Python 如何计算矩阵行的加权平均值，但每行的权重不同

正如标题所暗示的，我有一个数字矩阵(2d数组)，它的对角线恰好与0对称. 我想使用np.Average方法，以便使用来自相同长度的矩阵行的权重数组，将其行折叠为加权平均的一维列array. 但是，由于对角线是零，因此我不想将其计入行的加权平均值的结果中. 换句话说，我希望每一行都有一组不同的权重，这样对于行i，相应的权重[i]将为零，而其余的权重将保持不变.

Is it possible to do this without an explicit loop?
What is the best way to do it?

Code example-
Generate the matrix and the weights:

mat = np.array([[       0,     2436,     2434,     2428,     2416],
                [    2436,        0,     2454,     2446,     2435],
                [    2434,     2454,        0,     2447,     2436],
                [    2428,     2446,     2447,        0,     2428],
                [    2416,     2435,     2436,     2428,        0]])
weights = np.array([262140,   196608,   196608, 196608, 196608])

Current (wrong) implementation:
Calculate the weighted average:

weighted_avg = np.average(mat, axis=-1, weights=weights)
print(weighted_avg)

Out: [1821.38194802 1984.31077694 1984.18578409 1979.68578982 1972.56080841]

Loop implementation:个

weighted_avg = []
for i in range(mat.shape[0]):
    curr_weights = weights.copy()
    curr_weights[i] = 0
    weighted_avg.append(np.average(mat[i], axis=-1, weights=curr_weights))

weighted_avg = np.array(weighted_avg)
print(weighted_avg)

Out: [2428.5        2442.23079848 2442.076961   2436.53850163 2427.76928603]

我怎样才能使这个循环实现使用‘合适的NumPy’？