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 |
|---|---|---|---|---|---|
georgid/SourceFilterContoursMelody | smstools/software/models_interface/spsModel_function.py | 2 | 3560 | # function to call the extractHarmSpec analysis/synthesis functions in software/models/spsModel.py
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import spsModel as SPS
import... | gpl-3.0 |
russel1237/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 227 | 2520 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the Boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prett... | bsd-3-clause |
rajul/mne-python | mne/stats/tests/test_cluster_level.py | 8 | 20475 | import os
import os.path as op
import numpy as np
from numpy.testing import (assert_equal, assert_array_equal,
assert_array_almost_equal)
from nose.tools import assert_true, assert_raises
from scipy import sparse, linalg, stats
from mne.fixes import partial
import warnings
from mne.parallel i... | bsd-3-clause |
a301-teaching/a301_code | notebooks/python/resample.py | 1 | 7031 |
# coding: utf-8
# ## working with projections
#
# We have been using [fast_hist](https://github.com/a301-teaching/a301_code/blob/f8c2e8cc8dce36c9852ca99ccfe19250d1405b50/a301lib/geolocate.py#L124)
# and [fast_avg](https://github.com/a301-teaching/a301_code/blob/f8c2e8cc8dce36c9852ca99ccfe19250d1405b50/a301lib/geoloc... | mit |
nolanliou/tensorflow | tensorflow/python/estimator/canned/linear_testing_utils.py | 4 | 80247 | # 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 |
cbertinato/pandas | pandas/tests/arithmetic/conftest.py | 1 | 5813 | import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
# ------------------------------------------------------------------
# Helper Functions
def id_func(x):
if isinstance(x, tuple):
assert len(x) == 2
return x[0].__name__ + '-' + str(x[1])
else:
retur... | bsd-3-clause |
mgraffg/RGP | EvoDAG/tests/test_node.py | 2 | 21536 | # Copyright 2015 Mario Graff Guerrero
# 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 wri... | apache-2.0 |
gfyoung/pandas | pandas/tests/arithmetic/test_interval.py | 2 | 10345 | import operator
import numpy as np
import pytest
from pandas.core.dtypes.common import is_list_like
import pandas as pd
from pandas import (
Categorical,
Index,
Interval,
IntervalIndex,
Period,
Series,
Timedelta,
Timestamp,
date_range,
period_range,
timedelta_range,
)
impo... | bsd-3-clause |
yanlend/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 216 | 13290 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises_regexp
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
f... | bsd-3-clause |
heli522/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 228 | 11221 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
rexshihaoren/scikit-learn | examples/text/mlcomp_sparse_document_classification.py | 292 | 4498 | """
========================================================
Classification of text documents: using a MLComp dataset
========================================================
This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a s... | bsd-3-clause |
tawsifkhan/scikit-learn | sklearn/metrics/tests/test_classification.py | 83 | 49782 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import la... | bsd-3-clause |
pearsonlab/nipype | tools/make_examples.py | 10 | 2906 | #!/usr/bin/env python
"""Run the py->rst conversion and run all examples.
This also creates the index.rst file appropriately, makes figures, etc.
"""
from past.builtins import execfile
# -----------------------------------------------------------------------------
# Library imports
# ---------------------------------... | bsd-3-clause |
joernhees/scikit-learn | sklearn/metrics/classification.py | 4 | 72788 | """Metrics to assess performance on classification task given class prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramf... | bsd-3-clause |
rubikloud/scikit-learn | examples/applications/plot_out_of_core_classification.py | 255 | 13919 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
tmhm/scikit-learn | examples/ensemble/plot_partial_dependence.py | 249 | 4456 | """
========================
Partial Dependence Plots
========================
Partial dependence plots show the dependence between the target function [1]_
and a set of 'target' features, marginalizing over the
values of all other features (the complement features). Due to the limits
of human perception the size of t... | bsd-3-clause |
kadrlica/obztak | obztak/bliss.py | 1 | 23344 | #!/usr/bin/env python
"""
Code related to the Magellanic Satellites Survey (MagLiteS).
"""
import os,sys
import logging
import copy
from collections import OrderedDict as odict
import numpy as np
import fitsio
from obztak.field import FieldArray, SISPI_DICT, SEP
from obztak.survey import Survey
from obztak.scheduler ... | mit |
akionakamura/scikit-learn | examples/mixture/plot_gmm_classifier.py | 250 | 3918 | """
==================
GMM classification
==================
Demonstration of Gaussian mixture models for classification.
See :ref:`gmm` for more information on the estimator.
Plots predicted labels on both training and held out test data using a
variety of GMM classifiers on the iris dataset.
Compares GMMs with sp... | bsd-3-clause |
appapantula/scikit-learn | sklearn/ensemble/voting_classifier.py | 178 | 8006 | """
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>
#
# Licence: BSD 3 clause
import numpy as np
from ..base import BaseEstimator
f... | bsd-3-clause |
robin-lai/scikit-learn | examples/cluster/plot_lena_segmentation.py | 271 | 2444 | """
=========================================
Segmenting the picture of Lena in regions
=========================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous regions.
This procedure (spe... | bsd-3-clause |
chriscrosscutler/scikit-image | doc/examples/plot_join_segmentations.py | 14 | 1967 | """
==========================================
Find the intersection of two segmentations
==========================================
When segmenting an image, you may want to combine multiple alternative
segmentations. The `skimage.segmentation.join_segmentations` function
computes the join of two segmentations, in wh... | bsd-3-clause |
subutai/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_svg.py | 69 | 23593 | from __future__ import division
import os, codecs, base64, tempfile, urllib, gzip, cStringIO
try:
from hashlib import md5
except ImportError:
from md5 import md5 #Deprecated in 2.5
from matplotlib import verbose, __version__, rcParams
from matplotlib.backend_bases import RendererBase, GraphicsContextBase,\
... | agpl-3.0 |
person142/scipy | scipy/spatial/_plotutils.py | 8 | 6957 | 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 # type: ignore[import]
if ax is None:
fig = plt.figure()
... | bsd-3-clause |
louispotok/pandas | pandas/tests/io/msgpack/test_newspec.py | 22 | 2650 | # coding: utf-8
from pandas.io.msgpack import packb, unpackb, ExtType
def test_str8():
header = b'\xd9'
data = b'x' * 32
b = packb(data.decode(), use_bin_type=True)
assert len(b) == len(data) + 2
assert b[0:2] == header + b'\x20'
assert b[2:] == data
assert unpackb(b) == data
data = ... | bsd-3-clause |
rspavel/spack | var/spack/repos/builtin/packages/py-quast/package.py | 5 | 1332 | # Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PyQuast(PythonPackage):
"""Quality Assessment Tool for Genome Assemblies"""
homepage ... | lgpl-2.1 |
dkushner/zipline | tests/test_transforms_talib.py | 17 | 6080 | #
# Copyright 2013 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 |
Prasad9/incubator-mxnet | example/rcnn/rcnn/core/tester.py | 25 | 10193 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | apache-2.0 |
gclenaghan/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 102 | 2319 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | bsd-3-clause |
steinnp/Big-Data-Final | Classification/neural_network_classifier.py | 1 | 4297 | import sys, os
foo_dir = os.path.dirname(os.path.join(os.getcwd(), __file__))
sys.path.append(os.path.normpath(os.path.join(foo_dir, '../DataGathering', '..')))
sys.path.append(os.path.normpath(os.path.join(foo_dir, '../Classification', '..')))
sys.path.append(os.path.normpath(os.path.join(foo_dir, '../TextCleaning', '... | mit |
lancezlin/ml_template_py | lib/python2.7/site-packages/sklearn/ensemble/partial_dependence.py | 8 | 15443 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | mit |
ctogle/dilapidator | test/geometry/ray3_tests.py | 1 | 2623 | from dilap.geometry.vec3 import vec3
from dilap.geometry.ray3 import ray3
import matplotlib.pyplot as plt
import unittest,numpy,math
#python3 -m unittest discover -v ./ "*tests.py"
class test_ray3(unittest.TestCase):
def test_cp(self):
r1 = ray3(vec3(1,1,2),vec3(0,1,0))
r2 = ray3(vec3(1,1,2),ve... | mit |
machinelearningnanodegree/stanford-cs231 | solutions/levin/assignment2/convolutionalnetwork/convolutionalnetwork.py | 1 | 21893 | import sys
import os
from astropy.units import ys
from gevent.hub import sleep
sys.path.insert(0, os.path.abspath('..'))
import random
import numpy as np
import matplotlib.pyplot as plt
from assignment2.cs231n.layers import affine_forward
from assignment2.cs231n.layers import affine_backward
from assignment2.cs231n.l... | mit |
jpautom/scikit-learn | sklearn/cross_validation.py | 5 | 67227 | """
The :mod:`sklearn.cross_validation` module includes 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 3 clause
from... | bsd-3-clause |
IBT-FMI/SAMRI | samri/plotting/summary.py | 1 | 16538 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from itertools import product
from copy import deepcopy
import nibabel as nib
import numpy as np
import multiprocessing as mp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.formula.api as smf
from joblib import Parallel, delaye... | gpl-3.0 |
larsmans/scikit-learn | sklearn/covariance/graph_lasso_.py | 17 | 23130 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ im... | bsd-3-clause |
henrykironde/scikit-learn | examples/tree/plot_tree_regression.py | 206 | 1476 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
georgetown-analytics/travel-trends | prgm/NYTWrangler.py | 1 | 2670 | #!/usr/bin/env python
from os import getcwd
from os import listdir
from json import load
import pandas as pd
from pandas.io.json import json_normalize
import sqlite3
class NYT_Wrangle(object):
def __init__(self):
# self.json_indexer = ['pub_date', 'lead_paragraph', ['headline', 'main'], 'abstract', 'word_count'... | mit |
ocefpaf/system-test | Theme_2_Extreme_Events/Scenario_2B/HF_Radar_Currents/HF_Radar_Currents_west_coast.py | 2 | 23185 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <markdowncell>
# ># IOOS System Test: [HF Radar](https://github.com/ioos/system-test/wiki/Development-of-Test-Themes) Coastal Inundation
# <markdowncell>
# ### Can we obtain HF radar current data at stations located within a bounding box?
# This notebook is based... | unlicense |
hlin117/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 24 | 14430 | import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_dirichlet_expect... | bsd-3-clause |
grisaitis/sklearn-pmml | sklearn_pmml/convert/test/test_derived_fields.py | 3 | 2027 | import pytest
from sklearn.tree import DecisionTreeClassifier
from sklearn_pmml import EstimatorConverter, TransformationContext, pmml
from sklearn_pmml.convert.features import *
test_cases = [
(
[
RealNumericFeature(name='f1'),
],
[
DerivedFeature(
f... | mit |
toobaz/pandas | pandas/core/dtypes/base.py | 2 | 8839 | """Extend pandas with custom array types"""
from typing import List, Optional, Tuple, Type
import numpy as np
from pandas.errors import AbstractMethodError
from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
class ExtensionDtype:
"""
A custom data type, to be paired with an Extens... | bsd-3-clause |
massmutual/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 142 | 4467 | """
===============================================================
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 |
davidgbe/scikit-learn | sklearn/datasets/base.py | 196 | 18554 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.pa... | bsd-3-clause |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/tests/test_backend_ps.py | 5 | 4960 | # -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import io
import re
import numpy as np
import six
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import patheffects
from matplotlib.testing.decorators import cleanup... | bsd-2-clause |
nwillemse/nctrader | nctrader/price_handler/iterator/pandas/bar.py | 1 | 3338 | from ..base import AbstractBarEventIterator
class PandasDataFrameBarEventIterator(AbstractBarEventIterator):
"""
PandasDataFrameBarEventIterator is designed to read a Pandas DataFrame like
Open High Low Close Volume Adj Close
Date
2010-01-04 626.951088... | mit |
lazywei/scikit-learn | sklearn/ensemble/weight_boosting.py | 30 | 40648 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
YihaoLu/statsmodels | statsmodels/datasets/cpunish/data.py | 25 | 2597 | """US Capital Punishment dataset."""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Used with express permission from the original author,
who retains all rights."""
TITLE = __doc__
SOURCE = """
Jeff Gill's `Generalized Linear Models: A Unified Approach`
http://jgill.wustl.edu/research/books.html
"""... | bsd-3-clause |
synthicity/urbansim | urbansim/developer/sqftproforma.py | 4 | 29510 | from __future__ import division
import numpy as np
import pandas as pd
import logging
logger = logging.getLogger(__name__)
class SqFtProFormaConfig(object):
"""
This class encapsulates the configuration options for the square
foot based pro forma.
Parameters
----------
parcel_sizes : list
... | bsd-3-clause |
rubikloud/scikit-learn | sklearn/utils/tests/test_utils.py | 215 | 8100 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from itertools import chain
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest, ... | bsd-3-clause |
CompPhysics/ThesisProjects | doc/MSc/msc_students/former/sean/Thesis/Codes/PythonCode/CCD_alt_v2.py | 1 | 10945 | from sympy import *
#from pylab import *
import numpy as np
import math
import matplotlib.pyplot as plt
#CLASSES
class HEG: #Homogenous Electron Gas
def __init__(self):
pass
#3d box potential
def makeStateSpace(self): #N = num of particles, NB = size of basis (number of energy levels)
states = []
for n2 in... | cc0-1.0 |
cllamb0/dosenet-raspberrypi | rt_waterfall_D3S.py | 3 | 3186 | from auxiliaries import set_verbosity
import time
import numpy as np
import matplotlib.pyplot as plt
class Rt_Waterfall_D3S(object):
"""
Class for running the D3S in real-time waterfall mode
"""
def __init__(self,
manager=None,
verbosity=1,
log... | mit |
hsiaoyi0504/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 142 | 6276 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | bsd-3-clause |
aabadie/scikit-learn | sklearn/neighbors/tests/test_ball_tree.py | 159 | 10196 | import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dis... | bsd-3-clause |
Windy-Ground/scikit-learn | sklearn/utils/arpack.py | 265 | 64837 | """
This contains a copy of the future version of
scipy.sparse.linalg.eigen.arpack.eigsh
It's an upgraded wrapper of the ARPACK library which
allows the use of shift-invert mode for symmetric matrices.
Find a few eigenvectors and eigenvalues of a matrix.
Uses ARPACK: http://www.caam.rice.edu/software/ARPACK/
"""
#... | bsd-3-clause |
otmaneJai/Zipline | zipline/utils/factory.py | 1 | 10430 | #
# Copyright 2013 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 |
hdmetor/scikit-learn | sklearn/feature_selection/rfe.py | 137 | 17066 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import warnings
import numpy as np
from ..utils import check_X_y, safe_sqr
fro... | bsd-3-clause |
xya/sms-tools | lectures/03-Fourier-properties/plots-code/zero-padding.py | 26 | 1083 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming
from scipy.fftpack import fft, fftshift
plt.figure(1, figsize=(9.5, 6))
M = 8
N1 = 8
N2 = 16
N3 = 32
x = np.cos(2*np.pi*2/M*np.arange(M)) * np.hanning(M)
plt.subplot(4,1,1)
plt.title('x, M=8')
plt.plot(np.arange(-M/2.0,M/2), x, 'b', m... | agpl-3.0 |
zzcclp/spark | python/pyspark/pandas/tests/test_series.py | 9 | 118972 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
Ferouk/Twitter-Sentiment-Analysis | training.py | 1 | 4656 | from __future__ import absolute_import, print_function
import pickle
import random
import nltk
from nltk.classify.scikitlearn import SklearnClassifier
from nltk.tokenize import word_tokenize
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
f... | mit |
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated | python-packages/mne-python-0.10/mne/viz/tests/test_3d.py | 13 | 7481 | # 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>
# Mainak Jas <mainak@neuro.hut.fi>
# Mark Wronkiewicz <wronk.mark@gmail.c... | bsd-3-clause |
BrainTech/openbci | obci/analysis/balance/wii_preprocessing.py | 1 | 2905 | # -*- coding: utf-8 -*-
from __future__ import print_function, division
import numpy as np
import matplotlib.pyplot as py
from obci.analysis.obci_signal_processing import read_manager
from obci.analysis.obci_signal_processing.signal import read_data_source
from obci.analysis.obci_signal_processing.tags import smart_tag... | gpl-3.0 |
dingocuster/scikit-learn | sklearn/tests/test_naive_bayes.py | 70 | 17509 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits, load_iris
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
dsquareindia/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([T... | bsd-3-clause |
blackecho/Deep-Belief-Network | yadlt/models/recurrent/lstm.py | 2 | 6589 | """LSTM Tensorflow implementation."""
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from yadlt.core import Model
from yadlt.utils import utilities
class LSTM(Model):
"""Long Short-Term Memory Network tensorflow implementation.
The interfac... | apache-2.0 |
zaxtax/scikit-learn | examples/mixture/plot_gmm.py | 36 | 2875 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
duncanwp/iris | docs/iris/example_code/Meteorology/COP_1d_plot.py | 5 | 4186 | """
Global average annual temperature plot
======================================
Produces a time-series plot of North American temperature forecasts for 2
different emission scenarios. Constraining data to a limited spatial area also
features in this example.
The data used comes from the HadGEM2-AO model simulations... | lgpl-3.0 |
ngoix/OCRF | examples/applications/wikipedia_principal_eigenvector.py | 16 | 7819 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/lib/mpl_examples/pylab_examples/multiple_yaxis_with_spines.py | 6 | 1582 | import matplotlib.pyplot as plt
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.itervalues():
sp.set_visible(False)
fig, host = plt.subplots()
fig.subplots_adjust(right=0.75)
par1 = host.twinx()
par2 = host.twinx()
# Offset the right spi... | mit |
IL2HorusTeam/il2-heightmap-creator | il2fb/maps/heightmaps/rendering.py | 1 | 4124 | # -*- coding: utf-8 -*-
import argparse
import logging
from array import array
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
from pylab import contour, contourf
from il2fb.maps.heightmaps.constants import HEIGHT_PACK_FORMAT
from i... | mit |
OshynSong/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 225 | 6278 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | bsd-3-clause |
MartinSavc/scikit-learn | examples/linear_model/plot_iris_logistic.py | 283 | 1678 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <http://en.wikipedia.org/wiki/Iris_f... | bsd-3-clause |
Cophy08/ggplot | ggplot/stats/stat_vline.py | 12 | 1316 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
from ggplot.utils import pop, make_iterable, make_iterable_ntimes
from ggplot.utils.exceptions import GgplotError
from .stat import stat
class stat_vline(stat):
DEFAULT_PARAMS = {'geom... | bsd-2-clause |
MDAnalysis/pmda | pmda/rms/rmsd.py | 1 | 4773 | # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# PMDA
# Copyright (c) 2019 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
#
# Released under the GNU Public Licence, v2 or any higher ve... | gpl-2.0 |
wronk/mne-python | examples/time_frequency/plot_source_label_time_frequency.py | 4 | 3776 | """
=========================================================
Compute power and phase lock in label of the source space
=========================================================
Compute time-frequency maps of power and phase lock in the source space.
The inverse method is linear based on dSPM inverse operator.
The ex... | bsd-3-clause |
MyAOSP/external_chromium_org | chrome/browser/nacl_host/test/gdb_rsp.py | 99 | 2431 | # Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
# This file is based on gdb_rsp.py file from NaCl repository.
import re
import socket
import time
def RspChecksum(data):
checksum = 0
for char in ... | bsd-3-clause |
davidam/python-examples | scikit/confusion-matrix.py | 1 | 2514 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (C) 2018 David Arroyo Menéndez
# Author: David Arroyo Menéndez <davidam@gnu.org>
# Maintainer: David Arroyo Menéndez <davidam@gnu.org>
# This file is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as p... | gpl-3.0 |
kcavagnolo/astroML | book_figures/chapter2/fig_balltree_example.py | 3 | 3873 | """
Ball Tree Example
-----------------
Figure 2.5.
This example creates a simple Ball tree partition of a two-dimensional
parameter space, and plots a visualization of the result.
"""
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Minin... | bsd-2-clause |
roxyboy/scikit-learn | sklearn/datasets/species_distributions.py | 198 | 7923 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
dajusc/trimesh | tests/notebooks.py | 2 | 5546 | import os
import sys
import json
import inspect
import subprocess
import numpy as np
# current working directory
cwd = os.path.dirname(os.path.abspath(
inspect.getfile(inspect.currentframe())))
def load_notebook(file_obj):
"""
Load an ipynb file into a cleaned and stripped string that can
be ran with... | mit |
vibhorag/scikit-learn | sklearn/utils/tests/test_fixes.py | 281 | 1829 | # Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import numpy as np
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_true
from numpy.testing import (assert_almost_equal,
... | bsd-3-clause |
CCI-Tools/cate-core | cate/webapi/mpl.py | 2 | 12044 | # The MIT License (MIT)
# Copyright (c) 2016, 2017 by the ESA CCI Toolbox development team and contributors
#
# 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 wi... | mit |
MechCoder/scikit-learn | examples/covariance/plot_mahalanobis_distances.py | 348 | 6232 | r"""
================================================================
Robust covariance estimation and Mahalanobis distances relevance
================================================================
An example to show covariance estimation with the Mahalanobis
distances on Gaussian distributed data.
For Gaussian dis... | bsd-3-clause |
vernor1/vehicle_detection | classifier.py | 1 | 4380 | import cv2
import glob
import numpy as np
import os.path
import pickle
from feature_extraction import ExtractFeatures
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
class TClassifier():
""" Vehicle/non-vehicle classifier cla... | apache-2.0 |
mikecroucher/GPy | GPy/examples/dimensionality_reduction.py | 5 | 25399 | # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as _np
default_seed = 123344
# default_seed = _np.random.seed(123344)
def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
"""
model for testin... | bsd-3-clause |
ssaeger/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 83 | 5888 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
ShefaliGups11/Implementation-of-SFB-in-ns-3 | src/flow-monitor/examples/wifi-olsr-flowmon.py | 59 | 7427 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation;
#
# This program is distributed in the hope that it will be useful,
#... | gpl-2.0 |
jason-neal/equanimous-octo-tribble | octotribble/snr_calculations.py | 1 | 8067 | #!/usr/bin/env python
from __future__ import division
import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from astropy.io import fits
from numpy.lib import stride_tricks
# Testing for a snr of my spectra
import SpectralTools as s_tools
def _parser():
""... | mit |
beepee14/scikit-learn | examples/covariance/plot_outlier_detection.py | 235 | 3891 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates two
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assumin... | bsd-3-clause |
moutai/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
architecture-building-systems/CEAforArcGIS | cea/interfaces/cli/list_demand_graphs_fields.py | 2 | 1545 | """
List the fields that can be used for the demand-graphs ``--analysis-fields`` parameter given a scenario
"""
import os
import pandas as pd
import cea.config
import cea.inputlocator
def demand_graph_fields(scenario):
"""Lists the available fields for the demand graphs - these are fields that are present in ... | mit |
MSHallOpenSoft/plotter | sympyPlot_implicit.py | 1 | 15281 | """Implicit plotting module for SymPy
The module implements a data series called ImplicitSeries which is used by
``Plot`` class to plot implicit plots for different backends. The module,
by default, implements plotting using interval arithmetic. It switches to a
fall back algorithm if the expression cannot be plotted ... | gpl-2.0 |
dougalsutherland/py-sdm | sdm/features.py | 1 | 48075 | from __future__ import division, print_function
from collections import Counter, defaultdict
from contextlib import closing
from functools import partial
from glob import glob
import operator as op
import os
import cPickle as pickle
import shutil
import sys
import numpy as np
from .utils import (imap, izip, iterkeys... | bsd-3-clause |
thesuperzapper/tensorflow | tensorflow/python/estimator/inputs/queues/feeding_functions_test.py | 58 | 9375 | # 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 |
gweidner/incubator-systemml | src/main/python/systemml/mlcontext.py | 7 | 25911 | # -------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you unde... | apache-2.0 |
ucbtrans/sumo-project | car_following_model/macrosim.py | 1 | 4653 | '''
Macro-simulation...
'''
import sys
import numpy as np
import matplotlib.pyplot as plt
from Link import Link
import plot_routines as pr
import pickle
def initialize():
'''
Returns array of links, simulation step length in seconds and total simulation time.
'''
dt = 0.05 # seconds
total_t... | bsd-2-clause |
TomAugspurger/pandas | pandas/tests/io/test_html.py | 1 | 39222 | from functools import partial
from importlib import reload
from io import BytesIO, StringIO
import os
import re
import threading
from urllib.error import URLError
import numpy as np
from numpy.random import rand
import pytest
from pandas.compat import is_platform_windows
from pandas.errors import ParserError
import p... | bsd-3-clause |
SciTools/iris | lib/iris/tests/test_plot.py | 3 | 31896 | # Copyright Iris contributors
#
# This file is part of Iris and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
# import iris tests first so that some things can be initialised before
# importing anything else
import iris.tests as tests
... | lgpl-3.0 |
Windy-Ground/scikit-learn | examples/calibration/plot_calibration.py | 225 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
allafort/StatisticalMethods | examples/Cepheids/straightline_utils.py | 14 | 2858 | # numpy: numerical library
import numpy as np
# avoid broken installs by forcing Agg backend...
#import matplotlib
#matplotlib.use('Agg')
# pylab: matplotlib's matlab-like interface
import pylab as plt
# The data we will fit:
# x, y, sigma_y
data1 = np.array([[201,592,61],[244,401,25],[47,583,38],[287,402,15],[203,49... | gpl-2.0 |
montoyjh/pymatgen | pymatgen/io/abinit/tests/test_abiinspect.py | 3 | 4081 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
import os
import tempfile
from pymatgen.util.testing import PymatgenTest
from pymatgen.io.abinit.abiinspect import *
_test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", "..",
... | mit |
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