package udf;

import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.Text;
import org.testng.annotations.Test;

import java.util.Formatter;

public class Similar extends UDF {

    public Text evaluate(Text astr, Text bstr) {
        return new Text(new Formatter().format("%.2f", xiangsidu(astr.toString(), bstr.toString())).toString());
    }

    @Test
    public void test() {
        String rate = new Formatter().format("%.2f", xiangsidu("全世界无产者联合起来", "无产阶级")).toString();
        System.out.println(rate);
    }


    public double xiangsidu(String a, String b) {
        // 相似度公式：1 - 最小编辑距离/两个串中较长的长度
        int n = a.length();
        int m = b.length();
        if (n < 1 && m < 1) {
            return 0;
        }
        return 1 - minEditDist(a, b) / Math.max(n, m);
    }

    // 最小编辑距离计算
    public double minEditDist(String a, String b) {
        int n = a.length();
        int m = b.length();
        if (n < 1 || m < 1) {
            return (n + m) * 1.00;
        }

        double[][] d = new double[n + 1][m + 1];
        double cost;

        for (int i = 0; i <= n; ++i)
            d[i][0] = i;
        for (int i = 0; i <= m; ++i)
            d[0][i] = i;

        for (int i = 1; i <= n; i++) {
            for (int j = 1; j <= m; j++) {
                if (a.charAt(i - 1) == b.charAt(j - 1))
                    cost = 0;
                else
                    cost = 1;
                d[i][j] = Minimum(d[i - 1][j] + 1, d[i][j - 1] + 1,
                        d[i - 1][j - 1] + cost);
            }
        }
        return d[n][m];
    }

    private double Minimum(double a, double b, double c) {
        double mi = a;
        if (b < mi) {
            mi = b;
        }
        if (c < mi) {
            mi = c;
        }
        return mi;
    }

}