Argmax of numpy array returning non-flat indices
PythonMultidimensional ArrayNumpyPython Problem Overview
I'm trying to get the indices of the maximum element in a Numpy array.
This can be done using numpy.argmax. My problem is, that I would like to find the biggest element in the whole array and get the indices of that.
numpy.argmax can be either applied along one axis, which is not what I want, or on the flattened array, which is kind of what I want.
My problem is that using numpy.argmax with axis=None returns the flat index when I want the multi-dimensional index.
I could use divmod to get a non-flat index but this feels ugly. Is there any better way of doing this?
Python Solutions
Solution 1 - Python
You could use numpy.unravel_index() on the result of numpy.argmax():
>>> a = numpy.random.random((10, 10))
>>> numpy.unravel_index(a.argmax(), a.shape)
(6, 7)
>>> a[6, 7] == a.max()
True
Solution 2 - Python
np.where(a==a.max())
returns coordinates of the maximum element(s), but has to parse the array twice.
>>> a = np.array(((3,4,5),(0,1,2)))
>>> np.where(a==a.max())
(array([0]), array([2]))
This, comparing to argmax, returns coordinates of all elements equal to the maximum. argmax returns just one of them (np.ones(5).argmax() returns 0).
Solution 3 - Python
To get the non-flat index of all occurrences of the maximum value, you can modify eumiro's answer slightly by using argwhere instead of where:
np.argwhere(a==a.max())
>>> a = np.array([[1,2,4],[4,3,4]])
>>> np.argwhere(a==a.max())
array([[0, 2],
[1, 0],
[1, 2]])