root/bdm/stat/libDS.h @ 265

/*!
  \file
  \brief Common DataSources.
  \author Vaclav Smidl.

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

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

#ifndef DS_H
#define DS_H


#include "libBM.h"
#include "libEF.h"


namespace bdm {
        /*!
        * \brief Memory storage of off-line data column-wise

        The data are stored in an internal matrix \c Data . Each column of Data corresponds to one discrete time observation \f$t\f$. Access to this matrix is via indexes \c rowid and \c delays.

        The data can be loaded from a file.
        */
        class MemDS : public DS {
                //! internal matrix of data
                mat Data;
                //! active column in the Data matrix
                int time;
                //!  vector of rows that are presented in Dt
                ivec rowid;
                //! vector of delays that are presented in Dt
                ivec delays;

        public:
                void getdata ( vec &dt );
                void getdata ( vec &dt, const ivec &indeces );
                void linkrvs ( RV &drv, RV &urv );
                void write ( vec &ut ) {it_error ( "MemDS::write is not supported" );}
                void write ( vec &ut,ivec &indexes ) {it_error ( "MemDS::write is not supported" );}
                void step();
                //!Default constructor
                MemDS ( mat &Dat, ivec &rowid, ivec &delays );
        };

        /*!
        \brief Generator of ARX data

        */
        class ArxDS : public DS {
        protected:
                //! Rv of the regressor
                RV Rrv;
                //! Rv of the history (full regressor)
                RV Hrv;
                //! History, ordered as \f$[y_t, u_t, y_{t-1 }, u_{t-1}, \ldots]\f$
                vec H;
                //! (future) input
                vec U;
                //! temporary variable for regressor
                vec rgr;
                //! data link: H -> rgr
                datalink_e2e rgrlnk;
                //! model of Y - linear Gaussian
                mlnorm<chmat> model;
                //! options
                bool opt_L_theta;
                //! loggers
                int L_theta;
                int L_R;
                public:
                void getdata ( vec &dt ) {
                        it_assert_debug ( dt.length() ==Drv.count(),"ArxDS" );
                        dt=H.left ( Urv.count() +Drv.count() );
                };
                void getdata ( vec &dt, const ivec &indexes ) {
                        it_assert_debug ( dt.length() ==indeces.length(),"ArxDS" );
                        dt=H ( indexes );
                };
                void write ( vec &ut ) {
                        it_assert_debug ( ut.length() ==Urv.count(),"ArxDS" );
                        U=ut;
                };
                void write ( vec &ut, const ivec &indexes ) {
                        it_assert_debug ( ut.length() ==indeces.length(),"ArxDS" );
                        set_subvector ( U, indexes,ut );
                };
                void step();
                //!Default constructor
                ArxDS ( RV &drv, RV &urv, RV &rrv );
                //! Set parameters of the internal model
                void set_parameters ( const mat &Th0, const vec mu0, const chmat &sqR0 )
                { model.set_parameters ( Th0, mu0, sqR0 ); };
                //! set options from a string
                void set_options ( const string &s ) {
                        opt_L_theta= ( s.find ( "L_theta" ) !=string::npos );
                };
                virtual void log_add ( logger &L ) {
                        DS::log_add ( L );
                        mat &A =model._A();
                        mat R =model._R();
                        if ( opt_L_theta ) {L_theta=L.add ( RV("{theta }", vec_1(A.rows() *A.cols())),"t" );}
                        if ( opt_L_theta ) {L_R=L.add ( RV("{R }", vec_1(R.rows() *R.cols())),"r" );}
                }
                virtual void logit ( logger &L ) {
                        DS::logit ( L );
                        mat &A =model._A();
                        mat R =model._R();
                        if ( opt_L_theta ) {L.logit ( L_theta,vec ( A._data(), A.rows() *A.cols() ) );};
                        if ( opt_L_theta ) {L.logit ( L_R, vec ( R._data(), R.rows() *R.rows() ) );};
                }

        };

        class ARXDS : public ArxDS {
        public:
                ARXDS ( RV &drv, RV &urv, RV &rrv ) : ArxDS ( drv,urv,rrv ) {}

                void getdata ( vec &dt ) {dt=H;}
                void getdata ( vec &dt, const ivec &indeces ) {dt=H ( indeces );}
                virtual RV _drv() const {return Hrv;}

        };
}; //namespace

#endif // DS_H