Examples of VectorialCovariance

Examples of org.apache.commons.math.stat.descriptive.moment.VectorialCovariance

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            // store the points into the simplex
            buildSimplex(vertices);

            // compute the statistical properties of the simplex points
            VectorialMean meanStat = new VectorialMean(vertices[0].length);
            VectorialCovariance covStat = new VectorialCovariance(vertices[0].length, true);
            for (int i = 0; i < vertices.length; ++i) {
                meanStat.increment(vertices[i]);
                covStat.increment(vertices[i]);
            }
            double[] mean = meanStat.getResult();
            RealMatrix covariance = covStat.getResult();
           

            RandomGenerator rg = new JDKRandomGenerator();
            rg.setSeed(seed);
            RandomVectorGenerator rvg =
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Examples of org.apache.commons.math.stat.descriptive.moment.VectorialCovariance

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            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

    }
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Examples of org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance

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            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

    }
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Examples of org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance

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            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

    }
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Examples of org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance

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            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

    }
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Examples of org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance

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    @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),
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Examples of org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance

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    @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);
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Examples of org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance

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    @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),
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