xorbits.numpy.argmax#

xorbits.numpy.argmax(a, axis=None, out=None, combine_size=None)[源代码]#

Returns the indices of the maximum values along an axis.

参数

  • 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.

    1.22.0(numpy) 新版功能.

返回

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.

返回类型

ndarray of ints

参见

ndarray.argmax, argmin

amax

The maximum value along a given axis.

unravel_index

Convert a flat index into an index tuple.

take_along_axis

Apply np.expand_dims(index_array, axis) from argmax to an array as if by calling max.

提示

In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.

实际案例

>>> 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[1] = 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([[4],
       [3]])
>>> # 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.