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 |
|---|---|---|---|---|---|
ricklupton/sankeyview | floweaver/color_scales.py | 1 | 4260 | """Color scales for Sankey diagrams.
author: Rick Lupton
created: 2018-01-19
"""
import numpy as np
from palettable.colorbrewer import qualitative, sequential
# From matplotlib.colours
def rgb2hex(rgb):
'Given an rgb or rgba sequence of 0-1 floats, return the hex string'
if isinstance(rgb, str):
retu... | mit |
BenoitDamota/mempamal | mempamal/scripts/mapper.py | 1 | 2112 | #!/usr/bin/env python
# Author: Benoit Da Mota <damota.benoit@gmail.com>
#
# License: BSD 3 clause
"""
Generic mapper
"""
import json
from sklearn.externals import joblib
from sklearn.pipeline import Pipeline
from mempamal.arguments import get_map_argparser
from mempamal.crossval import get_fold, print_fold
from memp... | bsd-3-clause |
robert-giaquinto/air_pollution | build_dataset/Importing.py | 1 | 6250 | from __future__ import division
import os
from urllib2 import urlopen, URLError, HTTPError
import zipfile
import pandas as pd
import numpy as np
from data_utilities import *
# A list of all files for download can be found here:
# http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/file_list.csv
def dlfile(url, data_dir):
# ... | gpl-2.0 |
mhue/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 57 | 13752 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import assert_array_almost_equal, assert_array_equal
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis,
inplace_column_scale,
... | bsd-3-clause |
anntzer/scikit-learn | sklearn/decomposition/_pca.py | 2 | 24060 | """ Principal Component Analysis.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <denis-alexander.engemann@inria.fr>
# Michael Eickenberg <michael.eickenberg@inria.fr... | bsd-3-clause |
LiaoPan/scikit-learn | sklearn/utils/testing.py | 47 | 23587 | """Testing utilities."""
# Copyright (c) 2011, 2012
# Authors: Pietro Berkes,
# Andreas Muller
# Mathieu Blondel
# Olivier Grisel
# Arnaud Joly
# Denis Engemann
# License: BSD 3 clause
import os
import inspect
import pkgutil
import warnings
import sys
import re
import platf... | bsd-3-clause |
codevlabs/pandashells | pandashells/test/p_hist_tests.py | 7 | 2350 | #! /usr/bin/env python
from mock import patch, MagicMock
from unittest import TestCase
import pandas as pd
from pandashells.bin.p_hist import main, get_input_args, validate_args
class GetInputArgsTests(TestCase):
@patch('pandashells.bin.p_hist.sys.argv', 'p.hist -c x -n 30'.split())
def test_right_number_of_... | bsd-2-clause |
codles/UpDownMethods | UpDownMethods/plot.py | 1 | 1966 | import matplotlib.pyplot as plt
import UpDownMethods as ud
def plot_results(results, midpoints=False, figure=None, estimate=False,
reversals=False, runs=True):
if figure is None:
figure = plt.figure()
figure.clf()
figure.add_subplot(111)
plt.hold(True)
# Plot correct r... | mit |
hugobowne/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 392 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
altairpearl/scikit-learn | sklearn/datasets/svmlight_format.py | 9 | 16760 | """This module implements a loader and dumper for the svmlight format
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable to
predict.
This format is used as the... | bsd-3-clause |
dstndstn/astromalign | astrom_common.py | 1 | 57653 | from __future__ import print_function
import sys
import os
from glob import glob
from math import sqrt, ceil, pi, cos
import re
import numpy as np
from astrometry.util.fits import fits_table
from astrometry.util.util import Tan
from astrometry.libkd import spherematch
from astrometry.util.starutil_numpy import arcsec... | bsd-3-clause |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/spines.py | 10 | 19237 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import matplotlib
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
from matplotlib import docstring
import matplotlib.transforms as mtransforms
import matplotli... | bsd-2-clause |
aylward/ITKTubeTK | examples/archive/TubeGraphKernels/permtest.py | 7 | 9961 | ##############################################################################
#
# Library: TubeTK
#
# Copyright 2010 Kitware Inc. 28 Corporate Drive,
# Clifton Park, NY, 12065, USA.
#
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in comp... | apache-2.0 |
deepesch/scikit-learn | sklearn/datasets/tests/test_lfw.py | 230 | 7880 | """This test for the LFW require medium-size data dowloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
... | bsd-3-clause |
freemindhv/tq-python | docs/conf.py | 1 | 8134 | # -*- coding: utf-8 -*-
#
# 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; values that are commented out
# serve to show the default.
import sys
imp... | gpl-2.0 |
xuzhenqi/gardenia | scripts/shift_exp.py | 1 | 3841 | import numpy as np
import random
import cv2
import matplotlib.pyplot as plt
from util import get_index, get_index_mean, softmax
from inference import get_preds_multiple
caffe_root = '../caffe/'
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
caffe.set_mode_gpu()
mean_file = '../data/train_mean.blob'... | gpl-3.0 |
cmap/cmapPy | cmapPy/pandasGEXpress/tests/python2_tests/test_write_gctx.py | 1 | 11644 | import logging
import cmapPy.pandasGEXpress.setup_GCToo_logger as setup_logger
import unittest
import h5py
import os
import numpy
import cmapPy.pandasGEXpress.parse_gctx as parse_gctx
import cmapPy.pandasGEXpress.write_gctx as write_gctx
import cmapPy.pandasGEXpress.mini_gctoo_for_testing as mini_gctoo_for_testing
__... | bsd-3-clause |
AntoinePassemiers/Scythe | python/examples/rf_benchmark.py | 1 | 2815 | # -*- coding: utf-8 -*-
# rf_benchmark.py - Comparison with sklearn
# author : Antoine Passemiers
import time
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
from scythe.core import *
from scythe.plot import plot_feature_importances
... | apache-2.0 |
nelango/ViralityAnalysis | model/lib/sklearn/semi_supervised/label_propagation.py | 35 | 15442 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | mit |
mayblue9/scikit-learn | sklearn/metrics/scorer.py | 211 | 13141 | """
The :mod:`sklearn.metrics.scorer` submodule implements a flexible
interface for model selection and evaluation using
arbitrary score functions.
A scorer object is a callable that can be passed to
:class:`sklearn.grid_search.GridSearchCV` or
:func:`sklearn.cross_validation.cross_val_score` as the ``scoring`` parame... | bsd-3-clause |
TomTranter/OpenPNM | setup.py | 1 | 2601 | import os
import sys
from distutils.util import convert_path
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
sys.path.append(os.getcwd())
main_ = {}
ver_path = convert_path('openpnm/__init__.py')
with open(ver_path) as f:
for line in f:
if line.startswith('__... | mit |
asoliveira/NumShip | scripts/plot/beta-ace-v-cg-plt.py | 1 | 2033 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#É adimensional?
adi = False
#É para salvar as figuras(True|False)?
save = True
#Caso seja para salvar, qual é o formato desejado?
formato = 'jpg'
#Caso seja para salvar, qual é o diretório que devo salvar?
dircg = 'fig-sen'
#Caso seja para salvar, qual é o nome do arquivo... | gpl-3.0 |
tomography/tomobank | docs/demo/phantom_00004.py | 1 | 4114 | # -*- coding: utf-8 -*-
"""
Created on Sat Dec 3 15:35:30 2016
@author: decarlo
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from xdesign import *
import os
import time
import pytz
import datetime
import numpy as np
import m... | bsd-3-clause |
IndraVikas/scikit-learn | examples/mixture/plot_gmm_sin.py | 248 | 2747 | """
=================================
Gaussian Mixture Model Sine Curve
=================================
This example highlights the advantages of the Dirichlet Process:
complexity control and dealing with sparse data. The dataset is formed
by 100 points loosely spaced following a noisy sine curve. The fit by
the GMM... | bsd-3-clause |
mattmcd/PyAnalysis | scripts/doing_data_science/ch2_nyt_analysis.py | 1 | 2147 | import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sys.path.append(os.environ.get('MDA_CODE_DIR'))
import mda
def read():
df = pd.concat(
pd.read_csv(
mda.data_dir(
'doing_data_science', 'dds_datasets', 'nyt{}.csv'.... | apache-2.0 |
ZhenxingWu/luigi | examples/pyspark_wc.py | 56 | 3361 | # -*- coding: utf-8 -*-
#
# Copyright 2012-2015 Spotify AB
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | apache-2.0 |
zhenv5/scikit-learn | sklearn/utils/tests/test_shortest_path.py | 303 | 2841 | from collections import defaultdict
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.utils.graph import (graph_shortest_path,
single_source_shortest_path_length)
def floyd_warshall_slow(graph, directed=False):
N = graph.shape[0]
#set nonzer... | bsd-3-clause |
planaspa/Data-Mining | src/graphDb.py | 1 | 3347 | import sqlite3
import matplotlib.pyplot as plt
import sys
def text_format(text):
text = text.replace("&", "&")
text = text.replace(">", ">")
text = text.replace("<", "<")
return text
def creatingGroups(c, nGroups):
"""
This function returns a list which divides the tweets in differ... | mit |
ocefpaf/python-oceans | oceans/ocfis.py | 2 | 20695 | import re
import warnings
import gsw
import numpy as np
import numpy.ma as ma
def spdir2uv(spd, ang, deg=False):
"""
Computes u, v components from speed and direction.
Parameters
----------
spd : array_like
speed [m s :sup:`-1`]
ang : array_like
direction [deg]
deg : ... | bsd-3-clause |
henridwyer/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 251 | 1895 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samp... | bsd-3-clause |
adamgreenhall/scikit-learn | sklearn/preprocessing/tests/test_imputation.py | 213 | 11911 | import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.preprocessing.imputa... | bsd-3-clause |
alejandro-mc/trees | makeHistos.py | 1 | 4901 | import glob
import os
import sys
import matplotlib.pyplot as plt
from math import *
__stats__ = []#[min,max,histo]
#histogram settings
__min__ = 0
__max__ = 0
__bincount__ = 0
class histogram:
def __init__(self,bincount,maximum,minimum):
self.bincount = bincount
self.minimum = minimum
se... | mit |
gfyoung/pandas | pandas/io/excel/_xlsxwriter.py | 2 | 8066 | from typing import Dict, List, Tuple
import pandas._libs.json as json
from pandas._typing import StorageOptions
from pandas.io.excel._base import ExcelWriter
from pandas.io.excel._util import validate_freeze_panes
class _XlsxStyler:
# Map from openpyxl-oriented styles to flatter xlsxwriter representation
# ... | bsd-3-clause |
wllmtrng/udacity_data_analyst_nanodegree | P5 sklearn ML/tools/feature_format.py | 1 | 4386 | #!/usr/bin/python
"""
A general tool for converting data from the
dictionary format to an (n x k) python list that's
ready for training an sklearn algorithm
n--no. of key-value pairs in dictonary
k--no. of features being extracted
dictionary keys are names of persons in dataset
dictiona... | mit |
rs2/pandas | pandas/tests/indexes/datetimes/test_pickle.py | 4 | 1342 | import pytest
from pandas import NaT, date_range, to_datetime
import pandas._testing as tm
class TestPickle:
def test_pickle(self):
# GH#4606
idx = to_datetime(["2013-01-01", NaT, "2014-01-06"])
idx_p = tm.round_trip_pickle(idx)
assert idx_p[0] == idx[0]
assert idx_p[1] is... | bsd-3-clause |
binghongcha08/pyQMD | GWP/2D/1.0.4/c.py | 28 | 1767 | ##!/usr/bin/python
import numpy as np
import pylab as plt
import seaborn as sns
sns.set_context('poster')
#with open("traj.dat") as f:
# data = f.read()
#
# data = data.split('\n')
#
# x = [row.split(' ')[0] for row in data]
# y = [row.split(' ')[1] for row in data]
#
# fig = plt.figure()
#
# ax1 ... | gpl-3.0 |
jcalogovic/lightning | stormstats/storm.py | 1 | 2992 | import os
import numpy as np
import pandas as pd
import datetime as dt
from datetime import timedelta
import folium
from shapely.geometry import Point
import geopandas as gpd
import pkg_resources as pkg
class Storm(object):
"""Main analysis class. Optional kwargs, f can set file path and name
of csv file down... | mit |
s-gv/rnicu-webapp | ecg/ecg_visualizer_ble_PC/galry/test/test.py | 7 | 3947 | """Galry unit tests.
Every test shows a GalryWidget with a white square (non filled) and a black
background. Every test uses a different technique to show the same picture
on the screen. Then, the output image is automatically saved as a PNG file and
it is then compared to the ground truth.
"""
import unittest
import... | agpl-3.0 |
ch3ll0v3k/scikit-learn | benchmarks/bench_sample_without_replacement.py | 397 | 8008 | """
Benchmarks for sampling without replacement of integer.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import operator
import matplotlib.pyplot as plt
import numpy as np
import random
from sklearn.externals.six.moves i... | bsd-3-clause |
timothy1191xa/project-epsilon-1 | code/utils/scripts/multi_beta_script.py | 2 | 4214 |
"""
Purpose:
-----------------------------------------------------------------------------------
We generate beta values for each single voxels for each subject and save them to
files for multi-comparison test.
-----------------------------------------------------------------------------------
"""
import sys, os, ... | bsd-3-clause |
flag0010/pop_gen_cnn | data_prep_tricks/genet.data.matrix.prep.tricks.py | 1 | 1258 | from sklearn.neighbors import NearestNeighbors
def sort_min_diff(amat):
'''this function takes in a SNP matrix with indv on rows and returns the same matrix with indvs sorted by genetic similarity.
this problem is NP, so here we use a nearest neighbors approx. it's not perfect, but it's fast and generally perf... | gpl-3.0 |
jchodera/MSMs | shanson/mek-10488/msmbuilder-finding4/msmbuilder-finding4-chi1/msmbuilder-finding4-mek.py | 2 | 2180 | import matplotlib
matplotlib.use('Agg')
from msmbuilder.dataset import dataset
from msmbuilder import msm, featurizer, utils, decomposition
import numpy as np
import mdtraj as md
import matplotlib.pyplot as plt
from glob import glob
import os
# Source directory for MEK simulations
source_directory = '/cbio/jclab/pr... | gpl-2.0 |
Yiangos01/ADE2017 | hybrid_classifier.py | 1 | 5365 | from pandas import DataFrame
import pandas as pd
import re
import numpy
from sklearn import svm
from sklearn.preprocessing import Normalizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.calibration import CalibratedClassifierCV
from sklearn.feature_extraction import DictVectorizer
from sklea... | gpl-3.0 |
ankurankan/scikit-learn | examples/applications/plot_tomography_l1_reconstruction.py | 45 | 5463 | """
======================================================================
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
======================================================================
This example shows the reconstruction of an image from a set of parallel
projections, acquired along dif... | bsd-3-clause |
mfjb/scikit-learn | sklearn/tree/tree.py | 113 | 34767 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
ankurankan/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 15 | 3460 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
ch3ll0v3k/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 130 | 22974 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
dingocuster/scikit-learn | examples/svm/plot_svm_anova.py | 250 | 2000 | """
=================================================
SVM-Anova: SVM with univariate feature selection
=================================================
This example shows how to perform univariate feature before running a SVC
(support vector classifier) to improve the classification scores.
"""
print(__doc__)
import... | bsd-3-clause |
wrobstory/seaborn | seaborn/tests/test_distributions.py | 18 | 8426 | import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import nose.tools as nt
import numpy.testing as npt
from numpy.testing.decorators import skipif
from .. import distributions as dist
try:
import statsmodels.nonparametric.api
assert statsmodels.nonparametric.api
... | bsd-3-clause |
TomAugspurger/pandas | pandas/tests/indexes/multi/test_setops.py | 1 | 11417 | import numpy as np
import pytest
import pandas as pd
from pandas import MultiIndex, Series
import pandas._testing as tm
@pytest.mark.parametrize("case", [0.5, "xxx"])
@pytest.mark.parametrize(
"method", ["intersection", "union", "difference", "symmetric_difference"]
)
def test_set_ops_error_cases(idx, case, sort... | bsd-3-clause |
nsalomonis/AltAnalyze | visualization_scripts/umap_learn_single/spectral.py | 2 | 9570 | import numpy as np
import scipy.sparse
import scipy.sparse.csgraph
from sklearn.manifold import SpectralEmbedding
from sklearn.metrics import pairwise_distances
from warnings import warn
def component_layout(
data, n_components, component_labels, dim, metric="euclidean", metric_kwds={}
):
"""Provide a layou... | apache-2.0 |
sensbio/sensbiotk | examples/scripts/expe_prima.py | 1 | 4757 |
# -*- coding: utf-8 -*-
"""
Reconstruction angles example comparison
"""
import numpy as np
from sensbiotk.algorithms import martin_ahrs
from sensbiotk.algorithms.basic import find_static_periods
from sensbiotk.io.iofox import load_foxcsvfile
from sensbiotk.io.ahrs import save_ahrs_csvfile
import sensbiotk... | gpl-3.0 |
zhmxu/nyu_ml_lectures | fetch_data.py | 20 | 2545 | import os
try:
from urllib.request import urlopen
except ImportError:
from urllib import urlopen
import zipfile
SENTIMENT140_URL = ("http://cs.stanford.edu/people/alecmgo/"
"trainingandtestdata.zip")
SENTIMENT140_ARCHIVE_NAME = "trainingandtestdata.zip"
def get_datasets_folder():
he... | cc0-1.0 |
DonBeo/statsmodels | statsmodels/base/tests/test_shrink_pickle.py | 6 | 7890 | # -*- coding: utf-8 -*-
"""
Created on Fri Mar 09 16:00:27 2012
Author: Josef Perktold
"""
from __future__ import print_function
from statsmodels.compat.python import iterkeys, cPickle, BytesIO
import numpy as np
import statsmodels.api as sm
import pandas as pd
from numpy.testing import assert_
from nose import Ski... | bsd-3-clause |
nddsg/TreeDecomps | xplodnTree/tdec/td_ba_rnd_gm.py | 1 | 16969 | #!/usr/bin/env python
__version__="0.1.0"
# ToDo:
# [] process mult dimacs.trees to hrg
import sys
import math
import numpy as np
import traceback
import argparse
import os
from glob import glob
import networkx as nx
import pandas as pd
from tdec.PHRG import graph_checks
import subprocess
import math
import itertools... | mit |
dyoung418/tensorflow | tensorflow/contrib/learn/python/learn/estimators/kmeans.py | 10 | 10909 | # 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 |
jereze/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 249 | 1563 | """
==============
SGD: Penalties
==============
Plot the contours of the three penalties.
All of the above are supported by
:class:`sklearn.linear_model.stochastic_gradient`.
"""
from __future__ import division
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def l1(xs):
return np.array([np.... | bsd-3-clause |
sofianehaddad/gosa | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 408 | 8061 | import re
import inspect
import textwrap
import pydoc
from .docscrape import NumpyDocString
from .docscrape import FunctionDoc
from .docscrape import ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots... | lgpl-3.0 |
alshedivat/tensorflow | tensorflow/contrib/timeseries/examples/lstm.py | 24 | 13826 | # Copyright 2017 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 |
lancezlin/ml_template_py | lib/python2.7/site-packages/mpl_toolkits/tests/__init__.py | 8 | 2604 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import difflib
import os
from matplotlib import rcParams, rcdefaults, use
_multiprocess_can_split_ = True
# Check that the test directories exist
if not os.path.exists... | mit |
miloharper/neural-network-animation | matplotlib/artist.py | 11 | 41588 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import re
import warnings
import inspect
import matplotlib
import matplotlib.cbook as cbook
from matplotlib import docstring, rcParams
from .transforms import Bbox, IdentityTransform, TransformedBbo... | mit |
anurag313/scikit-learn | examples/calibration/plot_calibration.py | 225 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/tests/reshape/test_union_categoricals.py | 4 | 14309 | import pytest
import numpy as np
import pandas as pd
from pandas import Categorical, Series, CategoricalIndex
from pandas.core.dtypes.concat import union_categoricals
from pandas.util import testing as tm
class TestUnionCategoricals(object):
def test_union_categorical(self):
# GH 13361
data = [
... | apache-2.0 |
ilyes14/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
PrashntS/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 |
93lorenzo/software-suite-movie-market-analysis | LinearRegression.py | 1 | 5811 | from __future__ import division
import numpy as np
import json
import math
import itertools
import matplotlib.cm as cm
import scipy
import csv
from scipy.optimize import fmin
from scipy.optimize import minimize
import scipy.sparse
from sklearn.model_selection import train_test_split
from sklearn import linear_model
imp... | gpl-3.0 |
guoxiao/lwan | tools/benchmark.py | 6 | 4379 | #!/usr/bin/python
import sys
import json
import commands
import time
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
def clearstderrline():
sys.stderr.write('\033[2K')
def weighttp(url, n_threads, n_connections, n_requests, keep_alive):
keep_alive = '-k' if keep_alive else ''
command... | gpl-2.0 |
BV-DR/foamBazar | pythonScripts/upPost.py | 1 | 1848 | #!/usr/bin/env upython
#########################################################################
# Filename: upPost.py #
# Date: 2017-May-02 #
# Version: 1. #
# ... | gpl-3.0 |
jereze/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 387 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
andaag/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 |
IndraVikas/scikit-learn | examples/classification/plot_digits_classification.py | 289 | 2397 | """
================================
Recognizing hand-written digits
================================
An example showing how the scikit-learn can be used to recognize images of
hand-written digits.
This example is commented in the
:ref:`tutorial section of the user manual <introduction>`.
"""
print(__doc__)
# Autho... | bsd-3-clause |
soft-matter/mr | mr/motion.py | 1 | 16234 | # Copyright 2012 Daniel B. Allan
# dallan@pha.jhu.edu, daniel.b.allan@gmail.com
# http://pha.jhu.edu/~dallan
# http://www.danallan.com
#
# 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 ve... | gpl-3.0 |
maniteja123/sympy | sympy/physics/quantum/tensorproduct.py | 64 | 13572 | """Abstract tensor product."""
from __future__ import print_function, division
from sympy import Expr, Add, Mul, Matrix, Pow, sympify
from sympy.core.compatibility import u, range
from sympy.core.trace import Tr
from sympy.printing.pretty.stringpict import prettyForm
from sympy.physics.quantum.qexpr import QuantumEr... | bsd-3-clause |
harisbal/pandas | pandas/tests/dtypes/test_cast.py | 6 | 17619 | # -*- coding: utf-8 -*-
"""
These test the private routines in types/cast.py
"""
import pytest
from datetime import datetime, timedelta, date
import numpy as np
import pandas as pd
from pandas import (Timedelta, Timestamp, DatetimeIndex,
DataFrame, NaT, Period, Series)
from pandas.core.dtypes.c... | bsd-3-clause |
AIML/scikit-learn | sklearn/neighbors/tests/test_neighbors.py | 22 | 45265 | from itertools import product
import pickle
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.cross_validation import train_test_split, cross_val_score
from sklearn.utils.testing impor... | bsd-3-clause |
cl4rke/scikit-learn | examples/cluster/plot_birch_vs_minibatchkmeans.py | 333 | 3694 | """
=================================
Compare BIRCH and MiniBatchKMeans
=================================
This example compares the timing of Birch (with and without the global
clustering step) and MiniBatchKMeans on a synthetic dataset having
100,000 samples and 2 features generated using make_blobs.
If ``n_clusters... | bsd-3-clause |
ltiao/scikit-learn | benchmarks/bench_tree.py | 297 | 3617 | """
To run this, you'll need to have installed.
* scikit-learn
Does two benchmarks
First, we fix a training set, increase the number of
samples to classify and plot number of classified samples as a
function of time.
In the second benchmark, we increase the number of dimensions of the
training set, classify a sam... | bsd-3-clause |
rexshihaoren/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
allenai/allennlp | allennlp/commands/find_learning_rate.py | 1 | 12036 | """
The `find-lr` subcommand can be used to find a good learning rate for a model.
It requires a configuration file and a directory in
which to write the results.
"""
import argparse
import logging
import math
import os
import re
from typing import List, Tuple
import itertools
from overrides import overrides
from al... | apache-2.0 |
bureau14/qdb-benchmark | thirdparty/boost/libs/numeric/odeint/performance/plot_result.py | 43 | 2225 | """
Copyright 2011-2014 Mario Mulansky
Copyright 2011-2014 Karsten Ahnert
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE_1_0.txt or
copy at http://www.boost.org/LICENSE_1_0.txt)
"""
import numpy as np
from matplotlib import pyplot as plt
plt.rc("font", size=16)
def g... | bsd-2-clause |
DLunin/bayescraft | graphmodels/flow_analysis.py | 1 | 7775 | import networkx as nx
import numpy as np
from numpy import log, exp
import scipy as sp
import pymc
from scipy import stats
import emcee
import random
import matplotlib as mpl
import matplotlib.pyplot as plt
from itertools import product
from sklearn.metrics import mutual_info_score
from scipy.stats import gaussian_kde
... | mit |
B3AU/waveTree | sklearn/metrics/cluster/bicluster/tests/test_bicluster_metrics.py | 13 | 1145 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal
from ..bicluster_metrics import _jaccard
from ..bicluster_metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([True, True, True, True])
a3 ... | bsd-3-clause |
Vvucinic/Wander | venv_2_7/lib/python2.7/site-packages/pandas/tseries/tests/test_daterange.py | 9 | 26349 | from datetime import datetime
from pandas.compat import range
import nose
import numpy as np
from pandas.core.index import Index
from pandas.tseries.index import DatetimeIndex
from pandas import Timestamp
from pandas.tseries.offsets import generate_range
from pandas.tseries.index import cdate_range, bdate_range, date... | artistic-2.0 |
maartenbreddels/ipyvolume | ipyvolume/transferfunction.py | 1 | 6308 | """The transferfunction module of ipvyolume."""
from __future__ import absolute_import
__all__ = ['TransferFunction', 'TransferFunctionJsBumps', 'TransferFunctionWidgetJs3', 'TransferFunctionWidget3']
import numpy as np
import ipywidgets as widgets # we should not have widgets under two names
import traitlets
from ... | mit |
0todd0000/spm1d | spm1d/examples/stats1d_roi/ex_ttest2.py | 1 | 1033 |
import numpy as np
from matplotlib import pyplot
import spm1d
#(0) Load data:
dataset = spm1d.data.uv1d.t2.SimulatedTwoLocalMax()
YA,YB = dataset.get_data()
#(0a) Create region of interest(ROI):
roi = np.array([False]*YA.shape[1])
roi[15:35] = True
roi[65:85] = True
#(1) Conduct t test:
alp... | gpl-3.0 |
tomsilver/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/_cm.py | 70 | 375423 | """
Color data and pre-defined cmap objects.
This is a helper for cm.py, originally part of that file.
Separating the data (this file) from cm.py makes both easier
to deal with.
Objects visible in cm.py are the individual cmap objects ('autumn',
etc.) and a dictionary, 'datad', including all of these objects.
"""
im... | gpl-3.0 |
Clyde-fare/scikit-learn | sklearn/decomposition/pca.py | 192 | 23117 | """ Principal Component Analysis
"""
# 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>
# Michael Eickenberg <michael.eickenberg@inria.fr>
#
# Lice... | bsd-3-clause |
LumPenPacK/NetworkExtractionFromImages | osx_build/nefi2_osx_amd64_xcode_2015/site-packages/numpy_1.11/numpy/fft/fftpack.py | 22 | 45592 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | bsd-2-clause |
RWArunde/Mallet | vis/vis.py | 10 | 3086 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# ------------------------------------------------------------------------
# Filename : heatmap.py
# Date : 2013-04-19
# Updated : 2014-01-04
# Author : @LotzJoe >> Joe Lotz
# Description: My attempt at reproducing the FlowingData graphic in Python
# So... | epl-1.0 |
ssh0/growing-string | triangular_lattice/diecutting.py | 1 | 10266 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
#
# written by Shotaro Fujimoto
# 2016-08-24
from growing_string import Main
from surface import get_surface_points, set_labels, get_labeled_position
from optimize import Optimize_linear
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d imp... | mit |
ashhher3/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 |
ProkopHapala/SimpleSimulationEngine | python/pyApprox/radialIntegral2D.py | 1 | 1067 |
import numpy as np
def genPoints( n, sc, dphi0=0.0 ):
ps = np.empty((n+1,2))
phiMax = 2*np.pi
dPhi = phiMax/n
#phis = np.arange(0,phiMax,phiMax/n) + dPhi
phis = np.linspace(0,phiMax,n+1) + dphi0*dPhi
ps[:,0] = np.cos(phis)*sc
ps[:,1] = np.sin(phis)*sc
return ps
if __name__ == "... | mit |
Clyde-fare/scikit-learn | sklearn/tree/export.py | 78 | 15814 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Trevor... | bsd-3-clause |
siutanwong/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 |
liberatorqjw/scikit-learn | examples/datasets/plot_random_dataset.py | 348 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.... | bsd-3-clause |
krez13/scikit-learn | sklearn/metrics/pairwise.py | 14 | 45532 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck ... | bsd-3-clause |
mqyqlx/deeppy | examples/siamese_mnist.py | 9 | 2485 | #!/usr/bin/env python
"""
Siamese networks
================
"""
import random
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
import deeppy as dp
# Fetch MNIST data
dataset = dp.dataset.MNIST()
x_train, y_train, x_test, y_test = dataset.data(flat=True, dp_dtypes=True)
# Normaliz... | mit |
nmabhi/Webface | classifier_webcam.py | 1 | 7103 | #!/usr/bin/env python2
#
# Example to run classifier on webcam stream.
# Brandon Amos & Vijayenthiran
# 2016/06/21
#
# Copyright 2015-2016 Carnegie Mellon University
#
# 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 ... | apache-2.0 |
casimp/edi12 | pyxe/plotting.py | 2 | 16130 | # -*- coding: utf-8 -*-
"""
Created on Tue Oct 20 17:40:07 2015
@author: casimp
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import h5py
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpol... | mit |
nilmtk/nilmtk | nilmtk/feature_detectors/cluster.py | 1 | 5665 | import numpy as np
import pandas as pd
def cluster(X, max_num_clusters=3, exact_num_clusters=None):
'''Applies clustering on reduced data,
i.e. data where power is greater than threshold.
Parameters
----------
X : pd.Series or single-column pd.DataFrame
max_num_clusters : int
Returns
... | apache-2.0 |
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