{"id":18655908,"url":"https://github.com/michaelosthege/pyrff","last_synced_at":"2025-06-21T00:08:22.096Z","repository":{"id":39750464,"uuid":"262287508","full_name":"michaelosthege/pyrff","owner":"michaelosthege","description":"pyrff: Python implementation of random fourier feature approximations for gaussian processes","archived":false,"fork":false,"pushed_at":"2025-03-18T22:25:54.000Z","size":71860,"stargazers_count":28,"open_issues_count":1,"forks_count":2,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-04-11T20:45:53.470Z","etag":null,"topics":["bayesian-optimization","gaussian-processes","thompson-sampling"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![PyPI version](https://img.shields.io/pypi/v/pyrff)](https://pypi.org/project/pyrff)\n[![pipeline](https://github.com/michaelosthege/pyrff/workflows/pipeline/badge.svg)](https://github.com/michaelosthege/pyrff/actions)\n[![coverage](https://codecov.io/gh/michaelosthege/pyrff/branch/master/graph/badge.svg)](https://codecov.io/gh/michaelosthege/pyrff)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3831380.svg)](https://doi.org/10.5281/zenodo.3831380)\n\n\n# `pyrff`: Approximating Gaussian Process samples with Random Fourier Features\nThis project is a Python implementation of random fourier feature (RFF) approximations [1].\n\nIt is heavily inspired by the implementations from [2, 3] and generalizes the implementation to work with GP hyperparameters obtained from any GP library.\n\nExamples are given as Jupyter notebooks for GPs fitted with [PyMC3](https://github.com/pymc-devs/pymc3) and [scikit-learn](https://scikit-learn.org):\n+ [Example_RFF_PyMC3_1D](https://github.com/michaelosthege/pyrff/blob/master/notebooks/Example_RFF_1D_PyMC3.ipynb)\n+ [Example_RFF_PyMC3_2D](https://github.com/michaelosthege/pyrff/blob/master/notebooks/Example_RFF_2D_PyMC3.ipynb)\n+ [Thompson sampling, 1D with sklearn](https://github.com/michaelosthege/pyrff/blob/master/notebooks/TS_1D_sklearn.ipynb)\n\n# Installation\n`pyrff` is released on [PyPI](https://pypi.org/project/pyrff/):\n```\npip install pyrff\n```\n# Usage and Citing\n`pyrff` is licensed under the [GNU Affero General Public License v3.0](https://github.com/michaelosthege/pyrff/blob/master/LICENSE).\n\nWhen using `robotools` in your work, please cite the [corresponding software version](https://doi.org/10.5281/zenodo.3831380).\n\n```bibtex\n@software{pyrff,\n  author       = {Michael Osthege and\n                  Kobi Felton},\n  title        = {michaelosthege/pyrff: v2.0.1},\n  month        = dec,\n  year         = 2020,\n  publisher    = {Zenodo},\n  version      = {v2.0.1},\n  doi          = {10.5281/zenodo.4317685},\n  url          = {https://doi.org/10.5281/zenodo.4317685}\n}\n```\n\nHead over to Zenodo to [generate a BibTeX citation](https://doi.org/10.5281/zenodo.3831380) for the latest release.\n\n# References\n1. Hernández-Lobato, 2014 [paper](https://arxiv.org/pdf/1406.2541), [code](https://bitbucket.org/jmh233/codepesnips2014/src/ac843ba992ca1879190a472ac20c83a447e4e2c0/sourceFiles/sampleMinimum.m#lines-1)\n2. PES implementation in Cornell-MOE [code](https://github.com/wujian16/Cornell-MOE/blob/df299d1be882d2af9796d7a68b3f9505cac7a53e/pes/PES/sample_minimum.py#L23)\n3. Bradford, 2018 [paper](https://link.springer.com/article/10.1007/s10898-018-0609-2/), [code](https://github.com/Eric-Bradford/TS-EMO/blob/87151d94081db1d0f128a788ebdb789d2891ee9a/TSEMO_V4.m#L501)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmichaelosthege%2Fpyrff","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmichaelosthege%2Fpyrff","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmichaelosthege%2Fpyrff/lists"}