petal-decomposition

petal-decomposition provides matrix decomposition algorithms including PCA (principal component analysis) and ICA (independent component analysis).

Requirements

• Rust ≥ 1.38
• BLAS/LAPACK backend (OpenBLAS, Netlib, or Intel MKL)

Features

• PCA with exact, full SVD (singular value decomposition)
• PCA with randomized, truncated SVD
• FastICA

Examples

The following example shows how to apply PCA to an array of three samples, and obtain singular values as well as how much variance each component explains.

```rust use ndarray::arr2; use petal_decomposition::Pca;

let x = arr2(&[[0f64, 0f64], [1f64, 1f64], [2f64, 2f64]]); let mut pca = Pca::new(2); // Keep two dimensions. pca.fit(&x).unwrap();

let s = pca.singularvalues(); // [2f64, 0f64] let v = pca.explainedvarianceratio(); // [1f64, 0f64] let y = pca.transform(&x).unwrap(); // [-2f64.sqrt(), 0f64, 2f64.sqrt()] ```