Use 'num_' in all variable names

examples/glvq_iris.py

@@ -3,14 +3,13 @@
 import numpy as np
 import torch
 from matplotlib import pyplot as plt
-from sklearn.datasets import load_iris
-from sklearn.preprocessing import StandardScaler
-from torchinfo import summary
-
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules.losses import GLVQLoss
 from prototorch.modules.prototypes import Prototypes1D
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torchinfo import summary
 
 # Prepare and preprocess the data
 scaler = StandardScaler()
@@ -28,7 +27,7 @@ class Model(torch.nn.Module):
         self.proto_layer = Prototypes1D(
             input_dim=2,
             prototypes_per_class=3,
-            nclasses=3,
+            num_classes=3,
             prototype_initializer="stratified_random",
             data=[x_train, y_train],
         )

examples/gmlvq_tecator.py

@@ -2,13 +2,12 @@
 
 import matplotlib.pyplot as plt
 import torch
-from torch.utils.data import DataLoader
-
 from prototorch.datasets.tecator import Tecator
 from prototorch.functions.distances import sed
 from prototorch.modules import Prototypes1D
 from prototorch.modules.losses import GLVQLoss
 from prototorch.utils.colors import get_legend_handles
+from torch.utils.data import DataLoader
 
 # Prepare the dataset and dataloader
 train_data = Tecator(root="./artifacts", train=True)
@@ -23,7 +22,7 @@ class Model(torch.nn.Module):
         self.p1 = Prototypes1D(
             input_dim=100,
             prototypes_per_class=2,
-            nclasses=2,
+            num_classes=2,
             prototype_initializer="stratified_random",
             data=[x, y],
         )

examples/gtlvq_mnist.py

@@ -12,14 +12,13 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torchvision
-from torchvision import transforms
-
 from prototorch.functions.helper import calculate_prototype_accuracy
 from prototorch.modules.losses import GLVQLoss
 from prototorch.modules.models import GTLVQ
+from torchvision import transforms
 
 # Parameters and options
-n_epochs = 50
+num_epochs = 50
 batch_size_train = 64
 batch_size_test = 1000
 learning_rate = 0.1
@@ -141,7 +140,7 @@ optimizer = torch.optim.Adam(
 criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
 
 # Training loop
-for epoch in range(n_epochs):
+for epoch in range(num_epochs):
     for batch_idx, (x_train, y_train) in enumerate(train_loader):
         model.train()
         x_train, y_train = x_train.to(device), y_train.to(device)
@@ -161,7 +160,7 @@ for epoch in range(n_epochs):
         if batch_idx % log_interval == 0:
             acc = calculate_prototype_accuracy(distances, y_train, plabels)
             print(
-                f"Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
+                f"Epoch: {epoch + 1:02d}/{num_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
               Train Acc: {acc.item():02.02f}")
 
     # Test