mindspore.ops.cummin

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

Computation of the cumulative minimum of elements of ‘x’ in the dimension axis, and the index location of each maximum value found in the dimension ‘axis’.

It returns the cumulative minimum of elements and the index.

$\begin{split}\begin{array}{ll} \\ y{i} = min(x{1}, x{2}, ... , x{i}) \end{array}\end{split}$
Parameters
• x (Tensor) – The input tensor, rank of input_x > 0.

• axis (Int) – The dimension to do the operation, The axis is in the range from -len(input_x.shape) to len(input_x.shape) - 1. When it’s in the range from 0 to len(input_x.shape) - 1, it means starting from the first dimension and counting forwards, When it’s less than 0, it means we’re counting backwards from the last dimension. For example, -1 means the last dimension.

Outputs:
• output (Tensor) - The output tensor of the cumulative minimum of elements.

• indices (Tensor) - The result tensor of the index of each minimum value been found.

Raises
• TypeError – If input_x is not a Tensor.

• TypeError – If ‘axis’ is not an int.

• ValueError – If ‘axis’ is out the range of [-len(input_x.shape) to len(input_x.shape) - 1]

Supported Platforms:

Ascend

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]