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(RandomGenerator rng,
        List<Pair<Double, MultivariateNormalDistribution>> components)
        throws NotPositiveException, DimensionMismatchException 

Source Link

Document

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

Usage

From source file:rndvecgen.RandomVecGen.java

private void init() throws Exception {
    DocVector.initVectorRange(prop);/* www .  j  av  a 2 s. com*/

    numPoints = Integer.parseInt(prop.getProperty("syntheticdata.numsamples"));
    numGaussians = Integer.parseInt(prop.getProperty("syntheticdata.numgaussians"));
    maxSpan = Integer.parseInt(prop.getProperty("syntheticdata.maxSpan"));
    diagonalCovMatrix = Boolean.parseBoolean(prop.getProperty("syntheticdata.diagonalcov"));

    min = Float.parseFloat(prop.getProperty("syntheticdata.min"));
    max = Float.parseFloat(prop.getProperty("syntheticdata.max"));

    numDimensions = Integer.parseInt(prop.getProperty("vec.numdimensions"));

    final int NUM_MU_GEN = 5;
    float delta = (max - min) / NUM_MU_GEN;
    muGen = new NormalDistribution[NUM_MU_GEN];
    for (int i = 0; i < NUM_MU_GEN; i++) {
        muGen[i] = new NormalDistribution(min + i * delta, Math.random() * maxSpan);
    }

    List<Pair<Double, MultivariateNormalDistribution>> components = new ArrayList<>();
    for (int i = 0; i < numGaussians; i++) {
        components.add(new Pair(new Double(1 / (double) numGaussians), genRandom(i)));
    }

    // Ensure that we can reproduce the results...
    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(SEED);
    this.mixtureDist = new MixtureMultivariateNormalDistribution(rg, components);
}

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