root/bdm/stat/libDS.h @ 308

/*!
  \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 indices \c rowid and \c delays.

The data can be loaded from a file.
*/
class MemDS : public DS {
        protected:
        //! 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 set_rvs ( RV &drv, RV &urv );
        void write ( vec &ut ) {it_error ( "MemDS::write is not supported" );}
        void write ( vec &ut,ivec &indices ) {it_error ( "MemDS::write is not supported" );}
        void step();
        //!Default constructor
        MemDS () {};
        MemDS ( mat &Dat, ivec &rowid, ivec &delays );
};

/*! Pseudovirtual class for reading data from files

*/
class FileDS: public MemDS {

public:
        void getdata ( vec &dt ) {
                it_assert_debug ( dt.length() ==Data.rows(),"" );
                dt = Data.get_col(time);
        };
        void getdata ( vec &dt, const ivec &indeces ){
                it_assert_debug ( dt.length() ==indeces.length(),"" );
                vec tmp(indeces.length());
                tmp = Data.get_col(time);
                dt = tmp(indeces);
        };
        //! returns number of data in the file;
        int ndat(){return Data.cols();}
};

/*! 
* \brief Read Data Matrix from an IT file

The constructor creates an internal matrix \c Data from an IT++ file. The file is binary and can be made using the IT++ library or the Matlab/Octave function itsave. NB: the data are stored columnwise, i.e. each column contains the data for time \f$t\f$!

*/
class ItppFileDS: public FileDS {

public:
        ItppFileDS ( const string &fname, const string &varname ) :FileDS() {
                it_file it ( fname );
                it << Name ( varname ); 
                it >> Data;
                time = 0;
                //rowid and delays are ignored
        }
};

/*!
* \brief CSV file data storage
The constructor creates \c Data matrix from the records in a CSV file \c fname. The orientation can be of two types:
1. \c BY_COL which is default - the data are stored in columns; one column per time \f$t\f$, one row per data item.
2. \c BY_ROW if the data are stored the classical CSV style. Then each column stores the values for data item, for ex. \f$[y_{t} y_{t-1} ...]\f$, one row for each discrete time instant.

*/
class CsvFileDS: public FileDS {

public:
        //! Constructor - create DS from a CSV file.
        CsvFileDS ( const string& fname, const string& orientation = "BY_COL" );
};



/*!
\brief Generator of ARX data

*/
class ArxDS : public DS {
protected:
        //! Rv of the regressor
        RV Rrv;
        //! 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 rgrlnk;
        //! model of Y - linear Gaussian
        mlnorm<chmat> model;
        //! options
        bool opt_L_theta;
        //! loggers
        int L_theta;
        int L_R;
        int dt_size;
public:
        void getdata ( vec &dt ) {
                //it_assert_debug ( dt.length() ==Drv.count(),"ArxDS" );
                dt=H;
        };
        void getdata ( vec &dt, const ivec &indices ) {
                it_assert_debug ( dt.length() ==indices.length(),"ArxDS" );
                dt=H ( indices );
        };
        void write ( vec &ut ) {
                //it_assert_debug ( ut.length() ==Urv.count(),"ArxDS" );
                U=ut;
        };
        void write ( vec &ut, const ivec &indices ) {
                it_assert_debug ( ut.length() ==indices.length(),"ArxDS" );
                set_subvector ( U, indices,ut );
        };
        void step();
        //!Default constructor
        ArxDS ( ) {};
        //! Set parameters of the internal model, H is maximum time delay
        void set_parameters ( const mat &Th0, const vec mu0, const chmat &sqR0 )
        { model.set_parameters ( Th0, mu0, sqR0 );};
        //! Set
        void set_drv ( RV &yrv, RV &urv, RV &rrv ) {
                Rrv = rrv;
                Urv = urv;
                dt_size = yrv._dsize() +urv._dsize();

                RV drv = concat ( yrv,urv );
                Drv = drv;
                int td = rrv.mint();
                H.set_size ( drv._dsize() * ( -td+1 ) );
                U.set_size ( Urv._dsize() );
                for ( int i=-1;i>=td;i-- ) {
                        drv.t ( -1 );
                        Drv.add ( drv ); //shift u1
                }
                rgrlnk.set_connection ( rrv,Drv );

                dtsize = Drv._dsize();
                utsize = Urv._dsize();
        }
        //! 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 ); too long!!
                L_dt=L.add ( Drv ( 0,dt_size ),"" );
                L_ut=L.add ( Urv,"" );

                mat &A =model._A();
                mat R =model._R();
                if ( opt_L_theta ) {L_theta=L.add ( RV ( "{th }", 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 );
                L.logit ( L_dt, H.left ( dt_size ) );
                L.logit ( L_ut, U );

                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 stateDS : public DS {
protected:
        //!conditional pdf of the state evolution \f$ f(x_t|x_{t-1}) \f$
        mpdf* IM;
        //!conditional pdf of the observations \f$ f(d_t|x_t) \f$
        mpdf* OM;
        //! result storage
        vec dt;
        //! state storage
        vec xt;
        //! input storage
        vec ut;
        //! Logger
        int L_xt;
public:
        void getdata ( vec &dt0 ) {dt0=dt;}
        void getdata ( vec &dt0, const ivec &indeces ) {dt0=dt ( indeces );}

        stateDS ( mpdf* IM0, mpdf* OM0, int usize ) :DS ( ),IM ( IM0 ),OM ( OM0 ),
                        dt ( OM0->dimension() ), xt ( IM0->dimension() ), ut ( usize ) {}
        ~stateDS() {delete IM; delete OM;}
        virtual void step() {
                xt=IM->samplecond ( concat ( xt,ut ) );
                dt=OM->samplecond ( concat ( xt,ut ) );
        };

        virtual void log_add ( logger &L ) {
                DS::log_add ( L );
                L_xt=L.add ( IM->_rv(),"true" );
        }
        virtual void logit ( logger &L ) {
                DS::logit ( L );
                L.logit ( L_xt,xt );
        }

};

}; //namespace

#endif // DS_H