Package org.apache.commons.math.stat.descriptive.moment

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

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    }

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

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

    }
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    }

    public void testMeanAndCorrelation() throws DimensionMismatchException {

        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) {
            assertEquals(mean[i], estimatedMean[i], 0.07);
            for (int j = 0; j < i; ++j) {
                scale = standardDeviation[i] * standardDeviation[j];
                assertEquals(0, estimatedCorrelation.getEntry(i, j) / scale, 0.03);
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    }

    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),
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    }

    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),
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    }

    public void testMeanAndCorrelation() throws DimensionMismatchException {

        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) {
            assertEquals(mean[i], estimatedMean[i], 0.07);
            for (int j = 0; j < i; ++j) {
                scale = standardDeviation[i] * standardDeviation[j];
                assertEquals(0, estimatedCorrelation.getEntry(i, j) / scale, 0.03);
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            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

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

        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

    }
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    }

    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),
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    }

    public void testMeanAndCorrelation() throws DimensionMismatchException {

        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) {
            assertEquals(mean[i], estimatedMean[i], 0.07);
            for (int j = 0; j < i; ++j) {
                scale = standardDeviation[i] * standardDeviation[j];
                assertEquals(0, estimatedCorrelation.getEntry(i, j) / scale, 0.03);
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Related Classes of org.apache.commons.math.stat.descriptive.moment.VectorialCovariance

  • org.apache.commons.math.linear.RealMatrix
  • org.apache.commons.math.linear.RealMatrixImpl
  • org.apache.commons.math.optimization.DirectSearchOptimizer
  • org.apache.commons.math.random.CorrelatedRandomVectorGeneratorTest
  • org.apache.commons.math.random.UncorrelatedRandomVectorGeneratorTest
  • org.apache.commons.math.stat.descriptive.MultivariateSummaryStatistics
  • org.apache.commons.math.DimensionMismatchException
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