pyGPs is a Python project for Gaussian process (GP) regression and classification for machine learning.
We support two libraries: pyGP_OO and pyGP_PR. pyGP_OO is currently the default download, for pyGP_PR follow this link: https://github.com/marionmari/pyGP_PR/archive/v1.1.tar.gz.
pyGP_PR is a procedural implementation of GPs and follows structure and functionality of the gpml matlab implementaion by Carl Edward Rasmussen and Hannes Nickisch (Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-01-21).
pyGP_OO is an object-oriented implemetation of GP regression and classificaion additionally supporting useful routines for the practical use of GPs, such as cross validation functionalities for evaluation as well as basic routines for iterative restarts for the GP hyperparameter optimization.
Future extensions will be designed for pyGP_OO. pyGP_FN will be maintained as it is now.
- Changes to previous version:
pyGP_OO v1.0 is released and is the default download now. pyGP_PR v1.1 is released with substantial documentation updates and renamed (FN -> PR).
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