repo_name stringlengths 6 96 | path stringlengths 4 180 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 906 722k | license stringclasses 15
values |
|---|---|---|---|---|---|
kmike/scikit-learn | sklearn/utils/__init__.py | 3 | 10094 | """
The :mod:`sklearn.utils` module includes various utilites.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array, check_arrays, safe_asarray,
assert_all_fini... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.20/_downloads/76822bb92a8465181ec2a7ee96ca8cf4/plot_decoding_csp_timefreq.py | 1 | 6457 | """
============================================================================
Decoding in time-frequency space data using the Common Spatial Pattern (CSP)
============================================================================
The time-frequency decomposition is estimated by iterating over raw data that
has be... | bsd-3-clause |
bijanfallah/OI_CCLM | src/RMSE_MAPS_INGO.py | 1 | 2007 | # Program to show the maps of RMSE averaged over time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
import os
from netCDF4 import Dataset as NetCDFFile
import numpy as np
from CCLM_OUTS import Plot_CCLM
# option == 1 -> shift 4 with default cclm domain and nboundlines = 3
# option == 2... | mit |
lancezlin/ml_template_py | lib/python2.7/site-packages/sklearn/metrics/tests/test_score_objects.py | 15 | 17443 | import pickle
import tempfile
import shutil
import os
import numbers
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.t... | mit |
ChanChiChoi/scikit-learn | examples/model_selection/plot_roc.py | 146 | 3697 | """
=======================================
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 |
belltailjp/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 |
tosolveit/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
jblackburne/scikit-learn | sklearn/neural_network/rbm.py | 46 | 12291 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
jorik041/scikit-learn | sklearn/linear_model/randomized_l1.py | 95 | 23365 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import spar... | bsd-3-clause |
jingxiang-li/kaggle-yelp | model/level3_model_rf.py | 1 | 5669 | from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import f1_score
import... | mit |
JeanKossaifi/scikit-learn | sklearn/tree/tests/test_tree.py | 48 | 47506 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
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_rand... | bsd-3-clause |
kaczla/PJN | src/Przecinki/scikit.py | 1 | 1048 | #!/usr/bin/python2
# -*- coding: utf-8 -*-
import sys
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.cross_validation import cross_val_predict
from sklearn import linear_model
from sklearn import datasets
X = []
Y = []
for line in sys.stdin:
line = line.rstrip()
X... | gpl-2.0 |
chaluemwut/fbserver | venv/lib/python2.7/site-packages/sklearn/neighbors/base.py | 1 | 24541 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output... | apache-2.0 |
Vimos/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
siutanwong/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
COL-IU/XLSearch | xlsearch_train.py | 1 | 5042 | import sys
import pickle
import os
import getopt
from time import ctime
import numpy as np
usage = '''
USAGE: python xlsearch_train.py -l [path to xlsearch library]
-p [parameter file]
-o [output file]'''
(pairs, args) = getopt.getopt(sys.argv[1:], 'l:p:... | mit |
meduz/scikit-learn | examples/linear_model/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | bsd-3-clause |
PrashntS/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 103 | 4394 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
vermouthmjl/scikit-learn | sklearn/metrics/classification.py | 1 | 69294 | """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 |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/examples/decomposition/plot_sparse_coding.py | 1 | 4054 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | mit |
ChrisBeaumont/brut | bubbly/hyperopt.py | 2 | 2563 | """
A simple interface for random exploration of hyperparameter space
"""
import random
import numpy as np
from scipy import stats
from sklearn.metrics import auc
from sklearn import metrics as met
class Choice(object):
"""Randomly select from a list"""
def __init__(self, *choices):
self._choices = ... | mit |
zuku1985/scikit-learn | sklearn/utils/tests/test_multiclass.py | 58 | 14316 |
from __future__ import division
import numpy as np
import scipy.sparse as sp
from itertools import product
from sklearn.externals.six.moves import xrange
from sklearn.externals.six import iteritems
from scipy.sparse import issparse
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sp... | bsd-3-clause |
etkirsch/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 |
ominux/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 1 | 4105 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation me... | bsd-3-clause |
helloworldajou/webserver | demos/classifier_webcam.py | 4 | 7059 | #!/usr/bin/env python2
#
# Example to run classifier on webcam stream.
# Brandon Amos & Vijayenthiran
# 2016/06/21
#
# Copyright 2015-2016 Carnegie Mellon University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a ... | apache-2.0 |
roxyboy/scikit-learn | sklearn/mixture/tests/test_dpgmm.py | 261 | 4490 | import unittest
import sys
import numpy as np
from sklearn.mixture import DPGMM, VBGMM
from sklearn.mixture.dpgmm import log_normalize
from sklearn.datasets import make_blobs
from sklearn.utils.testing import assert_array_less, assert_equal
from sklearn.mixture.tests.test_gmm import GMMTester
from sklearn.externals.s... | bsd-3-clause |
igabriel85/dmon-adp | misc/keras_test.py | 1 | 1530 | import numpy
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import Lab... | apache-2.0 |
RachitKansal/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 |
tawsifkhan/scikit-learn | sklearn/linear_model/omp.py | 127 | 30417 | """Orthogonal matching pursuit algorithms
"""
# Author: Vlad Niculae
#
# License: BSD 3 clause
import warnings
from distutils.version import LooseVersion
import numpy as np
from scipy import linalg
from scipy.linalg.lapack import get_lapack_funcs
from .base import LinearModel, _pre_fit
from ..base import RegressorM... | bsd-3-clause |
lpeska/BRDTI | netlaprls.py | 1 | 2811 | '''
We base the NetLapRLS implementation on the one from PyDTI project, https://github.com/stephenliu0423/PyDTI, changes were made to the evaluation procedure
[1] Xia, Zheng, et al. "Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces." BMC systems biology 4.Suppl 2 (2010): S6.
De... | gpl-2.0 |
poryfly/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 |
abhishekgahlot/scikit-learn | examples/applications/topics_extraction_with_nmf.py | 106 | 2313 | """
========================================================
Topics extraction with Non-Negative Matrix Factorization
========================================================
This is a proof of concept application of Non Negative Matrix
Factorization of the term frequency matrix of a corpus of documents so
as to extra... | bsd-3-clause |
RachitKansal/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 |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/__init__.py | 27 | 3086 | """
Machine learning module for Python
==================================
sklearn is a Python module integrating classical machine
learning algorithms in the tightly-knit world of scientific Python
packages (numpy, scipy, matplotlib).
It aims to provide simple and efficient solutions to learning problems
that are acc... | unlicense |
etkirsch/scikit-learn | examples/model_selection/grid_search_text_feature_extraction.py | 253 | 4158 | """
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the do... | bsd-3-clause |
sunyihuan326/DeltaLab | shuwei_fengge/practice_one/model/tt.py | 1 | 3958 | # coding:utf-8
'''
Created on 2017/12/8.
@author: chk01
'''
import scipy.io as scio
# data = scio.loadmat(file)
# from sklearn.model_selection import train_test_split
#
# print(data['X'].shape)
# print(data['Y'].shape)
# X_train, X_test, Y_train, Y_test = train_test_split(data['X'], data['Y'], test_size=0.2)
# print(... | mit |
krahman/BuildingMachineLearningSystemsWithPython | ch04/build_lda.py | 1 | 2472 | # 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
from __future__ import print_function
try:
import nltk.corpus
except ImportError:
print("nltk n... | mit |
olologin/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
ishanic/scikit-learn | sklearn/manifold/tests/test_t_sne.py | 162 | 9771 | import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises_regexp
... | bsd-3-clause |
hmendozap/auto-sklearn | autosklearn/metalearning/metafeatures/plot_metafeatures.py | 1 | 20297 | from __future__ import print_function
import argparse
import cPickle
import itertools
import os
import StringIO
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
try:
from sklearn.manifold import TSNE
from sklearn.metrics.pairwise import P... | bsd-3-clause |
yaojenkuo/BuildingMachineLearningSystemsWithPython | ch03/rel_post_20news.py | 24 | 3903 | # 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
import scipy as sp
new_post = \
"""Disk drive problems. Hi, I have a probl... | mit |
hitszxp/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 40 | 12814 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
from sklearn.linear_model.ransac import _dynamic_max_tri... | bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/covariance/tests/test_covariance.py | 142 | 11068 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
AlexanderFabisch/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 46 | 2798 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
aabadie/scikit-learn | sklearn/utils/tests/test_testing.py | 24 | 7902 | import warnings
import unittest
import sys
from nose.tools import assert_raises
from sklearn.utils.testing import (
_assert_less,
_assert_greater,
assert_less_equal,
assert_greater_equal,
assert_warns,
assert_no_warnings,
assert_equal,
set_random_state,
assert_raise_message,
ig... | bsd-3-clause |
chaluemwut/fbserver | venv/lib/python2.7/site-packages/sklearn/feature_extraction/text.py | 1 | 49725 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gma... | apache-2.0 |
raghavrv/scikit-learn | sklearn/neighbors/tests/test_approximate.py | 12 | 20126 | """
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""
# Author: Maheshakya Wijewardena, Joel Nothman
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
parrt/lolviz | prince_dtree.py | 1 | 12296 | import IPython, graphviz, re
from io import StringIO
from IPython.display import Image
import numpy as np
import pandas as pd
import math
from sklearn import tree
from sklearn.datasets import load_boston, load_iris
from collections import defaultdict
import string
import re
YELLOW = "#fefecd" # "#fbfbd0" # "#FBFEB0"
B... | bsd-3-clause |
trankmichael/scikit-learn | sklearn/neighbors/approximate.py | 128 | 22351 | """Approximate nearest neighbor search"""
# Author: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Joel Nothman <joel.nothman@gmail.com>
import numpy as np
import warnings
from scipy import sparse
from .base import KNeighborsMixin, RadiusNeighborsMixin
from ..base import BaseEstimator
from ..utils.va... | bsd-3-clause |
sugartom/tensorflow-alien | tensorflow/examples/learn/text_classification.py | 39 | 5106 | # 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 appl... | apache-2.0 |
aflaxman/scikit-learn | sklearn/metrics/regression.py | 47 | 19967 | """Metrics to assess performance on regression task
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.gramfort@inria.fr>
# Ma... | bsd-3-clause |
trungnt13/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
anirudhjayaraman/scikit-learn | sklearn/utils/tests/test_extmath.py | 70 | 16531 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
SuperJohn/scikit-class | grid_search.py | 6 | 1243 | import pandas as pd
import numpy as np
df = pd.read_csv('tweets.csv')
target = df['is_there_an_emotion_directed_at_a_brand_or_product']
text = df['tweet_text']
fixed_text = text[pd.notnull(text)]
fixed_target = target[pd.notnull(text)]
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_b... | gpl-2.0 |
meduz/scikit-learn | examples/linear_model/plot_ransac.py | 73 | 1859 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
liyu1990/sklearn | examples/ensemble/plot_gradient_boosting_oob.py | 50 | 4764 | """
======================================
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 |
massmutual/scikit-learn | sklearn/utils/estimator_checks.py | 1 | 54609 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memory
from sklearn.utils.testing imp... | bsd-3-clause |
hrjn/scikit-learn | examples/cluster/plot_birch_vs_minibatchkmeans.py | 333 | 3694 | """
=================================
Compare BIRCH and MiniBatchKMeans
=================================
This example compares the timing of Birch (with and without the global
clustering step) and MiniBatchKMeans on a synthetic dataset having
100,000 samples and 2 features generated using make_blobs.
If ``n_clusters... | bsd-3-clause |
liberatorqjw/scikit-learn | sklearn/tests/test_multiclass.py | 8 | 21910 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing ... | bsd-3-clause |
liyu1990/sklearn | examples/manifold/plot_swissroll.py | 330 | 1446 | """
===================================
Swiss Roll reduction with LLE
===================================
An illustration of Swiss Roll reduction
with locally linear embedding
"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD 3 clause (C) INRIA 2011
print(__doc__)
import matplotlib.pyplot... | bsd-3-clause |
hmendozap/master-arbeit-projects | autosk_dev_test/component/LinReg.py | 1 | 8756 | import numpy as np
import scipy.sparse as sp
from HPOlibConfigSpace.configuration_space import ConfigurationSpace
from HPOlibConfigSpace.conditions import EqualsCondition, InCondition
from HPOlibConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter... | mit |
PrashntS/scikit-learn | sklearn/linear_model/ridge.py | 60 | 44642 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | bsd-3-clause |
dsquareindia/scikit-learn | sklearn/tests/test_cross_validation.py | 79 | 47914 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
tiagofrepereira2012/tensorflow | tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py | 52 | 69800 | # 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 |
shanzhenren/ClusType | src/algorithm.py | 1 | 10630 | from collections import defaultdict
from operator import itemgetter
from math import log, sqrt
import random as rn
import time
from numpy import * # install numpy
from scipy import * # install scipy
from numpy.linalg import norm
import numpy.linalg as npl
from scipy.sparse import *
import scipy.sparse.linalg as spsl
fr... | gpl-3.0 |
eg-zhang/scikit-learn | benchmarks/bench_mnist.py | 76 | 6136 | """
=======================
MNIST dataset benchmark
=======================
Benchmark on the MNIST dataset. The dataset comprises 70,000 samples
and 784 features. Here, we consider the task of predicting
10 classes - digits from 0 to 9 from their raw images. By contrast to the
covertype dataset, the feature space is... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-c604ac39ad0e5b066d964df3e8f31ba7ebda1e0e/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 |
clemkoa/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 39 | 7489 | r"""
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected i... | bsd-3-clause |
magic2du/contact_matrix | Contact_maps/DeepLearning/DeepLearningTool/DL_contact_matrix_load2-new10fold_01_09_2015_01.py | 1 | 25014 |
# coding: utf-8
# In[1]:
# this part imports libs and load data from csv file
import sys
sys.path.append('../../../libs/')
import csv
from dateutil import parser
from datetime import timedelta
from sklearn import svm
import numpy as np
import pandas as pd
import pickle
from sklearn.cross_validation import train_test... | gpl-2.0 |
rvraghav93/scikit-learn | sklearn/feature_extraction/tests/test_text.py | 8 | 35969 | from __future__ import unicode_literals
import warnings
from sklearn.feature_extraction.text import strip_tags
from sklearn.feature_extraction.text import strip_accents_unicode
from sklearn.feature_extraction.text import strip_accents_ascii
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.fe... | bsd-3-clause |
allinpaybusiness/ACS | allinpay projects/creditscoreMLP/classMLP.py | 1 | 9585 | # -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import sys;
import os;
sys.path.append("allinpay projects")
from creditscore.creditscore import CreditScore
import numpy as np
import pandas as pd
import time
from sklearn.model_selection import train_test_split
from sklearn.linear_model ... | apache-2.0 |
CVML/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 230 | 2649 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
ky822/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
DSLituiev/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 8 | 7473 | r"""
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected i... | bsd-3-clause |
bigdataelephants/scikit-learn | sklearn/datasets/tests/test_lfw.py | 50 | 6849 | """This test for the LFW require medium-size data dowloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
... | bsd-3-clause |
elkingtonmcb/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 103 | 22297 | """
Todo: cross-check the F-value with stats model
"""
from __future__ import division
import itertools
import warnings
import numpy as np
from scipy import stats, sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
scikit-learn-contrib/py-earth | examples/plot_feature_importance.py | 3 | 2142 | """
===========================
Plotting feature importance
===========================
A simple example showing how to compute and display
feature importances, it is also compared with the
feature importances obtained using random forests.
Feature importance is a measure of the effect of the features
on the outputs.... | bsd-3-clause |
hugobowne/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 |
Clyde-fare/scikit-learn | sklearn/metrics/ranking.py | 79 | 25426 | """Metrics to assess performance on classification task given scores
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.gramfort@inria.... | bsd-3-clause |
etkirsch/scikit-learn | examples/svm/plot_custom_kernel.py | 171 | 1546 | """
======================
SVM with custom kernel
======================
Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data... | bsd-3-clause |
fengzhyuan/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 |
ngoix/OCRF | sklearn/linear_model/ransac.py | 14 | 17163 | # coding: utf-8
# Author: Johannes Schönberger
#
# License: BSD 3 clause
import numpy as np
import warnings
from ..base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.random import sample_without_replacement
fr... | bsd-3-clause |
shikhardb/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
yanlend/scikit-learn | examples/datasets/plot_iris_dataset.py | 283 | 1928 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
The Iris Dataset
=========================================================
This data sets consists of 3 different types of irises'
(Setosa, Versicolour, and Virginica) petal and sepal
length, stored in a 150x4 numpy... | bsd-3-clause |
simon-pepin/scikit-learn | sklearn/semi_supervised/label_propagation.py | 128 | 15312 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
macks22/scikit-learn | examples/semi_supervised/plot_label_propagation_structure.py | 247 | 2432 | """
==============================================
Label Propagation learning a complex structure
==============================================
Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Be... | bsd-3-clause |
jkarnows/scikit-learn | benchmarks/bench_glm.py | 297 | 1493 | """
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
from sklearn.utils.bench import total_seconds
if __name__ == '__main__':
import pylab as pl
n_iter = 40
time_ridge = np.empty(n_it... | bsd-3-clause |
russel1237/scikit-learn | examples/svm/plot_weighted_samples.py | 188 | 1943 | """
=====================
SVM: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
The sample weighting rescales the C parameter, which means that the classifier
puts more emphasis on getting these points right. The effect might ... | bsd-3-clause |
LiaoPan/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 |
michigraber/scikit-learn | examples/linear_model/plot_bayesian_ridge.py | 248 | 2588 | """
=========================
Bayesian Ridge Regression
=========================
Computes a Bayesian Ridge Regression on a synthetic dataset.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the coefficient
weights are slightly shift... | bsd-3-clause |
xzh86/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
trungnt13/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 |
yilei0620/3D_Conditional_Gan | lib/data_utils.py | 1 | 1596 | import numpy as np
from sklearn import utils as skutils
from rng import np_rng, py_rng
def center_crop(x, ph, pw=None):
if pw is None:
pw = ph
h, w = x.shape[:2]
j = int(round((h - ph)/2.))
i = int(round((w - pw)/2.))
return x[j:j+ph, i:i+pw]
def patch(x, ph, pw=None):
if pw is None:
... | mit |
mayblue9/scikit-learn | benchmarks/bench_multilabel_metrics.py | 276 | 7138 | #!/usr/bin/env python
"""
A comparison of multilabel target formats and metrics over them
"""
from __future__ import division
from __future__ import print_function
from timeit import timeit
from functools import partial
import itertools
import argparse
import sys
import matplotlib.pyplot as plt
import scipy.sparse as... | bsd-3-clause |
Zing22/uemscode | tmp_test.py | 1 | 1634 | # -*- coding=utf-8 -*-
#### for testing steps
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.externals import joblib
from PIL import Image
from process import toBin, cropLetters
from img2feature import toFeature
from main import readAllFiles
TEMP_DIR = 'tmp/'
def test_onePi... | mit |
RPGOne/Skynet | scikit-learn-0.18.1/sklearn/linear_model/tests/test_ransac.py | 52 | 17482 | from scipy import sparse
import numpy as np
from scipy import sparse
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_raises_rege... | bsd-3-clause |
jjx02230808/project0223 | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
ChanderG/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 |
JPFrancoia/scikit-learn | sklearn/preprocessing/data.py | 13 | 70436 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# Giorgio Patrini <giorgio.patrini@anu.edu.au>
# Lic... | bsd-3-clause |
jm-begon/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
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 |
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