Consider this:
In [1]: np.argmax(np.array([[1,2,3],[1,9,3],[1,2,3]]).flat)
Out[2]: 4
Re:NumPy 1.20
by montebicyclelo on Sunday January 31, 2021 @07:37AM
(#25978351)
Alternatively you could use flat:
a = np.array([[1,2,3],[1,9,3],[1,2,3]])
idx = np.argmax(a)
a.flat[idx] # 9
Re:NumPy 1.20
by 6gvONxR4sf7o on Sunday January 31, 2021 @11:33AM
(#25979954)
Does that work the same way with strided arrays?
Re:NumPy 1.20
by montebicyclelo on Sunday January 31, 2021 @01:48PM
(#25981356)
Assuming you mean what I think you mean, it does work.
e.g. a[::2, ::3].flat[idx], where idx is from 0 to width*height of the view
(idx can also be a NumPy array, for getting multiple values)
However, I do have one request for it. Getting the argmax of a multi-dimensional array, in terms of the array's dimensions, is difficult for new users.
np.argmax(np.array([[1,2,3],[1,9,3],[1,2,3]])) is 4, rather than (1,1). I understand why, but it seems strange to me that argmax cannot return a value the user can use to index their array.
Having to then feed that `4` into unravel_index() with the array's shape as a parameter seems less elegant than say passing a parameter of "as_index=True" to the argmax.