Decomposition

Dimensionality reduction. Live in datarust::decomposition. Both implement Transformer.

PCA

Principal Component Analysis via Jacobi eigenvalue decomposition of the covariance matrix.

#![allow(unused)]
fn main() {
use datarust::decomposition::{PCA, PCAComponents};

// Select components by count, variance ratio, or keep all:
let mut pca = PCA::new(PCAComponents::Variance(0.95)) // keep 95% of variance
    .whiten(true);                                    // optional: whiten components
let projected = pca.fit_transform(&x)?;

// After fit:
pca.components();                // [[f64; n_features]; n_components]
pca.explained_variance_ratio();  // how much variance each component captures
pca.noise_variance();            // estimated noise variance
let reconstructed = pca.inverse_transform(&projected)?; // approximate recovery
}

Component selection

• PCAComponents::Count(k) — request k components, capped at min(n_samples, n_features).
• PCAComponents::Variance(0.95) — keep enough to explain 95% of variance.
• PCAComponents::All — keep all components.

Solver

#![allow(unused)]
fn main() {
use datarust::decomposition::PCASolver;

let mut pca = PCA::new(PCAComponents::Count(10))
    .solver(PCASolver::Randomized); // Halko–Martinsson–Tropp randomized SVD
}

• Auto (default) — exact eigensolver.
• Full — full Jacobi eigendecomposition.
• Randomized — randomized SVD, much faster for tall-and-wide, low-rank data.

Performance tip: enabling the matrixmultiply feature speeds up PCA significantly on large dense inputs by dispatching covariance and transform matmuls to a tuned pure-Rust GEMM.

TruncatedSVD

Dimensionality reduction via truncated SVD. Does not center the data, making it suitable for sparse or TF-IDF inputs.

#![allow(unused)]
fn main() {
use datarust::decomposition::{TruncatedSVD, SVDComponents};

// By count, variance threshold, or all:
let mut svd = TruncatedSVD::new(5).unwrap();                 // 5 components
let mut svd = TruncatedSVD::new(0.95).unwrap();              // 95% variance
let mut svd = TruncatedSVD::new(SVDComponents::All).unwrap();
let out = svd.fit_transform(&x)?;
}

PCA vs TruncatedSVD