PCAx is a minimal Principal Component Analysis (PCA) written in Jax. The aim of this project is to provide a JAX-based PCA implementation, eliminating the need for unnecessary data transfer to CPU or conversions to Numpy. This can provide performance benefits when working with large datasets or in GPU-intensive workflow
import pcax
# Fit the PCA model with 3 components on your data X
state = pcax.fit(X, n_components=3)
# Transform X to its principal components
X_pca = pcax.transform(state, X)
# Recover the original X from its principal components
X_recover = pcax.recover(state, X_pca)pcax can be installed from PyPI via pip
pip install pcax
If you use pcax in your research and need to reference it, please cite it as follows:
@software{alonso_pcax,
author = {Alonso, Albert},
title = {pcax: Minimal Principal Component Analysis (PCA) Implementation in JAX},
url = {https://github.com/alonfnt/pcax},
version = {0.0.1}
}