Struct KernelPCA 

pub struct KernelPCA { /* private fields */ }

Kernel Principal Component Analysis for non-linear dimensionality reduction

Implementations

impl KernelPCA

pub fn new() -> Self

Create a new KernelPCA instance with default parameters

pub fn with_n_components(self, n_components: usize) -> Self

Set the number of components

pub fn with_kernel(self, kernel: KernelType) -> Self

Set the kernel function

pub fn with_tol(self, tol: Float) -> Self

Set the tolerance for eigenvalue computation

pub fn with_max_iter(self, max_iter: usize) -> Self

Set the maximum number of iterations

pub fn with_fit_inverse_transform(self, fit_inverse_transform: bool) -> Self

Enable fitting of inverse transform

pub fn fit(&mut self, x: &Array2<Float>) -> Result<&mut Self, SklearsError>

Fit the kernel PCA model to the data

pub fn transform( &self, x: &Array2<Float>, ) -> Result<Array2<Float>, SklearsError>

Transform data to the kernel PCA space

pub fn fit_transform( &mut self, x: &Array2<Float>, ) -> Result<Array2<Float>, SklearsError>

Fit the model and transform the data

pub fn eigenvalues(&self) -> Option<&Array1<Float>>

Get the eigenvalues

pub fn eigenvectors(&self) -> Option<&Array2<Float>>

Get the eigenvectors

pub fn explained_variance_ratio(&self) -> Option<Array1<Float>>

Get explained variance ratio

Trait Implementations

impl Debug for KernelPCA

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

Formats the value using the given formatter.

impl Default for KernelPCA

fn default() -> Self

Returns the “default value” for a type.