hdu-1083-Courses

本文探讨了一道经典的二分图匹配问题,旨在通过N名学生和P门课程的组合来寻找是否能形成一个每门课程都有唯一代表的委员会。采用二分匹配算法解决这一问题,并提供了完整的代码实现。

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题目衔接:http://acm.hdu.edu.cn/showproblem.php?pid=1083

Courses

Time Limit: 20000/10000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 10314    Accepted Submission(s): 4877


 

Problem Description

Consider a group of N students and P courses. Each student visits zero, one or more than one courses. Your task is to determine whether it is possible to form a committee of exactly P students that satisfies simultaneously the conditions:

. every student in the committee represents a different course (a student can represent a course if he/she visits that course)

. each course has a representative in the committee

Your program should read sets of data from a text file. The first line of the input file contains the number of the data sets. Each data set is presented in the following format:

P N
Count1 Student1 1 Student1 2 ... Student1 Count1
Count2 Student2 1 Student2 2 ... Student2 Count2
...... 
CountP StudentP 1 StudentP 2 ... StudentP CountP

The first line in each data set contains two positive integers separated by one blank: P (1 <= P <= 100) - the number of courses and N (1 <= N <= 300) - the number of students. The next P lines describe in sequence of the courses . from course 1 to course P, each line describing a course. The description of course i is a line that starts with an integer Count i (0 <= Count i <= N) representing the number of students visiting course i. Next, after a blank, you'll find the Count i students, visiting the course, each two consecutive separated by one blank. Students are numbered with the positive integers from 1 to N.

There are no blank lines between consecutive sets of data. Input data are correct.

The result of the program is on the standard output. For each input data set the program prints on a single line "YES" if it is possible to form a committee and "NO" otherwise. There should not be any leading blanks at the start of the line.

An example of program input and output:

 

 

Sample Input

 

2 3 3 3 1 2 3 2 1 2 1 1 3 3 2 1 3 2 1 3 1 1

 

 

Sample Output

 

YES NO

 

 

Source

Southeastern Europe 2000

 题目大意:一共有N个学生跟P门课程,一个学生可以任意选一门或多门课,问是否达成:

  1.每个学生选的都是不同的课(即不能有两个学生选同一门课)

  2.每门课都有一个代表(即P门课都被成功选过)

思路:二分匹配,只要看懂了题意就不难,注意他的输入为:p行然后第一门先输入对这门功课感兴趣的学生总人数,然后是那几个学生,例如样例:表示课程1有3人感兴趣分别是学生1,学生2,学生3,然后敲个板子,最后判断能不能相等就可以了

代码:

#include<map>
#include<stack>
#include<cmath>
#include<queue>
#include<vector>
#include<string>
#include<cstdio>
#include<cstring>
#include<cstdlib>
#include<iostream>
#include<algorithm>
using namespace std;
#define maxn 1100
#define ll long long
#define inf 9999999
int p,n;
int e[maxn][maxn];
int book[maxn];
int match[maxn];
int dfs(int u)
{
    int i,j;
    for(i=1; i<=n; i++)
    {
        if(!book[i]&&e[u][i]==1)
        {
            book[i]=1;
            if(match[i]==0||dfs(match[i]))
            {
                match[i]=u;
                return 1;
            }
        }
    }
    return 0;
}
int main()
{
    int test;
    scanf("%d",&test);
    while(test--)
    {
        int i,j,k,t1,t2,ans=0;
        scanf("%d%d",&p,&n);
        memset(book,0,sizeof(book));
        memset(match,0,sizeof(match));
        memset(e,0,sizeof(e));
        for(i=1; i<=p; i++)
        {
            scanf("%d",&t1);
            while(t1--)
            {
                scanf("%d",&t2);
                e[i][t2]=1;
            }
        }
        for(i=1; i<=p; i++)
        {
            memset(book,0,sizeof(book));
            if(dfs(i))
            {
                ans++;
            }
        }
        if(ans==p)
            printf("YES\n");
        else
            printf("NO\n");
    }
    return 0;
}

 

内容概要:本文档详细介绍了一个基于MATLAB实现的跨尺度注意力机制(CSA)结合Transformer编码器的多变量时间序列预测项目。项目旨在精准捕捉多尺度时间序列特征,提升多变量时间序列的预测性能,降低模型计算复杂度与训练时间,增强模型的解释性和可视化能力。通过跨尺度注意力机制,模型可以同时捕获局部细节和全局趋势,显著提升预测精度和泛化能力。文档还探讨了项目面临的挑战,如多尺度特征融合、多变量复杂依赖关系、计算资源瓶颈等问题,并提出了相应的解决方案。此外,项目模型架构包括跨尺度注意力机制模块、Transformer编码器层和输出预测层,文档最后提供了部分MATLAB代码示例。 适合人群:具备一定编程基础,尤其是熟悉MATLAB和深度学习的科研人员、工程师和研究生。 使用场景及目标:①需要处理多变量、多尺度时间序列数据的研究和应用场景,如金融市场分析、气象预测、工业设备监控、交通流量预测等;②希望深入了解跨尺度注意力机制和Transformer编码器在时间序列预测中的应用;③希望通过MATLAB实现高效的多变量时间序列预测模型,提升预测精度和模型解释性。 其他说明:此项目不仅提供了一种新的技术路径来处理复杂的时间序列数据,还推动了多领域多变量时间序列应用的创新。文档中的代码示例和详细的模型描述有助于读者快速理解和复现该项目,促进学术和技术交流。建议读者在实践中结合自己的数据集进行调试和优化,以达到最佳的预测效果。
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