sort() can get the zero or more sorted elements and their indices of a 0D or more D tensor in ascending(Default) or descending order as shown below:
*Memos:
-
sort()
can be used with torch and a tensor. - Only the tensor of zero or more integers, floating-point numbers or boolean values can be used so the tensor of zero or more complex numbers cannot be used except the 0D tensor of a complex number.
- The 2nd argument(
int
) withtorch
or the 1st argument(int
) with a tensor isdim
(Optional-Defualt:-1
) which is a dimension. - The 3rd argument(
bool
) withtorch
or the 2nd argument(bool
) with a tensor isdescending
(Optional-Default:False
). *False
is ascending order andTrue
is descending order. - The 4th argument(
bool
) withtorch
or the 3rd argument(bool
) with a tensor isstable
(Optional-Default:False
). *Memos:-
True
can definitely sort the multiple same values whileFalse
cannot but evenFalse
basically can 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.sort(my_tensor)
my_tensor.sort()
torch.sort(my_tensor, dim=0, descending=False, stable=False)
my_tensor.sort(dim=0, descending=False, stable=False)
torch.sort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.sort(dim=-1, descending=False, stable=False)
torch.sort(my_tensor, dim=0, descending=False, stable=True)
my_tensor.sort(dim=0, descending=False, stable=True)
torch.sort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.sort(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(my_tensor, dim=0, descending=True, stable=False)
my_tensor.sort(0, dim=0, descending=True, stable=False)
torch.sort(my_tensor, dim=-1, descending=True, stable=False)
my_tensor.sort(0, 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(my_tensor)
my_tensor.sort()
torch.sort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.sort(dim=-1, descending=False, stable=False)
torch.sort(my_tensor, dim=1, descending=False, stable=False)
my_tensor.sort(dim=1, descending=False, stable=False)
torch.sort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.sort(dim=-1, descending=False, stable=True)
torch.sort(my_tensor, dim=1, descending=False, stable=True)
my_tensor.sort(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(my_tensor, dim=0, descending=False, stable=False)
my_tensor.sort(dim=0, descending=False, stable=False)
torch.sort(my_tensor, dim=-2, descending=False, stable=False)
my_tensor.sort(dim=-2, descending=False, stable=False)
torch.sort(my_tensor, dim=0, descending=False, stable=True)
my_tensor.sort(dim=0, descending=False, stable=True)
torch.sort(my_tensor, dim=-2, descending=False, stable=True)
my_tensor.sort(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., True, -5., 7.], [9., -3., False, -3.]])
torch.sort(my_tensor)
my_tensor.sort()
torch.sort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.sort(dim=-1, descending=False, stable=False)
torch.sort(my_tensor, dim=1, descending=False, stable=False)
my_tensor.sort(dim=1, descending=False, stable=False)
torch.sort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.sort(dim=-1, descending=False, stable=True)
torch.sort(my_tensor, dim=1, descending=False, stable=True)
my_tensor.sort(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]]))
my_tensor = torch.tensor(7+5j)
torch.sort(my_tensor)
my_tensor.sort()
torch.sort(my_tensor, dim=0, descending=False, stable=False)
my_tensor.sort(dim=0, descending=False, stable=False)
torch.sort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.sort(dim=-1, descending=False, stable=False)
torch.sort(my_tensor, dim=0, descending=False, stable=True)
my_tensor.sort(dim=0, descending=False, stable=True)
torch.sort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.sort(dim=-1, descending=False, stable=True)
torch.sort(my_tensor, dim=0, descending=True, stable=False)
my_tensor.sort(dim=0, descending=True, stable=False)
torch.sort(my_tensor, dim=-1, descending=True, stable=False)
my_tensor.sort(dim=-1, descending=True, stable=False)
torch.sort(my_tensor, dim=0, descending=True, stable=True)
my_tensor.sort(dim=0, descending=True, stable=True)
torch.sort(my_tensor, dim=-1, descending=True, stable=True)
my_tensor.sort(dim=-1, descending=True, stable=True)
# torch.return_types.sort(
# values=tensor(7.+5.j),
# indices=tensor(0))
argsort() can get the zero or more sorted elements' indices of a 0D or more D tensor in ascending(Default) or descending order as shown below:
*Memos:
-
argsort()
can be used withtorch
and a tensor. - Only the tensor of zero or more integers, floating-point numbers or boolean values can be used so the tensor of zero or more complex numbers cannot be used except the 0D tensor of a complex number.
- The 2nd argument(
int
) withtorch
or the 1st argument(int
) with a tensor isdim
(Optional-Defualt:-1
) which is a dimension. - The 3rd argument(
bool
) withtorch
or the 2nd argument(bool
) with a tensor isdescending
(Optional-Default:False
). *False
is ascending order andTrue
is descending order. - The 4th argument(
bool
) withtorch
or the 3rd argument(bool
) with a tensor isstable
(Optional-Default:False
). *Memos:-
True
can definitely sort the multiple same values whileFalse
cannot but evenFalse
basically can 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(my_tensor)
my_tensor.argsort()
torch.argsort(my_tensor, dim=0, descending=False, stable=False)
my_tensor.argsort(dim=0, descending=False, stable=False)
torch.argsort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.argsort(dim=-1, descending=False, stable=False)
torch.argsort(my_tensor, dim=0, descending=False, stable=True)
my_tensor.argsort(dim=0, descending=False, stable=True)
torch.argsort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.argsort(dim=-1, descending=False, stable=True)
# tensor([2, 5, 7, 6, 1, 0, 3, 4])
torch.argsort(my_tensor, dim=0, descending=True, stable=False)
my_tensor.argsort(0, dim=0, descending=True, stable=False)
torch.argsort(my_tensor, dim=-1, descending=True, stable=False)
my_tensor.argsort(0, 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(my_tensor)
my_tensor.argsort()
torch.argsort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.argsort(dim=-1, descending=False, stable=False)
torch.argsort(my_tensor, dim=1, descending=False, stable=False)
my_tensor.argsort(dim=1, descending=False, stable=False)
torch.argsort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.argsort(dim=-1, descending=False, stable=True)
torch.argsort(my_tensor, dim=1, descending=False, stable=True)
my_tensor.argsort(dim=1, descending=False, stable=True)
# tensor([[2, 1, 0, 3], [1, 3, 2, 0]])
torch.argsort(my_tensor, dim=0, descending=False, stable=False)
my_tensor.argsort(dim=0, descending=False, stable=False)
torch.argsort(my_tensor, dim=-2, descending=False, stable=False)
my_tensor.argsort(dim=-2, descending=False, stable=False)
torch.argsort(my_tensor, dim=0, descending=False, stable=True)
my_tensor.argsort(dim=0, descending=False, stable=True)
torch.argsort(my_tensor, dim=-2, descending=False, stable=True)
my_tensor.argsort(dim=-2, descending=False, stable=True)
# tensor([[0, 1, 0, 1], [1, 0, 1, 0]])
my_tensor = torch.tensor([[7., True, -5., 7.], [9., -3., False, -3.]])
torch.argsort(my_tensor)
my_tensor.argsort()
torch.argsort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.argsort(dim=-1, descending=False, stable=False)
torch.argsort(my_tensor, dim=1, descending=False, stable=False)
my_tensor.argsort(dim=1, descending=False, stable=False)
torch.argsort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.argsort(dim=-1, descending=False, stable=True)
torch.argsort(my_tensor, dim=1, descending=False, stable=True)
my_tensor.argsort(dim=1, descending=False, stable=True)
# tensor([[2, 1, 0, 3], [1, 3, 2, 0]])
my_tensor = torch.tensor(7+5j)
torch.argsort(my_tensor)
my_tensor.argsort()
torch.argsort(my_tensor, dim=0, descending=False, stable=False)
my_tensor.argsort(dim=0, descending=False, stable=False)
torch.argsort(my_tensor, dim=-1, descending=False, stable=False)
my_tensor.argsort(dim=-1, descending=False, stable=False)
torch.argsort(my_tensor, dim=0, descending=False, stable=True)
my_tensor.argsort(dim=0, descending=False, stable=True)
torch.argsort(my_tensor, dim=-1, descending=False, stable=True)
my_tensor.argsort(dim=-1, descending=False, stable=True)
torch.argsort(my_tensor, dim=0, descending=True, stable=False)
my_tensor.argsort(dim=0, descending=True, stable=False)
torch.argsort(my_tensor, dim=-1, descending=True, stable=False)
my_tensor.argsort(dim=-1, descending=True, stable=False)
torch.argsort(my_tensor, dim=0, descending=True, stable=True)
my_tensor.argsort(dim=0, descending=True, stable=True)
torch.argsort(my_tensor, dim=-1, descending=True, stable=True)
my_tensor.argsort(dim=-1, descending=True, stable=True)
# tensor(0)
msort() can get the zero or more sorted elements of a 0D or more D tensor in ascending as shown below:
*Memos:
-
msort()
can be used withtorch
and a tensor. - Only the tensor of zero or more integers, floating-point numbers or boolean values can be used so the tensor of zero or more complex numbers cannot be used except the 0D tensor of a complex number.
-
msort()
doesn't havedim
,descending
andstable
argument. -
msort()
is equivalent tosort()[0]
withdim=0
.
import torch
my_tensor = torch.tensor([7, 1, -5, 7, 9, -3, 0, -3])
torch.msort(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(my_tensor)
my_tensor.msort()
# tensor([[7, -3, -5, -3], [9, 1, 0, 7]])
my_tensor = torch.tensor([[7., True, -5., 7.], [9., -3., False, -3.]])
torch.msort(my_tensor)
my_tensor.msort()
# tensor([[7., -3., -5., -3.], [9., 1., 0., 7.]])
my_tensor = torch.tensor(7+5j)
torch.msort(my_tensor)
my_tensor.msort()
# tensor(7.+5.j)
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