Struct Pca 

#[non_exhaustive]
pub struct Pca { /* private fields */ }

Principal Component Analysis.

Projects data onto the top-k eigenvectors of the covariance matrix. Optionally whitens the output so each component has unit variance.

Implementations

impl Pca

pub fn new() -> Self

Create a PCA that retains all components.

pub fn with_n_components(k: usize) -> Self

Create a PCA that retains the top k components.

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

Enable whitening (scale components to unit variance).

pub fn explained_variance_ratio(&self) -> &[f64]

Fraction of total variance explained by each retained component.

pub fn explained_variance(&self) -> &[f64]

Absolute variance (eigenvalue) of each retained component.

pub fn components(&self) -> &[Vec<f64>]

Principal axes in feature space — [n_components][n_features].

pub fn n_components_fitted(&self) -> usize

Number of components actually retained after fitting.

Trait Implementations

impl Clone for Pca

fn clone(&self) -> Pca

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Pca

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

Formats the value using the given formatter.

impl Default for Pca

fn default() -> Self

Returns the “default value” for a type.

impl Transformer for Pca

fn fit(&mut self, data: &Dataset) -> Result<()>

Learn parameters from the training data.

fn transform(&self, data: &mut Dataset) -> Result<()>

Apply the learned transformation to a dataset (in-place).

fn inverse_transform(&self, data: &mut Dataset) -> Result<()>

Reverse the transformation (if invertible).

fn fit_transform(&mut self, data: &mut Dataset) -> Result<()>

Convenience: fit + transform in one call.