smile.stat.distribution

Class MultivariateExponentialFamilyMixture

java.lang.Object

smile.stat.distribution.AbstractMultivariateDistribution

smile.stat.distribution.MultivariateMixture

smile.stat.distribution.MultivariateExponentialFamilyMixture

All Implemented Interfaces:

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.

Nested Class Summary

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

MultivariateMixture.Component

Constructor Summary

Constructors

Constructor and Description
MultivariateExponentialFamilyMixture(java.util.List<MultivariateMixture.Component> mixture) Constructor.
MultivariateExponentialFamilyMixture(java.util.List<MultivariateMixture.Component> mixture, double[][] data) 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

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

MultivariateExponentialFamilyMixture

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

Constructor.

Parameters:

mixture - a list of multivariate exponential family distributions.

MultivariateExponentialFamilyMixture

public MultivariateExponentialFamilyMixture(java.util.List<MultivariateMixture.Component> mixture,
                                            double[][] data)

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

Parameters:

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

data - the training data.