Need to compare each value inside a numpy array and return 1 to the largest value and 0 to the others. I am having problem with different numbers of [].
Input Example:
[[[0.6673975 0.33333233]]
.
.
.
[[0.33260247 0.6673975]]]
Expected Output:
[[[1 0]]
.
.
.
[[0 1]]]
If, as suggested by Joe in the comments, you're looking for a maximum along an axis, then, for axis axis,
np.moveaxis((np.moveaxis(ar, axis, 0) == ar.max(axis)).astype(int), 0, axis)
or, a bit faster,
(ar == np.broadcast_to(np.expand_dims(ar.max(axis), axis), ar.shape)).astype(int)
Should cover the n-dimensional case.
Ex:
ar = np.random.randint(0, 100, (2, 3, 4))
ar
Out[157]:
array([[[17, 28, 22, 31],
[99, 51, 65, 65],
[46, 24, 93, 4]],
[[ 5, 84, 85, 79],
[ 7, 80, 27, 25],
[46, 80, 90, 3]]])
(ar == np.broadcast_to(np.expand_dims(ar.max(-1), -1), ar.shape)).astype(int)
Out[159]:
array([[[0, 0, 0, 1],
[1, 0, 0, 0],
[0, 0, 1, 0]],
[[0, 0, 1, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]])
ar.max(-1)
Out[160]:
array([[31, 99, 93],
[85, 80, 90]])
On the off-chance you're trying to identify elements equal to the maximum over the whole array,
(ar == ar.max()).astype(int)
should give what you're looking for.