I have an ndarray, A
, and I want to multiply this ndarray element wise by another 1D array b
where I assume that A.shape[i] = len(b)
for some i
. I need this generality in my application.
I can do this using np.tile
as follows:
A = np.random.rand(2,3,5,9)
b = np.random.rand(5)
i = 2
b_shape = np.ones(len(A.shape), dtype=np.int)
b_shape[i] = len(b)
b_reps = list(A.shape)
b_reps[i] = 1
B = np.tile(b.reshape(b_shape), b_reps)
# Here B.shape = A.shape and
# B[i,j,:,k] = b for all i,j,k
This strikes me as ugly. Is there a better way to do this?
For this particular example, the following code would do the trick:
result = A*b[:, np.newaxis]
For any value of i
, try this:
A2, B = np.broadcast_arrays(A, b)
result = A2*B
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