*Memos:
My post explains sum() and nansum().
My post explains prod() and cartesian_prod().
My post explains mean() and nanmean().
My post explains median() and nanmedian().
mode() can get two of the 0D or more D tensors of zero or more mode values(the most frequently appeared elements) and their indices from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
mode() 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 with a tensor is dim(Optional-Default:-1-Type:int).
The 3rd argument with torch or the 2nd argument with a tensor is keepdim(Optional-Default:False-Type:bool). *My post explains keepdim argument.
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.
The mode value of the last appeared element and its indice is taken.
If there are the multiple elements which appear with the same frequencies, the smaller last appeared one and its indice is taken as a mode value.
The empty 2D or more D input tensor or tensor doesn’t work if not setting dim.
The empty 1D or more D input tensor or tensor with the deepest dim doesn’t work.
my_tensor = torch.tensor([6, 3, 8, 3, 0, 3, 6, 6])
torch.mode(input=my_tensor)
my_tensor.mode()
torch.mode(input=my_tensor, dim=0)
torch.mode(input=my_tensor, dim=-1)
# torch.return_types.mode(
# values=tensor(3),
# indices=tensor(5))
my_tensor = torch.tensor([[6, 3, 8, 3],
[0, 3, 6, 6],
[8, 1, 1, 6]])
torch.mode(input=my_tensor)
torch.mode(input=my_tensor, dim=1)
torch.mode(input=my_tensor, dim=-1)
# torch.return_types.mode(
# values=tensor([3, 6, 1]),
# indices=tensor([3, 3, 2]))
torch.mode(input=my_tensor, dim=0)
torch.mode(input=my_tensor, dim=-2)
# torch.return_types.mode(
# values=tensor([0, 3, 1, 6]),
# indices=tensor([1, 1, 2, 2]))
my_tensor = torch.tensor([[[6, 3, 8], [3, 0, 3]],
[[6, 6, 8], [1, 1, 6]]])
torch.mode(input=my_tensor)
torch.mode(input=my_tensor, dim=2)
torch.mode(input=my_tensor, dim=-1)
# torch.return_types.mode(
# values=tensor([[3, 3], [6, 1]]),
# indices=tensor([[1, 2], [1, 1]]))
torch.mode(input=my_tensor, dim=0)
torch.mode(input=my_tensor, dim=-3)
# torch.return_types.mode(
# values=tensor([[6, 3, 8], [1, 0, 3]]),
# indices=tensor([[1, 0, 1], [1, 0, 0]]))
torch.mode(input=my_tensor, dim=1)
torch.mode(input=my_tensor, dim=-2)
# torch.return_types.mode(
# values=tensor([[3, 0, 3], [1, 1, 6]]),
# indices=tensor([[1, 1, 1], [1, 1, 1]]))
my_tensor = torch.tensor([[[6., 3., 8.], [3., 0., 3.]],
[[6., 6., 8.], [1., 1., 6.]]])
torch.mode(input=my_tensor)
# torch.return_types.mode(
# values=tensor([[3., 3.], [6., 1.]]),
# indices=tensor([[1, 2], [1, 1]]))
my_tensor = torch.tensor([[[True, False, True], [True, False, True]],
[[False, True, False], [False, True, False]]])
torch.mode(input=my_tensor)
# torch.return_types.mode(
# values=tensor([[True, True], [False, False]]),
# indices=tensor([[2, 2], [2, 2]]))
my_tensor = torch.tensor([])
torch.mode(input=my_tensor, dim=0) # Error
my_tensor = torch.tensor([[]])
torch.mode(input=my_tensor, dim=0)
# torch.return_types.mode(
# values=tensor([]),
# indices=tensor([], dtype=torch.int64))
torch.mode(input=my_tensor, dim=1) # Error
my_tensor = torch.tensor([[[]]])
torch.mode(input=my_tensor, dim=0)
torch.mode(input=my_tensor, dim=1)
# torch.return_types.mode(
# values=tensor([], size=(1, 0)),
# indices=tensor([], size=(1, 0), dtype=torch.int64))
torch.mode(input=my_tensor, dim=2) # Error
