pyGPs is a library hosting Python implementations of Gaussian processes (GPs) for machine learning. pyGPs bridges the gap between systems designed primarily for users, who mainly want to apply GPs and need basic machine learning routines for model training, evaluation, and visualiztion, and expressive systems for developers, who focus on extending the core functionalities as covariance and likelihood functions, as well as inference techniques.
The software package is released under the BSD 2-Clause (FreeBSD) License. Copyright (c) by Marion Neumann, Shan Huang, Daniel Marthaler, & Kristian Kersting, Feb.2014
Further, it includes implementations of
The most recent stable release is pyGPs v1.3.2. See changelog for a list of changes to the previous release. If you observe problems or bugs, please contact us. You can download the developer’s guide and manual containing an API here: API and manual. You can also download a procedual implementation of GP functionality from Github. However, the procedual version will not be supported in future.
The following persons helped to improve this software: Roman Garnett, Maciej Kurek, Hannes Nickisch, Zhao Xu, and Alejandro Molina.
This work is partly supported by the Fraunhofer ATTRACT fellowship STREAM.