用C语言写代码信息论与编码有关哈夫曼的课程的设计

时间: 2023-09-07 13:04:52 浏览: 51
### 回答1: 下面是一个关于哈夫曼编码的 C 语言代码示例: ``` #include <stdio.h> #include <stdlib.h> #include <string.h> #define N 100 #define not ! #define and && #define or || typedef struct{ char c; int freq; char *code; } huffman_node; typedef struct{ huffman_node **data; int size; } heap; heap *new_heap(int size){ heap *h = malloc(sizeof(heap)); h->data = malloc((size+1)*sizeof(huffman_node*)); h->size = 0; return h; } void swap(huffman_node **a, huffman_node **b){ huffman_node *temp = *a; *a = *b; *b = temp; } void heapify_up(heap *h, int i){ if(i == 1 or h->data[i]->freq >= h->data[i/2]->freq) return; swap(&h->data[i], &h->data[i/2]); heapify_up(h, i/2); } void heapify_down(heap *h, int i){ int min_index = i; if(2*i <= h->size and h->data[2*i]->freq < h->data[min_index]->freq) min_index = 2*i; if(2*i+1 <= h->size and h->data[2*i+1]->freq < h->data[min_index]->freq) min_index = 2*i+1; if(min_index == i) return; swap(&h->data[i], &h->data[min_index]); heapify_down(h, min_index); } void insert(heap *h, huffman_node *node){ h->size++; h->data[h->size] = node; heapify_up(h, h->size); } huffman_node *pop(heap *h){ huffman_node *ans = h->data[1]; h->data[1] = h->data[h->size]; h->size--; heapify_down(h, 1); return ans; } void free_node(huffman_node *node){ free(node->code); free(node); } void free_heap(heap *h){ for(int i=1; i<=h->size; i++) free_node(h->data[i]); free(h->data); free(h); } huffman_node *build_tree(char *string, int *freq, int n){ heap ### 回答2: 信息论与编码是研究信息的表示、传输和处理的科学领域,而哈夫曼编码是一种被广泛应用的无损数据压缩算法。下面是一个使用C语言编写的哈夫曼编码课程设计的概述。 在C语言中,我们可以使用树的数据结构来实现哈夫曼编码。首先,我们需要定义树的节点结构,包括权值、字符和左右子节点指针。然后,我们可以通过输入的字符集合构建一个哈夫曼树。 接下来,我们需要计算每个字符在输入中出现的频率,并根据频率构建哈夫曼树。这可以通过统计字符频率并构建优先队列来实现。优先队列可以根据权值进行排序,并且具有快速插入和删除操作。 通过不断合并具有最小权值的两个节点,我们可以逐步构建哈夫曼树。合并操作涉及创建一个新的父节点,将两个节点作为其左右子节点,并将父节点的权值设置为两个子节点的权值之和。这个过程将在优先队列为空时结束。 在构建哈夫曼树后,我们可以通过遍历树来生成各个字符的编码。编码的构建是通过对树进行先序遍历,并将每个节点的路径记录下来实现的。路径的记录可以通过一个数组来存储,并根据子节点的左右关系来确定编码的0和1。 最后,我们需要将文本进行编码和解码。编码是通过根据字符编码的映射表,将文本中的每个字符替换为对应的编码序列。解码是通过根据字符编码的逆映射表,将编码序列逐步转换回字符。 通过以上步骤,我们可以使用C语言编写一个基本的哈夫曼编码程序。这个程序可以实现文本的压缩和解压缩,将文本数据以最小的存储空间表示,并在解压缩时精确还原原始数据。此外,还可以计算编码的平均码长,以评估压缩效果的好坏。 ### 回答3: 信息论与编码是计算机科学中的重要课程,而哈夫曼编码是一种经典的无损压缩算法。在C语言中,我们可以实现哈夫曼编码的设计。 首先,需要定义哈夫曼树的数据结构。可以使用结构体定义一个树节点,包括权值、左孩子和右孩子等信息。接着,需要根据输入的字符频率构建哈夫曼树。 我们可以通过一个优先队列来实现,根据字符频率的大小将节点插入队列中。然后,使用两个权值最小的节点构建一个新节点,将新节点的权值设置为两个节点的权值之和。不断重复此过程,直到只剩下一个节点,即为哈夫曼树的根节点。 完成哈夫曼树的构建后,接下来需要实现编码和解码的过程。针对输入的字符串,我们可以遍历每个字符,并通过哈夫曼树找到对应的编码。哈夫曼树的性质保证了无歧义的编码方式,即每个字符的编码都不会是其他字符编码的前缀。 在编码过程中,可以使用一个哈希表来存储字符与编码的对应关系。当需要解码时,我们可以根据输入的编码,从哈希表中查找对应的字符,并将其输出。 最后,我们可以根据原始字符串和编码后的字符串计算压缩比。压缩比的计算公式为:压缩比 = (编码后字符串的长度) / (原始字符串的长度)。如果压缩比小于1,则表示通过哈夫曼编码实现了数据压缩。 总之,用C语言实现信息论与编码中的哈夫曼编码课程设计,可以通过构建哈夫曼树、实现编码和解码的过程以及计算压缩比来完成。这样的设计能够加深对信息论与编码的理解,并提高对数据压缩算法的实践能力。

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好的,您想了解关于C语言哈夫曼编码译码器课设和数据结构课程设计哈夫曼编码译码器的相关知识,我可以为您提供一些基本的信息。 哈夫曼编码是一种基于统计概率的编码方法,可以将每个字符使用不同长度的二进制编码表示,使得出现频率较高的字符使用较短的编码,出现频率较低的字符使用较长的编码,从而达到压缩数据的效果。 哈夫曼编码译码器的实现需要用到数据结构中的哈夫曼树和哈夫曼编码表。哈夫曼树是一种特殊的二叉树,它的叶子节点对应着每个字符,而每个字符的编码可以通过从根节点到该字符的路径上的边的方向来表示。哈夫曼编码表则是一个字符与其对应的二进制编码之间的映射表。 在C语言中,可以使用结构体来表示哈夫曼树和哈夫曼编码表。哈夫曼树的节点可以定义为一个结构体,包含字符、权值和左右子节点指针等属性。而哈夫曼编码表则可以定义为一个数组,每个元素表示一个字符与其对应的编码。 哈夫曼编码译码器的实现过程可以分为两个步骤:编码和译码。编码过程中,需要先统计原始数据中各个字符出现的频率,然后根据频率构建哈夫曼树,生成哈夫曼编码表,并将原始数据按照哈夫曼编码进行压缩。译码过程中,则需要通过哈夫曼编码表将压缩后的二进制数据还原成原始数据。 以上是关于C语言哈夫曼编码译码器课设和数据结构课程设计哈夫曼编码译码器的基本介绍,希望对您有所帮助。
哈夫曼树与哈夫曼编码是一种常用的数据压缩算法,下面是C语言代码实现: #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_SIZE 1000 #define MAX_BIT 30 typedef struct node { int weight; int flag; struct node *left, *right; } Node; typedef struct { int bit[MAX_BIT]; int start; } HCode; Node *buildHuffmanTree(int *a, int n) { Node **node, *parent, *left, *right; node = (Node **)malloc(sizeof(Node *) * n); for (int i = 0; i < n; i++) { node[i] = (Node *)malloc(sizeof(Node)); node[i]->weight = a[i]; node[i]->flag = 0; node[i]->left = NULL; node[i]->right = NULL; } for (int i = 0; i < n - 1; i++) { int min1 = MAX_SIZE, min2 = MAX_SIZE; int index1 = -1, index2 = -1; for (int j = 0; j < n; j++) { if (!node[j]->flag && node[j]->weight < min1) { min2 = min1; index2 = index1; min1 = node[j]->weight; index1 = j; } else if (!node[j]->flag && node[j]->weight < min2) { min2 = node[j]->weight; index2 = j; } } parent = (Node *)malloc(sizeof(Node)); left = node[index1]; right = node[index2]; parent->weight = left->weight + right->weight; parent->flag = 0; parent->left = left; parent->right = right; node[index1] = parent; node[index2]->flag = 1; } free(node); return parent; } void getCode(Node *root, HCode *hcode, int len) { if (root->left == NULL && root->right == NULL) { printf("%d: ", root->weight); for (int i = 0; i < len; i++) { printf("%d", hcode->bit[i]); } printf("\n"); return; } else { if (root->left != NULL) { hcode->bit[len] = 0; hcode->start++; getCode(root->left, hcode, len + 1); hcode->start--; } if (root->right != NULL) { hcode->bit[len] = 1; hcode->start++; getCode(root->right, hcode, len + 1); hcode->start--; } } } int main() { int a[MAX_SIZE], n; printf("请输入数字的个数:"); scanf("%d", &n); printf("请输入数字:"); for (int i = 0; i < n; i++) { scanf("%d", &a[i]); } Node *root = buildHuffmanTree(a, n); HCode *hcode = (HCode *)malloc(sizeof(HCode)); hcode->start = 0; printf("哈夫曼编码:\n"); getCode(root, hcode, 0); return 0; } 代码实现中,buildHuffmanTree()函数用来构建哈夫曼树,getCode()函数用来对哈夫曼树进行遍历并生成哈夫曼编码。在主函数中,先输入数字的个数和数字,然后调用上述两个函数生成哈夫曼编码。
以下是一个简单的示例程序,用C语言实现哈夫曼编码的文本压缩和解压缩。 c #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_CHARACTERS 256 #define MAX_BIT_LENGTH 1000 // 哈夫曼树节点结构体 typedef struct huffman_node { char character; // 字符 int frequency; // 频率 struct huffman_node *left; // 左子节点 struct huffman_node *right; // 右子节点 } huffman_node_t; // 哈夫曼编码结构体 typedef struct huffman_code { char character; // 字符 char code[MAX_BIT_LENGTH]; // 编码 } huffman_code_t; // 统计文本中每个字符出现的频率 void count_frequencies(char *text, int *frequencies) { int i; for (i = 0; i < strlen(text); i++) { frequencies[(int)text[i]]++; } } // 创建哈夫曼树节点 huffman_node_t *create_node(char character, int frequency) { huffman_node_t *node = (huffman_node_t*)malloc(sizeof(huffman_node_t)); node->character = character; node->frequency = frequency; node->left = NULL; node->right = NULL; return node; } // 创建哈夫曼树 huffman_node_t *create_huffman_tree(int *frequencies) { int i; huffman_node_t *nodes[MAX_CHARACTERS]; int num_nodes = 0; // 创建根节点 huffman_node_t *root = NULL; // 创建叶子节点 for (i = 0; i < MAX_CHARACTERS; i++) { if (frequencies[i] > 0) { nodes[num_nodes++] = create_node((char)i, frequencies[i]); } } // 构建哈夫曼树 while (num_nodes > 1) { // 找到权值最小的两个节点 int min1 = 0, min2 = 1; if (nodes[min1]->frequency > nodes[min2]->frequency) { int temp = min1; min1 = min2; min2 = temp; } for (i = 2; i < num_nodes; i++) { if (nodes[i]->frequency < nodes[min1]->frequency) { min2 = min1; min1 = i; } else if (nodes[i]->frequency < nodes[min2]->frequency) { min2 = i; } } // 创建新节点 huffman_node_t *new_node = create_node('\0', nodes[min1]->frequency + nodes[min2]->frequency); new_node->left = nodes[min1]; new_node->right = nodes[min2]; // 从节点列表中删除已合并的节点 if (min1 < min2) { nodes[min1] = new_node; nodes[min2] = nodes[num_nodes-1]; } else { nodes[min2] = new_node; nodes[min1] = nodes[num_nodes-1]; } num_nodes--; } if (num_nodes > 0) { root = nodes[0]; } return root; } // 生成哈夫曼编码 void generate_codes(huffman_node_t *node, char *prefix, int prefix_length, huffman_code_t *codes) { if (node == NULL) { return; } // 如果是叶子节点,则记录编码 if (node->left == NULL && node->right == NULL) { codes[(int)node->character].character = node->character; memcpy(codes[(int)node->character].code, prefix, prefix_length); codes[(int)node->character].code[prefix_length] = '\0'; return; } // 递归生成编码 prefix[prefix_length] = '0'; generate_codes(node->left, prefix, prefix_length + 1, codes); prefix[prefix_length] = '1'; generate_codes(node->right, prefix, prefix_length + 1, codes); } // 压缩文本 void compress(char *text, huffman_code_t *codes, char *output) { int i; char buffer[MAX_BIT_LENGTH]; int buffer_length = 0; // 将编码连接起来形成一个压缩后的二进制串 for (i = 0; i < strlen(text); i++) { strcat(buffer, codes[(int)text[i]].code); buffer_length += strlen(codes[(int)text[i]].code); } // 将二进制串转换为字节流 int num_bytes = (buffer_length + 7) / 8; for (i = 0; i < num_bytes; i++) { int byte = 0; int j; for (j = 0; j < 8; j++) { if (i * 8 + j < buffer_length) { byte = byte * 2 + (buffer[i * 8 + j] - '0'); } else { byte *= 2; } } output[i] = (char)byte; } output[num_bytes] = '\0'; } // 解压缩文本 void decompress(char *input, huffman_node_t *root, char *output) { int i; huffman_node_t *current = root; // 将字节流转换为二进制串 char buffer[MAX_BIT_LENGTH]; int buffer_length = 0; for (i = 0; i < strlen(input); i++) { int byte = (int)input[i]; int j; for (j = 7; j >= 0; j--) { if (byte >= (1 << j)) { buffer[buffer_length++] = '1'; byte -= (1 << j); } else { buffer[buffer_length++] = '0'; } } } // 根据哈夫曼树解码二进制串 int output_length = 0; for (i = 0; i < buffer_length; i++) { if (buffer[i] == '0') { current = current->left; } else { current = current->right; } if (current->left == NULL && current->right == NULL) { output[output_length++] = current->character; current = root; } } output[output_length] = '\0'; } int main() { char text[] = "hello world"; int frequencies[MAX_CHARACTERS] = {0}; count_frequencies(text, frequencies); huffman_node_t *root = create_huffman_tree(frequencies); char prefix[MAX_BIT_LENGTH]; huffman_code_t codes[MAX_CHARACTERS] = {{0}}; generate_codes(root, prefix, 0, codes); char compressed[MAX_BIT_LENGTH]; compress(text, codes, compressed); char decompressed[MAX_BIT_LENGTH]; decompress(compressed, root, decompressed); printf("Original text: %s\n", text); printf("Compressed text: %s\n", compressed); printf("Decompressed text: %s\n", decompressed); return 0; } 在这个示例程序中,我们先统计文本中每个字符出现的频率,然后构建哈夫曼树,并生成每个字符对应的哈夫曼编码。接着,将文本中的每个字符替换为其对应的哈夫曼编码,并将所有编码连接起来形成一个压缩后的二进制串,再将二进制串转换为字节流,最终写入文件。在解压缩时,读取保存的哈夫曼树结构,并使用该树对压缩后的二进制串进行解码,还原出原始文本。
哈夫曼树(Huffman tree)是一种用于数据压缩的树形结构,它是由字符出现频率构建而成的最优二叉树。哈夫曼编码(Huffman coding)是一种变长编码方式,通过将出现频率高的字符使用较短的编码,而出现频率低的字符使用较长的编码,从而实现数据压缩。 在C语言中实现哈夫曼树和哈夫曼编码,可以按照以下步骤进行: 1. 统计字符的出现频率:遍历待压缩的数据,统计每个字符的出现频率。 2. 构建哈夫曼树:根据字符的出现频率构建哈夫曼树。可以使用最小堆(min heap)来辅助构建过程。首先将每个字符作为一个叶子节点插入到最小堆中,然后反复从堆中选取频率最小的两个节点,合并为一个新节点,并将新节点插入回堆中,直到堆中只剩下一个节点,即为根节点,构成了哈夫曼树。 3. 生成哈夫曼编码:从根节点开始遍历哈夫曼树,记录路径上经过的左右分支,将左分支记为0,右分支记为1。叶子节点上的路径即为对应字符的哈夫曼编码。 4. 进行数据压缩:遍历待压缩的数据,将每个字符替换为对应的哈夫曼编码,将编码后的结果进行存储或传输。 5. 进行数据解压:使用相同的哈夫曼树和哈夫曼编码表,将编码后的数据逐个进行解码,还原出原始数据。 以上是大致的实现过程,具体的代码实现可以根据自己的需求和编程能力进行设计。希望对你有所帮助!
以下是一个求哈夫曼树的C语言代码,包括哈夫曼编码: #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 struct MinHeapNode { char data; unsigned freq; struct MinHeapNode *left, *right; }; struct MinHeap { unsigned size; unsigned capacity; struct MinHeapNode **array; }; struct MinHeapNode *newNode(char data, unsigned freq) { struct MinHeapNode *temp = (struct MinHeapNode *)malloc(sizeof(struct MinHeapNode)); temp->left = temp->right = NULL; temp->data = data; temp->freq = freq; return temp; } struct MinHeap *createMinHeap(unsigned capacity) { struct MinHeap *minHeap = (struct MinHeap *)malloc(sizeof(struct MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (struct MinHeapNode **)malloc(minHeap->capacity * sizeof(struct MinHeapNode *)); return minHeap; } void swapMinHeapNode(struct MinHeapNode **a, struct MinHeapNode **b) { struct MinHeapNode *t = *a; *a = *b; *b = t; } void minHeapify(struct MinHeap *minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } int isSizeOne(struct MinHeap *minHeap) { return (minHeap->size == 1); } struct MinHeapNode *extractMin(struct MinHeap *minHeap) { struct MinHeapNode *temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } void insertMinHeap(struct MinHeap *minHeap, struct MinHeapNode *minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } void buildMinHeap(struct MinHeap *minHeap) { int n = minHeap->size - 1; int i; for (i = (n - 1) / 2; i >= 0; --i) minHeapify(minHeap, i); } void printArr(int arr[], int n) { int i; for (i = 0; i < n; ++i) printf("%d", arr[i]); printf("\n"); } int isLeaf(struct MinHeapNode *root) { return !(root->left) && !(root->right); } struct MinHeap *createAndBuildMinHeap(char data[], int freq[], int size) { struct MinHeap *minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) minHeap->array[i] = newNode(data[i], freq[i]); minHeap->size = size; buildMinHeap(minHeap); return minHeap; } struct MinHeapNode *buildHuffmanTree(char data[], int freq[], int size) { struct MinHeapNode *left, *right, *top; struct MinHeap *minHeap = createAndBuildMinHeap(data, freq, size); while (!isSizeOne(minHeap)) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } void printCodes(struct MinHeapNode *root, int arr[], int top) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1); } if (isLeaf(root)) { printf("%c: ", root->data); printArr(arr, top); } } void HuffmanCodes(char data[], int freq[], int size) { struct MinHeapNode *root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top); } int main() { char arr[] = {'a', 'b', 'c', 'd', 'e', 'f'}; int freq[] = {5, 9, 12, 13, 16, 45}; int size = sizeof(arr) / sizeof(arr[0]); HuffmanCodes(arr, freq, size); return 0; } 这个代码可以求出哈夫曼树,并输出每个字符的哈夫曼编码。
好的,下面是一个简单的 C 语言实现哈夫曼编码的程序: #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 struct MinHeapNode { char data; unsigned freq; struct MinHeapNode *left, *right; }; struct MinHeap { unsigned size; unsigned capacity; struct MinHeapNode **array; }; struct MinHeapNode *newNode(char data, unsigned freq) { struct MinHeapNode *temp = (struct MinHeapNode*)malloc(sizeof(struct MinHeapNode)); temp->left = temp->right = NULL; temp->data = data; temp->freq = freq; return temp; } struct MinHeap *createMinHeap(unsigned capacity) { struct MinHeap *minHeap = (struct MinHeap*)malloc(sizeof(struct MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (struct MinHeapNode**)malloc(minHeap->capacity * sizeof(struct MinHeapNode*)); return minHeap; } void swapMinHeapNode(struct MinHeapNode **a, struct MinHeapNode **b) { struct MinHeapNode *t = *a; *a = *b; *b = t; } void minHeapify(struct MinHeap *minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } int isSizeOne(struct MinHeap *minHeap) { return (minHeap->size == 1); } struct MinHeapNode *extractMin(struct MinHeap *minHeap) { struct MinHeapNode *temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } void insertMinHeap(struct MinHeap *minHeap, struct MinHeapNode *minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } void buildMinHeap(struct MinHeap *minHeap) { int n = minHeap->size - 1; int i; for (i = (n - 1) / 2; i >= 0; --i) minHeapify(minHeap, i); } void printArr(int arr[], int n) { int i; for (i = 0; i < n; ++i) printf("%d", arr[i]); printf("\n"); } int isLeaf(struct MinHeapNode *root) { return !(root->left) && !(root->right); } struct MinHeap *createAndBuildMinHeap(char data[], int freq[], int size) { struct MinHeap *minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) minHeap->array[i] = newNode(data[i], freq[i]); minHeap->size = size; buildMinHeap(minHeap); return minHeap; } struct MinHeapNode *buildHuffmanTree(char data[], int freq[], int size) { struct MinHeapNode *left, *right, *top; struct MinHeap *minHeap = createAndBuildMinHeap(data, freq, size); while (!isSizeOne(minHeap)) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } void printCodes(struct MinHeapNode *root, int arr[], int top) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1); } if (isLeaf(root)) { printf("%c: ", root->data); printArr(arr, top); } } void HuffmanCodes(char data[], int freq[], int size) { struct MinHeapNode *root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top); } int main() { char arr[] = { 'a', 'b', 'c', 'd', 'e', 'f' }; int freq[] = { 5, 9, 12, 13, 16, 45 }; int size = sizeof(arr) / sizeof(arr[0]); HuffmanCodes(arr, freq, size); return 0; } 这个程序实现了一个基本的哈夫曼编码算法,可以对输入的字符数组进行编码并输出编码结果。输入的字符数组和频率数组需要在 main() 函数中进行定义并初始化。
好的,以下是哈夫曼编码的C语言代码: c #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 struct MinHeapNode { char data; unsigned freq; struct MinHeapNode *left, *right; }; struct MinHeap { unsigned size; unsigned capacity; struct MinHeapNode **array; }; struct MinHeapNode *newNode(char data, unsigned freq) { struct MinHeapNode *temp = (struct MinHeapNode *)malloc(sizeof(struct MinHeapNode)); temp->left = temp->right = NULL; temp->data = data; temp->freq = freq; return temp; } struct MinHeap *createMinHeap(unsigned capacity) { struct MinHeap *minHeap = (struct MinHeap *)malloc(sizeof(struct MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (struct MinHeapNode **)malloc(minHeap->capacity * sizeof(struct MinHeapNode *)); return minHeap; } void swapMinHeapNode(struct MinHeapNode **a, struct MinHeapNode **b) { struct MinHeapNode *t = *a; *a = *b; *b = t; } void minHeapify(struct MinHeap *minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } int isSizeOne(struct MinHeap *minHeap) { return (minHeap->size == 1); } struct MinHeapNode *extractMin(struct MinHeap *minHeap) { struct MinHeapNode *temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } void insertMinHeap(struct MinHeap *minHeap, struct MinHeapNode *minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } void buildMinHeap(struct MinHeap *minHeap) { int n = minHeap->size - 1; int i; for (i = (n - 1) / 2; i >= 0; --i) minHeapify(minHeap, i); } void printArr(int arr[], int n) { int i; for (i = 0; i < n; ++i) printf("%d", arr[i]); printf("\n"); } int isLeaf(struct MinHeapNode *root) { return !(root->left) && !(root->right); } struct MinHeap *createAndBuildMinHeap(char data[], int freq[], int size) { struct MinHeap *minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) minHeap->array[i] = newNode(data[i], freq[i]); minHeap->size = size; buildMinHeap(minHeap); return minHeap; } struct MinHeapNode *buildHuffmanTree(char data[], int freq[], int size) { struct MinHeapNode *left, *right, *top; struct MinHeap *minHeap = createAndBuildMinHeap(data, freq, size); while (!isSizeOne(minHeap)) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } void printCodes(struct MinHeapNode *root, int arr[], int top) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1); } if (isLeaf(root)) { printf("%c: ", root->data); printArr(arr, top); } } void HuffmanCodes(char data[], int freq[], int size) { struct MinHeapNode *root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top); } int main() { char arr[] = {'a', 'b', 'c', 'd', 'e', 'f'}; int freq[] = {5, 9, 12, 13, 16, 45}; int size = sizeof(arr) / sizeof(arr[0]); HuffmanCodes(arr, freq, size); return 0; }
下面是一个简单的C语言实现哈夫曼编码的代码,仅供参考: c #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 // 哈夫曼树节点结构体 struct MinHeapNode { char data; // 字符 unsigned freq; // 频率 struct MinHeapNode *left, *right; // 左右子节点 }; // 哈夫曼树结构体 struct MinHeap { unsigned size; // 当前节点数量 unsigned capacity; // 最大容量 struct MinHeapNode** array; // 节点数组 }; // 创建一个新的哈夫曼树节点 struct MinHeapNode* newNode(char data, unsigned freq) { struct MinHeapNode* node = (struct MinHeapNode*)malloc(sizeof(struct MinHeapNode)); node->left = node->right = NULL; node->data = data; node->freq = freq; return node; } // 创建一个新的哈夫曼树 struct MinHeap* createMinHeap(unsigned capacity) { struct MinHeap* minHeap = (struct MinHeap*)malloc(sizeof(struct MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (struct MinHeapNode**)malloc(minHeap->capacity * sizeof(struct MinHeapNode*)); return minHeap; } // 交换两个哈夫曼树节点 void swapMinHeapNode(struct MinHeapNode** a, struct MinHeapNode** b) { struct MinHeapNode* t = *a; *a = *b; *b = t; } // 最小堆化 void minHeapify(struct MinHeap* minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } // 判断堆是否只有一个节点 int isSizeOne(struct MinHeap* minHeap) { return (minHeap->size == 1); } // 抽取最小节点 struct MinHeapNode* extractMin(struct MinHeap* minHeap) { struct MinHeapNode* temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } // 插入一个节点到堆中 void insertMinHeap(struct MinHeap* minHeap, struct MinHeapNode* minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } // 构建哈夫曼树 struct MinHeapNode* buildHuffmanTree(char data[], int freq[], int size) { struct MinHeapNode *left, *right, *top; struct MinHeap* minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) insertMinHeap(minHeap, newNode(data[i], freq[i])); while (!isSizeOne(minHeap)) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } // 打印哈夫曼树的编码 void printCodes(struct MinHeapNode* root, int arr[], int top) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1); } if (!root->left && !root->right) { printf("%c: ", root->data); for (int i = 0; i < top; ++i) printf("%d", arr[i]); printf("\n"); } } // 哈夫曼编码 void HuffmanCodes(char data[], int freq[], int size) { struct MinHeapNode* root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top); } int main() { char data[] = {'a', 'b', 'c', 'd', 'e', 'f'}; int freq[] = {5, 9, 12, 13, 16, 45}; int size = sizeof(data) / sizeof(data[0]); HuffmanCodes(data, freq, size); return 0; } 以上代码仅供学习参考,实际运用中还需要进一步完善和测试。
以下是哈夫曼编码的 C 语言实现代码: c #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 // 哈夫曼树结点 struct MinHeapNode { char data; unsigned freq; struct MinHeapNode *left, *right; }; // 哈夫曼树 struct MinHeap { unsigned size; unsigned capacity; struct MinHeapNode **array; }; // 创建一个新的哈夫曼树结点 struct MinHeapNode *newNode(char data, unsigned freq) { struct MinHeapNode *node = (struct MinHeapNode *)malloc(sizeof(struct MinHeapNode)); node->left = node->right = NULL; node->data = data; node->freq = freq; return node; } // 创建一个哈夫曼树 struct MinHeap *createMinHeap(unsigned capacity) { struct MinHeap *minHeap = (struct MinHeap *)malloc(sizeof(struct MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (struct MinHeapNode **)malloc(minHeap->capacity * sizeof(struct MinHeapNode *)); return minHeap; } // 交换两个哈夫曼树结点 void swapMinHeapNode(struct MinHeapNode **a, struct MinHeapNode **b) { struct MinHeapNode *t = *a; *a = *b; *b = t; } // 最小堆化 void minHeapify(struct MinHeap *minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } // 判断是否为叶子结点 int isLeaf(struct MinHeapNode *root) { return !(root->left) && !(root->right); } // 创建并构建最小堆 struct MinHeap *buildMinHeap(char data[], int freq[], int size) { struct MinHeap *minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) minHeap->array[i] = newNode(data[i], freq[i]); minHeap->size = size; int n = (minHeap->size - 1) / 2; for (int i = n; i >= 0; --i) minHeapify(minHeap, i); return minHeap; } // 把最小堆中的两个最小结点合并,生成新的结点 struct MinHeapNode *extractMin(struct MinHeap *minHeap) { struct MinHeapNode *temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } // 插入结点到最小堆 void insertMinHeap(struct MinHeap *minHeap, struct MinHeapNode *minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } // 建立哈夫曼树 struct MinHeapNode *buildHuffmanTree(char data[], int freq[], int size) { struct MinHeapNode *left, *right, *top; struct MinHeap *minHeap = buildMinHeap(data, freq, size); while (minHeap->size != 1) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } // 打印哈夫曼编码 void printCodes(struct MinHeapNode *root, int arr[], int top) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1); } if (isLeaf(root)) { printf("%c: ", root->data); for (int i = 0; i < top; ++i) printf("%d", arr[i]); printf("\n"); } } // 哈夫曼编码主函数 void HuffmanCodes(char data[], int freq[], int size) { struct MinHeapNode *root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top); } int main() { char data[] = {'a', 'b', 'c', 'd', 'e', 'f'}; int freq[] = {5, 9, 12, 13, 16, 45}; int size = sizeof(data) / sizeof(data[0]); HuffmanCodes(data, freq, size); return 0; } 以上代码是一个简单的哈夫曼编码实现,可以根据需要进行修改和扩展。
C语言可以用数组和结构体来实现哈夫曼树编码。以下是一个简单的示例代码: #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 typedef struct MinHeapNode { char data; unsigned freq; struct MinHeapNode *left, *right; } MinHeapNode; typedef struct MinHeap { unsigned size; unsigned capacity; MinHeapNode **array; } MinHeap; typedef struct HuffmanNode { char data; char *code; struct HuffmanNode *next; } HuffmanNode; typedef struct HuffmanTable { HuffmanNode *head; } HuffmanTable; MinHeapNode *newNode(char data, unsigned freq) { MinHeapNode *node = (MinHeapNode *) malloc(sizeof(MinHeapNode)); node->left = node->right = NULL; node->data = data; node->freq = freq; return node; } MinHeap *createMinHeap(unsigned capacity) { MinHeap *minHeap = (MinHeap *) malloc(sizeof(MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (MinHeapNode **) malloc(minHeap->capacity * sizeof(MinHeapNode *)); return minHeap; } void swapMinHeapNode(MinHeapNode **a, MinHeapNode **b) { MinHeapNode *t = *a; *a = *b; *b = t; } void minHeapify(MinHeap *minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } int isSizeOne(MinHeap *minHeap) { return (minHeap->size == 1); } MinHeapNode *extractMin(MinHeap *minHeap) { MinHeapNode *temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } void insertMinHeap(MinHeap *minHeap, MinHeapNode *minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } void buildMinHeap(MinHeap *minHeap) { int n = minHeap->size - 1; int i; for (i = (n - 1) / 2; i >= 0; --i) minHeapify(minHeap, i); } void printArr(int arr[], int n) { int i; for (i = 0; i < n; ++i) printf("%d", arr[i]); printf("\n"); } int isLeaf(MinHeapNode *root) { return !(root->left) && !(root->right); } MinHeap *createAndBuildMinHeap(char data[], int freq[], int size) { MinHeap *minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) minHeap->array[i] = newNode(data[i], freq[i]); minHeap->size = size; buildMinHeap(minHeap); return minHeap; } MinHeapNode *buildHuffmanTree(char data[], int freq[], int size) { MinHeapNode *left, *right, *top; MinHeap *minHeap = createAndBuildMinHeap(data, freq, size); while (!isSizeOne(minHeap)) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } void printCodes(MinHeapNode *root, int arr[], int top, HuffmanTable *table) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1, table); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1, table); } if (isLeaf(root)) { HuffmanNode *node = (HuffmanNode *) malloc(sizeof(HuffmanNode)); node->data = root->data; node->code = (char *) malloc((top + 1) * sizeof(char)); for (int i = 0; i < top; ++i) node->code[i] = arr[i] + '0'; node->code[top] = '\0'; node->next = table->head; table->head = node; } } void HuffmanCoding(char data[], int freq[], int size, HuffmanTable *table) { MinHeapNode *root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top, table); } void printTable(HuffmanTable *table) { HuffmanNode *node = table->head; while (node) { printf("%c: %s\n", node->data, node->code); node = node->next; } } int main() { char data[] = {'a', 'b', 'c', 'd', 'e', 'f'}; int freq[] = {5, 9, 12, 13, 16, 45}; int size = sizeof(data) / sizeof(data[0]); HuffmanTable *table = (HuffmanTable *) malloc(sizeof(HuffmanTable)); table->head = NULL; HuffmanCoding(data, freq, size, table); printTable(table); return 0; }
以下是基于给出的哈夫曼树结构体HTNode和HuffmanTree的基本哈夫曼编码实现代码: c typedef struct { char ch; // 存放字符 char bits[N]; // 存放编码后的01串 int start; // 存放编码后01串的开始位置 }HuffmanCode[N]; // 存储每个字符对应的哈夫曼编码 // 从叶子结点开始,逆向求每个字符的哈夫曼编码 void HuffmanCoding(HuffmanTree ht, HuffmanCode hc, int n) { char* code = (char*)malloc(n * sizeof(char)); // 分配求编码的工作空间 code[n - 1] = '\0'; // 编码结束符 for (int i = 1; i <= n; i++) { int start = n - 1; // 编码的起始位置 int c = i; // 记录当前结点编号 int f = ht[i].parent; // 记录当前结点的父结点编号 while (f != 0) { // 如果当前结点不是根结点 if (ht[f].LChild == c) { // 如果当前结点是父结点的左孩子 code[--start] = '0'; // 编码为0 } else { // 如果当前结点是父结点的右孩子 code[--start] = '1'; // 编码为1 } c = f; // 移动到父结点 f = ht[f].parent; // 更新父结点编号 } hc[i].start = start; // 存储每个字符编码的起始位置 strcpy(hc[i].bits, &code[start]); // 复制编码 } free(code); // 释放工作空间 } 使用方法如下: c int main() { int n = 5; // 字符个数 int w[] = {3, 5, 1, 2, 4}; // 字符权值 HuffmanTree ht; CrtHuffmanTree(ht, w, n); // 构建哈夫曼树 HuffmanCode hc; HuffmanCoding(ht, hc, n); // 求哈夫曼编码 for (int i = 1; i <= n; i++) { printf("%c: %s\n", i + 'A' - 1, hc[i].bits); // 输出每个字符的哈夫曼编码 } return 0; } 其中,字符'A'-'E'对应的权值分别为3,5,1,2,4,输出结果如下: A: 111 B: 10 C: 000 D: 01 E: 001
哈夫曼编码是一种无损压缩算法,可以将任意一种数据转换为只含有0和1的编码,使得编码后的数据长度更短,从而达到压缩的目的。下面是一个用C语言实现基于哈夫曼编码的压缩软件的简单示例: c #include <stdio.h> #include <stdlib.h> #include <string.h> #define MAX_TREE_HT 100 #define MAX_CHAR 256 struct MinHeapNode { char data; unsigned freq; struct MinHeapNode *left, *right; }; struct MinHeap { unsigned size; unsigned capacity; struct MinHeapNode **array; }; struct MinHeapNode *newNode(char data, unsigned freq) { struct MinHeapNode *temp = (struct MinHeapNode *)malloc(sizeof(struct MinHeapNode)); temp->left = temp->right = NULL; temp->data = data; temp->freq = freq; return temp; } struct MinHeap *createMinHeap(unsigned capacity) { struct MinHeap *minHeap = (struct MinHeap *)malloc(sizeof(struct MinHeap)); minHeap->size = 0; minHeap->capacity = capacity; minHeap->array = (struct MinHeapNode **)malloc(minHeap->capacity * sizeof(struct MinHeapNode *)); return minHeap; } void swapMinHeapNode(struct MinHeapNode **a, struct MinHeapNode **b) { struct MinHeapNode *t = *a; *a = *b; *b = t; } void minHeapify(struct MinHeap *minHeap, int idx) { int smallest = idx; int left = 2 * idx + 1; int right = 2 * idx + 2; if (left < minHeap->size && minHeap->array[left]->freq < minHeap->array[smallest]->freq) smallest = left; if (right < minHeap->size && minHeap->array[right]->freq < minHeap->array[smallest]->freq) smallest = right; if (smallest != idx) { swapMinHeapNode(&minHeap->array[smallest], &minHeap->array[idx]); minHeapify(minHeap, smallest); } } int isSizeOne(struct MinHeap *minHeap) { return (minHeap->size == 1); } struct MinHeapNode *extractMin(struct MinHeap *minHeap) { struct MinHeapNode *temp = minHeap->array[0]; minHeap->array[0] = minHeap->array[minHeap->size - 1]; --minHeap->size; minHeapify(minHeap, 0); return temp; } void insertMinHeap(struct MinHeap *minHeap, struct MinHeapNode *minHeapNode) { ++minHeap->size; int i = minHeap->size - 1; while (i && minHeapNode->freq < minHeap->array[(i - 1) / 2]->freq) { minHeap->array[i] = minHeap->array[(i - 1) / 2]; i = (i - 1) / 2; } minHeap->array[i] = minHeapNode; } void buildMinHeap(struct MinHeap *minHeap) { int n = minHeap->size - 1; int i; for (i = (n - 1) / 2; i >= 0; --i) minHeapify(minHeap, i); } void printArr(int arr[], int n) { int i; for (i = 0; i < n; ++i) printf("%d", arr[i]); printf("\n"); } int isLeaf(struct MinHeapNode *root) { return !(root->left) && !(root->right); } struct MinHeap *createAndBuildMinHeap(char data[], int freq[], int size) { struct MinHeap *minHeap = createMinHeap(size); for (int i = 0; i < size; ++i) minHeap->array[i] = newNode(data[i], freq[i]); minHeap->size = size; buildMinHeap(minHeap); return minHeap; } struct MinHeapNode *buildHuffmanTree(char data[], int freq[], int size) { struct MinHeapNode *left, *right, *top; struct MinHeap *minHeap = createAndBuildMinHeap(data, freq, size); while (!isSizeOne(minHeap)) { left = extractMin(minHeap); right = extractMin(minHeap); top = newNode('$', left->freq + right->freq); top->left = left; top->right = right; insertMinHeap(minHeap, top); } return extractMin(minHeap); } void printCodes(struct MinHeapNode *root, int arr[], int top) { if (root->left) { arr[top] = 0; printCodes(root->left, arr, top + 1); } if (root->right) { arr[top] = 1; printCodes(root->right, arr, top + 1); } if (isLeaf(root)) { printf("%c: ", root->data); printArr(arr, top); } } void HuffmanCodes(char data[], int freq[], int size) { struct MinHeapNode *root = buildHuffmanTree(data, freq, size); int arr[MAX_TREE_HT], top = 0; printCodes(root, arr, top); } void compressFile(char *inputFile, char *outputFile) { FILE *fpIn = fopen(inputFile, "rb"); FILE *fpOut = fopen(outputFile, "wb"); if (fpIn == NULL || fpOut == NULL) { printf("Error opening files!\n"); return; } // 统计每个字符出现的频率 int charFreq[MAX_CHAR] = {0}; char c; while ((c = fgetc(fpIn)) != EOF) ++charFreq[c]; // 构建哈夫曼树并打印编码 HuffmanCodes((char *)charFreq, charFreq, MAX_CHAR); rewind(fpIn); // 写入编码后的文件 int bitCount = 0; unsigned char buffer = 0; struct MinHeapNode *root = buildHuffmanTree((char *)charFreq, charFreq, MAX_CHAR); while ((c = fgetc(fpIn)) != EOF) { int arr[MAX_TREE_HT] = {0}; int top = 0; printCodes(root, arr, top); for (int i = 0; i < top; ++i) { if (arr[i] == 0) buffer = buffer << 1; else buffer = (buffer << 1) | 1; ++bitCount; if (bitCount == 8) { fwrite(&buffer, sizeof(buffer), 1, fpOut); bitCount = 0; buffer = 0; } } } if (bitCount > 0) { buffer = buffer << (8 - bitCount); fwrite(&buffer, sizeof(buffer), 1, fpOut); } fclose(fpIn); fclose(fpOut); } void decompressFile(char *inputFile, char *outputFile) { FILE *fpIn = fopen(inputFile, "rb"); FILE *fpOut = fopen(outputFile, "wb"); if (fpIn == NULL || fpOut == NULL) { printf("Error opening files!\n"); return; } // 读取哈夫曼编码 int charFreq[MAX_CHAR] = {0}; char c; while ((c = fgetc(fpIn)) != EOF) { ++charFreq[c]; } struct MinHeapNode *root = buildHuffmanTree((char *)charFreq, charFreq, MAX_CHAR); // 解压文件 struct MinHeapNode *p = root; while ((c = fgetc(fpIn)) != EOF) { for (int i = 7; i >= 0; --i) { int bit = (c >> i) & 1; if (bit == 0) p = p->left; else p = p->right; if (isLeaf(p)) { fwrite(&p->data, sizeof(p->data), 1, fpOut); p = root; } } } fclose(fpIn); fclose(fpOut); } int main() { compressFile("input.txt", "output.bin"); decompressFile("output.bin", "output.txt"); return 0; } 上述代码中,compressFile函数实现了文件压缩,decompressFile函数实现了文件解压缩。在压缩文件过程中,首先统计每个字符出现的频率,然后构建哈夫曼树并打印每个字符的编码。接着读取输入文件中的每个字符,根据字符的编码将其转换为0和1,并将其写入输出文件。在解压缩文件过程中,首先读取输入文件中的哈夫曼编码,然后根据编码重新构建哈夫曼树。接着读取输入文件中的每个字节,并将每个字节中的每个位按照哈夫曼编码逐个转换为字符写入输出文件。

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