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*Memos:
- My post explains eye().
- My post explains diagflat().
- My post explains diag_embed().
- My post explains diagonal().
diag() can create the 2D tensor of zero or more elements on the diagonal and zero or more 0
, 0.
, 0.+0.j
or False
elsewhere from the 1D tensor of zero or more elements or can extract the 1D tensor of zero or more elements on the diagonal from the 2D tensor of zero or more elements as shown below:
*Memos:
-
diag()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,complex
orbool
). *Only a 2D or 1D tensor can be used. - The 2nd argument with
torch
or the 1st argument with a tensor isdiagonal
(Optional-Default:0
-Type:int
). - There is
out
argument with torch(Optional-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
- A 2D tensor creates a 1D tensor.
- A 1D tensor creates a 2D tensor.
import torch
my_tensor = torch.tensor([7, -4, 5])
torch.diag(input=my_tensor)
my_tensor.diag()
torch.diag(input=my_tensor, diagonal=0)
# tensor([[7, 0, 0],
# [0, -4, 0],
# [0, 0, 5]])
torch.diag(input=my_tensor, diagonal=1)
# tensor([[0, 7, 0, 0],
# [0, 0, -4, 0],
# [0, 0, 0, 5],
# [0, 0, 0, 0]])
torch.diag(input=my_tensor, diagonal=-1)
# tensor([[0, 0, 0, 0],
# [7, 0, 0, 0],
# [0, -4, 0, 0],
# [0, 0, 5, 0]])
torch.diag(input=my_tensor, diagonal=2)
# tensor([[0, 0, 7, 0, 0],
# [0, 0, 0, -4, 0],
# [0, 0, 0, 0, 5],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]])
torch.diag(input=my_tensor, diagonal=-2)
# tensor([[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0],
# [0, -4, 0, 0, 0],
# [0, 0, 5, 0, 0]])
my_tensor = torch.tensor([7., -4., 5.])
torch.diag(input=my_tensor)
# tensor([[7., 0., 0.],
# [0., -4., 0.],
# [0., 0., 5.]])
my_tensor = torch.tensor([7.+0.j, -4.+0.j, 5.+0.j])
torch.diag(input=my_tensor)
# tensor([[7.+0.j, 0.+0.j, 0.+0.j],
# [0.+0.j, -4.+0.j, 0.+0.j],
# [0.+0.j, 0.+0.j, 5.+0.j]])
my_tensor = torch.tensor([True, True, True])
torch.diag(input=my_tensor)
# tensor([[True, False, False],
# [False, True, False],
# [False, False, True]])
my_tensor = torch.tensor([[7, -4, 5],
[-6, -3, 8],
[9, 1, -2]])
torch.diag(input=my_tensor)
torch.diag(input=my_tensor, diagonal=0)
# tensor([7, -3, -2])
torch.diag(input=my_tensor, diagonal=1)
# tensor([-4, 8])
torch.diag(input=my_tensor, diagonal=-1)
# tensor([-6, -1])
torch.diag(input=my_tensor, diagonal=2)
# tensor([5])
torch.diag(input=my_tensor, diagonal=-2)
# tensor([9])
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