feat: add gmlvq example

it was necessary to update the pre-commit definition for a successfull
commit.

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.1.0
+  rev: v4.4.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
@@ -13,17 +13,17 @@ repos:
   - id: check-case-conflict
 
 - repo: https://github.com/myint/autoflake
-  rev: v1.4
+  rev: v2.1.1
   hooks:
   - id: autoflake
 
 - repo: http://github.com/PyCQA/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
   - id: isort
 
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.931
+  rev: v1.3.0
   hooks:
   - id: mypy
     files: prototorch
@@ -35,14 +35,14 @@ repos:
   - id: yapf
 
 - repo: https://github.com/pre-commit/pygrep-hooks
-  rev: v1.9.0
+  rev: v1.10.0
   hooks:
   - id: python-use-type-annotations
   - id: python-no-log-warn
   - id: python-check-blanket-noqa
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.31.0
+  rev: v3.7.0
   hooks:
   - id: pyupgrade
 

examples/gmlvq.py

@@ -0,0 +1,76 @@
+"""ProtoTorch CBC example using 2D Iris data."""
+
+import torch
+
+import prototorch as pt
+
+
+class GMLVQ(torch.nn.Module):
+    """
+    Implementation of Generalized Matrix Learning Vector Quantization.
+    """
+
+    def __init__(self, data, **kwargs):
+        super().__init__(**kwargs)
+
+        self.components_layer = pt.components.LabeledComponents(
+            distribution=[1, 1, 1],
+            components_initializer=pt.initializers.SMCI(data, noise=0.1),
+        )
+
+        self.backbone = pt.transforms.Omega(
+            len(data[0][0]),
+            len(data[0][0]),
+            pt.initializers.RandomLinearTransformInitializer(),
+        )
+
+    def forward(self, data):
+        """
+        Forward function that returns a tuple of dissimilarities and label information.
+        Feed into GLVQLoss to get a complete GMLVQ model.
+        """
+        components, label = self.components_layer()
+
+        latent_x = self.backbone(data)
+        latent_components = self.backbone(components)
+
+        distance = pt.distances.squared_euclidean_distance(
+            latent_x, latent_components)
+
+        return distance, label
+
+    def predict(self, data):
+        """
+        The GMLVQ has a modified prediction step, where a competition layer is applied.
+        """
+        components, label = self.components_layer()
+        distance = pt.distances.squared_euclidean_distance(data, components)
+        winning_label = pt.competitions.wtac(distance, label)
+        return winning_label
+
+
+if __name__ == "__main__":
+    train_ds = pt.datasets.Iris()
+
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
+
+    model = GMLVQ(train_ds)
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.05)
+    criterion = pt.losses.GLVQLoss()
+
+    for epoch in range(200):
+        correct = 0.0
+        for x, y in train_loader:
+            d, labels = model(x)
+            loss = criterion(d, y, labels).mean(0)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            with torch.no_grad():
+                y_pred = model.predict(x)
+                correct += (y_pred == y).float().sum(0)
+
+        acc = 100 * correct / len(train_ds)
+        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")