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Commons Math example source code file (CorrelatedRandomVectorGeneratorTest.java)

This example Commons Math source code file (CorrelatedRandomVectorGeneratorTest.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

correlatedrandomvectorgenerator, correlatedrandomvectorgenerator, correlatedrandomvectorgeneratortest, dimensionmismatchexception, dimensionmismatchexception, gaussianrandomgenerator, jdkrandomgenerator, jdkrandomgenerator, notpositivedefinitematrixexception, override, realmatrix, realmatrix, testcase, vectorialcovariance

The Commons Math CorrelatedRandomVectorGeneratorTest.java source code

//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.commons.math.random;

import junit.framework.TestCase;

import org.apache.commons.math.DimensionMismatchException;
import org.apache.commons.math.linear.MatrixUtils;
import org.apache.commons.math.linear.NotPositiveDefiniteMatrixException;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.stat.descriptive.moment.VectorialCovariance;
import org.apache.commons.math.stat.descriptive.moment.VectorialMean;

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 testMath226()
        throws DimensionMismatchException, NotPositiveDefiniteMatrixException {
        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);

        for (int i = 0; i < 10; i++) {
            double[] generated = sg.nextVector();
            assertTrue(Math.abs(generated[0] - 1) > 0.1);
        }

    }

    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])));
            }
        }

    }

    @Override
    public void setUp() {
        try {
            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);
        } catch (DimensionMismatchException e) {
            fail(e.getMessage());
        } catch (NotPositiveDefiniteMatrixException e) {
            fail("not positive definite matrix");
        }
    }

    @Override
    public void tearDown() {
        mean       = null;
        covariance = null;
        generator  = null;
    }

    private double[] mean;
    private RealMatrix covariance;
    private CorrelatedRandomVectorGenerator generator;

}

Other Commons Math examples (source code examples)

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