1.93e+04 -1.0 2.22e+02 -19.6 4.07e-03 2.60e-02f 1\n 183 8.4452994e+05 5.04e+00 1.99e+04 -1.0 2.17e+02 -19.1 4.96e-04 7.46e-03f 1\n 184 8.2921005e+05 4.98e+00 1.98e+04 -1.0 2.14e+02 -19.6 9.95e-03 1.09e-02f 1\n 185 8.2905253e+05 4.98e+00 1.90e+04 -1.0 2.09e+02 -19.1 5.75e-03 1.10e-04f 1\n 186 8.2904529e+05 4.98e+00 1.89e+04 -1.0 2.18e+02 -19.6 7.15e-05 5.41e-06f 1\n 187 8.2712955e+05 4.97e+00 1.92e+04 -1.0 2.14e+02 -19.1 5.72e-05 1.35e-03f 1\n 188 8.0950543e+05 4.91e+00 2.28e+04 -1.0 2.31e+02 -19.6 1.11e-03 1.34e-02f 1\n 189 8.0862616e+05 4.90e+00 2.24e+04 -1.0 2.12e+02 -19.1 1.92e-03 6.51e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 190 8.0425351e+05 4.89e+00 2.35e+04 -1.0 2.22e+02 -19.6 4.57e-04 3.51e-03f 1\n 191 7.9537870e+05 4.85e+00 2.31e+04 -1.0 2.15e+02 -19.1 7.37e-03 6.61e-03f 1\n 192 7.9489154e+05 4.85e+00 2.29e+04 -1.0 2.11e+02 -19.6 1.29e-03 3.92e-04f 1\n 193 7.8751827e+05 4.83e+00 2.29e+04 -1.0 2.01e+02 -19.1 2.56e-03 5.71e-03f 1\n 194 7.8736120e+05 4.82e+00 2.31e+04 -1.0 2.00e+02 -19.6 1.54e-04 1.32e-04f 1\n 195 7.8402757e+05 4.81e+00 2.31e+04 -1.0 1.94e+02 -19.1 2.19e-03 2.63e-03f 1\n 196 7.8388845e+05 4.81e+00 2.53e+04 -1.0 1.98e+02 -19.6 2.15e-04 1.18e-04f 1\n 197 7.8385262e+05 4.81e+00 2.50e+04 -1.0 1.94e+02 -19.1 4.34e-03 2.82e-05f 1\n 198 7.8236217e+05 4.81e+00 1.09e+04 -1.0 1.97e+02 -19.6 3.11e-04 1.27e-03f 1\n 199 7.7035462e+05 4.76e+00 1.08e+04 -1.0 1.91e+02 -19.1 3.91e-04 9.52e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 200 7.6826638e+05 4.75e+00 1.08e+04 -1.0 1.90e+02 -19.6 2.68e-02 1.77e-03f 1\n 201 7.6137802e+05 4.73e+00 1.07e+04 -1.0 1.80e+02 -19.1 5.08e-04 5.41e-03f 1\n 202 7.6092627e+05 4.72e+00 1.07e+04 -1.0 1.85e+02 -19.6 8.12e-03 3.85e-04f 1\n 203 6.8620409e+05 4.44e+00 1.26e+04 -1.0 1.80e+02 -19.1 1.57e-04 6.08e-02f 1\n 204 6.8223234e+05 4.42e+00 1.15e+04 -1.0 1.85e+02 -19.6 6.43e-03 3.54e-03f 1\n 205 6.7550321e+05 4.40e+00 1.16e+04 -1.0 1.74e+02 -19.1 5.10e-03 5.69e-03f 1\n 206 6.7512988e+05 4.39e+00 1.00e+04 -1.0 1.86e+02 -19.6 8.13e-03 3.47e-04f 1\n 207 6.6341460e+05 4.35e+00 9.84e+03 -1.0 1.72e+02 -19.1 1.30e-02 1.04e-02f 1\n 208 6.5075026e+05 4.30e+00 1.09e+04 -1.0 1.87e+02 -19.6 2.10e-04 1.19e-02f 1\n 209 6.4469528e+05 4.27e+00 1.05e+04 -1.0 1.72e+02 -19.1 7.52e-03 5.37e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 210 6.3074280e+05 4.22e+00 1.26e+04 -1.0 1.85e+02 -19.6 4.48e-04 1.32e-02f 1\n 211 6.3015403e+05 4.21e+00 1.26e+04 -1.0 3.38e+01 2.3 1.77e-03 7.51e-04f 1\n 212 6.3013669e+05 4.21e+00 1.47e+04 -1.0 1.21e+02 1.8 1.17e-03 1.79e-05f 1\n 213 6.2710947e+05 4.20e+00 1.45e+04 -1.0 1.26e+02 1.4 4.90e-04 2.86e-03f 1\n 214 6.2100935e+05 4.18e+00 1.49e+04 -1.0 1.53e+02 0.9 1.86e-03 5.85e-03f 1\n 215 6.1066877e+05 4.14e+00 1.59e+04 -1.0 1.97e+02 0.4 1.05e-05 8.89e-03f 1\n 216 6.0984620e+05 4.14e+00 1.57e+04 -1.0 1.84e+02 1.7 8.49e-03 1.01e-03f 1\n 217 6.0751052e+05 4.13e+00 1.57e+04 -1.0 2.02e+02 1.3 5.49e-03 2.08e-03f 1\n 218 6.0707832e+05 4.12e+00 1.57e+04 -1.0 2.01e+02 1.7 2.32e-04 5.32e-04f 1\n 219 6.0707327e+05 4.12e+00 1.53e+04 -1.0 1.96e+02 2.1 2.90e-03 7.52e-06f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 220 6.0555478e+05 4.12e+00 1.54e+04 -1.0 2.25e+02 1.6 9.57e-04 1.84e-03f 1\n 221 6.0553909e+05 4.12e+00 1.47e+04 -1.0 2.14e+02 2.1 5.83e-03 2.26e-05f 1\n 222 6.0387470e+05 4.11e+00 1.47e+04 -1.0 2.27e+02 1.6 2.04e-03 1.80e-03f 1\n 223 6.0325229e+05 4.11e+00 1.47e+04 -1.0 2.20e+02 2.0 4.35e-04 7.72e-04f 1\n 224 6.0322399e+05 4.11e+00 1.45e+04 -1.0 2.21e+02 1.5 4.10e-03 2.96e-05f 1\n 225 6.0185557e+05 4.10e+00 1.45e+04 -1.0 2.17e+02 2.0 1.59e-05 1.70e-03f 1\n 226 6.0180800e+05 4.10e+00 1.44e+04 -1.0 2.29e+02 1.5 1.37e-03 4.63e-05f 1\n 227 5.9923339e+05 4.09e+00 1.44e+04 -1.0 2.27e+02 1.9 2.84e-03 2.85e-03f 1\n 228 5.9851909e+05 4.09e+00 1.44e+04 -1.0 2.29e+02 1.4 9.28e-04 6.31e-04f 1\n 229 5.9692058e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.9 1.94e-03 1.77e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 230 5.9689878e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.4 1.22e-05 1.79e-05f 1\n 231 5.9684337e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.8 7.71e-05 6.11e-05f 1\n 232 5.9682024e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.3 2.55e-06 2.10e-05f 1\n 233 5.9680869e+05 4.08e+00 1.38e+04 -1.0 2.28e+02 1.8 7.05e-03 1.36e-05f 1\n 234 5.9076367e+05 4.05e+00 1.41e+04 -1.0 2.30e+02 1.3 5.00e-04 5.99e-03f 1\n 235 5.9073170e+05 4.05e+00 1.42e+04 -1.0 2.32e+02 1.7 4.66e-03 3.40e-05f 1\n 236 5.8521172e+05 4.03e+00 1.45e+04 -1.0 2.32e+02 1.2 8.81e-04 5.50e-03f 1\n 237 5.8376022e+05 4.03e+00 1.44e+04 -1.0 2.30e+02 0.8 2.07e-06 1.04e-03f 1\n 238 5.8372062e+05 4.03e+00 1.53e+04 -1.0 2.35e+02 2.1 3.62e-03 4.81e-05f 1\n 239 5.7779448e+05 4.00e+00 1.55e+04 -1.0 2.39e+02 1.6 1.33e-02 6.22e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 240 5.7771432e+05 4.00e+00 1.57e+04 -1.0 2.26e+02 2.0 1.00e-03 9.89e-05f 1\n 241 5.7665827e+05 4.00e+00 1.57e+04 -1.0 2.07e+02 1.6 9.92e-04 1.10e-03f 1\n 242 5.7511556e+05 3.99e+00 1.55e+04 -1.0 2.12e+02 2.0 1.09e-03 1.92e-03f 1\n 243 5.7052438e+05 3.97e+00 1.52e+04 -1.0 2.09e+02 1.5 2.73e-04 4.68e-03f 1\n 244 5.6998537e+05 3.97e+00 1.51e+04 -1.0 2.36e+02 1.9 3.75e-04 6.54e-04f 1\n 245 5.6612860e+05 3.95e+00 1.48e+04 -1.0 2.37e+02 1.5 3.47e-05 3.75e-03f 1\n 246 5.6572525e+05 3.95e+00 1.50e+04 -1.0 2.36e+02 1.9 2.57e-03 4.85e-04f 1\n 247 5.6516789e+05 3.95e+00 1.51e+04 -1.0 2.36e+02 1.4 1.32e-03 4.83e-04f 1\n 248 5.6316860e+05 3.94e+00 1.52e+04 -1.0 2.36e+02 1.8 3.05e-03 2.38e-03f 1\n 249 5.6254967e+05 3.94e+00 1.50e+04 -1.0 2.35e+02 1.3 7.91e-06 4.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 250 5.6077033e+05 3.93e+00 1.49e+04 -1.0 2.36e+02 1.8 1.29e-03 2.17e-03f 1\n 251 5.6022313e+05 3.93e+00 1.47e+04 -1.0 2.34e+02 1.3 2.29e-06 3.07e-04f 1\n 252 5.5692514e+05 3.91e+00 1.49e+04 -1.0 2.36e+02 1.7 8.13e-03 3.96e-03f 1\n 253 5.5673567e+05 3.91e+00 1.49e+04 -1.0 2.31e+02 1.2 2.68e-06 9.58e-05f 1\n 254 5.5535652e+05 3.91e+00 1.55e+04 -1.0 2.33e+02 1.7 1.14e-02 1.64e-03f 1\n 255 5.5534273e+05 3.91e+00 1.56e+04 -1.0 2.30e+02 2.1 4.75e-04 1.85e-05f 1\n 256 5.5514554e+05 3.90e+00 1.71e+04 -1.0 2.27e+02 1.6 1.32e-02 2.20e-04f 1\n 257 5.5141803e+05 3.90e+00 1.48e+04 -1.0 4.83e+02 1.1 1.93e-05 1.30e-03f 1\n 258 5.4693933e+05 3.88e+00 1.42e+04 -1.0 2.39e+02 1.6 2.91e-04 4.86e-03f 1\n 259 5.4687686e+05 3.88e+00 1.43e+04 -1.0 2.52e+02 1.1 2.34e-03 6.08e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 260 5.4353115e+05 3.87e+00 1.41e+04 -1.0 2.42e+02 1.5 1.34e-03 3.66e-03f 1\n 261 5.4346337e+05 3.87e+00 1.41e+04 -1.0 2.07e+02 1.0 1.56e-04 6.65e-05f 1\n 262 5.4335021e+05 3.87e+00 1.42e+04 -1.0 1.95e+02 1.5 2.22e-03 1.26e-04f 1\n 263 5.4296226e+05 3.86e+00 1.43e+04 -1.0 1.86e+02 1.0 2.63e-03 3.71e-04f 1\n 264 5.3812113e+05 3.84e+00 1.40e+04 -1.0 2.13e+02 1.4 2.17e-05 5.31e-03f 1\n 265 5.3811408e+05 3.84e+00 1.41e+04 -1.0 2.23e+02 1.8 3.29e-04 8.06e-06f 1\n 266 5.3810662e+05 3.84e+00 1.41e+04 -1.0 2.43e+02 1.4 9.90e-04 7.83e-06f 1\n 267 5.3692599e+05 3.84e+00 1.38e+04 -1.0 2.30e+02 1.8 8.84e-05 1.33e-03f 1\n 268 5.3689601e+05 3.84e+00 1.38e+04 -1.0 1.90e+02 1.3 1.05e-03 3.21e-05f 1\n 269 5.3480708e+05 3.83e+00 1.32e+04 -1.0 2.44e+02 0.8 2.23e-04 1.25e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 270 5.3431885e+05 3.83e+00 1.34e+04 -1.0 1.83e+02 1.3 4.31e-03 5.17e-04f 1\n 271 5.3310063e+05 3.83e+00 1.31e+04 -1.0 1.89e+02 1.7 2.86e-05 1.41e-03f 1\n 272 5.3150897e+05 3.82e+00 1.22e+04 -1.0 1.85e+02 2.1 1.96e-04 1.98e-03f 1\n 273 5.3147543e+05 3.82e+00 1.22e+04 -1.0 1.61e+02 1.6 1.35e-04 3.82e-05f 1\n 274 5.3137612e+05 3.82e+00 1.24e+04 -1.0 1.76e+02 1.2 3.23e-03 1.02e-04f 1\n 275 5.3106158e+05 3.82e+00 1.29e+04 -1.0 1.79e+02 1.6 5.34e-03 3.64e-04f 1\n 276 5.2929724e+05 3.81e+00 1.28e+04 -1.0 1.73e+02 1.1 5.52e-05 1.84e-03f 1\n 277 5.2907597e+05 3.81e+00 1.29e+04 -1.0 1.63e+02 2.4 4.41e-04 3.68e-04f 1\n 278 5.2816992e+05 3.80e+00 1.27e+04 -1.0 1.88e+02 2.0 5.12e-04 1.14e-03f 1\n 279 5.2807415e+05 3.80e+00 1.27e+04 -1.0 1.48e+03 1.5 1.26e-06 8.93e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 280 5.2805145e+05 3.80e+00 1.26e+04 -1.0 1.94e+02 1.0 1.55e-03 2.34e-05f 1\n 281 5.2494578e+05 3.79e+00 1.26e+04 -1.0 1.75e+02 1.4 7.38e-04 3.61e-03f 1\n 282 5.2382656e+05 3.79e+00 1.20e+04 -1.0 6.75e+02 1.0 1.61e-05 3.04e-04f 1\n 283 5.2294012e+05 3.78e+00 1.20e+04 -1.0 1.74e+02 1.4 1.78e-03 9.88e-04f 1\n 284 5.2002561e+05 3.77e+00 1.24e+04 -1.0 1.76e+02 1.8 7.11e-03 3.79e-03f 1\n 285 5.1038171e+05 3.73e+00 1.19e+04 -1.0 1.71e+02 1.3 6.29e-05 1.07e-02f 1\n 286 5.1029468e+05 3.73e+00 1.19e+04 -1.0 1.84e+02 1.8 7.42e-04 1.12e-04f 1\n 287 5.1028585e+05 3.73e+00 1.21e+04 -1.0 2.01e+02 2.2 4.17e-04 1.31e-05f 1\n 288 5.0835844e+05 3.72e+00 1.19e+04 -1.0 2.75e+02 1.7 1.15e-05 2.41e-03f 1\n 289 5.0829350e+05 3.72e+00 1.19e+04 -1.0 1.71e+02 1.2 3.97e-04 6.12e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 290 5.0786133e+05 3.72e+00 1.30e+04 -1.0 1.77e+02 1.7 1.37e-02 5.49e-04f 1\n 291 4.9734337e+05 3.69e+00 1.06e+04 -1.0 1.72e+02 1.2 8.48e-04 8.86e-03f 1\n 292 4.9689003e+05 3.68e+00 1.08e+04 -1.0 1.74e+02 1.6 3.20e-03 5.66e-04f 1\n 293 4.9257017e+05 3.67e+00 9.15e+03 -1.0 1.62e+02 1.1 7.85e-05 3.01e-03f 1\n 294 4.9140020e+05 3.67e+00 9.02e+03 -1.0 2.41e+02 1.6 4.58e-05 1.41e-03f 1\n 295 4.9127824e+05 3.67e+00 1.14e+04 -1.0 1.61e+02 1.1 2.66e-03 6.68e-05f 1\n 296 4.9066209e+05 3.66e+00 1.29e+04 -1.0 1.75e+02 1.5 1.82e-02 7.38e-04f 1\n 297 4.8899995e+05 3.66e+00 1.45e+04 -1.0 5.05e+02 1.0 9.31e-04 4.06e-04f 1\n 298 4.7750162e+05 3.61e+00 1.35e+04 -1.0 1.76e+02 1.5 4.98e-03 1.35e-02f 1\n 299 4.7434464e+05 3.61e+00 1.49e+04 -1.0 9.97e+02 1.0 6.27e-04 4.14e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 300 4.7339897e+05 3.61e+00 1.50e+04 -1.0 1.73e+02 1.4 2.13e-03 1.10e-03f 1\n 301 4.7161223e+05 3.61e+00 1.40e+04 -1.0 3.25e+02 0.9 1.03e-04 5.77e-04f 1\n 302 4.7150987e+05 3.60e+00 1.43e+04 -1.0 2.00e+02 0.4 6.17e-03 9.97e-05f 1\n 303 4.6068296e+05 3.57e+00 1.36e+04 -1.0 3.30e+02 -0.0 4.88e-05 1.06e-02f 1\n 304 4.5827805e+05 3.56e+00 1.49e+04 -1.0 1.68e+02 1.3 1.36e-02 2.70e-03f 1\n 305 4.5598068e+05 3.55e+00 1.41e+04 -1.0 4.79e+02 0.8 4.48e-04 7.06e-04f 1\n 306 4.5518400e+05 3.55e+00 1.43e+04 -1.0 1.59e+02 1.2 2.67e-03 8.88e-04f 1\n 307 4.5309751e+05 3.55e+00 1.72e+04 -1.0 2.98e+02 0.8 2.61e-03 8.91e-04f 1\n 308 4.4826799e+05 3.53e+00 1.86e+04 -1.0 1.66e+02 1.2 1.97e-02 5.39e-03f 1\n 309 4.4337229e+05 3.51e+00 1.70e+04 -1.0 1.62e+02 0.7 4.61e-05 4.04e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 310 4.3957043e+05 3.50e+00 2.22e+04 -1.0 1.64e+02 1.1 5.11e-02 4.22e-03f 1\n 311 4.3840763e+05 3.50e+00 2.35e+04 -1.0 1.68e+02 0.7 4.15e-03 9.47e-04f 1\n 312 4.2663652e+05 3.45e+00 2.43e+04 -1.0 1.62e+02 1.1 2.95e-02 1.34e-02f 1\n 313 4.1771582e+05 3.43e+00 2.40e+04 -1.0 1.75e+02 0.6 6.75e-03 7.00e-03f 1\n 314 4.1583852e+05 3.42e+00 2.27e+04 -1.0 8.29e+02 0.1 1.03e-04 3.62e-04f 1\n 315 4.0823036e+05 3.40e+00 2.35e+04 -1.0 1.82e+02 0.6 9.82e-03 6.83e-03f 1\n 316 4.0742102e+05 3.40e+00 2.31e+04 -1.0 2.88e+02 0.1 7.31e-05 5.39e-04f 1\n 317 4.0736672e+05 3.40e+00 2.36e+04 -1.0 4.84e+02 -0.4 7.32e-03 5.87e-05f 1\n 318 3.9419770e+05 3.35e+00 2.18e+04 -1.0 7.13e+02 -0.9 8.53e-05 1.33e-02f 1\n 319 3.8663282e+05 3.33e+00 1.92e+04 -1.0 8.56e+02 -1.3 2.10e-04 5.79e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 320 3.8579331e+05 3.33e+00 2.13e+04 -1.0 9.22e+02 -1.8 7.40e-03 7.22e-04f 1\n 321 3.7974323e+05 3.32e+00 1.94e+04 -1.0 9.50e+02 -2.3 1.97e-04 4.91e-03f 1\n 322 3.7938905e+05 3.31e+00 2.15e+04 -1.0 9.51e+02 -2.8 8.43e-03 3.27e-04f 1\n 323 3.7037180e+05 3.29e+00 1.92e+04 -1.0 9.55e+02 -3.3 1.13e-04 8.55e-03f 1\n 324 3.6908213e+05 3.28e+00 1.94e+04 -1.0 9.41e+02 -3.7 2.53e-03 1.30e-03f 1\n 325 3.6277620e+05 3.26e+00 1.81e+04 -1.0 2.17e+02 0.3 8.07e-04 7.29e-03f 1\n 326 3.5989666e+05 3.24e+00 1.79e+04 -1.0 1.55e+02 1.6 1.27e-03 5.36e-03f 1\n 327 3.5985661e+05 3.24e+00 1.79e+04 -1.0 1.59e+02 2.1 9.24e-04 1.04e-04f 1\n 328 3.5935266e+05 3.24e+00 1.79e+04 -1.0 1.62e+02 1.6 6.26e-05 8.73e-04f 1\n 329 3.5783375e+05 3.23e+00 1.78e+04 -1.0 1.63e+02 2.0 3.50e-03 3.63e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 330 3.5743587e+05 3.22e+00 1.78e+04 -1.0 1.61e+02 1.5 3.51e-05 6.78e-04f 1\n 331 3.5668635e+05 3.22e+00 1.78e+04 -1.0 1.52e+02 1.1 2.73e-04 1.08e-03f 1\n 332 3.5630331e+05 3.22e+00 1.78e+04 -1.0 1.67e+02 0.6 3.51e-03 4.92e-04f 1\n 333 3.5562828e+05 3.22e+00 1.78e+04 -1.0 2.82e+02 0.1 3.34e-03 7.59e-04f 1\n 334 3.5480287e+05 3.21e+00 1.78e+04 -1.0 4.82e+02 -0.4 4.21e-05 9.86e-04f 1\n 335 3.5438009e+05 3.21e+00 1.78e+04 -1.0 6.55e+02 -0.8 1.29e-04 5.04e-04f 1\n 336 3.5436086e+05 3.21e+00 1.78e+04 -1.0 8.12e+02 -1.3 2.56e-03 2.29e-05f 1\n 337 3.5193976e+05 3.20e+00 1.77e+04 -1.0 8.82e+02 -1.8 1.07e-03 2.89e-03f 1\n 338 3.5190047e+05 3.20e+00 1.77e+04 -1.0 9.10e+02 -2.3 7.21e-03 4.74e-05f 1\n 339 3.5093683e+05 3.20e+00 1.77e+04 -1.0 7.47e+02 -1.0 1.35e-05 1.13e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 340 3.5052498e+05 3.20e+00 1.77e+04 -1.0 2.07e+02 0.4 5.15e-04 5.29e-04f 1\n 341 3.3750946e+05 3.14e+00 1.74e+04 -1.0 3.58e+02 -0.1 1.67e-05 1.64e-02f 1\n 342 3.3747338e+05 3.14e+00 1.74e+04 -1.0 5.75e+02 -0.6 6.58e-03 4.69e-05f 1\n 343 3.3562091e+05 3.14e+00 1.74e+04 -1.0 7.66e+02 -1.1 1.05e-04 2.42e-03f 1\n 344 3.3510890e+05 3.13e+00 1.73e+04 -1.0 8.41e+02 -1.5 1.22e-03 6.80e-04f 1\n 345 3.2871807e+05 3.11e+00 1.72e+04 -1.0 8.84e+02 -2.0 3.02e-03 8.53e-03f 1\n 346 3.2777372e+05 3.10e+00 1.72e+04 -1.0 1.54e+02 1.1 7.57e-05 1.51e-03f 1\n 347 3.2772491e+05 3.10e+00 1.72e+04 -1.0 1.60e+02 1.6 2.40e-03 9.66e-05f 1\n 348 3.2718271e+05 3.10e+00 1.71e+04 -1.0 1.60e+02 2.0 4.86e-03 1.52e-03f 1\n 349 3.2706600e+05 3.10e+00 1.71e+04 -1.0 1.58e+02 1.5 1.31e-03 2.19e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 350 3.2406746e+05 3.08e+00 1.70e+04 -1.0 1.47e+02 1.0 1.75e-04 5.59e-03f 1\n 351 3.2366994e+05 3.08e+00 1.70e+04 -1.0 1.50e+02 1.5 4.30e-03 7.40e-04f 1\n 352 3.2356913e+05 3.08e+00 1.70e+04 -1.0 1.37e+02 1.0 1.11e-04 1.90e-04f 1\n 353 3.2355506e+05 3.08e+00 1.70e+04 -1.0 1.49e+02 1.4 3.22e-03 2.36e-05f 1\n 354 3.2316524e+05 3.07e+00 1.70e+04 -1.0 1.40e+02 0.9 1.38e-03 6.09e-04f 1\n 355r 3.2316524e+05 3.07e+00 1.00e+03 0.5 0.00e+00 0.4 0.00e+00 3.57e-07R 8\n 356r 3.2229084e+05 3.07e+00 1.00e+03 0.5 2.06e+03 - 6.18e-03 7.60e-04f 1\n 357r 3.2109567e+05 3.06e+00 9.98e+02 0.5 1.80e+03 - 1.71e-03 8.09e-04f 1\n 358r 3.1149974e+05 2.97e+00 9.95e+02 0.5 1.67e+03 - 3.74e-03 6.54e-03f 1\n 359r 3.1087190e+05 2.97e+00 9.95e+02 0.5 8.58e+02 - 9.83e-05 4.32e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 360r 3.0989090e+05 2.96e+00 9.97e+02 0.5 9.94e+02 - 6.50e-03 6.44e-04f 1\n 361r 3.0241262e+05 2.87e+00 9.92e+02 0.5 9.07e+02 - 2.36e-03 5.34e-03f 1\n 362r 2.9694919e+05 2.80e+00 9.80e+02 0.5 8.52e+02 - 1.55e-02 4.79e-03f 1\n 363r 2.8929254e+05 2.68e+00 9.72e+02 0.5 6.39e+02 - 1.25e-02 8.39e-03f 1\n 364 2.8919644e+05 2.68e+00 9.52e+03 -1.0 1.36e+02 -0.0 2.03e-04 1.66e-04f 1\n 365 2.8908661e+05 2.68e+00 9.52e+03 -1.0 1.38e+02 -0.5 3.89e-04 1.90e-04f 1\n 366 2.8861557e+05 2.68e+00 9.51e+03 -1.0 1.34e+02 -1.0 2.45e-04 8.16e-04f 1\n 367 2.8784152e+05 2.67e+00 9.50e+03 -1.0 1.29e+02 -1.5 5.67e-04 1.35e-03f 1\n 368 2.8557520e+05 2.66e+00 9.46e+03 -1.0 1.18e+02 -1.9 1.40e-03 3.97e-03f 1\n 369 2.8048140e+05 2.64e+00 9.38e+03 -1.0 1.31e+02 -2.4 2.51e-03 9.01e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 370 2.7807299e+05 2.63e+00 9.34e+03 -1.0 1.50e+02 -2.9 1.64e-03 4.32e-03f 1\n 371 2.7069758e+05 2.59e+00 9.21e+03 -1.0 1.60e+02 -3.4 3.85e-03 1.34e-02f 1\n 372 2.5748481e+05 2.53e+00 8.98e+03 -1.0 1.59e+02 -3.8 4.45e-03 2.48e-02f 1\n 373 2.5409647e+05 2.51e+00 8.92e+03 -1.0 1.53e+02 -4.3 1.21e-02 6.62e-03f 1\n 374 2.4261880e+05 2.45e+00 8.72e+03 -1.0 1.50e+02 -4.8 6.04e-03 2.29e-02f 1\n 375 2.2822588e+05 2.38e+00 8.45e+03 -1.0 1.46e+02 -5.3 1.51e-02 3.02e-02f 1\n 376 2.0965467e+05 2.28e+00 8.10e+03 -1.0 1.41e+02 -5.8 1.82e-02 4.16e-02f 1\n 377 2.0936932e+05 2.28e+00 8.10e+03 -1.0 1.37e+02 -6.2 3.27e-02 6.83e-04f 1\n 378 2.0780500e+05 2.27e+00 8.07e+03 -1.0 1.37e+02 -6.7 4.67e-03 3.76e-03f 1\n 379 1.9606770e+05 2.20e+00 7.84e+03 -1.0 1.38e+02 -7.2 7.84e-04 2.87e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 380 1.7935187e+05 2.11e+00 7.49e+03 -1.0 1.38e+02 -7.7 2.58e-02 4.37e-02f 1\n 381 1.7765925e+05 2.10e+00 7.46e+03 -1.0 1.36e+02 -8.1 4.29e-02 4.74e-03f 1\n 382 1.6495345e+05 2.02e+00 7.18e+03 -1.0 1.34e+02 -8.6 1.77e-02 3.65e-02f 1\n 383 1.6340175e+05 2.01e+00 7.15e+03 -1.0 1.30e+02 -9.1 1.84e-02 4.73e-03f 1\n 384 1.5788016e+05 1.98e+00 7.03e+03 -1.0 1.29e+02 -9.6 3.72e-03 1.71e-02f 1\n 385 1.5064127e+05 1.93e+00 6.87e+03 -1.0 1.28e+02 -10.1 8.10e-03 2.33e-02f 1\n 386 1.2939352e+05 1.79e+00 6.36e+03 -1.0 1.25e+02 -10.5 1.85e-02 7.34e-02f 1\n 387 1.2646803e+05 1.77e+00 6.29e+03 -1.0 1.18e+02 -11.0 4.15e-02 1.14e-02f 1\n 388 1.1471323e+05 1.68e+00 5.99e+03 -1.0 1.16e+02 -11.5 1.69e-02 4.78e-02f 1\n 389 1.0992888e+05 1.65e+00 5.86e+03 -1.0 1.11e+02 -12.0 7.97e-02 2.12e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 390 1.0212816e+05 1.59e+00 5.65e+03 -1.0 1.07e+02 -12.4 1.86e-03 3.63e-02f 1\n 391 8.6177113e+04 1.46e+00 5.19e+03 -1.0 1.04e+02 -12.9 7.26e-02 8.17e-02f 1\n 392 8.5370527e+04 1.45e+00 5.16e+03 -1.0 9.48e+01 -13.4 3.22e-02 4.71e-03f 1\n 393 7.4077318e+04 1.35e+00 4.81e+03 -1.0 9.38e+01 -13.9 1.85e-02 6.88e-02f 1\n 394 7.1177623e+04 1.33e+00 4.71e+03 -1.0 8.78e+01 -14.3 6.45e-02 1.99e-02f 1\n 395 6.4716733e+04 1.26e+00 4.49e+03 -1.0 8.51e+01 -14.8 4.54e-03 4.67e-02f 1\n 396 5.7488482e+04 1.19e+00 4.23e+03 -1.0 8.14e+01 -15.3 9.91e-03 5.78e-02f 1\n 397 4.8754351e+04 1.10e+00 3.90e+03 -1.0 7.88e+01 -15.8 6.42e-02 7.95e-02f 1\n 398 4.7745315e+04 1.08e+00 3.86e+03 -1.0 7.72e+01 -16.3 9.49e-02 1.05e-02f 1\n 399 4.1977413e+04 1.02e+00 3.61e+03 -1.0 7.89e+01 -16.7 6.18e-02 6.27e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 400 3.3677208e+04 9.10e-01 3.24e+03 -1.0 7.75e+01 -17.2 7.45e-03 1.05e-01f 1\n 401 3.0166245e+04 8.61e-01 3.06e+03 -1.0 7.30e+01 -17.7 2.38e-02 5.39e-02f 1\n 402 2.5223779e+04 7.87e-01 2.80e+03 -1.0 7.10e+01 -18.2 1.21e-01 8.61e-02f 1\n 403 2.2341250e+04 7.40e-01 2.63e+03 -1.0 6.89e+01 -18.6 1.27e-01 5.92e-02f 1\n 404 1.8863927e+04 6.80e-01 2.42e+03 -1.0 6.77e+01 -19.1 8.08e-02 8.17e-02f 1\n 405 1.5848263e+04 6.23e-01 2.21e+03 -1.0 6.47e+01 -19.6 5.68e-02 8.40e-02f 1\n 406 1.4057233e+04 5.86e-01 2.08e+03 -1.0 6.57e+01 -19.1 8.62e-02 5.84e-02f 1\n 407 1.1013361e+04 5.19e-01 1.84e+03 -1.0 5.68e+01 -19.6 1.68e-01 1.16e-01f 1\n 408 7.9952027e+03 4.41e-01 1.57e+03 -1.0 5.34e+01 -19.1 1.48e-01 1.49e-01f 1\n 409 5.4065322e+03 3.62e-01 1.29e+03 -1.0 4.45e+01 -19.6 1.93e-01 1.80e-01f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 410 2.4913122e+03 2.44e-01 8.68e+02 -1.0 4.14e+01 -19.1 1.52e-01 3.26e-01f 1\n 411 1.0310441e+03 1.55e-01 5.51e+02 -1.0 2.82e+01 -19.6 2.47e-01 3.66e-01f 1\n 412 2.7434337e+02 7.52e-02 4.80e+02 -1.0 2.93e+01 -19.1 1.96e-01 5.14e-01f 1\n 413 5.4776714e+01 2.33e-02 7.33e+02 -1.0 7.94e+00 -19.6 4.50e-01 6.90e-01f 1\n 414 2.8195260e+01 3.14e-04 1.32e+03 -1.0 1.06e+01 -19.1 5.84e-01 1.00e+00f 1\n 415 3.2622344e+01 3.87e-05 1.06e+03 -1.0 1.60e+01 -19.6 7.02e-01 9.94e-01f 1\n 416 3.0040029e+01 4.65e-05 2.31e+02 -1.0 4.17e+01 -19.1 9.04e-01 1.00e+00f 1\n 417 3.0266079e+01 4.64e-04 4.41e+02 -1.0 1.21e+02 -19.6 1.00e+00 9.30e-01h 1\n 418 2.9559008e+01 3.42e-05 5.33e+01 -1.0 1.42e+01 -19.1 6.03e-01 1.00e+00h 1\n 419 2.9904880e+01 2.39e-06 6.35e-03 -1.0 6.68e+00 -19.6 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 420 2.4902915e+01 2.75e-05 1.10e+00 -2.5 1.14e+01 -19.1 4.91e-01 4.44e-01f 1\n 421 2.2342576e+01 8.15e-05 1.58e+00 -2.5 6.79e+01 -19.6 1.30e-01 3.02e-01f 1\n 422 2.0275308e+01 8.22e-05 1.07e+00 -2.5 6.41e+00 -3.7 3.38e-01 3.24e-01h 1\n 423 1.9264876e+01 9.78e-05 8.99e-01 -2.5 2.12e+01 -4.2 3.83e-01 1.56e-01h 1\n 424 1.8289856e+01 7.85e-05 6.81e-01 -2.5 6.19e+00 -4.6 2.31e-01 2.42e-01h 1\n 425 1.6132778e+01 1.96e-04 8.21e-01 -2.5 1.24e+01 -5.1 4.78e-01 7.58e-01h 1\n 426 1.5611473e+01 1.11e-04 5.01e-01 -2.5 1.64e+01 -5.6 4.71e-01 5.11e-01h 1\n 427 1.5163933e+01 4.19e-05 3.81e-01 -2.5 2.85e+00 -3.4 1.00e+00 6.97e-01h 1\n 428 1.5050001e+01 3.15e-05 4.60e-01 -2.5 3.60e+00 -3.8 6.17e-01 2.96e-01h 1\n 429 1.4900715e+01 5.30e-06 1.02e+00 -2.5 1.96e+00 -4.3 6.36e-01 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 430 1.4825885e+01 2.55e-05 4.15e-01 -2.5 5.52e+00 -3.9 8.38e-01 5.50e-01h 1\n 431 1.4739809e+01 3.24e-05 3.56e-01 -2.5 1.98e+01 -4.4 1.99e-01 1.55e-01h 1\n 432 1.4596773e+01 2.88e-05 5.34e-01 -2.5 5.19e+00 -3.9 5.29e-01 7.94e-01h 1\n 433 1.4484823e+01 3.46e-05 3.39e-01 -2.5 3.05e+01 -4.4 2.32e-01 9.62e-02h 1\n 434 1.4369681e+01 4.72e-05 4.22e-01 -2.5 1.62e+01 -4.9 3.06e-01 5.62e-01h 1\n 435 1.4279373e+01 4.71e-05 3.94e+02 -2.5 3.10e+01 -5.4 4.70e-01 2.25e-01h 1\n 436 1.4208263e+01 2.19e-05 2.73e+02 -2.5 2.87e+01 -5.8 3.06e-01 1.00e+00h 1\n 437 1.4134986e+01 1.91e-05 1.24e+02 -2.5 1.93e+01 -6.3 5.46e-01 6.57e-01h 1\n 438 1.4091516e+01 4.37e-05 3.46e+01 -2.5 2.35e+01 -6.8 7.19e-01 1.00e+00h 1\n 439 1.4087170e+01 4.09e-06 2.76e-04 -2.5 7.32e+00 -7.3 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 440 1.4088533e+01 1.91e-09 4.39e-07 -2.5 4.32e-01 -7.8 1.00e+00 1.00e+00h 1\n 441 1.3804149e+01 3.76e-06 7.14e+00 -3.8 4.84e+00 -8.2 6.51e-01 8.20e-01f 1\n 442 1.3706304e+01 1.35e-06 1.69e-03 -3.8 1.67e+00 -8.7 1.00e+00 1.00e+00h 1\n 443 1.3684885e+01 4.66e-08 3.95e-05 -3.8 4.07e-02 -9.2 1.00e+00 1.00e+00h 1\n 444 1.3664347e+01 8.58e-08 6.51e-02 -5.7 3.79e-01 -9.7 9.45e-01 9.46e-01h 1\n 445 1.3660196e+01 4.91e-08 1.08e+01 -5.7 4.56e-02 -10.1 1.00e+00 9.19e-01h 1\n 446 1.3659000e+01 4.92e-08 1.36e-06 -5.7 9.38e-03 -10.6 1.00e+00 1.00e+00f 1\n 447 1.3658600e+01 4.94e-08 9.08e+01 -8.6 3.53e-03 -11.1 9.94e-01 8.63e-01h 1\n 448 1.3658471e+01 5.57e-08 6.82e+00 -8.6 1.22e-03 -11.6 1.00e+00 9.90e-01f 1\n 449 1.3658445e+01 4.72e-08 2.13e-09 -8.6 4.22e-04 -12.0 1.00e+00 1.00e+00f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 450 1.3658439e+01 4.92e-08 3.85e-12 -8.6 1.81e-05 -12.5 1.00e+00 1.00e+00h 1\n 451 1.3658437e+01 4.92e-08 1.82e-12 -8.6 1.52e-06 -13.0 1.00e+00 1.00e+00h 1\n 452 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.26e-07 -13.5 1.00e+00 1.00e+00h 1\n 453 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.49e-07 -14.0 1.00e+00 1.00e+00h 1\n 454 1.3658437e+01 4.92e-08 3.64e-12 -8.6 9.58e-09 -14.4 1.00e+00 1.00e+00h 1\n 455 1.3658437e+01 4.92e-08 9.09e-13 -8.6 3.90e-11 -14.9 1.00e+00 1.00e+00h 1\n 456 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.39e-12 -15.4 1.00e+00 1.00e+00h 1\n 457 1.3658437e+01 4.92e-08 9.09e-13 -8.6 9.57e-13 -15.9 1.00e+00 1.00e+00h 1\n 458 1.3658437e+01 4.92e-08 9.09e-13 -8.6 7.94e-13 -16.3 1.00e+00 1.00e+00h 1\n 459 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.30e-13 -16.8 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 460 1.3658437e+01 4.92e-08 4.55e-13 -8.6 2.07e-12 -17.3 1.00e+00 1.00e+00h 1\n 461 1.3658437e+01 4.92e-08 9.09e-13 -8.6 2.88e-12 -17.8 1.00e+00 1.00e+00h 1\n 462 1.3658437e+01 4.92e-08 9.09e-13 -8.6 5.03e-12 -18.2 1.00e+00 1.00e+00h 1\n 463 1.3658437e+01 4.92e-08 4.55e-13 -8.6 3.27e-12 -18.7 1.00e+00 1.00e+00h 1\n\nNumber of Iterations....: 463\n\n (scaled) (unscaled)\nObjective...............: 1.3658436881286921e+01 1.3658436881286921e+01\nDual infeasibility......: 4.5474735088646412e-13 4.5474735088646412e-13\nConstraint violation....: 4.9168770388519079e-08 4.9168770388519079e-08\nComplementarity.........: 2.5059067889663204e-09 2.5059067889663204e-09\nOverall NLP error.......: 4.9168770388519079e-08 4.9168770388519079e-08\n\n\nNumber of objective function evaluations = 483\nNumber of objective gradient evaluations = 395\nNumber of equality constraint evaluations = 483\nNumber of inequality constraint evaluations = 483\nNumber of equality constraint Jacobian evaluations = 467\nNumber of inequality constraint Jacobian evaluations = 467\nNumber of Lagrangian Hessian evaluations = 463\nTotal CPU secs in IPOPT (w/o function evaluations) = 4.565\nTotal CPU secs in NLP function evaluations = 10.021\n\nEXIT: Solved To Acceptable Level.\n solver : t_proc (avg) t_wall (avg) n_eval\n nlp_f | 21.33ms ( 44.17us) 21.30ms ( 44.11us) 483\n nlp_g | 520.21ms ( 1.08ms) 520.18ms ( 1.08ms) 483\n nlp_grad | 3.04ms ( 3.04ms) 3.04ms ( 3.04ms) 1\n nlp_grad_f | 34.11ms ( 86.15us) 34.12ms ( 86.15us) 396\n nlp_hess_l | 6.28 s ( 13.65ms) 6.26 s ( 13.60ms) 460\n nlp_jac_g | 3.17 s ( 6.78ms) 3.16 s ( 6.76ms) 468\n total | 14.66 s ( 14.66 s) 14.58 s ( 14.58 s) 1\nThis is Ipopt version 3.12.3, running with linear solver mumps.\nNOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n\nNumber of nonzeros in equality constraint Jacobian...: 4230\nNumber of nonzeros in inequality constraint Jacobian.: 3466\nNumber of nonzeros in Lagrangian Hessian.............: 3648\n\nTotal number of variables............................: 2293\n variables with only lower bounds: 0\n variables with lower and upper bounds: 0\n variables with only upper bounds: 0\nTotal number of equality constraints.................: 1377\nTotal number of inequality constraints...............: 967\n inequality constraints with only lower bounds: 204\n inequality constraints with lower and upper bounds: 355\n inequality constraints with only upper bounds: 408\n\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 0 0.0000000e+00 9.83e+00 7.50e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n 1 1.0399716e-02 9.83e+00 1.26e+00 -1.0 1.20e+02 -4.0 4.93e-04 3.26e-04h 1\n 2 2.0045559e-02 9.83e+00 2.20e+01 -1.0 1.82e+02 -4.5 3.56e-04 3.85e-05h 1\n 3 4.4710489e-01 9.83e+00 2.52e+01 -1.0 2.46e+02 -5.0 3.88e-04 3.37e-04h 1\n 4 2.5591113e+00 9.83e+00 6.47e+01 -1.0 3.34e+02 -5.4 1.11e-03 4.95e-04h 1\n 5 1.0304744e+01 9.83e+00 1.06e+02 -1.0 2.54e+02 -5.9 1.15e-03 5.03e-04h 1\n 6 7.9216962e+01 9.82e+00 1.08e+02 -1.0 1.86e+02 -6.4 1.38e-03 1.35e-03h 1\n 7 1.6205310e+02 9.82e+00 1.46e+02 -1.0 1.46e+02 -6.9 1.48e-03 8.87e-04h 1\n 8 2.2025207e+02 9.83e+00 1.56e+02 -1.0 3.22e+02 -7.3 6.46e-04 4.87e-04h 1\n 9 2.2441787e+02 9.83e+00 1.54e+02 -1.0 4.45e+03 -7.8 8.53e-06 3.28e-05h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 10 2.5182294e+02 9.82e+00 1.42e+02 -1.0 9.50e+02 -8.3 8.65e-06 2.07e-04h 1\n 11 2.8777432e+02 9.81e+00 1.66e+02 -1.0 4.28e+02 -8.8 6.37e-04 2.55e-04h 1\n 12 4.0157811e+02 9.80e+00 1.67e+02 -1.0 1.45e+02 -9.2 7.37e-04 7.08e-04h 1\n 13 4.1550901e+02 9.80e+00 1.78e+02 -1.0 1.56e+02 -9.7 2.52e-04 7.97e-05h 1\n 14 6.1114602e+02 9.80e+00 1.21e+02 -1.0 1.50e+02 -10.2 8.33e-05 1.00e-03h 1\n 15 6.1355211e+02 9.80e+00 1.82e+02 -1.0 1.68e+02 -10.7 9.96e-04 1.13e-05h 1\n 16 6.2428940e+02 9.80e+00 2.68e+02 -1.0 3.50e+02 -11.2 1.43e-03 5.02e-05h 1\n 17r 6.2428940e+02 9.80e+00 9.99e+02 1.0 0.00e+00 -11.6 0.00e+00 4.03e-07R 5\n 18r 8.6414419e+02 9.80e+00 9.99e+02 1.0 6.29e+03 - 3.71e-04 1.98e-05f 1\n 19r 3.3647143e+03 9.80e+00 9.99e+02 1.0 1.65e+03 - 3.45e-04 1.15e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 20r 5.1092797e+04 9.80e+00 9.98e+02 1.0 7.67e+02 - 3.88e-04 6.81e-04f 1\n 21r 6.3342770e+04 9.79e+00 1.07e+03 1.0 7.22e+00 2.0 1.50e-01 1.14e-02f 1\n 22r 1.7594664e+05 9.73e+00 1.03e+03 1.0 3.13e+00 2.4 2.75e-01 2.46e-01f 1\n 23r 1.6728527e+05 9.68e+00 1.07e+03 1.0 2.44e+00 2.9 3.31e-02 1.26e-01f 1\n 24r 1.7195513e+05 9.51e+00 1.09e+03 1.0 5.75e-01 3.3 8.31e-01 1.00e+00f 1\n 25r 1.7774546e+05 9.35e+00 1.07e+03 1.0 1.83e+00 2.8 3.09e-01 3.74e-01f 1\n 26r 1.7826098e+05 8.80e+00 1.06e+03 1.0 5.11e+00 2.3 3.60e-01 5.80e-01f 1\n 27 1.7790304e+05 8.75e+00 2.67e+03 -1.0 9.73e+01 -12.1 1.05e-03 4.89e-03f 1\n 28 1.7790581e+05 8.75e+00 2.67e+03 -1.0 9.99e+01 -12.6 4.15e-03 8.71e-05h 1\n 29 1.7805578e+05 8.74e+00 2.67e+03 -1.0 1.49e+02 -13.1 2.98e-03 1.65e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 30 1.7826045e+05 8.73e+00 2.66e+03 -1.0 1.00e+02 -13.5 5.02e-03 1.36e-03h 1\n 31 1.7833630e+05 8.72e+00 2.66e+03 -1.0 9.67e+01 -14.0 4.35e-03 3.57e-04h 1\n 32 1.7838366e+05 8.72e+00 2.66e+03 -1.0 9.66e+01 -14.5 5.53e-03 1.85e-04h 1\n 33 1.7897357e+05 8.70e+00 2.66e+03 -1.0 9.66e+01 -15.0 6.03e-03 1.99e-03h 1\n 34 1.7979138e+05 8.68e+00 2.65e+03 -1.0 9.65e+01 -15.5 5.02e-03 2.33e-03h 1\n 35 1.8083548e+05 8.66e+00 2.64e+03 -1.0 9.65e+01 -15.9 5.14e-03 2.59e-03h 1\n 36 1.8126368e+05 8.65e+00 2.64e+03 -1.0 9.68e+01 -16.4 1.38e-03 9.55e-04h 1\n 37 1.8126992e+05 8.65e+00 2.64e+03 -1.0 9.72e+01 -16.9 2.04e-05 1.32e-05h 1\n 38 1.8127749e+05 8.65e+00 2.64e+03 -1.0 9.75e+01 -17.4 5.22e-03 1.49e-05h 1\n 39 1.8272429e+05 8.63e+00 2.63e+03 -1.0 9.81e+01 -17.8 3.94e-04 2.72e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 40 1.8373146e+05 8.61e+00 2.63e+03 -1.0 9.79e+01 -18.3 2.78e-05 1.86e-03h 1\n 41 1.8460711e+05 8.60e+00 2.63e+03 -1.0 9.79e+01 -18.8 6.10e-03 1.58e-03h 1\n 42 1.8506845e+05 8.59e+00 3.03e+03 -1.0 9.77e+01 -19.3 3.60e-03 8.05e-04h 1\n 43 1.8630538e+05 8.57e+00 2.81e+03 -1.0 9.79e+01 -19.7 1.23e-03 2.15e-03h 1\n 44 1.8693912e+05 8.56e+00 2.69e+03 -1.0 9.91e+01 -19.1 6.31e-04 1.18e-03h 1\n 45 1.8714858e+05 8.56e+00 2.61e+03 -1.0 9.72e+01 -19.6 1.86e-05 3.60e-04h 1\n 46 1.8833659e+05 8.54e+00 3.03e+03 -1.0 9.83e+01 -19.1 4.39e-03 2.16e-03h 1\n 47 1.8900050e+05 8.53e+00 3.12e+03 -1.0 9.71e+01 -19.6 1.51e-03 1.12e-03h 1\n 48 1.8921851e+05 8.53e+00 3.04e+03 -1.0 9.85e+01 -19.1 2.64e-05 3.81e-04h 1\n 49 1.8974673e+05 8.52e+00 2.87e+03 -1.0 9.66e+01 -19.6 1.53e-04 8.26e-04h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 50 1.8975438e+05 8.52e+00 3.74e+03 -1.0 9.81e+01 -19.1 3.80e-03 1.32e-05h 1\n 51 1.9002449e+05 8.52e+00 4.95e+03 -1.0 9.84e+01 -19.6 3.14e-03 3.97e-04h 1\n 52 1.9113127e+05 8.50e+00 5.17e+03 -1.0 9.86e+01 -19.1 2.66e-03 1.79e-03h 1\n 53 1.9218965e+05 8.49e+00 4.74e+03 -1.0 9.66e+01 -19.6 1.49e-05 1.46e-03h 1\n 54 1.9235900e+05 8.49e+00 4.77e+03 -1.0 9.69e+01 -19.1 3.43e-04 2.55e-04h 1\n 55 1.9614877e+05 8.45e+00 3.24e+03 -1.0 9.55e+01 -19.6 4.82e-05 4.46e-03h 1\n 56 1.9767353e+05 8.43e+00 3.55e+03 -1.0 9.43e+01 -19.1 3.11e-03 2.00e-03h 1\n 57 2.0045953e+05 8.41e+00 3.54e+03 -1.0 9.38e+01 -19.6 3.09e-03 3.11e-03h 1\n 58 2.0058633e+05 8.41e+00 4.32e+03 -1.0 9.34e+01 -19.1 2.74e-03 1.60e-04h 1\n 59r 2.0058633e+05 8.41e+00 1.00e+03 0.9 0.00e+00 -19.6 0.00e+00 4.42e-07R 5\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 60r 2.0053275e+05 8.41e+00 1.00e+03 0.9 3.17e+03 - 3.55e-04 6.45e-05f 1\n 61r 2.0041460e+05 8.41e+00 1.00e+03 0.9 1.58e+03 - 3.29e-04 1.19e-04f 1\n 62r 2.0024799e+05 8.41e+00 9.99e+02 0.9 1.43e+03 - 7.29e-04 1.04e-03f 1\n 63r 2.0178166e+05 8.42e+00 9.97e+02 0.9 9.93e+02 - 2.24e-03 1.49e-03f 1\n 64r 2.0902033e+05 8.42e+00 9.92e+02 0.9 5.71e+02 - 1.11e-02 3.85e-03f 1\n 65r 2.3136420e+05 8.41e+00 9.83e+02 0.9 3.09e+02 - 8.14e-03 9.32e-03f 1\n 66r 2.6153891e+05 8.40e+00 9.88e+02 0.9 3.62e+02 - 7.16e-03 1.05e-02f 1\n 67r 2.7798903e+05 8.40e+00 9.64e+02 0.9 3.55e+02 - 1.32e-02 9.11e-03f 1\n 68r 2.8657577e+05 8.40e+00 9.59e+02 0.9 2.47e+02 - 2.08e-02 4.88e-03f 1\n 69r 2.9527415e+05 8.40e+00 9.55e+02 0.9 1.55e+02 - 2.61e-03 4.50e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 70r 3.0527057e+05 8.40e+00 9.50e+02 0.9 5.20e+02 - 6.42e-03 5.12e-03f 1\n 71r 3.3582481e+05 8.40e+00 9.58e+02 0.9 1.01e+02 - 3.07e-02 1.51e-02f 1\n 72r 4.1107309e+05 8.41e+00 9.04e+02 0.9 1.14e+02 - 2.39e-02 3.35e-02f 1\n 73r 4.5964398e+05 8.41e+00 8.95e+02 0.9 1.15e+02 - 3.62e-02 2.11e-02f 1\n 74r 5.2016472e+05 8.41e+00 8.67e+02 0.9 1.36e+02 - 4.89e-02 3.03e-02f 1\n 75r 5.9030592e+05 8.41e+00 8.31e+02 0.9 1.48e+02 - 6.01e-02 3.79e-02f 1\n 76r 6.7476964e+05 8.41e+00 7.88e+02 0.9 1.61e+02 - 7.73e-02 4.59e-02f 1\n 77r 7.7561624e+05 8.41e+00 7.44e+02 0.9 1.82e+02 - 1.18e-01 5.62e-02f 1\n 78r 8.4945381e+05 8.41e+00 7.06e+02 0.9 4.62e+02 - 4.89e-03 5.46e-02f 1\n 79r 9.0140487e+05 8.41e+00 6.78e+02 0.9 2.50e+02 - 1.14e-01 3.60e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 80r 9.7546515e+05 8.41e+00 6.36e+02 0.9 2.54e+02 - 1.43e-01 6.16e-02f 1\n 81r 1.1190008e+06 8.38e+00 5.68e+02 0.9 3.76e+02 - 3.36e-02 1.08e-01f 1\n 82r 1.1225339e+06 8.37e+00 5.49e+02 0.9 8.82e+02 - 8.26e-03 3.25e-02f 1\n 83r 1.1286850e+06 8.36e+00 5.46e+02 0.9 4.12e+02 - 3.25e-02 6.75e-03f 1\n 84r 1.2659338e+06 8.29e+00 3.86e+02 0.9 7.87e+00 0.0 3.42e-01 2.93e-01f 1\n 85r 1.2612169e+06 8.28e+00 3.74e+02 0.2 2.38e+02 - 1.60e-02 3.04e-02f 1\n 86r 1.3908950e+06 8.17e+00 3.95e+02 0.2 3.02e+02 - 2.38e-02 7.50e-02f 1\n 87r 1.4392216e+06 8.15e+00 2.99e+02 0.2 7.97e+00 0.4 1.84e-01 1.68e-01f 1\n 88r 1.4749437e+06 8.14e+00 2.77e+02 0.2 5.34e+01 -0.1 4.57e-02 3.68e-02f 1\n 89r 1.4776820e+06 8.12e+00 2.77e+02 0.2 8.08e+02 - 3.91e-03 5.72e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 90r 1.4904632e+06 8.09e+00 3.10e+02 0.2 2.23e+02 - 1.15e-01 3.01e-02f 1\n 91r 1.4780369e+06 8.03e+00 2.85e+02 0.2 2.60e+02 - 1.24e-02 4.77e-02f 1\n 92r 1.4633854e+06 7.98e+00 2.84e+02 0.2 2.35e+02 - 1.42e-01 5.45e-02f 1\n 93r 1.4468075e+06 7.94e+00 2.89e+02 0.2 3.39e+02 - 2.25e-02 4.04e-02f 1\n 94r 1.4177297e+06 7.84e+00 3.28e+02 0.2 3.67e+02 - 4.08e-02 1.06e-01f 1\n 95r 1.4208335e+06 7.83e+00 3.24e+02 0.2 3.41e+02 - 4.16e-02 2.22e-02f 1\n 96r 1.4035701e+06 7.79e+00 2.82e+02 0.2 3.36e+02 - 6.49e-02 9.28e-02f 1\n 97r 1.3981555e+06 7.76e+00 2.08e+02 0.2 3.03e+01 -0.5 1.71e-01 7.29e-02f 1\n 98r 1.3736085e+06 7.75e+00 2.02e+02 0.2 4.74e+02 - 2.70e-02 1.88e-02f 1\n 99r 1.3738774e+06 7.70e+00 1.87e+02 0.2 4.77e+02 - 7.99e-02 9.61e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 100r 1.3580286e+06 7.69e+00 2.14e+02 0.2 4.67e+02 - 1.03e-01 3.83e-02f 1\n 101r 1.3021262e+06 7.68e+00 2.15e+02 0.2 5.22e+02 - 1.39e-01 9.61e-02f 1\n 102r 1.2905134e+06 7.68e+00 2.12e+02 0.2 5.78e+02 - 5.56e-02 4.11e-02f 1\n 103r 1.2958912e+06 7.68e+00 1.61e+02 0.2 6.56e+02 - 2.71e-02 1.08e-01f 1\n 104r 1.3293811e+06 7.69e+00 1.47e+02 0.2 7.20e+02 - 8.99e-02 8.45e-02f 1\n 105r 1.4013282e+06 7.70e+00 1.56e+02 0.2 9.31e+02 - 7.51e-02 1.05e-01f 1\n 106r 1.5010415e+06 7.67e+00 2.27e+02 0.2 3.80e+01 -1.0 6.34e-02 1.79e-01f 1\n 107r 1.5310867e+06 7.66e+00 2.10e+02 0.2 5.35e+01 -1.5 5.32e-02 3.54e-02f 1\n 108r 1.6858235e+06 7.64e+00 2.07e+02 0.2 2.27e+02 -2.0 1.41e-02 1.65e-02f 1\n 109r 1.6992342e+06 7.64e+00 2.00e+02 0.2 4.36e+01 -0.6 3.64e-02 3.57e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 110r 1.6720537e+06 7.63e+00 1.42e+02 0.2 2.59e+00 0.7 3.58e-01 1.88e-01f 1\n 111r 1.6393971e+06 7.62e+00 1.61e+02 0.2 5.96e+00 0.2 6.27e-02 2.98e-01f 1\n 112r 1.5942856e+06 7.62e+00 9.69e+01 0.2 3.94e+00 0.6 5.59e-01 6.60e-01f 1\n 113r 1.5454439e+06 7.61e+00 8.14e+01 -0.5 9.57e+00 0.2 2.87e-01 3.13e-01f 1\n 114r 1.5348572e+06 7.52e+00 4.26e+01 -0.5 2.28e+01 -0.3 2.19e-01 1.39e-01f 1\n 115 1.5101190e+06 7.46e+00 1.69e+04 -1.0 7.31e+02 -19.1 1.74e-03 8.11e-03f 1\n 116 1.5081170e+06 7.45e+00 1.69e+04 -1.0 6.47e+02 -19.6 1.66e-03 6.70e-04f 1\n 117 1.5000973e+06 7.43e+00 1.68e+04 -1.0 6.33e+02 -19.1 1.83e-03 2.70e-03f 1\n 118 1.4972338e+06 7.43e+00 1.68e+04 -1.0 6.14e+02 -19.6 4.62e-03 9.87e-04f 1\n 119 1.4926222e+06 7.41e+00 1.68e+04 -1.0 5.92e+02 -19.1 4.76e-03 1.69e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 120 1.4897946e+06 7.40e+00 1.68e+04 -1.0 5.73e+02 -19.6 1.24e-03 1.25e-03f 1\n 121 1.4867964e+06 7.39e+00 1.67e+04 -1.0 5.67e+02 -19.1 1.24e-02 1.40e-03f 1\n 122 1.4811114e+06 7.37e+00 1.67e+04 -1.0 5.31e+02 -19.6 2.94e-03 2.99e-03f 1\n 123 1.4776862e+06 7.36e+00 1.67e+04 -1.0 5.21e+02 -19.1 1.64e-02 1.85e-03f 1\n 124 1.4722508e+06 7.34e+00 1.66e+04 -1.0 4.87e+02 -19.6 5.29e-03 3.07e-03f 1\n 125 1.4686209e+06 7.32e+00 1.66e+04 -1.0 4.76e+02 -19.1 8.82e-03 2.07e-03f 1\n 126 1.4549412e+06 7.26e+00 1.64e+04 -1.0 4.61e+02 -19.6 8.60e-03 7.87e-03f 1\n 127 1.4439147e+06 7.22e+00 1.63e+04 -1.0 4.42e+02 -19.1 3.68e-03 6.45e-03f 1\n 128 1.4318869e+06 7.17e+00 1.62e+04 -1.0 4.33e+02 -19.6 2.58e-04 7.02e-03f 1\n 129 1.4287865e+06 7.15e+00 1.62e+04 -1.0 4.28e+02 -19.1 9.28e-03 1.82e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 130 1.4117953e+06 7.08e+00 1.60e+04 -1.0 4.17e+02 -19.6 1.21e-02 9.95e-03f 1\n 131 1.4094629e+06 7.07e+00 1.60e+04 -1.0 3.99e+02 -19.1 1.22e-02 1.30e-03f 1\n 132 1.3898684e+06 6.99e+00 1.58e+04 -1.0 3.86e+02 -19.6 6.69e-03 1.09e-02f 1\n 133 1.3666966e+06 6.91e+00 1.56e+04 -1.0 3.73e+02 -19.1 1.23e-04 1.25e-02f 1\n 134 1.3653874e+06 6.90e+00 1.56e+04 -1.0 3.64e+02 -19.6 8.61e-03 7.06e-04f 1\n 135 1.3600774e+06 6.88e+00 1.56e+04 -1.0 3.58e+02 -19.1 4.77e-04 2.88e-03f 1\n 136 1.3599863e+06 6.88e+00 1.56e+04 -1.0 3.54e+02 -19.6 1.16e-04 4.90e-05f 1\n 137 1.3520824e+06 6.85e+00 1.55e+04 -1.0 3.56e+02 -19.1 2.25e-02 4.63e-03f 1\n 138 1.3198881e+06 6.72e+00 1.52e+04 -1.0 3.32e+02 -19.6 4.47e-03 1.93e-02f 1\n 139 1.3018334e+06 6.65e+00 1.51e+04 -1.0 3.23e+02 -19.1 3.31e-02 1.03e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 140 1.3001178e+06 6.64e+00 1.50e+04 -1.0 3.09e+02 -19.6 2.72e-03 1.11e-03f 1\n 141 1.2712885e+06 6.53e+00 1.48e+04 -1.0 3.10e+02 -19.1 1.01e-04 1.69e-02f 1\n 142 1.2672814e+06 6.51e+00 1.48e+04 -1.0 3.20e+02 -19.6 4.18e-03 2.32e-03f 1\n 143 1.2662613e+06 6.51e+00 1.47e+04 -1.0 2.90e+02 -19.1 1.94e-02 6.22e-04f 1\n 144 1.2251694e+06 6.34e+00 1.43e+04 -1.0 2.82e+02 -19.6 2.71e-03 2.66e-02f 1\n 145 1.2238974e+06 6.33e+00 1.43e+04 -1.0 2.99e+02 -19.1 1.32e-03 7.91e-04f 1\n 146 1.2218795e+06 6.32e+00 1.43e+04 -1.0 3.08e+02 -19.6 1.65e-03 1.32e-03f 1\n 147 1.2218327e+06 6.32e+00 1.43e+04 -1.0 3.05e+02 -19.1 3.84e-03 2.74e-05f 1\n 148 1.1933966e+06 6.22e+00 1.41e+04 -1.0 3.06e+02 -19.6 7.34e-04 1.67e-02f 1\n 149 1.1923484e+06 6.21e+00 1.41e+04 -1.0 3.08e+02 -19.1 8.24e-03 5.84e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 150 1.1851137e+06 6.19e+00 1.40e+04 -1.0 2.76e+02 -19.6 7.09e-04 4.39e-03f 1\n 151 1.1674398e+06 6.13e+00 1.39e+04 -1.0 2.77e+02 -19.1 4.82e-03 9.94e-03f 1\n 152 1.1671054e+06 6.12e+00 1.39e+04 -1.0 2.72e+02 -19.6 4.03e-03 2.01e-04f 1\n 153 1.1607724e+06 6.10e+00 1.38e+04 -1.0 2.70e+02 -19.1 6.82e-04 3.72e-03f 1\n 154 1.1521951e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.6 1.57e-03 5.21e-03f 1\n 155 1.1512500e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.1 7.99e-04 5.27e-04f 1\n 156 1.1507231e+06 6.06e+00 1.37e+04 -1.0 2.68e+02 -19.6 4.24e-04 3.27e-04f 1\n 157 1.1507040e+06 6.06e+00 1.37e+04 -1.0 2.81e+02 -19.1 5.41e-04 9.69e-06f 1\n 158 1.1467730e+06 6.05e+00 1.37e+04 -1.0 2.63e+02 -19.6 8.26e-06 2.11e-03f 1\n 159 1.0732380e+06 5.82e+00 1.32e+04 -1.0 2.63e+02 -19.1 2.67e-04 3.91e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 160 1.0714161e+06 5.81e+00 1.32e+04 -1.0 2.56e+02 -19.6 1.02e-02 9.30e-04f 1\n 161 1.0713627e+06 5.81e+00 1.32e+04 -1.0 2.54e+02 -19.1 8.09e-05 2.84e-05f 1\n 162 1.0580046e+06 5.76e+00 1.31e+04 -1.0 2.54e+02 -19.6 1.35e-04 7.69e-03f 1\n 163 1.0545534e+06 5.75e+00 1.30e+04 -1.0 2.54e+02 -19.1 3.39e-03 1.89e-03f 1\n 164 1.0341517e+06 5.68e+00 1.30e+04 -1.0 2.52e+02 -19.6 8.74e-04 1.22e-02f 1\n 165 1.0336429e+06 5.68e+00 1.29e+04 -1.0 2.51e+02 -19.1 6.22e-04 2.90e-04f 1\n 166 1.0275521e+06 5.66e+00 1.38e+04 -1.0 2.51e+02 -19.6 4.36e-04 3.68e-03f 1\n 167 1.0179732e+06 5.63e+00 1.32e+04 -1.0 2.50e+02 -19.1 7.95e-03 5.53e-03f 1\n 168 9.9624521e+05 5.55e+00 1.59e+04 -1.0 2.48e+02 -19.6 4.63e-03 1.40e-02f 1\n 169 9.9527357e+05 5.55e+00 1.58e+04 -1.0 2.44e+02 -19.1 5.96e-04 5.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 170 9.9489625e+05 5.55e+00 1.56e+04 -1.0 2.47e+02 -19.6 1.04e-03 2.42e-04f 1\n 171 9.9489118e+05 5.55e+00 1.50e+04 -1.0 2.53e+02 -19.1 1.24e-03 2.86e-06f 1\n 172 9.9421219e+05 5.54e+00 1.57e+04 -1.0 2.49e+02 -19.6 3.63e-06 4.37e-04f 1\n 173 9.8486650e+05 5.51e+00 1.68e+04 -1.0 2.46e+02 -19.1 5.48e-04 5.68e-03f 1\n 174 9.7308682e+05 5.47e+00 1.65e+04 -1.0 2.49e+02 -19.6 8.25e-03 7.61e-03f 1\n 175 9.7212896e+05 5.47e+00 1.65e+04 -1.0 2.44e+02 -19.1 3.20e-04 5.80e-04f 1\n 176 9.6540396e+05 5.44e+00 1.75e+04 -1.0 2.49e+02 -19.6 9.01e-04 4.34e-03f 1\n 177 9.4998516e+05 5.39e+00 1.51e+04 -1.0 2.44e+02 -19.1 1.93e-02 9.44e-03f 1\n 178 9.4904520e+05 5.39e+00 1.52e+04 -1.0 2.45e+02 -19.6 2.19e-04 6.06e-04f 1\n 179 9.4902274e+05 5.39e+00 1.51e+04 -1.0 2.75e+02 -19.1 1.62e-04 1.14e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 180 9.3880492e+05 5.35e+00 1.67e+04 -1.0 2.29e+02 -19.6 1.38e-05 6.45e-03f 1\n 181 8.9559690e+05 5.21e+00 1.79e+04 -1.0 2.29e+02 -19.1 1.06e-02 2.71e-02f 1\n 182 8.5549874e+05 5.07e+00 1.93e+04 -1.0 2.22e+02 -19.6 4.07e-03 2.60e-02f 1\n 183 8.4452994e+05 5.04e+00 1.99e+04 -1.0 2.17e+02 -19.1 4.96e-04 7.46e-03f 1\n 184 8.2921005e+05 4.98e+00 1.98e+04 -1.0 2.14e+02 -19.6 9.95e-03 1.09e-02f 1\n 185 8.2905253e+05 4.98e+00 1.90e+04 -1.0 2.09e+02 -19.1 5.75e-03 1.10e-04f 1\n 186 8.2904529e+05 4.98e+00 1.89e+04 -1.0 2.18e+02 -19.6 7.15e-05 5.41e-06f 1\n 187 8.2712955e+05 4.97e+00 1.92e+04 -1.0 2.14e+02 -19.1 5.72e-05 1.35e-03f 1\n 188 8.0950543e+05 4.91e+00 2.28e+04 -1.0 2.31e+02 -19.6 1.11e-03 1.34e-02f 1\n 189 8.0862616e+05 4.90e+00 2.24e+04 -1.0 2.12e+02 -19.1 1.92e-03 6.51e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 190 8.0425351e+05 4.89e+00 2.35e+04 -1.0 2.22e+02 -19.6 4.57e-04 3.51e-03f 1\n 191 7.9537870e+05 4.85e+00 2.31e+04 -1.0 2.15e+02 -19.1 7.37e-03 6.61e-03f 1\n 192 7.9489154e+05 4.85e+00 2.29e+04 -1.0 2.11e+02 -19.6 1.29e-03 3.92e-04f 1\n 193 7.8751827e+05 4.83e+00 2.29e+04 -1.0 2.01e+02 -19.1 2.56e-03 5.71e-03f 1\n 194 7.8736120e+05 4.82e+00 2.31e+04 -1.0 2.00e+02 -19.6 1.54e-04 1.32e-04f 1\n 195 7.8402757e+05 4.81e+00 2.31e+04 -1.0 1.94e+02 -19.1 2.19e-03 2.63e-03f 1\n 196 7.8388845e+05 4.81e+00 2.53e+04 -1.0 1.98e+02 -19.6 2.15e-04 1.18e-04f 1\n 197 7.8385262e+05 4.81e+00 2.50e+04 -1.0 1.94e+02 -19.1 4.34e-03 2.82e-05f 1\n 198 7.8236217e+05 4.81e+00 1.09e+04 -1.0 1.97e+02 -19.6 3.11e-04 1.27e-03f 1\n 199 7.7035462e+05 4.76e+00 1.08e+04 -1.0 1.91e+02 -19.1 3.91e-04 9.52e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 200 7.6826638e+05 4.75e+00 1.08e+04 -1.0 1.90e+02 -19.6 2.68e-02 1.77e-03f 1\n 201 7.6137802e+05 4.73e+00 1.07e+04 -1.0 1.80e+02 -19.1 5.08e-04 5.41e-03f 1\n 202 7.6092627e+05 4.72e+00 1.07e+04 -1.0 1.85e+02 -19.6 8.12e-03 3.85e-04f 1\n 203 6.8620409e+05 4.44e+00 1.26e+04 -1.0 1.80e+02 -19.1 1.57e-04 6.08e-02f 1\n 204 6.8223234e+05 4.42e+00 1.15e+04 -1.0 1.85e+02 -19.6 6.43e-03 3.54e-03f 1\n 205 6.7550321e+05 4.40e+00 1.16e+04 -1.0 1.74e+02 -19.1 5.10e-03 5.69e-03f 1\n 206 6.7512988e+05 4.39e+00 1.00e+04 -1.0 1.86e+02 -19.6 8.13e-03 3.47e-04f 1\n 207 6.6341460e+05 4.35e+00 9.84e+03 -1.0 1.72e+02 -19.1 1.30e-02 1.04e-02f 1\n 208 6.5075026e+05 4.30e+00 1.09e+04 -1.0 1.87e+02 -19.6 2.10e-04 1.19e-02f 1\n 209 6.4469528e+05 4.27e+00 1.05e+04 -1.0 1.72e+02 -19.1 7.52e-03 5.37e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 210 6.3074280e+05 4.22e+00 1.26e+04 -1.0 1.85e+02 -19.6 4.48e-04 1.32e-02f 1\n 211 6.3015403e+05 4.21e+00 1.26e+04 -1.0 3.38e+01 2.3 1.77e-03 7.51e-04f 1\n 212 6.3013669e+05 4.21e+00 1.47e+04 -1.0 1.21e+02 1.8 1.17e-03 1.79e-05f 1\n 213 6.2710947e+05 4.20e+00 1.45e+04 -1.0 1.26e+02 1.4 4.90e-04 2.86e-03f 1\n 214 6.2100935e+05 4.18e+00 1.49e+04 -1.0 1.53e+02 0.9 1.86e-03 5.85e-03f 1\n 215 6.1066877e+05 4.14e+00 1.59e+04 -1.0 1.97e+02 0.4 1.05e-05 8.89e-03f 1\n 216 6.0984620e+05 4.14e+00 1.57e+04 -1.0 1.84e+02 1.7 8.49e-03 1.01e-03f 1\n 217 6.0751052e+05 4.13e+00 1.57e+04 -1.0 2.02e+02 1.3 5.49e-03 2.08e-03f 1\n 218 6.0707832e+05 4.12e+00 1.57e+04 -1.0 2.01e+02 1.7 2.32e-04 5.32e-04f 1\n 219 6.0707327e+05 4.12e+00 1.53e+04 -1.0 1.96e+02 2.1 2.90e-03 7.52e-06f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 220 6.0555478e+05 4.12e+00 1.54e+04 -1.0 2.25e+02 1.6 9.57e-04 1.84e-03f 1\n 221 6.0553909e+05 4.12e+00 1.47e+04 -1.0 2.14e+02 2.1 5.83e-03 2.26e-05f 1\n 222 6.0387470e+05 4.11e+00 1.47e+04 -1.0 2.27e+02 1.6 2.04e-03 1.80e-03f 1\n 223 6.0325229e+05 4.11e+00 1.47e+04 -1.0 2.20e+02 2.0 4.35e-04 7.72e-04f 1\n 224 6.0322399e+05 4.11e+00 1.45e+04 -1.0 2.21e+02 1.5 4.10e-03 2.96e-05f 1\n 225 6.0185557e+05 4.10e+00 1.45e+04 -1.0 2.17e+02 2.0 1.59e-05 1.70e-03f 1\n 226 6.0180800e+05 4.10e+00 1.44e+04 -1.0 2.29e+02 1.5 1.37e-03 4.63e-05f 1\n 227 5.9923339e+05 4.09e+00 1.44e+04 -1.0 2.27e+02 1.9 2.84e-03 2.85e-03f 1\n 228 5.9851909e+05 4.09e+00 1.44e+04 -1.0 2.29e+02 1.4 9.28e-04 6.31e-04f 1\n 229 5.9692058e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.9 1.94e-03 1.77e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 230 5.9689878e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.4 1.22e-05 1.79e-05f 1\n 231 5.9684337e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.8 7.71e-05 6.11e-05f 1\n 232 5.9682024e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.3 2.55e-06 2.10e-05f 1\n 233 5.9680869e+05 4.08e+00 1.38e+04 -1.0 2.28e+02 1.8 7.05e-03 1.36e-05f 1\n 234 5.9076367e+05 4.05e+00 1.41e+04 -1.0 2.30e+02 1.3 5.00e-04 5.99e-03f 1\n 235 5.9073170e+05 4.05e+00 1.42e+04 -1.0 2.32e+02 1.7 4.66e-03 3.40e-05f 1\n 236 5.8521172e+05 4.03e+00 1.45e+04 -1.0 2.32e+02 1.2 8.81e-04 5.50e-03f 1\n 237 5.8376022e+05 4.03e+00 1.44e+04 -1.0 2.30e+02 0.8 2.07e-06 1.04e-03f 1\n 238 5.8372062e+05 4.03e+00 1.53e+04 -1.0 2.35e+02 2.1 3.62e-03 4.81e-05f 1\n 239 5.7779448e+05 4.00e+00 1.55e+04 -1.0 2.39e+02 1.6 1.33e-02 6.22e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 240 5.7771432e+05 4.00e+00 1.57e+04 -1.0 2.26e+02 2.0 1.00e-03 9.89e-05f 1\n 241 5.7665827e+05 4.00e+00 1.57e+04 -1.0 2.07e+02 1.6 9.92e-04 1.10e-03f 1\n 242 5.7511556e+05 3.99e+00 1.55e+04 -1.0 2.12e+02 2.0 1.09e-03 1.92e-03f 1\n 243 5.7052438e+05 3.97e+00 1.52e+04 -1.0 2.09e+02 1.5 2.73e-04 4.68e-03f 1\n 244 5.6998537e+05 3.97e+00 1.51e+04 -1.0 2.36e+02 1.9 3.75e-04 6.54e-04f 1\n 245 5.6612860e+05 3.95e+00 1.48e+04 -1.0 2.37e+02 1.5 3.47e-05 3.75e-03f 1\n 246 5.6572525e+05 3.95e+00 1.50e+04 -1.0 2.36e+02 1.9 2.57e-03 4.85e-04f 1\n 247 5.6516789e+05 3.95e+00 1.51e+04 -1.0 2.36e+02 1.4 1.32e-03 4.83e-04f 1\n 248 5.6316860e+05 3.94e+00 1.52e+04 -1.0 2.36e+02 1.8 3.05e-03 2.38e-03f 1\n 249 5.6254967e+05 3.94e+00 1.50e+04 -1.0 2.35e+02 1.3 7.91e-06 4.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 250 5.6077033e+05 3.93e+00 1.49e+04 -1.0 2.36e+02 1.8 1.29e-03 2.17e-03f 1\n 251 5.6022313e+05 3.93e+00 1.47e+04 -1.0 2.34e+02 1.3 2.29e-06 3.07e-04f 1\n 252 5.5692514e+05 3.91e+00 1.49e+04 -1.0 2.36e+02 1.7 8.13e-03 3.96e-03f 1\n 253 5.5673567e+05 3.91e+00 1.49e+04 -1.0 2.31e+02 1.2 2.68e-06 9.58e-05f 1\n 254 5.5535652e+05 3.91e+00 1.55e+04 -1.0 2.33e+02 1.7 1.14e-02 1.64e-03f 1\n 255 5.5534273e+05 3.91e+00 1.56e+04 -1.0 2.30e+02 2.1 4.75e-04 1.85e-05f 1\n 256 5.5514554e+05 3.90e+00 1.71e+04 -1.0 2.27e+02 1.6 1.32e-02 2.20e-04f 1\n 257 5.5141803e+05 3.90e+00 1.48e+04 -1.0 4.83e+02 1.1 1.93e-05 1.30e-03f 1\n 258 5.4693933e+05 3.88e+00 1.42e+04 -1.0 2.39e+02 1.6 2.91e-04 4.86e-03f 1\n 259 5.4687686e+05 3.88e+00 1.43e+04 -1.0 2.52e+02 1.1 2.34e-03 6.08e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 260 5.4353115e+05 3.87e+00 1.41e+04 -1.0 2.42e+02 1.5 1.34e-03 3.66e-03f 1\n 261 5.4346337e+05 3.87e+00 1.41e+04 -1.0 2.07e+02 1.0 1.56e-04 6.65e-05f 1\n 262 5.4335021e+05 3.87e+00 1.42e+04 -1.0 1.95e+02 1.5 2.22e-03 1.26e-04f 1\n 263 5.4296226e+05 3.86e+00 1.43e+04 -1.0 1.86e+02 1.0 2.63e-03 3.71e-04f 1\n 264 5.3812113e+05 3.84e+00 1.40e+04 -1.0 2.13e+02 1.4 2.17e-05 5.31e-03f 1\n 265 5.3811408e+05 3.84e+00 1.41e+04 -1.0 2.23e+02 1.8 3.29e-04 8.06e-06f 1\n 266 5.3810662e+05 3.84e+00 1.41e+04 -1.0 2.43e+02 1.4 9.90e-04 7.83e-06f 1\n 267 5.3692599e+05 3.84e+00 1.38e+04 -1.0 2.30e+02 1.8 8.84e-05 1.33e-03f 1\n 268 5.3689601e+05 3.84e+00 1.38e+04 -1.0 1.90e+02 1.3 1.05e-03 3.21e-05f 1\n 269 5.3480708e+05 3.83e+00 1.32e+04 -1.0 2.44e+02 0.8 2.23e-04 1.25e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 270 5.3431885e+05 3.83e+00 1.34e+04 -1.0 1.83e+02 1.3 4.31e-03 5.17e-04f 1\n 271 5.3310063e+05 3.83e+00 1.31e+04 -1.0 1.89e+02 1.7 2.86e-05 1.41e-03f 1\n 272 5.3150897e+05 3.82e+00 1.22e+04 -1.0 1.85e+02 2.1 1.96e-04 1.98e-03f 1\n 273 5.3147543e+05 3.82e+00 1.22e+04 -1.0 1.61e+02 1.6 1.35e-04 3.82e-05f 1\n 274 5.3137612e+05 3.82e+00 1.24e+04 -1.0 1.76e+02 1.2 3.23e-03 1.02e-04f 1\n 275 5.3106158e+05 3.82e+00 1.29e+04 -1.0 1.79e+02 1.6 5.34e-03 3.64e-04f 1\n 276 5.2929724e+05 3.81e+00 1.28e+04 -1.0 1.73e+02 1.1 5.52e-05 1.84e-03f 1\n 277 5.2907597e+05 3.81e+00 1.29e+04 -1.0 1.63e+02 2.4 4.41e-04 3.68e-04f 1\n 278 5.2816992e+05 3.80e+00 1.27e+04 -1.0 1.88e+02 2.0 5.12e-04 1.14e-03f 1\n 279 5.2807415e+05 3.80e+00 1.27e+04 -1.0 1.48e+03 1.5 1.26e-06 8.93e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 280 5.2805145e+05 3.80e+00 1.26e+04 -1.0 1.94e+02 1.0 1.55e-03 2.34e-05f 1\n 281 5.2494578e+05 3.79e+00 1.26e+04 -1.0 1.75e+02 1.4 7.38e-04 3.61e-03f 1\n 282 5.2382656e+05 3.79e+00 1.20e+04 -1.0 6.75e+02 1.0 1.61e-05 3.04e-04f 1\n 283 5.2294012e+05 3.78e+00 1.20e+04 -1.0 1.74e+02 1.4 1.78e-03 9.88e-04f 1\n 284 5.2002561e+05 3.77e+00 1.24e+04 -1.0 1.76e+02 1.8 7.11e-03 3.79e-03f 1\n 285 5.1038171e+05 3.73e+00 1.19e+04 -1.0 1.71e+02 1.3 6.29e-05 1.07e-02f 1\n 286 5.1029468e+05 3.73e+00 1.19e+04 -1.0 1.84e+02 1.8 7.42e-04 1.12e-04f 1\n 287 5.1028585e+05 3.73e+00 1.21e+04 -1.0 2.01e+02 2.2 4.17e-04 1.31e-05f 1\n 288 5.0835844e+05 3.72e+00 1.19e+04 -1.0 2.75e+02 1.7 1.15e-05 2.41e-03f 1\n 289 5.0829350e+05 3.72e+00 1.19e+04 -1.0 1.71e+02 1.2 3.97e-04 6.12e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 290 5.0786133e+05 3.72e+00 1.30e+04 -1.0 1.77e+02 1.7 1.37e-02 5.49e-04f 1\n 291 4.9734337e+05 3.69e+00 1.06e+04 -1.0 1.72e+02 1.2 8.48e-04 8.86e-03f 1\n 292 4.9689003e+05 3.68e+00 1.08e+04 -1.0 1.74e+02 1.6 3.20e-03 5.66e-04f 1\n 293 4.9257017e+05 3.67e+00 9.15e+03 -1.0 1.62e+02 1.1 7.85e-05 3.01e-03f 1\n 294 4.9140020e+05 3.67e+00 9.02e+03 -1.0 2.41e+02 1.6 4.58e-05 1.41e-03f 1\n 295 4.9127824e+05 3.67e+00 1.14e+04 -1.0 1.61e+02 1.1 2.66e-03 6.68e-05f 1\n 296 4.9066209e+05 3.66e+00 1.29e+04 -1.0 1.75e+02 1.5 1.82e-02 7.38e-04f 1\n 297 4.8899995e+05 3.66e+00 1.45e+04 -1.0 5.05e+02 1.0 9.31e-04 4.06e-04f 1\n 298 4.7750162e+05 3.61e+00 1.35e+04 -1.0 1.76e+02 1.5 4.98e-03 1.35e-02f 1\n 299 4.7434464e+05 3.61e+00 1.49e+04 -1.0 9.97e+02 1.0 6.27e-04 4.14e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 300 4.7339897e+05 3.61e+00 1.50e+04 -1.0 1.73e+02 1.4 2.13e-03 1.10e-03f 1\n 301 4.7161223e+05 3.61e+00 1.40e+04 -1.0 3.25e+02 0.9 1.03e-04 5.77e-04f 1\n 302 4.7150987e+05 3.60e+00 1.43e+04 -1.0 2.00e+02 0.4 6.17e-03 9.97e-05f 1\n 303 4.6068296e+05 3.57e+00 1.36e+04 -1.0 3.30e+02 -0.0 4.88e-05 1.06e-02f 1\n 304 4.5827805e+05 3.56e+00 1.49e+04 -1.0 1.68e+02 1.3 1.36e-02 2.70e-03f 1\n 305 4.5598068e+05 3.55e+00 1.41e+04 -1.0 4.79e+02 0.8 4.48e-04 7.06e-04f 1\n 306 4.5518400e+05 3.55e+00 1.43e+04 -1.0 1.59e+02 1.2 2.67e-03 8.88e-04f 1\n 307 4.5309751e+05 3.55e+00 1.72e+04 -1.0 2.98e+02 0.8 2.61e-03 8.91e-04f 1\n 308 4.4826799e+05 3.53e+00 1.86e+04 -1.0 1.66e+02 1.2 1.97e-02 5.39e-03f 1\n 309 4.4337229e+05 3.51e+00 1.70e+04 -1.0 1.62e+02 0.7 4.61e-05 4.04e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 310 4.3957043e+05 3.50e+00 2.22e+04 -1.0 1.64e+02 1.1 5.11e-02 4.22e-03f 1\n 311 4.3840763e+05 3.50e+00 2.35e+04 -1.0 1.68e+02 0.7 4.15e-03 9.47e-04f 1\n 312 4.2663652e+05 3.45e+00 2.43e+04 -1.0 1.62e+02 1.1 2.95e-02 1.34e-02f 1\n 313 4.1771582e+05 3.43e+00 2.40e+04 -1.0 1.75e+02 0.6 6.75e-03 7.00e-03f 1\n 314 4.1583852e+05 3.42e+00 2.27e+04 -1.0 8.29e+02 0.1 1.03e-04 3.62e-04f 1\n 315 4.0823036e+05 3.40e+00 2.35e+04 -1.0 1.82e+02 0.6 9.82e-03 6.83e-03f 1\n 316 4.0742102e+05 3.40e+00 2.31e+04 -1.0 2.88e+02 0.1 7.31e-05 5.39e-04f 1\n 317 4.0736672e+05 3.40e+00 2.36e+04 -1.0 4.84e+02 -0.4 7.32e-03 5.87e-05f 1\n 318 3.9419770e+05 3.35e+00 2.18e+04 -1.0 7.13e+02 -0.9 8.53e-05 1.33e-02f 1\n 319 3.8663282e+05 3.33e+00 1.92e+04 -1.0 8.56e+02 -1.3 2.10e-04 5.79e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 320 3.8579331e+05 3.33e+00 2.13e+04 -1.0 9.22e+02 -1.8 7.40e-03 7.22e-04f 1\n 321 3.7974323e+05 3.32e+00 1.94e+04 -1.0 9.50e+02 -2.3 1.97e-04 4.91e-03f 1\n 322 3.7938905e+05 3.31e+00 2.15e+04 -1.0 9.51e+02 -2.8 8.43e-03 3.27e-04f 1\n 323 3.7037180e+05 3.29e+00 1.92e+04 -1.0 9.55e+02 -3.3 1.13e-04 8.55e-03f 1\n 324 3.6908213e+05 3.28e+00 1.94e+04 -1.0 9.41e+02 -3.7 2.53e-03 1.30e-03f 1\n 325 3.6277620e+05 3.26e+00 1.81e+04 -1.0 2.17e+02 0.3 8.07e-04 7.29e-03f 1\n 326 3.5989666e+05 3.24e+00 1.79e+04 -1.0 1.55e+02 1.6 1.27e-03 5.36e-03f 1\n 327 3.5985661e+05 3.24e+00 1.79e+04 -1.0 1.59e+02 2.1 9.24e-04 1.04e-04f 1\n 328 3.5935266e+05 3.24e+00 1.79e+04 -1.0 1.62e+02 1.6 6.26e-05 8.73e-04f 1\n 329 3.5783375e+05 3.23e+00 1.78e+04 -1.0 1.63e+02 2.0 3.50e-03 3.63e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 330 3.5743587e+05 3.22e+00 1.78e+04 -1.0 1.61e+02 1.5 3.51e-05 6.78e-04f 1\n 331 3.5668635e+05 3.22e+00 1.78e+04 -1.0 1.52e+02 1.1 2.73e-04 1.08e-03f 1\n 332 3.5630331e+05 3.22e+00 1.78e+04 -1.0 1.67e+02 0.6 3.51e-03 4.92e-04f 1\n 333 3.5562828e+05 3.22e+00 1.78e+04 -1.0 2.82e+02 0.1 3.34e-03 7.59e-04f 1\n 334 3.5480287e+05 3.21e+00 1.78e+04 -1.0 4.82e+02 -0.4 4.21e-05 9.86e-04f 1\n 335 3.5438009e+05 3.21e+00 1.78e+04 -1.0 6.55e+02 -0.8 1.29e-04 5.04e-04f 1\n 336 3.5436086e+05 3.21e+00 1.78e+04 -1.0 8.12e+02 -1.3 2.56e-03 2.29e-05f 1\n 337 3.5193976e+05 3.20e+00 1.77e+04 -1.0 8.82e+02 -1.8 1.07e-03 2.89e-03f 1\n 338 3.5190047e+05 3.20e+00 1.77e+04 -1.0 9.10e+02 -2.3 7.21e-03 4.74e-05f 1\n 339 3.5093683e+05 3.20e+00 1.77e+04 -1.0 7.47e+02 -1.0 1.35e-05 1.13e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 340 3.5052498e+05 3.20e+00 1.77e+04 -1.0 2.07e+02 0.4 5.15e-04 5.29e-04f 1\n 341 3.3750946e+05 3.14e+00 1.74e+04 -1.0 3.58e+02 -0.1 1.67e-05 1.64e-02f 1\n 342 3.3747338e+05 3.14e+00 1.74e+04 -1.0 5.75e+02 -0.6 6.58e-03 4.69e-05f 1\n 343 3.3562091e+05 3.14e+00 1.74e+04 -1.0 7.66e+02 -1.1 1.05e-04 2.42e-03f 1\n 344 3.3510890e+05 3.13e+00 1.73e+04 -1.0 8.41e+02 -1.5 1.22e-03 6.80e-04f 1\n 345 3.2871807e+05 3.11e+00 1.72e+04 -1.0 8.84e+02 -2.0 3.02e-03 8.53e-03f 1\n 346 3.2777372e+05 3.10e+00 1.72e+04 -1.0 1.54e+02 1.1 7.57e-05 1.51e-03f 1\n 347 3.2772491e+05 3.10e+00 1.72e+04 -1.0 1.60e+02 1.6 2.40e-03 9.66e-05f 1\n 348 3.2718271e+05 3.10e+00 1.71e+04 -1.0 1.60e+02 2.0 4.86e-03 1.52e-03f 1\n 349 3.2706600e+05 3.10e+00 1.71e+04 -1.0 1.58e+02 1.5 1.31e-03 2.19e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 350 3.2406746e+05 3.08e+00 1.70e+04 -1.0 1.47e+02 1.0 1.75e-04 5.59e-03f 1\n 351 3.2366994e+05 3.08e+00 1.70e+04 -1.0 1.50e+02 1.5 4.30e-03 7.40e-04f 1\n 352 3.2356913e+05 3.08e+00 1.70e+04 -1.0 1.37e+02 1.0 1.11e-04 1.90e-04f 1\n 353 3.2355506e+05 3.08e+00 1.70e+04 -1.0 1.49e+02 1.4 3.22e-03 2.36e-05f 1\n 354 3.2316524e+05 3.07e+00 1.70e+04 -1.0 1.40e+02 0.9 1.38e-03 6.09e-04f 1\n 355r 3.2316524e+05 3.07e+00 1.00e+03 0.5 0.00e+00 0.4 0.00e+00 3.57e-07R 8\n 356r 3.2229084e+05 3.07e+00 1.00e+03 0.5 2.06e+03 - 6.18e-03 7.60e-04f 1\n 357r 3.2109567e+05 3.06e+00 9.98e+02 0.5 1.80e+03 - 1.71e-03 8.09e-04f 1\n 358r 3.1149974e+05 2.97e+00 9.95e+02 0.5 1.67e+03 - 3.74e-03 6.54e-03f 1\n 359r 3.1087190e+05 2.97e+00 9.95e+02 0.5 8.58e+02 - 9.83e-05 4.32e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 360r 3.0989090e+05 2.96e+00 9.97e+02 0.5 9.94e+02 - 6.50e-03 6.44e-04f 1\n 361r 3.0241262e+05 2.87e+00 9.92e+02 0.5 9.07e+02 - 2.36e-03 5.34e-03f 1\n 362r 2.9694919e+05 2.80e+00 9.80e+02 0.5 8.52e+02 - 1.55e-02 4.79e-03f 1\n 363r 2.8929254e+05 2.68e+00 9.72e+02 0.5 6.39e+02 - 1.25e-02 8.39e-03f 1\n 364 2.8919644e+05 2.68e+00 9.52e+03 -1.0 1.36e+02 -0.0 2.03e-04 1.66e-04f 1\n 365 2.8908661e+05 2.68e+00 9.52e+03 -1.0 1.38e+02 -0.5 3.89e-04 1.90e-04f 1\n 366 2.8861557e+05 2.68e+00 9.51e+03 -1.0 1.34e+02 -1.0 2.45e-04 8.16e-04f 1\n 367 2.8784152e+05 2.67e+00 9.50e+03 -1.0 1.29e+02 -1.5 5.67e-04 1.35e-03f 1\n 368 2.8557520e+05 2.66e+00 9.46e+03 -1.0 1.18e+02 -1.9 1.40e-03 3.97e-03f 1\n 369 2.8048140e+05 2.64e+00 9.38e+03 -1.0 1.31e+02 -2.4 2.51e-03 9.01e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 370 2.7807299e+05 2.63e+00 9.34e+03 -1.0 1.50e+02 -2.9 1.64e-03 4.32e-03f 1\n 371 2.7069758e+05 2.59e+00 9.21e+03 -1.0 1.60e+02 -3.4 3.85e-03 1.34e-02f 1\n 372 2.5748481e+05 2.53e+00 8.98e+03 -1.0 1.59e+02 -3.8 4.45e-03 2.48e-02f 1\n 373 2.5409647e+05 2.51e+00 8.92e+03 -1.0 1.53e+02 -4.3 1.21e-02 6.62e-03f 1\n 374 2.4261880e+05 2.45e+00 8.72e+03 -1.0 1.50e+02 -4.8 6.04e-03 2.29e-02f 1\n 375 2.2822588e+05 2.38e+00 8.45e+03 -1.0 1.46e+02 -5.3 1.51e-02 3.02e-02f 1\n 376 2.0965467e+05 2.28e+00 8.10e+03 -1.0 1.41e+02 -5.8 1.82e-02 4.16e-02f 1\n 377 2.0936932e+05 2.28e+00 8.10e+03 -1.0 1.37e+02 -6.2 3.27e-02 6.83e-04f 1\n 378 2.0780500e+05 2.27e+00 8.07e+03 -1.0 1.37e+02 -6.7 4.67e-03 3.76e-03f 1\n 379 1.9606770e+05 2.20e+00 7.84e+03 -1.0 1.38e+02 -7.2 7.84e-04 2.87e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 380 1.7935187e+05 2.11e+00 7.49e+03 -1.0 1.38e+02 -7.7 2.58e-02 4.37e-02f 1\n 381 1.7765925e+05 2.10e+00 7.46e+03 -1.0 1.36e+02 -8.1 4.29e-02 4.74e-03f 1\n 382 1.6495345e+05 2.02e+00 7.18e+03 -1.0 1.34e+02 -8.6 1.77e-02 3.65e-02f 1\n 383 1.6340175e+05 2.01e+00 7.15e+03 -1.0 1.30e+02 -9.1 1.84e-02 4.73e-03f 1\n 384 1.5788016e+05 1.98e+00 7.03e+03 -1.0 1.29e+02 -9.6 3.72e-03 1.71e-02f 1\n 385 1.5064127e+05 1.93e+00 6.87e+03 -1.0 1.28e+02 -10.1 8.10e-03 2.33e-02f 1\n 386 1.2939352e+05 1.79e+00 6.36e+03 -1.0 1.25e+02 -10.5 1.85e-02 7.34e-02f 1\n 387 1.2646803e+05 1.77e+00 6.29e+03 -1.0 1.18e+02 -11.0 4.15e-02 1.14e-02f 1\n 388 1.1471323e+05 1.68e+00 5.99e+03 -1.0 1.16e+02 -11.5 1.69e-02 4.78e-02f 1\n 389 1.0992888e+05 1.65e+00 5.86e+03 -1.0 1.11e+02 -12.0 7.97e-02 2.12e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 390 1.0212816e+05 1.59e+00 5.65e+03 -1.0 1.07e+02 -12.4 1.86e-03 3.63e-02f 1\n 391 8.6177113e+04 1.46e+00 5.19e+03 -1.0 1.04e+02 -12.9 7.26e-02 8.17e-02f 1\n 392 8.5370527e+04 1.45e+00 5.16e+03 -1.0 9.48e+01 -13.4 3.22e-02 4.71e-03f 1\n 393 7.4077318e+04 1.35e+00 4.81e+03 -1.0 9.38e+01 -13.9 1.85e-02 6.88e-02f 1\n 394 7.1177623e+04 1.33e+00 4.71e+03 -1.0 8.78e+01 -14.3 6.45e-02 1.99e-02f 1\n 395 6.4716733e+04 1.26e+00 4.49e+03 -1.0 8.51e+01 -14.8 4.54e-03 4.67e-02f 1\n 396 5.7488482e+04 1.19e+00 4.23e+03 -1.0 8.14e+01 -15.3 9.91e-03 5.78e-02f 1\n 397 4.8754351e+04 1.10e+00 3.90e+03 -1.0 7.88e+01 -15.8 6.42e-02 7.95e-02f 1\n 398 4.7745315e+04 1.08e+00 3.86e+03 -1.0 7.72e+01 -16.3 9.49e-02 1.05e-02f 1\n 399 4.1977413e+04 1.02e+00 3.61e+03 -1.0 7.89e+01 -16.7 6.18e-02 6.27e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 400 3.3677208e+04 9.10e-01 3.24e+03 -1.0 7.75e+01 -17.2 7.45e-03 1.05e-01f 1\n 401 3.0166245e+04 8.61e-01 3.06e+03 -1.0 7.30e+01 -17.7 2.38e-02 5.39e-02f 1\n 402 2.5223779e+04 7.87e-01 2.80e+03 -1.0 7.10e+01 -18.2 1.21e-01 8.61e-02f 1\n 403 2.2341250e+04 7.40e-01 2.63e+03 -1.0 6.89e+01 -18.6 1.27e-01 5.92e-02f 1\n 404 1.8863927e+04 6.80e-01 2.42e+03 -1.0 6.77e+01 -19.1 8.08e-02 8.17e-02f 1\n 405 1.5848263e+04 6.23e-01 2.21e+03 -1.0 6.47e+01 -19.6 5.68e-02 8.40e-02f 1\n 406 1.4057233e+04 5.86e-01 2.08e+03 -1.0 6.57e+01 -19.1 8.62e-02 5.84e-02f 1\n 407 1.1013361e+04 5.19e-01 1.84e+03 -1.0 5.68e+01 -19.6 1.68e-01 1.16e-01f 1\n 408 7.9952027e+03 4.41e-01 1.57e+03 -1.0 5.34e+01 -19.1 1.48e-01 1.49e-01f 1\n 409 5.4065322e+03 3.62e-01 1.29e+03 -1.0 4.45e+01 -19.6 1.93e-01 1.80e-01f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 410 2.4913122e+03 2.44e-01 8.68e+02 -1.0 4.14e+01 -19.1 1.52e-01 3.26e-01f 1\n 411 1.0310441e+03 1.55e-01 5.51e+02 -1.0 2.82e+01 -19.6 2.47e-01 3.66e-01f 1\n 412 2.7434337e+02 7.52e-02 4.80e+02 -1.0 2.93e+01 -19.1 1.96e-01 5.14e-01f 1\n 413 5.4776714e+01 2.33e-02 7.33e+02 -1.0 7.94e+00 -19.6 4.50e-01 6.90e-01f 1\n 414 2.8195260e+01 3.14e-04 1.32e+03 -1.0 1.06e+01 -19.1 5.84e-01 1.00e+00f 1\n 415 3.2622344e+01 3.87e-05 1.06e+03 -1.0 1.60e+01 -19.6 7.02e-01 9.94e-01f 1\n 416 3.0040029e+01 4.65e-05 2.31e+02 -1.0 4.17e+01 -19.1 9.04e-01 1.00e+00f 1\n 417 3.0266079e+01 4.64e-04 4.41e+02 -1.0 1.21e+02 -19.6 1.00e+00 9.30e-01h 1\n 418 2.9559008e+01 3.42e-05 5.33e+01 -1.0 1.42e+01 -19.1 6.03e-01 1.00e+00h 1\n 419 2.9904880e+01 2.39e-06 6.35e-03 -1.0 6.68e+00 -19.6 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 420 2.4902915e+01 2.75e-05 1.10e+00 -2.5 1.14e+01 -19.1 4.91e-01 4.44e-01f 1\n 421 2.2342576e+01 8.15e-05 1.58e+00 -2.5 6.79e+01 -19.6 1.30e-01 3.02e-01f 1\n 422 2.0275308e+01 8.22e-05 1.07e+00 -2.5 6.41e+00 -3.7 3.38e-01 3.24e-01h 1\n 423 1.9264876e+01 9.78e-05 8.99e-01 -2.5 2.12e+01 -4.2 3.83e-01 1.56e-01h 1\n 424 1.8289856e+01 7.85e-05 6.81e-01 -2.5 6.19e+00 -4.6 2.31e-01 2.42e-01h 1\n 425 1.6132778e+01 1.96e-04 8.21e-01 -2.5 1.24e+01 -5.1 4.78e-01 7.58e-01h 1\n 426 1.5611473e+01 1.11e-04 5.01e-01 -2.5 1.64e+01 -5.6 4.71e-01 5.11e-01h 1\n 427 1.5163933e+01 4.19e-05 3.81e-01 -2.5 2.85e+00 -3.4 1.00e+00 6.97e-01h 1\n 428 1.5050001e+01 3.15e-05 4.60e-01 -2.5 3.60e+00 -3.8 6.17e-01 2.96e-01h 1\n 429 1.4900715e+01 5.30e-06 1.02e+00 -2.5 1.96e+00 -4.3 6.36e-01 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 430 1.4825885e+01 2.55e-05 4.15e-01 -2.5 5.52e+00 -3.9 8.38e-01 5.50e-01h 1\n 431 1.4739809e+01 3.24e-05 3.56e-01 -2.5 1.98e+01 -4.4 1.99e-01 1.55e-01h 1\n 432 1.4596773e+01 2.88e-05 5.34e-01 -2.5 5.19e+00 -3.9 5.29e-01 7.94e-01h 1\n 433 1.4484823e+01 3.46e-05 3.39e-01 -2.5 3.05e+01 -4.4 2.32e-01 9.62e-02h 1\n 434 1.4369681e+01 4.72e-05 4.22e-01 -2.5 1.62e+01 -4.9 3.06e-01 5.62e-01h 1\n 435 1.4279373e+01 4.71e-05 3.94e+02 -2.5 3.10e+01 -5.4 4.70e-01 2.25e-01h 1\n 436 1.4208263e+01 2.19e-05 2.73e+02 -2.5 2.87e+01 -5.8 3.06e-01 1.00e+00h 1\n 437 1.4134986e+01 1.91e-05 1.24e+02 -2.5 1.93e+01 -6.3 5.46e-01 6.57e-01h 1\n 438 1.4091516e+01 4.37e-05 3.46e+01 -2.5 2.35e+01 -6.8 7.19e-01 1.00e+00h 1\n 439 1.4087170e+01 4.09e-06 2.76e-04 -2.5 7.32e+00 -7.3 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 440 1.4088533e+01 1.91e-09 4.39e-07 -2.5 4.32e-01 -7.8 1.00e+00 1.00e+00h 1\n 441 1.3804149e+01 3.76e-06 7.14e+00 -3.8 4.84e+00 -8.2 6.51e-01 8.20e-01f 1\n 442 1.3706304e+01 1.35e-06 1.69e-03 -3.8 1.67e+00 -8.7 1.00e+00 1.00e+00h 1\n 443 1.3684885e+01 4.66e-08 3.95e-05 -3.8 4.07e-02 -9.2 1.00e+00 1.00e+00h 1\n 444 1.3664347e+01 8.58e-08 6.51e-02 -5.7 3.79e-01 -9.7 9.45e-01 9.46e-01h 1\n 445 1.3660196e+01 4.91e-08 1.08e+01 -5.7 4.56e-02 -10.1 1.00e+00 9.19e-01h 1\n 446 1.3659000e+01 4.92e-08 1.36e-06 -5.7 9.38e-03 -10.6 1.00e+00 1.00e+00f 1\n 447 1.3658600e+01 4.94e-08 9.08e+01 -8.6 3.53e-03 -11.1 9.94e-01 8.63e-01h 1\n 448 1.3658471e+01 5.57e-08 6.82e+00 -8.6 1.22e-03 -11.6 1.00e+00 9.90e-01f 1\n 449 1.3658445e+01 4.72e-08 2.13e-09 -8.6 4.22e-04 -12.0 1.00e+00 1.00e+00f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 450 1.3658439e+01 4.92e-08 3.85e-12 -8.6 1.81e-05 -12.5 1.00e+00 1.00e+00h 1\n 451 1.3658437e+01 4.92e-08 1.82e-12 -8.6 1.52e-06 -13.0 1.00e+00 1.00e+00h 1\n 452 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.26e-07 -13.5 1.00e+00 1.00e+00h 1\n 453 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.49e-07 -14.0 1.00e+00 1.00e+00h 1\n 454 1.3658437e+01 4.92e-08 3.64e-12 -8.6 9.58e-09 -14.4 1.00e+00 1.00e+00h 1\n 455 1.3658437e+01 4.92e-08 9.09e-13 -8.6 3.90e-11 -14.9 1.00e+00 1.00e+00h 1\n 456 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.39e-12 -15.4 1.00e+00 1.00e+00h 1\n 457 1.3658437e+01 4.92e-08 9.09e-13 -8.6 9.57e-13 -15.9 1.00e+00 1.00e+00h 1\n 458 1.3658437e+01 4.92e-08 9.09e-13 -8.6 7.94e-13 -16.3 1.00e+00 1.00e+00h 1\n 459 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.30e-13 -16.8 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 460 1.3658437e+01 4.92e-08 4.55e-13 -8.6 2.07e-12 -17.3 1.00e+00 1.00e+00h 1\n 461 1.3658437e+01 4.92e-08 9.09e-13 -8.6 2.88e-12 -17.8 1.00e+00 1.00e+00h 1\n 462 1.3658437e+01 4.92e-08 9.09e-13 -8.6 5.03e-12 -18.2 1.00e+00 1.00e+00h 1\n 463 1.3658437e+01 4.92e-08 4.55e-13 -8.6 3.27e-12 -18.7 1.00e+00 1.00e+00h 1\n\nNumber of Iterations....: 463\n\n (scaled) (unscaled)\nObjective...............: 1.3658436881286921e+01 1.3658436881286921e+01\nDual infeasibility......: 4.5474735088646412e-13 4.5474735088646412e-13\nConstraint violation....: 4.9168770388519079e-08 4.9168770388519079e-08\nComplementarity.........: 2.5059067889663204e-09 2.5059067889663204e-09\nOverall NLP error.......: 4.9168770388519079e-08 4.9168770388519079e-08\n\n\nNumber of objective function evaluations = 483\nNumber of objective gradient evaluations = 395\nNumber of equality constraint evaluations = 483\nNumber of inequality constraint evaluations = 483\nNumber of equality constraint Jacobian evaluations = 467\nNumber of inequality constraint Jacobian evaluations = 467\nNumber of Lagrangian Hessian evaluations = 463\nTotal CPU secs in IPOPT (w/o function evaluations) = 4.521\nTotal CPU secs in NLP function evaluations = 10.181\n\nEXIT: Solved To Acceptable Level.\n solver : t_proc (avg) t_wall (avg) n_eval\n nlp_f | 30.64ms ( 63.43us) 30.63ms ( 63.42us) 483\n nlp_g | 523.37ms ( 1.08ms) 522.91ms ( 1.08ms) 483\n nlp_grad | 3.11ms ( 3.11ms) 3.11ms ( 3.11ms) 1\n nlp_grad_f | 34.94ms ( 88.23us) 34.97ms ( 88.32us) 396\n nlp_hess_l | 6.38 s ( 13.87ms) 6.36 s ( 13.82ms) 460\n nlp_jac_g | 3.21 s ( 6.87ms) 3.20 s ( 6.84ms) 468\n total | 14.79 s ( 14.79 s) 14.70 s ( 14.70 s) 1\nThis is Ipopt version 3.12.3, running with linear solver mumps.\nNOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n\nNumber of nonzeros in equality constraint Jacobian...: 4230\nNumber of nonzeros in inequality constraint Jacobian.: 3466\nNumber of nonzeros in Lagrangian Hessian.............: 3648\n\nTotal number of variables............................: 2293\n variables with only lower bounds: 0\n variables with lower and upper bounds: 0\n variables with only upper bounds: 0\nTotal number of equality constraints.................: 1377\nTotal number of inequality constraints...............: 967\n inequality constraints with only lower bounds: 204\n inequality constraints with lower and upper bounds: 355\n inequality constraints with only upper bounds: 408\n\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 0 0.0000000e+00 9.83e+00 7.50e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n 1 1.0399716e-02 9.83e+00 1.26e+00 -1.0 1.20e+02 -4.0 4.93e-04 3.26e-04h 1\n 2 2.0045559e-02 9.83e+00 2.20e+01 -1.0 1.82e+02 -4.5 3.56e-04 3.85e-05h 1\n 3 4.4710489e-01 9.83e+00 2.52e+01 -1.0 2.46e+02 -5.0 3.88e-04 3.37e-04h 1\n 4 2.5591113e+00 9.83e+00 6.47e+01 -1.0 3.34e+02 -5.4 1.11e-03 4.95e-04h 1\n 5 1.0304744e+01 9.83e+00 1.06e+02 -1.0 2.54e+02 -5.9 1.15e-03 5.03e-04h 1\n 6 7.9216962e+01 9.82e+00 1.08e+02 -1.0 1.86e+02 -6.4 1.38e-03 1.35e-03h 1\n 7 1.6205310e+02 9.82e+00 1.46e+02 -1.0 1.46e+02 -6.9 1.48e-03 8.87e-04h 1\n 8 2.2025207e+02 9.83e+00 1.56e+02 -1.0 3.22e+02 -7.3 6.46e-04 4.87e-04h 1\n 9 2.2441787e+02 9.83e+00 1.54e+02 -1.0 4.45e+03 -7.8 8.53e-06 3.28e-05h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 10 2.5182294e+02 9.82e+00 1.42e+02 -1.0 9.50e+02 -8.3 8.65e-06 2.07e-04h 1\n 11 2.8777432e+02 9.81e+00 1.66e+02 -1.0 4.28e+02 -8.8 6.37e-04 2.55e-04h 1\n 12 4.0157811e+02 9.80e+00 1.67e+02 -1.0 1.45e+02 -9.2 7.37e-04 7.08e-04h 1\n 13 4.1550901e+02 9.80e+00 1.78e+02 -1.0 1.56e+02 -9.7 2.52e-04 7.97e-05h 1\n 14 6.1114602e+02 9.80e+00 1.21e+02 -1.0 1.50e+02 -10.2 8.33e-05 1.00e-03h 1\n 15 6.1355211e+02 9.80e+00 1.82e+02 -1.0 1.68e+02 -10.7 9.96e-04 1.13e-05h 1\n 16 6.2428940e+02 9.80e+00 2.68e+02 -1.0 3.50e+02 -11.2 1.43e-03 5.02e-05h 1\n 17r 6.2428940e+02 9.80e+00 9.99e+02 1.0 0.00e+00 -11.6 0.00e+00 4.03e-07R 5\n 18r 8.6414419e+02 9.80e+00 9.99e+02 1.0 6.29e+03 - 3.71e-04 1.98e-05f 1\n 19r 3.3647143e+03 9.80e+00 9.99e+02 1.0 1.65e+03 - 3.45e-04 1.15e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 20r 5.1092797e+04 9.80e+00 9.98e+02 1.0 7.67e+02 - 3.88e-04 6.81e-04f 1\n 21r 6.3342770e+04 9.79e+00 1.07e+03 1.0 7.22e+00 2.0 1.50e-01 1.14e-02f 1\n 22r 1.7594664e+05 9.73e+00 1.03e+03 1.0 3.13e+00 2.4 2.75e-01 2.46e-01f 1\n 23r 1.6728527e+05 9.68e+00 1.07e+03 1.0 2.44e+00 2.9 3.31e-02 1.26e-01f 1\n 24r 1.7195513e+05 9.51e+00 1.09e+03 1.0 5.75e-01 3.3 8.31e-01 1.00e+00f 1\n 25r 1.7774546e+05 9.35e+00 1.07e+03 1.0 1.83e+00 2.8 3.09e-01 3.74e-01f 1\n 26r 1.7826098e+05 8.80e+00 1.06e+03 1.0 5.11e+00 2.3 3.60e-01 5.80e-01f 1\n 27 1.7790304e+05 8.75e+00 2.67e+03 -1.0 9.73e+01 -12.1 1.05e-03 4.89e-03f 1\n 28 1.7790581e+05 8.75e+00 2.67e+03 -1.0 9.99e+01 -12.6 4.15e-03 8.71e-05h 1\n 29 1.7805578e+05 8.74e+00 2.67e+03 -1.0 1.49e+02 -13.1 2.98e-03 1.65e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 30 1.7826045e+05 8.73e+00 2.66e+03 -1.0 1.00e+02 -13.5 5.02e-03 1.36e-03h 1\n 31 1.7833630e+05 8.72e+00 2.66e+03 -1.0 9.67e+01 -14.0 4.35e-03 3.57e-04h 1\n 32 1.7838366e+05 8.72e+00 2.66e+03 -1.0 9.66e+01 -14.5 5.53e-03 1.85e-04h 1\n 33 1.7897357e+05 8.70e+00 2.66e+03 -1.0 9.66e+01 -15.0 6.03e-03 1.99e-03h 1\n 34 1.7979138e+05 8.68e+00 2.65e+03 -1.0 9.65e+01 -15.5 5.02e-03 2.33e-03h 1\n 35 1.8083548e+05 8.66e+00 2.64e+03 -1.0 9.65e+01 -15.9 5.14e-03 2.59e-03h 1\n 36 1.8126368e+05 8.65e+00 2.64e+03 -1.0 9.68e+01 -16.4 1.38e-03 9.55e-04h 1\n 37 1.8126992e+05 8.65e+00 2.64e+03 -1.0 9.72e+01 -16.9 2.04e-05 1.32e-05h 1\n 38 1.8127749e+05 8.65e+00 2.64e+03 -1.0 9.75e+01 -17.4 5.22e-03 1.49e-05h 1\n 39 1.8272429e+05 8.63e+00 2.63e+03 -1.0 9.81e+01 -17.8 3.94e-04 2.72e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 40 1.8373146e+05 8.61e+00 2.63e+03 -1.0 9.79e+01 -18.3 2.78e-05 1.86e-03h 1\n 41 1.8460711e+05 8.60e+00 2.63e+03 -1.0 9.79e+01 -18.8 6.10e-03 1.58e-03h 1\n 42 1.8506845e+05 8.59e+00 3.03e+03 -1.0 9.77e+01 -19.3 3.60e-03 8.05e-04h 1\n 43 1.8630538e+05 8.57e+00 2.81e+03 -1.0 9.79e+01 -19.7 1.23e-03 2.15e-03h 1\n 44 1.8693912e+05 8.56e+00 2.69e+03 -1.0 9.91e+01 -19.1 6.31e-04 1.18e-03h 1\n 45 1.8714858e+05 8.56e+00 2.61e+03 -1.0 9.72e+01 -19.6 1.86e-05 3.60e-04h 1\n 46 1.8833659e+05 8.54e+00 3.03e+03 -1.0 9.83e+01 -19.1 4.39e-03 2.16e-03h 1\n 47 1.8900050e+05 8.53e+00 3.12e+03 -1.0 9.71e+01 -19.6 1.51e-03 1.12e-03h 1\n 48 1.8921851e+05 8.53e+00 3.04e+03 -1.0 9.85e+01 -19.1 2.64e-05 3.81e-04h 1\n 49 1.8974673e+05 8.52e+00 2.87e+03 -1.0 9.66e+01 -19.6 1.53e-04 8.26e-04h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 50 1.8975438e+05 8.52e+00 3.74e+03 -1.0 9.81e+01 -19.1 3.80e-03 1.32e-05h 1\n 51 1.9002449e+05 8.52e+00 4.95e+03 -1.0 9.84e+01 -19.6 3.14e-03 3.97e-04h 1\n 52 1.9113127e+05 8.50e+00 5.17e+03 -1.0 9.86e+01 -19.1 2.66e-03 1.79e-03h 1\n 53 1.9218965e+05 8.49e+00 4.74e+03 -1.0 9.66e+01 -19.6 1.49e-05 1.46e-03h 1\n 54 1.9235900e+05 8.49e+00 4.77e+03 -1.0 9.69e+01 -19.1 3.43e-04 2.55e-04h 1\n 55 1.9614877e+05 8.45e+00 3.24e+03 -1.0 9.55e+01 -19.6 4.82e-05 4.46e-03h 1\n 56 1.9767353e+05 8.43e+00 3.55e+03 -1.0 9.43e+01 -19.1 3.11e-03 2.00e-03h 1\n 57 2.0045953e+05 8.41e+00 3.54e+03 -1.0 9.38e+01 -19.6 3.09e-03 3.11e-03h 1\n 58 2.0058633e+05 8.41e+00 4.32e+03 -1.0 9.34e+01 -19.1 2.74e-03 1.60e-04h 1\n 59r 2.0058633e+05 8.41e+00 1.00e+03 0.9 0.00e+00 -19.6 0.00e+00 4.42e-07R 5\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 60r 2.0053275e+05 8.41e+00 1.00e+03 0.9 3.17e+03 - 3.55e-04 6.45e-05f 1\n 61r 2.0041460e+05 8.41e+00 1.00e+03 0.9 1.58e+03 - 3.29e-04 1.19e-04f 1\n 62r 2.0024799e+05 8.41e+00 9.99e+02 0.9 1.43e+03 - 7.29e-04 1.04e-03f 1\n 63r 2.0178166e+05 8.42e+00 9.97e+02 0.9 9.93e+02 - 2.24e-03 1.49e-03f 1\n 64r 2.0902033e+05 8.42e+00 9.92e+02 0.9 5.71e+02 - 1.11e-02 3.85e-03f 1\n 65r 2.3136420e+05 8.41e+00 9.83e+02 0.9 3.09e+02 - 8.14e-03 9.32e-03f 1\n 66r 2.6153891e+05 8.40e+00 9.88e+02 0.9 3.62e+02 - 7.16e-03 1.05e-02f 1\n 67r 2.7798903e+05 8.40e+00 9.64e+02 0.9 3.55e+02 - 1.32e-02 9.11e-03f 1\n 68r 2.8657577e+05 8.40e+00 9.59e+02 0.9 2.47e+02 - 2.08e-02 4.88e-03f 1\n 69r 2.9527415e+05 8.40e+00 9.55e+02 0.9 1.55e+02 - 2.61e-03 4.50e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 70r 3.0527057e+05 8.40e+00 9.50e+02 0.9 5.20e+02 - 6.42e-03 5.12e-03f 1\n 71r 3.3582481e+05 8.40e+00 9.58e+02 0.9 1.01e+02 - 3.07e-02 1.51e-02f 1\n 72r 4.1107309e+05 8.41e+00 9.04e+02 0.9 1.14e+02 - 2.39e-02 3.35e-02f 1\n 73r 4.5964398e+05 8.41e+00 8.95e+02 0.9 1.15e+02 - 3.62e-02 2.11e-02f 1\n 74r 5.2016472e+05 8.41e+00 8.67e+02 0.9 1.36e+02 - 4.89e-02 3.03e-02f 1\n 75r 5.9030592e+05 8.41e+00 8.31e+02 0.9 1.48e+02 - 6.01e-02 3.79e-02f 1\n 76r 6.7476964e+05 8.41e+00 7.88e+02 0.9 1.61e+02 - 7.73e-02 4.59e-02f 1\n 77r 7.7561624e+05 8.41e+00 7.44e+02 0.9 1.82e+02 - 1.18e-01 5.62e-02f 1\n 78r 8.4945381e+05 8.41e+00 7.06e+02 0.9 4.62e+02 - 4.89e-03 5.46e-02f 1\n 79r 9.0140487e+05 8.41e+00 6.78e+02 0.9 2.50e+02 - 1.14e-01 3.60e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 80r 9.7546515e+05 8.41e+00 6.36e+02 0.9 2.54e+02 - 1.43e-01 6.16e-02f 1\n 81r 1.1190008e+06 8.38e+00 5.68e+02 0.9 3.76e+02 - 3.36e-02 1.08e-01f 1\n 82r 1.1225339e+06 8.37e+00 5.49e+02 0.9 8.82e+02 - 8.26e-03 3.25e-02f 1\n 83r 1.1286850e+06 8.36e+00 5.46e+02 0.9 4.12e+02 - 3.25e-02 6.75e-03f 1\n 84r 1.2659338e+06 8.29e+00 3.86e+02 0.9 7.87e+00 0.0 3.42e-01 2.93e-01f 1\n 85r 1.2612169e+06 8.28e+00 3.74e+02 0.2 2.38e+02 - 1.60e-02 3.04e-02f 1\n 86r 1.3908950e+06 8.17e+00 3.95e+02 0.2 3.02e+02 - 2.38e-02 7.50e-02f 1\n 87r 1.4392216e+06 8.15e+00 2.99e+02 0.2 7.97e+00 0.4 1.84e-01 1.68e-01f 1\n 88r 1.4749437e+06 8.14e+00 2.77e+02 0.2 5.34e+01 -0.1 4.57e-02 3.68e-02f 1\n 89r 1.4776820e+06 8.12e+00 2.77e+02 0.2 8.08e+02 - 3.91e-03 5.72e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 90r 1.4904632e+06 8.09e+00 3.10e+02 0.2 2.23e+02 - 1.15e-01 3.01e-02f 1\n 91r 1.4780369e+06 8.03e+00 2.85e+02 0.2 2.60e+02 - 1.24e-02 4.77e-02f 1\n 92r 1.4633854e+06 7.98e+00 2.84e+02 0.2 2.35e+02 - 1.42e-01 5.45e-02f 1\n 93r 1.4468075e+06 7.94e+00 2.89e+02 0.2 3.39e+02 - 2.25e-02 4.04e-02f 1\n 94r 1.4177297e+06 7.84e+00 3.28e+02 0.2 3.67e+02 - 4.08e-02 1.06e-01f 1\n 95r 1.4208335e+06 7.83e+00 3.24e+02 0.2 3.41e+02 - 4.16e-02 2.22e-02f 1\n 96r 1.4035701e+06 7.79e+00 2.82e+02 0.2 3.36e+02 - 6.49e-02 9.28e-02f 1\n 97r 1.3981555e+06 7.76e+00 2.08e+02 0.2 3.03e+01 -0.5 1.71e-01 7.29e-02f 1\n 98r 1.3736085e+06 7.75e+00 2.02e+02 0.2 4.74e+02 - 2.70e-02 1.88e-02f 1\n 99r 1.3738774e+06 7.70e+00 1.87e+02 0.2 4.77e+02 - 7.99e-02 9.61e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 100r 1.3580286e+06 7.69e+00 2.14e+02 0.2 4.67e+02 - 1.03e-01 3.83e-02f 1\n 101r 1.3021262e+06 7.68e+00 2.15e+02 0.2 5.22e+02 - 1.39e-01 9.61e-02f 1\n 102r 1.2905134e+06 7.68e+00 2.12e+02 0.2 5.78e+02 - 5.56e-02 4.11e-02f 1\n 103r 1.2958912e+06 7.68e+00 1.61e+02 0.2 6.56e+02 - 2.71e-02 1.08e-01f 1\n 104r 1.3293811e+06 7.69e+00 1.47e+02 0.2 7.20e+02 - 8.99e-02 8.45e-02f 1\n 105r 1.4013282e+06 7.70e+00 1.56e+02 0.2 9.31e+02 - 7.51e-02 1.05e-01f 1\n 106r 1.5010415e+06 7.67e+00 2.27e+02 0.2 3.80e+01 -1.0 6.34e-02 1.79e-01f 1\n 107r 1.5310867e+06 7.66e+00 2.10e+02 0.2 5.35e+01 -1.5 5.32e-02 3.54e-02f 1\n 108r 1.6858235e+06 7.64e+00 2.07e+02 0.2 2.27e+02 -2.0 1.41e-02 1.65e-02f 1\n 109r 1.6992342e+06 7.64e+00 2.00e+02 0.2 4.36e+01 -0.6 3.64e-02 3.57e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 110r 1.6720537e+06 7.63e+00 1.42e+02 0.2 2.59e+00 0.7 3.58e-01 1.88e-01f 1\n 111r 1.6393971e+06 7.62e+00 1.61e+02 0.2 5.96e+00 0.2 6.27e-02 2.98e-01f 1\n 112r 1.5942856e+06 7.62e+00 9.69e+01 0.2 3.94e+00 0.6 5.59e-01 6.60e-01f 1\n 113r 1.5454439e+06 7.61e+00 8.14e+01 -0.5 9.57e+00 0.2 2.87e-01 3.13e-01f 1\n 114r 1.5348572e+06 7.52e+00 4.26e+01 -0.5 2.28e+01 -0.3 2.19e-01 1.39e-01f 1\n 115 1.5101190e+06 7.46e+00 1.69e+04 -1.0 7.31e+02 -19.1 1.74e-03 8.11e-03f 1\n 116 1.5081170e+06 7.45e+00 1.69e+04 -1.0 6.47e+02 -19.6 1.66e-03 6.70e-04f 1\n 117 1.5000973e+06 7.43e+00 1.68e+04 -1.0 6.33e+02 -19.1 1.83e-03 2.70e-03f 1\n 118 1.4972338e+06 7.43e+00 1.68e+04 -1.0 6.14e+02 -19.6 4.62e-03 9.87e-04f 1\n 119 1.4926222e+06 7.41e+00 1.68e+04 -1.0 5.92e+02 -19.1 4.76e-03 1.69e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 120 1.4897946e+06 7.40e+00 1.68e+04 -1.0 5.73e+02 -19.6 1.24e-03 1.25e-03f 1\n 121 1.4867964e+06 7.39e+00 1.67e+04 -1.0 5.67e+02 -19.1 1.24e-02 1.40e-03f 1\n 122 1.4811114e+06 7.37e+00 1.67e+04 -1.0 5.31e+02 -19.6 2.94e-03 2.99e-03f 1\n 123 1.4776862e+06 7.36e+00 1.67e+04 -1.0 5.21e+02 -19.1 1.64e-02 1.85e-03f 1\n 124 1.4722508e+06 7.34e+00 1.66e+04 -1.0 4.87e+02 -19.6 5.29e-03 3.07e-03f 1\n 125 1.4686209e+06 7.32e+00 1.66e+04 -1.0 4.76e+02 -19.1 8.82e-03 2.07e-03f 1\n 126 1.4549412e+06 7.26e+00 1.64e+04 -1.0 4.61e+02 -19.6 8.60e-03 7.87e-03f 1\n 127 1.4439147e+06 7.22e+00 1.63e+04 -1.0 4.42e+02 -19.1 3.68e-03 6.45e-03f 1\n 128 1.4318869e+06 7.17e+00 1.62e+04 -1.0 4.33e+02 -19.6 2.58e-04 7.02e-03f 1\n 129 1.4287865e+06 7.15e+00 1.62e+04 -1.0 4.28e+02 -19.1 9.28e-03 1.82e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 130 1.4117953e+06 7.08e+00 1.60e+04 -1.0 4.17e+02 -19.6 1.21e-02 9.95e-03f 1\n 131 1.4094629e+06 7.07e+00 1.60e+04 -1.0 3.99e+02 -19.1 1.22e-02 1.30e-03f 1\n 132 1.3898684e+06 6.99e+00 1.58e+04 -1.0 3.86e+02 -19.6 6.69e-03 1.09e-02f 1\n 133 1.3666966e+06 6.91e+00 1.56e+04 -1.0 3.73e+02 -19.1 1.23e-04 1.25e-02f 1\n 134 1.3653874e+06 6.90e+00 1.56e+04 -1.0 3.64e+02 -19.6 8.61e-03 7.06e-04f 1\n 135 1.3600774e+06 6.88e+00 1.56e+04 -1.0 3.58e+02 -19.1 4.77e-04 2.88e-03f 1\n 136 1.3599863e+06 6.88e+00 1.56e+04 -1.0 3.54e+02 -19.6 1.16e-04 4.90e-05f 1\n 137 1.3520824e+06 6.85e+00 1.55e+04 -1.0 3.56e+02 -19.1 2.25e-02 4.63e-03f 1\n 138 1.3198881e+06 6.72e+00 1.52e+04 -1.0 3.32e+02 -19.6 4.47e-03 1.93e-02f 1\n 139 1.3018334e+06 6.65e+00 1.51e+04 -1.0 3.23e+02 -19.1 3.31e-02 1.03e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 140 1.3001178e+06 6.64e+00 1.50e+04 -1.0 3.09e+02 -19.6 2.72e-03 1.11e-03f 1\n 141 1.2712885e+06 6.53e+00 1.48e+04 -1.0 3.10e+02 -19.1 1.01e-04 1.69e-02f 1\n 142 1.2672814e+06 6.51e+00 1.48e+04 -1.0 3.20e+02 -19.6 4.18e-03 2.32e-03f 1\n 143 1.2662613e+06 6.51e+00 1.47e+04 -1.0 2.90e+02 -19.1 1.94e-02 6.22e-04f 1\n 144 1.2251694e+06 6.34e+00 1.43e+04 -1.0 2.82e+02 -19.6 2.71e-03 2.66e-02f 1\n 145 1.2238974e+06 6.33e+00 1.43e+04 -1.0 2.99e+02 -19.1 1.32e-03 7.91e-04f 1\n 146 1.2218795e+06 6.32e+00 1.43e+04 -1.0 3.08e+02 -19.6 1.65e-03 1.32e-03f 1\n 147 1.2218327e+06 6.32e+00 1.43e+04 -1.0 3.05e+02 -19.1 3.84e-03 2.74e-05f 1\n 148 1.1933966e+06 6.22e+00 1.41e+04 -1.0 3.06e+02 -19.6 7.34e-04 1.67e-02f 1\n 149 1.1923484e+06 6.21e+00 1.41e+04 -1.0 3.08e+02 -19.1 8.24e-03 5.84e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 150 1.1851137e+06 6.19e+00 1.40e+04 -1.0 2.76e+02 -19.6 7.09e-04 4.39e-03f 1\n 151 1.1674398e+06 6.13e+00 1.39e+04 -1.0 2.77e+02 -19.1 4.82e-03 9.94e-03f 1\n 152 1.1671054e+06 6.12e+00 1.39e+04 -1.0 2.72e+02 -19.6 4.03e-03 2.01e-04f 1\n 153 1.1607724e+06 6.10e+00 1.38e+04 -1.0 2.70e+02 -19.1 6.82e-04 3.72e-03f 1\n 154 1.1521951e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.6 1.57e-03 5.21e-03f 1\n 155 1.1512500e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.1 7.99e-04 5.27e-04f 1\n 156 1.1507231e+06 6.06e+00 1.37e+04 -1.0 2.68e+02 -19.6 4.24e-04 3.27e-04f 1\n 157 1.1507040e+06 6.06e+00 1.37e+04 -1.0 2.81e+02 -19.1 5.41e-04 9.69e-06f 1\n 158 1.1467730e+06 6.05e+00 1.37e+04 -1.0 2.63e+02 -19.6 8.26e-06 2.11e-03f 1\n 159 1.0732380e+06 5.82e+00 1.32e+04 -1.0 2.63e+02 -19.1 2.67e-04 3.91e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 160 1.0714161e+06 5.81e+00 1.32e+04 -1.0 2.56e+02 -19.6 1.02e-02 9.30e-04f 1\n 161 1.0713627e+06 5.81e+00 1.32e+04 -1.0 2.54e+02 -19.1 8.09e-05 2.84e-05f 1\n 162 1.0580046e+06 5.76e+00 1.31e+04 -1.0 2.54e+02 -19.6 1.35e-04 7.69e-03f 1\n 163 1.0545534e+06 5.75e+00 1.30e+04 -1.0 2.54e+02 -19.1 3.39e-03 1.89e-03f 1\n 164 1.0341517e+06 5.68e+00 1.30e+04 -1.0 2.52e+02 -19.6 8.74e-04 1.22e-02f 1\n 165 1.0336429e+06 5.68e+00 1.29e+04 -1.0 2.51e+02 -19.1 6.22e-04 2.90e-04f 1\n 166 1.0275521e+06 5.66e+00 1.38e+04 -1.0 2.51e+02 -19.6 4.36e-04 3.68e-03f 1\n 167 1.0179732e+06 5.63e+00 1.32e+04 -1.0 2.50e+02 -19.1 7.95e-03 5.53e-03f 1\n 168 9.9624521e+05 5.55e+00 1.59e+04 -1.0 2.48e+02 -19.6 4.63e-03 1.40e-02f 1\n 169 9.9527357e+05 5.55e+00 1.58e+04 -1.0 2.44e+02 -19.1 5.96e-04 5.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 170 9.9489625e+05 5.55e+00 1.56e+04 -1.0 2.47e+02 -19.6 1.04e-03 2.42e-04f 1\n 171 9.9489118e+05 5.55e+00 1.50e+04 -1.0 2.53e+02 -19.1 1.24e-03 2.86e-06f 1\n 172 9.9421219e+05 5.54e+00 1.57e+04 -1.0 2.49e+02 -19.6 3.63e-06 4.37e-04f 1\n 173 9.8486650e+05 5.51e+00 1.68e+04 -1.0 2.46e+02 -19.1 5.48e-04 5.68e-03f 1\n 174 9.7308682e+05 5.47e+00 1.65e+04 -1.0 2.49e+02 -19.6 8.25e-03 7.61e-03f 1\n 175 9.7212896e+05 5.47e+00 1.65e+04 -1.0 2.44e+02 -19.1 3.20e-04 5.80e-04f 1\n 176 9.6540396e+05 5.44e+00 1.75e+04 -1.0 2.49e+02 -19.6 9.01e-04 4.34e-03f 1\n 177 9.4998516e+05 5.39e+00 1.51e+04 -1.0 2.44e+02 -19.1 1.93e-02 9.44e-03f 1\n 178 9.4904520e+05 5.39e+00 1.52e+04 -1.0 2.45e+02 -19.6 2.19e-04 6.06e-04f 1\n 179 9.4902274e+05 5.39e+00 1.51e+04 -1.0 2.75e+02 -19.1 1.62e-04 1.14e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 180 9.3880492e+05 5.35e+00 1.67e+04 -1.0 2.29e+02 -19.6 1.38e-05 6.45e-03f 1\n 181 8.9559690e+05 5.21e+00 1.79e+04 -1.0 2.29e+02 -19.1 1.06e-02 2.71e-02f 1\n 182 8.5549874e+05 5.07e+00 1.93e+04 -1.0 2.22e+02 -19.6 4.07e-03 2.60e-02f 1\n 183 8.4452994e+05 5.04e+00 1.99e+04 -1.0 2.17e+02 -19.1 4.96e-04 7.46e-03f 1\n 184 8.2921005e+05 4.98e+00 1.98e+04 -1.0 2.14e+02 -19.6 9.95e-03 1.09e-02f 1\n 185 8.2905253e+05 4.98e+00 1.90e+04 -1.0 2.09e+02 -19.1 5.75e-03 1.10e-04f 1\n 186 8.2904529e+05 4.98e+00 1.89e+04 -1.0 2.18e+02 -19.6 7.15e-05 5.41e-06f 1\n 187 8.2712955e+05 4.97e+00 1.92e+04 -1.0 2.14e+02 -19.1 5.72e-05 1.35e-03f 1\n 188 8.0950543e+05 4.91e+00 2.28e+04 -1.0 2.31e+02 -19.6 1.11e-03 1.34e-02f 1\n 189 8.0862616e+05 4.90e+00 2.24e+04 -1.0 2.12e+02 -19.1 1.92e-03 6.51e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 190 8.0425351e+05 4.89e+00 2.35e+04 -1.0 2.22e+02 -19.6 4.57e-04 3.51e-03f 1\n 191 7.9537870e+05 4.85e+00 2.31e+04 -1.0 2.15e+02 -19.1 7.37e-03 6.61e-03f 1\n 192 7.9489154e+05 4.85e+00 2.29e+04 -1.0 2.11e+02 -19.6 1.29e-03 3.92e-04f 1\n 193 7.8751827e+05 4.83e+00 2.29e+04 -1.0 2.01e+02 -19.1 2.56e-03 5.71e-03f 1\n 194 7.8736120e+05 4.82e+00 2.31e+04 -1.0 2.00e+02 -19.6 1.54e-04 1.32e-04f 1\n 195 7.8402757e+05 4.81e+00 2.31e+04 -1.0 1.94e+02 -19.1 2.19e-03 2.63e-03f 1\n 196 7.8388845e+05 4.81e+00 2.53e+04 -1.0 1.98e+02 -19.6 2.15e-04 1.18e-04f 1\n 197 7.8385262e+05 4.81e+00 2.50e+04 -1.0 1.94e+02 -19.1 4.34e-03 2.82e-05f 1\n 198 7.8236217e+05 4.81e+00 1.09e+04 -1.0 1.97e+02 -19.6 3.11e-04 1.27e-03f 1\n 199 7.7035462e+05 4.76e+00 1.08e+04 -1.0 1.91e+02 -19.1 3.91e-04 9.52e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 200 7.6826638e+05 4.75e+00 1.08e+04 -1.0 1.90e+02 -19.6 2.68e-02 1.77e-03f 1\n 201 7.6137802e+05 4.73e+00 1.07e+04 -1.0 1.80e+02 -19.1 5.08e-04 5.41e-03f 1\n 202 7.6092627e+05 4.72e+00 1.07e+04 -1.0 1.85e+02 -19.6 8.12e-03 3.85e-04f 1\n 203 6.8620409e+05 4.44e+00 1.26e+04 -1.0 1.80e+02 -19.1 1.57e-04 6.08e-02f 1\n 204 6.8223234e+05 4.42e+00 1.15e+04 -1.0 1.85e+02 -19.6 6.43e-03 3.54e-03f 1\n 205 6.7550321e+05 4.40e+00 1.16e+04 -1.0 1.74e+02 -19.1 5.10e-03 5.69e-03f 1\n 206 6.7512988e+05 4.39e+00 1.00e+04 -1.0 1.86e+02 -19.6 8.13e-03 3.47e-04f 1\n 207 6.6341460e+05 4.35e+00 9.84e+03 -1.0 1.72e+02 -19.1 1.30e-02 1.04e-02f 1\n 208 6.5075026e+05 4.30e+00 1.09e+04 -1.0 1.87e+02 -19.6 2.10e-04 1.19e-02f 1\n 209 6.4469528e+05 4.27e+00 1.05e+04 -1.0 1.72e+02 -19.1 7.52e-03 5.37e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 210 6.3074280e+05 4.22e+00 1.26e+04 -1.0 1.85e+02 -19.6 4.48e-04 1.32e-02f 1\n 211 6.3015403e+05 4.21e+00 1.26e+04 -1.0 3.38e+01 2.3 1.77e-03 7.51e-04f 1\n 212 6.3013669e+05 4.21e+00 1.47e+04 -1.0 1.21e+02 1.8 1.17e-03 1.79e-05f 1\n 213 6.2710947e+05 4.20e+00 1.45e+04 -1.0 1.26e+02 1.4 4.90e-04 2.86e-03f 1\n 214 6.2100935e+05 4.18e+00 1.49e+04 -1.0 1.53e+02 0.9 1.86e-03 5.85e-03f 1\n 215 6.1066877e+05 4.14e+00 1.59e+04 -1.0 1.97e+02 0.4 1.05e-05 8.89e-03f 1\n 216 6.0984620e+05 4.14e+00 1.57e+04 -1.0 1.84e+02 1.7 8.49e-03 1.01e-03f 1\n 217 6.0751052e+05 4.13e+00 1.57e+04 -1.0 2.02e+02 1.3 5.49e-03 2.08e-03f 1\n 218 6.0707832e+05 4.12e+00 1.57e+04 -1.0 2.01e+02 1.7 2.32e-04 5.32e-04f 1\n 219 6.0707327e+05 4.12e+00 1.53e+04 -1.0 1.96e+02 2.1 2.90e-03 7.52e-06f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 220 6.0555478e+05 4.12e+00 1.54e+04 -1.0 2.25e+02 1.6 9.57e-04 1.84e-03f 1\n 221 6.0553909e+05 4.12e+00 1.47e+04 -1.0 2.14e+02 2.1 5.83e-03 2.26e-05f 1\n 222 6.0387470e+05 4.11e+00 1.47e+04 -1.0 2.27e+02 1.6 2.04e-03 1.80e-03f 1\n 223 6.0325229e+05 4.11e+00 1.47e+04 -1.0 2.20e+02 2.0 4.35e-04 7.72e-04f 1\n 224 6.0322399e+05 4.11e+00 1.45e+04 -1.0 2.21e+02 1.5 4.10e-03 2.96e-05f 1\n 225 6.0185557e+05 4.10e+00 1.45e+04 -1.0 2.17e+02 2.0 1.59e-05 1.70e-03f 1\n 226 6.0180800e+05 4.10e+00 1.44e+04 -1.0 2.29e+02 1.5 1.37e-03 4.63e-05f 1\n 227 5.9923339e+05 4.09e+00 1.44e+04 -1.0 2.27e+02 1.9 2.84e-03 2.85e-03f 1\n 228 5.9851909e+05 4.09e+00 1.44e+04 -1.0 2.29e+02 1.4 9.28e-04 6.31e-04f 1\n 229 5.9692058e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.9 1.94e-03 1.77e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 230 5.9689878e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.4 1.22e-05 1.79e-05f 1\n 231 5.9684337e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.8 7.71e-05 6.11e-05f 1\n 232 5.9682024e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.3 2.55e-06 2.10e-05f 1\n 233 5.9680869e+05 4.08e+00 1.38e+04 -1.0 2.28e+02 1.8 7.05e-03 1.36e-05f 1\n 234 5.9076367e+05 4.05e+00 1.41e+04 -1.0 2.30e+02 1.3 5.00e-04 5.99e-03f 1\n 235 5.9073170e+05 4.05e+00 1.42e+04 -1.0 2.32e+02 1.7 4.66e-03 3.40e-05f 1\n 236 5.8521172e+05 4.03e+00 1.45e+04 -1.0 2.32e+02 1.2 8.81e-04 5.50e-03f 1\n 237 5.8376022e+05 4.03e+00 1.44e+04 -1.0 2.30e+02 0.8 2.07e-06 1.04e-03f 1\n 238 5.8372062e+05 4.03e+00 1.53e+04 -1.0 2.35e+02 2.1 3.62e-03 4.81e-05f 1\n 239 5.7779448e+05 4.00e+00 1.55e+04 -1.0 2.39e+02 1.6 1.33e-02 6.22e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 240 5.7771432e+05 4.00e+00 1.57e+04 -1.0 2.26e+02 2.0 1.00e-03 9.89e-05f 1\n 241 5.7665827e+05 4.00e+00 1.57e+04 -1.0 2.07e+02 1.6 9.92e-04 1.10e-03f 1\n 242 5.7511556e+05 3.99e+00 1.55e+04 -1.0 2.12e+02 2.0 1.09e-03 1.92e-03f 1\n 243 5.7052438e+05 3.97e+00 1.52e+04 -1.0 2.09e+02 1.5 2.73e-04 4.68e-03f 1\n 244 5.6998537e+05 3.97e+00 1.51e+04 -1.0 2.36e+02 1.9 3.75e-04 6.54e-04f 1\n 245 5.6612860e+05 3.95e+00 1.48e+04 -1.0 2.37e+02 1.5 3.47e-05 3.75e-03f 1\n 246 5.6572525e+05 3.95e+00 1.50e+04 -1.0 2.36e+02 1.9 2.57e-03 4.85e-04f 1\n 247 5.6516789e+05 3.95e+00 1.51e+04 -1.0 2.36e+02 1.4 1.32e-03 4.83e-04f 1\n 248 5.6316860e+05 3.94e+00 1.52e+04 -1.0 2.36e+02 1.8 3.05e-03 2.38e-03f 1\n 249 5.6254967e+05 3.94e+00 1.50e+04 -1.0 2.35e+02 1.3 7.91e-06 4.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 250 5.6077033e+05 3.93e+00 1.49e+04 -1.0 2.36e+02 1.8 1.29e-03 2.17e-03f 1\n 251 5.6022313e+05 3.93e+00 1.47e+04 -1.0 2.34e+02 1.3 2.29e-06 3.07e-04f 1\n 252 5.5692514e+05 3.91e+00 1.49e+04 -1.0 2.36e+02 1.7 8.13e-03 3.96e-03f 1\n 253 5.5673567e+05 3.91e+00 1.49e+04 -1.0 2.31e+02 1.2 2.68e-06 9.58e-05f 1\n 254 5.5535652e+05 3.91e+00 1.55e+04 -1.0 2.33e+02 1.7 1.14e-02 1.64e-03f 1\n 255 5.5534273e+05 3.91e+00 1.56e+04 -1.0 2.30e+02 2.1 4.75e-04 1.85e-05f 1\n 256 5.5514554e+05 3.90e+00 1.71e+04 -1.0 2.27e+02 1.6 1.32e-02 2.20e-04f 1\n 257 5.5141803e+05 3.90e+00 1.48e+04 -1.0 4.83e+02 1.1 1.93e-05 1.30e-03f 1\n 258 5.4693933e+05 3.88e+00 1.42e+04 -1.0 2.39e+02 1.6 2.91e-04 4.86e-03f 1\n 259 5.4687686e+05 3.88e+00 1.43e+04 -1.0 2.52e+02 1.1 2.34e-03 6.08e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 260 5.4353115e+05 3.87e+00 1.41e+04 -1.0 2.42e+02 1.5 1.34e-03 3.66e-03f 1\n 261 5.4346337e+05 3.87e+00 1.41e+04 -1.0 2.07e+02 1.0 1.56e-04 6.65e-05f 1\n 262 5.4335021e+05 3.87e+00 1.42e+04 -1.0 1.95e+02 1.5 2.22e-03 1.26e-04f 1\n 263 5.4296226e+05 3.86e+00 1.43e+04 -1.0 1.86e+02 1.0 2.63e-03 3.71e-04f 1\n 264 5.3812113e+05 3.84e+00 1.40e+04 -1.0 2.13e+02 1.4 2.17e-05 5.31e-03f 1\n 265 5.3811408e+05 3.84e+00 1.41e+04 -1.0 2.23e+02 1.8 3.29e-04 8.06e-06f 1\n 266 5.3810662e+05 3.84e+00 1.41e+04 -1.0 2.43e+02 1.4 9.90e-04 7.83e-06f 1\n 267 5.3692599e+05 3.84e+00 1.38e+04 -1.0 2.30e+02 1.8 8.84e-05 1.33e-03f 1\n 268 5.3689601e+05 3.84e+00 1.38e+04 -1.0 1.90e+02 1.3 1.05e-03 3.21e-05f 1\n 269 5.3480708e+05 3.83e+00 1.32e+04 -1.0 2.44e+02 0.8 2.23e-04 1.25e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 270 5.3431885e+05 3.83e+00 1.34e+04 -1.0 1.83e+02 1.3 4.31e-03 5.17e-04f 1\n 271 5.3310063e+05 3.83e+00 1.31e+04 -1.0 1.89e+02 1.7 2.86e-05 1.41e-03f 1\n 272 5.3150897e+05 3.82e+00 1.22e+04 -1.0 1.85e+02 2.1 1.96e-04 1.98e-03f 1\n 273 5.3147543e+05 3.82e+00 1.22e+04 -1.0 1.61e+02 1.6 1.35e-04 3.82e-05f 1\n 274 5.3137612e+05 3.82e+00 1.24e+04 -1.0 1.76e+02 1.2 3.23e-03 1.02e-04f 1\n 275 5.3106158e+05 3.82e+00 1.29e+04 -1.0 1.79e+02 1.6 5.34e-03 3.64e-04f 1\n 276 5.2929724e+05 3.81e+00 1.28e+04 -1.0 1.73e+02 1.1 5.52e-05 1.84e-03f 1\n 277 5.2907597e+05 3.81e+00 1.29e+04 -1.0 1.63e+02 2.4 4.41e-04 3.68e-04f 1\n 278 5.2816992e+05 3.80e+00 1.27e+04 -1.0 1.88e+02 2.0 5.12e-04 1.14e-03f 1\n 279 5.2807415e+05 3.80e+00 1.27e+04 -1.0 1.48e+03 1.5 1.26e-06 8.93e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 280 5.2805145e+05 3.80e+00 1.26e+04 -1.0 1.94e+02 1.0 1.55e-03 2.34e-05f 1\n 281 5.2494578e+05 3.79e+00 1.26e+04 -1.0 1.75e+02 1.4 7.38e-04 3.61e-03f 1\n 282 5.2382656e+05 3.79e+00 1.20e+04 -1.0 6.75e+02 1.0 1.61e-05 3.04e-04f 1\n 283 5.2294012e+05 3.78e+00 1.20e+04 -1.0 1.74e+02 1.4 1.78e-03 9.88e-04f 1\n 284 5.2002561e+05 3.77e+00 1.24e+04 -1.0 1.76e+02 1.8 7.11e-03 3.79e-03f 1\n 285 5.1038171e+05 3.73e+00 1.19e+04 -1.0 1.71e+02 1.3 6.29e-05 1.07e-02f 1\n 286 5.1029468e+05 3.73e+00 1.19e+04 -1.0 1.84e+02 1.8 7.42e-04 1.12e-04f 1\n 287 5.1028585e+05 3.73e+00 1.21e+04 -1.0 2.01e+02 2.2 4.17e-04 1.31e-05f 1\n 288 5.0835844e+05 3.72e+00 1.19e+04 -1.0 2.75e+02 1.7 1.15e-05 2.41e-03f 1\n 289 5.0829350e+05 3.72e+00 1.19e+04 -1.0 1.71e+02 1.2 3.97e-04 6.12e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 290 5.0786133e+05 3.72e+00 1.30e+04 -1.0 1.77e+02 1.7 1.37e-02 5.49e-04f 1\n 291 4.9734337e+05 3.69e+00 1.06e+04 -1.0 1.72e+02 1.2 8.48e-04 8.86e-03f 1\n 292 4.9689003e+05 3.68e+00 1.08e+04 -1.0 1.74e+02 1.6 3.20e-03 5.66e-04f 1\n 293 4.9257017e+05 3.67e+00 9.15e+03 -1.0 1.62e+02 1.1 7.85e-05 3.01e-03f 1\n 294 4.9140020e+05 3.67e+00 9.02e+03 -1.0 2.41e+02 1.6 4.58e-05 1.41e-03f 1\n 295 4.9127824e+05 3.67e+00 1.14e+04 -1.0 1.61e+02 1.1 2.66e-03 6.68e-05f 1\n 296 4.9066209e+05 3.66e+00 1.29e+04 -1.0 1.75e+02 1.5 1.82e-02 7.38e-04f 1\n 297 4.8899995e+05 3.66e+00 1.45e+04 -1.0 5.05e+02 1.0 9.31e-04 4.06e-04f 1\n 298 4.7750162e+05 3.61e+00 1.35e+04 -1.0 1.76e+02 1.5 4.98e-03 1.35e-02f 1\n 299 4.7434464e+05 3.61e+00 1.49e+04 -1.0 9.97e+02 1.0 6.27e-04 4.14e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 300 4.7339897e+05 3.61e+00 1.50e+04 -1.0 1.73e+02 1.4 2.13e-03 1.10e-03f 1\n 301 4.7161223e+05 3.61e+00 1.40e+04 -1.0 3.25e+02 0.9 1.03e-04 5.77e-04f 1\n 302 4.7150987e+05 3.60e+00 1.43e+04 -1.0 2.00e+02 0.4 6.17e-03 9.97e-05f 1\n 303 4.6068296e+05 3.57e+00 1.36e+04 -1.0 3.30e+02 -0.0 4.88e-05 1.06e-02f 1\n 304 4.5827805e+05 3.56e+00 1.49e+04 -1.0 1.68e+02 1.3 1.36e-02 2.70e-03f 1\n 305 4.5598068e+05 3.55e+00 1.41e+04 -1.0 4.79e+02 0.8 4.48e-04 7.06e-04f 1\n 306 4.5518400e+05 3.55e+00 1.43e+04 -1.0 1.59e+02 1.2 2.67e-03 8.88e-04f 1\n 307 4.5309751e+05 3.55e+00 1.72e+04 -1.0 2.98e+02 0.8 2.61e-03 8.91e-04f 1\n 308 4.4826799e+05 3.53e+00 1.86e+04 -1.0 1.66e+02 1.2 1.97e-02 5.39e-03f 1\n 309 4.4337229e+05 3.51e+00 1.70e+04 -1.0 1.62e+02 0.7 4.61e-05 4.04e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 310 4.3957043e+05 3.50e+00 2.22e+04 -1.0 1.64e+02 1.1 5.11e-02 4.22e-03f 1\n 311 4.3840763e+05 3.50e+00 2.35e+04 -1.0 1.68e+02 0.7 4.15e-03 9.47e-04f 1\n 312 4.2663652e+05 3.45e+00 2.43e+04 -1.0 1.62e+02 1.1 2.95e-02 1.34e-02f 1\n 313 4.1771582e+05 3.43e+00 2.40e+04 -1.0 1.75e+02 0.6 6.75e-03 7.00e-03f 1\n 314 4.1583852e+05 3.42e+00 2.27e+04 -1.0 8.29e+02 0.1 1.03e-04 3.62e-04f 1\n 315 4.0823036e+05 3.40e+00 2.35e+04 -1.0 1.82e+02 0.6 9.82e-03 6.83e-03f 1\n 316 4.0742102e+05 3.40e+00 2.31e+04 -1.0 2.88e+02 0.1 7.31e-05 5.39e-04f 1\n 317 4.0736672e+05 3.40e+00 2.36e+04 -1.0 4.84e+02 -0.4 7.32e-03 5.87e-05f 1\n 318 3.9419770e+05 3.35e+00 2.18e+04 -1.0 7.13e+02 -0.9 8.53e-05 1.33e-02f 1\n 319 3.8663282e+05 3.33e+00 1.92e+04 -1.0 8.56e+02 -1.3 2.10e-04 5.79e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 320 3.8579331e+05 3.33e+00 2.13e+04 -1.0 9.22e+02 -1.8 7.40e-03 7.22e-04f 1\n 321 3.7974323e+05 3.32e+00 1.94e+04 -1.0 9.50e+02 -2.3 1.97e-04 4.91e-03f 1\n 322 3.7938905e+05 3.31e+00 2.15e+04 -1.0 9.51e+02 -2.8 8.43e-03 3.27e-04f 1\n 323 3.7037180e+05 3.29e+00 1.92e+04 -1.0 9.55e+02 -3.3 1.13e-04 8.55e-03f 1\n 324 3.6908213e+05 3.28e+00 1.94e+04 -1.0 9.41e+02 -3.7 2.53e-03 1.30e-03f 1\n 325 3.6277620e+05 3.26e+00 1.81e+04 -1.0 2.17e+02 0.3 8.07e-04 7.29e-03f 1\n 326 3.5989666e+05 3.24e+00 1.79e+04 -1.0 1.55e+02 1.6 1.27e-03 5.36e-03f 1\n 327 3.5985661e+05 3.24e+00 1.79e+04 -1.0 1.59e+02 2.1 9.24e-04 1.04e-04f 1\n 328 3.5935266e+05 3.24e+00 1.79e+04 -1.0 1.62e+02 1.6 6.26e-05 8.73e-04f 1\n 329 3.5783375e+05 3.23e+00 1.78e+04 -1.0 1.63e+02 2.0 3.50e-03 3.63e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 330 3.5743587e+05 3.22e+00 1.78e+04 -1.0 1.61e+02 1.5 3.51e-05 6.78e-04f 1\n 331 3.5668635e+05 3.22e+00 1.78e+04 -1.0 1.52e+02 1.1 2.73e-04 1.08e-03f 1\n 332 3.5630331e+05 3.22e+00 1.78e+04 -1.0 1.67e+02 0.6 3.51e-03 4.92e-04f 1\n 333 3.5562828e+05 3.22e+00 1.78e+04 -1.0 2.82e+02 0.1 3.34e-03 7.59e-04f 1\n 334 3.5480287e+05 3.21e+00 1.78e+04 -1.0 4.82e+02 -0.4 4.21e-05 9.86e-04f 1\n 335 3.5438009e+05 3.21e+00 1.78e+04 -1.0 6.55e+02 -0.8 1.29e-04 5.04e-04f 1\n 336 3.5436086e+05 3.21e+00 1.78e+04 -1.0 8.12e+02 -1.3 2.56e-03 2.29e-05f 1\n 337 3.5193976e+05 3.20e+00 1.77e+04 -1.0 8.82e+02 -1.8 1.07e-03 2.89e-03f 1\n 338 3.5190047e+05 3.20e+00 1.77e+04 -1.0 9.10e+02 -2.3 7.21e-03 4.74e-05f 1\n 339 3.5093683e+05 3.20e+00 1.77e+04 -1.0 7.47e+02 -1.0 1.35e-05 1.13e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 340 3.5052498e+05 3.20e+00 1.77e+04 -1.0 2.07e+02 0.4 5.15e-04 5.29e-04f 1\n 341 3.3750946e+05 3.14e+00 1.74e+04 -1.0 3.58e+02 -0.1 1.67e-05 1.64e-02f 1\n 342 3.3747338e+05 3.14e+00 1.74e+04 -1.0 5.75e+02 -0.6 6.58e-03 4.69e-05f 1\n 343 3.3562091e+05 3.14e+00 1.74e+04 -1.0 7.66e+02 -1.1 1.05e-04 2.42e-03f 1\n 344 3.3510890e+05 3.13e+00 1.73e+04 -1.0 8.41e+02 -1.5 1.22e-03 6.80e-04f 1\n 345 3.2871807e+05 3.11e+00 1.72e+04 -1.0 8.84e+02 -2.0 3.02e-03 8.53e-03f 1\n 346 3.2777372e+05 3.10e+00 1.72e+04 -1.0 1.54e+02 1.1 7.57e-05 1.51e-03f 1\n 347 3.2772491e+05 3.10e+00 1.72e+04 -1.0 1.60e+02 1.6 2.40e-03 9.66e-05f 1\n 348 3.2718271e+05 3.10e+00 1.71e+04 -1.0 1.60e+02 2.0 4.86e-03 1.52e-03f 1\n 349 3.2706600e+05 3.10e+00 1.71e+04 -1.0 1.58e+02 1.5 1.31e-03 2.19e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 350 3.2406746e+05 3.08e+00 1.70e+04 -1.0 1.47e+02 1.0 1.75e-04 5.59e-03f 1\n 351 3.2366994e+05 3.08e+00 1.70e+04 -1.0 1.50e+02 1.5 4.30e-03 7.40e-04f 1\n 352 3.2356913e+05 3.08e+00 1.70e+04 -1.0 1.37e+02 1.0 1.11e-04 1.90e-04f 1\n 353 3.2355506e+05 3.08e+00 1.70e+04 -1.0 1.49e+02 1.4 3.22e-03 2.36e-05f 1\n 354 3.2316524e+05 3.07e+00 1.70e+04 -1.0 1.40e+02 0.9 1.38e-03 6.09e-04f 1\n 355r 3.2316524e+05 3.07e+00 1.00e+03 0.5 0.00e+00 0.4 0.00e+00 3.57e-07R 8\n 356r 3.2229084e+05 3.07e+00 1.00e+03 0.5 2.06e+03 - 6.18e-03 7.60e-04f 1\n 357r 3.2109567e+05 3.06e+00 9.98e+02 0.5 1.80e+03 - 1.71e-03 8.09e-04f 1\n 358r 3.1149974e+05 2.97e+00 9.95e+02 0.5 1.67e+03 - 3.74e-03 6.54e-03f 1\n 359r 3.1087190e+05 2.97e+00 9.95e+02 0.5 8.58e+02 - 9.83e-05 4.32e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 360r 3.0989090e+05 2.96e+00 9.97e+02 0.5 9.94e+02 - 6.50e-03 6.44e-04f 1\n 361r 3.0241262e+05 2.87e+00 9.92e+02 0.5 9.07e+02 - 2.36e-03 5.34e-03f 1\n 362r 2.9694919e+05 2.80e+00 9.80e+02 0.5 8.52e+02 - 1.55e-02 4.79e-03f 1\n 363r 2.8929254e+05 2.68e+00 9.72e+02 0.5 6.39e+02 - 1.25e-02 8.39e-03f 1\n 364 2.8919644e+05 2.68e+00 9.52e+03 -1.0 1.36e+02 -0.0 2.03e-04 1.66e-04f 1\n 365 2.8908661e+05 2.68e+00 9.52e+03 -1.0 1.38e+02 -0.5 3.89e-04 1.90e-04f 1\n 366 2.8861557e+05 2.68e+00 9.51e+03 -1.0 1.34e+02 -1.0 2.45e-04 8.16e-04f 1\n 367 2.8784152e+05 2.67e+00 9.50e+03 -1.0 1.29e+02 -1.5 5.67e-04 1.35e-03f 1\n 368 2.8557520e+05 2.66e+00 9.46e+03 -1.0 1.18e+02 -1.9 1.40e-03 3.97e-03f 1\n 369 2.8048140e+05 2.64e+00 9.38e+03 -1.0 1.31e+02 -2.4 2.51e-03 9.01e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 370 2.7807299e+05 2.63e+00 9.34e+03 -1.0 1.50e+02 -2.9 1.64e-03 4.32e-03f 1\n 371 2.7069758e+05 2.59e+00 9.21e+03 -1.0 1.60e+02 -3.4 3.85e-03 1.34e-02f 1\n 372 2.5748481e+05 2.53e+00 8.98e+03 -1.0 1.59e+02 -3.8 4.45e-03 2.48e-02f 1\n 373 2.5409647e+05 2.51e+00 8.92e+03 -1.0 1.53e+02 -4.3 1.21e-02 6.62e-03f 1\n 374 2.4261880e+05 2.45e+00 8.72e+03 -1.0 1.50e+02 -4.8 6.04e-03 2.29e-02f 1\n 375 2.2822588e+05 2.38e+00 8.45e+03 -1.0 1.46e+02 -5.3 1.51e-02 3.02e-02f 1\n 376 2.0965467e+05 2.28e+00 8.10e+03 -1.0 1.41e+02 -5.8 1.82e-02 4.16e-02f 1\n 377 2.0936932e+05 2.28e+00 8.10e+03 -1.0 1.37e+02 -6.2 3.27e-02 6.83e-04f 1\n 378 2.0780500e+05 2.27e+00 8.07e+03 -1.0 1.37e+02 -6.7 4.67e-03 3.76e-03f 1\n 379 1.9606770e+05 2.20e+00 7.84e+03 -1.0 1.38e+02 -7.2 7.84e-04 2.87e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 380 1.7935187e+05 2.11e+00 7.49e+03 -1.0 1.38e+02 -7.7 2.58e-02 4.37e-02f 1\n 381 1.7765925e+05 2.10e+00 7.46e+03 -1.0 1.36e+02 -8.1 4.29e-02 4.74e-03f 1\n 382 1.6495345e+05 2.02e+00 7.18e+03 -1.0 1.34e+02 -8.6 1.77e-02 3.65e-02f 1\n 383 1.6340175e+05 2.01e+00 7.15e+03 -1.0 1.30e+02 -9.1 1.84e-02 4.73e-03f 1\n 384 1.5788016e+05 1.98e+00 7.03e+03 -1.0 1.29e+02 -9.6 3.72e-03 1.71e-02f 1\n 385 1.5064127e+05 1.93e+00 6.87e+03 -1.0 1.28e+02 -10.1 8.10e-03 2.33e-02f 1\n 386 1.2939352e+05 1.79e+00 6.36e+03 -1.0 1.25e+02 -10.5 1.85e-02 7.34e-02f 1\n 387 1.2646803e+05 1.77e+00 6.29e+03 -1.0 1.18e+02 -11.0 4.15e-02 1.14e-02f 1\n 388 1.1471323e+05 1.68e+00 5.99e+03 -1.0 1.16e+02 -11.5 1.69e-02 4.78e-02f 1\n 389 1.0992888e+05 1.65e+00 5.86e+03 -1.0 1.11e+02 -12.0 7.97e-02 2.12e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 390 1.0212816e+05 1.59e+00 5.65e+03 -1.0 1.07e+02 -12.4 1.86e-03 3.63e-02f 1\n 391 8.6177113e+04 1.46e+00 5.19e+03 -1.0 1.04e+02 -12.9 7.26e-02 8.17e-02f 1\n 392 8.5370527e+04 1.45e+00 5.16e+03 -1.0 9.48e+01 -13.4 3.22e-02 4.71e-03f 1\n 393 7.4077318e+04 1.35e+00 4.81e+03 -1.0 9.38e+01 -13.9 1.85e-02 6.88e-02f 1\n 394 7.1177623e+04 1.33e+00 4.71e+03 -1.0 8.78e+01 -14.3 6.45e-02 1.99e-02f 1\n 395 6.4716733e+04 1.26e+00 4.49e+03 -1.0 8.51e+01 -14.8 4.54e-03 4.67e-02f 1\n 396 5.7488482e+04 1.19e+00 4.23e+03 -1.0 8.14e+01 -15.3 9.91e-03 5.78e-02f 1\n 397 4.8754351e+04 1.10e+00 3.90e+03 -1.0 7.88e+01 -15.8 6.42e-02 7.95e-02f 1\n 398 4.7745315e+04 1.08e+00 3.86e+03 -1.0 7.72e+01 -16.3 9.49e-02 1.05e-02f 1\n 399 4.1977413e+04 1.02e+00 3.61e+03 -1.0 7.89e+01 -16.7 6.18e-02 6.27e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 400 3.3677208e+04 9.10e-01 3.24e+03 -1.0 7.75e+01 -17.2 7.45e-03 1.05e-01f 1\n 401 3.0166245e+04 8.61e-01 3.06e+03 -1.0 7.30e+01 -17.7 2.38e-02 5.39e-02f 1\n 402 2.5223779e+04 7.87e-01 2.80e+03 -1.0 7.10e+01 -18.2 1.21e-01 8.61e-02f 1\n 403 2.2341250e+04 7.40e-01 2.63e+03 -1.0 6.89e+01 -18.6 1.27e-01 5.92e-02f 1\n 404 1.8863927e+04 6.80e-01 2.42e+03 -1.0 6.77e+01 -19.1 8.08e-02 8.17e-02f 1\n 405 1.5848263e+04 6.23e-01 2.21e+03 -1.0 6.47e+01 -19.6 5.68e-02 8.40e-02f 1\n 406 1.4057233e+04 5.86e-01 2.08e+03 -1.0 6.57e+01 -19.1 8.62e-02 5.84e-02f 1\n 407 1.1013361e+04 5.19e-01 1.84e+03 -1.0 5.68e+01 -19.6 1.68e-01 1.16e-01f 1\n 408 7.9952027e+03 4.41e-01 1.57e+03 -1.0 5.34e+01 -19.1 1.48e-01 1.49e-01f 1\n 409 5.4065322e+03 3.62e-01 1.29e+03 -1.0 4.45e+01 -19.6 1.93e-01 1.80e-01f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 410 2.4913122e+03 2.44e-01 8.68e+02 -1.0 4.14e+01 -19.1 1.52e-01 3.26e-01f 1\n 411 1.0310441e+03 1.55e-01 5.51e+02 -1.0 2.82e+01 -19.6 2.47e-01 3.66e-01f 1\n 412 2.7434337e+02 7.52e-02 4.80e+02 -1.0 2.93e+01 -19.1 1.96e-01 5.14e-01f 1\n 413 5.4776714e+01 2.33e-02 7.33e+02 -1.0 7.94e+00 -19.6 4.50e-01 6.90e-01f 1\n 414 2.8195260e+01 3.14e-04 1.32e+03 -1.0 1.06e+01 -19.1 5.84e-01 1.00e+00f 1\n 415 3.2622344e+01 3.87e-05 1.06e+03 -1.0 1.60e+01 -19.6 7.02e-01 9.94e-01f 1\n 416 3.0040029e+01 4.65e-05 2.31e+02 -1.0 4.17e+01 -19.1 9.04e-01 1.00e+00f 1\n 417 3.0266079e+01 4.64e-04 4.41e+02 -1.0 1.21e+02 -19.6 1.00e+00 9.30e-01h 1\n 418 2.9559008e+01 3.42e-05 5.33e+01 -1.0 1.42e+01 -19.1 6.03e-01 1.00e+00h 1\n 419 2.9904880e+01 2.39e-06 6.35e-03 -1.0 6.68e+00 -19.6 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 420 2.4902915e+01 2.75e-05 1.10e+00 -2.5 1.14e+01 -19.1 4.91e-01 4.44e-01f 1\n 421 2.2342576e+01 8.15e-05 1.58e+00 -2.5 6.79e+01 -19.6 1.30e-01 3.02e-01f 1\n 422 2.0275308e+01 8.22e-05 1.07e+00 -2.5 6.41e+00 -3.7 3.38e-01 3.24e-01h 1\n 423 1.9264876e+01 9.78e-05 8.99e-01 -2.5 2.12e+01 -4.2 3.83e-01 1.56e-01h 1\n 424 1.8289856e+01 7.85e-05 6.81e-01 -2.5 6.19e+00 -4.6 2.31e-01 2.42e-01h 1\n 425 1.6132778e+01 1.96e-04 8.21e-01 -2.5 1.24e+01 -5.1 4.78e-01 7.58e-01h 1\n 426 1.5611473e+01 1.11e-04 5.01e-01 -2.5 1.64e+01 -5.6 4.71e-01 5.11e-01h 1\n 427 1.5163933e+01 4.19e-05 3.81e-01 -2.5 2.85e+00 -3.4 1.00e+00 6.97e-01h 1\n 428 1.5050001e+01 3.15e-05 4.60e-01 -2.5 3.60e+00 -3.8 6.17e-01 2.96e-01h 1\n 429 1.4900715e+01 5.30e-06 1.02e+00 -2.5 1.96e+00 -4.3 6.36e-01 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 430 1.4825885e+01 2.55e-05 4.15e-01 -2.5 5.52e+00 -3.9 8.38e-01 5.50e-01h 1\n 431 1.4739809e+01 3.24e-05 3.56e-01 -2.5 1.98e+01 -4.4 1.99e-01 1.55e-01h 1\n 432 1.4596773e+01 2.88e-05 5.34e-01 -2.5 5.19e+00 -3.9 5.29e-01 7.94e-01h 1\n 433 1.4484823e+01 3.46e-05 3.39e-01 -2.5 3.05e+01 -4.4 2.32e-01 9.62e-02h 1\n 434 1.4369681e+01 4.72e-05 4.22e-01 -2.5 1.62e+01 -4.9 3.06e-01 5.62e-01h 1\n 435 1.4279373e+01 4.71e-05 3.94e+02 -2.5 3.10e+01 -5.4 4.70e-01 2.25e-01h 1\n 436 1.4208263e+01 2.19e-05 2.73e+02 -2.5 2.87e+01 -5.8 3.06e-01 1.00e+00h 1\n 437 1.4134986e+01 1.91e-05 1.24e+02 -2.5 1.93e+01 -6.3 5.46e-01 6.57e-01h 1\n 438 1.4091516e+01 4.37e-05 3.46e+01 -2.5 2.35e+01 -6.8 7.19e-01 1.00e+00h 1\n 439 1.4087170e+01 4.09e-06 2.76e-04 -2.5 7.32e+00 -7.3 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 440 1.4088533e+01 1.91e-09 4.39e-07 -2.5 4.32e-01 -7.8 1.00e+00 1.00e+00h 1\n 441 1.3804149e+01 3.76e-06 7.14e+00 -3.8 4.84e+00 -8.2 6.51e-01 8.20e-01f 1\n 442 1.3706304e+01 1.35e-06 1.69e-03 -3.8 1.67e+00 -8.7 1.00e+00 1.00e+00h 1\n 443 1.3684885e+01 4.66e-08 3.95e-05 -3.8 4.07e-02 -9.2 1.00e+00 1.00e+00h 1\n 444 1.3664347e+01 8.58e-08 6.51e-02 -5.7 3.79e-01 -9.7 9.45e-01 9.46e-01h 1\n 445 1.3660196e+01 4.91e-08 1.08e+01 -5.7 4.56e-02 -10.1 1.00e+00 9.19e-01h 1\n 446 1.3659000e+01 4.92e-08 1.36e-06 -5.7 9.38e-03 -10.6 1.00e+00 1.00e+00f 1\n 447 1.3658600e+01 4.94e-08 9.08e+01 -8.6 3.53e-03 -11.1 9.94e-01 8.63e-01h 1\n 448 1.3658471e+01 5.57e-08 6.82e+00 -8.6 1.22e-03 -11.6 1.00e+00 9.90e-01f 1\n 449 1.3658445e+01 4.72e-08 2.13e-09 -8.6 4.22e-04 -12.0 1.00e+00 1.00e+00f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 450 1.3658439e+01 4.92e-08 3.85e-12 -8.6 1.81e-05 -12.5 1.00e+00 1.00e+00h 1\n 451 1.3658437e+01 4.92e-08 1.82e-12 -8.6 1.52e-06 -13.0 1.00e+00 1.00e+00h 1\n 452 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.26e-07 -13.5 1.00e+00 1.00e+00h 1\n 453 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.49e-07 -14.0 1.00e+00 1.00e+00h 1\n 454 1.3658437e+01 4.92e-08 3.64e-12 -8.6 9.58e-09 -14.4 1.00e+00 1.00e+00h 1\n 455 1.3658437e+01 4.92e-08 9.09e-13 -8.6 3.90e-11 -14.9 1.00e+00 1.00e+00h 1\n 456 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.39e-12 -15.4 1.00e+00 1.00e+00h 1\n 457 1.3658437e+01 4.92e-08 9.09e-13 -8.6 9.57e-13 -15.9 1.00e+00 1.00e+00h 1\n 458 1.3658437e+01 4.92e-08 9.09e-13 -8.6 7.94e-13 -16.3 1.00e+00 1.00e+00h 1\n 459 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.30e-13 -16.8 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 460 1.3658437e+01 4.92e-08 4.55e-13 -8.6 2.07e-12 -17.3 1.00e+00 1.00e+00h 1\n 461 1.3658437e+01 4.92e-08 9.09e-13 -8.6 2.88e-12 -17.8 1.00e+00 1.00e+00h 1\n 462 1.3658437e+01 4.92e-08 9.09e-13 -8.6 5.03e-12 -18.2 1.00e+00 1.00e+00h 1\n 463 1.3658437e+01 4.92e-08 4.55e-13 -8.6 3.27e-12 -18.7 1.00e+00 1.00e+00h 1\n\nNumber of Iterations....: 463\n\n (scaled) (unscaled)\nObjective...............: 1.3658436881286921e+01 1.3658436881286921e+01\nDual infeasibility......: 4.5474735088646412e-13 4.5474735088646412e-13\nConstraint violation....: 4.9168770388519079e-08 4.9168770388519079e-08\nComplementarity.........: 2.5059067889663204e-09 2.5059067889663204e-09\nOverall NLP error.......: 4.9168770388519079e-08 4.9168770388519079e-08\n\n\nNumber of objective function evaluations = 483\nNumber of objective gradient evaluations = 395\nNumber of equality constraint evaluations = 483\nNumber of inequality constraint evaluations = 483\nNumber of equality constraint Jacobian evaluations = 467\nNumber of inequality constraint Jacobian evaluations = 467\nNumber of Lagrangian Hessian evaluations = 463\nTotal CPU secs in IPOPT (w/o function evaluations) = 4.510\nTotal CPU secs in NLP function evaluations = 10.087\n\nEXIT: Solved To Acceptable Level.\n solver : t_proc (avg) t_wall (avg) n_eval\n nlp_f | 29.34ms ( 60.74us) 29.31ms ( 60.69us) 483\n nlp_g | 524.38ms ( 1.09ms) 524.13ms ( 1.09ms) 483\n nlp_grad | 3.10ms ( 3.10ms) 3.10ms ( 3.10ms) 1\n nlp_grad_f | 36.14ms ( 91.26us) 36.16ms ( 91.31us) 396\n nlp_hess_l | 6.33 s ( 13.76ms) 6.31 s ( 13.71ms) 460\n nlp_jac_g | 3.20 s ( 6.85ms) 3.19 s ( 6.83ms) 468\n total | 14.70 s ( 14.70 s) 14.61 s ( 14.61 s) 1\nThis is Ipopt version 3.12.3, running with linear solver mumps.\nNOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n\nNumber of nonzeros in equality constraint Jacobian...: 4230\nNumber of nonzeros in inequality constraint Jacobian.: 3466\nNumber of nonzeros in Lagrangian Hessian.............: 3648\n\nTotal number of variables............................: 2293\n variables with only lower bounds: 0\n variables with lower and upper bounds: 0\n variables with only upper bounds: 0\nTotal number of equality constraints.................: 1377\nTotal number of inequality constraints...............: 967\n inequality constraints with only lower bounds: 204\n inequality constraints with lower and upper bounds: 355\n inequality constraints with only upper bounds: 408\n\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 0 0.0000000e+00 9.83e+00 7.50e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n 1 1.0399716e-02 9.83e+00 1.26e+00 -1.0 1.20e+02 -4.0 4.93e-04 3.26e-04h 1\n 2 2.0045559e-02 9.83e+00 2.20e+01 -1.0 1.82e+02 -4.5 3.56e-04 3.85e-05h 1\n 3 4.4710489e-01 9.83e+00 2.52e+01 -1.0 2.46e+02 -5.0 3.88e-04 3.37e-04h 1\n 4 2.5591113e+00 9.83e+00 6.47e+01 -1.0 3.34e+02 -5.4 1.11e-03 4.95e-04h 1\n 5 1.0304744e+01 9.83e+00 1.06e+02 -1.0 2.54e+02 -5.9 1.15e-03 5.03e-04h 1\n 6 7.9216962e+01 9.82e+00 1.08e+02 -1.0 1.86e+02 -6.4 1.38e-03 1.35e-03h 1\n 7 1.6205310e+02 9.82e+00 1.46e+02 -1.0 1.46e+02 -6.9 1.48e-03 8.87e-04h 1\n 8 2.2025207e+02 9.83e+00 1.56e+02 -1.0 3.22e+02 -7.3 6.46e-04 4.87e-04h 1\n 9 2.2441787e+02 9.83e+00 1.54e+02 -1.0 4.45e+03 -7.8 8.53e-06 3.28e-05h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 10 2.5182294e+02 9.82e+00 1.42e+02 -1.0 9.50e+02 -8.3 8.65e-06 2.07e-04h 1\n 11 2.8777432e+02 9.81e+00 1.66e+02 -1.0 4.28e+02 -8.8 6.37e-04 2.55e-04h 1\n 12 4.0157811e+02 9.80e+00 1.67e+02 -1.0 1.45e+02 -9.2 7.37e-04 7.08e-04h 1\n 13 4.1550901e+02 9.80e+00 1.78e+02 -1.0 1.56e+02 -9.7 2.52e-04 7.97e-05h 1\n 14 6.1114602e+02 9.80e+00 1.21e+02 -1.0 1.50e+02 -10.2 8.33e-05 1.00e-03h 1\n 15 6.1355211e+02 9.80e+00 1.82e+02 -1.0 1.68e+02 -10.7 9.96e-04 1.13e-05h 1\n 16 6.2428940e+02 9.80e+00 2.68e+02 -1.0 3.50e+02 -11.2 1.43e-03 5.02e-05h 1\n 17r 6.2428940e+02 9.80e+00 9.99e+02 1.0 0.00e+00 -11.6 0.00e+00 4.03e-07R 5\n 18r 8.6414419e+02 9.80e+00 9.99e+02 1.0 6.29e+03 - 3.71e-04 1.98e-05f 1\n 19r 3.3647143e+03 9.80e+00 9.99e+02 1.0 1.65e+03 - 3.45e-04 1.15e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 20r 5.1092797e+04 9.80e+00 9.98e+02 1.0 7.67e+02 - 3.88e-04 6.81e-04f 1\n 21r 6.3342770e+04 9.79e+00 1.07e+03 1.0 7.22e+00 2.0 1.50e-01 1.14e-02f 1\n 22r 1.7594664e+05 9.73e+00 1.03e+03 1.0 3.13e+00 2.4 2.75e-01 2.46e-01f 1\n 23r 1.6728527e+05 9.68e+00 1.07e+03 1.0 2.44e+00 2.9 3.31e-02 1.26e-01f 1\n 24r 1.7195513e+05 9.51e+00 1.09e+03 1.0 5.75e-01 3.3 8.31e-01 1.00e+00f 1\n 25r 1.7774546e+05 9.35e+00 1.07e+03 1.0 1.83e+00 2.8 3.09e-01 3.74e-01f 1\n 26r 1.7826098e+05 8.80e+00 1.06e+03 1.0 5.11e+00 2.3 3.60e-01 5.80e-01f 1\n 27 1.7790304e+05 8.75e+00 2.67e+03 -1.0 9.73e+01 -12.1 1.05e-03 4.89e-03f 1\n 28 1.7790581e+05 8.75e+00 2.67e+03 -1.0 9.99e+01 -12.6 4.15e-03 8.71e-05h 1\n 29 1.7805578e+05 8.74e+00 2.67e+03 -1.0 1.49e+02 -13.1 2.98e-03 1.65e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 30 1.7826045e+05 8.73e+00 2.66e+03 -1.0 1.00e+02 -13.5 5.02e-03 1.36e-03h 1\n 31 1.7833630e+05 8.72e+00 2.66e+03 -1.0 9.67e+01 -14.0 4.35e-03 3.57e-04h 1\n 32 1.7838366e+05 8.72e+00 2.66e+03 -1.0 9.66e+01 -14.5 5.53e-03 1.85e-04h 1\n 33 1.7897357e+05 8.70e+00 2.66e+03 -1.0 9.66e+01 -15.0 6.03e-03 1.99e-03h 1\n 34 1.7979138e+05 8.68e+00 2.65e+03 -1.0 9.65e+01 -15.5 5.02e-03 2.33e-03h 1\n 35 1.8083548e+05 8.66e+00 2.64e+03 -1.0 9.65e+01 -15.9 5.14e-03 2.59e-03h 1\n 36 1.8126368e+05 8.65e+00 2.64e+03 -1.0 9.68e+01 -16.4 1.38e-03 9.55e-04h 1\n 37 1.8126992e+05 8.65e+00 2.64e+03 -1.0 9.72e+01 -16.9 2.04e-05 1.32e-05h 1\n 38 1.8127749e+05 8.65e+00 2.64e+03 -1.0 9.75e+01 -17.4 5.22e-03 1.49e-05h 1\n 39 1.8272429e+05 8.63e+00 2.63e+03 -1.0 9.81e+01 -17.8 3.94e-04 2.72e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 40 1.8373146e+05 8.61e+00 2.63e+03 -1.0 9.79e+01 -18.3 2.78e-05 1.86e-03h 1\n 41 1.8460711e+05 8.60e+00 2.63e+03 -1.0 9.79e+01 -18.8 6.10e-03 1.58e-03h 1\n 42 1.8506845e+05 8.59e+00 3.03e+03 -1.0 9.77e+01 -19.3 3.60e-03 8.05e-04h 1\n 43 1.8630538e+05 8.57e+00 2.81e+03 -1.0 9.79e+01 -19.7 1.23e-03 2.15e-03h 1\n 44 1.8693912e+05 8.56e+00 2.69e+03 -1.0 9.91e+01 -19.1 6.31e-04 1.18e-03h 1\n 45 1.8714858e+05 8.56e+00 2.61e+03 -1.0 9.72e+01 -19.6 1.86e-05 3.60e-04h 1\n 46 1.8833659e+05 8.54e+00 3.03e+03 -1.0 9.83e+01 -19.1 4.39e-03 2.16e-03h 1\n 47 1.8900050e+05 8.53e+00 3.12e+03 -1.0 9.71e+01 -19.6 1.51e-03 1.12e-03h 1\n 48 1.8921851e+05 8.53e+00 3.04e+03 -1.0 9.85e+01 -19.1 2.64e-05 3.81e-04h 1\n 49 1.8974673e+05 8.52e+00 2.87e+03 -1.0 9.66e+01 -19.6 1.53e-04 8.26e-04h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 50 1.8975438e+05 8.52e+00 3.74e+03 -1.0 9.81e+01 -19.1 3.80e-03 1.32e-05h 1\n 51 1.9002449e+05 8.52e+00 4.95e+03 -1.0 9.84e+01 -19.6 3.14e-03 3.97e-04h 1\n 52 1.9113127e+05 8.50e+00 5.17e+03 -1.0 9.86e+01 -19.1 2.66e-03 1.79e-03h 1\n 53 1.9218965e+05 8.49e+00 4.74e+03 -1.0 9.66e+01 -19.6 1.49e-05 1.46e-03h 1\n 54 1.9235900e+05 8.49e+00 4.77e+03 -1.0 9.69e+01 -19.1 3.43e-04 2.55e-04h 1\n 55 1.9614877e+05 8.45e+00 3.24e+03 -1.0 9.55e+01 -19.6 4.82e-05 4.46e-03h 1\n 56 1.9767353e+05 8.43e+00 3.55e+03 -1.0 9.43e+01 -19.1 3.11e-03 2.00e-03h 1\n 57 2.0045953e+05 8.41e+00 3.54e+03 -1.0 9.38e+01 -19.6 3.09e-03 3.11e-03h 1\n 58 2.0058633e+05 8.41e+00 4.32e+03 -1.0 9.34e+01 -19.1 2.74e-03 1.60e-04h 1\n 59r 2.0058633e+05 8.41e+00 1.00e+03 0.9 0.00e+00 -19.6 0.00e+00 4.42e-07R 5\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 60r 2.0053275e+05 8.41e+00 1.00e+03 0.9 3.17e+03 - 3.55e-04 6.45e-05f 1\n 61r 2.0041460e+05 8.41e+00 1.00e+03 0.9 1.58e+03 - 3.29e-04 1.19e-04f 1\n 62r 2.0024799e+05 8.41e+00 9.99e+02 0.9 1.43e+03 - 7.29e-04 1.04e-03f 1\n 63r 2.0178166e+05 8.42e+00 9.97e+02 0.9 9.93e+02 - 2.24e-03 1.49e-03f 1\n 64r 2.0902033e+05 8.42e+00 9.92e+02 0.9 5.71e+02 - 1.11e-02 3.85e-03f 1\n 65r 2.3136420e+05 8.41e+00 9.83e+02 0.9 3.09e+02 - 8.14e-03 9.32e-03f 1\n 66r 2.6153891e+05 8.40e+00 9.88e+02 0.9 3.62e+02 - 7.16e-03 1.05e-02f 1\n 67r 2.7798903e+05 8.40e+00 9.64e+02 0.9 3.55e+02 - 1.32e-02 9.11e-03f 1\n 68r 2.8657577e+05 8.40e+00 9.59e+02 0.9 2.47e+02 - 2.08e-02 4.88e-03f 1\n 69r 2.9527415e+05 8.40e+00 9.55e+02 0.9 1.55e+02 - 2.61e-03 4.50e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 70r 3.0527057e+05 8.40e+00 9.50e+02 0.9 5.20e+02 - 6.42e-03 5.12e-03f 1\n 71r 3.3582481e+05 8.40e+00 9.58e+02 0.9 1.01e+02 - 3.07e-02 1.51e-02f 1\n 72r 4.1107309e+05 8.41e+00 9.04e+02 0.9 1.14e+02 - 2.39e-02 3.35e-02f 1\n 73r 4.5964398e+05 8.41e+00 8.95e+02 0.9 1.15e+02 - 3.62e-02 2.11e-02f 1\n 74r 5.2016472e+05 8.41e+00 8.67e+02 0.9 1.36e+02 - 4.89e-02 3.03e-02f 1\n 75r 5.9030592e+05 8.41e+00 8.31e+02 0.9 1.48e+02 - 6.01e-02 3.79e-02f 1\n 76r 6.7476964e+05 8.41e+00 7.88e+02 0.9 1.61e+02 - 7.73e-02 4.59e-02f 1\n 77r 7.7561624e+05 8.41e+00 7.44e+02 0.9 1.82e+02 - 1.18e-01 5.62e-02f 1\n 78r 8.4945381e+05 8.41e+00 7.06e+02 0.9 4.62e+02 - 4.89e-03 5.46e-02f 1\n 79r 9.0140487e+05 8.41e+00 6.78e+02 0.9 2.50e+02 - 1.14e-01 3.60e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 80r 9.7546515e+05 8.41e+00 6.36e+02 0.9 2.54e+02 - 1.43e-01 6.16e-02f 1\n 81r 1.1190008e+06 8.38e+00 5.68e+02 0.9 3.76e+02 - 3.36e-02 1.08e-01f 1\n 82r 1.1225339e+06 8.37e+00 5.49e+02 0.9 8.82e+02 - 8.26e-03 3.25e-02f 1\n 83r 1.1286850e+06 8.36e+00 5.46e+02 0.9 4.12e+02 - 3.25e-02 6.75e-03f 1\n 84r 1.2659338e+06 8.29e+00 3.86e+02 0.9 7.87e+00 0.0 3.42e-01 2.93e-01f 1\n 85r 1.2612169e+06 8.28e+00 3.74e+02 0.2 2.38e+02 - 1.60e-02 3.04e-02f 1\n 86r 1.3908950e+06 8.17e+00 3.95e+02 0.2 3.02e+02 - 2.38e-02 7.50e-02f 1\n 87r 1.4392216e+06 8.15e+00 2.99e+02 0.2 7.97e+00 0.4 1.84e-01 1.68e-01f 1\n 88r 1.4749437e+06 8.14e+00 2.77e+02 0.2 5.34e+01 -0.1 4.57e-02 3.68e-02f 1\n 89r 1.4776820e+06 8.12e+00 2.77e+02 0.2 8.08e+02 - 3.91e-03 5.72e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 90r 1.4904632e+06 8.09e+00 3.10e+02 0.2 2.23e+02 - 1.15e-01 3.01e-02f 1\n 91r 1.4780369e+06 8.03e+00 2.85e+02 0.2 2.60e+02 - 1.24e-02 4.77e-02f 1\n 92r 1.4633854e+06 7.98e+00 2.84e+02 0.2 2.35e+02 - 1.42e-01 5.45e-02f 1\n 93r 1.4468075e+06 7.94e+00 2.89e+02 0.2 3.39e+02 - 2.25e-02 4.04e-02f 1\n 94r 1.4177297e+06 7.84e+00 3.28e+02 0.2 3.67e+02 - 4.08e-02 1.06e-01f 1\n 95r 1.4208335e+06 7.83e+00 3.24e+02 0.2 3.41e+02 - 4.16e-02 2.22e-02f 1\n 96r 1.4035701e+06 7.79e+00 2.82e+02 0.2 3.36e+02 - 6.49e-02 9.28e-02f 1\n 97r 1.3981555e+06 7.76e+00 2.08e+02 0.2 3.03e+01 -0.5 1.71e-01 7.29e-02f 1\n 98r 1.3736085e+06 7.75e+00 2.02e+02 0.2 4.74e+02 - 2.70e-02 1.88e-02f 1\n 99r 1.3738774e+06 7.70e+00 1.87e+02 0.2 4.77e+02 - 7.99e-02 9.61e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 100r 1.3580286e+06 7.69e+00 2.14e+02 0.2 4.67e+02 - 1.03e-01 3.83e-02f 1\n 101r 1.3021262e+06 7.68e+00 2.15e+02 0.2 5.22e+02 - 1.39e-01 9.61e-02f 1\n 102r 1.2905134e+06 7.68e+00 2.12e+02 0.2 5.78e+02 - 5.56e-02 4.11e-02f 1\n 103r 1.2958912e+06 7.68e+00 1.61e+02 0.2 6.56e+02 - 2.71e-02 1.08e-01f 1\n 104r 1.3293811e+06 7.69e+00 1.47e+02 0.2 7.20e+02 - 8.99e-02 8.45e-02f 1\n 105r 1.4013282e+06 7.70e+00 1.56e+02 0.2 9.31e+02 - 7.51e-02 1.05e-01f 1\n 106r 1.5010415e+06 7.67e+00 2.27e+02 0.2 3.80e+01 -1.0 6.34e-02 1.79e-01f 1\n 107r 1.5310867e+06 7.66e+00 2.10e+02 0.2 5.35e+01 -1.5 5.32e-02 3.54e-02f 1\n 108r 1.6858235e+06 7.64e+00 2.07e+02 0.2 2.27e+02 -2.0 1.41e-02 1.65e-02f 1\n 109r 1.6992342e+06 7.64e+00 2.00e+02 0.2 4.36e+01 -0.6 3.64e-02 3.57e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 110r 1.6720537e+06 7.63e+00 1.42e+02 0.2 2.59e+00 0.7 3.58e-01 1.88e-01f 1\n 111r 1.6393971e+06 7.62e+00 1.61e+02 0.2 5.96e+00 0.2 6.27e-02 2.98e-01f 1\n 112r 1.5942856e+06 7.62e+00 9.69e+01 0.2 3.94e+00 0.6 5.59e-01 6.60e-01f 1\n 113r 1.5454439e+06 7.61e+00 8.14e+01 -0.5 9.57e+00 0.2 2.87e-01 3.13e-01f 1\n 114r 1.5348572e+06 7.52e+00 4.26e+01 -0.5 2.28e+01 -0.3 2.19e-01 1.39e-01f 1\n 115 1.5101190e+06 7.46e+00 1.69e+04 -1.0 7.31e+02 -19.1 1.74e-03 8.11e-03f 1\n 116 1.5081170e+06 7.45e+00 1.69e+04 -1.0 6.47e+02 -19.6 1.66e-03 6.70e-04f 1\n 117 1.5000973e+06 7.43e+00 1.68e+04 -1.0 6.33e+02 -19.1 1.83e-03 2.70e-03f 1\n 118 1.4972338e+06 7.43e+00 1.68e+04 -1.0 6.14e+02 -19.6 4.62e-03 9.87e-04f 1\n 119 1.4926222e+06 7.41e+00 1.68e+04 -1.0 5.92e+02 -19.1 4.76e-03 1.69e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 120 1.4897946e+06 7.40e+00 1.68e+04 -1.0 5.73e+02 -19.6 1.24e-03 1.25e-03f 1\n 121 1.4867964e+06 7.39e+00 1.67e+04 -1.0 5.67e+02 -19.1 1.24e-02 1.40e-03f 1\n 122 1.4811114e+06 7.37e+00 1.67e+04 -1.0 5.31e+02 -19.6 2.94e-03 2.99e-03f 1\n 123 1.4776862e+06 7.36e+00 1.67e+04 -1.0 5.21e+02 -19.1 1.64e-02 1.85e-03f 1\n 124 1.4722508e+06 7.34e+00 1.66e+04 -1.0 4.87e+02 -19.6 5.29e-03 3.07e-03f 1\n 125 1.4686209e+06 7.32e+00 1.66e+04 -1.0 4.76e+02 -19.1 8.82e-03 2.07e-03f 1\n 126 1.4549412e+06 7.26e+00 1.64e+04 -1.0 4.61e+02 -19.6 8.60e-03 7.87e-03f 1\n 127 1.4439147e+06 7.22e+00 1.63e+04 -1.0 4.42e+02 -19.1 3.68e-03 6.45e-03f 1\n 128 1.4318869e+06 7.17e+00 1.62e+04 -1.0 4.33e+02 -19.6 2.58e-04 7.02e-03f 1\n 129 1.4287865e+06 7.15e+00 1.62e+04 -1.0 4.28e+02 -19.1 9.28e-03 1.82e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 130 1.4117953e+06 7.08e+00 1.60e+04 -1.0 4.17e+02 -19.6 1.21e-02 9.95e-03f 1\n 131 1.4094629e+06 7.07e+00 1.60e+04 -1.0 3.99e+02 -19.1 1.22e-02 1.30e-03f 1\n 132 1.3898684e+06 6.99e+00 1.58e+04 -1.0 3.86e+02 -19.6 6.69e-03 1.09e-02f 1\n 133 1.3666966e+06 6.91e+00 1.56e+04 -1.0 3.73e+02 -19.1 1.23e-04 1.25e-02f 1\n 134 1.3653874e+06 6.90e+00 1.56e+04 -1.0 3.64e+02 -19.6 8.61e-03 7.06e-04f 1\n 135 1.3600774e+06 6.88e+00 1.56e+04 -1.0 3.58e+02 -19.1 4.77e-04 2.88e-03f 1\n 136 1.3599863e+06 6.88e+00 1.56e+04 -1.0 3.54e+02 -19.6 1.16e-04 4.90e-05f 1\n 137 1.3520824e+06 6.85e+00 1.55e+04 -1.0 3.56e+02 -19.1 2.25e-02 4.63e-03f 1\n 138 1.3198881e+06 6.72e+00 1.52e+04 -1.0 3.32e+02 -19.6 4.47e-03 1.93e-02f 1\n 139 1.3018334e+06 6.65e+00 1.51e+04 -1.0 3.23e+02 -19.1 3.31e-02 1.03e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 140 1.3001178e+06 6.64e+00 1.50e+04 -1.0 3.09e+02 -19.6 2.72e-03 1.11e-03f 1\n 141 1.2712885e+06 6.53e+00 1.48e+04 -1.0 3.10e+02 -19.1 1.01e-04 1.69e-02f 1\n 142 1.2672814e+06 6.51e+00 1.48e+04 -1.0 3.20e+02 -19.6 4.18e-03 2.32e-03f 1\n 143 1.2662613e+06 6.51e+00 1.47e+04 -1.0 2.90e+02 -19.1 1.94e-02 6.22e-04f 1\n 144 1.2251694e+06 6.34e+00 1.43e+04 -1.0 2.82e+02 -19.6 2.71e-03 2.66e-02f 1\n 145 1.2238974e+06 6.33e+00 1.43e+04 -1.0 2.99e+02 -19.1 1.32e-03 7.91e-04f 1\n 146 1.2218795e+06 6.32e+00 1.43e+04 -1.0 3.08e+02 -19.6 1.65e-03 1.32e-03f 1\n 147 1.2218327e+06 6.32e+00 1.43e+04 -1.0 3.05e+02 -19.1 3.84e-03 2.74e-05f 1\n 148 1.1933966e+06 6.22e+00 1.41e+04 -1.0 3.06e+02 -19.6 7.34e-04 1.67e-02f 1\n 149 1.1923484e+06 6.21e+00 1.41e+04 -1.0 3.08e+02 -19.1 8.24e-03 5.84e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 150 1.1851137e+06 6.19e+00 1.40e+04 -1.0 2.76e+02 -19.6 7.09e-04 4.39e-03f 1\n 151 1.1674398e+06 6.13e+00 1.39e+04 -1.0 2.77e+02 -19.1 4.82e-03 9.94e-03f 1\n 152 1.1671054e+06 6.12e+00 1.39e+04 -1.0 2.72e+02 -19.6 4.03e-03 2.01e-04f 1\n 153 1.1607724e+06 6.10e+00 1.38e+04 -1.0 2.70e+02 -19.1 6.82e-04 3.72e-03f 1\n 154 1.1521951e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.6 1.57e-03 5.21e-03f 1\n 155 1.1512500e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.1 7.99e-04 5.27e-04f 1\n 156 1.1507231e+06 6.06e+00 1.37e+04 -1.0 2.68e+02 -19.6 4.24e-04 3.27e-04f 1\n 157 1.1507040e+06 6.06e+00 1.37e+04 -1.0 2.81e+02 -19.1 5.41e-04 9.69e-06f 1\n 158 1.1467730e+06 6.05e+00 1.37e+04 -1.0 2.63e+02 -19.6 8.26e-06 2.11e-03f 1\n 159 1.0732380e+06 5.82e+00 1.32e+04 -1.0 2.63e+02 -19.1 2.67e-04 3.91e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 160 1.0714161e+06 5.81e+00 1.32e+04 -1.0 2.56e+02 -19.6 1.02e-02 9.30e-04f 1\n 161 1.0713627e+06 5.81e+00 1.32e+04 -1.0 2.54e+02 -19.1 8.09e-05 2.84e-05f 1\n 162 1.0580046e+06 5.76e+00 1.31e+04 -1.0 2.54e+02 -19.6 1.35e-04 7.69e-03f 1\n 163 1.0545534e+06 5.75e+00 1.30e+04 -1.0 2.54e+02 -19.1 3.39e-03 1.89e-03f 1\n 164 1.0341517e+06 5.68e+00 1.30e+04 -1.0 2.52e+02 -19.6 8.74e-04 1.22e-02f 1\n 165 1.0336429e+06 5.68e+00 1.29e+04 -1.0 2.51e+02 -19.1 6.22e-04 2.90e-04f 1\n 166 1.0275521e+06 5.66e+00 1.38e+04 -1.0 2.51e+02 -19.6 4.36e-04 3.68e-03f 1\n 167 1.0179732e+06 5.63e+00 1.32e+04 -1.0 2.50e+02 -19.1 7.95e-03 5.53e-03f 1\n 168 9.9624521e+05 5.55e+00 1.59e+04 -1.0 2.48e+02 -19.6 4.63e-03 1.40e-02f 1\n 169 9.9527357e+05 5.55e+00 1.58e+04 -1.0 2.44e+02 -19.1 5.96e-04 5.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 170 9.9489625e+05 5.55e+00 1.56e+04 -1.0 2.47e+02 -19.6 1.04e-03 2.42e-04f 1\n 171 9.9489118e+05 5.55e+00 1.50e+04 -1.0 2.53e+02 -19.1 1.24e-03 2.86e-06f 1\n 172 9.9421219e+05 5.54e+00 1.57e+04 -1.0 2.49e+02 -19.6 3.63e-06 4.37e-04f 1\n 173 9.8486650e+05 5.51e+00 1.68e+04 -1.0 2.46e+02 -19.1 5.48e-04 5.68e-03f 1\n 174 9.7308682e+05 5.47e+00 1.65e+04 -1.0 2.49e+02 -19.6 8.25e-03 7.61e-03f 1\n 175 9.7212896e+05 5.47e+00 1.65e+04 -1.0 2.44e+02 -19.1 3.20e-04 5.80e-04f 1\n 176 9.6540396e+05 5.44e+00 1.75e+04 -1.0 2.49e+02 -19.6 9.01e-04 4.34e-03f 1\n 177 9.4998516e+05 5.39e+00 1.51e+04 -1.0 2.44e+02 -19.1 1.93e-02 9.44e-03f 1\n 178 9.4904520e+05 5.39e+00 1.52e+04 -1.0 2.45e+02 -19.6 2.19e-04 6.06e-04f 1\n 179 9.4902274e+05 5.39e+00 1.51e+04 -1.0 2.75e+02 -19.1 1.62e-04 1.14e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 180 9.3880492e+05 5.35e+00 1.67e+04 -1.0 2.29e+02 -19.6 1.38e-05 6.45e-03f 1\n 181 8.9559690e+05 5.21e+00 1.79e+04 -1.0 2.29e+02 -19.1 1.06e-02 2.71e-02f 1\n 182 8.5549874e+05 5.07e+00 1.93e+04 -1.0 2.22e+02 -19.6 4.07e-03 2.60e-02f 1\n 183 8.4452994e+05 5.04e+00 1.99e+04 -1.0 2.17e+02 -19.1 4.96e-04 7.46e-03f 1\n 184 8.2921005e+05 4.98e+00 1.98e+04 -1.0 2.14e+02 -19.6 9.95e-03 1.09e-02f 1\n 185 8.2905253e+05 4.98e+00 1.90e+04 -1.0 2.09e+02 -19.1 5.75e-03 1.10e-04f 1\n 186 8.2904529e+05 4.98e+00 1.89e+04 -1.0 2.18e+02 -19.6 7.15e-05 5.41e-06f 1\n 187 8.2712955e+05 4.97e+00 1.92e+04 -1.0 2.14e+02 -19.1 5.72e-05 1.35e-03f 1\n 188 8.0950543e+05 4.91e+00 2.28e+04 -1.0 2.31e+02 -19.6 1.11e-03 1.34e-02f 1\n 189 8.0862616e+05 4.90e+00 2.24e+04 -1.0 2.12e+02 -19.1 1.92e-03 6.51e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 190 8.0425351e+05 4.89e+00 2.35e+04 -1.0 2.22e+02 -19.6 4.57e-04 3.51e-03f 1\n 191 7.9537870e+05 4.85e+00 2.31e+04 -1.0 2.15e+02 -19.1 7.37e-03 6.61e-03f 1\n 192 7.9489154e+05 4.85e+00 2.29e+04 -1.0 2.11e+02 -19.6 1.29e-03 3.92e-04f 1\n 193 7.8751827e+05 4.83e+00 2.29e+04 -1.0 2.01e+02 -19.1 2.56e-03 5.71e-03f 1\n 194 7.8736120e+05 4.82e+00 2.31e+04 -1.0 2.00e+02 -19.6 1.54e-04 1.32e-04f 1\n 195 7.8402757e+05 4.81e+00 2.31e+04 -1.0 1.94e+02 -19.1 2.19e-03 2.63e-03f 1\n 196 7.8388845e+05 4.81e+00 2.53e+04 -1.0 1.98e+02 -19.6 2.15e-04 1.18e-04f 1\n 197 7.8385262e+05 4.81e+00 2.50e+04 -1.0 1.94e+02 -19.1 4.34e-03 2.82e-05f 1\n 198 7.8236217e+05 4.81e+00 1.09e+04 -1.0 1.97e+02 -19.6 3.11e-04 1.27e-03f 1\n 199 7.7035462e+05 4.76e+00 1.08e+04 -1.0 1.91e+02 -19.1 3.91e-04 9.52e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 200 7.6826638e+05 4.75e+00 1.08e+04 -1.0 1.90e+02 -19.6 2.68e-02 1.77e-03f 1\n 201 7.6137802e+05 4.73e+00 1.07e+04 -1.0 1.80e+02 -19.1 5.08e-04 5.41e-03f 1\n 202 7.6092627e+05 4.72e+00 1.07e+04 -1.0 1.85e+02 -19.6 8.12e-03 3.85e-04f 1\n 203 6.8620409e+05 4.44e+00 1.26e+04 -1.0 1.80e+02 -19.1 1.57e-04 6.08e-02f 1\n 204 6.8223234e+05 4.42e+00 1.15e+04 -1.0 1.85e+02 -19.6 6.43e-03 3.54e-03f 1\n 205 6.7550321e+05 4.40e+00 1.16e+04 -1.0 1.74e+02 -19.1 5.10e-03 5.69e-03f 1\n 206 6.7512988e+05 4.39e+00 1.00e+04 -1.0 1.86e+02 -19.6 8.13e-03 3.47e-04f 1\n 207 6.6341460e+05 4.35e+00 9.84e+03 -1.0 1.72e+02 -19.1 1.30e-02 1.04e-02f 1\n 208 6.5075026e+05 4.30e+00 1.09e+04 -1.0 1.87e+02 -19.6 2.10e-04 1.19e-02f 1\n 209 6.4469528e+05 4.27e+00 1.05e+04 -1.0 1.72e+02 -19.1 7.52e-03 5.37e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 210 6.3074280e+05 4.22e+00 1.26e+04 -1.0 1.85e+02 -19.6 4.48e-04 1.32e-02f 1\n 211 6.3015403e+05 4.21e+00 1.26e+04 -1.0 3.38e+01 2.3 1.77e-03 7.51e-04f 1\n 212 6.3013669e+05 4.21e+00 1.47e+04 -1.0 1.21e+02 1.8 1.17e-03 1.79e-05f 1\n 213 6.2710947e+05 4.20e+00 1.45e+04 -1.0 1.26e+02 1.4 4.90e-04 2.86e-03f 1\n 214 6.2100935e+05 4.18e+00 1.49e+04 -1.0 1.53e+02 0.9 1.86e-03 5.85e-03f 1\n 215 6.1066877e+05 4.14e+00 1.59e+04 -1.0 1.97e+02 0.4 1.05e-05 8.89e-03f 1\n 216 6.0984620e+05 4.14e+00 1.57e+04 -1.0 1.84e+02 1.7 8.49e-03 1.01e-03f 1\n 217 6.0751052e+05 4.13e+00 1.57e+04 -1.0 2.02e+02 1.3 5.49e-03 2.08e-03f 1\n 218 6.0707832e+05 4.12e+00 1.57e+04 -1.0 2.01e+02 1.7 2.32e-04 5.32e-04f 1\n 219 6.0707327e+05 4.12e+00 1.53e+04 -1.0 1.96e+02 2.1 2.90e-03 7.52e-06f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 220 6.0555478e+05 4.12e+00 1.54e+04 -1.0 2.25e+02 1.6 9.57e-04 1.84e-03f 1\n 221 6.0553909e+05 4.12e+00 1.47e+04 -1.0 2.14e+02 2.1 5.83e-03 2.26e-05f 1\n 222 6.0387470e+05 4.11e+00 1.47e+04 -1.0 2.27e+02 1.6 2.04e-03 1.80e-03f 1\n 223 6.0325229e+05 4.11e+00 1.47e+04 -1.0 2.20e+02 2.0 4.35e-04 7.72e-04f 1\n 224 6.0322399e+05 4.11e+00 1.45e+04 -1.0 2.21e+02 1.5 4.10e-03 2.96e-05f 1\n 225 6.0185557e+05 4.10e+00 1.45e+04 -1.0 2.17e+02 2.0 1.59e-05 1.70e-03f 1\n 226 6.0180800e+05 4.10e+00 1.44e+04 -1.0 2.29e+02 1.5 1.37e-03 4.63e-05f 1\n 227 5.9923339e+05 4.09e+00 1.44e+04 -1.0 2.27e+02 1.9 2.84e-03 2.85e-03f 1\n 228 5.9851909e+05 4.09e+00 1.44e+04 -1.0 2.29e+02 1.4 9.28e-04 6.31e-04f 1\n 229 5.9692058e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.9 1.94e-03 1.77e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 230 5.9689878e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.4 1.22e-05 1.79e-05f 1\n 231 5.9684337e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.8 7.71e-05 6.11e-05f 1\n 232 5.9682024e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.3 2.55e-06 2.10e-05f 1\n 233 5.9680869e+05 4.08e+00 1.38e+04 -1.0 2.28e+02 1.8 7.05e-03 1.36e-05f 1\n 234 5.9076367e+05 4.05e+00 1.41e+04 -1.0 2.30e+02 1.3 5.00e-04 5.99e-03f 1\n 235 5.9073170e+05 4.05e+00 1.42e+04 -1.0 2.32e+02 1.7 4.66e-03 3.40e-05f 1\n 236 5.8521172e+05 4.03e+00 1.45e+04 -1.0 2.32e+02 1.2 8.81e-04 5.50e-03f 1\n 237 5.8376022e+05 4.03e+00 1.44e+04 -1.0 2.30e+02 0.8 2.07e-06 1.04e-03f 1\n 238 5.8372062e+05 4.03e+00 1.53e+04 -1.0 2.35e+02 2.1 3.62e-03 4.81e-05f 1\n 239 5.7779448e+05 4.00e+00 1.55e+04 -1.0 2.39e+02 1.6 1.33e-02 6.22e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 240 5.7771432e+05 4.00e+00 1.57e+04 -1.0 2.26e+02 2.0 1.00e-03 9.89e-05f 1\n 241 5.7665827e+05 4.00e+00 1.57e+04 -1.0 2.07e+02 1.6 9.92e-04 1.10e-03f 1\n 242 5.7511556e+05 3.99e+00 1.55e+04 -1.0 2.12e+02 2.0 1.09e-03 1.92e-03f 1\n 243 5.7052438e+05 3.97e+00 1.52e+04 -1.0 2.09e+02 1.5 2.73e-04 4.68e-03f 1\n 244 5.6998537e+05 3.97e+00 1.51e+04 -1.0 2.36e+02 1.9 3.75e-04 6.54e-04f 1\n 245 5.6612860e+05 3.95e+00 1.48e+04 -1.0 2.37e+02 1.5 3.47e-05 3.75e-03f 1\n 246 5.6572525e+05 3.95e+00 1.50e+04 -1.0 2.36e+02 1.9 2.57e-03 4.85e-04f 1\n 247 5.6516789e+05 3.95e+00 1.51e+04 -1.0 2.36e+02 1.4 1.32e-03 4.83e-04f 1\n 248 5.6316860e+05 3.94e+00 1.52e+04 -1.0 2.36e+02 1.8 3.05e-03 2.38e-03f 1\n 249 5.6254967e+05 3.94e+00 1.50e+04 -1.0 2.35e+02 1.3 7.91e-06 4.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 250 5.6077033e+05 3.93e+00 1.49e+04 -1.0 2.36e+02 1.8 1.29e-03 2.17e-03f 1\n 251 5.6022313e+05 3.93e+00 1.47e+04 -1.0 2.34e+02 1.3 2.29e-06 3.07e-04f 1\n 252 5.5692514e+05 3.91e+00 1.49e+04 -1.0 2.36e+02 1.7 8.13e-03 3.96e-03f 1\n 253 5.5673567e+05 3.91e+00 1.49e+04 -1.0 2.31e+02 1.2 2.68e-06 9.58e-05f 1\n 254 5.5535652e+05 3.91e+00 1.55e+04 -1.0 2.33e+02 1.7 1.14e-02 1.64e-03f 1\n 255 5.5534273e+05 3.91e+00 1.56e+04 -1.0 2.30e+02 2.1 4.75e-04 1.85e-05f 1\n 256 5.5514554e+05 3.90e+00 1.71e+04 -1.0 2.27e+02 1.6 1.32e-02 2.20e-04f 1\n 257 5.5141803e+05 3.90e+00 1.48e+04 -1.0 4.83e+02 1.1 1.93e-05 1.30e-03f 1\n 258 5.4693933e+05 3.88e+00 1.42e+04 -1.0 2.39e+02 1.6 2.91e-04 4.86e-03f 1\n 259 5.4687686e+05 3.88e+00 1.43e+04 -1.0 2.52e+02 1.1 2.34e-03 6.08e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 260 5.4353115e+05 3.87e+00 1.41e+04 -1.0 2.42e+02 1.5 1.34e-03 3.66e-03f 1\n 261 5.4346337e+05 3.87e+00 1.41e+04 -1.0 2.07e+02 1.0 1.56e-04 6.65e-05f 1\n 262 5.4335021e+05 3.87e+00 1.42e+04 -1.0 1.95e+02 1.5 2.22e-03 1.26e-04f 1\n 263 5.4296226e+05 3.86e+00 1.43e+04 -1.0 1.86e+02 1.0 2.63e-03 3.71e-04f 1\n 264 5.3812113e+05 3.84e+00 1.40e+04 -1.0 2.13e+02 1.4 2.17e-05 5.31e-03f 1\n 265 5.3811408e+05 3.84e+00 1.41e+04 -1.0 2.23e+02 1.8 3.29e-04 8.06e-06f 1\n 266 5.3810662e+05 3.84e+00 1.41e+04 -1.0 2.43e+02 1.4 9.90e-04 7.83e-06f 1\n 267 5.3692599e+05 3.84e+00 1.38e+04 -1.0 2.30e+02 1.8 8.84e-05 1.33e-03f 1\n 268 5.3689601e+05 3.84e+00 1.38e+04 -1.0 1.90e+02 1.3 1.05e-03 3.21e-05f 1\n 269 5.3480708e+05 3.83e+00 1.32e+04 -1.0 2.44e+02 0.8 2.23e-04 1.25e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 270 5.3431885e+05 3.83e+00 1.34e+04 -1.0 1.83e+02 1.3 4.31e-03 5.17e-04f 1\n 271 5.3310063e+05 3.83e+00 1.31e+04 -1.0 1.89e+02 1.7 2.86e-05 1.41e-03f 1\n 272 5.3150897e+05 3.82e+00 1.22e+04 -1.0 1.85e+02 2.1 1.96e-04 1.98e-03f 1\n 273 5.3147543e+05 3.82e+00 1.22e+04 -1.0 1.61e+02 1.6 1.35e-04 3.82e-05f 1\n 274 5.3137612e+05 3.82e+00 1.24e+04 -1.0 1.76e+02 1.2 3.23e-03 1.02e-04f 1\n 275 5.3106158e+05 3.82e+00 1.29e+04 -1.0 1.79e+02 1.6 5.34e-03 3.64e-04f 1\n 276 5.2929724e+05 3.81e+00 1.28e+04 -1.0 1.73e+02 1.1 5.52e-05 1.84e-03f 1\n 277 5.2907597e+05 3.81e+00 1.29e+04 -1.0 1.63e+02 2.4 4.41e-04 3.68e-04f 1\n 278 5.2816992e+05 3.80e+00 1.27e+04 -1.0 1.88e+02 2.0 5.12e-04 1.14e-03f 1\n 279 5.2807415e+05 3.80e+00 1.27e+04 -1.0 1.48e+03 1.5 1.26e-06 8.93e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 280 5.2805145e+05 3.80e+00 1.26e+04 -1.0 1.94e+02 1.0 1.55e-03 2.34e-05f 1\n 281 5.2494578e+05 3.79e+00 1.26e+04 -1.0 1.75e+02 1.4 7.38e-04 3.61e-03f 1\n 282 5.2382656e+05 3.79e+00 1.20e+04 -1.0 6.75e+02 1.0 1.61e-05 3.04e-04f 1\n 283 5.2294012e+05 3.78e+00 1.20e+04 -1.0 1.74e+02 1.4 1.78e-03 9.88e-04f 1\n 284 5.2002561e+05 3.77e+00 1.24e+04 -1.0 1.76e+02 1.8 7.11e-03 3.79e-03f 1\n 285 5.1038171e+05 3.73e+00 1.19e+04 -1.0 1.71e+02 1.3 6.29e-05 1.07e-02f 1\n 286 5.1029468e+05 3.73e+00 1.19e+04 -1.0 1.84e+02 1.8 7.42e-04 1.12e-04f 1\n 287 5.1028585e+05 3.73e+00 1.21e+04 -1.0 2.01e+02 2.2 4.17e-04 1.31e-05f 1\n 288 5.0835844e+05 3.72e+00 1.19e+04 -1.0 2.75e+02 1.7 1.15e-05 2.41e-03f 1\n 289 5.0829350e+05 3.72e+00 1.19e+04 -1.0 1.71e+02 1.2 3.97e-04 6.12e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 290 5.0786133e+05 3.72e+00 1.30e+04 -1.0 1.77e+02 1.7 1.37e-02 5.49e-04f 1\n 291 4.9734337e+05 3.69e+00 1.06e+04 -1.0 1.72e+02 1.2 8.48e-04 8.86e-03f 1\n 292 4.9689003e+05 3.68e+00 1.08e+04 -1.0 1.74e+02 1.6 3.20e-03 5.66e-04f 1\n 293 4.9257017e+05 3.67e+00 9.15e+03 -1.0 1.62e+02 1.1 7.85e-05 3.01e-03f 1\n 294 4.9140020e+05 3.67e+00 9.02e+03 -1.0 2.41e+02 1.6 4.58e-05 1.41e-03f 1\n 295 4.9127824e+05 3.67e+00 1.14e+04 -1.0 1.61e+02 1.1 2.66e-03 6.68e-05f 1\n 296 4.9066209e+05 3.66e+00 1.29e+04 -1.0 1.75e+02 1.5 1.82e-02 7.38e-04f 1\n 297 4.8899995e+05 3.66e+00 1.45e+04 -1.0 5.05e+02 1.0 9.31e-04 4.06e-04f 1\n 298 4.7750162e+05 3.61e+00 1.35e+04 -1.0 1.76e+02 1.5 4.98e-03 1.35e-02f 1\n 299 4.7434464e+05 3.61e+00 1.49e+04 -1.0 9.97e+02 1.0 6.27e-04 4.14e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 300 4.7339897e+05 3.61e+00 1.50e+04 -1.0 1.73e+02 1.4 2.13e-03 1.10e-03f 1\n 301 4.7161223e+05 3.61e+00 1.40e+04 -1.0 3.25e+02 0.9 1.03e-04 5.77e-04f 1\n 302 4.7150987e+05 3.60e+00 1.43e+04 -1.0 2.00e+02 0.4 6.17e-03 9.97e-05f 1\n 303 4.6068296e+05 3.57e+00 1.36e+04 -1.0 3.30e+02 -0.0 4.88e-05 1.06e-02f 1\n 304 4.5827805e+05 3.56e+00 1.49e+04 -1.0 1.68e+02 1.3 1.36e-02 2.70e-03f 1\n 305 4.5598068e+05 3.55e+00 1.41e+04 -1.0 4.79e+02 0.8 4.48e-04 7.06e-04f 1\n 306 4.5518400e+05 3.55e+00 1.43e+04 -1.0 1.59e+02 1.2 2.67e-03 8.88e-04f 1\n 307 4.5309751e+05 3.55e+00 1.72e+04 -1.0 2.98e+02 0.8 2.61e-03 8.91e-04f 1\n 308 4.4826799e+05 3.53e+00 1.86e+04 -1.0 1.66e+02 1.2 1.97e-02 5.39e-03f 1\n 309 4.4337229e+05 3.51e+00 1.70e+04 -1.0 1.62e+02 0.7 4.61e-05 4.04e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 310 4.3957043e+05 3.50e+00 2.22e+04 -1.0 1.64e+02 1.1 5.11e-02 4.22e-03f 1\n 311 4.3840763e+05 3.50e+00 2.35e+04 -1.0 1.68e+02 0.7 4.15e-03 9.47e-04f 1\n 312 4.2663652e+05 3.45e+00 2.43e+04 -1.0 1.62e+02 1.1 2.95e-02 1.34e-02f 1\n 313 4.1771582e+05 3.43e+00 2.40e+04 -1.0 1.75e+02 0.6 6.75e-03 7.00e-03f 1\n 314 4.1583852e+05 3.42e+00 2.27e+04 -1.0 8.29e+02 0.1 1.03e-04 3.62e-04f 1\n 315 4.0823036e+05 3.40e+00 2.35e+04 -1.0 1.82e+02 0.6 9.82e-03 6.83e-03f 1\n 316 4.0742102e+05 3.40e+00 2.31e+04 -1.0 2.88e+02 0.1 7.31e-05 5.39e-04f 1\n 317 4.0736672e+05 3.40e+00 2.36e+04 -1.0 4.84e+02 -0.4 7.32e-03 5.87e-05f 1\n 318 3.9419770e+05 3.35e+00 2.18e+04 -1.0 7.13e+02 -0.9 8.53e-05 1.33e-02f 1\n 319 3.8663282e+05 3.33e+00 1.92e+04 -1.0 8.56e+02 -1.3 2.10e-04 5.79e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 320 3.8579331e+05 3.33e+00 2.13e+04 -1.0 9.22e+02 -1.8 7.40e-03 7.22e-04f 1\n 321 3.7974323e+05 3.32e+00 1.94e+04 -1.0 9.50e+02 -2.3 1.97e-04 4.91e-03f 1\n 322 3.7938905e+05 3.31e+00 2.15e+04 -1.0 9.51e+02 -2.8 8.43e-03 3.27e-04f 1\n 323 3.7037180e+05 3.29e+00 1.92e+04 -1.0 9.55e+02 -3.3 1.13e-04 8.55e-03f 1\n 324 3.6908213e+05 3.28e+00 1.94e+04 -1.0 9.41e+02 -3.7 2.53e-03 1.30e-03f 1\n 325 3.6277620e+05 3.26e+00 1.81e+04 -1.0 2.17e+02 0.3 8.07e-04 7.29e-03f 1\n 326 3.5989666e+05 3.24e+00 1.79e+04 -1.0 1.55e+02 1.6 1.27e-03 5.36e-03f 1\n 327 3.5985661e+05 3.24e+00 1.79e+04 -1.0 1.59e+02 2.1 9.24e-04 1.04e-04f 1\n 328 3.5935266e+05 3.24e+00 1.79e+04 -1.0 1.62e+02 1.6 6.26e-05 8.73e-04f 1\n 329 3.5783375e+05 3.23e+00 1.78e+04 -1.0 1.63e+02 2.0 3.50e-03 3.63e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 330 3.5743587e+05 3.22e+00 1.78e+04 -1.0 1.61e+02 1.5 3.51e-05 6.78e-04f 1\n 331 3.5668635e+05 3.22e+00 1.78e+04 -1.0 1.52e+02 1.1 2.73e-04 1.08e-03f 1\n 332 3.5630331e+05 3.22e+00 1.78e+04 -1.0 1.67e+02 0.6 3.51e-03 4.92e-04f 1\n 333 3.5562828e+05 3.22e+00 1.78e+04 -1.0 2.82e+02 0.1 3.34e-03 7.59e-04f 1\n 334 3.5480287e+05 3.21e+00 1.78e+04 -1.0 4.82e+02 -0.4 4.21e-05 9.86e-04f 1\n 335 3.5438009e+05 3.21e+00 1.78e+04 -1.0 6.55e+02 -0.8 1.29e-04 5.04e-04f 1\n 336 3.5436086e+05 3.21e+00 1.78e+04 -1.0 8.12e+02 -1.3 2.56e-03 2.29e-05f 1\n 337 3.5193976e+05 3.20e+00 1.77e+04 -1.0 8.82e+02 -1.8 1.07e-03 2.89e-03f 1\n 338 3.5190047e+05 3.20e+00 1.77e+04 -1.0 9.10e+02 -2.3 7.21e-03 4.74e-05f 1\n 339 3.5093683e+05 3.20e+00 1.77e+04 -1.0 7.47e+02 -1.0 1.35e-05 1.13e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 340 3.5052498e+05 3.20e+00 1.77e+04 -1.0 2.07e+02 0.4 5.15e-04 5.29e-04f 1\n 341 3.3750946e+05 3.14e+00 1.74e+04 -1.0 3.58e+02 -0.1 1.67e-05 1.64e-02f 1\n 342 3.3747338e+05 3.14e+00 1.74e+04 -1.0 5.75e+02 -0.6 6.58e-03 4.69e-05f 1\n 343 3.3562091e+05 3.14e+00 1.74e+04 -1.0 7.66e+02 -1.1 1.05e-04 2.42e-03f 1\n 344 3.3510890e+05 3.13e+00 1.73e+04 -1.0 8.41e+02 -1.5 1.22e-03 6.80e-04f 1\n 345 3.2871807e+05 3.11e+00 1.72e+04 -1.0 8.84e+02 -2.0 3.02e-03 8.53e-03f 1\n 346 3.2777372e+05 3.10e+00 1.72e+04 -1.0 1.54e+02 1.1 7.57e-05 1.51e-03f 1\n 347 3.2772491e+05 3.10e+00 1.72e+04 -1.0 1.60e+02 1.6 2.40e-03 9.66e-05f 1\n 348 3.2718271e+05 3.10e+00 1.71e+04 -1.0 1.60e+02 2.0 4.86e-03 1.52e-03f 1\n 349 3.2706600e+05 3.10e+00 1.71e+04 -1.0 1.58e+02 1.5 1.31e-03 2.19e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 350 3.2406746e+05 3.08e+00 1.70e+04 -1.0 1.47e+02 1.0 1.75e-04 5.59e-03f 1\n 351 3.2366994e+05 3.08e+00 1.70e+04 -1.0 1.50e+02 1.5 4.30e-03 7.40e-04f 1\n 352 3.2356913e+05 3.08e+00 1.70e+04 -1.0 1.37e+02 1.0 1.11e-04 1.90e-04f 1\n 353 3.2355506e+05 3.08e+00 1.70e+04 -1.0 1.49e+02 1.4 3.22e-03 2.36e-05f 1\n 354 3.2316524e+05 3.07e+00 1.70e+04 -1.0 1.40e+02 0.9 1.38e-03 6.09e-04f 1\n 355r 3.2316524e+05 3.07e+00 1.00e+03 0.5 0.00e+00 0.4 0.00e+00 3.57e-07R 8\n 356r 3.2229084e+05 3.07e+00 1.00e+03 0.5 2.06e+03 - 6.18e-03 7.60e-04f 1\n 357r 3.2109567e+05 3.06e+00 9.98e+02 0.5 1.80e+03 - 1.71e-03 8.09e-04f 1\n 358r 3.1149974e+05 2.97e+00 9.95e+02 0.5 1.67e+03 - 3.74e-03 6.54e-03f 1\n 359r 3.1087190e+05 2.97e+00 9.95e+02 0.5 8.58e+02 - 9.83e-05 4.32e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 360r 3.0989090e+05 2.96e+00 9.97e+02 0.5 9.94e+02 - 6.50e-03 6.44e-04f 1\n 361r 3.0241262e+05 2.87e+00 9.92e+02 0.5 9.07e+02 - 2.36e-03 5.34e-03f 1\n 362r 2.9694919e+05 2.80e+00 9.80e+02 0.5 8.52e+02 - 1.55e-02 4.79e-03f 1\n 363r 2.8929254e+05 2.68e+00 9.72e+02 0.5 6.39e+02 - 1.25e-02 8.39e-03f 1\n 364 2.8919644e+05 2.68e+00 9.52e+03 -1.0 1.36e+02 -0.0 2.03e-04 1.66e-04f 1\n 365 2.8908661e+05 2.68e+00 9.52e+03 -1.0 1.38e+02 -0.5 3.89e-04 1.90e-04f 1\n 366 2.8861557e+05 2.68e+00 9.51e+03 -1.0 1.34e+02 -1.0 2.45e-04 8.16e-04f 1\n 367 2.8784152e+05 2.67e+00 9.50e+03 -1.0 1.29e+02 -1.5 5.67e-04 1.35e-03f 1\n 368 2.8557520e+05 2.66e+00 9.46e+03 -1.0 1.18e+02 -1.9 1.40e-03 3.97e-03f 1\n 369 2.8048140e+05 2.64e+00 9.38e+03 -1.0 1.31e+02 -2.4 2.51e-03 9.01e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 370 2.7807299e+05 2.63e+00 9.34e+03 -1.0 1.50e+02 -2.9 1.64e-03 4.32e-03f 1\n 371 2.7069758e+05 2.59e+00 9.21e+03 -1.0 1.60e+02 -3.4 3.85e-03 1.34e-02f 1\n 372 2.5748481e+05 2.53e+00 8.98e+03 -1.0 1.59e+02 -3.8 4.45e-03 2.48e-02f 1\n 373 2.5409647e+05 2.51e+00 8.92e+03 -1.0 1.53e+02 -4.3 1.21e-02 6.62e-03f 1\n 374 2.4261880e+05 2.45e+00 8.72e+03 -1.0 1.50e+02 -4.8 6.04e-03 2.29e-02f 1\n 375 2.2822588e+05 2.38e+00 8.45e+03 -1.0 1.46e+02 -5.3 1.51e-02 3.02e-02f 1\n 376 2.0965467e+05 2.28e+00 8.10e+03 -1.0 1.41e+02 -5.8 1.82e-02 4.16e-02f 1\n 377 2.0936932e+05 2.28e+00 8.10e+03 -1.0 1.37e+02 -6.2 3.27e-02 6.83e-04f 1\n 378 2.0780500e+05 2.27e+00 8.07e+03 -1.0 1.37e+02 -6.7 4.67e-03 3.76e-03f 1\n 379 1.9606770e+05 2.20e+00 7.84e+03 -1.0 1.38e+02 -7.2 7.84e-04 2.87e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 380 1.7935187e+05 2.11e+00 7.49e+03 -1.0 1.38e+02 -7.7 2.58e-02 4.37e-02f 1\n 381 1.7765925e+05 2.10e+00 7.46e+03 -1.0 1.36e+02 -8.1 4.29e-02 4.74e-03f 1\n 382 1.6495345e+05 2.02e+00 7.18e+03 -1.0 1.34e+02 -8.6 1.77e-02 3.65e-02f 1\n 383 1.6340175e+05 2.01e+00 7.15e+03 -1.0 1.30e+02 -9.1 1.84e-02 4.73e-03f 1\n 384 1.5788016e+05 1.98e+00 7.03e+03 -1.0 1.29e+02 -9.6 3.72e-03 1.71e-02f 1\n 385 1.5064127e+05 1.93e+00 6.87e+03 -1.0 1.28e+02 -10.1 8.10e-03 2.33e-02f 1\n 386 1.2939352e+05 1.79e+00 6.36e+03 -1.0 1.25e+02 -10.5 1.85e-02 7.34e-02f 1\n 387 1.2646803e+05 1.77e+00 6.29e+03 -1.0 1.18e+02 -11.0 4.15e-02 1.14e-02f 1\n 388 1.1471323e+05 1.68e+00 5.99e+03 -1.0 1.16e+02 -11.5 1.69e-02 4.78e-02f 1\n 389 1.0992888e+05 1.65e+00 5.86e+03 -1.0 1.11e+02 -12.0 7.97e-02 2.12e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 390 1.0212816e+05 1.59e+00 5.65e+03 -1.0 1.07e+02 -12.4 1.86e-03 3.63e-02f 1\n 391 8.6177113e+04 1.46e+00 5.19e+03 -1.0 1.04e+02 -12.9 7.26e-02 8.17e-02f 1\n 392 8.5370527e+04 1.45e+00 5.16e+03 -1.0 9.48e+01 -13.4 3.22e-02 4.71e-03f 1\n 393 7.4077318e+04 1.35e+00 4.81e+03 -1.0 9.38e+01 -13.9 1.85e-02 6.88e-02f 1\n 394 7.1177623e+04 1.33e+00 4.71e+03 -1.0 8.78e+01 -14.3 6.45e-02 1.99e-02f 1\n 395 6.4716733e+04 1.26e+00 4.49e+03 -1.0 8.51e+01 -14.8 4.54e-03 4.67e-02f 1\n 396 5.7488482e+04 1.19e+00 4.23e+03 -1.0 8.14e+01 -15.3 9.91e-03 5.78e-02f 1\n 397 4.8754351e+04 1.10e+00 3.90e+03 -1.0 7.88e+01 -15.8 6.42e-02 7.95e-02f 1\n 398 4.7745315e+04 1.08e+00 3.86e+03 -1.0 7.72e+01 -16.3 9.49e-02 1.05e-02f 1\n 399 4.1977413e+04 1.02e+00 3.61e+03 -1.0 7.89e+01 -16.7 6.18e-02 6.27e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 400 3.3677208e+04 9.10e-01 3.24e+03 -1.0 7.75e+01 -17.2 7.45e-03 1.05e-01f 1\n 401 3.0166245e+04 8.61e-01 3.06e+03 -1.0 7.30e+01 -17.7 2.38e-02 5.39e-02f 1\n 402 2.5223779e+04 7.87e-01 2.80e+03 -1.0 7.10e+01 -18.2 1.21e-01 8.61e-02f 1\n 403 2.2341250e+04 7.40e-01 2.63e+03 -1.0 6.89e+01 -18.6 1.27e-01 5.92e-02f 1\n 404 1.8863927e+04 6.80e-01 2.42e+03 -1.0 6.77e+01 -19.1 8.08e-02 8.17e-02f 1\n 405 1.5848263e+04 6.23e-01 2.21e+03 -1.0 6.47e+01 -19.6 5.68e-02 8.40e-02f 1\n 406 1.4057233e+04 5.86e-01 2.08e+03 -1.0 6.57e+01 -19.1 8.62e-02 5.84e-02f 1\n 407 1.1013361e+04 5.19e-01 1.84e+03 -1.0 5.68e+01 -19.6 1.68e-01 1.16e-01f 1\n 408 7.9952027e+03 4.41e-01 1.57e+03 -1.0 5.34e+01 -19.1 1.48e-01 1.49e-01f 1\n 409 5.4065322e+03 3.62e-01 1.29e+03 -1.0 4.45e+01 -19.6 1.93e-01 1.80e-01f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 410 2.4913122e+03 2.44e-01 8.68e+02 -1.0 4.14e+01 -19.1 1.52e-01 3.26e-01f 1\n 411 1.0310441e+03 1.55e-01 5.51e+02 -1.0 2.82e+01 -19.6 2.47e-01 3.66e-01f 1\n 412 2.7434337e+02 7.52e-02 4.80e+02 -1.0 2.93e+01 -19.1 1.96e-01 5.14e-01f 1\n 413 5.4776714e+01 2.33e-02 7.33e+02 -1.0 7.94e+00 -19.6 4.50e-01 6.90e-01f 1\n 414 2.8195260e+01 3.14e-04 1.32e+03 -1.0 1.06e+01 -19.1 5.84e-01 1.00e+00f 1\n 415 3.2622344e+01 3.87e-05 1.06e+03 -1.0 1.60e+01 -19.6 7.02e-01 9.94e-01f 1\n 416 3.0040029e+01 4.65e-05 2.31e+02 -1.0 4.17e+01 -19.1 9.04e-01 1.00e+00f 1\n 417 3.0266079e+01 4.64e-04 4.41e+02 -1.0 1.21e+02 -19.6 1.00e+00 9.30e-01h 1\n 418 2.9559008e+01 3.42e-05 5.33e+01 -1.0 1.42e+01 -19.1 6.03e-01 1.00e+00h 1\n 419 2.9904880e+01 2.39e-06 6.35e-03 -1.0 6.68e+00 -19.6 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 420 2.4902915e+01 2.75e-05 1.10e+00 -2.5 1.14e+01 -19.1 4.91e-01 4.44e-01f 1\n 421 2.2342576e+01 8.15e-05 1.58e+00 -2.5 6.79e+01 -19.6 1.30e-01 3.02e-01f 1\n 422 2.0275308e+01 8.22e-05 1.07e+00 -2.5 6.41e+00 -3.7 3.38e-01 3.24e-01h 1\n 423 1.9264876e+01 9.78e-05 8.99e-01 -2.5 2.12e+01 -4.2 3.83e-01 1.56e-01h 1\n 424 1.8289856e+01 7.85e-05 6.81e-01 -2.5 6.19e+00 -4.6 2.31e-01 2.42e-01h 1\n 425 1.6132778e+01 1.96e-04 8.21e-01 -2.5 1.24e+01 -5.1 4.78e-01 7.58e-01h 1\n 426 1.5611473e+01 1.11e-04 5.01e-01 -2.5 1.64e+01 -5.6 4.71e-01 5.11e-01h 1\n 427 1.5163933e+01 4.19e-05 3.81e-01 -2.5 2.85e+00 -3.4 1.00e+00 6.97e-01h 1\n 428 1.5050001e+01 3.15e-05 4.60e-01 -2.5 3.60e+00 -3.8 6.17e-01 2.96e-01h 1\n 429 1.4900715e+01 5.30e-06 1.02e+00 -2.5 1.96e+00 -4.3 6.36e-01 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 430 1.4825885e+01 2.55e-05 4.15e-01 -2.5 5.52e+00 -3.9 8.38e-01 5.50e-01h 1\n 431 1.4739809e+01 3.24e-05 3.56e-01 -2.5 1.98e+01 -4.4 1.99e-01 1.55e-01h 1\n 432 1.4596773e+01 2.88e-05 5.34e-01 -2.5 5.19e+00 -3.9 5.29e-01 7.94e-01h 1\n 433 1.4484823e+01 3.46e-05 3.39e-01 -2.5 3.05e+01 -4.4 2.32e-01 9.62e-02h 1\n 434 1.4369681e+01 4.72e-05 4.22e-01 -2.5 1.62e+01 -4.9 3.06e-01 5.62e-01h 1\n 435 1.4279373e+01 4.71e-05 3.94e+02 -2.5 3.10e+01 -5.4 4.70e-01 2.25e-01h 1\n 436 1.4208263e+01 2.19e-05 2.73e+02 -2.5 2.87e+01 -5.8 3.06e-01 1.00e+00h 1\n 437 1.4134986e+01 1.91e-05 1.24e+02 -2.5 1.93e+01 -6.3 5.46e-01 6.57e-01h 1\n 438 1.4091516e+01 4.37e-05 3.46e+01 -2.5 2.35e+01 -6.8 7.19e-01 1.00e+00h 1\n 439 1.4087170e+01 4.09e-06 2.76e-04 -2.5 7.32e+00 -7.3 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 440 1.4088533e+01 1.91e-09 4.39e-07 -2.5 4.32e-01 -7.8 1.00e+00 1.00e+00h 1\n 441 1.3804149e+01 3.76e-06 7.14e+00 -3.8 4.84e+00 -8.2 6.51e-01 8.20e-01f 1\n 442 1.3706304e+01 1.35e-06 1.69e-03 -3.8 1.67e+00 -8.7 1.00e+00 1.00e+00h 1\n 443 1.3684885e+01 4.66e-08 3.95e-05 -3.8 4.07e-02 -9.2 1.00e+00 1.00e+00h 1\n 444 1.3664347e+01 8.58e-08 6.51e-02 -5.7 3.79e-01 -9.7 9.45e-01 9.46e-01h 1\n 445 1.3660196e+01 4.91e-08 1.08e+01 -5.7 4.56e-02 -10.1 1.00e+00 9.19e-01h 1\n 446 1.3659000e+01 4.92e-08 1.36e-06 -5.7 9.38e-03 -10.6 1.00e+00 1.00e+00f 1\n 447 1.3658600e+01 4.94e-08 9.08e+01 -8.6 3.53e-03 -11.1 9.94e-01 8.63e-01h 1\n 448 1.3658471e+01 5.57e-08 6.82e+00 -8.6 1.22e-03 -11.6 1.00e+00 9.90e-01f 1\n 449 1.3658445e+01 4.72e-08 2.13e-09 -8.6 4.22e-04 -12.0 1.00e+00 1.00e+00f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 450 1.3658439e+01 4.92e-08 3.85e-12 -8.6 1.81e-05 -12.5 1.00e+00 1.00e+00h 1\n 451 1.3658437e+01 4.92e-08 1.82e-12 -8.6 1.52e-06 -13.0 1.00e+00 1.00e+00h 1\n 452 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.26e-07 -13.5 1.00e+00 1.00e+00h 1\n 453 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.49e-07 -14.0 1.00e+00 1.00e+00h 1\n 454 1.3658437e+01 4.92e-08 3.64e-12 -8.6 9.58e-09 -14.4 1.00e+00 1.00e+00h 1\n 455 1.3658437e+01 4.92e-08 9.09e-13 -8.6 3.90e-11 -14.9 1.00e+00 1.00e+00h 1\n 456 1.3658437e+01 4.92e-08 9.09e-13 -8.6 1.39e-12 -15.4 1.00e+00 1.00e+00h 1\n 457 1.3658437e+01 4.92e-08 9.09e-13 -8.6 9.57e-13 -15.9 1.00e+00 1.00e+00h 1\n 458 1.3658437e+01 4.92e-08 9.09e-13 -8.6 7.94e-13 -16.3 1.00e+00 1.00e+00h 1\n 459 1.3658437e+01 4.92e-08 9.09e-13 -8.6 6.30e-13 -16.8 1.00e+00 1.00e+00h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 460 1.3658437e+01 4.92e-08 4.55e-13 -8.6 2.07e-12 -17.3 1.00e+00 1.00e+00h 1\n 461 1.3658437e+01 4.92e-08 9.09e-13 -8.6 2.88e-12 -17.8 1.00e+00 1.00e+00h 1\n 462 1.3658437e+01 4.92e-08 9.09e-13 -8.6 5.03e-12 -18.2 1.00e+00 1.00e+00h 1\n 463 1.3658437e+01 4.92e-08 4.55e-13 -8.6 3.27e-12 -18.7 1.00e+00 1.00e+00h 1\n\nNumber of Iterations....: 463\n\n (scaled) (unscaled)\nObjective...............: 1.3658436881286921e+01 1.3658436881286921e+01\nDual infeasibility......: 4.5474735088646412e-13 4.5474735088646412e-13\nConstraint violation....: 4.9168770388519079e-08 4.9168770388519079e-08\nComplementarity.........: 2.5059067889663204e-09 2.5059067889663204e-09\nOverall NLP error.......: 4.9168770388519079e-08 4.9168770388519079e-08\n\n\nNumber of objective function evaluations = 483\nNumber of objective gradient evaluations = 395\nNumber of equality constraint evaluations = 483\nNumber of inequality constraint evaluations = 483\nNumber of equality constraint Jacobian evaluations = 467\nNumber of inequality constraint Jacobian evaluations = 467\nNumber of Lagrangian Hessian evaluations = 463\nTotal CPU secs in IPOPT (w/o function evaluations) = 4.499\nTotal CPU secs in NLP function evaluations = 9.988\n\nEXIT: Solved To Acceptable Level.\n solver : t_proc (avg) t_wall (avg) n_eval\n nlp_f | 28.65ms ( 59.32us) 28.66ms ( 59.35us) 483\n nlp_g | 513.61ms ( 1.06ms) 513.34ms ( 1.06ms) 483\n nlp_grad | 3.08ms ( 3.08ms) 3.08ms ( 3.08ms) 1\n nlp_grad_f | 34.91ms ( 88.17us) 34.96ms ( 88.29us) 396\n nlp_hess_l | 6.27 s ( 13.63ms) 6.25 s ( 13.58ms) 460\n nlp_jac_g | 3.15 s ( 6.74ms) 3.14 s ( 6.72ms) 468\n total | 14.60 s ( 14.60 s) 14.51 s ( 14.51 s) 1\nThis is Ipopt version 3.12.3, running with linear solver mumps.\nNOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n\nNumber of nonzeros in equality constraint Jacobian...: 4230\nNumber of nonzeros in inequality constraint Jacobian.: 3466\nNumber of nonzeros in Lagrangian Hessian.............: 3648\n\nTotal number of variables............................: 2293\n variables with only lower bounds: 0\n variables with lower and upper bounds: 0\n variables with only upper bounds: 0\nTotal number of equality constraints.................: 1377\nTotal number of inequality constraints...............: 967\n inequality constraints with only lower bounds: 204\n inequality constraints with lower and upper bounds: 355\n inequality constraints with only upper bounds: 408\n\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 0 0.0000000e+00 9.83e+00 7.50e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n 1 1.0399716e-02 9.83e+00 1.26e+00 -1.0 1.20e+02 -4.0 4.93e-04 3.26e-04h 1\n 2 2.0045559e-02 9.83e+00 2.20e+01 -1.0 1.82e+02 -4.5 3.56e-04 3.85e-05h 1\n 3 4.4710489e-01 9.83e+00 2.52e+01 -1.0 2.46e+02 -5.0 3.88e-04 3.37e-04h 1\n 4 2.5591113e+00 9.83e+00 6.47e+01 -1.0 3.34e+02 -5.4 1.11e-03 4.95e-04h 1\n 5 1.0304744e+01 9.83e+00 1.06e+02 -1.0 2.54e+02 -5.9 1.15e-03 5.03e-04h 1\n 6 7.9216962e+01 9.82e+00 1.08e+02 -1.0 1.86e+02 -6.4 1.38e-03 1.35e-03h 1\n 7 1.6205310e+02 9.82e+00 1.46e+02 -1.0 1.46e+02 -6.9 1.48e-03 8.87e-04h 1\n 8 2.2025207e+02 9.83e+00 1.56e+02 -1.0 3.22e+02 -7.3 6.46e-04 4.87e-04h 1\n 9 2.2441787e+02 9.83e+00 1.54e+02 -1.0 4.45e+03 -7.8 8.53e-06 3.28e-05h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 10 2.5182294e+02 9.82e+00 1.42e+02 -1.0 9.50e+02 -8.3 8.65e-06 2.07e-04h 1\n 11 2.8777432e+02 9.81e+00 1.66e+02 -1.0 4.28e+02 -8.8 6.37e-04 2.55e-04h 1\n 12 4.0157811e+02 9.80e+00 1.67e+02 -1.0 1.45e+02 -9.2 7.37e-04 7.08e-04h 1\n 13 4.1550901e+02 9.80e+00 1.78e+02 -1.0 1.56e+02 -9.7 2.52e-04 7.97e-05h 1\n 14 6.1114602e+02 9.80e+00 1.21e+02 -1.0 1.50e+02 -10.2 8.33e-05 1.00e-03h 1\n 15 6.1355211e+02 9.80e+00 1.82e+02 -1.0 1.68e+02 -10.7 9.96e-04 1.13e-05h 1\n 16 6.2428940e+02 9.80e+00 2.68e+02 -1.0 3.50e+02 -11.2 1.43e-03 5.02e-05h 1\n 17r 6.2428940e+02 9.80e+00 9.99e+02 1.0 0.00e+00 -11.6 0.00e+00 4.03e-07R 5\n 18r 8.6414419e+02 9.80e+00 9.99e+02 1.0 6.29e+03 - 3.71e-04 1.98e-05f 1\n 19r 3.3647143e+03 9.80e+00 9.99e+02 1.0 1.65e+03 - 3.45e-04 1.15e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 20r 5.1092797e+04 9.80e+00 9.98e+02 1.0 7.67e+02 - 3.88e-04 6.81e-04f 1\n 21r 6.3342770e+04 9.79e+00 1.07e+03 1.0 7.22e+00 2.0 1.50e-01 1.14e-02f 1\n 22r 1.7594664e+05 9.73e+00 1.03e+03 1.0 3.13e+00 2.4 2.75e-01 2.46e-01f 1\n 23r 1.6728527e+05 9.68e+00 1.07e+03 1.0 2.44e+00 2.9 3.31e-02 1.26e-01f 1\n 24r 1.7195513e+05 9.51e+00 1.09e+03 1.0 5.75e-01 3.3 8.31e-01 1.00e+00f 1\n 25r 1.7774546e+05 9.35e+00 1.07e+03 1.0 1.83e+00 2.8 3.09e-01 3.74e-01f 1\n 26r 1.7826098e+05 8.80e+00 1.06e+03 1.0 5.11e+00 2.3 3.60e-01 5.80e-01f 1\n 27 1.7790304e+05 8.75e+00 2.67e+03 -1.0 9.73e+01 -12.1 1.05e-03 4.89e-03f 1\n 28 1.7790581e+05 8.75e+00 2.67e+03 -1.0 9.99e+01 -12.6 4.15e-03 8.71e-05h 1\n 29 1.7805578e+05 8.74e+00 2.67e+03 -1.0 1.49e+02 -13.1 2.98e-03 1.65e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 30 1.7826045e+05 8.73e+00 2.66e+03 -1.0 1.00e+02 -13.5 5.02e-03 1.36e-03h 1\n 31 1.7833630e+05 8.72e+00 2.66e+03 -1.0 9.67e+01 -14.0 4.35e-03 3.57e-04h 1\n 32 1.7838366e+05 8.72e+00 2.66e+03 -1.0 9.66e+01 -14.5 5.53e-03 1.85e-04h 1\n 33 1.7897357e+05 8.70e+00 2.66e+03 -1.0 9.66e+01 -15.0 6.03e-03 1.99e-03h 1\n 34 1.7979138e+05 8.68e+00 2.65e+03 -1.0 9.65e+01 -15.5 5.02e-03 2.33e-03h 1\n 35 1.8083548e+05 8.66e+00 2.64e+03 -1.0 9.65e+01 -15.9 5.14e-03 2.59e-03h 1\n 36 1.8126368e+05 8.65e+00 2.64e+03 -1.0 9.68e+01 -16.4 1.38e-03 9.55e-04h 1\n 37 1.8126992e+05 8.65e+00 2.64e+03 -1.0 9.72e+01 -16.9 2.04e-05 1.32e-05h 1\n 38 1.8127749e+05 8.65e+00 2.64e+03 -1.0 9.75e+01 -17.4 5.22e-03 1.49e-05h 1\n 39 1.8272429e+05 8.63e+00 2.63e+03 -1.0 9.81e+01 -17.8 3.94e-04 2.72e-03h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 40 1.8373146e+05 8.61e+00 2.63e+03 -1.0 9.79e+01 -18.3 2.78e-05 1.86e-03h 1\n 41 1.8460711e+05 8.60e+00 2.63e+03 -1.0 9.79e+01 -18.8 6.10e-03 1.58e-03h 1\n 42 1.8506845e+05 8.59e+00 3.03e+03 -1.0 9.77e+01 -19.3 3.60e-03 8.05e-04h 1\n 43 1.8630538e+05 8.57e+00 2.81e+03 -1.0 9.79e+01 -19.7 1.23e-03 2.15e-03h 1\n 44 1.8693912e+05 8.56e+00 2.69e+03 -1.0 9.91e+01 -19.1 6.31e-04 1.18e-03h 1\n 45 1.8714858e+05 8.56e+00 2.61e+03 -1.0 9.72e+01 -19.6 1.86e-05 3.60e-04h 1\n 46 1.8833659e+05 8.54e+00 3.03e+03 -1.0 9.83e+01 -19.1 4.39e-03 2.16e-03h 1\n 47 1.8900050e+05 8.53e+00 3.12e+03 -1.0 9.71e+01 -19.6 1.51e-03 1.12e-03h 1\n 48 1.8921851e+05 8.53e+00 3.04e+03 -1.0 9.85e+01 -19.1 2.64e-05 3.81e-04h 1\n 49 1.8974673e+05 8.52e+00 2.87e+03 -1.0 9.66e+01 -19.6 1.53e-04 8.26e-04h 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 50 1.8975438e+05 8.52e+00 3.74e+03 -1.0 9.81e+01 -19.1 3.80e-03 1.32e-05h 1\n 51 1.9002449e+05 8.52e+00 4.95e+03 -1.0 9.84e+01 -19.6 3.14e-03 3.97e-04h 1\n 52 1.9113127e+05 8.50e+00 5.17e+03 -1.0 9.86e+01 -19.1 2.66e-03 1.79e-03h 1\n 53 1.9218965e+05 8.49e+00 4.74e+03 -1.0 9.66e+01 -19.6 1.49e-05 1.46e-03h 1\n 54 1.9235900e+05 8.49e+00 4.77e+03 -1.0 9.69e+01 -19.1 3.43e-04 2.55e-04h 1\n 55 1.9614877e+05 8.45e+00 3.24e+03 -1.0 9.55e+01 -19.6 4.82e-05 4.46e-03h 1\n 56 1.9767353e+05 8.43e+00 3.55e+03 -1.0 9.43e+01 -19.1 3.11e-03 2.00e-03h 1\n 57 2.0045953e+05 8.41e+00 3.54e+03 -1.0 9.38e+01 -19.6 3.09e-03 3.11e-03h 1\n 58 2.0058633e+05 8.41e+00 4.32e+03 -1.0 9.34e+01 -19.1 2.74e-03 1.60e-04h 1\n 59r 2.0058633e+05 8.41e+00 1.00e+03 0.9 0.00e+00 -19.6 0.00e+00 4.42e-07R 5\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 60r 2.0053275e+05 8.41e+00 1.00e+03 0.9 3.17e+03 - 3.55e-04 6.45e-05f 1\n 61r 2.0041460e+05 8.41e+00 1.00e+03 0.9 1.58e+03 - 3.29e-04 1.19e-04f 1\n 62r 2.0024799e+05 8.41e+00 9.99e+02 0.9 1.43e+03 - 7.29e-04 1.04e-03f 1\n 63r 2.0178166e+05 8.42e+00 9.97e+02 0.9 9.93e+02 - 2.24e-03 1.49e-03f 1\n 64r 2.0902033e+05 8.42e+00 9.92e+02 0.9 5.71e+02 - 1.11e-02 3.85e-03f 1\n 65r 2.3136420e+05 8.41e+00 9.83e+02 0.9 3.09e+02 - 8.14e-03 9.32e-03f 1\n 66r 2.6153891e+05 8.40e+00 9.88e+02 0.9 3.62e+02 - 7.16e-03 1.05e-02f 1\n 67r 2.7798903e+05 8.40e+00 9.64e+02 0.9 3.55e+02 - 1.32e-02 9.11e-03f 1\n 68r 2.8657577e+05 8.40e+00 9.59e+02 0.9 2.47e+02 - 2.08e-02 4.88e-03f 1\n 69r 2.9527415e+05 8.40e+00 9.55e+02 0.9 1.55e+02 - 2.61e-03 4.50e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 70r 3.0527057e+05 8.40e+00 9.50e+02 0.9 5.20e+02 - 6.42e-03 5.12e-03f 1\n 71r 3.3582481e+05 8.40e+00 9.58e+02 0.9 1.01e+02 - 3.07e-02 1.51e-02f 1\n 72r 4.1107309e+05 8.41e+00 9.04e+02 0.9 1.14e+02 - 2.39e-02 3.35e-02f 1\n 73r 4.5964398e+05 8.41e+00 8.95e+02 0.9 1.15e+02 - 3.62e-02 2.11e-02f 1\n 74r 5.2016472e+05 8.41e+00 8.67e+02 0.9 1.36e+02 - 4.89e-02 3.03e-02f 1\n 75r 5.9030592e+05 8.41e+00 8.31e+02 0.9 1.48e+02 - 6.01e-02 3.79e-02f 1\n 76r 6.7476964e+05 8.41e+00 7.88e+02 0.9 1.61e+02 - 7.73e-02 4.59e-02f 1\n 77r 7.7561624e+05 8.41e+00 7.44e+02 0.9 1.82e+02 - 1.18e-01 5.62e-02f 1\n 78r 8.4945381e+05 8.41e+00 7.06e+02 0.9 4.62e+02 - 4.89e-03 5.46e-02f 1\n 79r 9.0140487e+05 8.41e+00 6.78e+02 0.9 2.50e+02 - 1.14e-01 3.60e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 80r 9.7546515e+05 8.41e+00 6.36e+02 0.9 2.54e+02 - 1.43e-01 6.16e-02f 1\n 81r 1.1190008e+06 8.38e+00 5.68e+02 0.9 3.76e+02 - 3.36e-02 1.08e-01f 1\n 82r 1.1225339e+06 8.37e+00 5.49e+02 0.9 8.82e+02 - 8.26e-03 3.25e-02f 1\n 83r 1.1286850e+06 8.36e+00 5.46e+02 0.9 4.12e+02 - 3.25e-02 6.75e-03f 1\n 84r 1.2659338e+06 8.29e+00 3.86e+02 0.9 7.87e+00 0.0 3.42e-01 2.93e-01f 1\n 85r 1.2612169e+06 8.28e+00 3.74e+02 0.2 2.38e+02 - 1.60e-02 3.04e-02f 1\n 86r 1.3908950e+06 8.17e+00 3.95e+02 0.2 3.02e+02 - 2.38e-02 7.50e-02f 1\n 87r 1.4392216e+06 8.15e+00 2.99e+02 0.2 7.97e+00 0.4 1.84e-01 1.68e-01f 1\n 88r 1.4749437e+06 8.14e+00 2.77e+02 0.2 5.34e+01 -0.1 4.57e-02 3.68e-02f 1\n 89r 1.4776820e+06 8.12e+00 2.77e+02 0.2 8.08e+02 - 3.91e-03 5.72e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 90r 1.4904632e+06 8.09e+00 3.10e+02 0.2 2.23e+02 - 1.15e-01 3.01e-02f 1\n 91r 1.4780369e+06 8.03e+00 2.85e+02 0.2 2.60e+02 - 1.24e-02 4.77e-02f 1\n 92r 1.4633854e+06 7.98e+00 2.84e+02 0.2 2.35e+02 - 1.42e-01 5.45e-02f 1\n 93r 1.4468075e+06 7.94e+00 2.89e+02 0.2 3.39e+02 - 2.25e-02 4.04e-02f 1\n 94r 1.4177297e+06 7.84e+00 3.28e+02 0.2 3.67e+02 - 4.08e-02 1.06e-01f 1\n 95r 1.4208335e+06 7.83e+00 3.24e+02 0.2 3.41e+02 - 4.16e-02 2.22e-02f 1\n 96r 1.4035701e+06 7.79e+00 2.82e+02 0.2 3.36e+02 - 6.49e-02 9.28e-02f 1\n 97r 1.3981555e+06 7.76e+00 2.08e+02 0.2 3.03e+01 -0.5 1.71e-01 7.29e-02f 1\n 98r 1.3736085e+06 7.75e+00 2.02e+02 0.2 4.74e+02 - 2.70e-02 1.88e-02f 1\n 99r 1.3738774e+06 7.70e+00 1.87e+02 0.2 4.77e+02 - 7.99e-02 9.61e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 100r 1.3580286e+06 7.69e+00 2.14e+02 0.2 4.67e+02 - 1.03e-01 3.83e-02f 1\n 101r 1.3021262e+06 7.68e+00 2.15e+02 0.2 5.22e+02 - 1.39e-01 9.61e-02f 1\n 102r 1.2905134e+06 7.68e+00 2.12e+02 0.2 5.78e+02 - 5.56e-02 4.11e-02f 1\n 103r 1.2958912e+06 7.68e+00 1.61e+02 0.2 6.56e+02 - 2.71e-02 1.08e-01f 1\n 104r 1.3293811e+06 7.69e+00 1.47e+02 0.2 7.20e+02 - 8.99e-02 8.45e-02f 1\n 105r 1.4013282e+06 7.70e+00 1.56e+02 0.2 9.31e+02 - 7.51e-02 1.05e-01f 1\n 106r 1.5010415e+06 7.67e+00 2.27e+02 0.2 3.80e+01 -1.0 6.34e-02 1.79e-01f 1\n 107r 1.5310867e+06 7.66e+00 2.10e+02 0.2 5.35e+01 -1.5 5.32e-02 3.54e-02f 1\n 108r 1.6858235e+06 7.64e+00 2.07e+02 0.2 2.27e+02 -2.0 1.41e-02 1.65e-02f 1\n 109r 1.6992342e+06 7.64e+00 2.00e+02 0.2 4.36e+01 -0.6 3.64e-02 3.57e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 110r 1.6720537e+06 7.63e+00 1.42e+02 0.2 2.59e+00 0.7 3.58e-01 1.88e-01f 1\n 111r 1.6393971e+06 7.62e+00 1.61e+02 0.2 5.96e+00 0.2 6.27e-02 2.98e-01f 1\n 112r 1.5942856e+06 7.62e+00 9.69e+01 0.2 3.94e+00 0.6 5.59e-01 6.60e-01f 1\n 113r 1.5454439e+06 7.61e+00 8.14e+01 -0.5 9.57e+00 0.2 2.87e-01 3.13e-01f 1\n 114r 1.5348572e+06 7.52e+00 4.26e+01 -0.5 2.28e+01 -0.3 2.19e-01 1.39e-01f 1\n 115 1.5101190e+06 7.46e+00 1.69e+04 -1.0 7.31e+02 -19.1 1.74e-03 8.11e-03f 1\n 116 1.5081170e+06 7.45e+00 1.69e+04 -1.0 6.47e+02 -19.6 1.66e-03 6.70e-04f 1\n 117 1.5000973e+06 7.43e+00 1.68e+04 -1.0 6.33e+02 -19.1 1.83e-03 2.70e-03f 1\n 118 1.4972338e+06 7.43e+00 1.68e+04 -1.0 6.14e+02 -19.6 4.62e-03 9.87e-04f 1\n 119 1.4926222e+06 7.41e+00 1.68e+04 -1.0 5.92e+02 -19.1 4.76e-03 1.69e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 120 1.4897946e+06 7.40e+00 1.68e+04 -1.0 5.73e+02 -19.6 1.24e-03 1.25e-03f 1\n 121 1.4867964e+06 7.39e+00 1.67e+04 -1.0 5.67e+02 -19.1 1.24e-02 1.40e-03f 1\n 122 1.4811114e+06 7.37e+00 1.67e+04 -1.0 5.31e+02 -19.6 2.94e-03 2.99e-03f 1\n 123 1.4776862e+06 7.36e+00 1.67e+04 -1.0 5.21e+02 -19.1 1.64e-02 1.85e-03f 1\n 124 1.4722508e+06 7.34e+00 1.66e+04 -1.0 4.87e+02 -19.6 5.29e-03 3.07e-03f 1\n 125 1.4686209e+06 7.32e+00 1.66e+04 -1.0 4.76e+02 -19.1 8.82e-03 2.07e-03f 1\n 126 1.4549412e+06 7.26e+00 1.64e+04 -1.0 4.61e+02 -19.6 8.60e-03 7.87e-03f 1\n 127 1.4439147e+06 7.22e+00 1.63e+04 -1.0 4.42e+02 -19.1 3.68e-03 6.45e-03f 1\n 128 1.4318869e+06 7.17e+00 1.62e+04 -1.0 4.33e+02 -19.6 2.58e-04 7.02e-03f 1\n 129 1.4287865e+06 7.15e+00 1.62e+04 -1.0 4.28e+02 -19.1 9.28e-03 1.82e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 130 1.4117953e+06 7.08e+00 1.60e+04 -1.0 4.17e+02 -19.6 1.21e-02 9.95e-03f 1\n 131 1.4094629e+06 7.07e+00 1.60e+04 -1.0 3.99e+02 -19.1 1.22e-02 1.30e-03f 1\n 132 1.3898684e+06 6.99e+00 1.58e+04 -1.0 3.86e+02 -19.6 6.69e-03 1.09e-02f 1\n 133 1.3666966e+06 6.91e+00 1.56e+04 -1.0 3.73e+02 -19.1 1.23e-04 1.25e-02f 1\n 134 1.3653874e+06 6.90e+00 1.56e+04 -1.0 3.64e+02 -19.6 8.61e-03 7.06e-04f 1\n 135 1.3600774e+06 6.88e+00 1.56e+04 -1.0 3.58e+02 -19.1 4.77e-04 2.88e-03f 1\n 136 1.3599863e+06 6.88e+00 1.56e+04 -1.0 3.54e+02 -19.6 1.16e-04 4.90e-05f 1\n 137 1.3520824e+06 6.85e+00 1.55e+04 -1.0 3.56e+02 -19.1 2.25e-02 4.63e-03f 1\n 138 1.3198881e+06 6.72e+00 1.52e+04 -1.0 3.32e+02 -19.6 4.47e-03 1.93e-02f 1\n 139 1.3018334e+06 6.65e+00 1.51e+04 -1.0 3.23e+02 -19.1 3.31e-02 1.03e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 140 1.3001178e+06 6.64e+00 1.50e+04 -1.0 3.09e+02 -19.6 2.72e-03 1.11e-03f 1\n 141 1.2712885e+06 6.53e+00 1.48e+04 -1.0 3.10e+02 -19.1 1.01e-04 1.69e-02f 1\n 142 1.2672814e+06 6.51e+00 1.48e+04 -1.0 3.20e+02 -19.6 4.18e-03 2.32e-03f 1\n 143 1.2662613e+06 6.51e+00 1.47e+04 -1.0 2.90e+02 -19.1 1.94e-02 6.22e-04f 1\n 144 1.2251694e+06 6.34e+00 1.43e+04 -1.0 2.82e+02 -19.6 2.71e-03 2.66e-02f 1\n 145 1.2238974e+06 6.33e+00 1.43e+04 -1.0 2.99e+02 -19.1 1.32e-03 7.91e-04f 1\n 146 1.2218795e+06 6.32e+00 1.43e+04 -1.0 3.08e+02 -19.6 1.65e-03 1.32e-03f 1\n 147 1.2218327e+06 6.32e+00 1.43e+04 -1.0 3.05e+02 -19.1 3.84e-03 2.74e-05f 1\n 148 1.1933966e+06 6.22e+00 1.41e+04 -1.0 3.06e+02 -19.6 7.34e-04 1.67e-02f 1\n 149 1.1923484e+06 6.21e+00 1.41e+04 -1.0 3.08e+02 -19.1 8.24e-03 5.84e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 150 1.1851137e+06 6.19e+00 1.40e+04 -1.0 2.76e+02 -19.6 7.09e-04 4.39e-03f 1\n 151 1.1674398e+06 6.13e+00 1.39e+04 -1.0 2.77e+02 -19.1 4.82e-03 9.94e-03f 1\n 152 1.1671054e+06 6.12e+00 1.39e+04 -1.0 2.72e+02 -19.6 4.03e-03 2.01e-04f 1\n 153 1.1607724e+06 6.10e+00 1.38e+04 -1.0 2.70e+02 -19.1 6.82e-04 3.72e-03f 1\n 154 1.1521951e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.6 1.57e-03 5.21e-03f 1\n 155 1.1512500e+06 6.07e+00 1.37e+04 -1.0 2.70e+02 -19.1 7.99e-04 5.27e-04f 1\n 156 1.1507231e+06 6.06e+00 1.37e+04 -1.0 2.68e+02 -19.6 4.24e-04 3.27e-04f 1\n 157 1.1507040e+06 6.06e+00 1.37e+04 -1.0 2.81e+02 -19.1 5.41e-04 9.69e-06f 1\n 158 1.1467730e+06 6.05e+00 1.37e+04 -1.0 2.63e+02 -19.6 8.26e-06 2.11e-03f 1\n 159 1.0732380e+06 5.82e+00 1.32e+04 -1.0 2.63e+02 -19.1 2.67e-04 3.91e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 160 1.0714161e+06 5.81e+00 1.32e+04 -1.0 2.56e+02 -19.6 1.02e-02 9.30e-04f 1\n 161 1.0713627e+06 5.81e+00 1.32e+04 -1.0 2.54e+02 -19.1 8.09e-05 2.84e-05f 1\n 162 1.0580046e+06 5.76e+00 1.31e+04 -1.0 2.54e+02 -19.6 1.35e-04 7.69e-03f 1\n 163 1.0545534e+06 5.75e+00 1.30e+04 -1.0 2.54e+02 -19.1 3.39e-03 1.89e-03f 1\n 164 1.0341517e+06 5.68e+00 1.30e+04 -1.0 2.52e+02 -19.6 8.74e-04 1.22e-02f 1\n 165 1.0336429e+06 5.68e+00 1.29e+04 -1.0 2.51e+02 -19.1 6.22e-04 2.90e-04f 1\n 166 1.0275521e+06 5.66e+00 1.38e+04 -1.0 2.51e+02 -19.6 4.36e-04 3.68e-03f 1\n 167 1.0179732e+06 5.63e+00 1.32e+04 -1.0 2.50e+02 -19.1 7.95e-03 5.53e-03f 1\n 168 9.9624521e+05 5.55e+00 1.59e+04 -1.0 2.48e+02 -19.6 4.63e-03 1.40e-02f 1\n 169 9.9527357e+05 5.55e+00 1.58e+04 -1.0 2.44e+02 -19.1 5.96e-04 5.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 170 9.9489625e+05 5.55e+00 1.56e+04 -1.0 2.47e+02 -19.6 1.04e-03 2.42e-04f 1\n 171 9.9489118e+05 5.55e+00 1.50e+04 -1.0 2.53e+02 -19.1 1.24e-03 2.86e-06f 1\n 172 9.9421219e+05 5.54e+00 1.57e+04 -1.0 2.49e+02 -19.6 3.63e-06 4.37e-04f 1\n 173 9.8486650e+05 5.51e+00 1.68e+04 -1.0 2.46e+02 -19.1 5.48e-04 5.68e-03f 1\n 174 9.7308682e+05 5.47e+00 1.65e+04 -1.0 2.49e+02 -19.6 8.25e-03 7.61e-03f 1\n 175 9.7212896e+05 5.47e+00 1.65e+04 -1.0 2.44e+02 -19.1 3.20e-04 5.80e-04f 1\n 176 9.6540396e+05 5.44e+00 1.75e+04 -1.0 2.49e+02 -19.6 9.01e-04 4.34e-03f 1\n 177 9.4998516e+05 5.39e+00 1.51e+04 -1.0 2.44e+02 -19.1 1.93e-02 9.44e-03f 1\n 178 9.4904520e+05 5.39e+00 1.52e+04 -1.0 2.45e+02 -19.6 2.19e-04 6.06e-04f 1\n 179 9.4902274e+05 5.39e+00 1.51e+04 -1.0 2.75e+02 -19.1 1.62e-04 1.14e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 180 9.3880492e+05 5.35e+00 1.67e+04 -1.0 2.29e+02 -19.6 1.38e-05 6.45e-03f 1\n 181 8.9559690e+05 5.21e+00 1.79e+04 -1.0 2.29e+02 -19.1 1.06e-02 2.71e-02f 1\n 182 8.5549874e+05 5.07e+00 1.93e+04 -1.0 2.22e+02 -19.6 4.07e-03 2.60e-02f 1\n 183 8.4452994e+05 5.04e+00 1.99e+04 -1.0 2.17e+02 -19.1 4.96e-04 7.46e-03f 1\n 184 8.2921005e+05 4.98e+00 1.98e+04 -1.0 2.14e+02 -19.6 9.95e-03 1.09e-02f 1\n 185 8.2905253e+05 4.98e+00 1.90e+04 -1.0 2.09e+02 -19.1 5.75e-03 1.10e-04f 1\n 186 8.2904529e+05 4.98e+00 1.89e+04 -1.0 2.18e+02 -19.6 7.15e-05 5.41e-06f 1\n 187 8.2712955e+05 4.97e+00 1.92e+04 -1.0 2.14e+02 -19.1 5.72e-05 1.35e-03f 1\n 188 8.0950543e+05 4.91e+00 2.28e+04 -1.0 2.31e+02 -19.6 1.11e-03 1.34e-02f 1\n 189 8.0862616e+05 4.90e+00 2.24e+04 -1.0 2.12e+02 -19.1 1.92e-03 6.51e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 190 8.0425351e+05 4.89e+00 2.35e+04 -1.0 2.22e+02 -19.6 4.57e-04 3.51e-03f 1\n 191 7.9537870e+05 4.85e+00 2.31e+04 -1.0 2.15e+02 -19.1 7.37e-03 6.61e-03f 1\n 192 7.9489154e+05 4.85e+00 2.29e+04 -1.0 2.11e+02 -19.6 1.29e-03 3.92e-04f 1\n 193 7.8751827e+05 4.83e+00 2.29e+04 -1.0 2.01e+02 -19.1 2.56e-03 5.71e-03f 1\n 194 7.8736120e+05 4.82e+00 2.31e+04 -1.0 2.00e+02 -19.6 1.54e-04 1.32e-04f 1\n 195 7.8402757e+05 4.81e+00 2.31e+04 -1.0 1.94e+02 -19.1 2.19e-03 2.63e-03f 1\n 196 7.8388845e+05 4.81e+00 2.53e+04 -1.0 1.98e+02 -19.6 2.15e-04 1.18e-04f 1\n 197 7.8385262e+05 4.81e+00 2.50e+04 -1.0 1.94e+02 -19.1 4.34e-03 2.82e-05f 1\n 198 7.8236217e+05 4.81e+00 1.09e+04 -1.0 1.97e+02 -19.6 3.11e-04 1.27e-03f 1\n 199 7.7035462e+05 4.76e+00 1.08e+04 -1.0 1.91e+02 -19.1 3.91e-04 9.52e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 200 7.6826638e+05 4.75e+00 1.08e+04 -1.0 1.90e+02 -19.6 2.68e-02 1.77e-03f 1\n 201 7.6137802e+05 4.73e+00 1.07e+04 -1.0 1.80e+02 -19.1 5.08e-04 5.41e-03f 1\n 202 7.6092627e+05 4.72e+00 1.07e+04 -1.0 1.85e+02 -19.6 8.12e-03 3.85e-04f 1\n 203 6.8620409e+05 4.44e+00 1.26e+04 -1.0 1.80e+02 -19.1 1.57e-04 6.08e-02f 1\n 204 6.8223234e+05 4.42e+00 1.15e+04 -1.0 1.85e+02 -19.6 6.43e-03 3.54e-03f 1\n 205 6.7550321e+05 4.40e+00 1.16e+04 -1.0 1.74e+02 -19.1 5.10e-03 5.69e-03f 1\n 206 6.7512988e+05 4.39e+00 1.00e+04 -1.0 1.86e+02 -19.6 8.13e-03 3.47e-04f 1\n 207 6.6341460e+05 4.35e+00 9.84e+03 -1.0 1.72e+02 -19.1 1.30e-02 1.04e-02f 1\n 208 6.5075026e+05 4.30e+00 1.09e+04 -1.0 1.87e+02 -19.6 2.10e-04 1.19e-02f 1\n 209 6.4469528e+05 4.27e+00 1.05e+04 -1.0 1.72e+02 -19.1 7.52e-03 5.37e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 210 6.3074280e+05 4.22e+00 1.26e+04 -1.0 1.85e+02 -19.6 4.48e-04 1.32e-02f 1\n 211 6.3015403e+05 4.21e+00 1.26e+04 -1.0 3.38e+01 2.3 1.77e-03 7.51e-04f 1\n 212 6.3013669e+05 4.21e+00 1.47e+04 -1.0 1.21e+02 1.8 1.17e-03 1.79e-05f 1\n 213 6.2710947e+05 4.20e+00 1.45e+04 -1.0 1.26e+02 1.4 4.90e-04 2.86e-03f 1\n 214 6.2100935e+05 4.18e+00 1.49e+04 -1.0 1.53e+02 0.9 1.86e-03 5.85e-03f 1\n 215 6.1066877e+05 4.14e+00 1.59e+04 -1.0 1.97e+02 0.4 1.05e-05 8.89e-03f 1\n 216 6.0984620e+05 4.14e+00 1.57e+04 -1.0 1.84e+02 1.7 8.49e-03 1.01e-03f 1\n 217 6.0751052e+05 4.13e+00 1.57e+04 -1.0 2.02e+02 1.3 5.49e-03 2.08e-03f 1\n 218 6.0707832e+05 4.12e+00 1.57e+04 -1.0 2.01e+02 1.7 2.32e-04 5.32e-04f 1\n 219 6.0707327e+05 4.12e+00 1.53e+04 -1.0 1.96e+02 2.1 2.90e-03 7.52e-06f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 220 6.0555478e+05 4.12e+00 1.54e+04 -1.0 2.25e+02 1.6 9.57e-04 1.84e-03f 1\n 221 6.0553909e+05 4.12e+00 1.47e+04 -1.0 2.14e+02 2.1 5.83e-03 2.26e-05f 1\n 222 6.0387470e+05 4.11e+00 1.47e+04 -1.0 2.27e+02 1.6 2.04e-03 1.80e-03f 1\n 223 6.0325229e+05 4.11e+00 1.47e+04 -1.0 2.20e+02 2.0 4.35e-04 7.72e-04f 1\n 224 6.0322399e+05 4.11e+00 1.45e+04 -1.0 2.21e+02 1.5 4.10e-03 2.96e-05f 1\n 225 6.0185557e+05 4.10e+00 1.45e+04 -1.0 2.17e+02 2.0 1.59e-05 1.70e-03f 1\n 226 6.0180800e+05 4.10e+00 1.44e+04 -1.0 2.29e+02 1.5 1.37e-03 4.63e-05f 1\n 227 5.9923339e+05 4.09e+00 1.44e+04 -1.0 2.27e+02 1.9 2.84e-03 2.85e-03f 1\n 228 5.9851909e+05 4.09e+00 1.44e+04 -1.0 2.29e+02 1.4 9.28e-04 6.31e-04f 1\n 229 5.9692058e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.9 1.94e-03 1.77e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 230 5.9689878e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.4 1.22e-05 1.79e-05f 1\n 231 5.9684337e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.8 7.71e-05 6.11e-05f 1\n 232 5.9682024e+05 4.08e+00 1.44e+04 -1.0 2.28e+02 1.3 2.55e-06 2.10e-05f 1\n 233 5.9680869e+05 4.08e+00 1.38e+04 -1.0 2.28e+02 1.8 7.05e-03 1.36e-05f 1\n 234 5.9076367e+05 4.05e+00 1.41e+04 -1.0 2.30e+02 1.3 5.00e-04 5.99e-03f 1\n 235 5.9073170e+05 4.05e+00 1.42e+04 -1.0 2.32e+02 1.7 4.66e-03 3.40e-05f 1\n 236 5.8521172e+05 4.03e+00 1.45e+04 -1.0 2.32e+02 1.2 8.81e-04 5.50e-03f 1\n 237 5.8376022e+05 4.03e+00 1.44e+04 -1.0 2.30e+02 0.8 2.07e-06 1.04e-03f 1\n 238 5.8372062e+05 4.03e+00 1.53e+04 -1.0 2.35e+02 2.1 3.62e-03 4.81e-05f 1\n 239 5.7779448e+05 4.00e+00 1.55e+04 -1.0 2.39e+02 1.6 1.33e-02 6.22e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 240 5.7771432e+05 4.00e+00 1.57e+04 -1.0 2.26e+02 2.0 1.00e-03 9.89e-05f 1\n 241 5.7665827e+05 4.00e+00 1.57e+04 -1.0 2.07e+02 1.6 9.92e-04 1.10e-03f 1\n 242 5.7511556e+05 3.99e+00 1.55e+04 -1.0 2.12e+02 2.0 1.09e-03 1.92e-03f 1\n 243 5.7052438e+05 3.97e+00 1.52e+04 -1.0 2.09e+02 1.5 2.73e-04 4.68e-03f 1\n 244 5.6998537e+05 3.97e+00 1.51e+04 -1.0 2.36e+02 1.9 3.75e-04 6.54e-04f 1\n 245 5.6612860e+05 3.95e+00 1.48e+04 -1.0 2.37e+02 1.5 3.47e-05 3.75e-03f 1\n 246 5.6572525e+05 3.95e+00 1.50e+04 -1.0 2.36e+02 1.9 2.57e-03 4.85e-04f 1\n 247 5.6516789e+05 3.95e+00 1.51e+04 -1.0 2.36e+02 1.4 1.32e-03 4.83e-04f 1\n 248 5.6316860e+05 3.94e+00 1.52e+04 -1.0 2.36e+02 1.8 3.05e-03 2.38e-03f 1\n 249 5.6254967e+05 3.94e+00 1.50e+04 -1.0 2.35e+02 1.3 7.91e-06 4.87e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 250 5.6077033e+05 3.93e+00 1.49e+04 -1.0 2.36e+02 1.8 1.29e-03 2.17e-03f 1\n 251 5.6022313e+05 3.93e+00 1.47e+04 -1.0 2.34e+02 1.3 2.29e-06 3.07e-04f 1\n 252 5.5692514e+05 3.91e+00 1.49e+04 -1.0 2.36e+02 1.7 8.13e-03 3.96e-03f 1\n 253 5.5673567e+05 3.91e+00 1.49e+04 -1.0 2.31e+02 1.2 2.68e-06 9.58e-05f 1\n 254 5.5535652e+05 3.91e+00 1.55e+04 -1.0 2.33e+02 1.7 1.14e-02 1.64e-03f 1\n 255 5.5534273e+05 3.91e+00 1.56e+04 -1.0 2.30e+02 2.1 4.75e-04 1.85e-05f 1\n 256 5.5514554e+05 3.90e+00 1.71e+04 -1.0 2.27e+02 1.6 1.32e-02 2.20e-04f 1\n 257 5.5141803e+05 3.90e+00 1.48e+04 -1.0 4.83e+02 1.1 1.93e-05 1.30e-03f 1\n 258 5.4693933e+05 3.88e+00 1.42e+04 -1.0 2.39e+02 1.6 2.91e-04 4.86e-03f 1\n 259 5.4687686e+05 3.88e+00 1.43e+04 -1.0 2.52e+02 1.1 2.34e-03 6.08e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 260 5.4353115e+05 3.87e+00 1.41e+04 -1.0 2.42e+02 1.5 1.34e-03 3.66e-03f 1\n 261 5.4346337e+05 3.87e+00 1.41e+04 -1.0 2.07e+02 1.0 1.56e-04 6.65e-05f 1\n 262 5.4335021e+05 3.87e+00 1.42e+04 -1.0 1.95e+02 1.5 2.22e-03 1.26e-04f 1\n 263 5.4296226e+05 3.86e+00 1.43e+04 -1.0 1.86e+02 1.0 2.63e-03 3.71e-04f 1\n 264 5.3812113e+05 3.84e+00 1.40e+04 -1.0 2.13e+02 1.4 2.17e-05 5.31e-03f 1\n 265 5.3811408e+05 3.84e+00 1.41e+04 -1.0 2.23e+02 1.8 3.29e-04 8.06e-06f 1\n 266 5.3810662e+05 3.84e+00 1.41e+04 -1.0 2.43e+02 1.4 9.90e-04 7.83e-06f 1\n 267 5.3692599e+05 3.84e+00 1.38e+04 -1.0 2.30e+02 1.8 8.84e-05 1.33e-03f 1\n 268 5.3689601e+05 3.84e+00 1.38e+04 -1.0 1.90e+02 1.3 1.05e-03 3.21e-05f 1\n 269 5.3480708e+05 3.83e+00 1.32e+04 -1.0 2.44e+02 0.8 2.23e-04 1.25e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 270 5.3431885e+05 3.83e+00 1.34e+04 -1.0 1.83e+02 1.3 4.31e-03 5.17e-04f 1\n 271 5.3310063e+05 3.83e+00 1.31e+04 -1.0 1.89e+02 1.7 2.86e-05 1.41e-03f 1\n 272 5.3150897e+05 3.82e+00 1.22e+04 -1.0 1.85e+02 2.1 1.96e-04 1.98e-03f 1\n 273 5.3147543e+05 3.82e+00 1.22e+04 -1.0 1.61e+02 1.6 1.35e-04 3.82e-05f 1\n 274 5.3137612e+05 3.82e+00 1.24e+04 -1.0 1.76e+02 1.2 3.23e-03 1.02e-04f 1\n 275 5.3106158e+05 3.82e+00 1.29e+04 -1.0 1.79e+02 1.6 5.34e-03 3.64e-04f 1\n 276 5.2929724e+05 3.81e+00 1.28e+04 -1.0 1.73e+02 1.1 5.52e-05 1.84e-03f 1\n 277 5.2907597e+05 3.81e+00 1.29e+04 -1.0 1.63e+02 2.4 4.41e-04 3.68e-04f 1\n 278 5.2816992e+05 3.80e+00 1.27e+04 -1.0 1.88e+02 2.0 5.12e-04 1.14e-03f 1\n 279 5.2807415e+05 3.80e+00 1.27e+04 -1.0 1.48e+03 1.5 1.26e-06 8.93e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 280 5.2805145e+05 3.80e+00 1.26e+04 -1.0 1.94e+02 1.0 1.55e-03 2.34e-05f 1\n 281 5.2494578e+05 3.79e+00 1.26e+04 -1.0 1.75e+02 1.4 7.38e-04 3.61e-03f 1\n 282 5.2382656e+05 3.79e+00 1.20e+04 -1.0 6.75e+02 1.0 1.61e-05 3.04e-04f 1\n 283 5.2294012e+05 3.78e+00 1.20e+04 -1.0 1.74e+02 1.4 1.78e-03 9.88e-04f 1\n 284 5.2002561e+05 3.77e+00 1.24e+04 -1.0 1.76e+02 1.8 7.11e-03 3.79e-03f 1\n 285 5.1038171e+05 3.73e+00 1.19e+04 -1.0 1.71e+02 1.3 6.29e-05 1.07e-02f 1\n 286 5.1029468e+05 3.73e+00 1.19e+04 -1.0 1.84e+02 1.8 7.42e-04 1.12e-04f 1\n 287 5.1028585e+05 3.73e+00 1.21e+04 -1.0 2.01e+02 2.2 4.17e-04 1.31e-05f 1\n 288 5.0835844e+05 3.72e+00 1.19e+04 -1.0 2.75e+02 1.7 1.15e-05 2.41e-03f 1\n 289 5.0829350e+05 3.72e+00 1.19e+04 -1.0 1.71e+02 1.2 3.97e-04 6.12e-05f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 290 5.0786133e+05 3.72e+00 1.30e+04 -1.0 1.77e+02 1.7 1.37e-02 5.49e-04f 1\n 291 4.9734337e+05 3.69e+00 1.06e+04 -1.0 1.72e+02 1.2 8.48e-04 8.86e-03f 1\n 292 4.9689003e+05 3.68e+00 1.08e+04 -1.0 1.74e+02 1.6 3.20e-03 5.66e-04f 1\n 293 4.9257017e+05 3.67e+00 9.15e+03 -1.0 1.62e+02 1.1 7.85e-05 3.01e-03f 1\n 294 4.9140020e+05 3.67e+00 9.02e+03 -1.0 2.41e+02 1.6 4.58e-05 1.41e-03f 1\n 295 4.9127824e+05 3.67e+00 1.14e+04 -1.0 1.61e+02 1.1 2.66e-03 6.68e-05f 1\n 296 4.9066209e+05 3.66e+00 1.29e+04 -1.0 1.75e+02 1.5 1.82e-02 7.38e-04f 1\n 297 4.8899995e+05 3.66e+00 1.45e+04 -1.0 5.05e+02 1.0 9.31e-04 4.06e-04f 1\n 298 4.7750162e+05 3.61e+00 1.35e+04 -1.0 1.76e+02 1.5 4.98e-03 1.35e-02f 1\n 299 4.7434464e+05 3.61e+00 1.49e+04 -1.0 9.97e+02 1.0 6.27e-04 4.14e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 300 4.7339897e+05 3.61e+00 1.50e+04 -1.0 1.73e+02 1.4 2.13e-03 1.10e-03f 1\n 301 4.7161223e+05 3.61e+00 1.40e+04 -1.0 3.25e+02 0.9 1.03e-04 5.77e-04f 1\n 302 4.7150987e+05 3.60e+00 1.43e+04 -1.0 2.00e+02 0.4 6.17e-03 9.97e-05f 1\n 303 4.6068296e+05 3.57e+00 1.36e+04 -1.0 3.30e+02 -0.0 4.88e-05 1.06e-02f 1\n 304 4.5827805e+05 3.56e+00 1.49e+04 -1.0 1.68e+02 1.3 1.36e-02 2.70e-03f 1\n 305 4.5598068e+05 3.55e+00 1.41e+04 -1.0 4.79e+02 0.8 4.48e-04 7.06e-04f 1\n 306 4.5518400e+05 3.55e+00 1.43e+04 -1.0 1.59e+02 1.2 2.67e-03 8.88e-04f 1\n 307 4.5309751e+05 3.55e+00 1.72e+04 -1.0 2.98e+02 0.8 2.61e-03 8.91e-04f 1\n 308 4.4826799e+05 3.53e+00 1.86e+04 -1.0 1.66e+02 1.2 1.97e-02 5.39e-03f 1\n 309 4.4337229e+05 3.51e+00 1.70e+04 -1.0 1.62e+02 0.7 4.61e-05 4.04e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 310 4.3957043e+05 3.50e+00 2.22e+04 -1.0 1.64e+02 1.1 5.11e-02 4.22e-03f 1\n 311 4.3840763e+05 3.50e+00 2.35e+04 -1.0 1.68e+02 0.7 4.15e-03 9.47e-04f 1\n 312 4.2663652e+05 3.45e+00 2.43e+04 -1.0 1.62e+02 1.1 2.95e-02 1.34e-02f 1\n 313 4.1771582e+05 3.43e+00 2.40e+04 -1.0 1.75e+02 0.6 6.75e-03 7.00e-03f 1\n 314 4.1583852e+05 3.42e+00 2.27e+04 -1.0 8.29e+02 0.1 1.03e-04 3.62e-04f 1\n 315 4.0823036e+05 3.40e+00 2.35e+04 -1.0 1.82e+02 0.6 9.82e-03 6.83e-03f 1\n 316 4.0742102e+05 3.40e+00 2.31e+04 -1.0 2.88e+02 0.1 7.31e-05 5.39e-04f 1\n 317 4.0736672e+05 3.40e+00 2.36e+04 -1.0 4.84e+02 -0.4 7.32e-03 5.87e-05f 1\n 318 3.9419770e+05 3.35e+00 2.18e+04 -1.0 7.13e+02 -0.9 8.53e-05 1.33e-02f 1\n 319 3.8663282e+05 3.33e+00 1.92e+04 -1.0 8.56e+02 -1.3 2.10e-04 5.79e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 320 3.8579331e+05 3.33e+00 2.13e+04 -1.0 9.22e+02 -1.8 7.40e-03 7.22e-04f 1\n 321 3.7974323e+05 3.32e+00 1.94e+04 -1.0 9.50e+02 -2.3 1.97e-04 4.91e-03f 1\n 322 3.7938905e+05 3.31e+00 2.15e+04 -1.0 9.51e+02 -2.8 8.43e-03 3.27e-04f 1\n 323 3.7037180e+05 3.29e+00 1.92e+04 -1.0 9.55e+02 -3.3 1.13e-04 8.55e-03f 1\n 324 3.6908213e+05 3.28e+00 1.94e+04 -1.0 9.41e+02 -3.7 2.53e-03 1.30e-03f 1\n 325 3.6277620e+05 3.26e+00 1.81e+04 -1.0 2.17e+02 0.3 8.07e-04 7.29e-03f 1\n 326 3.5989666e+05 3.24e+00 1.79e+04 -1.0 1.55e+02 1.6 1.27e-03 5.36e-03f 1\n 327 3.5985661e+05 3.24e+00 1.79e+04 -1.0 1.59e+02 2.1 9.24e-04 1.04e-04f 1\n 328 3.5935266e+05 3.24e+00 1.79e+04 -1.0 1.62e+02 1.6 6.26e-05 8.73e-04f 1\n 329 3.5783375e+05 3.23e+00 1.78e+04 -1.0 1.63e+02 2.0 3.50e-03 3.63e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 330 3.5743587e+05 3.22e+00 1.78e+04 -1.0 1.61e+02 1.5 3.51e-05 6.78e-04f 1\n 331 3.5668635e+05 3.22e+00 1.78e+04 -1.0 1.52e+02 1.1 2.73e-04 1.08e-03f 1\n 332 3.5630331e+05 3.22e+00 1.78e+04 -1.0 1.67e+02 0.6 3.51e-03 4.92e-04f 1\n 333 3.5562828e+05 3.22e+00 1.78e+04 -1.0 2.82e+02 0.1 3.34e-03 7.59e-04f 1\n 334 3.5480287e+05 3.21e+00 1.78e+04 -1.0 4.82e+02 -0.4 4.21e-05 9.86e-04f 1\n 335 3.5438009e+05 3.21e+00 1.78e+04 -1.0 6.55e+02 -0.8 1.29e-04 5.04e-04f 1\n 336 3.5436086e+05 3.21e+00 1.78e+04 -1.0 8.12e+02 -1.3 2.56e-03 2.29e-05f 1\n 337 3.5193976e+05 3.20e+00 1.77e+04 -1.0 8.82e+02 -1.8 1.07e-03 2.89e-03f 1\n 338 3.5190047e+05 3.20e+00 1.77e+04 -1.0 9.10e+02 -2.3 7.21e-03 4.74e-05f 1\n 339 3.5093683e+05 3.20e+00 1.77e+04 -1.0 7.47e+02 -1.0 1.35e-05 1.13e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 340 3.5052498e+05 3.20e+00 1.77e+04 -1.0 2.07e+02 0.4 5.15e-04 5.29e-04f 1\n 341 3.3750946e+05 3.14e+00 1.74e+04 -1.0 3.58e+02 -0.1 1.67e-05 1.64e-02f 1\n 342 3.3747338e+05 3.14e+00 1.74e+04 -1.0 5.75e+02 -0.6 6.58e-03 4.69e-05f 1\n 343 3.3562091e+05 3.14e+00 1.74e+04 -1.0 7.66e+02 -1.1 1.05e-04 2.42e-03f 1\n 344 3.3510890e+05 3.13e+00 1.73e+04 -1.0 8.41e+02 -1.5 1.22e-03 6.80e-04f 1\n 345 3.2871807e+05 3.11e+00 1.72e+04 -1.0 8.84e+02 -2.0 3.02e-03 8.53e-03f 1\n 346 3.2777372e+05 3.10e+00 1.72e+04 -1.0 1.54e+02 1.1 7.57e-05 1.51e-03f 1\n 347 3.2772491e+05 3.10e+00 1.72e+04 -1.0 1.60e+02 1.6 2.40e-03 9.66e-05f 1\n 348 3.2718271e+05 3.10e+00 1.71e+04 -1.0 1.60e+02 2.0 4.86e-03 1.52e-03f 1\n 349 3.2706600e+05 3.10e+00 1.71e+04 -1.0 1.58e+02 1.5 1.31e-03 2.19e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 350 3.2406746e+05 3.08e+00 1.70e+04 -1.0 1.47e+02 1.0 1.75e-04 5.59e-03f 1\n 351 3.2366994e+05 3.08e+00 1.70e+04 -1.0 1.50e+02 1.5 4.30e-03 7.40e-04f 1\n 352 3.2356913e+05 3.08e+00 1.70e+04 -1.0 1.37e+02 1.0 1.11e-04 1.90e-04f 1\n 353 3.2355506e+05 3.08e+00 1.70e+04 -1.0 1.49e+02 1.4 3.22e-03 2.36e-05f 1\n 354 3.2316524e+05 3.07e+00 1.70e+04 -1.0 1.40e+02 0.9 1.38e-03 6.09e-04f 1\n 355r 3.2316524e+05 3.07e+00 1.00e+03 0.5 0.00e+00 0.4 0.00e+00 3.57e-07R 8\n 356r 3.2229084e+05 3.07e+00 1.00e+03 0.5 2.06e+03 - 6.18e-03 7.60e-04f 1\n 357r 3.2109567e+05 3.06e+00 9.98e+02 0.5 1.80e+03 - 1.71e-03 8.09e-04f 1\n 358r 3.1149974e+05 2.97e+00 9.95e+02 0.5 1.67e+03 - 3.74e-03 6.54e-03f 1\n 359r 3.1087190e+05 2.97e+00 9.95e+02 0.5 8.58e+02 - 9.83e-05 4.32e-04f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 360r 3.0989090e+05 2.96e+00 9.97e+02 0.5 9.94e+02 - 6.50e-03 6.44e-04f 1\n 361r 3.0241262e+05 2.87e+00 9.92e+02 0.5 9.07e+02 - 2.36e-03 5.34e-03f 1\n 362r 2.9694919e+05 2.80e+00 9.80e+02 0.5 8.52e+02 - 1.55e-02 4.79e-03f 1\n 363r 2.8929254e+05 2.68e+00 9.72e+02 0.5 6.39e+02 - 1.25e-02 8.39e-03f 1\n 364 2.8919644e+05 2.68e+00 9.52e+03 -1.0 1.36e+02 -0.0 2.03e-04 1.66e-04f 1\n 365 2.8908661e+05 2.68e+00 9.52e+03 -1.0 1.38e+02 -0.5 3.89e-04 1.90e-04f 1\n 366 2.8861557e+05 2.68e+00 9.51e+03 -1.0 1.34e+02 -1.0 2.45e-04 8.16e-04f 1\n 367 2.8784152e+05 2.67e+00 9.50e+03 -1.0 1.29e+02 -1.5 5.67e-04 1.35e-03f 1\n 368 2.8557520e+05 2.66e+00 9.46e+03 -1.0 1.18e+02 -1.9 1.40e-03 3.97e-03f 1\n 369 2.8048140e+05 2.64e+00 9.38e+03 -1.0 1.31e+02 -2.4 2.51e-03 9.01e-03f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 370 2.7807299e+05 2.63e+00 9.34e+03 -1.0 1.50e+02 -2.9 1.64e-03 4.32e-03f 1\n 371 2.7069758e+05 2.59e+00 9.21e+03 -1.0 1.60e+02 -3.4 3.85e-03 1.34e-02f 1\n 372 2.5748481e+05 2.53e+00 8.98e+03 -1.0 1.59e+02 -3.8 4.45e-03 2.48e-02f 1\n 373 2.5409647e+05 2.51e+00 8.92e+03 -1.0 1.53e+02 -4.3 1.21e-02 6.62e-03f 1\n 374 2.4261880e+05 2.45e+00 8.72e+03 -1.0 1.50e+02 -4.8 6.04e-03 2.29e-02f 1\n 375 2.2822588e+05 2.38e+00 8.45e+03 -1.0 1.46e+02 -5.3 1.51e-02 3.02e-02f 1\n 376 2.0965467e+05 2.28e+00 8.10e+03 -1.0 1.41e+02 -5.8 1.82e-02 4.16e-02f 1\n 377 2.0936932e+05 2.28e+00 8.10e+03 -1.0 1.37e+02 -6.2 3.27e-02 6.83e-04f 1\n 378 2.0780500e+05 2.27e+00 8.07e+03 -1.0 1.37e+02 -6.7 4.67e-03 3.76e-03f 1\n 379 1.9606770e+05 2.20e+00 7.84e+03 -1.0 1.38e+02 -7.2 7.84e-04 2.87e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 380 1.7935187e+05 2.11e+00 7.49e+03 -1.0 1.38e+02 -7.7 2.58e-02 4.37e-02f 1\n 381 1.7765925e+05 2.10e+00 7.46e+03 -1.0 1.36e+02 -8.1 4.29e-02 4.74e-03f 1\n 382 1.6495345e+05 2.02e+00 7.18e+03 -1.0 1.34e+02 -8.6 1.77e-02 3.65e-02f 1\n 383 1.6340175e+05 2.01e+00 7.15e+03 -1.0 1.30e+02 -9.1 1.84e-02 4.73e-03f 1\n 384 1.5788016e+05 1.98e+00 7.03e+03 -1.0 1.29e+02 -9.6 3.72e-03 1.71e-02f 1\n 385 1.5064127e+05 1.93e+00 6.87e+03 -1.0 1.28e+02 -10.1 8.10e-03 2.33e-02f 1\n 386 1.2939352e+05 1.79e+00 6.36e+03 -1.0 1.25e+02 -10.5 1.85e-02 7.34e-02f 1\n 387 1.2646803e+05 1.77e+00 6.29e+03 -1.0 1.18e+02 -11.0 4.15e-02 1.14e-02f 1\n 388 1.1471323e+05 1.68e+00 5.99e+03 -1.0 1.16e+02 -11.5 1.69e-02 4.78e-02f 1\n 389 1.0992888e+05 1.65e+00 5.86e+03 -1.0 1.11e+02 -12.0 7.97e-02 2.12e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 390 1.0212816e+05 1.59e+00 5.65e+03 -1.0 1.07e+02 -12.4 1.86e-03 3.63e-02f 1\n 391 8.6177113e+04 1.46e+00 5.19e+03 -1.0 1.04e+02 -12.9 7.26e-02 8.17e-02f 1\n 392 8.5370527e+04 1.45e+00 5.16e+03 -1.0 9.48e+01 -13.4 3.22e-02 4.71e-03f 1\n 393 7.4077318e+04 1.35e+00 4.81e+03 -1.0 9.38e+01 -13.9 1.85e-02 6.88e-02f 1\n 394 7.1177623e+04 1.33e+00 4.71e+03 -1.0 8.78e+01 -14.3 6.45e-02 1.99e-02f 1\n 395 6.4716733e+04 1.26e+00 4.49e+03 -1.0 8.51e+01 -14.8 4.54e-03 4.67e-02f 1\n 396 5.7488482e+04 1.19e+00 4.23e+03 -1.0 8.14e+01 -15.3 9.91e-03 5.78e-02f 1\n 397 4.8754351e+04 1.10e+00 3.90e+03 -1.0 7.88e+01 -15.8 6.42e-02 7.95e-02f 1\n 398 4.7745315e+04 1.08e+00 3.86e+03 -1.0 7.72e+01 -16.3 9.49e-02 1.05e-02f 1\n 399 4.1977413e+04 1.02e+00 3.61e+03 -1.0 7.89e+01 -16.7 6.18e-02 6.27e-02f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 400 3.3677208e+04 9.10e-01 3.24e+03 -1.0 7.75e+01 -17.2 7.45e-03 1.05e-01f 1\n 401 3.0166245e+04 8.61e-01 3.06e+03 -1.0 7.30e+01 -17.7 2.38e-02 5.39e-02f 1\n 402 2.5223779e+04 7.87e-01 2.80e+03 -1.0 7.10e+01 -18.2 1.21e-01 8.61e-02f 1\n 403 2.2341250e+04 7.40e-01 2.63e+03 -1.0 6.89e+01 -18.6 1.27e-01 5.92e-02f 1\n 404 1.8863927e+04 6.80e-01 2.42e+03 -1.0 6.77e+01 -19.1 8.08e-02 8.17e-02f 1\n 405 1.5848263e+04 6.23e-01 2.21e+03 -1.0 6.47e+01 -19.6 5.68e-02 8.40e-02f 1\n 406 1.4057233e+04 5.86e-01 2.08e+03 -1.0 6.57e+01 -19.1 8.62e-02 5.84e-02f 1\n 407 1.1013361e+04 5.19e-01 1.84e+03 -1.0 5.68e+01 -19.6 1.68e-01 1.16e-01f 1\n 408 7.9952027e+03 4.41e-01 1.57e+03 -1.0 5.34e+01 -19.1 1.48e-01 1.49e-01f 1\n 409 5.4065322e+03 3.62e-01 1.29e+03 -1.0 4.45e+01 -19.6 1.93e-01 1.80e-01f 1\niter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n 410 2.4913122e+03 2.44e-01 8.68e+02 -1.0 4.14e+01 -19.1 1.52e-01 3.26e-01f 1\n 411 1.0310441e+03 1.55e-01 5.51e+02 -1.0 2.82e+01 -19.6 2.47e-01 3.66e-01f 1\n 412 2.7434337e+02 7.52e-02 4.80e+02 -1.0 2.93e+01 -19.1 1.96e-01 5.14e-01f 1\n 413 5.4776714e+01 2.33e-02 7.33e+02 -1.0 7.94e+00 -19.6 4.50e-01 6.90e-01f 1\n 414 2.8195260e+01 3.14e-04 1.32e+03 -1.0 1.06e+01 -19.1 5.84e-01 1.00e+00f 1\n 415 3.2622344e+01 3.87e-05 1.06e+03 -1.0 1.60e+01 -19.6 7.02e-01 9.94e-01f 1\n 416 3.0040029e+01 4.65e-05 2.31e+02 -1.0 4.17e+01 -19.1 9.04e-01 1.00e+00f 1\n 417 3.0266079e+01 4.64e-04 4.41e+02 -1.0 1.21e+02 -19.6 1.00e+00 9.30e-01h 1\n 418 2.9559008e+01 3.42e-05 5.33e+01 -1.0 1.42e+01 -19.1 6.03e-01 1.00e+00h 1" ], [ "x1 = bri.opti.debug.value(bri.x_opt)\nplt.plot(x1[0,:], x1[1,:])", "_____no_output_____" ], [ "costs = [\"self.k_u_delta * self.u_delta_cost\",\n \"self.k_u_v * self.u_v_cost\",\n \"self.k_lat * self.lat_cost\", \n \"self.k_lon * self.lon_cost\", \n \"self.k_phi_error * self.phi_error_cost\",\n \"self.k_phi_dot * self.phidot_cost\",\n \"self.k_s * self.s_cost\",\n \"self.k_v * self.v_cost\",\n \"self.k_change_u_v * self.change_u_v\",\n \"self.k_change_u_delta * self.change_u_delta\", \n \"self.k_final * self.final_costs\",\n \"self.k_x * self.x_cost\"]\n\n\nfor i in range(len(bri.car1_costs_list)):\n amb_costs = bri.opti.debug.value(bri.car1_costs_list[i])\n print('%.03f'%amb_costs, costs[i])\n \nprint(bri.opti.debug.value(bri.slack_cost))", "0.157 self.k_u_delta * self.u_delta_cost\n3.246 self.k_u_v * self.u_v_cost\n296.473 self.k_lat * self.lat_cost\n0.000 self.k_lon * self.lon_cost\n25.852 self.k_phi_error * self.phi_error_cost\n186.438 self.k_phi_dot * self.phidot_cost\n0.000 self.k_s * self.s_cost\n0.000 self.k_v * self.v_cost\n0.373 self.k_change_u_v * self.change_u_v\n0.000 self.k_change_u_delta * self.change_u_delta\n50.985 self.k_final * self.final_costs\n-39.603 self.k_x * self.x_cost\n12634778.361273078\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# %load CommonFunctions.py\n\n\n# # COMMON ATOMIC AND ASTRING FUNCTIONS\n\n# In[14]:\n\n############### One String Pulse with width, shift and scale #############\ndef StringPulse(String1, t: float, a = 1., b = 0., c = 1., d = 0.) -> float:\n x = (t - b)/a\n if (x < -1):\n res = -0.5\n elif (x > 1):\n res = 0.5\n else:\n res = String1(x)\n res = d + res * c\n return res\n\n\n# In[16]:\n\n\n###### Atomic String Applied to list with width, shift and scale #############\ndef String(String1, x: list, a = 1., b = 0., c = 1., d = 0.) -> list:\n res = []\n for i in range(len(x)):\n res.append(StringPulse(String1, x[i], a, b, c, d))\n return res\n\n\n# In[17]:\n\n\n###### Summation of two lists #############\ndef Sum(x1: list, x2: list) -> list:\n res = []\n for i in range(len(x1)):\n res.append(x1[i] + x2[i])\n return res\n\n\n# In[18]:\n\n\n##########################################################\n##This script introduces Atomic Function \n################### One Pulse of atomic function\ndef up1(x: float) -> float:\n #Atomic function table\n up_y = [0.5, 0.48, 0.460000017,0.440000421,0.420003478,0.400016184, 0.380053256, 0.360139056, 0.340308139, 0.320605107,\n 0.301083436, 0.281802850, 0.262826445, 0.244218000, 0.226041554, 0.208361009, 0.191239338, 0.174736305, \n 0.158905389, 0.143991189, 0.129427260, 0.115840866, 0.103044024, 0.9110444278e-01, 0.798444445e-01, 0.694444445e-01, \n 0.598444445e-01, 0.510444877e-01, 0.430440239e-01, 0.358409663e-01, 0.294282603e-01, 0.237911889e-01, 0.189053889e-01, \n 0.147363055e-01, 0.112393379e-01, 0.836100883e-02, 0.604155412e-02, 0.421800000e-02, 0.282644445e-02, 0.180999032e-02, \n 0.108343562e-02, 0.605106267e-03, 0.308138660e-03, 0.139055523e-03, 0.532555251e-04, 0.161841328e-04, 0.347816874e-05, \n 0.420576116e-05, 0.167693347e-07, 0.354008603e-10, 0]\n up_x = np.arange(0.5, 1.01, 0.01)\n\n res = 0.\n if ((x >= 0.5) and (x <= 1)):\n for i in range(len(up_x) - 1):\n if (up_x[i] >= x) and (x < up_x[i+1]):\n N1 = 1 - (x - up_x[i])/0.01\n res = N1 * up_y[i] + (1 - N1) * up_y[i+1]\n return res\n return res\n\n\n# In[19]:\n############### Atomic Function Pulse with width, shift and scale #############\ndef pulse(up1, t: float, a = 1., b = 0., c = 1., d = 0.) -> float:\n x = (t - b)/a\n res = 0.\n if (x >= 0.5) and (x <= 1):\n res = up1(x)\n elif (x >= 0.0) and (x < 0.5):\n res = 1 - up1(1 - x)\n elif (x >= -1 and x <= -0.5):\n res = up1(-x)\n elif (x > -0.5) and (x < 0):\n res = 1 - up1(1 + x)\n res = d + res * c\n return res\n\n############### Atomic Function Applied to list with width, shift and scale #############\ndef up(up1, x: list, a = 1., b = 0., c = 1., d = 0.) -> list:\n res = []\n for i in range(len(x)):\n res.append(pulse(up1, x[i], a, b, c, d))\n return res\n\n############### Atomic String #############\ndef AString1(x: float) -> float:\n res = 1 * (pulse(up1, x/2.0 - 0.5) - 0.5)\n return res\n\n############### Atomic String Pulse with width, shift and scale #############\ndef AStringPulse(t: float, a = 1., b = 0., c = 1., d = 0.) -> float:\n x = (t - b)/a\n if (x < -1):\n res = -0.5\n elif (x > 1):\n res = 0.5\n else:\n res = AString1(x)\n res = d + res * c\n return res\n\n###### Atomic String Applied to list with width, shift and scale #############\ndef AString(x: list, a = 1., b = 0., c = 1., d = 0.) -> list:\n res = []\n for i in range(len(x)):\n res.append(AStringPulse(x[i], a, b, c, d))\n return res\n\n", "_____no_output_____" ], [ "import numpy as np\nimport pylab as pl", "_____no_output_____" ], [ "x = np.arange(-2.0, 2.0, 0.01)", "_____no_output_____" ], [ "pl.title('Atomic Function')\npl.plot(x, up(up1, x), label='Atomic Function')\npl.grid(True)\npl.show()", "_____no_output_____" ], [ "pl.title('Atomic String')\npl.plot(x, String(AString1, x, 1.0, 0, 1, 0), label='Atomic String')\npl.grid(True)\npl.show()", "_____no_output_____" ], [ "x = np.arange(-4.0, 4.0, 0.01)\ndx = x[1] - x[0]\n\npl.title('Atomic String')\npl.plot(x, String(AString1, x, 1., 0., 1., 1.), label='Atomic String')\n\nIntAString = np.cumsum(String(AString1, x, 1., 0., 1., 1.)) * dx\npl.plot(x, IntAString, label='AString Integral')\n\nInt2AString = np.cumsum(IntAString) * dx\npl.plot(x, Int2AString, label='AString Integral Integral')\n\npl.title('AString with Integrals')\npl.legend(loc='best', numpoints=1)\npl.grid(True)\npl.show()", "_____no_output_____" ] ], [ [ "## Summary and Observations", "_____no_output_____" ], [ "1) AString Integrals provide smooth curly connections between two straight lines\n\n2) Further integrals provide smooth curly connections between parabolas!!\n\n3) In general, AString integrals can provide smooth connections between any similar shapes!!!", "_____no_output_____" ] ], [ [ "AString1(0)", "_____no_output_____" ], [ "v=[0,0.1,0.25,0.5,0.9,1]\nfor i in v:\n print(AString1(i))", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "import h5py\nimport numpy as np\n\nwith h5py.File('/home/maxim/depth_dataset/train.h5', 'r') as hdf_train: \n center_train = np.array(hdf_train.get('center'))\n part_train = np.array(hdf_train.get('part'))\n scale_train = np.array(hdf_train.get('scale'))\n\nwith h5py.File('/home/maxim/depth_dataset/train_minimal.h5', 'w') as hdf_train_minimal:\n hdf_train_minimal.create_dataset('center', data=center_train)\n hdf_train_minimal.create_dataset('scale', data=scale_train)\n hdf_train_minimal.create_dataset('part', data=part_train)\n \nwith h5py.File('/home/maxim/depth_dataset/valid.h5', 'r') as hdf_valid: \n center_valid = np.array(hdf_valid.get('center'))\n part_valid = np.array(hdf_valid.get('part'))\n scale_valid = np.array(hdf_valid.get('scale'))\n\nwith h5py.File('/home/maxim/depth_dataset/valid_minimal.h5', 'w') as hdf_valid_minimal:\n hdf_valid_minimal.create_dataset('center', data=center_valid)\n hdf_valid_minimal.create_dataset('scale', data=scale_valid)\n hdf_valid_minimal.create_dataset('part', data=part_valid)\n", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]