缩放 3D 张量行 [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.我现在想将每个 n 3-by-3 矩阵的每一 row 与B中对应的标量，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)

这也可以不用循环来完成吗?

Can this be done without the loop as well?

推荐答案

您可以使用 NumPy 广播 在添加单例维度后将 B 扩展到 3D 后，让元素乘法以矢量化的方式发生最后是 np.newaxis 或其别名/速记 None.因此，实现将是 A*B[:,:,None] 或简单地 A*B[...,None].

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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