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# work/git/mixpp/bdm/stat/libEF.h

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    00001
    00013 #ifndef EF_H
    00014 #define EF_H
    00015
    00016 #include <itpp/itbase.h>
    00017 #include "../math/libDC.h"
    00018 #include "libBM.h"
    00019 #include "../itpp_ext.h"
    00020 //#include <std>
    00021
    00022 using namespace itpp;
    00023
    00024
    00026 extern Uniform_RNG UniRNG;
    00028 extern Normal_RNG NorRNG;
    00030 extern Gamma_RNG GamRNG;
    00031
    00038 class eEF : public epdf {
    00039 public:
    00040 //      eEF() :epdf() {};
    00042         eEF ( const RV &rv ) :epdf ( rv ) {};
    00044         virtual double lognc() const =0;
    00046         virtual void dupdate ( mat &v ) {it_error ( "Not implemented" );};
    00048         virtual double evallog_nn ( const vec &val ) const{it_error ( "Not implemented" );return 0.0;};
    00050         virtual double evallog ( const vec &val ) const {double tmp;tmp= evallog_nn ( val )-lognc();it_assert_debug(std::isfinite(tmp),"Infinite value"); return tmp;}
    00052         virtual vec evallog ( const mat &Val ) const {
    00053                 vec x ( Val.cols() );
    00054                 for ( int i=0;i<Val.cols();i++ ) {x ( i ) =evallog_nn ( Val.get_col ( i ) ) ;}
    00055                 return x-lognc();
    00056         }
    00058         virtual void pow ( double p ) {it_error ( "Not implemented" );};
    00059 };
    00060
    00067 class mEF : public mpdf {
    00068
    00069 public:
    00071         mEF ( const RV &rv0, const RV &rvc0 ) :mpdf ( rv0,rvc0 ) {};
    00072 };
    00073
    00075 class BMEF : public BM {
    00076 protected:
    00078         double frg;
    00080         double last_lognc;
    00081 public:
    00083         BMEF ( const RV &rv, double frg0=1.0 ) :BM ( rv ), frg ( frg0 ) {}
    00085         BMEF ( const BMEF &B ) :BM ( B ), frg ( B.frg ), last_lognc ( B.last_lognc ) {}
    00087         virtual void set_statistics ( const BMEF* BM0 ) {it_error ( "Not implemented" );};
    00089         virtual void bayes ( const vec &data, const double w ) {};
    00090         //original Bayes
    00091         void bayes ( const vec &dt );
    00093         virtual void flatten ( const BMEF * B ) {it_error ( "Not implemented" );}
    00095 //      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}
    00096
    00097         BMEF* _copy_ ( bool changerv=false ) {it_error ( "function _copy_ not implemented for this BM" ); return NULL;};
    00098 };
    00099
    00100 template<class sq_T>
    00101 class mlnorm;
    00102
    00108 template<class sq_T>
    00109 class enorm : public eEF {
    00110 protected:
    00112         vec mu;
    00114         sq_T R;
    00116         int dim;
    00117 public:
    00119         enorm ( const RV &rv );
    00121         void set_parameters ( const vec &mu,const sq_T &R );
    00123         //void tupdate ( double phi, mat &vbar, double nubar );
    00125         void dupdate ( mat &v,double nu=1.0 );
    00126
    00127         vec sample() const;
    00129         mat sample ( int N ) const;
    00130 //      double eval ( const vec &val ) const ;
    00131         double evallog_nn ( const vec &val ) const;
    00132         double lognc () const;
    00133         vec mean() const {return mu;}
    00134 //      mlnorm<sq_T>* condition ( const RV &rvn ) const ;
    00135         mpdf* condition ( const RV &rvn ) const ;
    00136 //      enorm<sq_T>* marginal ( const RV &rv ) const;
    00137         epdf* marginal ( const RV &rv ) const;
    00138 //Access methods
    00140         vec& _mu() {return mu;}
    00141
    00143         void set_mu ( const vec mu0 ) { mu=mu0;}
    00144
    00146         sq_T& _R() {return R;}
    00147         const sq_T& _R() const {return R;}
    00148
    00150 //      mat getR () {return R.to_mat();}
    00151 };
    00152
    00159 class egiw : public eEF {
    00160 protected:
    00162         ldmat V;
    00164         double nu;
    00166         int xdim;
    00168         int nPsi;
    00169 public:
    00171         egiw ( RV rv, mat V0, double nu0=-1.0 ) : eEF ( rv ), V ( V0 ), nu ( nu0 ) {
    00172                 xdim = rv.count() /V.rows();
    00173                 it_assert_debug ( rv.count() ==xdim*V.rows(),"Incompatible V0." );
    00174                 nPsi = V.rows()-xdim;
    00175                 //set mu to have proper normalization and
    00176                 if (nu0<0){
    00177                         nu = 0.1 +nPsi +2*xdim +2; // +2 assures finite expected value of R
    00178                         // terms before that are sufficient for finite normalization
    00179                 }
    00180         }
    00182         egiw ( RV rv, ldmat V0, double nu0=-1.0 ) : eEF ( rv ), V ( V0 ), nu ( nu0 ) {
    00183                 xdim = rv.count() /V.rows();
    00184                 it_assert_debug ( rv.count() ==xdim*V.rows(),"Incompatible V0." );
    00185                 nPsi = V.rows()-xdim;
    00186                 if (nu0<0){
    00187                         nu = 0.1 +nPsi +2*xdim +2; // +2 assures finite expected value of R
    00188                         // terms before that are sufficient for finite normalization
    00189                 }
    00190         }
    00191
    00192         vec sample() const;
    00193         vec mean() const;
    00194         void mean_mat ( mat &M, mat&R ) const;
    00196         double evallog_nn ( const vec &val ) const;
    00197         double lognc () const;
    00198
    00199         //Access
    00201         ldmat& _V() {return V;}
    00203         const ldmat& _V() const {return V;}
    00205         double& _nu()  {return nu;}
    00206         const double& _nu() const {return nu;}
    00207         void pow ( double p ) {V*=p;nu*=p;};
    00208 };
    00209
    00218 class eDirich: public eEF {
    00219 protected:
    00221         vec beta;
    00222 public:
    00224         eDirich ( const RV &rv, const vec &beta0 ) : eEF ( rv ),beta ( beta0 ) {it_assert_debug ( rv.count() ==beta.length(),"Incompatible statistics" ); };
    00226         eDirich ( const eDirich &D0 ) : eEF ( D0.rv ),beta ( D0.beta ) {};
    00227         vec sample() const {it_error ( "Not implemented" );return vec_1 ( 0.0 );};
    00228         vec mean() const {return beta/sum ( beta );};
    00230         double evallog_nn ( const vec &val ) const {double tmp; tmp=( beta-1 ) *log ( val );            it_assert_debug(std::isfinite(tmp),"Infinite value");
    00231         return tmp;};
    00232         double lognc () const {
    00233                 double tmp;
    00234                 double gam=sum ( beta );
    00235                 double lgb=0.0;
    00236                 for ( int i=0;i<beta.length();i++ ) {lgb+=lgamma ( beta ( i ) );}
    00237                 tmp= lgb-lgamma ( gam );
    00238                 it_assert_debug(std::isfinite(tmp),"Infinite value");
    00239                 return tmp;
    00240         };
    00242         vec& _beta()  {return beta;}
    00244         void set_parameters ( const vec &beta0 ) {
    00245                 if ( beta0.length() !=beta.length() ) {
    00246                         it_assert_debug ( rv.length() ==1,"Undefined" );
    00247                         rv.set_size ( 0,beta0.length() );
    00248                 }
    00249                 beta= beta0;
    00250         }
    00251 };
    00252
    00254 class multiBM : public BMEF {
    00255 protected:
    00257         eDirich est;
    00259         vec &beta;
    00260 public:
    00262         multiBM ( const RV &rv, const vec beta0 ) : BMEF ( rv ),est ( rv,beta0 ),beta ( est._beta() ) {if(beta.length()>0){last_lognc=est.lognc();}else{last_lognc=0.0;}}
    00264         multiBM ( const multiBM &B ) : BMEF ( B ),est ( rv,B.beta ),beta ( est._beta() ) {}
    00266         void set_statistics ( const BM* mB0 ) {const multiBM* mB=dynamic_cast<const multiBM*> ( mB0 ); beta=mB->beta;}
    00267         void bayes ( const vec &dt ) {
    00268                 if ( frg<1.0 ) {beta*=frg;last_lognc=est.lognc();}
    00269                 beta+=dt;
    00270                 if ( evalll ) {ll=est.lognc()-last_lognc;}
    00271         }
    00272         double logpred ( const vec &dt ) const {
    00273                 eDirich pred ( est );
    00274                 vec &beta = pred._beta();
    00275
    00276                 double lll;
    00277                 if ( frg<1.0 )
    00278                         {beta*=frg;lll=pred.lognc();}
    00279                 else
    00280                         if ( evalll ) {lll=last_lognc;}
    00281                         else{lll=pred.lognc();}
    00282
    00283                 beta+=dt;
    00284                 return pred.lognc()-lll;
    00285         }
    00286         void flatten ( const BMEF* B ) {
    00287                 const multiBM* E=dynamic_cast<const multiBM*> ( B );
    00288                 // sum(beta) should be equal to sum(B.beta)
    00289                 const vec &Eb=E->beta;//const_cast<multiBM*> ( E )->_beta();
    00290                 beta*= ( sum ( Eb ) /sum ( beta ) );
    00291                 if ( evalll ) {last_lognc=est.lognc();}
    00292         }
    00293         const epdf& _epdf() const {return est;};
    00294         const eDirich* _e() const {return &est;};
    00295         void set_parameters ( const vec &beta0 ) {
    00296                 est.set_parameters ( beta0 );
    00297                 rv = est._rv();
    00298                 if ( evalll ) {last_lognc=est.lognc();}
    00299         }
    00300 };
    00301
    00311 class egamma : public eEF {
    00312 protected:
    00314         vec alpha;
    00316         vec beta;
    00317 public :
    00319         egamma ( const RV &rv ) :eEF ( rv ) {};
    00321         void set_parameters ( const vec &a, const vec &b ) {alpha=a,beta=b;};
    00322         vec sample() const;
    00324 //      mat sample ( int N ) const;
    00325         double evallog ( const vec &val ) const;
    00326         double lognc () const;
    00328         void _param ( vec* &a, vec* &b ) {a=&alpha;b=&beta;};
    00329         vec mean() const {vec pom ( alpha ); pom/=beta; return pom;}
    00330 };
    00331 /*
    00333 class emix : public epdf {
    00334 protected:
    00335         int n;
    00336         vec &w;
    00337         Array<epdf*> Coms;
    00338 public:
    00340         emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};
    00341         void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}
    00342         vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};
    00343         vec sample() {it_error ( "Not implemented" );return 0;}
    00344 };
    00345 */
    00346
    00348
    00349 class euni: public epdf {
    00350 protected:
    00352         vec low;
    00354         vec high;
    00356         vec distance;
    00358         double nk;
    00360         double lnk;
    00361 public:
    00363         euni ( const RV rv ) :epdf ( rv ) {}
    00364         double eval ( const vec &val ) const  {return nk;}
    00365         double evallog ( const vec &val ) const  {return lnk;}
    00366         vec sample() const {
    00367                 vec smp ( rv.count() );
    00368 #pragma omp critical
    00369                 UniRNG.sample_vector ( rv.count(),smp );
    00370                 return low+elem_mult ( distance,smp );
    00371         }
    00373         void set_parameters ( const vec &low0, const vec &high0 ) {
    00374                 distance = high0-low0;
    00375                 it_assert_debug ( min ( distance ) >0.0,"bad support" );
    00376                 low = low0;
    00377                 high = high0;
    00378                 nk = prod ( 1.0/distance );
    00379                 lnk = log ( nk );
    00380         }
    00381         vec mean() const {vec pom=high; pom-=low; pom/=2.0; return pom;}
    00382 };
    00383
    00384
    00390 template<class sq_T>
    00391 class mlnorm : public mEF {
    00392 protected:
    00394         enorm<sq_T> epdf;
    00395         mat A;
    00396         vec mu_const;
    00397         vec& _mu; //cached epdf.mu;
    00398 public:
    00400         mlnorm ( const RV &rv, const RV &rvc );
    00402         void set_parameters ( const  mat &A, const vec &mu0, const sq_T &R );
    00403 //      //!Generate one sample of the posterior
    00404 //      vec samplecond (const vec &cond, double &lik );
    00405 //      //!Generate matrix of samples of the posterior
    00406 //      mat samplecond (const vec &cond, vec &lik, int n );
    00408         void condition ( const vec &cond );
    00409
    00411         vec& _mu_const() {return mu_const;}
    00413         mat& _A() {return A;}
    00415         mat _R() {return epdf._R().to_mat();}
    00416
    00417         template<class sq_M>
    00418         friend std::ostream &operator<< ( std::ostream &os,  mlnorm<sq_M> &ml );
    00419 };
    00420
    00423 class mlstudent : public mlnorm<ldmat> {
    00424 protected:
    00425         ldmat Lambda;
    00426         ldmat &_R;
    00427         ldmat Re;
    00428 public:
    00429         mlstudent ( const RV &rv0, const RV &rvc0 ) :mlnorm<ldmat> ( rv0,rvc0 ),
    00430                         Lambda ( rv0.count() ),
    00431                         _R ( epdf._R() ) {}
    00432         void set_parameters ( const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0) {
    00433                 epdf.set_parameters ( zeros ( rv.count() ),Lambda );
    00434                 A = A0;
    00435                 mu_const = mu0;
    00436                 Re=R0;
    00437                 Lambda = Lambda0;
    00438         }
    00439         void condition ( const vec &cond ) {
    00440                 _mu = A*cond + mu_const;
    00441                 double zeta;
    00442                 //ugly hack!
    00443                 if ((cond.length()+1)==Lambda.rows()){
    00444                         zeta = Lambda.invqform ( concat(cond, vec_1(1.0)) );
    00445                 } else {
    00446                         zeta = Lambda.invqform ( cond );
    00447                 }
    00448                 _R = Re;
    00449                 _R*=( 1+zeta );// / ( nu ); << nu is in Re!!!!!!
    00450         };
    00451
    00452 };
    00462 class mgamma : public mEF {
    00463 protected:
    00465         egamma epdf;
    00467         double k;
    00469         vec* _beta;
    00470
    00471 public:
    00473         mgamma ( const RV &rv,const RV &rvc );
    00475         void set_parameters ( double k );
    00476         void condition ( const vec &val ) {*_beta=k/val;};
    00477 };
    00478
    00490 class mgamma_fix : public mgamma {
    00491 protected:
    00493         double l;
    00495         vec refl;
    00496 public:
    00498         mgamma_fix ( const RV &rv,const RV &rvc ) : mgamma ( rv,rvc ),refl ( rv.count() ) {};
    00500         void set_parameters ( double k0 , vec ref0, double l0 ) {
    00501                 mgamma::set_parameters ( k0 );
    00502                 refl=pow ( ref0,1.0-l0 );l=l0;
    00503         };
    00504
    00505         void condition ( const vec &val ) {vec mean=elem_mult ( refl,pow ( val,l ) ); *_beta=k/mean;};
    00506 };
    00507
    00509 enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
    00515 class eEmp: public epdf {
    00516 protected :
    00518         int n;
    00520         vec w;
    00522         Array<vec> samples;
    00523 public:
    00525         eEmp ( const RV &rv0 ,int n0 ) :epdf ( rv0 ),n ( n0 ),w ( n ),samples ( n ) {};
    00527         void set_parameters ( const vec &w0, const epdf* pdf0 );
    00529         void set_samples ( const epdf* pdf0 );
    00531         void set_n ( int n0, bool copy=true ){w.set_size(n0,copy);samples.set_size(n0,copy);};
    00533         vec& _w()  {return w;};
    00535         const vec& _w() const {return w;};
    00537         Array<vec>& _samples() {return samples;};
    00539         const Array<vec>& _samples() const {return samples;};
    00541         ivec resample ( RESAMPLING_METHOD method = SYSTEMATIC );
    00543         vec sample() const {it_error ( "Not implemented" );return 0;}
    00545         double evallog ( const vec &val ) const {it_error ( "Not implemented" );return 0.0;}
    00546         vec mean() const {
    00547                 vec pom=zeros ( rv.count() );
    00548                 for ( int i=0;i<n;i++ ) {pom+=samples ( i ) *w ( i );}
    00549                 return pom;
    00550         }
    00551 };
    00552
    00553
    00555
    00556 template<class sq_T>
    00557 enorm<sq_T>::enorm ( const RV &rv ) :eEF ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {};
    00558
    00559 template<class sq_T>
    00560 void enorm<sq_T>::set_parameters ( const vec &mu0, const sq_T &R0 ) {
    00561 //Fixme test dimensions of mu0 and R0;
    00562         mu = mu0;
    00563         R = R0;
    00564 };
    00565
    00566 template<class sq_T>
    00567 void enorm<sq_T>::dupdate ( mat &v, double nu ) {
    00568         //
    00569 };
    00570
    00571 // template<class sq_T>
    00572 // void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {
    00573 //      //
    00574 // };
    00575
    00576 template<class sq_T>
    00577 vec enorm<sq_T>::sample() const {
    00578         vec x ( dim );
    00579         NorRNG.sample_vector ( dim,x );
    00580         vec smp = R.sqrt_mult ( x );
    00581
    00582         smp += mu;
    00583         return smp;
    00584 };
    00585
    00586 template<class sq_T>
    00587 mat enorm<sq_T>::sample ( int N ) const {
    00588         mat X ( dim,N );
    00589         vec x ( dim );
    00590         vec pom;
    00591         int i;
    00592
    00593         for ( i=0;i<N;i++ ) {
    00594                 NorRNG.sample_vector ( dim,x );
    00595                 pom = R.sqrt_mult ( x );
    00596                 pom +=mu;
    00597                 X.set_col ( i, pom );
    00598         }
    00599
    00600         return X;
    00601 };
    00602
    00603 // template<class sq_T>
    00604 // double enorm<sq_T>::eval ( const vec &val ) const {
    00605 //      double pdfl,e;
    00606 //      pdfl = evallog ( val );
    00607 //      e = exp ( pdfl );
    00608 //      return e;
    00609 // };
    00610
    00611 template<class sq_T>
    00612 double enorm<sq_T>::evallog_nn ( const vec &val ) const {
    00613         // 1.83787706640935 = log(2pi)
    00614         double tmp=-0.5* ( R.invqform ( mu-val ) );// - lognc();
    00615         return  tmp;
    00616 };
    00617
    00618 template<class sq_T>
    00619 inline double enorm<sq_T>::lognc () const {
    00620         // 1.83787706640935 = log(2pi)
    00621         double tmp=0.5* ( R.cols() * 1.83787706640935 +R.logdet() );
    00622         return tmp;
    00623 };
    00624
    00625 template<class sq_T>
    00626 mlnorm<sq_T>::mlnorm ( const RV &rv0, const RV &rvc0 ) :mEF ( rv0,rvc0 ),epdf ( rv0 ),A ( rv0.count(),rv0.count() ),_mu ( epdf._mu() ) {
    00627         ep =&epdf;
    00628 }
    00629
    00630 template<class sq_T>
    00631 void mlnorm<sq_T>::set_parameters ( const mat &A0, const vec &mu0, const sq_T &R0 ) {
    00632         epdf.set_parameters ( zeros ( rv.count() ),R0 );
    00633         A = A0;
    00634         mu_const = mu0;
    00635 }
    00636
    00637 // template<class sq_T>
    00638 // vec mlnorm<sq_T>::samplecond (const  vec &cond, double &lik ) {
    00639 //      this->condition ( cond );
    00640 //      vec smp = epdf.sample();
    00641 //      lik = epdf.eval ( smp );
    00642 //      return smp;
    00643 // }
    00644
    00645 // template<class sq_T>
    00646 // mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {
    00647 //      int i;
    00648 //      int dim = rv.count();
    00649 //      mat Smp ( dim,n );
    00650 //      vec smp ( dim );
    00651 //      this->condition ( cond );
    00652 //
    00653 //      for ( i=0; i<n; i++ ) {
    00654 //              smp = epdf.sample();
    00655 //              lik ( i ) = epdf.eval ( smp );
    00656 //              Smp.set_col ( i ,smp );
    00657 //      }
    00658 //
    00659 //      return Smp;
    00660 // }
    00661
    00662 template<class sq_T>
    00663 void mlnorm<sq_T>::condition ( const vec &cond ) {
    00664         _mu = A*cond + mu_const;
    00665 //R is already assigned;
    00666 }
    00667
    00668 template<class sq_T>
    00669 epdf* enorm<sq_T>::marginal ( const RV &rvn ) const {
    00670         ivec irvn = rvn.dataind ( rv );
    00671
    00672         sq_T Rn ( R,irvn );
    00673         enorm<sq_T>* tmp = new enorm<sq_T> ( rvn );
    00674         tmp->set_parameters ( mu ( irvn ), Rn );
    00675         return tmp;
    00676 }
    00677
    00678 template<class sq_T>
    00679 mpdf* enorm<sq_T>::condition ( const RV &rvn ) const {
    00680
    00681         RV rvc = rv.subt ( rvn );
    00682         it_assert_debug ( ( rvc.count() +rvn.count() ==rv.count() ),"wrong rvn" );
    00683         //Permutation vector of the new R
    00684         ivec irvn = rvn.dataind ( rv );
    00685         ivec irvc = rvc.dataind ( rv );
    00686         ivec perm=concat ( irvn , irvc );
    00687         sq_T Rn ( R,perm );
    00688
    00689         //fixme - could this be done in general for all sq_T?
    00690         mat S=Rn.to_mat();
    00691         //fixme
    00692         int n=rvn.count()-1;
    00693         int end=R.rows()-1;
    00694         mat S11 = S.get ( 0,n, 0, n );
    00695         mat S12 = S.get ( 0, n , rvn.count(), end );
    00696         mat S22 = S.get ( rvn.count(), end, rvn.count(), end );
    00697
    00698         vec mu1 = mu ( irvn );
    00699         vec mu2 = mu ( irvc );
    00700         mat A=S12*inv ( S22 );
    00701         sq_T R_n ( S11 - A *S12.T() );
    00702
    00703         mlnorm<sq_T>* tmp=new mlnorm<sq_T> ( rvn,rvc );
    00704
    00705         tmp->set_parameters ( A,mu1-A*mu2,R_n );
    00706         return tmp;
    00707 }
    00708
    00710
    00711 template<class sq_T>
    00712 std::ostream &operator<< ( std::ostream &os,  mlnorm<sq_T> &ml ) {
    00713         os << "A:"<< ml.A<<endl;
    00714         os << "mu:"<< ml.mu_const<<endl;
    00715         os << "R:" << ml.epdf._R().to_mat() <<endl;
    00716         return os;
    00717 };
    00718
    00719 #endif //EF_H