NumPy proposes a way to get the index of the maximum value of an array via np.argmax
.
I would like a similar thing, but returning the indexes of the N
maximum values.
For instance, if I have an array, [1, 3, 2, 4, 5]
, function(array, n=3)
would return the indices [4, 3, 1]
which correspond to the elements [5, 4, 3]
.
Newer NumPy versions (1.8 and up) have a function called argpartition
for this. To get the indices of the four largest elements, do
>>> a = np.array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0])
>>> a
array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0])
>>> ind = np.argpartition(a, -4)[-4:]
>>> ind
array([1, 5, 8, 0])
>>> a[ind]
array([4, 9, 6, 9])
Unlike argsort
, this function runs in linear time in the worst case, but the returned indices are not sorted, as can be seen from the result of evaluating a[ind]
. If you need that too, sort them afterwards:
>>> ind[np.argsort(a[ind])]
array([1, 8, 5, 0])
To get the top-k elements in sorted order in this way takes O(n + k log k) time.