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
Alternatively, it can be installed directly from the GitHub repository:
pip install git+git://github.com/alonfnt/pcax.git