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
正确的解出来方法是用二维的dp。假如我们要将字符串str1变成str2, sstr1(i)是str1的子串,范围[0到i),sstr1(0)是空串,sstr2(j)是str2的子串,同上d(i,j)表示将sstr1(i)变成sstr2(j)的编辑距离
首先d(0,t),0<=t<=str1.size()和d(k,0)是很显然的。
当我们要计算d(i,j)时,即计算sstr1(i)到sstr2(j)之间的编辑距离,
此时,设sstr1(i)形式是somestr1c;sstr2(i)形如somestr2d的话,
将somestr1变成somestr2的编辑距离已知是d(i-1,j-1)
将somestr1c变成somestr2的编辑距离已知是d(i,j-1)
将somestr1变成somestr2d的编辑距离已知是d(i-1,j)
那么利用这三个变量,就可以递推出d(i,j)了:
如果c==d,显然编辑距离和d(i-1,j-1)是一样的
如果c!=d,情况稍微复杂一点,
那最后只需要看着三种谁最小,就采用对应的编辑方案了。
递推公式出来了,程序也就出来了。
Thoughts:
First, we explain the recursive structure here.
Denote as
the edit distance between and .
For base case, we have:
Then, for the recursive step, we have:
public class Solution { public int minDistance(String word1, String word2) { int[][] table = new int[word1.length()+1][word2.length()+1]; for(int i = 0; i < table.length; i++){ for(int j = 0; j < table[i].length; j++){ if(i == 0){ table[i][j] = j; }else if(j == 0){ table[i][j] = i; }else{ if(word1.charAt(i-1) == word2.charAt(j-1)){ table[i][j] = table[i-1][j-1]; }else{ table[i][j] = 1 + Math.min(table[i-1][j-1], Math.min(table[i-1][j],table[i][j-1])); } } } }return table[word1.length()][word2.length()]; } }
原文:http://www.cnblogs.com/RazerLu/p/3536703.html