Example usage for org.apache.commons.math3.distribution MixtureMultivariateNormalDistribution MixtureMultivariateNormalDistribution

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution MixtureMultivariateNormalDistribution MixtureMultivariateNormalDistribution.

Prototype

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

Source Link

Document

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

Usage

From source file:sly.speakrecognizer.test.math.MultivariateNormalMixtureExpectationMaximizationFitterTest.java

@Test(expected = DimensionMismatchException.class)
public void testIncompatibleIntialMixture() {
    // Data has 3 columns
    double[][] data = new double[][] { { 1, 2, 3 }, { 4, 5, 6 }, { 7, 8, 9 } };
    double[] weights = new double[] { 0.5, 0.5 };

    // These distributions are compatible with 2-column data, not 3-column
    // data//w ww  .  j ava2 s  .  c om
    MultivariateNormalDistribution[] mvns = new MultivariateNormalDistribution[2];

    mvns[0] = new MultivariateNormalDistribution(new double[] { -0.0021722935000328823, 3.5432892936887908 },
            new double[][] { { 4.537422569229048, 3.5266152281729304 },
                    { 3.5266152281729304, 6.175448814169779 } });
    mvns[1] = new MultivariateNormalDistribution(new double[] { 5.090902706507635, 8.68540656355283 },
            new double[][] { { 2.886778573963039, 1.5257474543463154 },
                    { 1.5257474543463154, 3.3794567673616918 } });

    // Create components and mixture
    List<Pair<Double, MultivariateNormalDistribution>> components = new ArrayList<Pair<Double, MultivariateNormalDistribution>>();
    components.add(new Pair<Double, MultivariateNormalDistribution>(weights[0], mvns[0]));
    components.add(new Pair<Double, MultivariateNormalDistribution>(weights[1], mvns[1]));

    MixtureMultivariateNormalDistribution badInitialMix = new MixtureMultivariateNormalDistribution(components);

    MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(
            data);

    fitter.fit(badInitialMix);
}

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