二刷208

Author: diguage

Diff for: docs/0208-implement-trie-prefix-tree.adoc

@@ -1,47 +1,76 @@
 [#0208-implement-trie-prefix-tree]
-= 208. Implement Trie (Prefix Tree)
+= 208. 实现 Trie (前缀树)
 
-{leetcode}/problems/implement-trie-prefix-tree/[LeetCode - Implement Trie (Prefix Tree)^]
+https://leetcode.cn/problems/implement-trie-prefix-tree/[LeetCode - 208. 实现 Trie (前缀树) ^]
 
-没想到 Trie Tree 实现起来一点也不复杂！
-
-思考题：如何实现一个工业级的 Trie Tree？
+*link:https://baike.baidu.com/item/字典树/9825209?fr=aladdin[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，例如自动补全和拼写检查。
 
-image::images/0208-1.png[{image_attr}]
+请你实现 Trie 类：
 
-== 参考资料
+* `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` 。
 
-. https://en.wikipedia.org/wiki/Trie[Trie - Wikipedia^]
-. https://www.geeksforgeeks.org/trie-insert-and-search/[Trie | (Insert and Search) - GeeksforGeeks^]
 
-Implement a trie with `insert`, `search`, and `startsWith` methods.
+*示例：*
 
-*Example:*
+....
+输入
+["Trie", "insert", "search", "search", "startsWith", "insert", "search"]
+[[], ["apple"], ["apple"], ["app"], ["app"], ["app"], ["app"]]
+输出
+[null, null, true, false, true, null, true]
 
-[subs="verbatim,quotes,macros"]
-----
+解释
 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
-----
+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
+....
 
-*Note:*
+*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 * 10^4^` 次
 
-* You may assume that all inputs are consist of lowercase letters `a-z`.
-* All inputs are guaranteed to be non-empty strings.
+== 思路分析
 
+没想到 Trie Tree 实现起来一点也不复杂！
 
+思考题：如何实现一个工业级的 Trie Tree？
+
+image::images/0208-1.png[{image_attr}]
 
 
 [[src-0208]]
+[tabs]
+====
+一刷::
++
+--
 [{java_src_attr}]
 ----
 include::{sourcedir}/_0208_ImplementTriePrefixTree.java[tag=answer]
 ----
+--
+
+二刷::
++
+--
+[{java_src_attr}]
+----
+include::{sourcedir}/_0208_ImplementTriePrefixTree_2.java[tag=answer]
+----
+--
+====
+
+== 参考资料
+
+. https://en.wikipedia.org/wiki/Trie[Trie - Wikipedia^]
+. https://www.geeksforgeeks.org/trie-insert-and-search/[Trie | (Insert and Search) - GeeksforGeeks^]
 

Diff for: logbook/202503.adoc

@@ -125,6 +125,11 @@ endif::[]
 |{doc_base_url}/0025-reverse-nodes-in-k-group.adoc[题解]
 |✅ 分段递归反转，在拼接
 
+|{counter:codes2503}
+|{leetcode_base_url}/implement-trie-prefix-tree/[208. 实现 Trie (前缀树)^]
+|{doc_base_url}/0208-implement-trie-prefix-tree.adoc[题解]
+|✅ 竟然一次通过
+
 |===
 
 截止目前，本轮练习一共完成 {codes2503} 道题。

Diff for: src/main/java/com/diguage/algo/leetcode/_0208_ImplementTriePrefixTree.java

@@ -37,6 +37,9 @@ public class _0208_ImplementTriePrefixTree {
      * Runtime: 69 ms, faster than 11.59% of Java online submissions for Implement Trie (Prefix Tree).
      *
      * Memory Usage: 62.6 MB, less than 5.77% of Java online submissions for Implement Trie (Prefix Tree).
+     *
+     * @author D瓜哥 · https://www.diguage.com
+     * @since 2020-01-26 19:47
      */
     class Trie {
         private int ALPHABET_SIZE = 26;

Diff for: src/main/java/com/diguage/algo/leetcode/_0208_ImplementTriePrefixTree_2.java

@@ -0,0 +1,71 @@
+package com.diguage.algo.leetcode;
+
+import java.util.HashMap;
+import java.util.Map;
+
+public class _0208_ImplementTriePrefixTree_2 {
+  // tag::answer[]
+
+  /**
+   * 没想到竟然一次通过，😁
+   *
+   * @author D瓜哥 · https://www.diguage.com
+   * @since 2025-04-02 19:42:48
+   */
+  class Trie {
+    private Map<Character, Node> trie;
+
+    public Trie() {
+      trie = new HashMap<>();
+    }
+
+    public void insert(String word) {
+      Map<Character, Node> curr = trie;
+      Node node = null;
+      for (int i = 0; i < word.length(); i++) {
+        char c = word.charAt(i);
+        node = curr.get(c);
+        if (node == null) {
+          node = new Node(c);
+          curr.put(c, node);
+        }
+        curr = node.children;
+      }
+      node.isEnd = true;
+    }
+
+    public boolean search(String word) {
+      Node node = searchPrefix(word);
+      return node != null && node.isEnd;
+    }
+
+    public boolean startsWith(String prefix) {
+      return searchPrefix(prefix) != null;
+    }
+
+    private Node searchPrefix(String word) {
+      Map<Character, Node> curr = trie;
+      Node node = null;
+      for (int i = 0; i < word.length(); i++) {
+        char c = word.charAt(i);
+        node = curr.get(c);
+        if (node == null) {
+          return null;
+        }
+        curr = node.children;
+      }
+      return node;
+    }
+
+    private static class Node {
+      char data;
+      boolean isEnd;
+      Map<Character, Node> children = new HashMap<>();
+
+      public Node(char data) {
+        this.data = data;
+      }
+    }
+  }
+  // end::answer[]
+}