*Memos:
- My post explains min(), max(), argmin() and and argmax().
- My post explains aminmax(), amin() and amax().
minimum() can get the zero or more minimum values from 2 tensors as shown below:
*Memos:
-
minimum()
can be used with torch or a tensor. -
minimum()
can be used with 0D or more D tesnors. - 2 tensors must be the same size.
- 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.
import torch
tensor1 = torch.tensor([7, 1, 4])
tensor2 = torch.tensor([5, 3, 0])
torch.minimum(tensor1, tensor2)
tensor1.minimum(tensor2)
# tensor([5, 1, 0])
tensor1 = torch.tensor([[7, 1, 4], [5, 3, 0]])
tensor2 = torch.tensor([[3, 5, 1], [9, 0, 6]])
torch.minimum(tensor1, tensor2)
tensor1.minimum(tensor2)
# tensor([[3, 1, 1], [5, 0, 0]])
tensor1 = torch.tensor([[7., 1., 4.], [5., 3., 0.]])
tensor2 = torch.tensor([[3., 5., True], [9., False, 6.]])
torch.minimum(tensor1, tensor2)
tensor1.minimum(tensor2)
# tensor([[3., 1., 1.], [5., 0., 0.]])
maximum() can get the zero or more maximum values from 2 tensors as shown below:
*Memos:
-
maximum()
can be used withtorch
or a tensor. -
maximum()
can be used with 0D or more D tesnors. - 2 tensors must be the same size.
- 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.
import torch
tensor1 = torch.tensor([7, 1, 4])
tensor2 = torch.tensor([5, 3, 0])
torch.maximum(tensor1, tensor2)
tensor1.maximum(tensor2)
# tensor([7, 3, 4])
tensor1 = torch.tensor([[7, 1, 4], [5, 3, 0]])
tensor2 = torch.tensor([[3, 5, 1], [9, 0, 6]])
torch.maximum(tensor1, tensor2)
tensor1.maximum(tensor2)
# tensor([[7, 5, 4], [9, 3, 6]])
tensor1 = torch.tensor([[7., 1., 4.], [5., 3., 0.]])
tensor2 = torch.tensor([[3., 5., True], [9., False, 6.]])
torch.maximum(tensor1, tensor2)
tensor1.maximum(tensor2)
# tensor([[7., 5., 4.], [9., 3., 6.]])
kthvalue() can get the one or more k
th smallest elements and their indices of a 0D or more D tensor as shown below:
*Memos:
-
kthvalue()
can be used withtorch
or a tensor. - Only the tensor of one or more integers, floating-point numbers or boolean values can be used so the tensor of zero or more complex numbers cannot be used.
- The 2nd argument with
torch
or the 1st argument with a tensor isk
(Required). - The 3rd argument with
torch
or the 2nd argument with a tensor isdim
(Optional) which is a dimension. - The 4th argument with
torch
or the 3rd argument with a tensor iskeepdim
(Optional-Default:False
) which keeps the dimension of the input tensor. - If there are the multiple same
k
th values, one is returned nondeterministically.
import torch
my_tensor = torch.tensor([5, 1, 9, 7, 6, 8, 0, 5])
torch.kthvalue(my_tensor, 3)
my_tensor.kthvalue(3)
torch.kthvalue(my_tensor, 3, 0)
my_tensor.kthvalue(3, 0)
torch.kthvalue(my_tensor, 3, -1)
my_tensor.kthvalue(3, -1)
# torch.return_types.kthvalue(
# values=tensor(5),
# indices=tensor(7))
torch.kthvalue(my_tensor, 3, 0, True)
# torch.return_types.kthvalue(
# values=tensor([5]),
# indices=tensor([7]))
torch.kthvalue(my_tensor, 4)
my_tensor.kthvalue(4)
torch.kthvalue(my_tensor, 4, 0)
my_tensor.kthvalue(4, 0)
torch.kthvalue(my_tensor, 4, -1)
my_tensor.kthvalue(4, -1)
# torch.return_types.kthvalue(
# values=tensor(5),
# indices=tensor(0))
torch.kthvalue(my_tensor, 4, 0, True)
# torch.return_types.kthvalue(
values=tensor([5]),
indices=tensor([0]))
my_tensor = torch.tensor([[5, 1, 9, 7],
[6, 8, 0, 5]])
torch.kthvalue(my_tensor, 3)
my_tensor.kthvalue(3)
torch.kthvalue(my_tensor, 3, 1)
my_tensor.kthvalue(3, 1)
torch.kthvalue(my_tensor, 3, -1)
my_tensor.kthvalue(3, -1)
# torch.return_types.kthvalue(
# values=tensor([7, 6]),
# indices=tensor([3, 0]))
torch.kthvalue(my_tensor, 3, 1, True)
# torch.return_types.kthvalue(
# values=tensor([[7], [6]]),
# indices=tensor([[3], [0]]))
my_tensor = torch.tensor([[5., True, 9., 7.],
[6, 8, False, 5]])
torch.kthvalue(my_tensor, 1)
my_tensor.kthvalue(1)
# torch.return_types.kthvalue(
# values=tensor([1., 0.]),
# indices=tensor([1, 2]))
topk() can get the zero or more k
largest or smallest elements and their indices of a 0D or more D tensor as shown below:
*Memos:
-
topk()
can be used withtorch
or a tensor. - Only the tensor of one or more integers, floating-point numbers or boolean values can be used so the tensor of zero or more complex numbers cannot be used.
- The 2nd argument with
torch
or the 1st argument with a tensor isk
(Required). - The 3rd argument with
torch
or the 2nd argument with a tensor isdim
(Optional) which is a dimension. - The 4th argument with
torch
or the 3rd argument with a tensor islargest
(Optional-Default:True). *True
gets the zero or more largest elements whileFalse
gets the zero or more smallest elements. - The 5th argument with
torch
or the 4th argument with a tensor issorted
(Optional-Default:True). *Sometimes, a return tensor is sorted withFalse
but sometimes not so make itTrue
if you want to definitely get a sorted tensor. - If there are the multiple same
k
values, one or more ones are returned nondeterministically.
import torch
my_tensor = torch.tensor([5, 1, 9, 7, 6, 8, 0, 5])
torch.topk(my_tensor, 3)
my_tensor.topk(3)
torch.topk(my_tensor, 3, 0)
my_tensor.topk(3, 0)
torch.topk(my_tensor, 3, -1)
my_tensor.topk(3, -1)
# torch.return_types.topk(
# values=tensor([9, 8, 7]),
# indices=tensor([2, 5, 3]))
torch.topk(my_tensor, 3, 0, False)
my_tensor.topk(3, 0, False, True)
# torch.return_types.topk(
# values=tensor([0, 1, 5]),
# indices=tensor([6, 1, 0]))
torch.topk(my_tensor, 3, 0, False, False)
my_tensor.topk(3, 0, False, False)
# torch.return_types.topk(
# values=tensor([1, 0, 5]),
# indices=tensor([1, 6, 0]))
torch.topk(my_tensor, 4)
my_tensor.topk(4)
torch.topk(my_tensor, 4, 0)
my_tensor.topk(4, 0)
torch.topk(my_tensor, 4, -1)
my_tensor.topk(4, -1)
# torch.return_types.topk(
# values=tensor([9, 8, 7, 6]),
# indices=tensor([2, 5, 3, 4]))
torch.topk(my_tensor, 4, 0, False)
my_tensor.topk(4, 0, False)
# torch.return_types.topk(
# values=tensor([0, 1, 5, 5]),
# indices=tensor([6, 1, 0, 7]))
torch.topk(my_tensor, 4, 0, False, False)
my_tensor.topk(4, 0, False, False)
# torch.return_types.topk(
# values=tensor([1, 0, 5, 5]),
# indices=tensor([1, 6, 0, 7]))
my_tensor = torch.tensor([[5, 1, 9, 7],
[6, 8, 0, 5]])
torch.topk(my_tensor, 3)
my_tensor.topk(3)
torch.topk(my_tensor, 3, 1)
my_tensor.topk(3, 1)
torch.topk(my_tensor, 3, -1)
my_tensor.topk(3, -1)
# torch.return_types.topk(
# values=tensor([[9, 7, 5], [8, 6, 5]]),
# indices=tensor([[2, 3, 0], [1, 0, 3]]))
torch.topk(my_tensor, 3, 1, False)
my_tensor.topk(3, 1, False)
# torch.return_types.topk(
# values=tensor([[1, 5, 7], [0, 5, 6]]),
# indices=tensor([[1, 0, 3], [2, 3, 0]]))
torch.topk(my_tensor, 3, 1, False, False)
my_tensor.topk(3, 1, False, False)
# torch.return_types.topk(
# values=tensor([[1, 5, 7], [5, 0, 6]]),
# indices=tensor([[1, 0, 3], [3, 2, 0]]))
my_tensor = torch.tensor([[5., True, 9., 7.], [6, 8, False, 5]])
torch.topk(my_tensor, 4)
my_tensor.topk(4)
# torch.return_types.topk(
# values=tensor([[9., 7., 5., 1.], [8., 6., 5., 0.]]),
# indices=tensor([[2, 3, 0, 1], [1, 0, 3, 2]]))
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