smile.stat.distribution

Class GaussianMixture

java.lang.Object

smile.stat.distribution.AbstractDistribution

smile.stat.distribution.Mixture

smile.stat.distribution.ExponentialFamilyMixture

smile.stat.distribution.GaussianMixture

All Implemented Interfaces:

Distribution

public class GaussianMixture
extends ExponentialFamilyMixture

Finite univariate 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.Mixture

Mixture.Component

Constructor Summary

Constructors

Constructor and Description
GaussianMixture(double[] data) Constructor.
GaussianMixture(double[] data, int k) Constructor.
GaussianMixture(java.util.List<Mixture.Component> mixture) Constructor.

Method Summary

Methods inherited from class smile.stat.distribution.Mixture

bic, cdf, entropy, getComponents, logp, mean, npara, p, quantile, rand, sd, size, toString, var

Methods inherited from class smile.stat.distribution.AbstractDistribution

likelihood, logLikelihood

Methods inherited from class java.lang.Object

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

Constructor Detail

GaussianMixture

public GaussianMixture(java.util.List<Mixture.Component> mixture)

Constructor.

Parameters:

mixture - a list of multivariate Gaussian distributions.

GaussianMixture

public GaussianMixture(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.

GaussianMixture

public GaussianMixture(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.