ml-pca
ml-pca
Principal component analysis (PCA).
Maintained byZakodium
Installation
$ npm install ml-pca
Usage
const {PCA } =require('ml-pca');
constdataset =require('ml-dataset-iris').getNumbers();
// dataset is a two-dimensional array where rows represent the samples and columns the features
constpca =newPCA(dataset);
console.log(pca.getExplainedVariance());
/*
[ 0.9246187232017269,
0.05306648311706785,
0.017102609807929704,
0.005212183873275558 ]
*/
constnewPoints = [
[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 ]]
*/