added some ideas

backend/src/amenities/sightseeing.am

@@ -6,3 +6,5 @@ historic
 'amenity'='place_of_worship'
 'amenity'='fountain'
 'water'='reflecting_pool'
+# 'tourism'='attraction' might be a bit too broad
+# historic as well

backend/src/landmarks_manager.py

@@ -43,31 +43,6 @@ def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float
     return L, take_most_important(L)
 
 
-"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
-
-    l_sights, l_nature, l_shop = get_amenities()
-    L = []
-
-    # List for sightseeing
-    if preferences.sightseeing.score != 0 :
-        L1 = get_landmarks(l_sights, SIGHTSEEING, city_country=city_country, coordinates=coordinates)
-        correct_score(L1, preferences.sightseeing)
-        L += L1
-    
-    # List for nature
-    if preferences.nature.score != 0 :
-        L2 = get_landmarks(l_nature, NATURE, city_country=city_country, coordinates=coordinates)
-        correct_score(L2, preferences.nature)
-        L += L2
-    
-    # List for shopping
-    if preferences.shopping.score != 0 :
-        L3 = get_landmarks(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
-        correct_score(L3, preferences.shopping)
-        L += L3
-
-    return remove_duplicates(L), take_most_important(L)
-"""
 # Helper function to gather the amenities list
 def get_amenities() -> List[List[str]] :
     
@@ -87,6 +62,7 @@ def get_list(path: str) -> List[str] :
 
         amenities = []
         for line in content :
+            if not line.startswith('#') :
                 amenities.append(line.strip('\n'))
 
     return amenities
@@ -173,7 +149,7 @@ def correct_score(L: List[Landmark], preference: Preference) :
         raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
 
     for elem in L :
-        elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
+        elem.attractiveness = int(elem.attractiveness*preference.score/5)     # arbitrary computation
 
 
 # Function to count elements within a certain radius of a location
@@ -192,10 +168,14 @@ def count_elements_within_radius(coordinates: Tuple[float, float], radius: int)
     try : 
         overpass = Overpass()
         radius_result = overpass.query(radius_query)
-        return radius_result.countElements()
+        N_elem = radius_result.countWays() + radius_result.countRelations()
+        #print(f"There are {N_elem} ways/relations within 50m")
+        if N_elem is None :
+            return 0
+        return N_elem
     
     except :
-        return None
+        return 0
 
 
 # Creates a bounding box around given coordinates
@@ -267,99 +247,39 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: T
                 elem_type = landmarktype            # Add the landmark type as 'sightseeing
                 n_tags = len(elem.tags().keys())    # Add number of tags
 
+                # remove specific tags
+                skip = False
+                for tag in elem.tags().keys() :
+                    if "pay" in tag :
+                        n_tags -= 1             # discard payment options for tags
+                    if "disused" in tag :
+                        skip = True
+                        break
+                    if amenity not in ["'shop'='department_store'", "'shop'='mall'"] :
+                        if "shop" in tag :
+                            skip = True
+                            break
+                        if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
+                            skip = True
+                            break
+
+                if skip:
+                    continue
+
                 # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
                 if amenity == "'amenity'='place_of_worship'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                    score = int((count_elements_within_radius(location, radius) + (n_tags*tag_coeff) )*church_coeff)  
                 elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                    score = int((count_elements_within_radius(location, radius) + (n_tags*tag_coeff) )*park_coeff)  
                 else :
-                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+                    score = count_elements_within_radius(location, radius) + (n_tags*tag_coeff)
 
                 if score is not None :
                     # Generate the landmark and append it to the list
+                    #print(f"There are {n_tags} tags on this Landmark. Total score : {score}\n")
                     landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
                     L.append(landmark)
 
     return L
 
 
-
-"""def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
-    
-    if city_country is None and coordinates is None :
-        raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
-
-    if city_country is not None and coordinates is not None :
-        raise ValueError("Cannot specify both 'city_country' and 'coordinates' at the same time, please choose either one")
-
-    # Read the parameters from the file
-    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
-        parameters = json.loads(f.read())
-        tag_coeff = parameters['tag coeff']
-        park_coeff = parameters['park coeff']
-        church_coeff = parameters['church coeff']
-        radius = parameters['radius close to']
-        bbox_side = parameters['city bbox side']
-    
-    # If city_country is specified :
-    if city_country is not None :
-        nominatim = Nominatim()
-        areaId = nominatim.query(city_country).areaId()
-        bbox = None
-
-    # If coordinates are specified :
-    elif coordinates is not None :
-        bbox = create_bbox(coordinates, bbox_side)
-        areaId = None
-
-    else :
-        raise ValueError("Argument number is not corresponding.")
-    
-    # Initialize some variables
-    N = 0
-    L = []
-    overpass = Overpass()
-
-    for amenity in list_amenity :
-        query = overpassQueryBuilder(area=areaId, bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
-        result = overpass.query(query)
-        N += result.countElements()
-
-        for elem in result.elements():
-
-            name = elem.tag('name')                             # Add name
-            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
-
-            # skip if unprecise location
-            if name is None or location[0] is None:
-                continue
-
-            # skip if unused
-            if 'disused:leisure' in elem.tags().keys():
-                continue
-            
-            # skip if part of another building
-            if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
-                continue
-            
-            else :
-                osm_type = elem.type()              # Add type : 'way' or 'relation'
-                osm_id = elem.id()                  # Add OSM id 
-                elem_type = landmarktype            # Add the landmark type as 'sightseeing
-                n_tags = len(elem.tags().keys())    # Add number of tags
-                
-                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
-                if amenity == "'amenity'='place_of_worship'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
-                elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
-                else :
-                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
-
-                if score is not None :
-                    # Generate the landmark and append it to the list
-                    landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
-                    L.append(landmark)
-
-    return L
-"""

backend/src/main_example.py

@@ -1,23 +0,0 @@
-import fastapi
-from dataclasses import dataclass
-
-
-@dataclass
-class Destination:
-    name: str
-    location: tuple
-    attractiveness: int
-
-
-
-d = Destination()
-
-
-
-def get_route() -> list[Destination]:
-    return {"route": "Hello World"}
-
-endpoint = ("/get_route", get_route)
-end
-if __name__ == "__main__":
-    fastapi.run()

backend/src/optimizer_v2.py

@@ -5,23 +5,60 @@ from scipy.spatial import KDTree
 import numpy as np
 from itertools import combinations
 from structs.landmarks import Landmark
-from optimizer import print_res, link_list_simple
+from optimizer_v4 import print_res, link_list_simple, get_time
 import os
 import json
 import heapq
 
-
-# Define the get_distance function
-def get_distance(loc1: Tuple[float, float], loc2: Tuple[float, float], detour: float, speed: float) -> Tuple[float, float]:
-    # Placeholder implementation, should be replaced with the actual logic
-    distance = geodesic(loc1, loc2).meters
-    return distance, distance * detour / speed
-
 # Heuristic function: distance to the goal
-def heuristic(loc1: Tuple[float, float], loc2: Tuple[float, float]) -> float:
+def heuristic(loc1: Tuple[float, float], loc2: Tuple[float, float], score2: int) -> float:
   return geodesic(loc1, loc2).meters
 
 
+
+# A* planner to search through the graph
+def a_star2(G, start_id, end_id, max_walking_time, must_do_nodes, max_landmarks, detour, speed):
+  open_set = []
+  heapq.heappush(open_set, (0, start_id, 0, [start_id], set([start_id])))
+  best_path = None
+  max_attractiveness = 0
+  visited_must_do = set()
+
+  while open_set:
+    _, current_node, current_length, path, visited = heapq.heappop(open_set)
+
+    # If current node is a must_do node and hasn't been visited yet, mark it as visited
+    if current_node in must_do_nodes and current_node not in visited_must_do:
+      visited_must_do.add(current_node)
+
+    # Check if path includes all must_do nodes and reaches the end
+    if current_node == end_id and all(node in visited for node in must_do_nodes):
+      attractiveness = sum(G.nodes[node]['weight'] for node in path)
+      if attractiveness > max_attractiveness:
+        best_path = path
+        max_attractiveness = attractiveness
+      continue
+
+    if len(path) > max_landmarks + 1:
+      continue
+
+    for neighbor in G.neighbors(current_node):
+      if neighbor not in visited:
+        #distance = int(geodesic(G.nodes[current_node]['pos'], G.nodes[neighbor]['pos']).meters * detour / (speed * 16.6666))
+        distance = get_time(G.nodes[current_node]['pos'], G.nodes[neighbor]['pos'], detour, speed)
+        if current_length + distance <= max_walking_time:
+          new_path = path + [neighbor]
+          new_visited = visited | {neighbor}
+          estimated_cost = current_length + distance + get_time(G.nodes[neighbor]['pos'], G.nodes[end_id]['pos'], detour, speed)
+          heapq.heappush(open_set, (estimated_cost, neighbor, current_length + distance, new_path, new_visited))
+
+  # Check if all must_do_nodes have been visited
+  if all(node in visited_must_do for node in must_do_nodes):
+      return best_path, max_attractiveness
+  else:
+      return None, 0
+
+
 def a_star(G, start_id, end_id, max_walking_time, must_do_nodes, max_landmarks, detour, speed):
     open_set = []
     heapq.heappush(open_set, (0, start_id, 0, [start_id], set([start_id])))
@@ -49,11 +86,11 @@ def a_star(G, start_id, end_id, max_walking_time, must_do_nodes, max_landmarks,
 
         for neighbor in G.neighbors(current_node):
             if neighbor not in visited:
-        distance = int(geodesic(G.nodes[current_node]['pos'], G.nodes[neighbor]['pos']).meters * detour / (speed * 16.6666))
+                distance = get_time(G.nodes[current_node]['pos'], G.nodes[neighbor]['pos'], detour, speed)
                 if current_length + distance <= max_walking_time:
                     new_path = path + [neighbor]
                     new_visited = visited | {neighbor}
-          estimated_cost = current_length + distance + heuristic(G.nodes[neighbor]['pos'], G.nodes[end_id]['pos'])
+                    estimated_cost = current_length + distance + heuristic(G.nodes[neighbor]['pos'], G.nodes[end_id]['pos'], G.nodes[neighbor]['weight'])
                     heapq.heappush(open_set, (estimated_cost, neighbor, current_length + distance, new_path, new_visited))
 
     # Check if all must_do_nodes have been visited
@@ -64,31 +101,6 @@ def a_star(G, start_id, end_id, max_walking_time, must_do_nodes, max_landmarks,
     
 
 
-def dfs(G, current_node, end_id, current_length, path, visited, max_walking_time, must_do_nodes, max_landmarks, detour, speed):
-  # If the path includes all must_do nodes and reaches the end
-  if current_node == end_id and all(node in path for node in must_do_nodes):
-    return path, sum(G.nodes[node]['weight'] for node in path)
-
-  # If the number of landmarks exceeds the maximum allowed, return None
-  if len(path) > max_landmarks+1:
-    return None, 0
-
-  best_path = None
-  max_attractiveness = 0
-
-  for neighbor in G.neighbors(current_node):
-    if neighbor not in visited:
-      distance = int(geodesic(G.nodes[current_node]['pos'], G.nodes[neighbor]['pos']).meters * detour / (speed*16.6666))
-      if current_length + distance <= max_walking_time:
-        new_path = path + [neighbor]
-        new_visited = visited | {neighbor}
-        result_path, attractiveness = dfs(G, neighbor, end_id, current_length + distance, new_path, new_visited, max_walking_time, must_do_nodes, max_landmarks, detour, speed)
-        if attractiveness > max_attractiveness:
-          best_path = result_path
-          max_attractiveness = attractiveness
-  
-  return best_path, max_attractiveness
-
 def find_path(G, start_id, finish_id, max_walking_time, must_do_nodes, max_landmarks) -> List[str]:
     
   # Read the parameters from the file
@@ -97,15 +109,12 @@ def find_path(G, start_id, finish_id, max_walking_time, must_do_nodes, max_landm
     detour = parameters['detour factor']
     speed = parameters['average walking speed']
 
-
-  """if G[start_id]['pos'] == G[finish_id]['pos'] :
-    best_path, _ = dfs(G, start_id, finish_id, 0, [start_id], {start_id}, max_walking_time, must_do_nodes, max_landmarks, detour, speed)
-  else :"""
   best_path, _ = a_star(G, start_id, finish_id, max_walking_time, must_do_nodes, max_landmarks, detour, speed)
   
   return best_path if best_path else []
 
 
+
 # Function to dynamically adjust theta
 def adjust_theta(num_nodes, theta_opt, target_ratio=2.0):
   # Start with an initial guess
@@ -114,62 +123,8 @@ def adjust_theta(num_nodes, theta_opt, target_ratio=2.0):
   return initial_theta / (num_nodes ** (1 / target_ratio))
 
 
-# Create a graph using NetworkX and generate the path
+# Create a graph using NetworkX and generate the path without must_do
-def generate_path(landmarks: List[Landmark], max_walking_time: float, max_landmarks: int, theta_opt = 0.0008) -> List[List[Landmark]]:
+def generate_path(landmarks: List[Landmark], max_walking_time: float, max_landmarks: int) -> List[List[Landmark]]:
-    
-  landmap = {}
-  pos_dict = {}
-  weight_dict = {}
-  # Add nodes to the graph with attractiveness
-  for i, landmark in enumerate(landmarks):
-    #G.nodes[i]['attractiveness'] = landmark.attractiveness
-    pos_dict[i] = landmark.location
-    weight_dict[i] = landmark.attractiveness
-    #G.nodes[i]['pos'] = landmark.location
-    landmap[i] = landmark
-    if landmark.name == 'start' :
-      start_id = i
-    elif landmark.name == 'finish' :
-      end_id = i
-
-  # Lambda version of get_distance 
-  get_dist = lambda loc1, loc2: geodesic(loc1, loc2).meters + 0.001  #.meters*detour/speed +0.0000001
-
-  theta = adjust_theta(len(landmarks), theta_opt)
-  G = nx.geographical_threshold_graph(n=len(landmarks), theta=theta, pos=pos_dict, weight=weight_dict, metric=get_dist)
-
-  # good theta : 0.000125
-  # Define must_do nodes
-  must_do_nodes = [i for i in G.nodes() if landmap[i].must_do]
-
-  for node1, node2 in combinations(must_do_nodes, 2):
-    if not G.has_edge(node1, node2):
-      distance = geodesic(G.nodes[node1]['pos'], G.nodes[node2]['pos']).meters + 0.001
-      G.add_edge(node1, node2, weight=distance)
-  
-  print(f"Graph with {G.number_of_nodes()} nodes")
-  print(f"Graph with {G.number_of_edges()} edges")
-  print("Computing path...")
-
-  # Find the valid path using the greedy algorithm
-  valid_path = find_path(G, start_id, end_id, max_walking_time, must_do_nodes, max_landmarks)
-
-  if not valid_path:
-    return []  # No valid path found
-
-  lis = [landmap[id] for id in valid_path]
-
-  lis, tot_dist = link_list_simple(lis)
-
-  print_res(lis, len(landmarks))
-
-
-  return lis
-
-
-# Create a graph using NetworkX and generate the path
-def generate_path2(landmarks: List[Landmark], max_walking_time: float, max_landmarks: int) -> List[List[Landmark]]:
-  
 
   # Read the parameters from the file
   with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
@@ -177,11 +132,11 @@ def generate_path2(landmarks: List[Landmark], max_walking_time: float, max_landm
     detour = parameters['detour factor']
     speed = parameters['average walking speed']
 
-
   landmap = {}
   pos_dict = {}
   weight_dict = {}
   G = nx.Graph()
+
   # Add nodes to the graph with attractiveness
   for i, landmark in enumerate(landmarks):
     pos_dict[i] = landmark.location
@@ -193,40 +148,89 @@ def generate_path2(landmarks: List[Landmark], max_walking_time: float, max_landm
     elif landmark.name == 'finish' :
       finish_id = i
 
-  """# If start and finish are the same no need to add another node
-  if pos_dict[finish_id] == pos_dict[start_id] :
-    end_id = start_id
-  else :
-    G.add_node(finish_id, pos=pos_dict[finish_id], weight=weight_dict[finish_id])
-    end_id = finish_id"""
-
-  # Lambda version of get_distance 
-  #get_dist = lambda loc1, loc2: geodesic(loc1, loc2).meters + 0.001  #.meters*detour/speed +0.0000001
-
   coords = np.array(list(pos_dict.values()))
   kdtree = KDTree(coords)
 
-  k = 4
+  k = 5
+  if len(landmarks) <= k :
+     k = len(landmarks)-1
+  
   for node, coord in pos_dict.items():
     indices = kdtree.query(coord, k + 1)[1]   # k+1 because the closest neighbor is the node itself
     for idx in indices[1:]:                   # skip the first one (itself)
       neighbor = list(pos_dict.keys())[idx]
-      distance = get_distance(coord, pos_dict[neighbor], detour, speed)
+      distance = get_time(coord, pos_dict[neighbor], detour, speed)
+      G.add_edge(node, neighbor, weight=distance)
+  
+  print(f"Graph with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges")
+  print("Start computing path...")
+
+  # Find the valid path using the greedy algorithm
+  valid_path = find_path(G, start_id, finish_id, max_walking_time, [], max_landmarks)
+
+  if not valid_path:
+    return []  # No valid path found
+
+  lis = [landmap[id] for id in valid_path]
+
+  lis, _ = link_list_simple(lis)
+
+  print_res(lis, len(landmarks))
+
+
+  return lis
+
+
+
+# Create a graph using NetworkX and generate the path
+def generate_path2(landmarks: List[Landmark], max_walking_time: float, max_landmarks: int) -> List[List[Landmark]]:
+
+  # Read the parameters from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour = parameters['detour factor']
+    speed = parameters['average walking speed']
+
+
+  landmap = {}
+  pos_dict = {}
+  weight_dict = {}
+  must_do_nodes = []
+  G = nx.Graph()
+  # Add nodes to the graph with attractiveness
+  for i, landmark in enumerate(landmarks):
+    pos_dict[i] = landmark.location
+    weight_dict[i] = landmark.attractiveness
+    landmap[i] = landmark
+    if landmark.must_do :
+       must_do_nodes.append(i)
+    G.add_node(i, pos=landmark.location, weight=landmark.attractiveness)
+    if landmark.name == 'start' :
+      start_id = i
+    elif landmark.name == 'finish' :
+      finish_id = i
+
+  coords = np.array(list(pos_dict.values()))
+  kdtree = KDTree(coords)
+
+  k = 3
+  for node, coord in pos_dict.items():
+    indices = kdtree.query(coord, k + 1)[1]   # k+1 because the closest neighbor is the node itself
+    for idx in indices[1:]:                   # skip the first one (itself)
+      neighbor = list(pos_dict.keys())[idx]
+      distance = get_time(coord, pos_dict[neighbor], detour, speed)
       G.add_edge(node, neighbor, weight=distance)
 
 
-  # Define must_do nodes
-  must_do_nodes = [i for i in G.nodes() if landmap[i].must_do]
 
   # Add special edges between must_do nodes
   if len(must_do_nodes) > 0 :
     for node1, node2 in combinations(must_do_nodes, 2):
       if not G.has_edge(node1, node2):
-        distance = get_distance(G.nodes[node1]['pos'], G.nodes[node2]['pos'], detour, speed)
+        distance = get_time(G.nodes[node1]['pos'], G.nodes[node2]['pos'], detour, speed)
         G.add_edge(node1, node2, weight=distance)
   
-  print(f"Graph with {G.number_of_nodes()} nodes")
+  print(f"Graph with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges")
-  print(f"Graph with {G.number_of_edges()} edges")
   print("Computing path...")
 
   # Find the valid path using the greedy algorithm
@@ -249,12 +253,38 @@ def generate_path2(landmarks: List[Landmark], max_walking_time: float, max_landm
 
 def correct_path(tour: List[Landmark]) -> List[Landmark] :
   
+  # Read the parameters from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour = parameters['detour factor']
+    speed = parameters['average walking speed']
+
+  G = nx.Graph()
+
   coords = []
-  for landmark in tour :
+  landmap = {}
+  for i, landmark in enumerate(tour) :
     coords.append(landmark.location)
+    landmap[i] = landmark
+    G.add_node(i, pos=landmark.location, weight=landmark.attractiveness)
 
-  G = nx.circulant_graph(n=len(tour), create_using=coords)
+  kdtree = KDTree(coords)
 
-  path = nx.shortest_path(G=G, source=tour[0].location, target=tour[-1].location)
+  k = 3
+  for node, coord in coords:
+    indices = kdtree.query(coord, k + 1)[1]   # k+1 because the closest neighbor is the node itself
+    for idx in indices[1:]:                   # skip the first one (itself)
+      neighbor = list(coords)[idx]
+      distance = get_time(coord, coords[neighbor], detour, speed)
+      G.add_edge(node, neighbor, weight=distance)
+
+  path = nx.approximation.traveling_salesman_problem(G, weight='weight', cycle=True)
+
+  lis = [landmap[id] for id in path]
+
+  lis, tot_dist = link_list_simple(lis)
+
+  print_res(lis, len(tour))
 
   return path
+

backend/src/optimizer_v3.py

@@ -0,0 +1,326 @@
+import numpy as np
+import json, os
+
+from typing import List, Tuple
+from itertools import combinations
+from scipy.optimize import linprog
+from math import radians, sin, cos, acos
+from shapely import Polygon
+from geopy.distance import geodesic
+
+
+from structs.landmarks import Landmark
+
+
+# Function to print the result
+def print_res(L: List[Landmark], L_tot):
+
+    if len(L) == L_tot: 
+        print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
+    else :
+        print('Could not visit all the landmarks, the following order is suggested : ')
+
+    dist = 0
+    for elem in L : 
+        if elem.time_to_reach_next is not None :
+            print('- ' + elem.name + ', time to reach next = ' + str(elem.time_to_reach_next))
+            dist += elem.time_to_reach_next
+        else : 
+            print('- ' + elem.name)
+
+    print("\nMinutes walked : " + str(dist))
+    print(f"Visited {len(L)-2} out of {L_tot-2} landmarks")
+
+
+# Function that returns the distance in meters from one location to another
+def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
+    
+    # Compute the straight-line distance in m
+    if p1 == p2 :
+        return 0
+    else: 
+        #dist = 1000 * 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
+        dist = geodesic(p1, p2).meters
+
+    # Consider the detour factor for average city to determine walking distance (in m)
+    walk_dist = dist*detour
+
+    # Time to walk this distance (in minutes)
+    walk_time = walk_dist/speed*(60/1000)
+
+    """if walk_time > 15 :
+        walk_time = 5*round(walk_time/5)
+    else :
+        walk_time = round(walk_time)"""
+  
+
+    return round(walk_time)
+
+
+# Checks if the path is connected, returns a circle if it finds one and the RESULT
+def is_connected(resx) -> bool:
+    
+    
+    N = len(resx)               # length of res
+    L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
+    resx = resx[:L*L]
+
+    # first round the results to have only 0-1 values
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    n_edges = resx.sum()        # number of edges
+
+    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
+
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    ind_b = nonzero_tup[1].tolist()
+
+    edges = []
+    edges_visited = []
+    vertices_visited = []
+
+    edge1 = (ind_a[0], ind_b[0])
+    edges_visited.append(edge1)
+    vertices_visited.append(edge1[0])
+
+    for i, a in enumerate(ind_a) :
+        edges.append((a, ind_b[i]))      # Create the list of edges
+
+    remaining = edges
+    remaining.remove(edge1)
+
+    break_flag = False
+    while len(remaining) > 0 and not break_flag:
+        for edge2 in remaining :
+            if edge2[0] == edge1[1] :
+                if edge1[1] in vertices_visited :
+                    edges_visited.append(edge2)
+                    break_flag = True
+                    break
+                else :    
+                    vertices_visited.append(edge1[1])
+                    edges_visited.append(edge2)
+                    remaining.remove(edge2)
+                    edge1 = edge2
+
+            elif edge1[1] == L-1 or edge1[1] in vertices_visited:
+                        break_flag = True
+                        break
+
+    vertices_visited.append(edge1[1])
+
+
+    if len(vertices_visited) == n_edges +1 :
+      return vertices_visited, []
+    else: 
+      return vertices_visited, edges_visited
+
+
+# Computes the time to reach from each landmark to the next
+def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+    
+  # Read the parameters from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour_factor = parameters['detour factor']
+    speed = parameters['average walking speed']
+  
+  L =  []
+  j = 0
+  total_dist = 0
+  while j < len(order)-1 :
+    elem = landmarks[order[j]]
+    next = landmarks[order[j+1]]
+
+    d = get_time(elem.location, next.location, detour_factor, speed)
+    elem.time_to_reach_next = d
+    if elem.name not in ['start', 'finish'] :
+      elem.must_do = True
+    L.append(elem)
+    j += 1
+    total_dist += d
+
+  L.append(next)
+
+  return L, total_dist
+
+
+
+# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
+def respect_number(L:int, A_ub, b_ub):
+
+  ones = [1]*L
+  zeros = [0]*L
+  for i in range(L) :
+    h = zeros*i + ones + zeros*(L-1-i) + [0]*(L-1)
+    A_ub.append(h)
+    b_ub.append(1)
+
+  # Read the parameters from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    max_landmarks = parameters['max landmarks']
+
+  A_ub.append(ones*L + [0]*(L-1))
+  b_ub.append(max_landmarks+1)
+
+  return A_ub, b_ub
+
+
+
+def solve_optimizationv3(landmarks, max_walking_time):
+  L = len(landmarks)
+
+  # Read the parameters from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour = parameters['detour factor']
+    speed = parameters['average walking speed']
+
+  # Create distance matrix
+  A = np.zeros((L, L))
+  for i in range(L):
+    for j in range(L):
+      if i != j:
+        A[i, j] = get_time(landmarks[i].location, landmarks[j].location, detour, speed)
+
+  # Define the linear program
+  c = np.hstack((A.flatten(), [0]*(L-1)))
+  bounds = [(0, 1) for _ in range(L*L+L-1)]
+
+  # Flow conservation constraints
+  A_eq = []
+  b_eq = []
+
+  # Each node (except start and end) has one incoming and one outgoing edge
+  for i in range(L):
+    if i == 0 or i == L-1:
+      continue
+    A_eq.append([1 if j // L == i else 0 for j in range(L*L)] + [0]*(L-1))
+    b_eq.append(1)
+    A_eq.append([1 if j % L == i else 0 for j in range(L*L)] + [0]*(L-1))
+    b_eq.append(1)
+
+  # Start node constraint
+  A_eq.append([1 if j // L == 0 else 0 for j in range(L*L)] + [0]*(L-1))
+  b_eq.append(1)
+
+  # End node constraint
+  A_eq.append([1 if j % L == L-1 else 0 for j in range(L*L)] + [0]*(L-1))
+  b_eq.append(1)
+
+  # Subtour elimination constraints
+  A_ub = []
+  b_ub = []
+
+  # u_i - u_j + L*x_ij <= L-1 for all i != j
+  for i in range(1, L):
+    for j in range(1, L):
+      if i != j:
+        constraint = [0] * (L * L + L - 1)
+        constraint[i * L + j] = L
+        constraint[j * L + i] = -L
+        A_ub.append(constraint)
+        b_ub.append(L - 1)
+
+  
+  A_ub, b_ub = respect_number(L, A_ub, b_ub)                      # Respect max number of visits (no more possible stops than landmarks). 
+
+  # Convert constraints to numpy arrays
+  A_eq = np.array(A_eq)
+  A_ub = np.array(A_ub)
+  b_ub = np.array(b_ub)
+  b_eq = np.array(b_eq)
+
+  # Solve the linear program
+  result = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq, bounds=bounds, method='highs')
+
+  if result.success:
+    x = result.x[:L*L].reshape((L, L))
+    path = []
+    for i in range(L):
+      for j in range(L):
+        if x[i, j] > 0.5:
+          path.append((i, j))
+          print(f"({i}, {j})")
+
+    order, _ = is_connected(result.x)
+    L, _ = link_list(order, landmarks)
+    
+    print_res(L, len(landmarks))
+    print("\nTotal score : " + str(int(-result.fun)))
+
+    return L
+  else:
+    print("no results")
+    return []
+
+
+
+# Main optimization pipeline
+# def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
+
+#     L = len(landmarks)
+
+#     # SET CONSTRAINTS FOR INEQUALITY
+#     #c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
+#     A_ub, b_ub = respect_number(L, A_ub, b_ub)                      # Respect max number of visits (no more possible stops than landmarks). 
+#     #A_ub, b_ub = break_sym(L, A_ub, b_ub)                           # break the 'zig-zag' symmetry
+#     #A_ub, b_ub = prevent_subtours(L, A_ub, b_ub)
+
+#     # SET CONSTRAINTS FOR EQUALITY
+#     #A_eq, b_eq = init_eq_not_stay(L)                                # Force solution not to stay in same place
+#     #A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq)     # Check if there are user_defined must_see. Also takes care of start/goal
+#     #A_eq, b_eq = respect_start_finish(L, A_eq, b_eq)                # Force start and finish positions
+#     #A_eq, b_eq = respect_order(L, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
+
+#     # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
+#     x_bounds = [(0, 1)]*(L*L + L)
+
+#     # Solve linear programming problem
+#     res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+
+#     # Raise error if no solution is found
+#     if not res.success :
+#             raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
+
+#     # If there is a solution, we're good to go, just check for connectiveness
+#     else :
+#         order, circle = is_connected(res.x)
+#         i = 0
+#         timeout = 80
+#         """while len(circle) != 0 and i < timeout:
+#             A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
+#             #A_ub, b_ub = break_cricle(order, len(landmarks), A_ub, b_ub)
+#             res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+#             if not res.success :
+#                 raise ArithmeticError(f"No solution found after {timeout} iterations.")
+
+#             order, circle = is_connected(res.x)
+#             if len(circle) == 0 :
+#                 break
+#             print(i)
+#             i += 1
+        
+#         if i == timeout :
+#             raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
+#         """
+#         # Add the times to reach and stop optimizing
+#         L, total_dist = link_list(order, landmarks)
+            
+#         if printing_details is True :
+#             if i != 0 :
+#                 print(f"Neded to recompute paths {i} times because of unconnected loops...")
+#             print_res(L, len(landmarks))
+#             print("\nTotal score : " + str(int(-res.fun)))
+
+#         return L
+
+
+
+
+
+

backend/src/optimizer_v4.py

@@ -0,0 +1,422 @@
+import numpy as np
+import json, os
+
+from typing import List, Tuple
+from scipy.optimize import linprog
+from math import radians, sin, cos, acos
+from geopy.distance import geodesic
+from shapely import Polygon
+
+from structs.landmarks import Landmark
+
+    
+# Function to print the result
+def print_res(L: List[Landmark], L_tot):
+
+    if len(L) == L_tot: 
+        print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
+    else :
+        print('Could not visit all the landmarks, the following order is suggested : ')
+
+    dist = 0
+    for elem in L : 
+        if elem.time_to_reach_next is not None :
+            print('- ' + elem.name + ', time to reach next = ' + str(elem.time_to_reach_next))
+            dist += elem.time_to_reach_next
+        else : 
+            print('- ' + elem.name)
+
+    print("\nMinutes walked : " + str(dist))
+    print(f"Visited {len(L)-2} out of {L_tot-2} landmarks")
+
+
+# Prevent the use of a particular solution
+def prevent_config(resx, A_ub, b_ub) -> bool:
+    
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    N = len(resx)               # Number of edges
+    L = int(np.sqrt(N))         # Number of landmarks
+
+    nonzeroind = np.nonzero(resx)[0]                    # the return is a little funky so I use the [0]
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    vertices_visited = ind_a
+    vertices_visited.remove(0)
+
+    ones = [1]*L
+    h = [0]*N
+    for i in range(L) :
+        if i in vertices_visited :
+            h[i*L:i*L+L] = ones
+
+    A_ub = np.vstack((A_ub, h))
+    b_ub.append(len(vertices_visited)-1)
+
+    return A_ub, b_ub
+
+
+# Prevent the possibility of a given solution bit
+def break_cricle(circle_vertices: list, L: int, A_ub: list, b_ub: list) -> bool:
+
+    if L-1 in circle_vertices :
+        circle_vertices.remove(L-1)
+
+    h = [0]*L*L
+    for i in range(L) :
+        if i in circle_vertices :
+            h[i*L:i*L+L] = [1]*L
+
+    A_ub = np.vstack((A_ub, h))
+    b_ub.append(len(circle_vertices)-1)
+
+    return A_ub, b_ub
+
+
+# Checks if the path is connected, returns a circle if it finds one and the RESULT
+def is_connected(resx) -> bool:
+    
+    # first round the results to have only 0-1 values
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    N = len(resx)               # length of res
+    L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
+    n_edges = resx.sum()        # number of edges
+
+    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
+
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    ind_b = nonzero_tup[1].tolist()
+
+    edges = []
+    edges_visited = []
+    vertices_visited = []
+
+    edge1 = (ind_a[0], ind_b[0])
+    edges_visited.append(edge1)
+    vertices_visited.append(edge1[0])
+
+    for i, a in enumerate(ind_a) :
+        edges.append((a, ind_b[i]))      # Create the list of edges
+
+    remaining = edges
+    remaining.remove(edge1)
+
+    break_flag = False
+    while len(remaining) > 0 and not break_flag:
+        for edge2 in remaining :
+            if edge2[0] == edge1[1] :
+                if edge1[1] in vertices_visited :
+                    edges_visited.append(edge2)
+                    break_flag = True
+                    break
+                else :    
+                    vertices_visited.append(edge1[1])
+                    edges_visited.append(edge2)
+                    remaining.remove(edge2)
+                    edge1 = edge2
+
+            elif edge1[1] == L-1 or edge1[1] in vertices_visited:
+                        break_flag = True
+                        break
+
+    vertices_visited.append(edge1[1])
+
+
+    if len(vertices_visited) == n_edges +1 :
+        return vertices_visited, []
+    else: 
+        return vertices_visited, edges_visited
+
+
+# Function that returns the distance in meters from one location to another
+def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
+    
+    # Compute the straight-line distance in km
+    if p1 == p2 :
+        return 0
+    else: 
+        #dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
+        dist = geodesic(p1, p2).kilometers
+
+    # Consider the detour factor for average cityto deterline walking distance (in km)
+    walk_dist = dist*detour
+
+    # Time to walk this distance (in minutes)
+    walk_time = walk_dist/speed*60
+
+    """if walk_time > 15 :
+        walk_time = 5*round(walk_time/5)
+    else :
+        walk_time = round(walk_time)"""
+  
+
+    return round(walk_time)
+
+
+# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
+# We want to maximize the sightseeing :  max(c) st. A*x < b   and   A_eq*x = b_eq
+def init_ub_dist(landmarks: List[Landmark], max_steps: int):
+    
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
+    c = []
+    # Coefficients of inequality constraints (left-hand side)
+    A_ub = []
+
+    for spot1 in landmarks :
+        dist_table = [0]*len(landmarks)
+        c.append(-spot1.attractiveness)
+        for j, spot2 in enumerate(landmarks) :
+            t = get_time(spot1.location, spot2.location, detour, speed)
+            dist_table[j] = t
+        A_ub += dist_table
+    c = c*len(landmarks)
+
+    return c, A_ub, [max_steps]
+
+
+# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
+def respect_number(L:int, A_ub, b_ub):
+
+    ones = [1]*L
+    zeros = [0]*L
+    for i in range(L) :
+        h = zeros*i + ones + zeros*(L-1-i)
+        A_ub = np.vstack((A_ub, h))
+        b_ub.append(1)
+
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        max_landmarks = parameters['max landmarks']
+
+    A_ub = np.vstack((A_ub, ones*L))
+    b_ub.append(max_landmarks+1)
+
+    return A_ub, b_ub
+
+
+# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
+def break_sym(L, A_ub, b_ub):
+    upper_ind = np.triu_indices(L,0,L)
+
+    up_ind_x = upper_ind[0]
+    up_ind_y = upper_ind[1]
+
+    for i, _ in enumerate(up_ind_x) :
+        l = [0]*L*L
+        if up_ind_x[i] != up_ind_y[i] :
+            l[up_ind_x[i]*L + up_ind_y[i]] = 1
+            l[up_ind_y[i]*L + up_ind_x[i]] = 1
+
+            A_ub = np.vstack((A_ub,l))
+            b_ub.append(1)
+
+    return A_ub, b_ub
+
+
+# Constraint to not stay in position. Removes d11, d22, d33, etc.
+def init_eq_not_stay(L: int): 
+    l = [0]*L*L
+
+    for i in range(L) :
+        for j in range(L) :
+            if j == i :
+                l[j + i*L] = 1
+    
+    l = np.array(np.array(l))
+
+    return [l], [0]
+
+
+# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
+def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
+    L = len(landmarks)
+
+    for i, elem in enumerate(landmarks) :
+        if elem.must_do is True and elem.name not in ['finish', 'start']:
+            l = [0]*L*L
+            for j in range(L) :     # sets the horizontal ones (go from)
+                l[j +i*L] = 1       # sets the vertical ones (go to)        double check if good
+                
+            for k in range(L-1) :
+                l[k*L+L-1] = 1  
+
+            A_eq = np.vstack((A_eq,l))
+            b_eq.append(2)
+
+    return A_eq, b_eq
+
+
+# Constraint to ensure start at start and finish at goal
+def respect_start_finish(L: int, A_eq: list, b_eq: list):
+    ls = [1]*L + [0]*L*(L-1)    # sets only horizontal ones for start (go from)
+    ljump = [0]*L*L
+    ljump[L-1] = 1              # Prevent start finish jump
+    lg = [0]*L*L
+    ll = [0]*L*(L-1) + [1]*L
+    for k in range(L-1) :       # sets only vertical ones for goal (go to)
+        ll[k*L] = 1
+        if k != 0 :             # Prevent the shortcut start -> finish
+            lg[k*L+L-1] = 1 
+            
+
+    A_eq = np.vstack((A_eq,ls))
+    A_eq = np.vstack((A_eq,ljump))
+    A_eq = np.vstack((A_eq,lg))
+    A_eq = np.vstack((A_eq,ll))
+    b_eq.append(1)
+    b_eq.append(0)
+    b_eq.append(1)
+    b_eq.append(0)
+
+    return A_eq, b_eq
+
+
+# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
+def respect_order(N: int, A_eq, b_eq): 
+    for i in range(N-1) :           # Prevent stacked ones
+        if i == 0 or i == N-1:      # Don't touch start or finish
+            continue
+        else : 
+            l = [0]*N
+            l[i] = -1
+            l = l*N
+            for j in range(N) :
+                l[i*N + j] = 1
+
+            A_eq = np.vstack((A_eq,l))
+            b_eq.append(0)
+
+    return A_eq, b_eq
+
+
+# Computes the time to reach from each landmark to the next
+def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    L =  []
+    j = 0
+    total_dist = 0
+    while j < len(order)-1 :
+        elem = landmarks[order[j]]
+        next = landmarks[order[j+1]]
+
+        d = get_time(elem.location, next.location, detour_factor, speed)
+        elem.time_to_reach_next = d
+        elem.must_do = True
+        elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
+        L.append(elem)
+        j += 1
+        total_dist += d
+
+    next.location = (round(next.location[0], 5), round(next.location[1], 5))   
+    L.append(next)
+    
+    return L, total_dist
+
+
+def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+
+    L =  []
+    j = 0
+    total_dist = 0
+    while j < len(ordered_visit)-1 :
+        elem = ordered_visit[j]
+        next = ordered_visit[j+1]
+
+        elem.next_uuid = next.uuid
+        d = get_time(elem.location, next.location, detour_factor, speed)
+        elem.time_to_reach_next = d
+        if elem.name not in ['start', 'finish'] :
+            elem.must_do = True
+        L.append(elem)
+        j += 1
+        total_dist += d
+
+    L.append(next)
+    
+    return L, total_dist
+
+
+# Main optimization pipeline
+def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
+
+    L = len(landmarks)
+
+    # SET CONSTRAINTS FOR INEQUALITY
+    c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
+    A_ub, b_ub = respect_number(L, A_ub, b_ub)                      # Respect max number of visits (no more possible stops than landmarks). 
+    A_ub, b_ub = break_sym(L, A_ub, b_ub)                           # break the 'zig-zag' symmetry
+
+    # SET CONSTRAINTS FOR EQUALITY
+    A_eq, b_eq = init_eq_not_stay(L)                                # Force solution not to stay in same place
+    A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq)     # Check if there are user_defined must_see. Also takes care of start/goal
+    A_eq, b_eq = respect_start_finish(L, A_eq, b_eq)                # Force start and finish positions
+    A_eq, b_eq = respect_order(L, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
+
+    # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
+    x_bounds = [(0, 1)]*L*L
+
+    # Solve linear programming problem
+    res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+
+    # Raise error if no solution is found
+    if not res.success :
+            raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
+
+    # If there is a solution, we're good to go, just check for connectiveness
+    else :
+        order, circle = is_connected(res.x)
+        i = 0
+        timeout = 80
+        while len(circle) != 0 and i < timeout:
+            #A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
+            A_ub, b_ub = break_cricle(order, len(landmarks), A_ub, b_ub)
+            res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+            order, circle = is_connected(res.x)
+            if len(circle) == 0 :
+                break
+            print(i)
+            i += 1
+        
+        if i == timeout :
+            raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
+
+        # Add the times to reach and stop optimizing
+        L, total_dist = link_list(order, landmarks)
+            
+        if printing_details is True :
+            if i != 0 :
+                print(f"Neded to recompute paths {i} times because of unconnected loops...")
+            print_res(L, len(landmarks))
+            print("\nTotal score : " + str(int(-res.fun)))
+
+        return L
+
+
+
+
+
+

backend/src/parameters/landmarks_manager.params

@@ -1,8 +1,8 @@
 {
   "city bbox side" : 3,
-  "radius close to" : 27.5,
+  "radius close to" : 50,
-  "church coeff" : 0.7,
+  "church coeff" : 0.9,
-  "park coeff" : 1.5,
+  "park coeff" : 1.2,
-  "tag coeff" : 100,
+  "tag coeff" : 10,
-  "N important" : 40
+  "N important" : 30
 }

backend/src/parameters/optimizer.params

@@ -1,5 +1,5 @@
 {
   "detour factor" : 1.4,
   "average walking speed" : 4.8,
-  "max landmarks" : 8
+  "max landmarks" : 7
 }

backend/src/refiner.py

@@ -5,11 +5,13 @@ import os, json
 
 from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
 from typing import List, Tuple
+from scipy.spatial import KDTree
 from math import pi
+import networkx as nx
 
 from structs.landmarks import Landmark
 from landmarks_manager import take_most_important
-from optimizer import solve_optimization, link_list_simple, print_res, get_distance
+from optimizer_v4 import solve_optimization, link_list_simple, print_res, get_time
 from optimizer_v2 import generate_path, generate_path2
 
 
@@ -63,7 +65,7 @@ def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
   
   return landmarks
 
-def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
+def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> Tuple[List[Landmark], Polygon]:
     
   # Read from data
   with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
@@ -90,7 +92,7 @@ def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[
   
   # Step 4: Use nearest neighbor heuristic to visit all landmarks
   while unvisited_landmarks:
-    nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_distance(current_landmark.location, lm.location, detour, speed)[1])
+    nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time(current_landmark.location, lm.location, detour, speed))
     path.append(nearest_landmark)
     coordinates.append(nearest_landmark.location)
     current_landmark = nearest_landmark
@@ -120,23 +122,6 @@ def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[L
   return take_most_important(second_order_landmarks, len(visited_landmarks))
 
 
-def get_minor_landmarks2(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
-
-  second_order_landmarks = []
-  visited_names = []
-  area = create_corridor(visited_landmarks, width)
-
-  for visited in visited_landmarks :
-    visited_names.append(visited.name)
-  
-  for landmark in all_landmarks :
-    if is_in_area(area, landmark.location) and landmark.name not in visited_names:
-      second_order_landmarks.append(landmark)
-
-  return take_most_important(second_order_landmarks, len(visited_landmarks))
-
-
-
 
 """def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
 
@@ -152,6 +137,52 @@ def get_minor_landmarks2(all_landmarks: List[Landmark], visited_landmarks: List[
   return new_tour"""
 
 
+
+def fix_using_polygon(tour: List[Landmark])-> List[Landmark] :
+
+  coords = []
+  coords_dict = {}
+  for landmark in tour :
+    coords.append(landmark.location)
+    if landmark.name != 'finish' :
+      coords_dict[landmark.location] = landmark
+
+  tour_poly = Polygon(coords)
+  
+  better_tour_poly = tour_poly.buffer(0)
+  xs, ys = better_tour_poly.exterior.xy
+
+  if len(xs) != len(tour) :
+    better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
+    xs, ys = better_tour_poly.exterior.xy
+
+  # reverse the xs and ys
+  xs.reverse()
+  ys.reverse()
+
+  better_tour = []   # List of ordered visit
+  name_index = {}     # Maps the name of a landmark to its index in the concave polygon
+
+  # Loop through the polygon and generate the better (ordered) tour
+  for i,x in enumerate(xs[:-1]) :
+    y = ys[i]
+    better_tour.append(coords_dict[tuple((x,y))])
+    name_index[coords_dict[tuple((x,y))].name] = i
+
+
+  # Scroll the list to have start in front again
+  start_index = name_index['start']
+  better_tour = better_tour[start_index:] + better_tour[:start_index]
+
+  # Append the finish back and correct the time to reach
+  better_tour.append(tour[-1])
+
+  # Rearrange only if polygon
+  better_tour = rearrange(better_tour)
+  
+  return better_tour
+
+
 def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
 
   # Read from the file
@@ -159,10 +190,6 @@ def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], ma
     parameters = json.loads(f.read())
     max_landmarks = parameters['max landmarks'] + 4
 
-  if len(base_tour)-2 >= max_landmarks :
-    return base_tour
-  
-
   minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
 
   if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
@@ -175,66 +202,12 @@ def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], ma
   new_tour = solve_optimization(full_set, max_time, False)
   new_tour, new_dist = link_list_simple(new_tour)
   
-  """#if base_tour[0].location == base_tour[-1].location :
-  if False :
-    coords = []         # Coordinates of the new tour
-    coords_dict = {}    # maps the location of an element to the element itself. Used to access the elements back once we get the geometry
-
-    # Iterate through the new tour without finish
-    for elem in new_tour[:-1] :
-      coords.append(Point(elem.location))
-      coords_dict[elem.location] = elem         # if start = goal, only finish remains
-
-    # Create a concave polygon using the coordinates
-    better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
-    xs, ys = better_tour_poly.exterior.xy
-
-    # reverse the xs and ys
-    xs.reverse()
-    ys.reverse()
-
-    better_tour = []   # List of ordered visit
-    name_index = {}     # Maps the name of a landmark to its index in the concave polygon
-
-    # Loop through the polygon and generate the better (ordered) tour
-    for i,x in enumerate(xs[:-1]) :
-      better_tour.append(coords_dict[tuple((x,ys[i]))])
-      name_index[coords_dict[tuple((x,ys[i]))].name] = i
-
-
-    # Scroll the list to have start in front again
-    start_index = name_index['start']
-    better_tour = better_tour[start_index:] + better_tour[:start_index]
-
-    # Append the finish back and correct the time to reach
-    better_tour.append(new_tour[-1])
-
-    # Rearrange only if polygon
-    better_tour = rearrange(better_tour)
-
-    # Add the time to reach
-    better_tour = add_time_to_reach_simple(better_tour)
-  """
-  
-
-  """
-  if not better_poly.is_simple :
-    
-    coords_dict = {}
-    better_tour2 = []
-    for elem in better_tour :
-      coords_dict[elem.location] = elem
-
-    better_poly2 = better_poly.buffer(0)
-    new_coords = better_poly2.exterior.coords[:]
-    start_coords = base_tour[0].location
-    start_index = new_coords.
-
-    #for point in new_coords :
-  """
-
-
   better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
+
+  if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
+    better_tour = fix_using_polygon(better_tour)
+  
+
   better_tour, better_dist = link_list_simple(better_tour)
 
   if new_dist < better_dist :
@@ -258,12 +231,12 @@ def refine_path(landmarks: List[Landmark], base_tour: List[Landmark], max_time:
   # Read from the file
   with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
     parameters = json.loads(f.read())
-    max_landmarks = parameters['max landmarks'] + 4
+    max_landmarks = parameters['max landmarks'] + 3
   
   """if len(base_tour)-2 >= max_landmarks :
     return base_tour"""
 
-  minor_landmarks = get_minor_landmarks2(landmarks, base_tour, 200)
+  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
 
   if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
 
@@ -277,3 +250,46 @@ def refine_path(landmarks: List[Landmark], base_tour: List[Landmark], max_time:
 
 
 
+
+# If a tour is not connected
+def correct_path(tour: List[Landmark]) -> List[Landmark] :
+
+  # Read the parameters from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour = parameters['detour factor']
+    speed = parameters['average walking speed']
+
+  G = nx.Graph()
+
+  coords = []
+  landmap = {}
+  for i, landmark in enumerate(tour) :
+    coords.append(landmark.location)
+    landmap[i] = landmark
+    G.add_node(i, pos=landmark.location, weight=landmark.attractiveness)
+
+  kdtree = KDTree(coords)
+
+  k = 3
+  for node, coord in coords:
+    indices = kdtree.query(coord, k + 1)[1]   # k+1 because the closest neighbor is the node itself
+    for idx in indices[1:]:                   # skip the first one (itself)
+      neighbor = list(coords)[idx]
+      distance = get_time(coord, coords[neighbor], detour, speed)
+      G.add_edge(node, neighbor, weight=distance)
+
+  path = nx.approximation.traveling_salesman_problem(G, weight='weight', cycle=True)
+
+  if len(path) != len(tour) :
+    print("nope")
+
+  lis = [landmap[id] for id in path]
+
+  lis, tot_dist = link_list_simple(lis)
+
+  print_res(lis, len(tour))
+
+  return path
+
+

backend/src/structs/landmarks.py

@@ -7,8 +7,6 @@ from uuid import uuid4
 
 # Output to frontend
 class Landmark(BaseModel) :
-    # Unique ID of a given landmark
-    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
     
     # Properties of the landmark
     name : str
@@ -22,6 +20,9 @@ class Landmark(BaseModel) :
     description : Optional[str] = None                          # TODO future
     duration : Optional[int] = 0                                # TODO future
 
+    # Unique ID of a given landmark
+    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
+    
     # Additional properties depending on specific tour
     must_do : bool
     is_secondary : Optional[bool] = False                       # TODO future   

backend/src/tester.py

@@ -6,8 +6,8 @@ from typing import List
 from landmarks_manager import generate_landmarks
 from fastapi.encoders import jsonable_encoder
 
-from optimizer import solve_optimization
+from optimizer_v4 import solve_optimization
-from optimizer_v2 import generate_path, generate_path2
+from optimizer_v2 import generate_path
 from refiner import refine_optimization, refine_path
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
@@ -94,9 +94,10 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     
     # Generate the landmarks from the start location
     landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
-    #write_data(landmarks, "landmarks.txt")
+    #write_data(landmarks, "landmarks_Lyon.txt")
 
     # Insert start and finish to the landmarks list
+    #landmarks_short = landmarks_short[:4]
     landmarks_short.insert(0, start)
     landmarks_short.append(finish)
 
@@ -104,24 +105,29 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
         parameters = json.loads(f.read())
         max_landmarks = parameters['max landmarks']
-    max_walking_time = 45    # minutes
+    max_walking_time = 120    # minutes
-    detour = 10             # minutes
+    detour = 30             # minutes
 
 
     # First stage optimization
-    #base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
+    #base_tour = solve_optimization(landmarks_short, max_walking_time, True)
 
     
+    #base_tour = solve_optimization(landmarks_short, max_walking_time, True)
+
     # First stage using NetworkX
-    base_tour = generate_path2(landmarks_short, max_walking_time, max_landmarks)
+    base_tour = generate_path(landmarks_short, max_walking_time, max_landmarks)
     
     # Second stage using linear optimization
-    #refined_tour = refine_optimization(landmarks, base_tour, max_walking_time+detour, True)
+    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time+detour, True)
+
+    # Second stage using NetworkX
+    #refined_tour = refine_path(landmarks, base_tour, max_walking_time+detour, True)
 
     # Use NetworkX again to correct to shortest path
-    refined_tour = refine_path(landmarks, base_tour, max_walking_time+detour, True)
+    #refined_tour = refine_path(landmarks, base_tour, max_walking_time+detour, True)
     
-    return base_tour
+    return refined_tour
 
 
 #test4(tuple((48.8344400, 2.3220540)))       # Café Chez César