Implement Trie (Prefix Tree) 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.
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
Constraints:
1 <= word.length, prefix.length <= 2000
word
andprefix
consist only of lowercase English letters.- At most
3 * 104
calls in total will be made toinsert
,search
, andstartsWith
.
Implement Trie (Prefix Tree) Solutions
✅Time:
✅Space:
C++
struct TrieNode {
vector<TrieNode*> children;
bool isWord = false;
TrieNode() : children(26) {}
~TrieNode() {
for (TrieNode* child : children)
delete child;
}
};
class Trie {
public:
void insert(const string& word) {
TrieNode* node = &root;
for (const char c : word) {
const int i = c - 'a';
if (!node->children[i])
node->children[i] = new TrieNode;
node = node->children[i];
}
node->isWord = true;
}
bool search(const string& word) {
TrieNode* node = find(word);
return node && node->isWord;
}
bool startsWith(const string& prefix) {
return find(prefix);
}
private:
TrieNode root;
TrieNode* find(const string& prefix) {
TrieNode* node = &root;
for (const char c : prefix) {
const int i = c - 'a';
if (!node->children[i])
return nullptr;
node = node->children[i];
}
return node;
}
};
Java
class TrieNode {
public TrieNode[] children = new TrieNode[26];
public boolean isWord = false;
}
class Trie {
public void insert(String word) {
TrieNode node = root;
for (final char c : word.toCharArray()) {
final int i = c - 'a';
if (node.children[i] == null)
node.children[i] = new TrieNode();
node = node.children[i];
}
node.isWord = true;
}
public boolean search(String word) {
TrieNode node = find(word);
return node != null && node.isWord;
}
public boolean startsWith(String prefix) {
return find(prefix) != null;
}
private TrieNode root = new TrieNode();
private TrieNode find(String prefix) {
TrieNode node = root;
for (final char c : prefix.toCharArray()) {
final int i = c - 'a';
if (node.children[i] == null)
return null;
node = node.children[i];
}
return node;
}
}
Python
class Trie:
def __init__(self):
self.root = {}
def insert(self, word: str) -> None:
node = self.root
for c in word:
if c not in node:
node[c] = {}
node = node[c]
node['isWord'] = True
def search(self, word: str) -> bool:
node = self.find(word)
return node is not None and 'isWord' in node
def startsWith(self, prefix: str) -> bool:
return self.find(prefix) is not None
def find(self, prefix: str) -> dict:
node = self.root
for c in prefix:
if c not in node:
return None
node = node[c]
return node
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