Author: luc
Date: Mon Mar 25 15:47:31 2013
New Revision: 1460726
URL: http://svn.apache.org/r1460726
Log:
Fixed tests so they do not use equals on top level classes.
Patch submitted by Jared Becksfort.
JIRA: MATH-817
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java?rev=1460726&r1=1460725&r2=1460726&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
Mon Mar 25 15:47:31 2013
@@ -17,6 +17,7 @@
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;
@@ -25,10 +26,11 @@ import org.apache.commons.math3.exceptio
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;
-import org.junit.Ignore;
/**
* Test that demonstrates the use of
@@ -36,9 +38,6 @@ import org.junit.Ignore;
*/
public class MultivariateNormalMixtureExpectationMaximizationTest {
- // TODO reject initial mixes where means/covMats not computable with data
- // numCols
-
@Test(expected = NotStrictlyPositiveException.class)
public void testNonEmptyData() {
// Should not accept empty data
@@ -144,22 +143,34 @@ public class MultivariateNormalMixtureEx
fitter.fit(badInitialMix);
}
- @Ignore@Test
+ @Test
public void testInitialMixture() {
// Testing initial mixture estimated from data
- double[] correctWeights = new double[] { 0.5, 0.5 };
+ final double[] correctWeights = new double[] { 0.5, 0.5 };
- MultivariateNormalDistribution[] correctMVNs = new
MultivariateNormalDistribution[2];
+ final double[][] correctMeans = new double[][] {
+ {-0.0021722935000328823, 3.5432892936887908},
+ {5.090902706507635, 8.68540656355283},
+ };
- 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 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);
@@ -169,30 +180,41 @@ public class MultivariateNormalMixtureEx
.getComponents()) {
Assert.assertEquals(correctWeights[i], component.getFirst(),
Math.ulp(1d));
- Assert.assertEquals(correctMVNs[i], component.getSecond());
+
+ 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++;
}
}
- @Ignore@Test
+ @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 } });
+ 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);
@@ -209,10 +231,13 @@ public class MultivariateNormalMixtureEx
int i = 0;
for (Pair<Double, MultivariateNormalDistribution> component :
components) {
- double weight = component.getFirst();
- MultivariateNormalDistribution mvn = component.getSecond();
+ 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.assertEquals(correctMVNs[i], mvn);
+ Assert.assertTrue(Arrays.equals(correctMeans[i], mean));
+ Assert.assertEquals(correctCovMats[i], covMat);
i++;
}
}
