mindspore.mint.sort

mindspore.mint.sort(input, *, dim=- 1, descending=False, stable=False)

Sorts the elements of the input tensor along the given dimension in the specified order.

Warning

Currently, the data types of float16, uint8, int8, int16, int32, int64 are well supported. If use float32, it may cause loss of accuracy.

Parameters

input (Tensor) – The input tensor to sort. The shape is \((N,*)\) where \(*\) means, any number of additional dimensions.

Keyword Arguments

• dim (int, optional) – The dimension to sort along. Default: -1, means the last dimension.

• descending (bool, optional) – Controls the sort order. If descending is True, the elements are sorted in descending order, or else sorted in ascending order. Default: False .

• stable (bool, optional) – Controls the sort order. If stable is True then the sorting routine becomes stable, preserving the order of equivalent elements. Default: False .

Returns

• y1, a tensor whose values are the sorted values, with the same shape and data type as input.

• y2, a tensor that consists of the indices of the elements in the original input tensor. Data type is int64.

Raises

• TypeError – If dim is not an int.

• TypeError – If descending is not a bool.

• TypeError – If input not in float16, float32, uint8, int8, int16, int32, int64, bfloat16

• TypeError – If stable is not a bool.

• ValueError – If dim is not in range of [-len(input_x.shape), len(input_x.shape)).

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[8, 2, 1], [5, 9, 3], [4, 6, 7]]), mindspore.float16)
>>> output = mint.sort(x)
>>> # The output below is based on the Ascend platform.
>>> print(output)
(Tensor(shape=[3, 3], dtype=Float16, value=
[[ 1.0000e+00,  2.0000e+00,  8.0000e+00],
[ 3.0000e+00,  5.0000e+00,  9.0000e+00],
[ 4.0000e+00,  6.0000e+00,  7.0000e+00]]), Tensor(shape=[3, 3], dtype=Int64, value=
[[2, 1, 0],
[2, 0, 1],
[0, 1, 2]]))