def example2():
city1 = 'Arad'
city2 = 'Craiova'
heuristic = calculate_heuristics(city1, coordinates)
path1 = astar_search(romania_map, heuristic, city1, city2)
path2 = astar_search(romania_map, heuristic, city2, city1)
P1_task(path1, path2)
def main():
print("Choose city from list of possible cities:")
print(
"Arad, Bucharest, Craiova, Drobeta, Eforie, Fagaras, Giuegiu, Hirsova, Iasi, Lugoj, Mehadia, Neamt, Oradea,"
)
print("Pitesti, Rimnicu, Sibiu, Timisoara, Urziceni, Vaslui, Zerind",
end='\n')
city1 = input("Input the first city ")
city2 = input("Input the second city ")
heuristic = calculate_heuristics(city1, coordinates)
path1 = astar_search(romania_map, heuristic, city1, city2)
path2 = astar_search(romania_map, heuristic, city2, city1)
P1_task(path1, path2)
maze_graph = dict()
for node in maze:
c = dict()
for connected_node in node.connected_nodes:
c[str(connected_node.num)] = 1
maze_graph[str(node.num)] = c
undirectedGraph_maze = undirectedGraph(maze_graph)
undirectedGraph_maze.locations = dict()
for node in maze:
undirectedGraph_maze.locations[str(node.num)] = (node.col, node.row)
Maze_problem = GraphProblem('0', str(maze[-1].num), undirectedGraph_maze)
start = datetime.now()
search_result = astar_search(Maze_problem)
end = datetime.now()
print('search time cost: ' + str(end - start))
solution = search_result.solution()
h = w = 30
tl = turtle.Turtle()
screen = turtle.Screen()
screen.screensize(col * w, row * h)
screen.tracer(50000)
tl.hideturtle()
x0, y0 = draw(maze, row, col, tl, h, w)
'''
tl.setposition(x0 + w / 2, y0 - h / 2)
tl.pencolor('red')
tl.pensize(2)
tl.pendown()
avgnumExpandedNodesM = 0
avgeffectiveBranchingFactorM = 0
avgpenetranceM = 0
avgnumExpandedNodesPDB = 0
avgeffectiveBranchingFactorPDB = 0
avgpenetrancePDB = 0
for i in range(numTests):
# creo un problema
puzzleProblemM = Puzzle(n, nScrambles)
# copio il problema
puzzleProblemPDB = deepcopy(puzzleProblemM)
''' Manhattan heuristic '''
nodeResultM, numExpandedNodesM = astar_search(puzzleProblemM, h_manhattan)
avgnumExpandedNodesM += numExpandedNodesM
effectiveBranchingFactorM = calculateEffectiveBranchingFactor(numExpandedNodesM, nodeResultM.path_cost)
avgeffectiveBranchingFactorM += effectiveBranchingFactorM
penetranceM = float(nodeResultM.path_cost) / numExpandedNodesM
avgpenetranceM += penetranceM
''' Pattern databases heuristic '''
nodeResultPDB, numExpandedNodesPDB = astar_search(puzzleProblemPDB, h_dbPattern)
avgnumExpandedNodesPDB += numExpandedNodesPDB
penetrancePDB = float(nodeResultPDB.path_cost) / numExpandedNodesPDB
avgpenetrancePDB += penetrancePDB
path_backward = results[1].solution()
print('forward')
print(path_forward)
print('backward')
print(path_backward)
bi_path.extend(path_forward)
bi_path.extend(move_reverse(path_backward))
print(bi_path)
print(len(bi_path))
'''
start = datetime.now()
results = astar_search(puzzle1)
end = datetime.now()
print('time cost: ' + str(end - start))
as_path = results.solution()
print(as_path)
print(len(as_path))
results_move_bi = bi_path
results_move_as = as_path
state1 = initial
print(initial)
print('bi')