问题:

I'm having a numpy ndarray where I would like to check if each row vector is monotonically increasing.

Example:

a = np.asarray([[1,2,3],[1,5,7],[4,3,6]])
monotonically_increasing(a)

Expected return:

[True, True, False]

I'm not entirely sure how to efficiently do this, since the matrices are expected to be quite large (~1000x1000), and was hoping for some help.

回答1:

>>> import numpy as np
>>> a = np.asarray([[1,2,3],[1,5,7],[4,3,6]])

Find the difference between each element. np.diff has an argument that lets you specify the axis to perform the diff

>>> np.diff(a)
array([[ 1,  1],
       [ 4,  2],
       [-1,  3]])

Check to see if each difference is greater than 0.

>>> np.diff(a) > 0
array([[ True,  True],
       [ True,  True],
       [False,  True]], dtype=bool)

Check to see if all the differences are > 0

>>> np.all(np.diff(a) > 0)
False
>>>

As suggested by @Jaime - check that each element is greater than the element to its left:

np.all(a[:, 1:] >= a[:, :-1], axis=1)

Which appears to be about twice as fast/efficient as my diff solution.

You can make a function like this:

def monotonically_increasing(l):
    return all(x < y for x, y in zip(l, l[1:]))

and then check for it, sublist for sublist, so

[monotonically_increasing(sublist) for sublist in a]