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

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#ifndef EF_H
#define EF_H

#include <itpp/itbase.h>
#include "../math/libDC.h"
#include "libBM.h"
#include "../itpp_ext.h"
//#include <std>

using namespace itpp;


extern Uniform_RNG UniRNG;
extern Normal_RNG NorRNG;
extern Gamma_RNG GamRNG;

class eEF : public epdf {
public:
//      eEF() :epdf() {};
        eEF ( const RV &rv ) :epdf ( rv ) {};
        virtual double lognc() const =0;
        virtual void dupdate ( mat &v ) {it_error ( "Not implemneted" );};
        virtual double evalpdflog_nn ( const vec &val ) const{it_error ( "Not implemneted" );return 0.0;};
        virtual double evalpdflog ( const vec &val ) const {return evalpdflog_nn ( val )-lognc();}
        virtual vec evalpdflog ( const mat &Val ) const {
                vec x ( Val.cols() );
                for ( int i=0;i<Val.cols();i++ ) {x ( i ) =evalpdflog_nn ( Val.get_col ( i ) ) ;}
                return x-lognc();
        }
        virtual void pow ( double p ) {it_error ( "Not implemented" );};
};

class mEF : public mpdf {

public:
        mEF ( const RV &rv0, const RV &rvc0 ) :mpdf ( rv0,rvc0 ) {};
};

class BMEF : public BM {
protected:
        double frg;
        double last_lognc;
public:
        BMEF ( const RV &rv, double frg0=1.0 ) :BM ( rv ), frg ( frg0 ) {}
        BMEF ( const BMEF &B ) :BM ( B ), frg ( B.frg ), last_lognc ( B.last_lognc ) {}
        virtual void set_statistics ( const BMEF* BM0 ) {it_error ( "Not implemented" );};
        virtual void bayes ( const vec &data, const double w ) {};
        //original Bayes
        void bayes ( const vec &dt );
        virtual void flatten ( BMEF * B) {it_error ( "Not implemented" );}
};

template<class sq_T>

class enorm : public eEF {
protected:
        vec mu;
        sq_T R;
        int dim;
public:
//      enorm() :eEF() {};
        enorm ( RV &rv );
        void set_parameters ( const vec &mu,const sq_T &R );
        void tupdate ( double phi, mat &vbar, double nubar );
        void dupdate ( mat &v,double nu=1.0 );

        vec sample() const;
        mat sample ( int N ) const;
        double eval ( const vec &val ) const ;
        double evalpdflog ( const vec &val ) const;
        double lognc () const;
        vec mean() const {return mu;}

//Access methods
        vec& _mu() {return mu;}

        void set_mu ( const vec mu0 ) { mu=mu0;}

        sq_T& _R() {return R;}

        mat getR () {return R.to_mat();}
};

class egiw : public eEF {
protected:
        ldmat V;
        double nu;
        int xdim;
        int nPsi;
public:
        egiw ( RV rv, mat V0, double nu0 ) : eEF ( rv ), V ( V0 ), nu ( nu0 ) {
                xdim = rv.count() /V.rows();
                it_assert_debug ( rv.count() ==xdim*V.rows(),"Incompatible V0." );
                nPsi = V.rows()-xdim;
        }
        egiw ( RV rv, ldmat V0, double nu0 ) : eEF ( rv ), V ( V0 ), nu ( nu0 ) {
                xdim = rv.count() /V.rows();
                it_assert_debug ( rv.count() ==xdim*V.rows(),"Incompatible V0." );
                nPsi = V.rows()-xdim;
        }

        vec sample() const;
        vec mean() const;
        void mean_mat ( mat &M, mat&R ) const;
        double evalpdflog_nn ( const vec &val ) const;
        double lognc () const;

        //Access
        ldmat& _V() {return V;}
        double& _nu() {return nu;}
        void pow ( double p );
};

class eDirich: public eEF {
protected:
        vec beta;
public:
        eDirich ( const RV &rv, const vec &beta0 ) : eEF ( rv ),beta ( beta0 ) {it_assert_debug ( rv.count() ==beta.length(),"Incompatible statistics" ); };
        eDirich ( const eDirich &D0 ) : eEF ( D0.rv ),beta ( D0.beta ) {};
        vec sample() const {it_error ( "Not implemented" );return vec_1 ( 0.0 );};
        vec mean() const {return beta/sum ( beta );};
        double evalpdflog_nn ( const vec &val ) const {return ( beta-1 ) *log ( val );};
        double lognc () const {
                double gam=sum ( beta );
                double lgb=0.0;
                for ( int i=0;i<beta.length();i++ ) {lgb+=lgamma ( beta ( i ) );}
                return lgb-lgamma ( gam );
        };
        vec& _beta() {return beta;}
};

class multiBM : public BMEF {
protected:
        eDirich est;
        vec &beta;
public:
        multiBM ( const RV &rv, const vec beta0 ) : BMEF ( rv ),est ( rv,beta0 ),beta ( est._beta() ) {last_lognc=est.lognc();}
        multiBM ( const multiBM &B ) : BMEF ( B ),est ( rv,B.beta ),beta ( est._beta() ) {}

        void set_statistics ( const BM* mB0 ) {const multiBM* mB=dynamic_cast<const multiBM*> ( mB0 ); beta=mB->beta;}
        void bayes ( const vec &dt ) {
                if ( frg<1.0 ) {beta*=frg;last_lognc=est.lognc();}
                beta+=dt;
                if ( evalll ) {ll=est.lognc()-last_lognc;}
        }
        double logpred ( const vec &dt ) const {
                eDirich pred ( est );
                vec &beta = pred._beta();
                
                double lll;
                if ( frg<1.0 )
                        {beta*=frg;lll=pred.lognc();}
                else
                        if ( evalll ) {lll=last_lognc;}
                        else{lll=pred.lognc();}

                beta+=dt;
                return pred.lognc()-lll;
        }
        void flatten (BMEF* B ) {
                eDirich* E=dynamic_cast<eDirich*>(B);
                // sum(beta) should be equal to sum(B.beta)
                const vec &Eb=E->_beta();
                est.pow ( sum(beta)/sum(Eb) );
                if(evalll){last_lognc=est.lognc();}
        }
        const epdf& _epdf() const {return est;};
};

class egamma : public eEF {
protected:
        vec alpha;
        vec beta;
public :
        egamma ( const RV &rv ) :eEF ( rv ) {};
        void set_parameters ( const vec &a, const vec &b ) {alpha=a,beta=b;};
        vec sample() const;
//      mat sample ( int N ) const;
        double evalpdflog ( const vec &val ) const;
        double lognc () const;
        void _param ( vec* &a, vec* &b ) {a=&alpha;b=&beta;};
        vec mean() const {vec pom ( alpha ); pom/=beta; return pom;}
};
/*
class emix : public epdf {
protected:
        int n;
        vec &w;
        Array<epdf*> Coms;
public:
        emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};
        void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}
        vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};
        vec sample() {it_error ( "Not implemented" );return 0;}
};
*/


class euni: public epdf {
protected:
        vec low;
        vec high;
        vec distance;
        double nk;
        double lnk;
public:
        euni ( const RV rv ) :epdf ( rv ) {}
        double eval ( const vec &val ) const  {return nk;}
        double evalpdflog ( const vec &val ) const  {return lnk;}
        vec sample() const {
                vec smp ( rv.count() );
#pragma omp critical
                UniRNG.sample_vector ( rv.count(),smp );
                return low+elem_mult ( distance,smp );
        }
        void set_parameters ( const vec &low0, const vec &high0 ) {
                distance = high0-low0;
                it_assert_debug ( min ( distance ) >0.0,"bad support" );
                low = low0;
                high = high0;
                nk = prod ( 1.0/distance );
                lnk = log ( nk );
        }
        vec mean() const {vec pom=high; pom-=low; pom/=2.0; return pom;}
};


template<class sq_T>
class mlnorm : public mEF {
        enorm<sq_T> epdf;
        mat A;
        vec& _mu; //cached epdf.mu;
public:
        mlnorm ( RV &rv,RV &rvc );
        void set_parameters ( const  mat &A, const sq_T &R );
        vec samplecond ( vec &cond, double &lik );
        mat samplecond ( vec &cond, vec &lik, int n );
        void condition ( vec &cond );
};

class mgamma : public mEF {
protected:
        egamma epdf;
        double k;
        vec* _beta;

public:
        mgamma ( const RV &rv,const RV &rvc );
        void set_parameters ( double k );
        void condition ( const vec &val ) {*_beta=k/val;};
};

class mgamma_fix : public mgamma {
protected:
        double l;
        vec refl;
public:
        mgamma_fix ( const RV &rv,const RV &rvc ) : mgamma ( rv,rvc ),refl ( rv.count() ) {};
        void set_parameters ( double k0 , vec ref0, double l0 ) {
                mgamma::set_parameters ( k0 );
                refl=pow ( ref0,1.0-l0 );l=l0;
        };

        void condition ( const vec &val ) {vec mean=elem_mult ( refl,pow ( val,l ) ); *_beta=k/mean;};
};

enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
class eEmp: public epdf {
protected :
        int n;
        vec w;
        Array<vec> samples;
public:
        eEmp ( const RV &rv0 ,int n0 ) :epdf ( rv0 ),n ( n0 ),w ( n ),samples ( n ) {};
        void set_parameters ( const vec &w0, epdf* pdf0 );
        vec& _w()  {return w;};
        Array<vec>& _samples() {return samples;};
        ivec resample ( RESAMPLING_METHOD method = SYSTEMATIC );
        vec sample() const {it_error ( "Not implemented" );return 0;}
        double evalpdflog ( const vec &val ) const {it_error ( "Not implemented" );return 0.0;}
        vec mean() const {
                vec pom=zeros ( rv.count() );
                for ( int i=0;i<n;i++ ) {pom+=samples ( i ) *w ( i );}
                return pom;
        }
};



template<class sq_T>
enorm<sq_T>::enorm ( RV &rv ) :eEF ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {};

template<class sq_T>
void enorm<sq_T>::set_parameters ( const vec &mu0, const sq_T &R0 ) {
//Fixme test dimensions of mu0 and R0;
        mu = mu0;
        R = R0;
};

template<class sq_T>
void enorm<sq_T>::dupdate ( mat &v, double nu ) {
        //
};

template<class sq_T>
void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {
        //
};

template<class sq_T>
vec enorm<sq_T>::sample() const {
        vec x ( dim );
        NorRNG.sample_vector ( dim,x );
        vec smp = R.sqrt_mult ( x );

        smp += mu;
        return smp;
};

template<class sq_T>
mat enorm<sq_T>::sample ( int N ) const {
        mat X ( dim,N );
        vec x ( dim );
        vec pom;
        int i;

        for ( i=0;i<N;i++ ) {
                NorRNG.sample_vector ( dim,x );
                pom = R.sqrt_mult ( x );
                pom +=mu;
                X.set_col ( i, pom );
        }

        return X;
};

template<class sq_T>
double enorm<sq_T>::eval ( const vec &val ) const {
        double pdfl,e;
        pdfl = evalpdflog ( val );
        e = exp ( pdfl );
        return e;
};

template<class sq_T>
double enorm<sq_T>::evalpdflog ( const vec &val ) const {
        // 1.83787706640935 = log(2pi)
        return  -0.5* ( +R.invqform ( mu-val ) ) - lognc();
};

template<class sq_T>
inline double enorm<sq_T>::lognc () const {
        // 1.83787706640935 = log(2pi)
        return -0.5* ( R.cols() * 1.83787706640935 +R.logdet() );
};

template<class sq_T>
mlnorm<sq_T>::mlnorm ( RV &rv0,RV &rvc0 ) :mEF ( rv0,rvc0 ),epdf ( rv0 ),A ( rv0.count(),rv0.count() ),_mu ( epdf._mu() ) {
        ep =&epdf;
}

template<class sq_T>
void mlnorm<sq_T>::set_parameters ( const mat &A0, const sq_T &R0 ) {
        epdf.set_parameters ( zeros ( rv.count() ),R0 );
        A = A0;
}

template<class sq_T>
vec mlnorm<sq_T>::samplecond ( vec &cond, double &lik ) {
        this->condition ( cond );
        vec smp = epdf.sample();
        lik = epdf.eval ( smp );
        return smp;
}

template<class sq_T>
mat mlnorm<sq_T>::samplecond ( vec &cond, vec &lik, int n ) {
        int i;
        int dim = rv.count();
        mat Smp ( dim,n );
        vec smp ( dim );
        this->condition ( cond );

        for ( i=0; i<n; i++ ) {
                smp = epdf.sample();
                lik ( i ) = epdf.eval ( smp );
                Smp.set_col ( i ,smp );
        }

        return Smp;
}

template<class sq_T>
void mlnorm<sq_T>::condition ( vec &cond ) {
        _mu = A*cond;
//R is already assigned;
}



#endif //EF_H

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