我编写了一些实现Levensthein距离的java代码来计算两个字符串的相似度。我的代码是这样的：
public class LevenshteinDistance {
public LevenshteinDistance() {
}
public double similarity(String s1, String s2) {
if (s1.length() < s2.length()) { // s1 should always be bigger
String swap = s1;
s1 = s2;
s2 = swap;
}
int bigLen = s1.length();
if (bigLen == 0) {
return 1.0; /* both strings are zero length */ }
return (bigLen - computeEditDistance(s1, s2)) / (double) bigLen;
}
public int computeEditDistance(String s1, String s2) {
s1 = s1.toLowerCase();
s2 = s2.toLowerCase();
int[] costs = new int[s2.length() + 1];
for (int i = 0; i <= s1.length(); i++) {
int lastValue = i;
for (int j = 0; j <= s2.length(); j++) {
if (i == 0) {
costs[j] = j;
} else {
if (j > 0) {
int newValue = costs[j - 1];
if (s1.charAt(i - 1) != s2.charAt(j - 1)) {
newValue = Math.min(Math.min(newValue, lastValue), costs[j]) + 1;
}
costs[j - 1] = lastValue;
lastValue = newValue;
}
}
}
if (i > 0) {
costs[s2.length()] = lastValue;
}
}
return costs[s2.length()];
}
public double printDistance(String s1, String s2) {
System.out.println("[Edit Distance] : " + s1 + " and " + s2 + " have similarity is = " + similarity(s1, s2) * 100 + " %");
return similarity(s1, s2) * 100;
}
public static void main(String[] args) {
LevenshteinDistance lv = new LevenshteinDistance();
lv.printDistance("164164617044", "164164617044");
}
}
从我上面的代码中，我有一个这样的输出：
[Edit Distance] : 164164617044 and 164164617044 have similarity is = 100.0 %
如何获得代表Levensthein距离的matriks输出
1 6 4 1 6 4 6 1 7 0 4 4
0 1 2 3 4 5 6 7 8 9 10 11 12
1 1 0 1 2 3 4 5 6 7 8 9 10 11
6 2 1 0 1 2 3 4 5 6 7 8 9 10
4 3 2 1 0 1 2 3 4 5 6 7 8 9
1 4 3 2 1 0 1 2 3 4 5 6 7 8
6 5 4 3 2 1 0 1 2 3 4 5 6 7
4 6 5 4 3 2 1 0 1 2 3 4 5 6
6 7 6 5 4 3 2 1 0 1 2 3 4 5
1 8 7 6 5 4 3 2 1 0 1 2 3 4
7 9 8 7 6 5 4 3 2 1 0 1 2 3
0 10 9 8 7 6 5 4 3 2 1 0 1 2
4 11 10 9 8 7 6 5 4 3 2 1 0 1
4 12 11 10 9 8 7 6 5 4 3 2 1 0
寻求帮助，谢谢
答案 0 :(得分：0)
问题是你的实现只计算距离而不是结果矩阵。
此解决方案基于 Wagner–Fischer algorithm ：
public class LevenshteinDistance {
public static void main(String[] args) {
String str1 = "164164617044", str2 = "164164617044";
System.out.print("String 1: \"" + str1 + "\" String 2: \"" + str2
+ "\".\n\n");
int[][] resultingMatrix = resultingMatrix(str1, str2);
int distance = resultingMatrix[str1.length()][str2.length()];
double similarity = similarity(str1, str2, distance), similarityPercentage = similarity * 100.0;
System.out.print("Levenshtein distance = " + distance + ".\n\n");
System.out.print("Similarity = " + similarity + ".\n\n");
System.out.print("Similarity Percentage = " + similarityPercentage
+ "%.\n\n");
System.out.print("Resulting Matrix:\n");
for (int charsInStr2 = 0; charsInStr2 < str2.length(); charsInStr2++) {
if (charsInStr2 == 0) {
System.out.print(String.format("%-5s%-5s%-5s", " ", " ", "'" + str2.charAt(charsInStr2) + "'"));
} else {
System.out
.print(String.format("%-5s", "'" + str2.charAt(charsInStr2)+ "'"));
}
}
System.out.print("\n");
for (int i = 0; i < resultingMatrix.length; i++) {
String line = "";
for (int j = 0; j < resultingMatrix[i].length; j++) {
line += String.format("%-5s", resultingMatrix[i][j]);
}
if(i == 0)
System.out.printf("%-4s %s\n", " ", line);
else{
System.out.printf("%-4s %s\n", "'" + str1.charAt(i-1) + "'", line);
}
}
}
public static int[][] resultingMatrix(String str1, String str2) {
int[][] dist = new int[str1.length() + 1][str2.length() + 1];
for (int i = 0; i <= str1.length(); i++) {
dist[i][0] = i;
}
for (int j = 1; j <= str2.length(); j++) {
dist[0][j] = j;
}
for (int i = 1; i <= str1.length(); i++)
for (int j = 1; j <= str2.length(); j++)
dist[i][j] = Math
.min(Math.min(dist[i - 1][j] + 1, dist[i][j - 1] + 1),
dist[i - 1][j - 1]
+ ((str1.charAt(i - 1) == str2
.charAt(j - 1)) ? 0 : 1));
return dist;
}
public static double similarity(String str1, String str2, int distance) {
double bigLenght = Math.max(str1.length(), str2.length());
return (bigLenght - distance) / bigLenght;
}
}
示例：
String 1: "164164617044" String 2: "164164617044". Levenshtein distance = 0. Similarity = 1.0. Similarity Percentage = 100.0%. Resulting Matrix: '1' '6' '4' '1' '6' '4' '6' '1' '7' '0' '4' '4' 0 1 2 3 4 5 6 7 8 9 10 11 12 '1' 1 0 1 2 3 4 5 6 7 8 9 10 11 '6' 2 1 0 1 2 3 4 5 6 7 8 9 10 '4' 3 2 1 0 1 2 3 4 5 6 7 8 9 '1' 4 3 2 1 0 1 2 3 4 5 6 7 8 '6' 5 4 3 2 1 0 1 2 3 4 5 6 7 '4' 6 5 4 3 2 1 0 1 2 3 4 5 6 '6' 7 6 5 4 3 2 1 0 1 2 3 4 5 '1' 8 7 6 5 4 3 2 1 0 1 2 3 4 '7' 9 8 7 6 5 4 3 2 1 0 1 2 3 '0' 10 9 8 7 6 5 4 3 2 1 0 1 2 '4' 11 10 9 8 7 6 5 4 3 2 1 0 1 '4' 12 11 10 9 8 7 6 5 4 3 2 1 0
请注意，Levensthein距离始终生成矩阵右下角的元素（resulMatrix [ str1Len ] [ str2Len ] ）。另外，请看一下我的解决方案中计算相似度的方法。