*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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