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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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median and nanmedian in PyTorch

#python #pytorch #median #nanmedian

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*Memos:

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-Default:None-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
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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-Default:None-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
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