/*!
  \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 PARTICLES_H
#define PARTICLES_H


#include "../stat/exp_family.h"

namespace bdm {

/*!
* \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;

	//! which resampling method will be used
	RESAMPLING_METHOD resmethod;

	//! \name Options
	//!@{

	//! Log all samples
	bool opt_L_smp;
	//! Log all samples
	bool opt_L_wei;
	//!@}

public:
	//! \name Constructors
	//!@{
	PF ( ) : est(), _w ( est._w() ), _samples ( est._samples() ), opt_L_smp ( false ), opt_L_wei ( false ) {
		LIDs.set_size ( 5 );
	};
	/*	PF ( mpdf *par0, mpdf *obs0, epdf *epdf0, int n0 ) :
				est ( ),_w ( est._w() ),_samples ( est._samples() ),opt_L_smp(false), opt_L_wei(false)
		{ set_parameters ( par0,obs0,n0 ); set_statistics ( ones ( n0 ),epdf0 ); };*/
	void set_parameters ( mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm = SYSTEMATIC ) {
		par = par0;
		obs = obs0;
		n = n0;
		resmethod = rm;
	};
	void set_statistics ( const vec w0, const epdf &epdf0 ) {
		est.set_statistics ( w0, epdf0 );
	};
	//!@}
	//! Set posterior density by sampling from epdf0
//	void set_est ( const epdf &epdf0 );
	void set_options ( const string &opt ) {
		BM::set_options ( opt );
		opt_L_wei = ( opt.find ( "logweights" ) != string::npos );
		opt_L_smp = ( opt.find ( "logsamples" ) != string::npos );
	}
	void bayes ( const vec &dt );
	//!access function
	vec* __w() {
		return &_w;
	}
};

/*!
\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 {
	Array<BM_T*> BMs;

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

	class mpfepdf : public epdf  {
	protected:
		eEmp &E;
		vec &_w;
		Array<const epdf*> Coms;
	public:
		mpfepdf ( eEmp &E0 ) :
				epdf ( ), E ( E0 ),  _w ( E._w() ),
				Coms ( _w.length() ) {
		};
		//! read statistics from MPF
		void read_statistics ( Array<BM_T*> &A ) {
			dim = E.dimension() + A ( 0 )->posterior().dimension();
			for ( int i = 0; i < _w.length() ; i++ ) {
				Coms ( i ) = A ( i )->_e();
			}
		}
		//! needed in resampling
		void set_elements ( int &i, double wi, const epdf* ep ) {
			_w ( i ) = wi;
			Coms ( i ) = ep;
		};

		void set_parameters ( int n ) {
			E.set_parameters ( n, false );
			Coms.set_length ( n );
		}
		vec mean() const {
			// ugly
			vec pom = zeros ( Coms ( 0 )->dimension() );
			for ( int i = 0; i < _w.length(); i++ ) {
				pom += Coms ( i )->mean() * _w ( i );
			}
			return concat ( E.mean(), pom );
		}
		vec variance() const {
			// ugly
			vec pom = zeros ( Coms ( 0 )->dimension() );
			vec pom2 = zeros ( Coms ( 0 )->dimension() );
			for ( int i = 0; i < _w.length(); i++ ) {
				pom += Coms ( i )->mean() * _w ( i );
				pom2 += ( Coms ( i )->variance() + pow ( Coms ( i )->mean(), 2 ) ) * _w ( i );
			}
			return concat ( E.variance(), pom2 - pow ( pom, 2 ) );
		}
		void qbounds ( vec &lb, vec &ub, double perc = 0.95 ) const {
			//bounds on particles
			vec lbp;
			vec ubp;
			E.qbounds ( lbp, ubp );

			//bounds on Components
			int dimC = Coms ( 0 )->dimension();
			int j;
			// temporary
			vec lbc ( dimC );
			vec ubc ( dimC );
			// minima and maxima
			vec Lbc ( dimC );
			vec Ubc ( dimC );
			Lbc = std::numeric_limits<double>::infinity();
			Ubc = -std::numeric_limits<double>::infinity();

			for ( int i = 0; i < _w.length(); i++ ) {
				// check Coms
				Coms ( i )->qbounds ( lbc, ubc );
				for ( j = 0; j < dimC; j++ ) {
					if ( lbc ( j ) < Lbc ( j ) ) {
						Lbc ( j ) = lbc ( j );
					}
					if ( ubc ( j ) > Ubc ( j ) ) {
						Ubc ( j ) = ubc ( j );
					}
				}
			}
			lb = concat ( lbp, Lbc );
			ub = concat ( ubp, Ubc );
		}

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

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

	//! Density joining PF.est with conditional parts
	mpfepdf jest;

	//! Log means of BMs
	bool opt_L_mea;

public:
	//! Default constructor.
	MPF () : PF (), jest ( est ) {};
	void set_parameters ( mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm = SYSTEMATIC ) {
		PF::set_parameters ( par0, obs0, n0, rm );
		jest.set_parameters ( n0 );//duplication of rm
		BMs.set_length ( n0 );
	}
	void set_statistics ( const epdf &epdf0, const BM_T* BMcond0 ) {

		PF::set_statistics ( ones ( n ) / n, epdf0 );
		// copy
		for ( int i = 0; i < n; i++ ) {
			BMs ( i ) = new BM_T ( *BMcond0 );
			BMs ( i )->condition ( _samples ( i ) );
		}

		jest.read_statistics ( BMs );
		//options
	};

	void bayes ( const vec &dt );
	const epdf& posterior() const {
		return jest;
	}
	const epdf* _e() const {
		return &jest;    //Fixme: is it useful?
	}
	//! 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 ) );}
		}*/
	void set_options ( const string &opt ) {
		PF::set_options ( opt );
		opt_L_mea = ( opt.find ( "logmeans" ) != string::npos );
	}

	//!Access function
	BM* _BM ( int i ) {
		return BMs ( i );
	}
};

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();

#pragma omp parallel for
	for ( i = 0; i < n; i++ ) {
		//generate new samples from paramater evolution model;
		vec old_smp=_samples ( i );
		_samples ( i ) = par->samplecond ( old_smp );
		llsP ( i ) = par->evallogcond ( _samples ( i ), old_smp );
		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)
	}

	double sum_w = 0.0;
	// compute weights
#pragma omp parallel for
	for ( i = 0; i < n; i++ ) {
		_w ( i ) *= exp ( lls ( i ) - mlls ); // multiply w by likelihood
		sum_w += _w ( i );
	}

	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 ( resmethod );
		// Resample Bms!

#pragma omp parallel for
		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
				const epdf& pom = BMs ( i )->posterior();
				jest.set_elements ( i, 1.0 / n, &pom );
			}
		};
		cout << '.';
	}
}

}
#endif // KF_H


