elbow method
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@ -42,70 +42,3 @@ plt.xlabel('Number of clusters')
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plt.ylabel('Inertia')
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plt.show()
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for cluster in clusters.values():
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print(len(cluster))
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print("\n---- TIME ----")
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print("generate cities time: ", stop_time_generate - start_time_generate)
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print("split cities time: ", stop_time_split - start_time_split)
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# create new figure for annealing paths
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plt.figure()
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colors = [
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'#1f77b4', # Bleu moyen
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'#ff7f0e', # Orange
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'#2ca02c', # Vert
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'#d62728', # Rouge
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'#9467bd', # Violet
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'#8c564b', # Marron
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'#e377c2', # Rose
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'#7f7f7f', # Gris
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'#bcbd22', # Vert olive
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'#17becf', # Turquoise
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'#1b9e77', # Vert Teal
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'#d95f02', # Orange foncé
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'#7570b3', # Violet moyen
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'#e7298a', # Fuchsia
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'#66a61e', # Vert pomme
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'#e6ab02', # Jaune or
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'#a6761d', # Bronze
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'#666666', # Gris foncé
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'#f781bf', # Rose clair
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'#999999', # Gris moyen
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]
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best_routes = []
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for i, cluster_indices in enumerate(clusters.values()):
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# Sélection d'une couleur pour le cluster
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color = colors[i % len(colors)]
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# Récupération des coordonnées de la ville
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cluster_cities = [cities[index] for index in cluster_indices]
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# Appel de la fonction AntColony.run
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ant_colony = AntColony(cluster_cities, n_ants=nb_ants, max_time=max_time_per_cluster, alpha=1, beta=5)
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best_route = ant_colony.run()
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best_routes.append((best_route, color))
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print("Total distance for cluster", i, ": ", total_distance(best_route))
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# calculate total distance for all clusters
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full_total_distance = 0
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for route, color in best_routes:
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full_total_distance += total_distance(route)
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print("Total distance for all clusters: ", full_total_distance)
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for i, (route, color) in enumerate(best_routes):
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x = [city[0] for city in route]
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y = [city[1] for city in route]
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x.append(x[0])
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y.append(y[0])
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plt.plot(x, y, color="blue", marker='o', linestyle='-', label=f"Cluster {i}")
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# add title with nb_ville, nb_truck and max_time
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plt.title(f"nb_ville = {len(cities)}, nb_truck = {nb_truck}, max_time = {max_time}")
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plt.show()
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