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Dummy tests removed after revision 910475 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@910479 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Dimitri Pourbaix 2010-02-16 11:20:17 +00:00
parent 95627968c1
commit c1ce0e39e0
1 changed files with 0 additions and 81 deletions
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81
src/test/java/org/apache/commons/math/linear/SingularValueSolverTest.java
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@ -139,78 +139,6 @@ public class SingularValueSolverTest {
Assert.assertEquals(3.0, svd.getConditionNumber(), 1.5e-15);
}
// Forget about this test, SVD is no longer truncated!
// @Test
public void testTruncated() {
RealMatrix rm = new Array2DRowRealMatrix(new double[][] {
{ 1.0, 2.0, 3.0 }, { 2.0, 3.0, 4.0 }, { 3.0, 5.0, 7.0 }
});
double s439 = Math.sqrt(439.0);
double[] reference = new double[] {
Math.sqrt(3.0 * (21.0 + s439))
};
SingularValueDecomposition svd =
new SingularValueDecompositionImpl(rm, 1);
// check we get the expected theoretical singular values
double[] singularValues = svd.getSingularValues();
Assert.assertEquals(reference.length, singularValues.length);
for (int i = 0; i < reference.length; ++i) {
Assert.assertEquals(reference[i], singularValues[i], 4.0e-13);
}
// check the truncated decomposition DON'T allows to recover the original matrix
RealMatrix recomposed = svd.getU().multiply(svd.getS()).multiply(svd.getVT());
Assert.assertTrue(recomposed.subtract(rm).getNorm() > 1.4);
}
// Forget about this test, SVD is no longer truncated!
//@Test
public void testMath320A() {
RealMatrix rm = new Array2DRowRealMatrix(new double[][] {
{ 1.0, 2.0, 3.0 }, { 2.0, 3.0, 4.0 }, { 3.0, 5.0, 7.0 }
});
double s439 = Math.sqrt(439.0);
double[] reference = new double[] {
Math.sqrt(3.0 * (21.0 + s439)), Math.sqrt(3.0 * (21.0 - s439))
};
SingularValueDecomposition svd =
new SingularValueDecompositionImpl(rm);
// check we get the expected theoretical singular values
double[] singularValues = svd.getSingularValues();
Assert.assertEquals(reference.length, singularValues.length);
for (int i = 0; i < reference.length; ++i) {
Assert.assertEquals(reference[i], singularValues[i], 4.0e-13);
}
// check the decomposition allows to recover the original matrix
RealMatrix recomposed = svd.getU().multiply(svd.getS()).multiply(svd.getVT());
Assert.assertEquals(0.0, recomposed.subtract(rm).getNorm(), 5.0e-13);
// check we can solve a singular system
double[] b = new double[] { 5.0, 6.0, 7.0 };
double[] resSVD = svd.getSolver().solve(b);
Assert.assertEquals(rm.getColumnDimension(), resSVD.length);
// check the solution really minimizes the residuals
double svdMinResidual = residual(rm, resSVD, b);
double epsilon = 2 * Math.ulp(svdMinResidual);
double h = 0.1;
int k = 3;
for (double d0 = -k * h; d0 <= k * h; d0 += h) {
for (double d1 = -k * h ; d1 <= k * h; d1 += h) {
for (double d2 = -k * h; d2 <= k * h; d2 += h) {
double[] x = new double[] { resSVD[0] + d0, resSVD[1] + d1, resSVD[2] + d2 };
Assert.assertTrue((residual(rm, x, b) - svdMinResidual) > -epsilon);
}
}
}
}
@Test
public void testMath320B() {
RealMatrix rm = new Array2DRowRealMatrix(new double[][] {
@ -222,13 +150,4 @@ public class SingularValueSolverTest {
Assert.assertEquals(0.0, recomposed.subtract(rm).getNorm(), 2.0e-15);
}
private double residual(RealMatrix a, double[] x, double[] b) {
double[] ax = a.operate(x);
double sum = 0;
for (int i = 0; i < ax.length; ++i) {
sum += (ax[i] - b[i]) * (ax[i] - b[i]);
}
return Math.sqrt(sum);
}
}