缩放3D张量的行 [英] Scale rows of 3D-tensor

问题描述

我有一个n -by- 3 -by- 3 numpy数组A和一个n -by- 3 numpy数组B.我现在想将n 3 -by- 3矩阵中的每个矩阵的每个 row 与B中的相应标量(即

I have an n-by-3-by-3 numpy array A and an n-by-3 numpy array B. I'd now like to multiply every row of every one of the n 3-by-3 matrices with the corresponding scalar in B, i.e.,

import numpy as np

A = np.random.rand(10, 3, 3)
B = np.random.rand(10, 3)

for a, b in zip(A, B):
    a = (a.T * b).T
    print(a)

可以在没有循环的情况下完成此操作吗?

Can this be done without the loop as well?

推荐答案

您可以使用

You can use NumPy broadcasting to let the elementwise multiplication happen in a vectorized manner after extending B to 3D after adding a singleton dimension at the end with np.newaxis or its alias/shorthand None. Thus, the implementation would be A*B[:,:,None] or simply A*B[...,None].

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