当涉及复杂的高效C代码案例时,这些代码示例展示了C语言中一些复杂且高效的应用案例,涵盖了排序算法、图算法、位操作、文件操作、多线程编程等领域。这些案例体现了C语言在各个领域的广泛应用和高效性,以下是8个经典的例子:
1.快速排序算法:
void quicksort(int arr[], int low, int high) {
if (low < high) {
int pivot = partition(arr, low, high);
quicksort(arr, low, pivot - 1);
quicksort(arr, pivot + 1, high);
}
}
int partition(int arr[], int low, int high) {
int pivot = arr[high];
int i = low - 1;
for (int j = low; j <= high - 1; j++) {
if (arr[j] <= pivot) {
i++;
swap(&arr[i], &arr[j]);
}
}
swap(&arr[i + 1], &arr[high]);
return (i + 1);
}
void swap(int* a, int* b) {
int temp = *a;
*a = *b;
*b = temp;
}
这是经典的快速排序算法实现,采用分治思想,通过递归地将数组分成两部分并进行排序,从而实现快速的排序效果。
2.动态规划算法-最长公共子序列(Longest Common Subsequence):
int lcs(char* X, char* Y, int m, int n) {
int L[m + 1][n + 1];
for (int i = 0; i <= m; i++) {
for (int j = 0; j <= n; j++) {
if (i == 0 || j == 0)
L[i][j] = 0;
else if (X[i - 1] == Y[j - 1])
L[i][j] = L[i - 1][j - 1] + 1;
else
L[i][j] = max(L[i - 1][j], L[i][j - 1]);
}
}
return L[m][n];
}
int max(int a, int b) {
return (a > b) ? a : b;
}
这段代码实现了最长公共子序列问题的动态规划解法,通过构建一个二维数组,逐步计算最长公共子序列的长度。
3.哈夫曼编码(Huffman Coding):
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 buildMinHeap(struct MinHeap* minHeap, int size)
{
int i;
for (i = (size - 1) / 2; i >= 0; --i)
minHeapify(minHeap, i);
}
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);
}
}
void swapMinHeapNode(struct MinHeapNode** a, struct MinHeapNode** b)
{ struct MinHeapNode* t = *a; *a = *b; *b = t; }
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 buildHuffmanTree(char data[], int freq[], int size)
{ struct MinHeapNode* left, * right, * top;
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, size);
while (!isSizeOne(minHeap))
{
left = extractMin(minHeap);
right = extractMin(minHeap);
top = newNode('$', left->freq + right->freq);
top->left = left;
top->right = right;
insertMinHeap(minHeap, top);
}
printCodes(minHeap->array[0], "");
}
void printCodes(struct MinHeapNode* root, char* str)
{
if (root == NULL) return;
if (root->data != '$')
printf("%c: %sn", root->data, str);
printCodes(root->left, strcat(str, "0"));
str[strlen(str) - 1] = '