Reputation: 4828
I'm trying to implement a numpy function that replaces the max in each row of a 2D array with 1, and all other numbers with zero:
>>> a = np.array([[0, 1],
... [2, 3],
... [4, 5],
... [6, 7],
... [9, 8]])
>>> b = some_function(a)
>>> b
[[0. 1.]
[0. 1.]
[0. 1.]
[0. 1.]
[1. 0.]]
What I've tried so far
def some_function(x):
a = np.zeros(x.shape)
a[:,np.argmax(x, axis=1)] = 1
return a
>>> b = some_function(a)
>>> b
[[1. 1.]
[1. 1.]
[1. 1.]
[1. 1.]
[1. 1.]]
Upvotes: 38
Views: 34802
Reputation: 801
a==np.max(a)
will raise an error in the future, so here's a tweaked version that will continue to broadcast correctly.
I know this question is pretty ancient, but I think I have a decent solution that's a bit different from the other solutions.
# get max by row and convert from (n, ) -> (n, 1) which will broadcast
row_maxes = a.max(axis=1).reshape(-1, 1)
np.where(a == row_maxes, 1, 0)
np.where(a == row_maxes).astype(int)
if the update needs to be in place, you can do
a[:] = np.where(a == row_maxes, 1, 0)
Upvotes: 5
Reputation: 353199
Method #1, tweaking yours:
>>> a = np.array([[0, 1], [2, 3], [4, 5], [6, 7], [9, 8]])
>>> b = np.zeros_like(a)
>>> b[np.arange(len(a)), a.argmax(1)] = 1
>>> b
array([[0, 1],
[0, 1],
[0, 1],
[0, 1],
[1, 0]])
[Actually, range
will work just fine; I wrote arange
out of habit.]
Method #2, using max
instead of argmax
to handle the case where multiple elements reach the maximum value:
>>> a = np.array([[0, 1], [2, 2], [4, 3]])
>>> (a == a.max(axis=1)[:,None]).astype(int)
array([[0, 1],
[1, 1],
[1, 0]])
Upvotes: 44