/*!
  \file
  \brief TR 2525 file for testing Toy Problem of mpf for Covariance Estimation
  \author Vaclav Smidl.

  \ingroup PMSM

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

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



#include <estim/libKF.h>
#include <estim/libPF.h>
#include <stat/libFN.h>

#include "pmsm.h"
#include "simulator.h"
#include "sim_profiles.h"

using namespace bdm;

//!Extended Kalman filter with unknown \c Q and delta u
class EKFCh_du_kQ : public EKFCh , public BMcond {
	chmat Qref;
public:
	//! Default constructor
	EKFCh_du_kQ ( RV rx, RV ry,RV ru,RV rC ) :EKFCh ( rx,ry,ru ),BMcond ( rC ),Qref(rx.count()) {};
	void set_ref(const chmat &Qref0){Qref=Qref0;}
	void condition ( const vec &val ) {
		pfxu->condition ( val.left(2) );
		
		Q = Qref*std::abs(val(2));
		preA.set_submatrix ( dimy+dimx,dimy,Q._Ch() );
	};
};

class IMpmsm_delta :  public IMpmsm {
protected:
	vec ud;
public:
	IMpmsm_delta() :IMpmsm(),ud ( 2 ) {ud=zeros ( 2 );};
	//! Set mechanical and electrical variables

	void condition ( const vec &val ) {ud = val;}
	vec eval ( const vec &x0, const vec &u0 ) {
		// last state
		double iam = x0 ( 0 );
		double ibm = x0 ( 1 );
		double omm = x0 ( 2 );
		double thm = x0 ( 3 );
		double uam = u0 ( 0 );
		double ubm = u0 ( 1 );

		vec xk=zeros ( 4 );
		//ia
		xk ( 0 ) = ( 1.0- Rs/Ls*dt ) * iam + Ypm/Ls*dt*omm * sin ( thm ) + ( uam+ud ( 0 ) ) *dt/Ls;
		//ib
		xk ( 1 ) = ( 1.0- Rs/Ls*dt ) * ibm - Ypm/Ls*dt*omm * cos ( thm ) + ( ubm+ud ( 1 ) ) *dt/Ls;
		//om
		xk ( 2 ) = omm;// + kp*p*p * Ypm/J*dt* ( ibm * cos ( thm )-iam * sin ( thm ) ) - p/J*dt*Mz;
		//th
		xk ( 3 ) = thm + omm*dt; // <0..2pi>
		if ( xk ( 3 ) >pi ) xk ( 3 )-=2*pi;
		if ( xk ( 3 ) <-pi ) xk ( 3 ) +=2*pi;
		return xk;
	}

};


int main() {
	// Kalman filter
	int Ndat = 9000;
	double h = 1e-6;
	int Nsimstep = 125;
	int Npart = 20;

	mat Rnoise = randn ( 2,Ndat );

	// internal model
	IMpmsm2o fxu0;
	IMpmsm_delta fxu;
	//                  Rs    Ls        dt       Fmag(Ypm)    kp   p    J     Bf(Mz)
	fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 );
	fxu0.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 );
	// observation model
	OMpmsm hxu;

	vec mu0= "0.0 0.0 0.0 0.0";
	vec Qdiag ( "0.4 0.4 0.1 0.000001" ); //zdenek: 0.01 0.01 0.0001 0.0001
	vec Rdiag ( "0.1 0.1" ); //var(diff(xth)) = "0.034 0.034"
	chmat Q ( Qdiag );
	mat Q2o=diag(Qdiag);
	chmat R ( Rdiag );
	EKFCh KFE ( rx,ry,ru );
	KFE.set_parameters ( &fxu0,&hxu,Q,R );
	KFE.set_est ( mu0, chmat ( ones ( 4 ) ) );

	RV rUd ( "{ud k}", "2 1" );
	EKFCh_du_kQ KFEp ( rx,ry,ru,rUd );
	KFEp.set_parameters ( &fxu,&hxu,Q,R );
	KFEp.set_ref(Q);
	KFEp.set_est ( mu0, chmat ( ones ( 4 ) ) );

	mlnorm<ldmat> evolUd ( rUd,rUd );
	MPF<EKFCh_du_kQ> M ( rx,rUd,evolUd,evolUd,Npart,KFEp );
	// initialize
	vec Ud0="0 0 1.0";
	evolUd.set_parameters ( eye ( 3 ), zeros(3), ldmat ( vec( "1e-4 1e-4 10e-4" )));
	evolUd.condition ( Ud0 );
	epdf& pfinit=evolUd._epdf();
	M.set_est ( pfinit );
	evolUd.set_parameters ( eye ( 3 ), zeros(3), ldmat ( vec(" 7e-4 7e-4 1e-5" )));

	mat Xt=zeros ( Ndat ,4 ); //true state from simulator
	mat Dt=zeros ( Ndat,2+2+2 ); //observation
	mat XtE=zeros ( Ndat, 4 );
	mat Qtr=zeros ( Ndat, 4 );
	mat XtM=zeros ( Ndat, 3+4 ); //W + x
	mat XtMTh=zeros ( Ndat,1 );

	// SET SIMULATOR
	pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h );
	vec dt ( 2 );
	vec ut ( 2 );
	vec utm ( 2 );
	vec xt ( 4 );
	vec xtm=zeros ( 4 );
	double Ww=0.0;
	vec vecW="1 2 4 8 4 2 0 -4 -9 -16 -4 0 0 0";
	vecW*=10.0;

	for ( int tK=1;tK<Ndat;tK++ ) {
		//Number of steps of a simulator for one step of Kalman
		for ( int ii=0; ii<Nsimstep;ii++ ) {
			//simulator
			sim_profile_vec01t ( Ww,vecW );
			pmsmsim_step ( Ww );
		};
		ut ( 0 ) = KalmanObs[0];
		ut ( 1 ) = KalmanObs[1];
		dt ( 0 ) = KalmanObs[2]+0.3*Rnoise ( 0,t );
		dt ( 1 ) = KalmanObs[3]+0.3*Rnoise ( 1,t );
		xt = vec ( x,4 );

		vec x2o = fxu0.eval2o(utm-ut);
		utm =ut;
		
		Q2o*=0.99;
		Q2o+=outer_product(x2o,x2o);
		//estimator
		KFE.bayes ( concat ( dt,ut ) );
		for (int ii=0; ii<Npart; ii++){dynamic_cast<EKFCh_du_kQ*>(M._BM(ii))->set_ref(chmat(elem_mult(Qdiag,Qdiag)));}
		M.bayes ( concat ( dt,ut ) );

		Xt.set_row ( tK, xt ); //vec from C-array
		Dt.set_row ( tK, concat ( dt,ut.left(2), vec_2 ( KalmanObs[4],KalmanObs[5] ) ) );
		Qtr.set_row ( tK, Qdiag );
		XtE.set_row ( tK,KFE._e()->mean() );
		XtM.set_row ( tK,M._e()->mean() );
		// correction for theta

		{
			double sumSin=0.0;
			double sumCos=0.0;
			vec mea ( 4 );
			vec* _w;

			for ( int p=0; p<Npart;p++ ) {
				mea = M._BM ( p )->_e()->mean();
				_w = M.__w();
				sumSin += ( *_w ) ( p ) *sin ( mea ( 3 ) );
				sumCos += ( *_w ) ( p ) *cos ( mea ( 3 ) );
			}
			double Th = atan2 ( sumSin, sumCos );

			XtMTh.set_row ( tK,vec_1 ( Th ) );
		}

	}

	it_file fou ( "mpf_u_delta.it" );

	fou << Name ( "xth" ) << Xt;
	fou << Name ( "Dt" ) << Dt;
	fou << Name ( "Qtr" ) << Qtr;
	fou << Name ( "xthE" ) << XtE;
	fou << Name ( "xthM" ) << XtM;
	fou << Name ( "xthMTh" ) << XtMTh;
	//Exit program:

	return 0;
}