bucketize

paddle. bucketize ( x: Tensor, sorted_sequence: Tensor, out_int32: bool = False, right: bool = False, name: str | None = None ) → Tensor [source]

This API is used to find the index of the corresponding 1D tensor sorted_sequence in the innermost dimension based on the given x.

Parameters

x (Tensor) – An input N-D tensor value with type int32, int64, float32, float64.
sorted_sequence (Tensor) – An input 1-D tensor with type int32, int64, float32, float64. The value of the tensor monotonically increases in the innermost dimension.
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 x. 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|None, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to api_guide_Name.

Returns

Tensor (the same sizes of the x), 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([2, 4, 8, 16], dtype='int32')
>>> x = paddle.to_tensor([[0, 8, 4, 16], [-1, 2, 8, 4]], dtype='int32')
>>> out1 = paddle.bucketize(x, sorted_sequence)
>>> print(out1)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 2, 1, 3],
 [0, 0, 2, 1]])
>>> out2 = paddle.bucketize(x, sorted_sequence, right=True)
>>> print(out2)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 3, 2, 4],
 [0, 1, 3, 2]])
>>> out3 = x.bucketize(sorted_sequence)
>>> print(out3)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 2, 1, 3],
 [0, 0, 2, 1]])
>>> out4 = x.bucketize(sorted_sequence, right=True)
>>> print(out4)
Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 3, 2, 4],
 [0, 1, 3, 2]])

          