mindspore.ops.unsorted_segment_min

mindspore.ops.unsorted_segment_min(x, segment_ids, num_segments)

Computes the minimum of a tensor along segments.

The following figure shows the calculation process of unsorted_segment_min:

$${\text{ output }}_i={\text{min}}_{j\dots }\text{ data }[j\dots ]$$

where $min$ over tuples $j...$ such that $segment\mathrm{\_}ids[j...]==i$.

Note

• If the segment_id i is absent in the segment_ids, then output[i] will be filled with the maximum value of the x's type.

• The segment_ids must be non-negative tensor.

Parameters

• x (Tensor) – The shape is $(x_1,x_2,...,x_R)$. With float16, float32 or int32 data type.

• segment_ids (Tensor) – TThe label indicates the segment to which each element belongs. Set the shape as $(x_1,x_2,...,x_N)$, where 0 < N <= R.

• num_segments (Union[int, Tensor], optional) – Set $z$ as num_segments, it can be an int or 0-D Tensor.

Returns

Tensor, the shape is $(z,x_{N+1},...,x_R)$.

Raises

TypeError – If num_segments is not an int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor
>>> from mindspore import ops
>>> import numpy as np
>>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32))
>>> segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32))
>>> num_segments = 2
>>> output = ops.unsorted_segment_min(x, segment_ids, num_segments)
>>> print(output)
[[1. 2. 3.]
 [4. 2. 1.]]