root/bdm/stat/libBM.h @ 32

/*!
  \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 {
protected:
        //! size = sum of sizes
        int size;
        //! len = number of individual rvs
        int 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);
public:
        //! Full constructor which is called by the others
        RV ( ivec in_ids, Array<std::string> in_names, ivec in_sizes, ivec in_times);
        //! 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() const {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
        void add (const RV &rv2 );
        //! Add (concat) another variable to the current one
        friend RV concat (const RV &rv1, const 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 );
        //! generate a list of indeces, i.e. which
        ivec indexlist();
};


//! Class representing function $f(x)$ of variable $x$ represented by \c rv

class fnc {
protected:
        int dimy;
public:
        //! function evaluates numerical value of $f(x)$ at $x=cond$
        virtual vec eval ( const vec &cond ) {
                return vec ( 0 );
        }; //Fixme: virtual?

        //! access function
        int _dimy() const{return dimy;}

        //! Destructor for future use;
        virtual ~fnc() {};
};


//! Probability density function with numerical statistics, e.g. posterior density.

class epdf {
protected:
        RV rv;
public:
        //!default constructor
        epdf() :rv ( ivec ( 0 ) ) {};

        //!default constructor
        epdf ( const RV &rv0 ) :rv ( rv0 ) {};

        //! Returns the required moment of the epdf
//      virtual vec moment ( const int order = 1 );
        //! Returns a sample from the density, \f$x \sim epdf(rv)\f$
        virtual vec sample () const =0;
        //! Compute probability of argument \c val
        virtual double eval ( const vec &val ) const {return exp(this->evalpdflog(val));};

        //! Compute log-probability of argument \c val
        virtual double evalpdflog ( const vec &val ) const =0;

        //! return expected value 
        virtual vec mean() const =0;
        
        //! Destructor for future use;
        virtual ~epdf() {};
        //! access function
        RV _rv() const {return rv;}
};


//! Conditional probability density, e.g. modeling some dependencies.
//TODO Samplecond can be generalized

class mpdf {
protected:
        //! modeled random variable
        RV rv;
        //! random variable in condition
        RV rvc;
        //! pointer to internal epdf
        epdf* ep;
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, $x \sim epdf(rv|cond)$. \param ll is a return value of log-likelihood of the sample.
        virtual vec samplecond ( vec &cond, double &ll ) {this->condition(cond);vec temp= ep->sample();ll=ep->evalpdflog(temp);return temp;};

        virtual void condition ( const vec &cond ) {};
        
        virtual double evalcond (const vec &dt, const vec &cond ) {this->condition(cond);return ep->eval(dt);};

        //! Destructor for future use;
        virtual ~mpdf() {};

        //! Default constructor
        mpdf ( const RV &rv0, const RV &rvc0 ) :rv ( rv0 ),rvc ( rvc0 ) {};
        //! access function
        RV _rvc(){return rvc;}
        //!access function
        epdf& _epdf(){return *ep;}
};

/*! \brief Abstract class for discrete-time sources of data.

The class abstracts operations of: (i) data aquisition, (ii) data-preprocessing, (iii) scaling of data, and (iv) data resampling from the task of estimation and control.
Moreover, for controlled systems, it is able to receive the desired control action and perform it in the next step. (Or as soon as possible).

*/

class DS {
protected:
        //!Observed variables, returned by \c getdata().
        RV Drv;
        //!Action variables, accepted by \c write().
        RV Urv; //
public:
        //! Returns full vector of observed data
        void getdata ( vec &dt );
        //! Returns data records at indeces.
        void getdata ( vec &dt, ivec &indeces );
        //! Accepts action variable and schedule it for application.
        void write ( vec &ut );
        //! Accepts action variables at specific indeces
        void write ( vec &ut, ivec &indeces );
        /*! \brief Method that assigns random variables to the datasource.
        Typically, the datasource will be constructed without knowledge of random variables. This method will associate existing variables with RVs.

        (Inherited from m3k, may be deprecated soon).
        */
        void linkrvs ( RV &drv, RV &urv );

        //! Moves from $t$ to $t+1$, i.e. perfroms the actions and reads response of the system.
        void step();

};

/*! \brief Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.

*/

class BM {
protected:
        //!Random variable of the posterior
        RV rv;
        //!Logarithm of marginalized data likelihood.
 double ll;
        //!  If true, the filter will compute likelihood of the data record and store it in \c ll . Set to false if you want to save time.
        bool evalll;
public:

        //!Default constructor
        BM(const RV &rv0) :rv(rv0), ll ( 0 ),evalll ( true ) {//Fixme: test rv 
        };

        /*! \brief Incremental Bayes rule
        @param dt vector of input data
        */
        virtual void bayes ( const vec &dt ) = 0;
        //! Batch Bayes rule (columns of Dt are observations)
        void bayes ( mat Dt );
        //! Returns a pointer to the epdf representing posterior density on parameters. Use with care!
        virtual epdf& _epdf()=0;

        //! Destructor for future use;
        virtual ~BM() {};
        //!access function
        const RV& _rv() const {return rv;}
        //!access function
        double _ll() const {return ll;}
};

/*!
\brief Conditional Bayesian Filter

Evaluates conditional filtering density $f(rv|rvc,data)$ for a given \c rvc which is specified in each step by calling function \c condition.

This is an interface class used to assure that certain BM has operation \c condition .

*/

class BMcond {
protected:
        RV rvc;
public:
        //! Substitute \c val for \c rvc.
        virtual void condition ( const vec &val ) =0;
        BMcond(RV &rv0):rvc(rv0){};
        virtual ~BMcond(){};
        const RV& _rvc() const {return rvc;}
};

#endif // BM_H