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# torch.nanquantile¶

torch.nanquantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None)

This is a variant of torch.quantile() that “ignores” NaN values, computing the quantiles q as if NaN values in input did not exist. If all values in a reduced row are NaN then the quantiles for that reduction will be NaN. See the documentation for torch.quantile().

Parameters:
• input (Tensor) – the input tensor.

• q (float or Tensor) – a scalar or 1D tensor of quantile values in the range [0, 1]

• dim (int) – the dimension to reduce.

• keepdim (bool) – whether the output tensor has dim retained or not.

Keyword Arguments:
• interpolation (str) – interpolation method to use when the desired quantile lies between two data points. Can be linear, lower, higher, midpoint and nearest. Default is linear.

• out (Tensor, optional) – the output tensor.

Example:

>>> t = torch.tensor([float('nan'), 1, 2])
>>> t.quantile(0.5)
tensor(nan)
>>> t.nanquantile(0.5)
tensor(1.5000)
>>> t = torch.tensor([[float('nan'), float('nan')], [1, 2]])
>>> t
tensor([[nan, nan],
[1., 2.]])
>>> t.nanquantile(0.5, dim=0)
tensor([1., 2.])
>>> t.nanquantile(0.5, dim=1)
tensor([   nan, 1.5000])


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