I have a 3D numpy array and I want to multiply it with a 2D array, The 3D looks like follows:
C= np.zeros((3, 2, 2))
C[0][0] = [0,0]
C[0][1] = [0,1]
C[1][0] = [1,0]
C[1][1] = [1,1]
C[2][0] = [1,2]
C[2][1] = [2,1]
The 2D array looks like:
V = np.zeros((3,2))
V[0][0] = 1
V[0][1] = 2
V[1][0] = 1
V[1][1] = 3
V[2][0] = 4
V[2][1] = 5
The result R is to be a 2X2 2D array(4 elements in total) R=[[5,8],[13,10]] where:
R[0] = V[0][0]*C[0][0]+V[1][0]*C[1][0]+V[2][0]*C[2][0] = [5,8] (first row of R)
R[1] = V[0][1]*C[0][1]+V[1][1]*C[1][1]+V[2][1]*C[2][1] = [13,10] (second row of R)
This is just an example, How Can I get R using numpy matrix multiplication operation with V and C (with no for loop!). Please help!
Sorry I made some edit later, the comment showed an old example, it should be good now
Your example is confusing. Why do you say your expected result is [[1, 0], [5, 10]] but in your example you also say R should be [[5, 8], [13, 10]]?
I hope this was just a typo on your part because it's not clear from your example how you'd get from one to the other.
In any case:
(V.T * C.T).sum(axis=2).T
Output:
array([[ 5., 8.],
[13., 10.]])