Add comments for explanation

medium/K-Means_Clustering/K-Means_Clustering.py

@@ -19,9 +19,8 @@ def euc_dist(x1, x2):
 
     # Iterate for the given max_iterations
     for _ in range(max_iterations):
-        clusters = [[] for _ in range(k)] # Create empty lists for clusters
+        clusters = [[] for _ in range(k)]  # Create empty lists for clusters
 
-
         # Assign points to the nearest centroid (initial draft)
         for point in points:
             distance = [euc_dist(point, final_centroids[i]) for i in range(k)]
@@ -32,12 +31,14 @@ def euc_dist(x1, x2):
 
         # Update centroids by computing the mean of points in each cluster
         for cluster in clusters:
-            if cluster: # Only update centroids if cluster is not empty
+            if cluster:  # Only update centroids if cluster is not empty
                 new_centroid = tuple(np.round(np.mean(cluster, axis=0), 4))
                 new_centroids.append(new_centroid)
             else:
-                new_centroids.append(final_centroids[clusters.index(cluster)]) # Keep the same centroid if no points in the cluster
-
+                new_centroids.append(
+                    final_centroids[clusters.index(cluster)]
+                )  # Keep the same centroid if no points in the cluster
+
         # If centroids do not change, break early
         if new_centroids == final_centroids:
             break
@@ -69,7 +70,7 @@ def euc_dist(x1, x2):
 #         centroids = np.round(centroids,4)
 #     return [tuple(centroid) for centroid in centroids]
 
-
+# Example usage
 points = [(1, 2), (1, 4), (1, 0), (10, 2), (10, 4), (10, 0)]
 k = 2
 initial_centroids = [(1, 1), (10, 1)]