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package org.apache.commons.math3.random;
import java.util.Arrays;
import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.correlation.StorelessCovariance;
import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance;
import org.apache.commons.math3.stat.descriptive.moment.VectorialMean;
import org.apache.commons.math3.util.FastMath;
import org.junit.Test;
import org.junit.Assert;
public class CorrelatedRandomVectorGeneratorTest {
private double[] mean;
private RealMatrix covariance;
private CorrelatedRandomVectorGenerator generator;
public CorrelatedRandomVectorGeneratorTest() {
mean = new double[] { 0.0, 1.0, -3.0, 2.3 };
RealMatrix b = MatrixUtils.createRealMatrix(4, 3);
int counter = 0;
for (int i = 0; i < b.getRowDimension(); ++i) {
for (int j = 0; j < b.getColumnDimension(); ++j) {
b.setEntry(i, j, 1.0 + 0.1 * ++counter);
}
}
RealMatrix bbt = b.multiply(b.transpose());
covariance = MatrixUtils.createRealMatrix(mean.length, mean.length);
for (int i = 0; i < covariance.getRowDimension(); ++i) {
covariance.setEntry(i, i, bbt.getEntry(i, i));
for (int j = 0; j < covariance.getColumnDimension(); ++j) {
double s = bbt.getEntry(i, j);
covariance.setEntry(i, j, s);
covariance.setEntry(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);
}
@Test
public void testRank() {
Assert.assertEquals(2, generator.getRank());
}
@Test
public void testMath226() {
double[] mean = { 1, 1, 10, 1 };
double[][] cov = {
{ 1, 3, 2, 6 },
{ 3, 13, 16, 2 },
{ 2, 16, 38, -1 },
{ 6, 2, -1, 197 }
};
RealMatrix covRM = MatrixUtils.createRealMatrix(cov);
JDKRandomGenerator jg = new JDKRandomGenerator();
jg.setSeed(5322145245211l);
NormalizedRandomGenerator rg = new GaussianRandomGenerator(jg);
CorrelatedRandomVectorGenerator sg =
new CorrelatedRandomVectorGenerator(mean, covRM, 0.00001, rg);
double[] min = new double[mean.length];
Arrays.fill(min, Double.POSITIVE_INFINITY);
double[] max = new double[mean.length];
Arrays.fill(max, Double.NEGATIVE_INFINITY);
for (int i = 0; i < 10; i++) {
double[] generated = sg.nextVector();
for (int j = 0; j < generated.length; ++j) {
min[j] = FastMath.min(min[j], generated[j]);
max[j] = FastMath.max(max[j], generated[j]);
}
}
for (int j = 0; j < min.length; ++j) {
Assert.assertTrue(max[j] - min[j] > 2.0);
}
}
@Test
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) {
Assert.assertEquals(covariance.getEntry(i, j), bbt.getEntry(i, j), 1.0e-12);
}
}
}
@Test
public void testMeanAndCovariance() {
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) {
Assert.assertEquals(mean[i], estimatedMean[i], 0.07);
for (int j = 0; j <= i; ++j) {
Assert.assertEquals(covariance.getEntry(i, j),
estimatedCovariance.getEntry(i, j),
0.1 * (1.0 + FastMath.abs(mean[i])) * (1.0 + FastMath.abs(mean[j])));
}
}
}
@Test
public void testSampleWithZeroCovariance() {
final double[][] covMatrix1 = new double[][]{
{0.013445532, 0.010394690, 0.009881156, 0.010499559},
{0.010394690, 0.023006616, 0.008196856, 0.010732709},
{0.009881156, 0.008196856, 0.019023866, 0.009210099},
{0.010499559, 0.010732709, 0.009210099, 0.019107243}
};
final double[][] covMatrix2 = new double[][]{
{0.0, 0.0, 0.0, 0.0, 0.0},
{0.0, 0.013445532, 0.010394690, 0.009881156, 0.010499559},
{0.0, 0.010394690, 0.023006616, 0.008196856, 0.010732709},
{0.0, 0.009881156, 0.008196856, 0.019023866, 0.009210099},
{0.0, 0.010499559, 0.010732709, 0.009210099, 0.019107243}
};
final double[][] covMatrix3 = new double[][]{
{0.013445532, 0.010394690, 0.0, 0.009881156, 0.010499559},
{0.010394690, 0.023006616, 0.0, 0.008196856, 0.010732709},
{0.0, 0.0, 0.0, 0.0, 0.0},
{0.009881156, 0.008196856, 0.0, 0.019023866, 0.009210099},
{0.010499559, 0.010732709, 0.0, 0.009210099, 0.019107243}
};
testSampler(covMatrix1, 10000, 0.001);
testSampler(covMatrix2, 10000, 0.001);
testSampler(covMatrix3, 10000, 0.001);
}
private CorrelatedRandomVectorGenerator createSampler(double[][] cov) {
RealMatrix matrix = new Array2DRowRealMatrix(cov);
double small = 10e-12 * matrix.getNorm();
return new CorrelatedRandomVectorGenerator(
new double[cov.length],
matrix,
small,
new GaussianRandomGenerator(new JDKRandomGenerator()));
}
private void testSampler(final double[][] covMatrix, int samples, double epsilon) {
CorrelatedRandomVectorGenerator sampler = createSampler(covMatrix);
StorelessCovariance cov = new StorelessCovariance(covMatrix.length);
for (int i = 0; i < samples; ++i) {
cov.increment(sampler.nextVector());
}
double[][] sampleCov = cov.getData();
for (int r = 0; r < covMatrix.length; ++r) {
TestUtils.assertEquals(covMatrix[r], sampleCov[r], epsilon);
}
}
}