decomposition.PCA

Usage

import { PCA } from 'machinelearn/decomposition';

const pca = new PCA();
const X = [[1, 2], [3, 4], [5, 6]];
pca.fit(X);
console.log(pca.components); // result: [ [ 0.7071067811865476, 0.7071067811865474 ], [ 0.7071067811865474, -0.7071067811865476 ] ]
console.log(pca.explained_variance); // result: [ [ -0.3535533905932736, 0 ], [ 0, 0.5 ], [ 0.35355339059327373, 0 ] ]

Constructors

• constructor

Properties

• components

• explained_variance

Methods

• fit

• fromJSON

• predict

• toJSON

Constructors

constructor

⊕ PCA()

Defined in

Parameters:

Returns: PCA

Properties

▸ components

Defined in decomposition/pca.ts:31

▸ explained_variance

Defined in decomposition/pca.ts:38

Methods

λ fit

Fit the model with X. At the moment it does not take n_components into consideration so it will only calculate Singular value decomposition

Defined in decomposition/pca.ts:46

Parameters:

Returns:

void

λ fromJSON

Restores the model from given states

Defined in decomposition/pca.ts:91

Parameters:

Returns:

void

λ predict

Predict does nothing in PCA

Defined in decomposition/pca.ts:68

Parameters:

Returns:

number[][]

λ toJSON

Saves the model's states

Defined in decomposition/pca.ts:76

Returns: