【模糊搜索排序算法】基于KMP和Levenshtein的模糊搜索实现

KMP：字符间是否匹配

Levenshtein：字符间转换距离 

先贴代码，再补别的

/**
 * @Author: liuxs
 * @Description: 编辑距离算法工具类
 * @Date: Create in 18:47 2018/7/25.
 */
public class LevenshteinDistanceUtil {

    /**
     * 计算两字符间转换编辑距离
     * @param s1
     * @param s2
     * @return Levenshtein Distance
     */
    public static int getStringDistance(String s1, String s2) {

        int distance[][];// 定义距离表
        int s1_len = s1.length();
        int s2_len = s2.length();

        if (s1_len == 0) {
            return s2_len;
        }
        if (s2_len == 0) {
            return s1_len;
        }
        distance = new int[s1_len + 1][s2_len + 1];

        // 二维数组第一行和第一列放置自然数
        for (int i = 0; i <= s1_len; i++) {
            distance[i][0] = i;
        }
        for (int j = 0; j <= s2_len; j++) {
            distance[0][j] = j;
        }
        // 比较，若行列相同，则代价为0，否则代价为1；
        for (int i = 1; i <= s1_len; i++) {
            char s1_i = s1.charAt(i - 1);
            // 逐一比较
            for (int j = 1; j <= s2_len; j++) {
                char s2_j = s2.charAt(j - 1);
                // 若相等，则代价取0；直接取左上方值
                if (s1_i == s2_j) {
                    distance[i][j] = distance[i - 1][j - 1];
                } else {
                    // 否则代价取1，取左上角、左、上 最小值 + 代价（代价之和便是最终距离）
                    distance[i][j] = getMin(distance[i - 1][j], distance[i][j - 1], distance[i - 1][j - 1]) + 1;
                }
            }
        }
        // 取二位数组最后一位便是两个字符串之间的距离
        return distance[s1_len][s2_len];
    }

    // 求最小值
    private static int getMin(int a, int b, int c) {
        int min = a;
        if (b < min) {
            min = b;
        }
        if (c < min) {
            min = c;
        }
        return min;
    }

    /**
     * 计算相似度
     * @param s1
     * @param s2
     * @return
     */
    public static float calculateProximity(String s1, String s2) {
        float editDistance = getStringDistance(s1, s2);
        float proximity = 1 - editDistance / Math.max(s1.length(), s2.length());
        return (float) (Math.round(proximity * 100)) / 100;
    }

}

/**
 * @Author: liuxs
 * @Description: 字符匹配算法工具类
 * @Date: Create in 18:47 2018/7/25.
 */
public class KMPMatchUtil {

    /**
     * @param c
     *            主串（源串）中的字符
     * @param T
     *            模式串（目标串）字符数组
     * @return 滑动距离
     */
    private static int dist(char c, char T[]) {
        int n = T.length;
        if (c == T[n - 1]) {
            return n;// c出现在模式串最后一位时
        }
        for (int i = n; i >= 1; i--) {
            if (T[i - 1] == c)
                return n - i;// i=max{i|t[i-1]且0<=i<=n-2}
        }
        return n;// c不出现在模式中时
    }

    /**
     * @param p_s
     * @param p_t
     * @return -2错误，-1匹配不到，[0,p_s.length-p_t.length]表示t在s中位置,下标从0开始
     */
    public static int index(final String p_s, final String p_t) {
        if (p_s == null || p_t == null) {
            return -2;
        }
        char[] s = p_s.toCharArray();
        char[] t = p_t.toCharArray();
        int slen = s.length, tlen = t.length;

        if (slen < tlen) {
            return -1;
        }

        int i = tlen, j;
        while (i <= slen) {
            j = tlen;
            while (j > 0 && s[i - 1] == t[j - 1]) {// S[i-1]与T[j-1]若匹配，则进行下一组比较；反之离开循环。
                i--;
                j--;
            }
            if (0 == j) {// j=0时，表示完美匹配，返回其开始匹配的位置
                return i;// 如果要匹配多个，这里改为：int pos=i;i = i+tlen+1; --其中每次这个pos就是位置
            } else {
                // System.out.println(dist(s[i - 1], t));
                i = i + dist(s[i - 1], t);// 把主串和模式串均向右滑动一段距离dist(s[i-1]).即跳过dist(s[i-1])个字符无需比较
            }
        }

        return -1;// 模式串与主串无法匹配

    }

    /**
     * 两字符是否匹配
     * @param source
     * @param target
     * @return
     */
    public static boolean kmpMatch(String source, String target)
    {
        if(null == source || null == target || "".equals(source.trim()) || "".equals(target.trim()))
        {
            return false;
        }

        int bl = source.length();
        int al = target.length();

        for(int bi = 0,ai = 0;bi < al;ai++)
        {
            if(bi == al || ai == bl)
            {
                return false;
            }
            else if(source.charAt(ai) == target.charAt(bi))
            {
                bi++;
            }
        }
        return true;
    }

}

/**
 * @Author: liuxs
 * @Description: 模糊搜索支行信息
 * @Date: Create in 11:38 2018/7/26.
 */
@Service
public class FuzzyBankBranchService {

    private final static Logger logger = LoggerFactory.getLogger(FuzzyBankBranchService.class);

    @Autowired
    private BankBranchService bankBranchService;

    public List<BankBranch> fuzzyFindByBranchName(QueryBankBean queryBankBean) {
        String fuzzyWord = queryBankBean.getBranchName();
        List<BankBranch> allBranches = bankBranchService.findAll();
        List<String> resultStr = new ArrayList<>();
        List<String> relateCodes = new ArrayList<>();
        for (BankBranch branch : allBranches) {
            String code = branch.getRelateCode();
            String branchName = branch.getBranchName();
            if ((KMPMatchUtil.kmpMatch(branchName, fuzzyWord)/* || KMPMatchUtil.kmpMatch(fuzzyWord, branchName)*/) && !relateCodes.contains(code)) {
                resultStr.add(branch.objectToString(branch));
            }
        }
        if (CollectionUtils.isNotEmpty(resultStr)) {
            logger.info("通过支行名称:{}，匹配到{}记录。", fuzzyWord, resultStr.size());
            Collections.sort(resultStr, new Comparator<String>() {
                public int compare(String s1, String s2) {
                    return LevenshteinDistanceUtil.getStringDistance(s1.split(",")[2], fuzzyWord)
                            - LevenshteinDistanceUtil.getStringDistance(s2.split(",")[2], fuzzyWord);
                }
            });
            //若匹配数大于15条，则返回相似度较高的15条
            if (resultStr.size() > 15) {
                resultStr = resultStr.subList(0, 15);
            }
        }
        return convertBranch(resultStr, fuzzyWord);
    }

    /**
     * 整合返回信息，标记相似度
     * @param resultStr
     * @param fuzzyWord
     * @return
     */
    private List<BankBranch> convertBranch(List<String> resultStr, String fuzzyWord) {
        List<BankBranch> results = new ArrayList<>();
        for (String str : resultStr) {
            BankBranch branch = stringToObject(str);
            branch.setProximity(LevenshteinDistanceUtil.calculateProximity(branch.getBranchName(), fuzzyWord));
            results.add(branch);
        }
        return results;
    }

    private BankBranch stringToObject(String str) {
        BankBranch bankBranch = new BankBranch();
        bankBranch.setRelateCode(str.split(",")[0]);
        bankBranch.setBankName(str.split(",")[1]);
        bankBranch.setBranchName(str.split(",")[2]);
        bankBranch.setProvinceName(str.split(",")[3]);
        bankBranch.setCityName(str.split(",")[4]);
        return bankBranch;
    }
}