starting point to develop an example script

hello.py

@@ -1,6 +0,0 @@
-def main():
-    print("Hello from experiment!")
-
-
-if __name__ == "__main__":
-    main()

main.py

@@ -0,0 +1,79 @@
+"""Write a short description of your experiment here."""
+
+import logging
+import lightning as L
+import torch
+from torch.utils.data import DataLoader, random_split
+
+from protothor.callbacks.normalization import OmegaTraceNormalization
+from protothor.data.preprocessing import Standardizer
+from lightning.pytorch.callbacks import EarlyStopping
+from protothor.data.toy import Iris
+from protothor.functional.initialization import zero_initialization
+from protothor.lightning.metric_module import MetricModule
+from protothor.models.gmlvq import generate_model as generate_gmlvq
+from protothor.nn.collectable_loss import EvaluateExposedLosses
+from protothor.nn.container import find_instances
+
+logging.basicConfig(level=logging.INFO)
+
+
+def main():
+    # 1 - Get Dataset
+    data = Iris([0, 2])
+    train_ds, val_ds = random_split(data, [0.7, 0.3])
+
+    standardize = Standardizer(train_ds)
+
+    # 2 - Create Dataloaders
+    train_loader = DataLoader(
+        standardize(train_ds),
+        shuffle=True,
+        batch_size=len(train_ds),
+    )
+    val_loader = DataLoader(
+        standardize(val_ds),
+        shuffle=False,
+        batch_size=len(val_ds),
+    )
+
+    # 3 - Initialize Prototypes
+    labels, positions = zero_initialization(data)
+
+    # 4 - Generate Torch Model
+    model = generate_gmlvq(positions, labels)
+    omega_layer = find_instances(model, torch.nn.Linear)[0]
+
+    # 5 - Initialize Lightning Module
+    module = MetricModule(model, EvaluateExposedLosses(model))
+
+    # 6 - Define Callbacks
+
+    callbacks = [
+        OmegaTraceNormalization(omega_layer),
+    ]
+    callbacks.append(
+        EarlyStopping(
+            monitor="accuracy/validation",
+            min_delta=0.0002,
+            patience=50,
+            mode="max",
+            verbose=False,
+            check_on_train_epoch_end=True,
+        )
+    )
+
+    # 7 - Define Trainer
+    trainer = L.Trainer(detect_anomaly=True, max_epochs=1000, callbacks=callbacks)
+
+    # 8 - Train Model
+    trainer.fit(module, train_loader, val_loader)
+
+    # 9 - Analyse results
+    omega = omega_layer.weight.detach()
+    classification_correlation_matrix = omega.T @ omega
+    logging.info(classification_correlation_matrix)
+
+
+if __name__ == "__main__":
+    main()