better timing

backend/src/parameters/optimizer_parameters.yaml

@@ -2,5 +2,5 @@ detour_factor: 1.4
 detour_corridor_width: 300
 average_walking_speed: 4.8
 max_landmarks: 10
-max_landmarks_refiner: 20
-overshoot: 1.4
+max_landmarks_refiner: 30
+overshoot: 1.8

backend/src/tester.py

@@ -24,8 +24,8 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
         nature=Preference(type='nature', score = 5),
         shopping=Preference(type='shopping', score = 5),
 
-        max_time_minute=180,
-        detour_tolerance_minute=10
+        max_time_minute=100,
+        detour_tolerance_minute=0
     )
 
     # Create start and finish 
@@ -63,7 +63,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
     logger.info("Optimized route : ")
     for l in linked_tour :
         logger.info(f"{l}")
-    logger.info(f"Estimated length of tour : {linked_tour.total_time}")
+    logger.info(f"Estimated length of tour : {linked_tour.total_time} mintutes and visiting {len(linked_tour._landmarks)} landmarks.")
 
     # with open('linked_tour.yaml', 'w') as f:
     #     yaml.dump(linked_tour.asdict(), f)
@@ -74,6 +74,6 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
 # test(tuple((48.8344400, 2.3220540)))       # Café Chez César 
 # test(tuple((48.8375946, 2.2949904)))       # Point random
 # test(tuple((47.377859, 8.540585)))         # Zurich HB
-test(tuple((45.758217, 4.831814)))      # Lyon Bellecour
-# test(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
+# test(tuple((45.758217, 4.831814)))      # Lyon Bellecour
+test(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
 # test(tuple((48.2067858, 16.3692340)))      # Vienne

backend/src/utils/optimizer.py

@@ -195,7 +195,7 @@ class Optimizer:
             for j, spot2 in enumerate(landmarks) :
                 t = get_time(spot1.location, spot2.location) + spot1.duration
                 dist_table[j] = t
-            closest = sorted(dist_table)[:20]
+            closest = sorted(dist_table)[:25]
             for i, dist in enumerate(dist_table) :
                 if dist not in closest :
                     dist_table[i] = 32700

backend/src/utils/refiner.py

@@ -214,7 +214,7 @@ class Refiner :
             if self.is_in_area(area, landmark.location) and landmark.name not in visited_names:
                 second_order_landmarks.append(landmark)
 
-        return take_most_important.take_most_important(second_order_landmarks, len(visited_landmarks))
+        return take_most_important.take_most_important(second_order_landmarks, int(self.max_landmarks_refiner*0.75))
 
 
     # Try fix the shortest path using shapely