cummin() and cummax() in PyTorch

#pytorch #cummin #cummax #function

*Memos:

My post explains min() and max().
My post explains minimum() and maximum().
My post explains fmin() and fmax().
My post explains argmin() and argmax().
My post explains aminmax(), amin() and amax().
My post explains kthvalue() and topk().
My post explains cumsum() and cumprod().

cummin() can get two of the 0D or more D tensors of zero or more cumulative minima and their indices from the 0D or more D tensors of zero or more elements as shown below:

*Memos:

cummin() can be used with torch or a tensor.
The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or bool).
The 2nd argument with torch or the 1st argument is dim(Required-Type:int).
There is out argument with torch(Optional-Type:tuple(tensor, tensor) or list(tensor, tensor)): *Memos:
- out= must be used.
- My post explains out argument.

import torch

my_tensor = torch.tensor([5, 8, 4, 6, 9, 1, 7, 0])

torch.cummin(input=my_tensor, dim=0)
my_tensor.cummin(dim=0)
torch.cummin(input=my_tensor, dim=-1)
# torch.return_types.cummin(
# values=tensor([5, 5, 4, 4, 4, 1, 1, 0]),
# indices=tensor([0, 0, 2, 2, 2, 5, 5, 7]))

my_tensor = torch.tensor([[5, 8, 4, 6],
                          [9, 1, 7, 0],
                          [3, 6, 2, 4]])
torch.cummin(input=my_tensor, dim=0)
torch.cummin(input=my_tensor, dim=-2)
# torch.return_types.cummin(
# values=tensor([[5, 8, 4, 6], [5, 1, 4, 0], [3, 1, 2, 0]]),
# indices=tensor([[0, 0, 0, 0], [0, 1, 0, 1], [2, 1, 2, 1]]))

torch.cummin(input=my_tensor, dim=1)
torch.cummin(input=my_tensor, dim=-1)
# torch.return_types.cummin(
# values=tensor([[5, 5, 4, 4], [9, 1, 1, 0], [3, 3, 2, 2]]),
# indices=tensor([[0, 0, 2, 2], [0, 1, 1, 3], [0, 0, 2, 2]]))

my_tensor = torch.tensor([[5., 8., 4., 6.],
                          [9., 1., 7., 0.],
                          [3., 6., 2., 4.]])
torch.cummin(input=my_tensor, dim=0)
# torch.return_types.cummin(
# values=tensor([[5., 8., 4., 6.], [5., 1., 4., 0.], [3., 1., 2., 0.]]),
# indices=tensor([[0, 0, 0, 0], [0, 1, 0, 1], [2, 1, 2, 1]]))

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True],
                          [True, False, True, False]])
torch.cummin(input=my_tensor, dim=0)
# torch.return_types.cummin(
# values=tensor([[True, False, True, False],
#                [False, False, False, False],
#                [False, False, False, False]]),
# indices=tensor([[0, 0, 0, 0],
#                 [1, 0, 1, 0],
#                 [1, 2, 1, 2]]))

cummax() can get two of the 0D or more D tensors of zero or more cumulative maxima and their indices from the 0D or more D tensors of zero or more elements as shown below:

cummax() can be used with torch or a tensor.
The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or bool).
The 2nd argument with torch or the 1st argument is dim(Required-Type:int).
There is out argument with torch(Optional-Type:tuple(tensor, tensor) or list(tensor, tensor)): *Memos:
- out= must be used.
- My post explains out argument.

import torch

my_tensor = torch.tensor([5, 8, 4, 6, 9, 1, 7, 0])

torch.cummax(input=my_tensor, dim=0)
my_tensor.cummax(dim=0)
torch.cummax(input=my_tensor, dim=-1)
# torch.return_types.cummax(
# values=tensor([5, 8, 8, 8, 9, 9, 9, 9]),
# indices=tensor([0, 1, 1, 1, 4, 4, 4, 4]))

my_tensor = torch.tensor([[5, 8, 4, 6],
                          [9, 1, 7, 0],
                          [3, 6, 2, 4]])
torch.cummax(input=my_tensor, dim=0)
torch.cummax(input=my_tensor, dim=-2)
# torch.return_types.cummax(
# values=tensor([[5, 8, 4, 6], [9, 8, 7, 6], [9, 8, 7, 6]]),
# indices=tensor([[0, 0, 0, 0], [1, 0, 1, 0], [1, 0, 1, 0]]))

torch.cummax(input=my_tensor, dim=1)
torch.cummax(input=my_tensor, dim=-1)
# torch.return_types.cummax(
# values=tensor([[5, 8, 8, 8], [9, 9, 9, 9], [3, 6, 6, 6]]),
# indices=tensor([[0, 1, 1, 1], [0, 0, 0, 0], [0, 1, 1, 1]]))

my_tensor = torch.tensor([[5., 8., 4., 6.],
                          [9., 1., 7., 0.],
                          [3., 6., 2., 4.]])
torch.cummax(input=my_tensor, dim=0)
# torch.return_types.cummax(
# values=tensor([[5., 8., 4., 6.], [9., 8., 7., 6.], [9., 8., 7., 6.]]),
# indices=tensor([[0, 0, 0, 0], [1, 0, 1, 0], [1, 0, 1, 0]]))

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True],
                          [True, False, True, False]])
torch.cummax(input=my_tensor, dim=0)
# torch.return_types.cummax(
# values=tensor([[True, False, True, False],
#                [True, True, True, True],
#                [True, True, True, True]]),
# indices=tensor([[0, 0, 0, 0],
#                 [0, 1, 0, 1],
#                 [2, 1, 2, 1]]))

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cummin() and cummax() in PyTorch

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