Reputation: 419
I have a 4000 x 16 matrix A. I have 256 x 1 vector B. I need to get the elementwise addition for each element in A with every element of B and get a 3D array of size 4000 x 16 x 256. What is the most efficient way to achieve this without loops with numpy?
Upvotes: 2
Views: 252
Reputation: 59
This is where numpy's broadcasting and np.transpose() features really shine!
A[:,:, np.newaxis] + B[np.newaxis,:,:].transpose(2,0,1)
Upvotes: 4
Reputation: 476594
You can first reshape A
to a 4000×16×1 matrix, and B
to 1×1×256 matrix. Then you can perform addition:
A[:,:,None] + B.reshape(1, 1, -1)
For A
a 4×5 matrix, and B
a 3×1 matrix, for example, we get:
>>> A
array([[1, 2, 2, 2, 2],
[2, 1, 0, 3, 2],
[4, 1, 2, 1, 3],
[3, 2, 3, 2, 2]])
>>> B
array([[0],
[3],
[3],
[6],
[4],
[3]])
>>> A[:,:,None] + B.reshape(1, 1, -1)
array([[[ 1, 4, 4, 7, 5, 4],
[ 2, 5, 5, 8, 6, 5],
[ 2, 5, 5, 8, 6, 5],
[ 2, 5, 5, 8, 6, 5],
[ 2, 5, 5, 8, 6, 5]],
[[ 2, 5, 5, 8, 6, 5],
[ 1, 4, 4, 7, 5, 4],
[ 0, 3, 3, 6, 4, 3],
[ 3, 6, 6, 9, 7, 6],
[ 2, 5, 5, 8, 6, 5]],
[[ 4, 7, 7, 10, 8, 7],
[ 1, 4, 4, 7, 5, 4],
[ 2, 5, 5, 8, 6, 5],
[ 1, 4, 4, 7, 5, 4],
[ 3, 6, 6, 9, 7, 6]],
[[ 3, 6, 6, 9, 7, 6],
[ 2, 5, 5, 8, 6, 5],
[ 3, 6, 6, 9, 7, 6],
[ 2, 5, 5, 8, 6, 5],
[ 2, 5, 5, 8, 6, 5]]])
Performance: If I run the above 100 times with A
a 4000×16 matrix, and B
a 256×1 matrix of floating points, I get the following results:
>>> timeit(lambda: A[:,:,None] + B.reshape(1, 1, -1), number=100)
5.949456596048549
It thus takes roughly 59.49457ms for a single run. This looks reasonable to calculate 16'384'000 elements.
Upvotes: 4