smile.stat.distribution

Class MultivariateGaussianMixture

java.lang.Object

smile.stat.distribution.MultivariateMixture

smile.stat.distribution.MultivariateExponentialFamilyMixture

smile.stat.distribution.MultivariateGaussianMixture

All Implemented Interfaces:

java.io.Serializable, MultivariateDistribution

public class MultivariateGaussianMixture
extends MultivariateExponentialFamilyMixture

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

See Also:

Serialized Form

Nested Class Summary

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

MultivariateMixture.Component

Field Summary

Fields inherited from class smile.stat.distribution.MultivariateExponentialFamilyMixture

bic, L

Fields inherited from class smile.stat.distribution.MultivariateMixture

components

Constructor Summary

Constructors

Constructor and Description
MultivariateGaussianMixture(MultivariateMixture.Component... components) Constructor.

Method Summary

Modifier and Type	Method and Description
static MultivariateGaussianMixture	fit(double[][] data) Fits the Gaussian mixture model with the EM algorithm.
static MultivariateGaussianMixture	fit(double[][] data, boolean diagonal) Fits the Gaussian mixture model with the EM algorithm.
static MultivariateGaussianMixture	fit(int k, double[][] data) Fits the Gaussian mixture model with the EM algorithm.
static MultivariateGaussianMixture	fit(int k, double[][] data, boolean diagonal) Fits the Gaussian mixture model with the EM algorithm.

Methods inherited from class smile.stat.distribution.MultivariateExponentialFamilyMixture

fit, fit

Methods inherited from class smile.stat.distribution.MultivariateMixture

bic, cdf, cov, entropy, length, logp, map, mean, p, posteriori, size, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.stat.distribution.MultivariateDistribution

likelihood, logLikelihood

Constructor Detail

MultivariateGaussianMixture

public MultivariateGaussianMixture(MultivariateMixture.Component... components)

Constructor.

Parameters:

components - a list of multivariate Gaussian distributions.

Method Detail

fit

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

Fits the Gaussian mixture model with the EM algorithm.

Parameters:

data - the training data.

k - the number of components.

fit

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

Fits the Gaussian mixture model with the EM algorithm.

Parameters:

data - the training data.

k - the number of components.

diagonal - true if the components have diagonal covariance matrix.

fit

public static MultivariateGaussianMixture fit(double[][] data)

Fits the Gaussian mixture model with the EM algorithm. The number of components will be selected by BIC.

Parameters:

data - the training data.

fit

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

Fits the Gaussian mixture model 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.