/*! \file \brief Probability distributions for Mixtures of pdfs \author Vaclav Smidl. ----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty Using IT++ for numerical operations ----------------------------------- */ #ifndef EMIX_H #define EMIX_H #define LOG2 0.69314718055995 #include "../shared_ptr.h" #include "exp_family.h" namespace bdm { //this comes first because it is used inside emix! /*! \brief Class representing ratio of two densities which arise e.g. by applying the Bayes rule. It represents density in the form: \f[ f(rv|rvc) = \frac{f(rv,rvc)}{f(rvc)} \f] where \f$ f(rvc) = \int f(rv,rvc) d\ rv \f$. In particular this type of arise by conditioning of a mixture model. At present the only supported operation is evallogcond(). */ class mratio: public mpdf { protected: //! Nominator in the form of mpdf const epdf* nom; //!Denominator in the form of epdf epdf* den; //!flag for destructor bool destroynom; //!datalink between conditional and nom datalink_m2e dl; public: //!Default constructor. By default, the given epdf is not copied! //! It is assumed that this function will be used only temporarily. mratio ( const epdf* nom0, const RV &rv, bool copy = false ) : mpdf ( ), dl ( ) { // adjust rv and rvc rvc = nom0->_rv().subt ( rv ); dimc = rvc._dsize(); set_ep ( shared_ptr ( new epdf ) ); e()->set_parameters ( rv._dsize() ); e()->set_rv ( rv ); //prepare data structures if ( copy ) { it_error ( "todo" ); destroynom = true; } else { nom = nom0; destroynom = false; } it_assert_debug ( rvc.length() > 0, "Makes no sense to use this object!" ); // build denominator den = nom->marginal ( rvc ); dl.set_connection ( rv, rvc, nom0->_rv() ); }; double evallogcond ( const vec &val, const vec &cond ) { double tmp; vec nom_val ( e()->dimension() + dimc ); dl.pushup_cond ( nom_val, val, cond ); tmp = exp ( nom->evallog ( nom_val ) - den->evallog ( cond ) ); it_assert_debug ( std::isfinite ( tmp ), "Infinite value" ); return tmp; } //! Object takes ownership of nom and will destroy it void ownnom() { destroynom = true; } //! Default destructor ~mratio() { delete den; if ( destroynom ) { delete nom; } } }; /*! * \brief Mixture of epdfs Density function: \f[ f(x) = \sum_{i=1}^{n} w_{i} f_i(x), \quad \sum_{i=1}^n w_i = 1. \f] where \f$f_i(x)\f$ is any density on random variable \f$x\f$, called \a component, */ class emix : public epdf { protected: //! weights of the components vec w; //! Component (epdfs) Array Coms; //!Flag if owning Coms bool destroyComs; public: //!Default constructor emix ( ) : epdf ( ) {}; //! Set weights \c w and components \c Coms //!By default Coms are copied inside. Parameter \c copy can be set to false if Coms live externally. Use method ownComs() if Coms should be destroyed by the destructor. void set_parameters ( const vec &w, const Array &Coms, bool copy = false ); vec sample() const; vec mean() const { int i; vec mu = zeros ( dim ); for ( i = 0; i < w.length(); i++ ) { mu += w ( i ) * Coms ( i )->mean(); } return mu; } vec variance() const { //non-central moment vec mom2 = zeros ( dim ); for ( int i = 0; i < w.length(); i++ ) { mom2 += w ( i ) * ( Coms ( i )->variance() + pow ( Coms ( i )->mean(), 2 ) ); } //central moment return mom2 - pow ( mean(), 2 ); } double evallog ( const vec &val ) const { int i; double sum = 0.0; for ( i = 0; i < w.length(); i++ ) { sum += w ( i ) * exp ( Coms ( i )->evallog ( val ) ); } if ( sum == 0.0 ) { sum = std::numeric_limits::epsilon(); } double tmp = log ( sum ); it_assert_debug ( std::isfinite ( tmp ), "Infinite" ); return tmp; }; vec evallog_m ( const mat &Val ) const { vec x = zeros ( Val.cols() ); for ( int i = 0; i < w.length(); i++ ) { x += w ( i ) * exp ( Coms ( i )->evallog_m ( Val ) ); } return log ( x ); }; //! Auxiliary function that returns pdflog for each component mat evallog_M ( const mat &Val ) const { mat X ( w.length(), Val.cols() ); for ( int i = 0; i < w.length(); i++ ) { X.set_row ( i, w ( i ) *exp ( Coms ( i )->evallog_m ( Val ) ) ); } return X; }; emix* marginal ( const RV &rv ) const; mratio* condition ( const RV &rv ) const; //why not mratio!! //Access methods //! returns a pointer to the internal mean value. Use with Care! vec& _w() { return w; } virtual ~emix() { if ( destroyComs ) { for ( int i = 0; i < Coms.length(); i++ ) { delete Coms ( i ); } } } //! Auxiliary function for taking ownership of the Coms() void ownComs() { destroyComs = true; } //!access function epdf* _Coms ( int i ) { return Coms ( i ); } void set_rv ( const RV &rv ) { epdf::set_rv ( rv ); for ( int i = 0; i < Coms.length(); i++ ) { Coms ( i )->set_rv ( rv ); } } }; /*! * \brief Mixture of egiws */ class egiwmix : public egiw { protected: //! weights of the components vec w; //! Component (epdfs) Array Coms; //!Flag if owning Coms bool destroyComs; public: //!Default constructor egiwmix ( ) : egiw ( ) {}; //! Set weights \c w and components \c Coms //!By default Coms are copied inside. Parameter \c copy can be set to false if Coms live externally. Use method ownComs() if Coms should be destroyed by the destructor. void set_parameters ( const vec &w, const Array &Coms, bool copy = false ); //!return expected value vec mean() const; //!return a sample from the density vec sample() const; //!return the expected variance vec variance() const; // TODO!!! Defined to follow ANSI and/or for future development void mean_mat ( mat &M, mat&R ) const {}; double evallog_nn ( const vec &val ) const { return 0; }; double lognc () const { return 0; }; emix* marginal ( const RV &rv ) const; //Access methods //! returns a pointer to the internal mean value. Use with Care! vec& _w() { return w; } virtual ~egiwmix() { if ( destroyComs ) { for ( int i = 0; i < Coms.length(); i++ ) { delete Coms ( i ); } } } //! Auxiliary function for taking ownership of the Coms() void ownComs() { destroyComs = true; } //!access function egiw* _Coms ( int i ) { return Coms ( i ); } void set_rv ( const RV &rv ) { egiw::set_rv ( rv ); for ( int i = 0; i < Coms.length(); i++ ) { Coms ( i )->set_rv ( rv ); } } //! Approximation of a GiW mix by a single GiW pdf egiw* approx(); }; /*! \brief Chain rule decomposition of epdf Probability density in the form of Chain-rule decomposition: \[ f(x_1,x_2,x_3) = f(x_1|x_2,x_3)f(x_2,x_3)f(x_3) \] Note that */ class mprod: public compositepdf, public mpdf { protected: //! pointers to epdfs - shortcut to mpdfs().posterior() Array epdfs; //! Data link for each mpdfs Array dls; //! dummy ep shared_ptr dummy; public: /*!\brief Constructor from list of mFacs, */ mprod() : dummy ( new epdf() ) { } mprod ( Array mFacs ) : dummy ( new epdf() ) { set_elements ( mFacs ); } void set_elements ( Array mFacs , bool own = false ) { compositepdf::set_elements ( mFacs, own ); dls.set_size ( mFacs.length() ); epdfs.set_size ( mFacs.length() ); set_ep ( dummy ); RV rv = getrv ( true ); set_rv ( rv ); dummy->set_parameters ( rv._dsize() ); setrvc ( e()->_rv(), rvc ); // rv and rvc established = > we can link them with mpdfs for ( int i = 0; i < mpdfs.length(); i++ ) { dls ( i ) = new datalink_m2m; dls ( i )->set_connection ( mpdfs ( i )->_rv(), mpdfs ( i )->_rvc(), _rv(), _rvc() ); } for ( int i = 0; i < mpdfs.length(); i++ ) { epdfs ( i ) = mpdfs ( i )->e(); } }; double evallogcond ( const vec &val, const vec &cond ) { int i; double res = 0.0; for ( i = mpdfs.length() - 1; i >= 0; i-- ) { /* if ( mpdfs(i)->_rvc().count() >0) { mpdfs ( i )->condition ( dls ( i )->get_cond ( val,cond ) ); } // add logarithms res += epdfs ( i )->evallog ( dls ( i )->pushdown ( val ) );*/ res += mpdfs ( i )->evallogcond ( dls ( i )->pushdown ( val ), dls ( i )->get_cond ( val, cond ) ); } return res; } vec evallogcond_m ( const mat &Dt, const vec &cond ) { vec tmp ( Dt.cols() ); for ( int i = 0; i < Dt.cols(); i++ ) { tmp ( i ) = evallogcond ( Dt.get_col ( i ), cond ); } return tmp; }; vec evallogcond_m ( const Array &Dt, const vec &cond ) { vec tmp ( Dt.length() ); for ( int i = 0; i < Dt.length(); i++ ) { tmp ( i ) = evallogcond ( Dt ( i ), cond ); } return tmp; }; //TODO smarter... vec samplecond ( const vec &cond ) { //! Ugly hack to help to discover if mpfs are not in proper order. Correct solution = check that explicitely. vec smp = std::numeric_limits::infinity() * ones ( e()->dimension() ); vec smpi; // Hard assumption here!!! We are going backwards, to assure that samples that are needed from smp are already generated! for ( int i = ( mpdfs.length() - 1 ); i >= 0; i-- ) { if ( mpdfs ( i )->dimensionc() ) { mpdfs ( i )->condition ( dls ( i )->get_cond ( smp , cond ) ); // smp is val here!! } smpi = epdfs ( i )->sample(); // copy contribution of this pdf into smp dls ( i )->pushup ( smp, smpi ); // add ith likelihood contribution } return smp; } mat samplecond ( const vec &cond, int N ) { mat Smp ( dimension(), N ); for ( int i = 0; i < N; i++ ) { Smp.set_col ( i, samplecond ( cond ) ); } return Smp; } ~mprod() {}; //! Load from structure with elements: //! \code //! { class='mprod'; //! mpdfs = (..., ...); // list of mpdfs in the order of chain rule //! } //! \endcode //!@} void from_setting ( const Setting &set ) { Array Atmp; //temporary Array UI::get ( Atmp, set, "mpdfs", UI::compulsory ); set_elements ( Atmp, true ); } }; UIREGISTER ( mprod ); //! Product of independent epdfs. For dependent pdfs, use mprod. class eprod: public epdf { protected: //! Components (epdfs) Array epdfs; //! Array of indeces Array dls; public: eprod () : epdfs ( 0 ), dls ( 0 ) {}; void set_parameters ( const Array &epdfs0, bool named = true ) { epdfs = epdfs0;//.set_length ( epdfs0.length() ); dls.set_length ( epdfs.length() ); bool independent = true; if ( named ) { for ( int i = 0; i < epdfs.length(); i++ ) { independent = rv.add ( epdfs ( i )->_rv() ); it_assert_debug ( independent == true, "eprod:: given components are not independent." ); } dim = rv._dsize(); } else { dim = 0; for ( int i = 0; i < epdfs.length(); i++ ) { dim += epdfs ( i )->dimension(); } } // int cumdim = 0; int dimi = 0; int i; for ( i = 0; i < epdfs.length(); i++ ) { dls ( i ) = new datalink; if ( named ) { dls ( i )->set_connection ( epdfs ( i )->_rv() , rv ); } else { dimi = epdfs ( i )->dimension(); dls ( i )->set_connection ( dimi, dim, linspace ( cumdim, cumdim + dimi - 1 ) ); cumdim += dimi; } } } vec mean() const { vec tmp ( dim ); for ( int i = 0; i < epdfs.length(); i++ ) { vec pom = epdfs ( i )->mean(); dls ( i )->pushup ( tmp, pom ); } return tmp; } vec variance() const { vec tmp ( dim ); //second moment for ( int i = 0; i < epdfs.length(); i++ ) { vec pom = epdfs ( i )->mean(); dls ( i )->pushup ( tmp, pow ( pom, 2 ) ); } return tmp - pow ( mean(), 2 ); } vec sample() const { vec tmp ( dim ); for ( int i = 0; i < epdfs.length(); i++ ) { vec pom = epdfs ( i )->sample(); dls ( i )->pushup ( tmp, pom ); } return tmp; } double evallog ( const vec &val ) const { double tmp = 0; for ( int i = 0; i < epdfs.length(); i++ ) { tmp += epdfs ( i )->evallog ( dls ( i )->pushdown ( val ) ); } it_assert_debug ( std::isfinite ( tmp ), "Infinite" ); return tmp; } //!access function const epdf* operator () ( int i ) const { it_assert_debug ( i < epdfs.length(), "wrong index" ); return epdfs ( i ); } //!Destructor ~eprod() { for ( int i = 0; i < epdfs.length(); i++ ) { delete dls ( i ); } } }; /*! \brief Mixture of mpdfs with constant weights, all mpdfs are of equal type */ class mmix : public mpdf { protected: //! Component (epdfs) Array Coms; //!Internal epdf shared_ptr iepdf; public: //!Default constructor mmix() : iepdf ( new emix() ) { set_ep ( iepdf ); } //! Set weights \c w and components \c R void set_parameters ( const vec &w, const Array &Coms ) { Array Eps ( Coms.length() ); for ( int i = 0; i < Coms.length(); i++ ) { Eps ( i ) = Coms ( i )->e(); } iepdf->set_parameters ( w, Eps ); } void condition ( const vec &cond ) { for ( int i = 0; i < Coms.length(); i++ ) { Coms ( i )->condition ( cond ); } }; }; } #endif //MX_H