multiplication of 3-dimensional matrix in numpy

matrix, matrix-multiplication, numpy, python

Solution

Try using `numpy.einsum`, it has a little bit of a learning curve but it should give you what you want. Here is an example to get you started.

import numpy as np

A = np.random.random((2, 2, 3))
B = np.random.random((2, 2, 3))

C1 = np.empty((2, 2, 3))
for i in range(3):
    C1[:, :, i] = np.dot(A[:, :, i], B[:, :, i])

C2 = np.einsum('ijn,jkn->ikn', A, B)
np.allclose(C1, C2)

Problem

I think I asked the wrong question yesterday. What I actually want is to mutiply two `2x2xN` matrices `A` and `B`, so that ``` C[:,:,i] = dot(A[:,:,i], B[:,:,i]) ``` For example, if I have a matrix ``` A = np.arange(12).reshape(2, 2, 3) ``` How can I get `C = A x A` with the definition described above? Is there a built-in function to do this? Also, if I multiply `A (shape 2x2xN)` with `B (shape 2x2x1, instead of N)`, I want to get ``` C[:,:,i] = dot(A[:,:,i], B[:,:,1]) ```

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