Add examples/dynamic_components.py

examples/dynamic_components.py

@@ -0,0 +1,56 @@
+"""Dynamically update the number of prototypes in GLVQ."""
+
+import argparse
+
+import pytorch_lightning as pl
+import torch
+
+import prototorch as pt
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Dataset
+    train_ds = pt.datasets.Iris(dims=[0, 2])
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
+
+    # Hyperparameters
+    hparams = dict(
+        distribution=[1, 1, 1],
+        transfer_function="sigmoid_beta",
+        transfer_beta=10.0,
+        lr=0.01,
+    )
+
+    # Initialize the model
+    model = pt.models.GLVQ(
+        hparams,
+        prototype_initializer=pt.components.SMI(train_ds),
+    )
+
+    for _ in range(5):
+        # Callbacks
+        vis = pt.models.VisGLVQ2D(train_ds)
+
+        # Setup trainer
+        trainer = pl.Trainer.from_argparse_args(
+            args,
+            max_epochs=20,
+            callbacks=[vis],
+            terminate_on_nan=True,
+            weights_summary=None,
+        )
+
+        # Training loop
+        trainer.fit(model, train_loader)
+
+        # Increase prototypes
+        model.increase_prototypes(
+            pt.components.SMI(train_ds),
+            distribution=[1, 1, 1],
+        )

prototorch/models/abstract.py

@@ -1,6 +1,5 @@
 import pytorch_lightning as pl
 import torch
-from prototorch.functions.competitions import wtac
 from torch.optim.lr_scheduler import ExponentialLR
 
 

prototorch/models/glvq.py

@@ -120,6 +120,9 @@ class GLVQ(AbstractPrototypeModel):
     # def predict_step(self, batch, batch_idx, dataloader_idx=None):
     #     pass
 
+    def increase_prototypes(self, initializer, distribution):
+        self.proto_layer.increase_components(initializer, distribution)
+
     def __repr__(self):
         super_repr = super().__repr__()
         return f"{super_repr}"