[email protected] [208] Implement Trie (Prefix Tree)

Trie 树模板

https://leetcode.com/problems/implement-trie-prefix-tree/

class TrieNode {
public:
    char var;
    bool isWord;
    TrieNode *next[26];
    TrieNode() {
        var = 0;
        this->isWord = false;
        for(auto &c : next) c = NULL;
    }
    TrieNode(char c) {
        var = c;
        this->isWord = false;
        for(auto &c : next) c = NULL;
    }
};

class Trie {
public:
    Trie() {
        root = new TrieNode();
    }

    // Inserts a word into the trie.
    void insert(string word) {
        TrieNode *p = root;
        for(auto &a : word) {
            int idx = a - ‘a‘;
            if(!p->next[idx]) p->next[idx] = new TrieNode();
            p = p->next[idx];
        }
        p->isWord = true;
    }

    // Returns if the word is in the trie.
    bool search(string word) {
        TrieNode *p = root;
        for(auto &a : word) {
            int idx = a - ‘a‘;
            if(!p->next[idx]) return false;
            p = p->next[idx];
        }
        return p->isWord;
    }

    // Returns if there is any word in the trie
    // that starts with the given prefix.
    bool startsWith(string prefix) {
        TrieNode *p = root;
        for(auto &a : prefix) {
            int idx = a - ‘a‘;
            if(!p->next[idx]) return false;
            p = p->next[idx];
        }
        return true;
    }

private:
    TrieNode* root;
};

// Your Trie object will be instantiated and called as such:
// Trie trie;
// trie.insert("somestring");
// trie.search("key");
时间: 2024-12-18 11:56:54

[email protected] [208] Implement Trie (Prefix Tree)的相关文章

LeetCode 208. Implement Trie (Prefix Tree)

Implement a trie with insert, search, and startsWith methods. Note:You may assume that all inputs are consist of lowercase letters a-z. 这道题让我们实现一个重要但又有些复杂的数据结构-字典树, 又称前缀树或单词查找树,详细介绍可以参见网友董的博客,例如,一个保存了8个键的trie结构,"A", "to", "tea&quo

[leedcode 208] Implement Trie (Prefix Tree)

Trie树又被称为字典树.前缀树,是一种用于快速检索的多叉树.Tried树可以利用字符串的公共前缀来节省存储空间. 但如果系统存在大量没有公共前缀的字符串,相应的Trie树将非常消耗内存.(下图为Wiki上的Trie树示意图, https://en.wikipedia.org/wiki/Trie) 子节点用hashMap表示 isWord标记从根节点到该节点是否表示一个单词,还是另一单词的前缀. Implement a trie with insert, search, and startsWi

208. Implement Trie (Prefix Tree)

题目: Implement a trie with insert, search, and startsWith methods. 链接: http://leetcode.com/problems/implement-trie-prefix-tree/ 7/14/201760%,照着课件代码改写的. 将val cast成boolean,注意第21行需要设为false.以下代码可以不需要看,第一版虽然通过了,但是具体内容并不了解. 1 public class Trie { 2 private s

[LeetCode] 208. Implement Trie (Prefix Tree) Java

题目: Implement a trie with insert, search, and startsWith methods. Note:You may assume that all inputs are consist of lowercase letters a-z. 题意及分析:实现一个字典树或者叫前缀树的插入.删除和startsWith判断.这里定义一个trieNode类作为字典树的节点类.然后有一个chilrden数组保存子节点,一个isWord变量来判断从根节点到该节点是否是一

208. Implement Trie (Prefix Tree)字典树

Implement a trie with insert, search, and startsWith methods. Note: You may assume that all inputs are consist of lowercase letters a-z. 在Trie树中主要有3个操作,插入.查找和删除.一般情况下Trie树中很少存在删除单独某个结点的情况,因此只考虑删除整棵树. 1.插入 假设存在字符串str,Trie树的根结点为root.i=0,p=root. 1)取str[

Java for LeetCode 208 Implement Trie (Prefix Tree)

Implement a trie with insert, search, and startsWith methods. Note: You may assume that all inputs are consist of lowercase letters a-z. 解题思路: 参考百度百科:Trie树 已经给出了详细的代码: JAVA实现如下: class TrieNode { // Initialize your data structure here. int num;// 有多少单

208. Implement Trie (Prefix Tree) -- 键树

Implement a trie with insert, search, and startsWith methods. Note:You may assume that all inputs are consist of lowercase letters a-z. class TrieNode { public: // Initialize your data structure here. bool isWord; unordered_map<char, TrieNode*> alph

208. Implement Trie (Prefix Tree) 实现前缀树

Implement a trie with insert, search, and startsWith methods. Note: You may assume that all inputs are consist of lowercase letters a-z. var Trie = function() { this.nodes = {}; }; Trie.prototype.insert = function(word) { let node = this.nodes, cur;

[leetcode trie]208. Implement Trie (Prefix Tree)

实现一个字典树 1 class Trie(object): 2 3 def __init__(self): 4 self.root = TrieNode() 5 6 def insert(self, word): 7 cur = self.root 8 for i in word: 9 cur = cur.children[i] 10 cur.is_word = True 11 12 13 def search(self, word): 14 cur = self.root 15 for i i