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:
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
动态规划:
dp[i][j]
代表 word1
到 i
位置转换成 word2
到 j
位置需要最少步数
所以,
当 word1[i] == word2[j]
,dp[i][j] = dp[i-1][j-1]
;
当 word1[i] != word2[j]
,dp[i][j] = min(dp[i-1][j-1], dp[i-1][j], dp[i][j-1]) + 1
其中,dp[i-1][j-1]
表示替换操作,dp[i-1][j]
表示删除操作,dp[i][j-1]
表示插入操作。
注意,针对第一行,第一列要单独考虑,我们引入 ''
下图所示:
{:width=”360”}
{:align=center}
第一行,是 word1
为空变成 word2
最少步数,就是插入操作
第一列,是 word2
为空,需要的最少步数,就是删除操作
再附上自顶向下的方法,大家可以提供 Java 版吗?
代码:
自底向上
[1]1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
| 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 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]: dp[i][j] = dp[i-1][j-1] else: dp[i][j] = min(dp[i][j-1], dp[i-1][j], dp[i-1][j-1] ) + 1 return dp[-1][-1]
|
[1]1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
| class Solution { public int minDistance(String word1, String word2) { int n1 = word1.length(); int n2 = word2.length(); int[][] dp = new int[n1 + 1][n2 + 1]; for (int j = 1; j <= n2; j++) dp[0][j] = dp[0][j - 1] + 1; for (int i = 1; i <= n1; i++) dp[i][0] = dp[i - 1][0] + 1;
for (int i = 1; i <= n1; i++) { for (int j = 1; j <= n2; j++) { if (word1.charAt(i - 1) == word2.charAt(j - 1)) dp[i][j] = dp[i - 1][j - 1]; else dp[i][j] = Math.min(Math.min(dp[i - 1][j - 1], dp[i][j - 1]), dp[i - 1][j]) + 1; } } return dp[n1][n2]; } }
|
自顶向下
[2]1 2 3 4 5 6 7 8 9 10 11 12 13
| import functools class Solution: @functools.lru_cache(None) def minDistance(self, word1: str, word2: str) -> int: if not word1 or not word2: return len(word1) + len(word2) if word1[0] == word2[0]: return self.minDistance(word1[1:], word2[1:]) else: inserted = 1 + self.minDistance(word1, word2[1:]) deleted = 1 + self.minDistance(word1[1:], word2) replace = 1 + self.minDistance(word1[1:], word2[1:]) return min(inserted, deleted, replace)
|
@shu-xie-fan 的建议,由于字符串切片是 $O(n)$,所以改成用了索引号。
[2]1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
| 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)
|