Numpy matrix multiplication but instead of multiplying it XOR's elements

Question

as stated in the title I want to make a calculation where instead of multiplying corresponding elements I binary XOR them and then add them. Example for illustration:

EDIT: The big picture above IS the calculation but here we go: Take first row from the left [1 0 1] and first column from top matrix [1 0 0]. 1 XOR 1 = 0, 0 XOR 0 = 0, 1 XOR 0 = 1. Add them all 0 + 0 + 1 = 1. First row from the left [1 0 1], second column [0 0 0]: 1 XOR 0 = 1, 0 XOR 0 = 0, 1 XOR 0 = 1. Add them all 1 + 0 + 1 = 2. And so on

Is it possible to do that in numpy?

Answer 1

Try this:

M1 = np.array([[1, 0, 0], [0, 0, 0], [0, 0, 0]])
M2 = np.array([[1, 0, 1], [0, 0, 1], [1, 1, 1]])
(M1 ^ M2[:,None]).sum(-1)

Output:

array([[1, 2, 2],
       [2, 1, 1],
       [2, 3, 3]])

EDIT

If you want to preallocate memory:

intermediary = np.empty((3,3,3), dtype=np.int32)
np.bitwise_xor(M1, M2[:,None], out=intermediary).sum(-1)