数据压缩算法之哈夫曼编码(HUFFMAN)的实现

HUFFMAN编码可以很有效的压缩数据,通常可以压缩20%到90%的空间(算法导论)。具体的压缩率取决于数据的特性(词频)。如果采取标准的语料库进行编码,一般可以得到比较满意的编码结果(对不同文件产生不同压缩率的折中方法)。

本文采取对单独一个文件进行编码的方式来演示此压缩算法的使用。

分为下面几个步骤:

1.统计词频数据

2.词频数据转换成HUFFMAN算法能够处理的类型(本文为HuffmanNode,内部有存储词频和树节点的结构)

  (1)由输入的HuffmanNode[]数组创建最小优先级队列

  (2)依次取出队列中的每两个节点,然后由此两个节点构造一个新的节点,然后在重新插入回队列。直到队列中只剩唯一一个节点。

    此节点为编码树的根节点。

  (3)依次遍历原来输入的每个HUFFMAN节点,得到每个字符的对应编码(压缩使用)。

  (4)解码方式,依次输入0/1字符码到算法,算法遍历产生的编码树,如果有返回字符,则得到解码字符。

 词频统计的实现:

 public class FrequencyCounter
    {
        public IEnumerablechar, int>> MapReduce(string str)
        {
            //the GroupBy method is acting as the map, 
            //while the Select method does the job of reducing the intermediate results into the final list of results.
            var wordOccurrences = str
                .GroupBy(w => w)
                .Select(intermediate => new
                    {
                        Key = intermediate.Key,
                        Value = intermediate.Sum(w => 1)
                    })
                .OrderBy(kvp => kvp.Value);
            IEnumerablechar, int>> kvps = from wo in wordOccurrences select new KeyValuePair<char, int>(wo.Key, wo.Value);
            return kvps;
        }
    }
MapReduce

HUFFMAN编码类的实现:

 public class Huffman
    {
        private List originalNodes;
        private HuffmanNode rootNode;
        public Huffman(IEnumerablechar, int>> kvps)
        {
            //保存原始数据
            var tmpOriginalNodes = from kvp in kvps select new HuffmanNode(kvp.Key, kvp.Value);
            //创建最小优先队列,并输入数据
            MinPriorityQueue minQueue = new MinPriorityQueue();
            originalNodes = new List();
            foreach (var node in tmpOriginalNodes)
            {
                originalNodes.Add(node);
                minQueue.Insert(node);
            }
            //建造编码树,并取得编码树的根节点
            while (!minQueue.IsEmpty)
            {
                HuffmanNode left = minQueue.ExtractMin();
                if (minQueue.IsEmpty)
                {
                    rootNode = left;
                    break;
                }
                HuffmanNode right = minQueue.ExtractMin();
                HuffmanNode newNode = new HuffmanNode(null, left.Value + right.Value, left, right);
                left.Parent = newNode;
                right.Parent = newNode;
                minQueue.Insert(newNode);
            }
        }
        //只接受单个char的加密
        public string Encode(char sourceChar)
        {
            HuffmanNode hn = originalNodes.FirstOrDefault(n => n.Key == sourceChar);
            if (hn == null) return null;
            HuffmanNode parent = hn.Parent;
            StringBuilder rtn = new StringBuilder();
            while (parent != null)
            {
                if (Object.ReferenceEquals(parent.Left, hn))//左孩子,编码为0
                {
                    rtn.Insert(0, "0", 1);
                }
                else//右孩子,编码为1
                {
                    rtn.Insert(0, "1", 1);
                }
                hn = parent;
                parent = parent.Parent;
            }
            return rtn.ToString();
        }
        //只接受一个字符的解码输出
        public bool Decode(string string01, out char? output)
        {
            HuffmanNode tmpNode = rootNode;
            char[] chars = string01.Trim().ToCharArray();
            for (int i = 0; i < chars.Count(); i++)
            {
                if (chars[i] == '0') tmpNode = tmpNode.Left;
                if (chars[i] == '1') tmpNode = tmpNode.Right;
            }
            if (tmpNode != null && tmpNode.Left == null && tmpNode.Right==null)
            {
                output = tmpNode.Key;
                return true;
            }
            else
            {
                output = null;
                return false;
            }
        }

        class HuffmanNode : IHeapValue
        {
            public HuffmanNode(char? key, int value, HuffmanNode left = null, HuffmanNode right = null)
            {
                this.Left = left;
                this.Right = right;
                this.Key = key;
                this.Value = value;
            }
            public HuffmanNode Left { get; private set; }
            public HuffmanNode Right { get; private set; }
            public HuffmanNode Parent { get; set; }
            public char? Key { get; private set; }
            public int Value { get; set; }
        }
    }
View Code

对文本进行编码的用法:

 FrequencyCounter fc = new FrequencyCounter();
            var kvps = fc.MapReduce(这里是你的文本);
            hm = new Huffman(kvps);
            StringBuilder sb = new StringBuilder();
            string ori =这里是你的文本;
            char[] chararray = ori.ToCharArray();
            for (int i = 0; i < chararray.Length; i++)
            {
                sb.Append(hm.Encode(chararray[i]));
            }

对编码进行解码:

            string bstr =你的编码后的文本;
            StringBuilder sb = new StringBuilder();
            char? outchar = null;
            string tmpStr = null;
            for (int i = 0; i < bstr.Length; i++)
            {
                tmpStr = tmpStr + bstr[i];
                if (hm.Decode(tmpStr, out outchar))
                {
                    tmpStr = null;
                    sb.Append(outchar);
                }
            }       

测试效果,可以看到压缩效果还是很明显的:

数据压缩算法之哈夫曼编码(HUFFMAN)的实现_第1张图片

完毕。

作者:Andy Zeng

欢迎任何形式的转载,但请务必注明出处。

http://www.cnblogs.com/andyzeng/p/3703321.html

转载于:https://www.cnblogs.com/andyzeng/p/3703321.html

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