1. 题目

Trie(发音类似 "try")或者说 前缀树 是一种树形数据结构,用于高效地存储和检索字符串数据集中的键。这一数据结构有相当多的应用情景,例如自动补完和拼写检查。

请你实现 Trie 类:

Trie() 初始化前缀树对象。
void insert(String word) 向前缀树中插入字符串 word 。
boolean search(String word) 如果字符串 word 在前缀树中,返回 true(即,在检索之前已经插入);否则,返回 false
boolean startsWith(String prefix) 如果之前已经插入的字符串 word 的前缀之一为 prefix ,返回 true ;否则,返回 false


示例:
输入
["Trie", "insert", "search", "search", "startsWith", "insert", "search"]
[[], ["apple"], ["apple"], ["app"], ["app"], ["app"], ["app"]]
输出
[null, null, true, false, true, null, true]

解释
Trie trie = new Trie();
trie.insert("apple");
trie.search("apple"); // 返回 True
trie.search("app"); // 返回 False
trie.startsWith("app"); // 返回 True
trie.insert("app");
trie.search("app"); // 返回 True

提示:
1 <= word.length, prefix.length <= 2000
word 和 prefix 仅由小写英文字母组成
insert、search 和 startsWith 调用次数 总计 不超过 3 * 104

2. 题解

# 692
import collections


class Node:
def __init__(self):
self.children = collections.defaultdict(Node)
self.isword = False


class Trie:

def __init__(self):
"""
Initialize your data structure here.
"""
self.root = Node()

def insert(self, word: str) -> None:
"""
Inserts a word into the trie.
"""
cur = self.root
for w in word:
cur = cur.children[w]
cur.isword = True

def search(self, word: str) -> bool:
"""
Returns if the word is in the trie.
"""
cur = self.root
for w in word:
cur = cur.children.get(w)
if cur is None:
return False
return cur.isword

def startsWith(self, prefix: str) -> bool:
"""
Returns if there is any word in the trie that starts with the given prefix.
"""
cur = self.root
for w in prefix:
cur = cur.children.get(w)
if cur is None:
return False
return True



# Your Trie object will be instantiated and called as such:
# obj = Trie()
# obj.insert(word)
# param_2 = obj.search(word)
# param_3 = obj.startsWith(prefix)