Numpy - Multiple 3d Array With A 2d Array

I'm trying the following: Given a matrix A (x, y ,3) and another matrix B (3, 3), I would like to return a (x, y, 3) matrix in which the 3rd dimension of A is multiplied by the val

Solution 1:

You can use np.tensordot -

np.tensordot(A,B,axes=((2),(1)))

Related post to understand tensordot.

einsum equivalent would be -

np.einsum('ijk,lk->ijl', A, B)

We can also use A.dot(B.T), but that would be looping under the hoods. So, might not be the most preferred one, but it's a compact solution,

Solution 2:

Sorry for the confusion, I think you can do something like this, using simple numpy methods:

First you can reshape A in a way that its fibers (or depth vectors A[:,:,i]) will be placed as columns in matrix C:

C = A.reshape(x*y,3).T

Then using a simple matrix multiplication you can do:

D = numpy.dot(B,C)

Finally bring the result back to the original dimensions:

D.T.reshape([x,y,3])