A set of python modules for machine learning and data mining
Project description
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
The required dependencies to build the software are Python >= 2.6, setuptools, Numpy >= 1.3, SciPy >= 0.7 and a working C/C++ compiler. This configuration matches the Ubuntu 10.04 LTS release from April 2010.
To run the tests you will also need nose >= 0.10.
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 --home
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 git://github.com/scikit-learn/scikit-learn.git
or if you have write privileges:
git clone git@github.com:scikit-learn/scikit-learn.git
Testing
After installation, you can launch the test suite from outside the source directory (you will need to have nosetest installed):
$ nosetest --exe sklearn
See web page http://scikit-learn.org/stable/install.html#testing for more information.
Random number generation can be controled during testing by setting the SKLEARN_SEED environment variable
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