root/libKF.h @ 7

/*!
  \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 KF_H
#define KF_H

#include <itpp/itbase.h>
#include "libBM.h"
#include "libDC.h"


using namespace itpp;

/*!
* \brief Basic Kalman filter with full matrices (education purpose only)!
*/
class KalmanFull : public BM { 
        int dimx, dimy, dimu;
        mat A, B, C, D, R, Q;
        
        //cache 
        mat _Pp, _Ry, _iRy, _K;
public:
        //posterior 
        //! Mean value of the posterior density
        vec mu;
        //! Variance of the posterior density
        mat P;

public:
        //! Full constructor 
        KalmanFull ( mat A, mat B, mat C, mat D, mat R, mat Q, mat P0, vec mu0);
        //! Here dt = [yt;ut] of appropriate dimensions
        void bayes(const vec &dt, bool evalll=true); 

        friend std::ostream &operator<< ( std::ostream &os, const KalmanFull &kf );

};


/*!
* \brief Kalman filter with covaraince matrices in square root form.
*/
template<class sq_T>
class Kalman : public BM { 
        int dimx, dimy, dimu;
        mat A, B, C, D;
        sq_T R, Q;
        
        //cache
        mat _K, _yp;
        sq_T _Ry,_iRy;
public:
        //posterior 
        //! Mean value of the posterior density
        vec mu;
        //! Mean value of the posterior density
        sq_T P;

public:
        //! Full constructor 
        Kalman ( mat A0, mat B0, mat C0, mat D0, sq_T R0, sq_T Q0, sq_T P0, vec mu0 );
        //! Here dt = [yt;ut] of appropriate dimensions
        void bayes(const vec &dt, bool evalll=true); 

        friend std::ostream &operator<< ( std::ostream &os, const KalmanFull &kf );

};

//////// INstance

template<class sq_T>
Kalman<sq_T>::Kalman( mat A0, mat B0, mat C0, mat D0, sq_T R0, sq_T Q0, sq_T P0, vec mu0 ) {
        dimx = A0.rows();
        dimu = B0.cols();
        dimy = C0.rows();

        it_assert_debug( A0.cols()==dimx, "Kalman: A is not square" );
        it_assert_debug( B0.rows()==dimx, "Kalman: B is not compatible" );
        it_assert_debug( C0.cols()==dimx, "Kalman: C is not square" );
        it_assert_debug(( D0.rows()==dimy ) || ( D0.cols()==dimu ),     "Kalman: D is not compatible" );
        it_assert_debug(( R0.cols()==dimy ) || ( R0.rows()==dimy ), "Kalman: R is not compatible" );
        it_assert_debug(( Q0.cols()==dimx ) || ( Q0.rows()==dimx ), "Kalman: Q is not compatible" );

        A = A0;
        B = B0;
        C = C0;
        D = D0;
        R = R0;
        Q = Q0;
        mu = mu0;
        P = P0;

        ll = 0;
//Fixme should we assign cache??
}

template<class sq_T>
void Kalman<sq_T>::bayes( const vec &dt , bool evalll) {
        it_assert_debug( dt.length()==( dimy+dimu ),"KalmanFull::bayes wrong size of dt" );

        vec u = dt.get( dimy,dimy+dimu-1 );
        vec y = dt.get( 0,dimy-1 );
        //Time update
        mu = A*mu + B*u;
        //P  = A*P*A.transpose() + Q; in sq_T
        P.mult_qform( A );
        P+=Q;

        //Data update
        //_Ry = C*P*C.transpose() + R; in sq_T
        _Ry.mult_qform( C );
        _Ry+=R;

        mat Pfull = P.to_mat();
        
        _Ry.inv( _iRy ); // result is in _iRy;
        _K = Pfull*C.transpose()*(_iRy.to_mat());
        P -= _K*C*Pfull; // P = P -KCP;
        _yp = y-C*mu-D*u; //y prediction
        mu += _K*( _yp );
        
        if (evalll==true) {
        ll+= -0.5*_Ry.logdet() -0.5*_iRy.qform(_yp);
        }
};

//extern template class Kalman<ldmat>; 


#endif // KF_H