mindspore.Tensor.median

Tensor.median(global_median=False, axis=0, keep_dims=False)[source]

Computes the median of input tensor.

Warning

When attr global_median is True, the second output Tensor value is meaningless.

Parameters

  • global_median (bool) – Whether the output tensor is the global median of all input tensor elements or not. Default: False.

  • axis (int) – The dimension need to reduce. Default: 0.

  • keep_dims (bool) – Whether the output tensor need to retain axis dimension or not. Default: False.

Returns

y (Tensor), has the same dtype as the self Tensor. If global_median is true, the y has only one element. If keep_dims is true, the y has the same shape as the self Tensor except the shape of y in dimension axis is size 1. Otherwise, the y lacks axis dimension than input.

indices (Tensor), has the same shape as the y, but dtype is int64.

Raises

  • TypeError – If dtype of self Tensor is not one of the following: int16, int32, int64, float32, float64.

  • TypeError – If global_median is not a bool.

  • TypeError – If axis is not a int.

  • TypeError – If keep_dims is not a bool.

  • ValueError – If axis is not in range of [-len(self.shape), len(self.shape) - 1).

Supported Platforms:

GPU CPU

Examples

>>> # case 1 : common median compute
>>> 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 = x.median(global_median=False, axis=0, keep_dims=False)
>>> print(y)
(Tensor(shape=[3], dtype=Float32, value=[0.38, 0.16, 0.44]),
 Tensor(shape=[3], dtype=Int64, value=[1, 2, 2]))
>>> # case 2 : global median compute
>>> x = Tensor(np.array([1, 7, 6],[5, 1, 3],[9, 17, 1]), mindspore.int32)
>>> y = x.median(global_median=True)
>>> print(y)
(Tensor(shape=[1], dtype=Int32, value=[5]), Tensor(shape=[1], dtype=Int64, value=[1]))