root/bdm/estim/libPF.h @ 200

/*!
  \file
  \brief Bayesian Filtering using stochastic sampling (Particle Filters)
  \author Vaclav Smidl.

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

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

#ifndef PF_H
#define PF_H

#include <itpp/itbase.h>
#include "../stat/libEF.h"
#include "../math/libDC.h"

using namespace itpp;

//UGLY HACK
extern double PF_SSAT;//used for StrSim:06 test... if length>0 the value is written.

/*!
* \brief Trivial particle filter with proposal density equal to parameter evolution model.

Posterior density is represented by a weighted empirical density (\c eEmp ).
*/

class PF : public BM {
protected:
        //!number of particles;
        int n;
        //!posterior density
        eEmp est;
        //! pointer into \c eEmp
        vec &_w;
        //! pointer into \c eEmp
        Array<vec> &_samples;
        //! Parameter evolution model
        mpdf &par;
        //! Observation model
        mpdf &obs;
public:
        //! Default constructor
        PF ( const RV &rv0, mpdf &par0,  mpdf &obs0, int n0 ) :BM ( rv0 ),
                        n ( n0 ),est ( rv0,n ),_w ( est._w() ),_samples ( est._samples() ),
                        par ( par0 ), obs ( obs0 ) {};

        //! Set posterior density by sampling from epdf0
        void set_est ( const epdf &epdf0 );
        void bayes ( const vec &dt );
};

/*!
\brief Marginalized Particle filter

Trivial version: proposal = parameter evolution, observation model is not used. (it is assumed to be part of BM).
*/

template<class BM_T>

class MPF : public PF {
        BM_T* Bms[10000];

        //! internal class for MPDF providing composition of eEmp with external components

class mpfepdf : public epdf  {
        protected:
                eEmp &E;
                vec &_w;
                Array<const epdf*> Coms;
        public:
                mpfepdf ( eEmp &E0, const RV &rvc ) :
                                epdf ( RV( ) ), E ( E0 ),  _w ( E._w() ),
                                Coms ( _w.length() ) {
                        rv.add ( E._rv() );
                        rv.add ( rvc );
                };

                void set_elements ( int &i, double wi, const epdf* ep )
                {_w ( i ) =wi; Coms ( i ) =ep;};

                vec mean() const {
                        // ugly
                        vec pom=zeros ( ( Coms ( 0 )->_rv() ).count() );

                        for ( int i=0; i<_w.length(); i++ ) {pom += Coms ( i )->mean() * _w ( i );}

                        return concat ( E.mean(),pom );
                }

                vec sample() const {it_error ( "Not implemented" );return 0;}

                double evalpdflog ( const vec &val ) const {it_error ( "not implemented" ); return 0.0;}
        };

        //! estimate joining PF.est with conditional
        mpfepdf jest;

public:
        //! Default constructor.
        MPF ( const RV &rvlin, const RV &rvpf, mpdf &par0, mpdf &obs0, int n, const BM_T &BMcond0 ) : PF ( rvpf ,par0,obs0,n ),jest ( est,rvlin ) {
                //
                //TODO test if rv and BMcond.rv are compatible.
                rv.add ( rvlin );
                //

                if ( n>10000 ) {it_error ( "increase 10000 here!" );}

                for ( int i=0;i<n;i++ ) {
                        Bms[i] = new BM_T ( BMcond0 ); //copy constructor
                        const epdf& pom=Bms[i]->_epdf();
                        jest.set_elements ( i,1.0/n,&pom );
                }
        };

        ~MPF() {
        }

        void bayes ( const vec &dt );
        const epdf& _epdf() const {return jest;}
        const epdf* _e() const {return &jest;} //Fixme: is it useful?
        //! Set postrior of \c rvc to samples from epdf0. Statistics of Bms are not re-computed! Use only for initialization!
        void set_est ( const epdf& epdf0 ) {
                PF::set_est ( epdf0 );  // sample params in condition
                // copy conditions to BMs

                for ( int i=0;i<n;i++ ) {Bms[i]->condition ( _samples ( i ) );}
        }

//SimStr:
        double SSAT;
};

template<class BM_T>
void MPF<BM_T>::bayes ( const vec &dt ) {
        int i;
        vec lls ( n );
        vec llsP ( n );
        ivec ind;
        double mlls=-std::numeric_limits<double>::infinity();

        // StrSim:06
        double sumLWL=0.0;
        double sumL2WL=0.0;
        double WL = 0.0;

        #pragma omp parallel for
        for ( i=0;i<n;i++ ) {   
                //generate new samples from paramater evolution model;
                _samples ( i ) = par.samplecond ( _samples ( i ), llsP ( i ) );
                Bms[i]->condition ( _samples ( i ) );
                Bms[i]->bayes ( dt );
                lls ( i ) = Bms[i]->_ll(); // lls above is also in proposal her must be lls(i) =, not +=!!
                if ( lls ( i ) >mlls ) mlls=lls ( i ); //find maximum likelihood (for numerical stability)
        }

        if ( false) {
                #pragma omp parallel for reduction(+:sumLWL,sumL2WL) private(WL)
                for ( i=0;i<n;i++ ) {
                        WL = _w ( i ) *exp ( llsP ( i ) ); //using old weights!
                        sumLWL += exp ( lls ( i ) ) *WL;
                        sumL2WL += exp ( 2*lls ( i ) ) *WL;
                }
                SSAT  = sumL2WL/ ( sumLWL*sumLWL );
        }

        double sum_w=0.0;
        // compute weights
        #pragma omp parallel for
        for ( i=0;i<n;i++ ) {
                _w ( i ) *= exp ( lls ( i ) - mlls ); // multiply w by likelihood
                sum_w+=_w(i);
        }

        if ( sum_w  >0.0 ) {
                _w /=sum_w; //?
        } else {
                cout<<"sum(w)==0"<<endl;
        }


        double eff = 1.0/ ( _w*_w );
        if ( eff < ( 0.3*n ) ) {
                ind = est.resample();
                // Resample Bms!

                #pragma omp parallel for
                for ( i=0;i<n;i++ ) {
                        if ( ind ( i ) !=i ) {//replace the current Bm by a new one
                                //fixme this would require new assignment operator
                                // *Bms[i] = *Bms[ind ( i ) ];

                                // poor-man's solution: replicate constructor here
                                // copied from MPF::MPF
                                delete Bms[i];
                                Bms[i] = new BM_T ( *Bms[ind ( i ) ] ); //copy constructor
                                const epdf& pom=Bms[i]->_epdf();
                                jest.set_elements ( i,1.0/n,&pom );
                        }
                };
                cout << '.';
        }
}

#endif // KF_H