Argsmax
Sat 17 May 2025
import numpy as np
a = np.array(
[
[1,2,44,7],
[9,88,6,45],
[19,76,3,4]
]
)
a
array([[ 1, 2, 44, 7],
[ 9, 88, 6, 45],
[19, 76, 3, 4]])
a.size
12
a.shape
(3, 4)
a.ndim
2
np.argmax(a)
5
a.flatten()
array([ 1, 2, 44, 7, 9, 88, 6, 45, 19, 76, 3, 4])
Note:
argmax Returns the indices of the maximum values along an axis.
The np.argmax function by default works along the flattened array, unless you specify an axis
np.argmin(a) # returns the index of the minimum value
0
np.argmax(a, axis=0) # index of numbers 19, 88, 44, 45
array([2, 1, 0, 1])
Note
np.argmax(a, axis=0) returns the index of the maximum value in each of the four columns.
np.argmax(a, axis=1)
array([2, 1, 1])
Note:
That means np.argmax(a, axis=1) returns 2,1,1 because a has three rows. The index of the maximum value in the first row is 2 (44), the index of the maximum value of the second and third rows is 1 (88, 76)
b = np.array(
[
[2, 4],
[5, 3]
]
)
b
array([[2, 4],
[5, 3]])
b.size
4
b.shape
(2, 2)
b.flatten()
array([2, 4, 5, 3])
np.argmax(b)
2
np.argmax(b, axis=0)
array([1, 0])
np.argmax(b, axis=1)
array([1, 0])
https://stackoverflow.com/questions/28697993/numpy-what-is-the-logic-of-the-argmin-and-argmax-functions
Score: 20
Category: numpy