Struct ICA 

pub struct ICA { /* private fields */ }

Independent Component Analysis using FastICA algorithm.

ICA separates multivariate signals into independent, non-Gaussian components.

Implementations

impl ICA

pub fn new(n_components: usize) -> Self

Creates a new ICA model.

Arguments

• n_components - Number of independent components to extract

Examples

use aprender::decomposition::ICA;

let ica = ICA::new(3); // Extract 3 components

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

Sets the maximum number of iterations.

pub fn with_tolerance(self, tol: f32) -> Self

Sets the convergence tolerance.

pub fn with_random_state(self, seed: u64) -> Self

Sets the random state for reproducibility.

pub fn fit(&mut self, x: &Matrix<f32>) -> Result<()>

Fits the ICA model to the data.

Arguments

• x - Data matrix (n × p), where n is samples and p is features

Errors

Returns error if data dimensions are invalid or optimization fails.

pub fn transform(&self, x: &Matrix<f32>) -> Result<Matrix<f32>>

Transforms data using the fitted ICA model.

Arguments

• x - Data matrix (n × p)

Returns

Independent components (n × n_components)

Trait Implementations

impl Clone for ICA

fn clone(&self) -> ICA

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ICA

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

Formats the value using the given formatter.