/*!
  \file
  \brief Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/

#ifndef EKF_TEMPLATE_H
#define EKF_TEMPLATE_H

#include "kalman.h"

namespace bdm {

//!Extended Kalman filter with unknown \c Q and \c R
class EKFful_unQR : public EKFfull  {
public:
	void condition ( const vec &QR0 ) {
		Q = diag ( QR0 ( 0, dimx - 1 ) );
		R = diag ( QR0 ( dimx, dimx + dimy - 1 ) );
	};
};

//!Extended Kalman filter in Choleski form with unknown diagonal \c Q
class EKFCh_dQ : public EKFCh {
public:
	void condition ( const vec &Q0 ) {
		Q.setD ( Q0, 0 );
		//from EKF
		preA.set_submatrix ( dimy + dimx, dimy, Q._Ch() );
	};
};

//!Extended Kalman filter in Choleski form with unknown \c Q
class EKFCh_chQ : public EKFCh {
public:
	void condition ( const vec &chQ0 ) {
		Q.setCh ( chQ0 );
		//from EKF
		preA.set_submatrix ( dimy + dimx, dimy, Q._Ch() );
	};
};

//!Extended Kalman filter with unknown parameters in \c IM
class EKFCh_cond : public EKFCh  {
public:
	void condition ( const vec &val ) {
		pfxu->condition ( val );
	};
};

}
#endif //EKF_TEMP_H