Add LVQ1 and LVQ2.1 Models.

examples/lvq_iris.py

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+"""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)