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Java example source code file (MultivariateNormalMixtureExpectationMaximizationTest.java)

This example Java source code file (MultivariateNormalMixtureExpectationMaximizationTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

array2drowrealmatrix, arraylist, list, mixturemultivariatenormaldistribution, multivariatenormaldistribution, multivariatenormalmixtureexpectationmaximization, multivariatenormalmixtureexpectationmaximizationtest, pair, test, util

The MultivariateNormalMixtureExpectationMaximizationTest.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements. See the NOTICE file distributed with this
 * work for additional information regarding copyright ownership. The ASF
 * licenses this file to You under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations under
 * the License.
 */
package org.apache.commons.math3.distribution.fitting;

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

import org.apache.commons.math3.distribution.MixtureMultivariateNormalDistribution;
import org.apache.commons.math3.distribution.MultivariateNormalDistribution;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.util.Pair;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test that demonstrates the use of
 * {@link MultivariateNormalMixtureExpectationMaximization}.
 */
public class MultivariateNormalMixtureExpectationMaximizationTest {

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

    @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);
    }

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

    @Test(expected = NotStrictlyPositiveException.class)
    public void testMaxIterationsPositive() {
        // Maximum iterations for fit must be positive integer
        double[][] data = getTestSamples();
        MultivariateNormalMixtureExpectationMaximization fitter =
                new MultivariateNormalMixtureExpectationMaximization(data);

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

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

    @Test(expected = NotStrictlyPositiveException.class)
    public void testThresholdPositive() {
        // Maximum iterations for fit must be positive
        double[][] data = getTestSamples();
        MultivariateNormalMixtureExpectationMaximization fitter =
                new MultivariateNormalMixtureExpectationMaximization(
                    data);

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

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

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

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

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

    @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
        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 components =
                new ArrayList<Pair();
        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);
    }

    @Test
    public void testInitialMixture() {
        // Testing initial mixture estimated from data
        final double[] correctWeights = new double[] { 0.5, 0.5 };

        final double[][] correctMeans = new double[][] {
            {-0.0021722935000328823, 3.5432892936887908},
            {5.090902706507635, 8.68540656355283},
        };

        final RealMatrix[] correctCovMats = new Array2DRowRealMatrix[2];

        correctCovMats[0] = new Array2DRowRealMatrix(new double[][] {
                { 4.537422569229048, 3.5266152281729304 },
                { 3.5266152281729304, 6.175448814169779 } });

        correctCovMats[1] = new Array2DRowRealMatrix( new double[][] {
                { 2.886778573963039, 1.5257474543463154 },
                { 1.5257474543463154, 3.3794567673616918 } });

        final MultivariateNormalDistribution[] correctMVNs = new
                MultivariateNormalDistribution[2];

        correctMVNs[0] = new MultivariateNormalDistribution(correctMeans[0],
                correctCovMats[0].getData());

        correctMVNs[1] = new MultivariateNormalDistribution(correctMeans[1],
                correctCovMats[1].getData());

        final MixtureMultivariateNormalDistribution initialMix
            = MultivariateNormalMixtureExpectationMaximization.estimate(getTestSamples(), 2);

        int i = 0;
        for (Pair<Double, MultivariateNormalDistribution> component : initialMix
                .getComponents()) {
            Assert.assertEquals(correctWeights[i], component.getFirst(),
                    Math.ulp(1d));

            final double[] means = component.getValue().getMeans();
            Assert.assertTrue(Arrays.equals(correctMeans[i], means));

            final RealMatrix covMat = component.getValue().getCovariances();
            Assert.assertEquals(correctCovMats[i], covMat);
            i++;
        }
    }

    @Test
    public void testFit() {
        // Test that the loglikelihood, weights, and models are determined and
        // fitted correctly
        final double[][] data = getTestSamples();
        final double correctLogLikelihood = -4.292431006791994;
        final double[] correctWeights = new double[] { 0.2962324189652912, 0.7037675810347089 };

        final double[][] correctMeans = new double[][]{
            {-1.4213112715121132, 1.6924690505757753},
            {4.213612224374709, 7.975621325853645}
        };

        final RealMatrix[] correctCovMats = new Array2DRowRealMatrix[2];
        correctCovMats[0] = new Array2DRowRealMatrix(new double[][] {
            { 1.739356907285747, -0.5867644251487614 },
            { -0.5867644251487614, 1.0232932029324642 } }
                );
        correctCovMats[1] = new Array2DRowRealMatrix(new double[][] {
            { 4.245384898007161, 2.5797798966382155 },
            { 2.5797798966382155, 3.9200272522448367 } });

        final MultivariateNormalDistribution[] correctMVNs = new MultivariateNormalDistribution[2];
        correctMVNs[0] = new MultivariateNormalDistribution(correctMeans[0], correctCovMats[0].getData());
        correctMVNs[1] = new MultivariateNormalDistribution(correctMeans[1], correctCovMats[1].getData());

        MultivariateNormalMixtureExpectationMaximization fitter
            = new MultivariateNormalMixtureExpectationMaximization(data);

        MixtureMultivariateNormalDistribution initialMix
            = MultivariateNormalMixtureExpectationMaximization.estimate(data, 2);
        fitter.fit(initialMix);
        MixtureMultivariateNormalDistribution fittedMix = fitter.getFittedModel();
        List<Pair components = fittedMix.getComponents();

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

        int i = 0;
        for (Pair<Double, MultivariateNormalDistribution> component : components) {
            final double weight = component.getFirst();
            final MultivariateNormalDistribution mvn = component.getSecond();
            final double[] mean = mvn.getMeans();
            final RealMatrix covMat = mvn.getCovariances();
            Assert.assertEquals(correctWeights[i], weight, Math.ulp(1d));
            Assert.assertTrue(Arrays.equals(correctMeans[i], mean));
            Assert.assertEquals(correctCovMats[i], covMat);
            i++;
        }
    }

    private double[][] getTestSamples() {
        // generated using R Mixtools rmvnorm with mean vectors [-1.5, 2] and
        // [4, 8.2]
        return new double[][] { { 7.358553610469948, 11.31260831446758 },
                { 7.175770420124739, 8.988812210204454 },
                { 4.324151905768422, 6.837727899051482 },
                { 2.157832219173036, 6.317444585521968 },
                { -1.890157421896651, 1.74271202875498 },
                { 0.8922409354455803, 1.999119343923781 },
                { 3.396949764787055, 6.813170372579068 },
                { -2.057498232686068, -0.002522983830852255 },
                { 6.359932157365045, 8.343600029975851 },
                { 3.353102234276168, 7.087541882898689 },
                { -1.763877221595639, 0.9688890460330644 },
                { 6.151457185125111, 9.075011757431174 },
                { 4.281597398048899, 5.953270070976117 },
                { 3.549576703974894, 8.616038155992861 },
                { 6.004706732349854, 8.959423391087469 },
                { 2.802915014676262, 6.285676742173564 },
                { -0.6029879029880616, 1.083332958357485 },
                { 3.631827105398369, 6.743428504049444 },
                { 6.161125014007315, 9.60920569689001 },
                { -1.049582894255342, 0.2020017892080281 },
                { 3.910573022688315, 8.19609909534937 },
                { 8.180454017634863, 7.861055769719962 },
                { 1.488945440439716, 8.02699903761247 },
                { 4.813750847823778, 12.34416881332515 },
                { 0.0443208501259158, 5.901148093240691 },
                { 4.416417235068346, 4.465243084006094 },
                { 4.0002433603072, 6.721937850166174 },
                { 3.190113818788205, 10.51648348411058 },
                { 4.493600914967883, 7.938224231022314 },
                { -3.675669533266189, 4.472845076673303 },
                { 6.648645511703989, 12.03544085965724 },
                { -1.330031331404445, 1.33931042964811 },
                { -3.812111460708707, 2.50534195568356 },
                { 5.669339356648331, 6.214488981177026 },
                { 1.006596727153816, 1.51165463112716 },
                { 5.039466365033024, 7.476532610478689 },
                { 4.349091929968925, 7.446356406259756 },
                { -1.220289665119069, 3.403926955951437 },
                { 5.553003979122395, 6.886518211202239 },
                { 2.274487732222856, 7.009541508533196 },
                { 4.147567059965864, 7.34025244349202 },
                { 4.083882618965819, 6.362852861075623 },
                { 2.203122344647599, 7.260295257904624 },
                { -2.147497550770442, 1.262293431529498 },
                { 2.473700950426512, 6.558900135505638 },
                { 8.267081298847554, 12.10214104577748 },
                { 6.91977329776865, 9.91998488301285 },
                { 0.1680479852730894, 6.28286034168897 },
                { -1.268578659195158, 2.326711221485755 },
                { 1.829966451374701, 6.254187605304518 },
                { 5.648849025754848, 9.330002040750291 },
                { -2.302874793257666, 3.585545172776065 },
                { -2.629218791709046, 2.156215538500288 },
                { 4.036618140700114, 10.2962785719958 },
                { 0.4616386422783874, 0.6782756325806778 },
                { -0.3447896073408363, 0.4999834691645118 },
                { -0.475281453118318, 1.931470384180492 },
                { 2.382509690609731, 6.071782429815853 },
                { -3.203934441889096, 2.572079552602468 },
                { 8.465636032165087, 13.96462998683518 },
                { 2.36755660870416, 5.7844595007273 },
                { 0.5935496528993371, 1.374615871358943 },
                { -2.467481505748694, 2.097224634713005 },
                { 4.27867444328542, 10.24772361238549 },
                { -2.013791907543137, 2.013799426047639 },
                { 6.424588084404173, 9.185334939684516 },
                { -0.8448238876802175, 0.5447382022282812 },
                { 1.342955703473923, 8.645456317633556 },
                { 3.108712208751979, 8.512156853800064 },
                { 4.343205178315472, 8.056869549234374 },
                { -2.971767642212396, 3.201180146824761 },
                { 2.583820931523672, 5.459873414473854 },
                { 4.209139115268925, 8.171098193546225 },
                { 0.4064909057902746, 1.454390775518743 },
                { 3.068642411145223, 6.959485153620035 },
                { 6.085968972900461, 7.391429799500965 },
                { -1.342265795764202, 1.454550012997143 },
                { 6.249773274516883, 6.290269880772023 },
                { 4.986225847822566, 7.75266344868907 },
                { 7.642443254378944, 10.19914817500263 },
                { 6.438181159163673, 8.464396764810347 },
                { 2.520859761025108, 7.68222425260111 },
                { 2.883699944257541, 6.777960331348503 },
                { 2.788004550956599, 6.634735386652733 },
                { 3.331661231995638, 5.794191300046592 },
                { 3.526172276645504, 6.710802266815884 },
                { 3.188298528138741, 10.34495528210205 },
                { 0.7345539486114623, 5.807604004180681 },
                { 1.165044595880125, 7.830121829295257 },
                { 7.146962523500671, 11.62995162065415 },
                { 7.813872137162087, 10.62827008714735 },
                { 3.118099164870063, 8.286003148186371 },
                { -1.708739286262571, 1.561026755374264 },
                { 1.786163047580084, 4.172394388214604 },
                { 3.718506403232386, 7.807752990130349 },
                { 6.167414046828899, 10.01104941031293 },
                { -1.063477247689196, 1.61176085846339 },
                { -3.396739609433642, 0.7127911050002151 },
                { 2.438885945896797, 7.353011138689225 },
                { -0.2073204144780931, 0.850771146627012 }, };
    }
}

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