root/libBM.h @ 12

/*!
  \file
  \brief Bayesian Models (bm) that use Bayes rule to learn from observations
  \author Vaclav Smidl.

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

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

#ifndef BM_H
#define BM_H

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

using namespace itpp;

/*!
* \brief Class representing variables, most often random variables

* More?...
*/
class RV {
        int size,len;
        ivec ids;
        ivec sizes;
        ivec times;
        ivec obs;
        Array<std::string> names;

private:
        void init ( ivec in_ids, Array<std::string> in_names, ivec in_sizes, ivec in_times, ivec in_obs );
public:
        //! Full constructor which is called by the others
        RV ( ivec in_ids, Array<std::string> in_names, ivec in_sizes, ivec in_times, ivec in_obs );
        //! default constructor
        RV ( ivec ids );
        //! Empty constructor will be set later
        RV ();
        
        //! Printing output e.g. for debugging.
        friend std::ostream &operator<< ( std::ostream &os, const RV &rv );

        //! Return length (number of scalars) of the RV.
        int count() {return size;}
        //TODO why not inline and later??
        
        //! Find indexes of another rv in self
        ivec find(RV rv2);
        //! Add (concat) another variable to the current one
        RV add(RV rv2);
        //! Subtract  another variable from the current one
        RV subt(RV rv2);
        //! Select only variables at indeces ind
        RV subselect(ivec ind);
        //! Select only variables at indeces ind
        RV operator()(ivec ind);
        //! Generate new \c RV with \c time shifted by delta.
        void t(int delta);
};




//! Class representing function of variables
class fnc {
        RV rv;
};

//! Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.
class BM {
public:
        //!Logarithm of marginalized data likelihood.
        double ll;

        /*! \brief Incremental Bayes rule
        @param dt vector of input data
        @param evall If true, the filter will compute likelihood of the data record and store it in \c ll
        */
        virtual void bayes ( const vec &dt, bool evall=true ) = 0;
        //! Batch Bayes rule (columns of Dt are observations)
        void bayes ( mat Dt );
};

//! Probability density function with numerical statistics, e.g. posterior density.
class epdf {
        RV rv;
public:
        //! Returns the required moment of the epdf
//      virtual vec moment ( const int order = 1 );
        //! Returns a sample from the density, $x \sim epdf(rv)$
        virtual vec sample (){};
        virtual double eval(const vec &val){};
};

//! Conditional probability density, e.g. modeling some dependencies.
class mpdf {
        //! modeled random variable
        RV rv;
        //! random variable in condition
        RV rvc;
public:

        //! Returns the required moment of the epdf
//      virtual fnc moment ( const int order = 1 );
        //! Returns a sample from the density conditioned on \c cond, $x \sim epdf(rv|cond)$
        virtual vec samplecond (vec &cond, double lik){};
        virtual void condition (vec &cond){};
};



#endif // BM_H