Add knn

examples/knn_iris.py

@@ -0,0 +1,37 @@
+"""k-NN example using 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(k=20)
+
+    # Initialize the model
+    model = pt.models.KNN(hparams, data=train_ds)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=(x_train, y_train))
+
+    # Setup trainer
+    trainer = pl.Trainer(max_epochs=1, callbacks=[vis])
+
+    # Training loop
+    # This is only for visualization. k-NN has no training phase.
+    trainer.fit(model, train_loader)
+
+    # Recall
+    y_pred = model.predict(torch.tensor(x_train))
+    print(y_pred)

examples/siamese_glvq_iris.py

@@ -24,9 +24,7 @@ class Backbone(torch.nn.Module):
 
 if __name__ == "__main__":
     # Dataset
-    from sklearn.datasets import load_iris
-    x_train, y_train = load_iris(return_X_y=True)
-    train_ds = pt.datasets.NumpyDataset(x_train, y_train)
+    train_ds = pt.datasets.Iris()
 
     # Reproducibility
     pl.utilities.seed.seed_everything(seed=2)
@@ -39,7 +37,7 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         distribution=[1, 2, 3],
-        prototype_initializer=pt.components.SMI((x_train, y_train)),
+        prototype_initializer=pt.components.SMI(train_ds),
         proto_lr=0.01,
         bb_lr=0.01,
     )
@@ -54,7 +52,7 @@ if __name__ == "__main__":
     print(model)
 
     # Callbacks
-    vis = pt.models.VisSiameseGLVQ2D(data=(x_train, y_train), border=0.1)
+    vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
 
     # Setup trainer
     trainer = pl.Trainer(max_epochs=100, callbacks=[vis])