kuangbin专题十六 KMP&&扩展KMP HDU1686 Oulipo

本文深入探讨了KMP算法,一种高效的字符串匹配算法,用于在文本中查找特定模式的出现次数。文章通过具体实例展示了如何预处理模式串并进行快速匹配,避免了传统逐个字符比较的效率低下问题。
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.


InputThe 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.
OutputFor 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



在主串中匹配到j>=plen时,j继续回溯。


 1 #include<stdio.h>
 2 #include<string.h>
 3 int Next[10010],n,m,_,tlen,plen;
 4 char t[1000010],p[10010];
 5 
 6 void prekmp() {
 7     tlen=strlen(t);
 8     plen=strlen(p);
 9     int i,j;
10     j=Next[0]=-1;
11     i=0;
12     while(i<plen) {
13         while(j!=-1&&p[i]!=p[j]) j=Next[j];
14         if(p[++i]==p[++j]) Next[i]=Next[j]; //这里判断的是模式串
15         else Next[i]=j;
16     }
17 }
18 
19 int kmp() {
20     prekmp();
21     int i,j,ans=0;
22     i=j=0;
23     while(i<tlen) {
24         while(j!=-1&&t[i]!=p[j]) j=Next[j];
25         i++;j++;
26         if(j>=plen) {  //主要区别
27             ans++;
28             j=Next[j];
29         }
30     }
31     return ans;
32 }
33 
34 int main() {
35     for(scanf("%d",&_);_;_--) {
36         scanf("%s",p);
37         scanf("%s",t);
38         printf("%d\n",kmp());
39     }
40 }

 



转载于:https://www.cnblogs.com/ACMerszl/p/10263472.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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