用Python和Numpy计算协方差 [英] Calculating Covariance with Python and Numpy

问题描述

我正在尝试弄清楚如何使用Python Numpy函数cov计算协方差.当我将其传递给两个一维数组时，我得到了一个2x2的结果矩阵.我不知道该怎么办.我不太擅长统计，但我相信在这种情况下的协方差应该是一个整数. 这是我想要的.我写了我自己的:

I am trying to figure out how to calculate covariance with the Python Numpy function cov. When I pass it two one-dimentional arrays, I get back a 2x2 matrix of results. I don't know what to do with that. I'm not great at statistics, but I believe covariance in such a situation should be a single number. This is what I am looking for. I wrote my own:

def cov(a, b):

    if len(a) != len(b):
        return

    a_mean = np.mean(a)
    b_mean = np.mean(b)

    sum = 0

    for i in range(0, len(a)):
        sum += ((a[i] - a_mean) * (b[i] - b_mean))

    return sum/(len(a)-1)

那行得通，但我认为Numpy版本要有效得多，如果我能弄清楚如何使用它的话.

That works, but I figure the Numpy version is much more efficient, if I could figure out how to use it.

有人知道如何使Numpy cov函数像我写的那样执行吗?

Does anybody know how to make the Numpy cov function perform like the one I wrote?

谢谢

戴夫

推荐答案

当a和b是一维序列时，numpy.cov(a,b)[0][1]等同于您的cov(a,b).

When a and b are 1-dimensional sequences, numpy.cov(a,b)[0][1] is equivalent to your cov(a,b).

np.cov(a,b)返回的2x2数组的元素等于

The 2x2 array returned by np.cov(a,b) has elements equal to

cov(a,a)  cov(a,b)

cov(a,b)  cov(b,b)

(其中cov是您上面定义的功能.)

(where, again, cov is the function you defined above.)

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