Corrected normalization in repeated_sampling

EIVPackage/EIVData/repeated_sampling.py

@@ -34,7 +34,12 @@ class repeated_sampling():
         _, _, true_trainset, true_testset\
                 = self.dataclass.load_data(
                         seed=self.fixed_seed, splitting_part=splitting_part,
+                        normalize=False,
                         return_ground_truth=True)
+        full_noisy_x = torch.concat((true_trainset.tensors[2],
+                true_testset.tensors[2]), dim=0)
+        full_noisy_y = torch.concat((true_trainset.tensors[3],
+                true_testset.tensors[3]), dim=0)
         true_train_x, true_train_y = true_trainset.tensors[:2]
         true_test_x, true_test_y = true_testset.tensors[:2]
         random_generator = torch.Generator().manual_seed(seed)
@@ -45,13 +50,12 @@ class repeated_sampling():
                 add_noise((true_train_x, true_train_y),
                         (self.x_noise_strength, self.y_noise_strength), seeds,
                         normalize=normalize,
-                        normalization_list=true_trainset.tensors[2:])
+                        normalization_list=[full_noisy_x, full_noisy_y])
         (noisy_test_x, noisy_test_y), (true_test_x, true_test_y) =\
                 add_noise((true_test_x, true_test_y),
                         (self.x_noise_strength, self.y_noise_strength), seeds,
                         normalize=normalize,
-                        # normalize both datasets with train set
-                        normalization_list=true_trainset.tensors[2:])
+                        normalization_list=[full_noisy_x, full_noisy_y])
         trainset = TensorDataset(noisy_train_x, noisy_train_y)
         testset = TensorDataset(noisy_test_x, noisy_test_y)
         true_trainset = TensorDataset(true_train_x, true_train_y,
@@ -62,5 +66,3 @@ class repeated_sampling():
             return trainset, testset
         else:
             return trainset, testset, true_trainset, true_testset
-
-