HDU - 1022 Train Problem I STL 压栈

本文介绍了一个火车调度问题,探讨了如何使用数组模拟栈来验证一组火车能否按照特定顺序离开车站。通过具体的输入输出示例,文章详细展示了算法的实现过程。

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Train Problem I

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 41471    Accepted Submission(s): 15510


Problem Description
As the new term comes, the Ignatius Train Station is very busy nowadays. A lot of student want to get back to school by train(because the trains in the Ignatius Train Station is the fastest all over the world ^v^). But here comes a problem, there is only one railway where all the trains stop. So all the trains come in from one side and get out from the other side. For this problem, if train A gets into the railway first, and then train B gets into the railway before train A leaves, train A can't leave until train B leaves. The pictures below figure out the problem. Now the problem for you is, there are at most 9 trains in the station, all the trains has an ID(numbered from 1 to n), the trains get into the railway in an order O1, your task is to determine whether the trains can get out in an order O2.
 

Input
The input contains several test cases. Each test case consists of an integer, the number of trains, and two strings, the order of the trains come in:O1, and the order of the trains leave:O2. The input is terminated by the end of file. More details in the Sample Input.
 

Output
The output contains a string "No." if you can't exchange O2 to O1, or you should output a line contains "Yes.", and then output your way in exchanging the order(you should output "in" for a train getting into the railway, and "out" for a train getting out of the railway). Print a line contains "FINISH" after each test case. More details in the Sample Output.
 

Sample Input
3 123 321 3 123 312
 

Sample Output
Yes. in in in out out out FINISH No. FINISH

通过火车进站前的顺序判断是否能改变为所给的顺序,火车需先进站然后再出站,可用STL中的stack实现,这里用数组模拟压栈

#include<stdio.h>
#include<string.h>
int main()
{
	int n;
	char s1[20]={0},s2[20]={0},s3[20];
	int s1_in[20],s2_in[20];
	while(~scanf("%d %s %s",&n,s1,s2))	
	{
		memset(s3,0,sizeof(s3));//需对数组进行清空
		
		int s[20]={0};
		int i1=0,i2=0,i3=0,i=0;
		//scanf("%s" "%s",s1,s2);
		for(i1=0;i1<n;i1++)//将字符转换为数字
		{
			s1_in[20]=s1[i]-'0';
			s2_in[20]=s2[i]-'0';
			
		}
		for(i1=0;i1<n;i1++)
		{
			s3[i3]=s1[i1];//火车进站
			i3++;
			i++;
			while(s3[i3-1]==s2[i2]&&i2<n)
			{
				i3--;
				s[i]=1;
				i2++;
				i++;
			}
		}
		//printf("%d %d %d %d\n",i1,i2,i3,i);
		if(i2==n)//火车全部出站
		{
			printf("Yes.\n");
			for(i1=0;i1<i;i1++)
			{
				if(s[i1]==1)
				{
					printf("out\n");
				}
				else
				printf("in\n");
			}
			printf("FINISH\n");
		}
		else
		{
			printf("No.\n");
			printf("FINISH\n");
		}
	}
	return 0;
} 



内容概要:本文针对国内加密货币市场预测研究较少的现状,采用BP神经网络构建了CCi30指数预测模型。研究选取2018年3月1日至2019年3月26日共391天的数据作为样本,通过“试凑法”确定最优隐结点数目,建立三层BP神经网络模型对CCi30指数收盘价进行预测。论文详细介绍了数据预处理、模型构建、训练及评估过程,包括数据归一化、特征工程、模型架构设计(如输入层、隐藏层、输出层)、模型编译与训练、模型评估(如RMSE、MAE计算)以及结果可视化。研究表明,该模型在短期内能较准确地预测指数变化趋势。此外,文章还讨论了隐层节点数的优化方法及其对预测性能的影响,并提出了若干改进建议,如引入更多技术指标、优化模型架构、尝试其他时序模型等。 适合人群:对加密货币市场预测感兴趣的研究人员、投资者及具备一定编程基础的数据分析师。 使用场景及目标:①为加密货币市场投资者提供一种新的预测工具和方法;②帮助研究人员理解BP神经网络在时间序列预测中的应用;③为后续研究提供改进方向,如数据增强、模型优化、特征工程等。 其他说明:尽管该模型在短期内表现出良好的预测性能,但仍存在一定局限性,如样本量较小、未考虑外部因素影响等。因此,在实际应用中需谨慎对待模型预测结果,并结合其他分析工具共同决策。
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