A python module for learnign with operator-valued kernels
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.
Is available at: http://operalib.github.io/operalib/documentation/.
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
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 firstname.lastname@example.org:operalib/operalib.git
A non-exhaustive list of publications related to operator-valued kernel is available here:
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