85 lines
2.3 KiB
Python
85 lines
2.3 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
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from libs.clustering import split_tour_across_clusters
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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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def plot_clusters(cities, clusters):
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# Création d'une liste de couleurs pour les différents clusters
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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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# Création d'un nouveau graphique
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plt.figure()
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# Pour chaque cluster
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for i, cluster in clusters.items():
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# Sélection d'une couleur pour le cluster
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color = colors[i % len(colors)]
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# Pour chaque ville dans le cluster
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for city_index in cluster:
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# Récupération des coordonnées de la ville
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city = cities[city_index]
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# Ajout de la ville au graphique
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plt.scatter(city[0], city[1], c=color, s=20)
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# show first city in black and twice bigger
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plt.scatter(cities[0][0], cities[0][1], c='k', s=200)
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# Affichage du graphique
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plt.show()
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nb_ville = 1000
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max_coords = 1000
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nb_truck = 20
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# Define the coordinates of the cities
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# And set depot at the first city in the middle of the map
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start_time_generate = time.time()
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cities = generate_cities(nb_ville, max_coords)
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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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# Split the tour across clusters with nb_truck trucks
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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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# show the number of cities in each cluster
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for cluster in clusters.values():
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print(len(cluster))
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# show the time
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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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# show the clusters
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plot_clusters(cities, clusters) |