[LeetCode] 208. Implement Trie (Prefix Tree) 实现字典树(前缀树)
Posted 轻风舞动
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了[LeetCode] 208. Implement Trie (Prefix Tree) 实现字典树(前缀树)相关的知识,希望对你有一定的参考价值。
Implement a trie with insert
, search
, and startsWith
methods.
Example:
Trie trie = new Trie(); trie.insert("apple"); trie.search("apple"); // returns true trie.search("app"); // returns false trie.startsWith("app"); // returns true trie.insert("app"); trie.search("app"); // returns true
Note:
- You may assume that all inputs are consist of lowercase letters
a-z
. - All inputs are guaranteed to be non-empty strings.
实现一个数据结构:字典树(前缀树或单词查找树),具备insert, search, startsWith的功能。参考董的博客:数据结构之Trie树
Trie树,又称字典树,单词查找树或者前缀树,是一种用于快速检索的多叉树结构,如英文字母的字典树是一个26叉树,数字的字典树是一个10叉树。Trie树可以利用字符串的公共前缀来节约存储空间。
Trie树的基本性质可以归纳为:
(1)根节点不包含字符,除根节点以外每个节点只包含一个字符。
(2)从根节点到某一个节点,路径上经过的字符连接起来,为该节点对应的字符串。
(3)每个节点的所有子节点包含的字符串不相同。
下图是一个保存了8个键的trie结构,"A", "to", "tea", "ted", "ten", "i", "in", and "inn"
Trie树复杂度:
(1)插入、查找的时间复杂度均为O(n),其中n为字符串长度。
(2) 空间复杂度是26^n级别的,非常庞大(可采用双数组实现改善)。
实施方法:因为只用26个字母,所以可以用数组记录,数组元素为TrieNode。
Java:
class TrieNode { private TrieNode[] children; public boolean hasWord; // Initialize your data structure here. public TrieNode() { children = new TrieNode[26]; hasWord = false; } public void insert(String word, int index) { if (index == word.length()) { this.hasWord = true; return; } int pos = word.charAt(index) - \'a\'; if (children[pos] == null) { children[pos] = new TrieNode(); } children[pos].insert(word, index + 1); } public TrieNode find(String word, int index) { if (index == word.length()) { return this; } int pos = word.charAt(index) - \'a\'; if (children[pos] == null) { return null; } return children[pos].find(word, index + 1); } } public class Trie { private TrieNode root; public Trie() { root = new TrieNode(); } // Inserts a word into the trie. public void insert(String word) { root.insert(word, 0); } // Returns if the word is in the trie. public boolean search(String word) { TrieNode node = root.find(word, 0); return (node != null && node.hasWord); } // Returns if there is any word in the trie // that starts with the given prefix. public boolean startsWith(String prefix) { TrieNode node = root.find(prefix, 0); return node != null; } }
Python: Time: O(n), per operation, Space: O(1)
class TrieNode: def __init__(self): # Initialize your data structure here. self.childs = dict() self.isWord = False class Trie: def __init__(self): self.root = TrieNode() # @param {string} word # @return {void} # Inserts a word into the trie. def insert(self, word): node = self.root for letter in word: child = node.childs.get(letter) if child is None: child = TrieNode() node.childs[letter] = child node = child node.isWord = True # @param {string} word # @return {boolean} # Returns if the word is in the trie. def search(self, word): node = self.root for letter in word: node = node.childs.get(letter) if node is None: return False return node.isWord # @param {string} prefix # @return {boolean} # Returns if there is any word in the trie # that starts with the given prefix. def startsWith(self, prefix): node = self.root for letter in prefix: node = node.childs.get(letter) if node is None: return False return True
Python:
class TrieNode: def __init__(self): self.is_string = False self.leaves = {} class Trie: def __init__(self): self.root = TrieNode() def insert(self, word): cur = self.root for c in word: if not c in cur.leaves: cur.leaves[c] = TrieNode() cur = cur.leaves[c] cur.is_string = True def search(self, word): node = self.childSearch(word) if node: return node.is_string return False def startsWith(self, prefix): return self.childSearch(prefix) is not None def childSearch(self, word): cur = self.root for c in word: if c in cur.leaves: cur = cur.leaves[c] else: return None return cur
C++:
class TrieNode { public: // Initialize your data structure here. TrieNode() : is_string(false) { } bool is_string; unordered_map<char, TrieNode *> leaves; }; class Trie { public: Trie() { root_ = new TrieNode(); } void insert(string word) { auto *cur = root_; for (const auto& c : word) { if (!cur->leaves.count(c)) { cur->leaves[c] = new TrieNode(); } cur = cur->leaves[c]; } cur->is_string = true; } bool search(string word) { auto *node = childSearch(word); if (node) { return node->is_string; } return false; } bool startsWith(string prefix) { return childSearch(prefix); } TrieNode *childSearch(const string& word) { auto *cur = root_; for (const auto& c : word) { if (cur->leaves.count(c)) { cur = cur->leaves[c]; } else { return nullptr; } } return cur; } private: TrieNode *root_; };
All LeetCode Questions List 题目汇总
以上是关于[LeetCode] 208. Implement Trie (Prefix Tree) 实现字典树(前缀树)的主要内容,如果未能解决你的问题,请参考以下文章
[LeetCode] 208. Implement Trie (Prefix Tree)
leetcode No208. Implement Trie (Prefix Tree)
[LeetCode] 208(LintCode). Implement Trie(Prefix Tree)
LeetCode 208. Implement Trie (Prefix Tree)