Given a string S and a string T, count the number of distinct subsequences of T in S.
A subsequence of a string is a new string which is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (ie, "ACE"
is a subsequence of "ABCDE"
while "AEC"
is not).
Here is an example:
S = "rabbbit"
, T = "rabbit"
Return 3
.
看到有关字符串的子序列或者配准类的问题,首先应该考虑的就是用动态规划Dynamic Programming来求解,这个应成为条件发射。而所有DP问题的核心就是找出地推公式,想这道题就是递推一个二维的dp数组,下面我们从题目中给的例子来分析,这个二维dp数组应为:
Ø r a b b b i t Ø 1 1 1 1 1 1 1 1 r 0 1 1 1 1 1 1 1 a 0 0 1 1 1 1 1 1 b 0 0 0 1 2 3 3 3 b 0 0 0 0 1 3 3 3 i 0 0 0 0 0 0 3 3 t 0 0 0 0 0 0 0 3
首先,若原字符串和子序列都为空时,返回1,因为空串也是空串的一个子序列。若原字符串不为空,而子序列为空,也返回1,因为空串也是任意字符串的一个子序列。而当原字符串为空,子序列不为空时,返回0,因为非空字符串不能当空字符串的子序列。理清这些,二维数组dp的边缘变可以初始化了,下面只要找出递推式,就可以更新整个dp数组了。我们通过观察上面的二维数组可以发现,当更新到dp[i][j]时,dp[i][j] >= dp[i][j - 1] 总是成立,再进一步观察发现,当 T[i - 1] == S[j - 1] 时,dp[i][j] = dp[i][j - 1] + dp[i - 1][j - 1],若不等, dp[i][j] = dp[i][j - 1],所以,综合以上,递推式为:
dp[i][j] = dp[i][j - 1] + (T[i - 1] == S[j - 1] ? dp[i - 1][j - 1] : 0)
根据以上分析,可以写出代码如下:
class Solution { public: int numDistinct(string S, string T) { int dp[T.size() + 1][S.size() + 1]; for (int i = 0; i <= S.size(); ++i) dp[0][i] = 1; for (int i = 1; i <= T.size(); ++i) dp[i][0] = 0; for (int i = 1; i <= T.size(); ++i) { for (int j = 1; j <= S.size(); ++j) { dp[i][j] = dp[i][j - 1] + (T[i - 1] == S[j - 1] ? dp[i - 1][j - 1] : 0); } } return dp[T.size()][S.size()]; } };