diff --git a/05_mnist_vectorquant/vector_quantization.py b/05_mnist_vectorquant/vector_quantization.py
index fc1e91c..8c30fb0 100644
--- a/05_mnist_vectorquant/vector_quantization.py
+++ b/05_mnist_vectorquant/vector_quantization.py
@@ -4,26 +4,26 @@ import matplotlib.pyplot as plt
 
 # Load MNIST 
 print("Loading MNIST...") # debugging
-(X_train_raw, y_train), (X_test_raw, y_test) = mnist.load_data()
+(prototype_data_raw, prototype_labels_set), (test_data_raw, test_labels_set) = mnist.load_data()
 
-# print("X_train_raw[0]:")
-# print(X_train_raw[0]) # output: 28x28 vector spelling 5
-# print("y_train[0]:")
-# print(y_train[0]) # output: 5
-# print("X_test_raw[0]:")
-# print(X_test_raw[0]) # output: 28x28 vector spelling 7
-# print("y_test[0]:")
-# print(y_test[0]) # output: 7
+# print("prototype_data_raw[0]:")
+# print(prototype_data_raw[0]) # output: 28x28 vector spelling 5
+# print("prototype_labels_set[0]:")
+# print(prototype_labels_set[0]) # output: 5
+# print("test_data_raw[0]:")
+# print(test_data_raw[0]) # output: 28x28 vector spelling 7
+# print("test_labels_set[0]:")
+# print(test_labels_set[0]) # output: 7
 
 # Flatten images from (samples, 28, 28) to (samples, 784) -> one dimensional
-X_train = X_train_raw.reshape(X_train_raw.shape[0], -1).astype(np.float32)
-X_test = X_test_raw.reshape(X_test_raw.shape[0], -1).astype(np.float32)
+prototype_data = prototype_data_raw.reshape(prototype_data_raw.shape[0], -1).astype(np.float32)
+test_data = test_data_raw.reshape(test_data_raw.shape[0], -1).astype(np.float32)
 
-print("Train:", X_train.shape, "Test:", X_test.shape)
+print("Train:", prototype_data.shape, "Test:", test_data.shape)
 
 # Select first 1000 prototype vectors
-prototypes = X_train[:1000]
-prototype_labels = y_train[:1000]
+prototypes = prototype_data[:1000]
+prototype_labels = prototype_labels_set[:1000]
 
 print("Using", len(prototypes), "prototype vectors.") # debugging
 
@@ -47,7 +47,7 @@ def knn_predict_batch(X_batch, k=3):
             distance = np.sqrt(np.sum(diff ** 2))
             distances.append(distance)
         
-        # Find indices of k nearest neighbors (smallest distances)
+        # Find indices of k nearest neighbors
         distances = np.array(distances)
         nearest_k_indices = np.argsort(distances)[:k] # returns indices of array with sorted distances
         
@@ -65,8 +65,8 @@ def knn_predict_batch(X_batch, k=3):
 N_TEST = 1000
 print(f"Evaluating on {N_TEST} test samples...") # debugging
 
-X_eval = X_test[:N_TEST]
-y_eval = y_test[:N_TEST]
+X_eval = test_data[:N_TEST]
+y_eval = test_labels_set[:N_TEST]
 
 preds = knn_predict_batch(X_eval, k=5)