HDU 2955 Robberies(01背包)（动态规划）

Robberies

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 18375    Accepted Submission(s): 6797

Problem Description

The aspiring Roy the Robber has seen a lot of American movies, and knows that the bad guys usually gets caught in the end, often because they become too greedy. He has decided to work in the lucrative business of bank robbery only for a short while, before retiring to a comfortable job at a university.


For a few months now, Roy has been assessing the security of various banks and the amount of cash they hold. He wants to make a calculated risk, and grab as much money as possible.


His mother, Ola, has decided upon a tolerable probability of getting caught. She feels that he is safe enough if the banks he robs together give a probability less than this.

 

Input

The first line of input gives T, the number of cases. For each scenario, the first line of input gives a floating point number P, the probability Roy needs to be below, and an integer N, the number of banks he has plans for. Then follow N lines, where line j gives an integer Mj and a floating point number Pj . 
Bank j contains Mj millions, and the probability of getting caught from robbing it is Pj .

 

Output

For each test case, output a line with the maximum number of millions he can expect to get while the probability of getting caught is less than the limit set.

Notes and Constraints
0 < T <= 100
0.0 <= P <= 1.0
0 < N <= 100
0 < Mj <= 100
0.0 <= Pj <= 1.0
A bank goes bankrupt if it is robbed, and you may assume that all probabilities are independent as the police have very low funds.

 

Sample Input

30.04 31 0.022 0.033 0.050.06 32 0.032 0.033 0.050.10 31 0.032 0.023 0.05

 

Sample Output

246

 

  对于背包还是理解不是很透，看了某个人的博客，才对背包有了个大体稍微深入的了解，但现在还是不能独立自主的将思路和代码实现，希望近期努力能让我彻底掌握这类题型吧，至少独立的ac此类题目啊！！！

下面是某大神的简单解释http://blog.csdn.net/mu399/article/details/7722810

01背包问题，是用来介绍动态规划算法最经典的例子，网上关于01背包问题的讲解也很多，我写这篇文章力争做到用最简单的方式，最少的公式把01背包问题讲解透彻。

01背包的状态转换方程 f[i,j] = Max{ f[i-1,j-Wi]+Pi( j >= Wi ),  f[i-1,j] }

f[i,j]表示在前i件物品中选择若干件放在承重为 j 的背包中，可以取得的最大价值。

Pi表示第i件物品的价值。

决策：为了背包中物品总价值最大化，第 i件物品应该放入背包中吗 ？

题目描述：

有编号分别为a,b,c,d,e的五件物品，它们的重量分别是2,2,6,5,4，它们的价值分别是6,3,5,4,6，现在给你个承重为10的背包，如何让背包里装入的物品具有最大的价值总和？

nameweightvalue12345678910a26066991212151515b23033669991011c65000666661011d54000666661010e460006666666

只要你能通过找规律手工填写出上面这张表就算理解了01背包的动态规划算法。

首先要明确这张表是至底向上，从左到右生成的。

为了叙述方便，用e2单元格表示e行2列的单元格，这个单元格的意义是用来表示只有物品e时，有个承重为2的背包，那么这个背包的最大价值是0，因为e物品的重量是4，背包装不了。

对于d2单元格，表示只有物品e，d时,承重为2的背包,所能装入的最大价值，仍然是0，因为物品e,d都不是这个背包能装的。

同理，c2=0，b2=3,a2=6。

对于承重为8的背包，a8=15,是怎么得出的呢？

根据01背包的状态转换方程，需要考察两个值，

一个是f[i-1,j],对于这个例子来说就是b8的值9，另一个是f[i-1,j-Wi]+Pi；

在这里，

 f[i-1,j]表示我有一个承重为8的背包，当只有物品b,c,d,e四件可选时，这个背包能装入的最大价值

f[i-1,j-Wi]表示我有一个承重为6的背包（等于当前背包承重减去物品a的重量），当只有物品b,c,d,e四件可选时，这个背包能装入的最大价值

f[i-1,j-Wi]就是指单元格b6,值为9，Pi指的是a物品的价值，即6

由于f[i-1,j-Wi]+Pi = 9 + 6 = 15 大于f[i-1,j] = 9，所以物品a应该放入承重为8的背包

  我的代码如下：

#include<iostream>#include<cstring>#include<cstdio>#include<algorithm>using namespace std;double dp[10005];struct probability{    int value;    double chance;}rob[105];int main(){    int t,n;    double p;    cin>>t;    while(t--)    {        scanf("%lf%d",&p,&n);        int sum=0;        for(int i=0;i<n;i++)        {            scanf("%d%lf",&rob[i].value,&rob[i].chance);            sum+=rob[i].value;        }        memset(dp,0,sizeof(dp));        dp[0]=1;            for(int i=0;i<n;i++)                for(int j=sum;j>=rob[i].value;j--)                {                    dp[j]=max(dp[j],dp[j-rob[i].value]*(1-rob[i].chance));                }                for(int i=sum;i>=0;i--)                    if(dp[i]>1-p)                {                    cout<<i<<endl;                    break;                }    }    return 0;}