Add LVQ1 and LVQ2.1 Models.

examples/lvq_iris.py

@@ -0,0 +1,42 @@
+"""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)

prototorch/models/__init__.py

@@ -1,7 +1,7 @@
 from importlib.metadata import PackageNotFoundError, version
 
 from .cbc import CBC
-from .glvq import GLVQ, GMLVQ, GRLVQ, LVQMLN, ImageGLVQ, SiameseGLVQ
+from .glvq import GLVQ, GMLVQ, GRLVQ, LVQMLN, ImageGLVQ, SiameseGLVQ, LVQ1, LVQ21
 from .neural_gas import NeuralGas
 from .vis import *
 

prototorch/models/glvq.py

@@ -5,10 +5,12 @@ from prototorch.functions.activations import get_activation
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance, omega_distance,
                                             squared_euclidean_distance)
-from prototorch.functions.losses import glvq_loss
+from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
 
 from .abstract import AbstractPrototypeModel
 
+from torch.optim.lr_scheduler import ExponentialLR
+
 
 class GLVQ(AbstractPrototypeModel):
     """Generalized Learning Vector Quantization."""
@@ -30,6 +32,8 @@ class GLVQ(AbstractPrototypeModel):
         self.transfer_function = get_activation(self.hparams.transfer_function)
         self.train_acc = torchmetrics.Accuracy()
 
+        self.loss = glvq_loss
+
     @property
     def prototype_labels(self):
         return self.proto_layer.component_labels.detach().cpu()
@@ -44,7 +48,7 @@ class GLVQ(AbstractPrototypeModel):
         x = x.view(x.size(0), -1)  # flatten
         dis = self(x)
         plabels = self.proto_layer.component_labels
-        mu = glvq_loss(dis, y, prototype_labels=plabels)
+        mu = self.loss(dis, y, prototype_labels=plabels)
         batch_loss = self.transfer_function(mu,
                                             beta=self.hparams.transfer_beta)
         loss = batch_loss.sum(dim=0)
@@ -76,6 +80,42 @@ class GLVQ(AbstractPrototypeModel):
         return y_pred.numpy()
 
 
+class LVQ1(GLVQ):
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams, **kwargs)
+        self.loss = lvq1_loss
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.SGD(self.parameters(), lr=self.hparams.lr)
+        scheduler = ExponentialLR(optimizer,
+                                  gamma=0.99,
+                                  last_epoch=-1,
+                                  verbose=False)
+        sch = {
+            "scheduler": scheduler,
+            "interval": "step",
+        }  # called after each training step
+        return [optimizer], [sch]
+
+
+class LVQ21(GLVQ):
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams, **kwargs)
+        self.loss = lvq21_loss
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.SGD(self.parameters(), lr=self.hparams.lr)
+        scheduler = ExponentialLR(optimizer,
+                                  gamma=0.99,
+                                  last_epoch=-1,
+                                  verbose=False)
+        sch = {
+            "scheduler": scheduler,
+            "interval": "step",
+        }  # called after each training step
+        return [optimizer], [sch]
+
+
 class ImageGLVQ(GLVQ):
     """GLVQ for training on image data.