Suppose I have an ndarray, W of shape (m,n,n) and a vector C of dimension (m,n). I need to multiply these two in the following way
result = np.empty(m,n)
for i in range(m):
result[i] = W[i] @ C[i]
How do I do this in a vectorized way without loops and all?
Since, you need to keep the first axis from both W and C aligned, while loosing the last axis from them with the matrix-multiplication, I would suggest using np.einsum for a very efficient approach, like so -
np.einsum('ijk,ik->ij',W,C)
np.tensordot or np.dot doesn't have the feature to keep axes aligned and that's where np.einsum improves upon.
np.dot should be your friend.
http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.dot.html
It's done using np.tensordot
ans=np.tensordot(W,C,axes=[2,1])[np.arange(m),:,np.arange(m)]
assert np.all(result==ans)