将操作应用于python中二维numpy数组的每个向量

Question

I need it for 2d arrays but would also be interested in higher dimensions. If I have two numpy 2DArrays lets say A and B and a function f which takes 2 vectors and puts out a scalar. Performance is important and numpy array operations are really fast so how do I get the matrix:

(f(a1,b1),f(a1, b2)...)
(f(a2,b1), f(a2,b2)...)
(...                  )

a(i) and b(i) being 1d arrays/vectors

By applying something like f(A,B) and without using any loops.

With some methods I can use the axis=1 but how do I define my own function like that or is there an other way?

Answer 1

The easiest, and most efficient, solution is to use a for loop. np.vectorize won't help you here, because it passes scalars to a function.

Assuming your arrays have two columns:

for a,b in zip(A,B):
    print(f(a[0],b[0]))
    print(f(a[1],b[1]))

would print the values of f that you would want to return.