mindspore.mint.searchsorted
- mindspore.mint.searchsorted(sorted_sequence, values, *, out_int32=False, right=False, side=None, sorter=None)[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.
- Parameters
- Keyword Arguments
out_int32 (bool, optional) – Output datatype. If
True
, the output datatype will be int32; ifFalse
, the output datatype will be int64. Default:False
.right (bool, optional) – Search Strategy. If
True
, return the last suitable index found; ifFalse
, return the first such index. Default:False
.side (str, optional) – the same as right but preferred.
"left"
corresponds toFalse
for right and"right"
corresponds toTrue
for right. An error will be reported if this parameter is set to"left"
while right isTrue
. Default:None
.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
.
- Returns
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
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, mint >>> 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 = mint.searchsorted(sorted_sequence, values) >>> print(output) [[2 4 5] [1 2 4]]