Module rusty_machine::learning::gmm

Gaussian Mixture Models

Provides implementation of GMMs using the EM algorithm.

Usage

use rusty_machine::linalg::Matrix;
use rusty_machine::learning::gmm::{CovOption, GaussianMixtureModel};
use rusty_machine::learning::UnSupModel;

let inputs = Matrix::new(4, 2, vec![1.0, 2.0, -3.0, -3.0, 0.1, 1.5, -5.0, -2.5]);
let test_inputs = Matrix::new(3, 2, vec![1.0, 2.0, 3.0, 2.9, -4.4, -2.5]);

// Create gmm with k(=2) classes.
let mut model = GaussianMixtureModel::new(2);
model.set_max_iters(10);
model.cov_option = CovOption::Diagonal;

// Where inputs is a Matrix with features in columns.
model.train(&inputs).unwrap();

// Print the means and covariances of the GMM
println!("{:?}", model.means());
println!("{:?}", model.covariances());

// Where test_inputs is a Matrix with features in columns.
let post_probs = model.predict(&test_inputs).unwrap();

// Probabilities that each point comes from each Gaussian.
println!("{:?}", post_probs.data());

Structs

GaussianMixtureModel

A Gaussian Mixture Model

Enums

CovOption

Covariance options for GMMs.