content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
from random import shuffle
import random
import math
import string
import sys
import os
import string
import pdb
eng_punctuation = string.punctuation + '\xe2\x80\x9c' + '\xe2\x80\x9d'
hindi_punctuation = ['\xe0\xa5\xa4'] #danda
# words like don't are split into don ' t
# This version doesn't do anything about punctuation inside words
# and only considers punctuation at the beginning or end of a word
print sys.argv
if len(sys.argv) < 3:
print('python parseHindi.py nameOfSaveDir cutOff')
exit()
data_dir = '/deep/group/speech/asamar/nlp/data/hindi/source/'
save_dir = sys.argv[1] + '/'
cutOff = int(sys.argv[2])
print('Only sentences less than ' + str(cutOff) + ' will be taken from English.')
print('Only sentences less than ' + str(2*cutOff) + ' will be taken from Hindi.')
if not os.path.exists(save_dir):
print('Making directory ' + save_dir)
os.makedirs(save_dir)
print('Loading Original Hindi Dataset')
train_set_size = 273000
data = open(data_dir + 'hindencorp05.plaintext','r')
lines = data.readlines()
count = 0
print('Creating Train and Test Source Sets')
train_f = open(save_dir + 'ptb.train.txt', 'w')
test_f = open(save_dir + 'ptb.test.txt', 'w')
for line in lines:
if count < train_set_size:
train_f.write(line)
else:
test_f.write(line)
count = count + 1
data.close()
train_f.close()
test_f.close()
print('Building Vocabulary from Training Set')
punctuation = string.punctuation
eng_vocab = {}
hindi_vocab = {}
data = open(save_dir + 'ptb.train.txt','r')
num_lines = 0
max_eng_length = 0
max_hindi_length = 0
corresponding_line = ''
for line in data:
orig_line = line.lower().strip()
split_line = orig_line.split('\t')
eng_sent = splitSentence(split_line[3])
hindi_sent = splitSentence(split_line[4])
if len(eng_sent) < cutOff and len(hindi_sent) < 2*cutOff:
addToVocab(eng_vocab,eng_sent)
addToVocab(hindi_vocab,hindi_sent)
num_lines = num_lines + 1
if len(eng_sent) > max_eng_length:
max_eng_length = len(eng_sent)
if len(hindi_sent) > max_hindi_length:
max_hindi_length = len(hindi_sent)
corresponding_line = orig_line
print('Lines below cutoff: ' + str(num_lines))
print('Max English Length: ' + str(max_eng_length))
print('Max Hindi Length: ' + str(max_hindi_length))
print(corresponding_line)
data.close()
print('Sorting Eng Vocab')
eng_vocab = sorted(eng_vocab,key = lambda x: eng_vocab[x])
eng_vocab.reverse()
print('Sorting Hindi Vocab')
hindi_vocab = sorted(hindi_vocab,key = lambda x: hindi_vocab[x])
hindi_vocab.reverse()
print('Parsing Train and Test Set')
for a in [['ptb.train.txt','enc_train.txt','dec_train.txt'],['ptb.test.txt','enc_test.txt','dec_test.txt']]:
data = open(save_dir + a[0],'r')
enc_to = open(save_dir + a[1],'w')
dec_to = open(save_dir + a[2],'w')
count = 0
for line in data:
orig_line = line.lower().strip()
s = orig_line.split('\t')
eng_sent = splitSentence(s[3])
hindi_sent = splitSentence(s[4])
if len(eng_sent) < cutOff and len(hindi_sent) < 2*cutOff:
enc_to.write(removeOOV(eng_vocab,eng_sent).strip() + '\n')
dec_to.write(removeOOV(hindi_vocab,hindi_sent).strip() + '\n')
count = count + 1
if (count % 1000 == 0):
print('Finished parsing ' + str(count))
sys.stdout.flush()
data.close()
enc_to.close()
dec_to.close()
print('Total parsed: ' + str(count))
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] | 2.329808 | 1,513 |
# Solution A
# Solution B
| [
2,
28186,
317,
628,
198,
2,
28186,
347,
198
] | 3.111111 | 9 |
import asyncio
import logging
import socket
logger = logging.getLogger(__name__)
all_endpoints = []
def reset_pypeman_endpoints():
"""
clears book keeping of all endpoints
Can be useful for unit testing.
"""
all_endpoints.clear()
from pypeman.helpers import lazyload # noqa: E402
wrap = lazyload.Wrapper(__name__)
wrap.add_lazy('pypeman.contrib.http', 'HTTPEndpoint', ['aiohttp'])
wrap.add_lazy('pypeman.contrib.hl7', 'MLLPEndpoint', ['hl7'])
| [
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11401,
358,
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3256,
37250,
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] | 2.620879 | 182 |
import networkx as nx
import matplotlib.pyplot as plt
G_fb = nx.read_edgelist("facebook_combined.txt", create_using = nx.Graph(), nodetype=int)
print(nx.info(G_fb))
# if need drawing
ps = nx.spring_layout(G_fb)
nx.draw(G_fb, ps, with_labels = False, node_size = 5)
plt.show()
# ps = nx.spring_layout(G_fb)
# betCent = nx.betweenness_centrality(G_fb, normalized=True, endpoints=True)
# node_color = [20000.0 * G_fb.degree(v) for v in G_fb]
# node_size = [v * 10000 for v in betCent.values()]
# plt.figure(figsize=(20,20))
# nx.draw_networkx(G_fb, pos=ps, with_labels=False,
# node_color=node_color,
# node_size=node_size )
# plt.axis('off') | [
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] | 2.190939 | 309 |
from pyqtgraph.Qt import QtGui, QtCore
import numpy as np
import pyqtgraph as pg
import random
# Create the main application instance
pg.setConfigOption('background', 'w')
app = pg.mkQApp()
view = pg.PlotWidget()
view.resize(800, 600)
view.setWindowTitle('Scatter plot using pyqtgraph with PyQT5')
view.setAspectLocked(True)
view.show()
n = 1000
print('Number of points: ' + str(n))
data = np.random.normal(size=(2, n))
# Create the scatter plot and add it to the view
scatter = pg.ScatterPlotItem(pen=pg.mkPen(width=5, color='r'), symbol='d', size=2)
view.setXRange(-10, 10)
view.setYRange(-10, 10)
view.addItem(scatter)
pos = [{'pos': data[:, i]} for i in range(n)]
now = pg.ptime.time()
scatter.setData(pos)
print("Plot time: {} sec".format(pg.ptime.time() - now))
timer = QtCore.QTimer()
timer.timeout.connect(update)
timer.start(0)
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
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] | 2.604113 | 389 |
#!/usr/bin/env python
__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
import sys
from classes.dataset.Generator import *
from classes.model.pix2code import *
if __name__ == "__main__":
argv = sys.argv[1:]
if len(argv) < 2:
print "Error: not enough argument supplied:"
print "train.py <input path> <output path> <is memory intensive (default: 0)> <pretrained weights (optional)>"
exit(0)
else:
input_path = argv[0]
output_path = argv[1]
use_generator = False if len(argv) < 3 else True if int(argv[2]) == 1 else False
pretrained_weigths = None if len(argv) < 4 else argv[3]
run(input_path, output_path, is_memory_intensive=use_generator, pretrained_model=pretrained_weigths)
| [
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] | 2.5 | 342 |
# Copyright 2017 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Create function which removes the specified device policies
from ChromeDeviceSettingsProto.
This function is primarily intended to be used
for the implementation of the DeviceOffHours policy.
"""
from optparse import OptionParser
import sys
file_header = """//
// DO NOT MODIFY THIS FILE DIRECTLY!
// IT IS GENERATED BY generate_device_policy_remover.py
// FROM chrome_device_policy_pb2.py
//
#include "chrome/browser/ash/policy/core/device_policy_remover.h"
namespace policy {
void RemovePolicies(enterprise_management::ChromeDeviceSettingsProto* policies,
const std::vector<int>& policy_proto_tags_to_remove) {
for (const int tag : policy_proto_tags_to_remove) {
switch(tag) {
"""
file_footer = """ }
}
}
} // namespace policy
"""
if __name__ == '__main__':
sys.exit(main())
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198
] | 3.140575 | 313 |
import math
import os
import shutil
import torch
from collections import OrderedDict
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import json
from logging import getLogger
import attr
from sqlalchemy import Text, TypeDecorator, create_engine
from sqlalchemy.orm import sessionmaker
logger = getLogger(__name__)
@attr.s(auto_attribs=True)
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# This modules contains definitions used in Exercise Files
from typing import Any
def print_with_title(
output: Any,
title: str = '',
linebreaks_before: int = 2,
linebreaks_after: int = 2,
put_stars_after: bool = True,
) -> None:
"""
Print linebreak-padded output with a title and optional line of 80 stars
afterward
"""
print(
'\n' * linebreaks_before + title + '\n' * 2,
output,
'\n' * linebreaks_after,
sep='',
)
if put_stars_after:
print('*' * 80)
if __name__ == '__main__':
print_with_title('This module is working as expected!')
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355,
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] | 2.367273 | 275 |
'''MobileNetV3 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import os
import sys
sys.path.append(".")
from meta_utils.meta_quantized_module import MetaQuantConv, MetaQuantLinear
def conv3x3(in_planes, out_planes, kernel_size=3, stride=0, padding=1, bias=False, bitW=1):
" 3x3 convolution with padding "
return MetaQuantConv(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias, bitW=bitW)
'''
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(in_size // reduction),
nn.ReLU(inplace=True),
nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(in_size),
hsigmoid()
'''
class Block(nn.Module):
'''expand + depthwise + pointwise'''
from ptflops import get_model_complexity_info
test()
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# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# 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.
#
import proto # type: ignore
__protobuf__ = proto.module(
package="google.cloud.aiplatform.v1beta1.schema.trainingjob.definition",
manifest={
"AutoMlImageClassification",
"AutoMlImageClassificationInputs",
"AutoMlImageClassificationMetadata",
},
)
class AutoMlImageClassification(proto.Message):
r"""A TrainingJob that trains and uploads an AutoML Image
Classification Model.
Attributes:
inputs (~.automl_image_classification.AutoMlImageClassificationInputs):
The input parameters of this TrainingJob.
metadata (~.automl_image_classification.AutoMlImageClassificationMetadata):
The metadata information.
"""
inputs = proto.Field(
proto.MESSAGE, number=1, message="AutoMlImageClassificationInputs",
)
metadata = proto.Field(
proto.MESSAGE, number=2, message="AutoMlImageClassificationMetadata",
)
class AutoMlImageClassificationInputs(proto.Message):
r"""
Attributes:
model_type (~.automl_image_classification.AutoMlImageClassificationInputs.ModelType):
base_model_id (str):
The ID of the ``base`` model. If it is specified, the new
model will be trained based on the ``base`` model.
Otherwise, the new model will be trained from scratch. The
``base`` model must be in the same Project and Location as
the new Model to train, and have the same modelType.
budget_milli_node_hours (int):
The training budget of creating this model, expressed in
milli node hours i.e. 1,000 value in this field means 1 node
hour. The actual metadata.costMilliNodeHours will be equal
or less than this value. If further model training ceases to
provide any improvements, it will stop without using the
full budget and the metadata.successfulStopReason will be
``model-converged``. Note, node_hour = actual_hour \*
number_of_nodes_involved. For modelType
``cloud``\ (default), the budget must be between 8,000 and
800,000 milli node hours, inclusive. The default value is
192,000 which represents one day in wall time, considering 8
nodes are used. For model types ``mobile-tf-low-latency-1``,
``mobile-tf-versatile-1``, ``mobile-tf-high-accuracy-1``,
the training budget must be between 1,000 and 100,000 milli
node hours, inclusive. The default value is 24,000 which
represents one day in wall time on a single node that is
used.
disable_early_stopping (bool):
Use the entire training budget. This disables
the early stopping feature. When false the early
stopping feature is enabled, which means that
AutoML Image Classification might stop training
before the entire training budget has been used.
multi_label (bool):
If false, a single-label (multi-class) Model
will be trained (i.e. assuming that for each
image just up to one annotation may be
applicable). If true, a multi-label Model will
be trained (i.e. assuming that for each image
multiple annotations may be applicable).
"""
class ModelType(proto.Enum):
r""""""
MODEL_TYPE_UNSPECIFIED = 0
CLOUD = 1
MOBILE_TF_LOW_LATENCY_1 = 2
MOBILE_TF_VERSATILE_1 = 3
MOBILE_TF_HIGH_ACCURACY_1 = 4
model_type = proto.Field(proto.ENUM, number=1, enum=ModelType,)
base_model_id = proto.Field(proto.STRING, number=2)
budget_milli_node_hours = proto.Field(proto.INT64, number=3)
disable_early_stopping = proto.Field(proto.BOOL, number=4)
multi_label = proto.Field(proto.BOOL, number=5)
class AutoMlImageClassificationMetadata(proto.Message):
r"""
Attributes:
cost_milli_node_hours (int):
The actual training cost of creating this
model, expressed in milli node hours, i.e. 1,000
value in this field means 1 node hour.
Guaranteed to not exceed
inputs.budgetMilliNodeHours.
successful_stop_reason (~.automl_image_classification.AutoMlImageClassificationMetadata.SuccessfulStopReason):
For successful job completions, this is the
reason why the job has finished.
"""
class SuccessfulStopReason(proto.Enum):
r""""""
SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
BUDGET_REACHED = 1
MODEL_CONVERGED = 2
cost_milli_node_hours = proto.Field(proto.INT64, number=1)
successful_stop_reason = proto.Field(
proto.ENUM, number=2, enum=SuccessfulStopReason,
)
__all__ = tuple(sorted(__protobuf__.manifest))
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11235,
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834,
13,
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8409,
4008,
198
] | 2.580569 | 2,110 |
"""
This package is used to simulate RPi.GPIO library during development or if GPIO are not available.
""" | [
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] | 4.24 | 25 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 18 15:34:25 2018
@author: philld
"""
import requests
import json
import re
specfile="../../docs/spec/src/info_openapi.json"
#docsfile="../../docs/input/web_api.cm"
docsfile="/home/philld/reps/dhis2-docs/src/commonmark/en/content/developer/web-api.md"
ofile=open(specfile,'r')
openapi = json.load(ofile)
ofile.close()
docfile = open(docsfile, "r")
docs = docfile.read()
docfile.close()
recurseDict(openapi,"DESC")
apifile= open(specfile,'w')
apifile.write(json.dumps(openapi , sort_keys=False, indent=2, separators=(',', ': ')))
apifile.close()
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25,
705,
22305,
198,
499,
361,
576,
13,
19836,
3419,
628,
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] | 2.417625 | 261 |
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import rhinoscriptsyntax as rs
from compas_rhino.geometry import RhinoCurve
__all__ = [
'RhinoCurve'
]
| [
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834,
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62,
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834,
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198,
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9529,
11996,
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220,
220,
220,
705,
38576,
2879,
26628,
303,
6,
198,
60,
628
] | 3.235294 | 68 |
from rest_framework import serializers
from .models import CaseDetail
class CaseDetailSerializer(serializers.ModelSerializer):
"""CaseDetailSerializer use the CaseDetail Model"""
| [
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from __future__ import unicode_literals
from requests import auth
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from pyrocko import moment_tensor as mtm
magnitude = 5.4
exp = mtm.magnitude_to_moment(magnitude) # convert the mag to moment in [Nm]
# init pyrocko moment tensor
m = mtm.MomentTensor(
mnn = 0.04*exp,
mee = 0.6*exp,
mdd = -0.63*exp,
mne = 0.04*exp,
mnd = 0.5*exp,
med = 0.21*exp)
print(m) # print moment tensor
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# -*- coding: utf-8 -*-
"""Falcon app used for testing."""
# standard library
import logging
from typing import Any
# third-party
import falcon
# first-party
from falcon_provider_logger.middleware import LoggerMiddleware
class LoggerCustomLoggerResource:
"""Logger middleware testing resource."""
log = None
def on_get(self, req: falcon.Request, resp: falcon.Response) -> None:
"""Support GET method."""
key: str = req.get_param('key')
self.log.debug(f'DEBUG {key}')
self.log.info(f'INFO {key}')
self.log.warning(f'WARNING {key}')
self.log.error(f'ERROR {key}')
self.log.critical(f'CRITICAL {key}')
resp.body = f'Logged - {key}'
def on_post(self, req: falcon.Request, resp: falcon.Response) -> None:
"""Support POST method."""
key: str = req.get_param('key')
value: Any = req.get_param('value')
self.log.debug(f'DEBUG {key} {value}')
self.log.info(f'INFO {key} {value}')
self.log.warning(f'WARNING {key} {value}')
self.log.error(f'ERROR {key} {value}')
self.log.critical(f'CRITICAL {key} {value}')
resp.body = f'Logged - {key}'
logger: object = logging.getLogger('custom')
app_custom_logger = falcon.API(middleware=[LoggerMiddleware(logger=logger)])
app_custom_logger.add_route('/middleware', LoggerCustomLoggerResource())
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1574,
3256,
5972,
1362,
15022,
11187,
1362,
26198,
28955,
198
] | 2.3821 | 581 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Poshan Didi!
"""
import logging
import logging.handlers
import beneficiary_bot
import nurse_bot
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
from simple_settings import settings
from registration import registration_conversation
# Enable logging
logging.basicConfig(filename=settings.LOG_FILENAME,
format=settings.LOG_FORMAT,
level=settings.LOG_LEVEL)
logger = logging.getLogger(__name__)
handler = logging.handlers.RotatingFileHandler(
settings.LOG_FILENAME,
maxBytes=10*1024*1024,
backupCount=100
)
logger.addHandler(handler)
# Define a few command handlers. These usually take the two arguments bot and
# update. Error handlers also receive the raised TelegramError object in error.
def error(update, context):
"""Log Errors caused by Updates."""
logger.warning('Update "%s" caused error "%s"', update, context.error)
def main():
"""Start the bot."""
beneficiary_bot.setup_state_machines()
# Create the Updater and pass it the bot's token.
# Make sure to set use_context=True to use the new context based callbacks
updater = Updater(settings.TELEGRAM_TOKEN, use_context=True)
# Get the dispatcher to register handlers
dp = updater.dispatcher
# Add the regsitration conversation, which handles the /start command
dp.add_handler(registration_conversation)
# Add a nurse command to skip the current escalated message
# (only allow the nurse to access this command)
# dp.add_handler(CommandHandler('noreply', nurse_bot.skip,
# Filters.chat(settings.NURSE_CHAT_ID)))
# Add a nurse command to set state for a user (only allow the nurse to access this command)
# dp.add_handler(CommandHandler('state', nurse_bot.set_state,
# Filters.chat(settings.NURSE_CHAT_ID)))
# Add a nurse command to set state for a user (only allow the nurse to access this command)
# dp.add_handler(CommandHandler('state', nurse_bot.set_super_state,
# Filters.chat(settings.GOD_MODE)))
# dp.add_handler(CommandHandler('cohortstate', nurse_bot.set_cohort_super_state,
# Filters.chat(settings.GOD_MODE)))
# Add a nurse command to set state for a user (only allow the nurse to access this command)
# dp.add_handler(CommandHandler('send_next_modules', nurse_bot.send_next_modules,
# Filters.chat(settings.GOD_MODE)))
# Add a nurse command to set state for a user (only allow the nurse to access this command)
# dp.add_handler(CommandHandler('vhnd', nurse_bot.send_vhnd_reminder,
# Filters.chat(settings.GOD_MODE)))
# sign off messages
dp.add_handler(CommandHandler('sendglobal', nurse_bot.send_global_msg,
Filters.chat(settings.GOD_MODE)))
# on non-command i.e., a normal message message - process_user_input the
# message from Telegram. Use different handlers for the nurse and user
# messages
dp.add_handler(MessageHandler(
(Filters.text & (~ Filters.chat(settings.NURSE_CHAT_ID))), beneficiary_bot.all_done))
# dp.add_handler(MessageHandler(
# (Filters.text & (~ Filters.chat(settings.NURSE_CHAT_ID))), beneficiary_bot.process_user_input))
# dp.add_handler(MessageHandler(
# (Filters.text & Filters.chat(settings.NURSE_CHAT_ID)), nurse_bot.process_nurse_input))
# log all errors
dp.add_error_handler(error)
logger.info(
'************************** POSHAN DIDI HAS RETURNED **************************')
# Start the Bot.
updater.start_polling()
# Run the bot until you press Ctrl-C or the process receives SIGINT,
# SIGTERM or SIGABRT. This should be used most of the time, since
# start_polling() is non-blocking and will stop the bot gracefully.
updater.idle()
if __name__ == '__main__':
main()
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'''
Policy and intrinsic reward models.
'''
import numpy as np
import tensorflow as tf
from curiosity.models.model_building_blocks import ConvNetwithBypasses
from curiosity.models import explicit_future_prediction_base as fp_base
from curiosity.models import jerk_models
import distutils.version
use_tf1 = distutils.version.LooseVersion(tf.VERSION) >= distutils.version.LooseVersion('1.0.0')
#TODO replace all these makeshift helpers
def postprocess_depths(depths):
'''
Assumes depths is of shape [batch_size, time_number, height, width, 3]
'''
depths = tf.cast(depths, tf.float32)
depths = (depths[:,:,:,:,0:1] * 256. + depths[:,:,:,:,1:2] + \
depths[:,:,:,:,2:3] / 256.0) / 1000.0
depths /= 4. # normalization
return depths
default_damian_cfg = jerk_models.cfg_mom_complete_bypass(768, use_segmentation=False,
method='concat', nonlin='relu')
default_damian_cfg.update({'state_shape' : [2, 128, 170, 3], 'action_shape' : [2, 8]})
sample_depth_future_cfg = {
'state_shape' : [2, 64, 64, 3],
'action_shape' : [2, 8],
'action_join' : {
'reshape_dims' : [8, 8, 5],
'mlp' : {
'hidden_depth' : 2,
'hidden' : {
1 : {'num_features' : 320, 'dropout' : .75},
2 : {'num_features' : 320, 'activation' : 'identity'}
}
}
},
'encode' : {
'encode_depth' : 3,
'encode' : {
1 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}},
2 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}},
3 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 5}},
}
},
'deconv' : {
'deconv_depth' : 3,
'deconv' : {
1 : {'deconv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}, 'bypass' : 0},
2 : {'deconv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}, 'bypass' : 0},
3 : {'deconv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 1}, 'bypass' : 0}
}
}
}
a_bigger_depth_future_config = {
'state_shape' : [2, 64, 64, 3],
'action_shape' : [2, 8],
'action_join' : {
'reshape_dims' : [8, 8, 5],
'mlp' : {
'hidden_depth' : 3,
'hidden' : {
1 : {'num_features' : 320},
2 : {'num_features' : 320},
3 : {'num_features' : 320, 'activation' : 'identity'}
}
}
},
'encode' : {
'encode_depth' : 5,
'encode' : {
1 : {'conv' : {'filter_size' : 5, 'stride' : 2, 'num_filters' : 20}},
2 : {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 20}},
3 : {'conv' : {'filter_size' : 5, 'stride' : 2, 'num_filters' : 20}},
4 : {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 10}},
5 : {'conv' : {'filter_size' : 5, 'stride' : 2, 'num_filters' : 5}},
}
},
'deconv' : {
'deconv_depth' : 5,
'deconv' : {
1 : {'deconv' : {'filter_size' : 5, 'stride' : 2, 'num_filters' : 20}, 'bypass' : 4},
2 : {'deconv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 20}, 'bypass' : 3},
3 : {'deconv' : {'filter_size' : 5, 'stride' : 2, 'num_filters' : 20}, 'bypass' : 2},
4 : {'deconv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}, 'bypass' : 1},
5 : {'deconv' : {'filter_size' : 5, 'stride' : 1, 'num_filters' : 1}, 'bypass' : 0}
}
}
}
hourglass_latent_model_cfg = {
'state_shape' : [2, 64, 64, 3],
'action_shape' : [2, 8],
'encode' : {
'encode_depth' : 4,
'encode' : {
1: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}},
2: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}},
3: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}},
4: {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 8}}
}
},
'action_model' : {
'mlp' : {
'hidden_depth' : 2,
'hidden' : {
1: {'num_features' : 256},
2: {'num_features' : 16, 'activation' : 'identity'}
}
}
},
'future_model' : {
'mlp' : {
'hidden_depth' : 2,
'hidden' : {
1: {'num_features' : 256},
2: {'num_features' : 128, 'activation' : 'identity'}
}
},
'reshape_dims' : [4, 4, 8],
'deconv' : {
'deconv_depth' : 3,
'deconv' : {
1 : {'deconv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 16}, 'bypass' : 0},
2 : {'deconv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 16}, 'bypass' : 0},
3 : {'deconv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 8}, 'bypass' : 0}
}
}
}
}
mario_world_model_config = {
'state_shape' : [2, 64, 64, 3],
'action_shape' : [2, 8],
'encode' : {
'encode_depth' : 4,
'encode' : {
1: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}},
2: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}},
3: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}},
4: {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 32}}
}
},
'action_model' : {
'mlp' : {
'hidden_depth' : 2,
'hidden' : {
1: {'num_features' : 256},
2: {'num_features' : 16, 'activation' : 'identity'}
}
}
},
'future_model' : {
'mlp' : {
'hidden_depth' : 2,
'hidden' : {
1: {'num_features' : 512},
2: {'num_features' : 512, 'activation' : 'identity'}
}
}
}
}
class MixedUncertaintyModel:
'''For both action and future uncertainty prediction, simultaneously, as separate predictions.
Consider merging with UncertaintyModel, but right now that might look too messy. Want to leave that functionality alone.
'''
class ObjectThereWorldModel:
'''
A dummy oracle world model that just says the true value of whether an object is in the field of view.
'''
class ForceMagSquareWorldModel:
'''
Similar to the above, but just gives the square of the force.
'''
sample_cfg = {
'uncertainty_model' : {
'state_shape' : [2, 64, 64, 3],
'action_dim' : 8,
'n_action_samples' : 50,
'encode' : {
'encode_depth' : 3,
'encode' : {
1 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}},
2 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 10}},
3 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 5}},
}
},
'mlp' : {
'hidden_depth' : 2,
'hidden' : {1 : {'num_features' : 20, 'dropout' : .75},
2 : {'num_features' : 1, 'activation' : 'identity'}
}
}
},
'world_model' : sample_depth_future_cfg,
'seed' : 0
}
another_sample_cfg = {
'uncertainty_model' : {
'state_shape' : [2, 64, 64, 3],
'action_dim' : 8,
'n_action_samples' : 50,
'encode' : {
'encode_depth' : 5,
'encode' : {
1 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 20}},
2 : {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 20}},
3 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 20}},
4 : {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 10}},
5 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 5}},
}
},
'mlp' : {
'hidden_depth' : 2,
'hidden' : {1 : {'num_features' : 20, 'dropout' : .75},
2 : {'num_features' : 1, 'activation' : 'identity'}
}
}
},
'world_model' : a_bigger_depth_future_config,
'seed' : 0
}
default_damian_full_cfg = {
'uncertainty_model' : {
'state_shape' : [2, 128, 170, 3],
'action_dim' : 8,
'n_action_samples' : 50,
'encode' : {
'encode_depth' : 5,
'encode' : {
1 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 20}},
2 : {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 20}},
3 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 20}},
4 : {'conv' : {'filter_size' : 3, 'stride' : 1, 'num_filters' : 10}},
5 : {'conv' : {'filter_size' : 3, 'stride' : 2, 'num_filters' : 5}},
}
},
'mlp' : {
'hidden_depth' : 2,
'hidden' : {1 : {'num_features' : 20, 'dropout' : .75},
2 : {'num_features' : 1, 'activation' : 'identity'}
}
}
},
'world_model' : default_damian_cfg,
'seed' : 0
}
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] | 1.873996 | 4,730 |
print("part1包被导入了~") | [
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from dictdiffer import diff
from sqlalchemy import Column, Integer, String
from sqlalchemy.dialects.postgresql import JSONB
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
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import komand
from .schema import DecodeInput, DecodeOutput
import base64
| [
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] | 3.409091 | 22 |
import os
import shutil
import json
import time
import cv2
import numpy as np
import PIL
if __name__ == '__main__' :
erode_all(True) | [
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] | 2.622642 | 53 |
import sys
import unittest
import re
from operator import itemgetter
import argparse
import traceback
# print arg
if __name__=='__main__':
codes = []
for line in sys.stdin:
codes.append(line.strip())
print(part_one(codes))
print(part_two(codes))
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] | 2.628571 | 105 |
from collections import defaultdict
# name list obtained from: https://www.ssa.gov/oact/babynames/decades/century.html
# accessed on Nov 6th, 2018
if __name__ == '__main__':
lex = PronounLexicon()
all_words = lex.all_words()
in_file_path = "data/CBTest/data/cbt_train.txt"
all_lens = []
all_gaps = []
with open(in_file_path) as f:
for line in f:
line = line.strip()
marked_sentence = [1 if w in all_words else 0 for w in line.split(' ')]
all_lens.append(len(marked_sentence))
# print(marked_sentence)
gaps = find_gaps(marked_sentence)
# print(gaps)
all_gaps.extend(gaps)
import numpy as np
print(np.mean(all_lens), np.std(all_lens))
print(np.mean(all_gaps), np.std(all_gaps))
# l = 32 covers 81.5% of the sentences
# l = 64 covers 98.4% of the sentences
l = 64
print(len(list(filter(lambda x: x <= l, all_lens))) / float(len(all_lens)))
# l = 10 covers 82.7% of the gaps
# l = 20 covers 97.2% of the gaps
# l = 30 covers 99.4% of the gaps
l = 20
print(len(list(filter(lambda x: x <= l, all_gaps))) / float(len(all_gaps)))
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628,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198
] | 2.124783 | 577 |
# Generated by Django 3.2.2 on 2021-06-29 09:49
import django.core.validators
from django.db import migrations, models
| [
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602,
11,
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628
] | 2.95122 | 41 |
# Autogenerated file.
from .client import ButtonClient # type: ignore
| [
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198
] | 3.684211 | 19 |
import yaml
import os
from pathlib import Path
from django.conf import settings
from django.core.management.base import BaseCommand
from apps.findings import models
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import sys
from django.test import TestCase
error_while_importing = None
try:
from easymode import *
from easymode.admin import *
from easymode.admin.forms import *
from easymode.admin.models import *
from easymode.debug import *
from easymode.i18n import *
from easymode.i18n.admin import *
from easymode.management import *
from easymode.middleware import *
from easymode.management.commands import *
from easymode.templatetags import *
from easymode.tree import *
from easymode.tree.admin import *
from easymode.tree.admin.widgets import *
from easymode.urls import *
from easymode.utils import *
from easymode.views import *
from easymode.xslt import *
except Exception as e:
error_while_importing = e
__all__ = ('TestImportAll',)
class TestImportAll(TestCase):
"""Check if the import all works for every package in easymode"""
def test_import_all(self):
"""All easymode packages should be importable with * without any errors"""
if error_while_importing is not None:
self.fail("%s: %s" % (type(e).__name__, error_while_importing)) | [
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62,
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407,
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220,
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220,
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737,
834,
3672,
834,
11,
4049,
62,
4514,
62,
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278,
4008
] | 2.875312 | 401 |
from collections import Counter
| [
6738,
17268,
1330,
15034,
198
] | 6.4 | 5 |
from starlette.requests import Request
from typing import List, Optional
from viewmodels.shared.viewmodel import ViewModelBase
from services import episode_service
from data.episode import Episode
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] | 4.369565 | 46 |
#!/usr/bin/env python3
"""
Wal Steam
========================================
oooo oooo .
`888 .8P' .o8
888 d8' .ooooo. .o888oo .oooo.
88888[ d88' `88b 888 `P )88b
888`88b. 888 888 888 .oP"888
888 `88b. 888 888 888 . d8( 888
o888o o888o `Y8bod8P' "888" `Y888""8o
@nilsu.org
=== Copyright (C) 2019 Dakota Walsh ===
"""
import shutil # copying files
import os # getting paths
import urllib.request # downloading the zip files
import zipfile # extracting the zip files
import sys
import argparse # argument parsing
import textwrap
import time
import re
from distutils.dir_util import copy_tree # copytree from shutil is broken so use copy_tree
from argparse import RawTextHelpFormatter
# set some variables for the file locations
HOME_DIR = os.getenv("HOME", os.getenv("USERPROFILE")) # should be crossplatform
CACHE_DIR = os.path.join(HOME_DIR, ".cache", "wal_steam")
CONFIG_DIR = os.path.join(HOME_DIR, ".config", "wal_steam")
SKIN_VERSION = "4.4"
SKIN_NAME = "Metro %s Wal_Mod" % SKIN_VERSION
VERSION = "1.4"
CONFIG_FILE = "wal_steam.conf"
COLORS_FILE = os.path.join(CACHE_DIR, "custom.styles")
CONFIG_URL = "https://raw.githubusercontent.com/kotajacob/wal_steam_config/master/wal_steam.conf"
STEAM_DIR_OTHER = os.path.expanduser("~/.steam/steam/skins")
STEAM_DIR_OSX = os.path.expanduser("~/Library/Application Support/Steam/Steam.AppBundle/Steam/Contents/MacOS/skins")
STEAM_DIR_UBUNTU = os.path.expanduser("~/.steam/skins")
STEAM_DIR_WINDOWS = "C:\Program Files (x86)\Steam\skins"
WAL_COLORS = os.path.join(HOME_DIR, ".cache", "wal", "colors.css")
WPG_COLORS = os.path.join(HOME_DIR, ".config", "wpg", "formats", "colors.css")
METRO_URL = "https://github.com/minischetti/metro-for-steam/archive/v%s.zip" % SKIN_VERSION
METRO_ZIP = os.path.join(CACHE_DIR, "metroZip.zip")
METRO_DIR = os.path.join(CACHE_DIR, "metro-for-steam-%s" % SKIN_VERSION)
METRO_COLORS_FILE = os.path.join(METRO_DIR, "custom.styles")
METRO_PATCH_URL = "https://github.com/redsigma/UPMetroSkin/archive/9.1.12.zip" # A link to the version we've tested rather than the latest, just in case they break things upstream.
METRO_PATCH_ZIP = os.path.join(CACHE_DIR, "metroPatchZip.zip")
METRO_PATCH_DIR = os.path.join(CACHE_DIR, "metroPatchZip")
METRO_PATCH_COPY = os.path.join(METRO_PATCH_DIR, "UPMetroSkin-9.1.12", "Unofficial 4.x Patch", "Main Files [Install First]")
METRO_PATCH_HDPI = os.path.join(METRO_PATCH_DIR, "UPMetroSkin-9.1.12", "Unofficial 4.x Patch", "Extras", "High DPI", "Increased fonts", "Install")
MAX_PATCH_DL_ATTEMPTS = 5
# CLI colour and style sequences
CLI_RED = "\033[91m"
CLI_YELLOW = "\033[93m"
CLI_BOLD = "\033[1m"
CLI_END = "\033[0m"
###################
# color functions #
###################
def hexToRgb(hexColors):
"""Convert hex colors to rgb colors (takes a list)."""
return [tuple(bytes.fromhex(color.strip("#"))) for color in hexColors]
##########################
# checkInstall functions #
##########################
if __name__ == '__main__':
main()
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62,
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58,
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58,
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1,
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62,
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1,
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40,
62,
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220,
220,
220,
796,
37082,
44427,
58,
15,
76,
1,
628,
198,
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21017,
198,
2,
3124,
5499,
1303,
198,
14468,
21017,
198,
198,
4299,
17910,
2514,
49,
22296,
7,
33095,
5216,
669,
2599,
198,
220,
220,
220,
37227,
3103,
1851,
17910,
7577,
284,
46140,
7577,
357,
83,
1124,
257,
1351,
21387,
15931,
198,
220,
220,
220,
1441,
685,
83,
29291,
7,
33661,
13,
6738,
33095,
7,
8043,
13,
36311,
7203,
2,
1,
22305,
329,
3124,
287,
17910,
5216,
669,
60,
198,
198,
14468,
7804,
2235,
198,
2,
2198,
15798,
5499,
1303,
198,
14468,
7804,
2235,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 2.213861 | 1,515 |
import requests
def fetch(*, url, headers):
"""
Send a GET request and return response as Python object
"""
response = requests.get(url, headers=headers)
return response.json()
| [
11748,
7007,
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] | 3.225806 | 62 |
import torch
from shapely.geometry import Polygon
| [
11748,
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6738,
5485,
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13,
469,
15748,
1330,
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628,
628,
628,
628
] | 3.411765 | 17 |
# SPDX-License-Identifier: Apache-2.0
# Copyright 2019 Blue Cheetah Analog Design 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.
import argparse
from bag.core import BagProject
from bag.util.misc import register_pdb_hook
register_pdb_hook()
if __name__ == '__main__':
_args = parse_options()
local_dict = locals()
if 'bprj' not in local_dict:
print('creating BAG project')
_prj = BagProject()
else:
print('loading BAG project')
_prj = local_dict['bprj']
run_main(_prj, _args)
| [
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220,
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10786,
25138,
347,
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1628,
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198,
220,
220,
220,
220,
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220,
220,
4808,
1050,
73,
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62,
11600,
17816,
65,
1050,
73,
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628,
220,
220,
220,
1057,
62,
12417,
28264,
1050,
73,
11,
4808,
22046,
8,
198
] | 3.046784 | 342 |
#!/usr/bin/env python3
import argparse
import re
import sys
import requests
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2,
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] | 3.304348 | 23 |
#!/usr/bin/env python3
"""
Ensure that every security group tagged with “AllowCloudFlareIngress” has
permissions for every public CloudFlare netblock
"""
import sys
import boto3
import requests
from botocore.exceptions import ClientError
EC2_CLIENT = boto3.client("ec2")
CLOUDFLARE_IPV4 = requests.get("https://www.cloudflare.com/ips-v4").text.splitlines()
CLOUDFLARE_IPV6 = requests.get("https://www.cloudflare.com/ips-v6").text.splitlines()
if __name__ == "__main__":
for security_group_id, existing_permissions in get_security_groups():
add_ingess_rules_for_group(security_group_id, existing_permissions)
| [
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220,
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220,
220,
751,
62,
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62,
8094,
7,
12961,
62,
8094,
62,
312,
11,
4683,
62,
525,
8481,
8,
198
] | 2.828829 | 222 |
from piccolo.apps.migrations.auto import MigrationManager
from piccolo.columns.base import OnDelete, OnUpdate
from piccolo.columns.defaults.timestamp import TimestampNow
from piccolo.table import Table
ID = "2020-10-04T21:27:16"
VERSION = "0.13.4"
| [
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13,
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1,
628
] | 3.048193 | 83 |
# -*- coding: utf-8 -*-
from math import ceil
from .SolarMonth import SolarMonth
class SolarSeason:
"""
阳历季度
"""
MONTH_COUNT = 3
@staticmethod
@staticmethod
def getIndex(self):
"""
获取当月是第几季度
:return: 季度序号，从1开始
"""
return int(ceil(self.__month * 1.0 / SolarSeason.MONTH_COUNT))
def getMonths(self):
"""
获取本季度的阳历月列表
:return: 阳历月列表
"""
l = []
index = self.getIndex() - 1
for i in range(0, SolarSeason.MONTH_COUNT):
l.append(SolarMonth.fromYm(self.__year, SolarSeason.MONTH_COUNT * index + i + 1))
return l
def next(self, seasons):
"""
季度推移
:param seasons: 推移的季度数，负数为倒推
:return: 推移后的季度
"""
if 0 == seasons:
return SolarSeason.fromYm(self.__year, self.__month)
year = self.__year
month = self.__month
months = SolarSeason.MONTH_COUNT * seasons
if months == 0:
return SolarSeason.fromYm(year, month)
n = abs(months)
for i in range(1, n + 1):
if months < 0:
month -= 1
if month < 1:
month = 12
year -= 1
else:
month += 1
if month > 12:
month = 1
year += 1
return SolarSeason.fromYm(year, month)
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25,
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220,
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
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1227,
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352,
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220,
220,
220,
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220,
220,
220,
220,
220,
220,
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220,
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220,
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220,
614,
15853,
352,
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220,
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220,
1441,
12347,
18960,
13,
6738,
56,
76,
7,
1941,
11,
1227,
8,
198
] | 1.634615 | 884 |
# Generated by Django 2.0.5 on 2018-10-24 19:02
from django.db import migrations, models
| [
2,
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] | 2.84375 | 32 |
# -*- coding: utf-8 -*- line endings: unix -*-
__author__ = 'quixadhal'
import os
import sys
import time
import sysutils
import log_system
import db_system
logger = log_system.init_logging()
sys.path.append(os.getcwd())
if __name__ == '__main__':
logger.boot('System booting.')
snapshot = sysutils.ResourceSnapshot()
logger.info(snapshot.log_data())
db_system.init_db()
snapshot = sysutils.ResourceSnapshot()
logger.info(snapshot.log_data())
from db_system import Session
from option import Option
session = Session()
options = session.query(Option).first()
logger.boot('Using database version %s, created on %s', options.version, options.date_created)
#logger.boot('Port number is %d', options.port)
#logger.boot('Wizlock is %s', options.wizlock)
time.sleep(1)
snapshot = sysutils.ResourceSnapshot()
logger.info(snapshot.log_data())
logger.critical('System halted.')
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3256,
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8,
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220,
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13,
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7,
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10951,
7,
45380,
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13,
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62,
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28955,
198,
220,
220,
220,
49706,
13,
34666,
10786,
11964,
27771,
2637,
8,
198
] | 2.781711 | 339 |
import pandas as pa
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import Imputer,LabelEncoder,OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
#%%
datset=pa.read_csv("datasets/One.csv")
#datset_district=datset.drop_duplicates('District_Name')['District_Name']
datset_crop=datset.drop_duplicates('crop')['crop']
#%%
for i in datset_crop:
value=(datset.loc[(datset['crop'] == i)])
for j in headers:
mean_1=(value[j]).mean()
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25,
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220,
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13,
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58,
7,
67,
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316,
17816,
31476,
20520,
6624,
1312,
8,
12962,
198,
220,
220,
220,
329,
474,
287,
24697,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1612,
62,
16,
16193,
8367,
58,
73,
35944,
32604,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220
] | 2.534979 | 243 |
#!/usr/bin/env python
import numpy as np
if __name__ == "__main__":
import sys
data = load_data(sys.argv[1])
print("data: %s, %s" % (data, str(data.shape)))
| [
2,
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] | 2.293333 | 75 |
# flake8: noqa
from catalyst_rl.dl import AlchemyRunner
| [
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] | 3.111111 | 18 |
# -*- coding: utf-8 -*-
"""Python interface to GnuCash documents"""
from . import metadata
__version__ = metadata.version
__author__ = metadata.authors[0]
__license__ = metadata.license
__copyright__ = metadata.copyright
from ._common import (
GncNoActiveSession,
GnucashException, GncValidationError, GncImbalanceError,
Recurrence
)
from .core import (
Book,
Account, ACCOUNT_TYPES, AccountType,
Transaction, Split, ScheduledTransaction, Lot,
Commodity, Price,
create_book, open_book,
factories,
)
from .business import Vendor, Customer, Employee, Address
from .business import Invoice, Job
from .business import Taxtable, TaxtableEntry
from .budget import Budget, BudgetAmount
from .kvp import slot
from .ledger import ledger
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11,
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62,
2070,
11,
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11,
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11,
17917,
198,
6738,
764,
22680,
1330,
10001,
2942,
11,
15768,
198,
6738,
764,
22680,
1330,
11940,
742,
540,
11,
11940,
742,
540,
30150,
198,
6738,
764,
37315,
1330,
15401,
11,
15401,
31264,
198,
6738,
764,
74,
36133,
1330,
10852,
198,
6738,
764,
992,
1362,
1330,
37208,
198
] | 3.139344 | 244 |
from datetime import datetime
import numpy as np
import time
from pyrealtime.layer import ThreadLayer, TransformMixin, ProducerMixin, EncoderMixin
| [
6738,
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 20 12:17:30 2021
@author: jorge
"""
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] | 2.276596 | 47 |
# -*- coding: utf-8 -*-
##########################################################################
# NSAp - Copyright (C) CEA, 2019
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
"""
The U-Net is a convolutional encoder-decoder neural network.
"""
# Imports
import ast
import collections
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as func
from pynet.utils import tensor2im
class UNet(nn.Module):
""" UNet.
The U-Net is a convolutional encoder-decoder neural network.
Contextual spatial information (from the decoding,
expansive pathway) about an input tensor is merged with
information representing the localization of details
(from the encoding, compressive pathway).
Modifications to the original paper:
- padding is used in 3x3x3 convolutions to prevent loss
of border pixels
- merging outputs does not require cropping due to (1)
- residual connections can be used by specifying
UNet(merge_mode='add')
- if non-parametric upsampling is used in the decoder
pathway (specified by upmode='upsample'), then an
additional 1x1x1 3d convolution occurs after upsampling
to reduce channel dimensionality by a factor of 2.
This channel halving happens with the convolution in
the tranpose convolution (specified by upmode='transpose')
"""
def __init__(self, num_classes, in_channels=1, depth=5,
start_filts=16, up_mode="transpose", down_mode="maxpool",
merge_mode="concat", batchnorm=False, dim="3d",
skip_connections=False, mode="seg", input_size=None,
nb_regressors=None, freeze_encoder=False):
""" Init class.
Parameters
----------
num_classes: int
the number of features in the output segmentation map.
in_channels: int, default 1
number of channels in the input tensor.
depth: int, default 5
number of layers in the U-Net.
start_filts: int, default 16
number of convolutional filters for the first conv.
up_mode: string, default 'transpose'
type of upconvolution. Choices: 'transpose' for transpose
convolution, 'upsample' for nearest neighbour upsampling
down_mode: string, default 'maxpool'
Choices: 'maxpool' for maxpool, 'conv' for convolutions with stride=2
merge_mode: str, default 'concat', can be 'add' or None
the skip connections merging strategy.
skip_connections: bool, whether we add skip connections between conv layers or not
batchnorm: bool, default False
normalize the inputs of the activation function.
mode: 'str', default 'seg'
Whether the network is turned in 'segmentation' mode ("seg") or 'classification' mode ("classif") or both
("seg_classif"). Only the encoder can be also used in mode ("encoder")
The input_size is required for classification
input_size: tuple (optional) it is required for classification only. It should be a tuple (C, H, W, D) (for 3d)
or (C, H, W) (for 2d)
dim: str, default '3d'
'3d' or '2d' input data.
"""
# Inheritance
super(UNet, self).__init__()
# Check inputs
if dim in ("2d", "3d"):
self.dim = dim
else:
raise ValueError(
"'{}' is not a valid mode for merging up and down paths. Only "
"'3d' and '2d' are allowed.".format(dim))
if mode in ("seg", "classif", "seg_classif", "encoder", "simCLR"):
self.mode = mode
else:
raise ValueError("'{}' is not a valid mode. Should be in 'seg' "
"or 'classif' mode.".format(mode))
if up_mode in ("transpose", "upsample"):
self.up_mode = up_mode
else:
raise ValueError(
"'{}' is not a valid mode for upsampling. Only 'transpose' "
"and 'upsample' are allowed.".format(up_mode))
if merge_mode in ("concat", "add", None):
self.merge_mode = merge_mode
else:
raise ValueError(
"'{}' is not a valid mode for merging up and down paths. Only "
"'concat' and 'add' are allowed.".format(up_mode))
if down_mode in ("maxpool", "conv"):
self.down_mode = down_mode
else:
raise ValueError(
"'{}' is not a valid mode for down sampling. Only 'maxpool' "
"and 'conv' are allowed".format(down_mode)
)
if self.up_mode == "upsample" and self.merge_mode == "add":
raise ValueError(
"up_mode 'upsample' is incompatible with merge_mode 'add' at "
"the moment because it doesn't make sense to use nearest "
"neighbour to reduce depth channels (by half).")
# Declare class parameters
self.num_classes = num_classes
self.in_channels = in_channels
self.start_filts = start_filts
self.input_size = input_size
self.nb_regressors = nb_regressors
self.depth = depth
self.down = []
self.up = [] # Useful in seg mode
self.classifier = None # Useful in classif mode
self.freeze_encoder = freeze_encoder
self.name = "UNet_D%i_%s" % (self.depth, self.mode)
# Create the encoder pathway
for cnt in range(depth):
in_channels = self.in_channels if cnt == 0 else out_channels
out_channels = self.start_filts * (2**cnt)
down_sampling = False if cnt == 0 else True
self.down.append(
Down(in_channels, out_channels, self.dim, down_mode=self.down_mode,
pooling=down_sampling, batchnorm=batchnorm,
skip_connections=skip_connections))
# Freeze all the layers if necessary
if self.freeze_encoder:
for down_m in self.down:
for param in down_m.parameters():
param.requires_grad = False
if self.mode == "seg" or self.mode == "seg_classif":
# Create the decoder pathway
# - careful! decoding only requires depth-1 blocks
for cnt in range(depth - 1):
in_channels = out_channels
out_channels = in_channels // 2
self.up.append(
Up(in_channels, out_channels, up_mode=up_mode, dim=self.dim,
merge_mode=merge_mode, batchnorm=batchnorm,
skip_connections=skip_connections))
if self.mode == "classif" or self.mode == "seg_classif":
self.classifier = Classifier(self.nb_regressors, features=self.start_filts * 2**(self.depth-1))
elif self.mode == 'simCLR':
self.hidden_representation = nn.Linear(self.start_filts * (2**(self.depth-1)), 512)
self.head_projection = nn.Linear(512, 128)
# Add the list of modules to current module
self.down = nn.ModuleList(self.down)
self.up = nn.ModuleList(self.up)
# Get ouptut segmentation
if self.mode == "seg" or self.mode == "seg_classif":
self.conv_final = Conv1x1x1(out_channels, self.num_classes, self.dim)
# Kernel initializer
# Weight initialization
self.weight_initializer()
class Down(nn.Module):
"""
A helper Module that performs 2 convolutions and 1 MaxPool.
A ReLU activation and optionally a BatchNorm follows each convolution.
"""
class Up(nn.Module):
"""
A helper Module that performs 2 convolutions and 1 UpConvolution.
A ReLU activation and optionally a BatchNorm follows each convolution.
"""
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317,
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347,
963,
35393,
5679,
1123,
3063,
2122,
13,
198,
220,
220,
220,
37227,
198
] | 2.327105 | 3,479 |
n = int(input())
F = [0]*(n+1)
F[0], F[1] = 0, 1
for i in range(2, n+1):
F[i] = F[i-1] + F[i-2]
print(F[n])
| [
77,
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7,
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9,
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] | 1.527027 | 74 |
from typing import List, Optional
import numpy as np
import torch
import torch.nn as nn
from gluonts.core.component import validated
from gluonts.dataset.field_names import FieldName
from gluonts.torch.util import copy_parameters
from gluonts.torch.model.predictor import PyTorchPredictor
from gluonts.model.predictor import Predictor
from gluonts.transform import (
InstanceSplitter,
ValidationSplitSampler,
TestSplitSampler,
Transformation,
Chain,
ExpectedNumInstanceSampler,
AddObservedValuesIndicator,
AsNumpyArray,
)
from pts.model import PyTorchEstimator
from pts import Trainer
from pts.model.utils import get_module_forward_input_names
from .lstnet_network import LSTNetTrain, LSTNetPredict
| [
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198,
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62,
27349,
1330,
406,
2257,
7934,
44077,
11,
406,
2257,
7934,
47,
17407,
628
] | 2.99187 | 246 |
import numpy as np
from scipy.io import loadmat
import os | [
11748,
299,
32152,
355,
45941,
198,
6738,
629,
541,
88,
13,
952,
1330,
3440,
6759,
198,
11748,
28686
] | 3.166667 | 18 |
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
import grpc
from google.ads.google_ads.v4.proto.resources import income_range_view_pb2 as google_dot_ads_dot_googleads__v4_dot_proto_dot_resources_dot_income__range__view__pb2
from google.ads.google_ads.v4.proto.services import income_range_view_service_pb2 as google_dot_ads_dot_googleads__v4_dot_proto_dot_services_dot_income__range__view__service__pb2
class IncomeRangeViewServiceStub(object):
"""Proto file describing the Income Range View service.
Service to manage income range views.
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.GetIncomeRangeView = channel.unary_unary(
'/google.ads.googleads.v4.services.IncomeRangeViewService/GetIncomeRangeView',
request_serializer=google_dot_ads_dot_googleads__v4_dot_proto_dot_services_dot_income__range__view__service__pb2.GetIncomeRangeViewRequest.SerializeToString,
response_deserializer=google_dot_ads_dot_googleads__v4_dot_proto_dot_resources_dot_income__range__view__pb2.IncomeRangeView.FromString,
)
class IncomeRangeViewServiceServicer(object):
"""Proto file describing the Income Range View service.
Service to manage income range views.
"""
def GetIncomeRangeView(self, request, context):
"""Returns the requested income range view in full detail.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| [
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8,
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12061,
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0,
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628
] | 2.947467 | 533 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
import wagtail.images.models
| [
2,
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69,
12,
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9,
12,
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834,
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] | 2.956522 | 46 |
'''
Make two dictionaries: phr2sg_id and sg_id2phr
phr2sg_id["nice work']==6152
phr2sg_id["nicely done']==6152
phr2sg_id["nice going']==6152
sg_id2phr[6152]=="Well done."
'''
import json, os
import operator
import pickle
from hparams import hp
import re
from tqdm import tqdm
if __name__ == "__main__":
print("Determine the most frequent Synonym Groups")
data = json.load(open(hp.sg))
sg_id2cnt = dict()
for sg_id, sg in tqdm(data.items()):
sg_id = int(sg_id)
phrs = sg["phrases"] # [['i am mormon', 1], ["i'm a mormon", 1]]
sg_cnt = 0 # total cnt
for phr, cnt in phrs:
if cnt >= hp.min_cnt:
sg_cnt += cnt
sg_id2cnt[sg_id] = sg_cnt
sg_id_cnt = sorted(sg_id2cnt.items(), key=operator.itemgetter(1), reverse=True)
sg_ids = [sg_id for sg_id, _ in sg_id_cnt][:hp.n_phrs]
print("Determine the group of phrases")
sg_id2phr = dict()
phr2sg_id, phr2cnt = dict(), dict()
for sg_id in tqdm(sg_ids):
sg = data[str(sg_id)]
phrs = sg["phrases"] # [['i am mormon', 1], ["i'm a mormon", 1]]
sg_id2phr[sg_id] = phrs[0][0]
for phr, cnt in phrs:
if cnt >= hp.min_cnt:
phr = refine(phr)
if phr in phr2cnt and cnt > phr2cnt[phr]: # overwrite
phr2cnt[phr] = cnt
phr2sg_id[phr] = sg_id
else:
phr2cnt[phr] = cnt
phr2sg_id[phr] = sg_id
print("save")
os.makedirs(os.path.dirname(hp.phr2sg_id), exist_ok=True)
os.makedirs(os.path.dirname(hp.sg_id2phr), exist_ok=True)
pickle.dump(phr2sg_id, open(hp.phr2sg_id, 'wb'))
pickle.dump(sg_id2phr, open(hp.sg_id2phr, 'wb')) | [
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] | 1.809721 | 967 |
"""Tareas de Celery."""
# Django
from django.conf import settings
from django.template.loader import render_to_string
from django.utils import timezone
from django.core.mail import EmailMultiAlternatives
# Utilities
import jwt
from datetime import timedelta
# Celery
from celery.decorators import task, periodic_task
# Models
from cride.users.models import User
from cride.rides.models import Ride
def gen_verification_token(user):
"""Crea un token JWT que el usuario pueda usar para verificar su cuenta"""
# El self se utiliza para que la funcion pueda usar los atributos de la clase.
exp_date = timezone.now()+timedelta(days=3)
payload = {
'user': user.username,
'exp': int(exp_date.timestamp()),
'type': 'email_confirmation' # Creamos una variable que especifique de que es el token, se lo usa
# cuando tu proyecto genera mas JWT en otras aplicaciones y no queremos que se confundan.
}
token = jwt.encode(payload, settings.SECRET_KEY, algorithm='HS256')
return token.decode() # regresamos el token en cadena
@task(name='send_confirmation_email', max_retries=3) # Especificamos que estas son tareas de celery.
# Este decorator recibira el nombre de la tarea, y la otra es el maximo numero de veces que intentara
# ejecutar la tarea en caso de que ocurra errores
def send_confirmation_email(user_pk): # Quitamos self del metodo por que ya no estan dentro de una clase,
# Cuando usamos celery en funciones es recomendado no enviar datos complejos como instancias de clases.
# Es mejor usar solo datos nativos como enteros, strings,etc.
"""Envia un enlace de verificación de cuenta a usuario dado
Enviando un email al usuario para verificar la cuenta
"""
user = User.objects.get(pk=user_pk) # Obtenemos el usuario por su pk
verification_token = gen_verification_token(user)
subject = 'Bienvenido @{}! Verifica tu cuenta para empezar a usar Comparte-Ride'.format(user.username)
from_email = 'Comparte Ride <[email protected]>'
content = render_to_string(
'emails/users/account_verification.html',
{'token': verification_token, 'user': user}
) # Esta variable se usara en caso de que el usario no pueda interpretar el contenido html que se le
# envio, # El metodo render_to_string(), ayuda a no tener otra variable en caso de que no funcione el html
# html_content = '<p>This is an <strong>important</strong> message.</p>' # Esta variable era del
# contenido con html pero con la otra variable matamos 2 pajaros de un tiro.
msg = EmailMultiAlternatives(
subject,
content,
from_email,
[user.email] # Lista de direcciones de correos a enviar
) # El EmailMultiAlternative se utiliza para enviar emails que contengan un contenido de html,
msg.attach_alternative(
content, # En esta variable agregas la variable con el html pero enviamos content, que posee los 2.
"text/html")
msg.send()
# Usaremos los JWT para enviar la informacion del usuario sin necesidad de guardarlo en la base de datos.
@periodic_task(name='disable_finished_rides', run_every=timedelta(minutes=5))
# Esta tarea sera llamada cada 5 segundos
def disable_finished_rides():
"""Desactiva viajes terminados.
Este metodo servira para desactivar los rides una vez que termine
su hora de llegada, esto sera como un soporte para cuando el creador
del viaje se olvide desactivar el viaje.
"""
now = timezone.now()
offset = now + timedelta(seconds=5)
# Actualiza los paseos que ya han terminado // now <= arrival_date <= offset
rides = Ride.objects.filter(arrival_date__gte=now, is_active=True, arrival_date__lte=offset)
rides.update(is_active=False)
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] | 2.545101 | 1,541 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Balanced Brackets
#
# Write a function that accepts a string consisting entiring of brackets
# (`[](){}`) and returns whether it is balanced. Every "opening" bracket must
# be followed by a closing bracket of the same type. There can also be nested
# brackets, which adhere to the same rule.
#
# ```js
# f('()[]{}(([])){[()][]}') // true
# f('())[]{}') // false
# f('[(])') // false
# ```
pairs = {
"(": ")",
"[": "]",
"{": "}"
}
| [
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] | 2.647368 | 190 |
from django import forms
from django.conf import settings
from django.core import validators
from django.db import models
from django.utils import six, timezone
from django.utils.encoding import force_text
from django.utils.translation import ugettext_lazy as _
import django
from .base import aes_pad_key, armor, dearmor, pad, unpad
import datetime
import decimal
if django.VERSION >= (1, 7):
from django.db.models.lookups import Lookup
for lookup_name in ('exact', 'gt', 'gte', 'lt', 'lte'):
class_name = 'EncryptedLookup_%s' % lookup_name
lookup_class = type(class_name, (EncryptedLookup,), {'lookup_name': lookup_name})
BaseEncryptedField.register_lookup(lookup_class)
| [
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] | 2.853175 | 252 |
import os
import click
from .constants import VALID_CONFIG_DIR_FILE_EXTENSIONS
from .utils import get_config_dir
@click.command(
name="ls",
short_help="List configured sessions in :meth:`tmuxp.cli.utils.get_config_dir`.",
)
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] | 2.744186 | 86 |
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^$', views.examplePlugin, name='examplePlugin'),
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# -*- coding: utf-8 -*-
"""
Test resemblance of HM and RS trees
Created on Sun Jun 14 00:33:52 2015
@author: hoseung
"""
# Load HM
from astropy.io import fits
from astropy.table import Table
import tree
wdir = '/home/hoseung/Work/data/AGN2/'
data = fits.getdata(wdir + "halo/TMtree.fits", 1)
hmt = Table(data)
#%%
idgals = [5232, 5495, 5543, 6061, 5491, 6191]
for idgal in idgals:
prg_treex, prg_tree = tree.tmtree.get_main_prg(hmt, idgal, nout_ini=122, nout_fi=0)
print(prg_treex)
#%%
# Load RS
# 3D plot
tree.treeplots(hmt, thisgal, save=save_dir)
#%%
all_final_halo = tru.final_halo_list(hmt)
## ID list
#%%
# mass-produce plots of halo properties.
quantities=["sam_mvir","mvir","rvir","rs","vrms","vmax"
,"jx","jy","jz","spin","m200b","m200c","m500c","m2500c"
,"xoff","voff","btoc","ctoa","ax","ay","az"]
normalizer=np.array([1e-11,1e-11,1,1,1,1,1,1,1e-11
,1,1e-11,1e-11,1e-11,1e-11,1,1,1,1,1,1,1])
for i, hal in enumerate(all_final_halo[0:10]):
print(i, hal)
tree = tru.get_main_prg_tree(hmt, hal)
fn_save = str(hal) + 'halo_all.pdf'
# trp.plot_all(tree, hal, save=True, out_dir=work_dir, fn_save=fn_save,
# nrows=4, ncols=math.ceil(len(quantities)/4),
# quantities=quantities, normalizer=normalizer)
# trp.plot_all_multiPDF(tree, hal, out_dir=work_dir + 'RS_trees/', fn_save=fn_save,
# nrows=2, ncols=2,
# quantities=quantities, normalizer=normalizer)
trp.trajectory3D(tree, hal, save=work_dir + 'RS_trees/') | [
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] | 2.013038 | 767 |
from builtins import setattr, getattr
from fractions import Fraction
import math
from multiprocessing import Process, Manager
from warnings import warn, showwarning
try:
from sympy import *
import numpy as np
from scipy.stats import norm, beta, gamma, expon
from scipy.linalg import pascal
from scipy.integrate import quad
from sympy import (
symbols, zeros, integrate, N, factorial, sqrt, simplify, sympify, Abs
)
from sympy.core.numbers import NaN
from sympy.integrals.integrals import Integral
from sympy.parsing.sympy_parser import parse_expr
from sympy.solvers import solve
from sympy.utilities.lambdify import lambdify
from mpi4py import MPI
from mpi4py.MPI import (
COMM_WORLD as MPI_COMM_WORLD, DOUBLE as MPI_DOUBLE, MAX as MPI_MAX
)
except:
warn('Ensure that all required packages are installed.')
exit()
from PCE_Codes.custom_enums import Distribution, UncertaintyType
from PCE_Codes._helpers import _warn, uniform_hypercube
from PCE_Codes.variables.variable import Variable
from PCE_Codes.error import VariableInputError
class ContinuousVariable(Variable):
"""
Inputs: pdf- the equation that defines the pdf of the variable values
interval_low- the low interval of the variable
interval_high- the high interval of the variable
order- the order of the model to calculate the orthogonal
polynomials and norm squared values
type- the type of variable
name- the name of the variable
number- the number of the variable from the file
Class represents a continuous variable.
"""
def get_probability_density_func(self):
"""
Turns the input function into the corresponding probability density
function.
"""
diff_tol = 1e-5
tol = 1e-12
f = lambdify(self.x, self.distribution, ('numpy', 'sympy'))
const = quad(f, self.low_approx, self.high_approx, epsabs=tol, epsrel=tol)[0]
const_rnd = np.round(const)
if np.abs(const_rnd - const) < diff_tol:
const = const_rnd
self.distribution = self.distribution / const
def standardize(self, orig, std_vals):
"""
Inputs: orig- the un-standardized values
std_vals- the attribue name for the standardized vals
For each variable, it adds a new attribute for the standardized values
from the original input values.
"""
setattr(self, std_vals, getattr(self, orig))
return getattr(self, std_vals)
def standardize_points(self, values):
"""
Inputs: values- unstandardized points corresponding to the variable's
distribution
Standardizes and returns the inputs points.
"""
return values # general variable must already be standardized
def unstandardize_points(self, value):
"""
Inputs: value- the standardized value to be unstandardized
Calculates and returns the unscaled variable value from the
standardized value.
"""
return value # general variable must already be standardized
def check_distribution(self):
"""
Checks all values in an array to ensure that they are standardized.
"""
comm = MPI_COMM_WORLD
rank = comm.rank
mx = np.max(self.std_vals)
mn = np.min(self.std_vals)
if rank == 0 and mx > self.high_approx or mn < self.low_approx:
warn(
f'Large standardized value for variable {self.name} '
'with user distribution found. Check input and run matrix.'
)
return -1
def generate_samples(self, samp_size):
"""
Inputs: samp_size- the number of points needed to be generated
Generates points according to the Latin hypercube; each point is in an
interval of equal probability.
"""
decimals = 30
comm = MPI_COMM_WORLD
size = comm.size
rank = comm.rank
is_manager = (rank == 0)
base = samp_size // size
rem = samp_size % size
count = base + (rank < rem)
ranks = np.arange(0, size, dtype=int)
seq_count = (ranks < rem) + base
seq_disp = base * ranks + (ranks >= rem) * rem + (ranks < rem) * ranks
self.inverse_func = None
self.failed = None
try:
y = symbols('y')
if self.failed != None:
raise AttributeError # skip if has already gone through and failed
# solve for the cumulative density function with 10s timeout
if is_manager and not hasattr(self, 'cum_dens_func'):
manager = Manager()
proc_dict = manager.dict()
cdf_proc = Process(target=self._calc_cdf, args=(proc_dict,))
cdf_proc.start()
cdf_proc.join(10.0)
if cdf_proc.is_alive():
cdf_proc.terminate()
try:
self.cum_dens_func = proc_dict['cum_dens_func']
# solve for the inverse function with 10s timeout
inv_proc = Process(target=self._invert, args=(proc_dict,))
inv_proc.start()
inv_proc.join(10.0)
if inv_proc.is_alive():
inv_proc.terminate()
self.inverse_func = proc_dict['inverse_func']
except KeyError:
self.failed = 1
self.failed = comm.bcast(self.failed, root=0)
if not self.failed:
self.inverse_func = comm.bcast(self.inverse_func, root=0)
else:
raise ValueError
# plug in random uniform 0 -> 1 to solve for x vals
all_samples = np.zeros(samp_size)
for i in range(len(self.inverse_func)): # multiple solutions
inv_func = (
np.vectorize(
lambdify(y, str(self.inverse_func[i]), ('numpy', 'sympy'))
)
)
samples = N(inv_func(uniform_hypercube(0, 1, count)), decimals)
comm.Allgatherv(
[samples, count, MPI_DOUBLE],
[all_samples, seq_count, seq_disp, MPI_DOUBLE]
)
if np.min(all_samples) >= self.low_approx and np.max(all_samples) <= self.high_approx:
np.random.shuffle(all_samples)
return all_samples
if not (
(np.min(samples) >= self.low_approx) and (np.max(samples) <= self.high_approx)
):
raise ValueError
# if cdf or inverse func can't be found, use rejection-acceptance sampling
except (ValueError, NameError, AttributeError):
func = lambdify(self.x, self.distribution, ('numpy', 'sympy'))
try_total = 5000
tries = try_total // size + (rank < try_total % size)
max_all = np.zeros(1)
try:
max_val = (
np.max(func(
np.random.uniform(
self.low_approx, self.high_approx, tries
)
))
)
except RuntimeError:
max_val = np.max(
func(
np.random.uniform(
self.low_approx, self.high_approx, tries
)
)
).astype(float)
comm.Allreduce(
[max_val, MPI_DOUBLE], [max_all, MPI_DOUBLE], op=MPI_MAX
)
samples = np.zeros(count)
all_samples = np.zeros(samp_size)
i = 0
j = 0
y_vals = np.random.uniform(0, max_all, count)
x_vals = np.random.uniform(self.low_approx, self.high_approx, count)
func_vals = func(x_vals)
# while loop until all 'samp_size' samples have been generated
while i < count:
if j == count:
y_vals = np.random.uniform(0, max_all, count)
x_vals = np.random.uniform(self.low_approx, self.high_approx, count)
func_vals = func(x_vals)
j = 0
if y_vals[j] <= func_vals[j]:
samples[i] = x_vals[j]
i += 1
j += 1
comm.Allgatherv(
[samples, count, MPI_DOUBLE],
[all_samples, seq_count, seq_disp, MPI_DOUBLE]
)
np.random.shuffle(all_samples)
return all_samples
def create_norm_sq(self, low, high, func):
"""
Inputs: low- the low interval bound for the distribution
high- the high interval bound for the distribution
func- the function corresponding to the distribution
Calculates the norm squared values up to the order of polynomial
expansion based on the probability density function and its
corresponding orthogonal polynomials.
"""
orthopoly_count = len(self.var_orthopoly_vect)
self.norm_sq_vals = np.zeros(orthopoly_count)
tries = 2
zero = 0
# is rounded off at 50 decimals, requiring two decimals places
norm_sq_thresh = 1e-49
for i in range(orthopoly_count):
proc_dict = {}
for j in range(tries):
self._norm_sq(low, high, func, i, j, proc_dict)
try:
if (proc_dict['out'] is not None) and (not math.isclose(proc_dict['out'], zero)):
self.norm_sq_vals[i] = proc_dict['out']
break # only breaks inner loop
except KeyError:
pass
if (self.norm_sq_vals == zero).any():
warn(f'Finding the norm squared for variable {self.name} failed.')
if (self.norm_sq_vals <= norm_sq_thresh).any():
warn(
f'At least one norm squared value for variable {self.name} is '
f'very small. This can introduce error into the model.'
)
def _norm_sq(self, low, high, func, i, region, proc_dict):
"""
Inputs: low- the low interval bound for the distribution
high- the high interval bound for the distribution
func- the function corresponding to the distribution
i- the index of the norm squared to calculate
region- which sympy calculation to try for the norm squared
proc_dict- the dictionary in which the output will be stored
An assistant to create_norm_sq; allows the norm squared calculations to
have a timeout if an error isn't raised and the solution isn't found
reasonably quickly.
"""
proc_dict['out'] = None
# round 0.99999999 to 1 to reduce error; if value is small, don't round
thresh = 1e-2
tol = 1e-12
diff_tol = 1e-8
decimals = 30
if high == 'oo':
ul = np.inf
elif high == 'pi':
ul = np.pi
elif high == '-pi':
ul = -np.pi
else:
ul = high
if low == '-oo':
ll = -np.inf
elif low == 'pi':
ll = np.pi
elif low == '-pi':
ll = -np.pi
else:
ll = low
if region == 0:
try:
f = lambdify(self.x, func * self.var_orthopoly_vect[i] ** 2, ('numpy', 'sympy'))
ans = quad(f, ll, ul, epsabs=tol, epsrel=tol)[0]
if np.abs(int(ans) - ans) < diff_tol:
proc_dict['out'] = int(ans)
elif ans > thresh:
proc_dict['out'] = round(ans, 7)
else:
proc_dict['out'] = ans
except:
pass
elif region == 1:
try:
f = lambdify(
self.x,
N(func * self.var_orthopoly_vect[i] ** 2, decimals),
('numpy', 'sympy')
)
ans = quad(f, ll, ul, epsabs=tol, epsrel=tol)[0]
if np.abs(int(ans) - ans) < diff_tol:
proc_dict['out'] = int(ans)
elif ans > thresh:
proc_dict['out'] = round(ans, 7)
else:
proc_dict['out'] = ans
except:
pass
elif region == 2:
try:
f = lambdify(
self.x,
sympify(f'{func} * ({self.var_orthopoly_vect[i]}) ** 2'),
('numpy', 'sympy')
)
ans = quad(f, ll, ul, epsabs=tol, epsrel=tol)[0]
if np.abs(int(ans) - ans) < diff_tol:
proc_dict['out'] = int(ans)
elif ans > thresh:
proc_dict['out'] = round(ans, 7)
else:
proc_dict['out'] = ans
except:
pass
def recursive_var_basis(self, func, low, high, order):
"""
Inputs: func- the probability density function of the input equation
low- the low bound on the variable
high- the high bound on the variable
order- the order of polynomial expansion
Recursively calculates the variable basis up to the input 'order'.
"""
tol = 1e-12
if low == '-oo':
low = -np.inf
if high == 'oo':
high = np.inf
if order == 0:
self.poly_denom = np.zeros(self.order, dtype=object)
self.var_orthopoly_vect = np.zeros(self.order + 1, dtype=object)
self.var_orthopoly_vect[order] = 1
return
else:
self.recursive_var_basis(func, low, high, order - 1)
curr = self.x ** order
for i in range(order):
orthopoly = self.var_orthopoly_vect[i]
if self.poly_denom[i] == 0:
f = lambdify(self.x, orthopoly ** 2 * func, ('numpy', 'sympy'))
self.poly_denom[i] = quad(f, low, high, epsabs=tol, epsrel=tol)[0]
f = lambdify(self.x, self.x ** order * orthopoly * func, ('numpy', 'sympy'))
intergal_eval = (
quad(f, low, high, epsabs=tol, epsrel=tol)[0]
/ self.poly_denom[i]
) * orthopoly
curr -= intergal_eval
self.var_orthopoly_vect[order] = curr
if order == self.order and (self.var_orthopoly_vect == 0).any():
warn(
f'Variable {self.name} has at least one orthogonal polynomial '
f'that is zero. The model may not be accurate'
)
return
def get_resamp_vals(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Generates samp_size number of samples according to the pdf of the
Variable.
"""
self.resample = self.generate_samples(samp_size)
return self.resample
def _calc_cdf(self, proc_dict):
"""
Inputs: proc_dict- the dictionary in which the output will be stored
Calculates the cumulative density function of the distribution.
"""
try:
proc_dict['cum_dens_func'] = integrate(
self.distribution, (self.x, self.interval_low, self.x)
)
except RuntimeError:
pass
def _invert(self, proc_dict):
"""
Inputs: proc_dict- the dictionary in which the output will be stored
Solves for the inverse function of the cumulative density function.
"""
y = symbols('y')
try:
proc_dict['inverse_func'] = solve(f'{self.cum_dens_func}-y', self.x)
except (NameError, NotImplementedError, AttributeError, RuntimeError):
pass
def check_num_string(self):
"""
Checks for values in the input file that correspond to pi, -oo, or oo.
If these values exist, they are converted into values that Python can
use to create resampling points.
"""
decimals = 30
if self.interval_low == '-oo' or self.interval_high == 'oo':
x = self.x
integrate_tuple = (x, self.interval_low, self.interval_high)
self.mean = integrate(x * self.distribution, integrate_tuple)
stdev = (
math.sqrt(
integrate(x ** 2 * self.distribution, integrate_tuple)
-self.mean ** 2
)
)
if isinstance(self.interval_low, str):
if 'pi' in self.interval_low:
temp_low = float(self.interval_low.replace('pi', str(np.pi)))
self.interval_low = temp_low
self.low_approx = temp_low
elif self.interval_low == '-oo':
self.low_approx = N(self.mean - 10 * stdev, decimals)
if isinstance(self.interval_high, str):
if 'pi' in self.interval_high:
temp_high = float(self.interval_high.replace('pi', str(np.pi)))
self.interval_high = temp_high
self.high_approx = temp_high
elif self.interval_high == 'oo':
self.high_approx = N(self.mean + 10 * stdev, decimals)
def get_mean(self):
"""
Return the mean of the variable.
"""
decimals = 30
if not hasattr(self, 'mean'):
x = self.x
integrate_tuple = (x, self.interval_low, self.interval_high)
self.mean = integrate(x * self.distribution, integrate_tuple)
return N(self.mean, decimals)
class UniformVariable(ContinuousVariable):
"""
Inputs: interval_low- the low interval of the variable
interval_high- the high interval of the variable
order- the order of the model to calculate the orthogonal
polynomials and norm squared values
type- the type of variable
name- the name of the variable
number- the number of the variable from the file
Represents a uniform variable. The methods in this class correspond to
those of a uniform variable.
"""
def generate_orthopoly(self):
"""
Generates the orthogonal polynomials for a uniform variable up to the
order of polynomial expansion.
"""
self.var_orthopoly_vect = np.zeros(self.order + 1, dtype=object)
x = self.x
for n in range(self.order + 1):
if n == 0:
self.var_orthopoly_vect[n] = 1
elif n == 1:
self.var_orthopoly_vect[n] = x
else:
self.var_orthopoly_vect[n] = (
(
(2 * n - 1) * x
* self.var_orthopoly_vect[n - 1] - (n - 1)
* self.var_orthopoly_vect[n - 2]
)
/ n
)
def standardize(self, orig, std_vals):
"""
Inputs: orig- the un-standardized values
std_vals- the attribue name for the standardized vals
Overrides the Variable class standardize to align with
a uniform distribution.
"""
original = getattr(self, orig)
mean = (
(self.interval_high - self.interval_low) / 2 + self.interval_low
)
stdev = (self.interval_high - self.interval_low) / 2
standard = (original[:] - mean) / stdev
setattr(self, std_vals, standard)
return getattr(self, std_vals)
def standardize_points(self, values):
"""
Inputs: values- unstandardized points corresponding to the variable's
distribution
Standardizes and returns the inputs points.
"""
mean = (
(self.interval_high - self.interval_low) / 2 + self.interval_low
)
stdev = (self.interval_high - self.interval_low) / 2
return (values - mean) / stdev
def unstandardize_points(self, value):
"""
Inputs: value- the standardized value to be unstandardized
Calculates and returns the unscaled variable value from the
standardized value.
"""
mean = (
(self.interval_high - self.interval_low) / 2 + self.interval_low
)
stdev = (self.interval_high - self.interval_low) / 2
return (value * stdev) + mean
def check_distribution(self):
"""
Overrides the Variable class check_distribution to align with
a uniform distribution.
"""
if (
(np.max(self.std_vals) > 1 + 1e-5)
or (np.min(self.std_vals) < -1 - 1e-5)
):
warn(
f'Standardized value for variable {self.name} with uniform '
'distribution outside expected [-1, 1] bounds'
)
return -1
def generate_samples(self, samp_size):
"""
Inputs: samp_size- the number of points needed to be generated
Overrides the Variable class generate_samples to align with
a uniform distribution.
"""
vals = (
uniform_hypercube(self.interval_low, self.interval_high, samp_size)
)
return vals
def get_norm_sq_val(self, matrix_val):
"""
Inputs: matrix_val- the value in the model matrix to consider
Overrides the Variable class get_norm_sq_val to align with
a uniform distribution.
"""
return 1.0 / (2.0 * matrix_val + 1.0)
def get_resamp_vals(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Overrides the Variable class get_resamp_vals to align with
a uniform distribution.
"""
comm = MPI_COMM_WORLD
size = comm.size
rank = comm.rank
base = samp_size // size
rem = samp_size % size
count = base + (rank < rem)
ranks = np.arange(0, size, dtype=int)
seq_count = (ranks < rem) + base
seq_disp = base * ranks + (ranks >= rem) * rem + (ranks < rem) * ranks
self.resample = np.zeros(samp_size)
resample = np.random.uniform(-1, 1, count)
comm.Allgatherv(
[resample, count, MPI_DOUBLE],
[self.resample, seq_count, seq_disp, MPI_DOUBLE]
)
# The bound is included to help with ProbabilityBox convergence.
self.resample[0] = -1
self.resample[1] = 1
return self.resample
def check_num_string(self):
"""
Searches to replace sring 'pi' with its numpy equivalent in any of the
values that might contain it.
"""
if isinstance(self.interval_low, str) and 'pi' in self.interval_low:
self.interval_low = float(self.interval_low.replace('pi', str(np.pi)))
if isinstance(self.interval_high, str) and 'pi' in self.interval_high:
self.interval_high = float(self.interval_high.replace('pi', str(np.pi)))
def get_mean(self):
"""
Return the mean of the variable.
"""
return (self.interval_high - self.interval_low) / 2 + self.interval_low
class NormalVariable(ContinuousVariable):
"""
Inputs: mean- the mean of the variable
stdev- the standard deviation of the variable
order- the order of the model to calculate the orthogonal
polynomials and norm squared values
type- the type of variable
name- the name of the variable
number- the number of the variable from the file
Represents a normal variable. The methods in this class correspond to
those of a normal variable.
"""
__slots__ = ('mean', 'stdev')
def generate_orthopoly(self):
"""
Generates the orthogonal polynomials for a normal variable up to the
order of polynomial expansion.
"""
self.var_orthopoly_vect = zeros(self.order + 1, 1)
x = self.x
for n in range(self.order + 1):
if n == 0:
self.var_orthopoly_vect[n] = 1
elif n == 1:
self.var_orthopoly_vect[n] = 2 * x
else:
self.var_orthopoly_vect[n] = (
2 * x * self.var_orthopoly_vect[n - 1] - 2 * (n - 1)
* self.var_orthopoly_vect[n - 2]
)
for n in range(self.order + 1): # transform into probabalists Hermite poly
self.var_orthopoly_vect[n] = (
2 ** (-n / 2)
* self.var_orthopoly_vect[n].subs({x:x / math.sqrt(2)})
)
self.var_orthopoly_vect = np.array(self.var_orthopoly_vect).astype(object).T[0]
def standardize(self, orig, std_vals):
"""
Inputs: orig- the un-standardized values
std_vals- the attribue name for the standardized vals
Overrides the Variable class standardize to align with
a normal distribution.
"""
original = getattr(self, orig)
standard = (original[:] - self.mean) / (self.stdev)
setattr(self, std_vals, standard)
return getattr(self, std_vals)
def standardize_points(self, values):
"""
Inputs: values- unstandardized points corresponding to the variable's
distribution
Standardizes and returns the inputs points.
"""
return (values - self.mean) / (self.stdev)
def unstandardize_points(self, value):
"""
Inputs: value- the standardized value to be unstandardized
Calculates and returns the unscaled variable value from the
standardized value.
"""
return (value * self.stdev) + self.mean
def check_distribution(self):
"""
Overrides the Variable class check_distribution to align with
a normal distribution.
"""
comm = MPI_COMM_WORLD
rank = comm.rank
if rank == 0 and (np.max(self.std_vals) > 4.5) or (np.min(self.std_vals) < -4.5):
warn(
f'Large standardized value for variable {self.name} '
'with normal distribution found. Check input and run matrix.'
)
return -1
def generate_samples(self, samp_size):
"""
Inputs: samp_size- the number of points needed to be generated
Overrides the Variable class generate_samples to align with
a normal distribution.
"""
low_percent = 8e-17
high_percent = 1 - low_percent
dist = norm(loc=self.mean, scale=self.stdev)
rnd_hypercube = uniform_hypercube(low_percent, high_percent, samp_size)
vals = dist.ppf(rnd_hypercube)
return vals
def get_norm_sq_val(self, matrix_value):
"""
Inputs: matrix_val- the value in the model matrix to consider
Overrides the Variable class get_norm_sq_val to align with
a normal distribution.
"""
return math.factorial(matrix_value)
def get_resamp_vals(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Overrides the Variable class get_resamp_vals to align with
a normal distribution.
"""
comm = MPI_COMM_WORLD
size = comm.size
rank = comm.rank
base = samp_size // size
rem = samp_size % size
count = base + (rank < rem)
ranks = np.arange(0, size, dtype=int)
seq_count = (ranks < rem) + base
seq_disp = base * ranks + (ranks >= rem) * rem + (ranks < rem) * ranks
self.resample = np.zeros(samp_size)
resample = np.random.randn(count)
comm.Allgatherv(
[resample, count, MPI_DOUBLE],
[self.resample, seq_count, seq_disp, MPI_DOUBLE]
)
return self.resample
def check_num_string(self):
"""
Searches to replace sring 'pi' with its numpy equivalent in any of the
values that might contain it.
"""
if isinstance(self.mean, str) and 'pi' in self.mean:
self.mean = float(self.mean.replace('pi', str(np.pi)))
if isinstance(self.stdev, str) and 'pi' in self.stdev:
self.stdev = float(self.stdev.replace('pi', str(np.pi)))
def get_mean(self):
"""
Return the mean of the variable.
"""
return self.mean
class BetaVariable(ContinuousVariable):
"""
Inputs: alpha- the alpha parameter of the variable
beta- the beta parameter of the variable
interval_low- the low interval of the variable
interval_high- the high interval of the variable
order- the order of the model to calculate the orthogonal
polynomials and norm squared values
type- the type of variable
name- the name of the variable
number- the number of the variable from the file
Represents a beta variable. The methods in this class correspond to
those of a beta variable.
"""
__slots__ = ('alpha', 'beta')
equation = '((A+B-1)! * (x)**(A-1) * (1-x)**(B-1)) / ((A-1)! * (B-1)!)'
def generate_orthopoly(self):
"""
Generates the orthogonal polynomials for a beta variable up to the
self.self.order of polynomial expansion.
"""
var_orthopoly_vect = np.zeros(self.order + 1, dtype=object)
self.var_orthopoly_vect = np.zeros(self.order + 1, dtype=object)
x = self.x
a = parse_expr(str(Fraction(self.alpha)))
b = parse_expr(str(Fraction(self.beta)))
decimals = 30
for n in range(self.order + 1):
if n == 0:
var_orthopoly_vect[n] = 1
self.var_orthopoly_vect[n] = 1
elif n == 1:
var_orthopoly_vect[n] = x - (a / (a + b))
self.var_orthopoly_vect[n] = x - (a / (a + b))
else:
var_orthopoly_vect[n] = x ** n
pasc = pascal(self.order + 1, kind='lower')
for m in range(n):
var_orthopoly_vect[n] -= parse_expr(
f'{pasc[n, m]} * ((a+n-1)!*(a+b+2*m-1)!)/((a+m-1)!*(a+b+n+m-1)!)*({var_orthopoly_vect[m]})',
local_dict={'a':a, 'b':b, 'n':n, 'm':m, 'x':x}
)
self.var_orthopoly_vect[n] = N(var_orthopoly_vect[n], decimals)
return self.var_orthopoly_vect
def standardize(self, orig, std_vals):
"""
Inputs: orig- the un-standardized values
std_vals- the attribue name for the standardized vals
Overrides the Variable class standardize to align with
a beta distribution.
"""
original = getattr(self, orig)
standard = (
(original[:] - self.interval_low)
/ (self.interval_high - self.interval_low)
)
setattr(self, std_vals, standard)
return getattr(self, std_vals)
def standardize_points(self, values):
"""
Inputs: values- unstandardized points corresponding to the variable's
distribution
Standardizes and returns the inputs points.
"""
standard = (
(values - self.interval_low)
/ (self.interval_high - self.interval_low)
)
return standard
def unstandardize_points(self, value):
"""
Inputs: value- the standardized value to be unstandardized
Calculates and returns the unscaled variable value from the
standardized value.
"""
unscaled_value = value = (
value * (self.interval_high - self.interval_low)
+self.interval_low
)
return unscaled_value
def check_distribution(self):
"""
Overrides the Variable class check_distribution to align with
an beta distribution.
"""
shift = 8
comm = MPI_COMM_WORLD
rank = comm.rank
if rank == 0 and (np.max(self.std_vals) > shift) or (np.min(self.std_vals) < -shift):
warn(
f'Large standardized value for variable {self.name} '
'with Beta distribution found. Check input and run matrix.'
)
return -1
def generate_samples(self, samp_size):
"""
Inputs: samp_size- the number of points needed to be generated
Overrides the Variable class generate_samples to align with
an beta distribution.
"""
low_percent = 0
high_percent = 1
dist = beta(a=self.alpha, b=self.beta)
rnd_hypercube = uniform_hypercube(low_percent, high_percent, samp_size)
vals = (
(dist.ppf(rnd_hypercube) * (self.interval_high - self.interval_low))
+self.interval_low
)
return vals
def get_resamp_vals(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Overrides the Variable class get_resamp_vals to align with
an beta distribution.
"""
comm = MPI_COMM_WORLD
size = comm.size
rank = comm.rank
base = samp_size // size
rem = samp_size % size
count = base + (rank < rem)
ranks = np.arange(0, size, dtype=int)
seq_count = (ranks < rem) + base
seq_disp = base * ranks + (ranks >= rem) * rem + (ranks < rem) * ranks
self.resample = np.zeros(samp_size)
resample = np.random.beta(a=self.alpha, b=self.beta, size=count)
comm.Allgatherv(
[resample, count, MPI_DOUBLE],
[self.resample, seq_count, seq_disp, MPI_DOUBLE]
)
# The bound is included to help with ProbabilityBox convergence.
self.resample[0] = 0
self.resample[1] = 1
return self.resample
def check_num_string(self):
"""
Searches to replace sring 'pi' with its numpy equivalent in any of the
values that might contain it.
"""
if isinstance(self.alpha, str) and 'pi' in self.alpha:
self.alpha = float(self.alpha.replace('pi', str(np.pi)))
if isinstance(self.beta, str) and 'pi' in self.beta:
self.beta = float(self.beta.replace('pi', str(np.pi)))
if isinstance(self.interval_low, str) and 'pi' in self.interval_low:
self.interval_low = float(self.interval_low.replace('pi', str(np.pi)))
if isinstance(self.interval_high, str) and 'pi' in self.interval_high:
self.interval_high = float(self.interval_high.replace('pi', str(np.pi)))
def get_mean(self):
"""
Return the mean of the variable.
"""
scale = self.interval_high - self.interval_low
mean = (
self.interval_low + scale * (self.alpha / (self.alpha + self.beta))
)
return mean
class ExponentialVariable(ContinuousVariable):
"""
Inputs: lambd- the lambda parameter of the variable values
interval_low- the low interval of the variable
order- the order of the model to calculate the orthogonal
polynomials and norm squared values
type- the type of variable
name- the name of the variable
number- the number of the variable from the file
Represents an exponential variable. The methods in this class correspond to
those of an exponential variable.
"""
__slots__ = ('lambda')
equation = 'lambd * exp(-lambd * x)'
def standardize(self, orig, std_vals):
"""
Inputs: orig- the un-standardized values
std_vals- the attribue name for the standardized vals
Overrides the Variable class standardize to align with an exponential distribution.
"""
original = getattr(self, orig)
standard = (original[:] - self.interval_low)
setattr(self, std_vals, standard)
return getattr(self, std_vals)
def standardize_points(self, values):
"""
Inputs: values- unstandardized points corresponding to the variable's
distribution
Standardizes and returns the inputs points.
"""
return values - self.interval_low
def unstandardize_points(self, value):
"""
Inputs: value- the standardized value to be unstandardized
Calculates and returns the unscaled variable value from the
standardized value.
"""
return value + self.interval_low
def check_distribution(self):
"""
Overrides the Variable class check_distribution to align with
an exponential distribution.
"""
shift = 15
comm = MPI_COMM_WORLD
rank = comm.rank
if rank == 0 and ((np.min(self.std_vals) < 0)
or (np.max(self.std_vals) > shift)
):
warn(
f'Large standardized value for variable {self.name} '
'with exponential distribution found. Check input and run '
'matrix.'
)
return -1
def generate_samples(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Overrides the Variable class generate_samples to align with
an exponential distribution.
"""
percent_shift = 8e-17
low_percent = 0
high_percent = 1 - percent_shift
dist = expon(scale=1 / getattr(self, 'lambda'))
rnd_hypercube = uniform_hypercube(low_percent, high_percent, samp_size)
vals = dist.ppf(rnd_hypercube) + self.interval_low
np.random.shuffle(vals)
return vals
def get_resamp_vals(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Overrides the Variable class get_resamp_vals to align with
an exponential distribution.
"""
comm = MPI_COMM_WORLD
size = comm.size
rank = comm.rank
base = samp_size // size
rem = samp_size % size
count = base + (rank < rem)
ranks = np.arange(0, size, dtype=int)
seq_count = (ranks < rem) + base
seq_disp = base * ranks + (ranks >= rem) * rem + (ranks < rem) * ranks
self.resample = np.zeros(samp_size)
resample = np.random.exponential(
scale=(1 / getattr(self, 'lambda')), size=count
)
comm.Allgatherv(
[resample, count, MPI_DOUBLE],
[self.resample, seq_count, seq_disp, MPI_DOUBLE]
)
# The bound is included to help with ProbabilityBox convergence.
self.resample[0] = 0
return self.resample
def check_num_string(self):
"""
Searches to replace sring 'pi' with its numpy equivalent in any of the
values that might contain it.
"""
lambd = getattr(self, 'lambda')
if isinstance(lambd, str) and 'pi' in lambd:
setattr(self, 'lambda', float(lambd.replace('pi', str(np.pi))))
def get_mean(self):
"""
Return the mean of the variable.
"""
return self.interval_low + (1 / getattr(self, 'lambda'))
class GammaVariable(ContinuousVariable):
"""
Inputs: alpha- the alpha parameter of the variable
theta- the theta parameter of the variable
interval_low- the low interval of the variable
order- the order of the model to calculate the orthogonal
polynomials and norm squared values
type- the type of variable
name- the name of the variable
number- the number of the variable from the file
Represents a gamma variable. The methods in this class correspond to
those of a gamma variable.
"""
__slots__ = ('alpha', 'theta')
# This is the standardized form required for the UQPCE variable basis and
# norm squared.
equation = '(x**(A-1) * exp(-x)) / (A-1)!'
def standardize(self, orig, std_vals):
"""
Inputs: orig- the un-standardized values
std_vals- the attribue name for the standardized vals
Overrides the Variable class standardize to align with
a gamma distribution.
"""
standard = (getattr(self, orig) - self.interval_low) / self.theta
setattr(self, std_vals, standard)
return getattr(self, std_vals)
def standardize_points(self, values):
"""
Inputs: values- unstandardized points corresponding to the variable's
distribution
Standardizes and returns the inputs points.
"""
return (values - self.interval_low) / self.theta
def unstandardize_points(self, value):
"""
Inputs: value- the standardized value to be unstandardized
Calculates and returns the unscaled variable value from the
standardized value.
"""
return (value * self.theta) + self.interval_low
def check_distribution(self):
"""
Overrides the Variable class check_distribution to align with
a gamma distribution.
"""
shift = 15
comm = MPI_COMM_WORLD
rank = comm.rank
if rank == 0 and ((np.max(self.std_vals) > shift)
or (np.min(self.std_vals) < 0)
):
warn(
f'Large standardized value for variable {self.name} '
'with gamma distribution found. Check input and run matrix.'
)
return -1
def generate_samples(self, samp_size):
"""
Inputs: samp_size- the number of points needed to be generated
Overrides the Variable class generate_samples to align with
a gamma distribution.
"""
percent_shift = 8e-17
low_percent = 0
high_percent = 1 - percent_shift
dist = gamma(self.alpha, scale=self.theta)
rnd_hypercube = uniform_hypercube(low_percent, high_percent, samp_size)
vals = dist.ppf(rnd_hypercube) + self.interval_low
return vals
def get_resamp_vals(self, samp_size):
"""
Inputs: samp_size- the number of samples to generate according to the
distribution
Overrides the Variable class get_resamp_vals to align with
a gamma distribution.
"""
comm = MPI_COMM_WORLD
size = comm.size
rank = comm.rank
base = samp_size // size
rem = samp_size % size
count = base + (rank < rem)
ranks = np.arange(0, size, dtype=int)
seq_count = (ranks < rem) + base
seq_disp = base * ranks + (ranks >= rem) * rem + (ranks < rem) * ranks
self.resample = np.zeros(samp_size)
resample = np.random.gamma(shape=self.alpha, scale=1, size=count)
comm.Allgatherv(
[resample, count, MPI_DOUBLE],
[self.resample, seq_count, seq_disp, MPI_DOUBLE]
)
# The bound is included to help with ProbabilityBox convergence.
self.resample[0] = 0
return self.resample
def check_num_string(self):
"""
Searches to replace sring 'pi' with its numpy equivalent in any of the
values that might contain it.
"""
if isinstance(self.alpha, str) and 'pi' in self.alpha:
self.alpha = float(self.alpha.replace('pi', str(np.pi)))
if isinstance(self.theta, str) and 'pi' in self.theta:
self.theta = float(self.theta.replace('pi', str(np.pi)))
def get_mean(self):
"""
Return the mean of the variable.
"""
return self.interval_low + (self.alpha * self.theta)
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1267,
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6624,
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220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10558,
1958,
7,
69,
6,
90,
20786,
92,
1635,
37913,
944,
13,
7785,
62,
1506,
35894,
62,
303,
310,
58,
72,
60,
30072,
12429,
362,
33809,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
19203,
77,
32152,
3256,
705,
1837,
3149,
88,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1988,
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1988,
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657,
25,
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1288,
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64,
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275,
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198,
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220,
220,
220,
220,
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220,
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279,
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279,
27747,
7,
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13,
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1611,
11639,
21037,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
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7,
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62,
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58,
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7,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
6,
90,
79,
3372,
58,
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11,
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1635,
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12,
16,
31520,
9,
7,
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10,
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10,
17,
9,
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12,
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8,
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14,
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62,
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58,
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8,
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62,
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62,
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628,
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220,
220,
825,
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7,
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11,
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11,
14367,
62,
12786,
2599,
198,
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220,
220,
220,
220,
220,
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37227,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
82,
25,
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12,
262,
555,
12,
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1143,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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14367,
62,
12786,
12,
262,
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822,
518,
1438,
329,
262,
25713,
410,
874,
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220,
220,
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220,
220,
220,
3827,
81,
1460,
262,
35748,
1398,
3210,
1096,
284,
10548,
351,
198,
220,
220,
220,
220,
220,
220,
220,
257,
12159,
6082,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2656,
796,
651,
35226,
7,
944,
11,
1796,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3210,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
14986,
58,
47715,
532,
2116,
13,
3849,
2100,
62,
9319,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1220,
357,
944,
13,
3849,
2100,
62,
8929,
532,
2116,
13,
3849,
2100,
62,
9319,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
900,
35226,
7,
944,
11,
14367,
62,
12786,
11,
3210,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
651,
35226,
7,
944,
11,
14367,
62,
12786,
8,
628,
220,
220,
220,
825,
3210,
1096,
62,
13033,
7,
944,
11,
3815,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
82,
25,
3815,
12,
555,
20307,
1143,
2173,
11188,
284,
262,
7885,
338,
198,
220,
220,
220,
220,
220,
220,
220,
6082,
628,
220,
220,
220,
220,
220,
220,
220,
8997,
4340,
290,
5860,
262,
17311,
2173,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3210,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
27160,
532,
2116,
13,
3849,
2100,
62,
9319,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1220,
357,
944,
13,
3849,
2100,
62,
8929,
532,
2116,
13,
3849,
2100,
62,
9319,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
3210,
628,
220,
220,
220,
825,
555,
20307,
1096,
62,
13033,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
82,
25,
1988,
12,
262,
25713,
1988,
284,
307,
555,
20307,
1143,
628,
220,
220,
220,
220,
220,
220,
220,
27131,
689,
290,
5860,
262,
28594,
3021,
7885,
1988,
422,
262,
198,
220,
220,
220,
220,
220,
220,
220,
25713,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
28594,
3021,
62,
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796,
1988,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
1635,
357,
944,
13,
3849,
2100,
62,
8929,
532,
2116,
13,
3849,
2100,
62,
9319,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
944,
13,
3849,
2100,
62,
9319,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
28594,
3021,
62,
8367,
628,
220,
220,
220,
825,
2198,
62,
17080,
3890,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3827,
81,
1460,
262,
35748,
1398,
2198,
62,
17080,
3890,
284,
10548,
351,
198,
220,
220,
220,
220,
220,
220,
220,
281,
12159,
6082,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
6482,
796,
807,
198,
220,
220,
220,
220,
220,
220,
220,
725,
796,
4904,
40,
62,
9858,
44,
62,
45359,
11163,
198,
220,
220,
220,
220,
220,
220,
220,
4279,
796,
725,
13,
43027,
628,
220,
220,
220,
220,
220,
220,
220,
611,
4279,
6624,
657,
290,
357,
37659,
13,
9806,
7,
944,
13,
19282,
62,
12786,
8,
1875,
6482,
8,
393,
357,
37659,
13,
1084,
7,
944,
13,
19282,
62,
12786,
8,
1279,
532,
30846,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9828,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
6,
21968,
25713,
1988,
329,
7885,
1391,
944,
13,
3672,
92,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4480,
17993,
6082,
1043,
13,
6822,
5128,
290,
1057,
17593,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
532,
16,
628,
220,
220,
220,
825,
7716,
62,
82,
12629,
7,
944,
11,
264,
696,
62,
7857,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
82,
25,
264,
696,
62,
7857,
12,
262,
1271,
286,
2173,
2622,
284,
307,
7560,
628,
220,
220,
220,
220,
220,
220,
220,
3827,
81,
1460,
262,
35748,
1398,
7716,
62,
82,
12629,
284,
10548,
351,
198,
220,
220,
220,
220,
220,
220,
220,
281,
12159,
6082,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
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198,
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12,
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12,
16,
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25,
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12,
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220,
257,
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6082,
13,
198,
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220,
220,
220,
220,
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198,
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220,
220,
220,
220,
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35226,
7,
944,
11,
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8,
532,
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2100,
62,
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8,
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900,
35226,
7,
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62,
12786,
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3210,
8,
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7,
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1096,
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13033,
7,
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11,
3815,
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198,
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220,
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220,
220,
37227,
198,
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220,
220,
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220,
220,
23412,
82,
25,
3815,
12,
555,
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2173,
11188,
284,
262,
7885,
338,
198,
220,
220,
220,
220,
220,
220,
220,
6082,
628,
220,
220,
220,
220,
220,
220,
220,
8997,
4340,
290,
5860,
262,
17311,
2173,
13,
198,
220,
220,
220,
220,
220,
220,
220,
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198,
220,
220,
220,
220,
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532,
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2100,
62,
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8,
1220,
2116,
13,
1169,
8326,
628,
220,
220,
220,
825,
555,
20307,
1096,
62,
13033,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
82,
25,
1988,
12,
262,
25713,
1988,
284,
307,
555,
20307,
1143,
628,
220,
220,
220,
220,
220,
220,
220,
27131,
689,
290,
5860,
262,
28594,
3021,
7885,
1988,
422,
262,
198,
220,
220,
220,
220,
220,
220,
220,
25713,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
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1635,
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13,
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8,
1343,
2116,
13,
3849,
2100,
62,
9319,
628,
220,
220,
220,
825,
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62,
17080,
3890,
7,
944,
2599,
198,
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220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3827,
81,
1460,
262,
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1398,
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62,
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3890,
284,
10548,
351,
198,
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220,
220,
220,
220,
220,
257,
34236,
6082,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
6482,
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1315,
198,
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220,
220,
725,
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4904,
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62,
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44,
62,
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198,
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220,
220,
220,
220,
220,
220,
4279,
796,
725,
13,
43027,
628,
220,
220,
220,
220,
220,
220,
220,
611,
4279,
6624,
657,
290,
14808,
37659,
13,
9806,
7,
944,
13,
19282,
62,
12786,
8,
1875,
6482,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 2.102615 | 21,225 |
import os
import numpy as np
import cv2
from module.lightCNN_model_in_numpy import LightCNN9_in_numpy
def get_net(path):
'''
be used to get class net
:param para:
:return:
'''
print('Loading network...')
cfg = np.load(path, allow_pickle=True)
cfg = cfg.item()
net = LightCNN9_in_numpy(cfg)
return net
if __name__ == '__main__':
get_net() | [
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] | 2.299401 | 167 |
class ChildNodeIterator:
'''Iterates over a Humon node's children.'''
def __next__(self):
'''Iterate.'''
if self.idx < self.node.numChildren:
res = self.node.getChild(self.idx)
self.idx += 1
return res
raise StopIteration
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'''
Code of testing
'''
import argparse
import logging
import os
import sys
import csv
import time
import torch
import numpy as np
import pandas as pd
import torch.nn as nn
from tqdm import tqdm
from pycm import *
import matplotlib
import matplotlib.pyplot as plt
from dataset import BasicDataset_OUT
from torch.utils.data import DataLoader
from model import Resnet101_fl, InceptionV3_fl, Densenet161_fl, Resnext101_32x8d_fl, MobilenetV2_fl, Vgg16_bn_fl, Efficientnet_fl
#torch.distributed.init_process_group(backend="nccl")
font = {
'weight' : 'normal',
'size' : 18}
plt.rc('font',family='Times New Roman')
matplotlib.rc('font', **font)
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
args = get_args()
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
#logging.info(f'Using device {device}')
test_dir = args.test_dir
dataset=args.dataset
img_size= (512,512)
if args.model=='inceptionv3':
model_fl = InceptionV3_fl(pretrained=True)
if args.model=='densenet161':
model_fl = Densenet161_fl(pretrained=True)
if args.model == 'resnet101':
model_fl = Resnet101_fl(pretrained=True)
if args.model == 'resnext101':
model_fl_1 = Resnext101_32x8d_fl(pretrained=True)
model_fl_2 = Resnext101_32x8d_fl(pretrained=True)
model_fl_3 = Resnext101_32x8d_fl(pretrained=True)
model_fl_4 = Resnext101_32x8d_fl(pretrained=True)
model_fl_5 = Resnext101_32x8d_fl(pretrained=True)
model_fl_6 = Resnext101_32x8d_fl(pretrained=True)
model_fl_7 = Resnext101_32x8d_fl(pretrained=True)
model_fl_8 = Resnext101_32x8d_fl(pretrained=True)
if args.model == 'efficientnet':
model_fl_1 = Efficientnet_fl(pretrained=True)
model_fl_2 = Efficientnet_fl(pretrained=True)
model_fl_3 = Efficientnet_fl(pretrained=True)
model_fl_4 = Efficientnet_fl(pretrained=True)
model_fl_5 = Efficientnet_fl(pretrained=True)
model_fl_6 = Efficientnet_fl(pretrained=True)
model_fl_7 = Efficientnet_fl(pretrained=True)
model_fl_8 = Efficientnet_fl(pretrained=True)
if args.model == 'mobilenetv2':
model_fl = MobilenetV2_fl(pretrained=True)
if args.model == 'vgg16bn':
model_fl = Vgg16_bn_fl(pretrained=True)
checkpoint_path_1 = './{}/{}/{}/7_seed_28/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_2 = './{}/{}/{}/6_seed_30/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_3 = './{}/{}/{}/5_seed_32/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_4 = './{}/{}/{}/4_seed_34/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_5 = './{}/{}/{}/3_seed_36/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_6 = './{}/{}/{}/2_seed_38/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_7 = './{}/{}/{}/1_seed_40/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
checkpoint_path_8 = './{}/{}/{}/0_seed_42/best_loss_checkpoint.pth'.format(args.task, args.load, args.model )
model_fl_1.to(device=device)
model_fl_2.to(device=device)
model_fl_3.to(device=device)
model_fl_4.to(device=device)
model_fl_5.to(device=device)
model_fl_6.to(device=device)
model_fl_7.to(device=device)
model_fl_8.to(device=device)
map_location = {'cuda:%d' % 0: 'cuda:%d' % args.local_rank}
if args.load:
model_fl_1.load_state_dict(
torch.load(checkpoint_path_1, map_location="cuda:0")
)
model_fl_2.load_state_dict(
torch.load(checkpoint_path_2, map_location="cuda:0")
)
model_fl_3.load_state_dict(
torch.load(checkpoint_path_3, map_location="cuda:0")
)
model_fl_4.load_state_dict(
torch.load(checkpoint_path_4, map_location="cuda:0")
)
model_fl_5.load_state_dict(
torch.load(checkpoint_path_5, map_location="cuda:0")
)
model_fl_6.load_state_dict(
torch.load(checkpoint_path_6, map_location="cuda:0")
)
model_fl_7.load_state_dict(
torch.load(checkpoint_path_7, map_location="cuda:0")
)
model_fl_8.load_state_dict(
torch.load(checkpoint_path_8, map_location="cuda:0")
)
# faster convolutions, but more memory
# cudnn.benchmark = True
try:
test_net(model_fl_1,
model_fl_2,
model_fl_3,
model_fl_4,
model_fl_5,
model_fl_6,
model_fl_7,
model_fl_8,
test_dir,
device=device,
epochs=args.epochs,
batch_size=args.batchsize,
image_size=img_size)
except KeyboardInterrupt:
torch.save(model_fl.state_dict(), 'INTERRUPTED.pth')
logging.info('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)
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628,
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] | 2.032505 | 2,615 |
from googlesearch import search
query = "fastapi"
for i in search(query, tld="co.in", num=10, stop=10, pause=2):
print(i) | [
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] | 2.461538 | 52 |
# Created on 5/21/20
# Author: Ari Liloia and Michael Wehar
| [
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# -*- coding: utf-8 -*-
"""
Created on Sun Jan 17 04:01:48 2021
@author: Adham
"""
from .Model import model
import numpy as np
import pickle | [
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import numpy as np
| [
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] | 3.333333 | 6 |
from pulp import *
if __name__ == "main":
main() | [
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] | 2.65 | 20 |
import unittest
import os
import tempfile
from nose.tools import \
assert_equal
from bento import PackageDescription
from bento.core.package import file_list, static_representation
from bento.core.meta import PackageMetadata
from bento.core.pkg_objects import DataFiles
from bento.core.node import Node
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import pandas as pd
import math
from sklearn.ensemble import RandomForestRegressor
trainInput = pd.read_excel("FPL_Season_Data_Only_Inputs_Shuffled2.xlsx")
trainInput = trainInput.drop(trainInput.columns[0], axis=1)
trainOutput = pd.read_excel("FPL_Season_Data_Only_Outputs_Shuffled2.xlsx")
trainOutput = trainOutput.pop("Points")
position = pd.get_dummies(trainInput["Position"],prefix="Position")
trainInput = pd.concat([position,trainInput], axis =1)
trainInput.drop(["Position"], axis=1, inplace=True)
trainInput.drop(["YC"], axis=1, inplace=True)
trainInput.drop(["RC"], axis=1, inplace=True)
trainInput.drop(["Bonus Points"], axis=1, inplace=True)
df = pd.read_excel("PredictionsData.xlsx")
position = pd.get_dummies(df["Position"],prefix="Position")
df = pd.concat([position,df], axis =1)
df.drop(["Position"], axis=1, inplace=True)
df.drop(["YC"], axis=1, inplace=True)
df.drop(["RC"], axis=1, inplace=True)
df.drop(["Bonus Points"], axis=1, inplace=True)
names = df.pop("Name")
regressor = RandomForestRegressor(n_estimators = 10000, random_state=30)
regressor.fit(trainInput,trainOutput)
predictions = regressor.predict(df)
predictionsDF = pd.DataFrame(predictions, columns=["Points"])
predictionsDF = pd.concat([names, predictionsDF],axis=1)
predictionsDF.sort_values("Points",ascending=False, inplace=True)
predictionsDF.to_excel("PredictionsRF10k.xlsx")
# import numpy as np
# import tensorflow as tf
# import pandas as pd
# import wandb
# from wandb.keras import WandbCallback
# #initializing
# np.set_printoptions(precision=4, suppress=True)
# wandb.init(project="RandomForest", entity="matthewlchen",sync_tensorboard=True)
# #pulling data
# trainInput = pd.read_excel("FPL_Season_Data_Only_Inputs_Shuffled2.xlsx")
# trainInput = trainInput.drop(trainInput.columns[0], axis=1)
# trainOutput = pd.read_excel("FPL_Season_Data_Only_Outputs_Shuffled2.xlsx")
# trainOutput = trainOutput.pop("Points")
# #transforming categorical position data into one hot encoding
# position = pd.get_dummies(trainInput["Position"],prefix="Position")
# trainInput = pd.concat([position,trainInput], axis =1)
# trainInput.drop(["Position"], axis=1, inplace=True)
# #converting dataframes to numpy arrays
# inputNP = np.asarray(trainInput)
# outputNP = np.asarray(trainOutput)
# #train/test split
# #inpTrain, inpTest, outTrain, outTest = train_test_split(inputNP, outputNP, test_size=0.20)
# #define model
# ##parameters - tuning still in process
# nEpochs = 100
# batch = 32
# model =
# model.compile(
# optimizer="adam",
# loss = "mean_squared_error",
# metrics = ["accuracy"]
# )
# wandb.config = {
# "learning_rate": 0.001,
# "epochs": nEpochs,
# "batch_size": batch
# }
# #run
# model.fit(inputNP,outputNP,batch_size=batch, validation_split=0.20,verbose=2,epochs=nEpochs, callbacks=[WandbCallback()])
# #model.save_weights("weights")
# #print(model.evaluate(inpTest, outTest, verbose=2)) | [
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11,
503,
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11,
15942,
577,
28,
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] | 2.571185 | 1,173 |
import re
import os
from numpy.lib.function_base import insert
from utils import *
if __name__ =='__main__':
pass
#load_adfa_Attack_files("./ADFA-LD/Attack_Data_Master/")
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] | 2.48 | 75 |
import sublime, sublime_plugin
import json
import os
import platform
import subprocess
if platform.system() == 'Darwin':
os_name = 'osx'
elif platform.system() == 'Windows':
os_name = 'windows'
else:
os_name = 'linux'
# /usr/local/lib/node_modules/lebab/bin/index.js
BIN_PATH = os.path.join(
sublime.packages_path(),
os.path.dirname(os.path.realpath(__file__)),
'lebab-transform.js'
)
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] | 2.518293 | 164 |
import io
import os
from setuptools import find_packages, setup
here = os.path.abspath(os.path.dirname(__file__))
# Package meta-data.
NAME = 'chantilly'
DESCRIPTION = 'Deployment tool for online machine learning models'
LONG_DESCRIPTION_CONTENT_TYPE = 'text/markdown'
URL = 'https://github.com/creme-ml/chantilly'
EMAIL = '[email protected]'
AUTHOR = 'Max Halford'
REQUIRES_PYTHON = '>=3.7.0'
# Import the README and use it as the long-description.
with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = '\n' + f.read()
# Load the package's __version__.py module as a dictionary.
about = {}
with open(os.path.join(here, 'chantilly', '__version__.py')) as f:
exec(f.read(), about)
setup(
name=NAME,
version=about['__version__'],
description=DESCRIPTION,
long_description=long_description,
long_description_content_type=LONG_DESCRIPTION_CONTENT_TYPE,
author=AUTHOR,
author_email=EMAIL,
license='BSD-3',
classifiers=[
# Trove classifiers
# Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
'License :: OSI Approved :: BSD License',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy'
],
packages=find_packages(),
include_package_data=True,
python_requires=REQUIRES_PYTHON,
url=URL,
zip_safe=False,
install_requires=[
'cerberus>=1.3.2',
'river>=0.9.0',
'dill>=0.3.1.1',
'Flask>=1.1.1'
],
extras_require={
'redis': ['redis>=3.5'],
'dev': [
'flake8>=3.7.9',
'mypy>=0.770',
'pytest>=5.3.5',
'pytest-cov>=2.8.1'
]
},
entry_points={
'console_scripts': [
'chantilly=chantilly:cli_hook'
],
}
)
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220,
220,
220,
220,
220,
220,
16589,
198,
220,
220,
220,
1782,
198,
8,
198
] | 2.252183 | 916 |
import warnings
from marshmallow.warnings import ChangedInMarshmallow3Warning
from argschema import ArgSchema
from argschema.fields import (
InputDir, InputFile, Float,
Int, OutputFile, Str, Boolean)
warnings.simplefilter(
action='ignore',
category=ChangedInMarshmallow3Warning)
| [
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] | 3 | 104 |
import os
basedir = os.path.abspath(os.path.dirname(__file__))
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import tensorflow as tf
import numpy as np
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from django.shortcuts import render, redirect
from django.urls import reverse
from django.http import JsonResponse
from django.conf import settings
from django.contrib import messages
from django.core.mail import EmailMessage
from django.template.loader import render_to_string
from hknweb.events.views.aggregate_displays.calendar import calendar_helper
from hknweb.events.views.event_transactions.show_event import show_details_helper
from hknweb.utils import allow_public_access
from hknweb.studentservices.models import (
CourseGuideNode,
CourseGuideAdjacencyList,
CourseGuideGroup,
CourseGuideParam,
)
from hknweb.studentservices.forms import DocumentForm, TourRequest
@allow_public_access
@allow_public_access
@allow_public_access
@allow_public_access
@allow_public_access
@allow_public_access
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15526,
628,
198,
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] | 3.289683 | 252 |
# -*- coding: utf-8 -*-
__author__ = 'Marcin Usielski'
__copyright__ = 'Copyright (C) 2020, Nokia'
__email__ = '[email protected]'
import time
from moler.events.unix.ping_no_response import PingNoResponse
from moler.util.moler_test import MolerTest
import datetime
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"""
Test that the 'gui' displays long lines/names correctly without overruns.
"""
import lldb
from lldbsuite.test.decorators import *
from lldbsuite.test.lldbtest import *
from lldbsuite.test.lldbpexpect import PExpectTest
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import torch
from torch.utils.data.sampler import Sampler
from collections import defaultdict
import numpy as np
class MPerClassSampler(Sampler):
"""
Give m samples per class.
Args:
labels (np.ndarray):
Ground truth of datasets
m (int):
M samples per class
batch_size (int):
Batch size must be an interger multiple of m
"""
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1362,
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198,
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class Request(object):
"""
create a instance of Request
""" | [
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from django import forms
from django.contrib.auth import authenticate
from django.contrib.auth.models import User
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"""
A collection of common mathematical functions written for high performance with
the help of numpy and numba.
"""
import numpy as np
from math import sin as msin
from math import cos as mcos
from numba import jit
@jit
def dist(p, q):
"""
Compute distance between two 3D vectors
p: array
Cartesian coordinates for one of the vectors
q: array
Cartesian coordinates for one of the vectors
"""
return ((p[0] - q[0])**2 + (p[1] - q[1])**2 + (p[2] - q[2])**2)**0.5
@jit
def dot(p, q):
"""
Compute dot product between two 3D vectors
p: array
Cartesian coordinates for one of the vectors
q: array
Cartesian coordinates for one of the vectors
"""
return p[0] * q[0] + p[1] * q[1] + p[2] * q[2]
@jit
def cross(p, q):
"""
Compute cross product between two 3D vectors
p: array
Cartesian coordinates for one of the vectors
q: array
Cartesian coordinates for one of the vectors
"""
xyz = np.zeros(3)
xyz[0] = p[1] * q[2] - p[2] * q[1]
xyz[1] = p[2] * q[0] - p[0] * q[2]
xyz[2] = p[0] * q[1] - p[1] * q[0]
return xyz
@jit
def mod(p):
"""
Compute modulus of 3D vector
p: array
Cartesian coordinates
"""
return (p[0]**2 + p[1]**2 + p[2]**2)**0.5
@jit
def normalize(p):
"""
Compute a normalized 3D vector
p: array
Cartesian coordinates
"""
return p / mod(p)
@jit
def perp_vector(p, q, r):
"""
Compute perpendicular vector to (p-q) and (r-q) centered in q.
"""
v = cross(q - r, q - p)
return v / mod(v) + q
def get_angle(a, b, c):
"""
Compute the angle given 3 points
xyz: array
Cartesian coordinates
a-c: int
atom index for the three points defining the angle
"""
ba = a - b
cb = c - b
ba_mod = mod(ba)
cb_mod = mod(cb)
val = dot(ba, cb) / (ba_mod * cb_mod)
# better fix?
if val > 1:
val = 1
elif val < -1:
val = -1
return np.arccos(val)
# this function is the same as get_torsional_array, except that the last one need an xyz-array and
# this one do not.
def get_torsional(a, b, c, d):
"""
Compute the torsional angle given four points
a-d: int
atom index for the four points defining the torsional
"""
# Compute 3 vectors connecting the four points
ba = b - a
cb = c - b
dc = d - c
# Compute the normal vector to each plane
u_A = cross(ba, cb)
u_B = cross(cb, dc)
#Measure the angle between the two normal vectors
u_A_mod = mod(u_A)
u_B_mod = mod(u_B)
val = dot(u_A, u_B) / (u_A_mod * u_B_mod)
# better fix?
if val > 1:
val = 1
elif val < -1:
val = -1
tor_rad = np.arccos(val)
# compute the sign
sign = dot(u_A, dc)
if sign > 0:
return tor_rad
else:
return -tor_rad
def get_torsional_array(xyz, a, b, c, d):
"""
Compute the torsional angle given four points
xyz: array
Cartesian coordinates
a-d: int
atom index for the four points defining the torsional
"""
# Compute 3 vectors connecting the four points
ba = xyz[b] - xyz[a]
cb = xyz[c] - xyz[b]
dc = xyz[d] - xyz[c]
# Compute the normal vector to each plane
u_A = cross(ba, cb)
u_B = cross(cb, dc)
# Measure the angle between the two normal vectors
u_A_mod = mod(u_A)
u_B_mod = mod(u_B)
val = dot(u_A, u_B) / (u_A_mod * u_B_mod)
# better fix?
if val > 1:
val = 1
elif val < -1:
val = -1
tor_rad = np.arccos(val)
# compute the sign
sign = dot(u_A, dc)
if sign > 0:
return tor_rad
else:
return -tor_rad
@jit
def rotation_matrix_3d(u, theta):
"""Return the rotation matrix due to a right hand rotation of theta radians
around an arbitrary axis/vector u.
u: array
arbitrary axis/vector u
theta: float
rotation angle in radians
"""
x, y, z = normalize(u)
st = msin(theta)
ct = mcos(theta)
mct = 1 - ct
# filling the matrix by indexing each element is faster (with jit)
# than writting np.array([[, , ], [, , ], [, , ]])
R = np.zeros((3, 3))
R[0, 0] = ct + x * x * mct
R[0, 1] = y * x * mct - z * st
R[0, 2] = x * z * mct + y * st
R[1, 0] = y * x * mct + z * st
R[1, 1] = ct + y * y * mct
R[1, 2] = y * z * mct - x * st
R[2, 0] = x * z * mct - y * st
R[2, 1] = y * z * mct + x * st
R[2, 2] = ct + z * z * mct
return R
@jit
def set_torsional(xyz, i, j, idx_rot, theta_rad):
"""
rotate a set of coordinates around the i-j axis by theta_rad
xyz: array
Cartesian coordinates
i: int
atom i
j: int
atom j
idx_to_rot: array
indices of the atoms that will be rotated
theta_rad: float
rotation angle in radians
"""
xyz_s = xyz - xyz[i]
R = rotation_matrix_3d((xyz_s[j]), theta_rad)
xyz[:] = xyz_s[:]
xyz[idx_rot] = xyz_s[idx_rot] @ R
# TODO return to original position????
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] | 2.136724 | 2,399 |
from pymc import deterministic, stochastic, MvNormal, Normal, Lognormal, Uniform, \
MCMC
import pymc
import numpy as np
from pysb.examples.robertson import model
from pysb.integrate import odesolve, Solver
from matplotlib import pyplot as plt
# Generate the synthetic data
seed = 2
random = np.random.RandomState(seed)
sigma = 0.1;
ntimes = 20;
tspan = np.linspace(0, 40, ntimes);
ysim = odesolve(model, tspan)
ysim_array = ysim.view(float).reshape(len(ysim), -1)
yspecies = ysim_array[:, :len(model.species)]
ydata = yspecies * (random.randn(*yspecies.shape) * sigma + 1);
ysim_max = yspecies.max(0)
ydata_norm = ydata / ysim_max
solver = Solver(model, tspan)
# Set up the parameter vector for the solver
nominal_rates = [model.parameters[n].value for n in ('A_0', 'B_0', 'C_0')]
# Stochastic variables for the rate parameters.
# Given lognormal priors with their correct order-of-mag mean but with a
# variance of 10 base 10 log units
k1 = Lognormal('k1', mu=np.log(1e-2), tau=1/(np.log(10)*np.log(1e10)),
value=1e-2, plot=True)
k2 = Lognormal('k2', mu=np.log(1e7), tau=1/(np.log(10)*np.log(1e10)),
value=1e7, plot=True)
k3 = Lognormal('k3', mu=np.log(1e4), tau=1/(np.log(10)*np.log(1e10)),
value=1e4, plot=True)
# The model is set up as a deterministic variable
@deterministic(plot=False)
# The precision (1/variance) matrix
tau = np.eye(len(tspan)*3) * 10
output = MvNormal('output', mu=robertson_model, tau=tau, observed=True,
value=ydata_norm.flatten())
if __name__ == '__main__':
# Create the MCMC object and start sampling
pymc_model = pymc.Model([k1, k2, k3, robertson_model, output])
mcmc = MCMC(pymc_model)
mcmc.sample(iter=10000, burn=5000, thin=5)
# Show the pymc histograms and autocorrelation plots
plt.ion()
pymc.Matplot.plot(mcmc)
plt.show()
# Plot the original data along with the sampled trajectories
plt.figure()
plt.plot(tspan, ydata_norm[:,0], 'r')
plt.plot(tspan, ydata_norm[:,1], 'g')
plt.plot(tspan, ydata_norm[:,2], 'b')
num_timecourses = 1000
num_iterations_sampled = mcmc.trace('robertson_model')[:].shape[0]
plt.plot(tspan, mcmc.trace('robertson_model')[num_iterations_sampled -
num_timecourses:,0::3].T, alpha=0.05, color='r')
plt.plot(tspan, mcmc.trace('robertson_model')[num_iterations_sampled -
num_timecourses:,1::3].T, alpha=0.05, color='g')
plt.plot(tspan, mcmc.trace('robertson_model')[num_iterations_sampled -
num_timecourses:,2::3].T, alpha=0.05, color='b')
# Show k1/k3 scatter plot
plt.figure()
plt.scatter(mcmc.trace('k1')[:], mcmc.trace('k3')[:])
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] | 2.213995 | 1,229 |
from .PBXResolver import *
from .PBX_Base_Phase import *
from .PBX_Constants import *
from ...Helpers import logging_helper
from ...Helpers import xcrun_helper
from ...Helpers import path_helper | [
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] | 3.180328 | 61 |
'''
3. Faça um Programa que peça dois números e imprima a soma.
'''
num1 = int(input('Insira o primeiro número: '))
num2 = int(input('Insira o segundo número: '))
print(num1+num2)
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] | 2.207317 | 82 |
#!/usr/bin/python
#
# Copyright 2019 Google LLC
#
# 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.
"""Util functions for representation learning.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import re
import numpy as np
import tensorflow as tf
INPUT_DATA_STR = "input_data"
IS_TRAINING_STR = "is_training"
REPR_PREFIX_STR = "representation_"
TAGS_IS_TRAINING = ["is_training"]
def adaptive_pool(inp, num_target_dimensions=9000, mode="adaptive_max"):
"""Adaptive pooling layer.
This layer performs adaptive pooling, such that the total
dimensionality of output is not bigger than num_target_dimension
Args:
inp: input tensor
num_target_dimensions: maximum number of output dimensions
mode: one of {"adaptive_max", "adaptive_avg", "max", "avg"}
Returns:
Result of the pooling operation
Raises:
ValueError: mode is unexpected.
"""
size, _, k = inp.get_shape().as_list()[1:]
if mode in ["adaptive_max", "adaptive_avg"]:
if mode == "adaptive_max":
pool_fn = tf.nn.fractional_max_pool
else:
pool_fn = tf.nn.fractional_avg_pool
# Find the optimal target output tensor size
target_size = (num_target_dimensions / float(k)) ** 0.5
if (abs(num_target_dimensions - k * np.floor(target_size) ** 2) <
abs(num_target_dimensions - k * np.ceil(target_size) ** 2)):
target_size = max(np.floor(target_size), 1.0)
else:
target_size = max(np.ceil(target_size), 1.0)
# Get optimal stride. Subtract epsilon to ensure correct rounding in
# pool_fn.
stride = size / target_size - 1.0e-5
# Make sure that the stride is valid
stride = max(stride, 1)
stride = min(stride, size)
result = pool_fn(inp, [1, stride, stride, 1])[0]
elif mode in ["max", "avg"]:
if mode == "max":
pool_fn = tf.contrib.layers.max_pool2d
else:
pool_fn = tf.contrib.layers.avg_pool2d
total_size = float(np.prod(inp.get_shape()[1:].as_list()))
stride = int(np.ceil(np.sqrt(total_size / num_target_dimensions)))
stride = min(max(1, stride), size)
result = pool_fn(inp, kernel_size=stride, stride=stride)
else:
raise ValueError("Not supported %s pool." % mode)
return result
def append_multiple_rows_to_csv(dictionaries, csv_path):
"""Writes multiples rows to csv file from a list of dictionaries.
Args:
dictionaries: a list of dictionaries, mapping from csv header to value.
csv_path: path to the result csv file.
"""
keys = set([])
for d in dictionaries:
keys.update(d.keys())
if not tf.gfile.Exists(csv_path):
with tf.gfile.Open(csv_path, "w") as f:
writer = csv.DictWriter(f, sorted(keys))
writer.writeheader()
f.flush()
with tf.gfile.Open(csv_path, "a") as f:
writer = csv.DictWriter(f, sorted(keys))
writer.writerows(dictionaries)
f.flush()
def concat_dicts(dict_list):
"""Given a list of dicts merges them into a single dict.
This function takes a list of dictionaries as an input and then merges all
these dictionaries into a single dictionary by concatenating the values
(along the first axis) that correspond to the same key.
Args:
dict_list: list of dictionaries
Returns:
d: merged dictionary
"""
d = collections.defaultdict(list)
for e in dict_list:
for k, v in e.items():
d[k].append(v)
for k in d:
d[k] = tf.concat(d[k], axis=0)
return d
def str2intlist(s, repeats_if_single=None):
"""Parse a config's "1,2,3"-style string into a list of ints.
Args:
s: The string to be parsed, or possibly already an int.
repeats_if_single: If s is already an int or is a single element list,
repeat it this many times to create the list.
Returns:
A list of integers based on `s`.
"""
if isinstance(s, int):
result = [s]
else:
result = [int(i.strip()) if i != "None" else None
for i in s.split(",")]
if repeats_if_single is not None and len(result) == 1:
result *= repeats_if_single
return result
def tf_apply_to_image_or_images(fn, image_or_images):
"""Applies a function to a single image or each image in a batch of them.
Args:
fn: the function to apply, receives an image, returns an image.
image_or_images: Either a single image, or a batch of images.
Returns:
The result of applying the function to the image or batch of images.
Raises:
ValueError: if the input is not of rank 3 or 4.
"""
static_rank = len(image_or_images.get_shape().as_list())
if static_rank == 3: # A single image: HWC
return fn(image_or_images)
elif static_rank == 4: # A batch of images: BHWC
return tf.map_fn(fn, image_or_images)
elif static_rank > 4: # A batch of images: ...HWC
input_shape = tf.shape(image_or_images)
h, w, c = image_or_images.get_shape().as_list()[-3:]
image_or_images = tf.reshape(image_or_images, [-1, h, w, c])
image_or_images = tf.map_fn(fn, image_or_images)
return tf.reshape(image_or_images, input_shape)
else:
raise ValueError("Unsupported image rank: %d" % static_rank)
def tf_apply_with_probability(p, fn, x):
"""Apply function `fn` to input `x` randomly `p` percent of the time."""
return tf.cond(
tf.less(tf.random_uniform([], minval=0, maxval=1, dtype=tf.float32), p),
lambda: fn(x),
lambda: x)
def get_latest_hub_per_task(hub_module_paths):
"""Get latest hub module for each task.
The hub module path should match format ".*/hub/[0-9]*/module/.*".
Example usage:
get_latest_hub_per_task(expand_glob(["/cns/el-d/home/dune/representation/"
"xzhai/1899361/*/export/hub/*/module/"]))
returns 4 latest hub module from 4 tasks respectivley.
Args:
hub_module_paths: a list of hub module paths.
Returns:
A list of latest hub modules for each task.
"""
task_to_path = {}
for path in hub_module_paths:
task_name, module_name = path.split("/hub/")
timestamp = int(re.findall(r"([0-9]*)/module", module_name)[0])
current_path = task_to_path.get(task_name, "0/module")
current_timestamp = int(re.findall(r"([0-9]*)/module", current_path)[0])
if current_timestamp < timestamp:
task_to_path[task_name] = path
return sorted(task_to_path.values())
def get_classification_metrics(tensor_names):
"""Gets classification eval metric on input logits and labels.
Args:
tensor_names: a list of tensor names for _metrics input tensors.
Returns:
A function computes the metric result, from input logits and labels.
"""
def _metrics(labels, *tensors):
"""Computes the metric from logits and labels.
Args:
labels: ground truth labels.
*tensors: tensors to be evaluated.
Returns:
Result dict mapping from the metric name to the list of result tensor and
update_op used by tf.metrics.
"""
metrics = {}
assert len(tensor_names) == len(tensors), "Names must match tensors."
for i in range(len(tensors)):
tensor = tensors[i]
name = tensor_names[i]
for k in (1, 5):
metrics["top%d_accuracy_%s" % (k, name)] = _top_k_accuracy(
k, labels, tensor)
return metrics
return _metrics
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] | 2.650034 | 2,946 |
"""
Datos de entrada
edad del paciente = edad = int
sexo del paciente = sexo_paciente = int
Datos de salida
Resultado positivo o negativo acerca de si el paciente tiene anemia = resultado = str
"""
# Entradas
edad_paciente=int(input("Inserte la edad del paciente en su equivalente de años a meses "))
sexo_paciente=str(input("Inserte su sexo de la siguiente manera , femenino (F) o masculino (M) "))
nivel_hemoglobina=float(input("Inserte cual fue el resultado del nivel de hemoglobina en la sangre "))
sexo= sexo_paciente[0] # Es la posicion del caracter que quiero que tome
# Caja Negra
resultado=''
if (nivel_hemoglobina>=13 and nivel_hemoglobina<=26) and(edad_paciente>=0 and edad_paciente<=1):
resultado=(" El resultado es Negativo")
elif(nivel_hemoglobina>=10 and nivel_hemoglobina<=18)and(edad_paciente>1 and edad_paciente<=6):
resultado=(" El resultado es Negativo")
elif(nivel_hemoglobina>=11 and nivel_hemoglobina<=15)and(edad_paciente>6 and edad_paciente<=12):
resultado=(" El resultado es Negativo")
elif (nivel_hemoglobina>=11.5 and nivel_hemoglobina<=15) and (edad_paciente>12 and edad_paciente<=60): # Equivalencia entre 1 año y 5 años en meses
resultado=(" El resultado es Negativo")
elif (nivel_hemoglobina>=12.6 and nivel_hemoglobina<=15.5) and (edad_paciente>60 and edad_paciente<=120): # Equivalencia entre 5 años y 10 años en meses
resultado=(" El resultado es Negativo")
elif (nivel_hemoglobina>=13 and nivel_hemoglobina<=15.5) and(edad_paciente>120 and edad_paciente<=180): # Equivalencia entre 10 año y 15 años en meses
resultado=(" El resultado es Negativo")
elif (nivel_hemoglobina>=12 and nivel_hemoglobina<=16 ) and (edad_paciente>180 and sexo=="F"): # Equivalencia de 15 a meses
resultado=(" El resultado es Negativo")
elif (nivel_hemoglobina>=14 and nivel_hemoglobina<=18) and (edad_paciente>180 and sexo=="M"): # Equivalencia de 15
resultado=(" El resultado es Negativo")
else:
resultado= "El resultado es positivo"
#Salidas
print(resultado)
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] | 2.332953 | 877 |
from collections import namedtuple
from repositories.lines import LineORM
import cv2
import logging
from function_factory import FunctionFactory
from api import models
import time
logger = logging.getLogger(__name__)
_Line=namedtuple('_Line', 'name last_change nodes')
lines: list[_Line] = [_Line(name='asd', last_change=1413534, nodes=[])]
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] | 3.298077 | 104 |
import taichi as ti
import numpy as np
m_fluid_color = ti.Vector(list(np.random.rand(3) * 0.7 + 0.3))
m_dye_decay = 0.99
m_f_gravity = ti.Vector([0.0, -9.8]) | [
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] | 2.092105 | 76 |
import os
import math
import torch
from torch import optim
from models import BaseVAE
from models.types_ import *
from utils import data_loader
import pytorch_lightning as pl
from torchvision import transforms
import torchvision.utils as vutils
from torchvision.datasets import CelebA
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
# def sample_images(self):
# # Get sample reconstruction image
# test_input, test_label = next(iter(self.trainer.datamodule.test_dataloader()))
# test_input = test_input.to(self.curr_device)
# test_label = test_label.to(self.curr_device)
# # test_input, test_label = batch
# recons = self.model.generate(test_input, labels = test_label)
# vutils.save_image(recons.data,
# os.path.join(self.logger.log_dir ,
# "Reconstructions",
# f"recons_{self.logger.name}_Epoch_{self.current_epoch}.png"),
# normalize=True,
# nrow=12)
# try:
# samples = self.model.sample(144,
# self.curr_device,
# labels = test_label)
# vutils.save_image(samples.cpu().data,
# os.path.join(self.logger.log_dir ,
# "Samples",
# f"{self.logger.name}_Epoch_{self.current_epoch}.png"),
# normalize=True,
# nrow=12)
# except Warning:
# pass
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220,
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1208,
198
] | 1.778978 | 1,018 |
# coding=utf-8
"""A district is the voter population of a constituency or geographical area."""
import itertools as itls
import math as m
import random as rd
from .utils import generate_voter_groups, sort_dict_desc, make_table
from .candidate import Candidate
from .party import Party
from .voter_group import VoterGroup
from .result import ElectionResult
| [
2,
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] | 3.789474 | 95 |
from queue import Queue
from urllib.parse import urlparse
from threading import Thread
import requests
import threading
import re
import os
from requests import get
from requests import post
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
lock = threading.Lock()
if __name__ == '__main__':
main()
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] | 3.544643 | 112 |
from typing import Dict
from .contributions import Contributions
class ScheduledContributions(Contributions): # pylint: disable=too-few-public-methods
"""Contributions which occur at specific years in the life of the portfolio.
:param scheduled_contributions: contributions by year relative to inception of portfolio
"""
| [
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] | 3.930233 | 86 |
from __future__ import absolute_import
import os
from tenant_schemas_celery.app import CeleryApp
from django.conf import settings
from celery.schedules import crontab
# set the default Django settings module for the 'celery' program.
if os.path.isfile(os.path.join(os.path.abspath('.'), 'sgk', 'settings', 'local.py')):
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'sgk.settings.local')
else:
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'sgk.settings.production')
app = CeleryApp('sgk')
# Using a string here means the worker will not have to
# pickle the object when using Windows.
app.config_from_object('django.conf:settings')
app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)
# schedule for all environment
app.conf.beat_schedule = {
# 'send_sms_notifications': {
# 'task': 'send_sms_notifications',
# 'schedule': crontab(minute='0', hour='08') # Execute every day at 8:00 am
# },
}
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198,
220,
220,
220,
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8964,
198,
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] | 2.769912 | 339 |
# Copyright 2021 Alibaba Group Holding Limited. All Rights Reserved.
#
# 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.
# =============================================================================
r'''NCCL based collective commmunication.
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.platform import tf_logging as logging
from hybridbackend.tensorflow.distribute.communicator import CollectiveOps
from hybridbackend.tensorflow.distribute.communicator import Communicator
from hybridbackend.tensorflow.distribute.pubsub import PubSub
from hybridbackend.tensorflow.pywrap import _ops
ops.NotDifferentiable('GetNcclId')
ops.NotDifferentiable('NcclComm')
ops.NotDifferentiable('CreateNcclComm')
ops.NotDifferentiable('IsNcclCommInitialized')
@ops.RegisterGradient('ReduceWithNcclComm')
def _reduce_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL reduce op.
'''
comm = op.inputs[0]
grad_in = args[0]
reduce_op = op.get_attr('reduce_op')
root_rank = op.get_attr('root_rank')
if reduce_op != CollectiveOps.SUM:
raise NotImplementedError(
'Only reduce_op=SUM is supported for gradients computation.')
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.broadcast_with_nccl_comm(
comm, grad_in, root_rank=root_rank)
return None, grad_out
@ops.RegisterGradient('ReduceScatterWithNcclComm')
def _reduce_scatter_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL reduce scatter op.
'''
comm = op.inputs[0]
grad_in = args[0]
reduce_op = op.get_attr('reduce_op')
if reduce_op != CollectiveOps.SUM:
raise NotImplementedError(
'Only reduce_op=SUM is supported for gradients computation.')
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.allgather_with_nccl_comm(comm, grad_in)
return None, grad_out
@ops.RegisterGradient('AllreduceWithNcclComm')
def _allreduce_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL allreduce op.
'''
comm = op.inputs[0]
grad_in = args[0]
reduce_op = op.get_attr('reduce_op')
if reduce_op != CollectiveOps.SUM:
raise NotImplementedError(
'Only reduce_op=SUM is supported for gradients computation.')
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.allreduce_with_nccl_comm(
comm, grad_in, reduce_op=reduce_op)
return None, grad_out
ops.NotDifferentiable('BroadcastWithNcclComm')
ops.NotDifferentiable('ScatterWithNcclComm')
ops.NotDifferentiable('GatherWithNcclComm')
ops.NotDifferentiable('GathervWithNcclComm')
@ops.RegisterGradient('AllgatherWithNcclComm')
def _allgather_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL allgather op.
'''
comm = op.inputs[0]
grad_in = args[0]
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.reduce_scatter_with_nccl_comm(
comm, grad_in, reduce_op=CollectiveOps.SUM)
return None, grad_out
ops.NotDifferentiable('AllgathervWithNcclComm')
@ops.RegisterGradient('AlltoallWithNcclComm')
def _alltoall_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL alltoall op.
'''
comm = op.inputs[0]
grad_in = args[0]
wire_dtype = op.get_attr('wire_dtype')
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.alltoall_with_nccl_comm(
comm, grad_in,
wire_dtype=wire_dtype)
return None, grad_out
@ops.RegisterGradient('AlltoallvWithNcclComm')
def _nccl_alltoallv_grad(op, *args):
r'''Gradient for NCCL alltoallv op.
'''
comm = op.inputs[0]
grad_in = list(args)[0]
grad_sizes_in = op.outputs[1]
common_shape = op.get_attr('common_shape')
wire_dtype = op.get_attr('wire_dtype')
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out, grad_sizes_out = _ops.alltoallv_with_nccl_comm(
comm, grad_in, grad_sizes_in,
wire_dtype=wire_dtype,
common_shape=common_shape)
return None, grad_out, grad_sizes_out
@ops.RegisterGradient('GroupAlltoallvWithNcclComm')
def _group_alltoallv_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL group_alltoallv op.
'''
comm = op.inputs[0]
num_columns = op.get_attr('num_columns')
wire_dtype = op.get_attr('wire_dtype')
common_shapes = op.get_attr('common_shapes')
grad_in = args[:num_columns]
grad_sizes_in = op.outputs[num_columns:]
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out, _ = _ops.group_alltoallv_with_nccl_comm(
comm, grad_in, grad_sizes_in,
wire_dtype=wire_dtype,
common_shapes=common_shapes)
return (None, *grad_out, *[None for _ in range(num_columns)])
@ops.RegisterGradient('AlltoallwWithNcclComm')
def _alltoallw_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL alltoallw op.
'''
comm = op.inputs[0]
common_shape = op.get_attr('common_shape')
wire_dtype = op.get_attr('wire_dtype')
grad_in = list(args)
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.alltoallw_with_nccl_comm(
comm, grad_in,
wire_dtype=wire_dtype,
common_shape=common_shape)
return [None] + grad_out
@ops.RegisterGradient('GroupAlltoallwWithNcclComm')
def _group_alltoallw_with_nccl_comm_grad(op, *args):
r'''Gradient for NCCL group_alltoallw op.
'''
comm = op.inputs[0]
num_columns = op.get_attr('num_columns')
wire_dtype = op.get_attr('wire_dtype')
common_shapes = op.get_attr('common_shapes')
grad_in = list(args)
with ops.device(op.device):
with ops.control_dependencies(op.outputs):
grad_out = _ops.group_alltoallw_with_nccl_comm(
comm, grad_in,
num_columns=num_columns,
wire_dtype=wire_dtype,
common_shapes=common_shapes)
return [None] + grad_out
class NcclCommunicator(Communicator):
r'''A communicator using NCCL.
'''
NAME = 'NCCL'
@classmethod
Communicator.register(NcclCommunicator)
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7,
45,
535,
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30813,
26407,
8,
198
] | 2.558511 | 2,632 |
from django.db import models
BANDEIRA_CHOICES = (
('Visa', 'Visa'),
('Master', 'Master'),
('Hipercard', 'Hipercard'),
('Hiper', 'Hiper'),
('American Express', 'American Express'),
('Elo', 'Elo'),
('Diners Club', 'Diners Club'),
('American Express', 'American Express'),
('Discover', 'Discover'),
('JCB', 'JCB'),
('Aura', 'Aura'),
)
| [
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32,
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33809,
198,
220,
220,
220,
1267,
628
] | 2.119403 | 201 |
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