mindspore.mint.median

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mindspore.mint.median(input, dim=None, keepdim=False)[source]

Output the median on the specified dimension dim and its corresponding index. If dim is None, calculate the median of all elements in the Tensor.

Parameters

  • input (Tensor) – A Tensor of any dimension whose data type is uint8, int16, int32, int64, float16 or float32.

  • dim (int, optional) – Specify the axis for calculation. Default: None .

  • keepdim (bool, optional) – Whether the output tensor need to retain dim dimension or not. Default: False .

Returns

  • y (Tensor) - Output median, with the same data type as input .

    • If dim is None , y only has one element.

    • If keepdim is True , the y has the same shape as the input except the shape of y in dimension dim is size 1.

    • Otherwise, the y lacks dim dimension than input.

  • indices (Tensor) - The index of the median. Shape is consistent with y , with a data type of int64.

Raises

  • TypeError – If dtype of input is not one of the following: uint8, int16, int32, int64, float16 or float32.

  • TypeError – If input input is not a Tensor.

  • TypeError – If dim is not a int.

  • TypeError – If keepdim is not a bool.

  • ValueError – If dim is not in range of [-x.dim, x.dim-1].

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[0.57, 0.11, 0.21],[0.38, 0.50, 0.57], [0.36, 0.16, 0.44]]).astype(np.float32))
>>> y = mint.median(x, dim=0, keepdim=False)
>>> print(y)
(Tensor(shape=[3], dtype=Float32, value= [ 3.79999995e-01,  1.59999996e-01,  4.39999998e-01]),
Tensor(shape=[3], dtype=Int64, value= [1, 2, 2]))