import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
/**
 * Construct a MultivariateSummaryStatistics instance
 * @param k dimension of the data
 * @param isCovarianceBiasCorrected if true, the unbiased sample
 * covariance is computed, otherwise the biased population covariance
 * is computed
 */
public MultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) {
    this.k = k;

    sumImpl     = new StorelessUnivariateStatistic[k];
    sumSqImpl   = new StorelessUnivariateStatistic[k];
    minImpl     = new StorelessUnivariateStatistic[k];
    maxImpl     = new StorelessUnivariateStatistic[k];
    sumLogImpl  = new StorelessUnivariateStatistic[k];
    geoMeanImpl = new StorelessUnivariateStatistic[k];
    meanImpl    = new StorelessUnivariateStatistic[k];

    for (int i = 0; i < k; ++i) {
        sumImpl[i]     = new Sum();
        sumSqImpl[i]   = new SumOfSquares();
        minImpl[i]     = new Min();
        maxImpl[i]     = new Max();
        sumLogImpl[i]  = new SumOfLogs();
        geoMeanImpl[i] = new GeometricMean();
        meanImpl[i]    = new Mean();
    }

    covarianceImpl =
        new VectorialCovariance(k, isCovarianceBiasCorrected);

}
 

import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
@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])));
        }
    }

}
 

import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
@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);
    }
}
 

import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
public Gaussian fit(List<double[]> data) {
    k = data.get(0).length;
    int n = data.size();
    VectorialCovariance covCounter = new VectorialCovariance(k, true);
    double[] sumCounter = new double[k];

    for(double[] curDatum : data) {
        for (int i = 0; i < k; i++) {
            sumCounter[i] += curDatum[i];
        }
        covCounter.increment(curDatum);
    }
    for (int i = 0; i < k; i++) {
        sumCounter[i] /= n;
    }
    mean = sumCounter;
    cov = covCounter.getResult();
    initialize();
    return this;
}