题目描述:
给你两个单词 word1
和 word2
, 请返回将 word1
转换成 word2
所使用的最少操作数 。
你可以对一个单词进行如下三种操作:
- 插入一个字符
- 删除一个字符
- 替换一个字符
示例 1:
输入:word1 = "horse", word2 = "ros" 输出:3 解释: horse -> rorse (将 'h' 替换为 'r') rorse -> rose (删除 'r') rose -> ros (删除 'e')
示例 2:
输入:word1 = "intention", word2 = "execution" 输出:5 解释: intention -> inention (删除 't') inention -> enention (将 'i' 替换为 'e') enention -> exention (将 'n' 替换为 'x') exention -> exection (将 'n' 替换为 'c') exection -> execution (插入 'u')
提示:
0 <= word1.length, word2.length <= 500
word1
和word2
由小写英文字母组成
我的作答:
动态规划的递归公式究竟是怎么想出来的.............不看其他厉害的人的解析我抓耳挠腮半天也想不到捏。。
dp[i][j]为从word1的第i个字符到word2的第j个字符需要的步数(操作数)
class Solution:
def minDistance(self, word1: str, word2: str) -> int:
n1 = len(word1)
n2 = len(word2)
dp = [[0]*(n2+1) for _ in range(n1+1)]
for j in range(1, n2+1):
dp[0][j] = dp[0][j-1] + 1 #初始化
for i in range(1, n1+1):
dp[i][0] = dp[i-1][0] + 1
for i in range(1, n1+1):
for j in range(1, n2+1):
if word1[i-1]==word2[j-1]: #word1的第i个字符==word2的第j个字符
dp[i][j] = dp[i-1][j-1]
else: #dp[i][j-1]为插入,dp[i-1][j]是删除,dp[i-1][j-1]是替换
dp[i][j] = min(dp[i][j-1], dp[i-1][j], dp[i-1][j-1]) + 1
return dp[-1][-1]
参考:
自顶向下的方法,好巧妙
class Solution:
def minDistance(self, word1: str, word2: str) -> int:
import functools
@functools.lru_cache(None)
def helper(i, j):
if i == len(word1) or j == len(word2):
return len(word1) - i + len(word2) - j
if word1[i] == word2[j]:
return helper(i + 1, j + 1)
else:
inserted = helper(i, j + 1)
deleted = helper(i + 1, j)
replaced = helper(i + 1, j + 1)
return min(inserted, deleted, replaced) + 1
return helper(0, 0)