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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
    00299                 vec sample() const {
    00300                         bdm_error ("Not implemented");
    00301                         return vec();
    00302                 }
    00303
    00304                 vec mean() const {return beta / sum (beta);};
    00305                 vec variance() const {double gamma = sum (beta); return elem_mult (beta, (beta + 1)) / (gamma* (gamma + 1));}
    00307                 double evallog_nn (const vec &val) const {
    00308                         double tmp; tmp = (beta - 1) * log (val);
    00309                         return tmp;
    00310                 }
    00311
    00312                 double lognc () const {
    00313                         double tmp;
    00314                         double gam = sum (beta);
    00315                         double lgb = 0.0;
    00316                         for (int i = 0;i < beta.length();i++) {lgb += lgamma (beta (i));}
    00317                         tmp = lgb - lgamma (gam);
    00318                         return tmp;
    00319                 }
    00320
    00322                 vec& _beta()  {return beta;}
    00324 };
    00325
    00327 class multiBM : public BMEF
    00328 {
    00329         protected:
    00331                 eDirich est;
    00333                 vec &beta;
    00334         public:
    00336                 multiBM () : BMEF (), est (), beta (est._beta()) {
    00337                         if (beta.length() > 0) {last_lognc = est.lognc();}
    00338                         else{last_lognc = 0.0;}
    00339                 }
    00341                 multiBM (const multiBM &B) : BMEF (B), est (B.est), beta (est._beta()) {}
    00343                 void set_statistics (const BM* mB0) {const multiBM* mB = dynamic_cast<const multiBM*> (mB0); beta = mB->beta;}
    00344                 void bayes (const vec &dt) {
    00345                         if (frg < 1.0) {beta *= frg;last_lognc = est.lognc();}
    00346                         beta += dt;
    00347                         if (evalll) {ll = est.lognc() - last_lognc;}
    00348                 }
    00349                 double logpred (const vec &dt) const {
    00350                         eDirich pred (est);
    00351                         vec &beta = pred._beta();
    00352
    00353                         double lll;
    00354                         if (frg < 1.0)
    00355                                 {beta *= frg;lll = pred.lognc();}
    00356                         else
    00357                                 if (evalll) {lll = last_lognc;}
    00358                                 else{lll = pred.lognc();}
    00359
    00360                         beta += dt;
    00361                         return pred.lognc() - lll;
    00362                 }
    00363                 void flatten (const BMEF* B) {
    00364                         const multiBM* E = dynamic_cast<const multiBM*> (B);
    00365                         // sum(beta) should be equal to sum(B.beta)
    00366                         const vec &Eb = E->beta;//const_cast<multiBM*> ( E )->_beta();
    00367                         beta *= (sum (Eb) / sum (beta));
    00368                         if (evalll) {last_lognc = est.lognc();}
    00369                 }
    00371                 const eDirich& posterior() const {return est;};
    00373                 void set_parameters (const vec &beta0) {
    00374                         est.set_parameters (beta0);
    00375                         if (evalll) {last_lognc = est.lognc();}
    00376                 }
    00377 };
    00378
    00388 class egamma : public eEF
    00389 {
    00390         protected:
    00392                 vec alpha;
    00394                 vec beta;
    00395         public :
    00398                 egamma () : eEF (), alpha (0), beta (0) {};
    00399                 egamma (const vec &a, const vec &b) {set_parameters (a, b);};
    00400                 void set_parameters (const vec &a, const vec &b) {alpha = a, beta = b;dim = alpha.length();};
    00402
    00403                 vec sample() const;
    00404                 double evallog (const vec &val) const;
    00405                 double lognc () const;
    00407                 vec& _alpha() {return alpha;}
    00409                 vec& _beta() {return beta;}
    00410                 vec mean() const {return elem_div (alpha, beta);}
    00411                 vec variance() const {return elem_div (alpha, elem_mult (beta, beta)); }
    00412
    00423                 void from_setting (const Setting &set) {
    00424                         epdf::from_setting (set); // reads rv
    00425                         UI::get (alpha, set, "alpha", UI::compulsory);
    00426                         UI::get (beta, set, "beta", UI::compulsory);
    00427                         validate();
    00428                 }
    00429                 void validate() {
    00430                         bdm_assert (alpha.length() == beta.length(), "parameters do not match");
    00431                         dim = alpha.length();
    00432                 }
    00433 };
    00434 UIREGISTER (egamma);
    00435 SHAREDPTR ( egamma );
    00436
    00453 class eigamma : public egamma
    00454 {
    00455         protected:
    00456         public :
    00461
    00462                 vec sample() const {return 1.0 / egamma::sample();};
    00464                 vec mean() const {return elem_div (beta, alpha - 1);}
    00465                 vec variance() const {vec mea = mean(); return elem_div (elem_mult (mea, mea), alpha - 2);}
    00466 };
    00467 /*
    00469 class emix : public epdf {
    00470 protected:
    00471         int n;
    00472         vec &w;
    00473         Array<epdf*> Coms;
    00474 public:
    00476         emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};
    00477         void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}
    00478         vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};
    00479 };
    00480 */
    00481
    00483
    00484 class euni: public epdf
    00485 {
    00486         protected:
    00488                 vec low;
    00490                 vec high;
    00492                 vec distance;
    00494                 double nk;
    00496                 double lnk;
    00497         public:
    00500                 euni () : epdf () {}
    00501                 euni (const vec &low0, const vec &high0) {set_parameters (low0, high0);}
    00502                 void set_parameters (const vec &low0, const vec &high0) {
    00503                         distance = high0 - low0;
    00504                         low = low0;
    00505                         high = high0;
    00506                         nk = prod (1.0 / distance);
    00507                         lnk = log (nk);
    00508                         dim = low.length();
    00509                 }
    00511
    00512                 double evallog (const vec &val) const  {
    00513                         if (any (val < low) && any (val > high)) {return inf;}
    00514                         else return lnk;
    00515                 }
    00516                 vec sample() const {
    00517                         vec smp (dim);
    00518 #pragma omp critical
    00519                         UniRNG.sample_vector (dim , smp);
    00520                         return low + elem_mult (distance, smp);
    00521                 }
    00523                 vec mean() const {return (high -low) / 2.0;}
    00524                 vec variance() const {return (pow (high, 2) + pow (low, 2) + elem_mult (high, low)) / 3.0;}
    00535                 void from_setting (const Setting &set) {
    00536                         epdf::from_setting (set); // reads rv and rvc
    00537
    00538                         UI::get (high, set, "high", UI::compulsory);
    00539                         UI::get (low, set, "low", UI::compulsory);
    00540                         set_parameters(low,high);
    00541                         validate();
    00542                 }
    00543                 void validate() {
    00544                         bdm_assert(high.length()==low.length(), "Incompatible high and low vectors");
    00545                         dim = high.length();
    00546                         bdm_assert (min (distance) > 0.0, "bad support");
    00547                 }
    00548 };
    00549 UIREGISTER(euni);
    00550
    00556 template < class sq_T, template <typename> class TEpdf = enorm >
    00557 class mlnorm : public mpdf_internal< TEpdf<sq_T> >
    00558 {
    00559         protected:
    00561                 mat A;
    00563                 vec mu_const;
    00564 //                      vec& _mu; //cached epdf.mu; !!!!!! WHY NOT?
    00565         public:
    00568                 mlnorm() : mpdf_internal< TEpdf<sq_T> >() {};
    00569                 mlnorm (const mat &A, const vec &mu0, const sq_T &R) : mpdf_internal< TEpdf<sq_T> >() {
    00570                         set_parameters (A, mu0, R);
    00571                 }
    00572
    00574                 void set_parameters (const  mat &A0, const vec &mu0, const sq_T &R0) {
    00575                         this->iepdf.set_parameters (zeros (A0.rows()), R0);
    00576                         A = A0;
    00577                         mu_const = mu0;
    00578                         this->dimc = A0.cols();
    00579                 }
    00582                 void condition (const vec &cond) {
    00583                         this->iepdf._mu() = A * cond + mu_const;
    00584 //R is already assigned;
    00585                 }
    00586
    00588                 const vec& _mu_const() const {return mu_const;}
    00590                 const mat& _A() const {return A;}
    00592                 mat _R() const { return this->iepdf._R().to_mat(); }
    00593
    00595                 template<typename sq_M>
    00596                 friend std::ostream &operator<< (std::ostream &os,  mlnorm<sq_M, enorm> &ml);
    00597
    00608                 void from_setting (const Setting &set) {
    00609                         mpdf::from_setting (set);
    00610
    00611                         UI::get (A, set, "A", UI::compulsory);
    00612                         UI::get (mu_const, set, "const", UI::compulsory);
    00613                         mat R0;
    00614                         UI::get (R0, set, "R", UI::compulsory);
    00615                         set_parameters (A, mu_const, R0);
    00616                         validate();
    00617                 };
    00618                 void validate() {
    00619                         bdm_assert (A.rows() == mu_const.length(), "mlnorm: A vs. mu mismatch");
    00620                         bdm_assert (A.rows() == _R().rows(), "mlnorm: A vs. R mismatch");
    00621
    00622                 }
    00623 };
    00624 UIREGISTER2 (mlnorm,ldmat);
    00625 SHAREDPTR2 ( mlnorm, ldmat );
    00626 UIREGISTER2 (mlnorm,fsqmat);
    00627 SHAREDPTR2 ( mlnorm, fsqmat );
    00628 UIREGISTER2 (mlnorm, chmat);
    00629 SHAREDPTR2 ( mlnorm, chmat );
    00630
    00632 template<class sq_T>
    00633 class mgnorm : public mpdf_internal< enorm< sq_T > >
    00634 {
    00635         private:
    00636 //                      vec &mu; WHY NOT?
    00637                 shared_ptr<fnc> g;
    00638
    00639         public:
    00641                 mgnorm() : mpdf_internal<enorm<sq_T> >() { }
    00643                 inline void set_parameters (const shared_ptr<fnc> &g0, const sq_T &R0);
    00644                 inline void condition (const vec &cond);
    00645
    00646
    00665                 void from_setting (const Setting &set) {
    00666                         mpdf::from_setting(set);
    00667                         shared_ptr<fnc> g = UI::build<fnc> (set, "g", UI::compulsory);
    00668
    00669                         mat R;
    00670                         vec dR;
    00671                         if (UI::get (dR, set, "dR"))
    00672                                 R = diag (dR);
    00673                         else
    00674                                 UI::get (R, set, "R", UI::compulsory);
    00675
    00676                         set_parameters (g, R);
    00677                         validate();
    00678                 }
    00679                 void validate() {
    00680                         bdm_assert(g->dimension()==this->dimension(),"incompatible function");
    00681                 }
    00682 };
    00683
    00684 UIREGISTER2 (mgnorm, chmat);
    00685 SHAREDPTR2 ( mgnorm, chmat );
    00686
    00687
    00695 class mlstudent : public mlnorm<ldmat, enorm>
    00696 {
    00697         protected:
    00699                 ldmat Lambda;
    00701                 ldmat &_R;
    00703                 ldmat Re;
    00704         public:
    00705                 mlstudent () : mlnorm<ldmat, enorm> (),
    00706                                 Lambda (),      _R (iepdf._R()) {}
    00708                 void set_parameters (const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0) {
    00709                         iepdf.set_parameters (mu0, R0);// was Lambda, why?
    00710                         A = A0;
    00711                         mu_const = mu0;
    00712                         Re = R0;
    00713                         Lambda = Lambda0;
    00714                 }
    00715                 void condition (const vec &cond) {
    00716                         iepdf._mu() = A * cond + mu_const;
    00717                         double zeta;
    00718                         //ugly hack!
    00719                         if ( (cond.length() + 1) == Lambda.rows()) {
    00720                                 zeta = Lambda.invqform (concat (cond, vec_1 (1.0)));
    00721                         } else {
    00722                                 zeta = Lambda.invqform (cond);
    00723                         }
    00724                         _R = Re;
    00725                         _R *= (1 + zeta);// / ( nu ); << nu is in Re!!!!!!
    00726                 };
    00727
    00728                 void validate() {
    00729                         bdm_assert (A.rows() == mu_const.length(), "mlstudent: A vs. mu mismatch");
    00730                         bdm_assert (_R.rows() == A.rows(), "mlstudent: A vs. R mismatch");
    00731
    00732                 }
    00733 };
    00743 class mgamma : public mpdf_internal<egamma>
    00744 {
    00745         protected:
    00746
    00748                 double k;
    00749
    00751                 vec &_beta;
    00752
    00753         public:
    00755                 mgamma() : mpdf_internal<egamma>(), k (0),
    00756                                 _beta (iepdf._beta()) {
    00757                 }
    00758
    00760                 void set_parameters (double k, const vec &beta0);
    00761
    00762                 void condition (const vec &val) {_beta = k / val;};
    00774                 void from_setting (const Setting &set) {
    00775                         mpdf::from_setting (set); // reads rv and rvc
    00776                         vec betatmp; // ugly but necessary
    00777                         UI::get (betatmp, set, "beta", UI::compulsory);
    00778                         UI::get (k, set, "k", UI::compulsory);
    00779                         set_parameters (k, betatmp);
    00780                 }
    00781 };
    00782 UIREGISTER (mgamma);
    00783 SHAREDPTR (mgamma);
    00784
    00794 class migamma : public mpdf_internal<eigamma>
    00795 {
    00796         protected:
    00798                 double k;
    00799
    00801                 vec &_alpha;
    00802
    00804                 vec &_beta;
    00805
    00806         public:
    00809                 migamma() : mpdf_internal<eigamma>(),
    00810                                 k (0),
    00811                                 _alpha (iepdf._alpha()),
    00812                                 _beta (iepdf._beta()) {
    00813                 }
    00814
    00815                 migamma (const migamma &m) : mpdf_internal<eigamma>(),
    00816                                 k (0),
    00817                                 _alpha (iepdf._alpha()),
    00818                                 _beta (iepdf._beta()) {
    00819                 }
    00821
    00823                 void set_parameters (int len, double k0) {
    00824                         k = k0;
    00825                         iepdf.set_parameters ( (1.0 / (k*k) + 2.0) *ones (len) /*alpha*/, ones (len) /*beta*/);
    00826                         dimc = dimension();
    00827                 };
    00828                 void condition (const vec &val) {
    00829                         _beta = elem_mult (val, (_alpha - 1.0));
    00830                 };
    00831 };
    00832
    00833
    00845 class mgamma_fix : public mgamma
    00846 {
    00847         protected:
    00849                 double l;
    00851                 vec refl;
    00852         public:
    00854                 mgamma_fix () : mgamma (), refl () {};
    00856                 void set_parameters (double k0 , vec ref0, double l0) {
    00857                         mgamma::set_parameters (k0, ref0);
    00858                         refl = pow (ref0, 1.0 - l0);l = l0;
    00859                         dimc = dimension();
    00860                 };
    00861
    00862                 void condition (const vec &val) {vec mean = elem_mult (refl, pow (val, l)); _beta = k / mean;};
    00863 };
    00864
    00865
    00878 class migamma_ref : public migamma
    00879 {
    00880         protected:
    00882                 double l;
    00884                 vec refl;
    00885         public:
    00887                 migamma_ref () : migamma (), refl () {};
    00889                 void set_parameters (double k0 , vec ref0, double l0) {
    00890                         migamma::set_parameters (ref0.length(), k0);
    00891                         refl = pow (ref0, 1.0 - l0);
    00892                         l = l0;
    00893                         dimc = dimension();
    00894                 };
    00895
    00896                 void condition (const vec &val) {
    00897                         vec mean = elem_mult (refl, pow (val, l));
    00898                         migamma::condition (mean);
    00899                 };
    00900
    00901
    00914                 void from_setting (const Setting &set);
    00915
    00916                 // TODO dodelat void to_setting( Setting &set ) const;
    00917 };
    00918
    00919
    00920 UIREGISTER (migamma_ref);
    00921 SHAREDPTR (migamma_ref);
    00922
    00933 class elognorm: public enorm<ldmat>
    00934 {
    00935         public:
    00936                 vec sample() const {return exp (enorm<ldmat>::sample());};
    00937                 vec mean() const {vec var = enorm<ldmat>::variance();return exp (mu - 0.5*var);};
    00938
    00939 };
    00940
    00947 class mlognorm : public mpdf_internal<elognorm>
    00948 {
    00949         protected:
    00951                 double sig2;
    00952
    00954                 vec &mu;
    00955         public:
    00957                 mlognorm() : mpdf_internal<elognorm>(),
    00958                                 sig2 (0),
    00959                                 mu (iepdf._mu()) {
    00960                 }
    00961
    00963                 void set_parameters (int size, double k) {
    00964                         sig2 = 0.5 * log (k * k + 1);
    00965                         iepdf.set_parameters (zeros (size), 2*sig2*eye (size));
    00966
    00967                         dimc = size;
    00968                 };
    00969
    00970                 void condition (const vec &val) {
    00971                         mu = log (val) - sig2;//elem_mult ( refl,pow ( val,l ) );
    00972                 };
    00973
    00985                 void from_setting (const Setting &set);
    00986
    00987                 // TODO dodelat void to_setting( Setting &set ) const;
    00988
    00989 };
    00990
    00991 UIREGISTER (mlognorm);
    00992 SHAREDPTR (mlognorm);
    00993
    00997 class eWishartCh : public epdf
    00998 {
    00999         protected:
    01001                 chmat Y;
    01003                 int p;
    01005                 double delta;
    01006         public:
    01008                 void set_parameters (const mat &Y0, const double delta0) {Y = chmat (Y0);delta = delta0; p = Y.rows(); dim = p * p; }
    01010                 mat sample_mat() const {
    01011                         mat X = zeros (p, p);
    01012
    01013                         //sample diagonal
    01014                         for (int i = 0;i < p;i++) {
    01015                                 GamRNG.setup (0.5* (delta - i) , 0.5);   // no +1 !! index if from 0
    01016 #pragma omp critical
    01017                                 X (i, i) = sqrt (GamRNG());
    01018                         }
    01019                         //do the rest
    01020                         for (int i = 0;i < p;i++) {
    01021                                 for (int j = i + 1;j < p;j++) {
    01022 #pragma omp critical
    01023                                         X (i, j) = NorRNG.sample();
    01024                                 }
    01025                         }
    01026                         return X*Y._Ch();// return upper triangular part of the decomposition
    01027                 }
    01028                 vec sample () const {
    01029                         return vec (sample_mat()._data(), p*p);
    01030                 }
    01032                 void setY (const mat &Ch0) {copy_vector (dim, Ch0._data(), Y._Ch()._data());}
    01034                 void _setY (const vec &ch0) {copy_vector (dim, ch0._data(), Y._Ch()._data()); }
    01036                 const chmat& getY() const {return Y;}
    01037 };
    01038
    01040
    01042 class eiWishartCh: public epdf
    01043 {
    01044         protected:
    01046                 eWishartCh W;
    01048                 int p;
    01050                 double delta;
    01051         public:
    01053                 void set_parameters (const mat &Y0, const double delta0) {
    01054                         delta = delta0;
    01055                         W.set_parameters (inv (Y0), delta0);
    01056                         dim = W.dimension(); p = Y0.rows();
    01057                 }
    01058                 vec sample() const {mat iCh; iCh = inv (W.sample_mat()); return vec (iCh._data(), dim);}
    01060                 void _setY (const vec &y0) {
    01061                         mat Ch (p, p);
    01062                         mat iCh (p, p);
    01063                         copy_vector (dim, y0._data(), Ch._data());
    01064
    01065                         iCh = inv (Ch);
    01066                         W.setY (iCh);
    01067                 }
    01068                 virtual double evallog (const vec &val) const {
    01069                         chmat X (p);
    01070                         const chmat& Y = W.getY();
    01071
    01072                         copy_vector (p*p, val._data(), X._Ch()._data());
    01073                         chmat iX (p);X.inv (iX);
    01074                         // compute
    01075 //                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},
    01076                         mat M = Y.to_mat() * iX.to_mat();
    01077
    01078                         double log1 = 0.5 * p * (2 * Y.logdet()) - 0.5 * (delta + p + 1) * (2 * X.logdet()) - 0.5 * trace (M);
    01079                         //Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!
    01080
    01081                         /*                              if (0) {
    01082                                                                 mat XX=X.to_mat();
    01083                                                                 mat YY=Y.to_mat();
    01084
    01085                                                                 double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX));
    01086                                                                 cout << log1 << "," << log2 << endl;
    01087                                                         }*/
    01088                         return log1;
    01089                 };
    01090
    01091 };
    01092
    01094 class rwiWishartCh : public mpdf_internal<eiWishartCh>
    01095 {
    01096         protected:
    01098                 double sqd;
    01100                 vec refl;
    01102                 double l;
    01104                 int p;
    01105
    01106         public:
    01107                 rwiWishartCh() : sqd (0), l (0), p (0) {}
    01109                 void set_parameters (int p0, double k, vec ref0, double l0) {
    01110                         p = p0;
    01111                         double delta = 2 / (k * k) + p + 3;
    01112                         sqd = sqrt (delta - p - 1);
    01113                         l = l0;
    01114                         refl = pow (ref0, 1 - l);
    01115
    01116                         iepdf.set_parameters (eye (p), delta);
    01117                         dimc = iepdf.dimension();
    01118                 }
    01119                 void condition (const vec &c) {
    01120                         vec z = c;
    01121                         int ri = 0;
    01122                         for (int i = 0;i < p*p;i += (p + 1)) {//trace diagonal element
    01123                                 z (i) = pow (z (i), l) * refl (ri);
    01124                                 ri++;
    01125                         }
    01126
    01127                         iepdf._setY (sqd*z);
    01128                 }
    01129 };
    01130
    01132 enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
    01138 class eEmp: public epdf
    01139 {
    01140         protected :
    01142                 int n;
    01144                 vec w;
    01146                 Array<vec> samples;
    01147         public:
    01150                 eEmp () : epdf (), w (), samples () {};
    01152                 eEmp (const eEmp &e) : epdf (e), w (e.w), samples (e.samples) {};
    01154
    01156                 void set_statistics (const vec &w0, const epdf &pdf0);
    01158                 void set_statistics (const epdf &pdf0 , int n) {set_statistics (ones (n) / n, pdf0);};
    01160                 void set_samples (const epdf* pdf0);
    01162                 void set_parameters (int n0, bool copy = true) {n = n0; w.set_size (n0, copy);samples.set_size (n0, copy);};
    01164                 void set_parameters (const Array<vec> &Av) {
    01165                         bdm_assert(Av.size()>0,"Empty samples");
    01166                         n = Av.size();
    01167                         epdf::set_parameters(Av(0).length());
    01168                         w=1/n*ones(n);
    01169                         samples=Av;
    01170                 };
    01172                 vec& _w()  {return w;};
    01174                 const vec& _w() const {return w;};
    01176                 Array<vec>& _samples() {return samples;};
    01178                 const Array<vec>& _samples() const {return samples;};
    01180                 ivec resample (RESAMPLING_METHOD method = SYSTEMATIC);
    01181
    01183                 vec sample() const {
    01184                         bdm_error ("Not implemented");
    01185                         return vec();
    01186                 }
    01187
    01189                 double evallog (const vec &val) const {
    01190                         bdm_error ("Not implemented");
    01191                         return 0.0;
    01192                 }
    01193
    01194                 vec mean() const {
    01195                         vec pom = zeros (dim);
    01196                         for (int i = 0;i < n;i++) {pom += samples (i) * w (i);}
    01197                         return pom;
    01198                 }
    01199                 vec variance() const {
    01200                         vec pom = zeros (dim);
    01201                         for (int i = 0;i < n;i++) {pom += pow (samples (i), 2) * w (i);}
    01202                         return pom -pow (mean(), 2);
    01203                 }
    01205                 void qbounds (vec &lb, vec &ub, double perc = 0.95) const {
    01206                         // lb in inf so than it will be pushed below;
    01207                         lb.set_size (dim);
    01208                         ub.set_size (dim);
    01209                         lb = std::numeric_limits<double>::infinity();
    01210                         ub = -std::numeric_limits<double>::infinity();
    01211                         int j;
    01212                         for (int i = 0;i < n;i++) {
    01213                                 for (j = 0;j < dim; j++) {
    01214                                         if (samples (i) (j) < lb (j)) {lb (j) = samples (i) (j);}
    01215                                         if (samples (i) (j) > ub (j)) {ub (j) = samples (i) (j);}
    01216                                 }
    01217                         }
    01218                 }
    01219 };
    01220
    01221
    01223
    01224 template<class sq_T>
    01225 void enorm<sq_T>::set_parameters (const vec &mu0, const sq_T &R0)
    01226 {
    01227 //Fixme test dimensions of mu0 and R0;
    01228         mu = mu0;
    01229         R = R0;
    01230         validate();
    01231 };
    01232
    01233 template<class sq_T>
    01234 void enorm<sq_T>::from_setting (const Setting &set)
    01235 {
    01236         epdf::from_setting (set); //reads rv
    01237
    01238         UI::get (mu, set, "mu", UI::compulsory);
    01239         mat Rtmp;// necessary for conversion
    01240         UI::get (Rtmp, set, "R", UI::compulsory);
    01241         R = Rtmp; // conversion
    01242         validate();
    01243 }
    01244
    01245 template<class sq_T>
    01246 void enorm<sq_T>::dupdate (mat &v, double nu)
    01247 {
    01248         //
    01249 };
    01250
    01251 // template<class sq_T>
    01252 // void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {
    01253 //      //
    01254 // };
    01255
    01256 template<class sq_T>
    01257 vec enorm<sq_T>::sample() const
    01258 {
    01259         vec x (dim);
    01260 #pragma omp critical
    01261         NorRNG.sample_vector (dim, x);
    01262         vec smp = R.sqrt_mult (x);
    01263
    01264         smp += mu;
    01265         return smp;
    01266 };
    01267
    01268 // template<class sq_T>
    01269 // double enorm<sq_T>::eval ( const vec &val ) const {
    01270 //      double pdfl,e;
    01271 //      pdfl = evallog ( val );
    01272 //      e = exp ( pdfl );
    01273 //      return e;
    01274 // };
    01275
    01276 template<class sq_T>
    01277 double enorm<sq_T>::evallog_nn (const vec &val) const
    01278 {
    01279         // 1.83787706640935 = log(2pi)
    01280         double tmp = -0.5 * (R.invqform (mu - val));// - lognc();
    01281         return  tmp;
    01282 };
    01283
    01284 template<class sq_T>
    01285 inline double enorm<sq_T>::lognc () const
    01286 {
    01287         // 1.83787706640935 = log(2pi)
    01288         double tmp = 0.5 * (R.cols() * 1.83787706640935 + R.logdet());
    01289         return tmp;
    01290 };
    01291
    01292
    01293 // template<class sq_T>
    01294 // vec mlnorm<sq_T>::samplecond (const  vec &cond, double &lik ) {
    01295 //      this->condition ( cond );
    01296 //      vec smp = epdf.sample();
    01297 //      lik = epdf.eval ( smp );
    01298 //      return smp;
    01299 // }
    01300
    01301 // template<class sq_T>
    01302 // mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {
    01303 //      int i;
    01304 //      int dim = rv.count();
    01305 //      mat Smp ( dim,n );
    01306 //      vec smp ( dim );
    01307 //      this->condition ( cond );
    01308 //
    01309 //      for ( i=0; i<n; i++ ) {
    01310 //              smp = epdf.sample();
    01311 //              lik ( i ) = epdf.eval ( smp );
    01312 //              Smp.set_col ( i ,smp );
    01313 //      }
    01314 //
    01315 //      return Smp;
    01316 // }
    01317
    01318
    01319 template<class sq_T>
    01320 shared_ptr<epdf> enorm<sq_T>::marginal ( const RV &rvn ) const
    01321 {
    01322         enorm<sq_T> *tmp = new enorm<sq_T> ();
    01323         shared_ptr<epdf> narrow(tmp);
    01324         marginal ( rvn, *tmp );
    01325         return narrow;
    01326 }
    01327
    01328 template<class sq_T>
    01329 void enorm<sq_T>::marginal ( const RV &rvn, enorm<sq_T> &target ) const
    01330 {
    01331         bdm_assert (isnamed(), "rv description is not assigned");
    01332         ivec irvn = rvn.dataind (rv);
    01333
    01334         sq_T Rn (R, irvn);  // select rows and columns of R
    01335
    01336         target.set_rv ( rvn );
    01337         target.set_parameters (mu (irvn), Rn);
    01338 }
    01339
    01340 template<class sq_T>
    01341 shared_ptr<mpdf> enorm<sq_T>::condition ( const RV &rvn ) const
    01342 {
    01343         mlnorm<sq_T> *tmp = new mlnorm<sq_T> ();
    01344         shared_ptr<mpdf> narrow(tmp);
    01345         condition ( rvn, *tmp );
    01346         return narrow;
    01347 }
    01348
    01349 template<class sq_T>
    01350 void enorm<sq_T>::condition ( const RV &rvn, mpdf &target ) const
    01351 {
    01352         typedef mlnorm<sq_T> TMlnorm;
    01353
    01354         bdm_assert (isnamed(), "rvs are not assigned");
    01355         TMlnorm &uptarget = dynamic_cast<TMlnorm &>(target);
    01356
    01357         RV rvc = rv.subt (rvn);
    01358         bdm_assert ( (rvc._dsize() + rvn._dsize() == rv._dsize()), "wrong rvn");
    01359         //Permutation vector of the new R
    01360         ivec irvn = rvn.dataind (rv);
    01361         ivec irvc = rvc.dataind (rv);
    01362         ivec perm = concat (irvn , irvc);
    01363         sq_T Rn (R, perm);
    01364
    01365         //fixme - could this be done in general for all sq_T?
    01366         mat S = Rn.to_mat();
    01367         //fixme
    01368         int n = rvn._dsize() - 1;
    01369         int end = R.rows() - 1;
    01370         mat S11 = S.get (0, n, 0, n);
    01371         mat S12 = S.get (0, n , rvn._dsize(), end);
    01372         mat S22 = S.get (rvn._dsize(), end, rvn._dsize(), end);
    01373
    01374         vec mu1 = mu (irvn);
    01375         vec mu2 = mu (irvc);
    01376         mat A = S12 * inv (S22);
    01377         sq_T R_n (S11 - A *S12.T());
    01378
    01379         uptarget.set_rv (rvn);
    01380         uptarget.set_rvc (rvc);
    01381         uptarget.set_parameters (A, mu1 - A*mu2, R_n);
    01382 }
    01383
    01386 template<class sq_T>
    01387 void mgnorm<sq_T >::set_parameters (const shared_ptr<fnc> &g0, const sq_T &R0) {
    01388         g = g0;
    01389         this->iepdf.set_parameters (zeros (g->dimension()), R0);
    01390 }
    01391
    01392 template<class sq_T>
    01393 void mgnorm<sq_T >::condition (const vec &cond) {this->iepdf._mu() = g->eval (cond);};
    01394
    01396 template<class sq_T>
    01397 std::ostream &operator<< (std::ostream &os,  mlnorm<sq_T> &ml)
    01398 {
    01399         os << "A:" << ml.A << endl;
    01400         os << "mu:" << ml.mu_const << endl;
    01401         os << "R:" << ml._R() << endl;
    01402         return os;
    01403 };
    01404
    01405 }
    01406 #endif //EF_H