// Training SVM with feature vector X and label Y.
// Each row of X is a feature vector, with corresponding label in Y.
// Return a CV_32F weight Mat
Mat Objectness::trainSVM(CMat &X1f, const vecI &Y, int sT, double C, double bias, double eps)
{
// Set SVM parameters
parameter param; {
param.solver_type = sT; // L2R_L2LOSS_SVC_DUAL;
param.C = C;
param.eps = eps; // see setting below
param.p = 0.1;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;
set_print_string_function(print_null);
CV_Assert(X1f.rows == Y.size() && X1f.type() == CV_32F);
}
// Initialize a problem
feature_node *x_space = NULL;
problem prob;{
prob.l = X1f.rows;
prob.bias = bias;
prob.y = Malloc(double, prob.l);
prob.x = Malloc(feature_node*, prob.l);
const int DIM_FEA = X1f.cols;
prob.n = DIM_FEA + (bias >= 0 ? 1 : 0);
x_space = Malloc(feature_node, (prob.n + 1) * prob.l);
int j = 0;
for (int i = 0; i < prob.l; i++){
prob.y[i] = Y[i];
prob.x[i] = &x_space[j];
const float* xData = X1f.ptr<float>(i);
for (int k = 0; k < DIM_FEA; k++){
x_space[j].index = k + 1;
x_space[j++].value = xData[k];
}
if (bias >= 0){
x_space[j].index = prob.n;
x_space[j++].value = bias;
}
x_space[j++].index = -1;
}
CV_Assert(j == (prob.n + 1) * prob.l);
}
// Training SVM for current problem
const char* error_msg = check_parameter(&prob, ¶m);
if(error_msg){
fprintf(stderr,"ERROR: %s\n",error_msg);
exit(1);
}
model *svmModel = train(&prob, ¶m);
Mat wMat(1, prob.n, CV_64F, svmModel->w);
wMat.convertTo(wMat, CV_32F);
free_and_destroy_model(&svmModel);
destroy_param(¶m);
free(prob.y);
free(prob.x);
free(x_space);
return wMat;
}
bool isLucky(vecI mv){
int cont;
int n = mv.size();
for(int i = 0; i < n; i++)
{
if(mv[i]== 4 or mv[i] == 7)
cont++;
}
if(cont == n) return true;
else return false;
}
int CmAPCluster::ReMapIdx(vecI &mapIdx)
{
int N = (int)mapIdx.size(), newCount = 0;
map<int, int> idxCount, oldNewIdx;
vecI newIdx(N);
for (int i = 0; i < N; i++){
if (idxCount.find(mapIdx[i]) == idxCount.end())
oldNewIdx[mapIdx[i]] = newCount++, idxCount[mapIdx[i]]++;
mapIdx[i] = oldNewIdx[mapIdx[i]];
}
return (int)idxCount.size();
}
void DataSetVOC::loadBox(const FileNode &fn, vector<Vec4i> &boxes, vecI &clsIdx){
string isDifficult;
fn["difficult"]>>isDifficult;
if (isDifficult == "1")
return;
string strXmin, strYmin, strXmax, strYmax;
fn["bndbox"]["xmin"] >> strXmin;
fn["bndbox"]["ymin"] >> strYmin;
fn["bndbox"]["xmax"] >> strXmax;
fn["bndbox"]["ymax"] >> strYmax;
boxes.push_back(Vec4i(atoi(_S(strXmin)), atoi(_S(strYmin)), atoi(_S(strXmax)), atoi(_S(strYmax))));
string clsName;
fn["name"]>>clsName;
clsIdx.push_back(findFromList(clsName, classNames));
CV_Assert_(clsIdx[clsIdx.size() - 1] >= 0, ("Invalidate class name\n"));
}
