mindspore.ops.SearchSorted

class mindspore.ops.SearchSorted(dtype=mstype.int64, right=False)[source]

Return the position indices such that after inserting the values into the sorted_sequence, the order of innermost dimension of the sorted_sequence remains unchanged.

Warning

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

Refer to mindspore.ops.searchsorted() for more details.

Parameters

  • dtype (mindspore.dtype, optional) – The specified type of output tensor. Optional values are: mstype.int32 and mstype.int64. Default value: mstype.int64.

  • right (bool, optional) – Search Strategy. If True , return the last suitable index found; if False , return the first such index. Default: False .

Inputs:

  • sorted_sequence (Tensor) - The input tensor. It must contain a monotonically increasing sequence on the innermost dimension.

  • values (Tensor) - The value that should be inserted.

  • sorter (Tensor, optional) - if provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension and type must be int64. Default: None .

Outputs:

Tensor containing the indices from the innermost dimension of sorted_sequence such that, if insert the corresponding value in the values Tensor, the order of sorted_sequence would be preserved, whose datatype is int32 if out_int32 is True , otherwise int64, and shape is the same as the shape of values.

Raises

  • ValueError – If the dimension of sorted_sequence isn't 1 and all dimensions except the last dimension of sorted_sequence and values are different.

  • ValueError – If sorted_sequence value is a scalar.

  • ValueError – If values is a scalar when sorted_sequence dimension is not 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> searchsorted = ops.SearchSorted()
>>> sorted_sequence = Tensor(np.array([[0, 1, 3, 5, 7], [2, 4, 6, 8, 10]]), mindspore.float32)
>>> values = Tensor(np.array([[3, 6, 9], [3, 6, 9]]), mindspore.float32)
>>> output = searchsorted(sorted_sequence, values)
>>> print(output)
[[2 4 5]
[1 2 4]]