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Name: scikit-learn
Version: 0.17
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|_ |AppVeyor|_ |Coveralls|_
.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.png?branch=master
.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn
.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/github/scikit-learn/scikit-learn?branch=master&svg=true
.. _AppVeyor: https://ci.appveyor.com/project/sklearn-ci/scikit-learn/history
.. |Coveralls| image:: https://coveralls.io/repos/scikit-learn/scikit-learn/badge.svg?branch=master
.. _Coveralls: https://coveralls.io/r/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
For more detailed installation instructions,
see the web page http://scikit-learn.org/stable/install.html
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
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