ferrolearn-decomp 0.1.1

Dimensionality reduction and decomposition for the ferrolearn ML framework
Documentation

ferrolearn-decomp

Dimensionality reduction and matrix decomposition for the ferrolearn machine learning framework.

Algorithms

Linear methods

Model Description
PCA Principal Component Analysis — project onto directions of maximum variance
IncrementalPCA Incremental PCA for large datasets that don't fit in memory
TruncatedSVD Randomized SVD (Halko algorithm) — works on uncentered/sparse data
NMF Non-negative Matrix Factorization (coordinate descent and multiplicative update solvers)
FactorAnalysis Factor Analysis via EM algorithm
FastICA Independent Component Analysis

Manifold learning

Model Description
KernelPCA Non-linear PCA via RBF, polynomial, or sigmoid kernels
Isomap Isometric mapping via geodesic distances on a kNN graph
MDS Classical Multidimensional Scaling
SpectralEmbedding Laplacian Eigenmaps
LLE Locally Linear Embedding

Example

use ferrolearn_decomp::PCA;
use ferrolearn_core::{Fit, Transform};
use ndarray::array;

let x = array![[1.0_f64, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]];

let pca = PCA::<f64>::new(2);
let fitted = pca.fit(&x, &()).unwrap();
let projected = fitted.transform(&x).unwrap();
assert_eq!(projected.ncols(), 2);

// Inspect explained variance
let variance_ratio = fitted.explained_variance_ratio();

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.