Struct linfa_reduction::PcaParams

pub struct PcaParams { /* private fields */ }

Pincipal Component Analysis parameters

Implementations

impl PcaParams

pub fn whiten(self, apply: bool) -> Self

Apply whitening to the embedding vector

Whitening will scale the eigenvalues of the transformation such that the covariance will be unit diagonal for the original data.

Trait Implementations

impl Clone for PcaParams

fn clone(&self) -> PcaParams

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PcaParams

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T, D: Data<Elem = f64>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, ReductionError> for PcaParams

Fit a PCA model given a dataset

The Principal Component Analysis takes the records of a dataset and tries to find the best fit in a lower dimensional space such that the maximal variance is retained.

Parameters

• dataset: A dataset with records in N dimensions

Returns

A fitted PCA model with origin and hyperplane

type Object = Pca<f64>

fn fit(&self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>) -> Result<Pca<f64>>

impl PartialEq<PcaParams> for PcaParams

fn eq(&self, other: &PcaParams) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Eq for PcaParams

impl StructuralEq for PcaParams

impl StructuralPartialEq for PcaParams