[LintCode] Connecting Graph II

本文介绍了一种使用并查集数据结构处理图论中节点连接查询的问题。具体实现了一个UnionFind类来维护节点之间的连接关系,并通过connect方法添加边,query方法返回指定节点所在连通分量的节点数。

Given n nodes in a graph labeled from 1 to n. There is no edges in the graph at beginning.

You need to support the following method:
1. connect(a, b), an edge to connect node a and node b
2. query(a), Returns the number of connected component nodes which include node a.

 

Example
5 // n = 5
query(1) return 1
connect(1, 2)
query(1) return 2
connect(2, 4)
query(1) return 3
connect(1, 4)
query(1) return 3



 1 class UnionFind {
 2     private int[] father = null;
 3     private int[] numsInUnion = null;
 4     
 5     public UnionFind(int n){
 6         father = new int[n];
 7         numsInUnion = new int[n];
 8         for(int i = 0; i < n; i++){
 9             father[i] = i;
10             numsInUnion[i] = 1;
11         }
12     }
13     public int find(int x){
14         if(father[x] == x){
15             return x;
16         }
17         return father[x] = find(father[x]);
18     }
19     public void connect(int a, int b){
20         int root_a = find(a);
21         int root_b = find(b);
22         if(root_a != root_b){
23             father[root_a] = root_b;
24             numsInUnion[root_b] += numsInUnion[root_a];
25         }
26     }
27     public int queryNumInUnion(int a){
28         return numsInUnion[find(a)];
29     }
30 }
31 public class ConnectingGraph2 {
32     private UnionFind uf = null;
33     public ConnectingGraph2(int n) {
34         uf = new UnionFind(n);
35     }
36 
37     public void connect(int a, int b) {
38         uf.connect(a - 1, b - 1);
39     }
40         
41     public int query(int a) {
42         return uf.queryNumInUnion(a - 1);
43     }
44 }


转载于:https://www.cnblogs.com/lz87/p/7500090.html

基于模拟退火的计算器 在线运行 访问run.bcjh.xyz。 先展示下效果 https://pan.quark.cn/s/cc95c98c3760 参见此仓库。 使用方法(本地安装包) 前往Releases · hjenryin/BCJH-Metropolis下载最新 ,解压后输入游戏内校验码即可使用。 配置厨具 已在2.0.0弃用。 直接使用白菜菊花代码,保留高级厨具,新手池厨具可变。 更改迭代次数 如有需要,可以更改 中39行的数字来设置迭代次数。 本地编译 如果在windows平台,需要使用MSBuild编译,并将 改为ANSI编码。 如有条件,强烈建议这种本地运行(运行可加速、可多次重复)。 在 下运行 ,是游戏中的白菜菊花校验码。 编译、运行: - 在根目录新建 文件夹并 至build - - 使用 (linux) 或 (windows) 运行。 最后在命令行就可以得到输出结果了! (注意顺序)(得到厨师-技法,表示对应新手池厨具) 注:linux下不支持多任务选择 云端编译已在2.0.0弃用。 局限性 已知的问题: - 无法得到最优解! 只能得到一个比较好的解,有助于开阔思路。 - 无法选择菜品数量(默认拉满)。 可能有一定门槛。 (这可能有助于防止这类辅助工具的滥用导致分数膨胀? )(你问我为什么不用其他语言写? python一个晚上就写好了,结果因为有涉及json读写很多类型没法推断,jit用不了,算这个太慢了,所以就用c++写了) 工作原理 采用两层模拟退火来最大化总能量。 第一层为三个厨师,其能量用第二层模拟退火来估计。 也就是说,这套方法理论上也能算厨神(只要能够在非常快的时间内,算出一个厨神面板的得分),但是加上厨神的食材限制工作量有点大……以后再说吧。 (...
03-08
### Graphs in Computer Science and Technology Graph theory plays an essential role within computer science, providing a powerful framework to model relationships between objects or entities. A graph consists of vertices (also called nodes) connected by edges that can be directed or undirected depending on whether the relationship has directionality[^1]. In many applications such as social networks, transportation systems, and web pages linking structure, graphs serve as fundamental models representing these complex interconnections effectively. #### Representation Methods for Graph Data Structure Two primary methods exist for storing graph data structures: - **Adjacency Matrix**: This method uses a two-dimensional array where each entry indicates presence/absence of connection between pairs of vertices. - **Adjacency List**: An alternative approach involves maintaining lists associated with every vertex containing all directly reachable neighbors from it through existing connections represented via linked entries pointing towards adjacent elements forming chains emanating outwardly starting at source points until reaching terminal destinations along paths traversed during searches conducted over network topologies described mathematically using this formalism[^2]. #### Common Operations Performed on Graphs Several key operations frequently performed include but are not limited to: - Traversing entire components recursively visiting unvisited parts systematically ensuring full coverage without redundancy; - Searching specific items efficiently utilizing depth-first search (DFS), breadth-first search (BFS); - Finding shortest path solutions connecting distant locations optimally under varying constraints like distance minimization criteria applied across weighted links joining intermediate stops encountered en route when navigating large-scale interconnected frameworks modeled after real-world scenarios found commonly today inside modern software platforms built around robust computational paradigms supporting advanced analytics capabilities leveraging insights derived therefrom[^3]. ```python import collections def bfs(graph, start): visited = set() queue = collections.deque([start]) while queue: node = queue.popleft() if node not in visited: print(node) visited.add(node) for neighbor in graph[node]: if neighbor not in visited: queue.append(neighbor) # Example usage graph_example = { 'A': ['B', 'C'], 'B': ['D', 'E'], 'C': [], 'D': [], 'E': [] } bfs(graph_example, 'A') ```
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