LeetCode——Add and Search Word - Data structure design
Description:
Design a data structure that supports the following two operations:
void addWord(word)
bool search(word)
search(word) can search a literal word or a regular expression string containing only letters a-z
or .
. A .
means it can represent any one letter.
For example:
addWord("bad")
addWord("dad")
addWord("mad")
search("pad") -> false
search("bad") -> true
search(".ad") -> true
search("b..") -> true
Note:
You may assume that all words are consist of lowercase letters a-z
.
题目很好理解:添加单词查找单词。
首先想到的是用线性数据结构,然后逐个匹配查找。可以想象一定是超时的。
public class WordDictionary { private List<String> arr = new ArrayList<String>(); // Adds a word into the data structure.
public void addWord(String word) {
arr.add(word);
} // Returns if the word is in the data structure. A word could
// contain the dot character '.' to represent any one letter.
public boolean search(String word) {
boolean flag = true;
for(String str : arr) {
if(str.equals(word)) {
flag = true;
break;
}
else if(arr.contains(".")) {
if(word.length() != str.length()) {
flag = false;
break;
} for(int i=0; i<str.length();) {
char ch1 = str.charAt(i);
char ch2 = word.charAt(i);
if(ch1 == '.' || ch2 == '.' || ch1 == ch2) {
i ++;
}
else {
flag = false;
break;
}
} }
else {
flag = false;
break;
}
}
return flag;
}
} // Your WordDictionary object will be instantiated and called as such:
// WordDictionary wordDictionary = new WordDictionary();
// wordDictionary.addWord("word");
// wordDictionary.search("pattern");
要优化明显要使用Trie树来减少无用的比较次数,从而降低时间复杂度。
关于Trie树:http://www.cnblogs.com/wxisme/p/4876197.html
public class WordDictionary { private TrieNode root; public WordDictionary() {
this.root = new TrieNode();
} private class TrieNode {
private TrieNode[] son;
private char val;
private boolean isEnd;
public TrieNode() {
this.son = new TrieNode[26];
this.isEnd = false;
}
} // Adds a word into the data structure.
public void addWord(String word) { char[] wordChars = word.toCharArray();
TrieNode node = this.root; for(char ch : wordChars) {
int pos = ch - 'a';
if(node.son[pos] == null) {
node.son[pos] = new TrieNode();
node.son[pos].val = ch;
}
node = node.son[pos];
}
node.isEnd = true;
} public boolean patternSearch(String word, TrieNode node) {
char[] wordChars = word.toCharArray();
for(int at=0; at<word.length(); at++) {
char ch = wordChars[at];
if(ch != '.') {
int pos = ch - 'a';
if(node.son[pos] != null) { node = node.son[pos];
}
else {
return false;
}
}
else {
int flag = 0;
for(int i=0; i<26; i++) {
if(node.son[i] != null) {
boolean b =patternSearch(word.substring(at+1), node.son[i]);
if(b) return b;
else {
flag ++;
}
}
else {
flag ++;
continue;
}
}
if(flag == 26) {
return false;
}
}
}
return node.isEnd;
} // Returns if the word is in the data structure. A word could
// contain the dot character '.' to represent any one letter.
public boolean search(String word) { return patternSearch(word, this.root);
} }
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