Play the Dice - HDU 4586 期望dp

探讨一个特殊掷骰子游戏的数学模型,通过计算不同情况下的收益期望,解决何时可以获得额外掷骰机会的问题。

Play the Dice

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65535/32768 K (Java/Others)
Total Submission(s): 1689    Accepted Submission(s): 548
Special Judge


Problem Description
There is a dice with n sides, which are numbered from 1,2,...,n and have the equal possibility to show up when one rolls a dice. Each side has an integer ai on it. Now here is a game that you can roll this dice once, if the i-th side is up, you will get ai yuan. What's more, some sids of this dice are colored with a special different color. If you turn this side up, you will get once more chance to roll the dice. When you roll the dice for the second time, you still have the opportunity to win money and rolling chance. Now you need to calculate the expectations of money that we get after playing the game once.
 

Input
Input consists of multiple cases. Each case includes two lines.
The first line is an integer n (2<=n<=200), following with n integers ai(0<=ai<200)
The second line is an integer m (0<=m<=n), following with m integers bi(1<=bi<=n), which are the numbers of the special sides to get another more chance.
 

Output
Just a real number which is the expectations of the money one can get, rounded to exact two digits. If you can get unlimited money, print inf.
 

Sample Input
6 1 2 3 4 5 6 0 4 0 0 0 0 1 3
 

Sample Output
3.50 0.00
 

题意:一个骰子有n个面,每个面有一个权值,掷到m个面时可以继续掷,问得到的权值的期望是多少。

思路:ans=不能继续掷:ai/n +  继续掷:(ai+ans)/n ,最后结果是ans=(a1+a2+...+an)/(n/m)。再特判inf的情况。

AC代码如下:

#include<cstdio>
#include<cstring>
#include<iostream>
#include<algorithm>
#include<cmath>
using namespace std;
typedef long long ll;
int T,t,n,m,sum;
int main()
{
    int i,j,k;
    double ans;
    while(~scanf("%d",&n))
    {
        sum=0;
        for(i=1;i<=n;i++)
        {
            scanf("%d",&k);
            sum+=k;
        }
        scanf("%d",&m);
        for(j=1;j<=m;j++)
           scanf("%d",&k);
        if(sum==0)
           printf("0\n");
        else if(n==m)
          printf("inf\n");
        else
        {
            ans=round(100.0*sum/(n-m));
            printf("%.2f\n",ans/100);
        }
    }
}



内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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