/*********************************************   Levenshtein Distance Algorithm  *******************************/
/***************************************************************************************************************/
/*Author: TianLiang                                                                                            */
/*Time: 2011/10/14                                                                                             */
/*Function: Get the distance of the two given strings                                                        
   Step Description 
  1 Set n to be the length of s.
   Set m to be the length of t.
   If n = 0, return m and exit.
   If m = 0, return n and exit.
   Construct a matrix containing 0..m rows and 0..n columns.  
  2 Initialize the first row to 0..n.
   Initialize the first column to 0..m.
    
  3 Examine each character of s (i from 1 to n). 
  4 Examine each character of t (j from 1 to m). 
  5 If s[i] equals t[j], the cost is 0.
   If s[i] doesn't equal t[j], the cost is 1. 
  6 Set cell d[i,j] of the matrix equal to the minimum of:
   a. The cell immediately above plus 1: d[i-1,j] + 1.
   b. The cell immediately to the left plus 1: d[i,j-1] + 1.
   c. The cell diagonally above and to the left plus the cost: d[i-1,j-1] + cost.
           7 After the iteration steps (3, 4, 5, 6) are complete, the distance is found in cell d[n,m].     */
int CCalculateSimilarity::LevenshteinDistance(CString source,CString target)
{
    //step 1
 int n=source.GetLength(); //Set n to be the length of s.
 int m=target.GetLength(); //Set m to be the length of t
    if (m==0) return n; //If n = 0, return m and exit.
    if (n==0) return m; //If m = 0, return n and exit.
    //Construct a matrix
    typedef vector< vector<int> >  Tmatrix;
    Tmatrix matrix(n+1);
    for(int i=0; i<=n; i++)  
 {
  matrix[i].resize(m+1);
 }
    //step 2 Initialize
    for(int i=1;i<=n;i++) 
 {
  matrix[i][0]=i; //Initialize the first row to 0..n.
 }
    for(int i=1;i<=m;i++) 
 {
  matrix[0][i]=i; //Initialize the first column to 0..m.
 }
     //step 3 Examine each character of s (i from 1 to n).
     for(int i=1;i<=n;i++)
     {
        const TCHAR si=source[i-1];
        //step 4
        for(int j=1;j<=m;j++)
        {
            const TCHAR dj=target[j-1];
            //step 5
            int cost;
            if(si==dj)
   {
                cost=0;
            }
            else
   {
                cost=1;
            }
            //step 6
            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));

        }
     }
  //step7
      return matrix[n][m];
}
 

   在編寫詞語對齊軟體的時候，要用到一些相似度的計算，最簡單的莫過於2s/（s1+s2）這種計算方法了，當然還有上面呈現的這段程式碼：編輯距離相似度的計算方法。簡單的講，Levenshtein距離，用於比較字串的相似程度。

　　    LD(s1, s2)=把s1變成s2需要的最小單字元操作（插入、刪除和替換）的次數。

  這個相似度倒是幫了我不少忙，哈哈。分享下