Package smile.stat.distribution

Class MultivariateExponentialFamilyMixture

java.lang.Object

smile.stat.distribution.MultivariateMixture

smile.stat.distribution.MultivariateExponentialFamilyMixture

All Implemented Interfaces:

Serializable, MultivariateDistribution

Direct Known Subclasses:

MultivariateGaussianMixture

public class MultivariateExponentialFamilyMixture
extends MultivariateMixture

The finite mixture of distributions from multivariate exponential family. The EM algorithm can be used to learn the mixture model from data.

See Also:

• Serialized Form

Nested Class Summary

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

MultivariateMixture.Component

Field Summary

Fields

Modifier and Type	Field	Description
final double	bic	The BIC score when the distribution is fit on a sample data.
final double	L	The log-likelihood when the distribution is fit on a sample data.

Fields inherited from class smile.stat.distribution.MultivariateMixture

components

Constructor Summary

Constructors

Constructor	Description
MultivariateExponentialFamilyMixture(MultivariateMixture.Component... components)	Constructor.

Method Summary

Modifier and Type	Method	Description
static MultivariateExponentialFamilyMixture	fit(double[][] x, MultivariateMixture.Component... components)	Fits the mixture model with the EM algorithm.
static MultivariateExponentialFamilyMixture	fit(double[][] x, MultivariateMixture.Component[] components, double gamma, int maxIter, double tol)	Fits the mixture model with the EM algorithm.

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

Field Details

L

public final double L

The log-likelihood when the distribution is fit on a sample data.

bic

public final double bic

The BIC score when the distribution is fit on a sample data.

Constructor Details

MultivariateExponentialFamilyMixture

public MultivariateExponentialFamilyMixture(MultivariateMixture.Component... components)

Constructor.

Parameters:

components - a list of multivariate exponential family distributions.

Method Details

fit

public static MultivariateExponentialFamilyMixture fit(double[][] x,
 MultivariateMixture.Component... components)

Fits the mixture model with the EM algorithm.

Parameters:

x - the training data.

components - the initial configuration of mixture. Components may have different distribution form.

Returns:

the distribution.

fit

public static MultivariateExponentialFamilyMixture fit(double[][] x,
 MultivariateMixture.Component[] components,
 double gamma,
 int maxIter,
 double tol)

Fits the mixture model with the EM algorithm.

Parameters:

x - the training data.

components - the initial configuration of mixture. Components may have different distribution form.

gamma - the regularization parameter.

maxIter - the maximum number of iterations.

tol - the tolerance of convergence test.

Returns:

the distribution.