- xorbits.numpy.argmax(a, axis=None, out=None, combine_size=None)#
Returns the indices of the maximum values along an axis.
a (array_like) – Input array.
axis (int, optional) – By default, the index is into the flattened array, otherwise along the specified axis.
out (array, optional) – If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
keepdims (bool, optional (Not supported yet)) –
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array.
New in version 1.22.0(numpy).
index_array – Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed. If keepdims is set to True, then the size of axis will be 1 with the resulting array having same shape as a.shape.
- Return type
ndarray of ints
In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.
>>> a = np.arange(6).reshape(2,3) + 10 >>> a array([[10, 11, 12], [13, 14, 15]]) >>> np.argmax(a) 5 >>> np.argmax(a, axis=0) array([1, 1, 1]) >>> np.argmax(a, axis=1) array([2, 2])
Indexes of the maximal elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape) >>> ind (1, 2) >>> a[ind] 15
>>> b = np.arange(6) >>> b = 5 >>> b array([0, 5, 2, 3, 4, 5]) >>> np.argmax(b) # Only the first occurrence is returned. 1
>>> x = np.array([[4,2,3], [1,0,3]]) >>> index_array = np.argmax(x, axis=-1) >>> # Same as np.amax(x, axis=-1, keepdims=True) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1) array([, ]) >>> # Same as np.amax(x, axis=-1) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1) array([4, 3])
Setting keepdims to True,
>>> x = np.arange(24).reshape((2, 3, 4)) >>> res = np.argmax(x, axis=1, keepdims=True) >>> res.shape (2, 1, 4)
- combine_size: int, optional
The number of chunks to combine.
This docstring was copied from numpy.