/*!
  \file
  \brief Bayesian Filtering using stochastic sampling (Particle Filters)
  \author Vaclav Smidl.

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

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

#ifndef PF_H
#define PF_H

#include <itpp/itbase.h>
#include "../stat/libEF.h"
#include "../math/libDC.h"

using namespace itpp;

//UGLY HACK
extern double PF_SSAT;//used for StrSim:06 test... if length>0 the value is written.

/*!
* \brief Trivial particle filter with proposal density equal to parameter evolution model.

Posterior density is represented by a weighted empirical density (\c eEmp ).
*/

class PF : public BM {
protected:
	//!number of particles;
	int n;
	//!posterior density
	eEmp est;
	//! pointer into \c eEmp
	vec &_w;
	//! pointer into \c eEmp
	Array<vec> &_samples;
	//! Parameter evolution model
	mpdf &par;
	//! Observation model
	mpdf &obs;
public:
	//! Default constructor
	PF ( const RV &rv0, mpdf &par0,  mpdf &obs0, int n0 ) :BM ( rv0 ),
			n ( n0 ),est ( rv0,n ),_w ( est._w() ),_samples ( est._samples() ),
			par ( par0 ), obs ( obs0 ) {};

	//! Set posterior density by sampling from epdf0
	void set_est ( const epdf &epdf0 );
	void bayes ( const vec &dt );
};

/*!
\brief Marginalized Particle filter

Trivial version: proposal $=$ parameter evolution, observation model is not used. (it is assumed to be part of BM).
*/

template<class BM_T>

class MPF : public PF {
	BM_T* Bms[10000];

	//! internal class for MPDF providing composition of eEmp with external components

class mpfepdf : public epdf  {
	protected:
		eEmp &E;
		vec &_w;
		Array<epdf*> Coms;
	public:
		mpfepdf ( eEmp &E0, const RV &rvc ) :
				epdf ( RV( ) ), E ( E0 ),  _w ( E._w() ),
				Coms ( _w.length() ) {
			rv.add ( E._rv() );
			rv.add ( rvc );
		};

		void set_elements ( int &i, double wi, epdf* ep )
		{_w ( i ) =wi; Coms ( i ) =ep;};

		vec mean() const {
			// ugly
			vec pom=zeros ( ( Coms ( 0 )->_rv() ).count() );

			for ( int i=0; i<_w.length(); i++ ) {pom += Coms ( i )->mean() * _w ( i );}

			return concat ( E.mean(),pom );
		}

		vec sample() const {it_error ( "Not implemented" );return 0;}

		double evalpdflog ( const vec &val ) const {it_error ( "not implemented" ); return 0.0;}
	};

	//! estimate joining PF.est with conditional
	mpfepdf jest;

public:
	//! Default constructor.
	MPF ( const RV &rvlin, const RV &rvpf, mpdf &par0, mpdf &obs0, int n, const BM_T &BMcond0 ) : PF ( rvpf ,par0,obs0,n ),jest ( est,rvlin ) {
		//
		//TODO test if rv and BMcond.rv are compatible.
		rv.add ( rvlin );
		//

		if ( n>10000 ) it_error ( "increase 10000 here!" );

		for ( int i=0;i<n;i++ ) {
			Bms[i] = new BM_T ( BMcond0 ); //copy constructor
			epdf& pom=Bms[i]->_epdf();
			jest.set_elements ( i,1.0/n,&pom );
		}
	};

	~MPF() {
	}

	void bayes ( const vec &dt );
	epdf& _epdf() {return jest;}
	//! Set postrior of \c rvc to samples from epdf0. Statistics of Bms are not re-computed! Use only for initialization!
	void set_est ( const epdf& epdf0 ) {
		PF::set_est ( epdf0 );  // sample params in condition
		// copy conditions to BMs

		for ( int i=0;i<n;i++ ) {Bms[i]->condition ( _samples ( i ) );}
	}

//SimStr:
	double SSAT;	
};

template<class BM_T>
void MPF<BM_T>::bayes ( const vec &dt ) {
	int i;
	vec lls ( n );
	vec llsP ( n );
	ivec ind;
	double mlls=-std::numeric_limits<double>::infinity();

	// StrSim:06
	double sumLWL=0.0;
	double sumL2WL=0.0;
	double WL = 0.0;

	for ( i=0;i<n;i++ ) {
		//generate new samples from paramater evolution model;
		_samples ( i ) = par.samplecond ( _samples ( i ), llsP ( i ) );
		Bms[i]->condition ( _samples ( i ) );
		Bms[i]->bayes ( dt );
		lls ( i ) = Bms[i]->_ll(); // lls above is also in proposal her must be lls(i) =, not +=!!

		if ( lls ( i ) >mlls ) mlls=lls ( i ); //find maximum likelihood (for numerical stability)
	}
	
	if ( true ) {
		for ( i=0;i<n;i++ ) {
			WL = _w ( i ) *exp ( llsP ( i ) ); //using old weights!
			sumLWL += exp ( lls ( i ) ) *WL;
			sumL2WL += exp ( 2*lls ( i ) ) *WL;
		}
		SSAT  = sumL2WL/(sumLWL*sumLWL);
	}

	// compute weights
	for ( i=0;i<n;i++ ) {
		_w ( i ) *= exp ( lls ( i ) - mlls ); // multiply w by likelihood
	}

	if (sum(_w)>0.0){
		_w /=sum ( _w ); //?
		}
else
{
cout<<"sum(w)==0"<<endl;
}


	double eff = 1.0/ ( _w*_w );
	if ( eff < ( 0.3*n ) ) {
		ind = est.resample();
		// Resample Bms!

		for ( i=0;i<n;i++ ) {
			if ( ind ( i ) !=i ) {//replace the current Bm by a new one
				//fixme this would require new assignment operator
				// *Bms[i] = *Bms[ind ( i ) ];

				// poor-man's solution: replicate constructor here
				// copied from MPF::MPF
				delete Bms[i];
				Bms[i] = new BM_T ( *Bms[ind ( i ) ] ); //copy constructor
				epdf& pom=Bms[i]->_epdf();
				jest.set_elements ( i,1.0/n,&pom );
			}
		};
		cout << '.';
	}
}

#endif // KF_H