LeetCode: Distinct Subsequences 解题报告
Distinct Subsequences
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
.
SOLUTION 1(AC):
现在这种DP题目基本都是5分钟AC咯。主页君引一下别人的解释咯:
http://blog.csdn.net/fightforyourdream/article/details/17346385?reload#comments
http://blog.csdn.net/abcbc/article/details/8978146
引自以上的解释:
遇到这种两个串的问题,很容易想到DP。但是这道题的递推关系不明显。可以先尝试做一个二维的表int[][] dp,用来记录匹配子序列的个数(以S ="rabbbit"
,T = "rabbit"
为例):
r a b b b i t
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
从这个表可以看出,无论T的字符与S的字符是否匹配,dp[i][j] = dp[i][j - 1].就是说,假设S已经匹配了j - 1个字符,得到匹配个数为dp[i][j - 1].现在无论S[j]是不是和T[i]匹配,匹配的个数至少是dp[i][j - 1]。除此之外,当S[j]和T[i]相等时,我们可以让S[j]和T[i]匹配,然后让S[j - 1]和T[i - 1]去匹配。所以递推关系为:
dp[0][0] = 1; // T和S都是空串.
dp[0][1 ... S.length() - 1] = 1; // T是空串,S只有一种子序列匹配。
dp[1 ... T.length() - 1][0] = 0; // S是空串,T不是空串,S没有子序列匹配。
dp[i][j] = dp[i][j - 1] + (T[i - 1] == S[j - 1] ? dp[i - 1][j - 1] : 0).1 <= i <= T.length(), 1 <= j <= S.length()
这道题可以作为两个字符串DP的典型:
两个字符串:
先创建二维数组存放答案,如解法数量。注意二维数组的长度要比原来字符串长度+1,因为要考虑第一个位置是空字符串。
然后考虑dp[i][j]和dp[i-1][j],dp[i][j-1],dp[i-1][j-1]的关系,如何通过判断S.charAt(i)和T.charAt(j)的是否相等来看看如果移除了最后两个字符,能不能把问题转化到子问题。
最后问题的答案就是dp[S.length()][T.length()]
还有就是要注意通过填表来找规律。
注意:循环的时候,一定要注意i的取值要到len,这个出好几次错了。
1 public class Solution { 2 public int numDistinct(String S, String T) { 3 if (S == null || T == null) { 4 return 0; 5 } 6 7 int lenS = S.length(); 8 int lenT = T.length(); 9 10 if (lenS < lenT) { 11 return 0; 12 } 13 14 int[][] D = new int[lenS + 1][lenT + 1]; 15 16 // BUG 1: forget to use <= instead of <.... 17 for (int i = 0; i <= lenS; i++) { 18 for (int j = 0; j <= lenT; j++) { 19 // both are empty. 20 if (i == 0 && j == 0) { 21 D[i][j] = 1; 22 } else if (i == 0) { 23 // S is empty, can't form a non-empty string. 24 D[i][j] = 0; 25 } else if (j == 0) { 26 // T is empty. S is not empty. 27 D[i][j] = 1; 28 } else { 29 D[i][j] = 0; 30 // keep the last character of S. 31 if (S.charAt(i - 1) == T.charAt(j - 1)) { 32 D[i][j] += D[i - 1][j - 1]; 33 } 34 35 // discard the last character of S. 36 D[i][j] += D[i - 1][j]; 37 } 38 } 39 } 40 41 return D[lenS][lenT]; 42 } 43 }
运行时间:
Submit Time | Status | Run Time | Language |
---|---|---|---|
13 minutes ago | Accepted | 432 ms | java |
SOLUTION 2:
递归解法也写一下,蛮简单的:
但是这个解法过不了,TLE了。
1 // SOLUTION 2: recursion version. 2 public int numDistinct(String S, String T) { 3 if (S == null || T == null) { 4 return 0; 5 } 6 7 return rec(S, T, 0, 0); 8 } 9 10 public int rec(String S, String T, int indexS, int indexT) { 11 int lenS = S.length(); 12 int lenT = T.length(); 13 14 // base case: 15 if (indexT >= lenT) { 16 // T is empty. 17 return 1; 18 } 19 20 if (indexS >= lenS) { 21 // S is empty but T is not empty. 22 return 0; 23 } 24 25 int sum = 0; 26 // use the first character in S. 27 if (S.charAt(indexS) == T.charAt(indexT)) { 28 sum += rec(S, T, indexS + 1, indexT + 1); 29 } 30 31 // Don't use the first character in S. 32 sum += rec(S, T, indexS + 1, indexT); 33 34 return sum; 35 }
SOLUTION 3:
递归加上memory记忆之后,StackOverflowError. 可能还是不够优化。确实递归层次太多。
Runtime Error Message: | Line 125: java.lang.StackOverflowError |
Last executed input: | "zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz |
1 // SOLUTION 3: recursion version with memory. 2 public int numDistinct(String S, String T) { 3 if (S == null || T == null) { 4 return 0; 5 } 6 7 int lenS = S.length(); 8 int lenT = T.length(); 9 10 int[][] memory = new int[lenS + 1][lenT + 1]; 11 for (int i = 0; i <= lenS; i++) { 12 for (int j = 0; j <= lenT; j++) { 13 memory[i][j] = -1; 14 } 15 } 16 17 return rec(S, T, 0, 0, memory); 18 } 19 20 public int rec(String S, String T, int indexS, int indexT, int[][] memory) { 21 int lenS = S.length(); 22 int lenT = T.length(); 23 24 // base case: 25 if (indexT >= lenT) { 26 // T is empty. 27 return 1; 28 } 29 30 if (indexS >= lenS) { 31 // S is empty but T is not empty. 32 return 0; 33 } 34 35 if (memory[indexS][indexT] != -1) { 36 return memory[indexS][indexT]; 37 } 38 39 int sum = 0; 40 // use the first character in S. 41 if (S.charAt(indexS) == T.charAt(indexT)) { 42 sum += rec(S, T, indexS + 1, indexT + 1); 43 } 44 45 // Don't use the first character in S. 46 sum += rec(S, T, indexS + 1, indexT); 47 48 // record the solution. 49 memory[indexS][indexT] = sum; 50 return sum; 51 }
SOLUTION 4 (AC):
参考了http://blog.csdn.net/fightforyourdream/article/details/17346385?reload#comments的代码后,发现递归过程找解的过程可以优化。我们不需要沿用DP的思路
而应该与permutation之类差不多,把当前可能可以取的解都去尝试一次。就是在S中找到T的首字母,再进一步递归。
Submit Time | Status | Run Time | Language |
---|---|---|---|
0 minutes ago | Accepted | 500 ms | java |
1 // SOLUTION 4: improved recursion version 2 public int numDistinct(String S, String T) { 3 if (S == null || T == null) { 4 return 0; 5 } 6 7 int lenS = S.length(); 8 int lenT = T.length(); 9 10 int[][] memory = new int[lenS + 1][lenT + 1]; 11 for (int i = 0; i <= lenS; i++) { 12 for (int j = 0; j <= lenT; j++) { 13 memory[i][j] = -1; 14 } 15 } 16 17 return rec4(S, T, 0, 0, memory); 18 } 19 20 public int rec4(String S, String T, int indexS, int indexT, int[][] memory) { 21 int lenS = S.length(); 22 int lenT = T.length(); 23 24 // base case: 25 if (indexT >= lenT) { 26 // T is empty. 27 return 1; 28 } 29 30 if (indexS >= lenS) { 31 // S is empty but T is not empty. 32 return 0; 33 } 34 35 if (memory[indexS][indexT] != -1) { 36 return memory[indexS][indexT]; 37 } 38 39 int sum = 0; 40 for (int i = indexS; i < lenS; i++) { 41 // choose which character in S to choose as the first character of T. 42 if (S.charAt(i) == T.charAt(indexT)) { 43 sum += rec4(S, T, i + 1, indexT + 1, memory); 44 } 45 } 46 47 // record the solution. 48 memory[indexS][indexT] = sum; 49 return sum; 50 }
SOLUTION 5:
在SOLUTION 4的基础之上,把记忆体去掉之后,仍然是TLE
Last executed input: | "daacaedaceacabbaabdccdaaeaebacddadcaeaacadbceaecddecdeedcebcdacdaebccdeebcbdeaccabcecbeeaadbccbaeccbbdaeadecabbbedceaddcdeabbcdaeadcddedddcececbeeabcbecaeadddeddccbdbcdcbceabcacddbbcedebbcaccac", "ceadbaa" |
1 // SOLUTION 5: improved recursion version without memory. 2 public int numDistinct(String S, String T) { 3 if (S == null || T == null) { 4 return 0; 5 } 6 7 return rec5(S, T, 0, 0); 8 } 9 10 public int rec5(String S, String T, int indexS, int indexT) { 11 int lenS = S.length(); 12 int lenT = T.length(); 13 14 // base case: 15 if (indexT >= lenT) { 16 // T is empty. 17 return 1; 18 } 19 20 if (indexS >= lenS) { 21 // S is empty but T is not empty. 22 return 0; 23 } 24 25 int sum = 0; 26 for (int i = indexS; i < lenS; i++) { 27 // choose which character in S to choose as the first character of T. 28 if (S.charAt(i) == T.charAt(indexT)) { 29 sum += rec5(S, T, i + 1, indexT + 1); 30 } 31 } 32 33 return sum; 34 }
总结:
大家可以在SOLUTION 1和SOLUTION 4两个选择里用一个就好啦。
http://blog.csdn.net/fightforyourdream/article/details/17346385?reload#comments
这道题可以作为两个字符串DP的典型:
两个字符串:
先创建二维数组存放答案,如解法数量。注意二维数组的长度要比原来字符串长度+1,因为要考虑第一个位置是空字符串。
然后考虑dp[i][j]和dp[i-1][j],dp[i][j-1],dp[i-1][j-1]的关系,如何通过判断S.charAt(i)和T.charAt(j)的是否相等来看看如果移除了最后两个字符,能不能把问题转化到子问题。
最后问题的答案就是dp[S.length()][T.length()]
还有就是要注意通过填表来找规律。
GITHUB:
https://github.com/yuzhangcmu/LeetCode_algorithm/blob/master/dp/NumDistinct.java
posted on 2014-12-31 18:21 Yu's Garden 阅读(5076) 评论(0) 编辑 收藏 举报