iOS：使用莱文斯坦距离算法计算两串字符串的相似度

Levenshtein：莱文斯坦距离

Levenshtein的经典算法，参考http://en.wikipedia.org/wiki/Levenshtein_distance的伪代码实现的,同时参考了一些C++的实现，求字符串相似度。

下面求出结果是0.0~100.0,   表示为0%~100%。

static inline int min(int a, int b) {
    return a < b ? a : b;
}

+(float)likePercentByCompareOriginText:(NSString *)originText targetText:(NSString *)targetText{
    
    //length
    int n = (int)originText.length;
    int m = (int)targetText.length;
    if (n == 0 || m == 0) {
        return 0.0;
    }
    
    //Construct a matrix, need C99 support
    int N = n+1;
    int **matrix;
    matrix = (int **)malloc(sizeof(int *)*N);
    
    int M = m+1;
    for (int i = 0; i < N; i++) {
        matrix[i] = (int *)malloc(sizeof(int)*M);
    }
    
    for (int i = 0; i<N; i++) {
        for (int j=0; j<M; j++) {
            matrix[i][j]=0;
        }
    }
    
    for(int i=1; i<=n; i++) {
        matrix[i][0]=i;
    }
    for(int i=1; i<=m; i++) {
        matrix[0][i]=i;
    }
    for(int i=1;i<=n;i++)
    {
        unichar si = [originText characterAtIndex:i-1];
        for(int j=1;j<=m;j++)
        {
            unichar dj = [targetText characterAtIndex:j-1];
            int cost;
            if(si==dj){
                cost=0;
            }
            else{
                cost=1;
            }
            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));
        }
    }
    return 100.0 - 100.0*matrix[n][m]/MAX(m,n);
}