/*
  \file
  \brief Models for synchronous electric drive using IT++ and BDM
  \author Vaclav Smidl.

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

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

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

#include <stat/loggers.h>

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

using namespace itpp;
//!Extended Kalman filter with unknown \c Q
class EKF_unQ : public EKFCh , public BMcond {
public:
	//! Default constructor
	EKF_unQ ( RV rx, RV ry,RV ru,RV rQ ) :EKFCh ( rx,ry,ru ),BMcond ( rQ ) {};
	void condition ( const vec &Q0 ) {
		Q.setD ( Q0,0 );
		//from EKF
		preA.set_submatrix ( dimy+dimx,dimy,Q._Ch() );
	};
	void bayes(const vec dt){
	EKFCh::bayes(dt);
		
		vec xtrue(4);
		//UGLY HACK!!! reliance on a predictor!!
		xtrue(0)=x[0];
		xtrue(1)=x[1];
		xtrue(2)=x[2];
		xtrue(3)=x[3];
		
		ll = -0.5* ( 4 * 1.83787706640935 +_P.logdet() +_P.invqform(xtrue));
	}
};
class EKF_unQful : public EKFfull , public BMcond {
public:
	//! Default constructor
	EKF_unQful ( RV rx, RV ry,RV ru,RV rQ ) :EKFfull ( rx,ry,ru ),BMcond ( rQ ) {};
	void condition ( const vec &Q0 ) {
		Q=diag(Q0);
	};
	void bayes(const vec dt){
	EKFfull::bayes(dt);
		
		vec xtrue(4);
		//UGLY HACK!!! reliance on a predictor!!
		xtrue(0)=x[0];
		xtrue(1)=x[1];
		xtrue(2)=x[2];
		xtrue(3)=x[3];
		
		BM::ll = -0.5* ( 4 * 1.83787706640935 +log(det(P)) +xtrue* ( inv(P)*xtrue ) );
	}
};

int main() {
	// Kalman filter
	int Ndat = 90000;
	double h = 1e-6;
	int Nsimstep = 125;
	int Npart = 50;
	
	dirfilelog L("exp/pmsm_sim2",1000);
	
	// internal model
	IMpmsm 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 );
	// observation model
	OMpmsm hxu;

	vec mu0= "0.0 0.0 0.0 0.0";
//	vec Qdiag ( "0.01 0.01 0.01 0.0001" ); //zdenek: 0.01 0.01 0.0001 0.0001
	vec Qdiag ( "10 10 10 0.001" ); //zdenek: 0.01 0.01 0.0001 0.0001
	vec Rdiag ( "100 100" ); //var(diff(xth)) = "0.034 0.034"
	chmat Q ( Qdiag );
	chmat R ( Rdiag );
	EKFCh KFE ( rx,ry,ru );
	KFE.set_est ( mu0, chmat( 1*eye ( 4 ) ) );
	KFE.set_parameters ( &fxu,&hxu,Q,R);

	RV rQ ( "100","{Q}","4","0" );
	EKF_unQful KFEp ( rx,ry,ru,rQ );
	KFEp.set_est ( mu0,  1*ones ( 4 ) );
	KFEp.set_parameters ( &fxu,&hxu,diag(Qdiag),diag(Rdiag) );

	mgamma_fix evolQ ( rQ,rQ );
	MPF<EKF_unQful> M ( rx,rQ,evolQ,evolQ,Npart,KFEp );
	// initialize
	evolQ.set_parameters ( 1000.0 ,Qdiag, 0.5); //sigma = 1/10 mu
	evolQ.condition ( Qdiag ); //Zdenek default
	epdf& pfinit=evolQ._epdf();
	M.set_est ( pfinit );
	evolQ.set_parameters ( 100000.0, Qdiag, 0.9999 );
	//
	epdf& KFEep = KFE._epdf();
	epdf& Mep = M._epdf();

	int X_log = L.add(rx,"X");
	int Efix_log = L.add(rx,"XF");
	int M_log = L.add(concat(rQ,rx),"M");
	L.init();

	// SET SIMULATOR
	pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h );
	double Ww=0.0;
	vec dt ( 2 );
	vec ut ( 2 );

	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_steps1(Ww);
			pmsmsim_step ( Ww );
		};
		// collect data
		ut ( 0 ) = KalmanObs[0];
		ut ( 1 ) = KalmanObs[1];
		dt ( 0 ) = KalmanObs[2];
		dt ( 1 ) = KalmanObs[3];

		//estimator
		KFE.bayes ( concat ( dt,ut ) );
		M.bayes ( concat ( dt,ut ) );
		
		L.logit(X_log, 	vec(x,4)); //vec from C-array
		L.logit(Efix_log, KFEep.mean() ); 
		L.logit(M_log, 	Mep.mean() ); 
		
		L.step(false);
	}

	L.step(true); //final
	
	return 0;
}