【light oj】树的直径

Farthest Nodes in a Tree
Time Limit:2000MS     Memory Limit:32768KB     64bit IO Format:%lld & %llu

Description

Given a tree (a connected graph with no cycles), you have to find the farthest nodes in the tree. The edges of the tree are weighted and undirected. That means you have to find two nodes in the tree whose distance is maximum amongst all nodes.

Input

Input starts with an integer T (≤ 10), denoting the number of test cases.

Each case starts with an integer n (2 ≤ n ≤ 30000) denoting the total number of nodes in the tree. The nodes are numbered from 0 to n-1. Each of the next n-1 lines will contain three integers u v w (0 ≤ u, v < n, u ≠ v, 1 ≤ w ≤ 10000) denoting that node u and v are connected by an edge whose weight is w. You can assume that the input will form a valid tree.

Output

For each case, print the case number and the maximum distance.

Sample Input

2

4

0 1 20

1 2 30

2 3 50

5

0 2 20

2 1 10

0 3 29

0 4 50

Sample Output

Case 1: 100

Case 2: 80

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#include <cstdio>  
#include <cstring>  
#include <queue>   
#include <algorithm>   
using namespace std;  
struct Edge  
{  
    int from, to, val, next;  
};  
Edge edge[60000+10];  
int head[30000+10];//头'指针' 
int edgenum;//边数  
int dist[30000+10];  //以该点为尾点的最长距离 
bool vis[30000+10];  
int node; //记录端点值 
int ans;  //最终的最长路径 
int n;  
int k = 1;  
void init()  //初始化 
{  
    edgenum = 0;  
    memset(head, -1, sizeof(head));  
}  
void addedge(int u, int v, int w)  
{  
    edge[edgenum].from = u;//起点 
    edge[edgenum].to = v;//终点 
    edge[edgenum].val = w;//权值 
    edge[edgenum].next = head[u];//指向下一条边 
    head[u] = edgenum++;  
}  
void BFS(int s)  
{  
    queue<int> q;  
    memset(dist, 0, sizeof(dist));  
    memset(vis, false, sizeof(vis));  
    vis[s] = true;  
    ans = 0;  
    node = s;  
    q.push(s);  
    while(!q.empty())  
    {  
        int u = q.front();  
       q.pop();  
        for(int i = head[u]; i != -1; i = edge[i].next)  //遍历每一条边 
        {  
           int v = edge[i].to;  
            if(!vis[v] && dist[v] < dist[u] + edge[i].val)  
            {  
                vis[v] = true;  
                dist[v] = dist[u] + edge[i].val;  
                if(dist[v] > ans)  
                {  
                    ans = dist[v];  
                    node = v;  
                }  
               q.push(v);  
            }  
        }  
    }  
}   
int main()  
{  
    int t;  
    scanf("%d", &t);  
    while(t--)  
    {  
        scanf("%d", &n);  
        init();  
         int a, b, c;  
    for(int i = 1; i < n; i++)  
    {  
        scanf("%d%d%d", &a, &b, &c);  
        //a++, b++;  
        addedge(a,b,c);  
        addedge(b,a,c);  
    }   
         BFS(1);  //①任意从一个点u出发bfs,设其能到的最远点为v,0不可以 
         BFS(node);  //②从v出发重新bfs,设其能到达的最远点为s
    printf("Case %d: %d\n", k++, ans);  
  
    }  
    return 0;  
}  

### SDUOJ 图的直径数量算法 或 直径数量计算 在图论中,直径是指中距离最远的两个节点之间的路径长度。对于一棵,其直径可以通过两次深度优先搜索(DFS)或广度优先搜索(BFS)来计算[^1]。以下是具体方法: #### 直径计算 1. **选择任意节点作为起点**:从任意节点出发,执行一次 DFS 或 BFS,找到离该节点最远的节点 \( u \)。 2. **以 \( u \) 为起点再次搜索**:从 \( u \) 出发,再次执行 DFS 或 BFS,找到离 \( u \) 最远的节点 \( v \)。 3. **路径长度即为直径**:节点 \( u \) 和 \( v \) 之间的路径长度即为直径。 #### 直径数量计算 直径数量是指中所有路径中,与直径长度相等的路径数量。为了计算直径的数量,可以采用以下方法: - 在第一次 DFS/BFS 中记录每个节点的距离信息。 - 在第二次 DFS/BFS 中,记录到达直径终点的所有路径数量。 以下是基于 Python 的代码实现: ```python from collections import deque, defaultdict def bfs(graph, start): n = len(graph) visited = [False] * n distance = [-1] * n queue = deque() visited[start] = True distance[start] = 0 queue.append(start) farthest_node = start max_distance = 0 while queue: node = queue.popleft() for neighbor in graph[node]: if not visited[neighbor]: visited[neighbor] = True distance[neighbor] = distance[node] + 1 queue.append(neighbor) if distance[neighbor] > max_distance: max_distance = distance[neighbor] farthest_node = neighbor return farthest_node, max_distance, distance def count_diameter_paths(graph): n = len(graph) # 第一次 BFS 找到最远点 u u, _, _ = bfs(graph, 0) # 第二次 BFS 找到最远点 v 并得到直径 v, diameter, distance_u = bfs(graph, u) # 第三次 BFS 计算直径路径数量 _, _, distance_v = bfs(graph, v) count = 0 for i in range(n): if distance_u[i] + distance_v[i] == diameter: count += 1 return diameter, count # 示例图 graph = defaultdict(list) graph[0].append(1); graph[1].append(0) graph[0].append(2); graph[2].append(0) graph[1].append(3); graph[3].append(1) graph[2].append(4); graph[4].append(2) diameter, paths_count = count_diameter_paths(graph) print(f"Tree Diameter: {diameter}, Paths Count: {paths_count}") ``` 上述代码实现了直径及其路径数量的计算[^1]。 ### 图的直径数量计算 对于一般图(非结构),图的直径定义为任意两点间最短路径的最大值。计算图的直径需要求解所有节点对之间的最短路径,通常使用 Floyd-Warshall 算法或多次 Dijkstra 算法。图的直径数量则指所有最短路径中等于直径的路径数量。 以下是基于 Floyd-Warshall 算法的实现: ```python def floyd_warshall(graph, n): dist = [[float('inf')] * n for _ in range(n)] for i in range(n): dist[i][i] = 0 for u, neighbors in enumerate(graph): for v, w in neighbors: dist[u][v] = w for k in range(n): for i in range(n): for j in range(n): if dist[i][k] + dist[k][j] < dist[i][j]: dist[i][j] = dist[i][k] + dist[k][j] return dist def count_graph_diameter_paths(graph, n): dist = floyd_warshall(graph, n) diameter = max(max(row) for row in dist) count = sum(1 for i in range(n) for j in range(i+1, n) if dist[i][j] == diameter) return diameter, count # 示例图 n = 5 graph = [ [(1, 3), (2, 1)], [(0, 3), (3, 5)], [(0, 1), (4, 2)], [(1, 5), (4, 1)], [(2, 2), (3, 1)] ] diameter, paths_count = count_graph_diameter_paths(graph, n) print(f"Graph Diameter: {diameter}, Paths Count: {paths_count}") ``` 上述代码实现了图的直径及其路径数量的计算[^1]。
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