Metadata-Version: 2.4
Name: mini-gpr
Version: 0.0.0
Summary: a minimal reference implementation of gaussian process regression in pure numpy
Author-email: John Gardner <gardner.john97+dphil@gmail.com>
Requires-Python: >=3.11
Requires-Dist: jaxtyping>=0.3.2
Requires-Dist: matplotlib>=3.10.3
Requires-Dist: numpy>=2.3.0
Description-Content-Type: text/markdown

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# `mini-gpr`

[![Tests](https://github.com/jla-gardner/mini-gpr/actions/workflows/tests.yaml/badge.svg?branch=main)](https://github.com/jla-gardner/mini-gpr/actions/workflows/tests.yaml)
[![codecov](https://codecov.io/gh/jla-gardner/mini-gpr/branch/main/graph/badge.svg)](https://codecov.io/gh/jla-gardner/mini-gpr)
[![PyPI](https://img.shields.io/pypi/v/mini-gpr)](https://pypi.org/project/mini-gpr/)


<img src="docs/_static/1d-gpr.gif" alt="1D GPR" width="400">

</div>



## planned future work
- optimise sparse point locations
- implement the "Fully Independent Training Conditional" (FITC) approximation