How to get "dot addition" in numpy similar to dot product? [duplicate]

Question

I'm somewhat new to numpy and am strugging with this problem. I have two 2-dimensional numpy arrays:

array1 = [a1, a2, ..., an]
array2 = [b1, b2, ..., am]

a1, a2, b1, and b2 are all 1-d arrays with exactly 100 floats in them. However, array1 and array2 have different lengths. So array1 and array2 have shapes (n, 100) and (m, 100) respectively, where n and m are arbitrary lengths.

I'd like to perform some kind of modified dot product between them so I can output the following matrix:

array([[ a1+b1, a1+b2, a1+b3, ...],
       [ a2+b1, a2+b2, a2+b3, ...],
       [ a3+b1, a3+b2, a3+b3, ...],
       [...]])

I understand that np.dot(array1, array2.T) gets me really close. It just gives me a1•b1 instead of a1+b1 in the desired output array.

What's the most computationally efficient way for me to get my desired array with numpy? Thanks in advance!

Answer 1

use np.outer ufunc which is for this purpose:

np.add.outer(array1,array2)

example:

array1 = np.array([1,2,3])
array2 = np.array([1,2])

output:

[[2 3]
 [3 4]
 [4 5]]