HDU1686Oulipo

http://acm.hdu.edu.cn/showproblem.php?pid=1686

Oulipo

Time Limit: 3000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 5535    Accepted Submission(s): 2211


Problem Description
The French author Georges Perec (1936–1982) once wrote a book, La disparition, without the letter 'e'. He was a member of the Oulipo group. A quote from the book:

Tout avait Pair normal, mais tout s’affirmait faux. Tout avait Fair normal, d’abord, puis surgissait l’inhumain, l’affolant. Il aurait voulu savoir où s’articulait l’association qui l’unissait au roman : stir son tapis, assaillant à tout instant son imagination, l’intuition d’un tabou, la vision d’un mal obscur, d’un quoi vacant, d’un non-dit : la vision, l’avision d’un oubli commandant tout, où s’abolissait la raison : tout avait l’air normal mais…

Perec would probably have scored high (or rather, low) in the following contest. People are asked to write a perhaps even meaningful text on some subject with as few occurrences of a given “word” as possible. Our task is to provide the jury with a program that counts these occurrences, in order to obtain a ranking of the competitors. These competitors often write very long texts with nonsense meaning; a sequence of 500,000 consecutive 'T's is not unusual. And they never use spaces.

So we want to quickly find out how often a word, i.e., a given string, occurs in a text. More formally: given the alphabet {'A', 'B', 'C', …, 'Z'} and two finite strings over that alphabet, a word W and a text T, count the number of occurrences of W in T. All the consecutive characters of W must exactly match consecutive characters of T. Occurrences may overlap.

 

Input
The first line of the input file contains a single number: the number of test cases to follow. Each test case has the following format:

One line with the word W, a string over {'A', 'B', 'C', …, 'Z'}, with 1 ≤ |W| ≤ 10,000 (here |W| denotes the length of the string W).
One line with the text T, a string over {'A', 'B', 'C', …, 'Z'}, with |W| ≤ |T| ≤ 1,000,000.
 

Output
For every test case in the input file, the output should contain a single number, on a single line: the number of occurrences of the word W in the text T.

 

Sample Input
    
3 BAPC BAPC AZA AZAZAZA VERDI AVERDXIVYERDIAN
 

Sample Output
    
1 3 0
 

Source
 

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题目大意就是求子串在主串中出现的次数
这么熟悉。。原来在POJ做过。。
主要是NEXT数组的应用。NEXT数组是反应的是子串的前缀和后缀相同的长度。

#include<iostream>
#include<cstring>
#include<algorithm>
#include<cstdlib>
#include<vector>
#include<cmath>
#include<stdlib.h>
#include<iomanip>
#include<list>
#include<deque>
#include<map>
#include <stdio.h>
#include <queue>

#define maxn 10000+5
#define ull unsigned long long
#define ll long long
#define reP(i,n) for(i=1;i<=n;i++)
#define rep(i,n) for(i=0;i<n;i++)
#define cle(a) memset(a,0,sizeof(a))
#define mod 90001
#define PI 3.141592657
#define INF 1<<30
const ull inf = 1LL << 61;
const double eps=1e-5;

using namespace std;

bool cmp(int a,int b){
return a>b;
}
char t[1000005];
char p[10005];
int f[10005];
void getFail(char *p,int *f)
{
int i,j;
f[0]=0;
f[1]=0;
int m=strlen(p);
for(int i=1;i<m;i++)
{
j=f[i];
while(j&&p[i]!=p[j])
{
j=f[j];
}
f[i+1]=p[i]==p[j]?j+1:0;
}
}
int find(char * t,char *p,int *f)
{
int sum=0;
int n=strlen(t),m=strlen(p);
getFail(p,f);
int j=0;
for(int i=0;i<n;i++)
{
while(j&&p[j]!=t[i])
{
j=f[j];
}
if(p[j]==t[i])
j++;
if(j==m)
{
sum++;
j=f[j];///回溯找到上一个失配点(对称性)
}
}
return sum;
}
int main()
{
//freopen("in.txt","r",stdin);
//freopen("out.txt","w",stdout);
int n;
cin>>n;
while(n--)
{
scanf("%s",&p);
scanf("%s",&t);
cout<<find(t,p,f)<<endl;
}
return 0;
}


内容概要:本文档详细介绍了一个基于MATLAB实现的跨尺度注意力机制(CSA)结合Transformer编码器的多变量时间序列预测项目。项目旨在精准捕捉多尺度时间序列特征,提升多变量时间序列的预测性能,降低模型计算复杂度与训练时间,增强模型的解释性和可视化能力。通过跨尺度注意力机制,模型可以同时捕获局部细节和全局趋势,显著提升预测精度和泛化能力。文档还探讨了项目面临的挑战,如多尺度特征融合、多变量复杂依赖关系、计算资源瓶颈等问题,并提出了相应的解决方案。此外,项目模型架构包括跨尺度注意力机制模块、Transformer编码器层和输出预测层,文档最后提供了部分MATLAB代码示例。 适合人群:具备一定编程基础,尤其是熟悉MATLAB和深度学习的科研人员、工程师和研究生。 使用场景及目标:①需要处理多变量、多尺度时间序列数据的研究和应用场景,如金融市场分析、气象预测、工业设备监控、交通流量预测等;②希望深入了解跨尺度注意力机制和Transformer编码器在时间序列预测中的应用;③希望通过MATLAB实现高效的多变量时间序列预测模型,提升预测精度和模型解释性。 其他说明:此项目不仅提供了一种新的技术路径来处理复杂的时间序列数据,还推动了多领域多变量时间序列应用的创新。文档中的代码示例和详细的模型描述有助于读者快速理解和复现该项目,促进学术和技术交流。建议读者在实践中结合自己的数据集进行调试和优化,以达到最佳的预测效果。
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