第三十篇玩转数据结构—

第三十篇玩转数据结构——字典树（Trie）

1.. Trie通常被称为"字典树"或"前缀树"

Trie的形象化描述如下图：
Trie的优势和适用场景

2.. 实现Trie

实现Trie的业务无逻辑如下：

import java.util.TreeMap;

public class Trie {

    private class Node {

        public boolean isWord;
        public TreeMap<Character, Node> next;

        // 构造函数
        public Node(boolean isWord) {
            this.isWord = isWord;
            next = new TreeMap<>();
        }

        // 无参数构造函数
        public Node() {
            this(false);
        }
    }

    private Node root;
    private int size;

    // 构造函数
    public Trie() {
        root = new Node();
        size = 0;
    }

    // 实现getSize方法，获得Trie中存储的单词数量
    public int getSize() {
        return size;
    }

    // 实现add方法，向Trie中添加新的单词word
    public void add(String word) {

        Node cur = root;
        for (int i = 0; i < word.length(); i++) {
            char c = word.charAt(i);
            if (cur.next.get(c) == null) {
                cur.next.put(c, new Node());
            }
            cur = cur.next.get(c);
        }
        if (!cur.isWord) {
            cur.isWord = true;
            size++;
        }
    }

    // 实现contains方法，查询Trie中是否包含单词word
    public boolean contains(String word) {

        Node cur = root;
        for (int i = 0; i < word.length(); i++) {
            char c = word.charAt(i);
            if (cur.next.get(c) == null) {
                return false;
            }
            cur = cur.next.get(c);
        }
        return cur.isWord;  // 好聪明
    }

    // 实现isPrefix方法，查询Trie中时候保存了以prefix为前缀的单词
    public boolean isPrefix(String prefix) {

        Node cur = root;
        for (int i = 0; i < prefix.length(); i++) {
            char c = prefix.charAt(i);
            if (cur.next.get(c) == null) {
                return false;
            }
            cur = cur.next.get(c);
        }
        return true;
    }
}

3.. Trie和简单的模式匹配

实现的业务逻辑如下：

import java.util.TreeMap;

class WordDictionary {

    private class Node {

        public boolean isWord;
        public TreeMap<Character, Node> next;

        public Node(boolean isWord) {
            this.isWord = isWord;
            next = new TreeMap<>();
        }

        public Node() {
            this(false);
        }

    }

    /**
     * Initialize your data structure here.
     */
    private Node root;

    public WordDictionary() {
        root = new Node();
    }

    /**
     * Adds a word into the data structure.
     */
    public void addWord(String word) {
        Node cur = root;
        for (int i = 0; i < word.length(); i++) {
            char c = word.charAt(i);
            if (cur.next.get(c) == null) {
                cur.next.put(c, new Node());
            }
            cur = cur.next.get(c);
        }
        cur.isWord = true;
    }

    /**
     * Returns if the word is in the data structure. A word could contain the dot character '.' to represent any one letter.
     */
    public boolean search(String word) {
        return match(root, word, 0);
    }

    private boolean match(Node node, String word, int index) {
        if (index == word.length()) {
            return node.isWord;
        }

        char c = word.charAt(index);
        if (c != '.') {
            if (node.next.get(c) == null) {
                return false;
            }
            return match(node.next.get(c), word, index + 1);
        } else {
            for (char nextChar : node.next.keySet()) {
                if (match(node.next.get(nextChar), word, index + 1)) {
                    return true;
                }
            }
            return false;
        }
    }
}

第三十篇 玩转数据结构——字典树（Trie）

第三十篇玩转数据结构——字典树（Trie）