repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
CforED/Machine-Learning | sklearn/utils/tests/test_extmath.py | 19 | 21979 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
willettk/rgz-analysis | python/test_consensus.py | 2 | 15556 | from __future__ import division
# Local RGZ modules
import collinearity
from load_contours import get_contours,make_pathdict
# Default packages
import datetime
import operator
from collections import Counter
import cStringIO
import urllib
import json
import os.path
import time
import shutil
# Other packages
impor... | mit |
appapantula/scikit-learn | examples/applications/plot_stock_market.py | 227 | 8284 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
wzbozon/statsmodels | statsmodels/genmod/tests/test_glm.py | 19 | 37824 | """
Test functions for models.GLM
"""
from statsmodels.compat import range
import os
import numpy as np
from numpy.testing import (assert_almost_equal, assert_equal, assert_raises,
assert_allclose, assert_, assert_array_less, dec)
from scipy import stats
import statsmodels.api as sm
from st... | bsd-3-clause |
TNick/pylearn2 | pylearn2/cross_validation/dataset_iterators.py | 29 | 19389 | """
Cross-validation dataset iterators.
"""
__author__ = "Steven Kearnes"
__copyright__ = "Copyright 2014, Stanford University"
__license__ = "3-clause BSD"
import numpy as np
import warnings
try:
from sklearn.cross_validation import (KFold, StratifiedKFold, ShuffleSplit,
... | bsd-3-clause |
acapet/GHER-POSTPROC | Examples/Second.py | 1 | 1944 | import numpy as np
import numpy.ma as ma
from netCDF4 import Dataset
#from mpl_toolkits.basemap import Basemap
#from multiprocessing import Pool
#import gsw ... | gpl-3.0 |
cauchycui/scikit-learn | sklearn/cross_decomposition/cca_.py | 209 | 3150 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
Obus/scikit-learn | examples/calibration/plot_calibration.py | 225 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
rousseab/pymatgen | pymatgen/analysis/diffraction/xrd.py | 2 | 14724 | # coding: utf-8
from __future__ import division, unicode_literals
"""
This module implements an XRD pattern calculator.
"""
from six.moves import filter
from six.moves import map
from six.moves import zip
__author__ = "Shyue Ping Ong"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "0.1"
__mai... | mit |
kiyoto/statsmodels | statsmodels/examples/l1_demo/short_demo.py | 33 | 3737 | """
You can fit your LikelihoodModel using l1 regularization by changing
the method argument and adding an argument alpha. See code for
details.
The Story
---------
The maximum likelihood (ML) solution works well when the number of data
points is large and the noise is small. When the ML solution starts
"bre... | bsd-3-clause |
tttor/csipb-jamu-prj | predictor/connectivity/similarity/protein-kernel/gene-ontology/combine_go_sim.py | 1 | 2248 | import csv
import sys
import math
import time
import numpy as np
from sklearn.preprocessing import MinMaxScaler
def main():
if len(sys.argv)!=5:
print "Usage: python combine_go_sim.py [BP] [MF] [CC] [Output]"
CCDataDir = sys.argv[3]
outDir = sys.argv[4]
BPDataDir = sys.argv[1]
MFDataDir = ... | mit |
aliparsai/LittleDarwin | utils/HigherOrderExperiment/FormulaCalculator.py | 1 | 3395 | import math
from mpl_toolkits.mplot3d import axes3d
from itertools import izip
import matplotlib.pyplot as plt
import numpy as np
def calculate_formula(m, n, t):
poweroftwo = (1 - m + ((n + 1) // 2))
try:
# print m,n,t
top = math.factorial(t)
top *= math.factorial(m)
top *= m... | gpl-3.0 |
kjung/scikit-learn | examples/plot_kernel_approximation.py | 19 | 8004 | """
==================================================
Explicit feature map approximation for RBF kernels
==================================================
An example illustrating the approximation of the feature map
of an RBF kernel.
.. currentmodule:: sklearn.kernel_approximation
It shows how to use :class:`RBFSa... | bsd-3-clause |
Obus/scikit-learn | sklearn/utils/random.py | 234 | 10510 | # Author: Hamzeh Alsalhi <ha258@cornell.edu>
#
# License: BSD 3 clause
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ =... | bsd-3-clause |
centrofermi/e3sim | plot/plot_energy_cpu.py | 1 | 1300 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on 21/04/2015
@author: Fabrizio Coccetti (fabrizio.coccetti@centrofermi.it) [www.fc8.net]
"""
import matplotlib.pyplot as plt
import numpy as np
import os
import pkg_resources
from e3sim.config.specific_machine import machine
try:
# For Python 3.0 and later... | gpl-3.0 |
MechCoder/scikit-learn | examples/manifold/plot_mds.py | 88 | 2731 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.... | bsd-3-clause |
suchyta1/BalrogReconstruction | functions2.py | 1 | 15679 | #!/usr/bin/env python
import desdb
import numpy as np
import esutil
import pyfits
import sys
import healpy as hp
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
#import seaborn as sns
def CatMatch(c1, c2, band1, band2):
radius = 1/3600.0... | mit |
Tong-Chen/scikit-learn | sklearn/tests/test_common.py | 1 | 44279 | """
General tests for all estimators in sklearn.
"""
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
from __future__ import print_function
import os
import warnings
import sys
import traceback
import inspect
import pickle
import pkg... | bsd-3-clause |
pprett/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 26 | 6935 | import numpy as np
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal... | bsd-3-clause |
jaredweiss/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pyplot.py | 69 | 77521 | import sys
import matplotlib
from matplotlib import _pylab_helpers, interactive
from matplotlib.cbook import dedent, silent_list, is_string_like, is_numlike
from matplotlib.figure import Figure, figaspect
from matplotlib.backend_bases import FigureCanvasBase
from matplotlib.image import imread as _imread
from matplotl... | gpl-3.0 |
pkruskal/scikit-learn | sklearn/datasets/tests/test_rcv1.py | 322 | 2414 | """Test the rcv1 loader.
Skipped if rcv1 is not already downloaded to data_home.
"""
import errno
import scipy.sparse as sp
import numpy as np
from sklearn.datasets import fetch_rcv1
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing i... | bsd-3-clause |
fredhusser/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 248 | 6359 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guar... | bsd-3-clause |
allenlavoie/tensorflow | tensorflow/examples/learn/boston.py | 75 | 2549 | # Copyright 2016 The TensorFlow Authors. 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 appl... | apache-2.0 |
xwolf12/scikit-learn | examples/linear_model/plot_ols_3d.py | 350 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
mclaughlin6464/pylearn2 | pylearn2/training_algorithms/sgd.py | 1 | 48347 | """
Stochastic Gradient Descent and related functionality such as
learning rate adaptation, momentum, and Polyak averaging.
"""
from __future__ import division
__authors__ = "Ian Goodfellow"
__copyright__ = "Copyright 2010-2012, Universite de Montreal"
__credits__ = ["Ian Goodfellow, David Warde-Farley"]
__license__ =... | bsd-3-clause |
jmmease/pandas | pandas/tests/frame/test_block_internals.py | 3 | 20693 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime, timedelta
import itertools
from numpy import nan
import numpy as np
from pandas import (DataFrame, Series, Timestamp, date_range, compat,
option_context)
from pandas.compat import StringIO... | bsd-3-clause |
vibhorag/scikit-learn | sklearn/metrics/regression.py | 175 | 16953 | """Metrics to assess performance on regression task
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Ma... | bsd-3-clause |
DGrady/pandas | pandas/tests/dtypes/test_dtypes.py | 10 | 18488 | # -*- coding: utf-8 -*-
import pytest
from itertools import product
import numpy as np
import pandas as pd
from pandas import Series, Categorical, IntervalIndex, date_range
from pandas.core.dtypes.dtypes import (
DatetimeTZDtype, PeriodDtype,
IntervalDtype, CategoricalDtype)
from pandas.core.dtypes.common im... | bsd-3-clause |
stczhc/neupy | examples/gd/rectangles_mlp.py | 1 | 1025 | from sklearn import cross_validation, metrics
from skdata.larochelle_etal_2007 import dataset
from neupy import algorithms, layers, environment
environment.reproducible()
rectangle_dataset = dataset.Rectangles()
rectangle_dataset.fetch(download_if_missing=True)
data, target = rectangle_dataset.classification_task()... | mit |
rs2/pandas | pandas/tests/generic/test_frame.py | 2 | 7703 | from copy import deepcopy
from operator import methodcaller
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas._testing as tm
from .test_generic import Generic
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda se... | bsd-3-clause |
reminisce/mxnet | example/speech_recognition/stt_utils.py | 8 | 5838 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | apache-2.0 |
yanikou19/pymatgen | pymatgen/analysis/pourbaix/plotter.py | 4 | 24049 | # coding: utf-8
from __future__ import division, unicode_literals
"""
This module provides classes for plotting Pourbaix objects.
"""
import six
from six.moves import map
from six.moves import zip
__author__ = "Sai Jayaraman"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "1.1"
__maintainer__... | mit |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/tests/test_series.py | 9 | 288883 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import re
import sys
from datetime import datetime, timedelta
import operator
import string
from inspect import getargspec
from itertools import product, starmap
from distutils.version import LooseVersion
import warnings
import random
import nose
from numpy import nan... | gpl-2.0 |
alphaBenj/zipline | zipline/utils/security_list.py | 6 | 5399 | import warnings
from datetime import datetime
from os import listdir
import os.path
import pandas as pd
import pytz
import zipline
from zipline.errors import SymbolNotFound
from zipline.finance.asset_restrictions import SecurityListRestrictions
from zipline.zipline_warnings import ZiplineDeprecationWarning
DATE_FOR... | apache-2.0 |
psathyrella/partis | test/cf-tree-metrics.py | 1 | 70896 | #!/usr/bin/env python
import argparse
import operator
import os
import sys
import yaml
import json
import colored_traceback.always
import collections
import numpy
import math
import subprocess
import multiprocessing
# ----------------------------------------------------------------------------------------
linestyles =... | gpl-3.0 |
flightgong/scikit-learn | sklearn/linear_model/tests/test_base.py | 8 | 10058 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model.... | bsd-3-clause |
glos/glos-qartod | glos_qartod/run.py | 1 | 4052 | import os
import sys
import glob
import pandas as pd
from redis import Redis
from rq import Queue
from netCDF4 import Dataset
from glos_qartod import cli
from glos_qartod import get_logger
def main():
q = Queue(connection=Redis())
conf_file, proc_dir = sys.argv[1:3]
sheets = pd.read_excel(conf_file, None)... | apache-2.0 |
walterst/qiime | scripts/print_qiime_config.py | 15 | 35150 | #!/usr/bin/env python
from __future__ import division
__author__ = "Jens Reeder"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Jens Reeder", "Dan Knights", "Antonio Gonzalez Pena",
"Justin Kuczynski", "Jai Ram Rideout", "Greg Caporaso",
"Emily TerAvest"]
__license__ ... | gpl-2.0 |
anirudhjayaraman/scikit-learn | sklearn/ensemble/voting_classifier.py | 178 | 8006 | """
Soft Voting/Majority Rule classifier.
This module contains a Soft Voting/Majority Rule classifier for
classification estimators.
"""
# Authors: Sebastian Raschka <se.raschka@gmail.com>,
# Gilles Louppe <g.louppe@gmail.com>
#
# Licence: BSD 3 clause
import numpy as np
from ..base import BaseEstimator
f... | bsd-3-clause |
mblondel/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
leogulus/pisco_pipeline | run_rgb_pisco.py | 1 | 6622 | import os
import subprocess
import shlex
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.cosmology import Planck15 as cosmo
from astropy.visualization import make_lupton_rgb
import aplpy
def list_file_name(dir, name, end=0):
"""
lis... | mit |
manahl/arctic | tests/unit/serialization/test_incremental.py | 1 | 6189 | import itertools
import pytest
from arctic.exceptions import ArcticSerializationException
from arctic.serialization.incremental import IncrementalPandasToRecArraySerializer
from arctic.serialization.numpy_records import DataFrameSerializer
from tests.unit.serialization.serialization_test_data import _mixed_test_data,... | lgpl-2.1 |
masa-ito/ProtoToMET | src/test/benchmarkPlot.py | 1 | 1992 | import numpy as np
import matplotlib.pyplot as plt
# Create a figure of size 8x6 inches, 80 dots per inch
plt.figure(figsize=(8, 6), dpi=80)
# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1)
threadNums = [ 1, 2, 4,
8, 12, 16,
20, 24];
# elasped time (milisecond) for conj... | lgpl-3.0 |
Averroes/statsmodels | statsmodels/tools/print_version.py | 23 | 7951 | #!/usr/bin/env python
from __future__ import print_function
from statsmodels.compat.python import reduce
import sys
from os.path import dirname
def safe_version(module, attr='__version__'):
if not isinstance(attr, list):
attr = [attr]
try:
return reduce(getattr, [module] + attr)
except Att... | bsd-3-clause |
woozzu/tf_tutorials | 01_linear_regression_starter.py | 1 | 1652 | """
Simple linear regression example in TensorFlow
This program tries to predict the number of thefts from
the number of fire in the city of Chicago
"""
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import csv
DATA_FILE = 'data/fire_theft.csv'
# Step 1: read data
with open(DATA_FILE, 'r... | mit |
michigraber/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
kiyoto/statsmodels | tools/code_maintenance.py | 37 | 2307 | """
Code maintenance script modified from PyMC
"""
#!/usr/bin/env python
import sys
import os
# This is a function, not a test case, because it has to be run from inside
# the source tree to work well.
mod_strs = ['IPython', 'pylab', 'matplotlib', 'scipy','Pdb']
dep_files = {}
for mod_str in mod_strs:
dep_files... | bsd-3-clause |
etkirsch/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
xubenben/scikit-learn | examples/classification/plot_classification_probability.py | 242 | 2624 | """
===============================
Plot classification probability
===============================
Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, L1
and L2 penalized logistic regression with either a One-Vs-Rest or multinom... | bsd-3-clause |
wangwei7175878/tutorials | matplotlibTUT/plt19_animation.py | 3 | 1573 | # View more python tutorials on my Youtube and Youku channel!!!
# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial
# 19 - animation
"""
Please note, this script is for python3+.
If you are using python2+, please modify it accord... | mit |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/sklearn/semi_supervised/label_propagation.py | 12 | 18811 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semi-supervised classification algorithms. At a high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms perf... | mit |
vermouthmjl/scikit-learn | examples/plot_isotonic_regression.py | 303 | 1767 | """
===================
Isotonic Regression
===================
An illustration of the isotonic regression on generated data. The
isotonic regression finds a non-decreasing approximation of a function
while minimizing the mean squared error on the training data. The benefit
of such a model is that it does not assume a... | bsd-3-clause |
jseabold/scikit-learn | sklearn/tests/test_kernel_ridge.py | 342 | 3027 | import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert... | bsd-3-clause |
bhargav/scikit-learn | examples/linear_model/plot_huber_vs_ridge.py | 127 | 2206 | """
=======================================================
HuberRegressor vs Ridge on dataset with strong outliers
=======================================================
Fit Ridge and HuberRegressor on a dataset with outliers.
The example shows that the predictions in ridge are strongly influenced
by the outliers p... | bsd-3-clause |
jblackburne/scikit-learn | examples/manifold/plot_mds.py | 88 | 2731 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/utils/graph.py | 289 | 6239 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
franzpl/sweep | log_sweep_kaiser_window_script2/log_sweep_kaiser_window_script2.py | 2 | 2113 | #!/usr/bin/env python3
"""The influence of windowing of log. sweep signals when using a
Kaiser Window by fixing beta (=7) and fade_in (=0).
fstart = 1 Hz
fstop = 22050 Hz
"""
import sys
sys.path.append('..')
import measurement_chain
import plotting
import calculation
import generation
import matplotlib.py... | mit |
cmdunkers/DeeperMind | PythonEnv/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py | 3 | 112844 | #
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division, print_function, absolute_import
from scipy._lib.six import string_types, exec_
from scipy._lib._util import getargspec_no_self as _getargspec
import sys
import keyword
import re
imp... | bsd-3-clause |
wkfwkf/statsmodels | statsmodels/regression/tests/test_regression.py | 6 | 37622 | """
Test functions for models.regression
"""
# TODO: Test for LM
from statsmodels.compat.python import long, lrange
import warnings
import pandas
import numpy as np
from numpy.testing import (assert_almost_equal, assert_approx_equal,
assert_raises, assert_equal, assert_allclose)
from scipy.l... | bsd-3-clause |
leighpauls/k2cro4 | native_client/build/buildbot_chrome_nacl_stage.py | 1 | 11521 | #!/usr/bin/python
# Copyright (c) 2012 The Native Client Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Do all the steps required to build and test against nacl."""
import optparse
import os.path
import re
import shutil
import su... | bsd-3-clause |
mrjacobagilbert/gnuradio | gr-filter/examples/reconstruction.py | 5 | 4279 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from gnuradio import gr, digital
from gnuradio import filter
from gnuradio import blocks
from gnuradio.fft import window
import sys
import numpy
try:
... | gpl-3.0 |
mkness/TheCannon | code/makeplot_test.py | 1 | 3991 | #!/usr/bin/python
import scipy
import numpy
from numpy import *
from scipy import ndimage
from scipy import interpolate
from numpy import loadtxt
import os
import numpy as np
from numpy import *
from matplotlib import pyplot
import matplotlib.pyplot as plt
from matplotlib.pyplot import axes
from matplotlib.pyplo... | mit |
dkasak/pacal | pacal/depvars/copulas.py | 1 | 35176 | """Set of copulas different types"""
from pacal.integration import *
from pacal.interpolation import *
from matplotlib.collections import PolyCollection
import pacal.distr
#from pacal import *
from pacal.segments import PiecewiseDistribution, MInfSegment, PInfSegment, Segment, _segint
from pacal.segments imp... | gpl-3.0 |
dsm054/pandas | pandas/core/strings.py | 1 | 100158 | # -*- coding: utf-8 -*-
import numpy as np
from pandas.compat import zip
from pandas.core.dtypes.generic import ABCSeries, ABCIndex
from pandas.core.dtypes.missing import isna
from pandas.core.dtypes.common import (
ensure_object,
is_bool_dtype,
is_categorical_dtype,
is_object_dtype,
is_string_like... | bsd-3-clause |
wy36101299/NCKU_Machine-Learning-and-Bioinformatics | hw4_predictData/creatPredictdata.py | 1 | 2986 | import glob
import os
import pandas as pd
class CTD(object):
"""docstring for CTD"""
def __init__(self):
self.format_l = []
self.td_l = []
self.iternum = 0
self.formatname = ""
def feature(self,index):
format_l = self.format_l
feature = ((float(format_l[inde... | mit |
mumuwoyou/vnpy-master | vnpy/trader/gateway/tkproGateway/DataApi/data_api.py | 4 | 18709 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from builtins import *
import time
import numpy as np
from . import jrpc_py
# import jrpc
from . import utils
# def set_log_dir(log_dir):
# if log_dir:
# jr... | mit |
AlexGrig/GPy | GPy/core/parameterization/transformations.py | 10 | 20673 | # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from .domains import _POSITIVE,_NEGATIVE, _BOUNDED
import weakref
import sys
_exp_lim_val = np.finfo(np.float64).max
_lim_val = 36.0
epsilon = np.finfo(np.float64).resolution
#=======... | bsd-3-clause |
tawsifkhan/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
cblas_libs, blas_info = ... | bsd-3-clause |
voxlol/scikit-learn | sklearn/neighbors/tests/test_dist_metrics.py | 230 | 5234 | import itertools
import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
import scipy
from scipy.spatial.distance import cdist
from sklearn.neighbors.dist_metrics import DistanceMetric
from nose import SkipTest
def dist_func(x1, x2, p):
return np.sum((x1 - x2) ** p) ** (1. / p)
de... | bsd-3-clause |
anirudhjayaraman/Dato-Core | src/unity/python/graphlab/test/test_sarray.py | 13 | 60654 | # -*- coding: utf-8 -*-
'''
Copyright (C) 2015 Dato, Inc.
All rights reserved.
This software may be modified and distributed under the terms
of the BSD license. See the DATO-PYTHON-LICENSE file for details.
'''
from graphlab.data_structures.sarray import SArray
from graphlab_util.timezone import GMT
import pandas as p... | agpl-3.0 |
mhue/scikit-learn | examples/cluster/plot_agglomerative_clustering.py | 343 | 2931 | """
Agglomerative clustering with and without structure
===================================================
This example shows the effect of imposing a connectivity graph to capture
local structure in the data. The graph is simply the graph of 20 nearest
neighbors.
Two consequences of imposing a connectivity can be s... | bsd-3-clause |
JetBrains/intellij-community | python/helpers/pydev/pydev_ipython/inputhook.py | 21 | 19415 | # coding: utf-8
"""
Inputhook management for GUI event loop integration.
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distribu... | apache-2.0 |
rafael-radkowski/ME325 | ME325Common/InputHelpers.py | 1 | 2465 | import platform
import matplotlib
if platform.system() == 'Darwin':
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.patches import Circle, Arc
# tkinter for the display
from tkinter import *
from tkinter import Canvas
fro... | mit |
MdAsifKhan/DNGR-Keras | example.py | 1 | 1675 | import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import networkx as nx
from sklearn.cluster import KMeans
import matplotlib.colors as colors
from itertools import cycle
import time
import matplotlib.pyplot as plt
import subprocess
from utils import tsne
import pdb
import numpy as np
from sklea... | mit |
zbanga/trading-with-python | lib/interactiveBrokers/histData.py | 76 | 6472 | '''
Created on May 8, 2013
Copyright: Jev Kuznetsov
License: BSD
Module for downloading historic data from IB
'''
import ib
import pandas as pd
from ib.ext.Contract import Contract
from ib.opt import ibConnection, message
import logger as logger
from pandas import DataFrame, Index
import os
imp... | bsd-3-clause |
niamoto/niamoto-core | niamoto/data_providers/plantnote_provider/plantnote_plot_occurrence_provider.py | 2 | 1454 | # coding: utf-8
from sqlalchemy import *
import pandas as pd
from niamoto.data_providers.base_plot_occurrence_provider import \
BasePlotOccurrenceProvider
class PlantnotePlotOccurrenceProvider(BasePlotOccurrenceProvider):
"""
Pl@ntnote Plot-Occurrence provider.
"""
def __init__(self, data_provi... | gpl-3.0 |
jreback/pandas | pandas/tests/series/methods/test_argsort.py | 3 | 2248 | import numpy as np
import pytest
from pandas import Series, Timestamp, isna
import pandas._testing as tm
class TestSeriesArgsort:
def _check_accum_op(self, name, ser, check_dtype=True):
func = getattr(np, name)
tm.assert_numpy_array_equal(
func(ser).values, func(np.array(ser)), check_... | bsd-3-clause |
agiovann/Constrained_NMF | use_cases/CaImAnpaper/train_net_cifar_SNIPER.py | 2 | 10134 | #!/usr/bin/env python
"""
Created on Thu Aug 24 12:30:19 2017
@author: agiovann
"""
'''From keras example of convnet on the MNIST dataset.
TRAIN ON DATA EXTRACTED FROM RESIDUALS WITH generate_GT script
'''
#%%
import cv2
import glob
try:
cv2.setNumThreads(1)
except:
print('Open CV is naturally single threa... | gpl-2.0 |
isrohutamahopetechnik/MissionPlanner | Lib/site-packages/scipy/signal/filter_design.py | 53 | 63381 | """Filter design.
"""
import types
import warnings
import numpy
from numpy import atleast_1d, poly, polyval, roots, real, asarray, allclose, \
resize, pi, absolute, logspace, r_, sqrt, tan, log10, arctan, arcsinh, \
cos, exp, cosh, arccosh, ceil, conjugate, zeros, sinh
from numpy import mintypecode
from scipy... | gpl-3.0 |
glenioborges/ibis | ibis/util.py | 6 | 3963 | # Copyright 2014 Cloudera 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, so... | apache-2.0 |
rohanp/scikit-learn | sklearn/metrics/regression.py | 5 | 17399 | """Metrics to assess performance on regression task
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Ma... | bsd-3-clause |
duthchao/kaggle-galaxies | try_convnet_cc_multirotflip_3x69r45_maxout2048_extradense_dup3.py | 7 | 17439 | import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime_augmentation as ra
import time
import csv
import os
import cPickle as pickle
from datetime import datetime, timedelta
# import matplotlib.pyplot as plt
# plt.i... | bsd-3-clause |
nitishkd/Customer-Prediction | graph.py | 1 | 1975 | import numpy as np;
import matplotlib.pyplot as plt
from sklearn import svm
dset_file = open("extradata.txt");
##print(dset_file.readline())
row = 0;
col = 0;
dset = []
for info in dset_file.readlines():
col = 0;
s = info.strip()
sa = s.split("(")
sb = sa[1].split(")")
k = sb[0].split(",")
lst... | mit |
pdamodaran/yellowbrick | tests/test_meta.py | 1 | 5443 | # tests.test_meta
# Meta testing for testing helper functions!
#
# Author: Benjamin Bengfort <benjamin@bengfort.com>
# Created: Sat Apr 07 13:16:53 2018 -0400
#
# ID: test_meta.py [] benjamin@bengfort.com $
"""
Meta testing for testing helper functions!
"""
###########################################################... | apache-2.0 |
benschneider/sideprojects1 | Shotnoise-Calibration/SNfit1.py | 1 | 19844 | # -*- coding: utf-8 -*-
'''
@author: Ben Schneider
A script is used to readout mtx measurement data which also contains
a shotnoise responses.
Then fits them for G and Tn
'''
import numpy as np
from parsers import savemtx, loadmtx, make_header, read_header
# from scipy.optimize import curve_fit # , leastsq
from scipy.... | gpl-2.0 |
Erotemic/ibeis | ibeis/expt/test_result.py | 1 | 108427 | # -*- coding: utf-8 -*-
# TODO: find unused functions and kill them
from __future__ import absolute_import, division, print_function, unicode_literals
import six
import copy
import operator
import utool as ut
import vtool_ibeis as vt
import numpy as np
import itertools as it
from functools import partial
from six impor... | apache-2.0 |
michigraber/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 85 | 6377 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neigh... | bsd-3-clause |
meduz/scikit-learn | sklearn/svm/tests/test_sparse.py | 63 | 13366 | import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classification, load_digits, make_blobs
from sklearn.svm.tests import te... | bsd-3-clause |
eclee25/flu-SDI-exploratory-age | scripts/create_fluseverity_figs_v4/functions_v4.py | 1 | 67932 | #!/usr/bin/python
##############################################
###Python template
###Author: Elizabeth Lee
###Date: 10/19/14
## Purpose: script of functions for data cleaning and processing to draw flu severity figures; supports figures in create_fluseverity_figs
## v2: swap child:adult OR to adult:child OR
## v3: ... | mit |
slowvak/MachineLearningForMedicalImages | code/Module3.py | 1 | 11790 |
# coding: utf-8
# # Supervised Classification: SVM
#
# ## Import Libraries
# In[13]:
get_ipython().magic('matplotlib inline')
import warnings
warnings.filterwarnings('ignore')
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
import pandas as pd
from matplotlib.colors import Li... | mit |
rhattersley/iris | docs/iris/example_code/General/projections_and_annotations.py | 6 | 5396 | """
Plotting in different projections
=================================
This example shows how to overlay data and graphics in different projections,
demonstrating various features of Iris, Cartopy and matplotlib.
We wish to overlay two datasets, defined on different rotated-pole grids.
To display both together, we m... | lgpl-3.0 |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/sklearn/feature_selection/variance_threshold.py | 238 | 2594 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstim... | mit |
gengliangwang/spark | python/pyspark/pandas/data_type_ops/categorical_ops.py | 1 | 1062 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
stylianos-kampakis/scikit-learn | benchmarks/bench_mnist.py | 76 | 6136 | """
=======================
MNIST dataset benchmark
=======================
Benchmark on the MNIST dataset. The dataset comprises 70,000 samples
and 784 features. Here, we consider the task of predicting
10 classes - digits from 0 to 9 from their raw images. By contrast to the
covertype dataset, the feature space is... | bsd-3-clause |
stitchfix/pyxley | tests/app/components/plotly.py | 1 | 1349 |
from pyxley.charts.plotly import PlotlyAPI
from pyxley.filters import SelectButton
from pyxley import UILayout
import pandas as pd
from flask import jsonify, request
def make_plotly_ui():
filename = "../examples/metricsgraphics/project/fitbit_data.csv"
df = pd.read_csv(filename)
# Make a UI
ui = UIL... | mit |
ephes/scikit-learn | sklearn/metrics/setup.py | 299 | 1024 | import os
import os.path
import numpy
from numpy.distutils.misc_util import Configuration
from sklearn._build_utils import get_blas_info
def configuration(parent_package="", top_path=None):
config = Configuration("metrics", parent_package, top_path)
cblas_libs, blas_info = get_blas_info()
if os.name ==... | bsd-3-clause |
dieterich-lab/riboseq-utils | riboutils/extract_metagene_profiles.py | 1 | 10027 | #! /usr/bin/env python3
import argparse
import collections
import numpy as np
import pandas as pd
import sys
import bio_utils.bam_utils as bam_utils
import bio_utils.bed_utils as bed_utils
import misc.utils as utils
import misc.pandas_utils as pandas_utils
import logging
import misc.logging_utils as logging_utils
lo... | mit |
vmAggies/omniture-master | tests/testReports.py | 1 | 7658 | #!/usr/bin/python
import unittest
import omniture
import os
from datetime import date
import pandas
import datetime
import requests_mock
creds = {}
creds['username'] = os.environ['OMNITURE_USERNAME']
creds['secret'] = os.environ['OMNITURE_SECRET']
test_report_suite = 'omniture.api-gateway'
class ReportTest(unittes... | mit |
CrazyGuo/vincent | examples/bar_chart_examples.py | 11 | 2026 | # -*- coding: utf-8 -*-
"""
Vincent Bar Chart Example
"""
#Build a Bar Chart from scratch
from vincent import *
import pandas as pd
farm_1 = {'apples': 10, 'berries': 32, 'squash': 21, 'melons': 13, 'corn': 18}
farm_2 = {'apples': 15, 'berries': 43, 'squash': 17, 'melons': 10, 'corn': 22}
farm_3 = {'apples': 6, 'b... | mit |
djfan/why_yellow_taxi | Sjoin/Sjoin_Pyspark_1.py | 1 | 3068 | import pyproj
import csv
import shapely.geometry as geom
import fiona
import fiona.crs
import shapely
import rtree
import geopandas as gpd
import numpy as np
import operator
import pandas as pd
def countLine(partID, records):
import pyproj
import csv
import shapely.geometry as geom
import fiona
imp... | mit |
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