import pandas as pdfrom pandas import Series, DataFrame# 城市信息：city1 city2 path_cost_city_info = None# 按照路径消耗进行排序的FIFO,低路径消耗在前面# 优先队列_frontier_priority = []# 节点数据结构class Node:def __init__(self, state, parent, action, path_cost):self.state = stateself.parent = parentself.action = actionself.path_cost = path_costdef main():global _city_infoimport_city_info()while True:src_city = input('输入初始城市\n')dst_city = input('输入目的城市\n')# result = breadth_first_search(src_city, dst_city)result = uniform_cost_search(src_city, dst_city) # 搜索路径# print(result.state)if not result:print('从城市: %s 到城市 %s 查找失败' % (src_city, dst_city))else:print('从城市: %s 到城市 %s 查找成功' % (src_city, dst_city))path = [] # 记录路径while True: # 回溯 输出路径path.append(result.state)if result.parent is None:breakresult = result.parentsize = len(path)for i in range(size):if i < size - 1:print('%s->' % path.pop(), end='') # pop 出栈操作else:print(path.pop())
def import_city_info(): #初始化数据集global _city_info data = [{'city1': 'Oradea', 'city2': 'Zerind', 'path_cost': 71},{'city1': 'Oradea', 'city2': 'Sibiu', 'path_cost': 151},{'city1': 'Zerind', 'city2': 'Arad', 'path_cost': 75},{'city1': 'Arad', 'city2': 'Sibiu', 'path_cost': 140},{'city1': 'Arad', 'city2': 'Timisoara', 'path_cost': 118},{'city1': 'Timisoara', 'city2': 'Lugoj', 'path_cost': 111},{'city1': 'Lugoj', 'city2': 'Mehadia', 'path_cost': 70},{'city1': 'Mehadia', 'city2': 'Drobeta', 'path_cost': 75},{'city1': 'Drobeta', 'city2': 'Craiova', 'path_cost': 120},{'city1': 'Sibiu', 'city2': 'Fagaras', 'path_cost': 99},{'city1': 'Sibiu', 'city2': 'Rimnicu Vilcea', 'path_cost': 80},{'city1': 'Rimnicu Vilcea', 'city2': 'Craiova', 'path_cost': 146},{'city1': 'Rimnicu Vilcea', 'city2': 'Pitesti', 'path_cost': 97},{'city1': 'Craiova', 'city2': 'Pitesti', 'path_cost': 138},{'city1': 'Fagaras', 'city2': 'Bucharest', 'path_cost': 211},{'city1': 'Pitesti', 'city2': 'Bucharest', 'path_cost': 101},{'city1': 'Bucharest', 'city2': 'Giurgiu', 'path_cost': 90},{'city1': 'Bucharest', 'city2': 'Urziceni', 'path_cost': 85},{'city1': 'Urziceni', 'city2': 'Vaslui', 'path_cost': 142},{'city1': 'Urziceni', 'city2': 'Hirsova', 'path_cost': 98},{'city1': 'Neamt', 'city2': 'Iasi', 'path_cost': 87},{'city1': 'Iasi', 'city2': 'Vaslui', 'path_cost': 92},{'city1': 'Hirsova', 'city2': 'Eforie', 'path_cost': 86}]_city_info = DataFrame(data, columns=['city1', 'city2', 'path_cost'])# print(_city_info)
def breadth_first_search(src_state, dst_state): #bfs 广度优先搜索global _city_infonode = Node(src_state, None, None, 0)# 目标测试if node.state == dst_state:return nodefrontier = [node]explored = []while True:if len(frontier) == 0:return Falsenode = frontier.pop(0)explored.append(node.state)if node.parent is not None:print('处理城市节点:%s\t父节点:%s\t路径损失为:%d' % (node.state, node.parent.state, node.path_cost))else:print('处理城市节点:%s\t父节点:%s\t路径损失为:%d' % (node.state, None, node.path_cost))# 遍历子节点for i in range(len(_city_info)):dst_city = ''if _city_info['city1'][i] == node.state:dst_city = _city_info['city2'][i]elif _city_info['city2'][i] == node.state:dst_city = _city_info['city1'][i]if dst_city == '':continuechild = Node(dst_city, node, 'go', node.path_cost + _city_info['path_cost'][i])print('\t孩子节点:%s 路径损失为%d' % (child.state, child.path_cost))if child.state not in explored and not is_node_in_frontier(frontier, child):# 目标测试if child.state == dst_state:print('\t\t 这个孩子节点就是目的城市')return childfrontier.append(child)print('\t\t 添加孩子节点到这个孩子')def is_node_in_frontier(frontier, node): # 寻找node节点是否在frontier队列内for x in frontier:if node.state == x.state:return Truereturn Falsedef uniform_cost_search(src_state, dst_state): # uniform-cost search 一致代价搜索global _city_info, _frontier_prioritynode = Node(src_state, None, None, 0) # state, parent, action, path_costfrontier_priority_add(node) # node入队explored = [] # 搜索集：标记已经探索过的地区while True:if len(_frontier_priority) == 0: # 队列为空，返回错误return Falsenode = _frontier_priority.pop(0) # node为队首元素if node.parent is not None:print('处理城市节点:%s\t父节点:%s\t路径损失为:%d' % (node.state, node.parent.state, node.path_cost))else:print('处理城市节点:%s\t父节点:%s\t路径损失为:%d' % (node.state, None, node.path_cost))# 目标测试if node.state == dst_state:print('\t 目的地已经找到了')return nodeexplored.append(node.state) # 将该地区加入搜索集# 遍历子节点for i in range(len(_city_info)):dst_city = '' # 前往的下一个地区# 寻找当前地区可到达路径if _city_info['city1'][i] == node.state:dst_city = _city_info['city2'][i]elif _city_info['city2'][i] == node.state:dst_city = _city_info['city1'][i]if dst_city == '':continuechild = Node(dst_city, node, 'go', node.path_cost + _city_info['path_cost'][i]) # state, parent, action, path_costprint('\t孩子节点:%s 路径损失为:%d' % (child.state, child.path_cost))# 如果该地区未被探索过且不在优先队列内，该节点入队if child.state not in explored and not is_node_in_frontier(_frontier_priority, child):frontier_priority_add(child)print('\t\t 添加孩子到优先队列')# 如果该地区被搜索过且在优先队列内，替代为路径消耗少的节点elif is_node_in_frontier(_frontier_priority, child):frontier_priority_replace_by_priority(child)def frontier_priority_add(node): # 添加node节点到优先队列中""":param Node node::return:"""global _frontier_prioritysize = len(_frontier_priority)for i in range(size): # 花费从小到大排列，进行插入操作if node.path_cost < _frontier_priority[i].path_cost:_frontier_priority.insert(i, node)return_frontier_priority.append(node) # 如果未找到比node花费大的元素，node从队尾插入def frontier_priority_replace_by_priority(node):""":param Node node::return:"""global _frontier_prioritysize = len(_frontier_priority)for i in range(size):# 替换if _frontier_priority[i].state == node.state and _frontier_priority[i].path_cost > node.path_cost:print('\t\t 替换状态: %s 旧的损失:%d 新的损失:%d' % (node.state, _frontier_priority[i].path_cost,node.path_cost))_frontier_priority[i] = nodereturnif __name__ == '__main__':main()
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