repo_name stringlengths 6 103 | path stringlengths 5 191 | copies stringlengths 1 4 | size stringlengths 4 6 | content stringlengths 986 970k | license stringclasses 15
values |
|---|---|---|---|---|---|
glouppe/scikit-learn | sklearn/utils/tests/test_shortest_path.py | 292 | 2841 | from collections import defaultdict
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.utils.graph import (graph_shortest_path,
single_source_shortest_path_length)
def floyd_warshall_slow(graph, directed=False):
N = graph.shape[0]
#set nonzer... | bsd-3-clause |
bytedance/fedlearner | test/trainer/test_horizontal_nn_trainer.py | 1 | 18484 | # Copyright 2020 The FedLearner 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 |
DwangoMediaVillage/pqkmeans | pqkmeans/encoder/encoder_base.py | 2 | 1627 | import numpy
import sklearn
import typing
class EncoderBase(sklearn.base.BaseEstimator):
def fit_generator(self, x_train):
# type: (typing.Iterable[typing.Iterator[float]]) -> None
raise NotImplementedError()
def transform_generator(self, x_test):
# type: (typing.Iterable[typing.Itera... | mit |
LohithBlaze/scikit-learn | sklearn/feature_extraction/tests/test_dict_vectorizer.py | 274 | 3790 | # Authors: Lars Buitinck <L.J.Buitinck@uva.nl>
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
... | bsd-3-clause |
jzt5132/scikit-learn | sklearn/feature_extraction/tests/test_dict_vectorizer.py | 274 | 3790 | # Authors: Lars Buitinck <L.J.Buitinck@uva.nl>
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
... | bsd-3-clause |
lilleswing/deepchem | contrib/one_shot_models/examples/muv_attn_one_fold.py | 8 | 2521 | """
Train low-data attn models on MUV. Test last fold only.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import numpy as np
np.random.seed(123)
import tensorflow as tf
tf.set_random_seed(123)
import deepchem as dc
from datasets import load_muv_convmo... | mit |
ethen8181/machine-learning | projects/kaggle_rossman_store_sales/gbt_module/model.py | 1 | 5582 | import numpy as np
from sklearn.base import BaseEstimator
from sklearn.model_selection import RandomizedSearchCV, PredefinedSplit
__all__ = ['GBTPipeline']
class GBTPipeline(BaseEstimator):
"""
Gradient Boosted Tree Pipeline set up to do train/validation split
and hyperparameter search.
"""
def ... | mit |
glouppe/scikit-learn | sklearn/utils/testing.py | 13 | 27118 | """Testing utilities."""
# Copyright (c) 2011, 2012
# Authors: Pietro Berkes,
# Andreas Muller
# Mathieu Blondel
# Olivier Grisel
# Arnaud Joly
# Denis Engemann
# Giorgio Patrini
# License: BSD 3 clause
import os
import inspect
import pkgutil
import warnings
import... | bsd-3-clause |
glouppe/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 71 | 6451 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guar... | bsd-3-clause |
LohithBlaze/scikit-learn | sklearn/ensemble/__init__.py | 216 | 1307 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification and regression.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesClassifier
from .fores... | bsd-3-clause |
glouppe/scikit-learn | examples/model_selection/plot_validation_curve.py | 135 | 1931 | """
==========================
Plotting Validation Curves
==========================
In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. ... | bsd-3-clause |
luo66/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 250 | 2022 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
fx2003/tensorflow-study | TensorFlow实战/models/attention_ocr/python/datasets/fsns_test.py | 15 | 3374 | # 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 applicab... | mit |
automl/auto-sklearn | test/test_pipeline/components/feature_preprocessing/test_select_rates_classification.py | 1 | 4623 | import numpy as np
import scipy.sparse
import sklearn.preprocessing
from autosklearn.pipeline.components.feature_preprocessing.select_rates_classification import ( # noqa: E501
SelectClassificationRates,
)
from autosklearn.pipeline.util import _test_preprocessing, get_dataset
import unittest
class SelectClassi... | bsd-3-clause |
sgenoud/scikit-learn | benchmarks/bench_lasso.py | 6 | 3368 | """
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 |
MohammedWasim/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 |
sgenoud/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 1 | 1626 | """
Testing Recursive feature elimination
"""
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris
from sklearn.metrics import zero_one
from sklearn.svm import SVC
from skle... | bsd-3-clause |
manipopopo/tensorflow | tensorflow/contrib/learn/python/learn/datasets/base_test.py | 132 | 3072 | # 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 |
lukeiwanski/tensorflow | tensorflow/contrib/learn/python/learn/datasets/base_test.py | 132 | 3072 | # 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 |
CCI-Tools/cate-core | cate/ops/correlation.py | 1 | 14291 | # 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 |
oaelhara/numbbo | code-postprocessing/bbob_pproc/compall/ppfigs.py | 1 | 22256 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
"""Creates ERTs and convergence figures for multiple algorithms."""
from __future__ import absolute_import
import os
import matplotlib.pyplot as plt
import numpy
from pdb import set_trace
from .. import toolsdivers, toolsstats, bestalg, pproc, genericsettings, htmldesc, p... | bsd-3-clause |
LohithBlaze/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 326 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the ... | bsd-3-clause |
jzt5132/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 326 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the ... | bsd-3-clause |
glouppe/scikit-learn | sklearn/cross_decomposition/cca_.py | 145 | 3192 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
eadgarchen/tensorflow | tensorflow/contrib/learn/python/learn/estimators/stability_test.py | 110 | 6455 | # 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 |
glouppe/scikit-learn | sklearn/svm/tests/test_sparse.py | 21 | 13181 | from nose.tools import assert_raises, assert_true, assert_false
import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classif... | bsd-3-clause |
elkingtonmcb/h2o-2 | py/testdir_kevin/test_parse_specific_case5.py | 9 | 4807 | import unittest, random, sys, time, os
sys.path.extend(['.','..','../..','py'])
import h2o, h2o_cmd, h2o_import as h2i
import codecs, unicodedata
print "create some specific small datasets with exp row/col combinations"
print "This is trying a random sample of utf8 symbols inserted in an enum"
# 0x1 can be the hive s... | apache-2.0 |
glouppe/scikit-learn | examples/mixture/plot_gmm.py | 35 | 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 |
sgenoud/scikit-learn | sklearn/semi_supervised/label_propagation.py | 4 | 13783 | # 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 |
mlflow/mlflow | examples/ray_serve/train_model.py | 1 | 1302 | import mlflow
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import mean_squared_error
from sklearn.utils import shuffle
if __name__ == "__main__":
# Enable auto-logging
mlflow.set_tracking_uri("sqlite:///mlruns.db")
mlflow.sklearn.auto... | apache-2.0 |
mileistone/test | vedanet/models/yolo_abc.py | 1 | 2243 | #
# Darknet YOLOv2 model
# Copyright EAVISE
#
import os
from collections import OrderedDict, Iterable
import torch
import torch.nn as nn
from .. import data as vnd
from ._darknet import Darknet
__all__ = ['YoloABC']
class YoloABC(Darknet):
def __init__(self):
""" Network initialisation """
s... | mit |
glouppe/scikit-learn | examples/neighbors/plot_classification.py | 285 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
glouppe/scikit-learn | examples/cluster/plot_lena_segmentation.py | 269 | 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 |
mlperf/training_results_v0.5 | v0.5.0/nvidia/submission/code/translation/pytorch/eval_lm.py | 7 | 4361 | #!/usr/bin/env python3 -u
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import n... | apache-2.0 |
mlflow/mlflow | mlflow/mleap.py | 1 | 12700 | """
The ``mlflow.mleap`` module provides an API for saving Spark MLLib models using the
`MLeap <https://github.com/combust/mleap>`_ persistence mechanism.
NOTE:
You cannot load the MLeap model flavor in Python; you must download it using the
Java API method ``downloadArtifacts(String runId)`` and load the mod... | apache-2.0 |
LohithBlaze/scikit-learn | sklearn/ensemble/voting_classifier.py | 177 | 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 |
jnewland/home-assistant | homeassistant/components/android_ip_webcam/__init__.py | 5 | 9513 | """Support for Android IP Webcam."""
import asyncio
import logging
from datetime import timedelta
import voluptuous as vol
from homeassistant.core import callback
from homeassistant.const import (
CONF_NAME, CONF_HOST, CONF_PORT, CONF_USERNAME, CONF_PASSWORD,
CONF_SENSORS, CONF_SWITCHES, CONF_TIMEOUT, CONF_SC... | apache-2.0 |
eadgarchen/tensorflow | tensorflow/contrib/keras/api/keras/__init__.py | 128 | 1935 | # 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 |
mlperf/training_results_v0.5 | v0.5.0/google/cloud_v2.8/resnet-tpuv2-8/code/resnet/model/models/official/recommendation/neumf_model.py | 4 | 19328 | # Copyright 2018 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 |
luo66/scikit-learn | sklearn/utils/tests/test_seq_dataset.py | 93 | 2471 | # Author: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
from sklearn.utils.seq_dataset import ArrayDataset, CSRDataset
from sklearn.datasets import load_iris
from numpy.testing import assert_array_equal
from nose.tools import assert_equal
iris =... | bsd-3-clause |
lukeiwanski/tensorflow | tensorflow/python/data/kernel_tests/sequence_dataset_op_test.py | 18 | 7947 | # 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 |
mlperf/training_results_v0.5 | v0.5.0/google/cloud_v2.8/gnmt-tpuv2-8/code/gnmt/model/t2t/tensor2tensor/data_generators/algorithmic_math.py | 3 | 21962 | # coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# 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... | apache-2.0 |
manipopopo/tensorflow | tensorflow/python/data/kernel_tests/iterator_ops_test.py | 3 | 35917 | # 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 |
luo66/scikit-learn | sklearn/datasets/tests/test_base.py | 204 | 5878 | import os
import shutil
import tempfile
import warnings
import nose
import numpy
from pickle import loads
from pickle import dumps
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn... | bsd-3-clause |
automl/auto-sklearn | autosklearn/pipeline/classification.py | 1 | 14739 | from typing import Optional, Union
import copy
from itertools import product
import numpy as np
from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
from sklearn.base import ClassifierMixin
from autosklearn.askl... | bsd-3-clause |
tomsilver/nupic | tests/swarming/nupic/swarming/experiments/delta/description.py | 1 | 14792 | # ----------------------------------------------------------------------
# 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... | gpl-3.0 |
fx2003/tensorflow-study | TensorFlow实战/models/slim/datasets/download_and_convert_flowers.py | 7 | 7201 | # 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... | mit |
elkingtonmcb/h2o-2 | py/testdir_release/c5/test_c5_KMeans_sphere15_180GB_fvec.py | 9 | 6725 | import unittest, time, sys, random, math, json
sys.path.extend(['.','..','../..','py'])
import h2o, h2o_cmd, h2o_kmeans, h2o_import as h2i, h2o_common
import socket
print "Assumes you ran ../build_for_clone.py in this directory"
print "Using h2o-nodes.json. Also the sandbox dir"
DO_KMEANS = True
# assumes the cloud w... | apache-2.0 |
lilleswing/deepchem | examples/factors/FACTORS_datasets.py | 8 | 4118 | """
FACTORS dataset loader.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import shutil
import time
import numpy as np
import deepchem as dc
from factors_features import factors_descriptors
def remove_missing_entries(dataset):
"""Remove ... | mit |
rcln/tag.suggestion | code_python27/tfidfANDclasification/classByTags.py | 1 | 6011 | # -*- coding: utf-8 -*-
"""
Created on Sat Sep 19 15:21:27 2015
@author: ivan
"""
from __future__ import division
import numpy as np
from lxml import etree
import os
import argparse
from sklearn import svm
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics
from sklearn.neighbors import KNeighbor... | gpl-2.0 |
dahlstrom-g/intellij-community | python/helpers/third_party/thriftpy/_shaded_ply/ctokens.py | 206 | 3177 | # ----------------------------------------------------------------------
# ctokens.py
#
# Token specifications for symbols in ANSI C and C++. This file is
# meant to be used as a library in other tokenizers.
# ----------------------------------------------------------------------
# Reserved words
tokens = [
# Li... | apache-2.0 |
lukeiwanski/tensorflow | tensorflow/contrib/learn/python/learn/preprocessing/categorical.py | 40 | 4795 | # 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 |
farizrahman4u/keras-contrib | tests/tooling/test_codeowners.py | 2 | 4034 | import os
import pytest
from github import Github
try:
import pathlib
except ImportError:
import pathlib2 as pathlib
path_to_keras_contrib = pathlib.Path(__file__).resolve().parents[2]
path_to_codeowners = path_to_keras_contrib / 'CODEOWNERS'
authenticated = True
try:
github_client = Github(o... | mit |
ishanic/scikit-learn | sklearn/decomposition/base.py | 310 | 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 |
ChanChiChoi/scikit-learn | sklearn/decomposition/base.py | 310 | 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 |
dsquareindia/scikit-learn | benchmarks/bench_glm.py | 95 | 1515 | """
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 matplotlib.pyplot as plt
n_iter = 40
time_ridge = ... | bsd-3-clause |
dsquareindia/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 |
krdyke/OGP-metadata-py | src/arcgis2ogp.py | 1 | 9925 | from time import clock
from datetime import datetime
import os, math, sys
import pytz
import urllib
import StringIO
import glob
import json
from logger import Logger
from keyword_parse import keywordParse
from datatype_parse import *
import xml.etree.ElementTree as ET
from xml.etree.ElementTree import ElementTree, Elem... | mit |
ishanic/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 252 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
kylerbrown/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 252 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
fredhusser/scikit-learn | sklearn/cluster/tests/test_affinity_propagation.py | 338 | 2620 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/cluster/tests/test_affinity_propagation.py | 338 | 2620 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | bsd-3-clause |
BayesWatch/tf-variational-dropout | models/resnet.py | 1 | 4928 | '''
Functional definition for resnet inspired by
https://github.com/szagoruyko/functional-zoo
Unlike the original code, we're going to access the variables in the model in
the orthodox tensorflow way with variable_scopes. It wouldn't be hard to write
an iterator that accesses variables defined in the same scope and mo... | gpl-3.0 |
kylerbrown/scikit-learn | sklearn/utils/tests/test_fixes.py | 278 | 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 |
fredhusser/scikit-learn | examples/svm/plot_separating_hyperplane.py | 291 | 1273 | """
=========================================
SVM: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machine classifier with
linear kernel.
"""
print(__doc__)
import numpy as np
impor... | bsd-3-clause |
ishanic/scikit-learn | examples/applications/svm_gui.py | 285 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
andrewnc/scikit-learn | sklearn/__check_build/__init__.py | 342 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
ishanic/scikit-learn | sklearn/__check_build/__init__.py | 342 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/__check_build/__init__.py | 342 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 228 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <robertlayton@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random... | bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/__check_build/__init__.py | 342 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
fredhusser/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 228 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
kylerbrown/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 228 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
fredhusser/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 255 | 2406 | """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 |
dsquareindia/scikit-learn | benchmarks/bench_plot_neighbors.py | 96 | 6469 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
... | bsd-3-clause |
lukovnikov/qelos | qelos/scripts/webqa/preprocessing/checkentities.py | 1 | 2030 | from __future__ import print_function
import csv, re
import qelos as q
#####################################################################
## CHECK WHAT ENTITY LINKING FILE COVERS FROM IDEAL ENTITY LINKING ##
#####################################################################
def run(graphp="../../../../dataset... | mit |
dsquareindia/scikit-learn | examples/cluster/plot_segmentation_toy.py | 89 | 3522 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
dsquareindia/scikit-learn | examples/manifold/plot_manifold_sphere.py | 80 | 5055 | #!/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 |
distributed-system-analysis/pbench | lib/pbench/test/unit/server/query_apis/test_query_builder.py | 2 | 6545 | from typing import Optional
import pytest
from pbench.server import JSON
from pbench.server.api.resources import API_METHOD, API_OPERATION, ApiSchema
from pbench.server.api.resources.query_apis import ElasticBase
from pbench.server.auth.auth import Auth
from pbench.server.database.models.users import User
ADMIN_ID =... | gpl-3.0 |
ChanChiChoi/scikit-learn | sklearn/manifold/t_sne.py | 105 | 20057 | # Author: Alexander Fabisch -- <afabisch@informatik.uni-bremen.de>
# License: BSD 3 clause (C) 2014
# This is the standard t-SNE implementation. There are faster modifications of
# the algorithm:
# * Barnes-Hut-SNE: reduces the complexity of the gradient computation from
# N^2 to N log N (http://arxiv.org/abs/1301.... | bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/utils/metaestimators.py | 281 | 2353 | """Utilities for meta-estimators"""
# Author: Joel Nothman
# Andreas Mueller
# Licence: BSD
from operator import attrgetter
from functools import update_wrapper
__all__ = ['if_delegate_has_method']
class _IffHasAttrDescriptor(object):
"""Implements a conditional property using the descriptor protocol.
... | bsd-3-clause |
fredhusser/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 385 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
MegaShow/college-programming | Homework/Principles of Artificial Neural Networks/Week 15 Image Caption/src/agent.py | 1 | 6788 | """Agent"""
import torch
from torch.nn.utils.rnn import pack_padded_sequence
from net import Decoder, Encoder
from action import Action, AverageMeter
from dataset import Dataset
class Agent:
def __init__(self):
super(Agent, self).__init__()
#self.config = config
self.action = Action()
... | mit |
codeforsanjose/MobilityMapApi | src/sa_api_v2/south_migrations/0030_auto__add_field_submittedthing_submitter.py | 2 | 9106 | # -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'SubmittedThing.submitter'
db.add_column('sa_api_submittedthing', 'submitter',
... | gpl-3.0 |
andrewnc/scikit-learn | examples/ensemble/plot_ensemble_oob.py | 257 | 3265 | """
=============================
OOB Errors for Random Forests
=============================
The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average er... | bsd-3-clause |
fredhusser/scikit-learn | examples/ensemble/plot_ensemble_oob.py | 257 | 3265 | """
=============================
OOB Errors for Random Forests
=============================
The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average er... | bsd-3-clause |
google/uncertainty-baselines | baselines/diabetic_retinopathy_detection/jax_finetune_deterministic.py | 1 | 22801 | # coding=utf-8
# Copyright 2022 The Uncertainty Baselines Authors.
#
# 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 ap... | apache-2.0 |
UpSea/midProjects | 01_aat-ebook-full-source-code-20160430/chapter13/reuters-svm.py | 1 | 6726 | from __future__ import print_function
import pprint
import re
try:
from html.parser import HTMLParser
except ImportError:
from HTMLParser import HTMLParser
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import confusion_ma... | mit |
IndraVikas/scikit-learn | examples/applications/svm_gui.py | 285 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
frank-tancf/scikit-learn | sklearn/cluster/tests/test_k_means.py | 40 | 27789 | """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 |
raghavrv/scikit-learn | sklearn/__check_build/__init__.py | 342 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
IndraVikas/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | 154 | 8058 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
assert_equal, assert_not_equal,
assert_raises)
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import mak... | bsd-3-clause |
IndraVikas/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 228 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <robertlayton@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random... | bsd-3-clause |
frank-tancf/scikit-learn | sklearn/preprocessing/tests/test_imputation.py | 46 | 12381 |
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.preprocessing.imput... | bsd-3-clause |
IndraVikas/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 228 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
christianurich/VIBe2UrbanSim | 3rdparty/opus/src/psrc_parcel/models/water_demand_model.py | 2 | 5875 | # Opus/UrbanSim urban simulation software.
# Copyright (C) 2005-2009 University of Washington
# See opus_core/LICENSE
import re
from numpy import array, exp, arange, zeros
from opus_core.resources import Resources
from opus_core.regression_model import RegressionModel
from opus_core.variables.variable_name imp... | gpl-2.0 |
pkruskal/scikit-learn | sklearn/metrics/tests/test_pairwise.py | 104 | 22788 | import numpy as np
from numpy import linalg
from scipy.sparse import dok_matrix, csr_matrix, issparse
from scipy.spatial.distance import cosine, cityblock, minkowski, wminkowski
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing impo... | bsd-3-clause |
nightjean/Deep-Learning | tensorflow/contrib/keras/python/keras/datasets/__init__.py | 57 | 1290 | # Copyright 2015 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 |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/numpy/lib/function_base.py | 19 | 164441 | from __future__ import division, absolute_import, print_function
import collections
import operator
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d, transpose
from numpy.core.numeric import (
ones, zeros, arange, conc... | mit |
ageron/tensorflow | tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py | 44 | 4901 | # 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 |
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