C#使用哈夫曼编码实现压缩与解压

1using System;
2using System.Collections.Generic;
3
4// 定义节点类
5public class Node : IComparable<Node>
6{
7    public char Value { get; set; } // 节点值，如果是叶子节点则是字符
8    public int Frequency { get; set; } // 字符出现频率
9    public Node Left { get; set; } 
10    public Node Right { get; set; }
11    
12    public Node(char value, int frequency)
13    {
14        Value = value;
15        Frequency = frequency;
16    }
17
18    public int CompareTo(Node other)
19    {
20        return this.Frequency.CompareTo(other.Frequency);
21    }
22}
23
24public static class HuffmanCoding
25{
26    // 构建哈夫曼树
27    public static Node BuildHuffmanTree(Dictionary<char, int> frequencies)
28    {
29        var queue = new PriorityQueue<Node>(new List<Node>(frequencies.Select(x => new Node(x.Key, x.Value))));
30        while (queue.Count > 1)
31        {
32            var left = queue.Dequeue();
33            var right = queue.Dequeue();
34            
35            var newNode = new Node('\0', left.Frequency + right.Frequency);
36            newNode.Left = left;
37            newNode.Right = right;
38            
39            queue.Enqueue(newNode);
40        }
41        return queue.Dequeue();
42    }
43
44    // 从哈夫曼树生成编码字典
45    public static Dictionary<char, string> GenerateCodes(Node node, string codeSoFar = "", Dictionary<char, string> codes = null)
46    {
47        if (codes == null) codes = new Dictionary<char, string>();
48        
49        if (node.Left == null && node.Right == null) // 叶子节点
50        {
51            codes[node.Value] = codeSoFar;
52            return codes;
53        }
54
55        GenerateCodes(node.Left, codeSoFar + "0", codes);
56        GenerateCodes(node.Right, codeSoFar + "1", codes);
57
58        return codes;
59    }
60
61    // 压缩方法
62    public static string Compress(string input, Dictionary<char, string> huffmanCodes)
63    {
64        var compressedData = new StringBuilder();
65        foreach (char c in input)
66        {
67            compressedData.Append(huffmanCodes[c]);
68        }
69        return compressedData.ToString();
70    }
71
72    // 解压缩方法
73    public static string Decompress(string compressedData, Node huffmanTree)
74    {
75        var decompressedData = new StringBuilder();
76        var currentNode = huffmanTree;
77        foreach (char bit in compressedData)
78        {
79            if (bit == '0')
80            {
81                currentNode = currentNode.Left ?? throw new ArgumentException("Invalid Huffman tree or data");
82            }
83            else if (bit == '1')
84            {
85                currentNode = currentNode.Right ?? throw new ArgumentException("Invalid Huffman tree or data");
86            }
87
88            if (currentNode.Left == null && currentNode.Right == null)
89            {
90                decompressedData.Append(currentNode.Value);
91                currentNode = huffmanTree;
92            }
93        }
94        return decompressedData.ToString();
95    }
96}
97
98// 需要自定义优先队列PriorityQueue，这里仅示意，你可以自己实现或使用.NET中的堆结构
99public class PriorityQueue<T> where T : IComparable<T>
100{
101    // ... 实现优先队列相关方法 ...
102}
103
104// 使用：
105var frequencies = new Dictionary<char, int>();
106// 假设已统计好字符串中各字符频率并填充到frequencies中
107var huffmanTree = HuffmanCoding.BuildHuffmanTree(frequencies);
108var huffmanCodes = HuffmanCoding.GenerateCodes(huffmanTree);
109var inputString = "Your input string";
110var compressed = HuffmanCoding.Compress(inputString, huffmanCodes);
111var decompressed = HuffmanCoding.Decompress(compressed, huffmanTree);