数据结构入门（java版）（十三）

红黑树的实现

红黑树的五个重要性质：

实现代码：

import com.sun.org.apache.regexp.internal.RE;

import java.util.ArrayList;
import java.util.TreeMap;
import java.util.Random;

public class RBTree<K extends Comparable<K>, V>{


    // 创建静态变量
    private static final boolean RED = true;
    private static final boolean BLACK = false;

    private class Node{
        public K key;
        public V value;
        public Node left, right;
        public boolean color;

        public Node(K key, V value){
            this.key = key;
            this.value = value;
            left = null;
            right = null;
            color = RED;
        }
    }

    private Node root;
    private int size;

    public RBTree(){
        root = null;
        size = 0;
    }

    public int getSize() {
        return size;
    }

    public boolean isEmpty() {
        return size == 0;
    }

    // 判断节点node的颜色
    private boolean isRed(Node node){

        if (node == null)
            return BLACK;

        return node.color;
    }

    // 左旋转
    /*
            node                                x
            /  \            左旋转             /   \
           T1   x      ------------->       node  T3
               / \                          /  \
              T2  T3                       T1  T2
     */
    private Node leftRotate(Node node){

        Node x = node.right;

        // 左旋转
        node.right = x.left;
        x.left = node;

        x.color = node.color;
        node.color = RED;

        return x;
    }
    /*
    右旋转
            node                             x
            /  \         右旋转             /   \
           x    T1  ------------->       y     node
          / \                                  /  \
         y  T3                                T1  T2

     */
    private Node rightRotate(Node node){

        Node x = node.left;

        // 右旋转
        node.left = x.right;
        x.right = node;

        x.color = node.color;
        node.color = RED;

        return x;
    }

    // 颜色翻转
    private void flipColor(Node node){
        node.color = RED;
        node.left.color = BLACK;
        node.right.color = BLACK;
    }

    // 向红黑树中添加新的元素（key， value）
    public void add(K key, V value) {
        root = add(root, key, value);
        root.color = BLACK;   // 最终根节点为黑色节点
    }

    // 向以node为根的红黑树中插入元素（key， value），递归算法
    // 返回插入新节点后红黑树的根
    private Node add(Node node, K key, V value){

        if(node == null){
            size ++;
            return new Node(key, value);   // 默认为红色节点
        }

        if(key.compareTo(node.key) < 0)
            node.left = add(node.left, key, value);
        else if(key.compareTo(node.key) > 0)
            node.right = add(node.right, key, value);
        else    // key.compareTo(node.key) == 0
            node.value = value;

        // 维护红黑树性质
        if (isRed(node.right) && !isRed(node.left))
            node = leftRotate(node);

        // 黑节点左侧连续有两个红节点
        if (isRed(node.left) && isRed(node.left.left))
            node = rightRotate(node);

        if (isRed(node.left) && isRed(node.right))
            flipColor(node);



        return node;
    }

    // 返回以node为根节点的二分搜索树中，key所在的节点
    private Node getNode(Node node, K key){

        if (node == null)
            return null;

        if (key.compareTo(node.key) == 0)
            return node;
        else if (key.compareTo(node.key) < 0)
            return getNode(node.left, key);
        else    // if (key.compareTo(node.key)) > 0
            return getNode(node.right, key);
    }

    public boolean contains(K key) {
        return getNode(root, key) != null;
    }

    public V get(K key) {
        Node node = getNode(root, key);

        return node == null ? null : node.value;
    }

    // 更新操作
    public void set(K key, V newValue) {
        Node node = getNode(root, key);

        if (node == null)
            throw new IllegalArgumentException(key  + " deesn`t exists");

        node.value = newValue;
    }

    // 返回以node为根的二分搜索树的最小值所在的节点
    private Node minimum(Node node){
        if(node.left == null)
             return node;
        return minimum(node.left);
    }

    // 删除掉以node为根的二分搜索树中的最小节点
    // 返回删除节点后新的二分搜索树的根
    private Node removeMin(Node node){

        if(node.left == null){
            Node rightNode = node.right;
            node.right = null;
            size --;
            return rightNode;
        }

        node.left = removeMin(node.left);
        return node;
    }


    // 从二分搜索树中删除键为key的节点
    public V remove(K key) {

        Node node = getNode(root, key);
        if (node != null){
            root = remove(root, key);
            return node.value;
        }

        return null;
    }


    // 删除以node为根的二分搜索树中键为key的节点，递归算法
    // 返回删除节点后新的二分搜索树的根
    private Node remove(Node node, K key){

        if (node == null){
            return null;
        }

        if (key.compareTo(node.key) < 0){
            node.left = remove(node.left, key);
            return node;
        }
        else if (key.compareTo(node.key) > 0){
            node.right = remove(node.right, key);
            return node;
        }
        else{   // key.compareTo(node.key) == 0

            // 待删除节点左子树为空的情况
            if (node.left == null){
                // 此时的node为待删除节点
                Node rightNode = node.right;
                node.right = null;
                size --;
                return rightNode;   // 返回右子树根节点
            }

            // 待删除节点右子树为空的情况
            if (node.right == null){
                Node leftNode = node.left;
                node.left = null;
                size --;
                return leftNode;
            }

            // 待删除节点左右子树都不为空的情况
            // 找到比待删除节点大的节点, 即待删除节点右子树的最小节点
            // 或比待删除结点小的节点，即左子树最大节点
            // 用这个节点顶替待删除节点的位置
            Node successor = minimum(node.right);
            successor.right = removeMin(node.right);
            successor.left = node.left;

            node.left = node.right = null;

            return successor;
        }
    }

    public static void main(String[] args) {
        // write your code here

        System.out.println("Pride and Prejudice");

        ArrayList<String> words = new ArrayList<>();

        if (FileOperation.readFile("e:/java/Pride and Prejudice.txt", words)){

            System.out.println("Total words: " + words.size());

            RBTree<String, Integer> map = new RBTree<>();

            for (String word: words){
                if (map.contains(word))
                    map.set(word, map.get(word) + 1);
                else
                    map.add(word, 1);
            }
            System.out.println("Total different words: " + map.getSize());
            System.out.println("Frequency of By: " + map.get("By") );
        }
    }

}