root/bdm/stat/libBM.h @ 283

/*!
  \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_ext.h"
#include <map>

namespace bdm {
using namespace itpp;
using namespace std;

//! Root class of BDM objects
class bdmroot {
public:
        //! make sure this is a virtual object
        virtual ~bdmroot() {}
};

typedef std::map<string, int> RVmap;
extern ivec RV_SIZES;
extern Array<string> RV_NAMES;

//! Structure of RV (used internally), i.e. expanded RVs
class str {
public:
        //! vector id ids (non-unique!)
        ivec ids;
        //! vector of times
        ivec times;
        //!Default constructor
        str ( ivec ids0, ivec times0 ) :ids ( ids0 ),times ( times0 ) {
                it_assert_debug ( times0.length() ==ids0.length(),"Incompatible input" );
        };
};

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

The purpose of this class is to decribe a vector of data. Such description is used for connecting various vectors between each other, see class datalink.

The class is implemented using global variables to assure uniqueness of description:

 In is a vector
\dot
digraph datalink {
rankdir=LR;
subgraph cluster0 {
node [shape=record];
label = "RV_MAP \n std::map<string,int>";
map [label="{{\"a\"| \"b\" | \"c\"} | {<3> 3 |<1> 1|<2> 2}}"];
color = "white"
}
subgraph cluster1{
node [shape=record];
label = "RV_NAMES";
names [label="{<1> \"b\" | <2> \"c\" | <3>\"a\" }"];
color = "white"
}
subgraph cluster2{
node [shape=record];
label = "RV_SIZES";
labelloc = b;
sizes [label="{<1>1 |<2> 4 |<3> 1}"];
color = "white"
}
map:1 -> names:1;
map:1 -> sizes:1;
map:3 -> names:3;
map:3 -> sizes:3;
}
\enddot
*/

class RV :public bdmroot {
protected:
        //! size of the data vector
        int dsize;
        //! number of individual rvs
        int len;
        //! Vector of unique IDs
        ivec ids;
        //! Vector of shifts from current time
        ivec times;

private:
        //! auxiliary function used in constructor
        void init ( Array<std::string> in_names, ivec in_sizes, ivec in_times );
        int init ( const  string &name, int size );
public:
        //! \name Constructors
        //!@{

        //! Full constructor
        RV ( Array<std::string> in_names, ivec in_sizes, ivec in_times ) {init ( in_names,in_sizes,in_times );};
        //! Constructor with times=0
        RV ( Array<std::string> in_names, ivec in_sizes ) {init ( in_names,in_sizes,zeros_i ( in_names.length() ) );};
        //! Constructor with sizes=1, times=0
        RV ( Array<std::string> in_names ) {init ( in_names,ones_i ( in_names.length() ),zeros_i ( in_names.length() ) );}
        //! Constructor of empty RV
        RV () :dsize ( 0 ),len ( 0 ),ids ( 0 ),times ( 0 ) {};
        //! Constructor of a single RV with given id
        RV ( string name, int sz, int tm=0 );
        //!@}

        //! \name Access functions
        //!@{

        //! Printing output e.g. for debugging.
        friend std::ostream &operator<< ( std::ostream &os, const RV &rv );
        int _dsize() const {return dsize;} ;
        //! Recount size of the corresponding data vector
        int countsize() const;
        ivec cumsizes() const;
        int length() const {return len;} ;
        int id ( int at ) const{return ids ( at );};
        int size ( int at ) const {return RV_SIZES ( ids ( at ) );};
        int time ( int at ) const{return times ( at );};
        std::string name ( int at ) const {return RV_NAMES ( ids ( at ) );};
        void set_time ( int at, int time0 ) {times ( at ) =time0;};
        //!@}

        //TODO why not inline and later??

        //! \name Algebra on Random Variables
        //!@{

        //! Find indices of self in another rv, \return ivec of the same size as self.
        ivec findself ( const RV &rv2 ) const;
        //! Compare if \c rv2 is identical to this \c RV
        bool equal ( const RV &rv2 ) const;
        //! Add (concat) another variable to the current one, \return true if all rv2 were added, false if rv2 is in conflict
        bool add ( const RV &rv2 );
        //! Subtract  another variable from the current one
        RV subt ( const RV &rv2 ) const;
        //! Select only variables at indeces ind
        RV subselect ( const ivec &ind ) const;
        //! Select only variables at indeces ind
        RV operator() ( const ivec &ind ) const {return subselect ( ind );};
        //! Select from data vector starting at di1 to di2
        RV operator() ( int di1, int di2 ) const {
                ivec sz=cumsizes();
                int i1=0;
                while ( sz ( i1 ) <di1 ) i1++;
                int i2=i1;
                while ( sz ( i2 ) <di2 ) i2++;
                return subselect ( linspace ( i1,i2 ) );
        };
        //! Shift \c time shifted by delta.
        void t ( int delta );
        //!@}

        //!\name Relation to vectors
        //!@{

        //! generate \c str from rv, by expanding sizes
        str tostr() const;
        //! when this rv is a part of bigger rv, this function returns indeces of self in the data vector of the bigger crv.
        //! Then, data can be copied via: data_of_this = cdata(ind);
        ivec dataind ( const RV &crv ) const;
        //! generate mutual indeces when copying data betwenn self and crv.
        //! Data are copied via: data_of_this(selfi) = data_of_rv2(rv2i)
        void dataind ( const RV &rv2, ivec &selfi, ivec &rv2i ) const;
        //! Minimum time-offset
        int mint () const {return min ( times );};
        //!@}

};


//! Concat two random variables
RV concat ( const RV &rv1, const RV &rv2 );

//!Default empty RV that can be used as default argument
extern RV RV0;

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

class fnc :public bdmroot {
protected:
        //! Length of the output vector
        int dimy;
public:
        //!default constructor
        fnc ( ) {};
        //! function evaluates numerical value of \f$f(x)\f$ at \f$x=\f$ \c cond
        virtual vec eval ( const vec &cond ) {
                return vec ( 0 );
        };

        //! function substitutes given value into an appropriate position
        virtual void condition ( const vec &val ) {};

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

class mpdf;

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

class epdf :public bdmroot {
protected:
        //! dimension of the random variable
        int dim;
        //! Description of the random variable
        RV rv;

public:
        /*! \name Constructors
         Construction of each epdf should support two types of constructors:
        \li empty constructor,
        \li copy constructor,

        The following constructors should be supported for convenience:
        \li constructor followed by calling \c set_parameters()
        \li constructor accepting random variables calling \c set_rv()

         All internal data structures are constructed as empty. Their values (including sizes) will be set by method \c set_parameters(). This way references can be initialized in constructors.
        @{*/
        epdf() :dim ( 0 ),rv ( ) {};
        epdf ( const epdf &e ) :dim ( e.dim ),rv ( e.rv ) {};
        epdf ( const RV &rv0 ) {set_rv ( rv0 );};
        void set_parameters ( int dim0 ) {dim=dim0;}
        //!@}

        //! \name Matematical Operations
        //!@{

        //! Returns a sample, \f$ x \f$ from density \f$ f_x()\f$
        virtual vec sample () const {it_error ( "not implemneted" );return vec ( 0 );};
        //! Returns N samples, \f$ [x_1 , x_2 , \ldots \ \f$  from density \f$ f_x(rv)\f$
        virtual mat sample_m ( int N ) const;
        //! Compute log-probability of argument \c val
        virtual double evallog ( const vec &val ) const {it_error ( "not implemneted" );return 0.0;};
        //! Compute log-probability of multiple values argument \c val
        virtual vec evallog_m ( const mat &Val ) const {
                vec x ( Val.cols() );
                for ( int i=0;i<Val.cols();i++ ) {x ( i ) =evallog ( Val.get_col ( i ) ) ;}
                return x;
        }
        //! Return conditional density on the given RV, the remaining rvs will be in conditioning
        virtual mpdf* condition ( const RV &rv ) const  {it_warning ( "Not implemented" ); return NULL;}
        //! Return marginal density on the given RV, the remainig rvs are intergrated out
        virtual epdf* marginal ( const RV &rv ) const {it_warning ( "Not implemented" ); return NULL;}
        //! return expected value
        virtual vec mean() const {it_error ( "not implemneted" );return vec ( 0 );};
        //! return expected variance (not covariance!)
        virtual vec variance() const {it_error ( "not implemneted" );return vec ( 0 );};
        //! Lower and upper bounds of \c percentage % quantile, returns mean-2*sigma as default
        virtual void qbounds ( vec &lb, vec &ub, double percentage=0.95 ) const {
                vec mea=mean(); vec std=sqrt(variance()); 
                lb = mea-2*std; ub=mea+2*std;
        };
        //!@}

        //! \name Connection to other classes
        //! Description of the random quantity via attribute \c rv is optional.
        //! For operations such as sampling \c rv does not need to be set. However, for \c marginalization
        //! and \c conditioning \c rv has to be set. NB:
        //! @{

        //!Name its rv
        void set_rv ( const RV &rv0 ) {rv = rv0; }//it_assert_debug(isnamed(),""); };
        //! True if rv is assigned
        bool isnamed() const {bool b= ( dim==rv._dsize() );return b;}
        //! Return name (fails when isnamed is false)
        const RV& _rv() const {it_assert_debug ( isnamed(),"" ); return rv;}
        //!@}

        //! \name Access to attributes
        //! @{

        //! Size of the random variable
        int dimension() const {return dim;}
        //!@}

};


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

class mpdf : public bdmroot {
protected:
        //!dimension of the condition
        int dimc;
        //! random variable in condition
        RV rvc;
        //! pointer to internal epdf
        epdf* ep;
public:
        //! \name Constructors
        //! @{

        mpdf ( ) :dimc ( 0 ),rvc ( ) {};
        //! copy constructor does not set pointer \c ep - has to be done in offsprings!
        mpdf ( const mpdf &m ) :dimc ( m.dimc ),rvc ( m.rvc ) {};
        //!@}

        //! \name Matematical operations
        //!@{

        //! Returns a sample from the density conditioned on \c cond, \f$x \sim epdf(rv|cond)\f$. \param cond is numeric value of \c rv
        virtual vec samplecond ( const vec &cond ) {
                this->condition ( cond );
                vec temp= ep->sample();
                return temp;
        };
        //! Returns \param N samples from the density conditioned on \c cond, \f$x \sim epdf(rv|cond)\f$. \param cond is numeric value of \c rv \param ll is a return value of log-likelihood of the sample.
        virtual mat samplecond_m ( const vec &cond, int N ) {
                this->condition ( cond );
                mat temp ( ep->dimension(),N ); vec smp ( ep->dimension() );
                for ( int i=0;i<N;i++ ) {smp=ep->sample() ;temp.set_col ( i, smp );}
                return temp;
        };
        //! Update \c ep so that it represents this mpdf conditioned on \c rvc = cond
        virtual void condition ( const vec &cond ) {it_error ( "Not implemented" );};

        //! Shortcut for conditioning and evaluation of the internal epdf. In some cases,  this operation can be implemented efficiently.
        virtual double evallogcond ( const vec &dt, const vec &cond ) {
                double tmp; this->condition ( cond );tmp = ep->evallog ( dt );          it_assert_debug ( std::isfinite ( tmp ),"Infinite value" ); return tmp;
        };

        //! Matrix version of evallogcond
        virtual vec evallogcond_m ( const mat &Dt, const vec &cond ) {this->condition ( cond );return ep->evallog_m ( Dt );};

        //! \name Access to attributes
        //! @{

        RV _rv() {return ep->_rv();}
        RV _rvc() {it_assert_debug ( isnamed(),"" ); return rvc;}
        int dimension() {return ep->dimension();}
        int dimensionc() {return dimc;}
        epdf& _epdf() {return *ep;}
        epdf* _e() {return ep;}
        //!@}

        //! \name Connection to other objects
        //!@{
        void set_rvc ( const RV &rvc0 ) {rvc=rvc0;}
        void set_rv ( const RV &rv0 ) {ep->set_rv ( rv0 );}
        bool isnamed() {return ( ep->isnamed() ) && ( dimc==rvc._dsize() );}
        //!@}
};

/*! \brief DataLink is a connection between two data vectors Up and Down

Up can be longer than Down. Down must be fully present in Up (TODO optional)
See chart:
\dot
digraph datalink {
        node [shape=record];
        subgraph cluster0 {
                label = "Up";
        up [label="<1>|<2>|<3>|<4>|<5>"];
                color = "white"
}
        subgraph cluster1{
                label = "Down";
                labelloc = b;
        down [label="<1>|<2>|<3>"];
                color = "white"
}
    up:1 -> down:1;
    up:3 -> down:2;
    up:5 -> down:3;
}
\enddot

*/
class datalink {
protected:
        //! Remember how long val should be
        int downsize;
        //! Remember how long val of "Up" should be
        int upsize;
        //! val-to-val link, indeces of the upper val
        ivec v2v_up;
public:
        //! Constructor
        datalink () {};
        datalink ( const RV &rv, const RV &rv_up ) {set_connection ( rv,rv_up );};
        //! set connection, rv must be fully present in rv_up
        void set_connection ( const RV &rv, const RV &rv_up ) {
                downsize = rv._dsize();
                upsize = rv_up._dsize();
                v2v_up= ( rv.dataind ( rv_up ) );

                it_assert_debug ( v2v_up.length() ==downsize,"rv is not fully in rv_up" );
        }
        //! Get val for myself from val of "Up"
        vec pushdown ( const vec &val_up ) {
                it_assert_debug ( upsize==val_up.length(),"Wrong val_up" );
                return get_vec ( val_up,v2v_up );
        }
        //! Fill val of "Up" by my pieces
        void pushup ( vec &val_up, const vec &val ) {
                it_assert_debug ( downsize==val.length(),"Wrong val" );
                it_assert_debug ( upsize==val_up.length(),"Wrong val_up" );
                set_subvector ( val_up, v2v_up, val );
        }
};

//! data link between
class datalink_m2e: public datalink {
protected:
        //! Remember how long cond should be
        int condsize;
        //!upper_val-to-local_cond link, indeces of the upper val
        ivec v2c_up;
        //!upper_val-to-local_cond link, ideces of the local cond
        ivec v2c_lo;

public:
        //! Constructor
        datalink_m2e ( const RV &rv,  const RV &rvc, const RV &rv_up ) :
                        datalink ( rv,rv_up ), condsize ( rvc._dsize() ) {
                //establish v2c connection
                rvc.dataind ( rv_up, v2c_lo, v2c_up );
        }
        //!Construct condition
        vec get_cond ( const vec &val_up ) {
                vec tmp ( condsize );
                set_subvector ( tmp,v2c_lo,val_up ( v2c_up ) );
                return tmp;
        }
        void pushup_cond ( vec &val_up, const vec &val, const vec &cond ) {
                it_assert_debug ( downsize==val.length(),"Wrong val" );
                it_assert_debug ( upsize==val_up.length(),"Wrong val_up" );
                set_subvector ( val_up, v2v_up, val );
                set_subvector ( val_up, v2c_up, cond );
        }
};
//!DataLink is a connection between mpdf and its superordinate (Up)
//! This class links
class datalink_m2m: public datalink_m2e {
protected:
        //!cond-to-cond link, indeces of the upper cond
        ivec c2c_up;
        //!cond-to-cond link, indeces of the local cond
        ivec c2c_lo;
public:
        //! Constructor
        datalink_m2m ( const RV &rv, const RV &rvc, const RV &rv_up, const RV &rvc_up ) :
                        datalink_m2e ( rv, rvc, rv_up ) {
                //establish c2c connection
                rvc.dataind ( rvc_up, c2c_lo, c2c_up );
                it_assert_debug ( c2c_lo.length() +v2c_lo.length() ==condsize, "cond is not fully given" );
        }
        //! Get cond for myself from val and cond of "Up"
        vec get_cond ( const vec &val_up, const vec &cond_up ) {
                vec tmp ( condsize );
                set_subvector ( tmp,v2c_lo,val_up ( v2c_up ) );
                set_subvector ( tmp,c2c_lo,cond_up ( c2c_up ) );
                return tmp;
        }
        //! Fill

};

/*!
@brief Class for storing results (and semi-results) of an experiment

This class abstracts logging of results from implementation. This class replaces direct logging of results (e.g. to files or to global variables) by calling methods of a logger. Specializations of this abstract class for specific storage method are designed.
 */
class logger : public bdmroot {
protected:
        //! RVs of all logged variables.
        Array<RV> entries;
        //! Names of logged quantities, e.g. names of algorithm variants
        Array<string> names;
public:
        //!Default constructor
        logger ( ) : entries ( 0 ),names ( 0 ) {}

        //! returns an identifier which will be later needed for calling the \c logit() function
        //! For empty RV it returns -1, this entry will be ignored by \c logit().
        virtual int add ( const RV &rv, string name="" ) {
                int id;
                if ( rv._dsize() >0 ) {
                        id=entries.length();
                        names=concat ( names, name ); // diff
                        entries.set_length ( id+1,true );
                        entries ( id ) = rv;
                }
                else { id =-1;}
                return id; // identifier of the last entry
        }

        //! log this vector
        virtual void logit ( int id, const vec &v ) =0;

        //! Shifts storage position for another time step.
        virtual void step() =0;

        //! Finalize storing information
        virtual void finalize() {};

        //! Initialize the storage
        virtual void init() {};

};

/*! \brief Unconditional mpdf, allows using epdf in the role of mpdf.

*/
class mepdf : public mpdf {
public:
        //!Default constructor
        mepdf ( const epdf* em ) :mpdf ( ) {ep=const_cast<epdf*> ( em );};
        void condition ( const vec &cond ) {}
};

//!\brief Abstract composition of pdfs, will be used for specific classes
//!this abstract class is common to epdf and mpdf
class compositepdf {
protected:
        //!Number of mpdfs in the composite
        int n;
        //! Elements of composition
        Array<mpdf*> mpdfs;
public:
        compositepdf ( Array<mpdf*> A0 ) : n ( A0.length() ), mpdfs ( A0 ) {};
        //! find common rv, flag \param checkoverlap modifies whether overlaps are acceptable
        RV getrv ( bool checkoverlap=false );
        //! common rvc of all mpdfs is written to rvc
        void setrvc ( const RV &rv, RV &rvc );
};

/*! \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 : public bdmroot {
protected:
        int dtsize;
        int utsize;
        //!Description of data returned by \c getdata().
        RV Drv;
        //!Description of data witten by by \c write().
        RV Urv; //
        //! Remember its own index in Logger L
        int L_dt, L_ut;
public:
        //! default constructors
        DS() :Drv ( ),Urv ( ) {};
        //! Returns full vector of observed data=[output, input]
        virtual void getdata ( vec &dt ) {it_error ( "abstract class" );};
        //! Returns data records at indeces.
        virtual void getdata ( vec &dt, const ivec &indeces ) {it_error ( "abstract class" );};
        //! Accepts action variable and schedule it for application.
        virtual void write ( vec &ut ) {it_error ( "abstract class" );};
        //! Accepts action variables at specific indeces
        virtual void write ( vec &ut, const ivec &indeces ) {it_error ( "abstract class" );};

        //! Moves from \f$ t \f$ to \f$ t+1 \f$, i.e. perfroms the actions and reads response of the system.
        virtual void step() =0;

        //! Register DS for logging into logger L
        virtual void log_add ( logger &L ) {
                it_assert_debug ( dtsize==Drv._dsize(),"" );
                it_assert_debug ( utsize==Urv._dsize(),"" );

                L_dt=L.add ( Drv,"" );
                L_ut=L.add ( Urv,"" );
        }
        //! Register DS for logging into logger L
        virtual void logit ( logger &L ) {
                vec tmp ( Drv._dsize() +Urv._dsize() );
                getdata ( tmp );
                // d is first in getdata
                L.logit ( L_dt,tmp.left ( Drv._dsize() ) );
                // u follows after d in getdata
                L.logit ( L_ut,tmp.mid ( Drv._dsize(), Urv._dsize() ) );
        }
        //!access function
        virtual RV _drv() const {return concat ( Drv,Urv );}
        //!access function
        const RV& _urv() const {return Urv;}
};

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

This object represents exact or approximate evaluation of the Bayes rule:
\f[
f(\theta_t | d_1,\ldots,d_t) = \frac{f(y_t|\theta_t,\cdot) f(\theta_t|d_1,\ldots,d_{t-1})}{f(y_t|d_1,\ldots,d_{t-1})}
\f]

Access to the resulting posterior density is via function \c posterior().

As a "side-effect" it also evaluates log-likelihood of the data, which can be accessed via function _ll().
It can also evaluate predictors of future values of \f$y_t\f$, see functions epredictor() and predictor().

Alternatively, it can evaluate posterior density conditioned by a known constant, \f$ c_t \f$:
\f[
f(\theta_t | c_t, d_1,\ldots,d_t) \propto  f(y_t,\theta_t|c_t,\cdot, d_1,\ldots,d_{t-1})
\f]

The value of \f$ c_t \f$ is set by function condition().

*/

class BM :public bdmroot {
protected:
        //! Random variable of the data (optional)
        RV drv;
        //!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 computational time.
        bool evalll;
public:
        //! \name Constructors
        //! @{

        BM () :ll ( 0 ),evalll ( true ), LIDs ( 3 ), opt_L_bounds ( false ) {};
        BM ( const BM &B ) :  drv ( B.drv ), ll ( B.ll ), evalll ( B.evalll ) {}
        //! Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually!
        //! Prototype: \code BM* _copy_() const {return new BM(*this);} \endcode
        virtual BM* _copy_ () const {return NULL;};
        //!@}

        //! \name Mathematical operations
        //!@{

        /*! \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)
        virtual void bayesB ( const mat &Dt );
        //! Evaluates predictive log-likelihood of the given data record
        //! I.e. marginal likelihood of the data with the posterior integrated out.
        virtual double logpred ( const vec &dt ) const{it_error ( "Not implemented" );return 0.0;}
        //! Matrix version of logpred
        vec logpred_m ( const mat &dt ) const{vec tmp ( dt.cols() );for ( int i=0;i<dt.cols();i++ ) {tmp ( i ) =logpred ( dt.get_col ( i ) );}return tmp;}

        //!Constructs a predictive density \f$ f(d_{t+1} |d_{t}, \ldots d_{0}) \f$
        virtual epdf* epredictor ( ) const {it_error ( "Not implemented" );return NULL;};
        //!Constructs a conditional density 1-step ahead predictor \f$ f(d_{t+1} |d_{t+h-1}, \ldots d_{t})
        virtual mpdf* predictor ( ) const {it_error ( "Not implemented" );return NULL;};
        //!@}

        //! \name Extension to conditional BM
        //! This extension is useful e.g. in Marginalized Particle Filter (\ref bdm::MPF).
        //! Alternatively, it can be used for automated connection to DS when the condition is observed
        //!@{

        //! Name of extension variable
        RV rvc;
        //! access function
        const RV& _rvc() const {return rvc;}

        //! Substitute \c val for \c rvc.
        virtual void condition ( const vec &val ) {it_error ( "Not implemented!" );};

        //!@}


        //! \name Access to attributes
        //!@{

        const RV& _drv() const {return drv;}
        void set_drv ( const RV &rv ) {drv=rv;}
        void set_rv ( const RV &rv ) {const_cast<epdf&> ( posterior() ).set_rv ( rv );}
        double _ll() const {return ll;}
        void set_evalll ( bool evl0 ) {evalll=evl0;}
        virtual const epdf& posterior() const =0;
        virtual const epdf* _e() const =0;
        //!@}

        //! \name Logging of results
        //!@{

        //! Set boolean options from a string
        void set_options ( const string &opt ) {
                opt_L_bounds= ( opt.find ( "logbounds" ) !=string::npos );
        }
        //! IDs of storages in loggers
        ivec LIDs;

        //! Option for logging bounds
        bool opt_L_bounds;
        //! Add all logged variables to a logger
        void log_add ( logger *L, const string &name="" ) {
                // internal
                RV r;
                if ( posterior().isnamed() ) {r=posterior()._rv();}
                else{r=RV ( "est", posterior().dimension() );};

                // Add mean value
                LIDs ( 0 ) =L->add ( r,name );
                if ( opt_L_bounds ) {
                        LIDs ( 1 ) =L->add ( r,name+"_lb" );
                        LIDs ( 2 ) =L->add ( r,name+"_ub" );
                }
        }
        void logit ( logger *L ) {
                L->logit ( LIDs ( 0 ), posterior().mean() );
                if ( opt_L_bounds ) {
                        vec ub,lb;
                        posterior().qbounds(lb,ub);
                        L->logit ( LIDs ( 1 ), lb );
                        L->logit ( LIDs ( 2 ), ub );
                }
        }
        //!@}
};

}; //namespace
#endif // BM_H