repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/matplotlib/backends/backend_qt4agg.py | 10 | 2177 | """
Render to qt from agg
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import os # not used
import sys
import ctypes
import warnings
import matplotlib
from matplotlib.figure import Figure
from .backend_qt5agg import FigureCanvasQTAggBa... | gpl-3.0 |
nixphix/ml-projects | sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.py | 1 | 11757 |
# coding: utf-8
# ### Sentiment Analysis on "Jallikattu" with Twitter Data Feed <h3 style="color:red;">#DataScienceForSocialCause</h3>
#
# Twitter is flooded with Jallikattu issue, let us find peoples sentiment with Data Science tools. Following is the approach
# * Register a Twitter API handle for data feed
# * Pul... | mit |
pdamodaran/yellowbrick | tests/checks.py | 1 | 4707 | # tests.checks
# Performs checking that visualizers adhere to Yellowbrick conventions.
#
# Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Created: Mon May 22 11:18:06 2017 -0700
#
# Copyright (C) 2017 District Data Labs
# For license information, see LICENSE.txt
#
# ID: checks.py [4131cb1] benjamin@ben... | apache-2.0 |
TNT-Samuel/Coding-Projects | DNS Server/Source - Copy/Lib/site-packages/dask/dataframe/tests/test_merge_column_and_index.py | 5 | 5592 | import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from dask.dataframe.utils import assert_eq, PANDAS_VERSION
# Fixtures
# ========
@pytest.fixture
def df_left():
# Create frame with 10 partitions
# Frame has 11 distinct idx values
partition_sizes = np.array([3, 4, 2, 5, 3, ... | gpl-3.0 |
hakonsbm/nest-simulator | extras/ConnPlotter/examples/connplotter_tutorial.py | 18 | 27730 | # -*- coding: utf-8 -*-
#
# connplotter_tutorial.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the Li... | gpl-2.0 |
comprna/SUPPA | scripts/generate_boxplot_event.py | 1 | 5584 | # The next script will format a phenotype table (junctions, events, trasncripts...)
# for runnning FastQTL analysis
#This version is for formatting the SCLC phenotype
"""
@authors: Juan L. Trincado
@email: juanluis.trincado@upf.edu
generate_boxplot_event.py: Generates a boxplot with the PSI values, given which sampl... | mit |
OshynSong/scikit-learn | sklearn/utils/tests/test_fixes.py | 281 | 1829 | # Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import numpy as np
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_true
from numpy.testing import (assert_almost_equal,
... | bsd-3-clause |
kazemakase/scikit-learn | sklearn/feature_extraction/hashing.py | 183 | 6155 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if... | bsd-3-clause |
aflaxman/scikit-learn | examples/linear_model/plot_bayesian_ridge.py | 33 | 3875 | """
=========================
Bayesian Ridge Regression
=========================
Computes a Bayesian Ridge Regression on a synthetic dataset.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the coefficient
weights are slightly shift... | bsd-3-clause |
ueshin/apache-spark | python/run-tests.py | 15 | 13614 | #!/usr/bin/env python3
#
# 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 "L... | apache-2.0 |
nwjs/chromium.src | tools/perf/experimental/representative_perf_test_limit_adjuster/adjust_upper_limits.py | 1 | 6803 | # Copyright 2019 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from __future__ import print_function
import json
import os
import sys
import shutil
import subprocess
import tempfile
CHROMIUM_PATH = os.path.join(os.path... | bsd-3-clause |
iohannez/gnuradio | gr-filter/examples/synth_to_chan.py | 7 | 3891 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your ... | gpl-3.0 |
breisfeld/avoplot | examples/adv_sine_wave.py | 3 | 8650 | import numpy
import matplotlib.pyplot as plt
import math
from avoplot import plugins, series, controls, subplots
from avoplot.gui import widgets
import wx
plugin_is_GPL_compatible = True
class TrigFuncSubplot(subplots.AvoPlotXYSubplot):
def my_init(self):
"""
When defining your own subplot class... | gpl-3.0 |
mojoboss/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 240 | 6055 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
JeroenZegers/Nabu-MSSS | nabu/postprocessing/reconstructors/deepclusteringnoise_reconstructor.py | 1 | 5366 | """@file deepclusteringnoise_reconstructor.py
contains the reconstor class using deep clustering for modified noise architecture"""
from sklearn.cluster import KMeans
import mask_reconstructor
from nabu.postprocessing import data_reader
import numpy as np
import os
class DeepclusteringnoiseReconstructor(mask_reconst... | mit |
arg-hya/taxiCab | Tools/Misc/TaskPointGenerator.py | 1 | 1502 | import json
import shapefile as shp
import matplotlib.pyplot as plt
import random
def mean(numbers):
return float(sum(numbers)) / max(len(numbers), 1)
numbersX = []
numbersY = []
TaskPoints = {}
shpFilePath = r"D:\TaxiCab\mycode\Plots\ShapefileAndTrajectory\taxi_zones\taxi_zones"
sf = shp.Reader(shpFil... | gpl-3.0 |
dalejung/edamame | edamame/tools/follow.py | 1 | 9062 | import inspect
import gc
import sys
import os.path
import difflib
from collections import OrderedDict
import pandas as pd
from pandas.core.common import in_ipnb
def is_property(code):
"""
Using some CPython gc magics, check if a code object is a property
gc idea taken from trace.py from stdlib
"""
... | mit |
krasch/smart-assistants | examples/visualize_habits.py | 1 | 1689 | # -*- coding: UTF-8 -*-
"""
Plot visualization of user habits, i.e. show which actions typically follow some given user action.
Note: the figure for "Frontdoor=Closed" slightly deviates from Figure 1 in the paper and Figure 5.1 in the
dissertation (see paper_experiments.py for bibliographical information). The numb... | mit |
walterreade/scikit-learn | sklearn/datasets/tests/test_base.py | 33 | 7160 | 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 |
elidrc/PSO | test_pso.py | 1 | 1192 | from benchmark_functions import *
from pso import *
import matplotlib.pyplot as plt
iterations = 100
particles = 500
dimensions = 2
search_space = [[-5.12] * dimensions, [5.12] * dimensions]
# print init_pso(iterations, particles, search_space)
velocity, fitness, local_best, local_position, global_best, global_positi... | mit |
nmartensen/pandas | pandas/tests/scalar/test_interval.py | 7 | 4026 | from __future__ import division
from pandas import Interval
import pytest
import pandas.util.testing as tm
@pytest.fixture
def interval():
return Interval(0, 1)
class TestInterval(object):
def test_properties(self, interval):
assert interval.closed == 'right'
assert interval.left == 0
... | bsd-3-clause |
lbishal/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
rmhyman/DataScience | Lesson3/exploratory_data_analysis_subway_data.py | 1 | 1558 | import numpy as np
import pandas
import matplotlib.pyplot as plt
def entries_histogram(turnstile_weather):
'''
Before we perform any analysis, it might be useful to take a
look at the data we're hoping to analyze. More specifically, let's
examine the hourly entries in our NYC subway data and d... | mit |
josiahseaman/DNAResearch | Repeat_Graph.py | 1 | 8201 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
%matplotlib inline
from pylab import *
import matplotlib.pyplot as plt
from IPython.core.display import Image
# <codecell>
data = []
for y in range(10):
data.append([y+x for x in range(10)])
# print(data)
Image(data=data)
# <headingcell level=1>
... | apache-2.0 |
CharlesGulian/Deconv | fits_tools_tesla.py | 1 | 5575 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 14 21:18:54 2016
@author: charlesgulian
"""
import os
#os.chdir('/Users/annepstein/Work/Deconv')
curr_dir = os.getcwd()
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
import matplotlib
#from photutils import aperture_photometry
#from phot... | gpl-3.0 |
gdl-civestav-localization/cinvestav_location_fingerprinting | experimentation/__init__.py | 1 | 1691 | import os
import cPickle
import matplotlib.pyplot as plt
from datasets import DatasetManager
def plot_cost(results, data_name, plot_label):
plt.figure(plot_label)
plt.ylabel('Accuracy (m)', fontsize=30)
plt.xlabel('Epoch', fontsize=30)
plt.yscale('symlog')
plt.tick_params(axis='both', which='major... | gpl-3.0 |
Newsrecommender/newsrecommender | ArticleRecommendationProject/Recommendation/Collab_Content_Based.py | 1 | 5856 | import yaml
import pandas as pd
import numpy as np
import sys
import os
from math import sqrt
import matplotlib
import matplotlib.pyplot as plot
import networkx as nx
def get_script_directory():
"""
This function returns the directory of the script in scrip mode
In interactive mode returns interpreter name... | mit |
mrcslws/htmresearch | projects/feedback/feedback_sequences_additional.py | 7 | 24229 |
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the... | agpl-3.0 |
tracierenea/gnuradio | gr-filter/examples/fir_filter_ccc.py | 47 | 4019 | #!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your option)
# ... | gpl-3.0 |
jakevdp/klsh | klsh/hamming_ann.py | 1 | 6489 | """
This is a set of classes to perform fast (approximate) nearest neighbors
searches over Hamming spaces.
[1] M. Charikar. Similarity Estimation Techniques from Rounding Algorithms.
ACM Symposium on Theory of Computing, 2002.
"""
__all__ = ["HammingANN", "HammingBrute", "HammingBallTree"]
import numpy as np
from... | bsd-3-clause |
niltonlk/nest-simulator | pynest/examples/spatial/test_3d.py | 14 | 2140 | # -*- coding: utf-8 -*-
#
# test_3d.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the License, or
# (... | gpl-2.0 |
ChengeLi/VehicleTracking | utilities/embedding.py | 1 | 3427 | #### embedding
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE, MDS
from mpl_toolkits.mplot3d import Axes3D
class embeddings(obj):
def __init__(self, model,data):
self.modelChoice = model
self.data = data
# self.data = FeatureMtx_norm
def PCA_embedding(self,n_c... | mit |
ZENGXH/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 202 | 3757 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
iABC2XYZ/abc | DM_Twiss/TwissTrain3.py | 2 | 4285 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 20 13:37:16 2017
Author: Peiyong Jiang : jiangpeiyong@impcas.ac.cn
Function:
Check that the Distribution generation method is right.
"""
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from Orth import LambdaR,OrthT... | gpl-3.0 |
asi-uniovi/malloovia | malloovia/lpsolver.py | 1 | 33503 | # coding: utf-8
# import pandas as pd
"""Malloovia interface to LP solver"""
from typing import Sequence, List, Any
from itertools import product as cartesian_product
from inspect import ismethod
from collections import namedtuple
from uuid import uuid4
import os
import pulp # type: ignore
from pulp import (
L... | mit |
ClimbsRocks/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 299 | 1770 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.linear_model.bayes import BayesianRidge, ARDRegres... | bsd-3-clause |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/tseries/tests/test_frequencies.py | 9 | 25284 | from datetime import datetime, time, timedelta
from pandas.compat import range
import sys
import os
import nose
import numpy as np
from pandas import Index, DatetimeIndex, Timestamp, Series, date_range, period_range
import pandas.tseries.frequencies as frequencies
from pandas.tseries.tools import to_datetime
impor... | apache-2.0 |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_stats_cluster_methods.py | 6 | 8607 | # doc:slow-example
"""
.. _tut_stats_cluster_methods:
======================================================
Permutation t-test on toy data with spatial clustering
======================================================
Following the illustrative example of Ridgway et al. 2012,
this demonstrates some basic ideas behin... | bsd-3-clause |
fw1121/Roary | contrib/roary_plots/roary_plots.py | 1 | 5754 | #!/usr/bin/env python
# Copyright (C) <2015> EMBL-European Bioinformatics Institute
# 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 la... | gpl-3.0 |
OshynSong/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py | 254 | 2005 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
billy-inn/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 83 | 34544 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
nrhine1/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 260 | 1219 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as n... | bsd-3-clause |
hantek/BinaryConnect | mnist.py | 1 | 6258 | # Copyright 2015 Matthieu Courbariaux, Zhouhan Lin
"""
This file is adapted from BinaryConnect:
https://github.com/MatthieuCourbariaux/BinaryConnect
Running this script should reproduce the results of a feed forward net trained
on MNIST.
To train a vanilla feed forward net with ordinary backprop:
... | gpl-2.0 |
marscher/mdtraj | MDTraj/core/trajectory.py | 1 | 51903 | ##############################################################################
# MDTraj: A Python Library for Loading, Saving, and Manipulating
# Molecular Dynamics Trajectories.
# Copyright 2012-2014 Stanford University and the Authors
#
# Authors: Robert McGibbon
# Contributors: Kyle A. Beauchamp, TJ Lane, Jo... | lgpl-2.1 |
SergioGonzalezSanz/conformal_predictors | tests/nc_measures/SVMTest.py | 1 | 1492 | import unittest
from conformal_predictors.nc_measures.SVM import SVCDistanceNCMeasure
from sklearn.svm import SVC
from numpy import array
class SVMTest(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_1(self):
x = array([[1, 1], [2, 2]])
y = ar... | mit |
altairpearl/scikit-learn | sklearn/feature_extraction/tests/test_image.py | 25 | 11187 | # Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import numpy as np
import scipy as sp
from scipy import ndimage
from nose.tools import assert_equal, assert_true
from numpy.testing import assert_raises
from sklearn... | bsd-3-clause |
aewhatley/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 260 | 1219 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as n... | bsd-3-clause |
bmcfee/librosa | librosa/util/utils.py | 1 | 64787 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Utility functions"""
import warnings
import scipy.ndimage
import scipy.sparse
import numpy as np
import numba
from numpy.lib.stride_tricks import as_strided
from .._cache import cache
from .exceptions import ParameterError
# Constrain STFT block sizes to 256 KB
MAX_M... | isc |
YeoLab/anchor | anchor/simulate.py | 1 | 7366 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import six
from .visualize import violinplot, MODALITY_ORDER, MODALITY_TO_COLOR, barplot
def add_noise(data, iteration_per_noise=100,
noise_percentages=np.arange(0, 101, step=10), plot=True,
vio... | bsd-3-clause |
spacetelescope/stsci.tools | doc/source/conf.py | 1 | 7012 | # -*- coding: utf-8 -*-
#
# stsci.tools documentation build configuration file, created by
# sphinx-quickstart on Thu Oct 7 13:09:39 2010.
#
# 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.
#
#... | bsd-3-clause |
xuewei4d/scikit-learn | sklearn/preprocessing/_discretization.py | 5 | 13176 | # -*- coding: utf-8 -*-
# Author: Henry Lin <hlin117@gmail.com>
# Tom Dupré la Tour
# License: BSD
import numbers
import numpy as np
import warnings
from . import OneHotEncoder
from ..base import BaseEstimator, TransformerMixin
from ..utils.validation import check_array
from ..utils.validation import chec... | bsd-3-clause |
ngcurrier/ProteusCFD | GUI/dakotaHistogram.py | 1 | 1543 | #!/usr/bin/python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
#reads a space delimited file with a header and returns a dictionary
#attempts to cast dictionary entries into floats, if it fails, leaves as is
def readSpaceDelimitedFile(filename):
f = open(filename, 'r')
hea... | gpl-3.0 |
jmschrei/scikit-learn | examples/gaussian_process/plot_gpr_co2.py | 9 | 5718 | """
========================================================
Gaussian process regression (GPR) on Mauna Loa CO2 data.
========================================================
This example is based on Section 5.4.3 of "Gaussian Processes for Machine
Learning" [RW2006]. It illustrates an example of complex kernel engine... | bsd-3-clause |
Diyago/Machine-Learning-scripts | DEEP LEARNING/segmentation/Severstal-Steel-Defect-Detection-master/common_blocks/new_metrics.py | 1 | 15642 | from functools import partial
import numpy as np
import torch
from catalyst.dl import Callback, RunnerState, MetricCallback, CallbackOrder
from pytorch_toolbelt.utils.catalyst.visualization import get_tensorboard_logger
from pytorch_toolbelt.utils.torch_utils import to_numpy
from pytorch_toolbelt.utils.visualization i... | apache-2.0 |
mrshu/scikit-learn | sklearn/covariance/__init__.py | 10 | 1197 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
ThorbenJensen/wifi-locator | src/utils_classification.py | 1 | 2437 | """Module provides classification of signals and evaluates models."""
from random import randrange
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble.weight_boosting import AdaBoostClassifier
from sklearn.model_selection import c... | apache-2.0 |
sauloal/cnidaria | scripts/venv/lib/python2.7/site-packages/pandas/core/series.py | 1 | 89595 | """
Data structure for 1-dimensional cross-sectional and time series data
"""
from __future__ import division
# pylint: disable=E1101,E1103
# pylint: disable=W0703,W0622,W0613,W0201
import types
import warnings
from numpy import nan, ndarray
import numpy as np
import numpy.ma as ma
from pandas.core.common import (i... | mit |
asimshankar/tensorflow | tensorflow/contrib/learn/python/learn/preprocessing/tests/categorical_test.py | 137 | 2219 | # encoding: utf-8
# 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 r... | apache-2.0 |
malvikasharan/APRICOT | apricotlib/apricot_visualization.py | 1 | 22211 | #!/usr/bin/env python
# Description = Visualizes different output data from APRICOT analysis
from collections import defaultdict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import sys
try:
import subprocess
except ImportError:
print('Python package subprocess is missing. ... | isc |
jm-begon/scikit-learn | sklearn/datasets/base.py | 196 | 18554 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.pa... | bsd-3-clause |
joernhees/scikit-learn | examples/linear_model/plot_sgd_iris.py | 58 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
ndingwall/scikit-learn | sklearn/linear_model/_ridge.py | 2 | 77132 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | bsd-3-clause |
jensreeder/scikit-bio | skbio/diversity/beta/__init__.py | 1 | 6898 | """
Beta diversity measures (:mod:`skbio.diversity.beta`)
=====================================================
.. currentmodule:: skbio.diversity.beta
This package contains helper functions for working with scipy's pairwise
distance (``pdist``) functions in scikit-bio, and will eventually be expanded
to contain pair... | bsd-3-clause |
formath/mxnet | docs/mxdoc.py | 11 | 12953 | # 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 |
mbonsma/studyGroup | lessons/python/matplotlib/hwk3.1.py | 12 | 2149 | # -*- coding: utf-8 -*-
from numpy import float32
from numpy import linspace
from numpy import polyfit
from numpy import polyval
import matplotlib.pyplot as plt
#Read in data from csv
f=open('data.csv','r')
line=f.readlines()
#Empty array for data
FN=[]
EFN=[]
#This loop goes through every line, strips new line chara... | apache-2.0 |
mandli/multilayer-examples | 1d/setplot_shelf.py | 1 | 12827 |
"""
Set up the plot figures, axes, and items to be done for each frame.
This module is imported by the plotting routines and then the
function setplot is called to set the plot parameters.
"""
import numpy as np
# Plot customization
import matplotlib
# Markers and line widths
matplotlib.rcParams['lines.line... | mit |
camptocamp/QGIS | python/plugins/processing/algs/VectorLayerHistogram.py | 1 | 2809 | # -*- coding: utf-8 -*-
"""
***************************************************************************
EquivalentNumField.py
---------------------
Date : January 2013
Copyright : (C) 2013 by Victor Olaya
Email : volayaf at gmail dot com
*******************... | gpl-2.0 |
scholer/py2cytoscape | py2cytoscape/data/network_view.py | 1 | 6734 | # -*- coding: utf-8 -*-
import json
import pandas as pd
import requests
from py2cytoscape.data.edge_view import EdgeView
from py2cytoscape.data.node_view import NodeView
from . import BASE_URL, HEADERS
from py2cytoscape.data.util_network import NetworkUtil
BASE_URL_NETWORK = BASE_URL + 'networks'
class CyNetworkVi... | mit |
pompiduskus/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
nliolios24/textrank | share/doc/networkx-1.9.1/examples/algorithms/blockmodel.py | 32 | 3009 | #!/usr/bin/env python
# encoding: utf-8
"""
Example of creating a block model using the blockmodel function in NX. Data used is the Hartford, CT drug users network:
@article{,
title = {Social Networks of Drug Users in {High-Risk} Sites: Finding the Connections},
volume = {6},
shorttitle = {Social Networks of Drug ... | mit |
ggirelli/gpseq-img-py | pygpseq/anim/series.py | 1 | 12252 | # -*- coding: utf-8 -*-
'''
@author: Gabriele Girelli
@contact: gigi.ga90@gmail.com
@description: contains Series wrapper, which in turn contains Nucleus.
'''
# DEPENDENCIES =================================================================
import math
import os
import matplotlib.pyplot as plt
import numpy as np
fro... | mit |
raincoatrun/ThinkStats2 | code/thinkplot.py | 75 | 18140 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import math
import matplotlib
import matplotlib.pyplot as pyplot
import numpy as... | gpl-3.0 |
ltiao/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 30 | 44274 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | bsd-3-clause |
nelson-liu/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
OpenDrift/opendrift | tests/models/test_readers.py | 1 | 29038 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This file is part of OpenDrift.
#
# OpenDrift 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, version 2
#
# OpenDrift is distributed in the hope that it will b... | gpl-2.0 |
frank-tancf/scikit-learn | examples/feature_selection/plot_f_test_vs_mi.py | 75 | 1647 | """
===========================================
Comparison of F-test and mutual information
===========================================
This example illustrates the differences between univariate F-test statistics
and mutual information.
We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the
targ... | bsd-3-clause |
MattNolanLab/Ramsden_MEC | ABAFunctions/ABA_errors.py | 1 | 4010 | '''
Code for error analysis
Copyright (c) 2014, Helen Ramsden
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of cond... | bsd-3-clause |
ManuSchmi88/landlab | landlab/plot/imshow.py | 3 | 21050 | #! /usr/bin/env python
"""
Methods to plot data defined on Landlab grids.
Plotting functions
++++++++++++++++++
.. autosummary::
:toctree: generated/
~landlab.plot.imshow.imshow_grid
~landlab.plot.imshow.imshow_grid_at_cell
~landlab.plot.imshow.imshow_grid_at_node
"""
import numpy as np
import insp... | mit |
NelisVerhoef/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 260 | 1219 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as n... | bsd-3-clause |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/io/tests/parser/quoting.py | 7 | 5796 | # -*- coding: utf-8 -*-
"""
Tests that quoting specifications are properly handled
during parsing for all of the parsers defined in parsers.py
"""
import csv
import pandas.util.testing as tm
from pandas import DataFrame
from pandas.compat import PY3, StringIO, u
class QuotingTests(object):
def test_bad_quote_... | apache-2.0 |
mehdidc/scikit-learn | sklearn/neighbors/tests/test_kde.py | 17 | 5626 | import numpy as np
from sklearn.utils.testing import (assert_allclose, assert_raises,
assert_equal)
from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors
from sklearn.neighbors.ball_tree import kernel_norm
from sklearn.pipeline import make_pipeline
from sklearn.dataset... | bsd-3-clause |
jbloom/mutpath | src/plot.py | 1 | 10257 | """Module for performing plotting for ``mutpath`` package.
This module uses ``pylab`` and ``matplotlib`` to make plots. These plots will
fail if ``pylab`` and ``matplotlib`` are not available for importation. Before
running any function in this module, you can run the *PylabAvailable*
function to determine if ``pylab`... | gpl-3.0 |
xavierwu/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 |
AlexRobson/scikit-learn | sklearn/linear_model/coordinate_descent.py | 37 | 74167 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import n... | bsd-3-clause |
FernanOrtega/DAT210x | Module3/notes/2Dscatter_example.py | 1 | 1245 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 11 21:14:57 2017
@author: fernando
"""
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('ggplot')
df = pd.read_csv('concrete.csv')
print df.describe()
# Plot 1
df.plot.scatter(x='cement', y='strength')
plt.s... | mit |
Nelca/buildMLSystem | ch04/blei_lda.py | 3 | 1602 | # 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
from __future__ import print_function
from gensim import corpora, models, similarities
from mpltools im... | mit |
elijah513/scikit-learn | sklearn/decomposition/base.py | 313 | 5647 | """Principal Component Analysis Base Classes"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Kyle Kastner <kastnerkyle@gmail.com>
#
# Licen... | bsd-3-clause |
cbertinato/pandas | pandas/tests/indexes/test_setops.py | 1 | 2362 | '''
The tests in this package are to ensure the proper resultant dtypes of
set operations.
'''
import itertools as it
import numpy as np
import pytest
from pandas.core.dtypes.common import is_dtype_equal
import pandas as pd
from pandas import Int64Index, RangeIndex
from pandas.tests.indexes.conftest import indices_l... | bsd-3-clause |
Panos-Bletsos/spark-cost-model-optimizer | python/pyspark/sql/session.py | 11 | 24874 | #
# 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 |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_examples/widgets/menu.py | 3 | 4882 | import numpy as np
import matplotlib
import matplotlib.colors as colors
import matplotlib.patches as patches
import matplotlib.mathtext as mathtext
import matplotlib.pyplot as plt
import matplotlib.artist as artist
import matplotlib.image as image
class ItemProperties:
def __init__(self, fontsize=14, labelcolor='... | gpl-2.0 |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/tests/test_nanops.py | 7 | 44169 | # -*- coding: utf-8 -*-
from __future__ import division, print_function
from functools import partial
import warnings
import numpy as np
from pandas import Series, isnull
from pandas.types.common import is_integer_dtype
import pandas.core.nanops as nanops
import pandas.util.testing as tm
use_bn = nanops._USE_BOTTLEN... | gpl-3.0 |
musically-ut/statsmodels | statsmodels/tsa/vector_ar/dynamic.py | 27 | 9932 | # pylint: disable=W0201
from statsmodels.compat.python import iteritems, string_types, range
import numpy as np
from statsmodels.tools.decorators import cache_readonly
import pandas as pd
from . import var_model as _model
from . import util
from . import plotting
FULL_SAMPLE = 0
ROLLING = 1
EXPANDING = 2
def _get... | bsd-3-clause |
jblackburne/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 70 | 4523 | """
===============================================================
Model selection with Probabilistic PCA and Factor Analysis (FA)
===============================================================
Probabilistic PCA and Factor Analysis are probabilistic models.
The consequence is that the likelihood of new data can be u... | bsd-3-clause |
HeraclesHX/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 |
jstoxrocky/statsmodels | statsmodels/sandbox/tsa/fftarma.py | 30 | 16438 | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 14 19:53:25 2009
Author: josef-pktd
generate arma sample using fft with all the lfilter it looks slow
to get the ma representation first
apply arma filter (in ar representation) to time series to get white noise
but seems slow to be useful for fast estimation for nobs=1... | bsd-3-clause |
mmaelicke/scikit-gstat | skgstat/plotting/stvariogram_plot3d.py | 1 | 3989 | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
try:
import plotly.graph_objects as go
except ImportError:
pass
def __calculate_plot_data(stvariogram, **kwargs):
xx, yy = stvariogram.meshbins
z = stvariogram.experimental
# x = xx.flatten()
# y = yy.fla... | mit |
mjvakili/ccppabc | ccppabc/code/test_data.py | 1 | 4243 | '''
Test the data.py module
'''
import numpy as np
import matplotlib.pyplot as plt
import util
import data as Data
# --- Halotools ---
from halotools.empirical_models import PrebuiltHodModelFactory
from ChangTools.plotting import prettyplot
from ChangTools.plotting import prettycolors
def PlotCovariance(obvs, ... | mit |
Andreea-G/Codds_DarkMatter | src/experiment_HaloIndep_Band.py | 1 | 59260 | """
Copyright (c) 2015 Andreea Georgescu
Created on Wed Mar 4 00:47:37 2015
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 2 of the License, or
(at your option) any later version.
... | gpl-2.0 |
caiostringari/swantools | test.py | 1 | 2740 |
import swantools.io
import swantools.utils
import swantools.plot
import datetime
import matplotlib.pyplot as plt
import numpy as np
def readtable():
R = swantools.io.SwanIO()
P = swantools.plot.SwanPlot()
# Reading TABLE dada with headers:
df = R.read_swantable('data/table.txt')
y = df["Hsig... | gpl-2.0 |
tpsatish95/OCR-on-Indus-Seals | code/Test/TextROI.py | 1 | 16306 | # -*- coding: utf-8 -*-
import skimage.io
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import selectivesearch
import numpy as np
import skimage.transform
import os
import shutil
import caffe
from PIL import Image
candidates = set()
merged_candidates = set()
refined = set()
final = set()
final_... | apache-2.0 |
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