Air Raid POJ1422 & HDU1151 最小路径覆盖

本文介绍了一种基于最大匹配算法解决最小路径覆盖问题的方法。通过示例输入输出展示了如何使用程序来确定最少数量的路径以覆盖图中的所有节点。文章提供了一个C++实现示例。


Consider a town where all the streets are one-way and each street leads from one intersection to another. It is also known that starting from an intersection and walking through town's streets you can never reach the same intersection i.e. the town's streets form no cycles. 

With these assumptions your task is to write a program that finds the minimum number of paratroopers that can descend on the town and visit all the intersections of this town in such a way that more than one paratrooper visits no intersection. Each paratrooper lands at an intersection and can visit other intersections following the town streets. There are no restrictions about the starting intersection for each paratrooper. 
Input
Your program should read sets of data. The first line of the input file contains the number of the data sets. Each data set specifies the structure of a town and has the format: 

no_of_intersections 
no_of_streets 
S1 E1 
S2 E2 
...... 
Sno_of_streets Eno_of_streets 

The first line of each data set contains a positive integer no_of_intersections (greater than 0 and less or equal to 120), which is the number of intersections in the town. The second line contains a positive integer no_of_streets, which is the number of streets in the town. The next no_of_streets lines, one for each street in the town, are randomly ordered and represent the town's streets. The line corresponding to street k (k <= no_of_streets) consists of two positive integers, separated by one blank: Sk (1 <= Sk <= no_of_intersections) - the number of the intersection that is the start of the street, and Ek (1 <= Ek <= no_of_intersections) - the number of the intersection that is the end of the street. Intersections are represented by integers from 1 to no_of_intersections. 

There are no blank lines between consecutive sets of data. Input data are correct. 
Output
The result of the program is on standard output. For each input data set the program prints on a single line, starting from the beginning of the line, one integer: the minimum number of paratroopers required to visit all the intersections in the town. 
Sample Input
2
4
3
3 4
1 3
2 3
3
3
1 3
1 2
2 3
Sample Output
2
1


题意 : 最图中找到最少得路径使得覆盖了所有点


最小路径覆盖 = 顶点数 - 最大匹配数

模板题~

#include <iostream>
#include <cstring>
using namespace std;
int m, n;
bool vis[350];
int link[350], map[350][350];

bool dfs(int x) {
	for (int i = 1; i <= n; i++) {
		if (map[x][i] && !vis[i]) {
			vis[i] = 1;
			if (link[i] == -1 || dfs(link[i])) {
				link[i] = x;
				return 1;
			}
		}
	}
	return 0;
}


int main(void) {
	ios::sync_with_stdio(0);
	int t;
	cin >> t;
	while (t--) {
		cin >> n >> m;
		memset(map, 0, sizeof(map));
	    for (int i = 0,a,b; i < m; i++) {
	        cin >> a >> b;
	        map[a][b] = 1;
	    }
		
		int ans = 0;
		memset(link, -1, sizeof(link));
		for (int i = 1; i <= n; i++) {
			memset(vis, 0, sizeof(vis));
			if (dfs(i)) ans++;
		}
		cout << n-ans << '\n';
	}
	return 0;
}


内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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