Description
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 stringwordinto the trie.boolean search(String word)Returnstrueif the stringwordis in the trie (i.e., was inserted before), andfalseotherwise.boolean startsWith(String prefix)Returnstrueif there is a previously inserted stringwordthat has the prefixprefix, andfalseotherwise.
Β
Example 1:
Input
["Trie", "insert", "search", "search", "startsWith", "insert", "search"]
[[], ["apple"], ["apple"], ["app"], ["app"], ["app"], ["app"]]
Output
[null, null, true, false, true, null, true]
Explanation
Trie trie = new Trie();
trie.insert("apple");
trie.search("apple"); // return True
trie.search("app"); // return False
trie.startsWith("app"); // return True
trie.insert("app");
trie.search("app"); // return True
Β
Constraints:
1 <= word.length, prefix.length <= 2000wordandprefixconsist only of lowercase English letters.- At most
3 * 104calls in total will be made toinsert,search, andstartsWith.
Solution
Python3
class TrieNode:
def __init__(self):
self.children = defaultdict(TrieNode)
self.hasEnd = False
class Trie:
def __init__(self):
self.root = TrieNode()
def insert(self, word: str) -> None:
curr = self.root
for x in word:
curr = curr.children[x]
curr.hasEnd = True
def search(self, word: str) -> bool:
curr = self.root
for x in word:
if x not in curr.children:
return False
curr = curr.children[x]
return curr.hasEnd
def startsWith(self, prefix: str) -> bool:
curr = self.root
for x in prefix:
if x not in curr.children:
return False
curr = curr.children[x]
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)