text stringlengths 0 1.05M | meta dict |
|---|---|
import os, psutil
import threading
import time
class MonitorThread(threading.Thread):
"""
Monitors the CPU status
"""
def __init__(self, cpu_core, interval):
self.sampling_interval = interval; # sample time interval
self.sample = 0.5; # cpu load measurement sample
self.cpu =... | {
"repo_name": "GaetanoCarlucci/CPULoadGenerator",
"path": "utils/Monitor.py",
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"autogenerated": false,
"ratio": 3.8945454545454545,
"config_test": false,
... |
import threading
import time
class ControllerThread(threading.Thread):
"""
Controls the CPU status
"""
def __init__(self, interval, ki = None, kp = None):
self.running = 1; # thread status
self.sampling_interval = interval
self.period = 0.1 # actuation period in seconds
... | {
"repo_name": "GaetanoCarlucci/CPULoadGenerator",
"path": "utils/Controller.py",
"copies": "1",
"size": "2417",
"license": "mit",
"hash": -398276627672449900,
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"line_max": 119,
"alpha_frac": 0.6036408771,
"autogenerated": false,
"ratio": 3.800314465408805,
"config_tes... |
import time
import matplotlib.pyplot as plt
class realTimePlot():
"""
Plots the CPU load
"""
def __init__(self, duration, cpu, target):
plt.figure()
plt.axis([0, duration, 0, 100])
plt.ion()
plt.show()
plt.xlabel('Time(sec)')
plt.ylabel('%')
... | {
"repo_name": "GaetanoCarlucci/CPULoadGenerator",
"path": "utils/Plot.py",
"copies": "1",
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"alpha_frac": 0.5256578947,
"autogenerated": false,
"ratio": 3.2,
"config_test": false,
"has_no... |
import time
from Plot import realTimePlot
class openLoopActuator():
"""
Generates CPU load by tuning the sleep time
"""
def __init__(self, monitor, duration, cpu_core, plot):
self.sleep_time = 0.03
self.monitor = monitor
self.duration = duration
self.plot = plot
... | {
"repo_name": "GaetanoCarlucci/CPULoadGenerator",
"path": "utils/openLoopActuator.py",
"copies": "1",
"size": "1893",
"license": "mit",
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"line_max": 72,
"alpha_frac": 0.586899102,
"autogenerated": false,
"ratio": 3.9273858921161824,
"confi... |
import time
from Plot import realTimePlot
class closedLoopActuator():
"""
Generates CPU load by tuning the sleep time
"""
def __init__(self, controller, monitor, duration, cpu_core, target, plot):
self.controller = controller
self.monitor = monitor
self.duration = durati... | {
"repo_name": "GaetanoCarlucci/CPULoadGenerator",
"path": "utils/closedLoopActuator.py",
"copies": "1",
"size": "2552",
"license": "mit",
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"alpha_frac": 0.5885579937,
"autogenerated": false,
"ratio": 4,
"config_test": false,
"has... |
import numpy as np
import numbers
from .base import SelectorMixin
from ..base import BaseEstimator, clone, MetaEstimatorMixin
from ..externals import six
from ..exceptions import NotFittedError
from ..utils.metaestimators import if_delegate_has_method
def _get_feature_importances(estimator, norm_order=1):
"""R... | {
"repo_name": "vortex-ape/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "9",
"size": "8658",
"license": "bsd-3-clause",
"hash": 5436660436275882000,
"line_mean": 36.6434782609,
"line_max": 79,
"alpha_frac": 0.6029106029,
"autogenerated": false,
"ratio": 4.60042507970... |
import numpy as np
import numbers
from .base import SelectorMixin
from ..base import BaseEstimator, clone, MetaEstimatorMixin
from ..exceptions import NotFittedError
from ..utils.metaestimators import if_delegate_has_method
def _get_feature_importances(estimator, norm_order=1):
"""Retrieve or aggregate feature... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "11",
"size": "8617",
"license": "bsd-3-clause",
"hash": 5676329657540969000,
"line_mean": 36.6288209607,
"line_max": 79,
"alpha_frac": 0.6019496344,
"autogenerated": false,
"ratio": 4.6030982905982... |
import numpy as np
import numbers
from ._base import SelectorMixin
from ._base import _get_feature_importances
from ..base import BaseEstimator, clone, MetaEstimatorMixin
from ..utils._tags import _safe_tags
from ..utils.validation import check_is_fitted
from ..exceptions import NotFittedError
from ..utils.metaestim... | {
"repo_name": "anntzer/scikit-learn",
"path": "sklearn/feature_selection/_from_model.py",
"copies": "2",
"size": "11063",
"license": "bsd-3-clause",
"hash": 7235529517795028000,
"line_mean": 37.1482758621,
"line_max": 79,
"alpha_frac": 0.6017355148,
"autogenerated": false,
"ratio": 4.475323624595... |
import numpy as np
import numbers
from ._base import SelectorMixin
from ._base import _get_feature_importances
from ..base import BaseEstimator, clone, MetaEstimatorMixin
from ..utils.validation import check_is_fitted
from ..exceptions import NotFittedError
from ..utils.metaestimators import if_delegate_has_method
f... | {
"repo_name": "bnaul/scikit-learn",
"path": "sklearn/feature_selection/_from_model.py",
"copies": "2",
"size": "10636",
"license": "bsd-3-clause",
"hash": -5605500444734318000,
"line_mean": 37.1218637993,
"line_max": 79,
"alpha_frac": 0.5980631816,
"autogenerated": false,
"ratio": 4.4651553316540... |
import numpy as np
from .base import SelectorMixin
from ..base import BaseEstimator, clone, MetaEstimatorMixin
from ..externals import six
from ..exceptions import NotFittedError
from ..utils.fixes import norm
from ..utils.metaestimators import if_delegate_has_method
def _get_feature_importances(estimator, norm_or... | {
"repo_name": "Vimos/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "2",
"size": "7239",
"license": "bsd-3-clause",
"hash": 6123710103426010000,
"line_mean": 34.8366336634,
"line_max": 79,
"alpha_frac": 0.6190081503,
"autogenerated": false,
"ratio": 4.596190476190476,... |
import numpy as np
from .base import SelectorMixin
from ..base import BaseEstimator, clone, MetaEstimatorMixin
from ..externals import six
from ..exceptions import NotFittedError
from ..utils.metaestimators import if_delegate_has_method
def _get_feature_importances(estimator, norm_order=1):
"""Retrieve or aggr... | {
"repo_name": "ldirer/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "8",
"size": "7259",
"license": "bsd-3-clause",
"hash": -3989877826609810000,
"line_mean": 34.9356435644,
"line_max": 79,
"alpha_frac": 0.6150984984,
"autogenerated": false,
"ratio": 4.61768447837150... |
import numpy as np
from .base import SelectorMixin
from ..base import BaseEstimator, clone
from ..externals import six
from ..exceptions import NotFittedError
from ..utils.fixes import norm
def _get_feature_importances(estimator, norm_order=1):
"""Retrieve or aggregate feature importances from estimator"""
... | {
"repo_name": "mikebenfield/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "18",
"size": "6968",
"license": "bsd-3-clause",
"hash": 2252715953452257000,
"line_mean": 34.3705583756,
"line_max": 79,
"alpha_frac": 0.6090700344,
"autogenerated": false,
"ratio": 4.64843228... |
import numpy as np
from .base import SelectorMixin
from ..base import TransformerMixin, BaseEstimator, clone
from ..exceptions import NotFittedError
from ..externals import six
from ..utils import safe_mask, check_array, deprecated
from ..utils.validation import check_is_fitted
def _get_feature_importances(estimato... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/feature_selection/from_model.py",
"copies": "1",
"size": "9549",
"license": "mit",
"hash": -1177055967163521500,
"line_mean": 35.8687258687,
"line_max": 79,
"alpha_frac": 0.6093831815,
"autogenerated": false,
... |
import numpy as np
from .base import SelectorMixin
from ..base import TransformerMixin, BaseEstimator, clone
from ..externals import six
from ..utils import safe_mask, check_array, deprecated
from ..utils.validation import check_is_fitted
from ..exceptions import NotFittedError
from ..utils.fixes import norm
def _... | {
"repo_name": "waterponey/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "2",
"size": "9854",
"license": "bsd-3-clause",
"hash": 5407109031427295000,
"line_mean": 36.3257575758,
"line_max": 79,
"alpha_frac": 0.6131520195,
"autogenerated": false,
"ratio": 4.65910165484... |
import numpy as np
from ..base import TransformerMixin
from ..externals import six
from ..utils import safe_mask, atleast2d_or_csc
class _LearntSelectorMixin(TransformerMixin):
# Note because of the extra threshold parameter in transform, this does
# not naturally extend from SelectorMixin
"""Transforme... | {
"repo_name": "B3AU/waveTree",
"path": "sklearn/feature_selection/from_model.py",
"copies": "11",
"size": "4255",
"license": "bsd-3-clause",
"hash": 6907214902364467000,
"line_mean": 37.6818181818,
"line_max": 78,
"alpha_frac": 0.5696827262,
"autogenerated": false,
"ratio": 4.890804597701149,
"... |
import numpy as np
from ..base import TransformerMixin
from ..externals import six
from ..utils import safe_mask, check_array
from ..utils.validation import NotFittedError, check_is_fitted
class _LearntSelectorMixin(TransformerMixin):
# Note because of the extra threshold parameter in transform, this does
#... | {
"repo_name": "Jimmy-Morzaria/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "224",
"size": "4316",
"license": "bsd-3-clause",
"hash": -2550997177283025000,
"line_mean": 37.1946902655,
"line_max": 78,
"alpha_frac": 0.5817886932,
"autogenerated": false,
"ratio": 4.7169... |
import numpy as np
from ..base import TransformerMixin
from ..externals import six
from ..utils import safe_mask, check_array
class _LearntSelectorMixin(TransformerMixin):
# Note because of the extra threshold parameter in transform, this does
# not naturally extend from SelectorMixin
"""Transformer mix... | {
"repo_name": "eickenberg/scikit-learn",
"path": "sklearn/feature_selection/from_model.py",
"copies": "1",
"size": "4252",
"license": "bsd-3-clause",
"hash": 5229897829378882000,
"line_mean": 37.6545454545,
"line_max": 78,
"alpha_frac": 0.5689087488,
"autogenerated": false,
"ratio": 4.92699884125... |
import numpy as np
from ..base import TransformerMixin
from ..externals import six
from ..utils import safe_mask, atleast2d_or_csr
class SelectorMixin(TransformerMixin):
"""Transformer mixin selecting features based on importance weights.
This implementation can be mixin on any estimator that exposes a
... | {
"repo_name": "kmike/scikit-learn",
"path": "sklearn/feature_selection/selector_mixin.py",
"copies": "2",
"size": "4117",
"license": "bsd-3-clause",
"hash": 359951900457448450,
"line_mean": 37.1203703704,
"line_max": 78,
"alpha_frac": 0.5630313335,
"autogenerated": false,
"ratio": 4.9011904761904... |
import numpy as np
from ..base import TransformerMixin
from ..utils import safe_mask, atleast2d_or_csr
class SelectorMixin(TransformerMixin):
"""Transformer mixin selecting features based on importance weights.
This implementation can be mixin on any estimator that exposes a
``feature_importances_`` or... | {
"repo_name": "seckcoder/lang-learn",
"path": "python/sklearn/sklearn/feature_selection/selector_mixin.py",
"copies": "1",
"size": "3809",
"license": "unlicense",
"hash": -3123959474871177700,
"line_mean": 36.7128712871,
"line_max": 78,
"alpha_frac": 0.569178262,
"autogenerated": false,
"ratio": ... |
import itertools
from copy import copy
from twisted.web import client, _newclient, http_headers
from twisted.web._newclient import RequestNotSent, RequestGenerationFailed
from twisted.web._newclient import TransportProxyProducer, STATUS
from twisted.internet import reactor
from twisted.internet.defer import Deferred,... | {
"repo_name": "juga0/ooni-probe",
"path": "ooni/utils/trueheaders.py",
"copies": "1",
"size": "5890",
"license": "bsd-2-clause",
"hash": -6684001071561271000,
"line_mean": 31.5414364641,
"line_max": 77,
"alpha_frac": 0.621901528,
"autogenerated": false,
"ratio": 4.359733530717986,
"config_test"... |
import itertools
from copy import copy
from twisted.web import client, _newclient, http_headers
from twisted.web._newclient import RequestNotSent, RequestGenerationFailed, TransportProxyProducer, STATUS
from twisted.internet import reactor
from twisted.internet.defer import Deferred, fail, maybeDeferred, failure
fro... | {
"repo_name": "lordappsec/ooni-probe",
"path": "ooni/utils/trueheaders.py",
"copies": "3",
"size": "5405",
"license": "bsd-2-clause",
"hash": 8102901203983018000,
"line_mean": 31.7575757576,
"line_max": 106,
"alpha_frac": 0.6212765957,
"autogenerated": false,
"ratio": 4.362389023405973,
"config... |
"""A package for dealing with geometry."""
__version__ = '0.0.a'
#__all__ = [
# 'matrix',
# 'vector',
# 'point',
# 'affinematrix',
# 'line',
# 'polyline',
# 'circle',
# 'arc'
# ]
EPS = 1.0e-6
from matrix import Matrix
from vector import Vector
from point import Point
from affinematrix im... | {
"repo_name": "gfsmith/gears",
"path": "gears/geometry/__init__.py",
"copies": "1",
"size": "1799",
"license": "mit",
"hash": 8739113731544709000,
"line_mean": 25.8507462687,
"line_max": 120,
"alpha_frac": 0.5780989439,
"autogenerated": false,
"ratio": 2.495145631067961,
"config_test": false,
... |
from skmultilearn.base import MLClassifierBase
import numpy as np
import scipy.sparse as sp
from scipy.linalg import norm
from scipy.sparse.linalg import inv as inv_sparse
from scipy.linalg import inv as inv_dense
class MLTSVM(MLClassifierBase):
"""Twin multi-Label Support Vector Machines
Parameters
---... | {
"repo_name": "scikit-multilearn/scikit-multilearn",
"path": "skmultilearn/adapt/mltsvm.py",
"copies": "1",
"size": "6351",
"license": "bsd-2-clause",
"hash": 1284250646828732400,
"line_mean": 32.9625668449,
"line_max": 105,
"alpha_frac": 0.5836876083,
"autogenerated": false,
"ratio": 3.372809346... |
__authors__ = "Guillaume Desjardin, Xavier Muller"
__copyright__ = "(c) 2010, Universite de Montreal"
__license__ = "3-clause BSD License"
__contact__ = "Xavier Muller <xav.muller@gmail.com>"
import os
from optparse import OptionParser
from jobman.parse import filemerge
from jobman.parse import standard as jparse
fr... | {
"repo_name": "crmne/jobman",
"path": "jobman/findjob.py",
"copies": "1",
"size": "7652",
"license": "bsd-3-clause",
"hash": -3862384921149217300,
"line_mean": 33.1607142857,
"line_max": 122,
"alpha_frac": 0.5582854156,
"autogenerated": false,
"ratio": 3.9220912352639674,
"config_test": false,
... |
from ..backends._notebook \
import _NotebookInteractor as _PyVistaNotebookInteractor
class _NotebookInteractor(_PyVistaNotebookInteractor):
def __init__(self, brain):
self.brain = brain
super().__init__(self.brain._renderer)
def configure_controllers(self):
from ipywidgets import... | {
"repo_name": "larsoner/mne-python",
"path": "mne/viz/_brain/_notebook.py",
"copies": "4",
"size": "2301",
"license": "bsd-3-clause",
"hash": 8113060611981110000,
"line_mean": 33.3432835821,
"line_max": 67,
"alpha_frac": 0.5367231638,
"autogenerated": false,
"ratio": 4.094306049822064,
"config_... |
from ..backends._notebook \
import _NotebookInteractor as _PyVistaNotebookInteractor
class _NotebookInteractor(_PyVistaNotebookInteractor):
def __init__(self, time_viewer):
self.time_viewer = time_viewer
self.brain = self.time_viewer.brain
super().__init__(self.brain._renderer)
d... | {
"repo_name": "cjayb/mne-python",
"path": "mne/viz/_brain/_notebook.py",
"copies": "2",
"size": "2381",
"license": "bsd-3-clause",
"hash": -6191399142872381000,
"line_mean": 34.0147058824,
"line_max": 67,
"alpha_frac": 0.543049139,
"autogenerated": false,
"ratio": 4.0770547945205475,
"config_te... |
import matplotlib.pyplot as plt
from contextlib import contextmanager
from ...fixes import nullcontext
from ._pyvista import _Renderer as _PyVistaRenderer
from ._pyvista import \
_close_all, _set_3d_view, _set_3d_title # noqa: F401 analysis:ignore
class _Renderer(_PyVistaRenderer):
def __init__(self, *args,... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/viz/backends/_notebook.py",
"copies": "2",
"size": "5496",
"license": "bsd-3-clause",
"hash": 3001935578690518500,
"line_mean": 31.9101796407,
"line_max": 73,
"alpha_frac": 0.5669577875,
"autogenerated": false,
"ratio": 4.208269525267994,
"confi... |
import matplotlib.pyplot as plt
from contextlib import contextmanager
from ...fixes import nullcontext
from ._pyvista import _Renderer as _PyVistaRenderer
class _Renderer(_PyVistaRenderer):
def __init__(self, *args, **kwargs):
from IPython import get_ipython
ipython = get_ipython()
ipytho... | {
"repo_name": "cjayb/mne-python",
"path": "mne/viz/backends/_notebook.py",
"copies": "2",
"size": "5398",
"license": "bsd-3-clause",
"hash": 9137818348036910000,
"line_mean": 31.7151515152,
"line_max": 70,
"alpha_frac": 0.565024083,
"autogenerated": false,
"ratio": 4.257097791798107,
"config_te... |
__author__ = 'sh84.ahn@gmail.com'
__version__ = '0.1'
import logging
from logging import handlers
import inspect
import functools
import datetime
import os
from flask import request
""" LogLevel """
@property
def CRITICAL():
return logging.CRITICAL
@property
def FATAL():
return logging.FATAL
@property
... | {
"repo_name": "AhnSeongHyun/logis",
"path": "logis.py",
"copies": "1",
"size": "5988",
"license": "mit",
"hash": 3542163170834566000,
"line_mean": 22.8605577689,
"line_max": 106,
"alpha_frac": 0.5768203073,
"autogenerated": false,
"ratio": 4.109814687714482,
"config_test": false,
"has_no_keyw... |
# Desc : This file is the SQL generation core.
from .adapters import Adapter
def spacecat(*args):
return ' '.join(list(args))
_surround = lambda x, start, end: ''.join([start,x,end])
class Child:
def __init__(self, name, value, relation):
self.sub_tree = False
if value.__class__.__name__ ... | {
"repo_name": "shabinesh/Tabject",
"path": "tabject/tree.py",
"copies": "1",
"size": "2972",
"license": "bsd-3-clause",
"hash": -5855122166389519000,
"line_mean": 31.6593406593,
"line_max": 120,
"alpha_frac": 0.5349932705,
"autogenerated": false,
"ratio": 3.85473411154345,
"config_test": false,... |
__author__ = 'shabou'
import logging
import argparse
import os
import importlib
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(description='Make a submission.')
parser.add_argument('--c', type=str, help='competition name')
parser.add_argument('--a', type=str, help='author name')
args = par... | {
"repo_name": "aymen82/kaggler-competitions-scripts",
"path": "kaggler/scripts/run-submission.py",
"copies": "1",
"size": "1111",
"license": "bsd-3-clause",
"hash": 7553706089404071000,
"line_mean": 26.0975609756,
"line_max": 93,
"alpha_frac": 0.703870387,
"autogenerated": false,
"ratio": 3.25806... |
__author__ = 'shadoobie'
from socket import AF_INET, SOCK_STREAM, socket
from threading import Thread, Event
from Queue import Queue
from DistributedStorageBenchmarkTool.CountdownTask import CountdownTask
from DistributedStorageBenchmarkTool.LaunchAndMeasureDataTask import LaunchAndMeasureDataTask
from DistributedStora... | {
"repo_name": "shadoobie/dbench",
"path": "DistributedStorageBenchmarkTool/DiskPerformanceClient.py",
"copies": "1",
"size": "2963",
"license": "mit",
"hash": 4521584734919761400,
"line_mean": 39.602739726,
"line_max": 162,
"alpha_frac": 0.6888288896,
"autogenerated": false,
"ratio": 3.8281653746... |
__author__ = 'shadoobie'
import getopt, sys
import logging
import os.path
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from DistributedStorageBenchmarkTool.DiskPerformanceClient import DiskPerformanceClient
from DistributedStorageBenchmarkTool.StampyMcGetTheLog import StampyMcGetTheLog
def main(argv)... | {
"repo_name": "shadoobie/dbench",
"path": "DistributedStorageBenchmarkTool/TestDiskPerformance.py",
"copies": "1",
"size": "3763",
"license": "mit",
"hash": 239806508945842940,
"line_mean": 41.7613636364,
"line_max": 139,
"alpha_frac": 0.6183895828,
"autogenerated": false,
"ratio": 3.990455991516... |
__author__ = 'shadoobie'
import getopt, sys
import os.path
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from DistributedStorageBenchmarkTool.EchoHandler import EchoHandler
from DistributedStorageBenchmarkTool.DistributedStorageBenchmarkServer import DistributedStorageBenchmarkServer
from DistributedSt... | {
"repo_name": "shadoobie/dbench",
"path": "DistributedStorageBenchmarkTool/StartBenchmarkTestServer.py",
"copies": "1",
"size": "3100",
"license": "mit",
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"autogenerated": false,
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__author__ = 'shadoobie'
import time
class CountdownTask:
def __init__(self, log, sock, clientName):
self.log = log
self.sock = sock
self.flood("CountdownTask for heartbeat has been instantiated but not yet started.")
self.clientName = clientName
self._running = True
... | {
"repo_name": "shadoobie/dbench",
"path": "DistributedStorageBenchmarkTool/CountdownTask.py",
"copies": "1",
"size": "1100",
"license": "mit",
"hash": 9129258994812740000,
"line_mean": 33.40625,
"line_max": 135,
"alpha_frac": 0.5945454545,
"autogenerated": false,
"ratio": 3.678929765886288,
"co... |
__author__ = 'shadoobie'
import os
from DistributedStorageBenchmarkTool.StampyMcGetTheLog import StampyMcGetTheLog
from DistributedStorageBenchmarkTool.Timer import Timer
class DataTask:
def __init__(self, log, sock, clientName, chunkSize, maxFileSize, stopCountdownEvent):
self._running = True
sel... | {
"repo_name": "shadoobie/dbench",
"path": "DistributedStorageBenchmarkTool/DataTask.py",
"copies": "1",
"size": "2994",
"license": "mit",
"hash": 7983512047005656000,
"line_mean": 36.4375,
"line_max": 130,
"alpha_frac": 0.6022044088,
"autogenerated": false,
"ratio": 3.770780856423174,
"config_t... |
__author__ = 'SHADOWxCODEX'
#pip install requests
#or easy_install requests
#found under python_path\Scripts\pip
#if you get an python egg related error, try 'pip install --upgrade setuptools'
import requests
import itertools
print('Author: SHADOWxCODEX')
print('Date Created: 7-24-2015')
print('Source: https://github... | {
"repo_name": "PhantomBALR/pydomainfinder",
"path": "pydomainfinder.py",
"copies": "1",
"size": "5183",
"license": "mit",
"hash": 739841397947608300,
"line_mean": 34.2585034014,
"line_max": 156,
"alpha_frac": 0.5998456492,
"autogenerated": false,
"ratio": 3.6244755244755247,
"config_test": fals... |
__author__ = 'shafi'
import logging
import json,os
import socket
from json2html import *
from flask import Flask, url_for, request,render_template
from flask import Response
from flask import jsonify
from functools import wraps
app = Flask(__name__)
def check_auth(username, password):
return username == 'admin... | {
"repo_name": "shafi-codez/FlaskDemo",
"path": "run.py",
"copies": "1",
"size": "3470",
"license": "apache-2.0",
"hash": -5796967829035028000,
"line_mean": 25.0902255639,
"line_max": 141,
"alpha_frac": 0.6227665706,
"autogenerated": false,
"ratio": 3.497983870967742,
"config_test": false,
"ha... |
__author__ = 'shailesh'
import numpy as np
import csv
class UtilMethods(object):
@staticmethod
def LoadWordsSetFromFile(filePath):
words = set()
file = open(filePath,'r')
for word in file:
words.add(word.strip())
return words
@staticmethod
def LoadWordsListF... | {
"repo_name": "shaileshahuja/SentimentBlade",
"path": "src/Utils.py",
"copies": "1",
"size": "3905",
"license": "apache-2.0",
"hash": -609724267294153700,
"line_mean": 31.0081967213,
"line_max": 81,
"alpha_frac": 0.5513444302,
"autogenerated": false,
"ratio": 4.221621621621622,
"config_test": f... |
__author__ = 'Shailesh'
__author__ = 'Shailesh'
from sklearn.ensemble import RandomForestClassifier
import utils
from sklearn import cross_validation
from collections import defaultdict
import operator
import numpy as np
import math
import logging
import datetime
def preprocess(data):
processedData = [[] for _ i... | {
"repo_name": "shaileshahuja/MineRush",
"path": "Eye Movement Tracking/src/feature_extraction.py",
"copies": "1",
"size": "3420",
"license": "unlicense",
"hash": -3163962782088920000,
"line_mean": 35,
"line_max": 108,
"alpha_frac": 0.5926900585,
"autogenerated": false,
"ratio": 3.152073732718894,... |
__author__ = 'Shailesh'
from collections import defaultdict
import numpy
import csv
from Sentiment import Sentiment
from Angel import Angel
from Utils import UtilMethods as util
from PerformanceTest import PerformanceTest
def ImpactTraining(docPath, lexPath, lexiconID):
"""
Final score of the review is calcu... | {
"repo_name": "shaileshahuja/SentimentBlade",
"path": "src/ImpactTraining.py",
"copies": "1",
"size": "3681",
"license": "apache-2.0",
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"line_max": 122,
"alpha_frac": 0.6528117359,
"autogenerated": false,
"ratio": 4.108258928571429,
"config_test"... |
__author__ = 'Shailesh'
from sklearn import svm, cross_validation
import utils
import numpy as np
def main():
X, Y = utils.read_data("../files/train_10.csv")
Y = map(int, Y)
folds = 5
stf = cross_validation.StratifiedKFold(Y, folds)
loss = []
svc = svm.SVC(probability=True)
accs = []
c... | {
"repo_name": "shaileshahuja/MineRush",
"path": "Eye Movement Tracking/src/svmBenchmark.py",
"copies": "1",
"size": "1136",
"license": "unlicense",
"hash": 1624419493377083600,
"line_mean": 33.4545454545,
"line_max": 79,
"alpha_frac": 0.5704225352,
"autogenerated": false,
"ratio": 2.9053708439897... |
__author__ = 'shailesh'
from xml.etree import ElementTree as ET
from xml.dom import minidom
from YelpReview import Review
def Prettify(elem):
"""Return a pretty-printed XML string for the Element.
"""
rough_string = ET.tostring(elem, 'utf-8')
reparsed = minidom.parseString(rough_string)
return re... | {
"repo_name": "shaileshahuja/SentimentBlade",
"path": "src/XMLHandler.py",
"copies": "1",
"size": "3560",
"license": "apache-2.0",
"hash": -8631215972868792000,
"line_mean": 32.9047619048,
"line_max": 104,
"alpha_frac": 0.5904494382,
"autogenerated": false,
"ratio": 3.7355718782791185,
"config_... |
__author__ = 'Shailesh'
import math
def read_data(file_name):
f = open(file_name)
#ignore header
f.readline()
samples = []
target = []
for line in f:
line = line.strip().split(",")
sample = [float(x) for x in line[1:]]
samples.append(sample)
target.append(line[0... | {
"repo_name": "shaileshahuja/MineRush",
"path": "Eye Movement Tracking/src/utils.py",
"copies": "1",
"size": "2377",
"license": "unlicense",
"hash": 6107408170576256000,
"line_mean": 27.6506024096,
"line_max": 75,
"alpha_frac": 0.6066470341,
"autogenerated": false,
"ratio": 3.537202380952381,
"... |
__author__ = 'shailesh'
import os
import json
from Utils import UtilMethods as util
from YelpCrawler import YelpCrawler
from XMLHandler import LoadCrawledXMLFile,DumpSortedReviews
from Angel import Angel
from Sentiment import Sentiment
class SentimentBlade:
def __init__(self, url):
self.url = url
d... | {
"repo_name": "shaileshahuja/SentimentBlade",
"path": "src/SentimentBlade.py",
"copies": "1",
"size": "2877",
"license": "apache-2.0",
"hash": -7451752824779601000,
"line_mean": 34.975,
"line_max": 108,
"alpha_frac": 0.6478971151,
"autogenerated": false,
"ratio": 3.836,
"config_test": false,
... |
__author__ = 'shailesh'
import os
import json
posRoot = "/home/shailesh/nltk_data/corpora/movie_reviews/pos"
negRoot = "/home/shailesh/nltk_data/corpora/movie_reviews/neg"
outputPath = "/home/shailesh/webservice/src/classifier_v3.0/movie_reviews.txt"
def PrepareTextFile():
with open(outputPath, 'w') as outHand... | {
"repo_name": "shaileshahuja/SentimentBlade",
"path": "src/movie_reviews_test_prep.py",
"copies": "1",
"size": "2002",
"license": "apache-2.0",
"hash": 6186734174309490000,
"line_mean": 37.5192307692,
"line_max": 121,
"alpha_frac": 0.6178821179,
"autogenerated": false,
"ratio": 3.686924493554328,... |
__author__ = 'Shailesh'
class PredictionFunctions:
NegativeModifiers = {"not", "n't", "no", "nothing", "at"}
PositiveModifiers = {"very", "so", "really", "super", "extremely"}
NeutralModifiers = {"neither", "nor"}
TooExceptionList = {"good", "awesome", "brilliant", "kind", "great", "tempting", "big",... | {
"repo_name": "shaileshahuja/SentimentBlade",
"path": "src/PredictionFunctions.py",
"copies": "1",
"size": "4397",
"license": "apache-2.0",
"hash": 6696549591885527000,
"line_mean": 47.8666666667,
"line_max": 237,
"alpha_frac": 0.5774391631,
"autogenerated": false,
"ratio": 3.836823734729494,
"... |
__author__ = 'shako'
import os
import copy
import json
import time
import logging
import psutil
import commands
import datetime
import tempfile
from minions import Minion
class MtbfToRaptorMinion(Minion):
def update(self, **kwargs):
Minion.update(self, **kwargs)
self.conf = {}
if 'job_in... | {
"repo_name": "Mozilla-TWQA/mozMinions",
"path": "moz_minions/kevin.py",
"copies": "1",
"size": "9218",
"license": "mpl-2.0",
"hash": -503075646487018000,
"line_mean": 43.9658536585,
"line_max": 119,
"alpha_frac": 0.4798220872,
"autogenerated": false,
"ratio": 4.317564402810304,
"config_test": ... |
__author__ = 'Shane'
from unittest.mock import Mock
import ClassInteractor
import MethodInteractor
import MockeryInteractor
class DynamicMock(Mock):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if 'spec' in kwargs:
self.mockedClass = kwargs['spec']
# def ... | {
"repo_name": "smckee6192/MockyThingy",
"path": "DynamicMock.py",
"copies": "1",
"size": "2097",
"license": "mit",
"hash": -5003684980323817000,
"line_mean": 40.137254902,
"line_max": 109,
"alpha_frac": 0.6342393896,
"autogenerated": false,
"ratio": 3.8905380333951762,
"config_test": false,
"... |
__author__ = 'Shane'
from ClassToPass import ClassToPass
class ImportantClass:
def __init__(self):
pass
def doTheThing(self, number1=int(), number2=int(), classToPass=ClassToPass()) -> int:
print("TheThing")
added = classToPass.gimmeTheSum(number1, number2)
return added
... | {
"repo_name": "smckee6192/MockyThingy",
"path": "ImportantFile.py",
"copies": "1",
"size": "1064",
"license": "mit",
"hash": 8819676050588695000,
"line_mean": 27.7837837838,
"line_max": 89,
"alpha_frac": 0.6062030075,
"autogenerated": false,
"ratio": 3.4885245901639346,
"config_test": false,
... |
__author__ = 'Shane'
from DynamicMock import DynamicMock
import ClassInteractor
import MethodInteractor
from MockedTypes import MockedTypes
def createMockClassOfType(type_):
mockObject = DynamicMock(spec=type_)
class_ = type_()
for each in ClassInteractor.getAllMethodNames(class_):
method = Class... | {
"repo_name": "smckee6192/MockyThingy",
"path": "MockeryInteractor.py",
"copies": "1",
"size": "1969",
"license": "mit",
"hash": -5145854614057866000,
"line_mean": 35.462962963,
"line_max": 99,
"alpha_frac": 0.7110208228,
"autogenerated": false,
"ratio": 4.068181818181818,
"config_test": false,... |
__author__ = 'Shane'
import inspect
import MethodInteractor
def callUnboundMethodWithRandomValues(class_, methodName):
instantiatedClass = class_()
unboundMethod = getattr(instantiatedClass, methodName)
generatedParameters = MethodInteractor.getParameterInputs(unboundMethod)
unboundMethod(*generatedPa... | {
"repo_name": "smckee6192/MockyThingy",
"path": "ClassInteractor.py",
"copies": "1",
"size": "1044",
"license": "mit",
"hash": 5416961886091284000,
"line_mean": 29.7352941176,
"line_max": 76,
"alpha_frac": 0.7212643678,
"autogenerated": false,
"ratio": 4.078125,
"config_test": false,
"has_no_... |
__author__ = 'Shane'
import inspect
import ValueGenerator
import MockeryInteractor
def getParameterInputs(method):
methodInfo = inspect.getfullargspec(method)
defaultArgs = methodInfo[3]
argInputs = []
print("Testing", method.__name__)
if not (len(methodInfo[0]) == 1 and methodInfo[0][0] == 'sel... | {
"repo_name": "smckee6192/MockyThingy",
"path": "MethodInteractor.py",
"copies": "1",
"size": "2334",
"license": "mit",
"hash": -4611838771111313000,
"line_mean": 31.4305555556,
"line_max": 122,
"alpha_frac": 0.646101114,
"autogenerated": false,
"ratio": 4.26691042047532,
"config_test": false,
... |
__author__ = 'Shane'
import random
import string
import sys
def getRandomStrOfLen(length):
charList = [random.choice(string.printable) for _ in range(length)]
return ''.join(charList)
def createRandomListOfType(type_):
randList = []
listSize = random.randint(0, 1000)
for _ in range(listSize):
... | {
"repo_name": "smckee6192/MockyThingy",
"path": "ValueGenerator.py",
"copies": "1",
"size": "2050",
"license": "mit",
"hash": -9198534350607506000,
"line_mean": 30.5538461538,
"line_max": 94,
"alpha_frac": 0.6546341463,
"autogenerated": false,
"ratio": 3.9272030651340994,
"config_test": false,
... |
__author__ = 'Shaoteng Liu'
# Copyright 2015 SICS Swedish ICT AB
#
# 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 ... | {
"repo_name": "nigsics/perf-kvm",
"path": "server/sub.py",
"copies": "1",
"size": "3747",
"license": "apache-2.0",
"hash": -5000985328862769000,
"line_mean": 22.8662420382,
"line_max": 95,
"alpha_frac": 0.5866026154,
"autogenerated": false,
"ratio": 4.046436285097192,
"config_test": false,
"h... |
import direct.directbase.DirectStart
from panda3d.core import Texture
from direct.interval.LerpInterval import LerpFunc
from direct.gui.OnscreenText import OnscreenText
from direct.showbase.DirectObject import DirectObject
from direct.task.Task import Task
import sys
#Our specialized function to load texture movies a... | {
"repo_name": "toontownfunserver/Panda3D-1.9.0",
"path": "samples/Texture-Swapping/Tut-Texture-Swapping-II.py",
"copies": "3",
"size": "3258",
"license": "bsd-3-clause",
"hash": 2933382628222414000,
"line_mean": 39.2222222222,
"line_max": 85,
"alpha_frac": 0.718538981,
"autogenerated": false,
"ra... |
# The duck animation was created by Shane Liesegang and William Houng
# for the Entertainment Technology Center class Building Virtual Worlds
import direct.directbase.DirectStart
from panda3d.core import Texture
from panda3d.core import BillboardEffect
from panda3d.core import Camera
from panda3d.core import TextNode... | {
"repo_name": "ToonTownInfiniteRepo/ToontownInfinite",
"path": "Panda3D-1.9.0/samples/Texture-Swapping/Tut-Texture-Swapping.py",
"copies": "3",
"size": "8401",
"license": "mit",
"hash": -9088254001244020000,
"line_mean": 47.2816091954,
"line_max": 79,
"alpha_frac": 0.6928937031,
"autogenerated": fa... |
import direct.directbase.DirectStart
from panda3d.core import Fog
from panda3d.core import TextNode
from direct.gui.OnscreenText import OnscreenText
from direct.showbase.DirectObject import DirectObject
from direct.interval.MetaInterval import Sequence
from direct.interval.LerpInterval import LerpFunc
from direct.int... | {
"repo_name": "francholi/PandaExamples",
"path": "Infinite-Tunnel/Tut-Infinite-Tunnel.py",
"copies": "3",
"size": "8881",
"license": "mit",
"hash": 6179420144460110000,
"line_mean": 43.6281407035,
"line_max": 85,
"alpha_frac": 0.6935029839,
"autogenerated": false,
"ratio": 3.4489320388349514,
"... |
import direct.directbase.DirectStart #Initialize Panda and create a window
from panda3d.core import * #Contains most of Panda's modules
from direct.gui.DirectGui import * #Imports Gui objects we use for putting
#text on the screen
import sys
class World: ... | {
"repo_name": "toontownfunserver/Panda3D-1.9.0",
"path": "samples/Solar-System/Tut-Step-2-Basic-Setup.py",
"copies": "3",
"size": "2281",
"license": "bsd-3-clause",
"hash": 2022906311628869000,
"line_mean": 40.4727272727,
"line_max": 79,
"alpha_frac": 0.6773345024,
"autogenerated": false,
"ratio"... |
import direct.directbase.DirectStart
from panda3d.core import NodePath
from direct.gui.DirectGui import *
import sys
class World:
def __init__(self):
#This is the initialization we had before
self.title = OnscreenText( #Create the title
text="Panda3D: Tutorial 1 - Solar System",
style=1... | {
"repo_name": "toontownfunserver/Panda3D-1.9.0",
"path": "samples/Solar-System/Tut-Step-3-Load-Model.py",
"copies": "3",
"size": "4500",
"license": "bsd-3-clause",
"hash": -6542556713858582000,
"line_mean": 42.2692307692,
"line_max": 79,
"alpha_frac": 0.6906666667,
"autogenerated": false,
"ratio"... |
import direct.directbase.DirectStart
from direct.gui.DirectGui import *
from panda3d.core import Vec3, Vec4
import sys
class World:
def __init__(self):
#This is the initialization we had before
self.title = OnscreenText( #Create the title
text="Panda3D: Tutorial 1 - Solar System",
style... | {
"repo_name": "francholi/PandaExamples",
"path": "Solar-System/Tut-Step-5-Complete-Solar-System.py",
"copies": "3",
"size": "6726",
"license": "mit",
"hash": -4797329678987051000,
"line_mean": 38.798816568,
"line_max": 79,
"alpha_frac": 0.6775200714,
"autogenerated": false,
"ratio": 3.08673703533... |
import direct.directbase.DirectStart
from panda3d.physics import BaseParticleEmitter,BaseParticleRenderer
from panda3d.physics import PointParticleFactory,SpriteParticleRenderer
from panda3d.physics import LinearNoiseForce,DiscEmitter
from panda3d.core import TextNode
from panda3d.core import AmbientLight,DirectionalL... | {
"repo_name": "ToonTownInfiniteRepo/ToontownInfinite",
"path": "Panda3D-1.9.0/samples/Particles/Tut-Steam-Example.py",
"copies": "3",
"size": "3273",
"license": "mit",
"hash": -5893618172378569000,
"line_mean": 34.5760869565,
"line_max": 82,
"alpha_frac": 0.670944088,
"autogenerated": false,
"rat... |
import direct.directbase.DirectStart
from direct.showbase import DirectObject
from panda3d.core import *
from direct.interval.IntervalGlobal import *
from direct.gui.DirectGui import *
from direct.showbase.DirectObject import DirectObject
import sys
# We start this tutorial with the standard class. However, the clas... | {
"repo_name": "mweinberger-tgm/VenusAndMars",
"path": "prototyp/VenusAndMars.py",
"copies": "1",
"size": "11014",
"license": "apache-2.0",
"hash": -3591132263427912700,
"line_mean": 42.02734375,
"line_max": 100,
"alpha_frac": 0.633103323,
"autogenerated": false,
"ratio": 3.3910098522167487,
"co... |
import direct.directbase.DirectStart
from direct.showbase import DirectObject
from panda3d.core import TextNode, Vec3, Vec4
from direct.interval.IntervalGlobal import *
from direct.gui.DirectGui import *
from direct.showbase.DirectObject import DirectObject
import sys
#We start this tutorial with the standard class. ... | {
"repo_name": "toontownfunserver/Panda3D-1.9.0",
"path": "samples/Solar-System/Tut-Step-6-Controllable-System.py",
"copies": "3",
"size": "14595",
"license": "bsd-3-clause",
"hash": 7613119265882371000,
"line_mean": 42.4375,
"line_max": 82,
"alpha_frac": 0.6642685851,
"autogenerated": false,
"rat... |
import direct.directbase.DirectStart
from panda3d.core import AmbientLight,DirectionalLight
from panda3d.core import TextNode,NodePath,LightAttrib
from panda3d.core import Vec3,Vec4
from direct.actor.Actor import Actor
from direct.task.Task import Task
from direct.gui.OnscreenText import OnscreenText
from direct.showb... | {
"repo_name": "ToonTownInfiniteRepo/ToontownInfinite",
"path": "Panda3D-1.9.0/samples/Looking-and-Gripping/Tut-Looking-and-Gripping.py",
"copies": "3",
"size": "6366",
"license": "mit",
"hash": 6224943812424821000,
"line_mean": 47.5954198473,
"line_max": 81,
"alpha_frac": 0.6577128495,
"autogenerat... |
import direct.directbase.DirectStart
from panda3d.core import CollisionTraverser,CollisionNode
from panda3d.core import CollisionHandlerQueue,CollisionRay
from panda3d.core import AmbientLight,DirectionalLight,LightAttrib
from panda3d.core import TextNode
from panda3d.core import Point3,Vec3,Vec4,BitMask32
from direct... | {
"repo_name": "francholi/PandaExamples",
"path": "Chessboard/Tut-Chessboard.py",
"copies": "3",
"size": "10673",
"license": "mit",
"hash": 2793052011834429000,
"line_mean": 40.8549019608,
"line_max": 81,
"alpha_frac": 0.6891220838,
"autogenerated": false,
"ratio": 3.5062417871222076,
"config_te... |
import direct.directbase.DirectStart
from panda3d.core import AmbientLight,DirectionalLight
from panda3d.core import TextNode
from panda3d.core import Vec3,Vec4
from direct.showbase.DirectObject import DirectObject
from direct.gui.OnscreenText import OnscreenText
from direct.interval.MetaInterval import Sequence
from ... | {
"repo_name": "ToonTownInfiniteRepo/ToontownInfinite",
"path": "Panda3D-1.9.0/samples/Boxing-Robots/Tut-Boxing-Robots.py",
"copies": "3",
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... |
import direct.directbase.DirectStart
from panda3d.core import AmbientLight, DirectionalLight, LightAttrib
from panda3d.core import NodePath
from panda3d.core import Vec3, Vec4
from direct.interval.IntervalGlobal import * #Needed to use Intervals
from direct.gui.DirectGui import *
#Importing math constants and funct... | {
"repo_name": "francholi/PandaExamples",
"path": "Carousel/Tut-Carousel.py",
"copies": "3",
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"line_max": 79,
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"autogenerated": false,
"ratio": 3.7457627118644066,
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import direct.directbase.DirectStart
from panda3d.core import NodePath,TextNode
from panda3d.core import Vec3,Vec4
from direct.gui.OnscreenText import OnscreenText
from direct.showbase.DirectObject import DirectObject
from direct.interval.SoundInterval import SoundInterval
from direct.gui.DirectSlider import DirectSli... | {
"repo_name": "francholi/PandaExamples",
"path": "Music-Box/Tut-Music-Box.py",
"copies": "3",
"size": "6551",
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"line_max": 82,
"alpha_frac": 0.6647840024,
"autogenerated": false,
"ratio": 3.692784667418264,
"config_test": ... |
import direct.directbase.DirectStart
from panda3d.core import TextNode
from panda3d.core import Point2,Point3,Vec3,Vec4
from direct.gui.OnscreenText import OnscreenText
from direct.showbase.DirectObject import DirectObject
from direct.task.Task import Task
from math import sin, cos, pi
from random import randint, choi... | {
"repo_name": "francholi/PandaExamples",
"path": "Asteroids/Tut-Asteroids.py",
"copies": "3",
"size": "17665",
"license": "mit",
"hash": -7775464810289108000,
"line_mean": 45.0026041667,
"line_max": 80,
"alpha_frac": 0.650608548,
"autogenerated": false,
"ratio": 3.666459111664591,
"config_test"... |
import direct.directbase.DirectStart
from panda3d.core import CollisionTraverser,CollisionNode
from panda3d.core import CollisionHandlerQueue,CollisionRay
from panda3d.core import Material,LRotationf,NodePath
from panda3d.core import AmbientLight,DirectionalLight
from panda3d.core import TextNode
from panda3d.core imp... | {
"repo_name": "toontownfunserver/Panda3D-1.9.0",
"path": "samples/Ball-in-Maze/Tut-Ball-in-Maze.py",
"copies": "3",
"size": "15789",
"license": "bsd-3-clause",
"hash": 3988090727896132000,
"line_mean": 48.6509433962,
"line_max": 80,
"alpha_frac": 0.6988409652,
"autogenerated": false,
"ratio": 3.7... |
import numpy as np
from . import Differentiable
import matrix_ops
class Elementwise(Differentiable):
__slots__ = ['X']
def __init__(self, X):
super(Elementwise, self).__init__(X)
self.X = X
def _compute_shape(self, inputs=None):
return self.X.shape
# Just an alias for matrix addi... | {
"repo_name": "HIPS/Kayak",
"path": "kayak/elem_ops.py",
"copies": "3",
"size": "3194",
"license": "mit",
"hash": -1103919681223018400,
"line_mean": 29.1320754717,
"line_max": 79,
"alpha_frac": 0.6008140263,
"autogenerated": false,
"ratio": 3.3550420168067228,
"config_test": false,
"has_no_ke... |
import itertools
import numpy as np
import numpy.random as npr
class Fold(object):
def __init__(self, cv, train, valid):
self._cv = cv
self._train = train
self._valid = valid
def train(self):
if self._cv.targets is None:
return self._cv.inputs[self._... | {
"repo_name": "davek44/Kayak",
"path": "kayak/crossval.py",
"copies": "3",
"size": "2585",
"license": "mit",
"hash": -643063766234482400,
"line_mean": 33.9324324324,
"line_max": 161,
"alpha_frac": 0.5528046422,
"autogenerated": false,
"ratio": 3.7903225806451615,
"config_test": false,
"has_no... |
import numpy as np
from . import Differentiable
class DataNode(Differentiable):
__slots__ = ['_batcher', '_data','_children', '_value', '_grad', '_loss', '_parents']
def __init__(self, data, batcher=None):
if batcher is None:
super(DataNode, self).__init__([])
else:
sup... | {
"repo_name": "HIPS/Kayak",
"path": "kayak/root_nodes.py",
"copies": "2",
"size": "1734",
"license": "mit",
"hash": -5647018632082680000,
"line_mean": 28.8965517241,
"line_max": 89,
"alpha_frac": 0.6136101499,
"autogenerated": false,
"ratio": 3.5101214574898787,
"config_test": false,
"has_no_... |
import numpy as np
from numpy import exp
import util
from . import Differentiable
from kayak import EPSILON
class Nonlinearity(Differentiable):
__slots__ = ['X']
def __init__(self, X):
super(Nonlinearity, self).__init__((X,))
self.X = X
class SoftReLU(Nonlinearity):
__slots__ = ['scale'... | {
"repo_name": "HIPS/Kayak",
"path": "kayak/nonlinearities.py",
"copies": "2",
"size": "5492",
"license": "mit",
"hash": 5598647180222029000,
"line_mean": 32.6932515337,
"line_max": 121,
"alpha_frac": 0.5635469774,
"autogenerated": false,
"ratio": 2.9321943406300055,
"config_test": false,
"has... |
import numpy as np
from scipy.sparse import issparse
from . import Differentiable
class DataNode(Differentiable):
__slots__ = ['_batcher', '_data','_children', '_value', '_grad', '_loss', '_parents']
def __init__(self, data, batcher=None):
if batcher is None:
super(DataNode, self).__init__... | {
"repo_name": "davek44/Kayak",
"path": "kayak/root_nodes.py",
"copies": "1",
"size": "2032",
"license": "mit",
"hash": 2915009973305335000,
"line_mean": 30.2615384615,
"line_max": 89,
"alpha_frac": 0.6067913386,
"autogenerated": false,
"ratio": 3.6350626118067977,
"config_test": false,
"has_n... |
import numpy as np
import numpy.random as npr
import itertools as it
from . import EPSILON
from root_nodes import Parameter
def checkgrad(variable, output, epsilon=1e-4, verbose=False):
if not isinstance(variable, Parameter):
raise Exception("Cannot evaluate gradient in terms of non-Parameter ... | {
"repo_name": "davek44/Kayak",
"path": "kayak/util.py",
"copies": "3",
"size": "2292",
"license": "mit",
"hash": 7682779644727092000,
"line_mean": 36.5737704918,
"line_max": 126,
"alpha_frac": 0.6492146597,
"autogenerated": false,
"ratio": 3.1483516483516483,
"config_test": false,
"has_no_key... |
import numpy as np
import numpy.random as npr
from . import Differentiable, EPSILON
class Dropout(Differentiable):
__slots__ = ['X', 'drop_prob', '_rng', '_enhancement', '_mask']
def __init__(self, X, drop_prob=0.5, rng=None, batcher=None):
if batcher is not None:
super(Dropout, s... | {
"repo_name": "davek44/Kayak",
"path": "kayak/dropout.py",
"copies": "1",
"size": "2568",
"license": "mit",
"hash": -9064371998065624000,
"line_mean": 29.2117647059,
"line_max": 74,
"alpha_frac": 0.5638629283,
"autogenerated": false,
"ratio": 3.3612565445026177,
"config_test": false,
"has_no_... |
import numpy as np
import numpy.random as npr
from . import Differentiable
class Batcher(Differentiable):
"""Kayak class for managing batches of data.
This class is intended to provide a simple interface for managing
mini-batches of data, both on the input side and on the output
side. It... | {
"repo_name": "HIPS/Kayak",
"path": "kayak/batcher.py",
"copies": "3",
"size": "5008",
"license": "mit",
"hash": 9135207350775039000,
"line_mean": 33.0680272109,
"line_max": 79,
"alpha_frac": 0.6230031949,
"autogenerated": false,
"ratio": 4.055060728744939,
"config_test": false,
"has_no_keywo... |
import numpy as np
import numpy.random as npr
from . import Differentiable
class Take(Differentiable):
__slots__ = ['X', '_inds', '_axis']
def __init__(self, X, inds, axis=1):
super(Take, self).__init__([X])
self.X = X
self._inds = inds
self._axis = ax... | {
"repo_name": "HIPS/Kayak",
"path": "kayak/indexing.py",
"copies": "3",
"size": "1084",
"license": "mit",
"hash": -576241683916252600,
"line_mean": 31.8484848485,
"line_max": 65,
"alpha_frac": 0.6060885609,
"autogenerated": false,
"ratio": 3.2261904761904763,
"config_test": false,
"has_no_key... |
import numpy as np
import scipy.linalg as spla
from . import Differentiable
class MatMult(Differentiable):
__slots__ = ['A', 'B']
def __init__(self, A, B, *args):
# Recurse to handle lists of arguments.
if len(args) > 0:
B = MatMult(B, *args)
super(MatMult, self).__i... | {
"repo_name": "xebitstudios/Kayak",
"path": "kayak/matrix_ops.py",
"copies": "2",
"size": "10507",
"license": "mit",
"hash": -5447895233136876000,
"line_mean": 35.8666666667,
"line_max": 101,
"alpha_frac": 0.5860854668,
"autogenerated": false,
"ratio": 3.2752493765586035,
"config_test": false,
... |
import numpy as np
import weakref
class Differentiable(object):
__slots__ = ['_value', '_grad', '_loss', '_parents', '_children','__weakref__','_parent_indices']
def __init__(self, parents=()):
self._value = None # Cached value
self._grad = None # Cached grad
self._loss = None # Loss... | {
"repo_name": "davek44/Kayak",
"path": "kayak/differentiable.py",
"copies": "3",
"size": "6602",
"license": "mit",
"hash": 5500025730991600000,
"line_mean": 34.3048128342,
"line_max": 101,
"alpha_frac": 0.5958800364,
"autogenerated": false,
"ratio": 4.090458488228005,
"config_test": false,
"h... |
import numpy as np
from . import Differentiable
class Regularizer(Differentiable):
__slots__ = ['X', 'weight']
def __init__(self, X, weight):
super(Regularizer, self).__init__([X])
self.X = X
self.weight = weight
class L2Norm(Regularizer):
__slots__ = []
def __init__(sel... | {
"repo_name": "xebitstudios/Kayak",
"path": "kayak/regularizers.py",
"copies": "3",
"size": "2109",
"license": "mit",
"hash": -9223203991666046000,
"line_mean": 33.0161290323,
"line_max": 97,
"alpha_frac": 0.5926979611,
"autogenerated": false,
"ratio": 2.9049586776859506,
"config_test": false,
... |
import numpy as np
from input_checking import check_equal_ndims_for_broadcasting
from . import Differentiable
class Loss(Differentiable):
__slots__ = ['preds', 'targs']
def __init__(self, predictions, targets):
super(Loss, self).__init__((predictions, targets))
self.preds = predictions
... | {
"repo_name": "davek44/Kayak",
"path": "kayak/losses.py",
"copies": "1",
"size": "2672",
"license": "mit",
"hash": 8149545822819431000,
"line_mean": 37.7246376812,
"line_max": 82,
"alpha_frac": 0.625748503,
"autogenerated": false,
"ratio": 3.34,
"config_test": false,
"has_no_keywords": false,... |
import numpy as np
import util
from . import Differentiable
import sys
class Convolve1d(Differentiable):
__slots__ = ['A', 'B', 'ncolors', 'stride']
def __init__(self, A, B, ncolors=1, stride=1):
super(Convolve1d, self).__init__([A,B])
self.A = A
self.B = B
self.... | {
"repo_name": "xebitstudios/Kayak",
"path": "kayak/convolution.py",
"copies": "3",
"size": "7191",
"license": "mit",
"hash": 7327948249443548000,
"line_mean": 36.453125,
"line_max": 121,
"alpha_frac": 0.5462383535,
"autogenerated": false,
"ratio": 3.3555762949136723,
"config_test": false,
"ha... |
__author__ = 'sharvey'
from classifiers import Classifier
from corpus.mysql.reddit import RedditMySQLCorpus
from ppm import Trie
class RedditPPM(Classifier):
trie = None
def train(self, document, order=5):
if (self.trie is not None):
del self.trie
self.trie = Trie(order)
... | {
"repo_name": "worldwise001/stylometry",
"path": "classifiers/ppmc.py",
"copies": "1",
"size": "1106",
"license": "mit",
"hash": -6691101659347129000,
"line_mean": 28.1315789474,
"line_max": 81,
"alpha_frac": 0.5415913201,
"autogenerated": false,
"ratio": 3.8536585365853657,
"config_test": fals... |
__author__ = 'sharvey'
from parallel.thread import DataGetThread
from parallel.thread import DataPutThread
from parallel.thread import TaskThread
from parallel.thread import tprint
import feature.simple
import time
import multiprocessing
from corpus.mysql.reddit import RedditMySQLCorpus
from main import cred
def ... | {
"repo_name": "worldwise001/stylometry",
"path": "parallel/thread/__main__.py",
"copies": "1",
"size": "1885",
"license": "mit",
"hash": -3113085661225405400,
"line_mean": 24.4864864865,
"line_max": 95,
"alpha_frac": 0.576127321,
"autogenerated": false,
"ratio": 3.244406196213425,
"config_test"... |
__author__ = 'sharvey'
from threading import Thread
import threading
import traceback
import sys
import time
queue = []
queue_lock = threading.Lock()
queue_event = threading.Event()
result = []
result_lock = threading.Lock()
result_event = threading.Event()
exit_event = threading.Event()
end_event = threading.Even... | {
"repo_name": "worldwise001/stylometry",
"path": "parallel/thread/__init__.py",
"copies": "1",
"size": "4856",
"license": "mit",
"hash": 5291994031405490000,
"line_mean": 31.1589403974,
"line_max": 78,
"alpha_frac": 0.5045304778,
"autogenerated": false,
"ratio": 4.136286201022147,
"config_test"... |
__author__ = 'sharvey'
import json
import os, os.path
import requests
import shutil
import socket
import sys
import tarfile
import tempfile
import threading
import time
import SocketServer
import parser
from store import Store
import creds
from reddit import Reddit
datestart = '20131201'
dateend = '20131231'
tmpdir... | {
"repo_name": "aleboz/reddit-crawler",
"path": "py/crawler.py",
"copies": "1",
"size": "15020",
"license": "mit",
"hash": 4714313281090383000,
"line_mean": 32.8288288288,
"line_max": 139,
"alpha_frac": 0.5051930759,
"autogenerated": false,
"ratio": 4.061654948620876,
"config_test": false,
"ha... |
__author__ = 'sharvey'
import multiprocessing
import mysql.connector
import sys
import time
import traceback
from corpus import Corpus
class MySQLCorpus(Corpus):
cnx = None
def __init__(self, cpu_count=None):
super(MySQLCorpus, self).__init__(cpu_count)
def __del__(self):
if self.cnx i... | {
"repo_name": "worldwise001/stylometry",
"path": "corpus/mysql/__init__.py",
"copies": "1",
"size": "16496",
"license": "mit",
"hash": 7822352653279101000,
"line_mean": 42.875,
"line_max": 107,
"alpha_frac": 0.5497090204,
"autogenerated": false,
"ratio": 3.7237020316027087,
"config_test": false... |
__author__ = 'sharvey'
import multiprocessing
from corpus.mysql.reddit import RedditMySQLCorpus
from feature import ngram
from feature import lexical
import cred
from operator import itemgetter
import pprint
import re
def feature_to_numeric(features):
corpus = RedditMySQLCorpus()
corpus.setup(**(cred.kwargs... | {
"repo_name": "worldwise001/stylometry",
"path": "main/gen_feature_sparse1.py",
"copies": "1",
"size": "3599",
"license": "mit",
"hash": 3526522064027245000,
"line_mean": 38.1195652174,
"line_max": 115,
"alpha_frac": 0.5068074465,
"autogenerated": false,
"ratio": 3.430886558627264,
"config_test... |
__author__ = 'sharvey'
import multiprocessing
from corpus.mysql.reddit import RedditMySQLCorpus
from feature import ngram
from feature import lexical
import cred
import pprint
import re
def gen_feature(atuple):
text = re.sub(r'https?://([a-zA-Z0-9\.\-_]+)[\w\-\._~:/\?#@!\$&\'\*\+,;=%%]*',
'\\1... | {
"repo_name": "worldwise001/stylometry",
"path": "main/gen_features.py",
"copies": "1",
"size": "3363",
"license": "mit",
"hash": -1281558242600140800,
"line_mean": 37.6666666667,
"line_max": 139,
"alpha_frac": 0.4894439489,
"autogenerated": false,
"ratio": 3.4670103092783506,
"config_test": fa... |
__author__ = 'sharvey'
import random
from corpus.mysql import MySQLCorpus
from corpus.mysql import util
class RedditMySQLCorpus(MySQLCorpus):
def __init__(self, cpu_count=None):
super(RedditMySQLCorpus, self).__init__(cpu_count)
def __del__(self):
super(RedditMySQLCorpus, self).__del__()
... | {
"repo_name": "worldwise001/stylometry",
"path": "corpus/mysql/reddit.py",
"copies": "1",
"size": "19791",
"license": "mit",
"hash": -7756102918097358000,
"line_mean": 48.7286432161,
"line_max": 120,
"alpha_frac": 0.5024506089,
"autogenerated": false,
"ratio": 3.958991798359672,
"config_test": ... |
__author__ = 'Shashank'
import requests
import json
import os
import pandas as pd
zips = [94102, 94103, 94104, 94105, 94107, 94108, 94109, 94110, 94111, 94112, 94114, 94115, 94116, 94117, 94118, 94121, 94122, 94123, 94124, 94127, 94129, 94130, 94131, 94132, 94133, 94134, 94158]
coupon_data = []
for i in zips:
url =... | {
"repo_name": "vjadon/Front-End-workspace",
"path": "LHB - ETL Module/8Coupons.py",
"copies": "1",
"size": "3012",
"license": "mit",
"hash": -6217582585175369000,
"line_mean": 60.4897959184,
"line_max": 216,
"alpha_frac": 0.7104913679,
"autogenerated": false,
"ratio": 2.997014925373134,
"config... |
__author__ = 'shaunjl'
"""
Tastypie REST API tests for ResolveDOI(View)
"""
from tastypie.test import ResourceTestCase, TestApiClient
from django.contrib.auth.models import User
from hs_core import hydroshare
from tastypie.serializers import Serializer
class TestResolveDOIView(ResourceTestCase):
serializer = Se... | {
"repo_name": "hydroshare/hydroshare_temp",
"path": "hs_core/tests/api/http/test_resolve_doi_view.py",
"copies": "1",
"size": "1281",
"license": "bsd-3-clause",
"hash": 687444208259104900,
"line_mean": 25.6875,
"line_max": 79,
"alpha_frac": 0.6479313037,
"autogenerated": false,
"ratio": 3.5,
"c... |
__author__ = 'shaunjl'
"""
Tastypie API tests for update_account
comments- IMPORTANT- update_account(user, **kwargs) contains a 'blacklist,' that chucks
the username,password, and groups, if given. I only fixed it to work, but kept the blacklist
as a relic to hopefully jog the developers memory as to what he intended... | {
"repo_name": "hydroshare/hydroshare_temp",
"path": "hs_core/tests/api/native/test_update_account.py",
"copies": "1",
"size": "1577",
"license": "bsd-3-clause",
"hash": -6860299110180775000,
"line_mean": 36.5476190476,
"line_max": 92,
"alpha_frac": 0.6360177552,
"autogenerated": false,
"ratio": 3... |
__author__ = 'shaunjl'
"""
Tastypie REST API tests for CreateOrListAccounts.as_view() modeled after: https://github.com/hydroshare/hs_core/blob/master/tests/api/http/test_resource.py
comments-
post returns TypeError, put returns HttpResponseForbidden (403)
get expects a json query in a dictionary like data={'query': s... | {
"repo_name": "hydroshare/hydroshare_temp",
"path": "hs_core/tests/api/http/test_create_or_list_accounts.py",
"copies": "1",
"size": "4697",
"license": "bsd-3-clause",
"hash": 8867131220237410000,
"line_mean": 37.1869918699,
"line_max": 155,
"alpha_frac": 0.6242282308,
"autogenerated": false,
"ra... |
__author__ = 'shaunjl'
"""
Tastypie REST API tests for publish_resource(view)
comments-
"""
from tastypie.test import ResourceTestCase, TestApiClient
from django.contrib.auth.models import User
from hs_core import hydroshare
from tastypie.serializers import Serializer
class TestResolveDOIView(ResourceTestCase):
... | {
"repo_name": "hydroshare/hydroshare_temp",
"path": "hs_core/tests/api/http/test_publish_resource_view.py",
"copies": "1",
"size": "1078",
"license": "bsd-3-clause",
"hash": 2446350227678872000,
"line_mean": 24.0697674419,
"line_max": 79,
"alpha_frac": 0.6586270872,
"autogenerated": false,
"ratio... |
__author__ = 'shaunjl'
"""
Tastypie REST API tests for SetAccessRules.as_view()
comments- getting 404s for both
"""
from tastypie.test import ResourceTestCase, TestApiClient
from tastypie.serializers import Serializer
from django.contrib.auth.models import User, Group
from hs_core import hydroshare
from hs_core.view... | {
"repo_name": "hydroshare/hydroshare_temp",
"path": "hs_core/tests/api/http/test_set_access_rules_as_view.py",
"copies": "1",
"size": "1708",
"license": "bsd-3-clause",
"hash": -1845660646659926000,
"line_mean": 27.4666666667,
"line_max": 63,
"alpha_frac": 0.5884074941,
"autogenerated": false,
"r... |
__author__ = 'shaunjl'
"""
Tastypie REST API tests for SetResourceOwner.as_view
comments- set owner test gives 403
"""
from django.contrib.auth.models import User
from hs_core import hydroshare
from tastypie.test import ResourceTestCase, TestApiClient
from tastypie.serializers import Serializer
from hs_core.models im... | {
"repo_name": "hydroshare/hydroshare_temp",
"path": "hs_core/tests/api/http/test_set_resource_owner.py",
"copies": "1",
"size": "1567",
"license": "bsd-3-clause",
"hash": -7133582311801893000,
"line_mean": 30.9795918367,
"line_max": 94,
"alpha_frac": 0.6375239311,
"autogenerated": false,
"ratio":... |
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