root/library/bdm/estim/arx.cpp @ 741

#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 );
        if (cond.length()>0)
                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;
}

void ARX::flatten ( const BMEF* B ) {
        const ARX* A = dynamic_cast<const ARX*> ( B );
        // nu should be equal to B.nu
        est.pow ( A->posterior()._nu() / posterior()._nu() );
        if ( evalll ) {
                last_lognc = est.lognc();
        }
}

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;
}

enorm<ldmat>* ARX::epredictor() const {
        bdm_assert_debug ( dimy == posterior()._V().rows() - 1, "Regressor is not only 1" );
        return epredictor ( vec_1 ( 1.0 ) );
}

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();
}
}