Gaussian process with spherical harmonic features in JAX
Project description
$GP \mathcal{f} Y_\ell^m$
A lightweight library in JAX for Gaussian process with spherical kernels and sparse spherical harmonic inducing features.
$GP \mathcal{f} Y_\ell^m$ is based on the simple flax.struct dataclass. It implements (Eleftheriadis et al. 2023), which revisits the Sparse Gaussian Process with Spherical Harmonic features from Dutordoir et al. 2020, and introduces:
PolynomialDecay
kernel with "continuous" depth.- Sparse orthogonal basis derived from
SphericalHarmonics
features with phase truncation.
Installation
Latest (stable) release from PyPI
pip install gpfy
Development version
Alternatively, you can install the latest GitHub develop
version.
First create a virtual enviroment via conda:
conda create -n gpfy_env python=3.10.0
conda activate gpfy_env
Then clone a copy of the repository to your local machine and run the setup configuration in development mode:
git clone https://github.com/stefanosele/GPfY.git
cd GPfY
make install
This will automatically install all required dependencies.
Finally you can check the installation via running the tests:
make test
Project details
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