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 |
|---|---|---|---|---|---|
jemromerol/apasvo | apasvo/picking/apasvotrace.py | 1 | 30191 | # encoding: utf-8
'''
@author: Jose Emilio Romero Lopez
@copyright: Copyright 2013-2014, Jose Emilio Romero Lopez.
@license: GPL
@contact: jemromerol@gmail.com
This file is part of APASVO.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Pu... | gpl-3.0 |
roxyboy/scikit-learn | examples/linear_model/plot_robust_fit.py | 238 | 2414 | """
Robust linear estimator fitting
===============================
Here a sine function is fit with a polynomial of order 3, for values
close to zero.
Robust fitting is demoed in different situations:
- No measurement errors, only modelling errors (fitting a sine with a
polynomial)
- Measurement errors in X
- M... | bsd-3-clause |
TimothyHelton/k2datascience | k2datascience/cluster.py | 1 | 9060 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
""" Clustering Module
.. moduleauthor:: Timothy Helton <timothy.j.helton@gmail.com>
"""
import logging
import os.path as osp
import bokeh.io as bkio
import bokeh.models as bkm
import bokeh.plotting as bkplt
from bokeh.sampledata.us_states import data as states
import ma... | bsd-3-clause |
loli/sklearn-ensembletrees | sklearn/svm/tests/test_bounds.py | 42 | 2112 | import nose
from nose.tools import assert_true
import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression
dense_X = [[-1, 0], [0, 1], [1, 1], [1, 1]]
sparse_X = sp.csr_matrix(dense_X)
Y1 = ... | bsd-3-clause |
rexshihaoren/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 12 | 10796 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
sauloal/cnidaria | scripts/venv/lib/python2.7/site-packages/pandas/core/strings.py | 1 | 45903 | import numpy as np
from pandas.compat import zip
from pandas.core.common import isnull, _values_from_object, is_bool_dtype
import pandas.compat as compat
from pandas.util.decorators import Appender, deprecate_kwarg
import re
import pandas.lib as lib
import warnings
import textwrap
_shared_docs = dict()
def _get_ar... | mit |
anurag313/scikit-learn | sklearn/cluster/mean_shift_.py | 96 | 15434 | """Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to elim... | bsd-3-clause |
rmdort/clipper | containers/python/test_sklearn_cifar_container.py | 3 | 1871 | import numpy as np
import os
import pandas as pd
import rpc
import sys
from sklearn_cifar_container import SklearnCifarContainer
from sklearn.metrics import accuracy_score
classes = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
positive_class = classes.index('airp... | apache-2.0 |
cainiaocome/scikit-learn | examples/covariance/plot_covariance_estimation.py | 250 | 5070 | """
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================
When working with covariance estimation, the usual approach is to use
a maximum likelihood estimator,... | bsd-3-clause |
synthicity/activitysim | activitysim/abm/models/trip_destination.py | 2 | 19741 | # ActivitySim
# See full license in LICENSE.txt.
from __future__ import (absolute_import, division, print_function, )
from future.standard_library import install_aliases
install_aliases() # noqa: E402
from builtins import range
import logging
import numpy as np
import pandas as pd
from activitysim.core import trac... | agpl-3.0 |
alan-unravel/bokeh | examples/interactions/interactive_bubble/data.py | 49 | 1265 | import numpy as np
from bokeh.palettes import Spectral6
def process_data():
from bokeh.sampledata.gapminder import fertility, life_expectancy, population, regions
# Make the column names ints not strings for handling
columns = list(fertility.columns)
years = list(range(int(columns[0]), int(columns[-... | bsd-3-clause |
PiotrGrzybowski/NeuralNetworks | networks/neurons/optimizer.py | 1 | 5209 | import numpy as np
import matplotlib.pyplot as plt
from drawnow import drawnow
import time
from neurons.activations import bipolar
LEAST_MEAN_ERROR = 'least_mean_square'
DISCRETE_ERROR = 'discrete'
class Optimizer:
def __init__(self, loss, learning_rate, epochs, stop_error):
self.loss = loss
sel... | apache-2.0 |
badlogicmanpreet/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/font_manager.py | 69 | 42655 | """
A module for finding, managing, and using fonts across platforms.
This module provides a single :class:`FontManager` instance that can
be shared across backends and platforms. The :func:`findfont`
function returns the best TrueType (TTF) font file in the local or
system font path that matches the specified :class... | agpl-3.0 |
raoulbq/scipy | scipy/signal/waveforms.py | 17 | 14814 | # Author: Travis Oliphant
# 2003
#
# Feb. 2010: Updated by Warren Weckesser:
# Rewrote much of chirp()
# Added sweep_poly()
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy import asarray, zeros, place, nan, mod, pi, extract, log, sqrt, \
exp, cos, sin, polyval, po... | bsd-3-clause |
thomasmeagher/DS-501 | 3/text_analytics/solutions/exercise_02_sentiment.py | 46 | 2798 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | mit |
nrhine1/scikit-learn | sklearn/datasets/tests/test_base.py | 205 | 5878 | import os
import shutil
import tempfile
import warnings
import nose
import numpy
from pickle import loads
from pickle import dumps
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn... | bsd-3-clause |
pele-python/mcpele | examples/sfHS_WCA_fluid/radial_distribution_function.py | 2 | 4232 | from __future__ import division
import numpy as np
from scipy.special import gamma
import matplotlib.pyplot as plt
from pele.potentials import HS_WCA
from pele.optimize import LBFGS_CPP
from mcpele.monte_carlo import _BaseMCRunner
from mcpele.monte_carlo import RandomCoordsDisplacement
from mcpele.monte_carlo import Re... | gpl-3.0 |
gvanhorn38/active_neurofinder | movie.py | 1 | 1737 | """
Create a video out of the images
"""
from matplotlib import pyplot as plt
import matplotlib.animation as animation
import numpy as np
from scipy import interpolate
from glob import glob
from util import load_images, load_regions
import os
from scipy.misc import imread
from PIL import Image
def create_movie(datas... | mit |
zorroblue/scikit-learn | sklearn/datasets/tests/test_20news.py | 75 | 3266 | """Test the 20news downloader, if the data is available."""
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
from sklearn import datasets
def test_20news():
try:
data = dat... | bsd-3-clause |
sarathid/Learning | Intro_to_ML/tools/email_preprocess.py | 10 | 2628 | #!/usr/bin/python
import pickle
import cPickle
import numpy
from sklearn import cross_validation
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectPercentile, f_classif
def preprocess(words_file = "../tools/word_data.pkl", authors_file="../tools/email_authors.p... | gpl-3.0 |
icereval/modular-file-renderer | mfr/ext/tabular/configuration.py | 3 | 1191 | # -*- coding: utf-8 -*-
"""Configuration object for the mfr_tabular module."""
from .libs import (
csv_pandas,
tsv_pandas,
dta_pandas,
sav_pandas,
xlsx_xlrd
)
from mfr import Config
"""Defines a list of functions that can handle a particular file type. The
functions will be attempted in order, f... | apache-2.0 |
timqian/sms-tools | lectures/6-Harmonic-model/plots-code/f0Twm-piano.py | 19 | 1261 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackman
import math
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import utilFunctions as UF
import stft as STFT... | agpl-3.0 |
dilawar/moose-full | moose-examples/snippets/insertSpines.py | 2 | 2798 | #########################################################################
## This program is part of 'MOOSE', the
## Messaging Object Oriented Simulation Environment.
## Copyright (C) 2015 Upinder S. Bhalla. and NCBS
## It is made available under the terms of the
## GNU Lesser General Public License version 2... | gpl-2.0 |
kashif/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 83 | 5888 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
cbertinato/pandas | pandas/tests/plotting/test_datetimelike.py | 1 | 57288 | """ Test cases for time series specific (freq conversion, etc) """
from datetime import date, datetime, time, timedelta
import pickle
import sys
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import DataFrame, Index, NaT, Series, isna
from pandas.core.indexes.datetimes import ... | bsd-3-clause |
dbarbier/ot-svn | python/doc/sphinxext/numpydoc/tests/test_docscrape.py | 3 | 18530 | # -*- encoding:utf-8 -*-
from __future__ import division, absolute_import, print_function
import sys
import textwrap
from numpydoc.docscrape import NumpyDocString, FunctionDoc, ClassDoc
from numpydoc.docscrape_sphinx import SphinxDocString, SphinxClassDoc
from nose.tools import *
if sys.version_info[0] >= 3:
six... | gpl-3.0 |
AnasGhrab/scikit-learn | sklearn/tests/test_cross_validation.py | 27 | 41664 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn... | bsd-3-clause |
lgarren/spack | var/spack/repos/builtin/packages/py-yt/package.py | 3 | 4127 | ##############################################################################
# Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-64... | lgpl-2.1 |
YihaoLu/statsmodels | statsmodels/datasets/statecrime/data.py | 25 | 3128 | #! /usr/bin/env python
"""Statewide Crime Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Public domain."""
TITLE = """Statewide Crime Data 2009"""
SOURCE = """
All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below.
"""
DESCRSHORT = """State ... | bsd-3-clause |
JRock007/boxxy | dist/Boxxy.app/Contents/Resources/lib/python2.7/numpy/lib/twodim_base.py | 37 | 26758 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
from numpy.core.numeric import (
asanyarray, arange, zeros, greater_equal, multiply, ones, asarray,
where, int8, int16, int32, int64, empty, promote_types
)
from numpy.core import iinfo
__... | mit |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/core/sparse/scipy_sparse.py | 18 | 5516 | """
Interaction with scipy.sparse matrices.
Currently only includes SparseSeries.to_coo helpers.
"""
from pandas.core.index import MultiIndex, Index
from pandas.core.series import Series
from pandas.compat import OrderedDict, lmap
def _check_is_partition(parts, whole):
whole = set(whole)
parts = [set(x) for ... | mit |
tim777z/seaborn | doc/sphinxext/plot_generator.py | 38 | 10035 | """
Sphinx plugin to run example scripts and create a gallery page.
Lightly modified from the mpld3 project.
"""
from __future__ import division
import os
import os.path as op
import re
import glob
import token
import tokenize
import shutil
import json
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot... | bsd-3-clause |
jonpdx/gsoc2014 | contour sem tiff down sample.py | 1 | 2985 | # -*- coding: utf-8 -*-
# <nbformat>2</nbformat>
# <codecell>
###############################################################################
# Import Libraries
import time as time
import numpy as np
import scipy as sp
import pylab as pl
import Image
#
from sklearn.feature_extraction.image import grid_to_graph
from s... | gpl-2.0 |
cjermain/numpy | numpy/lib/twodim_base.py | 83 | 26903 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
from numpy.core.numeric import (
asanyarray, arange, zeros, greater_equal, multiply, ones, asarray,
where, int8, int16, int32, int64, empty, promote_types, diagonal,
)
from numpy.core import... | bsd-3-clause |
wangyum/tensorflow | tensorflow/contrib/learn/python/learn/estimators/__init__.py | 20 | 12315 | # 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 |
TNick/pylearn2 | pylearn2/scripts/papers/jia_huang_wkshp_11/evaluate.py | 44 | 3208 | from __future__ import print_function
from optparse import OptionParser
import warnings
try:
from sklearn.metrics import classification_report
except ImportError:
classification_report = None
warnings.warn("couldn't find sklearn.metrics.classification_report")
try:
from sklearn.metrics import confusion... | bsd-3-clause |
weigq/pytorch-pose | evaluation/utils.py | 2 | 1239 |
def visualize(oriImg, points, pa):
import matplotlib
import cv2 as cv
import matplotlib.pyplot as plt
import math
fig = matplotlib.pyplot.gcf()
# fig.set_size_inches(12, 12)
colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
... | gpl-3.0 |
sangwook236/SWDT | sw_dev/python/rnd/test/machine_vision/opencv/opencv_transformation.py | 2 | 12844 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import math
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
# REF [site] >> https://docs.opencv.org/master/da/d6e/tutorial_py_geometric_transformations.html
def geometric_transformation():
#--------------------
# Scaling.
img = cv.imread('../.... | gpl-3.0 |
magne-max/zipline-ja | zipline/lib/labelarray.py | 1 | 20669 | """
An ndarray subclass for working with arrays of strings.
"""
from functools import partial
from operator import eq, ne
import re
import numpy as np
from numpy import ndarray
import pandas as pd
from toolz import compose
from zipline.utils.compat import unicode
from zipline.utils.preprocess import preprocess
from z... | apache-2.0 |
giserh/mpld3 | mpld3/__init__.py | 20 | 1109 | """
Interactive D3 rendering of matplotlib images
=============================================
Functions: General Use
----------------------
:func:`fig_to_html`
convert a figure to an html string
:func:`fig_to_dict`
convert a figure to a dictionary representation
:func:`show`
launch a web server to view... | bsd-3-clause |
EnvGen/toolbox | scripts/concoct/nr_approved_bins_summary.py | 1 | 1775 | #!/usr/bin/env python
"""
Based on all checkm results, creates a table containing the nr of approved genomes
for all binning runs it can find within binning/*.
@author: alneberg
"""
from __future__ import print_function
import sys
import os
import argparse
import pandas as pd
import glob
def find_checkm_dirs():
... | mit |
landryb/QGIS | python/plugins/processing/algs/qgis/VectorLayerScatterplot.py | 15 | 3160 | # -*- coding: utf-8 -*-
"""
***************************************************************************
EquivalentNumField.py
---------------------
Date : January 2013
Copyright : (C) 2013 by Victor Olaya
Email : volayaf at gmail dot com
*******************... | gpl-2.0 |
saiwing-yeung/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
nvoron23/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | 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
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section L... | bsd-3-clause |
snfactory/cubefit | cubefit/tests/test_fitting.py | 2 | 12205 | #/usr/bin/env py.test
from __future__ import print_function
import os
import sys
import sysconfig
import numpy as np
from numpy.fft import fft2, ifft2
from numpy.testing import assert_allclose
from scipy.optimize import check_grad, approx_fprime
import cubefit
from cubefit.fitting import (sky_and_sn,
... | mit |
Averroes/statsmodels | statsmodels/iolib/summary2.py | 21 | 19583 | from statsmodels.compat.python import (lrange, iterkeys, iteritems, lzip,
reduce, itervalues, zip, string_types,
range)
from statsmodels.compat.collections import OrderedDict
import numpy as np
import pandas as pd
import datetime
import textw... | bsd-3-clause |
dialounke/pylayers | pylayers/gui/PylayersGui.py | 1 | 23274 | # -*- coding: utf-8 -*-
"""
PyLayers GUI
.. autommodule::
:members:
To run this code. type
python PylayersGui.py
"""
from pylayers.simul.link import *
import pylayers.util.pyutil as pyu
import pylayers.signal.standard as std
from pylayers.util.project import *
import json
# TEST
import matplotlib
matpl... | mit |
mxjl620/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 218 | 3893 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
Eric89GXL/scikit-learn | sklearn/metrics/cluster/bicluster/bicluster_metrics.py | 13 | 2597 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_arrays
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shape."""
a_rows, a_cols = check_arrays(*a)
b_rows,... | bsd-3-clause |
alberlab/alabtools | alabtools/plots.py | 1 | 10761 | # Copyright (C) 2017 University of Southern California and
# Nan Hua
#
# Authors: Nan Hua
#
# 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 Licen... | gpl-3.0 |
clovett/MissionPlanner | Lib/site-packages/numpy/lib/twodim_base.py | 70 | 23431 | """ Basic functions for manipulating 2d arrays
"""
__all__ = ['diag','diagflat','eye','fliplr','flipud','rot90','tri','triu',
'tril','vander','histogram2d','mask_indices',
'tril_indices','tril_indices_from','triu_indices','triu_indices_from',
]
from numpy.core.numeric import asanyarr... | gpl-3.0 |
matthiasmengel/sealevel | sealevel/print_projection_numbers.py | 1 | 4093 | # This file is part of SEALEVEL - a tool to estimates future sea-level rise
# constrained by past obervations and long-term sea-level commitment
# Copyright (C) 2016 Matthias Mengel working at PIK Potsdam
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Pub... | gpl-3.0 |
mattcaldwell/zipline | zipline/transforms/ta.py | 6 | 8006 | #
# Copyright 2013 Quantopian, 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 wr... | apache-2.0 |
alliemacleay/MachineLearning_CS6140 | Tests/hw4_tests.py | 1 | 4429 | from sklearn.metrics import roc_auc_score
__author__ = 'Allison MacLeay'
import CS6140_A_MacLeay.utils.Adaboost as adab
import CS6140_A_MacLeay.utils as utils
import CS6140_A_MacLeay.Homeworks.HW4 as decTree
import CS6140_A_MacLeay.Homeworks.HW3 as hw3
import CS6140_A_MacLeay.Homeworks.hw4 as hw4
import CS6140_A_MacL... | mit |
antoinecarme/pyaf | tests/HourOfWeek/test_Business_Hourly_LunchTime.py | 1 | 2087 | import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
np.random.seed(seed=1960)
#get_ipython().magic('matplotlib inline')
df = pd.DataFrame()
lTimeVar = 'Time'
lSignalVar = 'Signal'
N = 10000
df[lTimeVar + '_Hourly'] = pd.date_range('2000-1-1', periods=N, freq='1h')
df['Hour'] = df[lTimeVar ... | bsd-3-clause |
antworteffekt/EDeN | eden/iterated_semisupervised_feature_selection.py | 1 | 7729 | import random
import logging
import numpy as np
from sklearn.semi_supervised import LabelSpreading
from sklearn.feature_selection import RFECV
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import LabelEncoder
logger = logging.getLogger(__name__)
def semisupervised_target(target=None,
... | mit |
jjhelmus/scipy | scipy/fftpack/basic.py | 2 | 21565 | """
Discrete Fourier Transforms - basic.py
"""
# Created by Pearu Peterson, August,September 2002
from __future__ import division, print_function, absolute_import
__all__ = ['fft','ifft','fftn','ifftn','rfft','irfft',
'fft2','ifft2']
from numpy import zeros, swapaxes
import numpy
from . import _fftpack
im... | bsd-3-clause |
Ninad998/deepstylometry-python | crosslingual/MLModelCreatorWord.py | 1 | 4039 | #!/usr/bin/python
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np
np.random.seed(123)
databaseConnectionServer = 'srn01.cs.cityu.edu.hk'
documentTable = 'document'
def loadData(documentTable, samples ... | mit |
exa-analytics/atomic | exatomic/nwchem/inputs.py | 2 | 9896 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Input Generator and Parser
#############################
Every attempt is made to follow the Documentation on the
NWChem `website`_ with a general theme of the input Generator
ac... | apache-2.0 |
kcompher/BuildingMachineLearningSystemsWithPython | ch06/01_start.py | 6 | 3961 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
#
# This script trains multinomial Naive Bayes on the tweet corpus
# to find two different results:
# -... | mit |
IndraVikas/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 |
HaydenFaulkner/phd | processing/VEN/S2S/s2vt_captioner_old.py | 1 | 26012 | DEVICE_ID = 0
""" Script to generate captions from video features"""
from collections import OrderedDict
import argparse
import cPickle as pickle
import h5py
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import sys
sys.path.append('/home/hayden/caffe-recurrent/python')
# lib... | mit |
sourabhdattawad/BuildingMachineLearningSystemsWithPython | ch08/norm.py | 23 | 2242 | import numpy as np
class NormalizePositive(object):
def __init__(self, axis=0):
self.axis = axis
def fit(self, features, y=None):
# count features that are greater than zero in axis `self.axis`:
if self.axis == 1:
features = features.T
binary = (features > 0)
... | mit |
pianomania/scikit-learn | sklearn/neighbors/tests/test_dist_metrics.py | 36 | 6957 | 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 sklearn.neighbors import BallTree
from sklearn.utils.testing import SkipTest, assert_raises_regex
... | bsd-3-clause |
madgik/exareme | Exareme-Docker/src/mip-algorithms/CART/cart_lib.py | 1 | 34421 | from __future__ import division
from __future__ import print_function
import sys
from os import path
import numpy as np
import pandas as pd
import json
import itertools
import logging
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))) + '/utils/')
from algorithm_utils import TransferData, PRIVACY_MAGI... | mit |
jabeerahmed/testrepo | term1/Lessons/LaneDetection/hough_transform.py | 1 | 6249 | import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import os
COLOR_SWATCH = [
[255, 64, 0], [255, 128, 0], [255, 191, 0],
[255, 255, 0], [191, 255, 0], [128, 255, 0], [64, 255, 0],
[0, 255, 0], [0, 255, 64], [0, 255, 128],
[0, 255, 191], [0, 255, 255], [0, 191, 25... | gpl-2.0 |
mikekestemont/ruzicka | code/05latin_testviz.py | 1 | 1217 | from __future__ import print_function
import os
import time
import json
import sys
import pickle
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sb
from scipy.spatial.distance import squareform, pdist
import numpy as np
from sklearn.preprocessing import LabelEncoder
from ruzicka.distance_metrics i... | mit |
benoitsteiner/tensorflow | tensorflow/contrib/factorization/python/ops/gmm_test.py | 44 | 8747 | # 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 |
dancingdan/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_test.py | 21 | 54488 | # 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 |
DevinCharles/igpt | examples/mapGenerator.py | 1 | 3325 | # coding: utf-8
import folium
import pandas as pd
from os.path import splitext
def mapGenerator(files,data_info='default', map_opts='default',serve_files=False):
createMap(files,data_info, map_opts)
if serve_files:
try:
startServer()
except Exception as e:
... | gpl-2.0 |
jmschrei/scikit-learn | sklearn/semi_supervised/tests/test_label_propagation.py | 307 | 1974 | """ test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propa... | bsd-3-clause |
evgchz/scikit-learn | examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.py | 252 | 3490 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
==============================================================================
Gaussian Processes classification example: exploiting the probabilistic output
==============================================================================
A two-dimensional regression exerci... | bsd-3-clause |
ishanic/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 |
ArtsiomCh/tensorflow | tensorflow/contrib/training/python/training/feeding_queue_runner_test.py | 76 | 5052 | # Copyright 2015 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 |
nmayorov/scikit-learn | sklearn/mixture/gmm.py | 19 | 30655 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as... | bsd-3-clause |
rahul-c1/scikit-learn | sklearn/semi_supervised/tests/test_label_propagation.py | 307 | 1974 | """ test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propa... | bsd-3-clause |
heli522/scikit-learn | examples/classification/plot_classifier_comparison.py | 181 | 4699 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=====================
Classifier comparison
=====================
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.
This should be taken with ... | bsd-3-clause |
nonsk131/USRP2016 | generate_tests0000-0999.py | 1 | 3341 | from isochrones.dartmouth import Dartmouth_Isochrone
from isochrones.utils import addmags
import numpy as np
import pandas as pd
file = open('/tigress/np5/true_params.txt','a')
def get_index(n):
if n < 10:
return '000' + str(n)
elif n < 100:
return '00' + str(n)
elif n < 1000:
retu... | mit |
raysteam/zeppelin | python/src/main/resources/python/zeppelin_python.py | 1 | 9358 | #
# 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 |
fedhere/SESNCfAlib | getSpecOSNC.py | 1 | 1534 | import pandas as pd
import numpy as np
import json
import os
import glob
import inspect
import optparse
import time
import copy
import os
import pylab as pl
import numpy as np
import scipy
import json
import sys
import pickle as pkl
import scipy as sp
import numpy as np
from scipy import optimize
from scipy.interpola... | mit |
chenyyx/scikit-learn-doc-zh | examples/zh/svm/plot_iris.py | 1 | 4011 | # -*- coding:UTF-8 -*-
"""
===========================================================
在鸢尾花卉数据集上绘制不同的 SVM 分类器
===========================================================
在鸢尾花卉数据集的 2D 投影上的不同线性 SVM 分类器的比较。我们只考虑这个数据集的前 2 个特征:
- 萼片长度
- 萼片宽度
此示例显示如何绘制具有不同 kernel 的四个 SVM 分类器的决策表面。
线性模型 ``LinearSVC()`` 和 ``SVC(kernel='l... | gpl-3.0 |
lbishal/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
ningchi/scikit-learn | examples/ensemble/plot_adaboost_regression.py | 26 | 1523 | """
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tr... | bsd-3-clause |
PatrickChrist/scikit-learn | examples/feature_selection/plot_permutation_test_for_classification.py | 250 | 2233 | """
=================================================================
Test with permutations the significance of a classification score
=================================================================
In order to test if a classification score is significative a technique
in repeating the classification procedure aft... | bsd-3-clause |
tongwang01/tensorflow | tensorflow/examples/tutorials/input_fn/boston.py | 19 | 2448 | # 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 |
stverhae/incubator-airflow | airflow/contrib/hooks/bigquery_hook.py | 9 | 38929 | # -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
... | apache-2.0 |
antgonza/qiita | qiita_pet/handlers/api_proxy/tests/test_artifact.py | 1 | 15279 | # -----------------------------------------------------------------------------
# Copyright (c) 2014--, The Qiita Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | bsd-3-clause |
kyleabeauchamp/EnsemblePaper | code/figures/plot_1D_example.py | 1 | 2904 | import scipy.stats
import ALA3
import numpy as np
import matplotlib.pyplot as plt
from fitensemble import belt
import matplotlib
matplotlib.rcParams.update({'font.size': 18})
num_frames = 10000000
z = np.random.normal(size=(num_frames, 1))
rho = np.random.random_integers(0, 1, size=(num_frames, 1)) * 2 - 1
x = 0.5 * (... | gpl-3.0 |
lazywei/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 268 | 2723 | """
===================================================
Label Propagation digits: Demonstrating performance
===================================================
This example demonstrates the power of semisupervised learning by
training a Label Spreading model to classify handwritten digits
with sets of very few labels.... | bsd-3-clause |
kwentz10/Photosynthesis_Optimization_Modeling | Trait_Based_Photo_Model_Meadow_Plant_Traits_Plot_Each_Meadow.py | 1 | 19462 | # -*- coding: utf-8 -*-
"""
Photosynthesis and Stomatal Conductance Model
Created 9/27/2016
Katherine Wentz
This is a program that runs photosynthesis and
stomatal conductance given changes in leaf-
level traits. I derive photosynthesis from a
stomatal conductance model. That way I am
able to void the ci term. I am ... | mit |
CforED/Machine-Learning | sklearn/_build_utils/__init__.py | 21 | 1125 | """
Utilities useful during the build.
"""
# author: Andy Mueller, Gael Varoquaux
# license: BSD
from __future__ import division, print_function, absolute_import
HASH_FILE = 'cythonize.dat'
DEFAULT_ROOT = 'sklearn'
# WindowsError is not defined on unix systems
try:
WindowsError
except NameError:
WindowsError... | bsd-3-clause |
joernhees/scikit-learn | sklearn/cluster/tests/test_mean_shift.py | 23 | 3957 | """
Testing for mean shift clustering methods
"""
import numpy as np
import warnings
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.... | bsd-3-clause |
DBernardes/ProjetoECC | Eficiência_Quântica/Codigo/CCDinfo.py | 1 | 1100 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
Criado em 18 de Outubro de 2016
Descricao: este modulo tem como entrada o cabecalho de uma imagen fits e a quantidade de imagens da serie obtidam retornado uma string com as principais informacoes do CCD.
@author: Denis Varise Bernardes & Eder Martioli
... | mit |
ShawnMurd/MetPy | tests/calc/test_basic.py | 1 | 32604 | # Copyright (c) 2008,2015,2017,2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Test the `basic` module."""
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from metpy.calc import (add_height_to_pressure, add_pressure_... | bsd-3-clause |
vshtanko/scikit-learn | examples/ensemble/plot_forest_iris.py | 335 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/matplotlib/backends/backend_cairo.py | 10 | 19252 | """
A Cairo backend for matplotlib
Author: Steve Chaplin
Cairo is a vector graphics library with cross-device output support.
Features of Cairo:
* anti-aliasing
* alpha channel
* saves image files as PNG, PostScript, PDF
http://cairographics.org
Requires (in order, all available from Cairo website):
cairo, pyc... | gpl-3.0 |
blackball/an-test6 | util/compare-runs.py | 1 | 3995 | #! /usr/bin/env python
import matplotlib
matplotlib.use('Agg')
from astrometry.util.fits import *
from astrometry.util.file import *
from pylab import *
from optparse import *
import os
from glob import glob
from numpy import *
def get_field(fieldname, m1, m2, nil, preproc=None):
t1 = []
t2 = []
for k,v in m1.it... | gpl-2.0 |
noferini/AliceO2 | Analysis/Scripts/update_ccdb.py | 3 | 5974 | #!/usr/bin/env python3
# Copyright CERN and copyright holders of ALICE O2. This software is
# distributed under the terms of the GNU General Public License v3 (GPL
# Version 3), copied verbatim in the file "COPYING".
#
# See http://alice-o2.web.cern.ch/license for full licensing information.
#
# In applying this licen... | gpl-3.0 |
KevinHock/rtdpyt | profiling/test_projects/flaskbb_lite_2/mprof.py | 3 | 17606 | #!/usr/bin/env python
import glob
import os
import os.path as osp
import sys
import re
import copy
import time
import math
from optparse import OptionParser, OptionValueError
import memory_profiler as mp
ALL_ACTIONS = ("run", "rm", "clean", "list", "plot")
help_msg = """
Available commands:
run run a give... | gpl-2.0 |
tswast/google-cloud-python | securitycenter/docs/conf.py | 2 | 11946 | # -*- coding: utf-8 -*-
#
# google-cloud-securitycenter documentation build configuration file
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; v... | apache-2.0 |
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