我是python的新手，并且在函数定义方面存在一个奇怪的问题。我已经检查过论坛并确保在调用它之前定义我的函数，但是这没有帮助解决问题。当我尝试在这个特定方法中调用字面函数时，我不断得到一个名称未定义的错误。
from eight_puzzle import Puzzle
import math
################################################################
### Node class and helper functions provided for your convience.
### DO NOT EDIT!
################################################################
class Node:
"""
A class representing a node.
- 'state' holds the state of the node.
- 'parent' points to the node's parent.
- 'action' is the action taken by the parent to produce this node.
- 'path_cost' is the cost of the path from the root to this node.
"""
def __init__(self, state, parent, action, path_cost):
self.state = state
self.parent = parent
self.action = action
self.path_cost = path_cost
def gen_child(self, problem, action):
"""
Returns the child node resulting from applying 'action' to this node.
"""
return Node(state=problem.transitions(self.state, action),
parent=self,
action=action,
path_cost=self.path_cost + problem.step_cost(self.state, action))
@property
def state_hashed(self):
"""
Produces a hashed representation of the node's state for easy
lookup in a python 'set'.
"""
return hash(str(self.state))
################################################################
### Node class and helper functions provided for your convience.
### DO NOT EDIT!
################################################################
def retrieve_solution(node,num_explored,num_generated):
"""
Returns the list of actions and the list of states on the
path to the given goal_state node. Also returns the number
of nodes explored and generated.
"""
actions = []
states = []
while node.parent is not None:
actions += [node.action]
states += [node.state]
node = node.parent
states += [node.state]
return actions[::-1], states[::-1], num_explored, num_generated
################################################################
### Node class and helper functions provided for your convience.
### DO NOT EDIT!
################################################################
def print_solution(solution):
"""
Prints out the path from the initial state to the goal given
a tuple of (actions,states) corresponding to the solution.
"""
actions, states, num_explored, num_generated = solution
print('Start')
for step in range(len(actions)):
print(puzzle.board_str(states[step]))
print()
print(actions[step])
print()
print('Goal')
print(puzzle.board_str(states[-1]))
print()
print('Number of steps: {:d}'.format(len(actions)))
print('Nodes explored: {:d}'.format(num_explored))
print('Nodes generated: {:d}'.format(num_generated))
################################################################
### Skeleton code for your Astar implementation. Fill in here.
################################################################
class Astar:
"""
A* search.
- 'problem' is a Puzzle instance.
"""
def __init__(self, problem):
self.problem = problem
self.init_state = problem.init_state
self.num_explored = 0
self.num_generated = 1
def selectState(self, listOfStates):
'''
Selects the loweset cost node for expansion based on f(n) = g(n) + h(n)
'''
lowestCostPath = listOfStates[0].path_cost
index = int(1)
lowestNodeIndex = int(0)
while index != len(listOfStates):
scannedPathCost = listOfStates[index].path_cost
if index < scannedPathCost:
lowestCostPath = scannedPathCost
lowestNodeIndex = index
index += 1
return listOfStates[lowestNodeIndex]
def f(self,node, method):
'''
Returns a lower bound estimate on the cost from root through node
to the goal.
'''
return node.path_cost + self.h(node, method)
def getManhattanDistance(self, node):
'''
Evaluates the manhattan distance for a given state
'''
iterator = int(0)
misplacedCount = int(0)
totalDistance = int(0)
while iterator != len(node.state):
if iterator != node.state[iterator] and node.state[iterator] != 0:
misplacedCount = misplacedCount + 1
xCurrent = int(node.state[iterator]/3)
yCurrent = int(node.state[iterator]%3)
xDesired = int(iterator/3)
yDesired = int(iterator%3)
totalDistance = totalDistance + int(abs(xCurrent - xDesired)) + int(abs(yCurrent - yDesired))
iterator = iterator + 1
return totalDistance + misplacedCount
def h(self,node, method='man'):
'''
Returns a lower bound estimate on the cost from node to the goal
using the different heuristics.
'''
################################################################
### Your code here.
################################################################
if method == 'man':
self.getManhattanDistance(node)
return -1
elif method == 'rowcol':
return -1 # compute rowcol heuristic
elif method == 'misplaced':
return -1 # compute misplaced tiles the number of tiles out of place
elif method == 'null':
return -1 # compute null heuristic
else:
return 0
def method_stats(self, board, trials=100, method='man'):
'''
Returns an mean and standard deviation of the number of nodes expanded
'''
# write code here to randomly generate puzzles and
# compute the mean and standard deviation of the number
# nodes expanded. You can use np.mean() and np.std()
expanded_mean = 0.
expanded_std = 0.
for t in range(trials):
puzzle = Puzzle(board).shuffle()
solver = Astar(puzzle)
actions, states, num_explored, num_generated = solver.solve(method=method)
############################################################
### Compute upper bound for branching factor and update b_hi
### Your code here.
############################################################
return expanded_mean, expanded_std
def anotherFunction(self, node, method):
return 1
def generateStatesFor(self, node, method, listOfStates):
'''
Decides how to select an action from a list of available actions
'''
def solve(self, method='man'):
node = Node(state = self.init_state,
parent = None,
action = None,
path_cost = 0)
num_explored = int(0)
num_generated = int(0)
listOfStates = []
listOfStates.append(node)
print(listOfStates[0].state)
anotherFunction(self, node, method)
return retrieve_solution(node, num_explored=num_explored, num_generated=num_generated)
if __name__ == '__main__':
# Simple puzzle test
## board = [[3,1,2],
## [4,0,5],
## [6,7,8]]
board = [[7,2,4],
[5,0,6],
[8,3,1]]
puzzle = Puzzle(board)
solver = Astar(puzzle)
solution = solver.solve()
##print_solution(solution)
# Harder puzzle test
board = [[7,2,4],
[5,0,6],
[8,3,1]]
puzzle = Puzzle(board)
solver = Astar(puzzle)
##solution = solver.solve()
##print(len(solution[0]))
# branching factor test
method='man'
emean, estd = solver.method_stats(board, trials=100, method=method)
##print('mean and standard deviation: {0:.2f}, {1:.2f} using heuristic: {2}'.format(emean, estd, method))
错误代码：
Traceback (most recent call last): File "/Users/-/Downloads/HW1 2/code/my_hw1.py", line 214, in <module>
solution = solver.solve() File "/Users/-/Downloads/HW1 2/code/my_hw1.py", line 200, in solve
anotherFunction(self, node, method) NameError: name 'anotherFunction' is not defined [Finished in 0.1s with exit code 1]
如您所见，调用它的函数在第200行，函数在第185行定义。任何想法可能是什么问题？我也能够完全相同的＆＃34;另一个功能＆＃34;来自其他方法的方法，这些方法都没有解决。任何提示将不胜感激。
答案 0 :(得分：2)
当您使用“self”作为参数定义函数时，您需要从定义它的类中调用该函数。例如，如果您有一个类myClass的实例，其中定义了anotherFunction，则语法将为myClass.anotherFunction（node，method）。定义中的“self”参数表明anotherFunction是它定义的任何类的成员函数 - 我需要查看更多代码以了解它是什么类。