Module dimensionality_reduction 

Dimensionality Reduction Transformers

This module provides dimensionality reduction techniques for preprocessing pipelines:

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Independent Component Analysis (ICA)
• Non-negative Matrix Factorization (NMF)
• t-SNE (t-distributed Stochastic Neighbor Embedding)

Examples

use sklears_preprocessing::dimensionality_reduction::{PCA, PCAConfig};
use scirs2_core::ndarray::Array2;

fn example() -> Result<(), Box<dyn std::error::Error>> {
    let config = PCAConfig::new(2); // Reduce to 2 components
    let mut pca = PCA::new(config);
     
    let data = Array2::from_shape_vec((4, 3), vec![
        1.0, 2.0, 3.0,
        2.0, 4.0, 6.0,
        3.0, 6.0, 9.0,
        4.0, 8.0, 12.0,
    ])?;
     
    let pca_fitted = pca.fit(&data, &())?;
    let transformed = pca_fitted.transform(&data)?;
    println!("Reduced data shape: {:?}", transformed.dim());
     
    Ok(())
}

Structs

ICA

Independent Component Analysis transformer

ICAConfig

Configuration for Independent Component Analysis

ICAFitted

Fitted ICA with learned parameters

LDA

Linear Discriminant Analysis transformer

LDAConfig

Configuration for Linear Discriminant Analysis

LDAFitted

Fitted LDA with learned parameters

NMF

Non-negative Matrix Factorization transformer

NMFConfig

Configuration for Non-negative Matrix Factorization

NMFFitted

Fitted NMF with learned parameters

PCA

Principal Component Analysis transformer

PCAConfig

Configuration for Principal Component Analysis

PCAFitted

Fitted PCA with learned parameters

Enums

IcaAlgorithm

ICA algorithms

IcaFunction

Non-linearity functions for ICA

LdaSolver

Solver algorithms for LDA

NmfInit

Initialization methods for NMF

NmfSolver

Solver algorithms for NMF

PcaSolver

Solver algorithms for PCA