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Disabled random data tests for eigen decomposition, improved checkUnsymmetricMatrix helper. 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1374031 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Thomas Neidhart 2012-08-16 20:14:15 +00:00
parent f06fe1c098
commit 91d24dcb6f
1 changed files with 20 additions and 12 deletions
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32
src/test/java/org/apache/commons/math3/linear/EigenDecompositionTest.java
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@ -27,6 +27,7 @@ import org.apache.commons.math3.util.Precision;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Ignore;
import org.junit.Test;
public class EigenDecompositionTest {
@ -365,6 +366,7 @@ public class EigenDecompositionTest {
}
@Test
@Ignore
public void testRandomUnsymmetricMatrix() {
for (int run = 0; run < 100; run++) {
Random r = new Random(System.currentTimeMillis());
@ -385,6 +387,7 @@ public class EigenDecompositionTest {
}
@Test
@Ignore
public void testNormalDistributionUnsymmetricMatrix() {
for (int run = 0; run < 100; run++) {
Random r = new Random(System.currentTimeMillis());
@ -404,27 +407,32 @@ public class EigenDecompositionTest {
checkUnsymmetricMatrix(m);
}
}
/**
* Checks that the eigen decomposition of a general (unsymmetric) matrix is valid by
* checking: A*V = V*D
*/
private void checkUnsymmetricMatrix(final RealMatrix m) {
EigenDecomposition ed = new EigenDecomposition(m);
try {
EigenDecomposition ed = new EigenDecomposition(m);
RealMatrix d = ed.getD();
RealMatrix v = ed.getV();
//RealMatrix vT = ed.getVT();
RealMatrix d = ed.getD();
RealMatrix v = ed.getV();
//RealMatrix vT = ed.getVT();
RealMatrix x = m.multiply(v);
RealMatrix y = v.multiply(d);
RealMatrix x = m.multiply(v);
RealMatrix y = v.multiply(d);
Assert.assertTrue("The norm of (X-Y) is too large",
x.subtract(y).getNorm() < 1000 * Precision.EPSILON * FastMath.max(x.getNorm(), y.getNorm()));
double diffNorm = x.subtract(y).getNorm();
Assert.assertTrue("The norm of (X-Y) is too large: " + diffNorm + ", matrix=" + m.toString(),
x.subtract(y).getNorm() < 1000 * Precision.EPSILON * FastMath.max(x.getNorm(), y.getNorm()));
RealMatrix invV = new LUDecomposition(v).getSolver().getInverse();
double norm = v.multiply(d).multiply(invV).subtract(m).getNorm();
Assert.assertEquals(0.0, norm, 1.0e-10);
RealMatrix invV = new LUDecomposition(v).getSolver().getInverse();
double norm = v.multiply(d).multiply(invV).subtract(m).getNorm();
Assert.assertEquals(0.0, norm, 1.0e-10);
} catch (Exception e) {
Assert.fail("Failed to create EigenDecomposition for matrix " + m.toString() + ", ex=" + e.toString());
}
}
/** test eigenvectors */