nanmedian¶

paddle. nanmedian ( x, axis=None, keepdim=False, mode='avg', name=None ) [source]

Compute the median along the specified axis, while ignoring NaNs.

If the valid count of elements is a even number, the average value of both elements in the middle is calculated as the median.

Parameters

x (Tensor) – The input Tensor, it’s data type can be int32, int64, float16, bfloat16, float32, float64.
axis (None|int|list|tuple, optional) – The axis along which to perform median calculations axis should be int or list of int. axis should be in range [-D, D), where D is the dimensions of x . If axis is less than 0, it works the same way as \(axis + D\). If axis is None, median is calculated over all elements of x. Default is None.
keepdim (bool, optional) – Whether to reserve the reduced dimension(s) in the output Tensor. If keepdim is True, the dimensions of the output Tensor is the same as x except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in axis . Default is False.
mode (str, optional) – Whether to use mean or min operation to calculate the nanmedian values when the input tensor has an even number of non-NaN elements along the dimension axis. Support ‘avg’ and ‘min’. Default is ‘avg’.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor or tuple of Tensor. If mode == ‘min’ and axis is int, the result will be a tuple of two tensors (nanmedian value and nanmedian index). Otherwise, only nanmedian value will be returned.

Examples

           >>> import paddle
>>> x = paddle.to_tensor([[float('nan'), 2. , 3. ], [0. , 1. , 2. ]])

>>> y1 = x.nanmedian()
>>> print(y1.numpy())
2.0

>>> y2 = x.nanmedian(0)
>>> print(y2.numpy())
[0.  1.5 2.5]

>>> y3 = x.nanmedian(0, keepdim=True)
>>> print(y3.numpy())
[[0.  1.5 2.5]]

>>> y4 = x.nanmedian((0, 1))
>>> print(y4.numpy())
2.0

>>> y5 = x.nanmedian(mode='min')
>>> print(y5.numpy())
2.0

>>> y6, y6_index = x.nanmedian(0, mode='min')
>>> print(y6.numpy())
[0. 1. 2.]
>>> print(y6_index.numpy())
[1 1 1]

>>> y7, y7_index = x.nanmedian(1, mode='min')
>>> print(y7.numpy())
[2. 1.]
>>> print(y7_index.numpy())
[1 1]

>>> y8 = x.nanmedian((0,1), mode='min')
>>> print(y8.numpy())
2.0

          