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 |
|---|---|---|---|---|---|
libsmelt/libsmelt | scripts/plot-ab-bench.py | 1 | 26075 | #!/usr/bin/python3
import matplotlib
matplotlib.use('Agg')
import sys
import os
import config
import numpy
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import plotsetup
import json
import gzip
import numpy as np
#import topology_parser
import helpers
from extract_ab_bench i... | mit |
tkaitchuck/nupic | external/darwin64/lib/python2.6/site-packages/matplotlib/dviread.py | 69 | 29920 | """
An experimental module for reading dvi files output by TeX. Several
limitations make this not (currently) useful as a general-purpose dvi
preprocessor.
Interface::
dvi = Dvi(filename, 72)
for page in dvi: # iterate over pages
w, h, d = page.width, page.height, page.descent
for x,y,font,gl... | gpl-3.0 |
joshloyal/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 28 | 17934 | import numpy as np
import scipy.sparse as sp
import numbers
from scipy import linalg
from sklearn.decomposition import NMF, non_negative_factorization
from sklearn.decomposition import nmf # For testing internals
from scipy.sparse import csc_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.te... | bsd-3-clause |
dgketchum/MT_Rsense | obspnts/obsio/providers/wrcc.py | 1 | 17467 | from ..util.humidity import calc_pressure, convert_rh_to_tdew, \
convert_rh_to_vpd, convert_rh_to_vpd_daily
from .generic import ObsIO
from StringIO import StringIO
from calendar import monthrange
from datetime import datetime
from multiprocessing import Pool
import numpy as np
import os
import pandas as pd
import ... | apache-2.0 |
BiaDarkia/scikit-learn | sklearn/linear_model/base.py | 30 | 21031 | """
Generalized Linear models.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Vincent Michel <vincent.michel@inria.fr>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Mathieu Blond... | bsd-3-clause |
benjaminy/ThreadMeasurement | BlakesWork/csv_to_graphs.py | 1 | 4932 | #!/usr/bin/env python3
''' This generates some graphs from the csv data output by dat_to_csv.py '''
import sys, os, csv, yaml
import matplotlib.pyplot as plt
from overlaps import count_overlaps, pairwise_overlap_time, pairwise_time_sans_longs, time_any_overlaps, any_overlaps_sans_longs
config = yaml.safe_load(open('c... | mit |
saltastro/pipetools | saltslewstats.py | 1 | 6904 | ################################# LICENSE ##################################
# Copyright (c) 2009, South African Astronomical Observatory (SAAO) #
# All rights reserved. #
# #
# Redistribu... | bsd-3-clause |
zaxtax/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 18 | 3891 | """
=======================================================================================
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
=======================================================================================
This is an example of applying Non-negative Matrix ... | bsd-3-clause |
jonycgn/scipy | scipy/special/c_misc/struve_convergence.py | 76 | 3725 | """
Convergence regions of the expansions used in ``struve.c``
Note that for v >> z both functions tend rapidly to 0,
and for v << -z, they tend to infinity.
The floating-point functions over/underflow in the lower left and right
corners of the figure.
Figure legend
=============
Red region
Power series is clo... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/lib/mpl_examples/api/scatter_piecharts.py | 3 | 1196 | """
This example makes custom 'pie charts' as the markers for a scatter plotqu
Thanks to Manuel Metz for the example
"""
import math
import numpy as np
import matplotlib.pyplot as plt
# first define the ratios
r1 = 0.2 # 20%
r2 = r1 + 0.4 # 40%
# define some sizes of the scatter marker
sizes = [60,80,120]
# ca... | gpl-2.0 |
kjung/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 29 | 4470 | """
===============================================================
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 |
Universal-Model-Converter/UMC3.0a | data/Python/x86/Lib/site-packages/scipy/cluster/hierarchy.py | 2 | 94437 | """
========================================================
Hierarchical clustering (:mod:`scipy.cluster.hierarchy`)
========================================================
.. currentmodule:: scipy.cluster.hierarchy
These functions cut hierarchical clusterings into flat clusterings
or find the roots of the forest f... | mit |
uqyge/combustionML | FPV_ANN_pureResNet/data_reader.py | 1 | 5026 | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
from sklearn import preprocessing
#%%
def read_csv_data(path = 'premix_data', labels = ['T','CH4','O2','CO2','CO','H2O','H2','OH','PVs']):
df = pd.DataFrame()
# path = 'data'
for fn in os.listdir(path):
#... | mit |
dsm054/pandas | pandas/tests/indexes/datetimes/test_arithmetic.py | 4 | 4190 | # -*- coding: utf-8 -*-
from datetime import datetime
import pytest
import pytz
from pandas.errors import NullFrequencyError
import pandas as pd
from pandas import DatetimeIndex, Series, date_range
import pandas.util.testing as tm
class TestDatetimeIndexArithmetic(object):
# ----------------------------------... | bsd-3-clause |
Saran-nns/SORN | chartmann/plot_single.py | 2 | 101253 | from __future__ import division
from pylab import *
from scipy.optimize import curve_fit
from scipy import stats
import tables
import sys
sys.path.insert(0,"../")
import utils
utils.backup(__file__)
from scipy.io import savemat
import datetime
from utils.pca import pca
from mpl_toolkits.mplot3d import Axes3D
from sci... | mit |
davidgbe/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
ZhiangChen/soft_arm | example_nets/DQN.py | 1 | 7190 | """
This part of code is the Deep Q Network (DQN) brain.
view the tensorboard picture about this DQN structure on: https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/#modification
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: r1.2
"""
... | mit |
teolisitza/turtleart | ipython_histories/avagraph2.py | 1 | 23034 | Last login: Fri Mar 27 17:42:36 on ttys003
MBPro ~> ipython
Python 2.7.5 (default, Mar 9 2014, 22:15:05)
Type "copyright", "credits" or "license" for more information.
IPython 2.3.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help ... | apache-2.0 |
kcompher/thunder | thunder/utils/ec2.py | 3 | 31603 | #!/usr/bin/env python
"""
Wrapper for the Spark EC2 launch script that additionally
installs Anaconda, Thunder, and its dependencies, and optionally
loads example data sets
"""
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed wit... | apache-2.0 |
bnaul/scikit-learn | sklearn/utils/deprecation.py | 3 | 3624 | import warnings
import functools
__all__ = ["deprecated"]
class deprecated:
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra argument will be appended to the depreca... | bsd-3-clause |
seckcoder/lang-learn | python/sklearn/sklearn/naive_bayes.py | 1 | 14346 | # -*- coding: utf-8 -*-
"""
The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes' theorem with strong
(naive) feature independence assumptions.
"""
# Author: Vincent Michel <vincent.michel@inria.fr>
# Minor fixes by Fabian Pedre... | unlicense |
francois-durand/svvamp | setup.py | 1 | 1467 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import setuptools
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
with open('README.rst') as readme_file:
readme = readme_file.read()
with open('HISTORY.rst') as history_file:
history = history_file.read().replace... | gpl-3.0 |
Lysxia/dissemin | doc/sphinx/conf.py | 1 | 11447 | # -*- coding: utf-8 -*-
#
# Dissemin documentation build configuration file, created by
# sphinx-quickstart on Fri Jun 12 13:30:53 2015.
#
# 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.
#
# ... | agpl-3.0 |
bavardage/statsmodels | statsmodels/stats/tests/test_statstools.py | 3 | 5251 |
import numpy as np
import pandas as pd
from numpy.testing import assert_almost_equal
from statsmodels.stats.stattools import (omni_normtest, jarque_bera,
durbin_watson)
from statsmodels.stats.adnorm import normal_ad
#a random array, rounded to 4 decimals
x = np.array([-0.1184, -1.3403, ... | bsd-3-clause |
hrjn/scikit-learn | sklearn/ensemble/voting_classifier.py | 19 | 9888 | """
Soft Voting/Majority Rule classifier.
This module contains a Soft Voting/Majority Rule classifier for
classification estimators.
"""
# Authors: Sebastian Raschka <se.raschka@gmail.com>,
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ..base import BaseEstimator
f... | bsd-3-clause |
Erotemic/local | homelinks/ipython/profile_default/ipython_config.py | 1 | 3431 | # import six
c = get_config() # NOQA
c.InteractiveShellApp.exec_lines = []
# if six.PY2:
# future_line = (
# 'from __future__ import absolute_import, division, print_function, with_statement, unicode_literals')
# c.InteractiveShellApp.exec_lines.append(future_line)
# # Fix sip versions
# try:
#... | gpl-3.0 |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/tests/series/test_replace.py | 8 | 7896 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import numpy as np
import pandas as pd
import pandas.lib as lib
import pandas.util.testing as tm
from .common import TestData
class TestSeriesReplace(TestData, tm.TestCase):
_multiprocess_can_split_ = True
def test_replace(self):
N = 100
ser... | mit |
zuku1985/scikit-learn | sklearn/linear_model/ridge.py | 2 | 51487 | """
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 |
trenton3983/Data_Science_from_Scratch | code-python3/recommender_systems.py | 12 | 6248 | import math, random
from collections import defaultdict, Counter
from linear_algebra import dot
users_interests = [
["Hadoop", "Big Data", "HBase", "Java", "Spark", "Storm", "Cassandra"],
["NoSQL", "MongoDB", "Cassandra", "HBase", "Postgres"],
["Python", "scikit-learn", "scipy", "numpy", "statsmodels", "pa... | unlicense |
ky822/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
thypad/brew | test/dataset.py | 3 | 1583 | import numpy as np
import sklearn.datasets as datasets
# this indices will always be used so that we get reproduceable results in the tests
iris_index = np.array([ 69, 63, 32, 131, 13, 94, 10, 17, 4, 108, 29, 96, 100,
143, 20, 86, 35, 144, 78, 18, 11, 33, 72, 106, 24, 84,
... | mit |
statsmodels/statsmodels.github.io | devel/plots/graphics_gofplots_qqplot.py | 10 | 1927 | # -*- coding: utf-8 -*-
"""
Created on Sun May 06 05:32:15 2012
Author: Josef Perktold
editted by: Paul Hobson (2012-08-19)
"""
# example with the new ProbPlot class
from matplotlib import pyplot as plt
import numpy as np
from scipy import stats
import statsmodels.api as sm
#example from docstring
data = sm.datasets... | bsd-3-clause |
BillMills/AutoQC | util/dbutils.py | 1 | 8715 | import io, math
import numpy
import pandas
import sqlite3
import util.main as main
def unpack_qc(value):
'unpack a qc result from the db'
try:
qc = numpy.load(io.BytesIO(value), allow_pickle=True)
except:
print('failed to unpack qc data - check db for missing entries.')
qc = numpy.... | mit |
mavenlin/tensorflow | tensorflow/python/estimator/canned/linear_testing_utils.py | 11 | 67872 | # 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 |
odlgroup/dipp | examples/pytorch/haarpsi_eval.py | 1 | 3464 | """Demonstration of the components of the HaarPSI figure of merit.
This example shows the individual steps of computing the HaarPSI FOM,
including comparison images.
Required additional packages:
- ``scipy`` to generate the example image
- ``matplotlib`` to display images
"""
import matplotlib.pyplot as plt... | mpl-2.0 |
mmottahedi/neuralnilm_prototype | scripts/e512.py | 2 | 6453 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectiona... | mit |
yuxiang-zhou/deepmachine | deepmachine/contrib/training/3DMMCamera.py | 1 | 12537 | # basic library
import os
import shutil
import math
import time
import menpo.io as mio
import menpo3d.io as m3io
import numpy as np
import h5py
import pandas as pd
from menpo.shape import ColouredTriMesh, PointCloud
from menpo.transform import Homogeneous
from menpo3d.rasterize import rasterize_mesh
from pathlib impor... | mit |
Nyker510/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
robclewley/DataScotties | SnakesLadders/csv_utils.py | 1 | 1679 | """
Utilities for working with CSV files representing all game move data.
EXAMPLE USAGE:
all_data = multi_run(game, n)
save_csv(all_data, filename)
all_data = load_csv(filename)
... for a given number of runs, n, and a string filename parameter.
"""
import pandas as pd
def save_csv(all_data, filename):
"""Sav... | cc0-1.0 |
grehx/spark-tk | regression-tests/sparktkregtests/testcases/dicom/dicom_filter_keyword_test.py | 1 | 9362 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | apache-2.0 |
PrashntS/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/lib/matplotlib/tests/test_mlab.py | 3 | 1722 | import numpy as np
import matplotlib.mlab as mlab
import tempfile
from nose.tools import raises
def test_colinear_pca():
a = mlab.PCA._get_colinear()
pca = mlab.PCA(a)
assert(np.allclose(pca.fracs[2:], 0.))
assert(np.allclose(pca.Y[:,2:], 0.))
def test_recarray_csv_roundtrip():
expected = np.reca... | gpl-2.0 |
ghislainp/iris | lib/iris/tests/test_mapping.py | 11 | 8113 | # (C) British Crown Copyright 2010 - 2015, Met Office
#
# This file is part of Iris.
#
# Iris is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any l... | gpl-3.0 |
developerator/Maturaarbeit | GenerativeAdversarialNets/CelebA64 with DCGAN/CelebA64_dcgan.py | 1 | 6815 | '''
By Tim Ehrensberger
The base of the functions for the network's training is taken from https://github.com/Zackory/Keras-MNIST-GAN/blob/master/mnist_gan.py by Zackory Erickson
The network architecture is perhaps loosely inspired by https://github.com/aleju/face-generator by Alexander Jung
'''
import os
import num... | mit |
huzq/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 9 | 21205 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
import pytest
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
from sklearn.utils._testing import assert_a... | bsd-3-clause |
ZenDevelopmentSystems/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 83 | 17276 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_array_equal, assert_array_less
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal, assert_true
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
IEEE-NITK/DeepNLP | Training-Sessions/Session II - Word Embeddings/word2vec/run.py | 2 | 2166 | import random
import numpy as np
from cs224d.data_utils import *
import matplotlib.pyplot as plt
from word2vec import *
from sgd import *
# Reset the random seed to make sure that everyone gets the same results
random.seed(314)
dataset = StanfordSentiment()
tokens = dataset.tokens()
nWords = len(tokens)
# We are goi... | mit |
ywcui1990/htmresearch | projects/sequence_classification/run_encoder_with_union.py | 9 | 8995 | # ----------------------------------------------------------------------
# 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 progra... | agpl-3.0 |
MadsJensen/RP_scripts | sk_sliding_degrees_bin.py | 1 | 3121 | import numpy as np
from sklearn.externals import joblib
from mne import create_info, EpochsArray
from mne.decoding import GeneralizationAcrossTime
from sklearn.model_selection import (StratifiedKFold)
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
import matplotlib
matplo... | bsd-3-clause |
bradmontgomery/ml | book/ch10/neighbors.py | 21 | 1787 | # 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
import numpy as np
import mahotas as mh
from glob import glob
from features import texture, color_histogram
from matplotlib import pyplot as plt
from ... | mit |
rderradi/AliPhysics | PWGPP/FieldParam/fitsol.py | 39 | 8343 | #!/usr/bin/env python
debug = True # enable trace
def trace(x):
global debug
if debug: print(x)
trace("loading...")
from itertools import combinations, combinations_with_replacement
from glob import glob
from math import *
import operator
from os.path import basename
import matplotlib.pyplot as plt
import numpy as... | bsd-3-clause |
weissercn/MLTools | Dalitz_simplified/optimisation/bdt_complicated_base_estimator/classifier_eval_wrapper.py | 1 | 1411 | import numpy as np
import math
import sys
sys.path.insert(0,'../..')
import os
import classifier_eval_simplified
from sklearn import tree
from sklearn.ensemble import AdaBoostClassifier
# Write a function like this called 'main'
def main(job_id, params):
print 'Anything printed here will end up in the output dire... | mit |
NMTHydro/Recharge | utils/LandFire_TAW/raster_grouping_deprecated.py | 1 | 6028 | # ===============================================================================
# Copyright 2018 gabe-parrish
#
# 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/licen... | apache-2.0 |
cybernet14/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 234 | 12267 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
pySTEPS/pysteps | pysteps/tests/test_tracking_tdating.py | 1 | 1617 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from pysteps.tracking.tdating import dating
from pysteps.utils import to_reflectivity
from pysteps.tests.helpers import get_precipitation_fields
arg_names = ("source", "dry_input")
arg_values = [
("mch", False),
("mch", False),
("mch", True),
]
... | bsd-3-clause |
yask123/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
enakai00/ml4se | scripts/07-mix_em.py | 1 | 3089 | # -*- coding: utf-8 -*-
#
# 混合ベルヌーイ分布による手書き文字分類
#
# 2015/04/24 ver1.0
#
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
from numpy.random import randint, rand
#------------#
# Parameters #
#------------#
K = 3 # 分類する文字数
N = 10 # 反復回数
# 分類結果の表示
def show_... | gpl-2.0 |
joshbohde/scikit-learn | sklearn/cross_validation.py | 1 | 32337 | """Utilities for cross validation and performance evaluation"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD Style.
from math import ceil
import operator
import numpy as np
fro... | bsd-3-clause |
hlin117/statsmodels | statsmodels/examples/ex_feasible_gls_het.py | 34 | 4267 | # -*- coding: utf-8 -*-
"""Examples for linear model with heteroscedasticity estimated by feasible GLS
These are examples to check the results during developement.
The assumptions:
We have a linear model y = X*beta where the variance of an observation depends
on some explanatory variable Z (`exog_var`).
linear_model... | bsd-3-clause |
GbalsaC/bitnamiP | venv/lib/python2.7/site-packages/scipy/signal/spectral.py | 6 | 13467 | """Tools for spectral analysis.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy import fftpack
from . import signaltools
from .windows import get_window
from ._spectral import lombscargle
import warnings
from scipy.lib.six import string_types
__all__ = ['periodogra... | agpl-3.0 |
vighneshbirodkar/scikit-image | doc/examples/segmentation/plot_segmentations.py | 4 | 5367 | """
====================================================
Comparison of segmentation and superpixel algorithms
====================================================
This example compares four popular low-level image segmentation methods. As
it is difficult to obtain good segmentations, and the definition of "good"
ofte... | bsd-3-clause |
keiserlab/e3fp-paper | project/analysis/visualization/draw_graph.py | 1 | 3950 | """Draw graph showing fingerprint process from shell images.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
import os
import json
import sys
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import pygraphviz as pgv
sns.set_style("white")
DPI = 1200
SIZE = "11,4"
if __name__ == "__m... | lgpl-3.0 |
Mistobaan/tensorflow | tensorflow/tools/dist_test/python/census_widendeep.py | 48 | 11896 | # 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 |
julienmalard/Tikon | tikon/datos/dibs.py | 1 | 4687 | import os
import numpy as np
import pandas as pd
from matplotlib.backends.backend_agg import FigureCanvasAgg as TelaFigura
from matplotlib.figure import Figure as Figura
from pandas.plotting import register_matplotlib_converters
from tikon.utils import EJE_PARÁMS, EJE_ESTOC, EJE_TIEMPO
register_matplotlib_converters... | agpl-3.0 |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_decoding_time_generalization_conditions.py | 7 | 3200 | """
=========================================================================
Decoding sensor space data with generalization across time and conditions
=========================================================================
This example runs the analysis described in [1]_. It illustrates how one can
fit a linear cla... | bsd-3-clause |
cqychen/quants | quants/loaddata/skyeye_ods_classified_area.py | 1 | 1152 | #coding=utf8
import tushare as ts;
import pymysql;
import time as dt
from datashape.coretypes import string
from pandas.io.sql import SQLDatabase
import sqlalchemy
import datetime
from sqlalchemy import create_engine
from pandas.io import sql
import threading
import pandas as pd;
import sys
sys.path.append('../') #添加配... | epl-1.0 |
ajdawson/numpy | numpy/lib/function_base.py | 3 | 126640 | from __future__ import division, absolute_import, print_function
import warnings
import sys
import collections
import operator
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d
from numpy.core.numeric import (
ones, zeros, arange, concatenate, array, asarr... | bsd-3-clause |
eteq/bokeh | bokeh/charts/builder/tests/test_step_builder.py | 33 | 2495 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
dgwakeman/mne-python | mne/viz/tests/test_misc.py | 17 | 4858 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Cathy Nangini <cnangini@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
#... | bsd-3-clause |
dr-rodriguez/Exploring-Goodreads | explore.py | 1 | 4366 | # Script to explore the data
import pickle
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
from nltk.corpus import stopwords
from nltk.tokenize import wordpunct_tokenize
from nltk.stem.porter import PorterStemmer
import string
from utilities import my_replaceme... | mit |
L3nzian/AutoPermit | wxmpl_plus.py | 1 | 34441 | """
These are some features we've added to wxmpl (on which this code relies),
such as --
1) Simplified center mouse button events
2) allowed zoom function to be constrained to x direction only
3) allowed zoom function to be constrained to y direction only
4) cross-shaped cursor-related stuff
5) autoscaleUnzoom
Some... | gpl-2.0 |
vortex-ape/scikit-learn | sklearn/utils/tests/test_fixes.py | 7 | 1778 | # Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import pickle
import numpy as np
import pytest
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import ass... | bsd-3-clause |
vzmehta/BigData2016 | py/mapper_zip2.py | 1 | 3198 | #!/usr/bin/env python
import csv,sys,os
os.environ['MPLCONFIGDIR'] = '/tmp'
import numpy
from matplotlib.path import Path
from rtree import index as rtree
import shapefile
from pyproj import Proj, transform
def findNeighborhood(location, index_rtree, neighborhoods):
match = index_rtree.intersection((location[0],... | mit |
pratapvardhan/pandas | pandas/tests/frame/test_repr_info.py | 6 | 17679 | # -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import re
import sys
import textwrap
from numpy import nan
import numpy as np
import pytest
from pandas import (DataFrame, Series, compat, option_context,
date_range, period_range, Categorical)... | bsd-3-clause |
AlexanderFabisch/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
tosolveit/scikit-learn | sklearn/tests/test_kernel_ridge.py | 342 | 3027 | import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert... | bsd-3-clause |
mattgiguere/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 |
xubenben/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 253 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
Bioinformatics-Support-Unit/python-scripts | get_gipz_passengers.py | 1 | 1047 | import argparse
import sys
import pandas as pd
def get_passengers(gipz, fasta):
common5s = []
passengers = []
loops = []
dd = pd.read_table(gipz)
titles = dd['pSM2c Oligo ID']
hairpins = dd['Hairpin Sequence']
for hairpin in hairpins:
common5s.append(hairpin[0:18])
passenger... | mit |
toobaz/pandas | pandas/tests/sparse/test_format.py | 2 | 5759 | import warnings
import numpy as np
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
import pandas as pd
from pandas import option_context
import pandas.util.testing as tm
use_32bit_repr = is_platform_windows() or is_platform_32bit()
@pytest.mark.filterwarnings("ignore:Sparse:FutureWa... | bsd-3-clause |
pycomlink/pycomlink | pycomlink/processing/baseline.py | 2 | 5480 | from builtins import range
import numpy as np
import pandas as pd
from numba import jit
from .xarray_wrapper import xarray_loop_vars_over_dim
################################################
# Functions for setting the RSL baseline level #
################################################
@xarray_loop_vars_over_di... | bsd-3-clause |
starsriver/ML | 5/SVM Facial Recognition.py | 1 | 3110 | # -*- coding: utf-8 -*-
from __future__ import print_function
from time import time
from sklearn.cross_validation import train_test_split
from sklearn.datasets import fetch_lfw_people
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_mat... | bsd-2-clause |
brchiu/tensorflow | tensorflow/contrib/timeseries/examples/known_anomaly.py | 24 | 7880 | # 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 |
lukas/ml-class | examples/scikit/pipeline-custom-features.py | 2 | 1497 | import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
class NumBangExtractor(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def num_bang(self, str):
"""Helper code to compute number of exclamation points"""
return str.count('!')
... | gpl-2.0 |
phoenixstar7/pmtk3 | python/demos/ch02/gaussPlot2Ddemo.py | 7 | 1165 | #!/usr/bin/env python
import numpy as np
import matplotlib.pylab as pl
from mpl_toolkits.mplot3d import Axes3D
def mvn2d(x, y, sigma):
xx, yy = np.meshgrid(x, y)
u = np.array([np.mean(x), np.mean(y)])
xy = np.c_[xx.ravel(), yy.ravel()]
sigma_inv = np.linalg.inv(sigma)
z = np.dot((xy - u), sigma_i... | mit |
amirajdhawan/eYSIP_2015_Depth_Mapping_Kinect | Resources/Examples/Python freenect examples/demo_mp_async.py | 4 | 1034 | #!/usr/bin/env python
import freenect
import matplotlib.pyplot as mp
import signal
import frame_convert
mp.ion()
image_rgb = None
image_depth = None
keep_running = True
def display_depth(dev, data, timestamp):
global image_depth
data = frame_convert.pretty_depth(data)
mp.gray()
mp.figure(1)
if im... | cc0-1.0 |
MatthieuBizien/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
joshgabriel/dft-crossfilter | benchmark-api/api/endpoints/query_api.py | 1 | 12700 | import json
from flask.ext.api import status
import flask as fk
from api import app, API_URL, crossdomain, api_response
from benchdb.common.models import Row
from benchdb.common.models import Attribute
import mimetypes
import json
import traceback
import datetime
import random
import string
import os
from io import ... | mit |
dharmasam9/moose-core | tests/python/mus/rc19.py | 1 | 9791 | # -*- coding: utf-8 -*-
# rc19.py ---
#
# Filename: rc19.py
# Description:
# Author: Subhasis Ray
# Maintainer:
# Created: Sat May 24 14:10:22 2014 (+0530)
# Version:
# Last-Updated:
# By:
# Update #: 0
# URL:
# Keywords:
# Compatibility:
#
#
# Commentary:
#
#
#
#
# Change log:
#
#
#
#
# This program is... | gpl-3.0 |
MortimerGoro/servo | tests/heartbeats/process_logs.py | 139 | 16143 | #!/usr/bin/env python
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import argparse
import matplotlib.pyplot as plt
import numpy as np
import os
from os import path
... | mpl-2.0 |
saiwing-yeung/scikit-learn | sklearn/ensemble/tests/test_iforest.py | 9 | 6928 | """
Testing for Isolation Forest algorithm (sklearn.ensemble.iforest).
"""
# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.u... | bsd-3-clause |
e-q/scipy | scipy/interpolate/fitpack2.py | 4 | 73106 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
__all__ = [
'UnivariateSpline',
'Interpolate... | bsd-3-clause |
RedhawkSDR/integration-gnuhawk | gnuradio/gr-digital/examples/example_costas.py | 17 | 4430 | #!/usr/bin/env python
from gnuradio import gr, digital
from gnuradio import eng_notation
from gnuradio.eng_option import eng_option
from optparse import OptionParser
try:
import scipy
except ImportError:
print "Error: could not import scipy (http://www.scipy.org/)"
sys.exit(1)
try:
import pylab
excep... | gpl-3.0 |
sumspr/scikit-learn | examples/linear_model/plot_lasso_model_selection.py | 311 | 5431 | """
===================================================
Lasso model selection: Cross-Validation / AIC / BIC
===================================================
Use the Akaike information criterion (AIC), the Bayes Information
criterion (BIC) and cross-validation to select an optimal value
of the regularization paramet... | bsd-3-clause |
bsipocz/astropy | examples/coordinates/plot_galactocentric-frame.py | 3 | 8006 | # -*- coding: utf-8 -*-
"""
========================================================================
Transforming positions and velocities to and from a Galactocentric frame
========================================================================
This document shows a few examples of how to use and customize the
`~ast... | bsd-3-clause |
ngoix/OCRF | 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 |
ckuethe/gnuradio | gr-utils/python/utils/plot_data.py | 59 | 5818 | #
# Copyright 2007,2008,2011 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)
# any later ve... | gpl-3.0 |
tbenthompson/LMS_public | lms_code/analysis/qi_inverse.py | 1 | 2527 | import numpy as np
import matplotlib.pyplot as plt
import lms_code.lib.rep2 as rep2
import lms_code.plots.plot_all as lms_plot
solns = rep2.load("bem_all_details_inverse1")
width = 0.2
qi_gps = rep2.load('qi_gps_near_' + str(width))
# plt.plot(solns['detachment']['x'][0, :], solns['detachment']['u_soln'][0, :])
# plt.... | mit |
tedmeeds/tcga_encoder | tcga_encoder/analyses/kmeans_from_z_space_local_learn_survival.py | 1 | 18008 | from tcga_encoder.utils.helpers import *
from tcga_encoder.data.data import *
from tcga_encoder.definitions.tcga import *
#from tcga_encoder.definitions.nn import *
from tcga_encoder.definitions.locations import *
#from tcga_encoder.algorithms import *
import seaborn as sns
from sklearn.manifold import TSNE, locally_li... | mit |
shusenl/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
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