山东第四届省赛 I (基础类)

本文探讨了一个复杂的迷宫寻宝问题,通过概率动态规划(DP)方法求解预期步骤数以找到宝藏。文章详细介绍了状态转移方程,并提供了一段C++代码实现。适用于对算法和迷宫问题感兴趣的学习者。

Description

Mary stands in a strange maze, the maze looks like a triangle(the first layer have one room,the second layer have two rooms,the third layer have three rooms …). Now she stands at the top point(the first layer), and the KEY of this maze is in the lowest layer’s leftmost room. Known that each room can only access to its left room and lower left and lower right rooms .If a room doesn’t have its left room, the probability of going to the lower left room and lower right room are a and b (a + b = 1 ). If a room only has it’s left room, the probability of going to the room is 1. If a room has its lower left, lower right rooms and its left room, the probability of going to each room are c, d, e (c + d + e = 1). Now , Mary wants to know how many steps she needs to reach the KEY. Dear friend, can you tell Mary the expected number of steps required to reach the KEY?

 

Input

There are no more than 70 test cases. 
In each case , first Input a positive integer n(0<n<45), which means the layer of the maze, then Input five real number a, b, c, d, e. (0<=a,b,c,d,e<=1, a+b=1, c+d+e=1). 
The input is terminated with 0. This test case is not to be processed.

Output

Please calculate the expected number of steps required to reach the KEY room, there are 2 digits after the decimal point.

Sample Input

30.3 0.70.1 0.3 0.60 

Sample Output

3.41



本题的题意十分明显,状态转移也已经明确给出,作为初识概率dp把。有一点要注意概率转移步长加1

#include <map>
#include <set>
#include <list>
#include <deque>
#include <queue>
#include <stack>
#include <cmath>
#include <vector>
#include <string>
#include <cstdio>
#include <cstring>
#include <cstdlib>
#include <iostream>
#include <algorithm>
using namespace std;
typedef long long LL;
typedef unsigned long long ULL;
#define INF (1<<30)
#define prln(x) printf("%lld...\n",x)
#define pr(x) printf("%lld...",x)
#define EPS 1e-10
#define rep(i,n) for(int (i)=(0);(i)<(n);(i)++)
#define Rep(i,n) for(int (i)=(1);(i)<=(n);(i)++)

const int maxn = 50;
double dp[maxn][maxn];
double a,b,c,d,e;
int n;
int main()
{
    while(scanf("%d",&n)==1 && n){
      scanf("%lf %lf %lf %lf %lf",&a,&b,&c,&d,&e);
      for(int i=n;i>=1;i--)
        for(int j=1;j<=i;j++){
          if(i == n) dp[i][j] = (j-1);
          else {
            if(j == 1) dp[i][j] = a*(dp[i+1][j]+1)+b*(dp[i+1][j+1]+1);
            else dp[i][j] = c*(dp[i+1][j]+1)+d*(dp[i+1][j+1]+1)+e*(dp[i][j-1]+1);
          }
      }
      printf("%.2lf\n",dp[1][1]);
    }
    return 0;
}


【多变量输入超前多步预测】基于CNN-BiLSTM的光伏功率预测研究(Matlab代码实现)内容概要:本文介绍了基于CNN-BiLSTM模型的多变量输入超前多步光伏功率预测方法,并提供了Matlab代码实现。该研究结合卷积神经网络(CNN)强大的特征提取能力与双向长短期记忆网络(BiLSTM)对时间序列前后依赖关系的捕捉能力,构建了一个高效的深度学习预测模型。模型输入包含多个影响光伏发电的气象与环境变量,能够实现对未来多个时间步长的光伏功率进行精确预测,适用于复杂多变的实际应用场景。文中详细阐述了数据预处理、模型结构设计、训练流程及实验验证过程,展示了该方法相较于传统模型在预测精度和稳定性方面的优势。; 适合人群:具备一定机器学习和深度学习基础,熟悉Matlab编程,从事新能源预测、电力系统分析或相关领域研究的研发人员与高校研究生。; 使用场景及目标:①应用于光伏电站功率预测系统,提升电网调度的准确性与稳定性;②为可再生能源并网管理、能量存储规划及电力市场交易提供可靠的数据支持;③作为深度学习在时间序列多步预测中的典型案例,用于科研复现与教学参考。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注数据归一化、CNN特征提取层设计、BiLSTM时序建模及多步预测策略的实现细节,同时可尝试引入更多外部变量或优化网络结构以进一步提升预测性能。
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