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:

java.io.Serializable, 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.

See Also:

Serialized Form

Nested Class Summary

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

Mixture.Component

Field Summary

Fields inherited from class smile.stat.distribution.ExponentialFamilyMixture

bic, L

Fields inherited from class smile.stat.distribution.Mixture

components

Constructor Summary

Constructors

Constructor and Description
GaussianMixture(Mixture.Component... components) Constructor.

Method Summary

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

Methods inherited from class smile.stat.distribution.ExponentialFamilyMixture

fit, fit

Methods inherited from class smile.stat.distribution.Mixture

bic, cdf, entropy, length, logp, map, mean, p, posteriori, quantile, rand, size, toString, variance

Methods inherited from class smile.stat.distribution.AbstractDistribution

inverseTransformSampling, quantile, quantile, rejection

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.stat.distribution.Distribution

likelihood, logLikelihood, rand, sd

Constructor Detail

GaussianMixture

public GaussianMixture(Mixture.Component... components)

Constructor.

Parameters:

components - a list of multivariate Gaussian distributions.

Method Detail

fit

public static GaussianMixture fit(int k,
                                  double[] x)

Fits the Gaussian mixture model with the EM algorithm.

Parameters:

k - the number of components.

x - the training data.

fit

public static GaussianMixture fit(double[] x)

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

Parameters:

x - the training data.