moving most used functions to specitic python files
This commit is contained in:
Louis
parent
b21ecce51e
commit
0da622371f
@ -2,7 +2,7 @@ from sklearn.cluster import KMeans
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import random, time
|
||||
from clustering import split_tour_across_clusters
|
||||
from libs.clustering import split_tour_across_clusters
|
||||
|
||||
def generate_cities(nb, max_coords=1000):
|
||||
return [random.sample(range(max_coords), 2) for _ in range(nb)]
|
||||
|
||||
@ -2,7 +2,7 @@ from sklearn.cluster import KMeans
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import random, time, math
|
||||
from clustering import split_tour_across_clusters
|
||||
from libs.clustering import split_tour_across_clusters
|
||||
|
||||
random.seed(1)
|
||||
|
||||
@ -66,7 +66,7 @@ def simulated_annealing(cities, color='b', temperature=100000, cooling_rate=0.99
|
||||
plt.ioff()
|
||||
return best_solution
|
||||
|
||||
nb_ville = 100
|
||||
nb_ville = 200
|
||||
max_coords = 1000
|
||||
nb_truck = 4
|
||||
temperature = 10000
|
||||
|
||||
@ -2,7 +2,7 @@ from sklearn.cluster import KMeans
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import random, time, math
|
||||
from clustering import split_tour_across_clusters
|
||||
from libs.clustering import split_tour_across_clusters
|
||||
|
||||
def generate_cities(nb, max_coords=1000):
|
||||
return [random.sample(range(max_coords), 2) for _ in range(nb)]
|
||||
|
||||
@ -2,9 +2,9 @@ from sklearn.cluster import KMeans
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import random, time, math
|
||||
from clustering import split_tour_across_clusters
|
||||
from libs.clustering import split_tour_across_clusters
|
||||
|
||||
random.seed(2)
|
||||
random.seed(3)
|
||||
|
||||
def generate_cities(nb, max_coords=1000):
|
||||
return [random.sample(range(max_coords), 2) for _ in range(nb)]
|
||||
@ -60,9 +60,9 @@ class AntColony:
|
||||
return [self.cities[i] for i in best_ant]
|
||||
|
||||
nb_ville = 50
|
||||
max_coords = 10
|
||||
nb_truck = 1
|
||||
max_time = 1
|
||||
max_coords = 1000
|
||||
nb_truck = 2
|
||||
max_time = 3
|
||||
nb_ants = 10
|
||||
|
||||
max_time_per_cluster = max_time / nb_truck
|
||||
@ -123,6 +123,13 @@ for i, cluster_indices in enumerate(clusters.values()):
|
||||
|
||||
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]
|
||||
|
||||
@ -1,81 +0,0 @@
|
||||
from sklearn.cluster import KMeans
|
||||
import numpy as np
|
||||
|
||||
def split_tour_across_clusters(cities, nb_truck):
|
||||
if nb_truck == 1:
|
||||
return {0: list(range(len(cities)))}
|
||||
|
||||
# clustering initial
|
||||
kmeans = KMeans(n_clusters=nb_truck, random_state=0).fit(cities)
|
||||
clusters = {i:[] for i in range(nb_truck)}
|
||||
|
||||
# assignation des indices des villes aux clusters
|
||||
for i, label in enumerate(kmeans.labels_):
|
||||
clusters[label].append(i)
|
||||
|
||||
max_iterations = len(cities)
|
||||
iteration = 0
|
||||
|
||||
while True:
|
||||
iteration += 1
|
||||
if iteration > max_iterations:
|
||||
print("Le nombre maximum d'itérations a été atteint. La boucle a été interrompue.")
|
||||
break
|
||||
# calcul des tailles de clusters
|
||||
cluster_sizes = {i:len(clusters[i]) for i in range(nb_truck)}
|
||||
|
||||
# identification du cluster le plus grand et du plus petit
|
||||
max_cluster = max(cluster_sizes, key=cluster_sizes.get)
|
||||
min_cluster = min(cluster_sizes, key=cluster_sizes.get)
|
||||
|
||||
# s'il n'y a pas de grande disparité, on arrête la boucle
|
||||
if cluster_sizes[max_cluster] - cluster_sizes[min_cluster] <= 1:
|
||||
break
|
||||
|
||||
# calcul du centre de chaque cluster
|
||||
cluster_centers = {i:np.mean([cities[index] for index in clusters[i]], axis=0) for i in range(nb_truck)}
|
||||
|
||||
# calcul des distances entre le centre du cluster le plus grand et les autres
|
||||
distances = {i:np.linalg.norm(cluster_centers[max_cluster]-cluster_centers[i]) for i in range(nb_truck)}
|
||||
del distances[max_cluster] # on supprime la distance vers lui-même
|
||||
|
||||
if nb_truck >= 3:
|
||||
# on identifie les 2 clusters les plus proches
|
||||
closest_clusters = sorted(distances, key=distances.get)[:2]
|
||||
|
||||
|
||||
# parmi les deux clusters les plus proches, on choisit le plus petit
|
||||
if cluster_sizes[closest_clusters[0]] <= cluster_sizes[closest_clusters[1]]:
|
||||
target_cluster = closest_clusters[0]
|
||||
else:
|
||||
target_cluster = closest_clusters[1]
|
||||
else:
|
||||
closest_clusters = sorted(distances, key=distances.get)[:1]
|
||||
target_cluster = closest_clusters[0]
|
||||
|
||||
|
||||
|
||||
# si le transfert va créer une plus grande disparité, on arrête la boucle
|
||||
if cluster_sizes[target_cluster] >= cluster_sizes[max_cluster]:
|
||||
break
|
||||
|
||||
# calcul des distances entre le centre du cluster cible et les villes du cluster le plus grand
|
||||
distances_to_target = {index:np.linalg.norm(cluster_centers[target_cluster]-cities[index])
|
||||
for index in clusters[max_cluster]}
|
||||
|
||||
# on identifie la ville la plus proche du centre du cluster cible
|
||||
closest_city_index = min(distances_to_target, key=distances_to_target.get)
|
||||
|
||||
# on transfère la ville du cluster le plus grand au cluster cible
|
||||
clusters[target_cluster].append(closest_city_index)
|
||||
clusters[max_cluster].remove(closest_city_index)
|
||||
|
||||
# Ajout du point de départ et d'arrivée pour chaque cluster
|
||||
depot_index = 0
|
||||
for cluster in clusters.values():
|
||||
if cluster[0] != depot_index:
|
||||
cluster.insert(0, depot_index)
|
||||
if cluster[-1] != depot_index:
|
||||
cluster.append(depot_index)
|
||||
|
||||
return clusters
|
||||
@ -1,3 +1 @@
|
||||
[[6734.0, 1453.0], [2233.0, 10.0], [5530.0, 1424.0], [401.0, 841.0], [3082.0, 1644.0], [7608.0, 4458.0], [7573.0, 3716.0], [7265.0, 1268.0], [6898.0, 1885.0], [1112.0, 2049.0], [5468.0, 2606.0], [5989.0, 2873.0], [4706.0, 2674.0], [4612.0, 2035.0], [6347.0, 2683.0], [6107.0, 669.0], [7611.0, 5184.0], [7462.0, 3590.0],
|
||||
[7732.0, 4723.0], [5900.0, 3561.0], [4483.0, 3369.0], [6101.0, 1110.0], [5199.0, 2182.0], [1633.0, 2809.0], [4307.0, 2322.0], [675.0, 1006.0], [7555.0, 4819.0], [7541.0, 3981.0], [3177.0, 756.0], [7352.0, 4506.0], [7545.0, 2801.0], [3245.0, 3305.0], [6426.0, 3173.0], [4608.0, 1198.0], [23.0, 2216.0], [7248.0, 3779.0],
|
||||
[7762.0, 4595.0], [7392.0, 2244.0], [3484.0, 2829.0], [6271.0, 2135.0], [4985.0, 140.0], [1916.0, 1569.0], [7280.0, 4899.0], [7509.0, 3239.0], [10.0, 2676.0], [6807.0, 2993.0], [5185.0, 3258.0], [3023.0, 1942.0]]
|
||||
[['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']]
|
||||
51
tests/libs/aco.py
Normal file
51
tests/libs/aco.py
Normal file
@ -0,0 +1,51 @@
|
||||
import math, random, time, numpy as np
|
||||
|
||||
def distance(city1, city2):
|
||||
return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2) + 1e-10
|
||||
|
||||
def total_distance(cities):
|
||||
return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
|
||||
|
||||
class AntColony:
|
||||
def __init__(self, cities, n_ants, alpha=1, beta=2, evaporation=0.5, intensification=2, max_time=0.1):
|
||||
self.cities = cities
|
||||
self.n = len(cities)
|
||||
self.n_ants = n_ants
|
||||
self.alpha = alpha
|
||||
self.beta = beta
|
||||
self.evaporation = evaporation
|
||||
self.intensification = intensification
|
||||
self.max_time = max_time
|
||||
self.pheromones = [[1 / self.n for _ in range(self.n)] for __ in range(self.n)]
|
||||
|
||||
def choose_next_city(self, ant):
|
||||
unvisited_cities = [i for i in range(self.n) if i not in ant]
|
||||
probabilities = [self.pheromones[ant[-1]][i] ** self.alpha * ((1 / distance(self.cities[ant[-1]], self.cities[i])) ** self.beta) for i in unvisited_cities]
|
||||
total = sum(probabilities)
|
||||
if total == 0:
|
||||
probabilities = [1 / len(unvisited_cities) for _ in unvisited_cities]
|
||||
else:
|
||||
probabilities = [p / total for p in probabilities]
|
||||
return np.random.choice(unvisited_cities, p=probabilities)
|
||||
|
||||
def update_pheromones(self, ant):
|
||||
pheromones_delta = self.intensification / total_distance([self.cities[i] for i in ant])
|
||||
for i in range(len(ant) - 1):
|
||||
self.pheromones[ant[i]][ant[i+1]] += pheromones_delta
|
||||
|
||||
def run(self):
|
||||
best_ant = []
|
||||
best_distance = float('inf')
|
||||
start_time = time.time()
|
||||
while time.time() - start_time < self.max_time:
|
||||
ants = [[random.randint(0, self.n - 1)] for _ in range(self.n_ants)]
|
||||
for ant in ants:
|
||||
for _ in range(self.n - 1):
|
||||
ant.append(self.choose_next_city(ant))
|
||||
ant_distance = total_distance([self.cities[i] for i in ant])
|
||||
if ant_distance < best_distance:
|
||||
best_distance = ant_distance
|
||||
best_ant = ant.copy()
|
||||
self.update_pheromones(ant)
|
||||
self.pheromones = [[(1 - self.evaporation) * p for p in row] for row in self.pheromones]
|
||||
return [self.cities[i] for i in best_ant]
|
||||
81
tests/libs/clustering.py
Normal file
81
tests/libs/clustering.py
Normal file
@ -0,0 +1,81 @@
|
||||
from sklearn.cluster import KMeans
|
||||
import numpy as np
|
||||
|
||||
def split_tour_across_clusters(cities, nb_truck):
|
||||
if nb_truck == 1:
|
||||
return {0: list(range(len(cities)))}
|
||||
|
||||
# clustering initial
|
||||
kmeans = KMeans(n_clusters=nb_truck, random_state=0).fit(cities)
|
||||
clusters = {i:[] for i in range(nb_truck)}
|
||||
|
||||
# assignation des indices des villes aux clusters
|
||||
for i, label in enumerate(kmeans.labels_):
|
||||
clusters[label].append(i)
|
||||
|
||||
max_iterations = len(cities)
|
||||
iteration = 0
|
||||
|
||||
while True:
|
||||
iteration += 1
|
||||
if iteration > max_iterations:
|
||||
print("Le nombre maximum d'itérations a été atteint. La boucle a été interrompue.")
|
||||
break
|
||||
# calcul des tailles de clusters
|
||||
cluster_sizes = {i:len(clusters[i]) for i in range(nb_truck)}
|
||||
|
||||
# identification du cluster le plus grand et du plus petit
|
||||
max_cluster = max(cluster_sizes, key=cluster_sizes.get)
|
||||
min_cluster = min(cluster_sizes, key=cluster_sizes.get)
|
||||
|
||||
# s'il n'y a pas de grande disparité, on arrête la boucle
|
||||
if cluster_sizes[max_cluster] - cluster_sizes[min_cluster] <= 1:
|
||||
break
|
||||
|
||||
# calcul du centre de chaque cluster
|
||||
cluster_centers = {i:np.mean([cities[index] for index in clusters[i]], axis=0) for i in range(nb_truck)}
|
||||
|
||||
# calcul des distances entre le centre du cluster le plus grand et les autres
|
||||
distances = {i:np.linalg.norm(cluster_centers[max_cluster]-cluster_centers[i]) for i in range(nb_truck)}
|
||||
del distances[max_cluster] # on supprime la distance vers lui-même
|
||||
|
||||
if nb_truck >= 3:
|
||||
# on identifie les 2 clusters les plus proches
|
||||
closest_clusters = sorted(distances, key=distances.get)[:2]
|
||||
|
||||
|
||||
# parmi les deux clusters les plus proches, on choisit le plus petit
|
||||
if cluster_sizes[closest_clusters[0]] <= cluster_sizes[closest_clusters[1]]:
|
||||
target_cluster = closest_clusters[0]
|
||||
else:
|
||||
target_cluster = closest_clusters[1]
|
||||
else:
|
||||
closest_clusters = sorted(distances, key=distances.get)[:1]
|
||||
target_cluster = closest_clusters[0]
|
||||
|
||||
|
||||
|
||||
# si le transfert va créer une plus grande disparité, on arrête la boucle
|
||||
if cluster_sizes[target_cluster] >= cluster_sizes[max_cluster]:
|
||||
break
|
||||
|
||||
# calcul des distances entre le centre du cluster cible et les villes du cluster le plus grand
|
||||
distances_to_target = {index:np.linalg.norm(cluster_centers[target_cluster]-cities[index])
|
||||
for index in clusters[max_cluster]}
|
||||
|
||||
# on identifie la ville la plus proche du centre du cluster cible
|
||||
closest_city_index = min(distances_to_target, key=distances_to_target.get)
|
||||
|
||||
# on transfère la ville du cluster le plus grand au cluster cible
|
||||
clusters[target_cluster].append(closest_city_index)
|
||||
clusters[max_cluster].remove(closest_city_index)
|
||||
|
||||
# Ajout du point de départ et d'arrivée pour chaque cluster
|
||||
depot_index = 0
|
||||
for cluster in clusters.values():
|
||||
if cluster[0] != depot_index:
|
||||
cluster.insert(0, depot_index)
|
||||
if cluster[-1] != depot_index:
|
||||
cluster.append(depot_index)
|
||||
|
||||
return clusters
|
||||
33
tests/libs/simulated_annealing.py
Normal file
33
tests/libs/simulated_annealing.py
Normal file
@ -0,0 +1,33 @@
|
||||
import math, random
|
||||
|
||||
def distance(city1, city2):
|
||||
return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)
|
||||
|
||||
def total_distance(cities):
|
||||
return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
|
||||
|
||||
def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001, cluster_index=0):
|
||||
interration = 0
|
||||
current_solution = cities.copy()
|
||||
best_solution = cities.copy()
|
||||
while temperature > temperature_ok:
|
||||
new_solution = current_solution.copy()
|
||||
# Swap two cities in the route
|
||||
i = random.randint(0, len(new_solution) - 1)
|
||||
j = random.randint(0, len(new_solution) - 1)
|
||||
new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
|
||||
# Calculate the acceptance probability
|
||||
current_energy = total_distance(current_solution)
|
||||
new_energy = total_distance(new_solution)
|
||||
delta = new_energy - current_energy
|
||||
if delta < 0 or random.random() < math.exp(-delta / temperature):
|
||||
current_solution = new_solution
|
||||
if total_distance(current_solution) < total_distance(best_solution):
|
||||
best_solution = current_solution
|
||||
# Cool down
|
||||
temperature *= cooling_rate
|
||||
interration += 1
|
||||
# Print every 1000 iterations
|
||||
if interration % 1000 == 0:
|
||||
print("Cluster", cluster_index, ": iteration", interration, "with current total distance", total_distance(current_solution))
|
||||
return best_solution
|
||||
Loading…
x
Reference in New Issue
Block a user