首先我想说我搜索了我的问题，但由于问题的性质+我必须使用的代码，我没有找到解决方案。因此，这是我的问题：
Traceback (most recent call last):
File "AlienTilesMain.py", line 28, in <module>
goal = astar_search(prob, prob.h1)
File "/home/chris/Desktop/search.py", line 274, in astar_search
return best_first_graph_search(problem, lambda n: n.path_cost + h(n))
File "/home/chris/Desktop/search.py", line 213, in best_first_graph_search
frontier.append(node)
File "/home/chris/Desktop/utils.py", line 738, in append
bisect.insort(self.A, (self.f(item), item))
File "/home/chris/Desktop/utils.py", line 612, in memoized_fn
val = fn(obj, *args)
File "/home/chris/Desktop/search.py", line 274, in <lambda>
return best_first_graph_search(problem, lambda n: n.path_cost + h(n))
File "/home/chris/Desktop/utils.py", line 612, in memoized_fn
val = fn(obj, *args)
File "/home/chris/Desktop/AlienTiles.py", line 57, in h1
n = len(state[0])
AttributeError: Node instance has no attribute '__getitem__'
我的代码就是这个：
def h1(self, state):
n = len(state[0])
.
.
return value
在我的主文件中：
goal = astar_search(prob, prob.h1)
以下是问题，节点类和A *：
class Problem(object):
def __init__(self, initial, goal):
"""The constructor specifies the initial state, and possibly a goal
state, if there is a unique goal. Your subclass's constructor can add
other arguments."""
self.initial = initial; self.goal = goal
def actions(self, state):
"""Return the actions that can be executed in the given
state. The result would typically be a list, but if there are
many actions, consider yielding them one at a time in an
iterator, rather than building them all at once."""
abstract
def result(self, state, action):
"""Return the state that results from executing the given
action in the given state. The action must be one of
self.actions(state)."""
abstract
class Node:
"""A node in a search tree. Contains a pointer to the parent (the node
that this is a successor of) and to the actual state for this node. Note
that if a state is arrived at by two paths, then there are two nodes with
the same state. Also includes the action that got us to this state, and
the total path_cost (also known as g) to reach the node. Other functions
may add an f and h value; see best_first_graph_search and astar_search for
an explanation of how the f and h values are handled. You will not need to
subclass this class."""
def __init__(self, state, parent=None, action=None, path_cost=0):
"Create a search tree Node, derived from a parent by an action."
update(self, state=state, parent=parent, action=action,
path_cost=path_cost, depth=0)
if parent:
self.depth = parent.depth + 1
def __repr__(self):
return "<Node %s>" % (self.state,)
def expand(self, problem):
"List the nodes reachable in one step from this node."
return [self.child_node(problem, action)
for action in problem.actions(self.state)]
def child_node(self, problem, action):
"Fig. 3.10"
next = problem.result(self.state, action)
return Node(next, self, action,
problem.path_cost(self.path_cost, self.state, action, next))
def solution(self):
"Return the sequence of actions to go from the root to this node."
return [node.action for node in self.path()[1:]]
def path(self):
"Return a list of nodes forming the path from the root to this node."
node, path_back = self, []
while node:
path_back.append(node)
node = node.parent
return list(reversed(path_back))
def astar_search(problem, h=None):
"""A* search is best-first graph search with f(n) = g(n)+h(n).
You need to specify the h function when you call astar_search, or
else in your Problem subclass."""
h = memoize(h or problem.h, 'h')
return best_first_graph_search(problem, lambda n: n.path_cost + h(n))