Hi folks,
I need an efficient way to get both the min and argmin of a 2-d
array along one axis. It seemed to me that the way to do this was to
get the argmin and then use it to index into the array to get the min,
but I can't figure out how to do it. Here's my toy example:
>>> x = np.arange(25).reshape((5,5))
>>> x
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
>>> y = np.abs(x - x.T)
>>> y
array([[ 0, 4, 8, 12, 16],
[ 4, 0, 4, 8, 12],
[ 8, 4, 0, 4, 8],
[12, 8, 4, 0, 4],
[16, 12, 8, 4, 0]])
>>> np.argmin(y, axis=0)
array([0, 1, 2, 3, 4])
>>> np.min(y, axis=0)
array([0, 0, 0, 0, 0])
Here it seems like there should be a simple way to get the same array
that min() returns using the argmin result, which won't need to 'search'
in the array.
Thanks very much,
Ken
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