final = get_path(cameFrom, current);
时间: 2024-04-02 18:37:34 浏览: 8
这行代码通常用于从起点到目标节点恢复一条路径。在图搜索算法中,我们通常使用一个字典(如 Python 中的 dict,MATLAB 中的 containers.Map)来记录每个节点的前驱节点,即从起点到该节点的路径上的前一个节点。在 A* 算法中,这个字典通常称为 cameFrom。
当算法搜索到目标节点时,我们可以使用 cameFrom 字典来恢复一条从起点到目标节点的路径。具体来说,我们可以从目标节点开始,沿着 cameFrom 字典中的记录,一直追溯到起点。这个过程就是回溯路径,可以使用一个列表来保存路径上的节点。最终,这个列表中的元素顺序即为从起点到目标节点的路径。
在 A* 算法中,通常将回溯路径的过程封装在一个名为 get_path 的函数中。这个函数接受两个参数,即 cameFrom 字典和目标节点 current,返回从起点到目标节点的路径。因此,当执行代码 final = get_path(cameFrom, current); 时,会调用 get_path 函数,返回从起点到目标节点的路径,并将其赋值给变量 final。
需要注意的是,如果算法没有找到一条从起点到目标节点的路径,那么 get_path 函数通常会返回空列表或空数组。因此,在使用 get_path 函数返回的路径时,需要先判断其是否为空,以避免出现错误。
相关问题
while any(openSet(:) > 0) % Find the minimum fScore within the open set [~, current] = min(fScore(:)); % If we've reached the goal if current == goal % Get the full path and return it final = get_path(cameFrom, current); return end % Linear index -> row, col subscripts rc = rem(current - 1, mapSize(1)) + 1; cc = (current - rc) / mapSize(1) + 1; % Remove CURRENT from openSet openSet(rc, cc) = false; % Place CURRENT in closedSet closedSet(rc, cc) = true; fScore(rc, cc) = inf; gScoreCurrent = gScore(rc, cc) + costs(rc, cc); % Get all neighbors of CURRENT. Neighbors are adjacent indices on % the map, including diagonals. % Col 1 = Row, Col 2 = Col, Col 3 = Distance to the neighbor n_ss = [ ... rc + 1, cc + 1, S2 ; ... rc + 1, cc + 0, 1 ; ... rc + 1, cc - 1, S2 ; ... rc + 0, cc - 1, 1 ; ... rc - 1, cc - 1, S2 ; ... rc - 1, cc - 0, 1 ; ... rc - 1, cc + 1, S2 ; ... rc - 0, cc + 1, 1 ; ... ]; % keep valid indices only valid_row = n_ss(:,1) >= 1 & n_ss(:,1) <= mapSize(1); valid_col = n_ss(:,2) >= 1 & n_ss(:,2) <= mapSize(2); n_ss = n_ss(valid_row & valid_col, :); % subscripts -> linear indices neighbors = n_ss(:,1) + (n_ss(:,2) - 1) .* mapSize(1); % only keep neighbors in the map and not in the closed set ixInMap = map(neighbors) & ~closedSet(neighbors); neighbors = neighbors(ixInMap); % distance to each kept neighbor dists = n_ss(ixInMap, 3); % Add each neighbor to the open set openSet(neighbors) = true; % TENTATIVE_GSCORE is the score from START to NEIGHBOR. tentative_gscores = gScoreCurrent + costs(neighbors) .* dists; % IXBETTER indicates where a better path was found ixBetter = tentative_gscores < gScore(neighbors); bestNeighbors = neighbors(ixBetter); % For the better paths, update scores cameFrom(bestNeighbors) = current; gScore(bestNeighbors) = tentative_gscores(ixBetter); fScore(bestNeighbors) = gScore(bestNeighbors) + compute_cost(mapSize, bestNeighbors, gr, gc); end % while end
这段代码是 A* 算法的核心实现部分,用于寻找两点之间的最短路径。具体来说,它依次对起点周围的格子进行探索,计算每个格子到起点的代价(gScore)以及到终点的估计代价(fScore),并将其加入 openSet 中。然后从 openSet 中选取 fScore 最小的格子进行探索,直到找到终点或 openSet 中没有格子可供探索为止。
在探索每个格子时,首先将其从 openSet 中移除并加入 closedSet 中,然后计算该格子与周围格子的代价,并将未被探索过的格子加入 openSet 中。如果发现了新的更优路径,就更新该格子到起点的代价和 fScore,并将其加入到 cameFrom 列表中,表示它是从哪个格子转移而来的。
最终,如果找到了终点,就从 cameFrom 列表中回溯路径。
matlab 模拟退火算法tsp
以下是一个使用模拟退火算法解决TSP问题的MATLAB实现示例:
```matlab
% TSP问题求解的模拟退火算法
% 使用方法:[route, distance] = tsp_simulated_annealing(distance_matrix, initial_temperature, final_temperature, cooling_rate)
% 输入参数:
% - distance_matrix: 距离矩阵,即各个城市之间的距离矩阵,其中distance_matrix(i,j)表示第i个城市到第j个城市的距离
% - initial_temperature: 初始温度
% - final_temperature: 终止温度
% - cooling_rate: 降温速率
% 输出参数:
% - route: 最优路径,即访问所有城市的最优顺序
% - distance: 最优路径的总长度
function [route, distance] = tsp_simulated_annealing(distance_matrix, initial_temperature, final_temperature, cooling_rate)
n_cities = size(distance_matrix, 1); % 城市数量
current_route = randperm(n_cities); % 初始路径
current_distance = get_path_distance(current_route, distance_matrix); % 初始路径长度
best_route = current_route; % 最优路径
best_distance = current_distance; % 最优路径长度
temperature = initial_temperature; % 当前温度
while temperature > final_temperature
for i = 1 : n_cities
new_route = current_route;
% 随机交换两个城市
j = randi(n_cities);
while j == i
j = randi(n_cities);
end
new_route([i, j]) = new_route([j, i]);
new_distance = get_path_distance(new_route, distance_matrix); % 新路径长度
% 判断是否接受新路径
delta_distance = new_distance - current_distance;
if delta_distance < 0
% 新路径更优,直接接受
current_route = new_route;
current_distance = new_distance;
if new_distance < best_distance
% 更新最优路径
best_route = new_route;
best_distance = new_distance;
end
else
% 根据Metropolis准则接受新路径
p = exp(-delta_distance / temperature);
if rand() < p
current_route = new_route;
current_distance = new_distance;
end
end
end
temperature = temperature * cooling_rate; % 降温
end
route = best_route;
distance = best_distance;
end
% 计算路径长度
function distance = get_path_distance(route, distance_matrix)
n_cities = size(distance_matrix, 1);
distance = 0;
for i = 1 : n_cities - 1
distance = distance + distance_matrix(route(i), route(i+1));
end
distance = distance + distance_matrix(route(n_cities), route(1));
end
```
使用示例:
```matlab
% 构造距离矩阵
distance_matrix = [
0, 1, 2, 3;
1, 0, 4, 5;
2, 4, 0, 6;
3, 5, 6, 0;
];
% 调用模拟退火算法求解TSP问题
[route, distance] = tsp_simulated_annealing(distance_matrix, 100, 0.1, 0.99);
disp(route); % 输出最优路径
disp(distance); % 输出最优路径长度
```