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# work/mixpp/bdm/estim/libPF.h

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    00001
    00013 #ifndef PF_H
    00014 #define PF_H
    00015
    00016 #include <itpp/itbase.h>
    00017 #include "../stat/libEF.h"
    00018 #include "../math/libDC.h"
    00019
    00020 using namespace itpp;
    00021
    00028 class PF : public BM {
    00029 protected:
    00031         int n;
    00033         eEmp est;
    00035         vec &_w;
    00037         Array<vec> &_samples;
    00039         mpdf &par;
    00041         mpdf &obs;
    00042 public:
    00044         PF ( const RV &rv0, mpdf &par0,  mpdf &obs0, int n0 ) :BM ( rv0 ),
    00045                         n ( n0 ),est ( rv0,n ),_w ( est._w() ),_samples ( est._samples() ),
    00046                         par ( par0 ), obs ( obs0 ) {};
    00047
    00049         void set_est ( const epdf &epdf0 );
    00050         void bayes ( const vec &dt );
    00051 };
    00052
    00059 template<class BM_T>
    00060
    00061 class MPF : public PF {
    00062         BM_T* Bms[1000];
    00063
    00065
    00066 class mpfepdf : public epdf  {
    00067         protected:
    00068                 eEmp &E;
    00069                 vec &_w;
    00070                 Array<epdf*> Coms;
    00071         public:
    00072                 mpfepdf ( eEmp &E0, const RV &rvc ) :
    00073                                 epdf ( RV( ) ), E ( E0 ),  _w ( E._w() ),
    00074                                 Coms ( _w.length() ) {
    00075                         rv.add ( E._rv() );
    00076                         rv.add ( rvc );
    00077                 };
    00078
    00079                 void set_elements ( int &i, double wi, epdf* ep )
    00080                 {_w ( i ) =wi; Coms ( i ) =ep;};
    00081
    00082                 vec mean() const {
    00083                         // ugly
    00084                         vec pom=zeros ( ( Coms ( 0 )->_rv() ).count() );
    00085
    00086                         for ( int i=0; i<_w.length(); i++ ) {pom += Coms ( i )->mean() * _w ( i );}
    00087
    00088                         return concat ( E.mean(),pom );
    00089                 }
    00090
    00091                 vec sample() const {it_error ( "Not implemented" );return 0;}
    00092
    00093                 double evalpdflog ( const vec &val ) const {it_error ( "not implemented" ); return 0.0;}
    00094         };
    00095
    00097         mpfepdf jest;
    00098
    00099 public:
    00101         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 ) {
    00102                 //
    00103                 //TODO test if rv and BMcond.rv are compatible.
    00104                 rv.add ( rvlin );
    00105                 //
    00106
    00107                 if ( n>1000 ) it_error ( "increase 1000 here!" );
    00108
    00109                 for ( int i=0;i<n;i++ ) {
    00110                         Bms[i] = new BM_T ( BMcond0 ); //copy constructor
    00111                         epdf& pom=Bms[i]->_epdf();
    00112                         jest.set_elements ( i,1.0/n,&pom );
    00113                 }
    00114         };
    00115
    00116         ~MPF() {
    00117         }
    00118
    00119         void bayes ( const vec &dt );
    00120         epdf& _epdf() {return jest;}
    00122         void set_est ( const epdf& epdf0 ) {
    00123                 PF::set_est ( epdf0 );  // sample params in condition
    00124                 // copy conditions to BMs
    00125
    00126                 for ( int i=0;i<n;i++ ) {Bms[i]->condition ( _samples ( i ) );}
    00127         }
    00128 };
    00129
    00130 template<class BM_T>
    00131 void MPF<BM_T>::bayes ( const vec &dt ) {
    00132         int i;
    00133         vec lls ( n );
    00134         ivec ind;
    00135         double mlls=-std::numeric_limits<double>::infinity();
    00136
    00137         for ( i=0;i<n;i++ ) {
    00138                 //generate new samples from paramater evolution model;
    00139                 _samples ( i ) = par.samplecond ( _samples ( i ), lls ( i ) );
    00140                 Bms[i]->condition ( _samples ( i ) );
    00141                 Bms[i]->bayes ( dt );
    00142                 lls ( i ) = Bms[i]->_ll(); // lls above is also in proposal her must be lls(i) =, not +=!!
    00143
    00144                 if ( lls ( i ) >mlls ) mlls=lls ( i ); //find maximum likelihood (for numerical stability)
    00145         }
    00146
    00147         // compute weights
    00148         for ( i=0;i<n;i++ ) {
    00149                 _w ( i ) *= exp ( lls ( i ) - mlls ); // multiply w by likelihood
    00150         }
    00151
    00152         _w /=sum(_w); //?
    00153
    00154         double eff = 1.0/( _w*_w );
    00155         if ( eff < ( 0.1*n ) ) {
    00156                 ind = est.resample();
    00157                 // Resample Bms!
    00158
    00159                 for ( i=0;i<n;i++ ) {
    00160                         if ( ind ( i ) !=i ) {//replace the current Bm by a new one
    00161                                 //fixme this would require new assignment operator
    00162                                 // *Bms[i] = *Bms[ind ( i ) ];
    00163
    00164                                 // poor-man's solution: replicate constructor here
    00165                                 // copied from MPF::MPF
    00166                                 delete Bms[i];
    00167                                 Bms[i] = new BM_T ( *Bms[ind ( i ) ] ); //copy constructor
    00168                                 epdf& pom=Bms[i]->_epdf();
    00169                                 jest.set_elements ( i,1.0/n,&pom );
    00170                         }
    00171                 };
    00172                 cout << '.';
    00173         }
    00174 }
    00175
    00176 #endif // KF_H
    00177
    00178