学习Opencv2.4.9(四)---SVM支持向量机

#include <cv.h>#include <highgui.h>#include <ml.h>#include <cxcore.h>#include <iostream>using namespace std;int main(){ // step 1://训练数据的分类标记，即4类 float labels[16] = {1.0, 1.0,1.0,1.0,2.0,2.0,2.0,2.0,3.0,3.0,3.0,3.0,4.0,4.0,4.0,4.0}; CvMat labelsMat = cvMat(16, 1, CV_32FC1, labels); //训练数据矩阵 float trainingData[16][2] = { {0, 0}, {4, 1}, {4, 5}, {-1, 6},{3,11},{-2,10},{4,30},{0,25},{10,13},{15,12},{25,40},{11,35},{8,1},{9,6},{15,5},{20,-1} }; CvMat trainingDataMat = cvMat(16, 2, CV_32FC1, trainingData); // step 2: //训练参数设定 CvSVMParams params; params.svm_type = CvSVM::C_SVC;                 //SVM类型 params.kernel_type = CvSVM::LINEAR;             //核函数的类型 //SVM训练过程的终止条件, max_iter:最大迭代次数  epsilon:结果的精确性 params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, FLT_EPSILON ); // step 3: //启动训练过程 CvSVM SVM; SVM.train( &trainingDataMat, &labelsMat, NULL,NULL, params); // step 4: //使用训练所得模型对新样本进行分类测试 for (int i=-5; i<15; i++) {      for (int j=-5; j<15; j++)      {           float a[] = {i,j};           CvMat sampleMat;           cvInitMatHeader(&sampleMat,1,2,CV_32FC1,a);           cvmSet(&sampleMat,0,0,i);                                        // Set M(i,j)           cvmSet(&sampleMat,0,1,j);                                        // Set M(i,j)           float response = SVM.predict(&sampleMat);           cout<<response<<" ";      }      cout<<endl; } // step 5: //获取支持向量 int c = SVM.get_support_vector_count(); cout<<endl; for (int i=0; i<c; i++) {      const float* v = SVM.get_support_vector(i);      cout<<*v<<" "; } cout<<endl; system("pause"); return 0;}#include <cv.h>#include <highgui.h>#include <ml.h>