CodeForces 471B MUH and Important Things

本文介绍了一种针对极地动物的任务分配算法。该算法需为每只动物生成不同的任务执行计划,并确保这些计划各不相同。输入包含任务数量及难度,输出则是判断是否可以生成三个不同的任务序列,以及具体的任务安排方案。
B. MUH and Important Things
time limit per test
1 second
memory limit per test
256 megabytes
input
standard input
output
standard output

It's time polar bears Menshykov and Uslada from the zoo of St. Petersburg and elephant Horace from the zoo of Kiev got down to business. In total, there aren tasks for the day and each animal should do each of these tasks. For each task, they have evaluated its difficulty. Also animals decided to do the tasks in order of their difficulty. Unfortunately, some tasks can have the same difficulty, so the order in which one can perform the tasks may vary.

Menshykov, Uslada and Horace ask you to deal with this nuisance and come up with individual plans for each of them. The plan is a sequence describing the order in which an animal should do all then tasks. Besides, each of them wants to have its own unique plan. Therefore three plans must form three different sequences. You are to find the required plans, or otherwise deliver the sad news to them by stating that it is impossible to come up with three distinct plans for the given tasks.

Input

The first line contains integer n (1 ≤ n ≤ 2000) — the number of tasks. The second line containsn integersh1, h2, ..., hn (1 ≤ hi ≤ 2000), wherehi is the difficulty of thei-th task. The larger numberhi is, the more difficult thei-th task is.

Output

In the first line print "YES" (without the quotes), if it is possible to come up with three distinct plans of doing the tasks. Otherwise print in the first line "NO" (without the quotes). If three desired plans do exist, print in the second line n distinct integers that represent the numbers of the tasks in the order they are done according to the first plan. In the third and fourth line print two remaining plans in the same form.

If there are multiple possible answers, you can print any of them.

Sample test(s)
Input
4
1 3 3 1
Output
YES
1 4 2 3 
4 1 2 3 
4 1 3 2 
Input
5
2 4 1 4 8
Output
NO
Note

In the first sample the difficulty of the tasks sets one limit: tasks 1 and 4 must be done before tasks 2 and 3. That gives the total of four possible sequences of doing tasks : [1, 4, 2, 3], [4, 1, 2, 3], [1, 4, 3, 2], [4, 1, 3, 2]. You can print any three of them in the answer.

In the second sample there are only two sequences of tasks that meet the conditions — [3, 1, 2, 4, 5] and [3, 1, 4, 2, 5]. Consequently, it is impossible to make three distinct sequences of tasks.


#include<cstring>
#include<iostream>
#include<algorithm>
using namespace std;

int main(){
    int n,i,j,equalnum,flag;
    while(cin>>n){
        equalnum=0;
        pair<int,int>a[n];
        for(i=0;i<n;i++){
            cin>>a[i].first,a[i].second=i;
        }
        sort(a,a+n);
        for(i=0;i<n-1;i++){
            if(a[i].first==a[i+1].first){
                equalnum++;
            }
        }
        if(equalnum<2){
            cout<<"NO"<<endl;
            return 0;
        }
        else{
            cout<<"YES"<<endl;
            for(i=0;i<n;i++){
                if(i==n-1){
                    cout<<a[i].second+1<<endl;
                }
                else{
                    cout<<a[i].second+1<<" ";
                }
            }
            flag=0;
            for(i=0;i<n,flag!=2;i++){
                if(a[i].first==a[i+1].first){
                    swap(a[i],a[i+1]);
                    for(j=0;j<n;j++){
                        if(j==n-1){
                            cout<<a[j].second+1<<endl;
                        }
                        else{
                            cout<<a[j].second+1<<" ";
                        }
                    }
                    flag++;
                }
            }
        }
    }
    return 0;
}


【电能质量扰动】基于ML和DWT的电能质量扰动分类方法研究(Matlab实现)内容概要:本文研究了一种基于机器学习(ML)和离散小波变换(DWT)的电能质量扰动分类方法,并提供了Matlab实现方案。首先利用DWT对电能质量信号进行多尺度分解,提取信号的时频域特征,有效捕捉电压暂降、暂升、中断、谐波、闪变等常见扰动的关键信息;随后结合机器学习分类器(如SVM、BP神经网络等)对提取的特征进行训练与分类,实现对不同类型扰动的自动识别与准确区分。该方法充分发挥DWT在信号去噪与特征提取方面的优势,结合ML强大的模式识别能力,提升了分类精度与鲁棒性,具有较强的实用价值。; 适合人群:电气工程、自动化、电力系统及其自动化等相关专业的研究生、科研人员及从事电能质量监测与分析的工程技术人员;具备一定的信号处理基础和Matlab编程能力者更佳。; 使用场景及目标:①应用于智能电网中的电能质量在线监测系统,实现扰动类型的自动识别;②作为高校或科研机构在信号处理、模式识别、电力系统分析等课程的教学案例或科研实验平台;③目标是提高电能质量扰动分类的准确性与效率,为后续的电能治理与设备保护提供决策依据。; 阅读建议:建议读者结合Matlab代码深入理解DWT的实现过程与特征提取步骤,重点关注小波基选择、分解层数设定及特征向量构造对分类性能的影响,并尝试对比不同机器学习模型的分类效果,以全面掌握该方法的核心技术要点。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值