elbow algorithm

tests/04_cluster_ant_colony_no_animation_elbow_algo.py

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+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import numpy as np
+import random, time, math
+from libs.clustering import split_tour_across_clusters
+from libs.aco import AntColony, total_distance
+
+def generate_cities(nb, max_coords=1000):
+    return [random.sample(range(max_coords), 2) for _ in range(nb)]
+
+nb_ville = 1500
+max_coords = 1000
+nb_truck = 2
+max_time = 10
+nb_ants = 10
+
+max_time_per_cluster = max_time / nb_truck
+
+start_time_generate = time.time()
+cities = generate_cities(nb_ville, max_coords)
+stop_time_generate = time.time()
+
+
+start_time_split = time.time()
+clusters = split_tour_across_clusters(cities, nb_truck)
+stop_time_split = time.time()
+
+def elbow_method(cities, max_clusters):
+    inertias = []
+    for i in range(1, max_clusters+1):
+        kmeans = KMeans(n_clusters=i, random_state=0).fit(cities)
+        inertias.append(kmeans.inertia_)
+    return inertias
+
+cities = generate_cities(nb_ville, max_coords) # Assurez-vous que les villes sont générées avant de les passer à la méthode du coude
+inertias = elbow_method(cities, max_clusters=50)
+
+plt.figure()
+plt.plot(range(1, len(inertias)+1), inertias, marker='o')
+plt.title('Elbow Method')
+plt.xlabel('Number of clusters')
+plt.ylabel('Inertia')
+plt.show()
+
+
+
+
+for cluster in clusters.values():
+    print(len(cluster))
+print("\n---- TIME ----")
+print("generate cities time: ", stop_time_generate - start_time_generate)
+print("split cities time: ", stop_time_split - start_time_split)
+
+# create new figure for annealing paths
+plt.figure()
+colors = [
+    '#1f77b4',  # Bleu moyen
+    '#ff7f0e',  # Orange
+    '#2ca02c',  # Vert
+    '#d62728',  # Rouge
+    '#9467bd',  # Violet
+    '#8c564b',  # Marron
+    '#e377c2',  # Rose
+    '#7f7f7f',  # Gris
+    '#bcbd22',  # Vert olive
+    '#17becf',  # Turquoise
+    '#1b9e77',  # Vert Teal
+    '#d95f02',  # Orange foncé
+    '#7570b3',  # Violet moyen
+    '#e7298a',  # Fuchsia
+    '#66a61e',  # Vert pomme
+    '#e6ab02',  # Jaune or
+    '#a6761d',  # Bronze
+    '#666666',  # Gris foncé
+    '#f781bf',  # Rose clair
+    '#999999',  # Gris moyen
+]
+
+best_routes = []
+
+for i, cluster_indices in enumerate(clusters.values()):
+    # Sélection d'une couleur pour le cluster
+    color = colors[i % len(colors)]
+    
+    # Récupération des coordonnées de la ville
+    cluster_cities = [cities[index] for index in cluster_indices]
+
+    # Appel de la fonction AntColony.run
+    ant_colony = AntColony(cluster_cities, n_ants=nb_ants, max_time=max_time_per_cluster, alpha=1, beta=5)
+    best_route = ant_colony.run()
+    best_routes.append((best_route, color))
+
+    print("Total distance for cluster", i, ": ", total_distance(best_route))
+
+# calculate total distance for all clusters
+full_total_distance = 0
+for route, color in best_routes:
+    full_total_distance += total_distance(route)
+
+print("Total distance for all clusters: ", full_total_distance)
+
+for i, (route, color) in enumerate(best_routes):
+    x = [city[0] for city in route]
+    y = [city[1] for city in route]
+    x.append(x[0])
+    y.append(y[0])
+    plt.plot(x, y, color="blue", marker='o', linestyle='-', label=f"Cluster {i}")
+    # add title with nb_ville, nb_truck and max_time
+    plt.title(f"nb_ville = {len(cities)}, nb_truck = {nb_truck}, max_time = {max_time}")   
+
+plt.show()