Example usage for org.apache.commons.math3.distribution.fitting MultivariateNormalMixtureExpectationMaximization MultivariateNormalMixtureExpectationMaximization

Introduction

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

Prototype

public MultivariateNormalMixtureExpectationMaximization(double[][] data)
        throws NotStrictlyPositiveException, DimensionMismatchException, NumberIsTooSmallException 

Source Link

Document

Creates an object to fit a multivariate normal mixture model to data.

Usage

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

@Test(expected = NotStrictlyPositiveException.class)
public void testNonEmptyData() {
    // Should not accept empty data
    new MultivariateNormalMixtureExpectationMaximization(new double[][] {});
}

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

@Test(expected = DimensionMismatchException.class)
public void testNonJaggedData() {
    // Reject data with nonconstant numbers of columns
    double[][] data = new double[][] { { 1, 2, 3 }, { 4, 5, 6, 7 }, };
    new MultivariateNormalMixtureExpectationMaximization(data);
}

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

@Test(expected = NumberIsTooSmallException.class)
public void testMultipleColumnsRequired() {
    // Data should have at least 2 columns
    double[][] data = new double[][] { { 1 }, { 2 } };
    new MultivariateNormalMixtureExpectationMaximization(data);
}

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

@Test(expected = NotStrictlyPositiveException.class)
public void testMaxIterationsPositive() {
    // Maximum iterations for fit must be positive integer
    double[][] data = getTestSamples();
    MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(
            data);/*from  ww  w  .j  a  va 2s. c  om*/

    MixtureMultivariateNormalDistribution initialMix = MultivariateNormalMixtureExpectationMaximization
            .estimate(data, 2);

    fitter.fit(initialMix, 0, 1E-5);
}

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

@Test(expected = NotStrictlyPositiveException.class)
public void testThresholdPositive() {
    // Maximum iterations for fit must be positive
    double[][] data = getTestSamples();
    MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(
            data);/*from w w  w . j  a v a  2 s  .c om*/

    MixtureMultivariateNormalDistribution initialMix = MultivariateNormalMixtureExpectationMaximization
            .estimate(data, 2);

    fitter.fit(initialMix, 1000, 0);
}

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

@Test(expected = ConvergenceException.class)
public void testConvergenceException() {
    // ConvergenceException thrown if fit terminates before threshold met
    double[][] data = getTestSamples();
    MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(
            data);/*from  ww  w . java2s .  c  om*/

    MixtureMultivariateNormalDistribution initialMix = MultivariateNormalMixtureExpectationMaximization
            .estimate(data, 2);

    // 5 iterations not enough to meet convergence threshold
    fitter.fit(initialMix, 5, 1E-5);
}

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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//from  w w w .jav a2  s.co  m
    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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From source file:sly.speakrecognizer.test.math.MultivariateNormalMixtureExpectationMaximizationFitterTest.java

@Test
public void testFit() {
    // Test that the loglikelihood, weights, and models are determined and
    // fitted correctly
    double[][] data = getTestSamples();
    double correctLogLikelihood = -4.292431006791994;
    double[] correctWeights = new double[] { 0.2962324189652912, 0.7037675810347089 };
    MultivariateNormalDistribution[] correctMVNs = new MultivariateNormalDistribution[2];
    correctMVNs[0] = new MultivariateNormalDistribution(
            new double[] { -1.4213112715121132, 1.6924690505757753 }, new double[][] {
                    { 1.739356907285747, -0.5867644251487614 }, { -0.5867644251487614, 1.0232932029324642 } });

    correctMVNs[1] = new MultivariateNormalDistribution(new double[] { 4.213612224374709, 7.975621325853645 },
            new double[][] { { 4.245384898007161, 2.5797798966382155 },
                    { 2.5797798966382155, 3.9200272522448367 } });
    //=========================================
    MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(
            data);//from www  .  ja v  a 2s  . c  o m
    MixtureMultivariateNormalDistribution initialMix = MultivariateNormalMixtureExpectationMaximization
            .estimate(data, 2);
    fitter.fit(initialMix);
    MixtureMultivariateNormalDistribution fittedMix = fitter.getFittedModel();
    printMMND(fittedMix);
    List<Pair<Double, MultivariateNormalDistribution>> components = fittedMix.getComponents();

    Assert.assertEquals(correctLogLikelihood, fitter.getLogLikelihood(), Math.ulp(1d));

    int i = 0;
    for (Pair<Double, MultivariateNormalDistribution> component : components) {
        double weight = component.getFirst();
        MultivariateNormalDistribution mvn = component.getSecond();
        Assert.assertEquals(correctWeights[i], weight, Math.ulp(1d));
        assertMultivariateNormalDistribution(correctMVNs[i], mvn, 0);
        i++;
    }
}

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

@Test
public void testFitForDifferentSizesData() {
    System.out.println("TEST DLA ROZNYCH ROZMAROW DANYCH");
    double[][][] covariances = { new double[][] { new double[] { 1.74, -0.59 }, new double[] { -0.59, 1.02 }, },
            new double[][] { new double[] { 4.24, 2.58 }, new double[] { 2.58, 3.92 }, }, };
    double[][] means = { new double[] { -1.42, 1.69 }, new double[] { 4.21, 7.98 }, };

    double[] weights = new double[] { 0.296, 0.704 };
    MixtureMultivariateNormalDistribution mmnd = new MixtureMultivariateNormalDistribution(weights, means,
            covariances);//w w w  .  j  av a2  s. c  om

    int[] lengths = new int[] { 10, 50, 250, 1500, 10000, 100000 };
    double[] errors = new double[lengths.length];
    int counter = 0;
    for (int length : lengths) {
        System.out.println("Dla dugoci danych: " + length);
        double[][] data = getTestSamples(length, mmnd);
        MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(
                data);
        MixtureMultivariateNormalDistribution initialMix = MultivariateNormalMixtureExpectationMaximization
                .estimate(data, 2);
        fitter.fit(initialMix);
        MixtureMultivariateNormalDistribution fittedMix = fitter.getFittedModel();
        printMMND(fittedMix);
        errors[counter++] = printErrorsMMND(mmnd, fittedMix);
    }
    System.out.println("Podsumowanie");
    for (int x = 0; x < lengths.length; x++) {
        System.out.println("Dla length = " + lengths[x] + " bd wynosi = " + errors[x]);
    }

}

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