Added Vis for GMLVQ with more then 2 dims using PCA (#11)

* Added Vis for GMLVQ with more then 2 dims using PCA

* Added initialization possibility to GMlVQ with PCA and one example with omega init + PCA vis of 3 dims

* test(githooks): Add githooks for automatic commit checks

Co-authored-by: staps@hs-mittweida.de <staps@hs-mittweida.de>
Co-authored-by: Alexander Engelsberger <alexanderengelsberger@gmail.com>

examples/gmlvq_iris.py

@@ -0,0 +1,59 @@
+"""GLVQ example using the Iris dataset."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from torch.optim.lr_scheduler import ExponentialLR
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Dataset
+    train_ds = pt.datasets.Iris()
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
+
+    # Hyperparameters
+    hparams = dict(
+        input_dim=4,
+        latent_dim=3,
+        distribution={
+            "num_classes": 3,
+            "prototypes_per_class": 2
+        },
+        proto_lr=0.0005,
+        bb_lr=0.0005,
+    )
+
+    # Initialize the model
+    model = pt.models.GMLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototype_initializer=pt.components.SSI(train_ds),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+        omega_initializer=pt.components.PCA(train_ds.data)
+    )
+
+    # Compute intermediate input and output sizes
+    #model.example_input_array = torch.zeros(4, 2)
+
+    # Callbacks
+    vis = pt.models.VisGMLVQ2D(data=train_ds, border=0.1)
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)