add dataframe for variance

iris.py

 import pandas as pd
 import numpy as np
 from collections import Counter
-
+import matplotlib.pyplot as plt
 
 def manhattan_distance(x1: np.ndarray, x2: np.ndarray) -> float:
     distance = 0
@@ -26,6 +26,7 @@ if __name__ == '__main__':
     centroids = X[np.random.choice(X.shape[0], k, replace=False)]
     # Nombre maximal d'itérations
     max_iter = 100
+    total_variances = []
 
     # Algorithme K-Means
     for i in range(max_iter):
@@ -45,15 +46,17 @@ if __name__ == '__main__':
             if len(clusters[j]) > 0:
                 centroids[j] = np.mean(clusters[j], axis=0)
 
+        # Calculer la variance totale à cette itération
+        total_variance = 0.0
+        for j in range(k):
+            for point in clusters[j]:
+                total_variance += manhattan_distance(point, centroids[j]) ** 2
+
+        total_variances.append(total_variance)
+
         # Convergence ?
         if np.all(old_centroids == centroids):
             break
-    # Calculer la variance totale
-    total_variance = 0.0
-    for cluster_index, cluster_points in enumerate(clusters):
-        cluster_center = centroids[cluster_index]
-        for point in cluster_points:
-            total_variance += manhattan_distance(point, cluster_center) ** 2
 
     # Calculer le taux de classification par cluster et la classe majoritaire par cluster
     cluster_classifications = {}
@@ -71,3 +74,11 @@ if __name__ == '__main__':
     for cluster_index in range(k):
         print(f'Cluster {cluster_index + 1} - Taux de classification : {cluster_classifications[cluster_index]}')
         print(f'Cluster {cluster_index + 1} - Classe majoritaire : {cluster_majority_class[cluster_index]}')
+
+    # Afficher les variances totales à chaque itération
+    plt.plot(range(1, len(total_variances) + 1), total_variances, marker='o')
+    plt.xlabel('Itération')
+    plt.ylabel('Variance totale')
+    plt.title('Variance totale à chaque itération de K-Means')
+    plt.grid(True)
+    plt.show()