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//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,
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package org.apache.commons.math.random;
import org.apache.commons.math.DimensionMismatchException;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.RealMatrixImpl;
import org.apache.commons.math.stat.descriptive.moment.VectorialCovariance;
import org.apache.commons.math.stat.descriptive.moment.VectorialMean;
import junit.framework.*;
public class CorrelatedRandomVectorGeneratorTest
extends TestCase {
public CorrelatedRandomVectorGeneratorTest(String name) {
super(name);
mean = null;
covariance = null;
generator = null;
}
public void testRank() {
assertEquals(3, generator.getRank());
}
public void testRootMatrix() {
RealMatrix b = generator.getRootMatrix();
RealMatrix bbt = b.multiply(b.transpose());
for (int i = 0; i < covariance.getRowDimension(); ++i) {
for (int j = 0; j < covariance.getColumnDimension(); ++j) {
assertEquals(covariance.getEntry(i, j), bbt.getEntry(i, j), 1.0e-12);
}
}
}
public void testMeanAndCovariance() throws DimensionMismatchException {
VectorialMean meanStat = new VectorialMean(mean.length);
VectorialCovariance covStat = new VectorialCovariance(mean.length, true);
for (int i = 0; i < 5000; ++i) {
double[] v = generator.nextVector();
meanStat.increment(v);
covStat.increment(v);
}
double[] estimatedMean = meanStat.getResult();
RealMatrix estimatedCovariance = covStat.getResult();
for (int i = 0; i < estimatedMean.length; ++i) {
assertEquals(mean[i], estimatedMean[i], 0.07);
for (int j = 0; j <= i; ++j) {
assertEquals(covariance.getEntry(i, j),
estimatedCovariance.getEntry(i, j),
0.1 * (1.0 + Math.abs(mean[i])) * (1.0 + Math.abs(mean[j])));
}
}
}
public void setUp() {
try {
mean = new double[] { 0.0, 1.0, -3.0, 2.3};
RealMatrixImpl b = new RealMatrixImpl(4, 3);
double[][] bData = b.getDataRef();
int counter = 0;
for (int i = 0; i < bData.length; ++i) {
double[] bi = bData[i];
for (int j = 0; j < b.getColumnDimension(); ++j) {
bi[j] = 1.0 + 0.1 * ++counter;
}
}
RealMatrix bbt = b.multiply(b.transpose());
covariance = new RealMatrixImpl(mean.length, mean.length);
double[][] covData = covariance.getDataRef();
for (int i = 0; i < covariance.getRowDimension(); ++i) {
covData[i][i] = bbt.getEntry(i, i);
for (int j = 0; j < covariance.getColumnDimension(); ++j) {
double s = bbt.getEntry(i, j);
covData[i][j] = s;
covData[j][i] = s;
}
}
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(17399225432l);
GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
generator = new CorrelatedRandomVectorGenerator(mean,
covariance,
1.0e-12 * covariance.getNorm(),
rawGenerator);
} catch (DimensionMismatchException e) {
fail(e.getMessage());
} catch (NotPositiveDefiniteMatrixException e) {
fail("not positive definite matrix");
}
}
public void tearDown() {
mean = null;
covariance = null;
generator = null;
}
public static Test suite() {
return new TestSuite(CorrelatedRandomVectorGeneratorTest.class);
}
private double[] mean;
private RealMatrixImpl covariance;
private CorrelatedRandomVectorGenerator generator;
}