This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

Operalib

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. It also relies on dill for lambda pickling.

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

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

GIT

You can check the latest sources with the command:

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

or 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.

Release History

Release History

0.2b2

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.2b1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.2b0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
operalib-0.2b2.tar.gz (1.4 MB) Copy SHA256 Checksum SHA256 Source Jul 8, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS HPE HPE Development Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting