def __best_first_graph_search(self, problem, f):
"""Search the nodes with the lowest f scores first.
You specify the function f(node) that you want to minimize; for example,
if f is a heuristic estimate to the goal, then we have greedy best
first search; if f is node.depth then we have breadth-first search.
There is a subtlety: the line "f = memoize(f, 'f')" means that the f
values will be cached on the nodes as they are computed. So after doing
a best first search you can examine the f values of the path returned."""
f = self.__memoize(f, 'f')
node = Node(problem.initial)
assert node != None and node.state != None
if problem.goal_test(node.state):
return node
frontier = PriorityQueue(min, f)
frontier.append(node)
explored = set()
step = 0
while frontier:
step+=1
node = frontier.pop()
assert node != None and node.state != None, "Estratto un nodo None"
#print '---- CURRENT NODE ----'
#print node.state
if problem.goal_test(node.state):
return node, len(explored)+1
explored.add(node.state)
for child in node.expand(problem):
assert child != None and child.state != None
if child.state not in explored and child not in frontier:
frontier.append(child)
elif child in frontier:
incumbent = frontier[child]
if f(child) < f(incumbent):
del frontier[incumbent]
frontier.append(child)
return None
