Changeset 283 for bdm/estim

Timestamp:

02/24/09 14:14:01 (15 years ago)

Author:

smidl

Message:

get rid of BMcond + adaptation in doprava/

Location:

bdm/estim

Files:

• arx.cpp (modified) (1 diff)
• arx.h (modified) (1 diff)
• ekf_templ.h (modified) (3 diffs)
• libKF.cpp (modified) (3 diffs)
• libKF.h (modified) (21 diffs)
• libPF.cpp (modified) (3 diffs)
• libPF.h (modified) (9 diffs)
• merger.cpp (modified) (1 diff)
• merger.h (modified) (1 diff)

bdm/estim/arx.cpp

@@ -44 +44 @@
 }
 
-ARX* ARX::_copy_ ( ) {
+ARX* ARX::_copy_ ( ) const {
         ARX* Tmp=new ARX ( *this );
         return Tmp;

bdm/estim/arx.h

@@ -55 +55 @@
         ARX ( const double frg0=1.0 ) : BMEF ( frg0 ),est (), V ( est._V() ), nu ( est._nu() ) {};
         ARX ( const ARX &A0 ) : BMEF (),est ( A0.est ), V ( est._V() ), nu ( est._nu() ) {};
-        ARX* _copy_();
+        ARX* _copy_() const;
         void set_parameters ( double frg0 ) {frg=frg0;}
         void set_statistics ( int dimx0, const ldmat V0, double nu0=-1.0 ) {est.set_parameters ( dimx0,V0,nu0 );last_lognc=est.lognc();dimx=dimx0;}

bdm/estim/ekf_templ.h

@@ -19 +19 @@
 
 //!Extended Kalman filter with unknown \c Q and \c R
-class EKFful_unQR : public EKFfull , public BMcond {
+class EKFful_unQR : public EKFfull  {
 public:
-        //! Default constructor
-        EKFful_unQR ( ) :EKFfull ( ),BMcond ( ) {};
         void condition ( const vec &QR0 ) {
                 Q=diag(QR0(0,dimx-1));
@@ -30 +28 @@
 
 //!Extended Kalman filter in Choleski form with unknown \c Q
-class EKFCh_unQ : public EKFCh , public BMcond {
+class EKFCh_unQ : public EKFCh {
 public:
-        //! Default constructor
-        EKFCh_unQ (  ) :EKFCh ( ),BMcond ( ) {};
         void condition ( const vec &Q0 ) {
                 Q.setD ( Q0,0 );
@@ -42 +38 @@
 
 //!Extended Kalman filter with unknown parameters in \c IM
-class EKFCh_cond : public EKFCh , public BMcond {
+class EKFCh_cond : public EKFCh  {
         public:
-        //! Default constructor
-                EKFCh_cond ( ) :EKFCh ( ),BMcond ( ) {};
                 void condition ( const vec &val ) {
                         pfxu->condition ( val );

bdm/estim/libKF.cpp

@@ -69 +69 @@
 
         dimx = pfxu0->_dimx();
-        dimy = phxu0->_dimy();
+        dimy = phxu0->dimension();
         dimu = pfxu0->_dimu();
 
@@ -169 +169 @@
         phxu = phxu0;
 
-        dimx = pfxu0->_dimy();
-        dimy = phxu0->_dimy();
+        dimx = pfxu0->dimension();
+        dimy = phxu0->dimension();
         dimu = pfxu0->_dimu();
         // set size of mu - just for constant terms of A and C 
@@ -249 +249 @@
 }
 
-void KFcondQR::condition ( const vec &QR ) {
-        it_assert_debug ( QR.length() == ( dimx+dimy ),"KFcondRQ: conditioning by incompatible vector" );
-
-        Q.setD ( QR ( 0, dimx-1 ) );
-        R.setD ( QR ( dimx, -1 ) );
-};
-
-void KFcondR::condition ( const vec &R0 ) {
-        it_assert_debug ( R0.length() == ( dimy ),"KFcondR: conditioning by incompatible vector" );
-
-        R.setD ( R0 );
-};
-
-
-}
+}

bdm/estim/libKF.h

@@ -19 +19 @@
 #include "../math/chmat.h"
 
-namespace bdm{
+namespace bdm {
 
 /*!
@@ -49 +49 @@
         friend std::ostream &operator<< ( std::ostream &os, const KalmanFull &kf );
         //! For EKFfull;
-        KalmanFull(){};
+        KalmanFull() {};
 };
 
@@ -56 +56 @@
 * \brief Kalman filter with covariance matrices in square root form.
 
-Parameter evolution model:\f[ x_t = A x_{t-1} + B u_t + Q^{1/2} e_t \f] 
+Parameter evolution model:\f[ x_t = A x_{t-1} + B u_t + Q^{1/2} e_t \f]
 Observation model: \f[ y_t = C x_{t-1} + C u_t + Q^{1/2} w_t. \f]
-Where $e_t$ and $w_t$ are independent vectors Normal(0,1)-distributed disturbances. 
+Where $e_t$ and $w_t$ are independent vectors Normal(0,1)-distributed disturbances.
 */
 template<class sq_T>
@@ -77 +77 @@
         mat A;
         //! Matrix B
-        mat B; 
+        mat B;
         //! Matrix C
         mat C;
@@ -112 +112 @@
         //! Set estimate values, used e.g. in initialization.
         void set_est ( const vec &mu0, const sq_T &P0 ) {
-                sq_T pom(dimy);
+                sq_T pom ( dimy );
                 est.set_parameters ( mu0,P0 );
-                P0.mult_sym(C,pom);
+                P0.mult_sym ( C,pom );
                 fy.set_parameters ( C*mu0, pom );
         };
@@ -133 +133 @@
 Trivial example:
 \include kalman_simple.cpp
- 
-*/
-class KalmanCh : public Kalman<chmat>{
+
+*/
+class KalmanCh : public Kalman<chmat> {
 protected:
 //! pre array (triangular matrix)
-mat preA;
+        mat preA;
 //! post array (triangular matrix)
-mat postA;
-
-public:
-        //! Default constructor
-        KalmanCh ():Kalman<chmat>(),preA(),postA(){};
+        mat postA;
+
+public:
+        //! copy constructor
+        BM* _copy_() const {
+                KalmanCh* K=new KalmanCh;
+                K->set_parameters ( A,B,C,D,Q,R );
+                K->set_statistics ( _mu,_P );
+                return K;
+        }
         //! Set parameters with check of relevance
         void set_parameters ( const mat &A0,const mat &B0,const mat &C0,const mat &D0,const chmat &Q0,const chmat &R0 );
@@ -150 +155 @@
                 est.set_parameters ( mu0,P0 );
         };
-        
-        
+
+
         /*!\brief  Here dt = [yt;ut] of appropriate dimensions
-        
+
         The following equality hold::\f[
-\left[\begin{array}{cc}
-R^{0.5}\\
-P_{t|t-1}^{0.5}C' & P_{t|t-1}^{0.5}CA'\\
- & Q^{0.5}\end{array}\right]<\mathrm{orth.oper.}>=\left[\begin{array}{cc}
-R_{y}^{0.5} & KA'\\
- & P_{t+1|t}^{0.5}\\
-\\\end{array}\right]\f]
-
-Thus this object evaluates only predictors! Not filtering densities.
+        \left[\begin{array}{cc}
+        R^{0.5}\\
+        P_{t|t-1}^{0.5}C' & P_{t|t-1}^{0.5}CA'\\
+        & Q^{0.5}\end{array}\right]<\mathrm{orth.oper.}>=\left[\begin{array}{cc}
+        R_{y}^{0.5} & KA'\\
+        & P_{t+1|t}^{0.5}\\
+        \\\end{array}\right]\f]
+
+        Thus this object evaluates only predictors! Not filtering densities.
         */
         void bayes ( const vec &dt );
@@ -179 +184 @@
         //! Observation Model h(x,u)
         diffbifn* phxu;
-        
-        enorm<fsqmat> E; 
+
+        enorm<fsqmat> E;
 public:
         //! Default constructor
@@ -189 +194 @@
         void bayes ( const vec &dt );
         //! set estimates
-        void set_est (vec mu0, mat P0){mu=mu0;P=P0;};
+        void set_est ( vec mu0, mat P0 ) {mu=mu0;P=P0;};
         //!dummy!
-        const epdf& posterior()const{return E;};
-        const enorm<fsqmat>* _e()const{return &E;};
-        const mat _R(){return P;}
+        const epdf& posterior() const{return E;};
+        const enorm<fsqmat>* _e() const{return &E;};
+        const mat _R() {return P;}
 };
 
@@ -210 +215 @@
         //! Default constructor
         EKF ( RV rvx, RV rvy, RV rvu );
+        //! copy constructor
+        EKF<sq_T>* _copy_() const { return new EKF<sq_T>(this); }
         //! Set nonlinear functions for mean values and covariance matrices.
         void set_parameters ( diffbifn* pfxu, diffbifn* phxu, const sq_T Q0, const sq_T R0 );
@@ -223 +230 @@
 
 class EKFCh : public KalmanCh {
-        protected:
+protected:
         //! Internal Model f(x,u)
         diffbifn* pfxu;
@@ -229 +236 @@
         diffbifn* phxu;
 public:
+        //! copy constructor duplicated - calls different set_parameters
+        BM* _copy_() const {
+                EKFCh* E=new EKFCh;
+                E->set_parameters ( pfxu,phxu,Q,R );
+                E->set_statistics ( _mu,_P );
+                return E;
+        }
         //! Set nonlinear functions for mean values and covariance matrices.
         void set_parameters ( diffbifn* pfxu, diffbifn* phxu, const chmat Q0, const chmat R0 );
@@ -239 +253 @@
 */
 
-class KFcondQR : public Kalman<ldmat>, public BMcond {
+class KFcondQR : public Kalman<ldmat> {
 //protected:
 public:
+        void condition ( const vec &QR ) {
+                it_assert_debug ( QR.length() == ( dimx+dimy ),"KFcondRQ: conditioning by incompatible vector" );
+
+                Q.setD ( QR ( 0, dimx-1 ) );
+                R.setD ( QR ( dimx, -1 ) );
+        };
+};
+
+/*!
+\brief Kalman Filter with conditional diagonal matrices R and Q.
+*/
+
+class KFcondR : public Kalman<ldmat> {
+//protected:
+public:
         //!Default constructor
-        KFcondQR ( ) : Kalman<ldmat> ( ),BMcond ( ) {};
-
-        void condition ( const vec &RQ );
-};
-
-/*!
-\brief Kalman Filter with conditional diagonal matrices R and Q.
-*/
-
-class KFcondR : public Kalman<ldmat>, public BMcond {
-//protected:
-public:
-        //!Default constructor
-        KFcondR ( ) : Kalman<ldmat> ( ),BMcond ( ) {};
-
-        void condition ( const vec &R );
+        KFcondR ( ) : Kalman<ldmat> ( ) {};
+
+        void condition ( const vec &R0 ) {
+                it_assert_debug ( R0.length() == ( dimy ),"KFcondR: conditioning by incompatible vector" );
+
+                R.setD ( R0 );
+        };
+
 };
 
@@ -267 +288 @@
                 dimx ( K0.dimx ), dimy ( K0.dimy ),dimu ( K0.dimu ),
                 A ( K0.A ), B ( K0.B ), C ( K0.C ), D ( K0.D ),
-                Q(K0.Q), R(K0.R),
-                est ( K0.est ), fy ( K0.fy ), _yp(fy._mu()),_Ry(fy._R()), _mu(est._mu()), _P(est._R()) {
+                Q ( K0.Q ), R ( K0.R ),
+                est ( K0.est ), fy ( K0.fy ), _yp ( fy._mu() ),_Ry ( fy._R() ), _mu ( est._mu() ), _P ( est._R() ) {
 
 // copy values in pointers
@@ -279 +300 @@
 
 template<class sq_T>
-Kalman<sq_T>::Kalman ( ) : BM (), est ( ), fy (),  _yp(fy._mu()), _Ry(fy._R()), _mu(est._mu()), _P(est._R()) {
+Kalman<sq_T>::Kalman ( ) : BM (), est ( ), fy (),  _yp ( fy._mu() ), _Ry ( fy._R() ), _mu ( est._mu() ), _P ( est._R() ) {
 };
 
@@ -287 +308 @@
         dimy = C0.rows();
         dimu = B0.cols();
-        
+
         it_assert_debug ( A0.cols() ==dimx, "Kalman: A is not square" );
         it_assert_debug ( B0.rows() ==dimx, "Kalman: B is not compatible" );
@@ -307 +328 @@
         it_assert_debug ( dt.length() == ( dimy+dimu ),"KalmanFull::bayes wrong size of dt" );
 
-        sq_T iRy(dimy);
+        sq_T iRy ( dimy );
         vec u = dt.get ( dimy,dimy+dimu-1 );
         vec y = dt.get ( 0,dimy-1 );
@@ -328 +349 @@
         sq_T pom ( ( int ) Pfull.rows() );
         iRy.mult_sym_t ( C*Pfull,pom );
-        (_P ) -= pom; // P = P -PC'iRy*CP;
-        (_yp ) = C* _mu  +D*u; //y prediction
-        (_mu ) += _K* ( y- _yp  );
+        ( _P ) -= pom; // P = P -PC'iRy*CP;
+        ( _yp ) = C* _mu  +D*u; //y prediction
+        ( _mu ) += _K* ( y- _yp );
 
 
@@ -340 +361 @@
 
 };
- 
+
 
 
@@ -369 +390 @@
         it_assert_debug ( dt.length() == ( dimy+dimu ),"KalmanFull::bayes wrong size of dt" );
 
-        sq_T iRy(dimy,dimy);
+        sq_T iRy ( dimy,dimy );
         vec u = dt.get ( dimy,dimy+dimu-1 );
         vec y = dt.get ( 0,dimy-1 );
@@ -393 +414 @@
         sq_T pom ( ( int ) Pfull.rows() );
         iRy.mult_sym_t ( C*Pfull,pom );
-        (_P ) -= pom; // P = P -PC'iRy*CP;
+        ( _P ) -= pom; // P = P -PC'iRy*CP;
         _yp = phxu->eval ( _mu,u ); //y prediction
         ( _mu ) += _K* ( y-_yp );

bdm/estim/libPF.cpp

@@ -1 +1 @@
 #include "libPF.h"
 
-namespace bdm{
+namespace bdm {
 
 using std::endl;
@@ -13 +13 @@
         for ( i=0;i<n;i++ ) {
                 //generate new samples from paramater evolution model;
-                _samples ( i ) = par->samplecond ( _samples ( i ));
-                lls(i )= par->_e()->evallog(_samples(i));
+                _samples ( i ) = par->samplecond ( _samples ( i ) );
+                lls ( i ) = par->_e()->evallog ( _samples ( i ) );
                 lls ( i ) *= obs->evallogcond ( dt,_samples ( i ) );
 
@@ -30 +30 @@
         _w ( i ) /=sum; //?
 
-        ind = est.resample();
+        ind = est.resample(resmethod);
 
 }
 
-void PF::set_est ( const epdf &epdf0 ) {
-        int i;
+// void PF::set_est ( const epdf &epdf0 ) {
+//      int i;
+// 
+//      for ( i=0;i<n;i++ ) {
+//              _samples ( i ) = epdf0.sample();
+//      }
+// }
 
-        for ( i=0;i<n;i++ ) {
-                _samples ( i ) = epdf0.sample();
-        }
-}
 
 }

bdm/estim/libPF.h

@@ -40 +40 @@
         mpdf *obs;
 
+        //! which resampling method will be used
+        RESAMPLING_METHOD resmethod;
+
         //! \name Options
         //!@{
 
-        //! Log resampling times
-        bool opt_L_res;
         //! Log all samples
         bool opt_L_smp;
+        //! Log all samples
+        bool opt_L_wei;
         //!@}
 
@@ -52 +55 @@
         //! \name Constructors
         //!@{
-        PF ( ) :est(), _w ( est._w() ),_samples ( est._samples() ) {};
-        PF ( mpdf *par0, mpdf *obs0, epdf *epdf0, int n0 ) :
-                        est ( ),_w ( est._w() ),_samples ( est._samples() )
-        { set_parameters ( par0,obs0,n0 ); set_statistics ( ones ( n0 ),epdf0 ); };
-        void set_parameters ( mpdf *par0, mpdf *obs0, int n0 )
-        { par = par0; obs=obs0; n=n0; est.set_n ( n );};
-        void set_statistics ( const vec w0, epdf *epdf0 ) {est.set_parameters ( w0,epdf0 );};
+        PF ( ) :est(), _w ( est._w() ),_samples ( est._samples() ), opt_L_smp ( false ), opt_L_wei ( false ) {LIDs.set_size ( 5 );};
+        /*      PF ( mpdf *par0, mpdf *obs0, epdf *epdf0, int n0 ) :
+                                est ( ),_w ( est._w() ),_samples ( est._samples() ),opt_L_smp(false), opt_L_wei(false)
+                { set_parameters ( par0,obs0,n0 ); set_statistics ( ones ( n0 ),epdf0 ); };*/
+        void set_parameters ( mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC )
+        { par = par0; obs=obs0; n=n0; resmethod= rm;};
+        void set_statistics ( const vec w0, epdf *epdf0 ) {est.set_statistics ( w0,epdf0 );};
         //!@}
         //! Set posterior density by sampling from epdf0
-        void set_est ( const epdf &epdf0 );
-        void set_options(const string &opt){
-                opt_L_res= ( s.find ( "logres" ) !=string::npos );
-                opt_L_smp= ( s.find ( "logsmp" ) !=string::npos );              
+//      void set_est ( const epdf &epdf0 );
+        void set_options ( const string &opt ) {
+                BM::set_options(opt);
+                opt_L_wei= ( opt.find ( "logweights" ) !=string::npos );
+                opt_L_smp= ( opt.find ( "logsamples" ) !=string::npos );
         }
         void bayes ( const vec &dt );
@@ -78 +82 @@
 
 template<class BM_T>
-
 class MPF : public PF {
-        BM_T* Bms[10000];
+        Array<BM_T*> BMs;
 
         //! internal class for MPDF providing composition of eEmp with external components
@@ -94 +97 @@
                                 Coms ( _w.length() ) {
                 };
+                //! read statistics from MPF
+                void read_statistics ( Array<BM_T*> &A ) {
+                        dim = E.dimension() +A ( 0 )->posterior().dimension();
+                        for ( int i=0; i<_w.length() ;i++ ) {Coms ( i ) = A ( i )->_e();}
+                }
+                //! needed in resampling
                 void set_elements ( int &i, double wi, const epdf* ep )
                 {_w ( i ) =wi; Coms ( i ) =ep;};
 
-                void set_n ( int n ) {E.set_n ( n ); Coms.set_length ( n );}
+                void set_parameters ( int n ) {
+                        E.set_parameters ( n, false );
+                        Coms.set_length ( n );
+                }
                 vec mean() const {
                         // ugly
@@ -114 +126 @@
                         return concat ( E.variance(),pom2-pow ( pom,2 ) );
                 }
+                void qbounds ( vec &lb, vec &ub, double perc=0.95 ) const {
+                        //bounds on particles
+                        vec lbp;
+                        vec ubp;
+                        E.qbounds ( lbp,ubp );
+
+                        //bounds on Components
+                        int dimC=Coms ( 0 )->dimension();
+                        int j;
+                        // temporary
+                        vec lbc(dimC);
+                        vec ubc(dimC);
+                        // minima and maxima
+                        vec Lbc(dimC);
+                        vec Ubc(dimC);
+                        Lbc = std::numeric_limits<double>::infinity();
+                        Ubc = -std::numeric_limits<double>::infinity();
+
+                        for ( int i=0;i<_w.length();i++ ) {
+                                // check Coms
+                                Coms ( i )->qbounds ( lbc,ubc );
+                                for ( j=0;j<dimC; j++ ) {
+                                        if ( lbc ( j ) <Lbc ( j ) ) {Lbc ( j ) =lbc ( j );}
+                                        if ( ubc ( j ) >Ubc ( j ) ) {Ubc ( j ) =ubc ( j );}
+                                }
+                        }
+                        lb=concat(lbp,Lbc);
+                        ub=concat(ubp,Ubc);
+                }
 
                 vec sample() const {it_error ( "Not implemented" );return 0;}
@@ -125 +166 @@
         //! Log means of BMs
         bool opt_L_mea;
-        
+
 public:
         //! Default constructor.
-        MPF ( mpdf *par0, mpdf *obs0, int n, const BM_T &BMcond0 ) : PF (), jest ( est ) {
-                PF::set_parameters ( par0,obs0,n );
-                jest.set_n ( n );
-                //
-                //TODO test if rv and BMcond.rv are compatible.
-//              rv.add ( rvlin );
-                //
-
-                if ( n>10000 ) {it_error ( "increase 10000 here!" );}
-
-                for ( int i=0;i<n;i++ ) {
-                        Bms[i] = new BM_T ( BMcond0 ); //copy constructor
-                        const epdf& pom=Bms[i]->posterior();
-                        jest.set_elements ( i,1.0/n,&pom );
-                }
+        MPF () : PF (), jest ( est ) {};
+        void set_parameters ( mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC ) {
+                PF::set_parameters ( par0, obs0, n0, rm );
+                jest.set_parameters ( n0 );//duplication of rm
+                BMs.set_length ( n0 );
+        }
+        void set_statistics ( epdf *epdf0, const BM_T* BMcond0 ) {
+
+                PF::set_statistics ( ones ( n ) /n, epdf0 );
+                // copy
+                for ( int i=0;i<n;i++ ) { BMs ( i ) = new BM_T ( *BMcond0 ); BMs ( i )->condition ( _samples ( i ) );}
+
+                jest.read_statistics ( BMs );
+                //options
         };
-
-        ~MPF() {
-        }
 
         void bayes ( const vec &dt );
         const epdf& posterior() const {return jest;}
         const epdf* _e() const {return &jest;} //Fixme: is it useful?
-        //! Set postrior of \c rvc to samples from epdf0. Statistics of Bms are not re-computed! Use only for initialization!
-        void set_est ( const epdf& epdf0 ) {
-                PF::set_est ( epdf0 );  // sample params in condition
-                // copy conditions to BMs
-
-                for ( int i=0;i<n;i++ ) {Bms[i]->condition ( _samples ( i ) );}
-        }
-        void set_options(const string &opt){
-                PF:set_options(opt);
-                opt_L_mea = ( s.find ( "logmeans" ) !=string::npos );
-        }
-        
+        //! Set postrior of \c rvc to samples from epdf0. Statistics of BMs are not re-computed! Use only for initialization!
+        /*      void set_est ( const epdf& epdf0 ) {
+                        PF::set_est ( epdf0 );  // sample params in condition
+                        // copy conditions to BMs
+
+                        for ( int i=0;i<n;i++ ) {BMs(i)->condition ( _samples ( i ) );}
+                }*/
+        void set_options ( const string &opt ) {
+                PF::set_options ( opt );
+                opt_L_mea = ( opt.find ( "logmeans" ) !=string::npos );
+        }
+
         //!Access function
-        BM* _BM ( int i ) {return Bms[i];}
+        BM* _BM ( int i ) {return BMs ( i );}
 };
 
@@ -180 +217 @@
                 _samples ( i ) = par->samplecond ( _samples ( i ) );
                 llsP ( i ) = par->_e()->evallog ( _samples ( i ) );
-                Bms[i]->condition ( _samples ( i ) );
-                Bms[i]->bayes ( dt );
-                lls ( i ) = Bms[i]->_ll(); // lls above is also in proposal her must be lls(i) =, not +=!!
+                BMs ( i )->condition ( _samples ( i ) );
+                BMs ( i )->bayes ( dt );
+                lls ( i ) = BMs ( i )->_ll(); // lls above is also in proposal her must be lls(i) =, not +=!!
                 if ( lls ( i ) >mlls ) mlls=lls ( i ); //find maximum likelihood (for numerical stability)
         }
@@ -204 +241 @@
         double eff = 1.0/ ( _w*_w );
         if ( eff < ( 0.3*n ) ) {
-                ind = est.resample();
+                ind = est.resample ( resmethod );
                 // Resample Bms!
 
@@ -215 +252 @@
                                 // poor-man's solution: replicate constructor here
                                 // copied from MPF::MPF
-                                delete Bms[i];
-                                Bms[i] = new BM_T ( *Bms[ind ( i ) ] ); //copy constructor
-                                const epdf& pom=Bms[i]->posterior();
+                                delete BMs ( i );
+                                BMs ( i ) = new BM_T ( *BMs ( ind ( i ) ) ); //copy constructor
+                                const epdf& pom=BMs ( i )->posterior();
                                 jest.set_elements ( i,1.0/n,&pom );
                         }

bdm/estim/merger.cpp

@@ -33 +33 @@
         it_assert_debug ( rv.equal ( g0->_rv() ),"Incompatible g0" );
         //Empirical density - samples
-        eSmp.set_parameters ( ones ( Ns ), g0 );
+        eSmp.set_statistics ( ones ( Ns ), g0 );
         Array<vec> &Smp = eSmp._samples(); //aux
         vec &w = eSmp._w(); //aux

bdm/estim/merger.h

@@ -71 +71 @@
         }
 //! Set internal parameters used in approximation
-        void set_parameters ( double beta0, int Ns0, int Nc0 ) {beta=beta0;Ns=Ns0;Nc=Nc0;eSmp.set_n(Ns0,false);}
+        void set_parameters ( double beta0, int Ns0, int Nc0 ) {beta=beta0;Ns=Ns0;Nc=Nc0;eSmp.set_parameters(Ns0,false);}
 //!Initialize the proposal density. This function must be called before merge()!
         void init() { ////////////// NOT FINISHED