def astar_search(problem):
node = Node(problem.initial)
frontier = []
explored = set()
h = problem.manhattanDist(node)
g = node.path_cost
f = h + g
heapq.heappush(frontier, (h, node))
while frontier:
node = heapq.heappop(frontier)[1]
if problem.goal_test(node.state):
print(node.solution())
return node
explored.add(node.state)
for child in node.expand(problem):
h = problem.manhattanDist(child)
g = node.path_cost + 1
f = g + h
if child.state not in explored and child not in frontier:
child.path_cost = g
heapq.heappush(frontier, (f, child))
elif child in frontier and child.path_cost > g:
new_child = child
new_child.cost = g
frontier.remove(child)
heapq.heappush(frontier, (f, new_child))
return None
def main():
global default_cube
global goal
scrambled = scrambler(default, 3)
cube = Rubiks(scrambled, goal)
startTime = time.time()
result = astar_search(cube)
endTime = time.time()
print('solution is', Node.solution(result))
print('path is', Node.path(result))
print('걸린 시간 :', endTime - startTime)
def best_first_greedy_search(problem):
node = Node(problem.initial)
frontier = []
explored = set()
h = problem.manhattanDist(node)
heapq.heappush(frontier, (h, node))
while frontier:
node = heapq.heappop(frontier)[1]
if problem.goal_test(node.state):
print(node.solution())
return node
explored.add(node.state)
for child in node.expand(problem):
if child.state not in explored and child not in frontier:
h = problem.manhattanDist(child)
heapq.heappush(frontier, (h, child))
return None
def main():
global default_problem
global goal
global goal2
puzzle = EightPuzzle(default_problem, goal)
startTime = time.time()
result = depth_limited_search(puzzle, 30)
#result = breadth_first_tree_search(puzzle)
endTime = time.time()
print('solution is ', Node.solution(result))
print('path is', Node.path(result))
print('걸린 시간 :', endTime - startTime)
def printPathAndSolution(result):
print('path is ...')
print(Node.path(result))
print('solution is ...')
print(Node.solution(result))
def output(self, node: Node):
actions = node.solution()
print(len(actions))
for action in actions:
print(action[0] + " " + action[1])