/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.math.solver;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.MatrixSlice;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.DoubleFunction;
import org.apache.mahout.math.function.Functions;
import org.junit.Assert;
import org.junit.Test;
import java.util.Random;
public class EigenDecompositionTest {
@Test
public void testDegenerateMatrix() {
double[][] m = {
new double[]{0.641284, 0.767303, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000},
new double[]{0.767303, 3.050159, 2.561342, 0.000000, 0.000000, 0.000000, 0.000000},
new double[]{0.000000, 2.561342, 5.000609, 0.810507, 0.000000, 0.000000, 0.000000},
new double[]{0.000000, 0.000000, 0.810507, 0.550477, 0.142853, 0.000000, 0.000000},
new double[]{0.000000, 0.000000, 0.000000, 0.142853, 0.254566, 0.000000, 0.000000},
new double[]{0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.256073, 0.000000},
new double[]{0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000}};
Matrix x = new DenseMatrix(m);
EigenDecomposition eig = new EigenDecomposition(x, true);
Matrix d = eig.getD();
Matrix v = eig.getV();
check("EigenvalueDecomposition (evil)...", x.times(v), v.times(d));
}
@Test
public void testDeficientRank() {
Matrix a = new DenseMatrix(10, 3).assign(new DoubleFunction() {
private final Random gen = RandomUtils.getRandom();
@Override
public double apply(double arg1) {
return gen.nextGaussian();
}
});
a = a.transpose().times(a);
EigenDecomposition eig = new EigenDecomposition(a);
Matrix d = eig.getD();
Matrix v = eig.getV();
check("EigenvalueDecomposition (rank deficient)...", a.times(v), v.times(d));
Assert.assertEquals(0, eig.getImagEigenvalues().norm(1), 1.0e-10);
Assert.assertEquals(3, eig.getRealEigenvalues().norm(0), 1.0e-10);
}
@Test
public void testEigen() {
double[] evals =
{0.0, 1.0, 0.0, 0.0,
1.0, 0.0, 2.0e-7, 0.0,
0.0, -2.0e-7, 0.0, 1.0,
0.0, 0.0, 1.0, 0.0};
int i = 0;
Matrix a = new DenseMatrix(4, 4);
for (MatrixSlice row : a) {
for (Vector.Element element : row.vector().all()) {
element.set(evals[i++]);
}
}
EigenDecomposition eig = new EigenDecomposition(a);
Matrix d = eig.getD();
Matrix v = eig.getV();
check("EigenvalueDecomposition (nonsymmetric)...", a.times(v), v.times(d));
}
@Test
public void testSequential() {
int validld = 3;
Matrix A = new DenseMatrix(validld, validld);
double[] columnwise = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0};
int i = 0;
for (MatrixSlice row : A) {
for (Vector.Element element : row.vector().all()) {
element.set(columnwise[i++]);
}
}
EigenDecomposition Eig = new EigenDecomposition(A);
Matrix D = Eig.getD();
Matrix V = Eig.getV();
check("EigenvalueDecomposition (nonsymmetric)...", A.times(V), V.times(D));
A = A.transpose().times(A);
Eig = new EigenDecomposition(A);
D = Eig.getD();
V = Eig.getV();
check("EigenvalueDecomposition (symmetric)...", A.times(V), V.times(D));
}
private static void check(String msg, Matrix a, Matrix b) {
Assert.assertEquals(msg, 0, a.minus(b).aggregate(Functions.PLUS, Functions.ABS), 1.0e-10);
}
}