关于struct SVM的介绍

该博客内容为转载,给出了转载博客的链接http://bubblexc.com/y2011/503/ ,未包含其他关键信息技术信息。

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转自博客:

http://bubblexc.com/y2011/503/


#include <stdio.h> #include <stdlib.h> #include <math.h> #include "svm.h" // 简单的HOG特征提取示例,实际应用需要更复杂的实现 #define CELL_SIZE 8 #define BLOCK_SIZE 2 #define NUM_BINS 9 // 计算HOG特征 void compute_hog(float *image, int width, int height, float *hog_features) { // 这里只是简单示例,实际HOG计算更复杂 int num_cells_x = width / CELL_SIZE; int num_cells_y = height / CELL_SIZE; int num_blocks_x = num_cells_x - BLOCK_SIZE + 1; int num_blocks_y = num_cells_y - BLOCK_SIZE + 1; int index = 0; for (int by = 0; by < num_blocks_y; by++) { for (int bx = 0; bx < num_blocks_x; bx++) { for (int cby = 0; cby < BLOCK_SIZE; cby++) { for (int cbx = 0; cbx < BLOCK_SIZE; cbx++) { for (int bin = 0; bin < NUM_BINS; bin++) { hog_features[index++] = (float)rand() / RAND_MAX; } } } } } } // 训练SVM模型 void train_svm(struct svm_problem *prob, struct svm_parameter *param) { struct svm_model *model = svm_train(prob, param); svm_save_model("model.svm", model); svm_free_and_destroy_model(&model); } // 预测 double predict(struct svm_model *model, struct svm_node *x) { return svm_predict(model, x); } int main() { // 模拟数据 int num_samples = 100; int width = 64; int height = 64; int num_features = ((width / CELL_SIZE) - BLOCK_SIZE + 1) * ((height / CELL_SIZE) - BLOCK_SIZE + 1) * BLOCK_SIZE * BLOCK_SIZE * NUM_BINS; struct svm_problem prob; prob.l = num_samples; prob.y = (double *)malloc(num_samples * sizeof(double)); prob.x = (struct svm_node **)malloc(num_samples * sizeof(struct svm_node *)); for (int i = 0; i < num_samples; i++) { float *image = (float *)malloc(width * height * sizeof(float)); float *hog_features = (float *)malloc(num_features * sizeof(float)); compute_hog(image, width, height, hog_features); prob.x[i] = (struct svm_node *)malloc((num_features + 1) * sizeof
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