smile.stat.distribution

Class MultivariateGaussianMixture

java.lang.Object

smile.stat.distribution.AbstractMultivariateDistribution

smile.stat.distribution.MultivariateMixture

smile.stat.distribution.MultivariateExponentialFamilyMixture

smile.stat.distribution.MultivariateGaussianMixture

All Implemented Interfaces:

MultivariateDistribution

public class MultivariateGaussianMixture
extends MultivariateExponentialFamilyMixture

Finite multivariate Gaussian mixture. The EM algorithm is provide to learned the mixture model from data. BIC score is employed to estimate the number of components.

Nested Class Summary

Nested classes/interfaces inherited from class smile.stat.distribution.MultivariateMixture

MultivariateMixture.Component

Constructor Summary

Constructors

Constructor and Description
MultivariateGaussianMixture(double[][] data) Constructor.
MultivariateGaussianMixture(double[][] data, boolean diagonal) Constructor.
MultivariateGaussianMixture(double[][] data, int k) Constructor.
MultivariateGaussianMixture(double[][] data, int k, boolean diagonal) Constructor.
MultivariateGaussianMixture(java.util.List<MultivariateMixture.Component> mixture) Constructor.

Method Summary

Methods inherited from class smile.stat.distribution.MultivariateMixture

bic, cdf, cov, entropy, getComponents, logp, mean, npara, p, size, toString

Methods inherited from class smile.stat.distribution.AbstractMultivariateDistribution

likelihood, logLikelihood

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

MultivariateGaussianMixture

public MultivariateGaussianMixture(java.util.List<MultivariateMixture.Component> mixture)

Constructor.

Parameters:

mixture - a list of multivariate Gaussian distributions.

MultivariateGaussianMixture

public MultivariateGaussianMixture(double[][] data,
                                   int k)

Constructor. The Gaussian mixture model will be learned from the given data with the EM algorithm.

Parameters:

data - the training data.

k - the number of components.

MultivariateGaussianMixture

public MultivariateGaussianMixture(double[][] data,
                                   int k,
                                   boolean diagonal)

Constructor. The Gaussian mixture model will be learned from the given data with the EM algorithm.

Parameters:

data - the training data.

k - the number of components.

diagonal - true if the components have diagonal covariance matrix.

MultivariateGaussianMixture

public MultivariateGaussianMixture(double[][] data)

Constructor. The Gaussian mixture model will be learned from the given data with the EM algorithm. The number of components will be selected by BIC.

Parameters:

data - the training data.

MultivariateGaussianMixture

public MultivariateGaussianMixture(double[][] data,
                                   boolean diagonal)

Constructor. The Gaussian mixture model will be learned from the given data with the EM algorithm. The number of components will be selected by BIC.

Parameters:

data - the training data.

diagonal - true if the components have diagonal covariance matrix.