Add RSLVQ and LikelihoodLVQ

examples/probabilistic.py

@@ -0,0 +1,53 @@
+"""GLVQ example using the Iris dataset."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from sklearn.datasets import load_iris
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Dataset
+    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
+    num_classes = 3
+    prototypes_per_class = 2
+    hparams = dict(
+        distribution=(num_classes, prototypes_per_class),
+        lr=0.05,
+        variance=1,
+    )
+
+    # Initialize the model
+    model = pt.models.probabilistic.RSLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototype_initializer=pt.components.SSI(train_ds, noise=2),
+        #prototype_initializer=pt.components.UniformInitializer(2),
+    )
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=(x_train, y_train), block=False)
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)