fixed timing and optimizer speed
This commit is contained in:
		| @@ -7,5 +7,5 @@ tag_exponent: 1.15 | ||||
| image_bonus: 10 | ||||
| viewpoint_bonus: 15 | ||||
| wikipedia_bonus: 6 | ||||
| N_important: 50 | ||||
| N_important: 40 | ||||
| pay_bonus: -1 | ||||
|   | ||||
| @@ -1,5 +1,5 @@ | ||||
| detour_factor: 1.4 | ||||
| detour_corridor_width: 200 | ||||
| detour_corridor_width: 300 | ||||
| average_walking_speed: 4.8 | ||||
| max_landmarks: 10 | ||||
| max_landmarks_refiner: 20 | ||||
|   | ||||
| @@ -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=1000, | ||||
|         detour_tolerance_minute=0 | ||||
|         max_time_minute=120, | ||||
|         detour_tolerance_minute=10 | ||||
|     ) | ||||
|  | ||||
|     # Create start and finish  | ||||
| @@ -70,9 +70,9 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = | ||||
|     return linked_tour | ||||
|  | ||||
|  | ||||
| test(tuple((48.8344400, 2.3220540)))       # Café Chez César  | ||||
| # 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((45.7576485, 4.8330241)))      # Lyon Bellecour | ||||
| 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((48.2067858, 16.3692340)))      # Vienne | ||||
|   | ||||
| @@ -318,6 +318,7 @@ class LandmarkManager: | ||||
|  | ||||
|                     if "viewpoint" in tag: | ||||
|                         score += self.viewpoint_bonus | ||||
|                         duration = 10 | ||||
|  | ||||
|                     if "image" in tag: | ||||
|                         score += self.image_bonus | ||||
| @@ -334,7 +335,6 @@ class LandmarkManager: | ||||
|                         if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']: | ||||
|                             skip = True | ||||
|                             break | ||||
|  | ||||
|                      | ||||
|                     # Get additional information | ||||
|                     # if tag == 'wikipedia' : | ||||
| @@ -352,14 +352,14 @@ class LandmarkManager: | ||||
|                 score = score_function(score) | ||||
|                 if "place_of_worship" in elem.tags().values() : | ||||
|                     score = int(score*self.church_coeff) | ||||
|                     duration = 20 | ||||
|                     duration = 15 | ||||
|                  | ||||
|                 elif "museum" in elem.tags().values() : | ||||
|                     score = int(score*self.church_coeff) | ||||
|                     duration = 60 | ||||
|                  | ||||
|                 else :  | ||||
|                     duration = 30 | ||||
|                     duration = 5 | ||||
|  | ||||
|                 # Generate the landmark and append it to the list | ||||
|                 landmark = Landmark( | ||||
|   | ||||
| @@ -193,7 +193,7 @@ 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)[:22] | ||||
|             closest = sorted(dist_table)[:20] | ||||
|             for i, dist in enumerate(dist_table) : | ||||
|                 if dist not in closest : | ||||
|                     dist_table[i] = 32700 | ||||
|   | ||||
		Reference in New Issue
	
	Block a user