Update example scripts

examples/glvq_iris.py

@@ -17,15 +17,16 @@ if __name__ == "__main__":
                                                batch_size=150)
 
     # Hyperparameters
+    nclasses = 3
+    prototypes_per_class = 2
     hparams = dict(
-        nclasses=3,
+        distribution=(nclasses, prototypes_per_class),
-        prototypes_per_class=2,
         prototype_initializer=pt.components.SMI(train_ds),
         lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.GLVQ(hparams)
+    model = pt.models.GLVQ(hparams, optimizer=torch.optim.Adam)
 
     # Callbacks
     vis = pt.models.VisGLVQ2D(data=(x_train, y_train))

examples/glvq_spiral.py

@@ -25,10 +25,11 @@ if __name__ == "__main__":
                                                batch_size=256)
 
     # Hyperparameters
+    nclasses = 2
+    prototypes_per_class = 20
     hparams = dict(
-        nclasses=2,
+        distribution=(nclasses, prototypes_per_class),
-        prototypes_per_class=20,
+        prototype_initializer=pt.components.SSI(train_ds, noise=1e-1),
-        prototype_initializer=pt.components.SSI(train_ds, noise=1e-7),
         transfer_function="sigmoid_beta",
         transfer_beta=10.0,
         lr=0.01,

examples/gmlvq_iris.py

@@ -15,9 +15,10 @@ if __name__ == "__main__":
                                                num_workers=0,
                                                batch_size=150)
     # Hyperparameters
+    nclasses = 3
+    prototypes_per_class = 1
     hparams = dict(
-        nclasses=3,
+        distribution=(nclasses, prototypes_per_class),
-        prototypes_per_class=1,
         input_dim=x_train.shape[1],
         latent_dim=x_train.shape[1],
         prototype_initializer=pt.components.SMI(train_ds),

examples/liramlvq_tecator.py

@@ -17,9 +17,10 @@ if __name__ == "__main__":
                                                batch_size=32)
 
     # Hyperparameters
+    nclasses = 2
+    prototypes_per_class = 2
     hparams = dict(
-        nclasses=2,
+        distribution=(nclasses, prototypes_per_class),
-        prototypes_per_class=2,
         input_dim=100,
         latent_dim=2,
         prototype_initializer=pt.components.SMI(train_ds),

examples/lvq_iris.py

@@ -1,42 +0,0 @@
-"""Classical LVQ using GLVQ example on the Iris dataset."""
-
-import prototorch as pt
-import pytorch_lightning as pl
-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)
-
-    # Dataloaders
-    train_loader = torch.utils.data.DataLoader(train_ds,
-                                               num_workers=0,
-                                               batch_size=150)
-
-    # Hyperparameters
-    hparams = dict(
-        nclasses=3,
-        prototypes_per_class=2,
-        prototype_initializer=pt.components.SMI(train_ds),
-        #prototype_initializer=pt.components.Random(2),
-        lr=0.005,
-    )
-
-    # Initialize the model
-    model = pt.models.LVQ1(hparams)
-    #model = pt.models.LVQ21(hparams)
-
-    # Callbacks
-    vis = pt.models.VisGLVQ2D(data=(x_train, y_train))
-
-    # Setup trainer
-    trainer = pl.Trainer(
-        max_epochs=200,
-        callbacks=[vis],
-    )
-
-    # Training loop
-    trainer.fit(model, train_loader)

examples/siamese_glvq_iris.py

@@ -38,11 +38,10 @@ if __name__ == "__main__":
 
     # Hyperparameters
     hparams = dict(
-        nclasses=3,
+        distribution=[1, 2, 3],
-        prototypes_per_class=2,
         prototype_initializer=pt.components.SMI((x_train, y_train)),
-        proto_lr=0.001,
+        proto_lr=0.01,
-        bb_lr=0.001,
+        bb_lr=0.01,
     )
 
     # Initialize the model