Closed cifkao closed 4 years ago
In some cases, when some of the values in x are equal, the output will be nan. For example:
x
nan
>>> Interp1d()(x=torch.tensor([2., 2.]), y=torch.tensor([0., 1.]), xnew=torch.tensor([2.])) tensor([[nan]])
In some cases, when some of the values in
x
are equal, the output will benan
. For example: