Example analysis of Huffman tree in java

Huffman tree

1. Basic introduction

2. Several important concepts and examples of Huffman tree

Steps to construct a Huffman tree

For example: take arr = {1 3 6 7 8 13 29}

public class HuffmanTree {
	public static void main(String[] args) {
		int[] arr = { 13, 7, 8, 3, 29, 6, 1 };
		Node root = createHuffmanTree(arr);
		preOrder(root);
	}
	// 编写一个前序遍历的方法
	public static void preOrder(Node root) {
		if (root != null) {
			root.preOrder();
		} else {
			System.out.println("树是空树，无法遍历~~");
		}
	}
	// 创建赫夫曼树的方法
	/**
	 * @param arr 需要创建成霍夫曼树的数组
	 * @return 创建好后的霍夫曼树的root节点
	 */
	public static Node createHuffmanTree(int[] arr) {
		// 第一步为了操作方便
		// 1.遍历 arr 数组
		// 2.将 arr 的每个元素构成一个Node
		// 3.将Node 放入到ArrayList中
		List<Node> nodes = new ArrayList<Node>();
		for (int value : arr) {
			nodes.add(new Node(value));
		}
		while (nodes.size() > 1) {
			// 排序从小到大
			Collections.sort(nodes);
			System.out.println("nodes = " + nodes); 
			// 取出根节点权值最小的两颗二叉树
			//注意：如果是从大到小排列的：就应该取倒数第一个和倒数第二个
			// (1) 取出权值最小的节点(二叉树)
			Node leftNode = nodes.get(0);
			// (2) 取出权值第二小的节点(二叉树)
			Node rightNode = nodes.get(1);
			// (3) 构建一颗新的二叉树
			Node parent = new Node(leftNode.value + rightNode.value);
			parent.left = leftNode;
			parent.right = rightNode;
			// (4) 从ArrayList删除处理过的二叉树
			nodes.remove(leftNode);
			nodes.remove(rightNode);
			// (5) 将parent加入到nodes
			nodes.add(parent);
		}
		// 返回赫夫曼树的root节点
		return nodes.get(0);
	}
}
//创建节点类
//为了让Node对象支持排序Collections集合排序
//让Node实现Comparable接口
class Node implements Comparable<Node> {
	int value;// 节点权值
	Node left;// 指向左子节点
	Node right;// 指向右子节点
 
	public Node(int value) {
		this.value = value;
	}
	// 写一个前序遍历
	public void preOrder() {
		System.out.println(this);
		if (this.left != null) {
			this.left.preOrder();
		}
		if (this.right != null) {
			this.right.preOrder();
		}
	}
	@Override
	public String toString() {
		return "Node [value=" + value + "]";
	}
	@Override
	public int compareTo(Node o) {
		// 表示从小到大排列
		return this.value - o.value;
	}
}

Huffman coding

1. Basic introduction

2. Principle analysis

## 6) Note:

The original length is 359, compressed (359 - 133) / 359 = 62.9%

This encoding satisfies the prefix encoding, that is, the encoding of the characters cannot Is a prefix for other character encodings. It will not cause matching ambiguity;

Huffman coding is a lossless compression processing solution

Note:

Notes on Huffman encoding compressed files

1) If the file itself is compressed, Then using Huffman coding will not significantly change the compression efficiency, such as videos, ppt and other files

2) Huffman coding is processed by bytes, so it can process all files (binary File, text file)

3) If the content in a file does not have much repeated data, the compression effect will not be obvious.

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