Numpy, change max value in each row to 1 without changing others

user_14989028 0
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I'm trying to change max value of each rows to 1 and leave others.

Each values is between 0 to 1.

I want to change this

>>> a = np.array([[0.5, 0.2, 0.1], 
...               [0.6, 0.3, 0.8], 
...               [0.3, 0.4, 0.2]])

into this

>>> new_a = np.array([[1, 0.2, 0.1],
...                   [0.6, 0.3, 1],
...                   [0.3, 1, 0.2]])

Is there any good solution for this problem using np.where maybe? (without using for loop)

numpy python
5 Answers

Use np.argmax and slice assignment:

>>> a[np.arange(len(a)), np.argmax(a, axis=1)] = 1
>>> a
array([[1. , 0.2, 0.1],
       [1. , 0.3, 0.6],
       [1. , 0.3, 0.2]])
>>>
user_87083640

the question differes from desired output. the author says he wants to replace max value and leave others but actualy he replaces max value and some others.

this is the solution for replacing max value only.

np.where(arr == np.amax(arr), 1, arr)
user_44500900

U12-Forward's answer does it perfectly. Here is another answer using numpy.where

np.where(a[0]==a.max(1), 1, a)
# `a[0]==a.max(1)` -> ​for each row, find element that is equal to max element in that row
# `1` -> set it to `1`
# `a` -> others remain the same
user_106660660

Here's a more detailed step by step process that gives us the desired output:

# input array
a = np.array([[0.5, 0.8, 0.1],
              [0.8, 0.9, 0.6],
               [0.4, 0.3, 12]])

# finding the max element for each row
# axis=1 is given because we want to find the max for each row
max_elements = np.amax(a, axis=1)

# this changes the shape of max_elements array so that it matches with input array(a)
# this shape change is done so that we can compare directly
max_elements = max_elements[:, None]

# this code is checking the main condition
# if the value in a row matches with the max element of that row, change it to 1
# else keep it the same
new_arr = np.where(a == max_elements, 1, a)

print(new_arr)
user_107843150

U12-Forward's and AcaNg's answers are perfect. Here's another way to do it usng numpy.where

new_a = np.where(a==[[i] for i in np.amax(a,axis=1)],1,a)
user_98853720
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