0208. Implement Trie (Prefix Tree)
Medium | Design + String | 112 ms (99.40%), 29.6 MB (93.89%)
Source: LeetCode - Implement Trie (Prefix Tree) GitHub: Solution / Performance
A trie (pronounced as "try") or prefix tree is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. There are various applications of this data structure, such as autocomplete and spellchecker.
Implement the Trie class:
Trie()
Initializes the trie object.void insert(String word)
Inserts the stringword
into the trie.boolean search(String word)
Returnstrue
if the stringword
is in the trie (i.e., was inserted before), andfalse
otherwise.boolean startsWith(String prefix)
Returnstrue
if there is a previously inserted stringword
that has the prefixprefix
, andfalse
otherwise.
Insert
Iterate each char in the word and insert each of them into the hash table. Note that the symbol that represents the end of the word also needs to be added. (Here I use #)
Search
To search for a certain word, we iterate each char in the word and check the existence of each of them. Besides, if the search operation is done for each char, check whether the symbol # exists in the current node or not.
StartWith
Almost the same as the search operation. Except that we don't need to check the symbol # since the input string is only a prefix.
class Trie:
def __init__(self):
self.root = {}
def insert(self, word: str) -> None:
# time : O(n), n is the lenght of str
# space : O(n)
cur = self.root
for char in word:
if char not in cur: cur[char] = {}
cur = cur[char]
cur['#'] = True
def search(self, word: str) -> bool:
# time : O(n)
# space : O(1)
cur = self.root
for char in word:
if char not in cur: return False
cur = cur[char]
return '#' in cur
def startsWith(self, prefix: str) -> bool:
# time : O(n)
# space : O(1)
cur = self.root
for char in prefix:
if char not in cur: return False
cur = cur[char]
return True
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