zoj 3822 Domination 概率DP

教育者埃德华购买了一块大小为N行M列的装饰棋盘,并每天随机放置一枚棋子。几天后,棋盘被棋子全部占据。埃德华想计算将空白棋盘变为完全占据状态所需的平均天数。

Domination

Time Limit: 8 Seconds      Memory Limit: 131072 KB      Special Judge

Edward is the headmaster of Marjar University. He is enthusiastic about chess and often plays chess with his friends. What's more, he bought a large decorative chessboard with N rows and M columns.

Every day after work, Edward will place a chess piece on a random empty cell. A few days later, he found the chessboard was dominated by the chess pieces. That means there is at least one chess piece in every row. Also, there is at least one chess piece in every column.

"That's interesting!" Edward said. He wants to know the expectation number of days to make an empty chessboard of N × M dominated. Please write a program to help him.

Input

There are multiple test cases. The first line of input contains an integer T indicating the number of test cases. For each test case:

There are only two integers N and M (1 <= N, M <= 50).

Output

For each test case, output the expectation number of days.

Any solution with a relative or absolute error of at most 10-8 will be accepted.

Sample Input
2
1 3
2 2
Sample Output
3.000000000000
2.666666666667

Author: JIANG, Kai

#include <cstdio>
#include <cstring>
#include <iostream>
#include <algorithm>

using namespace std;
#define N 55
#define M 2505

int n,m;
double dp[N][N][M];


int main()
{
    int _T;
    scanf("%d", &_T);
    while(_T--)
    {
        scanf("%d%d", &n, &m);
        memset(dp, 0, sizeof dp);
        dp[0][0][0] = 1.0;
        int tt = n * m;
        for(int i = 1; i <= n; i++)
            for(int j = 1; j <= m; j++)
                for(int k = 1; k <= i * j; k++)
                {
                    int tmp = tt - k + 1;
                    dp[i][j][k] += dp[i - 1][j - 1][k - 1] * (double)(n - i + 1) * (m - j + 1) ;
                    dp[i][j][k] += dp[i][j - 1][k - 1] * (double)i * (m - j + 1) ;
                    dp[i][j][k] += dp[i - 1][j][k - 1] * (double)(n - i + 1) * j ;
                    if(i != n || j != m)
                        dp[i][j][k] += dp[i][j][k - 1] * (double)(i * j - k + 1) ;
                    dp[i][j][k] /= tmp;
                    //printf("dp[%d][%d][%d] = %.10f\n", i, j, k, dp[i][j][k]);

                }
        double ans = 0.0;
        for(int i = 0; i <= tt; i++)
            ans += dp[n][m][i] * i;
        printf("%.10f\n", ans);
    }
    return 0;
}

/*

2
1 3
2 2

10
1 4
1 5
2 4
2 5
3 4
3 5
4 4

*/

根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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