mindspore.mint.unique_consecutive

mindspore.mint.unique_consecutive(input, return_inverse=False, return_counts=False, dim=None)[source]

Returns the elements that are unique in each consecutive group of equivalent elements in the input tensor.

When return_inverse=True , it returns a tensor containing the indices of the elements in the input tensor within the output tensor.

When return_counts=True , it returns a tensor representing the number of occurrences of each output element in the input.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • input (Tensor) – The input tensor.

  • return_inverse (bool, optional) – Whether to return the index of where the element in the original input maps to the position in the output. Default: False .

  • return_counts (bool, optional) – Whether to return the counts of each unique element. Default: False .

  • dim (int, optional) – The dimension to apply unique. If None , the unique of the flattened input is returned. If specified, it must be int32 or int64. Default: None .

Returns

A tensor or a tuple of tensors containing tensor objects (output, inverse_indices, counts).

  • output (Tensor): the output tensor has the same type as input and represents the output list of unique scalar elements.

  • inverse_indices (Tensor, optional): if return_inverse is True, there will be an additional returned tensor inverse_indices. inverse_indices has the same shape as input and represents the index of where the element in the original input maps to the position in the output.

  • counts (Tensor, optional): if return_counts is True, there will be an additional returned tensor counts. counts has the same shape as output or output.shape[dim] if dim was specified and represents the number of occurrences for each unique value or tensor.

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If dtype of input is not supported.

  • TypeError – If return_inverse is not a bool.

  • TypeError – If return_counts is not a bool.

  • TypeError – If dim is not an int.

  • ValueError – If dim is not in the range of \([-ndim, ndim-1]\).

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> from mindspore import dtype as mstype
>>> x = Tensor(np.array([1, 1, 2, 2, 3, 1, 1, 2]), mstype.int64)
>>> output, inverse_indices, counts = mint.unique_consecutive(x, True, True, None)
>>> print(output)
[1 2 3 1 2]
>>> print(inverse_indices)
[0 0 1 1 2 3 3 4]
>>> print(counts)
[2 2 1 2 1]