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Gaussian Processes for Regression and Classification

Project description

pyGPs is a library containing code for Gaussian Process (GP) Regression and Classification.

The online documentation can be found at: http://www-ai.cs.uni-dortmund.de/weblab/static/api_docs/pyGPs/

pyGPs is an object-oriented implementation of GPs. Its functionality follows roughly the gpml matlab implementation by Carl Edward Rasmussen and Hannes Nickisch (Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-01-21).

Standard GP regression and (binary) classification as well as FITC (sparse GPs) inference is implemented. For a list of implemented covariance, mean, likelihood, and inference functions see list_of_functions.txt. The current implementation is optimized and tested, however, the work on this library is still in progress. We appreciate any feedback.

For a comprehensive introduction to the current functionality and demonstrations, just open /doc/build/html/index.html in your browser to get to the html documentation of the whole package.

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