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Merge branch 'master' into model_selection_touchups
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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 <olivier.grisel@ensta.org> <olivier.grisel@ensta.org>
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Olivier Hervieu <olivier.hervieu@gmail.com> <olivier.hervieu@tinyclues.com>
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Paul Butler <paulgb@gmail.com>
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Peter Prettenhofer <peter.prettenhofer@gmail.com>
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Raghav R V <rvraghav93@gmail.com>
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Raghav RV <rvraghav93@gmail.com>
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Raghav RV <rvraghav93@gmail.com> <ragvrv@gmail.com>
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Robert Layton <robertlayton@gmail.com>
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Roman Sinayev <roman.sinayev@gmail.com>
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Roman Sinayev <roman.sinayev@gmail.com> <roman@y570.(none)>

‎.travis.yml

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- DISTRIB="ubuntu" PYTHON_VERSION="2.7" CYTHON_VERSION="0.23.4"
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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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- 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.

‎CONTRIBUTING.md

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How to contribute
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-----------------
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The preferred workflow for contributing to scikit-learn is to fork the
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The preferred workflow for contributing to scikit-learn is to fork the
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[main repository](https://github.com/scikit-learn/scikit-learn) on
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GitHub, clone, and develop on a branch. Steps:
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$ git clone git@github.com:YourLogin/scikit-learn.git
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$ cd scikit-learn
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```
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3. Create a ``feature`` branch to hold your development changes:
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```bash
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$ git checkout -b my-feature
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```
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Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch!
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4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files:
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Click the 'Pull request' button to send your changes to the project's maintainers for
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review. This will send an email to the committers.
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(If any of the above seems like magic to you, please look up the
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(If any of the above seems like magic to you, please look up the
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[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
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Pull Request Checklist
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import scipy; print("SciPy", scipy.__version__)
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import sklearn; print("Scikit-Learn", sklearn.__version__)
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```
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- Please be specific about what estimators and/or functions are involved
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and the shape of the data, as appropriate; please include a
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[reproducible](http://stackoverflow.com/help/mcve) code snippet
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-------------------
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Visit the [Contributing Code](http://scikit-learn.org/stable/developers/index.html#coding-guidelines)
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section of the website for more information including conforming to the
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section of the website for more information including conforming to the
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API spec and profiling contributed code.

‎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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Website: http://scikit-learn.org
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Installation
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------------
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scikit-learn requires:
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- Python (>= 2.6 or >= 3.3)
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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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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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The project was started in 2007 by David Cournapeau as a Google Summer
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of Code project, and since then many volunteers have contributed. See
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the AUTHORS.rst file for a complete list of contributors.
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the `AUTHORS.rst <AUTHORS.rst>`_ file for a complete list of contributors.
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The project is currently maintained by a team of volunteers.
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‎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()
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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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t for (label, t) in six.iteritems(results)
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fig = plt.figure('scikit-learn K-Means benchmark results')
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sorted(six.iteritems(results))):
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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)

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