Numpy time-dependent 2D matrix multiplication

Question

I am looking for an efficient way to perform a matrix multiplication (dot product) of two time-dependent 2D matrices to end up with one time-dependent 2D matrix.

For example:

a = np.zeros([7200,13,4])
b = np.zeros([7200,4,7])

And I want to end up with

c = np.zeros([7200,13,7])

I already found np.tensordot, however this yields me a 4D matrix instead of a 3D matrix. Also other numpy functions did not yield me the required shape. So I wonder if there is any way to perform this matrix multiplication without the use of for-loops?

Best regards,

Timothy Van Daele

Answer 1

I just digged a little bit deeper and I found the numpy function einsum. This gives a lot of freedom for doing vector multiplications.

a = np.zeros([7200,13,4])
b = np.zeros([7200,4,7])

c = np.einsum('ijk,ikl->ijl',a,b) 
c.shape (7200, 13, 7)