Example usage for org.apache.commons.math3.distribution MultivariateNormalDistribution density

Introduction

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

Prototype

public double density(final double[] vals) throws DimensionMismatchException 

Source Link

Usage

From source file:com.datumbox.framework.core.statistics.distributions.ContinuousDistributions.java

/**
 * Calculates the PDF of Multinomial Normal Distribution for a particular x.
 * // ww w .  jav a2 s.  c  om
 * @param mean
 * @param covariance
 * @param x             The x record
 * @return              The multinomialGaussianPdf of x
 */
public static double multinomialGaussianPdf(double[] mean, double[][] covariance, double[] x) {
    MultivariateNormalDistribution gaussian = new MultivariateNormalDistribution(mean, covariance);
    return gaussian.density(x);
}

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From source file:common.matrix.GaussianProbaTest.java

@Test
public void MultinomialTest() {
    final Graph graph = new GraphBuilder().withPlugin(new MLXPlugin()).withScheduler(new NoopScheduler())
            .build();/*from   www.  j  a  v  a2  s  . c o m*/
    graph.connect(new Callback<Boolean>() {
        @Override
        public void on(Boolean result) {

            GaussianMixtureNode gaussianNodeLive = (GaussianMixtureNode) graph.newTypedNode(0, 0,
                    GaussianMixtureNode.NAME);

            gaussianNodeLive.set(GaussianMixtureNode.FROM, "f1;f2");

            int test = 100;
            int feat = 2;

            double[][] b = new double[test][feat];
            double[] v = new double[feat];
            Random random = new Random();

            for (int i = 0; i < test; i++) {
                for (int j = 0; j < feat; j++) {
                    v[j] = random.nextDouble() * (1 + 100 * j);
                    b[i][j] = v[j];
                }
                gaussianNodeLive.set("f1", b[i][0]);
                gaussianNodeLive.set("f2", b[i][1]);

                gaussianNodeLive.learn(new Callback<Boolean>() {
                    @Override
                    public void on(Boolean result) {

                    }
                });
            }

            double[] ravg = gaussianNodeLive.getAvg();
            Matrix rcovData = gaussianNodeLive.getCovariance(ravg, null);

            double[][] temp = new double[rcovData.rows()][];
            for (int i = 0; i < rcovData.rows(); i++) {
                temp[i] = new double[rcovData.columns()];
                for (int j = 0; j < rcovData.columns(); j++) {
                    temp[i][j] = rcovData.get(i, j);
                }
            }

            //Test probability calculation.
            MultivariateNormalDistribution apache = new MultivariateNormalDistribution(ravg, temp);

            double eps = 1e-8;
            double d = apache.density(v);
            //System.out.println("apache: " + d);

            double y = gaussianNodeLive.getProbability(v, null, false);
            //System.out.println("live: " + y);

            Assert.assertTrue(Math.abs(d - y) < eps);

        }
    });
}

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