How to compare 3 numpy arrays elementwise and get the results as the array with maximum values?

Question

The numpy arrays contain prediction probabilities which looks like this:

predict_prob1 =([[0.95602106, 0.04397894],
                 [0.93332366, 0.06667634],
                 [0.97311459, 0.02688541],
                 [0.97323962, 0.02676038]])

predict_prob2 =([[0.70425144, 0.29574856],
                 [0.69751251, 0.30248749],
                 [0.7072872 , 0.2927128 ],
                 [0.68683139, 0.31316861]])

predict_prob3 =([[0.56551921, 0.43448079],
                 [0.93321106, 0.06678894],
                 [0.92345399, 0.07654601],
                 [0.88396842, 0.11603158]])

I want to compare these three numpy.ndarray elementwise and find out which array has the maximum probability as a result. Three of the arrays are of the same length. I have tried to implement something like this which is not correct.

for i in range(len(predict_prob1)):
    if(predict_prob1[i] > predict_prob2[i])
        c = predict_prob1[i]
    else
        c = predict_prob2[i]
    if(c > predict_prob3[i])
        result = c
    else
        result = array[i]

Please help!!

Answer 1

For me, it's not completely clear what you're asking — If your desired result is a 4x2 array that indexes which of the three arrays has the max value in position i,j then you want to use np.argmax

>>> import numpy as np
>>> predict_prob1 =([[0.95602106, 0.04397894],
    [0.93332366, 0.06667634],
    [0.97311459, 0.02688541],
    [0.97323962, 0.02676038]])
>>> predict_prob2 =([[0.70425144, 0.29574856],
    [0.69751251, 0.30248749],
    [0.7072872 , 0.2927128 ],
    [0.68683139, 0.31316861]])
>>> predict_prob3 =([[0.56551921, 0.43448079],
    [0.93321106, 0.06678894],
    [0.92345399, 0.07654601],
    [0.88396842, 0.11603158]])
>>> np.argmax((predict_prob1,predict_prob2,predict_prob3), 0)
array([[0, 2],
       [0, 1],
       [0, 1],
       [0, 1]])
>>>

---

Addendum

Having read a comment of the OP I add the following to my answer

>>> names = np.array(['predict_prob%d'%(i+1) for i in range(3)])
>>> names[np.argmax((predict_prob1,predict_prob2,predict_prob3),0)]
array([['predict_prob1', 'predict_prob3'],
       ['predict_prob1', 'predict_prob2'],
       ['predict_prob1', 'predict_prob2'],
       ['predict_prob1', 'predict_prob2']], dtype='<U13')
>>> 

Answer 2

You could do with np.maximum.reduce:

np.maximum.reduce([A, B, C])

where A, B, C are numpy.ndarray

For your example it results:

[[0.95602106 0.43448079]
 [0.93332366 0.30248749]
 [0.97311459 0.2927128 ]
 [0.97323962 0.31316861]]