Huffman coding哈夫曼编码

Description

In computer science and information theory, a Huffman code is an optimal prefix code algorithm.

In this exercise, please use Huffman coding to encode a given data.

You should output the number of bits, denoted as B(T), to encode the data:

B(T)=∑f(c)d_T(c),

where f(c) is the frequency of character c, and d_T(c) is be the depth of character c's leaf in the tree T.

Input

The first line is the number of characters n.

The following n lines, each line gives the character c and its frequency f(c).

Output

 Output a single number B(T).

题目解释：对给出的节点构建哈夫曼树

输入：第一行表示有节点数n，后面n行表示n个节点，每一个节点（也就是每一行）使用节点号和出现频率来表示

输出：B(T) = f(c)d(c)总和（f(c)表示节点c的频率，d(c)表示节点c在哈弗曼树中的深度）

#include 
#include 
#include 
using namespace std;

struct HuffmanNode{     //构建哈夫曼节点
    char character;
    int frequency;
    HuffmanNode *lchild,*rchild;
    HuffmanNode():character(0),frequency(0),lchild(NULL),rchild(NULL){}
};
typedef HuffmanNode *HTNode;

struct cmp{          //重定义排序
    bool operator()(HuffmanNode *a,HuffmanNode *b)
    {
        return a->frequency > b->frequency;
    }
};
int main()
{
    int n;
    priority_queue,cmp> pq;    //使用priority_queue进行节点存储，能够及时排序
    while( cin >> n)
    {
        for(int i = 0; i < n; i++)
        {
            HTNode tmpNode = new HuffmanNode;       //对节点进行存储
            cin >> tmpNode->character >> tmpNode->frequency;
            pq.push(tmpNode);
        }
        int BT = 0;
        while(pq.size()>1)
        {
            HTNode tmp1 = new HuffmanNode;  //获得频率最小的两个节点进行合并
            tmp1 = pq.top();
            pq.pop();
            HTNode tmp2 = new HuffmanNode;
            tmp2 = pq.top();
            pq.pop();
            HTNode tmp = new HuffmanNode;
            tmp->frequency = tmp1->frequency +tmp2->frequency;
            tmp->lchild = tmp1;
            tmp->rchild = tmp2;
            BT +=  tmp1->frequency + tmp2->frequency;
            pq.push(tmp);                 //每一次对priority_queue进行操作都会自动更新排序
        }
        cout << BT << endl;
    }
    return 0;
}                                 

后记：

1.priority_queue能够及时对节点进行排序，可以最快地获得更新后的最小两个节点，实现新的更新

代码新手，欢迎各位大神提出宝贵的意见和建议