root/library/bdm/base/datasources.h @ 529

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

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

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

#ifndef DATASOURCE_H
#define DATASOURCE_H


#include "../base/bdmbase.h"
#include "../stat/exp_family.h"
#include "../base/user_info.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 &rowid0, ivec &delays0 );
};

/*! 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();
        }
        //! no sense to log this type
        void log_add ( logger &L ) {};
        //! no sense to log this type
        void logit ( logger &L ) {};
};

/*!
* \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
        };

        ITppFileDS () : FileDS() {
        };

        void from_setting ( const Setting &set );

        // TODO dodelat void to_setting( Setting &set ) const;

};

UIREGISTER ( ITppFileDS );
SHAREDPTR ( ITppFileDS );

/*!
* \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 ( const RV &yrv, const RV &urv, const 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() ) );
                };
        }

        // TODO dokumentace - aktualizovat
        /*! UI for ArxDS using factorized description!

        The ArxDS is constructed from a structure with fields:
        \code
        system = {
                type = "ArxDS";
                // description of y variables
                y = {type="rv"; names=["y", "u"];};
                // description of u variable
                u = {type="rv"; names=[];}
                // description of regressor
                rgr = {type="rv";
                        names = ["y","y","y","u"];
                        times = [-1, -2, -3, -1];
                }

                // theta
                theta = [0.8, -0.3, 0.4, 1.0,
                                 0.0, 0.0, 0.0, 0.0];
                // offset (optional)
                offset = [0.0, 0.0];
                //variance
                r = [0.1, 0.0,
                         0.0, 1.0];
                //options: L_theta = log value of theta,
                opt = "L_theta";
        };
        \endcode

        Result is ARX data source offering with full history as Drv.
        */
        void from_setting ( const Setting &set );

        // TODO dodelat void to_setting( Setting &set ) const;
};

UIREGISTER ( ArxDS );
SHAREDPTR ( ArxDS );

class stateDS : public DS {
private:
        //!conditional pdf of the state evolution \f$ f(x_t|x_{t-1}) \f$
        shared_ptr<mpdf> IM;

        //!conditional pdf of the observations \f$ f(d_t|x_t) \f$
        shared_ptr<mpdf> OM;

protected:
        //! 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 ( const shared_ptr<mpdf> &IM0, const shared_ptr<mpdf> &OM0, int usize ) : IM ( IM0 ), OM ( OM0 ),
                dt ( OM0->dimension() ), xt ( IM0->dimension() ),
                ut ( usize ), L_xt(0) { }

        stateDS() : L_xt(0) { }

        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 );
        }

        /*! UI for stateDS

        The DS is constructed from a structure with fields:
        \code
        system = {
                type = "stateDS";
                //Internal model
                IM = { type = "mpdf"; //<-- valid offspring! e.g. "mlnorm"
                        rv = { //description of x_t
                                names=["name1",...];
                                sizes=[2,1]; // optional default=[1,1...];
                                times=[0,0]; // optional default=[0,0...];
                                }
                        rvu= { //description of  u_t
                                //optional default=empty
                                }

                        // remaining fields depending on the chosen type
                        };
                //Observation model
                OM = { type = "mpdf-offspring";
                        rv = {}; //description of d_t
                        rvu = {type="internal", path="system.IM.rvu"}; //description of u_t

                        //remaining fields
                }
        };
        \endcode
        */
        void from_setting ( const Setting &set );

        // TODO dodelat void to_setting( Setting &set ) const;

};

UIREGISTER ( stateDS );
SHAREDPTR ( stateDS );

}; //namespace

#endif // DS_H