添加一个矩阵的倍数而不建立一个新的矩阵 [英] Add multiple of a matrix without build a new one

问题描述

说我有两个矩阵B和M，我想执行以下语句:

Say I have two matrices B and M and I want to execute the following statement:

B += 3*M

我重复执行此指令，所以我不想每次都构建矩阵3*M(3可能会改变，只是为了安慰我只做一个标量矩阵乘积).是使该计算就地"的numpy函数吗?

I execute this instruction repeatedly so I don't want to build each time the matrix 3*M (3 may change, it is just to make cleat that I only do a scalar-matrix product). Is it a numpy-function which makes this computation "in place"?

更确切地说，我有一个标量as列表和一个矩阵Ms列表，我想执行点积"(由于两个操作数的类型不同，所以实际上不是一个)这两个，就是说:

More precisely, I have a list of scalars as and a list of matrices Ms, I would like to perform the "dot product" (which is not really one since the two operands are of different type) of the two, that is to say:

sum(a*M for a, M in zip(as, Ms))

np.dot函数除了我没有做......

The np.dot function does not do what I except...

推荐答案

您可以使用 np.tensordot -

You can use np.tensordot -

np.tensordot(As,Ms,axes=(0,0))

或 np.einsum -

np.einsum('i,ijk->jk',As,Ms)

样品运行-

In [41]: As = [2,5,6]

In [42]: Ms = [np.random.rand(2,3),np.random.rand(2,3),np.random.rand(2,3)]

In [43]: sum(a*M for a, M in zip(As, Ms))
Out[43]: 
array([[  6.79630284,   5.04212877,  10.76217631],
       [  4.91927651,   1.98115548,   6.13705742]])

In [44]: np.tensordot(As,Ms,axes=(0,0))
Out[44]: 
array([[  6.79630284,   5.04212877,  10.76217631],
       [  4.91927651,   1.98115548,   6.13705742]])

In [45]: np.einsum('i,ijk->jk',As,Ms)
Out[45]: 
array([[  6.79630284,   5.04212877,  10.76217631],
       [  4.91927651,   1.98115548,   6.13705742]])

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