#include <estim/libKF.h>
using namespace bdm;
	
// estimation of AR(0) model
int main() {
	//dimensions
	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);
	//prior
	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);
	// Estimation loop
	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;
}