Implementation of random fourier feature (RFF) approximations and Thompson sampling.
Project description
pyrff
: Approximating Gaussian Process samples with Random Fourier Features
This project is a Python implementation of random fourier feature (RFF) approximations [1].
It is heavily inspired by the implementations from [2, 3] and generalizes the implementation to work with GP hyperparameters obtained from any GP library.
Examples are given as Jupyter notebooks for GPs fitted with PyMC3 and scikit-learn:
Installation
pyrff
is released on PyPI:
pip install pyrff
Usage and Citing
pyrff
is licensed under the GNU Affero General Public License v3.0.
When using robotools
in your work, please cite the corresponding software version.
@software{pyrff,
author = {Michael Osthege and
Kobi Felton},
title = {michaelosthege/pyrff: v2.0.1},
month = dec,
year = 2020,
publisher = {Zenodo},
version = {v2.0.1},
doi = {10.5281/zenodo.4317685},
url = {https://doi.org/10.5281/zenodo.4317685}
}
Head over to Zenodo to generate a BibTeX citation for the latest release.
References
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.