/*!
  \file
  \brief Probability distributions for Exponential Family models.
  \author Vaclav Smidl.

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

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

#ifndef EF_H
#define EF_H

#include <itpp/itbase.h>
//#include <std>

using namespace itpp;

/*!
* \brief General conjugate exponential family posterior density.

* More?...
*/
class eEF :public epdf {

public:
	virtual void tupdate( double phi, mat &vbar, double nubar ){};
	virtual void dupdate( mat &v,double nu=1.0 ){};
};

class mEF :public mpdf {

public:

};

/*!
* \brief General exponential family density

* More?...
*/
template<class sq_T>
class enorm : public eEF {
	vec mu;
	sq_T R;
public:
	enorm( RV &rv, vec &mu, sq_T &R );
	enorm();
	void tupdate( double phi, mat &vbar, double nubar );
	void dupdate( mat &v,double nu=1.0 );
	vec sample();
	double eval( const vec &val );

};

/*!
 \brief
*/
template<class sq_T>
class mlnorm : public mEF {
	enorm<sq_T> epdf;
	mat A;
public:
	//! Constructor
	mlnorm( RV &rv,RV &rvc, mat &A, sq_T &R );
	//!Generate one sample of the posterior
	vec samplecond( vec &cond, double &lik );
	mat samplecond( vec &cond, vec &lik, int n );
	void condition( vec &cond );
};

////////////////////////

template<class sq_T>
enorm<sq_T>::enorm( RV &rv, vec &mu0, sq_T &R0 ) {
	mu = mu0;
	R = R0;
};

template<class sq_T>
void enorm<sq_T>::dupdate( mat &v, double nu ) {
	//
};

template<class sq_T>
void enorm<sq_T>::tupdate( double phi, mat &vbar, double nubar ) {
	//
};

template<class sq_T>
vec enorm<sq_T>::sample() {
	//
};

template<class sq_T>
double enorm<sq_T>::eval( const vec &val ) {
	//
};


template<class sq_T>
enorm<sq_T>::enorm() {};

template<class sq_T>
mlnorm<sq_T>::mlnorm( RV &rv,RV &rvc, mat &A, sq_T &R ) {
	int dim = rv.length();
	vec mu( dim );

	epdf = enorm<sq_T>( rv,mu,R );
}

template<class sq_T>
vec mlnorm<sq_T>::samplecond( vec &cond, double &lik ) {
	this->condition( cond );
	vec smp = epdf.sample();
	lik = epdf.eval(smp);
	return smp;
}

template<class sq_T>
mat mlnorm<sq_T>::samplecond( vec &cond, vec &lik, int n ) {
	int i;
	int dim = rv.length();
	mat Smp( dim,n );
	vec smp( dim);
	this->condition( cond );
	for ( i=0; i<dim; i++ ) {
		smp = epdf.sample();
		lik(i) = epdf.eval(smp);
		Smp.set_col( i ,smp);
	}
	return Smp;
}

template<class sq_T>
void mlnorm<sq_T>::condition( vec &cond) {
}

#endif //EF_H