root/applications/mpdm/merg_giw.cpp @ 1290

#include <estim/arx.h>
#include <estim/merger.h>
#include <stat/exp_family.h>
#include <stat/loggers.h>
//#include <stat/merger.h>
using namespace bdm;

//These lines are needed for use of cout and endl
using std::cout;
using std::endl;

int main() {
        // Setup model
        RV y ( "{y }" );
        RV u1 ( "{u1 }" );
        RV u2 ( "{u2 }" );
        RV uu=u1; uu.add ( u2 );

        double a1t = 1.5;
        double a2t = 0.8;
        double sqr=0.10;
        // Full system
        vec thg =vec_2 ( a1t,a2t ); //Simulated system - zero for constant term
        vec Th = concat ( thg, sqr ); //Full parameter

        // Estimated systems ARX(2)
        RV a1 ( "{a1 }" );
        RV a2 ( "{a2 }" );
        RV r ( "{r }" );
        RV all =a1; all.add ( a2 ); all.add ( r );
        RV allj =a1; allj.add ( r ); allj.add ( a2 );
        vec Thj=vec_3 ( a1t,sqr,a2t );
        // Setup values

        //ARX constructor
        mat V0 = 0.001*eye ( 2 ); V0 ( 0,0 ) = 1; //
        mat V0g = 0.001*eye ( 3 ); V0g ( 0,0 ) = 1; //

        ARX P1; P1.set_statistics(2, V0, -1 );
        ARX P2; P2.set_statistics(2, V0, -1 );
        ARX PG; PG.set_statistics(3, V0g, -1 ); //or -1?
//      ARX PGk ( all, V0g, -1 );

        //Test estimation
        int ndat = 100;
        int t;

        // Logging
        dirfilelog L ( "exp/merg_giw",ndat );
        int Li_Data = L.add ( RV ( "{Y U1 U2 }" ), "" );
        int Li_LL   = L.add ( RV ( "{G M }" ), "LL" );
        int Li_P1m   = L.add ( RV ( "{a1 r }" ), "P1" );
        int Li_P2m   = L.add ( RV ( "{a2 r }" ), "P2" );
        int Li_Gm   = L.add ( RV ( "{a1 a2 r }" ), "G" );
        int Li_Mm   = L.add ( RV ( "{a1 r a2 }" ), "M" );
        L.init();

        vec Yt ( ndat );
        vec yt ( 1 );

        vec LLs ( 2 );
        vec rgrg ( 2 );

        //Proposal
        enorm<ldmat> g0; g0.set_rv( a1 ); g0.set_parameters ( "1  ",mat ( "1" ) );
        egamma g1; g1.set_rv ( r ); g1.set_parameters ( "2  ", "2" );
        enorm<ldmat> g2; g2.set_rv ( a2 ); g2.set_parameters ( "1  ",mat ( "1" ) );

        Array<const epdf*> A ( 3 ); A ( 0 ) = &g0; A ( 1 ) =&g1; A ( 2 ) = &g2;
        eprod G0; G0.set_parameters ( A );

        for ( t=0; t<ndat; t++ ) {
                // True system
                rgrg ( 0 ) = pow ( sin ( ( t/40.0 ) *pi ),3 );
                rgrg ( 1 ) = pow ( cos ( ( t/40.0 +0.1 ) *pi ),3 );

                Yt ( t ) = thg*rgrg + sqr * NorRNG();

                // Bayes for all
                P1.bayes ( concat ( Yt ( t ),vec_1 ( rgrg ( 0 ) ) ) );
                P2.bayes ( concat ( Yt ( t ),vec_1 ( rgrg ( 1 ) ) ) );
                PG.bayes ( concat ( Yt ( t ),rgrg ) );


                // crippling PGk by substituting zeros.
                /*      ldmat &Vk=const_cast<egiw*>(PGk._e())->_V();  //PG ldmat does not like 0!
                        mat fVk=PG._e()->_V().to_mat();
                        fVk(1,2) = 0.0;
                        fVk(2,1) = 0.0;
                        Vk = ldmat(fVk);
                */      //PGk is now krippled

                // Merge estimates
                Array<pdf*> A ( 2 ); A ( 0 ) =&P1;A ( 1 ) =&P2;
                merger M ( A );
                M.set_parameters ( 1.5, 100,3 ); //M._Mix().set_method(QB);
                //M2.set_parameters ( 100.0, 1000,3 ); //M2._Mix().set_method(QB);
                M.merge ( &G0 );
                //M2.merge ( &G0 );

                //Likelihood
                yt ( 0 ) = Yt ( t );

//              LLs ( 0 ) = P1._e()->evallog ( get_vec(Th, "1 2") );
//              LLs ( 1 ) = P2._e()->evallog ( get_vec(Th, "3 2")  );
                LLs ( 0 ) = PG._e()->evallog ( Th );
                LLs ( 1 ) = M._Mix().logpred ( concat ( Thj, vec_1 ( 1.0 ) ) );
//              LLs ( 3 ) = M2._Mix().logpred ( concat(Thj, vec_1(1.0)) );
                L.logit ( Li_LL, LLs ); //log-normal

                //Logger
                L.logit ( Li_Data, vec_3 ( Yt ( t ), rgrg ( 0 ), rgrg ( 1 ) ) );
                L.logit ( Li_P1m, P1._e()->mean() );
                L.logit ( Li_P2m, P2._e()->mean() );
                L.logit ( Li_Gm, PG._e()->mean() );
                L.logit ( Li_Mm, M.mean() );

                L.step ( );

                cout << "Vg: " << PG._e()->_V().to_mat() <<endl;
                vec mea = M.mean();
                cout << "Ve: " << M.variance() <<endl;
        }
        L.finalize( );
        L.itsave ( "merg_egiw.it" );
        cout << endl;
}