updating notebook

tests/03_cluster_recuit_no_animation_no_random_colling_rate_variation.py

@@ -30,8 +30,6 @@ print("\n---- TIME ----")
 print("generate cities time: ", stop_time_generate - start_time_generate)
 print("split cities time: ", stop_time_split - start_time_split)
 
-# create new figure for annealing paths
-plt.figure()
 colors = [
     '#1f77b4',  # Bleu moyen
     '#ff7f0e',  # Orange

tests/libs/simulated_annealing_stats.py

@@ -0,0 +1,43 @@
+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))])
+
+class SimulatedAnnealing:
+    def __init__(self, cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001):
+        self.cities = cities
+        self.temperature = temperature
+        self.cooling_rate = cooling_rate
+        self.temperature_ok = temperature_ok
+        self.distances = []
+        self.temperatures = []
+
+    def run(self):
+        interration = 0
+        current_solution = self.cities.copy()
+        best_solution = self.cities.copy()
+        while self.temperature > self.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 / self.temperature):
+                current_solution = new_solution
+                if total_distance(current_solution) < total_distance(best_solution):
+                    best_solution = current_solution
+
+            if interration % 10 == 0:
+                self.distances.append(total_distance(current_solution))
+            # Cool down
+            self.temperature *= self.cooling_rate
+            interration += 1
+        return best_solution, self.distances