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        "author": "Andreas Mueller",
        "author_email": "amueller@ais.uni-bonn.de",
        "bugtrack_url": null,
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: C",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": ".. -*- mode: rst -*-\n\n|Travis|_ |AppVeyor|_ |Coveralls|_\n\n.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.png?branch=master\n.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn\n\n.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/github/scikit-learn/scikit-learn?branch=master&svg=true\n.. _AppVeyor: https://ci.appveyor.com/project/sklearn-ci/scikit-learn/history\n\n.. |Coveralls| image:: https://coveralls.io/repos/scikit-learn/scikit-learn/badge.svg?branch=master\n.. _Coveralls: https://coveralls.io/r/scikit-learn/scikit-learn\n\nscikit-learn\n============\n\nscikit-learn is a Python module for machine learning built on top of\nSciPy and distributed under the 3-Clause BSD license.\n\nThe project was started in 2007 by David Cournapeau as a Google Summer\nof Code project, and since then many volunteers have contributed. See\nthe AUTHORS.rst file for a complete list of contributors.\n\nIt is currently maintained by a team of volunteers.\n\n**Note** `scikit-learn` was previously referred to as `scikits.learn`.\n\n\nImportant links\n===============\n\n- Official source code repo: https://github.com/scikit-learn/scikit-learn\n- HTML documentation (stable release): http://scikit-learn.org\n- HTML documentation (development version): http://scikit-learn.org/dev/\n- Download releases: http://sourceforge.net/projects/scikit-learn/files/\n- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues\n- Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general\n- IRC channel: ``#scikit-learn`` at ``irc.freenode.net``\n\nDependencies\n============\n\nscikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4.\n(using the same codebase thanks to an embedded copy of\n`six <http://pythonhosted.org/six/>`_). It should also work with Python 3.3.\n\nThe required dependencies to build the software are NumPy >= 1.6.1,\nSciPy >= 0.9 and a working C/C++ compiler.\n\nFor running the examples Matplotlib >= 1.1.1 is required and for running the\ntests you need nose >= 1.1.2.\n\nThis configuration matches the Ubuntu Precise 12.04 LTS release from April\n2012.\n\nscikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra\nSubprograms library. scikit-learn comes with a reference implementation, but\nthe system CBLAS will be detected by the build system and used if present.\nCBLAS exists in many implementations; see `Linear algebra libraries\n<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_\nfor known issues.\n\n\nInstall\n=======\n\nThis package uses distutils, which is the default way of installing\npython modules. To install in your home directory, use::\n\n  python setup.py install --user\n\nTo install for all users on Unix/Linux::\n\n  python setup.py build\n  sudo python setup.py install\n\n\nDevelopment\n===========\n\nCode\n----\n\nGIT\n~~~\n\nYou can check the latest sources with the command::\n\n    git clone https://github.com/scikit-learn/scikit-learn.git\n\nor if you have write privileges::\n\n    git clone git@github.com:scikit-learn/scikit-learn.git\n\n\nContributing\n~~~~~~~~~~~~\n\nQuick tutorial on how to go about setting up your environment to\ncontribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md\n\nBefore opening a Pull Request, have a look at the\nfull Contributing page to make sure your code complies\nwith our guidelines: http://scikit-learn.org/stable/developers/index.html\n\n\nTesting\n-------\n\nAfter installation, you can launch the test suite from outside the\nsource directory (you will need to have the ``nose`` package installed)::\n\n   $ nosetests -v sklearn\n\nUnder Windows, it is recommended to use the following command (adjust the path\nto the ``python.exe`` program) as using the ``nosetests.exe`` program can badly\ninteract with tests that use ``multiprocessing``::\n\n   C:\\Python34\\python.exe -c \"import nose; nose.main()\" -v sklearn\n\nSee the web page http://scikit-learn.org/stable/install.html#testing\nfor more information.\n\n    Random number generation can be controlled during testing by setting\n    the ``SKLEARN_SEED`` environment variable.",
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