root/library/bdm/estim/arx.cpp @ 700

#include "arx.h"
namespace bdm {

void ARX::bayes_weighted ( const vec &yt, const vec &cond, const double w ) {
                
        bdm_assert_debug(yt.length()>=dimy,"ARX::bayes yt is smaller then dimc");       
        bdm_assert_debug(cond.length()>=dimc,"ARX::bayes cond is smaller then dimc");   
        double lnc;
        //cache
        ldmat &V=est._V(); 
        double &nu=est._nu();

        dyad.set_subvector(0,yt);
        dyad.set_subvector(dimy,cond);
        // possible "1" is there from the beginning
        
        if ( frg < 1.0 ) {
                est.pow ( frg ); // multiply V and nu

                
                //stabilize
                ldmat V0=alter_est._V(); //$ copy
                double &nu0=alter_est._nu();
                
                V0*=(1-frg);
                V += V0; //stabilization
                nu +=(1-frg)*nu0;
                
                // recompute loglikelihood of new "prior"
                if ( evalll ) {
                        last_lognc = est.lognc();
                }
        }
        V.opupdt ( dyad, w );
        nu += w;

        // log(sqrt(2*pi)) = 0.91893853320467
        if ( evalll ) {
                lnc = est.lognc();
                ll = lnc - last_lognc - 0.91893853320467;
                last_lognc = lnc;
        }
}

double ARX::logpred ( const vec &yt ) const {
        egiw pred ( est );
        ldmat &V = pred._V();
        double &nu = pred._nu();

        double lll;
        vec dyad_p = dyad;
        dyad_p.set_subvector(0,yt);

        if ( frg < 1.0 ) {
                pred.pow ( frg );
                lll = pred.lognc();
        } else//should be save: last_lognc is changed only by bayes;
                if ( evalll ) {
                        lll = last_lognc;
                } else {
                        lll = pred.lognc();
                }

        V.opupdt ( dyad_p, 1.0 );
        nu += 1.0;
        // log(sqrt(2*pi)) = 0.91893853320467
        return pred.lognc() - lll - 0.91893853320467;
}

ARX* ARX::_copy_ ( ) const {
        ARX* Tmp = new ARX ( *this );
        return Tmp;
}

void ARX::set_statistics ( const BMEF* B0 ) {
        const ARX* A0 = dynamic_cast<const ARX*> ( B0 );

        bdm_assert_debug ( dimension() == A0->dimension(), "Statistics of different dimensions" );
        set_statistics ( A0->dimensiony(), A0->posterior()._V(), A0->posterior()._nu() );
}

enorm<ldmat>* ARX::epredictor ( const vec &rgr ) const {
        mat mu ( dimy, posterior()._V().rows() - dimy );
        mat R ( dimy, dimy );

        enorm<ldmat>* tmp;
        tmp = new enorm<ldmat> ( );
        //TODO: too hackish
        if ( yrv._dsize() > 0 ) {
        }

        est.mean_mat ( mu, R ); //mu =
        //correction for student-t  -- TODO check if correct!!
        //R*=nu/(nu-2);
        mat p_mu = mu.T() * rgr;        //the result is one column
        tmp->set_parameters ( p_mu.get_col ( 0 ), ldmat ( R ) );
        return tmp;
}

mlnorm<ldmat>* ARX::predictor ( ) const {
        mat mu ( dimy, posterior()._V().rows() - dimy );
        mat R ( dimy, dimy );
        mlnorm<ldmat>* tmp;
        tmp = new mlnorm<ldmat> ( );

        est.mean_mat ( mu, R ); //mu =
        mu = mu.T();
        //correction for student-t  -- TODO check if correct!!

        if ( have_constant) { // constant term
                //Assume the constant term is the last one:
                tmp->set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ) );
        } else {
                tmp->set_parameters ( mu, zeros ( dimy ), ldmat ( R ) );
        }
        return tmp;
}

mlstudent* ARX::predictor_student ( ) const {
        const ldmat &V = posterior()._V();
        
        mat mu ( dimy, V.rows() - dimy );
        mat R ( dimy, dimy );
        mlstudent* tmp;
        tmp = new mlstudent ( );

        est.mean_mat ( mu, R ); //
        mu = mu.T();

        int end = V._L().rows() - 1;
        ldmat Lam ( V._L() ( dimy, end, dimy, end ), V._D() ( dimy, end ) );  //exp val of R


        if ( have_constant) { // no constant term
                //Assume the constant term is the last one:
                if ( mu.cols() > 1 ) {
                        tmp->set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ), Lam );
                } else {
                        tmp->set_parameters ( mat ( dimy, dimc ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ), Lam );
                }
        } else {
                // no constant term
                tmp->set_parameters ( mu, zeros ( dimy ), ldmat ( R ), Lam );
        }
        return tmp;
}



/*! \brief Return the best structure
@param Eg a copy of GiW density that is being examined
@param Eg0 a copy of prior GiW density before estimation
@param Egll likelihood of the current Eg
@param indeces current indeces
\return best likelihood in the structure below the given one
*/
double egiw_bestbelow ( egiw Eg, egiw Eg0, double Egll, ivec &indeces ) { //parameter Eg is a copy!
        ldmat Vo = Eg._V(); //copy
        ldmat Vo0 = Eg._V(); //copy
        ldmat& Vp = Eg._V(); // pointer into Eg
        ldmat& Vp0 = Eg._V(); // pointer into Eg
        int end = Vp.rows() - 1;
        int i;
        mat Li;
        mat Li0;
        double maxll = Egll;
        double tmpll = Egll;
        double belll = Egll;

        ivec tmpindeces;
        ivec maxindeces = indeces;


        cout << "bb:(" << indeces << ") ll=" << Egll << endl;

        //try to remove only one rv
        for ( i = 0; i < end; i++ ) {
                //copy original
                Li = Vo._L();
                Li0 = Vo0._L();
                //remove stuff
                Li.del_col ( i + 1 );
                Li0.del_col ( i + 1 );
                Vp.ldform ( Li, Vo._D() );
                Vp0.ldform ( Li0, Vo0._D() );
                tmpll = Eg.lognc() - Eg0.lognc(); // likelihood is difference of norm. coefs.

                cout << "i=(" << i << ") ll=" << tmpll << endl;

                //
                if ( tmpll > Egll ) { //increase of the likelihood
                        tmpindeces = indeces;
                        tmpindeces.del ( i );
                        //search for a better match in this substructure
                        belll = egiw_bestbelow ( Eg, Eg0, tmpll, tmpindeces );
                        if ( belll > maxll ) { //better match found
                                maxll = belll;
                                maxindeces = tmpindeces;
                        }
                }
        }
        indeces = maxindeces;
        return maxll;
}

ivec ARX::structure_est ( egiw est0 ) {
        ivec ind = linspace ( 1, est.dimension() - 1 );
        egiw_bestbelow ( est, est0, est.lognc() - est0.lognc(), ind );
        return ind;
}



ivec ARX::structure_est_LT ( egiw est0 ) {
        //some stuff with beliefs etc.
//      ivec ind = bdm::straux1(V,nu, est0._V(), est0._nu());
        return ivec();//ind;
}

void ARX::from_setting ( const Setting &set ) {
        shared_ptr<RV> yrv_ = UI::build<RV> ( set, "rv", UI::compulsory );
        shared_ptr<RV> rrv = UI::build<RV> ( set, "rgr", UI::compulsory );
        dimy = yrv_->_dsize();
        // rgrlen - including constant!!!
        dimc = rrv->_dsize();
        
        yrv = *yrv_;
        rvc = *rrv;
        
        string opt;
        if ( UI::get(opt, set,  "options", UI::optional) ) {
                BM::set_options(opt);
        }
        int constant;
        if (!UI::get(constant, set, "constant", UI::optional)){
                have_constant=true;
        } else {
                have_constant=constant>0;
        }
        int rgrlen = dimc+int(have_constant==true);

        //init
        shared_ptr<egiw> pri=UI::build<egiw>(set, "prior", UI::optional);
        if (pri) {
                bdm_assert(pri->_dimx()==dimy,"prior is not compatible");
                bdm_assert(pri->_V().rows()==dimy+rgrlen,"prior is not compatible");
                est.set_parameters( pri->_dimx(),pri->_V(), pri->_nu());
        }else{
                est.set_parameters( dimy, zeros(dimy+rgrlen));
                set_prior_default(est);
        }
                
        shared_ptr<egiw> alt=UI::build<egiw>(set, "alternative", UI::optional);
        if (alt) {
                bdm_assert(alt->_dimx()==dimy,"alternative is not compatible");
                bdm_assert(alt->_V().rows()==dimy+rgrlen,"alternative is not compatible");
                alter_est.set_parameters( alt->_dimx(),alt->_V(), alt->_nu());
        } else {
                alter_est = est;
        }

        double frg;
        if ( !UI::get ( frg, set, "frg" ) )
                frg = 1.0;

        set_parameters ( frg );
        
        //name results (for logging)
        shared_ptr<RV> rv_par=UI::build<RV>(set, "rv_param",UI::optional );
        if (!rv_par){
                est.set_rv ( RV ( "{theta r }", vec_2 ( dimy*rgrlen, dimy*dimy ) ) );
        } else {
                est.set_rv ( *rv_par );
        }
        validate();
}
}