我最近在尝试执行一些数据清理算法。当我试图计算数据集中的点和平均向量之间的马氏距离时，似乎是一样的。
例如，我有这样一个数据集：

{{2,2,3},{4,5,9},{7,8,9}}

平均向量为：

{13/3,5,7}

协方差矩阵为：

{{6.333333333333333,7.5,7.0},{7.5,9.0,9.0},{7.0,9.0,12.0}}

那么{2,2,3}，{4,5,9}，{7,8,9}和平均向量之间的距离都是8290542，这很奇怪。经过纸上计算，结果是一致的。
有人知道我的代码或想法有什么问题吗？如果有人能帮我，我会非常感激的。下面是我用来处理这个问题的一些代码。

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.correlation.Covariance;
import org.apache.mahout.math.*;
import org.apache.mahout.common.distance.MahalanobisDistanceMeasure;

public class Test {

    public static void main(String[] args) {
        double[] a = {2,2,3};
        Vector aVector = new DenseVector(a);

        double[] b = {4,5,9};
        Vector bVector = new DenseVector(b);

        double[] c = {7,8,9};
        Vector cVector = new DenseVector(b);

        double[] mean = {13/3,5,7};
        Vector meanVector = new DenseVector(mean);

        MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();

        double[][] ma = {{2,2,3},{4,5,9},{7,8,9}};
        RealMatrix matrix = new Covariance(ma).getCovarianceMatrix();
        Matrix math = new DenseMatrix(matrix.getData());

        measure.setCovarianceMatrix(math);

        measure.setMeanVector(meanVector);
        System.out.println(matrix.toString());
        System.out.println(measure.distance(meanVector,cVector));

    }

}