numpy vectorize use on (2,) array

Question

I have a numpy array of (m, 2) and I want to transform it to shape of (m, 1) using a function below.

def func(x):
    if x == [1., 1.]:
        return 0.
    if x == [-1., 1.] or x == [-1., -1.]:
        return 1.
    if x == [1., -1.]:
        return 2.

I want this for applied on each (2,) vector inside the (m, 2) array resulting an (m, 1) array. I tried to use numpy.vectorize but it seems that the function gets applied in each element of a array (which makes sense in general purpose case). So I have failed to apply it.

My intension is not to use for loop. Can anyone help me with this? Thanks.

Each condition has to be wrapped in (x == [1., 1.]).all(), then np.vectorize(func, signature='(n)->()')(arr) or np.apply_along_axis(func, 1, arr). Both are slower than the comprehension np.fromiter([func(x) for x in arr], dtype=float). — Michael Szczesny
– Michael Szczesny, Commented Jun 14, 2022 at 6:59

Lorenzo Palloni · Accepted Answer · 2022-06-14 05:42:51Z

1

import numpy as np


def f(a, b):
    return a + b


F = np.vectorize(f)
x = np.asarray([[1, 2], [3, 4], [5, 6]]).T

print(F(*x))

Output:

[3, 7, 11]

edited Jun 14, 2022 at 5:42

answered Jun 14, 2022 at 5:27

Lorenzo Palloni

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2 Comments

Stat_prob_001 Over a year ago

hi, your approach maybe right, but I don't think 1+2 = 5

Lorenzo Palloni Over a year ago

Damn, you're right! Sorry, I've changed my answer.

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