- Main Page
- Related Pages
- Classes
- Files

- File List
- File Members

# exp_family.h

Go to the documentation of this file.

    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) {
    00220                         dimx = dimx0;
    00221                         nPsi = V0.rows() - dimx;
    00222                         dim = dimx * (dimx + nPsi); // size(R) + size(Theta)
    00223
    00224                         V = V0;
    00225                         if (nu0 < 0) {
    00226                                 nu = 0.1 + nPsi + 2 * dimx + 2; // +2 assures finite expected value of R
    00227                                 // terms before that are sufficient for finite normalization
    00228                         } else {
    00229                                 nu = nu0;
    00230                         }
    00231                 }
    00233
    00234                 vec sample() const;
    00235                 vec mean() const;
    00236                 vec variance() const;
    00237
    00239                 vec est_theta() const;
    00240
    00242                 ldmat est_theta_cov() const;
    00243
    00245                 void mean_mat (mat &M, mat&R) const;
    00247                 double evallog_nn (const vec &val) const;
    00248                 double lognc () const;
    00249                 void pow (double p) {V *= p;nu *= p;};
    00250
    00253
    00254                 ldmat& _V() {return V;}
    00255                 const ldmat& _V() const {return V;}
    00256                 double& _nu()  {return nu;}
    00257                 const double& _nu() const {return nu;}
    00272                 void from_setting (const Setting &set) {
    00273                         epdf::from_setting(set);
    00274                         if (!UI::get (nu, set, "nu", UI::compulsory)) {nu=-1;}
    00275                         UI::get (dimx, set, "dimx", UI::compulsory);
    00276                         mat V;
    00277                         UI::get (V, set, "V", UI::compulsory);
    00278                         set_parameters (dimx, V, nu);
    00279                 }
    00281 };
    00282 UIREGISTER ( egiw );
    00283 SHAREDPTR ( egiw );
    00284
    00293 class eDirich: public eEF
    00294 {
    00295         protected:
    00297                 vec beta;
    00298         public:
    00301
    00302                 eDirich () : eEF () {};
    00303                 eDirich (const eDirich &D0) : eEF () {set_parameters (D0.beta);};
    00304                 eDirich (const vec &beta0) {set_parameters (beta0);};
    00305                 void set_parameters (const vec &beta0) {
    00306                         beta = beta0;
    00307                         dim = beta.length();
    00308                 }
    00310
    00311                 vec sample() const {
    00312                         bdm_error ("Not implemented");
    00313                         return vec();
    00314                 }
    00315
    00316                 vec mean() const {return beta / sum (beta);};
    00317                 vec variance() const {double gamma = sum (beta); return elem_mult (beta, (beta + 1)) / (gamma* (gamma + 1));}
    00319                 double evallog_nn (const vec &val) const {
    00320                         double tmp; tmp = (beta - 1) * log (val);
    00321                         return tmp;
    00322                 }
    00323
    00324                 double lognc () const {
    00325                         double tmp;
    00326                         double gam = sum (beta);
    00327                         double lgb = 0.0;
    00328                         for (int i = 0;i < beta.length();i++) {lgb += lgamma (beta (i));}
    00329                         tmp = lgb - lgamma (gam);
    00330                         return tmp;
    00331                 }
    00332
    00334                 vec& _beta()  {return beta;}
    00336 };
    00337
    00339 class multiBM : public BMEF
    00340 {
    00341         protected:
    00343                 eDirich est;
    00345                 vec &beta;
    00346         public:
    00348                 multiBM () : BMEF (), est (), beta (est._beta()) {
    00349                         if (beta.length() > 0) {last_lognc = est.lognc();}
    00350                         else{last_lognc = 0.0;}
    00351                 }
    00353                 multiBM (const multiBM &B) : BMEF (B), est (B.est), beta (est._beta()) {}
    00355                 void set_statistics (const BM* mB0) {const multiBM* mB = dynamic_cast<const multiBM*> (mB0); beta = mB->beta;}
    00356                 void bayes (const vec &dt) {
    00357                         if (frg < 1.0) {beta *= frg;last_lognc = est.lognc();}
    00358                         beta += dt;
    00359                         if (evalll) {ll = est.lognc() - last_lognc;}
    00360                 }
    00361                 double logpred (const vec &dt) const {
    00362                         eDirich pred (est);
    00363                         vec &beta = pred._beta();
    00364
    00365                         double lll;
    00366                         if (frg < 1.0)
    00367                                 {beta *= frg;lll = pred.lognc();}
    00368                         else
    00369                                 if (evalll) {lll = last_lognc;}
    00370                                 else{lll = pred.lognc();}
    00371
    00372                         beta += dt;
    00373                         return pred.lognc() - lll;
    00374                 }
    00375                 void flatten (const BMEF* B) {
    00376                         const multiBM* E = dynamic_cast<const multiBM*> (B);
    00377                         // sum(beta) should be equal to sum(B.beta)
    00378                         const vec &Eb = E->beta;//const_cast<multiBM*> ( E )->_beta();
    00379                         beta *= (sum (Eb) / sum (beta));
    00380                         if (evalll) {last_lognc = est.lognc();}
    00381                 }
    00383                 const eDirich& posterior() const {return est;};
    00385                 void set_parameters (const vec &beta0) {
    00386                         est.set_parameters (beta0);
    00387                         if (evalll) {last_lognc = est.lognc();}
    00388                 }
    00389 };
    00390
    00400 class egamma : public eEF
    00401 {
    00402         protected:
    00404                 vec alpha;
    00406                 vec beta;
    00407         public :
    00410                 egamma () : eEF (), alpha (0), beta (0) {};
    00411                 egamma (const vec &a, const vec &b) {set_parameters (a, b);};
    00412                 void set_parameters (const vec &a, const vec &b) {alpha = a, beta = b;dim = alpha.length();};
    00414
    00415                 vec sample() const;
    00416                 double evallog (const vec &val) const;
    00417                 double lognc () const;
    00419                 vec& _alpha() {return alpha;}
    00421                 vec& _beta() {return beta;}
    00422                 vec mean() const {return elem_div (alpha, beta);}
    00423                 vec variance() const {return elem_div (alpha, elem_mult (beta, beta)); }
    00424
    00435                 void from_setting (const Setting &set) {
    00436                         epdf::from_setting (set); // reads rv
    00437                         UI::get (alpha, set, "alpha", UI::compulsory);
    00438                         UI::get (beta, set, "beta", UI::compulsory);
    00439                         validate();
    00440                 }
    00441                 void validate() {
    00442                         bdm_assert (alpha.length() == beta.length(), "parameters do not match");
    00443                         dim = alpha.length();
    00444                 }
    00445 };
    00446 UIREGISTER (egamma);
    00447 SHAREDPTR ( egamma );
    00448
    00465 class eigamma : public egamma
    00466 {
    00467         protected:
    00468         public :
    00473
    00474                 vec sample() const {return 1.0 / egamma::sample();};
    00476                 vec mean() const {return elem_div (beta, alpha - 1);}
    00477                 vec variance() const {vec mea = mean(); return elem_div (elem_mult (mea, mea), alpha - 2);}
    00478 };
    00479 /*
    00481 class emix : public epdf {
    00482 protected:
    00483         int n;
    00484         vec &w;
    00485         Array<epdf*> Coms;
    00486 public:
    00488         emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};
    00489         void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}
    00490         vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};
    00491 };
    00492 */
    00493
    00495
    00496 class euni: public epdf
    00497 {
    00498         protected:
    00500                 vec low;
    00502                 vec high;
    00504                 vec distance;
    00506                 double nk;
    00508                 double lnk;
    00509         public:
    00512                 euni () : epdf () {}
    00513                 euni (const vec &low0, const vec &high0) {set_parameters (low0, high0);}
    00514                 void set_parameters (const vec &low0, const vec &high0) {
    00515                         distance = high0 - low0;
    00516                         low = low0;
    00517                         high = high0;
    00518                         nk = prod (1.0 / distance);
    00519                         lnk = log (nk);
    00520                         dim = low.length();
    00521                 }
    00523
    00524                 double evallog (const vec &val) const  {
    00525                         if (any (val < low) && any (val > high)) {return inf;}
    00526                         else return lnk;
    00527                 }
    00528                 vec sample() const {
    00529                         vec smp (dim);
    00530 #pragma omp critical
    00531                         UniRNG.sample_vector (dim , smp);
    00532                         return low + elem_mult (distance, smp);
    00533                 }
    00535                 vec mean() const {return (high -low) / 2.0;}
    00536                 vec variance() const {return (pow (high, 2) + pow (low, 2) + elem_mult (high, low)) / 3.0;}
    00547                 void from_setting (const Setting &set) {
    00548                         epdf::from_setting (set); // reads rv and rvc
    00549
    00550                         UI::get (high, set, "high", UI::compulsory);
    00551                         UI::get (low, set, "low", UI::compulsory);
    00552                         set_parameters(low,high);
    00553                         validate();
    00554                 }
    00555                 void validate() {
    00556                         bdm_assert(high.length()==low.length(), "Incompatible high and low vectors");
    00557                         dim = high.length();
    00558                         bdm_assert (min (distance) > 0.0, "bad support");
    00559                 }
    00560 };
    00561 UIREGISTER(euni);
    00562
    00568 template < class sq_T, template <typename> class TEpdf = enorm >
    00569 class mlnorm : public mpdf_internal< TEpdf<sq_T> >
    00570 {
    00571         protected:
    00573                 mat A;
    00575                 vec mu_const;
    00576 //                      vec& _mu; //cached epdf.mu; !!!!!! WHY NOT?
    00577         public:
    00580                 mlnorm() : mpdf_internal< TEpdf<sq_T> >() {};
    00581                 mlnorm (const mat &A, const vec &mu0, const sq_T &R) : mpdf_internal< TEpdf<sq_T> >() {
    00582                         set_parameters (A, mu0, R);
    00583                 }
    00584
    00586                 void set_parameters (const  mat &A0, const vec &mu0, const sq_T &R0) {
    00587                         this->iepdf.set_parameters (zeros (A0.rows()), R0);
    00588                         A = A0;
    00589                         mu_const = mu0;
    00590                         this->dimc = A0.cols();
    00591                 }
    00594                 void condition (const vec &cond) {
    00595                         this->iepdf._mu() = A * cond + mu_const;
    00596 //R is already assigned;
    00597                 }
    00598
    00600                 const vec& _mu_const() const {return mu_const;}
    00602                 const mat& _A() const {return A;}
    00604                 mat _R() const { return this->iepdf._R().to_mat(); }
    00605
    00607                 template<typename sq_M>
    00608                 friend std::ostream &operator<< (std::ostream &os,  mlnorm<sq_M, enorm> &ml);
    00609
    00620                 void from_setting (const Setting &set) {
    00621                         mpdf::from_setting (set);
    00622
    00623                         UI::get (A, set, "A", UI::compulsory);
    00624                         UI::get (mu_const, set, "const", UI::compulsory);
    00625                         mat R0;
    00626                         UI::get (R0, set, "R", UI::compulsory);
    00627                         set_parameters (A, mu_const, R0);
    00628                         validate();
    00629                 };
    00630                 void validate() {
    00631                         bdm_assert (A.rows() == mu_const.length(), "mlnorm: A vs. mu mismatch");
    00632                         bdm_assert (A.rows() == _R().rows(), "mlnorm: A vs. R mismatch");
    00633
    00634                 }
    00635 };
    00636 UIREGISTER2 (mlnorm,ldmat);
    00637 SHAREDPTR2 ( mlnorm, ldmat );
    00638 UIREGISTER2 (mlnorm,fsqmat);
    00639 SHAREDPTR2 ( mlnorm, fsqmat );
    00640 UIREGISTER2 (mlnorm, chmat);
    00641 SHAREDPTR2 ( mlnorm, chmat );
    00642
    00644 template<class sq_T>
    00645 class mgnorm : public mpdf_internal< enorm< sq_T > >
    00646 {
    00647         private:
    00648 //                      vec &mu; WHY NOT?
    00649                 shared_ptr<fnc> g;
    00650
    00651         public:
    00653                 mgnorm() : mpdf_internal<enorm<sq_T> >() { }
    00655                 inline void set_parameters (const shared_ptr<fnc> &g0, const sq_T &R0);
    00656                 inline void condition (const vec &cond);
    00657
    00658
    00677                 void from_setting (const Setting &set) {
    00678                         mpdf::from_setting(set);
    00679                         shared_ptr<fnc> g = UI::build<fnc> (set, "g", UI::compulsory);
    00680
    00681                         mat R;
    00682                         vec dR;
    00683                         if (UI::get (dR, set, "dR"))
    00684                                 R = diag (dR);
    00685                         else
    00686                                 UI::get (R, set, "R", UI::compulsory);
    00687
    00688                         set_parameters (g, R);
    00689                         validate();
    00690                 }
    00691                 void validate() {
    00692                         bdm_assert(g->dimension()==this->dimension(),"incompatible function");
    00693                 }
    00694 };
    00695
    00696 UIREGISTER2 (mgnorm, chmat);
    00697 SHAREDPTR2 ( mgnorm, chmat );
    00698
    00699
    00707 class mlstudent : public mlnorm<ldmat, enorm>
    00708 {
    00709         protected:
    00711                 ldmat Lambda;
    00713                 ldmat &_R;
    00715                 ldmat Re;
    00716         public:
    00717                 mlstudent () : mlnorm<ldmat, enorm> (),
    00718                                 Lambda (),      _R (iepdf._R()) {}
    00720                 void set_parameters (const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0) {
    00721                         iepdf.set_parameters (mu0, R0);// was Lambda, why?
    00722                         A = A0;
    00723                         mu_const = mu0;
    00724                         Re = R0;
    00725                         Lambda = Lambda0;
    00726                 }
    00727                 void condition (const vec &cond) {
    00728                         iepdf._mu() = A * cond + mu_const;
    00729                         double zeta;
    00730                         //ugly hack!
    00731                         if ( (cond.length() + 1) == Lambda.rows()) {
    00732                                 zeta = Lambda.invqform (concat (cond, vec_1 (1.0)));
    00733                         } else {
    00734                                 zeta = Lambda.invqform (cond);
    00735                         }
    00736                         _R = Re;
    00737                         _R *= (1 + zeta);// / ( nu ); << nu is in Re!!!!!!
    00738                 };
    00739
    00740                 void validate() {
    00741                         bdm_assert (A.rows() == mu_const.length(), "mlstudent: A vs. mu mismatch");
    00742                         bdm_assert (_R.rows() == A.rows(), "mlstudent: A vs. R mismatch");
    00743
    00744                 }
    00745 };
    00755 class mgamma : public mpdf_internal<egamma>
    00756 {
    00757         protected:
    00758
    00760                 double k;
    00761
    00763                 vec &_beta;
    00764
    00765         public:
    00767                 mgamma() : mpdf_internal<egamma>(), k (0),
    00768                                 _beta (iepdf._beta()) {
    00769                 }
    00770
    00772                 void set_parameters (double k, const vec &beta0);
    00773
    00774                 void condition (const vec &val) {_beta = k / val;};
    00786                 void from_setting (const Setting &set) {
    00787                         mpdf::from_setting (set); // reads rv and rvc
    00788                         vec betatmp; // ugly but necessary
    00789                         UI::get (betatmp, set, "beta", UI::compulsory);
    00790                         UI::get (k, set, "k", UI::compulsory);
    00791                         set_parameters (k, betatmp);
    00792                 }
    00793 };
    00794 UIREGISTER (mgamma);
    00795 SHAREDPTR (mgamma);
    00796
    00806 class migamma : public mpdf_internal<eigamma>
    00807 {
    00808         protected:
    00810                 double k;
    00811
    00813                 vec &_alpha;
    00814
    00816                 vec &_beta;
    00817
    00818         public:
    00821                 migamma() : mpdf_internal<eigamma>(),
    00822                                 k (0),
    00823                                 _alpha (iepdf._alpha()),
    00824                                 _beta (iepdf._beta()) {
    00825                 }
    00826
    00827                 migamma (const migamma &m) : mpdf_internal<eigamma>(),
    00828                                 k (0),
    00829                                 _alpha (iepdf._alpha()),
    00830                                 _beta (iepdf._beta()) {
    00831                 }
    00833
    00835                 void set_parameters (int len, double k0) {
    00836                         k = k0;
    00837                         iepdf.set_parameters ( (1.0 / (k*k) + 2.0) *ones (len) /*alpha*/, ones (len) /*beta*/);
    00838                         dimc = dimension();
    00839                 };
    00840                 void condition (const vec &val) {
    00841                         _beta = elem_mult (val, (_alpha - 1.0));
    00842                 };
    00843 };
    00844
    00845
    00857 class mgamma_fix : public mgamma
    00858 {
    00859         protected:
    00861                 double l;
    00863                 vec refl;
    00864         public:
    00866                 mgamma_fix () : mgamma (), refl () {};
    00868                 void set_parameters (double k0 , vec ref0, double l0) {
    00869                         mgamma::set_parameters (k0, ref0);
    00870                         refl = pow (ref0, 1.0 - l0);l = l0;
    00871                         dimc = dimension();
    00872                 };
    00873
    00874                 void condition (const vec &val) {vec mean = elem_mult (refl, pow (val, l)); _beta = k / mean;};
    00875 };
    00876
    00877
    00890 class migamma_ref : public migamma
    00891 {
    00892         protected:
    00894                 double l;
    00896                 vec refl;
    00897         public:
    00899                 migamma_ref () : migamma (), refl () {};
    00901                 void set_parameters (double k0 , vec ref0, double l0) {
    00902                         migamma::set_parameters (ref0.length(), k0);
    00903                         refl = pow (ref0, 1.0 - l0);
    00904                         l = l0;
    00905                         dimc = dimension();
    00906                 };
    00907
    00908                 void condition (const vec &val) {
    00909                         vec mean = elem_mult (refl, pow (val, l));
    00910                         migamma::condition (mean);
    00911                 };
    00912
    00913
    00926                 void from_setting (const Setting &set);
    00927
    00928                 // TODO dodelat void to_setting( Setting &set ) const;
    00929 };
    00930
    00931
    00932 UIREGISTER (migamma_ref);
    00933 SHAREDPTR (migamma_ref);
    00934
    00945 class elognorm: public enorm<ldmat>
    00946 {
    00947         public:
    00948                 vec sample() const {return exp (enorm<ldmat>::sample());};
    00949                 vec mean() const {vec var = enorm<ldmat>::variance();return exp (mu - 0.5*var);};
    00950
    00951 };
    00952
    00959 class mlognorm : public mpdf_internal<elognorm>
    00960 {
    00961         protected:
    00963                 double sig2;
    00964
    00966                 vec &mu;
    00967         public:
    00969                 mlognorm() : mpdf_internal<elognorm>(),
    00970                                 sig2 (0),
    00971                                 mu (iepdf._mu()) {
    00972                 }
    00973
    00975                 void set_parameters (int size, double k) {
    00976                         sig2 = 0.5 * log (k * k + 1);
    00977                         iepdf.set_parameters (zeros (size), 2*sig2*eye (size));
    00978
    00979                         dimc = size;
    00980                 };
    00981
    00982                 void condition (const vec &val) {
    00983                         mu = log (val) - sig2;//elem_mult ( refl,pow ( val,l ) );
    00984                 };
    00985
    00997                 void from_setting (const Setting &set);
    00998
    00999                 // TODO dodelat void to_setting( Setting &set ) const;
    01000
    01001 };
    01002
    01003 UIREGISTER (mlognorm);
    01004 SHAREDPTR (mlognorm);
    01005
    01009 class eWishartCh : public epdf
    01010 {
    01011         protected:
    01013                 chmat Y;
    01015                 int p;
    01017                 double delta;
    01018         public:
    01020                 void set_parameters (const mat &Y0, const double delta0) {Y = chmat (Y0);delta = delta0; p = Y.rows(); dim = p * p; }
    01022                 mat sample_mat() const {
    01023                         mat X = zeros (p, p);
    01024
    01025                         //sample diagonal
    01026                         for (int i = 0;i < p;i++) {
    01027                                 GamRNG.setup (0.5* (delta - i) , 0.5);   // no +1 !! index if from 0
    01028 #pragma omp critical
    01029                                 X (i, i) = sqrt (GamRNG());
    01030                         }
    01031                         //do the rest
    01032                         for (int i = 0;i < p;i++) {
    01033                                 for (int j = i + 1;j < p;j++) {
    01034 #pragma omp critical
    01035                                         X (i, j) = NorRNG.sample();
    01036                                 }
    01037                         }
    01038                         return X*Y._Ch();// return upper triangular part of the decomposition
    01039                 }
    01040                 vec sample () const {
    01041                         return vec (sample_mat()._data(), p*p);
    01042                 }
    01044                 void setY (const mat &Ch0) {copy_vector (dim, Ch0._data(), Y._Ch()._data());}
    01046                 void _setY (const vec &ch0) {copy_vector (dim, ch0._data(), Y._Ch()._data()); }
    01048                 const chmat& getY() const {return Y;}
    01049 };
    01050
    01052
    01054 class eiWishartCh: public epdf
    01055 {
    01056         protected:
    01058                 eWishartCh W;
    01060                 int p;
    01062                 double delta;
    01063         public:
    01065                 void set_parameters (const mat &Y0, const double delta0) {
    01066                         delta = delta0;
    01067                         W.set_parameters (inv (Y0), delta0);
    01068                         dim = W.dimension(); p = Y0.rows();
    01069                 }
    01070                 vec sample() const {mat iCh; iCh = inv (W.sample_mat()); return vec (iCh._data(), dim);}
    01072                 void _setY (const vec &y0) {
    01073                         mat Ch (p, p);
    01074                         mat iCh (p, p);
    01075                         copy_vector (dim, y0._data(), Ch._data());
    01076
    01077                         iCh = inv (Ch);
    01078                         W.setY (iCh);
    01079                 }
    01080                 virtual double evallog (const vec &val) const {
    01081                         chmat X (p);
    01082                         const chmat& Y = W.getY();
    01083
    01084                         copy_vector (p*p, val._data(), X._Ch()._data());
    01085                         chmat iX (p);X.inv (iX);
    01086                         // compute
    01087 //                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},
    01088                         mat M = Y.to_mat() * iX.to_mat();
    01089
    01090                         double log1 = 0.5 * p * (2 * Y.logdet()) - 0.5 * (delta + p + 1) * (2 * X.logdet()) - 0.5 * trace (M);
    01091                         //Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!
    01092
    01093                         /*                              if (0) {
    01094                                                                 mat XX=X.to_mat();
    01095                                                                 mat YY=Y.to_mat();
    01096
    01097                                                                 double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX));
    01098                                                                 cout << log1 << "," << log2 << endl;
    01099                                                         }*/
    01100                         return log1;
    01101                 };
    01102
    01103 };
    01104
    01106 class rwiWishartCh : public mpdf_internal<eiWishartCh>
    01107 {
    01108         protected:
    01110                 double sqd;
    01112                 vec refl;
    01114                 double l;
    01116                 int p;
    01117
    01118         public:
    01119                 rwiWishartCh() : sqd (0), l (0), p (0) {}
    01121                 void set_parameters (int p0, double k, vec ref0, double l0) {
    01122                         p = p0;
    01123                         double delta = 2 / (k * k) + p + 3;
    01124                         sqd = sqrt (delta - p - 1);
    01125                         l = l0;
    01126                         refl = pow (ref0, 1 - l);
    01127
    01128                         iepdf.set_parameters (eye (p), delta);
    01129                         dimc = iepdf.dimension();
    01130                 }
    01131                 void condition (const vec &c) {
    01132                         vec z = c;
    01133                         int ri = 0;
    01134                         for (int i = 0;i < p*p;i += (p + 1)) {//trace diagonal element
    01135                                 z (i) = pow (z (i), l) * refl (ri);
    01136                                 ri++;
    01137                         }
    01138
    01139                         iepdf._setY (sqd*z);
    01140                 }
    01141 };
    01142
    01144 enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
    01150 class eEmp: public epdf
    01151 {
    01152         protected :
    01154                 int n;
    01156                 vec w;
    01158                 Array<vec> samples;
    01159         public:
    01162                 eEmp () : epdf (), w (), samples () {};
    01164                 eEmp (const eEmp &e) : epdf (e), w (e.w), samples (e.samples) {};
    01166
    01168                 void set_statistics (const vec &w0, const epdf &pdf0);
    01170                 void set_statistics (const epdf &pdf0 , int n) {set_statistics (ones (n) / n, pdf0);};
    01172                 void set_samples (const epdf* pdf0);
    01174                 void set_parameters (int n0, bool copy = true) {n = n0; w.set_size (n0, copy);samples.set_size (n0, copy);};
    01176                 void set_parameters (const Array<vec> &Av) {
    01177                         bdm_assert(Av.size()>0,"Empty samples");
    01178                         n = Av.size();
    01179                         epdf::set_parameters(Av(0).length());
    01180                         w=1/n*ones(n);
    01181                         samples=Av;
    01182                 };
    01184                 vec& _w()  {return w;};
    01186                 const vec& _w() const {return w;};
    01188                 Array<vec>& _samples() {return samples;};
    01190                 const Array<vec>& _samples() const {return samples;};
    01192                 ivec resample (RESAMPLING_METHOD method = SYSTEMATIC);
    01193
    01195                 vec sample() const {
    01196                         bdm_error ("Not implemented");
    01197                         return vec();
    01198                 }
    01199
    01201                 double evallog (const vec &val) const {
    01202                         bdm_error ("Not implemented");
    01203                         return 0.0;
    01204                 }
    01205
    01206                 vec mean() const {
    01207                         vec pom = zeros (dim);
    01208                         for (int i = 0;i < n;i++) {pom += samples (i) * w (i);}
    01209                         return pom;
    01210                 }
    01211                 vec variance() const {
    01212                         vec pom = zeros (dim);
    01213                         for (int i = 0;i < n;i++) {pom += pow (samples (i), 2) * w (i);}
    01214                         return pom -pow (mean(), 2);
    01215                 }
    01217                 void qbounds (vec &lb, vec &ub, double perc = 0.95) const {
    01218                         // lb in inf so than it will be pushed below;
    01219                         lb.set_size (dim);
    01220                         ub.set_size (dim);
    01221                         lb = std::numeric_limits<double>::infinity();
    01222                         ub = -std::numeric_limits<double>::infinity();
    01223                         int j;
    01224                         for (int i = 0;i < n;i++) {
    01225                                 for (j = 0;j < dim; j++) {
    01226                                         if (samples (i) (j) < lb (j)) {lb (j) = samples (i) (j);}
    01227                                         if (samples (i) (j) > ub (j)) {ub (j) = samples (i) (j);}
    01228                                 }
    01229                         }
    01230                 }
    01231 };
    01232
    01233
    01235
    01236 template<class sq_T>
    01237 void enorm<sq_T>::set_parameters (const vec &mu0, const sq_T &R0)
    01238 {
    01239 //Fixme test dimensions of mu0 and R0;
    01240         mu = mu0;
    01241         R = R0;
    01242         validate();
    01243 };
    01244
    01245 template<class sq_T>
    01246 void enorm<sq_T>::from_setting (const Setting &set)
    01247 {
    01248         epdf::from_setting (set); //reads rv
    01249
    01250         UI::get (mu, set, "mu", UI::compulsory);
    01251         mat Rtmp;// necessary for conversion
    01252         UI::get (Rtmp, set, "R", UI::compulsory);
    01253         R = Rtmp; // conversion
    01254         validate();
    01255 }
    01256
    01257 template<class sq_T>
    01258 void enorm<sq_T>::dupdate (mat &v, double nu)
    01259 {
    01260         //
    01261 };
    01262
    01263 // template<class sq_T>
    01264 // void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {
    01265 //      //
    01266 // };
    01267
    01268 template<class sq_T>
    01269 vec enorm<sq_T>::sample() const
    01270 {
    01271         vec x (dim);
    01272 #pragma omp critical
    01273         NorRNG.sample_vector (dim, x);
    01274         vec smp = R.sqrt_mult (x);
    01275
    01276         smp += mu;
    01277         return smp;
    01278 };
    01279
    01280 // template<class sq_T>
    01281 // double enorm<sq_T>::eval ( const vec &val ) const {
    01282 //      double pdfl,e;
    01283 //      pdfl = evallog ( val );
    01284 //      e = exp ( pdfl );
    01285 //      return e;
    01286 // };
    01287
    01288 template<class sq_T>
    01289 double enorm<sq_T>::evallog_nn (const vec &val) const
    01290 {
    01291         // 1.83787706640935 = log(2pi)
    01292         double tmp = -0.5 * (R.invqform (mu - val));// - lognc();
    01293         return  tmp;
    01294 };
    01295
    01296 template<class sq_T>
    01297 inline double enorm<sq_T>::lognc () const
    01298 {
    01299         // 1.83787706640935 = log(2pi)
    01300         double tmp = 0.5 * (R.cols() * 1.83787706640935 + R.logdet());
    01301         return tmp;
    01302 };
    01303
    01304
    01305 // template<class sq_T>
    01306 // vec mlnorm<sq_T>::samplecond (const  vec &cond, double &lik ) {
    01307 //      this->condition ( cond );
    01308 //      vec smp = epdf.sample();
    01309 //      lik = epdf.eval ( smp );
    01310 //      return smp;
    01311 // }
    01312
    01313 // template<class sq_T>
    01314 // mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {
    01315 //      int i;
    01316 //      int dim = rv.count();
    01317 //      mat Smp ( dim,n );
    01318 //      vec smp ( dim );
    01319 //      this->condition ( cond );
    01320 //
    01321 //      for ( i=0; i<n; i++ ) {
    01322 //              smp = epdf.sample();
    01323 //              lik ( i ) = epdf.eval ( smp );
    01324 //              Smp.set_col ( i ,smp );
    01325 //      }
    01326 //
    01327 //      return Smp;
    01328 // }
    01329
    01330
    01331 template<class sq_T>
    01332 shared_ptr<epdf> enorm<sq_T>::marginal ( const RV &rvn ) const
    01333 {
    01334         enorm<sq_T> *tmp = new enorm<sq_T> ();
    01335         shared_ptr<epdf> narrow(tmp);
    01336         marginal ( rvn, *tmp );
    01337         return narrow;
    01338 }
    01339
    01340 template<class sq_T>
    01341 void enorm<sq_T>::marginal ( const RV &rvn, enorm<sq_T> &target ) const
    01342 {
    01343         bdm_assert (isnamed(), "rv description is not assigned");
    01344         ivec irvn = rvn.dataind (rv);
    01345
    01346         sq_T Rn (R, irvn);  // select rows and columns of R
    01347
    01348         target.set_rv ( rvn );
    01349         target.set_parameters (mu (irvn), Rn);
    01350 }
    01351
    01352 template<class sq_T>
    01353 shared_ptr<mpdf> enorm<sq_T>::condition ( const RV &rvn ) const
    01354 {
    01355         mlnorm<sq_T> *tmp = new mlnorm<sq_T> ();
    01356         shared_ptr<mpdf> narrow(tmp);
    01357         condition ( rvn, *tmp );
    01358         return narrow;
    01359 }
    01360
    01361 template<class sq_T>
    01362 void enorm<sq_T>::condition ( const RV &rvn, mpdf &target ) const
    01363 {
    01364         typedef mlnorm<sq_T> TMlnorm;
    01365
    01366         bdm_assert (isnamed(), "rvs are not assigned");
    01367         TMlnorm &uptarget = dynamic_cast<TMlnorm &>(target);
    01368
    01369         RV rvc = rv.subt (rvn);
    01370         bdm_assert ( (rvc._dsize() + rvn._dsize() == rv._dsize()), "wrong rvn");
    01371         //Permutation vector of the new R
    01372         ivec irvn = rvn.dataind (rv);
    01373         ivec irvc = rvc.dataind (rv);
    01374         ivec perm = concat (irvn , irvc);
    01375         sq_T Rn (R, perm);
    01376
    01377         //fixme - could this be done in general for all sq_T?
    01378         mat S = Rn.to_mat();
    01379         //fixme
    01380         int n = rvn._dsize() - 1;
    01381         int end = R.rows() - 1;
    01382         mat S11 = S.get (0, n, 0, n);
    01383         mat S12 = S.get (0, n , rvn._dsize(), end);
    01384         mat S22 = S.get (rvn._dsize(), end, rvn._dsize(), end);
    01385
    01386         vec mu1 = mu (irvn);
    01387         vec mu2 = mu (irvc);
    01388         mat A = S12 * inv (S22);
    01389         sq_T R_n (S11 - A *S12.T());
    01390
    01391         uptarget.set_rv (rvn);
    01392         uptarget.set_rvc (rvc);
    01393         uptarget.set_parameters (A, mu1 - A*mu2, R_n);
    01394 }
    01395
    01398 template<class sq_T>
    01399 void mgnorm<sq_T >::set_parameters (const shared_ptr<fnc> &g0, const sq_T &R0) {
    01400         g = g0;
    01401         this->iepdf.set_parameters (zeros (g->dimension()), R0);
    01402 }
    01403
    01404 template<class sq_T>
    01405 void mgnorm<sq_T >::condition (const vec &cond) {this->iepdf._mu() = g->eval (cond);};
    01406
    01408 template<class sq_T>
    01409 std::ostream &operator<< (std::ostream &os,  mlnorm<sq_T> &ml)
    01410 {
    01411         os << "A:" << ml.A << endl;
    01412         os << "mu:" << ml.mu_const << endl;
    01413         os << "R:" << ml._R() << endl;
    01414         return os;
    01415 };
    01416
    01417 }
    01418 #endif //EF_H