Cleanup and created main

backend/src/main.py

@@ -1,23 +1,26 @@
-import fastapi
+from optimizer import solve_optimization
-from dataclasses import dataclass
+from optimizer import landmark
+
+def main():
+    
+    # CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
+    max_steps = 16
 
 
-@dataclass
+    # Initialize all landmarks (+ start and goal). Order matters here
-class Destination:
+    landmarks = []
-    name: str
+    landmarks.append(landmark("départ", -1, (0, 0)))
-    location: tuple
+    landmarks.append(landmark("tour eiffel", 99, (0,2)))                           # PUT IN JSON
-    attractiveness: int
+    landmarks.append(landmark("arc de triomphe", 99, (0,4)))
+    landmarks.append(landmark("louvre", 99, (0,6)))
+    landmarks.append(landmark("montmartre", 99, (0,10)))
+    landmarks.append(landmark("concorde", 99, (0,8)))
+    landmarks.append(landmark("arrivée", -1, (0, 0)))
+
+
+    visiting_order = solve_optimization(landmarks, max_steps, True)
 
 
 
-d = Destination()
-
-
-
-def get_route() -> list[Destination]:
-    return {"route": "Hello World"}
-
-endpoint = ("/get_route", get_route)
-end
 if __name__ == "__main__":
-    fastapi.run()
+    main()

backend/src/main_example.py

@@ -0,0 +1,23 @@
+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.py

@@ -11,9 +11,8 @@ class landmark :
         self.loc = loc
 
 
-
+# Convert the solution of the optimization into the list of edges to follow. Order is taken into account
-
+def untangle(resx: list) :
-def untangle2(resx: list) :
     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
@@ -40,65 +39,31 @@ def untangle2(resx: list) :
 
     return order
 
-# Convert the result (edges from j to k like d_25 = edge between vertex 2 and vertex 5) into the list of indices corresponding to the landmarks
-def untangle(resx: list) :
-    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_landmarks = resx.sum()     # number of edges
-    visit_order = []
-    cnt = 0
-
-    if n_landmarks % 2 == 1 :                                     # if odd number of visited checkpoints
-        for i in range(L) :
-            for j in range(L) :
-                if res[i*L + j] == 1 :              # if index is 1
-                    cnt += 1                        # increment counter
-                    if cnt % 2 == 1 :               # if counter odd
-                        visit_order.append(i)
-                        visit_order.append(j)
-    else :                                   # if even number of ones
-        for i in range(L) :
-            for j in range(L) :
-                if res[i*L + j] == 1 :              # if index is one
-                    cnt += 1                        # increment counter
-                    if j % (L-1) == 0 :             # if last node
-                        visit_order.append(j)       # append only the last index
-                        return visit_order          # return
-                    if cnt % 2 == 1 : 
-                        visit_order.append(i)
-                        visit_order.append(j)
-    return visit_order
-
 # Just to print the result
-def print_res(res: list, landmarks: list, P) :
+def print_res(res, landmarks: list, P) :
     X = abs(res.x)
+    order = untangle(X)
 
-    N = int(np.sqrt(len(X)))
+    """N = int(np.sqrt(len(X)))
     for i in range(N):
         print(X[i*N:i*N+N])
-
+    print("Optimal value:", -res.fun)  # Minimization, so we negate to get the maximum
-    order = untangle2(X)
+    print("Optimal point:", res.x)
-
+    for i,x in enumerate(X) : X[i] = round(x,0)
-    order_ideal = [0, 0, 0, 0, 0, 0, 1, 0]
+    print(order)"""
-
-    # print("Optimal value:", -res.fun)  # Minimization, so we negate to get the maximum
-    # print("Optimal point:", res.x)
-    
-    #for i,x in enumerate(X) : X[i] = round(x,0)
-    
-    #print(order)
 
     if (X.sum()+1)**2 == len(X) : 
-        print('\nAll landmarks can be visited within max_steps, the following order is most likely not the fastest')
+        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 could be the fastest but not sure')
+        print('Could not visit all the landmarks, the following order is suggested : ')
-    print("Order of visit :")
+
     for idx in order : 
         print('- ' + landmarks[idx].name)
 
     steps = path_length(P, abs(res.x))
     print("\nSteps walked : " + str(steps))
 
+    return order
 
 # Checks for cases of circular symmetry in the result
 def has_circle(resx: list) :
@@ -164,8 +129,8 @@ def break_sym(landmarks, 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 prevent_circle(landmarks, A_ub, b_ub, circle) :
+def break_circle(landmarks, A_ub, b_ub, circle) :
     N = len(landmarks)
     l = [0]*N*N
 
@@ -195,7 +160,7 @@ def respect_number(landmarks, A_ub, b_ub):
         print("\n")"""
     return np.vstack((A_ub, T)), b_ub + [1]*len(landmarks)
 
-# Constraint to tie the problem together and have a connected path
+# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
 def respect_order(landmarks: list, A_eq, b_eq): 
     N = len(landmarks)
     for i in range(N-1) :     # Prevent stacked ones
@@ -294,56 +259,43 @@ def respect_user_mustsee(landmarks: list, A_eq: list, b_eq: list) :
 def path_length(P: list, resx: list) :
     return np.dot(P, resx)
 
-# Initialize all landmarks (+ start and goal). Order matters here
+# Main optimization pipeline
-landmarks = []
+def solve_optimization (landmarks, max_steps, printing) :
-landmarks.append(landmark("départ", -1, (0, 0)))
-landmarks.append(landmark("tour eiffel", 99, (0,2)))                           # PUT IN JSON
-landmarks.append(landmark("arc de triomphe", 99, (0,4)))
-landmarks.append(landmark("louvre", 99, (0,6)))
-landmarks.append(landmark("montmartre", 99, (0,10)))
-landmarks.append(landmark("concorde", 99, (0,8)))
-landmarks.append(landmark("arrivée", -1, (0, 0)))
 
+    # 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(landmarks, A_ub, b_ub)              # Respect max number of visits. 
 
+    # TODO : Problems with circular symmetry
+    A_ub, b_ub = break_sym(landmarks, A_ub, b_ub)                  # break the symmetry. Only use the upper diagonal values
 
-# CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
+    # SET CONSTRAINTS FOR EQUALITY
-max_steps = 16
+    A_eq, b_eq = init_eq_not_stay(landmarks)                       # 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
 
-# SET CONSTRAINTS FOR INEQUALITY
+    A_eq, b_eq = respect_order(landmarks, A_eq, b_eq)              # Respect order of visit (only works when max_steps is limiting factor)
-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(landmarks, A_ub, b_ub)              # Respect max number of visits. 
 
-# TODO : Problems with circular symmetry
+    # Bounds for variables (x can only be 0 or 1)
-A_ub, b_ub = break_sym(landmarks, A_ub, b_ub)                  # break the symmetry. Only use the upper diagonal values
+    x_bounds = [(0, 1)] * len(c)
 
-# SET CONSTRAINTS FOR EQUALITY
+    # Solve linear programming problem
-A_eq, b_eq = init_eq_not_stay(landmarks)                       # 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_order(landmarks, 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)
-x_bounds = [(0, 1)] * len(c)
-
-# 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)
-circle = has_circle(res.x)
-
-while len(circle) != 0 :
-    print("The solution has a circular path. Not interpretable.")
-    print("Need to add constraints until no circle ")
-
-    A_ub, b_ub = prevent_circle(landmarks, A_ub, b_ub, circle)
     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)
+    i = 0
+
+    # Break the circular symmetry if needed
+    while len(circle) != 0 :
+        A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
+        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)
+        i += 1
 
 
+    # Raise error if no solution is found
+    if not res.success :
 
-# Raise error if no solution is found
-if not res.success :
-    print(f"No solution has been found within given timeframe.\nMinimum steps to visit all must_see is : {H}")
         # Override the max_steps using the heuristic
         for i, val in enumerate(b_ub) :
             if val == max_steps : b_ub[i] = H
@@ -351,11 +303,18 @@ if not res.success :
         # Solve problem again :
         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 ValueError("No solution could be found, even when increasing max_steps using the heuristic")
     
-# Print result
+
-print_res(res, landmarks, P)
+    if printing is True :
-
+        if i != 0 :
-
+            print(f"Neded to recompute paths {i} times because of unconnected loops...")
+            X = print_res(res, landmarks, P)
+            return X
+
+    else :
+        return untangle(res.x)