/**
* 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.hadoop.stochasticsvd;
import java.util.Random;
import junit.framework.Assert;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.DoubleFunction;
import org.junit.Test;
/**
* Tests parts of of Stochastic SVD solver code in local mode
* using "prototype" code (class that simulates processes
* actually happenning in the MR jobs).
*
*
*/
public class SSVDPrototypeTest extends MahoutTestCase {
private static final double SCALE = 1000;
private static final double SVD_EPSILON = 1.0e-10;
public void testSSVDPrototype() throws Exception {
SSVDPrototype.main(null);
}
@Test
public void testGivensQR() throws Exception {
// DenseMatrix m = new DenseMatrix(dims<<2,dims);
Matrix m = new DenseMatrix(3, 3);
m.assign(new DoubleFunction() {
private final Random rnd = RandomUtils.getRandom();
@Override
public double apply(double arg0) {
return rnd.nextDouble() * SCALE;
}
});
m.setQuick(0, 0, 1);
m.setQuick(0, 1, 2);
m.setQuick(0, 2, 3);
m.setQuick(1, 0, 4);
m.setQuick(1, 1, 5);
m.setQuick(1, 2, 6);
m.setQuick(2, 0, 7);
m.setQuick(2, 1, 8);
m.setQuick(2, 2, 9);
GivensThinSolver qrSolver = new GivensThinSolver(m.rowSize(), m.columnSize());
qrSolver.solve(m);
Matrix qtm = new DenseMatrix(qrSolver.getThinQtTilde());
assertOrthonormality(qtm.transpose(), false, SVD_EPSILON);
Matrix aClone = new DenseMatrix(qrSolver.getThinQtTilde()).transpose()
.times(qrSolver.getRTilde());
System.out.println("aclone : " + aClone);
}
public static void assertOrthonormality(Matrix mtx, boolean insufficientRank, double epsilon) {
int n = mtx.columnSize();
int rank = 0;
for (int i = 0; i < n; i++) {
Vector ei = mtx.getColumn(i);
double norm = ei.norm(2);
if (Math.abs(1 - norm) < epsilon) {
rank++;
} else {
Assert.assertTrue(Math.abs(norm) < epsilon);
}
for (int j = 0; j <= i; j++) {
Vector e_j = mtx.getColumn(j);
double dot = ei.dot(e_j);
Assert
.assertTrue(Math.abs((i == j && rank > j ? 1 : 0) - dot) < epsilon);
}
}
Assert.assertTrue((!insufficientRank && rank == n) || (insufficientRank && rank < n));
}
}