python:有效地将矩阵堆栈的切片i乘以矩阵的第i列 [英] python: Multiply slice i of a matrix stack by column i of a matrix efficiently
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问题描述
import numpy as np
M,N,R = 2,3,4
# For a 3-dimensional array A:
A = np.reshape(np.r_[0:M*N*R], [M,N,R], order = 'C')
# and 2-dimensional array B:
B = np.reshape(np.r_[0:M*R], [R,M], order = 'C')
我希望将A
的切片i
与B
的列i
相乘而得到的N*M
矩阵.我尝试过np.dot
和np.einsum
,但无法获得所需的信息.
I would like the N*M
matrix that results from multiplying slice i
of A
by column i
of B
. I have tried np.dot
, and np.einsum
and have been unable to obtain what I need.
有人可以帮忙吗?谢谢!
Could anybody help, please? Thanks!
推荐答案
With np.einsum
, we would have -
np.einsum('ijk,ki->ji',A,B)
让我们使用给定的样本并使用np.dot
-
Let's verify the results using the given sample and using matrix-multiplication with np.dot
-
In [35]: A.shape
Out[35]: (2, 3, 4)
In [36]: B.shape
Out[36]: (4, 2)
In [37]: A[0].dot(B[:,0])
Out[37]: array([ 28, 76, 124])
In [38]: A[1].dot(B[:,1])
Out[38]: array([226, 290, 354])
In [39]: np.einsum('ijk,ki->ji',A,B)
Out[39]:
array([[ 28, 226],
[ 76, 290],
[124, 354]])
关于何时在np.dot
/np.tensordot
之类的dot-based
工具上使用einsum
的方面,这是
For aspects related to when to use einsum
over dot-based
tools like np.dot
/np.tensordot
, here's a related post
.
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