Awesome
ml-pca
Principal component analysis (PCA).
<h3 align="center"> <a href="https://www.zakodium.com"> <img src="https://www.zakodium.com/brand/zakodium-logo-white.svg" width="50" alt="Zakodium logo" /> </a> <p> Maintained by <a href="https://www.zakodium.com">Zakodium</a> </p>
Installation
$ npm install ml-pca
Usage
const { PCA } = require('ml-pca');
const dataset = require('ml-dataset-iris').getNumbers();
// dataset is a two-dimensional array where rows represent the samples and columns the features
const pca = new PCA(dataset);
console.log(pca.getExplainedVariance());
/*
[ 0.9246187232017269,
0.05306648311706785,
0.017102609807929704,
0.005212183873275558 ]
*/
const newPoints = [
[4.9, 3.2, 1.2, 0.4],
[5.4, 3.3, 1.4, 0.9],
];
console.log(pca.predict(newPoints)); // project new points into the PCA space
/*
[
[ -2.830722471866897,
0.01139060953209596,
0.0030369648815961603,
-0.2817812120420965 ],
[ -2.308002707614927,
-0.3175048770719249,
0.059976053412802766,
-0.688413413360567 ]]
*/