mindspore.ops.UnsortedSegmentMin

class mindspore.ops.UnsortedSegmentMin[source]

Computes the minimum of a tensor along segments.

The following figure shows the calculation process of UnsortedSegmentMin:

../../_images/UnsortedSegmentMin.png
\[\text { output }_i=\text{min}_{j \ldots} \text { data }[j \ldots]\]

where \(min\) over tuples \(j...\) such that \(segment_ids[j...] == i\).

Note

If the segment_id i is absent in the segment_ids, then output[i] will be filled with the maximum value of the input_x’s type. The segment_ids must be non-negative tensor.

Inputs:

  • input_x (Tensor) - The shape is \((x_1, x_2, ..., x_R)\). The data type must be float16, float32 or int32.

  • segment_ids (Tensor) - A 1-D tensor whose shape is \((x_1)\), the value must be non-negative tensor. The data type must be int32.

  • num_segments (int) - The value specifies the number of distinct segment_ids.

Outputs:

Tensor, set the number of num_segments as N, the shape is \((N, x_2, ..., x_R)\).

Raises

  • TypeError – If num_segments is not an int.

  • ValueError – If length of shape of segment_ids is not equal to 1.

Supported Platforms:

Ascend GPU

Examples

>>> input_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
>>> unsorted_segment_min = ops.UnsortedSegmentMin()
>>> output = unsorted_segment_min(input_x, segment_ids, num_segments)
>>> print(output)
[[1. 2. 3.]
 [4. 2. 1.]]