Struct rusty_machine::learning::gmm::GaussianMixtureModel [] [src]

pub struct GaussianMixtureModel {
    pub cov_option: CovOption,
    // some fields omitted
}

A Gaussian Mixture Model

Fields

The covariance options for the GMM.

Methods

impl GaussianMixtureModel
[src]

Constructs a new Gaussian Mixture Model

Defaults to 100 maximum iterations and full covariance structure.

Examples

use rusty_machine::learning::gmm::GaussianMixtureModel;

let gmm = GaussianMixtureModel::new(3);

Constructs a new GMM with the specified prior mixture weights.

The mixture weights must have the same length as the number of components. Each element of the mixture weights must be non-negative.

Examples

use rusty_machine::learning::gmm::GaussianMixtureModel;
use rusty_machine::linalg::Vector;

let mix_weights = Vector::new(vec![0.25, 0.25, 0.5]);

let gmm = GaussianMixtureModel::with_weights(3, mix_weights).unwrap();

Failures

Fails if either of the following conditions are met:

  • Mixture weights do not have length k.
  • Mixture weights have a negative entry.

The model means

Returns an Option<&Matrix> containing the model means. Each row represents the mean of one of the Gaussians.

The model covariances

Returns an Option<&Vec>> containing the model covariances. Each Matrix in the vector is the covariance of one of the Gaussians.

The model mixture weights

Returns a reference to the model mixture weights. These are the weighted contributions of each underlying Gaussian to the model distribution.

Sets the max number of iterations for the EM algorithm.

Examples

use rusty_machine::learning::gmm::GaussianMixtureModel;

let mut gmm = GaussianMixtureModel::new(2);
gmm.set_max_iters(5);

Trait Implementations

impl Debug for GaussianMixtureModel
[src]

Formats the value using the given formatter.

impl UnSupModel<Matrix<f64>, Matrix<f64>> for GaussianMixtureModel
[src]

Train the model using inputs.

Predict output from inputs.