ml-pca 2.1.0 | Documentation

ml-pca

2.1.0

PCA ▸
- Static members
- .load
- Instance members
- #predict
- #getExplainedVariance
- #getCumulativeVariance
- #getEigenvectors
- #getEigenvalues
- #getStandardDeviations
- #getLoadings
- #toJSON

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PCA

src/pca.js

Creates new PCA (Principal Component Analysis) from the dataset

new PCA(dataset: Matrix, options: Object)

Parameters

dataset (Matrix) dataset or covariance matrix

options (Object)

Name	Description
options.isCovarianceMatrix `boolean` (default `false`)	true if the dataset is a covariance matrix
options.center `boolean` (default `true`)	should the data be centered (subtract the mean)
options.scale `boolean` (default `false`)	should the data be scaled (divide by the standard deviation)

Static Members

▸ load(model)

src/pca.js

Load a PCA model from JSON

load(model: Object): PCA

Parameters

model (Object)

Returns

PCA:

Instance Members

▸ predict(dataset, options)

src/pca.js

Project the dataset into the PCA space

predict(dataset: Matrix, options: Object): Matrix

Parameters

dataset (Matrix)

options

(Object
            = {})

Returns

Matrix: dataset projected in the PCA space

▸ getExplainedVariance()

src/pca.js

Returns the proportion of variance for each component

getExplainedVariance(): [number]

Returns

[number]:

▸ getCumulativeVariance()

src/pca.js

Returns the cumulative proportion of variance

getCumulativeVariance(): [number]

Returns

[number]:

▸ getEigenvectors()

src/pca.js

Returns the Eigenvectors of the covariance matrix

getEigenvectors(): Matrix

Returns

Matrix:

▸ getEigenvalues()

src/pca.js

Returns the Eigenvalues (on the diagonal)

getEigenvalues(): [number]

Returns

[number]:

▸ getStandardDeviations()

src/pca.js

Returns the standard deviations of the principal components

getStandardDeviations(): [number]

Returns

[number]:

▸ getLoadings()

src/pca.js

Returns the loadings matrix

getLoadings(): Matrix

Returns

Matrix:

▸ toJSON()

src/pca.js

Export the current model to a JSON object

toJSON(): Object

Returns

Object: model