feat: add GMLVQ with new architecture

examples/y_architecture_example.py

@@ -0,0 +1,89 @@
+import prototorch as pt
+import pytorch_lightning as pl
+import torchmetrics
+from prototorch.core import SMCI
+from prototorch.models.y_arch.callbacks import (
+    LogTorchmetricCallback,
+    PlotLambdaMatrixToTensorboard,
+    VisGMLVQ2D,
+)
+from prototorch.models.y_arch.library.gmlvq import GMLVQ
+from pytorch_lightning.callbacks import EarlyStopping
+from torch.utils.data import DataLoader
+
+# ##############################################################################
+
+if __name__ == "__main__":
+
+    # ------------------------------------------------------------
+    # DATA
+    # ------------------------------------------------------------
+
+    # Dataset
+    train_ds = pt.datasets.Iris()
+
+    # Dataloader
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=32,
+        num_workers=0,
+        shuffle=True,
+    )
+
+    # ------------------------------------------------------------
+    # HYPERPARAMETERS
+    # ------------------------------------------------------------
+
+    # Select Initializer
+    components_initializer = SMCI(train_ds)
+
+    # Define Hyperparameters
+    hyperparameters = GMLVQ.HyperParameters(
+        lr=0.1,
+        backbone_lr=5,
+        input_dim=4,
+        distribution=dict(
+            num_classes=3,
+            per_class=1,
+        ),
+        component_initializer=components_initializer,
+    )
+
+    # Create Model
+    model = GMLVQ(hyperparameters)
+
+    print(model)
+
+    # ------------------------------------------------------------
+    # TRAINING
+    # ------------------------------------------------------------
+
+    # Controlling Callbacks
+    stopping_criterion = LogTorchmetricCallback(
+        'recall',
+        torchmetrics.Recall,
+        num_classes=3,
+    )
+
+    es = EarlyStopping(
+        monitor=stopping_criterion.name,
+        mode="max",
+        patience=10,
+    )
+
+    # Visualization Callback
+    vis = VisGMLVQ2D(data=train_ds)
+
+    # Define trainer
+    trainer = pl.Trainer(
+        callbacks=[
+            vis,
+            stopping_criterion,
+            es,
+            PlotLambdaMatrixToTensorboard(),
+        ],
+        max_epochs=1000,
+    )
+
+    # Train
+    trainer.fit(model, train_loader)