.. -- mode: rst --

    |Travis|_

    .. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.png?branch=master
    .. _Travis: https://travis-ci.org/scikit-learn/scikit-learn

    scikit-learn
    ============

    scikit-learn is a Python module for machine learning built on top of
    SciPy and distributed under the 3-Clause BSD license.

    The project was started in 2007 by David Cournapeau as a Google Summer
    of Code project, and since then many volunteers have contributed. See
    the AUTHORS.rst file for a complete list of contributors.

    It is currently maintained by a team of volunteers.

    **Note** `scikit-learn` was previously referred to as `scikits.learn`.


    Important links
    ===============

    - Official source code repo: https://github.com/scikit-learn/scikit-learn
    - HTML documentation (stable release): http://scikit-learn.org
    - HTML documentation (development version): http://scikit-learn.org/dev/
    - Download releases: http://sourceforge.net/projects/scikit-learn/files/
    - Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
    - Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
    - IRC channel: ``#scikit-learn`` at ``irc.freenode.net``

    Dependencies
    ============

    scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4.
    (using the same codebase thanks to an embedded copy of
    `six <http://pythonhosted.org/six/>`_). It should also work with Python 3.3.

    The required dependencies to build the software are NumPy >= 1.6.2,
    SciPy >= 0.9 and a working C/C++ compiler.

    For running the examples Matplotlib >= 1.1.1 is required and for running the
    tests you need nose >= 1.1.2.

    This configuration matches the Ubuntu Precise 12.04 LTS release from April
    2012.

    scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
    Subprograms library. scikit-learn comes with a reference implementation, but
    the system CBLAS will be detected by the build system and used if present.
    CBLAS exists in many implementations; see `Linear algebra libraries
    <http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
    for known issues.


    Install
    =======

    This package uses distutils, which is the default way of installing
    python modules. To install in your home directory, use::

      python setup.py install --user

    To install for all users on Unix/Linux::

      python setup.py build
      sudo python setup.py install


    Development
    ===========

    Code
    ----

    GIT
    ~~~

    You can check the latest sources with the command::

        git clone https://github.com/scikit-learn/scikit-learn.git

    or if you have write privileges::

        git clone [email protected]:scikit-learn/scikit-learn.git


    Contributing
    ~~~~~~~~~~~~

    Quick tutorial on how to go about setting up your environment to
    contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

    Before opening a Pull Request, have a look at the
    full Contributing page to make sure your code complies
    with our guidelines: http://scikit-learn.org/stable/developers/index.html


    Testing
    -------

    After installation, you can launch the test suite from outside the
    source directory (you will need to have the ``nose`` package installed)::

       $ nosetests -v sklearn

    Under Windows, it is recommended to use the following command (adjust the path
    to the ``python.exe`` program) as using the ``nosetests.exe`` program can badly
    interact with tests that use ``multiprocessing``::

       C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

    See the web page http://scikit-learn.org/stable/install.html#testing
    for more information.

        Random number generation can be controlled during testing by setting
        the ``SKLEARN_SEED`` environment variable.