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//to you under the Apache License, Version 2.0 (the
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//http://www.apache.org/licenses/LICENSE-2.0

//Unless required by applicable law or agreed to in writing,
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//"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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package org.apache.commons.math3.random;

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance;
import org.apache.commons.math3.stat.descriptive.moment.VectorialMean;

import org.junit.Test;
import org.junit.Assert;

public class UncorrelatedRandomVectorGeneratorTest {
    private double[] mean;
    private double[] standardDeviation;
    private UncorrelatedRandomVectorGenerator generator;

    public UncorrelatedRandomVectorGeneratorTest() {
        mean              = new double[] {0.0, 1.0, -3.0, 2.3};
        standardDeviation = new double[] {1.0, 2.0, 10.0, 0.1};
        RandomGenerator rg = new JDKRandomGenerator();
        rg.setSeed(17399225432l);
        generator =
            new UncorrelatedRandomVectorGenerator(mean, standardDeviation,
                    new GaussianRandomGenerator(rg));
    }

    @Test
    public void testMeanAndCorrelation() {

        VectorialMean meanStat = new VectorialMean(mean.length);
        VectorialCovariance covStat = new VectorialCovariance(mean.length, true);
        for (int i = 0; i < 10000; ++i) {
            double[] v = generator.nextVector();
            meanStat.increment(v);
            covStat.increment(v);
        }

        double[] estimatedMean = meanStat.getResult();
        double scale;
        RealMatrix estimatedCorrelation = 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) {
                scale = standardDeviation[i] * standardDeviation[j];
                Assert.assertEquals(0, estimatedCorrelation.getEntry(i, j) / scale, 0.03);
            }
            scale = standardDeviation[i] * standardDeviation[i];
            Assert.assertEquals(1, estimatedCorrelation.getEntry(i, i) / scale, 0.025);
        }
    }
}