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# exp_family.h

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    00001
    00013 #ifndef EF_H
    00014 #define EF_H
    00015
    00016
    00017 #include "../shared_ptr.h"
    00018 #include "../base/bdmbase.h"
    00019 #include "../math/chmat.h"
    00020
    00021 namespace bdm
    00022 {
    00023
    00024
    00026 extern Uniform_RNG UniRNG;
    00028 extern Normal_RNG NorRNG;
    00030 extern Gamma_RNG GamRNG;
    00031
    00038 class eEF : public epdf
    00039 {
    00040         public:
    00041 //      eEF() :epdf() {};
    00043                 eEF () : epdf () {};
    00045                 virtual double lognc() const = 0;
    00046
    00048                 virtual double evallog_nn (const vec &val) const {
    00049                         bdm_error ("Not implemented");
    00050                         return 0.0;
    00051                 }
    00052
    00054                 virtual double evallog (const vec &val) const {
    00055                         double tmp;
    00056                         tmp = evallog_nn (val) - lognc();
    00057                         return tmp;
    00058                 }
    00060                 virtual vec evallog_m (const mat &Val) const {
    00061                         vec x (Val.cols());
    00062                         for (int i = 0;i < Val.cols();i++) {x (i) = evallog_nn (Val.get_col (i)) ;}
    00063                         return x -lognc();
    00064                 }
    00066                 virtual vec evallog_m (const Array<vec> &Val) const {
    00067                         vec x (Val.length());
    00068                         for (int i = 0;i < Val.length();i++) {x (i) = evallog_nn (Val (i)) ;}
    00069                         return x -lognc();
    00070                 }
    00071
    00073                 virtual void pow (double p) {
    00074                         bdm_error ("Not implemented");
    00075                 }
    00076 };
    00077
    00078
    00080 class BMEF : public BM
    00081 {
    00082         protected:
    00084                 double frg;
    00086                 double last_lognc;
    00087         public:
    00089                 BMEF (double frg0 = 1.0) : BM (), frg (frg0) {}
    00091                 BMEF (const BMEF &B) : BM (B), frg (B.frg), last_lognc (B.last_lognc) {}
    00093                 virtual void set_statistics (const BMEF* BM0) {
    00094                         bdm_error ("Not implemented");
    00095                 }
    00096
    00098                 virtual void bayes (const vec &data, const double w) {};
    00099                 //original Bayes
    00100                 void bayes (const vec &dt);
    00101
    00103                 virtual void flatten (const BMEF * B) {
    00104                         bdm_error ("Not implemented");
    00105                 }
    00106
    00107                 BMEF* _copy_ () const {
    00108                         bdm_error ("function _copy_ not implemented for this BM");
    00109                         return NULL;
    00110                 }
    00111 };
    00112
    00113 template<class sq_T, template <typename> class TEpdf>
    00114 class mlnorm;
    00115
    00121 template<class sq_T>
    00122 class enorm : public eEF
    00123 {
    00124         protected:
    00126                 vec mu;
    00128                 sq_T R;
    00129         public:
    00132
    00133                 enorm () : eEF (), mu (), R () {};
    00134                 enorm (const vec &mu, const sq_T &R) {set_parameters (mu, R);}
    00135                 void set_parameters (const vec &mu, const sq_T &R);
    00145                 void from_setting (const Setting &root);
    00146                 void validate() {
    00147                         bdm_assert (mu.length() == R.rows(), "mu and R parameters do not match");
    00148                         dim = mu.length();
    00149                 }
    00151
    00154
    00156                 void dupdate (mat &v, double nu = 1.0);
    00157
    00158                 vec sample() const;
    00159
    00160                 double evallog_nn (const vec &val) const;
    00161                 double lognc () const;
    00162                 vec mean() const {return mu;}
    00163                 vec variance() const {return diag (R.to_mat());}
    00164 //      mlnorm<sq_T>* condition ( const RV &rvn ) const ; <=========== fails to cmpile. Why?
    00165                 shared_ptr<mpdf> condition ( const RV &rvn ) const;
    00166
    00167                 // target not typed to mlnorm<sq_T, enorm<sq_T> > &
    00168                 // because that doesn't compile (perhaps because we
    00169                 // haven't finished defining enorm yet), but the type
    00170                 // is required
    00171                 void condition ( const RV &rvn, mpdf &target ) const;
    00172
    00173                 shared_ptr<epdf> marginal (const RV &rvn ) const;
    00174                 void marginal ( const RV &rvn, enorm<sq_T> &target ) const;
    00176
    00179
    00180                 vec& _mu() {return mu;}
    00181                 const vec& _mu() const {return mu;}
    00182                 void set_mu (const vec mu0) { mu = mu0;}
    00183                 sq_T& _R() {return R;}
    00184                 const sq_T& _R() const {return R;}
    00186
    00187 };
    00188 UIREGISTER2 (enorm, chmat);
    00189 SHAREDPTR2 ( enorm, chmat );
    00190 UIREGISTER2 (enorm, ldmat);
    00191 SHAREDPTR2 ( enorm, ldmat );
    00192 UIREGISTER2 (enorm, fsqmat);
    00193 SHAREDPTR2 ( enorm, fsqmat );
    00194
    00195
    00202 class egiw : public eEF
    00203 {
    00204         protected:
    00206                 ldmat V;
    00208                 double nu;
    00210                 int dimx;
    00212                 int nPsi;
    00213         public:
    00216                 egiw() : eEF() {};
    00217                 egiw (int dimx0, ldmat V0, double nu0 = -1.0) : eEF() {set_parameters (dimx0, V0, nu0);};
    00218
    00219                 void set_parameters (int dimx0, ldmat V0, double nu0 = -1.0);
    00221
    00222                 vec sample() const;
    00223                 vec mean() const;
    00224                 vec variance() const;
    00225
    00227                 vec est_theta() const;
    00228
    00230                 ldmat est_theta_cov() const;
    00231
    00233                 void mean_mat (mat &M, mat&R) const;
    00235                 double evallog_nn (const vec &val) const;
    00236                 double lognc () const;
    00237                 void pow (double p) {V *= p;nu *= p;};
    00238
    00241
    00242                 ldmat& _V() {return V;}
    00243                 const ldmat& _V() const {return V;}
    00244                 double& _nu()  {return nu;}
    00245                 const double& _nu() const {return nu;}
    00260                 void from_setting (const Setting &set) {
    00261                         epdf::from_setting(set);
    00262                         if (!UI::get (nu, set, "nu", UI::compulsory)) {nu=-1;}
    00263                         UI::get (dimx, set, "dimx", UI::compulsory);
    00264                         mat V;
    00265                         UI::get (V, set, "V", UI::compulsory);
    00266                         set_parameters (dimx, V, nu);
    00267                 }
    00269 };
    00270 UIREGISTER ( egiw );
    00271 SHAREDPTR ( egiw );
    00272
    00281 class eDirich: public eEF
    00282 {
    00283         protected:
    00285                 vec beta;
    00286         public:
    00289
    00290                 eDirich () : eEF () {};
    00291                 eDirich (const eDirich &D0) : eEF () {set_parameters (D0.beta);};
    00292                 eDirich (const vec &beta0) {set_parameters (beta0);};
    00293                 void set_parameters (const vec &beta0) {
    00294                         beta = beta0;
    00295                         dim = beta.length();
    00296                 }
    00298
    00300                 vec sample() const {
    00301                         vec y(beta.length());
    00302                         for (int i=0; i<beta.length(); i++){
    00303                                 GamRNG.setup(beta(i),1);
    00304                                 y(i)=GamRNG.sample();
    00305                         }
    00306                         return y/sum(y);
    00307                 }
    00308
    00309                 vec mean() const {return beta / sum (beta);};
    00310                 vec variance() const {double gamma = sum (beta); return elem_mult (beta, (gamma-beta)) / (gamma*gamma* (gamma + 1));}
    00312                 double evallog_nn (const vec &val) const {
    00313                         double tmp; tmp = (beta - 1) * log (val);
    00314                         return tmp;
    00315                 }
    00316
    00317                 double lognc () const {
    00318                         double tmp;
    00319                         double gam = sum (beta);
    00320                         double lgb = 0.0;
    00321                         for (int i = 0;i < beta.length();i++) {lgb += lgamma (beta (i));}
    00322                         tmp = lgb - lgamma (gam);
    00323                         return tmp;
    00324                 }
    00325
    00327                 vec& _beta()  {return beta;}
    00334                 void from_setting(const Setting &set){
    00335                         epdf::from_setting(set);
    00336                         UI::get(beta,set, "beta", UI::compulsory);
    00337                         validate();
    00338                 }
    00339                 void validate() {
    00340                         //check rv
    00341                         dim = beta.length();
    00342                 }
    00343 };
    00344 UIREGISTER(eDirich);
    00345
    00357 class mDirich: public mpdf_internal<eDirich> {
    00358         protected:
    00360                 double k;
    00362                 vec &_beta;
    00364                 vec betac;
    00365         public:
    00366                 mDirich(): mpdf_internal<eDirich>(), _beta(iepdf._beta()){};
    00367                 void condition (const vec &val) {_beta =  val/k+betac; };
    00380                 void from_setting (const Setting &set) {
    00381                         mpdf::from_setting (set); // reads rv and rvc
    00382                         if (_rv()._dsize()>0){
    00383                                 rvc = _rv().copy_t(-1);
    00384                         }
    00385                         vec beta0;
    00386                         if (!UI::get (beta0, set, "beta0", UI::optional)){
    00387                                 beta0 = ones(_rv()._dsize());
    00388                         }
    00389                         if (!UI::get (betac, set, "betac", UI::optional)){
    00390                                 betac = 0.1*ones(_rv()._dsize());
    00391                         }
    00392                         _beta = beta0;
    00393
    00394                         UI::get (k, set, "k", UI::compulsory);
    00395                         validate();
    00396                 }
    00397                 void validate() {
    00398                         iepdf.validate();
    00399                         bdm_assert(_beta.length()==betac.length(),"beta0 and betac are not compatible");
    00400                         if (_rv()._dsize()>0){
    00401                                 bdm_assert( (_rv()._dsize()==dimension()) , "Size of rv does not match with beta");
    00402                         }
    00403                         dimc = _beta.length();
    00404                 };
    00405 };
    00406 UIREGISTER(mDirich);
    00407
    00409 class multiBM : public BMEF
    00410 {
    00411         protected:
    00413                 eDirich est;
    00415                 vec &beta;
    00416         public:
    00418                 multiBM () : BMEF (), est (), beta (est._beta()) {
    00419                         if (beta.length() > 0) {last_lognc = est.lognc();}
    00420                         else{last_lognc = 0.0;}
    00421                 }
    00423                 multiBM (const multiBM &B) : BMEF (B), est (B.est), beta (est._beta()) {}
    00425                 void set_statistics (const BM* mB0) {const multiBM* mB = dynamic_cast<const multiBM*> (mB0); beta = mB->beta;}
    00426                 void bayes (const vec &dt) {
    00427                         if (frg < 1.0) {beta *= frg;last_lognc = est.lognc();}
    00428                         beta += dt;
    00429                         if (evalll) {ll = est.lognc() - last_lognc;}
    00430                 }
    00431                 double logpred (const vec &dt) const {
    00432                         eDirich pred (est);
    00433                         vec &beta = pred._beta();
    00434
    00435                         double lll;
    00436                         if (frg < 1.0)
    00437                                 {beta *= frg;lll = pred.lognc();}
    00438                         else
    00439                                 if (evalll) {lll = last_lognc;}
    00440                                 else{lll = pred.lognc();}
    00441
    00442                         beta += dt;
    00443                         return pred.lognc() - lll;
    00444                 }
    00445                 void flatten (const BMEF* B) {
    00446                         const multiBM* E = dynamic_cast<const multiBM*> (B);
    00447                         // sum(beta) should be equal to sum(B.beta)
    00448                         const vec &Eb = E->beta;//const_cast<multiBM*> ( E )->_beta();
    00449                         beta *= (sum (Eb) / sum (beta));
    00450                         if (evalll) {last_lognc = est.lognc();}
    00451                 }
    00453                 const eDirich& posterior() const {return est;};
    00455                 void set_parameters (const vec &beta0) {
    00456                         est.set_parameters (beta0);
    00457                         if (evalll) {last_lognc = est.lognc();}
    00458                 }
    00459 };
    00460
    00470 class egamma : public eEF
    00471 {
    00472         protected:
    00474                 vec alpha;
    00476                 vec beta;
    00477         public :
    00480                 egamma () : eEF (), alpha (0), beta (0) {};
    00481                 egamma (const vec &a, const vec &b) {set_parameters (a, b);};
    00482                 void set_parameters (const vec &a, const vec &b) {alpha = a, beta = b;dim = alpha.length();};
    00484
    00485                 vec sample() const;
    00486                 double evallog (const vec &val) const;
    00487                 double lognc () const;
    00489                 vec& _alpha() {return alpha;}
    00491                 vec& _beta() {return beta;}
    00492                 vec mean() const {return elem_div (alpha, beta);}
    00493                 vec variance() const {return elem_div (alpha, elem_mult (beta, beta)); }
    00494
    00505                 void from_setting (const Setting &set) {
    00506                         epdf::from_setting (set); // reads rv
    00507                         UI::get (alpha, set, "alpha", UI::compulsory);
    00508                         UI::get (beta, set, "beta", UI::compulsory);
    00509                         validate();
    00510                 }
    00511                 void validate() {
    00512                         bdm_assert (alpha.length() == beta.length(), "parameters do not match");
    00513                         dim = alpha.length();
    00514                 }
    00515 };
    00516 UIREGISTER (egamma);
    00517 SHAREDPTR ( egamma );
    00518
    00535 class eigamma : public egamma
    00536 {
    00537         protected:
    00538         public :
    00543
    00544                 vec sample() const {return 1.0 / egamma::sample();};
    00546                 vec mean() const {return elem_div (beta, alpha - 1);}
    00547                 vec variance() const {vec mea = mean(); return elem_div (elem_mult (mea, mea), alpha - 2);}
    00548 };
    00549 /*
    00551 class emix : public epdf {
    00552 protected:
    00553         int n;
    00554         vec &w;
    00555         Array<epdf*> Coms;
    00556 public:
    00558         emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};
    00559         void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}
    00560         vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};
    00561 };
    00562 */
    00563
    00565
    00566 class euni: public epdf
    00567 {
    00568         protected:
    00570                 vec low;
    00572                 vec high;
    00574                 vec distance;
    00576                 double nk;
    00578                 double lnk;
    00579         public:
    00582                 euni () : epdf () {}
    00583                 euni (const vec &low0, const vec &high0) {set_parameters (low0, high0);}
    00584                 void set_parameters (const vec &low0, const vec &high0) {
    00585                         distance = high0 - low0;
    00586                         low = low0;
    00587                         high = high0;
    00588                         nk = prod (1.0 / distance);
    00589                         lnk = log (nk);
    00590                         dim = low.length();
    00591                 }
    00593
    00594                 double evallog (const vec &val) const  {
    00595                         if (any (val < low) && any (val > high)) {return inf;}
    00596                         else return lnk;
    00597                 }
    00598                 vec sample() const {
    00599                         vec smp (dim);
    00600 #pragma omp critical
    00601                         UniRNG.sample_vector (dim , smp);
    00602                         return low + elem_mult (distance, smp);
    00603                 }
    00605                 vec mean() const {return (high -low) / 2.0;}
    00606                 vec variance() const {return (pow (high, 2) + pow (low, 2) + elem_mult (high, low)) / 3.0;}
    00617                 void from_setting (const Setting &set) {
    00618                         epdf::from_setting (set); // reads rv and rvc
    00619
    00620                         UI::get (high, set, "high", UI::compulsory);
    00621                         UI::get (low, set, "low", UI::compulsory);
    00622                         set_parameters(low,high);
    00623                         validate();
    00624                 }
    00625                 void validate() {
    00626                         bdm_assert(high.length()==low.length(), "Incompatible high and low vectors");
    00627                         dim = high.length();
    00628                         bdm_assert (min (distance) > 0.0, "bad support");
    00629                 }
    00630 };
    00631 UIREGISTER(euni);
    00632
    00638 template < class sq_T, template <typename> class TEpdf = enorm >
    00639 class mlnorm : public mpdf_internal< TEpdf<sq_T> >
    00640 {
    00641         protected:
    00643                 mat A;
    00645                 vec mu_const;
    00646 //                      vec& _mu; //cached epdf.mu; !!!!!! WHY NOT?
    00647         public:
    00650                 mlnorm() : mpdf_internal< TEpdf<sq_T> >() {};
    00651                 mlnorm (const mat &A, const vec &mu0, const sq_T &R) : mpdf_internal< TEpdf<sq_T> >() {
    00652                         set_parameters (A, mu0, R);
    00653                 }
    00654
    00656                 void set_parameters (const  mat &A0, const vec &mu0, const sq_T &R0) {
    00657                         this->iepdf.set_parameters (zeros (A0.rows()), R0);
    00658                         A = A0;
    00659                         mu_const = mu0;
    00660                         this->dimc = A0.cols();
    00661                 }
    00664                 void condition (const vec &cond) {
    00665                         this->iepdf._mu() = A * cond + mu_const;
    00666 //R is already assigned;
    00667                 }
    00668
    00670                 const vec& _mu_const() const {return mu_const;}
    00672                 const mat& _A() const {return A;}
    00674                 mat _R() const { return this->iepdf._R().to_mat(); }
    00675
    00677                 template<typename sq_M>
    00678                 friend std::ostream &operator<< (std::ostream &os,  mlnorm<sq_M, enorm> &ml);
    00679
    00690                 void from_setting (const Setting &set) {
    00691                         mpdf::from_setting (set);
    00692
    00693                         UI::get (A, set, "A", UI::compulsory);
    00694                         UI::get (mu_const, set, "const", UI::compulsory);
    00695                         mat R0;
    00696                         UI::get (R0, set, "R", UI::compulsory);
    00697                         set_parameters (A, mu_const, R0);
    00698                         validate();
    00699                 };
    00700                 void validate() {
    00701                         bdm_assert (A.rows() == mu_const.length(), "mlnorm: A vs. mu mismatch");
    00702                         bdm_assert (A.rows() == _R().rows(), "mlnorm: A vs. R mismatch");
    00703
    00704                 }
    00705 };
    00706 UIREGISTER2 (mlnorm,ldmat);
    00707 SHAREDPTR2 ( mlnorm, ldmat );
    00708 UIREGISTER2 (mlnorm,fsqmat);
    00709 SHAREDPTR2 ( mlnorm, fsqmat );
    00710 UIREGISTER2 (mlnorm, chmat);
    00711 SHAREDPTR2 ( mlnorm, chmat );
    00712
    00714 template<class sq_T>
    00715 class mgnorm : public mpdf_internal< enorm< sq_T > >
    00716 {
    00717         private:
    00718 //                      vec &mu; WHY NOT?
    00719                 shared_ptr<fnc> g;
    00720
    00721         public:
    00723                 mgnorm() : mpdf_internal<enorm<sq_T> >() { }
    00725                 inline void set_parameters (const shared_ptr<fnc> &g0, const sq_T &R0);
    00726                 inline void condition (const vec &cond);
    00727
    00728
    00747                 void from_setting (const Setting &set) {
    00748                         mpdf::from_setting(set);
    00749                         shared_ptr<fnc> g = UI::build<fnc> (set, "g", UI::compulsory);
    00750
    00751                         mat R;
    00752                         vec dR;
    00753                         if (UI::get (dR, set, "dR"))
    00754                                 R = diag (dR);
    00755                         else
    00756                                 UI::get (R, set, "R", UI::compulsory);
    00757
    00758                         set_parameters (g, R);
    00759                         validate();
    00760                 }
    00761                 void validate() {
    00762                         bdm_assert(g->dimension()==this->dimension(),"incompatible function");
    00763                 }
    00764 };
    00765
    00766 UIREGISTER2 (mgnorm, chmat);
    00767 SHAREDPTR2 ( mgnorm, chmat );
    00768
    00769
    00777 class mlstudent : public mlnorm<ldmat, enorm>
    00778 {
    00779         protected:
    00781                 ldmat Lambda;
    00783                 ldmat &_R;
    00785                 ldmat Re;
    00786         public:
    00787                 mlstudent () : mlnorm<ldmat, enorm> (),
    00788                                 Lambda (),      _R (iepdf._R()) {}
    00790                 void set_parameters (const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0) {
    00791                         iepdf.set_parameters (mu0, R0);// was Lambda, why?
    00792                         A = A0;
    00793                         mu_const = mu0;
    00794                         Re = R0;
    00795                         Lambda = Lambda0;
    00796                 }
    00797                 void condition (const vec &cond) {
    00798                         iepdf._mu() = A * cond + mu_const;
    00799                         double zeta;
    00800                         //ugly hack!
    00801                         if ( (cond.length() + 1) == Lambda.rows()) {
    00802                                 zeta = Lambda.invqform (concat (cond, vec_1 (1.0)));
    00803                         } else {
    00804                                 zeta = Lambda.invqform (cond);
    00805                         }
    00806                         _R = Re;
    00807                         _R *= (1 + zeta);// / ( nu ); << nu is in Re!!!!!!
    00808                 };
    00809
    00810                 void validate() {
    00811                         bdm_assert (A.rows() == mu_const.length(), "mlstudent: A vs. mu mismatch");
    00812                         bdm_assert (_R.rows() == A.rows(), "mlstudent: A vs. R mismatch");
    00813
    00814                 }
    00815 };
    00825 class mgamma : public mpdf_internal<egamma>
    00826 {
    00827         protected:
    00828
    00830                 double k;
    00831
    00833                 vec &_beta;
    00834
    00835         public:
    00837                 mgamma() : mpdf_internal<egamma>(), k (0),
    00838                                 _beta (iepdf._beta()) {
    00839                 }
    00840
    00842                 void set_parameters (double k, const vec &beta0);
    00843
    00844                 void condition (const vec &val) {_beta = k / val;};
    00856                 void from_setting (const Setting &set) {
    00857                         mpdf::from_setting (set); // reads rv and rvc
    00858                         vec betatmp; // ugly but necessary
    00859                         UI::get (betatmp, set, "beta", UI::compulsory);
    00860                         UI::get (k, set, "k", UI::compulsory);
    00861                         set_parameters (k, betatmp);
    00862                 }
    00863 };
    00864 UIREGISTER (mgamma);
    00865 SHAREDPTR (mgamma);
    00866
    00876 class migamma : public mpdf_internal<eigamma>
    00877 {
    00878         protected:
    00880                 double k;
    00881
    00883                 vec &_alpha;
    00884
    00886                 vec &_beta;
    00887
    00888         public:
    00891                 migamma() : mpdf_internal<eigamma>(),
    00892                                 k (0),
    00893                                 _alpha (iepdf._alpha()),
    00894                                 _beta (iepdf._beta()) {
    00895                 }
    00896
    00897                 migamma (const migamma &m) : mpdf_internal<eigamma>(),
    00898                                 k (0),
    00899                                 _alpha (iepdf._alpha()),
    00900                                 _beta (iepdf._beta()) {
    00901                 }
    00903
    00905                 void set_parameters (int len, double k0) {
    00906                         k = k0;
    00907                         iepdf.set_parameters ( (1.0 / (k*k) + 2.0) *ones (len) /*alpha*/, ones (len) /*beta*/);
    00908                         dimc = dimension();
    00909                 };
    00910                 void condition (const vec &val) {
    00911                         _beta = elem_mult (val, (_alpha - 1.0));
    00912                 };
    00913 };
    00914
    00915
    00927 class mgamma_fix : public mgamma
    00928 {
    00929         protected:
    00931                 double l;
    00933                 vec refl;
    00934         public:
    00936                 mgamma_fix () : mgamma (), refl () {};
    00938                 void set_parameters (double k0 , vec ref0, double l0) {
    00939                         mgamma::set_parameters (k0, ref0);
    00940                         refl = pow (ref0, 1.0 - l0);l = l0;
    00941                         dimc = dimension();
    00942                 };
    00943
    00944                 void condition (const vec &val) {vec mean = elem_mult (refl, pow (val, l)); _beta = k / mean;};
    00945 };
    00946
    00947
    00960 class migamma_ref : public migamma
    00961 {
    00962         protected:
    00964                 double l;
    00966                 vec refl;
    00967         public:
    00969                 migamma_ref () : migamma (), refl () {};
    00971                 void set_parameters (double k0 , vec ref0, double l0) {
    00972                         migamma::set_parameters (ref0.length(), k0);
    00973                         refl = pow (ref0, 1.0 - l0);
    00974                         l = l0;
    00975                         dimc = dimension();
    00976                 };
    00977
    00978                 void condition (const vec &val) {
    00979                         vec mean = elem_mult (refl, pow (val, l));
    00980                         migamma::condition (mean);
    00981                 };
    00982
    00983
    00996                 void from_setting (const Setting &set);
    00997
    00998                 // TODO dodelat void to_setting( Setting &set ) const;
    00999 };
    01000
    01001
    01002 UIREGISTER (migamma_ref);
    01003 SHAREDPTR (migamma_ref);
    01004
    01015 class elognorm: public enorm<ldmat>
    01016 {
    01017         public:
    01018                 vec sample() const {return exp (enorm<ldmat>::sample());};
    01019                 vec mean() const {vec var = enorm<ldmat>::variance();return exp (mu - 0.5*var);};
    01020
    01021 };
    01022
    01029 class mlognorm : public mpdf_internal<elognorm>
    01030 {
    01031         protected:
    01033                 double sig2;
    01034
    01036                 vec &mu;
    01037         public:
    01039                 mlognorm() : mpdf_internal<elognorm>(),
    01040                                 sig2 (0),
    01041                                 mu (iepdf._mu()) {
    01042                 }
    01043
    01045                 void set_parameters (int size, double k) {
    01046                         sig2 = 0.5 * log (k * k + 1);
    01047                         iepdf.set_parameters (zeros (size), 2*sig2*eye (size));
    01048
    01049                         dimc = size;
    01050                 };
    01051
    01052                 void condition (const vec &val) {
    01053                         mu = log (val) - sig2;//elem_mult ( refl,pow ( val,l ) );
    01054                 };
    01055
    01067                 void from_setting (const Setting &set);
    01068
    01069                 // TODO dodelat void to_setting( Setting &set ) const;
    01070
    01071 };
    01072
    01073 UIREGISTER (mlognorm);
    01074 SHAREDPTR (mlognorm);
    01075
    01079 class eWishartCh : public epdf
    01080 {
    01081         protected:
    01083                 chmat Y;
    01085                 int p;
    01087                 double delta;
    01088         public:
    01090                 void set_parameters (const mat &Y0, const double delta0) {Y = chmat (Y0);delta = delta0; p = Y.rows(); dim = p * p; }
    01092                 mat sample_mat() const {
    01093                         mat X = zeros (p, p);
    01094
    01095                         //sample diagonal
    01096                         for (int i = 0;i < p;i++) {
    01097                                 GamRNG.setup (0.5* (delta - i) , 0.5);   // no +1 !! index if from 0
    01098 #pragma omp critical
    01099                                 X (i, i) = sqrt (GamRNG());
    01100                         }
    01101                         //do the rest
    01102                         for (int i = 0;i < p;i++) {
    01103                                 for (int j = i + 1;j < p;j++) {
    01104 #pragma omp critical
    01105                                         X (i, j) = NorRNG.sample();
    01106                                 }
    01107                         }
    01108                         return X*Y._Ch();// return upper triangular part of the decomposition
    01109                 }
    01110                 vec sample () const {
    01111                         return vec (sample_mat()._data(), p*p);
    01112                 }
    01114                 void setY (const mat &Ch0) {copy_vector (dim, Ch0._data(), Y._Ch()._data());}
    01116                 void _setY (const vec &ch0) {copy_vector (dim, ch0._data(), Y._Ch()._data()); }
    01118                 const chmat& getY() const {return Y;}
    01119 };
    01120
    01122
    01124 class eiWishartCh: public epdf
    01125 {
    01126         protected:
    01128                 eWishartCh W;
    01130                 int p;
    01132                 double delta;
    01133         public:
    01135                 void set_parameters (const mat &Y0, const double delta0) {
    01136                         delta = delta0;
    01137                         W.set_parameters (inv (Y0), delta0);
    01138                         dim = W.dimension(); p = Y0.rows();
    01139                 }
    01140                 vec sample() const {mat iCh; iCh = inv (W.sample_mat()); return vec (iCh._data(), dim);}
    01142                 void _setY (const vec &y0) {
    01143                         mat Ch (p, p);
    01144                         mat iCh (p, p);
    01145                         copy_vector (dim, y0._data(), Ch._data());
    01146
    01147                         iCh = inv (Ch);
    01148                         W.setY (iCh);
    01149                 }
    01150                 virtual double evallog (const vec &val) const {
    01151                         chmat X (p);
    01152                         const chmat& Y = W.getY();
    01153
    01154                         copy_vector (p*p, val._data(), X._Ch()._data());
    01155                         chmat iX (p);X.inv (iX);
    01156                         // compute
    01157 //                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},
    01158                         mat M = Y.to_mat() * iX.to_mat();
    01159
    01160                         double log1 = 0.5 * p * (2 * Y.logdet()) - 0.5 * (delta + p + 1) * (2 * X.logdet()) - 0.5 * trace (M);
    01161                         //Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!
    01162
    01163                         /*                              if (0) {
    01164                                                                 mat XX=X.to_mat();
    01165                                                                 mat YY=Y.to_mat();
    01166
    01167                                                                 double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX));
    01168                                                                 cout << log1 << "," << log2 << endl;
    01169                                                         }*/
    01170                         return log1;
    01171                 };
    01172
    01173 };
    01174
    01176 class rwiWishartCh : public mpdf_internal<eiWishartCh>
    01177 {
    01178         protected:
    01180                 double sqd;
    01182                 vec refl;
    01184                 double l;
    01186                 int p;
    01187
    01188         public:
    01189                 rwiWishartCh() : sqd (0), l (0), p (0) {}
    01191                 void set_parameters (int p0, double k, vec ref0, double l0) {
    01192                         p = p0;
    01193                         double delta = 2 / (k * k) + p + 3;
    01194                         sqd = sqrt (delta - p - 1);
    01195                         l = l0;
    01196                         refl = pow (ref0, 1 - l);
    01197
    01198                         iepdf.set_parameters (eye (p), delta);
    01199                         dimc = iepdf.dimension();
    01200                 }
    01201                 void condition (const vec &c) {
    01202                         vec z = c;
    01203                         int ri = 0;
    01204                         for (int i = 0;i < p*p;i += (p + 1)) {//trace diagonal element
    01205                                 z (i) = pow (z (i), l) * refl (ri);
    01206                                 ri++;
    01207                         }
    01208
    01209                         iepdf._setY (sqd*z);
    01210                 }
    01211 };
    01212
    01214 enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
    01220 class eEmp: public epdf
    01221 {
    01222         protected :
    01224                 int n;
    01226                 vec w;
    01228                 Array<vec> samples;
    01229         public:
    01232                 eEmp () : epdf (), w (), samples () {};
    01234                 eEmp (const eEmp &e) : epdf (e), w (e.w), samples (e.samples) {};
    01236
    01238                 void set_statistics (const vec &w0, const epdf &pdf0);
    01240                 void set_statistics (const epdf &pdf0 , int n) {set_statistics (ones (n) / n, pdf0);};
    01242                 void set_samples (const epdf* pdf0);
    01244                 void set_parameters (int n0, bool copy = true) {n = n0; w.set_size (n0, copy);samples.set_size (n0, copy);};
    01246                 void set_parameters (const Array<vec> &Av) {
    01247                         bdm_assert(Av.size()>0,"Empty samples");
    01248                         n = Av.size();
    01249                         epdf::set_parameters(Av(0).length());
    01250                         w=1/n*ones(n);
    01251                         samples=Av;
    01252                 };
    01254                 vec& _w()  {return w;};
    01256                 const vec& _w() const {return w;};
    01258                 Array<vec>& _samples() {return samples;};
    01260                 const vec& _sample(int i) const {return samples(i);};
    01262                 const Array<vec>& _samples() const {return samples;};
    01265                 void resample ( ivec &index, RESAMPLING_METHOD method = SYSTEMATIC);
    01266
    01268                 void resample (RESAMPLING_METHOD method = SYSTEMATIC){ivec ind; resample(ind,method);};
    01269
    01271                 vec sample() const {
    01272                         bdm_error ("Not implemented");
    01273                         return vec();
    01274                 }
    01275
    01277                 double evallog (const vec &val) const {
    01278                         bdm_error ("Not implemented");
    01279                         return 0.0;
    01280                 }
    01281
    01282                 vec mean() const {
    01283                         vec pom = zeros (dim);
    01284                         for (int i = 0;i < n;i++) {pom += samples (i) * w (i);}
    01285                         return pom;
    01286                 }
    01287                 vec variance() const {
    01288                         vec pom = zeros (dim);
    01289                         for (int i = 0;i < n;i++) {pom += pow (samples (i), 2) * w (i);}
    01290                         return pom -pow (mean(), 2);
    01291                 }
    01293                 void qbounds (vec &lb, vec &ub, double perc = 0.95) const {
    01294                         // lb in inf so than it will be pushed below;
    01295                         lb.set_size (dim);
    01296                         ub.set_size (dim);
    01297                         lb = std::numeric_limits<double>::infinity();
    01298                         ub = -std::numeric_limits<double>::infinity();
    01299                         int j;
    01300                         for (int i = 0;i < n;i++) {
    01301                                 for (j = 0;j < dim; j++) {
    01302                                         if (samples (i) (j) < lb (j)) {lb (j) = samples (i) (j);}
    01303                                         if (samples (i) (j) > ub (j)) {ub (j) = samples (i) (j);}
    01304                                 }
    01305                         }
    01306                 }
    01307 };
    01308
    01309
    01311
    01312 template<class sq_T>
    01313 void enorm<sq_T>::set_parameters (const vec &mu0, const sq_T &R0)
    01314 {
    01315 //Fixme test dimensions of mu0 and R0;
    01316         mu = mu0;
    01317         R = R0;
    01318         validate();
    01319 };
    01320
    01321 template<class sq_T>
    01322 void enorm<sq_T>::from_setting (const Setting &set)
    01323 {
    01324         epdf::from_setting (set); //reads rv
    01325
    01326         UI::get (mu, set, "mu", UI::compulsory);
    01327         mat Rtmp;// necessary for conversion
    01328         UI::get (Rtmp, set, "R", UI::compulsory);
    01329         R = Rtmp; // conversion
    01330         validate();
    01331 }
    01332
    01333 template<class sq_T>
    01334 void enorm<sq_T>::dupdate (mat &v, double nu)
    01335 {
    01336         //
    01337 };
    01338
    01339 // template<class sq_T>
    01340 // void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {
    01341 //      //
    01342 // };
    01343
    01344 template<class sq_T>
    01345 vec enorm<sq_T>::sample() const
    01346 {
    01347         vec x (dim);
    01348 #pragma omp critical
    01349         NorRNG.sample_vector (dim, x);
    01350         vec smp = R.sqrt_mult (x);
    01351
    01352         smp += mu;
    01353         return smp;
    01354 };
    01355
    01356 // template<class sq_T>
    01357 // double enorm<sq_T>::eval ( const vec &val ) const {
    01358 //      double pdfl,e;
    01359 //      pdfl = evallog ( val );
    01360 //      e = exp ( pdfl );
    01361 //      return e;
    01362 // };
    01363
    01364 template<class sq_T>
    01365 double enorm<sq_T>::evallog_nn (const vec &val) const
    01366 {
    01367         // 1.83787706640935 = log(2pi)
    01368         double tmp = -0.5 * (R.invqform (mu - val));// - lognc();
    01369         return  tmp;
    01370 };
    01371
    01372 template<class sq_T>
    01373 inline double enorm<sq_T>::lognc () const
    01374 {
    01375         // 1.83787706640935 = log(2pi)
    01376         double tmp = 0.5 * (R.cols() * 1.83787706640935 + R.logdet());
    01377         return tmp;
    01378 };
    01379
    01380
    01381 // template<class sq_T>
    01382 // vec mlnorm<sq_T>::samplecond (const  vec &cond, double &lik ) {
    01383 //      this->condition ( cond );
    01384 //      vec smp = epdf.sample();
    01385 //      lik = epdf.eval ( smp );
    01386 //      return smp;
    01387 // }
    01388
    01389 // template<class sq_T>
    01390 // mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {
    01391 //      int i;
    01392 //      int dim = rv.count();
    01393 //      mat Smp ( dim,n );
    01394 //      vec smp ( dim );
    01395 //      this->condition ( cond );
    01396 //
    01397 //      for ( i=0; i<n; i++ ) {
    01398 //              smp = epdf.sample();
    01399 //              lik ( i ) = epdf.eval ( smp );
    01400 //              Smp.set_col ( i ,smp );
    01401 //      }
    01402 //
    01403 //      return Smp;
    01404 // }
    01405
    01406
    01407 template<class sq_T>
    01408 shared_ptr<epdf> enorm<sq_T>::marginal ( const RV &rvn ) const
    01409 {
    01410         enorm<sq_T> *tmp = new enorm<sq_T> ();
    01411         shared_ptr<epdf> narrow(tmp);
    01412         marginal ( rvn, *tmp );
    01413         return narrow;
    01414 }
    01415
    01416 template<class sq_T>
    01417 void enorm<sq_T>::marginal ( const RV &rvn, enorm<sq_T> &target ) const
    01418 {
    01419         bdm_assert (isnamed(), "rv description is not assigned");
    01420         ivec irvn = rvn.dataind (rv);
    01421
    01422         sq_T Rn (R, irvn);  // select rows and columns of R
    01423
    01424         target.set_rv ( rvn );
    01425         target.set_parameters (mu (irvn), Rn);
    01426 }
    01427
    01428 template<class sq_T>
    01429 shared_ptr<mpdf> enorm<sq_T>::condition ( const RV &rvn ) const
    01430 {
    01431         mlnorm<sq_T> *tmp = new mlnorm<sq_T> ();
    01432         shared_ptr<mpdf> narrow(tmp);
    01433         condition ( rvn, *tmp );
    01434         return narrow;
    01435 }
    01436
    01437 template<class sq_T>
    01438 void enorm<sq_T>::condition ( const RV &rvn, mpdf &target ) const
    01439 {
    01440         typedef mlnorm<sq_T> TMlnorm;
    01441
    01442         bdm_assert (isnamed(), "rvs are not assigned");
    01443         TMlnorm &uptarget = dynamic_cast<TMlnorm &>(target);
    01444
    01445         RV rvc = rv.subt (rvn);
    01446         bdm_assert ( (rvc._dsize() + rvn._dsize() == rv._dsize()), "wrong rvn");
    01447         //Permutation vector of the new R
    01448         ivec irvn = rvn.dataind (rv);
    01449         ivec irvc = rvc.dataind (rv);
    01450         ivec perm = concat (irvn , irvc);
    01451         sq_T Rn (R, perm);
    01452
    01453         //fixme - could this be done in general for all sq_T?
    01454         mat S = Rn.to_mat();
    01455         //fixme
    01456         int n = rvn._dsize() - 1;
    01457         int end = R.rows() - 1;
    01458         mat S11 = S.get (0, n, 0, n);
    01459         mat S12 = S.get (0, n , rvn._dsize(), end);
    01460         mat S22 = S.get (rvn._dsize(), end, rvn._dsize(), end);
    01461
    01462         vec mu1 = mu (irvn);
    01463         vec mu2 = mu (irvc);
    01464         mat A = S12 * inv (S22);
    01465         sq_T R_n (S11 - A *S12.T());
    01466
    01467         uptarget.set_rv (rvn);
    01468         uptarget.set_rvc (rvc);
    01469         uptarget.set_parameters (A, mu1 - A*mu2, R_n);
    01470 }
    01471
    01474 template<class sq_T>
    01475 void mgnorm<sq_T >::set_parameters (const shared_ptr<fnc> &g0, const sq_T &R0) {
    01476         g = g0;
    01477         this->iepdf.set_parameters (zeros (g->dimension()), R0);
    01478 }
    01479
    01480 template<class sq_T>
    01481 void mgnorm<sq_T >::condition (const vec &cond) {this->iepdf._mu() = g->eval (cond);};
    01482
    01484 template<class sq_T>
    01485 std::ostream &operator<< (std::ostream &os,  mlnorm<sq_T> &ml)
    01486 {
    01487         os << "A:" << ml.A << endl;
    01488         os << "mu:" << ml.mu_const << endl;
    01489         os << "R:" << ml._R() << endl;
    01490         return os;
    01491 };
    01492
    01493 }
    01494 #endif //EF_H