mindspore.ops.searchsorted

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mindspore.ops.searchsorted(sorted_sequence, values, *, out_int32=False, 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.

Parameters

  • 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.

Keyword Arguments

  • out_int32 (bool, optional) – Output datatype. If True , the output datatype will be int32; if False , the output datatype will be int64. Default: False .

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

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.

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]]