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 |
|---|---|---|---|---|---|
wzbozon/scikit-learn | sklearn/tests/test_learning_curve.py | 225 | 10791 | # Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.u... | bsd-3-clause |
irblsensitivity/irblsensitivity | scripts/analysis/MWU_Project_EMSE.py | 1 | 9231 | #-*- coding: utf-8 -*-
'''
Created on 2017. 02. 12
Updated on 2017. 02. 12
'''
from __future__ import print_function
import os
import re
import matplotlib
# Force matplotlib to not use any Xwindows backend.
matplotlib.use('Agg')
from scipy.stats import mannwhitneyu, pearsonr
from ExpBase import ExpBase
... | apache-2.0 |
fmfn/UnbalancedDataset | examples/under-sampling/plot_illustration_tomek_links.py | 2 | 3180 | """
==============================================
Illustration of the definition of a Tomek link
==============================================
This example illustrates what is a Tomek link.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
# %%
print(__doc__)
import matplotlib.pyplot as pl... | mit |
waylonflinn/bquery | bquery/benchmarks/bench_groupby.py | 2 | 2465 | from __future__ import print_function
# bench related imports
import numpy as np
import shutil
import bquery
import pandas as pd
import itertools as itt
import cytoolz
import cytoolz.dicttoolz
from toolz import valmap, compose
from cytoolz.curried import pluck
import blaze as blz
# other imports
import contextlib
impor... | bsd-3-clause |
HiSPARC/sapphire | scripts/simulations/analyze_shower_front.py | 1 | 5153 | import numpy as np
import tables
from scipy.optimize import curve_fit
from scipy.stats import scoreatpercentile
from artist import GraphArtist
from pylab import *
import matplotlib.pyplot as plt
import utils
USE_TEX = False
# For matplotlib plots
if USE_TEX:
rcParams['font.serif'] = 'Computer Modern'
rcPar... | gpl-3.0 |
jmbeuken/abinit | scripts/post_processing/abinit_eignc_to_bandstructure.py | 3 | 47417 | #!/usr/bin/python
#=================================================================#
# Script to plot the bandstructure from an abinit bandstructure #
# _EIG.nc netcdf file or from a wannier bandstructure, or from #
# an _EIG.nc file+GW file+ bandstructure _EIG.nc file #
#=============================... | gpl-3.0 |
robios/PyTES | pytes/Util.py | 1 | 32573 | import warnings
import numpy as np
import time
from struct import unpack
from scipy.stats import norm
from scipy.signal import tukey
from Filter import median_filter
import Analysis, Filter, Constants
def savefits(data, filename, vmax=1.0, sps=1e6, bits=14, noise=False, clobber=True):
"""
Save pulse/noise to F... | mit |
mwv/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 249 | 1095 | """
==========================
SGD: convex loss functions
==========================
A plot that compares the various convex loss functions supported by
:class:`sklearn.linear_model.SGDClassifier` .
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def modified_huber_loss(y_true, y_pred):
z ... | bsd-3-clause |
openfisca/openfisca-france-indirect-taxation | openfisca_france_indirect_taxation/examples/transports/plot_legislation/plot_ticpe_taux_implicite.py | 4 | 2264 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 17 18:06:45 2015
@author: thomas.douenne
TICPE: Taxe intérieure sur la consommation des produits énergétiques
"""
# L'objectif de ce script est d'illustrer graphiquement l'évolution du taux implicite de la TICPE depuis 1993.
# On étudie ce taux pour le diesel, et pour l... | agpl-3.0 |
jmontgom10/Mimir_pyPol | oldCode/04b_avgBAABditherHWPimages.py | 1 | 17054 | # -*- coding: utf-8 -*-
"""
Combines all the images for a given (TARGET, FILTER, HWP) combination to
produce a single, average image.
Estimates the sky background level of the on-target position at the time of the
on-target observation using a bracketing pair of off-target observations through
the same HWP polaroid ro... | mit |
jldbc/pybaseball | pybaseball/standings.py | 1 | 3820 | from typing import List, Optional
import pandas as pd
import requests
from bs4 import BeautifulSoup, Comment, PageElement, ResultSet
from . import cache
from .utils import most_recent_season
def get_soup(year: int) -> BeautifulSoup:
url = f'http://www.baseball-reference.com/leagues/MLB/{year}-standings.shtml'
... | mit |
BrainTech/openbci | obci/analysis/csp/MLogit.py | 1 | 11792 | #!/usr/bin/env python
#-*- coding:utf-8 -*-
"""This is a class for Multinomial Logit Regression
Class uses scipy.optimize package for minimalization of a cost function.
The gradient of the cost function is passed to the minimizer.
Piotr Milanowski, November 2011, Warsaw
"""
from scipy.optimize import fmin_ncg, fmin_... | gpl-3.0 |
vortex-exoplanet/VIP | vip_hci/negfc/utils_negfc.py | 2 | 8821 | #! /usr/bin/env python
"""
Module with post-processing related functions called from within the NFC
algorithm.
"""
__author__ = 'Carlos Alberto Gomez Gonzalez'
__all__ = ['cube_planet_free']
import numpy as np
from ..metrics import cube_inject_companions
import math
from matplotlib.pyplot import plot, xlim, ylim, ax... | mit |
chaluemwut/smcdemo | demo_filter.py | 1 | 2602 | import pickle
from feature_process import FeatureMapping
import feature_process
from text_processing import TextProcessing
from sklearn.cross_validation import train_test_split
is_not_important = {9:0,
13:0,
18:0,
19:0,
23:0,
28:0,
29:0,
33:0,
34:0,
37:0,
40:0,
44:0,
46:0,
50:0,
55:0,
59:0,
61:0,
62:0,
63:0,
72:0,
73:... | apache-2.0 |
ChanChiChoi/scikit-learn | examples/exercises/plot_iris_exercise.py | 323 | 1602 | """
================================
SVM Exercise
================================
A tutorial exercise for using different SVM kernels.
This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
"""
print(__doc__)
import numpy as np
i... | bsd-3-clause |
ianatpn/nupictest | external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py | 70 | 10245 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow ... | gpl-3.0 |
ShujiaHuang/AsmVar | src/AsmvarGenotype/GMM/GMM2D.py | 2 | 18363 | """
================================================
My own Gaussion Mixture Model for SV genotyping.
Learn form scikit-learn
================================================
Author : Shujia Huang
Date : 2014-01-06 14:33:45
"""
import sys
import numpy as np
from scipy import linalg
from sklearn import cluster
from ... | mit |
kcyu1993/ML_course_kyu | projects/project1/scripts/model.py | 1 | 19450 | from __future__ import absolute_import
from abc import ABCMeta, abstractmethod
import copy
from data_utils import build_k_indices
from learning_model import *
from regularizer import *
from helpers import save_numpy_array
import numpy as np
class Model(object):
"""
Author: Kaicheng Yu
Machine learning mo... | mit |
jmetzen/scikit-learn | sklearn/base.py | 22 | 18131 | """Base classes for all estimators."""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import copy
import warnings
import numpy as np
from scipy import sparse
from .externals import six
from .utils.fixes import signature
from .utils.deprecation import deprecated
from .exceptions impor... | bsd-3-clause |
htygithub/bokeh | bokeh/sampledata/gapminder.py | 41 | 2655 | from __future__ import absolute_import
import pandas as pd
from os.path import join
import sys
from . import _data_dir
'''
This module provides a pandas DataFrame instance of four
of the datasets from gapminder.org.
These are read in from csvs that have been downloaded from Bokeh's
sample data on S3. But the origina... | bsd-3-clause |
huongttlan/bokeh | bokeh/compat/mplexporter/renderers/base.py | 44 | 14355 | import warnings
import itertools
from contextlib import contextmanager
import numpy as np
from matplotlib import transforms
from .. import utils
from .. import _py3k_compat as py3k
class Renderer(object):
@staticmethod
def ax_zoomable(ax):
return bool(ax and ax.get_navigate())
@staticmethod
... | bsd-3-clause |
joelfrederico/SciSalt | scisalt/qt/mplwidget.py | 1 | 13557 | from PyQt4 import QtGui
from PyQt4 import QtCore
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as _FigureCanvas
from matplotlib.backends.backend_qt4 import NavigationToolbar2QT as _NavigationToolbar
import matplotlib as _mpl
import numpy as _np
from .Rectangle import Rectangle
import pdb
import tra... | mit |
mathhun/scipy_2015_sklearn_tutorial | notebooks/figures/plot_kneighbors_regularization.py | 25 | 1363 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsRegressor
def make_dataset(n_samples=100):
rnd = np.random.RandomState(42)
x = np.linspace(-3, 3, n_samples)
y_no_noise = np.sin(4 * x) + x
y = y_no_noise + rnd.normal(size=len(x))
return x, y
def plot_re... | cc0-1.0 |
qifeigit/scikit-learn | examples/decomposition/plot_pca_3d.py | 354 | 2432 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to ch... | bsd-3-clause |
sonyahanson/assaytools | examples/ipynbs/data-analysis/spectra/2015-12-18/xml2png4scans-spectra.py | 8 | 5636 | # This script takes xml data file output from the Tecan Infinite m1000 Pro plate reader
# and makes quick and dirty images of the raw data.
# But with scans and not just singlet reads.
# This script specifically combines four spectrum scripts (AB, CD, EF, GH) into a single dataframe and plot.
# The same procedure can... | lgpl-2.1 |
nikitasingh981/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 93 | 3243 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
RomainBrault/scikit-learn | sklearn/neighbors/graph.py | 36 | 6650 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def _check_params(X, metric, p, metric_params):
"""C... | bsd-3-clause |
micahcochran/geopandas | geopandas/_version.py | 3 | 16750 |
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains t... | bsd-3-clause |
zycdragonball/tensorflow | tensorflow/contrib/learn/python/learn/estimators/linear_test.py | 58 | 71789 | # 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 |
yqzhang/OpenANN | benchmarks/iris/benchmark.py | 5 | 3308 | ## \page IrisBenchmark Iris Flower Dataset
#
# The iris dataset is a standard machine learning dataset.
# See e.g. the <a href="http://en.wikipedia.org/wiki/Iris_flower_data_set"
# target=_blank>Wikipedia article</a> for more details.
#
# You can start the benchmark with the script:
# \verbatim
# python benchmark... | gpl-3.0 |
alexei-matveev/ase-local | doc/exercises/siesta1/answer1.py | 3 | 1197 | # -*- coding: utf-8 -*-
# creates: ener.png distance.png angle.png
import os
import matplotlib
matplotlib.use('Agg')
import pylab as plt
e_s = [0.01,0.1,0.2,0.3,0.4,0.5]
E = [-463.2160, -462.9633, -462.4891, -462.0551,
-461.5426, -461.1714]
d = [1.1131, 1.1046, 1.0960, 1.0901,
1.0857, 1.0810]
alpha = [100.... | gpl-2.0 |
mugwizaleon/PCRasterMapstacks | pcrastermapstackvisualisation.py | 1 | 17920 | # -*- coding: utf-8 -*-
"""
/***************************************************************************
PcrasterMapstackVisualisation
A QGIS plugin
PCRaster Mapstack visualisation
-------------------
begin : 2014-06-28
copy... | apache-2.0 |
jreback/pandas | pandas/io/formats/latex.py | 2 | 25201 | """
Module for formatting output data in Latex.
"""
from abc import ABC, abstractmethod
from typing import Iterator, List, Optional, Sequence, Tuple, Type, Union
import numpy as np
from pandas.core.dtypes.generic import ABCMultiIndex
from pandas.io.formats.format import DataFrameFormatter
def _split_into_full_shor... | bsd-3-clause |
trungnt13/scikit-learn | examples/feature_selection/plot_rfe_with_cross_validation.py | 226 | 1384 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
michelp/pywt | util/refguide_check.py | 2 | 27051 | #!/usr/bin/env python
"""
refguide_check.py [OPTIONS] [-- ARGS]
Check for a PyWavelets submodule whether the objects in its __all__ dict
correspond to the objects included in the reference guide.
Example of usage::
$ python refguide_check.py optimize
Note that this is a helper script to be able to check if thin... | mit |
samklr/spark-timeseries | python/sparkts/test/test_timeseriesrdd.py | 6 | 5407 | from test_utils import PySparkTestCase
from sparkts.timeseriesrdd import *
from sparkts.timeseriesrdd import _TimeSeriesSerializer
from sparkts.datetimeindex import *
import pandas as pd
import numpy as np
from unittest import TestCase
from io import BytesIO
from pyspark.sql import SQLContext
class TimeSeriesSerialize... | apache-2.0 |
jblackburne/scikit-learn | sklearn/tree/tests/test_tree.py | 7 | 55471 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import struct
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_random... | bsd-3-clause |
andaag/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 286 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
liberatorqjw/scikit-learn | sklearn/tree/export.py | 30 | 4529 | """
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>
# Licence: BSD 3 ... | bsd-3-clause |
crichardson17/starburst_atlas | Low_resolution_sims/DustFree_LowRes/Padova_inst/padova_inst_6/Optical1.py | 33 | 7366 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
rphlypo/parietalretreat | setup_data_path_salma.py | 1 | 6001 | import glob
import os.path
from pandas import DataFrame
import pandas
def get_all_paths(data_set=None, root_dir="/"):
# TODO
# if data_set ... collections.Sequence
# iterate over list
if data_set is None:
data_set = {"hcp", "henson2010faces", "ds105", "ds107"}
list_ = list()
head, tail... | bsd-2-clause |
debsankha/bedtime-programming | ls222/visual-lotka.py | 1 | 5120 | #!/usr/bin/env python
from math import *
import thread
import random
import time
import pygtk
pygtk.require("2.0")
import gtk
import gtk.glade
import commands
import matplotlib.pyplot
class rodent:
def __init__(self):
self.time_from_last_childbirth=0
class felix:
def __init__(self):
self.size=0
self.is_virgin... | gpl-3.0 |
blaze/distributed | distributed/protocol/tests/test_collection_cuda.py | 1 | 2448 | import pytest
from distributed.protocol import serialize, deserialize
from dask.dataframe.utils import assert_eq
import pandas as pd
@pytest.mark.parametrize("collection", [tuple, dict])
@pytest.mark.parametrize("y,y_serializer", [(50, "cuda"), (None, "pickle")])
def test_serialize_cupy(collection, y, y_serializer):... | bsd-3-clause |
nicholaschris/landsatpy | utils.py | 1 | 2693 | import operator
import pandas as pd
import numpy as np
from numpy import ma
from scipy.misc import imresize
import scipy.ndimage as ndimage
from skimage.morphology import disk, dilation
def get_truth(input_one, input_two, comparison): # too much abstraction
ops = {'>': operator.gt,
'<': operator.lt,
... | mit |
kwilliams-mo/iris | lib/iris/tests/test_plot.py | 1 | 32122 | # (C) British Crown Copyright 2010 - 2013, 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 |
kaichogami/scikit-learn | sklearn/utils/multiclass.py | 40 | 12966 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
ONEcampaign/humanitarian-data-service | displacement_tracker_data.py | 1 | 27157 | import requests
import pandas as pd
import os.path
import resources.constants
import json
from pandas.io.json import json_normalize
from utils.data_utils import get_ordinal_number
"""
This script aggregates data from multiple endpoints and returns a single .json file containing all data
used in the displacement tracke... | mit |
Tong-Chen/scikit-learn | sklearn/tests/test_cross_validation.py | 4 | 30858 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing im... | bsd-3-clause |
ahoyosid/scikit-learn | examples/applications/plot_stock_market.py | 227 | 8284 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
DESatAPSU/DAWDs | python/origBandpass_FITSToCSV.py | 1 | 1930 | # Converts STD_BANDPASSES_Y3A1_FGCM_20170630_extend3000.fits to
# y3a2_std_passband_extend3000_ugrizYatm.csv
#
# To run (bash):
# python origBandpass_FITSToCSV.py > origBandpass_FITSToCSV.log 2>&1 &
#
# To run (tcsh):
# python origBandpass_FITSToCSV.py >& origBandpass_FITSToCSV.log &
#
# DLT, 2017-06-30
# bas... | mit |
nmartensen/pandas | asv_bench/benchmarks/gil.py | 7 | 11003 | from .pandas_vb_common import *
from pandas.core.algorithms import take_1d
try:
from cStringIO import StringIO
except ImportError:
from io import StringIO
try:
from pandas._libs import algos
except ImportError:
from pandas import algos
try:
from pandas.util.testing import test_parallel
have... | bsd-3-clause |
great-expectations/great_expectations | tests/datasource/test_batch_generators.py | 1 | 6706 | import os
from great_expectations.datasource.batch_kwargs_generator import (
DatabricksTableBatchKwargsGenerator,
GlobReaderBatchKwargsGenerator,
SubdirReaderBatchKwargsGenerator,
)
try:
from unittest import mock
except ImportError:
from unittest import mock
def test_file_kwargs_generator(
d... | apache-2.0 |
jblackburne/scikit-learn | sklearn/gaussian_process/gpc.py | 42 | 31571 | """Gaussian processes classification."""
# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
from operator import itemgetter
import numpy as np
from scipy.linalg import cholesky, cho_solve, solve
from scipy.optimize import fmin_l_bfgs_b
from scipy.special import erf... | bsd-3-clause |
dsquareindia/scikit-learn | sklearn/decomposition/tests/test_fastica.py | 70 | 7808 | """
Test the fastica algorithm.
"""
import itertools
import warnings
import numpy as np
from scipy import stats
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
untom/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 |
dmargala/qusp | examples/compare_delta.py | 1 | 7364 | #!/usr/bin/env python
import argparse
import numpy as np
import numpy.ma as ma
import h5py
import qusp
import matplotlib.pyplot as plt
import scipy.interpolate
import fitsio
class DeltaLOS(object):
def __init__(self, thing_id):
path = '/data/lya/deltas/delta-%d.fits' % thing_id
hdulist = fitsio.... | mit |
guildai/guild | examples/iris-svm/plot_iris_exercise.py | 1 | 1702 | """
A tutorial exercise for using different SVM kernels.
Adapted from:
https://scikit-learn.org/stable/auto_examples/exercises/plot_iris_exercise.html
"""
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn import datasets, svm
kernel = 'linear' # choice of line... | apache-2.0 |
jzt5132/scikit-learn | examples/svm/plot_rbf_parameters.py | 132 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
etkirsch/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 268 | 2723 | """
===================================================
Label Propagation digits: Demonstrating performance
===================================================
This example demonstrates the power of semisupervised learning by
training a Label Spreading model to classify handwritten digits
with sets of very few labels.... | bsd-3-clause |
nmartensen/pandas | asv_bench/benchmarks/categoricals.py | 3 | 2803 | from .pandas_vb_common import *
try:
from pandas.api.types import union_categoricals
except ImportError:
try:
from pandas.types.concat import union_categoricals
except ImportError:
pass
class Categoricals(object):
goal_time = 0.2
def setup(self):
N = 100000
self.s ... | bsd-3-clause |
YinongLong/scikit-learn | examples/preprocessing/plot_function_transformer.py | 158 | 1993 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
newville/scikit-image | doc/examples/plot_rank_mean.py | 17 | 1499 | """
============
Mean filters
============
This example compares the following mean filters of the rank filter package:
* **local mean**: all pixels belonging to the structuring element to compute
average gray level.
* **percentile mean**: only use values between percentiles p0 and p1
(here 10% and 90%).
* *... | bsd-3-clause |
dipanjanS/text-analytics-with-python | Old-First-Edition/Ch06_Text_Similarity_and_Clustering/utils.py | 1 | 1097 | # -*- coding: utf-8 -*-
"""
Created on Sun Sep 11 23:06:06 2016
@author: DIP
"""
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
def build_feature_matrix(documents, feature_type='frequency',
ngram_range=(1, 1), min_df=0.0, max_df=1.0):
feature_type = featur... | apache-2.0 |
billy-inn/scikit-learn | examples/decomposition/plot_ica_vs_pca.py | 306 | 3329 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
hrjn/scikit-learn | sklearn/cluster/tests/test_hierarchical.py | 33 | 20167 | """
Several basic tests for hierarchical clustering procedures
"""
# Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
# Matteo Visconti di Oleggio Castello 2014
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
from functools import partial
import numpy as np
from scipy import sparse
from... | bsd-3-clause |
mne-tools/mne-python | mne/viz/circle.py | 14 | 15879 | """Functions to plot on circle as for connectivity."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: Simplified BSD
from itertools import cycle
from functools import partial
import ... | bsd-3-clause |
zorroblue/scikit-learn | examples/model_selection/plot_roc.py | 102 | 5056 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
harshaneelhg/scikit-learn | examples/cluster/plot_lena_compress.py | 271 | 2229 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Vector Quantization Example
=========================================================
The classic image processing example, Lena, an 8-bit grayscale
bit-depth, 512 x 512 sized image, is used here to illustrate
how ... | bsd-3-clause |
JosmanPS/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
smorante/continuous-goal-directed-actions | demonstration-feature-selection/src/alternatives/main_dtw_mds_norm.py | 2 | 3731 | # -*- coding: utf-8 -*-
"""
Author: Santiago Morante
Robotics Lab. Universidad Carlos III de Madrid
"""
########################## DTW ####################################
import libmddtw
import matplotlib.pyplot as plt
from dtw import dtw
########################## MDS ####################################
impo... | mit |
wavycloud/pyboto3 | pyboto3/glue.py | 1 | 692979 | '''
The MIT License (MIT)
Copyright (c) 2016 WavyCloud
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, p... | mit |
alekz112/statsmodels | statsmodels/datasets/tests/test_utils.py | 26 | 1697 | import os
import sys
from statsmodels.datasets import get_rdataset, webuse, check_internet
from numpy.testing import assert_, assert_array_equal, dec
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_get_rdataset():
# smoke test
if sys.version_info[0] >= 3:
#NOTE: there's no way to test bo... | bsd-3-clause |
vene/ambra | ambra/cross_validation.py | 1 | 9371 | import numbers
import time
import numpy as np
from sklearn.utils import safe_indexing
from sklearn.base import is_classifier, clone
from sklearn.metrics.scorer import check_scoring
from sklearn.externals.joblib import Parallel, delayed, logger
from ambra.backports import _num_samples, indexable
from sklearn.cross_val... | bsd-2-clause |
jlegendary/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 47 | 8095 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
... | bsd-3-clause |
glouppe/scikit-learn | examples/model_selection/plot_roc.py | 49 | 5041 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
dahlstrom-g/intellij-community | python/helpers/pydev/_pydevd_bundle/pydevd_vars.py | 7 | 26282 | """ pydevd_vars deals with variables:
resolution/conversion to XML.
"""
import math
import pickle
from _pydev_bundle.pydev_imports import quote
from _pydev_imps._pydev_saved_modules import thread
from _pydevd_bundle.pydevd_constants import get_frame, get_current_thread_id, xrange, NUMPY_NUMERIC_TYPES, NUMPY_FLOATI... | apache-2.0 |
rmm-fcul/workshops | 2015_graz/binary_choice/two_arenas_real_real/casu_utils.py | 5 | 8116 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
a library of functions used in CASU controller dynamics. Got a lot of
messy code that would be neater like this
RM, Feb 2015
'''
import numpy as np
from assisipy import casu
#import matplotlib.cm as cm
from datetime import datetime
import parsing
import time
### ==... | lgpl-3.0 |
zooniverse/aggregation | docs/source/conf.py | 1 | 9778 | # -*- coding: utf-8 -*-
#
# Zooniverse Aggregation Engine documentation build configuration file, created by
# sphinx-quickstart on Mon Mar 14 11:15:07 2016.
#
# 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
# aut... | apache-2.0 |
akloster/bokeh | bokeh/properties.py | 20 | 42601 | """ Properties are objects that can be assigned as class level
attributes on Bokeh models, to provide automatic serialization
and validation.
For example, the following defines a model that has integer,
string, and list[float] properties::
class Model(HasProps):
foo = Int
bar = String
baz ... | bsd-3-clause |
trustedanalytics/spark-tk | regression-tests/sparktkregtests/testcases/frames/boxcox_test.py | 12 | 5074 | # 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 |
yonglehou/scikit-learn | examples/applications/plot_stock_market.py | 227 | 8284 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
phoebe-project/phoebe2-docs | 2.1/examples/minimal_contact_binary.py | 1 | 5694 | #!/usr/bin/env python
# coding: utf-8
# Minimal Contact Binary System
# ============================
#
# Setup
# -----------------------------
# Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update ... | gpl-3.0 |
jereze/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 218 | 3893 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
jeremiedecock/snippets | python/matplotlib/hist_logscale_x.py | 1 | 1804 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Make a histogram using a logarithmic scale on X axis
See:
- http://stackoverflow.com/questions/6855710/how-to-have-logarithmic-bins-in-a-python-histogram
"""
import numpy as np
import matplotlib.pyplot as plt
# SETUP ###############################################... | mit |
daodaoliang/bokeh | bokeh/charts/builder/tests/test_line_builder.py | 33 | 2376 | """ 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 |
pypot/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
JackKelly/neuralnilm_prototype | scripts/experiment029.py | 2 | 3262 | from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import theano
import theano.tensor as T
import lasagne
from gen_data_029 import gen_data, N_BATCH, LENGTH
theano.config.compute_test_value = 'raise'
# Number of units in the hidden (recurrent) layer
N_HIDDEN = 5
# SGD learning rate
LEA... | mit |
zmr/namsel | accuracy_test.py | 1 | 2139 | #encoding: utf-8
import cPickle as pickle
from classify import load_cls, label_chars
from cv2 import GaussianBlur
from feature_extraction import get_zernike_moments, get_hu_moments, \
extract_features, normalize_and_extract_features
from functools import partial
import glob
from multiprocessing.pool import Pool
im... | mit |
ephes/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
aguirrea/lucy | tests/lfootGraph.py | 1 | 6007 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# Andrés Aguirre Dorelo
#
# MINA/INCO/UDELAR
#
# module for finding the steps in the tutors
#
# 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; eith... | gpl-3.0 |
otmaneJai/Zipline | zipline/utils/tradingcalendar_bmf.py | 17 | 7576 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
kylerbrown/scikit-learn | examples/feature_selection/plot_rfe_with_cross_validation.py | 226 | 1384 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
nasseralkmim/SaPy | sapy/plotter.py | 1 | 4743 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Line3D
from matplotlib.lines import Line2D
import numpy as np
def window(name):
return plt.figure(name)
def show():
plt.show()
return None
def undeformed(model):
"""Plot the undeformed st... | gpl-3.0 |
hennersz/pySpace | basemap/doc/users/figures/omerc.py | 6 | 1065 | from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# setup oblique mercator basemap.
# width is width of map projection region in km (xmax-xmin_
# height is height of map projection region in km (ymax-ymin)
# lon_0, lat_0 are the central longitude and latitude of the projection.... | gpl-3.0 |
jlegendary/scikit-learn | examples/plot_multilabel.py | 87 | 4279 | # 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 |
ephes/scikit-learn | sklearn/decomposition/dict_learning.py | 83 | 44062 | """ Dictionary learning
"""
from __future__ import print_function
# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
# License: BSD 3 clause
import time
import sys
import itertools
from math import sqrt, ceil
import numpy as np
from scipy import linalg
from numpy.lib.stride_tricks import as_strided
from ..b... | bsd-3-clause |
melqkiades/yelp | source/python/topicmodeling/external/topicensemble/unsupervised/nmf.py | 2 | 1622 | import numpy as np
from sklearn import decomposition
import logging as log
# --------------------------------------------------------------
class SklNMF:
"""
Wrapper class backed by the scikit-learn package NMF implementation.
"""
def __init__( self, max_iters = 100, init_strategy = "random" ):
self.max_iters =... | lgpl-2.1 |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/matplotlib/tri/triplot.py | 8 | 3150 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import numpy as np
from matplotlib.tri.triangulation import Triangulation
def triplot(ax, *args, **kwargs):
"""
Draw a unstructured triangular grid as lines and/o... | apache-2.0 |
joshgabriel/dft-crossfilter | CompleteApp/crossfilter_app/old_mains/old_main.py | 3 | 10263 | # main.py that controls the whole app
# to run: just run bokeh serve --show crossfilter_app in the benchmark-view repo
from random import random
import os
from bokeh.layouts import column
from bokeh.models import Button
from bokeh.models.widgets import Select, MultiSelect, Slider
from bokeh.palettes import RdYlBu3
fr... | mit |
Tuyki/TT_RNN | MNISTSeq.py | 1 | 14227 | __author__ = "Yinchong Yang"
__copyright__ = "Siemens AG, 2018"
__licencse__ = "MIT"
__version__ = "0.1"
"""
MIT License
Copyright (c) 2018 Siemens AG
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Softwa... | mit |
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