leetcode 72. Edit Distance DP动态规划 + 编辑距离

Given two words word1 and word2, find the minimum number of steps required to convert word1 to word2。 (each operation is counted as 1 step。)

You have the following 3 operations permitted on a word:

a) Insert a character
b) Delete a character
c) Replace a character

这个就是经典的编辑距离的问题，就是经典的DP解决，具体为什么这么做就不多说了，我这里就简单的描述一下DP的过程。

用dp[i][j]表示从word1[i]变为word2[j]所需要的最少步骤。那么主要分为一下几种情况处理：
1) 替换代价：若word1[i]=word2[j] , 则只需把word1[0…i-1]变为word2[0…j-1]，equalCost = dp[i-1][j-1]，否者equalCost = dp[i-1][j-1]+1；

2) 插入代价：若在word1[i]插入某字符是word1到word2[的最小改动，insertCost= dp[i][j-1] + 1;

3) 删除代价：若删除word1[i]是word1到word2的最小改动，则deleteCost = dp[i-1][j] + 1。

最后dp[i][j]是替换代价、插入代价和删除代价的最小值。

代码如下：

public class Solution {    /*     * http://www.bubuko.com/infodetail-644271.html     *      * 这个题吧比较难，动态规划DP解决                                                        * */    public int minDistance(String word1, String word2)     {        if(word1==null || word1.length()<=0)            return word2.length();        if(word2==null || word2.length()<=0)            return word1.length();        int [][]dp=new int[word1.length()+1][word2.length()+1];        for(int i=0;i<dp.length;i++)            dp[i][0]=i;        for(int i=0;i<dp[0].length;i++)            dp[0][i]=i;/*      用dp[i][j]表示从word1[i]变为word2[j]所需要的最少步骤。        1) 匹配：若word1[i]=word2[j] , 则只需把word1[0...i-1]变为word2[0...j-1]                即可. ==> dp[i][j] = dp[i-1][j-1].        2) 替换：把word1[i]替换word2[j]是从word1到word2的最小改动，        则 先把word1[0...i-1]以最小代价变为word2[0...j-1]，再替换. ==> dp[i][j] = dp[i-1][j-1] + 1.        3) 插入：若在word1[i]插入某字符是word1到word2[的最小改动，                                          则先把word1[0...i]以最小代价变为word2[0...j-1]，在此基础上加上word2[j], ==> dp[i][j] = dp[i][j-1] + 1.        4) 删除：若删除word1[i]是word1到word2的最小改动，        则先把word1[0...i-1]以最小代价变为word2[0...j], 再删除word1[j]即可，        dp[i][j] = dp[i-1][j] + 1.*/          for(int i=1;i<=word1.length();i++)          {              for(int j=1;j<=word2.length();j++)              {                  int addA=dp[i][j-1]+1;                  int deleteA=dp[i-1][j]+1;                  int eqA=0;                  if(word1.charAt(i-1)==word2.charAt(j-1))                      eqA=dp[i-1][j-1];                  else                      eqA=dp[i-1][j-1]+1;                  dp[i][j]=Math.min(addA, Math.min(deleteA, eqA));               }          }        return dp[word1.length()][word2.length()];    }}

下面是C++的做法，就是一个很经典但是也很难想的DP动态规划的做法

代码如下：

#include <iostream>#include <vector>#include <string>#include <algorithm>using namespace std;class Solution{public:    int minDistance(string word1, string word2)     {        int m = word1.length(), n = word2.length();        vector<vector<int> > dp(m + 1, vector<int>(n + 1, 0));        for (int i = 1; i <= m; i++)            dp[i][0] = i;        for (int j = 1; j <= n; j++)            dp[0][j] = j;        for (int i = 1; i <= m; i++)         {            for (int j = 1; j <= n; j++)             {                if (word1[i - 1] == word2[j - 1])                    dp[i][j] = dp[i - 1][j - 1];                else                     dp[i][j] = min(dp[i - 1][j - 1] + 1, min(dp[i][j - 1] + 1, dp[i - 1][j] + 1));            }        }        return dp[m][n];    }};