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* 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,
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* See the License for the specific language governing permissions and
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package org.apache.mahout.math.ssvd;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.DiagonalMatrix;
import org.apache.mahout.math.MahoutTestCase;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.RandomTrinaryMatrix;
import org.apache.mahout.math.SingularValueDecomposition;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.Functions;
import org.junit.Test;
public final class SequentialBigSvdTest extends MahoutTestCase {
@Test
public void testSingularValues() {
Matrix A = lowRankMatrix();
SequentialBigSvd s = new SequentialBigSvd(A, 8);
SingularValueDecomposition svd = new SingularValueDecomposition(A);
Vector reference = new DenseVector(svd.getSingularValues()).viewPart(0, 8);
assertEquals(reference, s.getSingularValues());
assertEquals(A, s.getU().times(new DiagonalMatrix(s.getSingularValues())).times(s.getV().transpose()));
}
@Test
public void testLeftVectors() {
Matrix A = lowRankMatrix();
SequentialBigSvd s = new SequentialBigSvd(A, 8);
SingularValueDecomposition svd = new SingularValueDecomposition(A);
// can only check first few singular vectors because once the singular values
// go to zero, the singular vectors are not uniquely determined
Matrix u1 = svd.getU().viewPart(0, 20, 0, 4).assign(Functions.ABS);
Matrix u2 = s.getU().viewPart(0, 20, 0, 4).assign(Functions.ABS);
assertEquals(0, u1.minus(u2).aggregate(Functions.PLUS, Functions.ABS), 1.0e-9);
}
private static void assertEquals(Matrix u1, Matrix u2) {
assertEquals(0, u1.minus(u2).aggregate(Functions.MAX, Functions.ABS), 1.0e-10);
}
private static void assertEquals(Vector u1, Vector u2) {
assertEquals(0, u1.minus(u2).aggregate(Functions.MAX, Functions.ABS), 1.0e-10);
}
@Test
public void testRightVectors() {
Matrix A = lowRankMatrix();
SequentialBigSvd s = new SequentialBigSvd(A, 6);
SingularValueDecomposition svd = new SingularValueDecomposition(A);
Matrix v1 = svd.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
Matrix v2 = s.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
assertEquals(v1, v2);
}
private static Matrix lowRankMatrix() {
Matrix u = new RandomTrinaryMatrix(1, 20, 4, false);
Matrix d = new DiagonalMatrix(new double[]{5, 3, 1, 0.5});
Matrix v = new RandomTrinaryMatrix(2, 23, 4, false);
return u.times(d).times(v.transpose());
}
}