if (visited.find(next_state) == visited.end()) { found = dfs(next_state, path, visited); }

这段代码是一个深度优先搜索算法中的关键代码。在搜索过程中，我们需要对每个状态进行判断是否已经访问过，避免重复搜索造成时间浪费和死循环。这里使用了一个名为visited的哈希表来存储已经访问过的状态。如果当前状态next_state没有被visited访问过，那么就可以继续对next_state进行深度优先搜索。如果已经访问过，就直接返回false，表示该状态已经被搜索过了。这样可以保证搜索过程不会陷入死循环，同时也避免了重复搜索造成的时间和空间浪费。

相关问题

class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序，使其能返回所有已经探索过的目标点visited_points的数量，请用代码表示

可以在 `solution()` 方法中添加一个变量来记录已经探索过的目标点数量，然后在每次更新 `visited_points` 后更新这个变量。下面是修改后的代码：

class AbstractGreedyAndPrune():
    def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True):
        self.aoi = aoi
        self.max_rounds = max_rounds
        self.debug = debug
        self.graph = aoi.graph
        self.nnodes = self.aoi.n_targets
        self.uavs = list(uavs_tours.keys())
        self.nuavs = len(self.uavs)
        self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)}
        self.__check_depots()
        self.reachable_points = self.__reachable_points()
        
    def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution:
        return utility.pruning_multiroundsolution(mr_solution)
    
    def solution(self) -> Tuple[MultiRoundSolution, int]:
        mrs_builder = MultiRoundSolutionBuilder(self.aoi)
        
        for uav in self.uavs:
            mrs_builder.add_drone(uav)
            
        residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)}
        tour_to_assign = self.max_rounds * self.nuavs
        visited_points = set()
        explored_points = 0
        
        while not self.greedy_stop_condition(visited_points, tour_to_assign):
            itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign)
            residual_ntours_to_assign[itd_uav] -= 1
            tour_to_assign -= 1
            opt_tour = self.uavs_tours[itd_uav][ind_tour]
            new_points = set(opt_tour.targets_indexes) - visited_points
            explored_points += len(new_points)
            visited_points |= new_points  # update visited points
            mrs_builder.append_tour(self.uavs[itd_uav], opt_tour)
        
        return self.__pruning(mrs_builder.build()), explored_points
    
    
class CumulativeGreedyCoverage(AbstractGreedyAndPrune):
    def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set):
        new_points = set(tour.targets_indexes) - visited_points
        return round_count * len(new_points)
    
    def local_optimal_choice(self, visited_points, residual_ntours_to_assign):
        choice_dict = {}
        for ind_uav in range(self.nuavs):
            uav_residual_rounds = residual_ntours_to_assign[ind_uav]
            if uav_residual_rounds > 0:
                uav_tours = self.uavs_tours[ind_uav]
                for ind_tour in range(len(uav_tours)):
                    tour = uav_tours[ind_tour]
                    quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points)
                    choice_dict[quality_tour] = (ind_uav, ind_tour)
        
        best_value = max(choice_dict, key=int)
        return choice_dict[best_value]

import matplotlib.pyplot as plt from queue import Queue s = (0, 0, 0) # (人所在位置, 距起点距离, 已经走的步数) t = (4, 4, -1) # 终点为平台（4，4） movable = [(1, 0), (0, 1), (-1, 0), (0, -1)] step = 6 lake = [[1,1,1,1,1,1], [1,0,0,0,0,1], [1,0,1,1,0,1], [1,0,0,1,0,1], [1,0,1,0,0,1], [1,1,1,1,1,1]] def bfs(start, end): queue = Queue() queue.put(start) visited = set() visited.add(start) while not queue.empty(): curr = queue.get() if curr == end: return curr for i, j in movable: x0, y0, z0 = curr x, y = x0 + i, y0 + j if 0 <= x < 6 and 0 <= y < 6 and lake[x][y] == 0: if z0 + 1 <= step: next_state = (x, y, z0 + 1) if next_state not in visited: visited.add(next_state) queue.put(next_state) parent[next_state] = curr return None def find_path(parent, end): path = [] while end: path.append(end) end = parent.get(end) path.reverse() return path parent = {} end_state = bfs(s, t) if end_state: path = find_path(parent, end_state) for x, y, _ in path: lake[x][y] = 2 fig, ax = plt.subplots(figsize=(8, 8)) for row in range(6): for col in range(6): if lake[row][col] == 0: ax.plot(row, col, '.', color='white', markersize=35) elif lake[row][col] == 1: ax.plot(row, col, '#', color='brown', markersize=35) elif lake[row][col] == 2: ax.plot(row, col, '*', color='blue', markersize=35) ax.plot(row, col, color='black', linestyle='-', linewidth=2) ax.set_xticks([]) ax.set_yticks([]) ax.set_xlim([-0.5, 5.5]) ax.set_ylim([-0.5, 5.5]) plt.show()修正这个代码

在这段代码中，有一些问题。首先，你的导入语句和第一行代码应该分开写。其次，`parent`变量在`find_path`函数中未定义，需要传递给该函数。最后，你需要将`col`的值在循环内部更新，而不是在循环外部。下面是修正后的代码：

import matplotlib.pyplot as plt
from queue import Queue

s = (0, 0, 0)  # (人所在位置, 距起点距离, 已经走的步数)
t = (4, 4, -1)  # 终点为平台（4，4）

movable = [(1, 0), (0, 1), (-1, 0), (0, -1)]
step = 6

lake = [[1,1,1,1,1,1],
        [1,0,0,0,0,1],
        [1,0,1,1,0,1],
        [1,0,0,1,0,1],
        [1,0,1,0,0,1],
        [1,1,1,1,1,1]]

def bfs(start, end):
    queue = Queue()
    queue.put(start)
    visited = set()
    visited.add(start)
    parent = {}
    
    while not queue.empty():
        curr = queue.get()
        if curr == end:
            return curr
        for i, j in movable:
            x0, y0, z0 = curr
            x, y = x0 + i, y0 + j
            if 0 <= x < 6 and 0 <= y < 6 and lake[x][y] == 0:
                if z0 + 1 <= step:
                    next_state = (x, y, z0 + 1)
                    if next_state not in visited:
                        visited.add(next_state)
                        queue.put(next_state)
                        parent[next_state] = curr
    return None

def find_path(parent, end):
    path = []
    while end:
        path.append(end)
        end = parent.get(end)
    path.reverse()
    return path

parent = {}
end_state = bfs(s, t)

if end_state:
    path = find_path(parent, end_state)
    for row in range(6):
        for col in range(6):
            if lake[row][col] == 0:
                plt.plot(row, col, '.', color='white', markersize=35)
            elif lake[row][col] == 1:
                plt.plot(row, col, '#', color='brown', markersize=35)
            elif lake[row][col] == 2:
                plt.plot(row, col, '*', color='blue', markersize=35)
        plt.plot(row, col, color='black', linestyle='-', linewidth=2)
        col = 0
        
    plt.xticks([])
    plt.yticks([])
    plt.xlim([-0.5, 5.5])
    plt.ylim([-0.5, 5.5])

    plt.show()

这个修正后的代码将绘制一个可行路径的图形。