mindspore.ops.SearchSorted

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

Returns the indices correspond to the positions where the given numbers in values should be inserted into sorted_sequence so that the order of the sequence is maintained.

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) – Output data type. An optional data type of mstype.int32 and mstype.int64. Default: 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 shape of tensor is \((x_1, x_2, ..., x_R-1, x_R)\) or (x_1). It must contain a monotonically increasing sequence on the innermost dimension.

  • values (Tensor) - The value that should be inserted. The shape of tensor is \((x_1, x_2, ..., x_R-1, x_S)\).

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.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> 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 = ops.SearchSorted()(sorted_sequence, values)
>>> print(output)
[[2 4 5]
 [1 2 4]]