cummin

paddle. cummin ( x: Tensor, axis: int | None = None, dtype: DTypeLike = 'int64', name: str | None = None ) → tuple[Tensor, Tensor] [source]

The cumulative min of the elements along a given axis.

Note

The first element of the result is the same as the first element of the input.

Parameters

• x (Tensor) – The input tensor needed to be cummined.

• axis (int, optional) – The dimension to accumulate along. -1 means the last dimension. The default (None) is to compute the cummin over the flattened array.

• dtype (str|paddle.dtype|np.dtype, optional) – The data type of the indices tensor, can be int32, int64. The default value is int64.

• name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

Returns

out (Tensor), The result of cummin operation. The dtype of cummin result is same with input x.

indices (Tensor), The corresponding index results of cummin operation.

Examples

>>> import paddle
>>> data = paddle.to_tensor([-1, 5, 0, -2, -3, 2])
>>> data = paddle.reshape(data, (2, 3))

>>> value, indices = paddle.cummin(data)
>>> value
Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True,
[-1, -1, -1, -2, -3, -3])
>>> indices
Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 0, 0, 3, 4, 4])

>>> value, indices = paddle.cummin(data, axis=0)
>>> value
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[-1,  5,  0],
 [-2, -3,  0]])
>>> indices
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 0, 0],
 [1, 1, 0]])

>>> value, indices = paddle.cummin(data, axis=-1)
>>> value
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[-1, -1, -1],
 [-2, -3, -3]])
>>> indices
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 0, 0],
 [0, 1, 1]])

>>> value, indices = paddle.cummin(data, dtype='int64')
>>> assert indices.dtype == paddle.int64