[WIP] Update CBC implementation to use SiameseGLVQ

examples/cbc_iris.py

@@ -6,13 +6,10 @@ import torch
 
 if __name__ == "__main__":
     # Dataset
-    from sklearn.datasets import load_iris
-    x_train, y_train = load_iris(return_X_y=True)
-    x_train = x_train[:, [0, 2]]
-    train_ds = pt.datasets.NumpyDataset(x_train, y_train)
+    train_ds = pt.datasets.Iris(dims=[0, 2])
 
     # Reproducibility
-    pl.utilities.seed.seed_everything(seed=2)
+    pl.utilities.seed.seed_everything(seed=3)
 
     # Dataloaders
     train_loader = torch.utils.data.DataLoader(train_ds,
@@ -21,18 +18,19 @@ if __name__ == "__main__":
 
     # Hyperparameters
     hparams = dict(
-        input_dim=x_train.shape[1],
-        nclasses=3,
-        num_components=5,
-        component_initializer=pt.components.SSI(train_ds, noise=0.01),
-        lr=0.01,
+        distribution=[3, 2, 2],
+        proto_lr=0.01,
+        bb_lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.CBC(hparams)
+    model = pt.models.CBC(
+        hparams,
+        prototype_initializer=pt.components.SSI(train_ds, noise=0.01),
+    )
 
     # Callbacks
-    dvis = pt.models.VisCBC2D(data=(x_train, y_train),
+    dvis = pt.models.VisCBC2D(data=train_ds,
                               title="CBC Iris Example",
                               resolution=300,
                               axis_off=True)