median() and nanmedian() in PyTorch

#pytorch #median #nanmedian #function

*Memos:

My post explains sum() and nansum().
My post explains prod() and cartesian_prod().
My post explains mean() and nanmean().
My post explains cumsum() and cumprod().
My post explains torch.nan and torch.inf.

median() can get the 0 or more D tensor of one median element or two of the 0D or more D tensors of zero or more median elements and their indices, normally treating zero or more NaNs(Not a Numbers) from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

median() can be used with torch or a tensor.
The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int or float).
The 2nd argument with torch or the 1st argument with a tensor is dim(Optional-Type:int). *It can get two of the 0D or more D tensors of the zero or more median elements and their indices.
The 3rd argument with torch or the 2nd argument with a tensor is keepdim(Optional-Default:False-Type:bool): *Memos:
- It must be used with dim.
- My post explains keepdim argument.
There is out argument with torch(Optional-Type:tuple(tensor, tensor) or list(tensor, tensor): *Memos:
- It must be used with dim.
- out= must be used.
- My post explains out argument.
Normally, the arithmetic operation with a NaN results in a NaN.
The empty 1D or more D input tensor or tensor without dim gets a NaN.
The empty 1D or more D input tensor or tensor with the deepest dim doesn't work to get a NaN.

import torch

my_tensor = torch.tensor([5., 4., 7., 7.])

torch.median(input=my_tensor)
my_tensor.median()
# tensor(5.)

torch.median(input=my_tensor, dim=0)
torch.median(input=my_tensor, dim=-1)
# torch.return_types.median(
# values=tensor(5.),
# indices=tensor(0))

my_tensor = torch.tensor([5., 4., torch.nan, 7., 7.])

torch.median(input=my_tensor)
# tensor(nan)

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.median(input=my_tensor)
# tensor(5.)

torch.median(input=my_tensor, dim=0)
torch.median(input=my_tensor, dim=-2)
# torch.return_types.median(
# values=tensor([5., 5., 7., 5.]),
# indices=tensor([0, 1, 0, 1]))

torch.median(input=my_tensor, dim=1)
torch.median(input=my_tensor, dim=-1)
# torch.return_types.median(
# values=tensor([5., 5., 3.]),
# indices=tensor([0, 1, 3]))

my_tensor = torch.tensor([[torch.nan, 5., 4., torch.nan, 7., 7., torch.nan],
                          [6., torch.nan, 5., torch.nan, 3., 5., torch.nan],
                          [3., 8., torch.nan, torch.nan, 9., 3., torch.nan]])
torch.median(input=my_tensor)
# tensor(nan)

torch.median(input=my_tensor, dim=0)
# torch.return_types.median(
# values=tensor([nan, nan, nan, nan, 7., 5., nan]),
# indices=tensor([0, 1, 2, 0, 0, 1, 0]))

torch.median(input=my_tensor, dim=1)
# torch.return_types.median(
# values=tensor([nan, nan, nan]),
# indices=tensor([0, 1, 2]))

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.median(input=my_tensor)
# tensor(5)

my_tensor = torch.tensor([])

torch.median(input=my_tensor)
# tensor(nan)

torch.median(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.median(input=my_tensor)
# tensor(nan)

torch.median(input=my_tensor, dim=0)
# torch.return_types.median(
# values=tensor([]),
# indices=tensor([], dtype=torch.int64))

torch.median(input=my_tensor, dim=1) # Error

my_tensor = torch.tensor([[[]]])

torch.median(input=my_tensor)
# tensor(nan)

torch.median(input=my_tensor, dim=0)
torch.median(input=my_tensor, dim=1)
# torch.return_types.median(
# values=tensor([], size=(1, 0)),
# indices=tensor([], size=(1, 0), dtype=torch.int64))

torch.median(input=my_tensor, dim=2) # Error

nanmedian() can get the 0D or more D tensor of one median element or two of the 0D or more D tensors of zero or more median elements and their indices, ignoring zero or more NaNs(Not a Numbers) only if they are with non-NaNs from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

nanmedian() can be used with torch or a tensor.
The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int or float).
The 2nd argument with torch or the 1st argument with a tensor is dim(Optional-Type:int). *It can get two of the 0D or more D tensors of the zero or more median elements and their indices.
The 3rd argument with torch or the 2nd argument with a tensor is keepdim(Optional-Default:False-Type:bool): *Memos:
- It must be used with dim.
- My post explains keepdim argument.
There is out argument with torch(Optional-Type:tuple(tensor, tensor) or list(tensor, tensor): *Memos:
- It must be used with dim.
- out= must be used.
- My post explains out argument.
Normally, the arithmetic operation with a NaN results in a NaN.
The empty 1D or more D input tensor or tensor without dim gets a NaN.
The empty 1D or more D input tensor or tensor with the deepest dim doesn't work to get a NaN.

import torch

my_tensor = torch.tensor(torch.nan)
my_tensor = torch.tensor([torch.nan, torch.nan])
my_tensor = torch.tensor([torch.nan, torch.nan, torch.nan])

torch.nanmedian(input=my_tensor)
my_tensor.nanmedian()
# tensor(nan)

torch.nanmedian(input=my_tensor, dim=0)
torch.nanmedian(input=my_tensor, dim=-1)
# torch.return_types.nanmedian(
# values=tensor(nan),
# indices=tensor(0))

my_tensor = torch.tensor([5., 4., 7., 7.])
my_tensor = torch.tensor([5., 4., torch.nan, 7., 7.])
my_tensor = torch.tensor([5., 4., 7., 7., torch.nan])

torch.nanmedian(input=my_tensor)
# tensor(5.)

torch.nanmedian(input=my_tensor, dim=0)
torch.nanmedian(input=my_tensor, dim=-1)
# torch.return_types.nanmedian(
# values=tensor(5.),
# indices=tensor(0))

my_tensor = torch.tensor([[torch.nan, 5., 4., torch.nan, 7., 7., torch.nan],
                          [6., torch.nan, 5., torch.nan, 3., 5., torch.nan],
                          [3., 8., torch.nan, torch.nan, 9., 3., torch.nan]])
torch.nanmedian(input=my_tensor)
# tensor(5.)

torch.nanmedian(input=my_tensor, dim=0)
torch.nanmedian(input=my_tensor, dim=-2)
# torch.return_types.nanmedian(
# values=tensor([3., 5., 4., nan, 7., 5., nan]),
# indices=tensor([2, 0, 0, 0, 0, 1, 0]))

torch.nanmedian(input=my_tensor, dim=1)
torch.nanmedian(input=my_tensor, dim=-1)
# torch.return_types.nanmedian(
# values=tensor([5., 5., 3.]),
# indices=tensor([1, 2, 5]))

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.nanmedian(input=my_tensor)
# tensor(5)

my_tensor = torch.tensor([])

torch.nanmedian(input=my_tensor)
# tensor(nan)

torch.nanmedian(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.nanmedian(input=my_tensor)
# tensor(nan)

torch.nanmedian(input=my_tensor, dim=0)
# torch.return_types.nanmedian(
# values=tensor([]),
# indices=tensor([], dtype=torch.int64))

torch.nanmedian(input=my_tensor, dim=1) # Error

my_tensor = torch.tensor([[[]]])

torch.nanmedian(input=my_tensor)
# tensor(nan)

torch.nanmedian(input=my_tensor, dim=0)
torch.nanmedian(input=my_tensor, dim=1)
# torch.return_types.nanmedian(
# values=tensor([], size=(1, 0)),
# indices=tensor([], size=(1, 0), dtype=torch.int64))

torch.nanmedian(input=my_tensor, dim=2) # Error

DEV Community

median() and nanmedian() in PyTorch

Top comments (0)

Read next

NLP Challenges and Semantic Savior

How to Set Up Email Alerts for Schedule Task Errors in .NET

Mindful Activities

Restart Access in Microsoft Access