def heuristic_search(root_node: Node, heuristic: Callable[[Node], int]) -> (Node, Monitor):
X = set()
F = []
heapq.heapify(F)
heapq.heappush(F, root_node)
monitor = Monitor(X, F)
monitor.start()
while True:
if not F:
monitor.finish()
return False, monitor
# with MonitorPerformance():
v = heapq.heappop(F)
if v.state == parameters.objective_state:
monitor.finish()
return v, monitor
elif v.state not in X: #rever isso, o estado pode estar aqui já, mas ter chego por outro caminho
X.add(v.state)
monitor.count()
for node in expand_node(v):
node.set_heuristic_cost(heuristic(node))
heapq.heappush(F, node)
def breadth_first_search(root_node: Node) -> (Node, Monitor):
X = set() # <-- Explorados
F: Deque[Node] = deque() # <-- Fronteira - deque eh uma implementacao para fila
F.append(root_node)
monitor = Monitor(X, F)
monitor.start()
while True:
if not F: # <-- Se a fronteira fica vazia, nao existe caminho
monitor.finish()
return False, monitor
v = F.popleft() # <-- Retira um nodo v da fronteira
if v.state == parameters.objective_state: # <-- Chegou no estado final?
monitor.finish()
return v, monitor # <-- Entao retorna o ultimo nó
elif v.state not in X: # <-- Adiciona v aos explorados caso ainda nao esteja
X.add(v.state)
monitor.count()
for node in expand_node(v): # <-- Adiciona os vizinhos de V a fronteira
F.append(node)
