| Name | pcax JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | Minimal Principal Component Analysis (PCA) implementation using JAX. |
| upload_time | 2023-05-29 12:20:21 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2023 Albert Alonso Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
jax
pca
machine-learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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
```python
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
```
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