qfeval_functions.functions.nanones

Create a tensor filled with ones, preserving NaN positions from the input tensor.

This function creates a new tensor with the same shape, dtype, and device as the input tensor, filled with ones. However, positions where the input tensor contains NaN values are preserved as NaN in the output tensor. This is useful for creating mask-like tensors that maintain the NaN structure of the original data while providing ones for valid positions.

The function performs the following operation:

\[\begin{split}\text{nanones}(X)_i = \begin{cases} \text{NaN} & \text{if } X_i = \text{NaN} \\ 1 & \text{otherwise} \end{cases}\end{split}\]

Parameters:: like (Tensor) – The input tensor whose shape, dtype, and device will be used for the output tensor. NaN positions in this tensor will be preserved in the output.
Returns:: A tensor with the same shape, dtype, and device as like, filled with ones except for positions where like contains NaN values, which remain NaN.
Return type:: Tensor

Example

>>> # Simple 1D tensor with NaN
>>> x = torch.tensor([1.0, nan, 3.0, 4.0])
>>> QF.nanones(x)
tensor([1., nan, 1., 1.])

>>> # 2D tensor with mixed NaN positions
>>> x = torch.tensor([[1.0, nan, 3.0],
...                   [nan, 5.0, 6.0]])
>>> QF.nanones(x)
tensor([[1., nan, 1.],
        [nan, 1., 1.]])

>>> # All NaN tensor
>>> x = torch.tensor([nan, nan, nan])
>>> QF.nanones(x)
tensor([nan, nan, nan])

>>> # No NaN tensor
>>> x = torch.tensor([1.0, 2.0, 3.0])
>>> QF.nanones(x)
tensor([1., 1., 1.])

>>> # Different dtypes preserved
>>> x = torch.tensor([1.0, nan, 3.0], dtype=torch.float32)
>>> result = QF.nanones(x)
>>> result.dtype
torch.float32