import numpy as np
a = np.array([[[ 0.25, 0.10 , 0.50 , 0.15],
[ 0.50, 0.60 , 0.70 , 0.30]],
[[ 0.25, 0.50 , 0.20 , 0.70],
[ 0.80, 0.10 , 0.50 , 0.15]]])
I need to find the row and column of the max value in a[i]. If i=0, a[0,1,2] is max, so I need to code a method that gives [1,2] as the output for max in a[0]. Any pointers, please? NB: np.argmax flattens the a[i] 2D array and when axis=0 is used, it gives the index of max in each row of a[0]
You can also use argmax with unravel_index:
def max_by_index(idx, arr):
return (idx,) + np.unravel_index(np.argmax(arr[idx]), arr.shape[1:])
e.g.
import numpy as np
a = np.array([[[ 0.25, 0.10 , 0.50 , 0.15],
[ 0.50, 0.60 , 0.70 , 0.30]],
[[ 0.25, 0.50 , 0.20 , 0.70],
[ 0.80, 0.10 , 0.50 , 0.15]]])
def max_by_index(idx, arr):
return (idx,) + np.unravel_index(np.argmax(arr[idx]), arr.shape[1:])
print(max_by_index(0, a))
gives
(0, 1, 2)
You can use numpy.where, which you can wrap in a simple function to meet your requirements:
def max_by_index(idx, arr):
return np.where(arr[idx] == np.max(arr[idx]))
In action:
>>> max_by_index(0, a)
(array([1], dtype=int64), array([2], dtype=int64))
You can index your array with this result to access the maximum value:
>>> a[0][max_by_index(0, a)]
array([0.7])
This will return all locations of the maximum value, if you only want a single occurrence, you may replace np.max with np.argmax.
np.where may not return anything at all when comparing float numbers with ==col = (np.argmax(a[i])) % (a[i].shape[1])
row = (np.argmax(a[i])) // (a[i].shape[1])
This also helped