python - NumPy广播: Calculating sum of squared differences between two arrays

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

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

最佳答案

您可以在计算出 np.einsum 中的差异后使用 broadcasted way ，就像这样-

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

或使用 scipy's cdist 及其可选的度量参数设置为'sqeuclidean'来为我们的问题提供所需的平方欧几里德距离，如下所示-

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

关于python - NumPy广播: Calculating sum of squared differences between two arrays，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/36241608/