// h = train(dummy, X,Y, var_cat_mask, T,J,v, node_size);
void train(int nlhs,mxArray *plhs[], int nrhs,const mxArray *prhs[])
{
/* Input */
MLData tr;
// training tr X
set_X(prhs[1], tr.X);
// response Y
set_Y(prhs[2], tr.Y);
// var_cat_mask
set_mask(prhs[3], tr.var_type);
// T
int T = (int)mxGetScalar(prhs[4]);
// J
int J = (int)mxGetScalar(prhs[5]);
// v
double v = (double)mxGetScalar(prhs[6]);
// node_size
int node_size = (int)mxGetScalar(prhs[7]);
/* train */
tr.problem_type = PROBLEM_CLS;
tr.preprocess();
booster_t* pbooster = new booster_t;
pbooster->param_.T = T;
pbooster->param_.v = v;
pbooster->param_.J = J;
pbooster->param_.ns = node_size;
pbooster->train(&tr);
/*Output*/
plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL);
double* pp = mxGetPr(plhs[0]);
*pp = (long long) pbooster;
}
/*!
Initialize the 3D point coordinates.
\param X,Y,Z : \f$(X,Y,Z)\f$ coordinates in the camera frame of the
3D point visual feature.
\sa set_X(), set_Y(), set_Z()
*/
void
vpFeaturePoint3D::set_XYZ(const double X,
const double Y,
const double Z)
{
set_X(X) ;
set_Y(Y) ;
set_Z(Z) ;
for(unsigned int i = 0; i < nbParameters; i++) flags[i] = true;
}
