https://github.com/operalib/operalib

Learning with operator-valued kernels
https://github.com/operalib/operalib

features fourier kernel kernel-methods learning-algorithm machine-learning-library operator-valued random rff scikit-learn

Last synced: 2 months ago
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Learning with operator-valued kernels

• Host: GitHub
• URL: https://github.com/operalib/operalib
• Owner: operalib
• License: bsd-3-clause
• Created: 2016-03-10T15:55:28.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2023-01-31T08:47:06.000Z (almost 2 years ago)
• Last Synced: 2024-04-26T05:07:11.612Z (9 months ago)
• Topics: features, fourier, kernel, kernel-methods, learning-algorithm, machine-learning-library, operator-valued, random, rff, scikit-learn
• Language: Python
• Size: 32.6 MB
• Stars: 19
• Watchers: 9
• Forks: 3
• Open Issues: 6
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
.. -*- mode: rst -*-

.. |Travis| image:: https://travis-ci.org/operalib/operalib.svg?branch=master

.. _Travis: https://travis-ci.org/operalib/operalib

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.. _Codecov: https://codecov.io/gh/operalib/operalib

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.. _CircleCI: https://circleci.com/gh/operalib/operalib

.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg

.. _Python27: https://github.com/operalib/operalib

.. |Python36| image:: https://img.shields.io/badge/python-3.6-blue.svg

.. _Python36: https://github.com/operalib/operalib

.. |PyPi| image:: https://badge.fury.io/py/operalib.svg

.. _PyPi: https://badge.fury.io/py/operalib

Operalib

========

|PyPi|_ |Travis|_ |Codecov|_ |CircleCI|_ |Python27|_ |Python36|_

Operalib is a library for structured learning and prediction for

`python `_ 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 `_ interface and conventions.

It uses `numpy `_,

`scipy `_ and `cvxopt `_ as

underlying libraries.

The project is developed by the

`AROBAS `_ group of the

`IBISC laboratory `_ 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 [email protected]: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.