Metadata-Version: 1.1
Name: scikit-learn
Version: 0.19.1
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: https://pypi.org/project/scikit-learn/#files
Description: .. -*- mode: rst -*-
        
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        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 <AUTHORS.rst>`_ file for a complete list of contributors.
        
        It is currently maintained by a team of volunteers.
        
        Website: http://scikit-learn.org
        
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        scikit-learn requires:
        
        - Python (>= 2.7 or >= 3.3)
        - NumPy (>= 1.8.2)
        - SciPy (>= 0.13.3)
        
        For running the examples Matplotlib >= 1.1.1 is required.
        
        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.
        
        User installation
        ~~~~~~~~~~~~~~~~~
        
        If you already have a working installation of numpy and scipy,
        the easiest way to install scikit-learn is using ``pip`` ::
        
            pip install -U scikit-learn
        
        or ``conda``::
        
            conda install scikit-learn
        
        The documentation includes more detailed `installation instructions <http://scikit-learn.org/stable/install.html>`_.
        
        
        Development
        -----------
        
        We welcome new contributors of all experience levels. The scikit-learn
        community goals are to be helpful, welcoming, and effective. The
        `Development Guide <http://scikit-learn.org/stable/developers/index.html>`_
        has detailed information about contributing code, documentation, tests, and
        more. We've included some basic information in this README.
        
        Important links
        ~~~~~~~~~~~~~~~
        
        - Official source code repo: https://github.com/scikit-learn/scikit-learn
        - Download releases: https://pypi.python.org/pypi/scikit-learn
        - Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
        
        Source code
        ~~~~~~~~~~~
        
        You can check the latest sources with the command::
        
            git clone https://github.com/scikit-learn/scikit-learn.git
        
        Setting up a development environment
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        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
        
        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/developers/advanced_installation.html#testing
        for more information.
        
            Random number generation can be controlled during testing by setting
            the ``SKLEARN_SEED`` environment variable.
        
        Submitting a Pull Request
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        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
        
        
        Project History
        ---------------
        
        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 <AUTHORS.rst>`_ file for a complete list of contributors.
        
        The project is currently maintained by a team of volunteers.
        
        **Note**: `scikit-learn` was previously referred to as `scikits.learn`.
        
        
        Help and Support
        ----------------
        
        Documentation
        ~~~~~~~~~~~~~
        
        - HTML documentation (stable release): http://scikit-learn.org
        - HTML documentation (development version): http://scikit-learn.org/dev/
        - FAQ: http://scikit-learn.org/stable/faq.html
        
        Communication
        ~~~~~~~~~~~~~
        
        - Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
        - IRC channel: ``#scikit-learn`` at ``webchat.freenode.net``
        - Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
        - Website: http://scikit-learn.org
        
        Citation
        ~~~~~~~~
        
        If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn
        
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 :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
