- dask.array.argmax(a, axis=None, keepdims=False, split_every=None, out=None)[source]¶
Returns the indices of the maximum values along an axis.
This docstring was copied from numpy.argmax.
Some inconsistencies with the Dask version may exist.
- axisint, optional
By default, the index is into the flattened array, otherwise along the specified axis.
- outarray, optional
If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
- keepdimsbool, optional
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.
- index_arrayndarray of ints
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.
The maximum value along a given axis.
Convert a flat index into an index tuple.
np.expand_dims(index_array, axis)from argmax to an array as if by calling max.
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)