92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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import numpy as np
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import random, time, math
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from libs.clustering import split_tour_across_clusters
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from libs.aco import AntColony, total_distance
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def generate_cities(nb, max_coords=1000):
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return [random.sample(range(max_coords), 2) for _ in range(nb)]
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nb_ville = 2000
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max_coords = 1000
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nb_truck = 1
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max_time = 2
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nb_ants = 10
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max_time_per_cluster = max_time / nb_truck
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start_time_generate = time.time()
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cities = [[6734, 1453], [2233, 10], [5530, 1424], [401, 841], [3082, 1644], [7608, 4458], [7573, 3716], [7265, 1268], [6898, 1885], [1112, 2049], [5468, 2606], [5989, 2873], [4706, 2674], [4612, 2035], [6347, 2683], [6107, 669], [7611, 5184], [7462, 3590], [7732, 4723], [5900, 3561], [4483, 3369], [6101, 1110], [5199, 2182], [1633, 2809], [4307, 2322], [675, 1006], [7555, 4819], [7541, 3981], [3177, 756], [7352, 4506], [7545, 2801], [3245, 3305], [6426, 3173], [4608, 1198], [23, 2216], [7248, 3779], [7762, 4595], [7392, 2244], [3484, 2829], [6271, 2135], [4985, 140], [1916, 1569], [7280, 4899], [7509, 3239], [10, 2676], [6807, 2993], [5185, 3258], [3023, 1942]]
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#cities[0] = [max_coords/2, max_coords/2]
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stop_time_generate = time.time()
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start_time_split = time.time()
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clusters = split_tour_across_clusters(cities, nb_truck)
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stop_time_split = time.time()
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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=color, marker='x', 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 = {nb_ville}, nb_truck = {nb_truck}, max_time = {max_time}")
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plt.show()
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