Metadata-Version: 1.1
Name: scikit-learn
Version: 0.16.1
Summary: A set of python modules for machine learning and data mining
Home-page: http://scikit-learn.org
Author: Andreas Mueller
Author-email: amueller@ais.uni-bonn.de
License: new BSD
Download-URL: http://sourceforge.net/projects/scikit-learn/files/
Description: .. -*- 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 >= 1.6.1,
        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 git@github.com: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.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
