NumPy广播:计算两个数组之间的平方差之和 [英] NumPy Broadcasting: Calculating sum of squared differences between two arrays

问题描述

我有以下代码.它在Python中永远存在.必须有一种方法可以将该计算结果转换为广播...

I have the following code. It is taking forever in Python. There must be a way to translate this calculation into a broadcast...

def euclidean_square(a,b):
    squares = np.zeros((a.shape[0],b.shape[0]))
    for i in range(squares.shape[0]):
        for j in range(squares.shape[1]):
            diff = a[i,:] - b[j,:]
            sqr = diff**2.0
            squares[i,j] = np.sum(sqr)
    return squares

推荐答案

您可以使用

You can use np.einsum after calculating the differences in a broadcasted way, like so -

ab = a[:,None,:] - b
out = np.einsum('ijk,ijk->ij',ab,ab)

或使用 ，其可选的度量参数设置为'sqeuclidean'，以根据问题的需要为我们提供平方的欧几里德距离，就像这样-

Or use scipy's cdist with its optional metric argument set as 'sqeuclidean' to give us the squared euclidean distances as needed for our problem, like so -

from scipy.spatial.distance import cdist
out = cdist(a,b,'sqeuclidean')

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