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

Crate Features

• intel-mkl, netlib, and openblas are used to select a BLAS/LAPACK backend. By default, intel-mkl is used.
• serialization enables serialization/deserialization using serde.

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.

use ndarray::arr2;
use petal_decomposition::Pca;

let x = arr2(&[[0_f64, 0_f64], [1_f64, 1_f64], [2_f64, 2_f64]]);
let mut pca = Pca::new(2);               // Keep two dimensions.
pca.fit(&x).unwrap();

let s = pca.singular_values();           // [2_f64, 0_f64]
let v = pca.explained_variance_ratio();  // [1_f64, 0_f64]
let y = pca.transform(&x).unwrap();      // [-2_f64.sqrt(), 0_f64, 2_f64.sqrt()]