feat: Improve 2D visualization with Voronoi Cells

examples/cbc_iris.py

@@ -38,10 +38,12 @@ if __name__ == "__main__":
     )
 
     # Callbacks
-    vis = pt.models.VisCBC2D(data=train_ds,
-                             title="CBC Iris Example",
-                             resolution=100,
-                             axis_off=True)
+    vis = pt.models.Visualize2DVoronoiCallback(
+        data=train_ds,
+        title="CBC Iris Example",
+        resolution=100,
+        axis_off=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(

examples/glvq_iris.py

@@ -3,7 +3,7 @@
 import argparse
 
 import prototorch as pt
-import prototorch.models.expanded
+import prototorch.models.clcc
 import pytorch_lightning as pl
 import torch
 from torch.optim.lr_scheduler import ExponentialLR
@@ -30,7 +30,7 @@ if __name__ == "__main__":
     )
 
     # Initialize the model
-    model = prototorch.models.expanded.GLVQ(
+    model = prototorch.models.GLVQ(
         hparams,
         optimizer=torch.optim.Adam,
         prototypes_initializer=pt.initializers.SMCI(train_ds),
@@ -42,7 +42,13 @@ if __name__ == "__main__":
     model.example_input_array = torch.zeros(4, 2)
 
     # Callbacks
-    vis = pt.models.VisGLVQ2D(data=train_ds)
+    vis = pt.models.Visualize2DVoronoiCallback(
+        data=train_ds,
+        resolution=200,
+        title="Example: GLVQ on Iris",
+        x_label="sepal length",
+        y_label="petal length",
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(