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 |
|---|---|---|---|---|---|
rcrowder/nupic | examples/opf/tools/MirrorImageViz/mirrorImageViz.py | 50 | 7221 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, 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 |
ArnaudKOPP/BioREST | BioREST/QuickGo.py | 1 | 12406 | # coding=utf-8
"""
REST class to QuicGo services
"""
__author__ = "Arnaud KOPP"
__copyright__ = "© 2015-2016 KOPP Arnaud All Rights Reserved"
__credits__ = ["KOPP Arnaud"]
__license__ = "GNU GPL V3.0"
__maintainer__ = "Arnaud KOPP"
__email__ = "kopp.arnaud@gmail.com"
__status__ = "Production"
import logging
from BioR... | gpl-3.0 |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/tests/series/test_analytics.py | 4 | 63970 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
from itertools import product
from distutils.version import LooseVersion
import pytest
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, Categorical, DataFrame, isnull, notnull,
bdate_range, date_range, _np_v... | mit |
manashmndl/scikit-learn | benchmarks/bench_plot_neighbors.py | 287 | 6433 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import pylab as pl
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
return np.... | bsd-3-clause |
heidtn/evolutionary_robotics | ANN/test.py | 1 | 1281 | import sys
sys.path.append("../hill_climber")
import hill_climber as hc
import math
import matplotlib.pyplot as plt
import random
import ANN
num_neurons = 10
neuronPositions = hc.MatrixCreate(2,num_neurons)
angle = 0.0
angleUpdate = 2 * math.pi / num_neurons
for i in range(0, num_neurons):
x = math.sin(angle)... | gpl-3.0 |
ilayn/scipy | scipy/stats/_continuous_distns.py | 7 | 296915 | # -*- coding: utf-8 -*-
#
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
import warnings
from collections.abc import Iterable
import ctypes
import numpy as np
from scipy._lib.doccer import (extend_notes_in_docstring,
replace_notes_i... | bsd-3-clause |
ssaeger/scikit-learn | sklearn/feature_extraction/hashing.py | 41 | 6175 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if... | bsd-3-clause |
gfyoung/pandas | pandas/tests/arrays/boolean/test_comparison.py | 9 | 3103 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.arrays import BooleanArray
from pandas.tests.extension.base import BaseOpsUtil
@pytest.fixture
def data():
return pd.array(
[True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
d... | bsd-3-clause |
geomf/omf-fork | omf/common/plot.py | 1 | 1137 | #
# Open Modeling Framework (OMF) Software for simulating power systems behavior
# Copyright (c) 2015, Intel Corporation.
#
# This program is free software; you can redistribute it and/or modify it
# under the terms and conditions of the GNU General Public License,
# version 2, as published by the Free Software Foundat... | gpl-2.0 |
Radymus/QMetric | QMetric.py | 1 | 40492 | #!/usr/bin/env python -W ignore::DeprecationWarning
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 3 21:24:06 2014
@author: Radim Spigel
"""
from __future__ import division
import shutil
import os
import logging
import re
from pandas import DataFrame
from gittle import Gittle, InvalidRemoteUrl
from argparse import A... | gpl-3.0 |
Averroes/statsmodels | statsmodels/sandbox/rls.py | 33 | 5179 | """Restricted least squares
from pandas
License: Simplified BSD
"""
from __future__ import print_function
import numpy as np
from statsmodels.regression.linear_model import WLS, GLS, RegressionResults
class RLS(GLS):
"""
Restricted general least squares model that handles linear constraints
Parameters
... | bsd-3-clause |
matthewdippel/pandas-utils | plots/plotutils.py | 1 | 2507 | import matplotlib.pyplot as plt
import seaborn as sns
__author__ = 'mdippel'
def prettify_axis(ax):
ax.patch.set_facecolor('grey')
ax.patch.set_alpha(0.5)
ax.grid(linestyle='-', linewidth='0.5', color='white')
def pretty_scatter(df, x, y):
plt.clf()
ax = df.plot(kind='scatter', x=x, y=y)
... | mit |
chrisburr/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 62 | 6762 | import numpy as np
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.ut... | bsd-3-clause |
Bismarrck/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | 39 | 32726 | # 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 |
robin-lai/scikit-learn | examples/svm/plot_svm_scale_c.py | 223 | 5375 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classificati... | bsd-3-clause |
mcstrother/dicom-sr-qi | inquiries/cpt_box_plots.py | 2 | 3170 | from srqi.core import inquiry, Parse_Syngo, my_utils
import matplotlib.pyplot as plt
import heapq
import math
class Cpt_Box_Plots(inquiry.Inquiry):
NUM_PROCEDURE_TYPES = inquiry.Inquiry_Parameter(5, "Number of Procedure Types",
"The Maximum number of proce... | bsd-2-clause |
jakobworldpeace/scikit-learn | examples/ensemble/plot_gradient_boosting_oob.py | 82 | 4768 | """
======================================
Gradient Boosting Out-of-Bag estimates
======================================
Out-of-bag (OOB) estimates can be a useful heuristic to estimate
the "optimal" number of boosting iterations.
OOB estimates are almost identical to cross-validation estimates but
they can be compute... | bsd-3-clause |
jayflo/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py | 254 | 2005 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
gregstarr/anomaly-detection | test.py | 1 | 1597 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 30 16:42:25 2017
@author: greg
"""
import numpy as np
from ranksvm_k import ranksvm_k
import matplotlib.pyplot as plt
from scipy.sparse import csr_matrix
from gregAD import knn_score
from scipy.io import loadmat
dic = np.load('arrays.npz')
l = len... | gpl-3.0 |
MartinSavc/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/tests/test_errors.py | 6 | 1133 | # -*- coding: utf-8 -*-
import pytest
from warnings import catch_warnings
import pandas # noqa
import pandas as pd
@pytest.mark.parametrize(
"exc", ['UnsupportedFunctionCall', 'UnsortedIndexError',
'OutOfBoundsDatetime',
'ParserError', 'PerformanceWarning', 'DtypeWarning',
'E... | mit |
cauchycui/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.spars... | bsd-3-clause |
jeromecn/caravel_viz_full | caravel/models.py | 1 | 88506 | """A collection of ORM sqlalchemy models for Caravel"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from collections import OrderedDict
import functools
import json
import logging
import pickle
import re
import tex... | apache-2.0 |
BeiLuoShiMen/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backend_bases.py | 69 | 69740 | """
Abstract base classes define the primitives that renderers and
graphics contexts must implement to serve as a matplotlib backend
:class:`RendererBase`
An abstract base class to handle drawing/rendering operations.
:class:`FigureCanvasBase`
The abstraction layer that separates the
:class:`matplotlib.fi... | agpl-3.0 |
rexshihaoren/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
kernc/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 73 | 1854 | """
===================================================================
Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees <tree>`
is used to predict simultaneously the ... | bsd-3-clause |
sauliusl/seaborn | seaborn/palettes.py | 3 | 33251 | from __future__ import division
import colorsys
from itertools import cycle
import numpy as np
import matplotlib as mpl
from .external import husl
from .external.six import string_types
from .external.six.moves import range
from .utils import desaturate, set_hls_values, get_color_cycle
from .colors import xkcd_rgb, ... | bsd-3-clause |
gviejo/ThalamusPhysio | python/pyfigures/main_fig3.py | 1 | 9231 |
import numpy as np
import pandas as pd
# from matplotlib.pyplot import plot,show,draw
import scipy.io
import sys
sys.path.append("../")
from functions import *
from pylab import *
from sklearn.decomposition import PCA
import _pickle as cPickle
import matplotlib.cm as cm
import os
####################################... | gpl-3.0 |
rahuldhote/scikit-learn | sklearn/utils/multiclass.py | 83 | 12343 |
# 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 |
INM-6/elephant | elephant/current_source_density_src/icsd.py | 2 | 35175 | # -*- coding: utf-8 -*-
"""
py-iCSD toolbox!
Translation of the core functionality of the CSDplotter MATLAB package
to python.
The methods were originally developed by Klas H. Pettersen, as described in:
Klas H. Pettersen, Anna Devor, Istvan Ulbert, Anders M. Dale, Gaute T. Einevoll,
Current-source density estimation ... | bsd-3-clause |
HyperloopTeam/FullOpenMDAO | lib/python2.7/site-packages/matplotlib/tests/test_basic.py | 10 | 1264 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from nose.tools import assert_equal
from matplotlib.testing.decorators import knownfailureif
from pylab import *
def test_simple():
assert_equal(1 + 1, 2)
@knownfailureif(True)
def test_sim... | gpl-2.0 |
grahesh/Stock-Market-Event-Analysis | Tools/Visualizer/Visualizer.py | 3 | 62630 | '''
(c) 2011, 2012 Georgia Tech Research Corporation
This source code is released under the New BSD license. Please see
http://wiki.quantsoftware.org/index.php?title=QSTK_License
for license details.
Created on April, 20, 2012
@author: Sourabh Bajaj
@contact: sourabhbajaj90@gmail.com
@summary: Visualizer Main Code
... | bsd-3-clause |
Newman101/scipy | scipy/integrate/odepack.py | 62 | 9420 | # Author: Travis Oliphant
from __future__ import division, print_function, absolute_import
__all__ = ['odeint']
from . import _odepack
from copy import copy
import warnings
class ODEintWarning(Warning):
pass
_msgs = {2: "Integration successful.",
1: "Nothing was done; the integration time was 0.",
... | bsd-3-clause |
tobanw/py-econ-examples | timeseries.py | 1 | 2106 | #Time series example: generate series, visualize, regression
import numpy as np
from statsmodels.tsa.stattools import acf,pacf
from statsmodels.tsa import ar_model
import matplotlib.pyplot as plt
import pandas as pd
def ar1(phi,n):
"""
Generates an instance of an AR(1) process with Gaussian white noise
phi... | mit |
sillvan/hyperspy | doc/sphinxext/gen_rst.py | 2 | 4933 | # This is a modified version of matplotlib's file of the same name
# The matplotlib license of choice applies to this file
"""
generate the rst files for the examples by iterating over the examples
"""
import os
import glob
import os
import re
import sys
fileList = []
def out_of_date(original, derived):
"""
... | gpl-3.0 |
AnoshZahir/bad_boids | boids/boids.py | 1 | 3762 | from matplotlib import pyplot as plt
from matplotlib import animation
import numpy as np
class Boids(object):
def __init__(self, no_of_boids = 50, position_limits = [-450, 300, 50, 600],
velocity_limits = [0, -20, 10, 20], move_to_middle_strength = 0.01,
alert_distance = 100, formation_flying_dist... | mit |
sssllliang/edx-analytics-pipeline | edx/analytics/tasks/tests/test_course_subjects.py | 1 | 10412 | """
Test the course subjects task which processes from the course catalog.
Testing strategy:
Empty catalog (expect empty output)
Catalog with one course listed which has no subjects listed (expect output with null values?)
Catalog with one course listed which has one subject
Catalog with one course lis... | agpl-3.0 |
roystgnr/queso | examples/gp/scalar/cobra-manual.py | 5 | 1375 | import sys
import re
from StringIO import StringIO
import numpy as np
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
chain = str(sys.argv[2])
with open(str(sys.argv[1])+'/ip_raw_chain_sub'+chain+'.m', 'r') as fin:
header = fin.readline()
# Sort out the dimension of the 2D array of s... | lgpl-2.1 |
kdebrab/pandas | pandas/tests/frame/test_sorting.py | 4 | 22928 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import random
import numpy as np
import pandas as pd
from pandas.compat import lrange
from pandas.api.types import CategoricalDtype
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range, NaT, IntervalIn... | bsd-3-clause |
MihawkHu/Gene_Chip | dnn/dnn.py | 1 | 2261 | from load import *
from sklearn import decomposition
import sys
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.utils import np_utils
# pca feature dimension
ndim = 100
if len(sys.argv) != 1:
ndim = int... | mit |
jmcorgan/gnuradio | gr-analog/examples/fmtest.py | 18 | 7986 | #!/usr/bin/env python
#
# Copyright 2009,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your ... | gpl-3.0 |
bthirion/scikit-learn | examples/neural_networks/plot_mlp_training_curves.py | 58 | 3692 | """
========================================================
Compare Stochastic learning strategies for MLPClassifier
========================================================
This example visualizes some training loss curves for different stochastic
learning strategies, including SGD and Adam. Because of time-constrai... | bsd-3-clause |
jrkerns/pylinac | pylinac/core/geometry.py | 1 | 13651 | """Module for classes that represent common geometric objects or patterns."""
from itertools import zip_longest
import math
from typing import Union, Optional, List, Iterable, Tuple
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle as mpl_Circle
from matplotlib.patches import Rec... | mit |
Titan-C/scikit-learn | sklearn/neighbors/regression.py | 7 | 10971 | """Nearest Neighbor Regression"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck
# Multi-output support by Arnaud Joly <a.joly@ulg.ac... | bsd-3-clause |
justacec/bokeh | bokeh/charts/builders/donut_builder.py | 9 | 9623 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Donut builder which lets you build your Donut plots just passing
the arguments to the Chart class and calling the proper functions.
"""
#--------------------------------------------------------------... | bsd-3-clause |
bsipocz/scikit-image | doc/examples/plot_rag.py | 25 | 2139 | """
=======================
Region Adjacency Graphs
=======================
This example demonstrates the use of the `merge_nodes` function of a Region
Adjacency Graph (RAG). The `RAG` class represents a undirected weighted graph
which inherits from `networkx.graph` class. When a new node is formed by
merging two node... | bsd-3-clause |
timnon/pyschedule | examples/shift-bounds.py | 1 | 1450 | # test artefact for the case that pyschedule is
# read from folder
import sys
sys.path += ['../src','src']
import getopt
opts, _ = getopt.getopt(sys.argv[1:], 't:', ['test'])
from pyschedule import Scenario, solvers, plotters, Task
S = Scenario('shift_bounds',horizon=8)
# define two employees
empl0 = S.Resource('empl... | apache-2.0 |
ysasaki6023/NeuralNetworkStudy | bayes_opt/helpers.py | 1 | 5447 | from __future__ import print_function
from __future__ import division
import numpy as np
from datetime import datetime
from scipy.stats import norm
class UtilityFunction(object):
"""
An object to compute the acquisition functions.
"""
def __init__(self, kind, kappa, xi):
"""
If UCB is... | mit |
Phlya/adjustText | docs/source/conf.py | 1 | 5095 | import sys
import os
import matplotlib
sys.path.insert(0, os.path.abspath('../..'))
matplotlib.use('Agg')
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sph... | mit |
trankmichael/scikit-learn | examples/model_selection/plot_learning_curve.py | 250 | 4171 | """
========================
Plotting Learning Curves
========================
On the left side the learning curve of a naive Bayes classifier is shown for
the digits dataset. Note that the training score and the cross-validation score
are both not very good at the end. However, the shape of the curve can be found
in ... | bsd-3-clause |
scw/geopandas | geopandas/geodataframe.py | 7 | 15536 | try:
from collections import OrderedDict
except ImportError:
# Python 2.6
from ordereddict import OrderedDict
import json
import os
import sys
import numpy as np
from pandas import DataFrame, Series
from shapely.geometry import mapping, shape
from shapely.geometry.base import BaseGeometry
from six import s... | bsd-3-clause |
plissonf/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 128 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivari... | bsd-3-clause |
shusenl/scikit-learn | examples/ensemble/plot_forest_importances.py | 241 | 1761 | """
=========================================
Feature importances with forests of trees
=========================================
This examples shows the use of forests of trees to evaluate the importance of
features on an artificial classification task. The red bars are the feature
importances of the forest, along wi... | bsd-3-clause |
pypot/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 |
maxlz/ML | tf_simple_LR.py | 1 | 1144 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 1 19:50:54 2016
@author: max
"""
import tensorflow as tf
import numpy as np
import matplotlib.pylab as m
x_data = np.linspace(0.0,1.0,num = 500,dtype='float32')
x_data = np.reshape(x_data,(500,))
y_data = np.linspace(0.0,1.0,num = 500,dtype='float32')
y_data = y_data ... | apache-2.0 |
Shaswat27/scipy | scipy/spatial/_plotutils.py | 11 | 4843 | from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
import matplotlib.pyplot as plt
if ax... | bsd-3-clause |
AlexanderFabisch/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 |
dimroc/tensorflow-mnist-tutorial | lib/python3.6/site-packages/matplotlib/backends/backend_qt5agg.py | 10 | 9036 | """
Render to qt from agg
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import ctypes
import sys
import traceback
from matplotlib.figure import Figure
from .backend_agg import FigureCanvasAgg
from .backend_qt5 import QtCore
from .backend_... | apache-2.0 |
anntzer/scikit-learn | sklearn/linear_model/_perceptron.py | 7 | 6205 | # Author: Mathieu Blondel
# License: BSD 3 clause
from ..utils.validation import _deprecate_positional_args
from ._stochastic_gradient import BaseSGDClassifier
class Perceptron(BaseSGDClassifier):
"""Perceptron
Read more in the :ref:`User Guide <perceptron>`.
Parameters
----------
penalty : {'... | bsd-3-clause |
karlnapf/kernel_exp_family | kernel_exp_family/examples/demo_xvalidation_grid_search_manual.py | 1 | 2087 | from kernel_exp_family.estimators.lite.gaussian import KernelExpLiteGaussian
from kernel_exp_family.examples.tools import visualise_fit_2d
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
"""
This simple demo demonstrates how to select the kernel parameter for the lite
estimat... | bsd-3-clause |
bhillmann/gingivere | tests/ensemble_gradient.py | 2 | 2184 | import random
import pandas as pd
from sklearn.cross_validation import StratifiedKFold
import numpy as np
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
from sklearn.neighbors import KNeighborsClassifier
from tests import shelve_api
def yield_patient_names(name, d):
... | mit |
futurulus/scipy | scipy/stats/tests/test_morestats.py | 17 | 50896 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (TestCase, run_module_suite, assert_array_equal,
... | bsd-3-clause |
CartoDB/crankshaft | release/python/0.5.2/crankshaft/setup.py | 1 | 1300 |
"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.5.2',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Se... | bsd-3-clause |
derdav3/tf-sparql | meta-learner.py | 1 | 3028 | import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
DIMS = 10 # Dimensions of the parabola
LAYERS = 2
STATE_SIZE = 20
TRAINING_STEPS = 20 # This is 100 in the paper
scale = tf.random_uniform([DIMS], 0.5, 1.5)
# This represents the network/function we are trying to optimize,... | mit |
IssamLaradji/scikit-learn | examples/feature_stacker.py | 246 | 1906 | """
=================================================
Concatenating multiple feature extraction methods
=================================================
In many real-world examples, there are many ways to extract features from a
dataset. Often it is beneficial to combine several methods to obtain good
performance. Th... | bsd-3-clause |
henridwyer/scikit-learn | examples/cluster/plot_digits_linkage.py | 369 | 2959 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | bsd-3-clause |
wdbm/shijian | setup.py | 1 | 1580 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import setuptools
def main():
setuptools.setup(
name = 'shijian',
version = '2020.01.29.1857',
description = 'change, time, file, list, statistics, language and other utilities',
long_description = long_d... | gpl-3.0 |
masml/masmlblog | Naive Bayes/Naive_bayes_MASML.py | 1 | 4678 |
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import scipy as sp
df1=pd.read_csv("C:\\Users\\hp\\Desktop\\MAS ML\\datasets\\t20.csv")#rem to use double backslash
#booleans[]
dfx=df1[df1.Innings1Team==df1.Winner]
dfy= df1[df1.Innings1Team!=df1.Winner]
dfx['Winner']=0
dfy['Winner']=1
df1=pd.concat(... | mit |
blisseth/ThinkStats2 | code/thinkplot.py | 75 | 18140 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import math
import matplotlib
import matplotlib.pyplot as pyplot
import numpy as... | gpl-3.0 |
ashhher3/scikit-learn | examples/manifold/plot_manifold_sphere.py | 258 | 5101 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
XiaoLiuAI/RUPEE | src/python/model/statsmodel_wrapper.py | 1 | 1509 | import copy
import numpy as np
import statsmodels.api as sm
import sklearn
class MNLogit(sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator):
def __init__(self):
self.algoModule = sm.MNLogit
def choose_opt_params(self, X, y, params, cross_val_score, average_measure, metric_func):
score... | gpl-2.0 |
hdmetor/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | 67 | 14842 | from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
fr... | bsd-3-clause |
DomiDre/SASModels | tests/Simulate_distribution.py | 1 | 4707 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
import matplotlib.colors as mcolors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import integrate
from scipy import constants
# Stochastic solver for a colloidal magnetic rod
pi = np.pi
sqrt = np.sqrt
# Set up mea... | gpl-3.0 |
anne-urai/RT_RDK | old_code/HDDM_RunStopos.py | 2 | 6676 | #!/usr/bin/env python
# encoding: utf-8
"""
Anne Urai, 2016
takes input arguments from stopos
Important: on Cartesius, call module load python2.7.9 before running
(the only environment where HDDM is installed)
"""
# ============================================ #
# HDDM cheat sheet
# ==================================... | mit |
nguyentu1602/statsmodels | statsmodels/datasets/spector/data.py | 25 | 2000 | """Spector and Mazzeo (1980) - Program Effectiveness Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Used with express permission of the original author, who
retains all rights. """
TITLE = __doc__
SOURCE = """
http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
The raw data was d... | bsd-3-clause |
kinshuk4/MoocX | k2e/dev/libs/python/numpy-and-pandas/average_gold_silver_and_bronze.py | 1 | 1802 | import numpy
from pandas import DataFrame, Series
def avg_medal_count():
'''
Using the dataframe's apply method, create a new Series called
avg_medal_count that indicates the average number of gold, silver,
and bronze medals earned amongst countries who earned at
least one medal of any kind at the... | mit |
walterreade/scikit-learn | sklearn/gaussian_process/gpr.py | 43 | 18642 | """Gaussian processes regression. """
# 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_triangular
from scipy.optimize import fmin_l_bfgs_b
from sklearn.base im... | bsd-3-clause |
yunfeilu/scikit-learn | sklearn/kernel_ridge.py | 155 | 6545 | """Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
# Authors: Mathieu Blondel <mathieu@mblondel.org>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from .base import BaseEstimator, RegressorMixin
from .metrics.pairwise import pairwise... | bsd-3-clause |
deepakantony/sms-tools | lectures/09-Sound-description/plots-code/soundAnalysis.py | 25 | 5271 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import os, sys
import json
from scipy.cluster.vq import vq, kmeans, whiten
def fetchDataDetails(inputDir, descExt = '.json'):
dataDetails = {}
for path, dname, fnames in os.walk(inputDir):
for fname in fnames:
if des... | agpl-3.0 |
MohammedWasim/scikit-learn | sklearn/cluster/tests/test_bicluster.py | 226 | 9457 | """Testing for Spectral Biclustering methods"""
import numpy as np
from scipy.sparse import csr_matrix, issparse
from sklearn.grid_search import ParameterGrid
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from... | bsd-3-clause |
tseaver/google-cloud-python | bigquery/samples/load_table_dataframe.py | 1 | 3122 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | apache-2.0 |
allenai/allennlp | tests/training/metrics/fbeta_multi_label_measure_test.py | 1 | 18049 | from typing import Dict, List, Tuple, Union, Any
import pytest
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.common.testing import (
AllenNlpTestCase,
global_distributed_metric,
multi_device,
run_distributed_test,
)
from sklearn.metrics import precision_recall_fscore_... | apache-2.0 |
wwf5067/statsmodels | statsmodels/nonparametric/_kernel_base.py | 29 | 18238 | """
Module containing the base object for multivariate kernel density and
regression, plus some utilities.
"""
from statsmodels.compat.python import range, string_types
import copy
import numpy as np
from scipy import optimize
from scipy.stats.mstats import mquantiles
try:
import joblib
has_joblib = True
exce... | bsd-3-clause |
jpautom/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 58 | 17158 | """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 |
mikecroucher/GPy | GPy/testing/plotting_tests.py | 3 | 21811 | #===============================================================================
# Copyright (c) 2015, Max Zwiessele
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source... | bsd-3-clause |
vibhorag/scikit-learn | sklearn/linear_model/setup.py | 146 | 1713 | 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('linear_model', parent_package, top_path)
cblas_libs, blas_info = get_blas_info... | bsd-3-clause |
simon-pepin/scikit-learn | sklearn/qda.py | 140 | 7682 | """
Quadratic Discriminant Analysis
"""
# Author: Matthieu Perrot <matthieu.perrot@gmail.com>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import BaseEstimator, ClassifierMixin
from .externals.six.moves import xrange
from .utils import check_array, check_X_y
from .utils.validation import ... | bsd-3-clause |
Jakub21/EU4-Province-Editor | src/meta.py | 1 | 7228 | from platform import system as psys
from os import getcwd
################################
# SETTINGS AND CONSTANT VARIABLES
def get_const():
global const
const = {
# General
'auto_sort' : True,
'auto_apply' : True,
'autoprint_atselect' : False,
... | mit |
pandeyadarsh/sympy | sympy/external/importtools.py | 85 | 7294 | """Tools to assist importing optional external modules."""
from __future__ import print_function, division
import sys
# Override these in the module to change the default warning behavior.
# For example, you might set both to False before running the tests so that
# warnings are not printed to the console, or set bo... | bsd-3-clause |
enlighter/learnML | learn/sklearn/label_data_preprocessing_labelencoder.py | 1 | 1248 | # In this exercise we'll load the titanic data (from Project 0)
# And then perform one-hot encoding on the feature names
import numpy as np
import pandas as pd
# Load the dataset
X = pd.read_csv('titanic_data.csv')
# Limit to categorical data
X = X.select_dtypes(include=[object])
from sklearn.preprocessing import La... | mit |
amolkahat/pandas | pandas/tests/extension/base/missing.py | 5 | 4326 | import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
from .base import BaseExtensionTests
class BaseMissingTests(BaseExtensionTests):
def test_isna(self, data_missing):
expected = np.array([True, False])
result = pd.isna(data_missing)
tm.assert_numpy_arr... | bsd-3-clause |
KeithYue/StockTrading | st_select.py | 1 | 6527 | # coding=utf-8
import pandas as pd
import pandas.io.data as web
import os
import numpy as np
import logging
# config the logging system
logging.basicConfig(level=logging.DEBUG)
from scipy.signal import argrelextrema, argrelmin, argrelextrema
from talib.abstract import *
# load the utility function
from utility impor... | apache-2.0 |
zuku1985/scikit-learn | sklearn/linear_model/coordinate_descent.py | 4 | 81531 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import n... | bsd-3-clause |
mikebenfield/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 55 | 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 |
zhenv5/scikit-learn | sklearn/cluster/tests/test_k_means.py | 63 | 26190 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
bradmontgomery/ml | book/ch03/noise_analysis.py | 24 | 2412 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
import sklearn.datasets
groups = [
'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.ha... | mit |
funbaker/astropy | astropy/visualization/tests/test_histogram.py | 2 | 1772 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from numpy.testing import assert_allclose
try:
import matplotlib.pyplot as plt
HAS_PLT = True
except ImportError:
HAS_PLT = False
try:
import scipy # noqa
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
import pytest
im... | bsd-3-clause |
kalyons11/kevin | kevin/playground/gpa.py | 1 | 2017 | # Quick script to calculate GPA given a class list file.
# Class list file should be a csv with COURSE_ID,NUM_UNITS,GRADE
# GRADE should be LETTER with potential modifiers after that
# registrar.mit.edu/classes-grades-evaluations/grades/calculating-gpa
import argparse
import pandas as pd
def get_parser():
# Get ... | mit |
evidation-health/bokeh | examples/interactions/interactive_bubble/gapminder.py | 8 | 4161 | import pandas as pd
from jinja2 import Template
from bokeh.browserlib import view
from bokeh.models import (
ColumnDataSource, Plot, Circle, Range1d,
LinearAxis, HoverTool, Text,
SingleIntervalTicker, CustomJS, Slider
)
from bokeh.palettes import Spectral6
from bokeh.plotting import vplot
from bokeh.resou... | bsd-3-clause |
sinhrks/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
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