repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
nesterione/scikit-learn | examples/linear_model/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | bsd-3-clause |
clavicule/keras-exp | tmp/rnn.py | 1 | 7006 | import argparse
import numpy as np
import os.path
# from progressbar import ProgressBar
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.callbacks import ModelCheckpoint
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
def baseline_mode... | mit |
mattgiguere/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optim... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/sklearn/ensemble/tests/test_voting_classifier.py | 5 | 17078 | """Testing for the VotingClassifier"""
import numpy as np
from sklearn.utils.testing import assert_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_equal, assert_true, assert_false
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_warns_message
fro... | mit |
BigDataforYou/movie_recommendation_workshop_1 | big_data_4_you_demo_1/venv/lib/python2.7/site-packages/numpy/core/tests/test_multiarray.py | 4 | 238592 | from __future__ import division, absolute_import, print_function
import collections
import tempfile
import sys
import shutil
import warnings
import operator
import io
import itertools
import ctypes
import os
if sys.version_info[0] >= 3:
import builtins
else:
import __builtin__ as builtins
from decimal import D... | mit |
pavanramkumar/pyglmnet | pyglmnet/externals/sklearn/utils/validation.py | 2 | 27669 | """Utilities for input validation"""
# adapt from scikit-learn
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# Sylvain Marie
# License: BSD 3 clause
import warnings
import numbers
import nump... | mit |
henrykironde/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | 142 | 5990 | # Author: Vlad Niculae
# License: BSD 3 clause
import sys
import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import ass... | bsd-3-clause |
woutdenolf/spectrocrunch | spectrocrunch/utils/lut.py | 1 | 5768 | # -*- coding: utf-8 -*-
import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
from copy import copy
from . import instance
from . import units
from . import listtools
from .copyable import Copyable
class LUT(Copyable):
"""Lookup table with sorted/unique keys and keys/values with units... | mit |
peterfpeterson/mantid | scripts/MultiPlotting/subplot/subplot.py | 3 | 10459 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
from... | gpl-3.0 |
googleinterns/cabby | cabby/geo/util.py | 1 | 19295 | # coding=utf-8
# Copyright 2020 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... | apache-2.0 |
andaag/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 297 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
... | bsd-3-clause |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/matplotlib/animation.py | 4 | 50572 | # TODO:
# * Loop Delay is broken on GTKAgg. This is because source_remove() is not
# working as we want. PyGTK bug?
# * Documentation -- this will need a new section of the User's Guide.
# Both for Animations and just timers.
# - Also need to update http://www.scipy.org/Cookbook/Matplotlib/Animations
# * Blit
... | mit |
mfjb/scikit-learn | sklearn/metrics/cluster/supervised.py | 207 | 27395 | """Utilities to evaluate the clustering performance of models
Functions named as *_score return a scalar value to maximize: the higher the
better.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Wei LI <kuantkid@gmail.com>
# Diego Molla <dmolla-aliod@gmail.com>
# License: BSD 3 clause
fr... | bsd-3-clause |
linebp/pandas | bench/bench_take_indexing.py | 2 | 1194 | from __future__ import print_function
import numpy as np
from pandas import *
import pandas._tseries as lib
from pandas import DataFrame
import timeit
from pandas.compat import zip
setup = """
from pandas import Series
import pandas._tseries as lib
import random
import numpy as np
import random
n = %d
k = %d
arr = ... | bsd-3-clause |
zephirefaith/AI_Fall15_Assignments | A2/lib/networkx/drawing/tests/test_pylab.py | 45 | 1137 | """
Unit tests for matplotlib drawing functions.
"""
import os
from nose import SkipTest
import networkx as nx
class TestPylab(object):
@classmethod
def setupClass(cls):
global plt
try:
import matplotlib as mpl
mpl.use('PS',warn=False)
import matplotli... | mit |
nkcr/WebIndex | app/venv/lib/python3.5/site-packages/nltk/tbl/demo.py | 7 | 14715 | # -*- coding: utf-8 -*-
# Natural Language Toolkit: Transformation-based learning
#
# Copyright (C) 2001-2016 NLTK Project
# Author: Marcus Uneson <marcus.uneson@gmail.com>
# based on previous (nltk2) version by
# Christopher Maloof, Edward Loper, Steven Bird
# URL: <http://nltk.org/>
# For license information, see... | mit |
ODM2/ODM2YODAParser | yodatools/converter/Outputs/dbOutput.py | 2 | 8074 | from sqlalchemy.exc import IntegrityError
from yodatools.converter.Abstract import iOutputs
from odm2api.ODMconnection import dbconnection
from odm2api.services import *
from odm2api.models import setSchema, _changeSchema, Sites, Results, SamplingFeatures, Specimens, MeasurementResults, TimeSeriesResults
import logging... | bsd-3-clause |
ankurankan/scikit-learn | examples/covariance/plot_sparse_cov.py | 6 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
phdowling/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 48 | 12645 | import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_dirichlet_expect... | bsd-3-clause |
mlperf/training_results_v0.7 | Google/benchmarks/gnmt/implementations/gnmt-research-TF-tpu-v3-1024/nmt.py | 2 | 22970 | # Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | apache-2.0 |
Barmaley-exe/scikit-learn | examples/neighbors/plot_nearest_centroid.py | 264 | 1804 | """
===============================
Nearest Centroid Classification
===============================
Sample usage of Nearest Centroid classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
f... | bsd-3-clause |
mojoboss/scikit-learn | sklearn/cross_validation.py | 96 | 58309 | """
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from... | bsd-3-clause |
Solid-Mechanics/matplotlib-4-abaqus | matplotlib/axes.py | 3 | 334534 | from __future__ import division, print_function
import math
import warnings
from operator import itemgetter
import itertools
import numpy as np
from numpy import ma
import matplotlib
rcParams = matplotlib.rcParams
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
import matplotlib... | mit |
computationalEpidemiology/biobankAccelerometerAnalysis | setup.py | 2 | 1064 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="accelerometer",
version="3.1",
author="Aiden Doherty",
author_email="aiden.doherty@bdi.ox.ac.uk",
description="A package to extract meaningful health information from large accelerometer d... | bsd-2-clause |
dremio/arrow | js/test/data/tables/generate.py | 13 | 2324 | # 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 |
larsoner/mne-python | tutorials/sample-datasets/plot_sleep.py | 7 | 12214 | # -*- coding: utf-8 -*-
"""
.. _tut-sleep-stage-classif:
Sleep stage classification from polysomnography (PSG) data
==========================================================
.. note:: This code is taken from the analysis code used in [3]_. If you reuse
this code please consider citing this work.
This tuto... | bsd-3-clause |
juditacs/morph-segmentation-experiments | morph_seg/sequence_tagger/train.py | 2 | 11252 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2017 Judit Acs <judit@sch.bme.hu>
#
# Distributed under terms of the MIT license.
from argparse import ArgumentParser
from sys import stdin, stdout
import os
from six.moves import cPickle
from datetime import datetime
import copy
import ya... | mit |
chrisburr/scikit-learn | sklearn/feature_extraction/tests/test_feature_hasher.py | 258 | 2861 | from __future__ import unicode_literals
import numpy as np
from sklearn.feature_extraction import FeatureHasher
from nose.tools import assert_raises, assert_true
from numpy.testing import assert_array_equal, assert_equal
def test_feature_hasher_dicts():
h = FeatureHasher(n_features=16)
assert_equal("dict",... | bsd-3-clause |
AnnieJumpCannon/RAVE | article/figures/plot_bensby.py | 1 | 1775 |
"""
Make label comparisons with Bensby et al. (2014).
"""
import numpy as np
import matplotlib.pyplot as plt
try:
bensby
except NameError: # Do you know who I am?
from rave_io import get_cannon_dr1, get_literature_bensby
rave_cannon_dr1 = get_cannon_dr1()
#OK = (data["SNRK"] > 10) * (data["R_CHI... | mit |
fbagirov/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
RomainBrault/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 41 | 12562 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
rabernat/xray | xarray/core/dataarray.py | 1 | 71555 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import warnings
import numpy as np
import pandas as pd
from ..plot.plot import _PlotMethods
from . import duck_array_ops
from . import indexing
from . import groupby
from . import resample
fr... | apache-2.0 |
alexei-matveev/ase-local | ase/optimize/minimahopping.py | 1 | 28613 | import os
import numpy as np
from ase import io, units
from ase.optimize import QuasiNewton
from ase.parallel import paropen, rank, world
from ase.md import VelocityVerlet
from ase.md import MDLogger
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
class MinimaHopping:
"""Implements the minima... | gpl-2.0 |
glemaitre/protoclass | protoclass/validation/tests/.dummy_validation.py | 1 | 3150 | #title :test_validation.py
#description :This will create a header for a python script.
#author :Guillaume Lemaitre
#date :2015/05/18
#version :0.1
#notes :
#python_version :2.7.6
#==============================================================================
# Im... | gpl-2.0 |
rajul/tardis | tardis/plasma/properties/radiative_properties.py | 2 | 7371 | import logging
import numpy as np
import pandas as pd
from astropy import units as u, constants as const
from tardis.plasma.properties.base import ProcessingPlasmaProperty
from tardis import macro_atom
logger = logging.getLogger(__name__)
__all__ = ['StimulatedEmissionFactor', 'TauSobolev', 'BetaSobolev',
'Tran... | bsd-3-clause |
jdavidrcamacho/Tests_GP | 07 - MCMC results/kernel_ess_1day.py | 1 | 16967 | # -*- coding: utf-8 -*-
import Gedi as gedi
import numpy as np; #np.random.seed(13042017)
import matplotlib.pylab as pl; pl.close("all")
from matplotlib.ticker import MaxNLocator
import astropy.table as Table
from time import time
import sys
print
print 'It has began.'
print
for ijk in range(1,41):
#file to use
... | mit |
Bismarrck/pymatgen | pymatgen/analysis/chemenv/coordination_environments/tests/test_voronoi.py | 1 | 14923 | #!/usr/bin/env python
__author__ = 'waroquiers'
import unittest
from pymatgen.analysis.chemenv.coordination_environments.voronoi import DetailedVoronoiContainer
from pymatgen.core.structure import Structure
from pymatgen.core.lattice import Lattice
import os
import shutil
import random
import numpy as np
<<<<<<< HEA... | mit |
nelango/ViralityAnalysis | model/lib/pandas/tseries/tests/test_resample.py | 9 | 65806 | # pylint: disable=E1101
from datetime import datetime, timedelta
from functools import partial
from pandas.compat import range, lrange, zip, product
import numpy as np
from pandas import (Series, TimeSeries, DataFrame, Panel, Index,
isnull, notnull, Timestamp)
from pandas.core.groupby import Dat... | mit |
y0sh1/iozone-results-comparator | src/plotter.py | 8 | 8195 | #!/usr/bin/python
# Copyright (C) 2011, 2013
# Adam Okuliar aokuliar at redhat dot com
# Jiri Hladky hladky dot jiri at gmail dot com
# Petr Benas petrbenas at gmail dot com
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Gene... | gpl-3.0 |
zentol/flink | flink-python/pyflink/table/table.py | 4 | 32961 | ################################################################################
# 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... | apache-2.0 |
kmather73/zipline | tests/history_cases.py | 20 | 21166 | """
Test case definitions for history tests.
"""
import pandas as pd
import numpy as np
from zipline.finance.trading import TradingEnvironment
from zipline.history.history import HistorySpec
from zipline.protocol import BarData
from zipline.utils.test_utils import to_utc
def mixed_frequency_expected_index(count, fr... | apache-2.0 |
gojira/tensorflow | tensorflow/contrib/learn/python/learn/estimators/linear_test.py | 23 | 77821 | # 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 applica... | apache-2.0 |
imperial-genomics-facility/data-management-python | test/utils/projectutils_test.py | 1 | 16333 | import os, unittest
import pandas as pd
from sqlalchemy import create_engine
from igf_data.igfdb.igfTables import Base,Project,User,ProjectUser,Sample,Experiment,Run,Collection,Collection_group,File
from igf_data.igfdb.baseadaptor import BaseAdaptor
from igf_data.igfdb.projectadaptor import ProjectAdaptor
from igf_data... | apache-2.0 |
cbertinato/pandas | pandas/tests/extension/test_datetime.py | 1 | 7308 | import numpy as np
import pytest
from pandas.core.dtypes.dtypes import DatetimeTZDtype
import pandas as pd
from pandas.core.arrays import DatetimeArray
from pandas.tests.extension import base
@pytest.fixture(params=["US/Central"])
def dtype(request):
return DatetimeTZDtype(unit="ns", tz=request.param)
@pytest... | bsd-3-clause |
Sapphirine/Real-time-Risk-Management-System | GetVar.py | 1 | 5402 | '''
Copyright [2014] [Iljoon Hwang, ih138@columbia.edu
Sung Joon Huh, sh3246@columbia.edu]
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 ... | apache-2.0 |
lokik/sfepy | examples/linear_elasticity/its2D_4.py | 5 | 4331 | r"""
Diametrically point loaded 2-D disk with postprocessing and probes. See
:ref:`sec-primer`.
Use it as follows (assumes running from the sfepy directory; on Windows, you
may need to prefix all the commands with "python " and remove "./"):
1. solve the problem::
./simple.py examples/linear_elasticity/its2D_4.py... | bsd-3-clause |
sanja7s/SR_Twitter | src_general/explain_bar_plot_edge_FORMATION_REL_SIMPLE7s.py | 1 | 6311 | #!/usr/bin/env python
# a bar plot with errorbars
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from pylab import *
width=0.28 # the width of the bars
font = {'family' : 'sans-serif',
'variant' : 'normal',
'weight' : 'light',
... | mit |
suiyuan2009/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 72 | 12865 | # 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 applica... | apache-2.0 |
zhenv5/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 69 | 8605 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
osvaldshpengler/BuildingMachineLearningSystemsWithPython | ch08/chapter.py | 21 | 6372 | import numpy as np # NOT IN BOOK
from matplotlib import pyplot as plt # NOT IN BOOK
def load():
import numpy as np
from scipy import sparse
data = np.loadtxt('data/ml-100k/u.data')
ij = data[:, :2]
ij -= 1 # original data is in 1-based system
values = data[:, 2]
reviews = sparse.csc_matri... | mit |
cdemulde/wwdata | wwdata/Class_HydroData.py | 1 | 84432 | # -*- coding: utf-8 -*-
"""
Class_HydroData provides functionalities for handling data obtained in the context of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Fre... | agpl-3.0 |
wangmiao1981/spark | python/pyspark/sql/session.py | 8 | 31156 | #
# 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 |
marcocaccin/scikit-learn | sklearn/utils/tests/test_linear_assignment.py | 421 | 1349 | # Author: Brian M. Clapper, G Varoquaux
# License: BSD
import numpy as np
# XXX we should be testing the public API here
from sklearn.utils.linear_assignment_ import _hungarian
def test_hungarian():
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/examples/cluster/plot_digits_linkage.py | 1 | 2967 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | mit |
followyourheart/airflow | airflow/hooks/hive_hooks.py | 17 | 14064 | from __future__ import print_function
from builtins import zip
from past.builtins import basestring
import csv
import logging
import subprocess
from tempfile import NamedTemporaryFile
from thrift.transport import TSocket
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol
from hive_ser... | apache-2.0 |
pkruskal/scikit-learn | examples/svm/plot_separating_hyperplane.py | 294 | 1273 | """
=========================================
SVM: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machine classifier with
linear kernel.
"""
print(__doc__)
import numpy as np
impor... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/lib/mpl_examples/api/scatter_piecharts.py | 3 | 1214 | """
This example makes custom 'pie charts' as the markers for a scatter plotqu
Thanks to Manuel Metz for the example
"""
import math
import numpy as np
import matplotlib.pyplot as plt
# first define the ratios
r1 = 0.2 # 20%
r2 = r1 + 0.4 # 40%
# define some sizes of the scatter marker
sizes = [60,80,120]
# ca... | mit |
Sentient07/scikit-learn | sklearn/cluster/tests/test_k_means.py | 26 | 32656 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
bssrdf/sklearn-theano | examples/plot_comparison.py | 8 | 1823 | """
================================================
Comparison of GoogLeNet and Overfeat Classifiers
================================================
In this example, an image is input to an OverfeatClassifier and a
GoogLeNetClassifier, and the top N
probability outputs are compared for both classifiers.
"""
print(_... | bsd-3-clause |
robin-lai/scikit-learn | examples/cluster/plot_digits_linkage.py | 369 | 2959 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | bsd-3-clause |
ishank08/scikit-learn | sklearn/neighbors/tests/test_neighbors.py | 49 | 46769 | from itertools import product
import numpy as np
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix)
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.... | bsd-3-clause |
hamish2014/optTune | optTune/terminationCriteria.py | 1 | 5882 | '''
OptTunes standard termination crition is based on the gammaBudget, this module constrains additional termination critera
'''
import numpy, pickle
from tMOPSO_code import tMOPSO
from paretoArchives import paretoArchive2D
def noTransform(x):
return x
class hyperVolume_stagation:
def __init__(self, ratioTh... | gpl-3.0 |
kagayakidan/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
NextThought/pypy-numpy | numpy/lib/polynomial.py | 35 | 37641 | """
Functions to operate on polynomials.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['poly', 'roots', 'polyint', 'polyder', 'polyadd',
'polysub', 'polymul', 'polydiv', 'polyval', 'poly1d',
'polyfit', 'RankWarning']
import re
import warnings
import numpy.core.... | bsd-3-clause |
equialgo/scikit-learn | benchmarks/bench_sparsify.py | 323 | 3372 | """
Benchmark SGD prediction time with dense/sparse coefficients.
Invoke with
-----------
$ kernprof.py -l sparsity_benchmark.py
$ python -m line_profiler sparsity_benchmark.py.lprof
Typical output
--------------
input data sparsity: 0.050000
true coef sparsity: 0.000100
test data sparsity: 0.027400
model sparsity:... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/sklearn/cross_decomposition/tests/test_pls.py | 24 | 14995 | import numpy as np
from numpy.testing import assert_approx_equal
from sklearn.utils.testing import (assert_equal, assert_array_almost_equal,
assert_array_equal, assert_true,
assert_raise_message)
from sklearn.datasets import load_linnerud
from sklea... | mit |
studywolf/control | studywolf_control/sim_and_plot.py | 1 | 7267 | '''
Copyright (C) 2014 Terry Stewart and Travis DeWolf
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distrib... | gpl-3.0 |
xhappy/selfMachineLearning | cs231n-assignment/assigment1/CifarDataUtil.py | 1 | 2549 | import pickle
import numpy as np
import matplotlib.pyplot as plt
class CifarDataUtil(object):
def __init__(self, path):
self.path = path
def loadBatchData(self, fileName):
with open(self.path+'/'+fileName, 'rb') as batchFile:
pickleObj = pickle.load(batchFil... | mit |
brainiak/brainiak | brainiak/factoranalysis/htfa.py | 6 | 28942 | # Copyright 2016 Intel Corporation
#
# 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... | apache-2.0 |
JackKelly/neuralnilm_prototype | scripts/e435.py | 2 | 7298 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
mhue/scikit-learn | sklearn/neighbors/tests/test_neighbors.py | 103 | 41083 | from itertools import product
import numpy as np
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix)
from sklearn.cross_validation import train_test_split
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing impo... | bsd-3-clause |
NunoEdgarGub1/scikit-learn | examples/model_selection/plot_precision_recall.py | 249 | 6150 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
akrherz/iem | scripts/ingestors/isusm/ingest_isusm.py | 1 | 13145 | """ISUSM ingest."""
import datetime
import os
import subprocess
import traceback
import inotify.adapters
import pytz
from metpy.units import units
from metpy.calc import dewpoint_from_relative_humidity
from pyiem.observation import Observation
from pyiem.util import get_dbconn, logger, convert_value, c2f, mm2inch
impo... | mit |
mjgrav2001/scikit-learn | sklearn/qda.py | 140 | 7682 | """
Quadratic Discriminant Analysis
"""
# Author: Matthieu Perrot <matthieu.perrot@gmail.com>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import BaseEstimator, ClassifierMixin
from .externals.six.moves import xrange
from .utils import check_array, check_X_y
from .utils.validation import ... | bsd-3-clause |
Weihonghao/ECM | Vpy34/lib/python3.5/site-packages/pandas/tests/series/test_api.py | 7 | 11504 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
import pandas as pd
from pandas import Index, Series, DataFrame, date_range
from pandas.core.indexes.datetimes import Timestamp
from pandas.compat import range
from pandas import compat
import pandas.io.formats.printing as printing
fr... | agpl-3.0 |
dhermyt/WONS | analysis/textclassification/SklearnClassifierWrapper.py | 1 | 4179 | import re
from sklearn.feature_extraction.text import TfidfVectorizer
from analysis import corpus
from analysis.textclassification import bagofwords
from helpers.file import File
from tools.SentimentAnalysisToolbox import SentimentAnalysisToolbox
class SklearnClassifierWrapper(object):
__classifier = None
_... | bsd-2-clause |
dsm054/pandas | asv_bench/benchmarks/stat_ops.py | 3 | 3231 | import numpy as np
import pandas as pd
ops = ['mean', 'sum', 'median', 'std', 'skew', 'kurt', 'mad', 'prod', 'sem',
'var']
class FrameOps(object):
params = [ops, ['float', 'int'], [0, 1], [True, False]]
param_names = ['op', 'dtype', 'axis', 'use_bottleneck']
def setup(self, op, dtype, axis, use... | bsd-3-clause |
h2educ/scikit-learn | sklearn/tests/test_pipeline.py | 35 | 15221 | """
Test the pipeline module.
"""
import numpy as np
from scipy import sparse
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_raises, assert_raises_regex, assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn... | bsd-3-clause |
cl4rke/scikit-learn | benchmarks/bench_glmnet.py | 297 | 3848 | """
To run this, you'll need to have installed.
* glmnet-python
* scikit-learn (of course)
Does two benchmarks
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of... | bsd-3-clause |
index-0/Geometric-Shapes | 3D/Tri-Surface/ellipsoid.py | 1 | 1059 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.tri as mtri
fig = plt.figure(figsize=plt.figaspect(1))
a = float(input("a: "))
b = float(input("b: "))
c = float(input("c: "))
u = np.linspace(0, 2 * np.pi, endp... | gpl-3.0 |
linyc74/CaMNIST | dnn.py | 1 | 19966 | """
Version 0.3
Give the option to compute cost or not for each epoch
"""
__version__ = '0.3'
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import math, cv2, json, time
class NeuralNet(object):
"""
There are two main groups of attributes for this neural network model.
--- G... | mit |
ASPP/svg_utils | docs/source/tutorial/anscombe.py | 1 | 1899 | #!/usr/bin/env python
"""
Edward Tufte uses this example from Anscombe to show 4 datasets of x
and y that have the same mean, standard deviation, and regression
line, but which are qualitatively different.
matplotlib fun for a rainy day
Downloaded from: http://matplotlib.sourceforge.net/examples/pylab_examples/ansco... | mit |
mne-tools/mne-tools.github.io | stable/_downloads/4872a48890443799edd4c7e80b4327d9/60_visualize_stc.py | 6 | 8504 | """
.. _tut-viz-stcs:
Visualize source time courses (stcs)
====================================
This tutorial focuses on visualization of :term:`source estimates<STC>`.
Surface Source Estimates
------------------------
First, we get the paths for the evoked data and the time courses (stcs).
"""
import os
import os.... | bsd-3-clause |
AIML/scikit-learn | examples/plot_digits_pipe.py | 250 | 1809 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
kzky/python-online-machine-learning-library | cw/multiclass_soft_confidence_weighted_1_diag.py | 1 | 13277 | import numpy as np
import logging as logger
import time
from base import ConfidenceWeightedModel
from collections import defaultdict
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_svmlight_file
from scipy.stats import norm
from scipy.sparse import csr_matrix
from scipy import sparse
cl... | bsd-3-clause |
simpeg/simpegem | simpegEM/Examples/CylInversion.py | 2 | 3124 | from SimPEG import *
import simpegEM as EM
from scipy.constants import mu_0
import matplotlib.pyplot as plt
plotIt = False
cs, ncx, ncz, npad = 5., 25, 15, 15
hx = [(cs,ncx), (cs,npad,1.3)]
hz = [(cs,npad,-1.3), (cs,ncz), (cs,npad,1.3)]
mesh = Mesh.CylMesh([hx,1,hz], '00C')
active = mesh.vectorCCz<0.
layer = (mesh.v... | mit |
vshtanko/scikit-learn | examples/linear_model/plot_iris_logistic.py | 283 | 1678 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <http://en.wikipedia.org/wiki/Iris_f... | bsd-3-clause |
rohanp/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
jereze/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 272 | 6972 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, wher... | bsd-3-clause |
arjun-krishna/Face_Recognition | src/facial_feature_extract_inceptionv3.py | 1 | 1175 | import os
import re
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.python.platform import gfile
frozen_graph_filename = 'models/classify_image_graph_def.pb' # define with libraries
def load_graph(frozen_graph_filename):
with tf.gfile.GFile(frozen_graph_filename, "rb") a... | mit |
hsiaoyi0504/scikit-learn | examples/model_selection/grid_search_text_feature_extraction.py | 253 | 4158 | """
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the do... | bsd-3-clause |
DGrady/pandas | pandas/tests/io/json/test_ujson.py | 7 | 55996 | # -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, zip, StringIO, u
import pandas._libs.j... | bsd-3-clause |
shennjia/home | .script/myTPC.py | 1 | 4549 | #!/usr/bin/env python
import sys
from tempfile import mkstemp
from shutil import move
from os import remove, close
import fileinput
from subprocess import call
import os
import re
import matplotlib.pyplot as plt
import numpy as np
def filteroutTpcSamples(file_path, rePattern):
import re
#Create temp file
... | unlicense |
phievo/phievo | phievo/AnalysisTools/palette.py | 1 | 4432 | from phievo import __silent__,__verbose__
if __verbose__:
print("Execute palette.py")
from matplotlib import pylab,colors
default_colormap = "gist_rainbow"
def update_default_colormap(colormap):
"""
Update the color map used by the palette modules
Arg:
colormap (str): name of the matplotlib c... | lgpl-3.0 |
skandavivek/tracking-cars-highway | common.py | 11 | 6701 | #!/usr/bin/env python
'''
This module contains some common routines used by other samples.
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
from functools import reduce
import numpy as np
import cv2
# built-in modules
import os
import iterto... | apache-2.0 |
dhomeier/astropy | astropy/visualization/tests/test_units.py | 7 | 3529 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import io
import pytest
from astropy.utils.compat.optional_deps import HAS_PLT
if HAS_PLT:
import matplotlib.pyplot as plt
from astropy import units as u
from astropy.coordinates import Angle
from astropy.visualization.units... | bsd-3-clause |
myt00seven/svrg | cifar/alexnet_lasagne/lasagne-googlenet-master/googlenet_bn.py | 1 | 57162 | from __future__ import division, absolute_import
from __future__ import unicode_literals
import theano
import theano.tensor as T
import numpy as np
import lasagne
from lasagne.layers import dnn
from lasagne.objectives import categorical_crossentropy, aggregate
# custom layers and functions
import googlenet
#from uti... | mit |
sameeptandon/sail-car-log | mapping/sandbox/graphslam/scripts/eval_maps.py | 1 | 4146 | import h5py
import json
import numpy as np
from os.path import splitext, basename, join as pjoin
from pipeline_utils import print_and_call
from pipeline_config import MAPPING_PATH
from graphslam_config import GRAPHSLAM_EVAL_DIR, GRAPHSLAM_MAPS_DIR, MATCH_JSON_DATA
from sklearn.neighbors import NearestNeighbors
import m... | bsd-2-clause |
wanggang3333/scikit-learn | examples/classification/plot_lda.py | 164 | 2224 | """
====================================================================
Normal and Shrinkage Linear Discriminant Analysis for classification
====================================================================
Shows how shrinkage improves classification.
"""
from __future__ import division
import numpy as np
import... | bsd-3-clause |
vrok/gucdb | test/comparative.py | 1 | 14232 | #!/usr/bin/python
from os import system
import os
from collections import defaultdict
import subprocess
import sys
TEMP_PRE_INPUT = '/tmp/pre_input.txt'
TEMP_INPUT = '/tmp/input.txt'
GRANULARITY = 4
REPEATS = 1
MEMORY_LABEL = 'Memory (MB)'
files = [
'test_data/Wikipedia_titles',
'test_data/Length_from_150_to_... | gpl-3.0 |
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