字符串相似度算法
来源:互联网 发布:连杆机构设计软件 编辑:程序博客网 时间:2024/05/17 01:58
字符串相似度算法( Levenshtein Distance算法)
题目: 一个字符串可以通过增加一个字符,删除一个字符,替换一个字符得到另外一个字符串,假设,我们把从字符串A转换成字符串B,前面3种操作所执行的最少次数称为AB相似度
如 abc adc 度为 1
ababababa babababab 度为 2
abcd acdb 度为2
字符串相似度算法可以使用 Levenshtein Distance算法(中文翻译:编辑距离算法) 这算法是由俄国科学家Levenshtein提出的。其步骤
Set m to be the length of t.
If n = 0, return m and exit.
If m = 0, return n and exit.
Construct a matrix containing 0..m rows and 0..n columns.2Initialize the first row to 0..n.
Initialize the first column to 0..m.3Examine each character of s (i from 1 to n).4Examine each character of t (j from 1 to m).5If s[i] equals t[j], the cost is 0.
If s[i] doesn't equal t[j], the cost is 1.6Set cell d[i,j] of the matrix equal to the minimum of:
a. The cell immediately above plus 1: d[i-1,j] + 1.
b. The cell immediately to the left plus 1: d[i,j-1] + 1.
c. The cell diagonally above and to the left plus the cost: d[i-1,j-1] + cost.7After the iteration steps (3, 4, 5, 6) are complete, the distance is found in cell d[n,m].
C++实现如下
#include <iostream>
#include <vector>
#include <string>
using namespace std;
//算法
int ldistance(const string source,const string target)
{
//step 1
int n=source.length();
int m=target.length();
if (m==0) return n;
if (n==0) return m;
//Construct a matrix
typedef vector< vector<int> > Tmatrix;
Tmatrix matrix(n+1);
for(int i=0; i<=n; i++) matrix[i].resize(m+1);
//step 2 Initialize
for(int i=1;i<=n;i++) matrix[i][0]=i;
for(int i=1;i<=m;i++) matrix[0][i]=i;
//step 3
for(int i=1;i<=n;i++)
{
const char si=source[i-1];
//step 4
for(int j=1;j<=m;j++)
{
const char dj=target[j-1];
//step 5
int cost;
if(si==dj){
cost=0;
}
else{
cost=1;
}
//step 6
const int above=matrix[i-1][j]+1;
const int left=matrix[i][j-1]+1;
const int diag=matrix[i-1][j-1]+cost;
matrix[i][j]=min(above,min(left,diag));
}
}//step7
return matrix[n][m];
}
int main(){
string s;
string d;
cout<<"source=";
cin>>s;
cout<<"diag=";
cin>>d;
int dist=ldistance(s,d);
cout<<"dist="<<dist<<endl;
}
#include <iostream>
#include <vector>
#include <string>
using namespace std;
//算法
int ldistance(const string source,const string target)
{
//step 1
int n=source.length();
int m=target.length();
if (m==0) return n;
if (n==0) return m;
//Construct a matrix
typedef vector< vector<int> > Tmatrix;
Tmatrix matrix(n+1);
for(int i=0; i<=n; i++) matrix[i].resize(m+1);
//step 2 Initialize
for(int i=1;i<=n;i++) matrix[i][0]=i;
for(int i=1;i<=m;i++) matrix[0][i]=i;
//step 3
for(int i=1;i<=n;i++)
{
const char si=source[i-1];
//step 4
for(int j=1;j<=m;j++)
{
const char dj=target[j-1];
//step 5
int cost;
if(si==dj){
cost=0;
}
else{
cost=1;
}
//step 6
const int above=matrix[i-1][j]+1;
const int left=matrix[i][j-1]+1;
const int diag=matrix[i-1][j-1]+cost;
matrix[i][j]=min(above,min(left,diag));
}
}//step7
return matrix[n][m];
}
int main(){
string s;
string d;
cout<<"source=";
cin>>s;
cout<<"diag=";
cin>>d;
int dist=ldistance(s,d);
cout<<"dist="<<dist<<endl;
}
#include <vector>
#include <string>
using namespace std;
//算法
int ldistance(const string source,const string target)
{
//step 1
int n=source.length();
int m=target.length();
if (m==0) return n;
if (n==0) return m;
//Construct a matrix
typedef vector< vector<int> > Tmatrix;
Tmatrix matrix(n+1);
for(int i=0; i<=n; i++) matrix[i].resize(m+1);
//step 2 Initialize
for(int i=1;i<=n;i++) matrix[i][0]=i;
for(int i=1;i<=m;i++) matrix[0][i]=i;
//step 3
for(int i=1;i<=n;i++)
{
const char si=source[i-1];
//step 4
for(int j=1;j<=m;j++)
{
const char dj=target[j-1];
//step 5
int cost;
if(si==dj){
cost=0;
}
else{
cost=1;
}
//step 6
const int above=matrix[i-1][j]+1;
const int left=matrix[i][j-1]+1;
const int diag=matrix[i-1][j-1]+cost;
matrix[i][j]=min(above,min(left,diag));
}
}//step7
return matrix[n][m];
}
int main(){
string s;
string d;
cout<<"source=";
cin>>s;
cout<<"diag=";
cin>>d;
int dist=ldistance(s,d);
cout<<"dist="<<dist<<endl;
}
#include <iostream>
#include <vector>
#include <string>
using namespace std;
//算法
int ldistance(const string source,const string target)
{
//step 1
int n=source.length();
int m=target.length();
if (m==0) return n;
if (n==0) return m;
//Construct a matrix
typedef vector< vector<int> > Tmatrix;
Tmatrix matrix(n+1);
for(int i=0; i<=n; i++) matrix[i].resize(m+1);
//step 2 Initialize
for(int i=1;i<=n;i++) matrix[i][0]=i;
for(int i=1;i<=m;i++) matrix[0][i]=i;
//step 3
for(int i=1;i<=n;i++)
{
const char si=source[i-1];
//step 4
for(int j=1;j<=m;j++)
{
const char dj=target[j-1];
//step 5
int cost;
if(si==dj){
cost=0;
}
else{
cost=1;
}
//step 6
const int above=matrix[i-1][j]+1;
const int left=matrix[i][j-1]+1;
const int diag=matrix[i-1][j-1]+cost;
matrix[i][j]=min(above,min(left,diag));
}
}//step7
return matrix[n][m];
}
int main(){
string s;
string d;
cout<<"source=";
cin>>s;
cout<<"diag=";
cin>>d;
int dist=ldistance(s,d);
cout<<"dist="<<dist<<endl;
}
java 字符串编辑距离算法实现:
public static int getLevenshteinDistance (String s, String t) { if (s == null || t == null) { throw new IllegalArgumentException("Strings must not be null"); } /* The difference between this impl. and the previous is that, rather than creating and retaining a matrix of size s.length()+1 by t.length()+1, we maintain two single-dimensional arrays of length s.length()+1. The first, d, is the 'current working' distance array that maintains the newest distance cost counts as we iterate through the characters of String s. Each time we increment the index of String t we are comparing, d is copied to p, the second int[]. Doing so allows us to retain the previous cost counts as required by the algorithm (taking the minimum of the cost count to the left, up one, and diagonally up and to the left of the current cost count being calculated). (Note that the arrays aren't really copied anymore, just switched...this is clearly much better than cloning an array or doing a System.arraycopy() each time through the outer loop.) Effectively, the difference between the two implementations is this one does not cause an out of memory condition when calculating the LD over two very large strings. */ int n = s.length(); // length of s int m = t.length(); // length of t if (n == 0) { return m; } else if (m == 0) { return n; } int p[] = new int[n+1]; //'previous' cost array, horizontally int d[] = new int[n+1]; // cost array, horizontally int _d[]; //placeholder to assist in swapping p and d // indexes into strings s and t int i; // iterates through s int j; // iterates through t char t_j; // jth character of t int cost; // cost for (i = 0; i<=n; i++) { p[i] = i; } for (j = 1; j<=m; j++) { t_j = t.charAt(j-1); d[0] = j; for (i=1; i<=n; i++) { cost = s.charAt(i-1)==t_j ? 0 : 1; // minimum of cell to the left+1, to the top+1, diagonally left and up +cost d[i] = Math.min(Math.min(d[i-1]+1, p[i]+1), p[i-1]+cost); } // copy current distance counts to 'previous row' distance counts _d = p; p = d; d = _d; } // our last action in the above loop was to switch d and p, so p now // actually has the most recent cost counts return p[n];}字符串相似度=1-(编辑距离/(MAX(字符串1长度,字符串2的长度))
oracle 11提供了计算字符串编辑距离和相似度的函数:
参见http://psoug.org/reference/utl_match.html
Oracle UTL_MATCHVersion 11.1 General InformationThe four functions included in the package use different methods to compare a source string and destination string, and return an assessment of what it would take to turn the source string into the destination string.Source$ORACLE_HOME/rdbms/admin/utlmatch.sql EDIT_DISTANCEReturns the number of changes required to turn the source string into the destination string using the Levenshtein Distance algorithm.utl_match.edit_distance(s1 IN VARCHAR2, s2 IN VARCHAR2)RETURN PLS_INTEGER;SELECT utl_match.edit_distance('expresso', 'espresso') DIST
FROM dual; EDIT_DISTANCE_SIMILARITYReturns an integer between 0 and 100, where 0 indicates no similarity at all and 100 indicates a perfect match.utl_match.edit_distance_similarity(
s1 IN VARCHAR2, s2 IN VARCHAR2) RETURN PLS_INTEGER;SELECT utl_match.edit_distance_similarity('expresso', 'espresso') SIM
FROM dual; JARO_WINKLERInstead of simply calculating the number of steps required to change the source string to the destination string, determines how closely the two strings agree with each other and tries to take into account the possibility of a data entry error.utl_match.jaro_winkler(s1 IN VARCHAR2, s2 IN VARCHAR2)
RETURN BINARY_DOUBLE;SELECT utl_match.jaro_winkler('expresso', 'espresso') DIST
FROM dual; JARO_WINKLER_SIMILARITYReturns an integer between 0 and 100, where 0 indicates no similarity at all and 100 indicates a perfect match but tries to take into account possible data entry errors.utl_match.jaro_winkler_similarity(
s1 IN VARCHAR2, s2 IN VARCHAR2) RETURN PLS_INTEGER;SELECT utl_match.jaro_winkler_similarity('expresso', 'expresso') SIM
FROM dual;
0 0
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度算法
- 字符串相似度的算法
- 字符串相似度Levenshtein算法
- 字符串相似度算法介绍
- 字符串相似度算法介绍
- 字符串相似度算法介绍(整理)
- 字符串相似度算法(Levenshtein Distance)
- 字符串相似度算法介绍(整理)
- 比较两字符串相似度算法
- 菜鸟程序员的成长之路(三)——2014,逝去的半年,奋斗的半年
- coco中实现按钮屏蔽层的拙劣的处理方法
- 深入理解js闭包
- Codeforces 464 A. No to Palindromes!
- android Textview颜色渐变
- 字符串相似度算法
- 文件操作
- 嵌入式Qtopia-2.2.0开发环境的搭建和使用
- [寒江孤叶丶的CrossApp之旅_10][入门系列]CrossApp中CATextField的使用
- STL map
- Tokyo Cabinet碎片整理
- JAVA安装及配置(WIN732位)
- 上海庆科苏琼:智能硬件背后的连接和交互者
- 【C语言】[指针]:指针函数 和 函数指针