repo_name stringlengths 6 103 | path stringlengths 4 209 | copies stringclasses 325
values | size stringlengths 4 7 | content stringlengths 838 1.04M | license stringclasses 15
values |
|---|---|---|---|---|---|
kastnerkyle/pylearn2 | pylearn2/scripts/datasets/make_mnistplus.py | 5 | 8862 | """
Script to generate the MNIST+ dataset. The purpose of this dataset is to make a
more challenging MNIST-like dataset, with multiple factors of variation. These
factors can serve to evaluate a model's performance at learning invariant
features, or its ability to disentangle factors of variation in a multi-task
classi... | bsd-3-clause |
nicproulx/mne-python | tutorials/plot_brainstorm_auditory.py | 3 | 16597 | # -*- coding: utf-8 -*-
"""
====================================
Brainstorm auditory tutorial dataset
====================================
Here we compute the evoked from raw for the auditory Brainstorm
tutorial dataset. For comparison, see [1]_ and:
http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory
Experi... | bsd-3-clause |
alvarouc/polyssifier | polyssifier/poly_utils.py | 1 | 10338 | from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC, SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import (LogisticRegression,
LinearRegression,
... | gpl-2.0 |
UMWRG/HydraPlatform | HydraServer/python/HydraServer/plugins/timeseries_functions.py | 2 | 5406 | # (c) Copyright 2013, 2014, University of Manchester
#
# HydraPlatform is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HydraPlatfor... | gpl-3.0 |
kastnerkyle/pylearn2 | pylearn2/scripts/datasets/step_through_small_norb.py | 49 | 3123 | #! /usr/bin/env python
"""
A script for sequentially stepping through SmallNORB, viewing each image and
its label.
Intended as a demonstration of how to iterate through NORB images,
and as a way of testing SmallNORB's StereoViewConverter.
If you just want an image viewer, consider
pylearn2/scripts/show_binocular_gra... | bsd-3-clause |
OpenMined/PySyft | benchmarks/macro_executor.py | 1 | 3935 | # stdlib
from datetime import date
import json
import os
from pathlib import Path
import subprocess
from time import time
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
# third party
import pyarrow.parquet as pq
# syft absolute
import syft as sy
from syft.core.adp... | apache-2.0 |
shangwuhencc/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 265 | 6813 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process ... | bsd-3-clause |
lakshayg/tensorflow | tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py | 53 | 4253 | # 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 |
wavycloud/pyboto3 | pyboto3/glue.py | 1 | 692979 | '''
The MIT License (MIT)
Copyright (c) 2016 WavyCloud
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, p... | mit |
shangwuhencc/scikit-learn | examples/linear_model/plot_ransac.py | 249 | 1673 | """
===========================================
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 |
CredoReference/edx-platform | lms/djangoapps/course_api/blocks/tests/test_serializers.py | 4 | 8709 | """
Tests for Course Blocks serializers
"""
from mock import MagicMock
from lms.djangoapps.course_blocks.api import get_course_block_access_transformers, get_course_blocks
from openedx.core.djangoapps.content.block_structure.transformers import BlockStructureTransformers
from student.roles import CourseStaffRole
from ... | agpl-3.0 |
nicproulx/mne-python | tutorials/plot_artifacts_detection.py | 5 | 5377 | """
.. _tut_artifacts_detect:
Introduction to artifacts and artifact detection
================================================
Since MNE supports the data of many different acquisition systems, the
particular artifacts in your data might behave very differently from the
artifacts you can observe in our tutorials and... | bsd-3-clause |
shangwuhencc/scikit-learn | examples/exercises/plot_iris_exercise.py | 320 | 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 |
NUKnightLab/panda | panda/migrations/0025_add_subscription_permissions.py | 6 | 14192 | # -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import DataMigration
from django.db import models
class Migration(DataMigration):
def forwards(self, orm):
"""
This migration will fail if run against a clean database (fresh setup)
This is fine because the permi... | mit |
nicproulx/mne-python | tutorials/plot_stats_cluster_methods.py | 5 | 8797 | # doc:slow-example
"""
.. _tut_stats_cluster_methods:
======================================================
Permutation t-test on toy data with spatial clustering
======================================================
Following the illustrative example of Ridgway et al. 2012 [1]_,
this demonstrates some basic ideas ... | bsd-3-clause |
kastnerkyle/pylearn2 | pylearn2/tests/test_theano.py | 45 | 4805 | """ Include tests related to Theano.
1) One test on one thing Pylearn2 depend to be done by Theano.
2) One test for a rare corner case crash in Theano that we where not
able to reproduce rapidly enough without having this tests depend on
Pylearn2.
"""
__authors__ = "Ian Goodfellow"
__copyright__ = "Copyright 2010-20... | bsd-3-clause |
gacarrillor/QGIS | tests/src/python/featuresourcetestbase.py | 8 | 57101 | # -*- coding: utf-8 -*-
"""QGIS Unit test utils for QgsFeatureSource subclasses.
.. note:: This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any lat... | gpl-2.0 |
shangwuhencc/scikit-learn | examples/cluster/plot_cluster_iris.py | 347 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
lakshayg/tensorflow | tensorflow/contrib/boosted_trees/examples/mnist.py | 61 | 5840 | # 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 |
aalmah/pylearn2 | pylearn2/utils/utlc.py | 49 | 7347 | """Several utilities for experimenting upon utlc datasets"""
# Standard library imports
import logging
import os
import inspect
import zipfile
from tempfile import TemporaryFile
# Third-party imports
import numpy
import theano
from pylearn2.datasets.utlc import load_ndarray_dataset, load_sparse_dataset
from pylearn2.u... | bsd-3-clause |
ephes/scikit-learn | examples/linear_model/plot_ols_3d.py | 347 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
roxyboy/scikit-learn | sklearn/utils/multiclass.py | 83 | 12343 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
roxyboy/scikit-learn | sklearn/metrics/pairwise.py | 103 | 42995 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck ... | bsd-3-clause |
rv816/lightfm | examples/movielens/data.py | 11 | 3560 | import itertools
import os
import zipfile
import numpy as np
import requests
import scipy.sparse as sp
def _get_movielens_path():
"""
Get path to the movielens dataset file.
"""
return os.path.join(os.path.dirname(os.path.abspath(__file__)),
'movielens.zip')
def _download... | apache-2.0 |
dotsdl/msmbuilder | msmbuilder/tests/test_metzner_mcmc.py | 2 | 3416 | import numpy as np
from msmbuilder.cluster import NDGrid
from msmbuilder.example_datasets import load_doublewell
from msmbuilder.msm import BayesianMarkovStateModel
from msmbuilder.msm import MarkovStateModel
from msmbuilder.msm._metzner_mcmc_fast import metzner_mcmc_fast
from msmbuilder.msm._metzner_mcmc_slow import m... | lgpl-2.1 |
unreal666/namebench | nb_third_party/dns/rdata.py | 215 | 14860 | # Copyright (C) 2001-2007, 2009, 2010 Nominum, Inc.
#
# Permission to use, copy, modify, and distribute this software and its
# documentation for any purpose with or without fee is hereby granted,
# provided that the above copyright notice and this permission notice
# appear in all copies.
#
# THE SOFTWARE IS PROVIDED ... | apache-2.0 |
ephes/scikit-learn | examples/applications/plot_prediction_latency.py | 233 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
yinwenpeng/rescale | en/parser/nltk_lite/probability.py | 10 | 59364 | # Natural Language Toolkit: Probability and Statistics
#
# Copyright (C) 2001-2006 University of Pennsylvania
# Author: Edward Loper <edloper@gradient.cis.upenn.edu>
# Steven Bird <sb@csse.unimelb.edu.au> (additions)
# Trevor Cohn <tacohn@cs.mu.oz.au> (additions)
# URL: <http://nltk.sf.net>
# For licens... | gpl-3.0 |
roxyboy/scikit-learn | examples/neighbors/plot_nearest_centroid.py | 263 | 1804 | """
===============================
Nearest Centroid Classification
===============================
Sample usage of Nearest Centroid 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 ListedColormap
f... | bsd-3-clause |
boscotsang/BayesDigitClassify | classify_ternary.py | 1 | 2776 | import numpy
from sklearn.metrics import confusion_matrix
def load_data():
train_labels = []
with open('digitdata/traininglabels', 'rb') as f:
for i, line in enumerate(f):
train_labels.append(int(line))
train_labels = numpy.array(train_labels, dtype=int)
train_x = numpy.zeros((trai... | mit |
ephes/scikit-learn | sklearn/utils/tests/test_class_weight.py | 139 | 11909 | import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testin... | bsd-3-clause |
fberanizo/neural_network | tests/ibovespa/mlp.py | 1 | 5708 | # -*- coding: utf-8 -*-
import sys, os
sys.path.insert(0, os.path.abspath('../..'))
import unittest, pandas, numpy, datetime, itertools, mlp
from sklearn import cross_validation, preprocessing
class MLP(unittest.TestCase):
"""Test cases for Ibovespa tendency problem."""
grid_search = True
def test_1(sel... | bsd-2-clause |
moverlan/LOTlib | LOTlib/Examples/SymbolicRegression/Galileo/Run.py | 1 | 1312 | # -*- coding: utf-8 -*-
"""
This uses Galileo's data on a falling ball.
See: http://www.amstat.org/publications/jse/v3n1/datasets.dickey.html
See also: Jeffreys, W. H., and Berger, J. O. (1992), "Ockham's Razor and Bayesian Analysis," American
Scientist, 80, 64-72 (Erratum, p. 116).
"""
from LOTlib.Hypotheses.Gau... | gpl-3.0 |
cpausmit/IntelROCCS | Detox/python/siteProperties.py | 3 | 12964 | #====================================================================================================
# C L A S S E S concerning the site description
#====================================================================================================
#----------------------------------------------------------------... | mit |
google-research/selfstudy-adversarial-robustness | defense_randomneuron/model.py | 1 | 2860 | # Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
roxyboy/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 302 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
JustinNoel1/ML-Course | linear-regression/python/linreg.py | 1 | 1895 |
from sklearn.linear_model import LinearRegression
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from sklearn.metrics import mean_squared_error
# Fix the number of samples and our seed
NUM_SAMPLES = 200
np.random.seed(42)
# Our "true function"
def f(x):
return 1.5*x + 0.5
#Const... | apache-2.0 |
SKA-INAF/caesar | scripts/skymodel_generator.py | 1 | 65709 | #!/usr/bin/env python
##################################################
### MODULE IMPORT
##################################################
## STANDARD MODULES
import os
import sys
import subprocess
import string
import time
import signal
from threading import Thread
import datetime
import numpy as np
impor... | gpl-3.0 |
aalmah/pylearn2 | pylearn2/train_extensions/plots.py | 34 | 9617 | """
Plot monitoring extensions while training.
"""
__authors__ = "Laurent Dinh"
__copyright__ = "Copyright 2014, Universite de Montreal"
__credits__ = ["Laurent Dinh"]
__license__ = "3-clause BSD"
__maintainer__ = "Laurent Dinh"
__email__ = "dinhlaur@iro"
import logging
import os
import os.path
import stat
import num... | bsd-3-clause |
a-parhom/edx-platform | lms/djangoapps/course_api/blocks/tests/test_api.py | 2 | 11195 | """
Tests for Blocks api.py
"""
from itertools import product
from mock import patch
import ddt
from django.test.client import RequestFactory
from django.test.utils import override_settings
import course_blocks.api as course_blocks_api
from openedx.core.djangoapps.content.block_structure.api import clear_course_fro... | agpl-3.0 |
roxyboy/scikit-learn | examples/cluster/plot_segmentation_toy.py | 257 | 3336 | """
===========================================
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 |
aalmah/pylearn2 | pylearn2/utils/datasets.py | 44 | 9068 | """
Several utilities to evaluate an ALC on the dataset, to iterate over
minibatches from a dataset, or to merge three data with given proportions
"""
# Standard library imports
import logging
import os
import functools
from itertools import repeat
import warnings
# Third-party imports
import numpy
import scipy
from t... | bsd-3-clause |
gibiansky/tensorflow | tensorflow/examples/learn/iris_with_pipeline.py | 13 | 1854 | # 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 |
roxyboy/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 294 | 8265 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
ephes/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 294 | 8265 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
ephes/scikit-learn | sklearn/externals/joblib/__init__.py | 85 | 4795 | """ Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast*... | bsd-3-clause |
edx/edx-platform | openedx/features/course_experience/tests/views/test_course_outline.py | 2 | 34442 | """
Tests for the Course Outline view and supporting views.
"""
import datetime
import re
from unittest.mock import Mock, patch
import ddt
from completion.waffle import ENABLE_COMPLETION_TRACKING_SWITCH
from completion.models import BlockCompletion
from completion.test_utils import CompletionWaffleTestMixin
from dja... | agpl-3.0 |
rgommers/statsmodels | statsmodels/tsa/filters/bk_filter.py | 28 | 3112 | from __future__ import absolute_import
import numpy as np
from scipy.signal import fftconvolve
from ._utils import _maybe_get_pandas_wrapper
def bkfilter(X, low=6, high=32, K=12):
"""
Baxter-King bandpass filter
Parameters
----------
X : array-like
A 1 or 2d ndarray. If 2d, variables are ... | bsd-3-clause |
jeffreyliu3230/osf.io | website/addons/dataverse/tests/test_client.py | 16 | 8789 | from nose.tools import *
import mock
import unittest
from dataverse import Connection, Dataverse, DataverseFile, Dataset
from dataverse.exceptions import UnauthorizedError
from framework.exceptions import HTTPError
from website.addons.dataverse.tests.utils import DataverseAddonTestCase
from website.addons.dataverse.t... | apache-2.0 |
gdementen/larray | larray/tests/generate_data.py | 2 | 9260 | import os
from larray import ndtest, open_excel, Session, X
DATA_DIR = os.path.join(os.path.dirname(__file__), 'data')
def generate_tests_files():
tests = {'1d': 3,
'2d': "a=1..3; b=b0,b1",
'2d_classic': "a=a0..a2;b=b0..b2",
'3d': "a=1..3; b=b0,b1; c=c0..c2",
... | gpl-3.0 |
LohithBlaze/scikit-learn | sklearn/utils/tests/test_multiclass.py | 128 | 12853 |
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 |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Kaggle Avito Demand Prediction Challenge/image feat. extraction/avito_deepIQA/deepIQA/evaluate_back.py | 1 | 2442 | #!/usr/bin/python2
import argparse
import cv2
import numpy as np
import six
from chainer import cuda
from chainer import serializers
from sklearn.feature_extraction.image import extract_patches
from deepIQA.fr_model import FRModel
from deepIQA.nr_model import Model
parser = argparse.ArgumentParser(description="evalu... | apache-2.0 |
mlperf/training_results_v0.5 | v0.5.0/google/cloud_v3.8/resnet-tpuv3-8/code/resnet/model/tpu/models/official/amoeba_net/amoeba_net.py | 5 | 15390 | # 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 |
grehujt/SmallPythonProjects | ClusteringRelatedPosts/solution.py | 1 | 2849 |
import scipy as sp
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk.stem
stemmer = nltk.stem.SnowballStemmer('english')
class StemmedCountVectorizer(CountVectorizer):
def build_analyzer(self):
analyzer = super(CountVectorizer, self).build_analyzer()
return... | mit |
jzt5132/scikit-learn | sklearn/__init__.py | 59 | 3038 | """
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... | bsd-3-clause |
LohithBlaze/scikit-learn | sklearn/tree/tests/test_export.py | 75 | 9318 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
# toy sa... | bsd-3-clause |
jzt5132/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 380 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
aspidites/django | django/contrib/gis/geoip2/base.py | 73 | 8630 | import os
import socket
import geoip2.database
from django.conf import settings
from django.core.validators import ipv4_re
from django.utils import six
from django.utils.ipv6 import is_valid_ipv6_address
from .resources import City, Country
# Creating the settings dictionary with any settings, if needed.
GEOIP_SETT... | bsd-3-clause |
qinjian623/dlnotes | tutorials/pytorch/drl/enduro.py | 1 | 5020 | import argparse
import gym
import numpy as np
import cv2
from itertools import count
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.autograd as autograd
from torch.autograd import Variable
parser = argparse.ArgumentParser(description='PyTorch REINFORCE exa... | gpl-3.0 |
jzt5132/scikit-learn | examples/mixture/plot_gmm_selection.py | 247 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
MohammedWasim/scikit-learn | sklearn/mixture/tests/test_gmm.py | 199 | 17427 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_ma... | bsd-3-clause |
LohithBlaze/scikit-learn | sklearn/datasets/svmlight_format.py | 113 | 15826 | """This module implements a loader and dumper for the svmlight format
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable to
predict.
This format is used as the... | bsd-3-clause |
jzt5132/scikit-learn | examples/feature_selection/plot_feature_selection.py | 248 | 2827 | """
===============================
Univariate Feature Selection
===============================
An example showing univariate feature selection.
Noisy (non informative) features are added to the iris data and
univariate feature selection is applied. For each feature, we plot the
p-values for the univariate feature s... | bsd-3-clause |
JSilva90/MITWS | Asynchronous_SFS/new_async.py | 1 | 5082 | from __future__ import division
import multiprocessing as mp
import time
import pandas as pd
import numpy as np
import sys
import utils
#from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from ML_test import ML_test
from ML_data import ML_instance
max_search_leve... | gpl-3.0 |
glouppe/scikit-learn | examples/covariance/plot_lw_vs_oas.py | 150 | 2951 | """
=============================
Ledoit-Wolf vs OAS estimation
=============================
The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding th... | bsd-3-clause |
glouppe/scikit-learn | sklearn/neighbors/tests/test_neighbors.py | 22 | 45330 | from itertools import product
import pickle
import numpy as np
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix)
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_scor... | bsd-3-clause |
automl/auto-sklearn | autosklearn/pipeline/components/data_preprocessing/rescaling/power_transformer.py | 1 | 1715 | from typing import Dict, Optional, Tuple, Union
import numpy as np
from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE
from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E50... | bsd-3-clause |
fx2003/tensorflow-study | TensorFlow实战/models/cognitive_mapping_and_planning/scripts/script_preprocess_annoations_S3DIS.py | 14 | 7024 | # 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 applicab... | mit |
switowski/invenio | invenio/modules/indexer/tokenizers/BibIndexDOITokenizer.py | 12 | 2004 | # -*- coding: utf-8 -*-
#
# This file is part of Invenio.
# Copyright (C) 2013, 2015 CERN.
#
# Invenio is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or (at your option) any... | gpl-2.0 |
MatthieuCourbariaux/BinaryConnect | svhn.py | 3 | 10981 | # Copyright 2015 Matthieu Courbariaux
# This file is part of BinaryConnect.
# BinaryConnect is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any l... | gpl-2.0 |
sgenoud/scikit-learn | examples/linear_model/plot_sgd_iris.py | 3 | 2170 | """
========================================
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__
i... | bsd-3-clause |
glouppe/scikit-learn | sklearn/cluster/__init__.py | 359 | 1228 | """
The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
algorithms.
"""
from .spectral import spectral_clustering, SpectralClustering
from .mean_shift_ import (mean_shift, MeanShift,
estimate_bandwidth, get_bin_seeds)
from .affinity_propagation_ import affinity_propagati... | bsd-3-clause |
luo66/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 296 | 1770 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.linear_model.bayes import BayesianRidge, ARDRegres... | bsd-3-clause |
elkingtonmcb/h2o-2 | bench/BMscripts/glmBench.py | 11 | 9671 | #GLM bench
import os, sys, time, csv, socket, string
sys.path.append('../py/')
sys.path.extend(['.','..'])
import h2o_cmd, h2o, h2o_hosts, h2o_browse as h2b, h2o_import as h2i, h2o_rf, h2o_jobs
csv_header = ('h2o_build','nMachines','nJVMs','Xmx/JVM','dataset','nTrainRows','nTestRows','nCols','trainParseWallTime','nfol... | apache-2.0 |
codeworldprodigy/lab2 | lib/jinja2/jinja2/visitor.py | 1402 | 3316 | # -*- coding: utf-8 -*-
"""
jinja2.visitor
~~~~~~~~~~~~~~
This module implements a visitor for the nodes.
:copyright: (c) 2010 by the Jinja Team.
:license: BSD.
"""
from jinja2.nodes import Node
class NodeVisitor(object):
"""Walks the abstract syntax tree and call visitor functions for every... | apache-2.0 |
davidhstocker/Graphyne | Smoketest.py | 1 | 217442 | #!/usr/bin/env python3
"""
Smoketest.py: Regression testing utility for Graphyne. Multiprocessing wrapper for Smokest, allowing multiple simultaneous tests against different persistence types.
"""
from tkinter.test.runtktests import this_dir_path
from graphyne.DatabaseDrivers.DriverTermplate import linkTypes
__au... | apache-2.0 |
mlperf/training_results_v0.5 | v0.5.0/google/cloud_v2.8/gnmt-tpuv2-8/code/gnmt/model/t2t/tensor2tensor/layers/transformer_layers.py | 3 | 13829 | # 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 |
tomsilver/nupic | tests/swarming/nupic/swarming/experiments/input_predicted_field/description.py | 1 | 14270 | # ----------------------------------------------------------------------
# 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 |
LohithBlaze/scikit-learn | benchmarks/bench_plot_svd.py | 322 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.s... | bsd-3-clause |
jzt5132/scikit-learn | sklearn/linear_model/bayes.py | 219 | 15248 | """
Various bayesian regression
"""
from __future__ import print_function
# Authors: V. Michel, F. Pedregosa, A. Gramfort
# License: BSD 3 clause
from math import log
import numpy as np
from scipy import linalg
from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, p... | bsd-3-clause |
MohammedWasim/scikit-learn | sklearn/linear_model/bayes.py | 219 | 15248 | """
Various bayesian regression
"""
from __future__ import print_function
# Authors: V. Michel, F. Pedregosa, A. Gramfort
# License: BSD 3 clause
from math import log
import numpy as np
from scipy import linalg
from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, p... | bsd-3-clause |
MohammedWasim/scikit-learn | examples/mixture/plot_gmm_sin.py | 247 | 2747 | """
=================================
Gaussian Mixture Model Sine Curve
=================================
This example highlights the advantages of the Dirichlet Process:
complexity control and dealing with sparse data. The dataset is formed
by 100 points loosely spaced following a noisy sine curve. The fit by
the GMM... | bsd-3-clause |
jzt5132/scikit-learn | sklearn/utils/tests/test_testing.py | 106 | 4210 | 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)
from ... | bsd-3-clause |
jzt5132/scikit-learn | sklearn/neighbors/base.py | 71 | 31147 | """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... | bsd-3-clause |
Vict0rSch/deep_learning | keras/feedforward/feedforward_keras_mnist.py | 1 | 2623 | import time
import numpy as np
from matplotlib import pyplot as plt
from keras.utils import np_utils
import keras.callbacks as cb
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.datasets import mnist
class LossHistory(cb.Call... | gpl-2.0 |
luo66/scikit-learn | examples/applications/plot_outlier_detection_housing.py | 241 | 5577 | """
====================================
Outlier detection on a real data set
====================================
This example illustrates the need for robust covariance estimation
on a real data set. It is useful both for outlier detection and for
a better understanding of the data structure.
We selected two sets o... | bsd-3-clause |
sgenoud/scikit-learn | sklearn/setup.py | 1 | 2735 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
LohithBlaze/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 248 | 2211 | #!/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 |
glouppe/scikit-learn | sklearn/utils/tests/test_validation.py | 54 | 18600 | """Tests for input validation functions"""
import warnings
from tempfile import NamedTemporaryFile
from itertools import product
import numpy as np
from numpy.testing import assert_array_equal
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true, assert_false, assert_equal
from sklearn.utils.... | bsd-3-clause |
RensaProject/nodebox_linguistics_extended | nodebox_linguistics_extended/parser/nltk_lite/__init__.py | 10 | 2228 | # Natural Language Toolkit (NLTK-Lite)
#
# Copyright (C) 2001-2006 University of Pennsylvania
# Authors: Steven Bird <sb@csse.unimelb.edu.au>
# Edward Loper <edloper@gradient.cis.upenn.edu>
# URL: <http://nltk.sf.net>
# For license information, see LICENSE.TXT
"""
NLTK-Lite is a collection of lightweight NLP ... | gpl-2.0 |
lukeiwanski/tensorflow | tensorflow/contrib/losses/python/metric_learning/metric_loss_ops_test.py | 40 | 20535 | # 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 |
MohammedWasim/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 102 | 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 |
jfconavarrete/kaggle-facebook | src/data/pre_process_k_means.py | 1 | 1226 | import pandas as pd
import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
import cv2
from sklearn import metrics
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
from sklearn import cross_validation as cv
from sklearn im... | mit |
tomsilver/nupic | examples/opf/experiments/spatial_classification/category_1/description.py | 17 | 1557 | # ----------------------------------------------------------------------
# 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 |
MohammedWasim/scikit-learn | examples/covariance/plot_lw_vs_oas.py | 247 | 2903 | """
=============================
Ledoit-Wolf vs OAS estimation
=============================
The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding th... | bsd-3-clause |
LohithBlaze/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 250 | 1895 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samp... | bsd-3-clause |
jzt5132/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 |
jzt5132/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 274 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` f... | bsd-3-clause |
automl/auto-sklearn | test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py | 1 | 1879 | import logging
import os
import autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner as metalearner # noqa: E501
import autosklearn.pipeline.classification
from autosklearn.metalearning.metalearning.meta_base import MetaBase
import unittest
logging.basicConfig()
class MetalearningConfiguration(unit... | bsd-3-clause |
sgenoud/scikit-learn | sklearn/cluster/tests/test_k_means.py | 3 | 18449 | """Testing for K-means"""
import numpy as np
import warnings
from scipy import sparse as sp
from numpy.testing import assert_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
from nose import SkipTest
from nose.tools import assert_almost_equal
from nose.tools import... | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.