0.1,0.12,0.43,0.11,0.13,0
0.1,0.2,0.4,0.1,0.3,0
0.12,0.22,0.41,0.12,0.32,0
0.31,0.23,0.44,0.21,0.23,0
0.13,0.23,0.43,0.13,0.23,0
0.14,0.24,0.44,0.14,0.34,0
0.14,0.42,0.44,0.14,0.34,0
0.51,0.52,0.54,0.51,0.53,0
0.51,0.52,0.54,0.51,0.53,1
0.52,0.58,0.57,0.54,0.55,1
0.53,0.54,0.55,0.56,0.54,1
0.53,0.57,0.59,0.59,0.59,1
0.69,0.62,0.69,0.69,0.53,2
0.61,0.62,0.54,0.59,0.53,2
0.71,0.72,0.74,0.51,0.53,2
0.81,0.82,0.84,0.81,0.83,2
0.85,0.82,0.85,0.81,0.83,2
0.85,0.92,0.85,0.81,0.93,2
0.95,0.82,0.85,0.91,0.83,2
- last columns represent classes and first columns represent features
vector
vector
ifstream f;
f.open("c:\\bayes.txt");
while(f)
{
string str = "";
f>>str;
vector
size_t found = str.find(",");
while(found<100 && found>0)
{
string t = str.substr(0,found);
desc.push_back(strtod(t.c_str(), NULL));
str = str.substr(found+1);
found = str.find(",");
}
if(desc.size()>0)
{
descriptor.push_back(desc);
classes.push_back(strtod(str.c_str(), NULL));
}
}
f.close();
vector
v.push_back(0.9481);
v.push_back(0.9432);
v.push_back(0.434);
v.push_back(0.0531);
v.push_back(0.533);
int num_samples = descriptor.size();
int num_features = descriptor[0].size();
CvMat* data = cvCreateMat( num_samples, num_features, CV_32F );
CvMat* responses = cvCreateMat( descriptor.size(), 1, CV_32F );
for(int i=0;i
{
for(int j=0;j
CV_MAT_ELEM(*data,float,i,j)=descriptor[i][j];
CV_MAT_ELEM(*responses,float,i,0)=classes[i];
}
// initialize parameters
CvTermCriteria crit;
crit.epsilon = CV_TERMCRIT_EPS;
crit.max_iter = 1000;
crit.type = 1;
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::RBF;
params.gamma = 0.1;
params.C = 3;
params.nu = 0.1;
params.term_crit = crit;
CvMat* sample = cvCreateMat( 1,num_features, CV_32F );
for(int j=0;j
CV_MAT_ELEM(*sample,float,0,j)=v[j];
CvSVM SVM;
SVM.train(data,responses,0,0,params);
float r = SVM.predict( sample );
SVM.clear();
cvReleaseMat(&sample);
cvReleaseMat(&data);
cvReleaseMat(&responses);