[BUG] LVQ1 is broken

examples/lvq1_iris.py

@@ -0,0 +1,74 @@
+"""LVQ1 example using the Iris dataset."""
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+
+if __name__ == "__main__":
+    # Acquire data
+    from sklearn.datasets import load_iris
+    x_train, y_train = load_iris(return_X_y=True)
+    x_train = x_train[:, [0, 2]]
+
+    # Relabel classes
+    # y_train[y_train == 0] = 3
+    # y_train[y_train == 1] = 4
+    # y_train[y_train == 2] = 6
+
+    # Dataset
+    train_ds = pt.datasets.NumpyDataset(x_train, y_train)
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds, shuffle=True)
+
+    # Hyperparameters
+    num_classes = 3
+    prototypes_per_class = 10
+    hparams = dict(
+        distribution={
+            # class_label: num_prototypes
+            # 3: 1,
+            # 4: 2,
+            # 6: 3,
+            0: 1,
+            2: 2,
+            3: 3,
+        },
+        lr=0.001,
+    )
+
+    # Initialize the model
+    model = pt.models.LVQ1(
+        hparams,
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
+    )
+
+    # Check if `num_classes` is correct
+    print(f"{model.num_classes=}")
+    assert model.num_classes == 3
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=(x_train, y_train),
+                              cmap="viridis",
+                              resolution=200,
+                              block=False)
+
+    # Setup trainer
+    trainer = pl.Trainer(
+        gpus=0,
+        max_epochs=50,
+        callbacks=[vis],
+        # fast_dev_run=1,
+    )
+
+    # Get prototype labels
+    print(f"Protoype Labels are: ", model.prototype_labels.tolist())
+
+    # Training loop
+    trainer.fit(model, train_loader)

prototorch/models/lvq.py

@@ -9,7 +9,7 @@ class LVQ1(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
+        plabels = self.proto_layer.labels
 
         x, y = train_batch
         dis = self.compute_distances(x)
@@ -28,6 +28,8 @@ class LVQ1(NonGradientMixin, GLVQ):
             self.proto_layer.load_state_dict({"_components": updated_protos},
                                              strict=False)
 
+        print(f"{dis=}")
+        print(f"{y=}")
         # Logging
         self.log_acc(dis, y, tag="train_acc")
 
@@ -38,7 +40,7 @@ class LVQ21(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 2.1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
+        plabels = self.proto_layer.labels
 
         x, y = train_batch
         dis = self.compute_distances(x)