numpy: what is the logic of the argmin() and argmax() functions?
""" ....READ THE COMMENTS FOR CLARIFICATION....."""
import numpy as np
a = np.array([[1,2,4,7], [9,88,6,45], [9,76,3,4]])
"""np.argmax(a) will give index of max value in flatted array of given matrix """
>>np.argmax(a)
5
"""np.argmax(a,axis=0) will return list of indexes of max value column-wise"""
>>print(np.argmax(a,axis=0))
[1,1,1,1]
"""np.argmax(a,axis=1) will return list of indexes of max value row-wise"""
>>print(np.argmax(a,axis=1))
[3,1,1]
"""np.argmin(a) will give index of min value in flatted array of given matrix """
>>np.argmin(a)
0
"""np.argmin(a,axis=0) will return list of indexes of min value column-wise"""
>>print(np.argmin(a,axis=0))
[0,0,2,2]
"""np.argmin(a,axis=0) will return list of indexes of min value row-wise"""
>>print(np.argmin(a,axis=1))
[0,2,2]
As a side note: if you want to find the coordinates of your maximum value in the full array, you can use
a=np.array([[1,2,4,7],[9,88,6,45],[9,76,3,4]])
>>> a
[[ 1 2 4 7]
[ 9 88 6 45]
[ 9 76 3 4]]
c=(np.argmax(a)/len(a[0]),np.argmax(a)%len(a[0]))
>>> c
(1, 1)
The np.argmax
function by default works along the flattened array, unless you specify an axis. To see what is happening you can use flatten
explicitly:
np.argmax(a)
>>> 5
a.flatten()
>>>> array([ 1, 2, 4, 7, 9, 88, 6, 45, 9, 76, 3, 4])
0 1 2 3 4 5
I've numbered the indices under the array above to make it clearer. Note that indices are numbered from zero in numpy
.
In the cases where you specify the axis, it is also working as expected:
np.argmax(a,axis=0)
>>> array([1, 1, 1, 1])
This tells you that the largest value is in row 1
(2nd value), for each column along axis=0
(down). You can see this more clearly if you change your data a bit:
a=np.array([[100,2,4,7],[9,88,6,45],[9,76,3,100]])
a
>>> array([[100, 2, 4, 7],
[ 9, 88, 6, 45],
[ 9, 76, 3, 100]])
np.argmax(a, axis=0)
>>> array([0, 1, 1, 2])
As you can see it now identifies the maximum value in row 0 for column 1, row 1 for column 2 and 3 and row 3 for column 4.
There is a useful guide to numpy
indexing in the documentation.
By adding the axis
argument, NumPy looks at the rows and columns individually. When it's not given, the array a
is flattened into a single 1D array.
axis=0
means that the operation is performed down the columns of a 2D array a
in turn.
For example np.argmin(a, axis=0)
returns the index of the minimum value in each of the four columns. The minimum value in each column is shown in bold below:
>>> a
array([[ 1, 2, 4, 7], # 0
[ 9, 88, 6, 45], # 1
[ 9, 76, 3, 4]]) # 2
>>> np.argmin(a, axis=0)
array([0, 0, 2, 2])
On the other hand, axis=1
means that the operation is performed across the rows of a
.
That means np.argmin(a, axis=1)
returns [0, 2, 2]
because a
has three rows. The index of the minimum value in the first row is 0, the index of the minimum value of the second and third rows is 2:
>>> a
# 0 1 2 3
array([[ 1, 2, 4, 7],
[ 9, 88, 6, 45],
[ 9, 76, 3, 4]])
>>> np.argmin(a, axis=1)
array([0, 2, 2])