fix: fill missing values in empirical_mean with 0, make grud more rob…

…ust;

pypots/classification/grud/data.py

@@ -63,6 +63,8 @@ def __init__(
             self.empirical_mean = torch.sum(
                 self.missing_mask * self.X, dim=[0, 1]
             ) / torch.sum(self.missing_mask, dim=[0, 1])
+            # fill nan with 0, in case some features have no observations
+            self.empirical_mean = torch.nan_to_num(self.empirical_mean, 0)
 
     def _fetch_data_from_array(self, idx: int) -> Iterable:
         """Fetch data according to index.