You can use numpy broadcasting to do calculation on the two arrays, turning a
into a vertical 2D array using newaxis
:
In [11]: a = np.array([1, 2, 3]) # n1 = 3
...: b = np.array([4, 5]) # n2 = 2
...: #if function is c(i, j) = a(i) + b(j)*2:
...: c = a[:, None] + b*2
In [12]: c
Out[12]:
array([[ 9, 11],
[10, 12],
[11, 13]])
To benchmark:
In [28]: a = arange(100)
In [29]: b = arange(222)
In [30]: timeit r = np.array([[f(i, j) for j in b] for i in a])
10 loops, best of 3: 29.9 ms per loop
In [31]: timeit c = a[:, None] + b*2
10000 loops, best of 3: 71.6 us per loop
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