*Memos:
- My post explains eye().
- My post explains diag().
- My post explains diag_embed().
- My post explains diagonal().
diagflat() 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 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
diagflat()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - The 2nd argument with
torchor the 1st argument with a tensor isoffset(Optional-Default:0-Type:int).
import torch
my_tensor = torch.tensor([7, -4, 5])
torch.diagflat(input=my_tensor)
my_tensor.diagflat()
torch.diagflat(input=my_tensor, offset=0)
# tensor([[7, 0, 0],
# [0, -4, 0],
# [0, 0, 5]])
torch.diagflat(input=my_tensor, offset=1)
# tensor([[0, 7, 0, 0],
# [0, 0, -4, 0],
# [0, 0, 0, 5],
# [0, 0, 0, 0]])
torch.diagflat(input=my_tensor, offset=-1)
# tensor([[0, 0, 0, 0],
# [7, 0, 0, 0],
# [0, -4, 0, 0],
# [0, 0, 5, 0]])
torch.diagflat(input=my_tensor, offset=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.diagflat(input=my_tensor, offset=-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.diagflat(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.diagflat(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.diagflat(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.diagflat(input=my_tensor)
torch.diagflat(input=my_tensor, offset=0)
# tensor([[7, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, -4, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 5, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -6, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -3, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 8, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 9, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, -2]])
torch.diagflat(input=my_tensor, offset=1)
# tensor([[0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, -4, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 5, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -6, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, -3, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 8, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 9, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, -2],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
torch.diagflat(input=my_tensor, offset=-1)
# tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0,-4, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -6, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -3, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 8, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 9, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, -2, 0]])
torch.diagflat(input=my_tensor, offset=2)
# tensor([[0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -4, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, -6, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
torch.diagflat(input=my_tensor, offset=-2)
# tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, -4, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -6, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0]])
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