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Operalib

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Operalib is a library for structured learning and prediction for python <https://www.python.org>_ based on operator-valued kernels (OVKs). OVKs are an extension of scalar kernels to matrix-valued kernels. The idea is to predict silmultaneously several targets while, for instance, encoding the output structure with the operator-valued kernel.

We aim at providing an easy-to-use standard implementation of operator-valued kernel methods. Operalib is designed for compatilibity to scikit-learn <http://scikit-learn.org>_ interface and conventions. It uses numpy <http://www.numpy.org>, scipy <http://www.scipy.org> and cvxopt <http://www.cvxopt.org>_ as underlying libraries.

The project is developed by the AROBAS <https://www.ibisc.univ-evry.fr/arobas>_ group of the IBISC laboratory <https://www.ibisc.univ-evry.fr/en/start>_ of the University of Evry, France.

Documentation

Is available at: http://operalib.github.io/operalib/documentation/.

Install

The package is available on PyPi, and the installation should be as simple as::

pip install operalib

To install from the sources in your home directory, use::

pip install .

To install for all users on Unix/Linux::

python setup.py build python setup.py install

.. For more detailed installation instructions, .. see the web page http://scikit-learn.org/stable/install.html

GIT

You can check the latest sources with the command::

    git clone https://github.com/operalib/operalib

or through ssh, instead of https, if you have write privileges::

    git clone git@github.com:operalib/operalib.git

References
==========
A non-exhaustive list of publications related to operator-valued kernel is
available here:

http://operalib.github.io/operalib/documentation/reference_papers/index.html.