A Python package for inference with Gaussian processes.
The goal is for this to be a relatively self-contained python package for using
Gaussian Processes (GPs) that is loosely based on Carl Rasmussen's
toolbox. The structure and ideas are based on that toolbox's implementations,
but some changes have been made to make this package more pythonic.
The easiest way to install this package is by running
pip install -r https://github.com/mwhoffman/pygp/raw/master/requirements.txt pip install git+https://github.com/mwhoffman/pygp.git
The first line installs any dependencies of the package and the second line installs the package itself. Alternatively the repository can be cloned directly in order to make any local modifications to the code. In this case the dependencies can easily be installed by running
pip install -r requirements.txt
from the main directory. The package itself can be installed by running
python setup.py or by symlinking the directory into somewhere on the
Once the package is installed the included demos can be run directly via python.
For example, by running
python -m pygp.demos.basic
A full list of demos can be viewed here.