supervised working

pina/data/sample_dataset.py

@@ -0,0 +1,43 @@
+from torch.utils.data import Dataset
+import torch
+
+from ..label_tensor import LabelTensor
+
+
+class SamplePointDataset(Dataset):
+    """
+    This class is used to create a dataset of sample points.
+    """
+
+    def __init__(self, problem, device) -> None:
+        """
+        :param dict input_pts: The input points.
+        """
+        super().__init__()
+        pts_list = []
+        self.condition_names = []
+
+        for name, condition in problem.conditions.items():
+            if not hasattr(condition, "output_points"):
+                pts_list.append(problem.input_pts[name])
+                self.condition_names.append(name)
+
+        self.pts = LabelTensor.stack(pts_list)
+
+        if self.pts != []:
+            self.condition_indeces = torch.cat(
+                [
+                    torch.tensor([i] * len(pts_list[i]))
+                    for i in range(len(self.condition_names))
+                ],
+                dim=0,
+            )
+        else:  # if there are no sample points
+            self.condition_indeces = torch.tensor([])
+            self.pts = torch.tensor([])
+
+        self.pts = self.pts.to(device)
+        self.condition_indeces = self.condition_indeces.to(device)
+
+    def __len__(self):
+        return self.pts.shape[0]