root/applications/mpdm/SYSID09/merg_2a.cpp @ 1305

#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;

/*! \file
Experiment for distributed identification using log-normal merging

The purpose of this experiment is to test merging fragmental pdfs between two participants sharing the same parameter, a. However, this parameter has a different role in each model.

Lets assume that:
\dot
digraph compart{

  edge [fontname="FreeSans",fontsize=10,labelfontname="FreeSans",labelfontsize=10];
  node [fontname="FreeSans",fontsize=10,shape=record];

rankdir=LR;

 U [label="u",height=0.2,width=0.4,color="white", fillcolor="white", style="filled" fontcolor="black"];
 AR1 [label="a,b,r",height=0.2,width=0.4,color="black", fillcolor="white", style="filled" fontcolor="black"]
 U -> AR1 [color="midnightblue",style="solid"]; 
 AR2 [label="a,c,r",height=0.2,width=0.4,color="black", fillcolor="white", style="filled" fontcolor="black"]
 AR1 -> AR2 [color="midnightblue",style="solid",label="y"]; 
 Z [label="z",height=0.2,width=0.4,color="white", fillcolor="white", style="filled" fontcolor="black"];
 AR2 -> Z [color="midnightblue",style="solid"]; 

 
}
\enddot 
*/

int main() {
        // Setup model
        RV y ( "{y }" );
        RV u ( "{u }" );
        RV z ( "{z }" );
        RV a ("{a }"); 
        RV b ("{b }"); RV ab = a; ab.add(b);
        RV c ("{c }"); RV ac = a; ac.add(c);
        RV r ("{r }");
        
        double at = 0.9;
        double bt = 5;
        double ct = -0.5;
        double rt = 0.50;
        // Full system
        vec thy =vec_2 ( at,bt ); //Simulated system - zero for constant term
        vec thz =vec_2 ( at,ct ); //Simulated system - zero for constant term
        vec Thy = concat ( thy, vec_1(rt) ); //Full parameter
        vec Thz = concat ( thz, vec_1(rt) ); //Full parameter

        //ARX constructor
        mat V0 = 0.01*eye ( 3 ); V0 ( 0,0 ) = 1; //

        ARX P1; P1.set_rv(concat(ab,r));
        P1.set_statistics(1, V0, -1 );
        P1.set_parameters(0.9);
        ARX P2; P2.set_rv(concat(ac,r));
        P2.set_statistics(1, V0, -1 );
        P2.set_parameters(0.9);

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

        // Logging
        dirfilelog L ( "exp/merg_2a",100 );
        int Li_Data = L.add ( RV ( "{U Y Z }" ), "" );
//      int Li_LL   = L.add ( RV ( "{P1 P2 M1 M2 }" ), "LL" );
        int Li_P1m   = L.add ( concat ( ab,r ), "P1m" );
        int Li_P2m   = L.add ( concat ( ac,r ), "P2m" );
        int Li_Mm   = L.add ( concat ( ab,concat(r,c) ), "Mm" );
        int Li_Th   = L.add ( concat ( ab,concat(c,r) ), "T" );
        L.init();

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

        //Proposal
        enorm<ldmat> g0; g0.set_rv(ab); 
        g0.set_parameters ( "1 1 ",mat ( "1 0; 0 1" ) );
        egamma g1;g1.set_rv(r); 
        g1.set_parameters ( "2  ", "2" );
        enorm<ldmat> g2; g2.set_rv(c); 
        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 );

        epdf* proposal=&G0;
        
        vec rgry(2);
        vec rgrz(2);
        Yt(0) = 0.1;
        Ut(0) = 0.0;
        for ( t=1; t<ndat; t++ ) {
                // True system
                Ut ( t ) = 0.1*pow ( sin ( ( t/40.0 ) *pi ),3 );
                
                rgry(0) = Ut(t); rgry(1) = Ut(t-1);
                Yt ( t ) = thy*rgry + rt * NorRNG();
                
                rgrz(0) = Yt(t); rgrz(1) = Yt(t-1);
                Zt ( t ) = thz*rgrz + rt * NorRNG();

                // Bayes for all
                P1.bayes ( concat ( Yt ( t ),rgry ) );
                P2.bayes ( concat ( Zt ( t ),rgrz ) );

                if (t>50) {bt+=0.1;
                        thy(1)=bt;}
                if (t>50) {ct-=0.01;
                        thz(1)=ct;}
                
                // Merge estimates
                Array<pdf*> A ( 2 ); A ( 0 ) =&P1;A ( 1 ) =&P2;
                merger_mix M ( A );
                M.set_parameters ( 20 ,0.99); 
                M.set_method(LOGNORMAL, 1.2);
                M._Mix().set_method(QB);
                //M2.set_parameters ( 100.0, 1000,3 ); //M2._Mix().set_method(QB);
/*              char fnm[100];
                sprintf(fnm,"m2a_dbg%d.it",t);
                M.debug_file(fnm);*/
                M.set_support ( *proposal,100 );
                M.merge();
                //proposal = M.proposal();
                //Likelihood
                yt ( 0 ) = Yt ( t );

                //Logger
                L.logit(Li_Data, vec_3(Ut(t), Yt(t), Zt(t)));
                L.logit(Li_P1m, P1._e()->mean());
                L.logit(Li_P2m, P2._e()->mean());
                L.logit(Li_Mm, M.mean());
                L.logit(Li_Th, concat(thy,vec_2(ct,rt*rt)));
                L.step ( );

                cout << t << "," << endl;
        }
        L.finalize( );
        L.itsave ( "merg_2a.it" );
        cout << endl;
}