mindspore.ops.cummin

mindspore.ops.cummin(input, axis)[source]

Returns a tuple (values,indices) where 'values' is the cumulative minimum value of input Tensor input along the dimension axis, and indices is the index location of each minimum value.

\[\begin{split}\begin{array}{ll} \\ y_{i} = \min(x_{1}, x_{2}, ... , x_{i}) \end{array}\end{split}\]
Parameters

  • input (Tensor) – The input Tensor, rank of input > 0.

  • axis (int) – The dimension to do the operation over. The value of axis must be in the range [-input.ndim, input.ndim - 1].

Returns

tuple [Tensor], tuple of 2 Tensors, containing the cumulative minimum of elements and the index. The shape of each output tensor is the same as input input.

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If input is a Tensor, but the type is complex or bool.

  • TypeError – If axis is not an int.

  • ValueError – If axis is out the range of [-input.ndim, input.ndim - 1].

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor, ops
>>> import mindspore
>>> a = Tensor([-0.2284, -0.6628,  0.0975,  0.2680, -1.3298, -0.4220], mindspore.float32)
>>> output = ops.cummin(a, axis=0)
>>> print(output[0])
[-0.2284 -0.6628 -0.6628 -0.6628 -1.3298 -1.3298]
>>> print(output[1])
[0 1 1 1 4 4]