Use 'num_' in all variable names

examples/glvq_iris.py

@@ -24,10 +24,10 @@ if __name__ == "__main__":
                                                batch_size=150)
 
     # Hyperparameters
-    nclasses = 3
+    num_classes = 3
     prototypes_per_class = 2
     hparams = dict(
-        distribution=(nclasses, prototypes_per_class),
+        distribution=(num_classes, prototypes_per_class),
         lr=0.01,
     )
 

examples/glvq_spiral.py

@@ -13,7 +13,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     # Dataset
-    train_ds = pt.datasets.Spiral(n_samples=600, noise=0.6)
+    train_ds = pt.datasets.Spiral(num_samples=600, noise=0.6)
 
     # Dataloaders
     train_loader = torch.utils.data.DataLoader(train_ds,
@@ -21,10 +21,10 @@ if __name__ == "__main__":
                                                batch_size=256)
 
     # Hyperparameters
-    nclasses = 2
+    num_classes = 2
     prototypes_per_class = 20
     hparams = dict(
-        distribution=(nclasses, prototypes_per_class),
+        distribution=(num_classes, prototypes_per_class),
         transfer_function="sigmoid_beta",
         transfer_beta=10.0,
         lr=0.01,

examples/gmlvq_iris.py

@@ -22,10 +22,10 @@ if __name__ == "__main__":
                                                num_workers=0,
                                                batch_size=150)
     # Hyperparameters
-    nclasses = 3
+    num_classes = 3
     prototypes_per_class = 1
     hparams = dict(
-        distribution=(nclasses, prototypes_per_class),
+        distribution=(num_classes, prototypes_per_class),
         input_dim=x_train.shape[1],
         latent_dim=x_train.shape[1],
         proto_lr=0.01,

examples/gmlvq_mnist.py

@@ -41,12 +41,12 @@ if __name__ == "__main__":
                                               batch_size=256)
 
     # Hyperparameters
-    nclasses = 10
+    num_classes = 10
     prototypes_per_class = 2
     hparams = dict(
         input_dim=28 * 28,
         latent_dim=28 * 28,
-        distribution=(nclasses, prototypes_per_class),
+        distribution=(num_classes, prototypes_per_class),
         proto_lr=0.01,
         bb_lr=0.01,
     )
@@ -61,7 +61,7 @@ if __name__ == "__main__":
     # Callbacks
     vis = pt.models.VisImgComp(
         data=train_ds,
-        nrow=5,
+        num_columns=5,
         show=False,
         tensorboard=True,
         random_data=20,

examples/liramlvq_tecator.py

@@ -24,10 +24,10 @@ if __name__ == "__main__":
     test_loader = torch.utils.data.DataLoader(test_ds, batch_size=32)
 
     # Hyperparameters
-    nclasses = 2
+    num_classes = 2
     prototypes_per_class = 2
     hparams = dict(
-        distribution=(nclasses, prototypes_per_class),
+        distribution=(num_classes, prototypes_per_class),
         input_dim=100,
         latent_dim=2,
         proto_lr=0.001,