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法律研究针对已排序输入的高效赫夫曼编码
在之前的赫夫曼编码问题中，频率是未排序的。如果频率列表以排序顺序给出，则分配编码的任务将变得更加高效。
在此问题中，我们将使用两个空队列。然后为每个唯一字符创建叶节点，并按频率递增顺序将其插入队列。
在此方法中，该算法的复杂度为 O(n)。
输入和输出
Input:
Different letters and their frequency in sorted order
Letters: {L, K, X, C, E, B, A, F}
Frequency: {1, 1, 2, 2, 2, 2, 3, 4}
Output:
Codes for the letters
L: 0000
K: 0001
X: 001
C: 010
E: 011
F: 10
B: 110
A: 111算法
huffmanCodes(dataList, freqList, n)
输入：数据列表、频率列表以及列表中的数据数目 n。
输出 −分配给代码的字符。
Begin root := huffmanTree(dataList, freqList, n) //create root of Huffman tree create an array to store codes, and top pointer for that array. call getCodes(root, array, top) to find codes for each character. End
getCodes(root :node, array, top)
输入：根节点、用于存储代码的数组、数组的顶部。
输出 −每个字符的代码
Begin if leftChild(root) ≠φ then array[top] := 0 getCodes(leftChild(root), array, top) if rightChild(root) ≠φ then array[top] = 1 getCode(rightChild(root), array, top) if leftChild(root) = φ AND rightChild(root) = φ then display the character ch of root for all entries of the array do display the code in array[i] for character ch done End
huffmanTree(dataList, freqList, n)
输入 −数据列表、频率列表以及列表中的数据数目 n。
输出 −创建一棵赫夫曼树
Begin for all different character ch do add node with ch and frequency of ch into queue q1 done while q1 is not empty OR size of q2 ≠ 1 do find two minimum node using q1 and q2 and add them as left and right child of a new node. add new node in q2 done delete node from q2 and return that node. End
示例
#include<iostream>
#include<queue>
using namespace std;
struct node {
char data;
int freq;
node *child0, *child1;
};
node *getNode(char d, int f) {
node *newNode = new node;
newNode->data = d;
newNode->freq = f;
newNode->child0 = NULL;
newNode->child1 = NULL;
return newNode;
}
node *findMinNode(queue<node*>&q1, queue<node*>&q2) {
node *minNode;
if(q1.empty()) { //if first queue is empty, delete and return node from second queue
minNode = q2.front();
q2.pop();
return minNode;
}
if(q2.empty()) { //if second queue is empty, delete and return node from first queue
minNode = q1.front();
q1.pop();
return minNode;
}
if((q1.front()->freq) < (q2.front()->freq)) { //find smaller from two queues
minNode = q1.front();
q1.pop();
return minNode;
}else {
minNode = q2.front();
q2.pop();
return minNode;
}
}
node *huffmanTree(char data[], int frequency[], int n) {
node *c0, *c1, *par;
node *newNode;
queue<node*> qu1, qu2;
for(int i = 0; i<n; i++) { //add all node to queue 1
newNode = getNode(data[i], frequency[i]);
qu1.push(newNode);
}
while(!(qu1.empty() && (qu2.size() == 1))) {
c0 = findMinNode(qu1, qu2); //find two minimum as two child
c1 = findMinNode(qu1, qu2);
node *newNode = getNode('#', c0->freq+c1->freq);
//intermediate node holds special character
par = newNode;
par->child0 = c0;
par->child1 = c1;
qu2.push(par); //add sub tree into queue 2
}
node *retNode = qu2.front();
qu2.pop();
return retNode;
}
void getCodes(node *rootNode, int array[], int n) { //array to store the code
if(rootNode->child0 != NULL) {
array[n] = 0;
getCodes(rootNode->child0, array, n+1);
}
if(rootNode->child1 != NULL) {
array[n] = 1;
getCodes(rootNode->child1, array, n+1);
}
if(rootNode->child0 == NULL && rootNode->child1 == NULL) { // when root is leaf node
cout << rootNode->data << ": ";
for(int i = 0; i<n; i++)
cout << array[i];
cout << endl;
}
}
void huffmanCodes(char data[], int frequency[], int n) {
node *rootNode = huffmanTree(data, frequency, n);
int array[50], top = 0;
getCodes(rootNode, array, top);
}
int main() {
char data[] = {'L', 'K', 'X', 'C', 'E', 'B', 'A', 'F'};
int frequency[] = {1, 1, 2, 2, 2, 2, 3, 4};
int n = sizeof(data)/sizeof(data[0]);
huffmanCodes(data, frequency, n);
}输出
L: 0000 K: 0001 X: 001 C: 010 E: 011 F: 10 B: 110 A: 111
更新于：15-6 月-2020
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