sort() can get two of the 0D or more D tensors of zero or more sorted elements and their indices in ascending(Default) or descending order from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
sort()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orbool
). *The 0D tensor of a complex number can be used. - The 2nd argument with
torch
or the 1st argument with a tensor isdim
(Optional-Defualt:-1
-Type:int
). - The 3rd argument with
torch
or the 2nd argument with a tensor isdescending
(Optional-Default:False
-Type:bool
). *False
is ascending order andTrue
is descending order. - The 4th argument with
torch
or the 3rd argument with a tensor isstable
(Optional-Default:False
-Type:bool
). *Memos:-
True
can definitely sort the multiple same values whileFalse
cannot but evenFalse
basically can sort the multiple same values. - You must use
stable=
. *Settingstable
,dim
anddescending
also needsdim=
anddescending=
respectively.
-
- There is
out
argument withtorch
(Optional-Type:tuple
(tensor
,tensor
) orlist
(tensor
,tensor
)): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
import torch
my_tensor = torch.tensor([7, 1, -5, 7, 9, -3, 0, -3])
torch.sort(my_tensor)
my_tensor.sort()
torch.sort(input=my_tensor, dim=0, descending=False, stable=False)
torch.sort(input=my_tensor, dim=-1, descending=False, stable=False)
torch.sort(input=my_tensor, dim=0, descending=False, stable=True)
torch.sort(input=my_tensor, dim=-1, descending=False, stable=True)
# torch.return_types.sort(
# values=tensor([-5, -3, -3, 0, 1, 7, 7, 9]),
# indices=tensor([2, 5, 7, 6, 1, 0, 3, 4]))
torch.sort(input=my_tensor, dim=0, descending=True, stable=False)
torch.sort(input=my_tensor, dim=-1, descending=True, stable=False)
# torch.return_types.sort(
# values=tensor([9, 7, 7, 1, 0, -3, -3, -5]),
# indices=tensor([4, 0, 3, 1, 6, 5, 7, 2]))
my_tensor = torch.tensor([[7, 1, -5, 7], [9, -3, 0, -3]])
torch.sort(input=my_tensor)
torch.sort(input=my_tensor, dim=-1, descending=False, stable=False)
torch.sort(input=my_tensor, dim=1, descending=False, stable=False)
torch.sort(input=my_tensor, dim=-1, descending=False, stable=True)
torch.sort(input=my_tensor, dim=1, descending=False, stable=True)
# torch.return_types.sort(
# values=tensor([[-5, 1, 7, 7], [-3, -3, 0, 9]]),
# indices=tensor([[2, 1, 0, 3], [1, 3, 2, 0]]))
torch.sort(input=my_tensor, dim=0, descending=False, stable=False)
torch.sort(input=my_tensor, dim=-2, descending=False, stable=False)
torch.sort(input=my_tensor, dim=0, descending=False, stable=True)
torch.sort(input=my_tensor, dim=-2, descending=False, stable=True)
# torch.return_types.sort(
# values=tensor([[7, -3, -5, -3], [9, 1, 0, 7]]),
# indices=tensor([[0, 1, 0, 1], [1, 0, 1, 0]]))
my_tensor = torch.tensor([[7., 1, -5., 7.], [9., -3., 0, -3.]])
torch.sort(input=my_tensor)
# torch.return_types.sort(
# values=tensor([[-5., 1., 7., 7.], [-3., -3., 0., 9.]]),
# indices=tensor([[2, 1, 0, 3], [1, 3, 2, 0]]))
my_tensor = torch.tensor([[True, False, True, False],
[False, True, False, True]])
torch.sort(input=my_tensor)
# torch.return_types.sort(
# values=tensor([[False, False, True, True],
# [False, False, True, True]]),
# indices=tensor([[1, 3, 0, 2],
# [0, 2, 1, 3]]))
my_tensor = torch.tensor(7.+0.j)
torch.sort(input=my_tensor)
# torch.return_types.sort(
# values=tensor(7.+0.j),
# indices=tensor(0))
argsort() can get the 0D or more D tensor of zero or more sorted elements' indices in ascending(Default) or descending order from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
argsort()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orbool
). *The 0D tensor of a complex number can be used. - The 2nd argument with
torch
or the 1st argument with a tensor isdim
(Optional-Defualt:-1
Type:int
). - The 3rd argument with
torch
or the 2nd argument with a tensor isdescending
(Optional-Default:False
-Type:bool
). *False
is ascending order andTrue
is descending order. - The 4th argument with
torch
or the 3rd argument with a tensor isstable
(Optional-Default:False
-Type:bool
). *Memos:-
True
can definitely sort the multiple same values whileFalse
cannot but evenFalse
can basically sort the multiple same values. - You must use
stable=
. *When settingstable
,dim
anddescending
also needdim=
anddescending=
respectively.
-
import torch
my_tensor = torch.tensor([7, 1, -5, 7, 9, -3, 0, -3])
torch.argsort(input=my_tensor)
my_tensor.argsort()
torch.argsort(input=my_tensor, dim=0, descending=False, stable=False)
torch.argsort(input=my_tensor, dim=-1, descending=False, stable=False)
torch.argsort(input=my_tensor, dim=0, descending=False, stable=True)
torch.argsort(input=my_tensor, dim=-1, descending=False, stable=True)
# tensor([2, 5, 7, 6, 1, 0, 3, 4])
torch.argsort(input=my_tensor, dim=0, descending=True, stable=False)
torch.argsort(input=my_tensor, dim=-1, descending=True, stable=False)
# tensor([4, 0, 3, 1, 6, 5, 7, 2])
my_tensor = torch.tensor([[7, 1, -5, 7], [9, -3, 0, -3]])
torch.argsort(input=my_tensor)
torch.argsort(input=my_tensor, dim=-1, descending=False, stable=False)
torch.argsort(input=my_tensor, dim=1, descending=False, stable=False)
torch.argsort(input=my_tensor, dim=-1, descending=False, stable=True)
torch.argsort(input=my_tensor, dim=1, descending=False, stable=True)
# tensor([[2, 1, 0, 3], [1, 3, 2, 0]])
torch.argsort(input=my_tensor, dim=0, descending=False, stable=False)
torch.argsort(input=my_tensor, dim=-2, descending=False, stable=False)
torch.argsort(input=my_tensor, dim=0, descending=False, stable=True)
torch.argsort(input=my_tensor, dim=-2, descending=False, stable=True)
# tensor([[0, 1, 0, 1], [1, 0, 1, 0]])
my_tensor = torch.tensor([[7., 1., -5., 7.], [9., -3., 0., -3.]])
torch.argsort(input=my_tensor)
# tensor([[2, 1, 0, 3], [1, 3, 2, 0]])
my_tensor = torch.tensor([[True, False, True, False],
[False, True, False, True]])
torch.argsort(input=my_tensor)
# tensor([[1, 3, 0, 2],
# [0, 2, 1, 3]])
my_tensor = torch.tensor(7.+0.j)
torch.argsort(input=my_tensor)
# tensor(0)
msort() can get the 0D or more D tensor of zero or more sorted elements in ascending order from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
msort()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orbool
). *The 0D tensor of a complex number can be used. -
msort()
doesn't havedim
,descending
andstable
argument. -
msort()
is equivalent tosort()[0]
withdim=0
. - There is
out
argument withtorch
(Optional-Type:tuple
(tensor
,tensor
) orlist
(tensor
,tensor
)): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
import torch
my_tensor = torch.tensor([7, 1, -5, 7, 9, -3, 0, -3])
torch.msort(input=my_tensor)
my_tensor.msort()
# tensor([-5, -3, -3, 0, 1, 7, 7, 9])
my_tensor = torch.tensor([[7, 1, -5, 7], [9, -3, 0, -3]])
torch.msort(input=my_tensor)
# tensor([[7, -3, -5, -3], [9, 1, 0, 7]])
my_tensor = torch.tensor([[7., 1., -5., 7.], [9., -3., 0., -3.]])
torch.msort(input=my_tensor)
# tensor([[7., -3., -5., -3.], [9., 1., 0., 7.]])
my_tensor = torch.tensor([[True, False, True, False],
[False, True, False, True]])
torch.msort(input=my_tensor)
# tensor([[False, False, False, False],
# [True, True, True, True]])
my_tensor = torch.tensor(7.+5.j)
torch.msort(input=my_tensor)
# tensor(7.+5.j)
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