Changeset 263 for tests/arx_test.cpp

Timestamp:

02/09/09 23:11:35 (15 years ago)

Author:

smidl

Message:

UIArxDS test

Files:

• tests/arx_test.cpp (modified) (1 diff)

tests/arx_test.cpp

@@ +1 @@
+/*!
+\file 
+\brief Test of basic elements of the ARX class
+
+See file \ref arx for mathematical background.
+
+This class tests functions ARX::bayes (Bayes rule) ARX::structure_est and ARX::predictor_student
+
+Untested functions: none.
+
+ */
+
 #include <estim/arx.h>
-#include <stat/libEF.h>
 using namespace bdm;
-
-//These lines are needed for use of cout and endl
-using std::cout;
-using std::endl;
 
 int main() {
         // Setup model
-        vec th("0.8 -0.3 0.4 0.01");
-        int ord=th.length();
+        vec th ( "0.8 -0.3 0.4 0.01" );
+        int ord=th.length(); //auxiliary variable
         double sqr=0.1;
-        
+
         //Test constructor
-        mat V0 = 0.00001*eye(ord+1); V0(0.0)*= 100; //
-        double nu0 = ord+4;
-        
-        RV thr("{theta_and_r }",vec_1(ord+1));
-        ARX Ar(thr, V0, nu0);
-        const epdf& Ar_ep = Ar._epdf();
-                                
+        mat V0 = 0.00001*eye ( ord+1 ); V0 ( 0.0 ) = 1; //
+        double nu0 = ord+5.0;
+
+        RV thr ( "{theta_and_r }",vec_1 ( ord+1 ) );   // Descriptive random variable
+        ARX Ar ( thr, V0, nu0 );                 // Estimator
+        const epdf& f_thr = Ar._epdf();          // refrence to posterior of the estimator
+
         //Test estimation
-        int ndat = 100;
-        int t,j;
-        vec Yt(ndat);
-        vec LL(ndat);
-        Yt.set_subvector(0,randn(ord)); //initial values
-        vec rgr(ord);
-        
-        
-        cout << Ar_ep.mean()<<endl;
-        for (t=ord; t<ndat; t++) {
-                for(j=0;j<(ord);j++){rgr(j)=Yt(t-j-1);}
-                Yt(t) = th*rgr + sqr * NorRNG();
-                
-                vec Psi = concat(Yt(t), rgr);
-                Ar.bayes(Psi);
-                LL(t) = Ar._ll();
-                
-                cout << "y: " << Yt(t) << endl;
-                mlstudent*      Pr = Ar.predictor_student(RV("{y }"),RV("{y1 y2 y3 y4 }"));
-                cout << Ar._ll() <<" , " << log(Pr->evallogcond(vec_1(Yt(t)),rgr)) <<endl;
+        int ndat = 100;                 // number of data records
+        vec Yt ( ndat );                // Store generated data
+        Yt.set_subvector ( 0,randn ( ord ) ); //initial values
+        vec rgr ( ord );                // regressor
+        vec Psi ( ord+1 );              // extended regressor
+
+        //print moments of the prior distribution
+        cout << "prior mean: " << f_thr.mean() <<endl;
+        cout << "prior variance: " << f_thr.variance() <<endl;
+
+        // cycle over time:
+        for ( int t=ord; t<ndat; t++ ) {
+                //Generate regressor
+                for ( int j=0;j< ( ord );j++ ) {rgr ( j ) =Yt ( t-j-1 );}
+                //model
+                Yt ( t ) = th*rgr + sqr * NorRNG();
+
+                Psi = concat ( Yt ( t ), rgr ); // Inefficient! Used for compatibility with Matlab!
+                Ar.bayes ( Psi );             // Bayes rule
+
+                // Build predictor
+                mlstudent*      Pr = Ar.predictor_student ( RV ( "{y }" ),RV ( "{y1 y2 y3 y4 }" ) );
+                // Test similarity of likelihoods from the Bayes rule and the predictor
+                cout << "BR log-lik: " << Ar._ll();
+                cout <<" , predictor ll: " <<  Pr->evallogcond ( vec_1 ( Yt ( t ) ),rgr )  <<endl;
                 delete Pr;
         }
-        cout << Ar_ep.mean()<<endl;
+        //print posterior moments
+        cout << "posterior mean: " << f_thr.mean() <<endl;
+        cout << "posterior variance: " << f_thr.variance() <<endl;
+
+        // Test brute-froce structure estimation
         
-        // Test brute-froce structure estimation
-        cout << Ar.structure_est(egiw(thr,V0,nu0)) <<endl;
+        cout << "Structure estimation: " <<endl;
+        cout <<Ar.structure_est ( egiw ( thr,V0,nu0 ) ) <<endl;
 }