searchsorted

paddle. searchsorted ( sorted_sequence: Tensor, values: Tensor, out_int32: bool = False, right: bool = False, name: str | None = None, *, side: str | None = None, out: Tensor | None = None, sorter: Tensor | None = None ) → Tensor [source]

Find the index of the corresponding sorted_sequence in the innermost dimension based on the given values.

Parameters

  • sorted_sequence (Tensor) – An input N-D or 1-D tensor with type int32, int64, float16, float32, float64, bfloat16. The value of the tensor monotonically increases in the innermost dimension.

  • values (Tensor) – An input N-D tensor value with type int32, int64, float16, float32, float64, bfloat16. alias: input.

  • out_int32 (bool, optional) – Data type of the output tensor which can be int32, int64. The default value is False, and it indicates that the output data type is int64.

  • right (bool, optional) – Find the upper or lower bounds of the sorted_sequence range in the innermost dimension based on the given values. If the value of the sorted_sequence is nan or inf, return the size of the innermost dimension. The default value is False and it shows the lower bounds.

  • name (str, optional) – For details, please refer to api_guide_Name. Generally, no setting is required. Default: None.

  • side (str|None, optional) – The same as right but preferred. left corresponds to False for right and right corresponds to True for right. It will error if this is set to left while right is True. Default value is None.

  • sorter (Tensor|None, 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

  • out (Tensor|None, optional) – The output tensor. Default: None.

Returns

Tensor (the same sizes of the values), return the tensor of int32 if set out_int32 is True, otherwise return the tensor of int64.

Examples

>>> import paddle

>>> sorted_sequence = paddle.to_tensor([[1, 3, 5, 7, 9, 11],
...                                     [2, 4, 6, 8, 10, 12]], dtype='int32')
>>> values = paddle.to_tensor([[3, 6, 9, 10], [3, 6, 9, 10]], dtype='int32')
>>> out1 = paddle.searchsorted(sorted_sequence, values)
>>> print(out1)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[1, 3, 4, 5],
 [1, 2, 4, 4]])
>>> out2 = paddle.searchsorted(sorted_sequence, values, right=True)
>>> print(out2)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[2, 3, 5, 5],
 [1, 3, 4, 5]])
>>> sorted_sequence_1d = paddle.to_tensor([1, 3, 5, 7, 9, 11, 13])
>>> out3 = paddle.searchsorted(sorted_sequence_1d, values)
>>> print(out3)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[1, 3, 4, 5],
 [1, 3, 4, 5]])