Struct PCA 

pub struct PCA { /* private fields */ }

Principal Component Analysis (PCA) for dimensionality reduction.

PCA reduces dimensionality by projecting data onto principal components (directions of maximum variance).

Example

use aprender::preprocessing::PCA;
use aprender::traits::Transformer;
use aprender::primitives::Matrix;

let data = Matrix::from_vec(4, 3, vec![
    1.0, 2.0, 3.0,
    4.0, 5.0, 6.0,
    7.0, 8.0, 9.0,
    10.0, 11.0, 12.0,
]).expect("valid matrix dimensions");

let mut pca = PCA::new(2); // Reduce to 2 components
let transformed = pca.fit_transform(&data).expect("fit_transform should succeed");
assert_eq!(transformed.shape(), (4, 2));

Implementations

impl PCA

pub fn new(n_components: usize) -> Self

Creates a new PCA transformer.

Arguments

• n_components - Number of principal components to keep

pub fn explained_variance(&self) -> Option<&[f32]>

Returns the variance explained by each component.

pub fn explained_variance_ratio(&self) -> Option<&[f32]>

Returns the ratio of variance explained by each component.

pub fn components(&self) -> Option<&Matrix<f32>>

Returns the principal components.

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

Reconstructs data from principal component space.

Errors

Returns error if PCA is not fitted.

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 Transformer for PCA

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

Fits the transformer to data.

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

Transforms data using fitted parameters.

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

Fits and transforms in one step.