Package org.hipparchus.distribution.multivariate

Class MixtureMultivariateNormalDistribution

java.lang.Object

org.hipparchus.distribution.multivariate.AbstractMultivariateRealDistribution

org.hipparchus.distribution.multivariate.MixtureMultivariateRealDistribution<MultivariateNormalDistribution>

org.hipparchus.distribution.multivariate.MixtureMultivariateNormalDistribution

All Implemented Interfaces:

MultivariateRealDistribution

public class MixtureMultivariateNormalDistribution
extends MixtureMultivariateRealDistribution<MultivariateNormalDistribution>

Multivariate normal mixture distribution. This class is mainly syntactic sugar.

See Also:

MixtureMultivariateRealDistribution

Field Summary

Fields inherited from class org.hipparchus.distribution.multivariate.AbstractMultivariateRealDistribution

random

Constructor Summary

Constructors

Constructor	Description
MixtureMultivariateNormalDistribution(double[] weights, double[][] means, double[][][] covariances)	Creates a multivariate normal mixture distribution.
MixtureMultivariateNormalDistribution(List<Pair<Double,MultivariateNormalDistribution>> components)	Creates a mixture model from a list of distributions and their associated weights.
MixtureMultivariateNormalDistribution(RandomGenerator rng, List<Pair<Double,MultivariateNormalDistribution>> components)	Creates a mixture model from a list of distributions and their associated weights.

Method Summary

Methods inherited from class org.hipparchus.distribution.multivariate.AbstractMultivariateRealDistribution

getDimension, sample

Methods inherited from class org.hipparchus.distribution.multivariate.MixtureMultivariateRealDistribution

density, getComponents, reseedRandomGenerator, sample

Methods inherited from class java.lang.Object

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

Constructor Detail

MixtureMultivariateNormalDistribution

public MixtureMultivariateNormalDistribution(double[] weights,
                                             double[][] means,
                                             double[][][] covariances)

Creates a multivariate normal mixture distribution.

Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see MixtureMultivariateRealDistribution.sample() and AbstractMultivariateRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to pass null as random generator via the appropriate constructors to avoid the additional initialisation overhead.

Parameters:

weights - Weights of each component.

means - Mean vector for each component.

covariances - Covariance matrix for each component.

MixtureMultivariateNormalDistribution

public MixtureMultivariateNormalDistribution(List<Pair<Double,MultivariateNormalDistribution>> components)

Creates a mixture model from a list of distributions and their associated weights.

Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see MixtureMultivariateRealDistribution.sample() and AbstractMultivariateRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to pass null as random generator via the appropriate constructors to avoid the additional initialisation overhead.

Parameters:

components - List of (weight, distribution) pairs from which to sample.

MixtureMultivariateNormalDistribution

public MixtureMultivariateNormalDistribution(RandomGenerator rng,
                                             List<Pair<Double,MultivariateNormalDistribution>> components)
                                      throws MathIllegalArgumentException

Creates a mixture model from a list of distributions and their associated weights.

Parameters:

rng - Random number generator.

components - Distributions from which to sample.

Throws:

MathIllegalArgumentException - if any of the weights is negative.

MathIllegalArgumentException - if not all components have the same number of variables.