mindspore.mint.cummin

mindspore.mint.cummin(input, dim)

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

$$\begin{array}{r}\begin{array}{l}\\ y_i=min(x_1,x_2,...,x_i)\end{array}\end{array}$$

Note

O2 mode is not supported in Ascend.

Parameters

• input (Tensor) – The input Tensor, The dimension must be greater than 0.

• dim (int) – Operation dimension. The value of dim 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 that of 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 dim is not an int.

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

Supported Platforms:

Ascend

Examples

>>> from mindspore import Tensor, mint
>>> import mindspore
>>> a = Tensor([-0.2284, -0.6628,  0.0975,  0.2680, -1.3298, -0.4220], mindspore.float32)
>>> output = mint.cummin(a, dim=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]