*Memos:
- My post explains min() and max().
- 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 cummin() and cummax().
minimum() can get the 0D or more D tensor of zero or more minimum elements prioritizing nan from two of the 0D or more D tensors of zero or more elements as shown below:
*Memos:
-
minimum()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,floatorbool). - The 2nd argument with
torchor the 1st argument isother(Required-Type:tensorofint,floatorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. - My post explains
outargument.
-
-
nanis taken if there are a number andnan.
import torch tensor1 = torch.tensor([5., float('nan'), 4., float('nan')]) tensor2 = torch.tensor([[7., 8., float('nan'), float('nan')], [-9., 2., 0., -6.]]) torch.minimum(input=tensor1, other=tensor2) tensor1.minimum(other=tensor2) # tensor([[5., nan, nan, nan], # [-9., nan, 0., nan]]) tensor1 = torch.tensor(5.) tensor2 = torch.tensor([[[7., 8.], [float('nan'), float('nan')]], [[-9., 2.], [0., -6.]]]) torch.minimum(input=tensor1, other=tensor2) # tensor([[[5., 5.], [nan, nan]], # [[-9., 2.], [0., -6.]]]) tensor1 = torch.tensor(5) tensor2 = torch.tensor([[[7, 8], [-5, -1]], [[-9, 2], [0, -6]]]) torch.minimum(input=tensor1, other=tensor2) # tensor([[[5, 5], [-5, -1]], # [[-9, 2], [0, -6]]]) tensor1 = torch.tensor(True) tensor2 = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.minimum(input=tensor1, other=tensor2) # tensor([[[True, False], [True, False]], # [[False, True], [False, True]]]) maximum() can get the 0D or more D tensor of zero or more maximum elements prioritizing nan from two of the 0D or more D tensors of zero or more elements as shown below:
*Memos:
-
maximum()can be used withtorchor a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,floatorbool). - The 2nd argument with
torchor the 1st argument isother(Required-Type:tensorofint,floatorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. - My post explains
outargument.
-
-
nanis taken if there are a number andnan.
import torch tensor1 = torch.tensor([5., float('nan'), 4., float('nan')]) tensor2 = torch.tensor([[7., 8., float('nan'), float('nan')], [-9., 2., 0., -6.]]) torch.maximum(input=tensor1, other=tensor2) tensor1.maximum(other=tensor2) # tensor([[7., nan, nan, nan], # [5., nan, 4., nan]]) tensor1 = torch.tensor(5.) tensor2 = torch.tensor([[[7., 8.], [float('nan'), float('nan')]], [[-9., 2.], [0., -6.]]]) torch.maximum(input=tensor1, other=tensor2) # tensor([[[7., 8.], [nan, nan]], # [[5., 5.], [5., 5.]]]) tensor1 = torch.tensor(5) tensor2 = torch.tensor([[[7, 8], [-5, -1]], [[-9, 2], [0, -6]]]) torch.maximum(input=tensor1, other=tensor2) # tensor([[[7, 8], [5, 5]], # [[5, 5], [5, 5]]]) tensor1 = torch.tensor(True) tensor2 = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.maximum(input=tensor1, other=tensor2) # tensor([[[True, True], [True, True]], # [[True, True], [True, True]]]) 
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