/*
* Copyright 1999-2002 Carnegie Mellon University.
* Portions Copyright 2002 Sun Microsystems, Inc.
* Portions Copyright 2002 Mitsubishi Electric Research Laboratories.
* All Rights Reserved. Use is subject to license terms.
*
* See the file "license.terms" for information on usage and
* redistribution of this file, and for a DISCLAIMER OF ALL
* WARRANTIES.
*
*/
package edu.cmu.sphinx.linguist.acoustic.tiedstate.test;
import static java.lang.Math.PI;
import static java.lang.Math.exp;
import static java.lang.Math.sqrt;
import org.testng.Assert;
import org.testng.annotations.BeforeSuite;
import org.testng.annotations.Test;
import edu.cmu.sphinx.frontend.FloatData;
import edu.cmu.sphinx.linguist.acoustic.tiedstate.MixtureComponent;
import edu.cmu.sphinx.util.LogMath;
/**
* Some tests which test the functionality of <code>MixtureComponentt</code>s.
* <p/>
* Currently testing is restricted to univariate gaussians. It should be extended to test highdimensional gaussians as
* well.
*/
public class MixtureComponentTest {
@BeforeSuite
public void setupLogMath() {
LogMath.setUseTable(true);
}
/**
* Compute the density values of a sampled interval with an univariate <code>MixtureComponent</code> and compare
* values with the precomputed-computed ones.
*/
@Test
public void testUnivariateDensity() {
float minX = 10;
float maxX = 30;
float resolution = 0.1f;
float mean = 20;
float var = 3;
MixtureComponent gaussian = new MixtureComponent(new float[]{mean}, new float[]{var});
for (float curX = minX; curX <= maxX; curX += resolution) {
double gauLogScore = gaussian.getScore(new FloatData(new float[]{curX}, 16000, 0));
double manualScore = (1 / sqrt(var * 2 * PI)) * exp((-0.5 / var) * (curX - mean) * (curX - mean));
double gauScore = LogMath.getLogMath().logToLinear((float) gauLogScore);
Assert.assertEquals(manualScore, gauScore, 1E-5);
}
}
/** Tests whether working with different types transformations works properly. */
@Test
public void testUnivariateMeanTransformation() {
float mean = 20;
float var = 0.001f;
MixtureComponent gaussian = new MixtureComponent(new float[]{mean}, new float[][]{{2}}, new float[]{5}, new float[]{var}, null, null);
Assert.assertTrue(LogMath.getLogMath().logToLinear(gaussian.getScore(new float[]{2 * mean + 5})) > 10);
}
/** Tests whether a <code>MixtureComponent</code>s can be cloned (using deep copying). */
@Test
public void testClone() throws CloneNotSupportedException {
MixtureComponent gaussian = new MixtureComponent(new float[]{2}, new float[][]{{3}}, new float[]{4}, new float[]{5}, new float[][]{{6}}, new float[]{7});
MixtureComponent clonedGaussian = gaussian.clone();
Assert.assertTrue(!clonedGaussian.equals(gaussian));
Assert.assertTrue(gaussian.getMean() != clonedGaussian.getMean());
Assert.assertTrue(gaussian.getVariance() != clonedGaussian.getVariance());
Assert.assertTrue(gaussian.getScore(new float[]{2}) == clonedGaussian.getScore(new float[]{2}));
}
}