cleaned up
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@ -55,6 +55,7 @@ def prevent_config(resx):
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return h, [len(vertices_visited)-1]
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# Prevents the creation of the same circle (both directions)
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def prevent_circle(circle_vertices: list, L: int) :
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l1 = [0]*L*L
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@ -73,42 +74,9 @@ def prevent_circle(circle_vertices: list, L: int) :
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return np.vstack((l1, l2)), [0, 0]
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# Prevent the possibility of a given solution bit
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def break_circle(circle_vertices: list, L: int, A_ub: list, b_ub: list):
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if L-1 in circle_vertices :
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circle_vertices.remove(L-1)
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h = [0]*L*L
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for i in range(L) :
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if i in circle_vertices :
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h[i*L:i*L+L] = [1]*L
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A_ub = np.vstack((A_ub, h))
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b_ub.append(len(circle_vertices)-1)
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return A_ub, b_ub
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"""
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def break_circles(circle_edges_list: list, L: int, A_ub: list, b_ub: list):
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for circle_edges in circle_edges_list :
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if L-1 in circle_vertices :
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circle_vertices.remove(L-1)
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h = [0]*L*L
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for i in range(L) :
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if i in circle_vertices :
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h[i*L:i*L+L] = [1]*L
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A_ub = np.vstack((A_ub, h))
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b_ub.append(len(circle_vertices)-1)
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return A_ub, b_ub
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"""
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# Checks if the path is connected, returns a circle if it finds one and the RESULT
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def is_connected(resx):
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# Returns the order of visit as well as any circles if there are some
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def is_connected(resx) :
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# first round the results to have only 0-1 values
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for i, elem in enumerate(resx):
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@ -125,105 +93,6 @@ def is_connected(resx):
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ind_a = nonzero_tup[0].tolist()
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ind_b = nonzero_tup[1].tolist()
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edges = []
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edges_visited = []
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vertices_visited = []
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for i, a in enumerate(ind_a) :
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edges.append((a, ind_b[i])) # Create the list of edges
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edge1 = (ind_a[0], ind_b[0])
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del ind_a[0]
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edges_visited.append(edge1)
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vertices_visited.append(edge1[0])
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remaining = edges
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remaining.remove(edge1)
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break_flag = False
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while len(remaining) > 0 and not break_flag:
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for edge2 in remaining :
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if edge2[0] == edge1[1] :
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"""if edge1[1] in vertices_visited :
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ind_b.remove(edge2[1])
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#edges_visited.append(edge2)
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break_flag = True
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break
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else : """
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vertices_visited.append(edge1[1])
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ind_a.remove(edge2[0])
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ind_b.remove(edge2[0])
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#edges_visited.append(edge2)
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remaining.remove(edge2)
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edge1 = edge2
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elif edge1[1] == L-1 or edge1[1] in vertices_visited:
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ind_b.remove(edge1[1])
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break_flag = True
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break
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vertices_visited.append(edge1[1])
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# Return order of visit if all good
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if len(vertices_visited) == n_edges +1 :
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return vertices_visited, []
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"""edge1 = (ind_a[0], ind_b[0])
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vertices_visited.clear()
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edges_visited.clear()
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edges_visited.append(edge1)
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vertices_visited.append(edge1[0])
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remaining = edges
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remaining.remove(edge1)
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break_flag = False
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while len(remaining) > 0 and not break_flag:
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for edge2 in remaining :
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if edge2[0] == edge1[1] :
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# if edge1[1] in vertices_visited :
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# edges_visited.append(edge2)
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# break_flag = True
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# break
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# else :
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vertices_visited.append(edge1[1])
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edges_visited.append(edge2)
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remaining.remove(edge2)
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edge1 = edge2
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elif edge1[1] == L-1 or edge1[1] in vertices_visited:
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break_flag = True
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break
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vertices_visited.append(edge1[1])
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"""
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return vertices_visited, edges_visited
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def is_connected2(resx) :
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# first round the results to have only 0-1 values
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for i, elem in enumerate(resx):
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resx[i] = round(elem)
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N = len(resx) # length of res
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L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
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n_edges = resx.sum() # number of edges
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nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
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nonzero_tup = np.unravel_index(nonzeroind, (L,L))
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ind_a = nonzero_tup[0].tolist()
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ind_b = nonzero_tup[1].tolist()
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# print(f"ind_a = {ind_a}")
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# print(f"ind_b = {ind_b}")
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# Step 1: Create a graph representation
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graph = defaultdict(list)
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for a, b in zip(ind_a, ind_b):
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@ -232,7 +101,6 @@ def is_connected2(resx) :
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# Step 2: Function to perform BFS/DFS to extract journeys
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def get_journey(start):
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journey_nodes = []
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#journey_edges = []
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visited = set()
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stack = deque([start])
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@ -242,26 +110,21 @@ def is_connected2(resx) :
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visited.add(node)
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journey_nodes.append(node)
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for neighbor in graph[node]:
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#journey_edges.append((node, neighbor))
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if neighbor not in visited:
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stack.append(neighbor)
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return journey_nodes#, journey_edges
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return journey_nodes
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# Step 3: Extract all journeys
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all_journeys_nodes = []
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#all_journeys_edges = []
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visited_nodes = set()
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for node in ind_a:
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if node not in visited_nodes:
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journey_nodes = get_journey(node)
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all_journeys_nodes.append(journey_nodes)
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#all_journeys_edges.append(journey_edges)
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visited_nodes.update(journey_nodes)
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for l in all_journeys_nodes :
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if 0 in l :
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order = l
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@ -274,16 +137,13 @@ def is_connected2(resx) :
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return order, all_journeys_nodes
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# Function that returns the distance in meters from one location to another
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# Function that returns the time in minutes from one location to another
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def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
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# Compute the straight-line distance in km
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if p1 == p2 :
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return 0
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else:
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#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])))
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dist = geodesic(p1, p2).kilometers
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# Consider the detour factor for average cityto deterline walking distance (in km)
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@ -292,12 +152,6 @@ def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, sp
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# Time to walk this distance (in minutes)
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walk_time = walk_dist/speed*60
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"""if walk_time > 15 :
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walk_time = 5*round(walk_time/5)
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else :
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walk_time = round(walk_time)"""
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return round(walk_time)
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@ -394,8 +248,8 @@ def init_eq_not_stay(L: int):
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return [l], [0]
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# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
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def respect_user_mustsee(landmarks: List[Landmark]) :
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# Go through the landmarks and force the optimizer to use landmarks marked as must_do
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def respect_user_must_do(landmarks: List[Landmark]) :
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L = len(landmarks)
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A = [0]*L*L
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@ -482,6 +336,7 @@ def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
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return L, total_dist
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# Same as link_list but does it on a already ordered list
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def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
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# Read the parameters from the file
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@ -528,7 +383,7 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
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# SET CONSTRAINTS FOR EQUALITY
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A_eq, b_eq = init_eq_not_stay(L) # Force solution not to stay in same place
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A, b = respect_user_mustsee(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
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A, b = respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
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A_eq = np.vstack((A_eq, A))
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b_eq += b
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A, b = respect_start_finish(L) # Force start and finish positions
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@ -550,8 +405,8 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
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# If there is a solution, we're good to go, just check for connectiveness
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else :
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order, circles = is_connected2(res.x)
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#nodes, edges = is_connected2(res.x)
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order, circles = is_connected(res.x)
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#nodes, edges = is_connected(res.x)
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i = 0
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timeout = 80
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while circles is not None and i < timeout:
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@ -564,8 +419,8 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
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A_eq = np.vstack((A_eq, A))
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b_eq += b
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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)
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order, circles = is_connected2(res.x)
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#nodes, edges = is_connected2(res.x)
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order, circles = is_connected(res.x)
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#nodes, edges = is_connected(res.x)
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if circles is None :
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break
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print(i)
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@ -575,7 +430,7 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
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raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
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# Add the times to reach and stop optimizing
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L, total_dist = link_list(order, landmarks)
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L, _ = link_list(order, landmarks)
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if printing_details is True :
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if i != 0 :
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@ -1,6 +1,4 @@
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import pandas as pd
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import os
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import json
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from typing import List
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from landmarks_manager import generate_landmarks
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@ -25,45 +23,6 @@ def write_data(L: List[Landmark], file_name: str):
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data.to_json(file_name, indent = 2, force_ascii=False)
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def test3(city_country: str) -> List[Landmark]:
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preferences = Preferences(
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sightseeing=Preference(
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name='sightseeing',
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type=LandmarkType(landmark_type='sightseeing'),
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score = 5),
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nature=Preference(
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name='nature',
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type=LandmarkType(landmark_type='nature'),
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score = 0),
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shopping=Preference(
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name='shopping',
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type=LandmarkType(landmark_type='shopping'),
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score = 5))
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coordinates = None
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landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
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#write_data(landmarks)
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start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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test = landmarks_short
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test.insert(0, start)
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test.append(finish)
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max_walking_time = 2 # hours
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visiting_list = solve_optimization(test, max_walking_time*60, True)
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def test4(coordinates: tuple[float, float]) -> List[Landmark]:
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@ -117,4 +76,3 @@ test4(tuple((48.8344400, 2.3220540))) # Café Chez César
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#test4(tuple((48.8375946, 2.2949904))) # Point random
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#test4(tuple((47.377859, 8.540585))) # Zurich HB
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#test4(tuple((45.7576485, 4.8330241))) # Lyon Bellecour
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#test3('Vienna, Austria')
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