64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
from sklearn.cluster import KMeans
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import random, time
|
|
from clustering import split_tour_across_clusters
|
|
|
|
def generate_cities(nb, max_coords=1000):
|
|
return [random.sample(range(max_coords), 2) for _ in range(nb)]
|
|
|
|
def plot_clusters(cities, clusters):
|
|
# Création d'une liste de couleurs pour les différents clusters
|
|
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
|
|
|
|
# Création d'un nouveau graphique
|
|
plt.figure()
|
|
|
|
# Pour chaque cluster
|
|
for i, cluster in clusters.items():
|
|
# Sélection d'une couleur pour le cluster
|
|
color = colors[i % len(colors)]
|
|
|
|
# Pour chaque ville dans le cluster
|
|
for city_index in cluster:
|
|
# Récupération des coordonnées de la ville
|
|
city = cities[city_index]
|
|
|
|
# Ajout de la ville au graphique
|
|
plt.scatter(city[0], city[1], c=color, s=20)
|
|
|
|
# show first city in black and twice bigger
|
|
plt.scatter(cities[0][0], cities[0][1], c='k', s=200)
|
|
|
|
|
|
# Affichage du graphique
|
|
plt.show()
|
|
|
|
|
|
nb_ville = 100
|
|
max_coords = 1000
|
|
nb_truck = 5
|
|
|
|
# Define the coordinates of the cities
|
|
# And set depot at the first city in the middle of the map
|
|
start_time_generate = time.time()
|
|
cities = generate_cities(nb_ville, max_coords)
|
|
cities[0] = [max_coords/2, max_coords/2]
|
|
stop_time_generate = time.time()
|
|
|
|
# Split the tour across clusters with nb_truck trucks
|
|
start_time_split = time.time()
|
|
clusters = split_tour_across_clusters(cities, nb_truck)
|
|
stop_time_split = time.time()
|
|
|
|
# show the number of cities in each cluster
|
|
for cluster in clusters.values():
|
|
print(len(cluster))
|
|
|
|
# show the time
|
|
print("\n---- TIME ----")
|
|
print("generate cities time: ", stop_time_generate - start_time_generate)
|
|
print("split cities time: ", stop_time_split - start_time_split)
|
|
|
|
# show the clusters
|
|
plot_clusters(cities, clusters) |