package com.xiaozhi.blog;
/**
* 字符相似度比较
* @author xiaozhi
*
*/
public class Levenshtein {
private static int compare(String str, String target) {
int d[][]; // 矩阵
int n = str.length();
int m = target.length();
int i; // 遍历str的
int j; // 遍历target的
char ch1; // str的
char ch2; // target的
int temp; // 记录相同字符,在某个矩阵位置值的增量,不是0就是1
if (n == 0) {
return m;
}
if (m == 0) {
return n;
}
d = new int[n + 1][m + 1];
for (i = 0; i <= n; i++) { // 初始化第一列
d[i][0] = i;
}
for (j = 0; j <= m; j++) { // 初始化第一行
d[0][j] = j;
}
for (i = 1; i <= n; i++) { // 遍历str
ch1 = str.charAt(i - 1);
// 去匹配target
for (j = 1; j <= m; j++) {
ch2 = target.charAt(j - 1);
if (ch1 == ch2) {
temp = 0;
} else {
temp = 1;
}
// 左边+1,上边+1, 左上角+temp取最小
d[i][j] = min(d[i - 1][j] + 1, d[i][j - 1] + 1, d[i - 1][j - 1]+ temp);
}
}
return d[n][m];
}
private static int min(int one, int two, int three) {
return (one = one < two ? one : two) < three ? one : three;
}
/**
*
* 获取两字符串的相似度
* @param str
* @param target
* @return
*/
public static float getSimilarityRatio(String str, String target) {
return 1 - (float) compare(str, target)/ Math.max(str.length(), target.length());
}
/**
* 短距离算法加权平均获得用户相似度
* @param me
* @param u
* @return
*/
// public static float getUserSimilarityRatio(TianJiUser me,TianJiUser u){
// float name = Levenshtein.getSimilarityRatio(me.getName(), u.getName());
// float headline = Levenshtein.getSimilarityRatio(me.getHeadline(), u.getHeadline());
// float country = Levenshtein.getSimilarityRatio(me.getLocation().getCountry(), u.getLocation().getCountry());
// float city = Levenshtein.getSimilarityRatio(me.getLocation().getCity(), u.getLocation().getCity());
//
// return (name*10+country*5+city*10+headline*20)/(10+5+10+20);
// }
public static void main(String[] args) {
String str = "北京";
String target = "北京地区的人";
System.out.println("similarityRatio="+ Levenshtein.getSimilarityRatio(str, target));
}
}