Java tutorial
/* * 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 sly.speakrecognizer.test.math; 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.distribution.fitting.MultivariateNormalMixtureExpectationMaximization; 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.util.Pair; import org.junit.Assert; import org.junit.Test; /** * Test that demonstrates the use of * {@link MultivariateNormalMixtureExpectationMaximizationFitter}. */ public class MultivariateNormalMixtureExpectationMaximizationFitterTest { // TODO reject initial mixes where means/covMats not compatable with data // numCols @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<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); } @Test public void testInitialMixture() { // Testing initial mixture estimated from data double[] correctWeights = new double[] { 0.5, 0.5 }; MultivariateNormalDistribution[] correctMVNs = new MultivariateNormalDistribution[2]; correctMVNs[0] = new MultivariateNormalDistribution( new double[] { -0.0021722935000328823, 3.5432892936887908 }, new double[][] { { 4.537422569229048, 3.5266152281729304 }, { 3.5266152281729304, 6.175448814169779 } }); correctMVNs[1] = new MultivariateNormalDistribution(new double[] { 5.090902706507635, 8.68540656355283 }, new double[][] { { 2.886778573963039, 1.5257474543463154 }, { 1.5257474543463154, 3.3794567673616918 } }); 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)); assertMultivariateNormalDistribution(correctMVNs[i], component.getSecond(), 0); //=========================== i++; } } private void assertMultivariateNormalDistribution(MultivariateNormalDistribution expected, MultivariateNormalDistribution actual, double delta) { Assert.assertEquals(expected.getCovariances(), actual.getCovariances()); Assert.assertArrayEquals(expected.getMeans(), actual.getMeans(), delta); Assert.assertArrayEquals(expected.getStandardDeviations(), actual.getStandardDeviations(), delta); Assert.assertEquals(expected.getDimension(), actual.getDimension()); } @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); 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++; } } @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); 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]); } } public static void printMMND(MixtureMultivariateNormalDistribution mmnd) { System.out.println("MixtureMultivariateNormalDistribution"); int counter = 0; for (Pair<Double, MultivariateNormalDistribution> pair : mmnd.getComponents()) { System.out.println("MND " + ++counter); System.out.println(" Weight: " + pair.getFirst()); System.out.println(" Means: " + Arrays.toString(pair.getSecond().getMeans())); System.out.println(" StandardDeviations: " + Arrays.toString(pair.getSecond().getStandardDeviations())); System.out.println(" Covariance: "); for (double[] covR : pair.getSecond().getCovariances().getData()) { System.out.println(" : " + Arrays.toString(covR)); } pair.getSecond().getCovariances().getData(); } System.out.println("End MixtureMultivariateNormalDistribution"); } public double[][] getErrors(double[] data, double[] pattern) { double[] error = new double[pattern.length]; double sumError = 0.0; for (int x = 0; x < pattern.length; x++) { double err = data[x] - pattern[x]; sumError += Math.abs(err); error[x] = err; } return new double[][] { error, new double[] { sumError } }; } public double printErrorsMMND(MixtureMultivariateNormalDistribution pattern, MixtureMultivariateNormalDistribution mmnd) { System.out.println("ErrorsMixtureMultivariateNormalDistribution"); double sumErrors = 0.0; double error; double[][] error2; for (int counter = 0; counter < pattern.getComponents().size(); counter++) { Pair<Double, MultivariateNormalDistribution> pairPattern = pattern.getComponents().get(counter); Pair<Double, MultivariateNormalDistribution> pair = mmnd.getComponents().get(counter); System.out.println("MND " + ++counter); error = pair.getFirst() - pairPattern.getFirst(); sumErrors += Math.abs(error); System.out.println(" WeightError: " + error); error2 = getErrors(pair.getSecond().getMeans(), pairPattern.getSecond().getMeans()); sumErrors += error2[1][0]; System.out.println(" MeansError: " + Arrays.toString(error2[0])); error2 = getErrors(pair.getSecond().getStandardDeviations(), pairPattern.getSecond().getStandardDeviations()); sumErrors += error2[1][0]; System.out.println(" StandardDeviationsError: " + Arrays.toString(error2[0])); System.out.println(" CovarianceError: "); for (int counter1 = 0; counter1 < pair.getSecond().getDimension(); counter1++) { double[] covPattern = pairPattern.getSecond().getCovariances().getRow(counter1); double[] cov = pair.getSecond().getCovariances().getRow(counter1); error2 = getErrors(cov, covPattern); sumErrors += error2[1][0]; System.out.println(" : " + Arrays.toString(error2[0])); } } System.out.println("Calkowity blad: " + sumErrors); System.out.println("End ErrorsMixtureMultivariateNormalDistribution"); return sumErrors; } private double[][] getTestSamples(int length, double[] weights, double[][] means, double[][][] covariances) { return getTestSamples(length, new MixtureMultivariateNormalDistribution(weights, means, covariances)); } private double[][] getTestSamples(int length, MixtureMultivariateNormalDistribution mmnd) { return mmnd.sample(length); } 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 }, }; } }