root/library/bdm/estim/arx.h @ 979

/*!
  \file
  \brief Bayesian Filtering for generalized autoregressive (ARX) model
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/

#ifndef AR_H
#define AR_H

#include "../math/functions.h"
#include "../stat/exp_family.h"
#include "../base/user_info.h"
//#include "../estim/kalman.h"
#include "arx_straux.h"

namespace bdm {

/*!
* \brief Linear Autoregressive model with Gaussian noise

Regression of the following kind:
\f[
y_t = \theta_1 \psi_1 + \theta_2 + \psi_2 +\ldots + \theta_n \psi_n + r e_t
\f]
where unknown parameters \c rv are \f$[\theta r]\f$, regression vector \f$\psi=\psi(y_{1:t},u_{1:t})\f$ is a known function of past outputs and exogeneous variables \f$u_t\f$. Distrubances \f$e_t\f$ are supposed to be normally distributed:
\f[
e_t \sim \mathcal{N}(0,1).
\f]

See \ref tut_arx for mathematical treatment.

The easiest way how to use the class is:
\include arx_simple.cpp

        \todo sort out constant terms - bayes should accept vec without additional 1s
*/
class ARX: public BMEF {
protected:
        //! switch if constant is modelled or not
        bool have_constant;
        //! vector of dyadic update
        vec dyad;
        //! RV of regressor
        RV rgr;
        //! length of the regressor
        int rgrlen;
        //! posterior density
        egiw est;
        //! Alternative estimate of parameters, used in stabilized forgetting, see [Kulhavy]
        egiw alter_est;
public:
        //! \name Constructors
        //!@{
        ARX ( const double frg0 = 1.0 ) : BMEF ( frg0 ),  have_constant ( true ), dyad(), rgrlen(),est(), alter_est() {};
        ARX ( const ARX &A0 ) : BMEF ( A0 ),  have_constant ( A0.have_constant ), dyad ( A0.dyad ),rgrlen(A0.rgrlen), est ( A0.est ), alter_est ( A0.alter_est ) { };

        ARX* _copy() const;

        void set_frg ( double frg0 ) {
                frg = frg0;
        }
        void set_constant ( bool const0 ) {
                have_constant = const0;
        }
        void set_statistics ( int dimy0, const ldmat V0, double nu0 = -1.0 ) {
                est.set_parameters ( dimy0, V0, nu0 );
                est.validate();
                last_lognc = est.lognc();
                dimy = dimy0;
        }
        //!@}

        //! Set sufficient statistics
        void set_statistics ( const BMEF* BM0 );

        //!\name Mathematical operations
        //!@{

        //! Weighted Bayes \f$ dt = [y_t psi_t] \f$.
        void bayes_weighted ( const vec &yt, const vec &cond = empty_vec, const double w = 1.0 );
        void bayes ( const vec &yt, const vec &cond = empty_vec ) {
                bayes_weighted ( yt, cond, 1.0 );
        };
        double logpred ( const vec &yt ) const;
        void flatten ( const BMEF* B );
        //! Conditioned version of the predictor
        enorm<ldmat>* epredictor ( const vec &rgr ) const;
        //! Predictor for empty regressor
        enorm<ldmat>* epredictor() const;
        //! conditional version of the predictor
        template<class sq_T>
        shared_ptr<mlnorm<sq_T> > ml_predictor() const;
        //! fast version of predicto
        template<class sq_T>
        void ml_predictor_update ( mlnorm<sq_T> &pred ) const;
        mlstudent* predictor_student() const;
        //! Brute force structure estimation.\return indices of accepted regressors.
        ivec structure_est ( egiw Eg0 );
        //! Smarter structure estimation by Ludvik Tesar.\return indices of accepted regressors.
        ivec structure_est_LT ( egiw Eg0 );
        //!@}

        //!\name Access attributes
        //!@{
        //! return correctly typed posterior (covariant return)
        const egiw& posterior() const {
                return est;
        }
        //!@}

        /*! UI for ARX estimator

        \code
        class = 'ARX';
        yrv   = RV({names_of_dt} )                 // description of output variables
        rgr   = RV({names_of_regressors}, [-1,-2]} // description of regressor variables
        constant = 1;                              // 0/1 switch if the constant term is modelled or not

        --- optional ---
        prior = {class='egiw',...};                // Prior density, when given default is used instead
        alternative = {class='egiw',...};          // Alternative density in stabilized estimation, when not given prior is used

        frg = 1.0;                                 // forgetting, default frg=1.0

        rv  = RV({names_of_parameters}}            // description of parametetr names
                                                                                           // default: [""]
        \endcode
        */
        void from_setting ( const Setting &set );

        void validate() {
                BMEF::validate();       
                est.validate();
                //if dimc not set set it from V
                if ( dimc == 0 ) {
                        dimc = posterior()._V().rows() - dimy - int ( have_constant == true );
                }

                if ( have_constant ) {
                        dyad = ones ( dimy + dimc + 1 );
                } else {
                        dyad = zeros ( dimy + dimc );
                }

        }
        //! function sets prior and alternative density
        void set_prior ( const epdf *prior );
        //! build default prior and alternative when all values are set
        void set_prior_default ( egiw &prior ) {
                //assume
                vec dV0 ( prior._V().rows() );
                dV0.set_subvector ( 0, prior._dimx() - 1, 1.0 );
                if (dV0.length()>prior._dimx())
                        dV0.set_subvector ( prior._dimx(), dV0.length() - 1, 1e-5 );
                
                prior.set_parameters ( prior._dimx(), ldmat ( dV0 ) );
                prior.validate();
        }

        void to_setting ( Setting &set ) const
        {                       
                BMEF::to_setting( set ); // takes care of rv, yrv, rvc
                UI::save(rgr, set, "rgr");
                int constant = have_constant ? 1 : 0;
                UI::save(constant, set, "constant");
                UI::save(&alter_est, set, "alternative");
                UI::save(&posterior(), set, "posterior");
                
        } 
};
UIREGISTER ( ARX );
SHAREDPTR ( ARX );

/*! ARX model conditined by knowledge of the forgetting factor
\f[ f(\theta| d_1 \ldots d_t , \phi_t) \f]

The symbol \f$ \phi \f$ is assumed to be the last of the conditioning variables.
*/
class ARXfrg : public ARX {
public:
        ARXfrg() : ARX() {};
        //! copy constructor
        ARXfrg ( const ARXfrg &A0 ) : ARX ( A0 ) {};
        virtual ARXfrg* _copy() const {
                ARXfrg *A = new ARXfrg ( *this );
                return A;
        }

        void bayes ( const vec &val, const vec &cond ) {
                int arx_cond_size=rgrlen -int(have_constant==true);
                bdm_assert_debug(cond.size()>arx_cond_size, "ARXfrg: Insufficient conditioning, frg not given.");
                frg = cond ( arx_cond_size ); // the first part after rgrlen
                ARX::bayes ( val, cond.left(arx_cond_size) );
        }
        void validate() {
                ARX::validate();
                rvc.add ( RV ( "{phi }", vec_1 ( 1 ) ) );
                dimc += 1;
        }
};
UIREGISTER ( ARXfrg );


////////////////////
template<class sq_T>
shared_ptr< mlnorm<sq_T> > ARX::ml_predictor ( ) const {
        shared_ptr< mlnorm<sq_T> > tmp = new mlnorm<sq_T> ( );
        tmp->set_rv ( yrv );
        tmp->set_rvc ( _rvc() );

        ml_predictor_update ( *tmp );
        tmp->validate();
        return tmp;
}

template<class sq_T>
void ARX::ml_predictor_update ( mlnorm<sq_T> &pred ) const {
        mat mu ( dimy, posterior()._V().rows() - dimy );
        mat R ( dimy, dimy );

        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:
                pred.set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), sq_T ( R ) );
        } else {
                pred.set_parameters ( mu, zeros ( dimy ), sq_T ( R ) );
        }
}

};
#endif // AR_H