以下回复参考：皆我百晓生、券券喵儿等免费微信小程序作答:
在C#中实现路径规划算法时，我们通常会使用Dijkstra算法、A*算法等，这些算法都是基于优先队列的。在这个问题中，我们正在使用F-Heuristic（欧几里得距离）作为启发式函数，这使得我们的路径规划算法更加高效。
以下是将F-Heuristic应用于您的代码片段中的解决方案：
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Numerics;
using System.Text;
class Problem
{
public enum HeuristicType
{
Euclidean,
Manhattan
}
public class Node
{
public int ID { get; set; }
public string Label { get; set; }
public List<Node> Children { get; set; }
}
public class Edge
{
public int From { get; set; }
public int To { get; set; }
public float Weight { get; set; }
}
public class Graph
{
private Dictionary<int, Node> Nodes;
private List<Edge> Edges;
public Graph()
{
Nodes = new Dictionary<int, Node>();
Edges = new List<Edge>();
}
public void AddEdge(int from, int to, float weight)
{
var edge = new Edge { From = from, To = to, Weight = weight };
Edges.Add(edge);
}
public Node GetNode(int id)
{
return Nodes[id];
}
public List<Node> GetNodes()
{
return Nodes.Values.ToList();
}
public void AddNode(Node node)
{
Nodes[node.ID] = node;
}
}
public class AStarSearchProblem : IProblem
{
public readonly Graph Graph;
public readonly int StartId;
public readonly int EndId;
public readonly HeuristicType HeuristicType;
public AStarSearchProblem(Graph graph, int startId, int endId, HeuristicType heuristicType)
{
Graph = graph;
StartId = startId;
EndId = endId;
HeuristicType = heuristicType;
}
public bool IsGoal(Node node)
{
return node.ID == EndId;
}
public List<Node> GenerateSolution()
{
var solution = new List<Node>();
Node current = Graph.GetNode(StartId);
solution.Add(current);
while (current != null && !IsGoal(current))
{
current = current.Children[0];
solution.Add(current);
}
return solution;
}
public float ComputeTotalDistance(Node start, Node goal)
{
var totalDistance = 0f;
Node current = start;
while (current != goal)
{
totalDistance += Math.Abs((int)(current.Weight * 100));
current = current.Children[0];
}
return totalDistance / 100;
}
public float ComputePathCost(Node start, Node goal)
{
return ComputeTotalDistance(start, goal);
}
public float ComputeHCost(Node node)
{
switch (HeuristicType)
{
case HeuristicType.Euclidean:
return ComputeTotalDistance(node, Graph.GetNode(EndId));
case HeuristicType.Manhattan:
return ComputeTotalDistance(node, Graph.GetNode(EndId)) + Math.Abs(node.ID - EndId);
default:
throw new ArgumentOutOfRangeException(nameof(HeuristicType), HeuristicType, null);
}
}
}
public class Solution
{
public Node Node { get; set; }
public float Cost { get; set; }
public float Distance { get; set; }
}
public class SearchResult
{
public List<Solution> Solutions { get; set; }
public float BestScore { get; set; }
public float AverageScore { get; set; }
public float TotalDistance { get; set; }
}
public class Solver
{
public static SearchResult Solve(AStarSearchProblem problem)
{
var result = new SearchResult();
var fHeap = new FHeap(problem.Graph.Nodes, problem.ComputeHCost);
var nodesToVisit = new Queue<Node>(problem.Graph.GetNodes());
nodesToVisit.Enqueue(problem.Graph.GetNode(problem.StartId));
var solutions = new List<Solution>();
while (nodesToVisit.Count > 0)
{
var node = nodesToVisit.Dequeue();
var solution = problem.GenerateSolution().FirstOrDefault(s => s.ID == problem.EndId);
if (solution != null)
{
var score = problem.ComputePathCost(solution, problem.Graph.GetNode(problem.EndId));
var distance = problem.ComputeHCost(solution);
if (score < result.BestScore || (score == result.BestScore && distance < result.TotalDistance))
{
result.Solutions.Clear();
result.Solutions.Add(new Solution { Node = solution, Cost = score, Distance = distance });
result.BestScore = score;
result.TotalDistance = distance;
}
else if (score == result.BestScore && distance == result.TotalDistance)
{
result.Solutions.Add(new Solution { Node = solution, Cost = score, Distance = distance });
}
if (solution.ID == problem.EndId)
{
break;
}
foreach (var child in problem.Graph.GetNode(solution.ID).Children)
{
if (!nodesToVisit.Contains(child))
{
nodesToVisit.Enqueue(child);
}
}
}
}
return result;
}
}
public class FHeap
{
private PriorityQueue<SpLabel> Heap;
private SortedDictionary<int, int> ProcessedIDs;
public FHeap(Dictionary<int, int> processedIDs, Func<SpLabel, float> heuristicFunc)
{
Heap = new PriorityQueue<SpLabel>(heuristicFunc);
ProcessedIDs = processedIDs;
}
public void Insert(SpLabel label)
{
Heap.Push(label);
}
public SpLabel ExtractMin()
{
var min = Heap.Min;
Heap.Remove(min);
ProcessedIDs[min.NodeID]++;
return min;
}
public void Delete(SpLabel label)
{
Heap.Remove(label);
ProcessedIDs[label.NodeID]--;
}
}
public class PriorityQueue<T>
{
private SortedList<int, T> Items;
private Comparator<T> Comparer;
public PriorityQueue(Comparator<T> comparer)
{
Items = new SortedList<int, T>();
Comparer = comparer;
}
public T Min
{
get { return Items.MinValue; }
}
public void Push(T item)
{
Items.Add(item.Key, item);
}
public T Pop()
{
var item = Items.MinValue;
Items.RemoveAt(item.Key);
return item.Value;
}
}
public interface IProblem
{
Graph Graph { get; }
int StartId { get; }
int EndId { get; }
HeuristicType HeuristicType { get; }
}
public class AStarSearchProblem : IProblem
{
public Graph Graph { get; set; }
public int StartId { get; set; }
public int EndId { get; set; }
public HeuristicType HeuristicType { get; set; }
}
public class SpLabel
{
public int NodeID { get; set; }
public float PathCost { get; set; }
public float HeuristicDistance { get; set; }
public SpLabel Parent { get; set; }
public SpLabel AssociatedNode { get; set; }
}
}
// Example usage
public class Program
{
public static void Main(string[] args)
{
var graph = new Graph();
graph.AddEdge(1, 2, 10f);
graph.AddEdge(1, 3, 8f);
graph.AddEdge(2, 3, 6f);
graph.AddEdge(3, 4, 7f);
graph.AddEdge(4, 5, 9f);
var aStarProblem = new AStarSearchProblem(graph, 1, 5, AStarSearchProblem.HeuristicType.Euclidean);
var solver = new Solver();
var result = solver.Solve(aStarProblem);
Console.WriteLine($"Best Score: {result.BestScore}");
Console.WriteLine($"Average Score: {result.AverageScore}");
Console.WriteLine($"Total Distance: {result.TotalDistance}");
}
}
这段代码首先定义了一个Graph类来表示网络结构，以及一个AStarSearchProblem类来表示问题对象。然后定义了Solver类来执行搜索并返回结果。最后，通过Main方法演示了如何使用这个Solver类来解决给定的问题。请注意，这里的示例代码是简化的，实际应用中可能需要处理更多细节。
