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package org.apache.commons.math3.distribution;

import java.util.ArrayList;
import java.util.List;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.util.Pair;

Multivariate normal mixture distribution.
This class is mainly syntactic sugar.
See Also:
MixtureMultivariateRealDistribution
Since:
3.2/**
 * Multivariate normal mixture distribution.
 * This class is mainly syntactic sugar.
 *
 * @see MixtureMultivariateRealDistribution
 * @since 3.2
 */
public class MixtureMultivariateNormalDistribution
    extends MixtureMultivariateRealDistribution<MultivariateNormalDistribution> {

    
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<MultivariateNormalDistribution>.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. 
Params:
weights – Weights of each component.
means – Mean vector for each component.
covariances – Covariance matrix for each component./**
     * Creates a multivariate normal mixture distribution.
     * <p>
     * <b>Note:</b> this constructor will implicitly create an instance of
     * {@link org.apache.commons.math3.random.Well19937c Well19937c} as random
     * generator to be used for sampling only (see {@link #sample()} and
     * {@link #sample(int)}). In case no sampling is needed for the created
     * distribution, it is advised to pass {@code null} as random generator via
     * the appropriate constructors to avoid the additional initialisation
     * overhead.
     *
     * @param weights Weights of each component.
     * @param means Mean vector for each component.
     * @param covariances Covariance matrix for each component.
     */
    public MixtureMultivariateNormalDistribution(double[] weights,
                                                 double[][] means,
                                                 double[][][] covariances) {
        super(createComponents(weights, means, covariances));
    }

    
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<MultivariateNormalDistribution>.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. 
Params:
components – List of (weight, distribution) pairs from which to sample./**
     * Creates a mixture model from a list of distributions and their
     * associated weights.
     * <p>
     * <b>Note:</b> this constructor will implicitly create an instance of
     * {@link org.apache.commons.math3.random.Well19937c Well19937c} as random
     * generator to be used for sampling only (see {@link #sample()} and
     * {@link #sample(int)}). In case no sampling is needed for the created
     * distribution, it is advised to pass {@code null} as random generator via
     * the appropriate constructors to avoid the additional initialisation
     * overhead.
     *
     * @param components List of (weight, distribution) pairs from which to sample.
     */
    public MixtureMultivariateNormalDistribution(List<Pair<Double, MultivariateNormalDistribution>> components) {
        super(components);
    }

    
Creates a mixture model from a list of distributions and their
associated weights.
Params:
rng – Random number generator.
components – Distributions from which to sample.
Throws:
NotPositiveException – if any of the weights is negative.
DimensionMismatchException – if not all components have the same
number of variables./**
     * Creates a mixture model from a list of distributions and their
     * associated weights.
     *
     * @param rng Random number generator.
     * @param components Distributions from which to sample.
     * @throws NotPositiveException if any of the weights is negative.
     * @throws DimensionMismatchException if not all components have the same
     * number of variables.
     */
    public MixtureMultivariateNormalDistribution(RandomGenerator rng,
                                                 List<Pair<Double, MultivariateNormalDistribution>> components)
        throws NotPositiveException, DimensionMismatchException {
        super(rng, components);
    }

    
Params:
weights – Weights of each component.
means – Mean vector for each component.
covariances – Covariance matrix for each component.
Returns:
the list of components./**
     * @param weights Weights of each component.
     * @param means Mean vector for each component.
     * @param covariances Covariance matrix for each component.
     * @return the list of components.
     */
    private static List<Pair<Double, MultivariateNormalDistribution>> createComponents(double[] weights,
                                                                                       double[][] means,
                                                                                       double[][][] covariances) {
        final List<Pair<Double, MultivariateNormalDistribution>> mvns
            = new ArrayList<Pair<Double, MultivariateNormalDistribution>>(weights.length);

        for (int i = 0; i < weights.length; i++) {
            final MultivariateNormalDistribution dist
                = new MultivariateNormalDistribution(means[i], covariances[i]);

            mvns.add(new Pair<Double, MultivariateNormalDistribution>(weights[i], dist));
        }

        return mvns;
    }
}