mindspore.ops.SearchSorted
- 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 ofmstype.int32
andmstype.int64
. Default:mstype.int64
.right (bool, optional) – Search Strategy. If
True
, return the last suitable index found; ifFalse
, 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]]