运行（一遍）协方差计算

问题描述

我有一组3d向量（x，y，z），我想在不存储向量的情况下计算协方差矩阵。

I got a set of 3d vectors (x,y,z), and I want to calculate the covariance matrix without storing the vectors.

我会这样做用C＃，但最终我将在微控制器上用C实现它，因此我需要算法本身，而不是库。

I will do it in C#, but eventually I will implement it in C on a microcontroller, so I need the algorithm in itself, and not a library.

伪代码也很好。

推荐答案

我想我已经找到了解决方案。它基于这篇有关的文章，以及有关。然后，根据我从第一篇文章开始对它的理解，我对后者中的算法进行了调整，以计算协方差而不是方差。

I think I have found the solution. It is based on this article about how to calculate covariance manually and this one about calculating running variance. And then I adapted the algorithm in the latter to calculate covariance instead of variance, given my understanding of it from the first article.

public class CovarianceMatrix
{
    private int _n;
    private Vector _oldMean, _newMean,
                    _oldVarianceSum, _newVarianceSum,
                    _oldCovarianceSum, _newCovarianceSum;

    public void Push(Vector x)
    {
        _n++;
        if (_n == 1)
        {
            _oldMean = _newMean = x;
            _oldVarianceSum = new Vector(0, 0, 0);
            _oldCovarianceSum = new Vector(0, 0, 0);
        }
        else
        {
            //_newM = _oldM + (x - _oldM) / _n;
            _newMean = new Vector(
                _oldMean.X + (x.X - _oldMean.X) / _n,
                _oldMean.Y + (x.Y - _oldMean.Y) / _n,
                _oldMean.Z + (x.Z - _oldMean.Z) / _n);

            //_newS = _oldS + (x - _oldM) * (x - _newM);
            _newVarianceSum = new Vector(
                _oldVarianceSum.X + (x.X - _oldMean.X) * (x.X - _newMean.X),
                _oldVarianceSum.Y + (x.Y - _oldMean.Y) * (x.Y - _newMean.Y),
                _oldVarianceSum.Z + (x.Z - _oldMean.Z) * (x.Z - _newMean.Z));

            /* .X is X vs Y
             * .Y is Y vs Z
             * .Z is Z vs X
             */
            _newCovarianceSum = new Vector(
                _oldCovarianceSum.X + (x.X - _oldMean.X) * (x.Y - _newMean.Y),
                _oldCovarianceSum.Y + (x.Y - _oldMean.Y) * (x.Z - _newMean.Z),
                _oldCovarianceSum.Z + (x.Z - _oldMean.Z) * (x.X - _newMean.X));

            // set up for next iteration
            _oldMean = _newMean;
            _oldVarianceSum = _newVarianceSum;
        }
    }
    public int NumDataValues()
    {
        return _n;
    }

    public Vector Mean()
    {
        return (_n > 0) ? _newMean : new Vector(0, 0, 0);
    }

    public Vector Variance()
    {
        return _n <= 1 ? new Vector(0, 0, 0) : _newVarianceSum.DivideBy(_n - 1);
    }
}

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