Word Break II
update Sep 16 2018, 11:05
Given a non-empty string s and a dictionary wordDict containing a list of non-empty words, add spaces in s to construct a sentence where each word is a valid dictionary word. Return all such possible sentences.
Note:
The same word in the dictionary may be reused multiple times in the segmentation. You may assume the dictionary does not contain duplicate words.
Example 1:
Input:
s = "catsanddog"
wordDict = ["cat", "cats", "and", "sand", "dog"]
Output:
[
"cats and dog",
"cat sand dog"
]
Example 2:
Input:
s = "pineapplepenapple"
wordDict = ["apple", "pen", "applepen", "pine", "pineapple"]
Output:
[
"pine apple pen apple",
"pineapple pen apple",
"pine applepen apple"
]
Explanation: Note that you are allowed to reuse a dictionary word.
Example 3:
Input:
s = "catsandog"
wordDict = ["cats", "dog", "sand", "and", "cat"]
Output:
[]
Basic Idea:
Word Break I 求的是给定字符串能否被分割成字典中的元素,因为只求一个boolean,可以用DP的思路求解,对于一个string s,我们只要考虑能否将其分解为子问题 s[0:k] 能被成功分割, 且s[k:]是dict中的一个word,k from 0 to end
。
而对于这道题目,要求的是所有可能的解,我们就需要使用DFS了。与之前dp的思路略有不同,这次我们每次从前面开始考虑,s[0:k]
为 dict 中的word,后面部分为下一个子问题。dfs function 的返回值为当前问题的所有可能的结果 List<String>
,则对于一个子问题 string s,我们尝试用dict中的每个word来匹配s最前面的部分,然后调用dfs function计算其后面部分的所有可能解,再将prefix放在每个解的前面,返回即可。需要注意的是后面部分可能会被重复计算,所以我们需要用一个 Map<Integer, List<String>>
来存储已经计算过的右边部分的解。
时间复杂度分析:
如果不考虑优化,时间复杂度为
O(n * 2^n)
,因为解集的大小可以是O(2^n)
,即怎么分都valid,共有n-1个空可以分割,总分割可能为O(2^(n - 1))
,而每种情况生成解集的时间为O(n)
.
Java Code:
class Solution {
public List<String> wordBreak(String s, List<String> wordDict) {
Set<String> dict = new HashSet<>(wordDict);
Map<Integer, List<String>> memory = new HashMap<>();
List<String> res = dfs(s, 0, dict, memory);
return res;
}
private List<String> dfs(String s, int start, Set<String> dict, Map<Integer, List<String>> memory) {
if (memory.containsKey(start)) return memory.get(start);
List<String> ret = new ArrayList<>();
if (start == s.length()) {
ret.add("");
memory.put(start, ret);
return ret;
}
for (int i = start; i < s.length(); ++i) {
String prefix = s.substring(start, i + 1);
if (dict.contains(prefix)) {
List<String> rightPaths = dfs(s, i + 1, dict, memory);
for (String rightPath : rightPaths) {
if (rightPath.length() == 0) {
ret.add(prefix);
} else {
ret.add(prefix + " " + rightPath);
}
}
}
}
memory.put(start, ret);
return ret;
}
}