pcax
Minimal Principal Component Analsys (PCA) implementation using 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
Usage
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)
Installation
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
1 maintainer
Metadata
1 maintainer
Release files for pcax 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pcax-0.1.0.tar.gz | 4.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pcax-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.3 kB
Release files / pcax-0.1.0.tar.gz
| Download URL | pcax-0.1.0.tar.gz |
|---|---|
| Size | 4.0 kB |
| Tags | Source |
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Release files / pcax-0.1.0-py3-none-any.whl
| Download URL | pcax-0.1.0-py3-none-any.whl |
|---|---|
| Size | 4.3 kB |
| Tags | Python 3 |
|
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