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Added rigging for checking eigenvalues and eigenvectors and some easy…

… test cases.

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/branches/MATH_2_0@721943 13f79535-47bb-0310-9956-ffa450edef68
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psteitz committed Dec 1, 2008
1 parent 2924cfd commit 543a3ca57eade833b7bc3d2deec6fc0c16436f41
Showing with 101 additions and 0 deletions.
  1. +101 −0 src/test/org/apache/commons/math/linear/EigenDecompositionImplTest.java
@@ -255,6 +255,107 @@ public void testSolve() {
}
}
+
+ /**
+ * Matrix with eigenvalues {8, -1, -1}
+ */
+ public void testRepeatedEigenvalue() {
+ RealMatrix repeated = new RealMatrixImpl(new double[][] {
+ {3, 2, 4},
+ {2, 0, 2},
+ {4, 2, 3}
+ });
+ EigenDecomposition ed = new EigenDecompositionImpl(repeated);
+ checkEigenValues((new double[] {8, -1, -1}), ed, 1E-12);
+ checkEigenVector((new double[] {2, 1, 2}), ed, 1E-12);
+ }
+
+ /**
+ * Matrix with eigenvalues {2, 0, 12}
+ */
+ public void testDistinctEigenvalues() {
+ RealMatrix distinct = new RealMatrixImpl(new double[][] {
+ {3, 1, -4},
+ {1, 3, -4},
+ {-4, -4, 8}
+ });
+ EigenDecomposition ed = new EigenDecompositionImpl(distinct);
+ checkEigenValues((new double[] {2, 0, 12}), ed, 1E-12);
+ checkEigenVector((new double[] {1, -1, 0}), ed, 1E-12);
+ checkEigenVector((new double[] {1, 1, 1}), ed, 1E-12);
+ checkEigenVector((new double[] {-1, -1, 2}), ed, 1E-12);
+ }
+
+ /**
+ * Verifies that the given EigenDecomposition has eigenvalues equivalent to
+ * the targetValues, ignoring the order of the values and allowing
+ * values to differ by tolerance.
+ */
+ protected void checkEigenValues(double[] targetValues,
+ EigenDecomposition ed, double tolerance) {
+ double[] observed = ed.getEigenvalues();
+ for (int i = 0; i < observed.length; i++) {
+ assertTrue(isIncludedValue(observed[i], targetValues, tolerance));
+ assertTrue(isIncludedValue(targetValues[i], observed, tolerance));
+ }
+ }
+
+ /**
+ * Returns true iff there is an entry within tolerance of value in
+ * searchArray.
+ */
+ private boolean isIncludedValue(double value, double[] searchArray,
+ double tolerance) {
+ boolean found = false;
+ int i = 0;
+ while (!found && i < searchArray.length) {
+ if (Math.abs(value - searchArray[i]) < tolerance) {
+ found = true;
+ }
+ i++;
+ }
+ return found;
+ }
+
+ /**
+ * Returns true iff eigenVector is a scalar multiple of one of the columns
+ * of ed.getV(). Does not try linear combinations - i.e., should only be
+ * used to find vectors in one-dimensional eigenspaces.
+ */
+ protected void checkEigenVector(double[] eigenVector,
+ EigenDecomposition ed, double tolerance) {
+ assertTrue(isIncludedColumn(eigenVector, ed.getV(), tolerance));
+ }
+
+ /**
+ * Returns true iff there is a column that is a scalar multiple of column
+ * in searchMatrix (modulo tolerance)
+ */
+ private boolean isIncludedColumn(double[] column, RealMatrix searchMatrix,
+ double tolerance) {
+ boolean found = false;
+ int i = 0;
+ while (!found && i < searchMatrix.getColumnDimension()) {
+ double multiplier = 1d;
+ boolean matching = true;
+ int j = 0;
+ while (matching && j < searchMatrix.getRowDimension()) {
+ double colEntry = searchMatrix.getEntry(j, i);
+ // Use the first entry where both are non-zero as scalar
+ if (multiplier == 1d && Math.abs(colEntry) > 1E-14
+ && Math.abs(column[j]) > 1e-14) {
+ multiplier = colEntry / column[j];
+ }
+ if (Math.abs(column[j] * multiplier - colEntry) > tolerance) {
+ matching = false;
+ }
+ j++;
+ }
+ found = matching;
+ i++;
+ }
+ return found;
+ }
public void setUp() {
refValues = new double[] {

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