HDU-1087-Super Jumping! Jumping! Jumping!

本文介绍了一款名为“SuperJumping!Jumping!Jumping!”的游戏,玩家需从起点跳跃到终点,途中经过的棋子数值总和即为得分。文章提供了实现这一游戏逻辑的C++代码示例,通过动态规划算法寻找最大得分路径。

Super Jumping! Jumping! Jumping!

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 40385    Accepted Submission(s): 18636
Problem Description
Nowadays, a kind of chess game called “Super Jumping! Jumping! Jumping!” is very popular in HDU. Maybe you are a good boy, and know little about this game, so I introduce it to you now.



The game can be played by two or more than two players. It consists of a chessboard(棋盘)and some chessmen(棋子), and all chessmen are marked by a positive integer or “start” or “end”. The player starts from start-point and must jumps into end-point finally. In the course of jumping, the player will visit the chessmen in the path, but everyone must jumps from one chessman to another absolutely bigger (you can assume start-point is a minimum and end-point is a maximum.). And all players cannot go backwards. One jumping can go from a chessman to next, also can go across many chessmen, and even you can straightly get to end-point from start-point. Of course you get zero point in this situation. A player is a winner if and only if he can get a bigger score according to his jumping solution. Note that your score comes from the sum of value on the chessmen in you jumping path.
Your task is to output the maximum value according to the given chessmen list.
Input
Input contains multiple test cases. Each test case is described in a line as follow:
N value_1 value_2 …value_N 
It is guarantied that N is not more than 1000 and all value_i are in the range of 32-int.
A test case starting with 0 terminates the input and this test case is not to be processed.
Output
For each case, print the maximum according to rules, and one line one case.
Sample Input
3 1 3 2
4 1 2 3 4
4 3 3 2 1
0
Sample Output
4
10
3
Author
lcy


转移状态方程式 :dp[i]=max(dp[i],dp[j]+a[i])

#include<cstdio>
#include<cstring>
#include<algorithm>
using namespace std;
int main()
{
	int n,a[1001],dp[1001],sum;
	while(scanf("%d",&n)&&n){
		for(int i=1;i<=n;i++){
			scanf("%d",&a[i]);
		}
		sum=0;
		for(int i=1;i<=n;i++){
			dp[i]=a[i]; 
			for(int j=1;j<i;j++){
				if(a[i]>a[j]){
					dp[i]=max(dp[i],dp[j]+a[i]);//比较其大小; 
				}
				sum=max(sum,dp[i]);
			}
		}
		printf("%d\n",sum);
	}
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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