fixed optimizer. works fine now

backend/src/landmarks_manager.py

@@ -1,46 +1,80 @@
+import math as m
+
 from OSMPythonTools.api import Api
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder, Nominatim
-from dataclasses import dataclass
-from pydantic import BaseModel
-import math as m
 from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
 from typing import List
 from typing import Tuple
 
-RADIUS = 0.0005             # size of the bbox in degrees. 0.0005 ~ 50m
-BBOX_SIDE = 10              # size of bbox in km for general area, 10km
-RADIUS_CLOSE_TO = 50       # size of area in m for close features, 5àm radius
-MIN_SCORE = 100             # discard elements with score < 100
+
+BBOX_SIDE = 10              # size of bbox in *km* for general area, 10km
+RADIUS_CLOSE_TO = 25        # size of area in *m* for close features, 5àm radius
+MIN_SCORE = 30             # discard elements with score < 100
 MIN_TAGS = 5                # discard elements withs less than 5 tags
 
+SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
+NATURE = LandmarkType(landmark_type='nature')
+SHOPPING = LandmarkType(landmark_type='shopping')
 
-# Include th json here
+
+# Include the json here
 # Create a list of all things to visit given some preferences and a city. Ready for the optimizer
-def generate_landmarks(coordinates: Tuple[float, float], preferences: Preferences) :
+def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None)->Tuple[List[Landmark], List[Landmark]] :
 
     l_sights = ["'tourism'='museum'", "'tourism'='attraction'", "'tourism'='gallery'", 'historic', "'amenity'='arts_centre'", "'amenity'='planetarium'", "'amenity'='place_of_worship'", "'amenity'='fountain'", '"water"="reflecting_pool"'] 
     l_nature = ["'leisure'='park'", 'geological', "'natural'='geyser'", "'natural'='hot_spring'", '"natural"="arch"', '"natural"="cave_entrance"', '"natural"="volcano"', '"natural"="stone"', '"tourism"="alpine_hut"', '"tourism"="picnic_site"', '"tourism"="viewpoint"', '"tourism"="zoo"', '"waterway"="waterfall"'] 
     l_shop = ["'shop'='department_store'", "'shop'='mall'"] #, '"shop"="collector"', '"shop"="antiques"'] 
   
+    L = []
+
+    # Use 'City, Country'
+    if city_country is not None :
+
         # List for sightseeing
-    L1 = get_landmarks(coordinates, l_sights, LandmarkType(landmark_type='sightseeing'))
+        if preferences.sightseeing.score != 0 :
+            L1 = get_landmarks_nominatim(city_country, l_sights, SIGHTSEEING)
             correct_score(L1, preferences.sightseeing)
+            L += L1
         
         # List for nature
-    L2 = get_landmarks(coordinates, l_nature, LandmarkType(landmark_type='nature'))
+        if preferences.nature.score != 0 :
+            L2 = get_landmarks_nominatim(city_country, l_nature, NATURE)
             correct_score(L2, preferences.nature)
+            L += L2
         
         # List for shopping
-    L3 = get_landmarks(coordinates, l_shop, LandmarkType(landmark_type='shopping'))
+        if preferences.shopping.score != 0 :
+            L3 = get_landmarks_nominatim(city_country, l_shop, SHOPPING)
             correct_score(L3, preferences.shopping)
+            L += L3
 
-    L = L1 + L2 + L3
+    # Use coordinates
+    elif coordinates is not None :
 
-    return cleanup_list(L)
+        # List for sightseeing
+        if preferences.sightseeing.score != 0 :
+            L1 = get_landmarks_coords(coordinates, l_sights, SIGHTSEEING)
+            correct_score(L1, preferences.sightseeing)
+            L += L1
+        
+        # List for nature
+        if preferences.nature.score != 0 :
+            L2 = get_landmarks_coords(coordinates, l_nature, NATURE)
+            correct_score(L2, preferences.nature)
+            L += L2
+        
+        # List for shopping
+        if preferences.shopping.score != 0 :
+            L3 = get_landmarks_coords(coordinates, l_shop, SHOPPING)
+            correct_score(L3, preferences.shopping)
+            L += L3
+
+
+    return L, cleanup_list(L)
 
 # Determines if two locations are close to each other
-def is_close_to(loc1: Tuple[float, float], loc2: Tuple[float, float]) :
+def is_close_to(loc1: Tuple[float, float], loc2: Tuple[float, float])->bool :
 
     alpha = (180*RADIUS_CLOSE_TO)/(6371000*m.pi)
     if abs(loc1[0] - loc2[0]) + abs(loc1[1] - loc2[1]) < alpha*2 :
@@ -49,7 +83,7 @@ def is_close_to(loc1: Tuple[float, float], loc2: Tuple[float, float]) :
         return False
 
 # Remove duplicate elements and elements with low score
-def cleanup_list(L: List[Landmark]) :
+def cleanup_list(L: List[Landmark])->List[Landmark] :
     L_clean = []
     names = []
 
@@ -70,7 +104,6 @@ def cleanup_list(L: List[Landmark]) :
     
     return L_clean
 
-
 # Correct the score of a list of landmarks by taking into account preferences and the number of tags
 def correct_score(L: List[Landmark], preference: Preference) :
 
@@ -81,8 +114,8 @@ 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/100) + elem.n_tags      # arbitrary correction of the balance score vs number of tags
-        elem.attractiveness = elem.attractiveness*preference.score        # arbitrary computation
+        elem.attractiveness = int(elem.attractiveness) + elem.n_tags*100        # arbitrary correction of the balance score vs number of tags
+        elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
 
 # Correct the score of a list of landmarks by taking into account preferences and the number of tags
 def correct_score_test(L: List[Landmark], preference: Preference) :
@@ -103,7 +136,9 @@ def count_elements_within_radius(coordinates: Tuple[float, float]) -> int:
     lat = coordinates[0]
     lon = coordinates[1]
 
-    bbox = {'latLower':lat-RADIUS,'lonLower':lon-RADIUS,'latHigher':lat+RADIUS,'lonHigher': lon+RADIUS}
+    alpha = (180*RADIUS_CLOSE_TO)/(6371000*m.pi)
+
+    bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
     overpass = Overpass()
     
     # Build the query to find elements within the radius
@@ -148,11 +183,11 @@ def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[flo
     return min_lat, min_lon, max_lat, max_lon
 
 # Generates the list of landmarks for a given Landmarktype. Needs coordinates, a list of amenities and the corresponding LandmarkType
-def get_landmarks(coordinates: Tuple[float, float], l: List[Landmark], landmarktype: LandmarkType):
+def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], landmarktype: LandmarkType)->List[Landmark]:
 
     overpass = Overpass()
 
-    # Generate a bbox around currunt coordinates
+    # Generate a bbox around current coordinates
     bbox = create_bbox(coordinates, BBOX_SIDE)
 
     # Initialize some variables
@@ -164,6 +199,46 @@ def get_landmarks(coordinates: Tuple[float, float], l: List[Landmark], landmarkt
         result = overpass.query(query)
         N += result.countElements()
 
+        for elem in result.elements():
+
+            name = elem.tag('name')                             # Add name, decode to ASCII
+            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+
+            # skip if unprecise location
+            if name is None or location[0] is None:
+                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
+                score = count_elements_within_radius(location)
+
+                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
+
+def get_landmarks_nominatim(city_country: str, l: List[Landmark], landmarktype: LandmarkType)->List[Landmark] :
+    
+    overpass = Overpass()
+    nominatim = Nominatim()
+    areaId = nominatim.query(city_country).areaId()
+    
+    # Initialize some variables
+    N = 0
+    L = []
+
+    for amenity in l :
+        query = overpassQueryBuilder(area=areaId, 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
@@ -188,4 +263,3 @@ def get_landmarks(coordinates: Tuple[float, float], l: List[Landmark], landmarkt
                     L.append(landmark)
 
     return L
-

backend/src/main.py

@@ -12,13 +12,13 @@ app = FastAPI()
 
 # Assuming frontend is calling like this : 
 #"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
-# This should become main at some point
-@app.post("/optimizer/{longitude}/{latitude}")
-def main(longitude: float, latitude: float, preferences: Preferences = Body(...)) -> List[Landmark]:
+@app.post("/optimizer_coords/{longitude}/{latitude}/{city_country}")
+def main1(preferences: Preferences = Body(...), longitude: float = None, latitude: float = None, city_country: str = None) -> List[Landmark]:
+    
     # From frontend get longitude, latitude and prefence list
     
     # Generate the landmark list
-    landmarks = generate_landmarks(tuple((longitude, latitude)), preferences)
+    landmarks = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=tuple((longitude, latitude)))
 
     # Set the max distance
     max_steps = 90

backend/src/optimizer.py

@@ -1,8 +1,40 @@
-from scipy.optimize import linprog
 import numpy as np
+
+from typing import List
+from typing import Tuple
+from scipy.optimize import linprog
 from scipy.linalg import block_diag
-from structs.landmarks import Landmark, LandmarkType
-from structs.preferences import Preference, Preferences
+from structs.landmarks import Landmark
+from math import radians, sin, cos, acos
+
+
+
+DETOUR_FACTOR = 1.3         # detour factor for straightline distance
+AVG_WALKING_SPEED = 4.8     # average walking speed in km/h
+
+
+# Function that returns the distance in meters from one location to another
+def get_distance(p1: Tuple[float, float], p2: Tuple[float, float]) :
+    
+    # Compute the straight-line distance in km
+    if p1 == p2 :
+        return 0, 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])))
+
+    # Consider the detour factor for average city
+    wdist = dist*DETOUR_FACTOR
+
+    # Time to walk this distance (in minutes)
+    wtime = wdist/AVG_WALKING_SPEED*60
+
+    if wtime > 15 :
+        wtime = 5*round(wtime/5)
+    else :
+        wtime = round(wtime)
+  
+
+    return round(wdist, 1), wtime
 
 
 # landmarks = [Landmark_1, Landmark_2, ...]
@@ -35,10 +67,10 @@ def untangle(resx: list) -> list:
 
     return order
 
+    
 # Just to print the result
-def print_res(res, landmarks: list, P) -> list:
-    X = abs(res.x)
-    order = untangle(X)
+def print_res(res, order, landmarks: List[Landmark], P) -> list:
+
     things = []
 
     """N = int(np.sqrt(len(X)))
@@ -49,7 +81,7 @@ def print_res(res, landmarks: list, P) -> list:
     for i,x in enumerate(X) : X[i] = round(x,0)
     print(order)"""
 
-    if (X.sum()+1)**2 == len(X) : 
+    if len(order) == len(landmarks): 
         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 : ')
@@ -59,10 +91,141 @@ def print_res(res, landmarks: list, P) -> list:
         things.append(landmarks[idx].name)
 
     steps = path_length(P, abs(res.x))
-    print("\nSteps walked : " + str(steps))
+    print("\nMinutes walked : " + str(steps))
+    print(f"\nVisited {len(order)} out of {len(landmarks)} landmarks")
 
     return things
 
+
+
+# prevent the creation of similar circles
+def prevent_circle(resx, landmarks: List[Landmark], A_ub, b_ub) -> bool:
+    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()
+    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
+
+
+def break_circle2(circle_vertices, landmarks: List[Landmark], A_ub, b_ub) -> bool:
+    
+    L = len(landmarks)         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
+
+
+    if L-1 in circle_vertices :
+        circle_vertices.remove(L-1)
+
+    ones = [1]*L
+    h = [0]*L*L
+    for i in range(L) :
+        if i in circle_vertices :
+            h[i*L:i*L+L] = ones
+
+    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
+def is_connected(resx, landmarks: List[Landmark]) -> bool:
+    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
+
+    flag = False
+
+    remaining = edges
+    remaining.remove(edge1)
+    # This can be further optimized
+    #while len(vertices_visited) < n_edges + 1 :
+    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
+                    #continue # continue vs break vs needed at all ?
+                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
+                #break
+        #if flag is True :
+        #    break
+    vertices_visited.append(edge1[1])
+
+
+    if len(vertices_visited) == n_edges +1 :
+        flag = True
+        circle = []
+    else: 
+        flag = False
+        circle = edges_visited
+
+    """j = 0
+    for i in vertices_visited :
+        if landmarks[i].name == 'start' :
+            ordered_visit = vertices_visited[j:] + vertices_visited[:j]
+            break
+        j+=1"""
+
+
+    return flag, vertices_visited, circle
+
+
+
+
 # Checks for cases of circular symmetry in the result
 def has_circle(resx: list) :
     N = len(resx)               # length of res
@@ -127,13 +290,13 @@ def break_sym(N, A_ub, b_ub):
     return A_ub, b_ub
 
 # Constraint to not have circular paths. Want to go from start -> finish without unconnected loops
-def break_circle(N, A_ub, b_ub, circle) :
-    l = [0]*N*N
+def break_circle(L, A_ub, b_ub, circle) :
+    l = [0]*L*L
 
     for index in circle :
         x = index[0]
         y = index[1]
-        l[x*N+y] = 1
+        l[x*L+y] = 1
 
     A_ub = np.vstack((A_ub,l))
     b_ub.append(len(circle)-1)
@@ -147,19 +310,29 @@ def break_circle(N, A_ub, b_ub, circle) :
 
 # Constraint to respect max number of travels
 def respect_number(N, A_ub, b_ub):
-    h = []
+    """h = []
     for i in range(N) : h.append([1]*N)
     T = block_diag(*h)
-    """for l in T :
+    for l in T :
         for i in range(7):
             print(l[i*7:i*7+7])
         print("\n")"""
-    return np.vstack((A_ub, T)), b_ub + [1]*N
+    #return np.vstack((A_ub, T)), b_ub + [1]*N
+    ones = [1]*N
+    zeros = [0]*N
+    for i in range(N) :
+        h = zeros*i + ones + zeros*(N-1-i)
+
+        A_ub = np.vstack((A_ub, h))
+        b_ub.append(1)
+
+    return A_ub, b_ub
+        
 
 # 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 :
+        if i == 0 or i == N-1:  # Don't touch start or finish
             continue
         else : 
             l = [0]*N
@@ -171,10 +344,6 @@ def respect_order(N: int, A_eq, b_eq):
             A_eq = np.vstack((A_eq,l))
             b_eq.append(0)
 
-            """for i in range(7):
-                print(l[i*7:i*7+7])
-            print("\n")"""
-
     return A_eq, b_eq
 
 # Compute manhattan distance between 2 locations
@@ -183,27 +352,41 @@ def manhattan_distance(loc1: tuple, loc2: tuple):
     x2, y2 = loc2
     return abs(x1 - x2) + abs(y1 - y2)
 
-# Constraint to not stay in position
+# Constraint to not stay in position. Removes d11, d22, d33, etc.
 def init_eq_not_stay(N: int): 
     l = [0]*N*N
 
-
     for i in range(N) :
         for j in range(N) :
             if j == i :
                 l[j + i*N] = 1
-    l[N-1] = 1      # cannot skip from start to finish
-    #A_eq = np.array([np.array(xi) for xi in A_eq])                  # Must convert A_eq into an np array
-    l = np.array(np.array(l))
     
-    """for i in range(7):
-        print(l[i*7:i*7+7])"""
+    l = np.array(np.array(l))
 
     return [l], [0]
 
+# Constraint to ensure start at start and finish at goal
+def respect_start_finish(N, A_eq: list, b_eq: list):
+    ls = [1]*N + [0]*N*(N-1)    # sets only horizontal ones for start (go from)
+    ljump = [0]*N*N
+    ljump[N-1] = 1              # Prevent start finish jump
+    lg = [0]*N*N
+    for k in range(N-1) :       # sets only vertical ones for goal (go to)
+        if k != 0 :             # Prevent the shortcut start -> finish
+            lg[k*N+N-1] = 1 
+
+    A_eq = np.vstack((A_eq,ls))
+    A_eq = np.vstack((A_eq,ljump))
+    A_eq = np.vstack((A_eq,lg))
+    b_eq.append(1)
+    b_eq.append(0)
+    b_eq.append(1)
+
+    return A_eq, b_eq
+
 # 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, max_steps: int):
+def init_ub_dist(landmarks: List[Landmark], max_steps: int):
     # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
     c = []
     # Coefficients of inequality constraints (left-hand side)
@@ -212,7 +395,8 @@ def init_ub_dist(landmarks: list, max_steps: int):
         dist_table = [0]*len(landmarks)
         c.append(-spot1.attractiveness)
         for j, spot2 in enumerate(landmarks) :
-            dist_table[j] = manhattan_distance(spot1.location, spot2.location)
+            d, t = get_distance(spot1.location, spot2.location)
+            dist_table[j] = t
         A.append(dist_table)
     c = c*len(landmarks)
     A_ub = []
@@ -222,31 +406,31 @@ def init_ub_dist(landmarks: list, max_steps: int):
     return c, A_ub, [max_steps]
 
 # Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: list, A_eq: list, b_eq: list) :
+def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
     L = len(landmarks)
     H = 0       # sort of heuristic to get an idea of the number of steps needed
-    for i in landmarks : 
-        if i.name == "départ" : elem_prev = i              # list of all matches
-    for i, elem in enumerate(landmarks) :
-        if elem.attractiveness == -1 :
-            l = [0]*L*L
-            if elem.name != "arrivée" :
-                for j in range(L) :
-                    l[j +i*L] = 1
     
-            else :                          # This ensures we go to goal
+    elem_prev = landmarks[0]
+    
+    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  
 
-            H += manhattan_distance(elem.location, elem_prev.location)
-            elem_prev = elem
-
-            """for i in range(7):
-                print(l[i*7:i*7+7])
-            print("\n")"""
 
             A_eq = np.vstack((A_eq,l))
-            b_eq.append(1)
+            b_eq.append(2)
+
+        d, t = get_distance(elem.location, elem_prev.location)
+        H += t
+        elem_prev = elem
+
+            
+
     return A_eq, b_eq, H
 
 # Computes the path length given path matrix (dist_table) and a result
@@ -261,7 +445,7 @@ def solve_optimization (landmarks, max_steps, printing_details) :
     # SET CONSTRAINTS FOR INEQUALITY
     c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)      # Add the distances from each landmark to the other
     P = A_ub                                                # store the paths for later. Needed to compute path length
-    A_ub, b_ub = respect_number(N, A_ub, b_ub)              # Respect max number of visits. 
+    A_ub, b_ub = respect_number(N, A_ub, b_ub)              # Respect max number of visits (no more possible stops than landmarks). 
 
     # TODO : Problems with circular symmetry
     A_ub, b_ub = break_sym(N, A_ub, b_ub)                  # break the symmetry. Only use the upper diagonal values
@@ -269,7 +453,7 @@ def solve_optimization (landmarks, max_steps, printing_details) :
     # SET CONSTRAINTS FOR EQUALITY
     A_eq, b_eq = init_eq_not_stay(N)                                # Force solution not to stay in same place
     A_eq, b_eq, H = 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(N, A_eq, b_eq)                # Force start and finish positions
     A_eq, b_eq = respect_order(N, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
 
     # Bounds for variables (x can only be 0 or 1)
@@ -298,20 +482,27 @@ def solve_optimization (landmarks, max_steps, printing_details) :
     
     # If there is a solution, we're good to go, just check for
     else :
-        circle = has_circle(res.x)
+        t, order, circle = is_connected(res.x, landmarks)
         i = 0
 
         # Break the circular symmetry if needed
         while len(circle) != 0 :
-            A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
+            A_ub, b_ub = prevent_circle(res.x, landmarks, A_ub, b_ub)
+            A_ub, b_ub = break_circle(len(landmarks), A_ub, b_ub, circle)
+            A_ub, b_ub = break_circle2(order, 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)
-            circle = has_circle(res.x)
+            t, order, circle = is_connected(res.x, landmarks)
+            if t :
+                break
+            #circle = has_circle(res.x)
+            print(i)
             i += 1
 
         if printing_details is True :
             if i != 0 :
                 print(f"Neded to recompute paths {i} times because of unconnected loops...")
-            X = print_res(res, landmarks, P)
+            t, order, [] = is_connected(res.x, landmarks)
+            X = print_res(res, order, landmarks, P)
             return X
         else :
             return untangle(res.x)

backend/src/tester.py

@@ -1,13 +1,25 @@
+import pandas as pd
 from optimizer import solve_optimization
 from landmarks_manager import generate_landmarks
-from structs.landmarks import LandmarkTest
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
-from fastapi import FastAPI, Query, Body
+from fastapi.encoders import jsonable_encoder
 from typing import List
 
 
+# Helper function to create a .txt file with results
+def write_data(L: List[Landmark]): 
+
+    data = pd.DataFrame()
+    i = 0
+
+    for landmark in L :
+        data[i] = jsonable_encoder(landmark)
+        i += 1
+
+    data.to_json('landmarks.txt', indent = 2, force_ascii=False)
+
 def test3(city_country: str) -> List[Landmark]:
 
 
@@ -25,7 +37,9 @@ def test3(city_country: str) -> List[Landmark]:
                                   type=LandmarkType(landmark_type='shopping'),
                                   score = 5))
 
-    landmarks = generate_landmarks(city_country, preferences)
+    coords = None
+
+    landmarks = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coords)
 
     max_steps = 9
 
@@ -47,21 +61,33 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
                     nature=Preference(
                                   name='nature', 
                                   type=LandmarkType(landmark_type='nature'),
-                                  score = 0),
+                                  score = 5),
                     shopping=Preference(
                                   name='shopping', 
                                   type=LandmarkType(landmark_type='shopping'),
                                   score = 5))
 
-    landmarks = generate_landmarks(coordinates, preferences)
+    city_country = None
 
-    max_steps = 90
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
 
-    visiting_order = solve_optimization(landmarks, max_steps, True)
+    #write_data(landmarks)
+
+
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    
+
+    test = landmarks_short
+    test.append(finish)
+    test.insert(0, start)
+
+    max_walking_time = 4 # hours
+
+    visiting_order = solve_optimization(test, max_walking_time*60, True)
 
-    print(len(visiting_order))
 
     return len(visiting_order)
 
 
-test3(tuple((48.834378, 2.322113)))
+test4(tuple((48.834378, 2.322113)))