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TomDLTTomDLT
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solve conflict with master
2 parents 057c70c + 6cbef3e commit a9ac84a

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‎.gitattributes‎

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/sklearn/__check_build/_check_build.c -diff
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/sklearn/_isotonic.c -diff
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/sklearn/cluster/_dbscan_inner.cpp -diff
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/sklearn/cluster/_hierarchical.cpp -diff
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/sklearn/cluster/_k_means.c -diff
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/sklearn/cluster/_k_means_elkan.c -diff
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/sklearn/datasets/_svmlight_format.c -diff
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/sklearn/decomposition/_online_lda.c -diff
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/sklearn/decomposition/cdnmf_fast.c -diff
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/sklearn/ensemble/_gradient_boosting.c -diff
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/sklearn/feature_extraction/_hashing.c -diff
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/sklearn/linear_model/cd_fast.c -diff
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/sklearn/linear_model/sgd_fast.c -diff
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/sklearn/linear_model/sag_fast.c -diff
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/sklearn/metrics/pairwise_fast.c -diff
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/sklearn/neighbors/ball_tree.c -diff
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/sklearn/neighbors/kd_tree.c -diff
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/sklearn/svm/liblinear.c -diff
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/sklearn/svm/libsvm.c -diff
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/sklearn/svm/libsvm_sparse.c -diff
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/sklearn/tree/_tree.c -diff
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/sklearn/tree/_utils.c -diff
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/sklearn/utils/arrayfuncs.c -diff
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/sklearn/utils/graph_shortest_path.c -diff
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/sklearn/utils/lgamma.c -diff
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/sklearn/utils/_logistic_sigmoid.c -diff
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/sklearn/utils/murmurhash.c -diff
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/sklearn/utils/seq_dataset.c -diff
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/sklearn/utils/sparsefuncs_fast.c -diff
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/sklearn/utils/weight_vector.c -diff
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/doc/whats_new.rst merge=union

‎.gitignore‎

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.pydevproject
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.idea
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cythonize.dat
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*.c
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*.cpp
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‎.mailmap‎

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@@ -93,7 +93,8 @@ Olivier Grisel <[email protected]> <[email protected]>
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Olivier Hervieu <[email protected]> <[email protected]>
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Paul Butler <[email protected]>
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Peter Prettenhofer <[email protected]>
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Raghav R V <[email protected]>
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Raghav RV <[email protected]>
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Raghav RV <[email protected]> <[email protected]>
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Robert Layton <[email protected]>
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Roman Sinayev <[email protected]>
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Roman Sinayev <[email protected]> <roman@y570.(none)>

‎.travis.yml‎

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# Pre-install packages for the ubuntu distribution
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cache:
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apt: true
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# We use three different cache directory
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# to work around a Travis bug with multi-platform cache
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directories:
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- $HOME/sklearn_build_ubuntu
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- $HOME/sklearn_build_oldest
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- $HOME/sklearn_build_latest
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- $HOME/sklearn_build_numpy_dev
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- $HOME/.cache/pip
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- $HOME/download
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addons:
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# This environment tests that scikit-learn can be built against
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# versions of numpy, scipy with ATLAS that comes with Ubuntu Precise 12.04
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- DISTRIB="ubuntu" PYTHON_VERSION="2.7" CYTHON_VERSION="0.23.4"
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CACHED_BUILD_DIR="$HOME/sklearn_build_ubuntu" COVERAGE=true
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COVERAGE=true
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# This environment tests the oldest supported anaconda env
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- DISTRIB="conda" PYTHON_VERSION="2.6" INSTALL_MKL="false"
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NUMPY_VERSION="1.6.2" SCIPY_VERSION="0.11.0" CYTHON_VERSION="0.21"
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CACHED_BUILD_DIR="$HOME/sklearn_build_oldest"
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- DISTRIB="conda" PYTHON_VERSION="2.7" INSTALL_MKL="false"
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NUMPY_VERSION="1.6.2" SCIPY_VERSION="0.11.0" CYTHON_VERSION="0.23"
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# This environment tests the newest supported anaconda env
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# It also runs tests requiring Pandas.
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- DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="true"
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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.17.0" PANDAS_VERSION="0.18.0"
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CYTHON_VERSION="0.23.4" CACHED_BUILD_DIR="$HOME/sklearn_build_latest"
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CYTHON_VERSION="0.23.4"
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# flake8 linting on diff wrt common ancestor with upstream/master
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- RUN_FLAKE8="true" SKIP_TESTS="true"
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DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="true"
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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.17.0" CYTHON_VERSION="0.23.4"
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CACHED_BUILD_DIR="$HOME/dummy"
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matrix:
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# the before_install step with and addons/apt/packages declaration.
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- python: 3.5
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env: DISTRIB="scipy-dev-wheels"
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CACHED_BUILD_DIR="$HOME/sklearn_build_numpy_dev"
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sudo: True
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before_install: sudo apt-get install -yqq libatlas3gf-base libatlas-dev
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‎Makefile‎

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find sklearn -name "*.py" -exec perl -pi -e 's/[ \t]*$$//' {} \;
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cython:
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python build_tools/cythonize.py sklearn
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python setup.py build_src
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ctags:
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# make tags for symbol based navigation in emacs and vim

‎README.rst‎

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.. -*- mode: rst -*-
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|Travis|_ |AppVeyor|_ |Coveralls|_ |CircleCI|_ |Python27|_ |Python35|_ |PyPi|_ |DOI|_
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.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.svg?branch=master
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Dependencies
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~~~~~~~~~~~~
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Scikit-learn requires::
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scikit-learn requires:
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- Python (>= 2.6 or >= 3.3),
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- NumPy (>= 1.6.1),
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- SciPy (>= 0.9).
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- Python (>= 2.7 or >= 3.3)
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- NumPy (>= 1.6.1)
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- SciPy (>= 0.9)
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scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
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Subprograms library. scikit-learn comes with a reference implementation, but
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We welcome new contributors of all experience levels. The scikit-learn
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community goals are to be helpful, welcoming, and effective. The
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`Development Guide <http://scikit-learn.org/stable/developers/index.html>`_
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`Development Guide <http://scikit-learn.org/stable/developers/index.html>`_
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has detailed information about contributing code, documentation, tests, and
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more. We've included some basic information in this README.
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After installation, you can launch the test suite from outside the
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source directory (you will need to have the ``nose`` package installed)::
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$ nosetests -v sklearn
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nosetests -v sklearn
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Under Windows, it is recommended to use the following command (adjust the path
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to the ``python.exe`` program) as using the ``nosetests.exe`` program can badly
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interact with tests that use ``multiprocessing``::
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C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn
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C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn
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See the web page http://scikit-learn.org/stable/install.html#testing
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See the web page http://scikit-learn.org/stable/developers/advanced_installation.html#testing
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for more information.
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Random number generation can be controlled during testing by setting
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with our guidelines: http://scikit-learn.org/stable/developers/index.html
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Project history
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Project History
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---------------
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The project was started in 2007 by David Cournapeau as a Google Summer
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The project is currently maintained by a team of volunteers.
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**Note** `scikit-learn` was previously referred to as `scikits.learn`.
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**Note**: `scikit-learn` was previously referred to as `scikits.learn`.
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Help and Support
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- IRC channel: ``#scikit-learn`` at ``irc.freenode.net``
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- Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
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- Website: http://scikit-learn.org
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Citation
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~~~~~~~~
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If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn

‎benchmarks/bench_plot_fastkmeans.py‎

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from collections import defaultdict
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from time import time
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import six
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import numpy as np
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from numpy import random as nr
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results = compute_bench(samples_range, features_range)
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results_2 = compute_bench_2(chunks)
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max_time = max([max(i) for i in [t for (label, t) in results.iteritems()
106-
if "speed" in label]])
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max_time = max([max(i) for i in [t for (label, t) in six.iteritems(results)
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if "speed" in label]])
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max_inertia = max([max(i) for i in [
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t for (label, t) in results.iteritems()
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if "speed" not in label]])
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t for (label, t) in six.iteritems(results)
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if "speed" not in label]])
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fig = plt.figure('scikit-learn K-Means benchmark results')
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for c, (label, timings) in zip('brcy',
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sorted(results.iteritems())):
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sorted(six.iteritems(results))):
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if 'speed' in label:
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ax = fig.add_subplot(2, 2, 1, projection='3d')
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ax.set_zlim3d(0.0, max_time * 1.1)
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i = 0
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for c, (label, timings) in zip('br',
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sorted(results_2.iteritems())):
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sorted(six.iteritems(results_2))):
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i += 1
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ax = fig.add_subplot(2, 2, i + 2)
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y = np.asarray(timings)

‎benchmarks/bench_plot_nmf.py‎

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import gc
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from time import time
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import six
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import numpy as np
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from scipy.linalg import norm
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fig = plt.figure('scikit-learn Non-Negative Matrix Factorization'
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'benchmark results')
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ax = fig.gca(projection='3d')
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for c, (label, timings) in zip('rbgcm', sorted(results.iteritems())):
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for c, (label, timings) in zip('rbgcm', sorted(six.iteritems(results))):
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X, Y = np.meshgrid(samples_range, features_range)
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Z = np.asarray(timings).reshape(samples_range.shape[0],
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features_range.shape[0])

‎benchmarks/bench_plot_omp_lars.py‎

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import sys
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from time import time
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import six
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import numpy as np
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from sklearn.linear_model import lars_path, orthogonal_mp
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fig = plt.figure('scikit-learn OMP vs. LARS benchmark results')
110-
for i, (label, timings) in enumerate(sorted(results.iteritems())):
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for i, (label, timings) in enumerate(sorted(six.iteritems(results))):
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ax = fig.add_subplot(1, 2, i+1)
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vmax = max(1 - timings.min(), -1 + timings.max())
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plt.matshow(timings, fignum=False, vmin=1 - vmax, vmax=1 + vmax)
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ax.set_xticklabels([''] + map(str, samples_range))
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ax.set_yticklabels([''] + map(str, features_range))
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ax.set_xticklabels([''] + [str(each) for each in samples_range])
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ax.set_yticklabels([''] + [str(each) for each in features_range])
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plt.xlabel('n_samples')
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plt.ylabel('n_features')
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plt.title(label)

‎benchmarks/bench_plot_svd.py‎

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import numpy as np
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import six
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from scipy.linalg import svd
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from sklearn.utils.extmath import randomized_svd
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from sklearn.datasets.samples_generator import make_low_rank_matrix
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label = 'scikit-learn singular value decomposition benchmark results'
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fig = plt.figure(label)
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ax = fig.gca(projection='3d')
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for c, (label, timings) in zip('rbg', sorted(results.iteritems())):
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for c, (label, timings) in zip('rbg', sorted(six.iteritems(results))):
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X, Y = np.meshgrid(samples_range, features_range)
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Z = np.asarray(timings).reshape(samples_range.shape[0],
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features_range.shape[0])

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