POJ 1611 The Suspects

本文介绍如何使用并查集算法解决SARS病毒传播问题。通过输入学生人数、学生分组信息,输出所有被怀疑感染病毒的学生数量。

The Suspects
Time Limit: 1000MS Memory Limit: 20000K
Total Submissions: 24044 Accepted: 11741

Description

Severe acute respiratory syndrome (SARS), an atypical pneumonia of unknown aetiology, was recognized as a global threat in mid-March 2003. To minimize transmission to others, the best strategy is to separate the suspects from others. 
In the Not-Spreading-Your-Sickness University (NSYSU), there are many student groups. Students in the same group intercommunicate with each other frequently, and a student may join several groups. To prevent the possible transmissions of SARS, the NSYSU collects the member lists of all student groups, and makes the following rule in their standard operation procedure (SOP). 
Once a member in a group is a suspect, all members in the group are suspects. 
However, they find that it is not easy to identify all the suspects when a student is recognized as a suspect. Your job is to write a program which finds all the suspects.

Input

The input file contains several cases. Each test case begins with two integers n and m in a line, where n is the number of students, and m is the number of groups. You may assume that 0 < n <= 30000 and 0 <= m <= 500. Every student is numbered by a unique integer between 0 and n−1, and initially student 0 is recognized as a suspect in all the cases. This line is followed by m member lists of the groups, one line per group. Each line begins with an integer k by itself representing the number of members in the group. Following the number of members, there are k integers representing the students in this group. All the integers in a line are separated by at least one space. 
A case with n = 0 and m = 0 indicates the end of the input, and need not be processed.

Output

For each case, output the number of suspects in one line.

Sample Input

100 4
2 1 2
5 10 13 11 12 14
2 0 1
2 99 2
200 2
1 5
5 1 2 3 4 5
1 0
0 0

Sample Output

4
1
1

题目大意:有n个人,m组。0是病毒携带者,和0一组的人被怀疑感染病毒,和被怀疑的人一组的人也被怀疑。求所有被怀疑者的个数。


解题思路:并查集。将0作为祖先,祖先为0的都是被怀疑者。


代码如下:


#include <cstdio>
int const maxn=30003;
int father[maxn];
int find(int x)
{
	return x==father[x]?x:father[x]=find(father[x]);
}
void Union(int a,int b)
{
	int a1=find(a);
	int b1=find(b);
	if(a1>b1)
	father[a1]=b1;
	else
		father[b1]=a1;
}
int main()
{
	int n,m,cnt,i,j,num,f,x;
	while(scanf("%d%d",&n,&m)!=EOF&&(n||m))
	{
		cnt=0;
		for(i=0;i<n;i++)
			father[i]=i;
		for(i=0;i<m;i++)
		{
			scanf("%d%d",&num,&f);
			for(j=0;j<num-1;j++)
			{
				scanf("%d",&x);
				Union(f,x);
			}
		}
		for(i=0;i<n;i++)
			if(find(i)==0)
				cnt++;
		printf("%d\n", cnt);
	}
	return 0;
}


基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
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