root/library/bdm/estim/particles.h @ 971

/*!
  \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 PARTICLES_H
#define PARTICLES_H


#include "../estim/arx_ext.h"
#include "../stat/emix.h"

namespace bdm {
        
//! class used in PF
class MarginalizedParticleBase : public BM{
        protected:
        //! discrte particle
        dirac est_emp;
        //! internal Bayes Model
        shared_ptr<BM> bm; 
        
        //! custom posterior - product of empirical and exact part
        class eprod_2:public eprod_base {
                protected:
                MarginalizedParticleBase &mp;
                public:
                eprod_2(MarginalizedParticleBase &m):mp(m){}
                const epdf* factor(int i) const {return (i==0) ? &mp.bm->posterior() : &mp.est_emp;}
                const int no_factors() const {return 2;}
        } est;
        
        public:
                MarginalizedParticleBase():est(*this){};
                MarginalizedParticleBase(const MarginalizedParticleBase &m2):est(*this){
                        bm = m2.bm->_copy();
                        est_emp = m2.est_emp;
                        validate();
                };
                void bayes(const vec &dt, const vec &cond) NOT_IMPLEMENTED_VOID;
                
                const eprod_2& posterior() const {return est;}
        
                void set_prior(const epdf *pdf0){
                        const eprod *ep=dynamic_cast<const eprod*>(pdf0);
                        if (ep){ // full prior
                                bdm_assert(ep->no_factors()==2,"Incompatible prod");
                                bm->set_prior(ep->factor(0));
                                est_emp.set_point(ep->factor(1)->sample());
                        } else {
                                // assume prior is only for emp;
                                est_emp.set_point(pdf0->sample());
                        }
                }
                void from_setting(const Setting &set){
                        BM::from_setting ( set ); 
                        bm = UI::build<BM> ( set, "bm", UI::compulsory );
                }
                void validate() {
                        BM::validate();
                        bdm_assert(bm,"Internal BM is not given");
                }
};

class MarginalizedParticle : public MarginalizedParticleBase{
        protected:
                //! pdf with for transitional par
                shared_ptr<pdf> par; // pdf for non-linear part
                //! link from this to bm
                shared_ptr<datalink_part> cond2bm;
                //! link from cond to par
                shared_ptr<datalink_part> cond2par;
                //! link from emp 2 par
                shared_ptr<datalink_part> emp2bm;
                //! link from emp 2 par
                shared_ptr<datalink_part> emp2par;
                
        public:
                BM* _copy() const{return new MarginalizedParticle(*this);};
                void bayes(const vec &dt, const vec &cond){
                        vec par_cond(par->dimensionc());
                        cond2par->filldown(cond,par_cond); // copy ut
                        emp2par->filldown(est_emp._point(),par_cond); // copy xt-1
                        
                        //sample new particle
                        est_emp.set_point(par->samplecond(par_cond));
                        //if (evalll)
                        vec bm_cond(bm->dimensionc());
                        cond2bm->filldown(cond, bm_cond);// set e.g. ut
                        emp2bm->filldown(est_emp._point(), bm_cond);// set e.g. ut
                        bm->bayes(dt, bm_cond);
                        ll=bm->_ll();
                }
                                
                /*! parse structure
                \code
                class = "MarginalizedParticle";
                parameter_pdf = {class = 'epdf_offspring', ...};
                bm = {class = 'bm_offspring',...};
                \endcode
                If rvs are set, then it checks for compatibility.
                */
                void from_setting(const Setting &set){
                        MarginalizedParticleBase::from_setting ( set );
                        par = UI::build<pdf> ( set, "parameter_pdf", UI::compulsory );
                }
                
                void to_setting(Setting &set){
                        MarginalizedParticleBase::to_setting(set);
                        UI::save(par,set,"parameter_pdf");
                }
                void validate(){
                        if (est_emp.point.length()!=par->dimension())
                                est_emp.set_point(zeros(par->dimension()));
                        est.validate();
                        
                        yrv = bm->_yrv();
                        dimy = bm->dimensiony();
                        set_rv( concat(bm->_rv(), par->_rv()));
                        set_dim( par->dimension()+bm->dimension());
                        
                        rvc = par->_rvc();
                        rvc.add(bm->_rvc());
                        rvc=rvc.subt(par->_rv());
                        rvc=rvc.subt(par->_rv().copy_t(-1));
                        rvc=rvc.subt(bm->_rv().copy_t(-1)); //
                        
                        cond2bm=new datalink_part;
                        cond2par=new datalink_part;
                        emp2bm  =new datalink_part;
                        emp2par =new datalink_part;
                        cond2bm->set_connection(bm->_rvc(), rvc);
                        cond2par->set_connection(par->_rvc(), rvc);
                        emp2bm->set_connection(bm->_rvc(), par->_rv());
                        emp2par->set_connection(par->_rvc(), par->_rv().copy_t(-1));
                        
                        dimc = rvc._dsize();
                };
};
UIREGISTER(MarginalizedParticle);

//! class used in PF
class BootstrapParticle : public BM{
        dirac est;
        shared_ptr<pdf> par;
        shared_ptr<pdf> obs;
        shared_ptr<datalink_part> cond2par;
        shared_ptr<datalink_part> cond2obs;
        shared_ptr<datalink_part> xt2obs;
        shared_ptr<datalink_part> xtm2par;
        public:
                BM* _copy() const{return new BootstrapParticle(*this);};
                void bayes(const vec &dt, const vec &cond){
                        vec par_cond(par->dimensionc());
                        cond2par->filldown(cond,par_cond); // copy ut
                        xtm2par->filldown(est._point(),par_cond); // copy xt-1
                        
                        //sample new particle
                        est.set_point(par->samplecond(par_cond));
                        //if (evalll)
                        vec obs_cond(obs->dimensionc());
                        cond2obs->filldown(cond, obs_cond);// set e.g. ut
                        xt2obs->filldown(est._point(), obs_cond);// set e.g. ut
                        ll=obs->evallogcond(dt,obs_cond);
                }
                const dirac& posterior() const {return est;}
                
                void set_prior(const epdf *pdf0){est.set_point(pdf0->sample());}
                
                /*! parse structure
                \code
                class = "BootstrapParticle";
                parameter_pdf = {class = 'epdf_offspring', ...};
                observation_pdf = {class = 'epdf_offspring',...};
                \endcode
                If rvs are set, then it checks for compatibility.
                */
                void from_setting(const Setting &set){
                        BM::from_setting ( set );
                        par = UI::build<pdf> ( set, "parameter_pdf", UI::compulsory );
                        obs = UI::build<pdf> ( set, "observation_pdf", UI::compulsory );
                }
                void validate(){
                        yrv = obs->_rv();
                        dimy = obs->dimension();
                        set_rv( par->_rv());
                        set_dim( par->dimension());
                        
                        rvc = par->_rvc().subt(par->_rv().copy_t(-1));
                        rvc.add(obs->_rvc()); //
                        
                        cond2obs=new datalink_part;
                        cond2par=new datalink_part;
                        xt2obs  =new datalink_part;
                        xtm2par =new datalink_part;
                        cond2obs->set_connection(obs->_rvc(), rvc);
                        cond2par->set_connection(par->_rvc(), rvc);
                        xt2obs->set_connection(obs->_rvc(), _rv());
                        xtm2par->set_connection(par->_rvc(), _rv().copy_t(-1));
                        
                        dimc = rvc._dsize();
                };
};
UIREGISTER(BootstrapParticle);


/*!
* \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 {
        //! \var log_level_enums weights
        //! all weightes will be logged

        //! \var log_level_enums menas
        //! means of particles will be logged
        LOG_LEVEL(PF,logweights,logmeans,logvars);
        
        class pf_mix: public emix_base{
                Array<BM*> &bms;
                public:
                        pf_mix(vec &w0, Array<BM*> &bms0):emix_base(w0),bms(bms0){}
                        const epdf* component(const int &i)const{return &(bms(i)->posterior());}
                        int no_coms() const {return bms.length();}
        };
protected:
        //!number of particles;
        int n;
        //!posterior density
        pf_mix est;
        //! weights;
        vec w;
        //! particles
        Array<BM*> particles;
        //! internal structure storing loglikelihood of predictions
        vec lls;

        //! which resampling method will be used
        RESAMPLING_METHOD resmethod;
        //! resampling threshold; in this case its meaning is minimum ratio of active particles
        //! For example, for 0.5 resampling is performed when the numebr of active aprticles drops belo 50%.
        double res_threshold;

        //! \name Options
        //!@{
        //!@}

public:
        //! \name Constructors
        //!@{
        PF ( ) : est(w,particles) { };

        void set_parameters ( int n0, double res_th0 = 0.5, RESAMPLING_METHOD rm = SYSTEMATIC ) {
                n = n0;
                res_threshold = res_th0;
                resmethod = rm;
        };
        void set_model ( const BM *particle0, const epdf *prior) {
                if (n>0){
                        particles.set_length(n);
                        for (int i=0; i<n;i++){
                                particles(i) = particle0->_copy();
                                particles(i)->set_prior(prior);
                        }
                }
                // set values for posterior
                est.set_rv ( particle0->posterior()._rv() );
        };
        void set_statistics ( const vec w0, const epdf &epdf0 ) {
                //est.set_statistics ( w0, epdf0 );
        };
/*      void set_statistics ( const eEmp &epdf0 ) {
                bdm_assert_debug ( epdf0._rv().equal ( par->_rv() ), "Incompatible input" );
                est = epdf0;
        };*/
        //!@}

        //! bayes compute weights of the
        virtual void bayes_weights();
        //! important part of particle filtering - decide if it is time to perform resampling
        virtual bool do_resampling() {
                double eff = 1.0 / ( w * w );
                return eff < ( res_threshold*n );
        }
        void bayes ( const vec &yt, const vec &cond );
        //!access function
        vec& _lls() {
                return lls;
        }
        //!access function
        RESAMPLING_METHOD _resmethod() const {
                return resmethod;
        }
        //! return correctly typed posterior (covariant return)
        const pf_mix& posterior() const {
                return est;
        }

        /*! configuration structure for basic PF
        \code
        parameter_pdf   = pdf_class;         // parameter evolution pdf
        observation_pdf = pdf_class;         // observation pdf
        prior           = epdf_class;         // prior probability density
        --- optional ---
        n               = 10;                 // number of particles
        resmethod       = 'systematic', or 'multinomial', or 'stratified'
                                                                                  // resampling method
        res_threshold   = 0.5;                // resample when active particles drop below 50%
        \endcode
        */
        void from_setting ( const Setting &set ) {
                BM::from_setting ( set );
                UI::get ( log_level, set, "log_level", UI::optional );
                
                shared_ptr<BM> bm0 = UI::build<BM>(set, "particle",UI::compulsory);
                
                shared_ptr<epdf> pri = UI::build<epdf> ( set, "prior", UI::compulsory );
                n =0;
                UI::get(n,set,"n",UI::optional);;
                if (n>0){
                        particles.set_length(n);
                        for(int i=0;i<n;i++){particles(i)=bm0->_copy();}
                        w = ones(n)/n;
                }
                set_prior(pri.get());
                // set resampling method
                resmethod_from_set ( set );
                //set drv

                rvc = bm0->_rvc();
                dimc = bm0->dimensionc();
                BM::set_rv(bm0->_rv());
                yrv=bm0->_yrv();
                dimy = bm0->dimensiony();
        }
        
        void log_register ( bdm::logger& L, const string& prefix ){
                BM::log_register(L,prefix);
                if (log_level[logweights]){
                        L.add_vector( log_level, logweights, RV ( particles.length()), prefix); 
                }
                if (log_level[logmeans]){
                        for (int i=0; i<particles.length(); i++){
                                L.add_vector( log_level, logmeans, RV ( particles(i)->dimension() ), prefix , i);
                        }
                }
                if (log_level[logvars]){
                        for (int i=0; i<particles.length(); i++){
                                L.add_vector( log_level, logvars, RV ( particles(i)->dimension() ), prefix , i);
                        }
                }
        };
        void log_write ( ) const {
                BM::log_write();
                if (log_level[logweights]){
                        log_level.store( logweights, w); 
                }
                if (log_level[logmeans]){
                        for (int i=0; i<particles.length(); i++){
                                log_level.store( logmeans, particles(i)->posterior().mean(), i);
                        }
                }
                if (log_level[logvars]){
                        for (int i=0; i<particles.length(); i++){
                                log_level.store( logvars, particles(i)->posterior().variance(), i);
                        }
                }
                
        }
        
        void set_prior(const epdf *pri){
                const emix_base *emi=dynamic_cast<const emix_base*>(pri);
                if (emi) {
                        bdm_assert(particles.length()>0, "initial particle is not assigned");
                        n = emi->_w().length();
                        int old_n = particles.length();
                        if (n!=old_n){
                                particles.set_length(n,true);
                        } 
                        for(int i=old_n;i<n;i++){particles(i)=particles(0)->_copy();}
                        
                        for (int i =0; i<n; i++){
                                particles(i)->set_prior(emi->_com(i));
                        }
                } else {
                        // try to find "n"
                        bdm_assert(n>0, "Field 'n' must be filled when prior is not of type emix");
                        for (int i =0; i<n; i++){
                                particles(i)->set_prior(pri);
                        }
                        
                }
        }
        //! auxiliary function reading parameter 'resmethod' from configuration file
        void resmethod_from_set ( const Setting &set ) {
                string resmeth;
                if ( UI::get ( resmeth, set, "resmethod", UI::optional ) ) {
                        if ( resmeth == "systematic" ) {
                                resmethod = SYSTEMATIC;
                        } else  {
                                if ( resmeth == "multinomial" ) {
                                        resmethod = MULTINOMIAL;
                                } else {
                                        if ( resmeth == "stratified" ) {
                                                resmethod = STRATIFIED;
                                        } else {
                                                bdm_error ( "Unknown resampling method" );
                                        }
                                }
                        }
                } else {
                        resmethod = SYSTEMATIC;
                };
                if ( !UI::get ( res_threshold, set, "res_threshold", UI::optional ) ) {
                        res_threshold = 0.9;
                }
                //validate();
        }

        void validate() {
                BM::validate();
                est.validate();
                bdm_assert ( n>0, "empty particle pool" );
                n = w.length();
                lls = zeros ( n );

                if ( particles(0)->_rv()._dsize() > 0 ) {
                        bdm_assert (  particles(0)->_rv()._dsize() == est.dimension(), "Mismatch of RV and dimension of posterior" );
                }
        }
        //! resample posterior density (from outside - see MPF)
        void resample ( ) {
                ivec ind = zeros_i ( n );
                bdm::resample(w,ind,resmethod);
                // copy the internals according to ind
                for (int i = 0; i < n; i++ ) {
                        if ( ind ( i ) != i ) {
                                particles( i ) = particles( ind ( i ) )->_copy();
                        }
                        w ( i ) = 1.0 / n;
                }
        }
        //! access function
        Array<BM*>& _particles() {
                return particles;
        }

};
UIREGISTER ( PF );

/*! Marginalized particle for state-space models with unknown parameters of residues distribution

\f[
\begin{eqnarray}
x_t = g(x_{t-1}) + v_t,\\
z_t = h(x_{t-1}) + w_t,\\
\end{eqnarray}
\f]

This particle is a only a shell creating the residues calling internal estimator of their parameters. The internal estimator can be of any compatible type, e.g. ARX for Gaussian residues with unknown mean and variance.

*/
class NoiseParticle : public MarginalizedParticleBase{
        protected:
                //! function transforming xt, ut -> x_t+1
                shared_ptr<fnc> g; // pdf for non-linear part
                //! function transforming xt,ut -> yt
                shared_ptr<fnc> h; // pdf for non-linear part

                RV rvv;
                
                RV rvx;
                RV rvxc;
                RV rvyc;
                
                //!link from condition to f
                datalink_part cond2g;
                //!link from condition to h
                datalink_part cond2h;
                //!link from xt to f
                datalink_part x2g;
                //!link from xt to h
                datalink_part x2h;
                
        public:
                BM* _copy() const{return new NoiseParticle();};
                void bayes(const vec &dt, const vec &cond){
                        epdf* pred_vw=bm->epredictor();
                        shared_ptr<epdf> pred_v = pred_vw->marginal(rvv);
                        
                        vec vt=pred_v->sample();
                        
                        //new sample
                        vec &xtm=est_emp.point;
                        vec g_args(g->dimensionc());
                        x2g.filldown(xtm,g_args);
                        cond2g.filldown(cond,g_args);
                        vec xt = g->eval(g_args) + vt;
                        
                        // residue of observation
                        vec h_args(h->dimensionc());
                        x2h.filldown(xt,h_args);
                        cond2h.filldown(cond,h_args);
                        vec wt = dt-h->eval(h_args);
                        // the vector [v_t,w_t] is now complete
                        bm->bayes(concat(vt,wt));
                        ll=bm->_ll();
                }
                void from_setting(const Setting &set){
                        MarginalizedParticleBase::from_setting(set); //reads bm, yrv,rvc, bm_rv, etc...
                                
                        UI::get(g,set,"g",UI::compulsory);
                        UI::get(h,set,"h",UI::compulsory);
                        RV rvx;
                        UI::get(rvx,set,"rvx",UI::compulsory);
                        est_emp.set_rv(rvx);
                        
                        RV rvxc;
                        UI::get(rvxc,set,"rvxc",UI::compulsory);
                        RV rvyc;
                        UI::get(rvyc,set,"rvyc",UI::compulsory);
                }
                void validate(){
                        MarginalizedParticleBase::validate();
                        //check dimensions
                        rvc = rvxc;
                        rvc.add( rvyc);
                        rvc.subt(rvx);
                
                        //establish datalinks
                        x2g.set_connection(rvxc, rvx.copy_t(-1));
                        cond2g.set_connection(rvxc, rvc);
                        
                        x2h.set_connection(rvyc, rvx);
                        cond2h.set_connection(rvyc, rvc);
                }
};
UIREGISTER(NoiseParticle);



}
#endif // KF_H