comparing values in numpy array of n-dimension

Question

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]]]

Answer 1

Max over axis:

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]])

Max over full array:

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.

Answer 2

Let's create an example case to work with:

a = np.array([[1, 2, 3], [4, 6, 5], [9, 1, 1]])

then we can iterate through with a for-loop over the last row (second last axis) and modify that axis accordingly:

for i in range(a.shape[-2]):
    a[..., i, :] = a[..., i, :] == max(a[..., i, :])

And this will modify a to the correct result:

array([[0, 0, 1],
       [0, 1, 0],
       [1, 0, 0]])

---

And this same method will work for any rectangular array, such as:

a = np.array([[1, 2], [4, 3], [9, 7]])

giving:

array([[0, 1],
       [1, 0],
       [1, 0]])