编码过程中,踩了一些小坑,做下记录:
- 1.全局变量
count与std:count矛盾,建议用其他变量名。 - 2.内存泄漏问题 注意空间要开够 指针不可越界
main函数内开辟的栈空间大小一般为8MB 若要开辟较大的数组 请去main函数之外 - 3.编译器错误 推荐大家使用教新的较稳定的编译器
- 4.文件操作 打开后记得关闭 否则会占用系统资源
- 5.申请完空间,要记得释放,养成习惯。释放函数不可张冠李戴(留心编译器的
Warning)。malloc/free,new/delete要配对使用。具体原因可参考 这篇文章
准备一个字符文件,要求:
- 统计该文件中各种字符的频率
- 对各字符进行 Huffman编码,显示每个字符的编码
- 以及将该文件翻译成 Huffman编码文件
- 再将 Huffman编码文件翻译成源文件
- 显示每个字符以一个字节进行二进制编码后的编码文件
实现步骤可分为:
- 统计被编码文件中个字符出现的频数,即统计权重
- 根据权重,构造哈夫曼树,进行哈夫曼编码
- 读取文件进行二进制编码
- 读取文件,将每个字符匹配哈夫曼编码,写入新文件,即完成编码
- 读取编码文件,根据哈夫曼编码进行解码,并写入新文件
- 对比二进制编码和哈夫曼编码后的文件字节大小,并计算压缩率
这里我准备了一首小诗,写入文件,并将其命名为poem.txt
If I could save time in a bottle the first thing that I'd like to do is to save every day until eternity passes away just to spend them with you if I could make days last forever if words could make wishes come true I'd save every day like a treasure and then again I would spend them with you
// 定义哈夫曼树节点 typedef struct { int weight; int parent; int l_child; int r_child; char data; } HTNode, * HuffmanTree; typedef char** HuffmanCode;
//统计该文件中各种字符的频率 void frequencyRecord(HuffmanTree& HT) { HuffmanTree TEMP; TEMP = new HTNode[130]; for (int i = 0; i < 130; ++i) { TEMP[i].weight = 0; } ifstream originFile("poem.txt"); originFile.seekg(0); if (!originFile) { cout << "Can't find the file!" << endl; } else { char _data; cin.unsetf(ios::skipws); while (!originFile.eof()) { if (originFile.get(_data)) { TEMP[_data].data = _data; TEMP[_data].weight++; } } originFile.close(); } for (int i = 0; i < 130; ++i) { if (TEMP[i].weight != 0) { N++; } } HT = new HTNode[2 * N]; int k = 1; for (int i = 0; i < 130; ++i) { if (TEMP[i].weight != 0) { HT[k++] = TEMP[i]; } } }
//对各字符进行 Huffman编码,显示每个字符的编码 void HuffmanCoding(HuffmanTree& HT, HuffmanCode& HC) { int m = 2 * N - 1; for (int i = 1; i <= N; ++i) { HT[i].parent = 0; HT[i].l_child = 0; HT[i].r_child = 0; } for (int i = N + 1; i <= m; ++i) { HT[i].weight = 0; HT[i].parent = 0; HT[i].l_child = 0; HT[i].r_child = 0; HT[i].data = '#'; } int child1, child2; for (int i = N + 1; i <= m; i++) { select(HT, i - 1, child1, child2); HT[child1].parent = i; HT[child2].parent = i; HT[i].l_child = child1; HT[i].r_child = child2; HT[i].weight = HT[child1].weight + HT[child2].weight; } HC = new char* [N + 1]; char* cd = new char[N]; cd[N - 1] = '0円'; int start, c, f; for (int i = 1; i <= N; i++) { start = N - 1; for (c = i, f = HT[i].parent; f != 0; c = f, f = HT[f].parent) { if (HT[f].l_child == c) cd[--start] = '0'; else cd[--start] = '1'; } HC[i] = new char[N - start]; strcpy(HC[i], &cd[start]); } delete[] cd; for (int i = 1; i <= N; i++) { if (HT[i].data == '\n') { cout << "回车" << " " << HC[i] << endl; } else if (HT[i].data == ' ') { cout << "空格" << " " << HC[i] << endl; } else { cout << HT[i].data << " " << HC[i] << endl;; } } }
//找出最小的两个叶子节点 void select(HuffmanTree HT, int num, int& child1, int& child2) { child1 = child2 = 0; int w1 = 0, w2 = 0; //Start finding... for (int i = 1; i <= num; ++i) { if (HT[i].parent == 0) { if (child1 == 0) { child1 = i; w1 = HT[i].weight; continue; } if (child2 == 0) { child2 = i; w2 = HT[i].weight; continue; } if (w1 > w2 && w1 > HT[i].weight) { child1 = i; w1 = HT[i].weight; continue; } if (w2 > w1 && w2 > HT[i].weight) { child2 = i; w2 = HT[i].weight; continue; } } } // 使得w1永远小于w2 int temp; if (w1 > w2) { temp = child1; child1 = child2; child2 = temp; } }
//将该文件翻译成 Huffman编码文件 void zip(HuffmanTree& HT, HuffmanCode& HC, vector<string>& code) { ofstream codeFile("codefile.txt"); ifstream originFile("poem.txt"); if (!codeFile) { cout << "Can't find the file!" << endl; } else { char _data; cin.unsetf(ios::skipws); while (!originFile.eof()) { if (originFile.get(_data)) { for (int i = 1; i <= N; ++i) { if (HT[i].data == _data) { codeFile << HC[i]; code.push_back(HC[i]); } } } } } codeFile.close(); }
//再将 Huffman编码文件翻译成源文件 void unzip(HuffmanTree& HT, HuffmanCode& HC, vector<string>& code) { ofstream decodeFile("decodefile.txt"); if (!decodeFile) { cout << "Can't find the file!" << endl; } else { vector<string>::iterator v = code.begin(); while (v != code.end()) { for (int i = 1; i <= N; ++i) { if (HC[i] == *v) { decodeFile << HT[i].data; } } v++; } } decodeFile.close(); }
//显示每个字符以一个字节进行二进制编码后的编码文件 void binaryCode() { ofstream binaryFile("binaryfile.txt"); ifstream originFile("poem.txt"); originFile.seekg(0); if (!originFile) { cout << "Can't find the file!" << endl; } else { char _data; cin.unsetf(ios::skipws); while (!originFile.eof()) { if (originFile.get(_data)) { bitset<8> data(_data); binaryFile << data; } } originFile.close(); } }
发现这里实际大小与占用空间不一样?这篇文章可以解答你的疑惑
通过编写利用哈夫曼算法实现的文件编码解码小工具,可加深对哈夫曼算法的理解,以及编码的熟练度。
同时,体会到通过算法减少文本空间,降低计算机磁盘负荷的妙处,我们需要优秀的算法来提升计算机性能。在实际的压缩算法中虽然很少听到哈夫曼编码,但其实它并没有被淘汰,而是作为核心的算法,结合了其他的压缩算法,实现对文件(文本,PPT,图片,电影等)的压缩,给日常生活带来极大便利。
本人的小工具仅针对英文大小字母及' '\n' ' '字符针对性的进行了哈夫曼编码,若想实现中文及各种支持语言的编码,可在此代码基础上,进行优化,源码地址为https://github.com/Cheung0-bit/HuffmanTreeCoding,欢迎PR;