added comments and added target as feature aswell

Author: Gerhardsa0

src/safeds/data/tabular/containers/_timeseries_table.py

@@ -431,7 +431,7 @@ def _as_table(self: TimeSeries) -> Table:
 
 
     # ------------------------------------------------------------------------------------------------------------------
-    # Windiw Functions and needed class:
+    # Window Functions and needed class:
     # for testing purposes they are here
     # ------------------------------------------------------------------------------------------------------------------
 
@@ -458,8 +458,12 @@ def _generate_label_windows( ):
 
 
     def into_DataLoader(self):
+        #code below concatenate the column like the following 
+        #f1:[w1, w2, w3] f2[w1, w2, w3] -> [w1+w1, w2+w2, w3+w3]
         x_train = np.concatenate(list(self._create_all_windows_for_column()), axis=1)
+        #for target this will be created:  [   t1,    t2,    t3]
         y_train = np.array(self._create_all_labels_for_target_column())
+        #load them into PyTorch 
         dataset = TimeSeriesDataset(x_train,y_train)
         return DataLoader(dataset, batch_size=1) 
 

tests/safeds/data/tabular/containers/_table/_timeseries_table/test_timeseries_creation.py

@@ -30,12 +30,12 @@ def test_create_timeseries() -> None:
                     date_name="f1",
                     window_size=2,
                     forecast_horizon=1,
-                    feature_names=["f1", "f2"])
+                    feature_names=["f1", "f2", "target"])
 
 
     # ein Modell erstellen ist in safeDS noch nicht definiert darum low level in PyTorch
     # 2 ist hier die number der feature Columns
-    input_dim = ts._window_size * 2 
+    input_dim = ts._window_size * len(ts._feature_names)
     hidden_dim = 1
     output_dim = ts._forecast_horizon
     model = LSTMModel(input_dim, hidden_dim, output_dim)
@@ -50,4 +50,4 @@ def test_create_timeseries() -> None:
 
 
     #wenn durchläuft wurde korrekt Table in Dataloader geladen
-    assert False
+    #assert False