Changeset 477 for library/tests/tutorial

Timestamp:

08/05/09 14:40:03 (16 years ago)

Author:

mido

Message:

panove, vite, jak jsem peclivej na upravu kodu.. snad se vam bude libit:) konfigurace je v souboru /system/astylerc

Location:

library/tests/tutorial

Files:

• arx_simple.cpp (modified) (1 diff)
• kalman_simple.cpp (modified) (1 diff)

library/tests/tutorial/arx_simple.cpp

@@ -1 +1 @@
 #include "estim/arx.h"
 using namespace bdm;
-        
+
 // estimation of AR(0) model
 int main() {
-        //prior 
-        mat V0 = 0.00001*eye(2); V0(0,0)= 0.1; //
-        ARX Ar; 
-        Ar.set_statistics(1, V0); //nu is default (set to have finite moments)
-                                                          // forgetting is default: 1.0
-        mat Data = concat_vertical( randn(1,100), ones(1,100) );
-        Ar.bayesB( Data); 
-                        
-        cout << "Expected value of Theta is: " << Ar.posterior().mean() <<endl;
+        //prior
+        mat V0 = 0.00001 * eye ( 2 );
+        V0 ( 0, 0 ) = 0.1; //
+        ARX Ar;
+        Ar.set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
+        // forgetting is default: 1.0
+        mat Data = concat_vertical ( randn ( 1, 100 ), ones ( 1, 100 ) );
+        Ar.bayesB ( Data );
+
+        cout << "Expected value of Theta is: " << Ar.posterior().mean() << endl;
 }

library/tests/tutorial/kalman_simple.cpp

@@ -1 +1 @@
 #include "estim/kalman.h"
 using namespace bdm;
-        
+
 // estimation of AR(0) model
 int main() {
         //dimensions
-        int dx=3, dy=3, du=1;
+        int dx = 3, dy = 3, du = 1;
         // matrices
-        mat A = eye(dx);
-        mat B = zeros(dx,du);
-        mat C = eye(dx);
-        mat D = zeros(dy,du);
-        mat Q = eye(dx);
-        mat R = 0.1*eye(dy);
+        mat A = eye ( dx );
+        mat B = zeros ( dx, du );
+        mat C = eye ( dx );
+        mat D = zeros ( dy, du );
+        mat Q = eye ( dx );
+        mat R = 0.1 * eye ( dy );
         //prior
-        mat P0 = 100*eye(dx);
-        vec mu0 = zeros(dx);
+        mat P0 = 100 * eye ( dx );
+        vec mu0 = zeros ( dx );
         // Estimator
         KalmanCh KF;
-        KF.set_parameters(A,B,C,D,/*covariances*/ Q,R);
-        KF.set_statistics(mu0,P0);
+        KF.set_parameters ( A, B, C, D,/*covariances*/ Q, R );
+        KF.set_statistics ( mu0, P0 );
         // Estimation loop
-        for (int i=0;i<100;i++){
-                KF.bayes(randn(dx+du));
+        for ( int i = 0; i < 100; i++ ) {
+                KF.bayes ( randn ( dx + du ) );
         }
         //print results
         cout << "Posterior estimate of x is: "  << endl;
-        cout << "mean: "<< KF.posterior().mean()<< endl;
-        cout << "variance: "<< KF.posterior().variance()<< endl;
+        cout << "mean: " << KF.posterior().mean() << endl;
+        cout << "variance: " << KF.posterior().variance() << endl;
 }