import heapqSTART, END = (), () # 起点和终点的位置
OBSTRUCTION = 1 # 障碍物标记class Node:def __init__(self, x, y, parent):self.x = x  # 节点的行号self.y = y  # 节点的列号self.parent = parent  # 父节点self.h = 0self.g = 0self.f = 0def get_G(self):''' 当前节点到起点的代价 '''if self.g != 0:return self.gelif self.parent is None:self.g = 0# 当前节点在parent的垂直或水平方向elif self.parent.x == self.x or self.parent.y == self.y:self.g = self.parent.get_G() + 10# 当前节点在parent的斜对角else:self.g = self.parent.get_G() + 14return self.gdef get_H(self):'''节点到终点的距离估值 '''if self.h == 0:self.h = self.manhattan(self.x, self.y, END[0], END[1]) * 10return self.hdef get_F(self):''' 节点的评估值 '''if self.f == 0:self.f = self.get_G() + self.get_H()return self.fdef manhattan(self, from_x, from_y, to_x, to_y):''' 曼哈顿距离 '''return abs(to_x - from_x) + abs(to_y - from_y)def __lt__(self, other):''' 用于堆比较，返回堆中f最小的一个 '''return self.get_F() < other.get_F()def __eq__(self, other):''' 判断Node是否相等 '''return self.x == other.x and self.y == other.ydef __ne__(self, other):''' 判断Node是否不等 '''return not self.__eq__(other)class Tank_way:''' 使用A*搜索找到坦克的最短移动路径 '''def __init__(self, map2d):self.map2d = map2d # 地图数据self.x_edge, self.y_edge = len(map2d), len(map2d[0]) # 地图边界# 垂直和水平方向的差向量self.v_hv = [(-1, 0), (0, 1), (1, 0), (0, -1)]# 斜对角的差向量self.v_diagonal = [(-1, 1), (1, 1), (1, -1), (-1, -1)]self.openlist = [] # openlist使用基于堆的优先队列self.closelist = set()self.answer = Nonedef is_in_map(self, x, y):''' (x, y)是否中地图内 '''return 0 <= x < self.x_edge and 0 <= y < self.y_edgedef in_closelist(self, x, y):''' (x, y) 方格是否在closeList中 '''return (x, y) in self.closelistdef add_in_openlist(self, node):''' 将node添加到 openlist '''heapq.heappush(self.openlist, node)def add_in_closelist(self, node):''' 将node添加到 closelist '''self.closelist.add((node.x, node.y))def pop_min_F(self):''' 弹出openlist中F值最小的节点 '''return heapq.heappop(self.openlist)def append_Q(self, P):''' 找到P周围可以探索的节点,将其加入openlist，并返回这些节点 '''Q = {}# 将水平或垂直方向的相应方格加入到Qfor dir in self.v_hv:x, y = P.x + dir[0], P.y + dir[1]# 如果(x,y)不是障碍物并且不在closelist中，将(x,y)加入到Qif self.is_in_map(x, y) \and self.map2d[x][y] != OBSTRUCTION \and not self.in_closelist(x, y):node = Node(x, y, P)Q[(x, y)] = nodeheapq.heappush(self.openlist, node) # 将node同时放入openlist中# 将斜对角的相应方格加入到Qfor dir in self.v_diagonal:x, y = P.x + dir[0], P.y + dir[1]# 如果(x,y)不是障碍物，且(x,y)能够与P联通，且(x,y)不在closelist中，将(x,y)加入到Qif self.is_in_map(x, y) \and self.map2d[x][y] != OBSTRUCTION \and self.map2d[x][P.y] != OBSTRUCTION \and self.map2d[P.x][y] != OBSTRUCTION \and not self.in_closelist(x, y):node = Node(x, y, P)Q[(x, y)] = nodeheapq.heappush(self.openlist, node)  # 将node同时放入openlist中return Qdef a_search(self):while self.openlist:# 找到openlist中F值最小的节点作为探索节点P = self.pop_min_F()# 如果P在closelist中，执行下一次循环if self.in_closelist(P.x, P.y):continue# P加入closelist
            self.add_in_closelist(P)# P周围待探索的节点Q = self.append_Q(P)# Q中没有任何节点，表示该路径一定不是最短路径，重新从openlist中选择if not Q:continue# 找到了终点， 退出循环if Q.get(END) is not None:self.answer = Node(END[0], END[1], P)breakdef start(self):node_start = Node(START[0], START[1], None)self.add_in_openlist(node_start)self.a_search()def paint(self):''' 打印最短路线 '''node = self.answerwhile node is not None:print((node.x, node.y),'G={0}, H={1}, F={2}'.format(node.g, node.h, node.get_F()))node = node.parentif __name__ == '__main__':map2d = [[0] * 8 for i in range(8)]map2d[5][4] = 1map2d[5][5] = 1map2d[4][5] = 1map2d[3][5] = 1map2d[2][5] = 1START, END = (3, 2), (5, 7)a_way = Tank_way(map2d)a_way.start()a_way.paint()

import random
import copy
import heapqIMG_END = []  # 拼图的复原状态
EYE_VAL = ' ' # 图眼的值
DIST = {}def get_hash_value(img):''' 获取img的哈希值 '''return hash(str(img))class Node:def __init__(self, img, x=0, y=0, parent=None):self.img = img # 当前拼图self.x, self.y = x, y # 图眼在img中的位置self.parent = parent  # 父节点self.hash_value = get_hash_value(img) # Node的哈希值self.h = 0self.g = 0self.f = 0def get_G(self):''' 当前节点到起点的代价 '''if self.g != 0:return self.gelif self.parent is None:self.g = 0else:self.g = self.parent.get_G() + 1return self.gdef get_H(self):''' 节点到终点的距离估值 '''if self.h == 0:self.h = self.manhattan()return self.hdef get_F(self):''' 节点的评估值 '''if self.f == 0:self.f = self.get_G() + self.get_H()# self.f = self.get_H()return self.fdef manhattan(self):'' '当前拼图到复原状态的距离 '''d = DIST.get(self.hash_value)if d is not None:return ddist = 0x_end, y_end = 0, 0  # img_end 中某一个碎片的位置n = len(self.img)for x, row in enumerate(self.img):for y, piece in enumerate(row):if piece == IMG_END[x][y]:continue# 计算piece碎片在img_end中的位置if piece == EYE_VAL:x_end = n - 1y_end = n - 1else:x_end = piece // ny_end = piece - n * x_enddist += abs(x - x_end) + abs(y - y_end)DIST[self.hash_value] = distreturn distdef __lt__(self, other):''' 用于堆比较，返回堆中f最小的一个 '''return self.get_F() < other.get_F()def __eq__(self, other):''' 判断Node是否相等 '''return self.img.hash_value == other.img.hash_valuedef __ne__(self, other):''' 判断Node是否不等 '''return not self.__eq__(other)def __hash__(self):return self.hash_valueclass JigsawPuzzle_A:''' 用A*搜索复原拼图 '''def __init__(self, level=1, img_start=None):self.level = level # 难度系数self.n = len(IMG_END)  # 拼图的维度self.end_hash_value = get_hash_value(IMG_END) # 复原状态的哈希值# “图眼”移动的方向, 上、左、下、右self.v_move = [(0, 1), (-1, 0), (0, -1), (1, 0)]# 设置拼图的初始状态和图眼的位置if img_start is not None:self.img_start = img_startself.eye_x, self.eye_y = self.search_eye(img_start)else:self.img_start, self.eye_x, self.eye_y = self.confuse()self.openlist = []self.closelist = set()# 拼图复原步骤self.answer = Nonedef confuse(self):''' 创建一个n*n的拼图，返回打乱状态和图眼位置 '''# 拼图的初始状态img_start = copy.deepcopy(IMG_END)from_x, from_y = self.search_eye(IMG_END)to_x, to_y = from_x, from_y# 将图眼随机移动 n * n * level次for i in range(self.n * self.n * self.level):# 选择一个随机方向v_x, v_y = random.choice(self.v_move)to_x, to_y = from_x + v_x, from_y + v_yif self.enable(to_x, to_y):# 向选择的随机方向移动
                self.move(img_start, from_x, from_y, to_x, to_y)from_x, from_y = to_x, to_yelse:to_x, to_y = from_x, from_yreturn img_start, to_x, to_ydef search_eye(self, img):''' 找到img中图眼的位置 '''# “图眼”的值是eye_val，打乱顺序后需要寻找到图眼的位置for x in range(self.n):for y in range(self.n):if EYE_VAL == img[x][y]:return  x, ydef in_closelist(self, node):''' node 是否在closelist中 '''return node.hash_value in self.closelistdef add_in_openlist(self, node):''' node节点加入openlist '''heapq.heappush(self.openlist, node)def add_in_closelist(self, node):''' node节点加入closelist '''self.closelist.add(node.hash_value)def pop_min_F(self):''' 找到openlist中F值最小的节点 '''return heapq.heappop(self.openlist)def enable(self, to_x, to_y):''' 图眼是否能够移动到x,y的位置 '''return 0 <= to_x < self.n and 0 <= to_y < self.ndef move(self, img, from_x, from_y, to_x, to_y):''' 将图眼从from_x, from_y移动到to_x, to_y '''img[from_x][from_y], img[to_x][to_y] = img[to_x][to_y], img[from_x][from_y]def append_Q(self, P):''' 找到P周围可以探索的节点,将其加入openlist，并返回这些节点 '''Q = {}for v_x, v_y in self.v_move:to_x, to_y = P.x + v_x, P.y + v_y# 检验是否可以向to_x, to_y方向移动if not self.enable(to_x, to_y):continuecurr_img = copy.deepcopy(P.img)self.move(curr_img, P.x, P.y, to_x, to_y)# 如果node是不在closelist中，把node添加到Q中if not self.in_closelist(Node(curr_img)):node = Node(curr_img, x=to_x, y=to_y, parent=P)Q[node.hash_value] = nodeself.add_in_openlist(node)return Qdef a_search(self):''' A*搜索拼图的解 '''while self.openlist:# 找到openlist中F值最小的节点作为探索节点P = self.pop_min_F()# 如果P在closelist中，执行下一次循环if self.in_closelist(P):continue# P加入closelist
            self.add_in_closelist(P)# P周围待探索的节点Q = self.append_Q(P)# Q中没有任何节点，表示该路径一定不是最短路径，重新从openlist中选择if not Q:continue# 找到了终点， 退出循环if Q.get(self.end_hash_value) is not None:self.answer = Node(IMG_END, parent=P)breakdef start(self):if self.img_start == IMG_END:print('start = end')returnnode_start = Node(img=self.img_start, x=self.eye_x, y=self.eye_y)self.add_in_openlist(node_start)self.a_search()def display(self):if self.answer is None:print('No answer')node = self.answerwhile node is not None:print(node.img)node = node.parentdef create_img_end(n):''' 创建一个n*n的拼图，将右下角的碎片图指定为图眼 '''img = []for i in range(n):img.append(list(range(n * i, n * i + n)))img[n - 1][n - 1] = EYE_VALreturn imgif __name__ == '__main__':n = 9IMG_END = create_img_end(n)# img_start = [[3, 0, 2], [1, 7, EYE_VAL], [6, 5, 4]]jigsaw = JigsawPuzzle_A(level=5)print('start=', jigsaw.img_start, ',eye =', (jigsaw.eye_y, jigsaw.eye_x))jigsaw.start()jigsaw.display()

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