[97] | 1 | /*! |
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| 2 | \file |
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| 3 | \brief Bayesian Filtering for generalized autoregressive (ARX) model |
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| 4 | \author Vaclav Smidl. |
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| 5 | |
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| 6 | ----------------------------------- |
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| 7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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| 8 | |
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| 9 | Using IT++ for numerical operations |
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| 10 | ----------------------------------- |
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| 11 | */ |
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| 12 | |
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| 13 | #ifndef AR_H |
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| 14 | #define AR_H |
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| 15 | |
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[384] | 16 | #include "../math/functions.h" |
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| 17 | #include "../stat/exp_family.h" |
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| 18 | #include "../base/user_info.h" |
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[585] | 19 | //#include "../estim/kalman.h" |
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| 20 | #include "arx_straux.h" |
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[97] | 21 | |
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[270] | 22 | namespace bdm { |
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[97] | 23 | |
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| 24 | /*! |
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| 25 | * \brief Linear Autoregressive model with Gaussian noise |
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| 26 | |
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| 27 | Regression of the following kind: |
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| 28 | \f[ |
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[1077] | 29 | y_t = heta_1 \psi_1 + heta_2 + \psi_2 +\ldots + heta_n \psi_n + r e_t |
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[97] | 30 | \f] |
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[1077] | 31 | where unknown parameters \c rv are \f$[ heta r]\f$, regression vector \f$\psi=\psi(y_{1:t},u_{1:t})\f$ is a known function of past outputs and exogeneous variables \f$u_t\f$. Distrubances \f$e_t\f$ are supposed to be normally distributed: |
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[97] | 32 | \f[ |
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| 33 | e_t \sim \mathcal{N}(0,1). |
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| 34 | \f] |
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| 35 | |
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[271] | 36 | See \ref tut_arx for mathematical treatment. |
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| 37 | |
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| 38 | The easiest way how to use the class is: |
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| 39 | \include arx_simple.cpp |
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| 40 | |
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[1077] | 41 | odo sort out constant terms - bayes should accept vec without additional 1s |
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[97] | 42 | */ |
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[170] | 43 | class ARX: public BMEF { |
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[97] | 44 | protected: |
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[1064] | 45 | //! switch if constant is modelled or not |
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| 46 | bool have_constant; |
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| 47 | //! vector of dyadic update |
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| 48 | vec dyad; |
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| 49 | //! RV of regressor |
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| 50 | RV rgr; |
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| 51 | //! length of the regressor (without optional constant) |
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| 52 | int rgrlen; |
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| 53 | //! posterior density |
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| 54 | egiw est; |
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| 55 | //! Alternative estimate of parameters, used in stabilized forgetting, see [Kulhavy] |
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| 56 | egiw alter_est; |
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[97] | 57 | public: |
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[1064] | 58 | //! \name Constructors |
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| 59 | //!@{ |
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| 60 | ARX ( const double frg0 = 1.0 ) : BMEF ( frg0 ), have_constant ( true ), dyad(), rgrlen(),est(), alter_est() {}; |
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| 61 | ARX ( const ARX &A0 ) : BMEF ( A0 ), have_constant ( A0.have_constant ), dyad ( A0.dyad ),rgrlen(A0.rgrlen), est ( A0.est ), alter_est ( A0.alter_est ) { }; |
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[766] | 62 | |
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[1064] | 63 | ARX* _copy() const; |
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[766] | 64 | |
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[1064] | 65 | void set_frg ( double frg0 ) { |
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| 66 | frg = frg0; |
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| 67 | } |
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| 68 | void set_constant ( bool const0 ) { |
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| 69 | have_constant = const0; |
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| 70 | } |
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| 71 | void set_statistics ( int dimy0, const ldmat V0, double nu0 = -1.0 ) { |
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| 72 | est.set_parameters ( dimy0, V0, nu0 ); |
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| 73 | est.validate(); |
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| 74 | last_lognc = est.lognc(); |
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| 75 | dimy = dimy0; |
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| 76 | } |
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| 77 | //!@} |
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[170] | 78 | |
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[1064] | 79 | //! Set sufficient statistics |
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| 80 | void set_statistics ( const BMEF* BM0 ); |
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[625] | 81 | |
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[1064] | 82 | //!\name Mathematical operations |
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| 83 | //!@{ |
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[270] | 84 | |
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[1064] | 85 | //! Weighted Bayes \f$ dt = [y_t psi_t] \f$. |
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| 86 | void bayes_weighted ( const vec &yt, const vec &cond = empty_vec, const double w = 1.0 ); |
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| 87 | void bayes ( const vec &yt, const vec &cond = empty_vec ) { |
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| 88 | bayes_weighted ( yt, cond, 1.0 ); |
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| 89 | }; |
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[1166] | 90 | double logpred ( const vec &yt, const vec &cond ) const; |
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| 91 | vec samplepred ( const vec &cond ) const; |
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| 92 | void flatten ( const BMEF* B , double weight ); |
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[1064] | 93 | //! Conditioned version of the predictor |
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| 94 | enorm<ldmat>* epredictor ( const vec &rgr ) const; |
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[1176] | 95 | estudent<ldmat>* epredictor_student ( const vec &rgr ) const; |
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| 96 | //! conditional version of the predictor |
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[1064] | 97 | template<class sq_T> |
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| 98 | shared_ptr<mlnorm<sq_T> > ml_predictor() const; |
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| 99 | //! fast version of predicto |
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| 100 | template<class sq_T> |
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| 101 | void ml_predictor_update ( mlnorm<sq_T> &pred ) const; |
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[1176] | 102 | mlstudent* predictor() const; |
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[1064] | 103 | //! Brute force structure estimation.\return indices of accepted regressors. |
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| 104 | ivec structure_est ( const egiw &Eg0 ); |
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| 105 | //! Smarter structure estimation by Ludvik Tesar.\return indices of accepted regressors. |
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| 106 | ivec structure_est_LT ( const egiw &Eg0 ); |
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| 107 | //! reduce structure to the given ivec of matrix V |
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| 108 | void reduce_structure(ivec &inds_in_V) { |
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| 109 | ldmat V = posterior()._V(); |
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| 110 | if (max(inds_in_V)>=V.rows()) { |
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| 111 | bdm_error("Incompatible structure"); |
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| 112 | } |
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[270] | 113 | |
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[1064] | 114 | ldmat newV(V,inds_in_V); |
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| 115 | est.set_parameters(dimy,newV, posterior()._nu()); |
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[270] | 116 | |
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[1064] | 117 | if (have_constant) { |
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| 118 | ivec rgr_elem= find(inds_in_V<(V.rows()-1)); // < -- find non-constant |
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| 119 | rgr = rgr.subselect(rgr_elem); |
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| 120 | rgrlen = rgr_elem.length(); |
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| 121 | } else { |
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| 122 | rgr = rgr.subselect(inds_in_V); |
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| 123 | } |
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| 124 | validate(); |
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| 125 | } |
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| 126 | //!@} |
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[357] | 127 | |
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[1064] | 128 | //!\name Access attributes |
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| 129 | //!@{ |
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| 130 | //! return correctly typed posterior (covariant return) |
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| 131 | const egiw& posterior() const { |
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| 132 | return est; |
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| 133 | } |
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| 134 | //!@} |
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[357] | 135 | |
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[1077] | 136 | /*! Create object from the following structure |
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[737] | 137 | |
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[1064] | 138 | \code |
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| 139 | class = 'ARX'; |
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[1077] | 140 | rgr = RV({'names',...},[sizes,...],[times,...]); % description of regressor variables |
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| 141 | --- optional fields --- |
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| 142 | prior = configuration of bdm::egiw; % any offspring of eqiw for prior density, bdm::egiw::from_setting |
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| 143 | alternative = configuration of bdm::egiw; % any offspring of eqiw for alternative density in stabilized estimation of prior density |
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| 144 | constant = []; % 0/1 switch if the constant term is modelled or not |
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| 145 | --- inherited fields --- |
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| 146 | bdm::BMEF::from_setting |
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[1064] | 147 | \endcode |
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[1077] | 148 | If the optional fields are not given, they will be filled as follows: |
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| 149 | \code |
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| 150 | prior = posterior; % when prior is not given the posterior is used (TODO it is unclear) |
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| 151 | alternative = prior; % when alternative is not given the prior is used |
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| 152 | constant = 1; % constant term is modelled on default |
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| 153 | \endcode |
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[1064] | 154 | */ |
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| 155 | void from_setting ( const Setting &set ); |
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[746] | 156 | |
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[1064] | 157 | void validate() { |
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| 158 | BMEF::validate(); |
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| 159 | est.validate(); |
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| 160 | |
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| 161 | // When statistics is defined, it has priority |
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| 162 | if(posterior()._dimx()>0) {//statistics is assigned |
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| 163 | dimy = posterior()._dimx(); |
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| 164 | rgrlen=posterior()._V().rows() - dimy - int ( have_constant == true ); |
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| 165 | dimc = rgrlen; |
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| 166 | } else { // statistics is not assigned - build it from dimy and rgrlen |
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| 167 | bdm_assert(dimy>0,"No way to validate egiw: empty statistics and empty dimy"); |
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| 168 | est.set_parameters(dimy, zeros(dimy+rgrlen+int(have_constant==true))); |
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| 169 | set_prior_default(est); |
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| 170 | } |
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| 171 | if (alter_est.dimension()==0) alter_est=est; |
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| 172 | |
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| 173 | dyad = ones ( est._V().rows() ); |
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| 174 | } |
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| 175 | //! function sets prior and alternative density |
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| 176 | void set_prior ( const epdf *prior ); |
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| 177 | //! build default prior and alternative when all values are set |
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| 178 | void set_prior_default ( egiw &prior ) { |
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| 179 | //assume |
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| 180 | vec dV0 ( prior._V().rows() ); |
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| 181 | dV0.set_subvector ( 0, prior._dimx() - 1, 1.0 ); |
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| 182 | if (dV0.length()>prior._dimx()) |
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| 183 | dV0.set_subvector ( prior._dimx(), dV0.length() - 1, 1e-5 ); |
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| 184 | |
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| 185 | prior.set_parameters ( prior._dimx(), ldmat ( dV0 ) ); |
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| 186 | prior.validate(); |
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| 187 | } |
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| 188 | |
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| 189 | void to_setting ( Setting &set ) const |
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| 190 | { |
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| 191 | BMEF::to_setting( set ); // takes care of rv, yrv, rvc |
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| 192 | UI::save(rgr, set, "rgr"); |
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| 193 | int constant = have_constant ? 1 : 0; |
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| 194 | UI::save(constant, set, "constant"); |
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| 195 | UI::save(&alter_est, set, "alternative"); |
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| 196 | UI::save(&posterior(), set, "posterior"); |
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| 197 | |
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| 198 | } |
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| 199 | //! access function |
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| 200 | RV & _rgr() { |
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| 201 | return rgr; |
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| 202 | } |
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| 203 | bool _have_constant() { |
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| 204 | return have_constant; |
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| 205 | } |
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| 206 | int _rgrlen() { |
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| 207 | return rgrlen; |
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| 208 | } |
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[97] | 209 | }; |
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[477] | 210 | UIREGISTER ( ARX ); |
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[529] | 211 | SHAREDPTR ( ARX ); |
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[357] | 212 | |
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[1121] | 213 | //! \brief ARX moidel with parameters in LS form |
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| 214 | class ARXls : public BMEF{ |
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[1131] | 215 | public: |
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[1121] | 216 | egw_ls<ldmat> est; |
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| 217 | |
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[1131] | 218 | egw_ls<ldmat> alternative; |
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| 219 | |
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[1121] | 220 | const egw_ls<ldmat>& posterior() {return est;}; |
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| 221 | |
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| 222 | void bayes(const vec &dt, const vec &psi){ |
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[1131] | 223 | ldmat &Pbeta = est.P; |
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| 224 | ldmat &Palpha = alternative.P; |
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| 225 | |
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| 226 | |
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[1121] | 227 | } |
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| 228 | }; |
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| 229 | |
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[1077] | 230 | /*! \brief ARX model conditined by knowledge of the forgetting factor |
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| 231 | \f[ f( heta| d_1 \ldots d_t , \phi_t) \f] |
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[700] | 232 | |
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| 233 | The symbol \f$ \phi \f$ is assumed to be the last of the conditioning variables. |
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[639] | 234 | */ |
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[737] | 235 | class ARXfrg : public ARX { |
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| 236 | public: |
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[1064] | 237 | ARXfrg() : ARX() {}; |
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| 238 | //! copy constructor |
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| 239 | ARXfrg ( const ARXfrg &A0 ) : ARX ( A0 ) {}; |
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| 240 | virtual ARXfrg* _copy() const { |
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| 241 | ARXfrg *A = new ARXfrg ( *this ); |
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| 242 | return A; |
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| 243 | } |
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[700] | 244 | |
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[1064] | 245 | void bayes ( const vec &val, const vec &cond ) { |
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| 246 | bdm_assert_debug(cond.size()>rgrlen, "ARXfrg: Insufficient conditioning, frg not given."); |
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| 247 | frg = cond ( rgrlen); // the first part after rgrlen |
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| 248 | ARX::bayes ( val, cond.left(rgrlen) ); |
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| 249 | } |
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| 250 | void validate() { |
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| 251 | ARX::validate(); |
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| 252 | rvc.add ( RV ( "{phi }", vec_1 ( 1 ) ) ); |
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| 253 | dimc += 1; |
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| 254 | } |
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[639] | 255 | }; |
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[737] | 256 | UIREGISTER ( ARXfrg ); |
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[723] | 257 | |
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| 258 | |
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[1063] | 259 | /*! \brief ARX with partial forgetting |
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[996] | 260 | |
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[1025] | 261 | Implements partial forgetting when <tt>bayes(const vec &yt, const vec &cond=empty_vec)</tt> is called, where \c cond is a vector <em>(regressor', forg.factor')</em>. |
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| 262 | |
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[1036] | 263 | Note, that the weights have the same order as hypotheses in partial forgetting, and follow this scheme: |
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[1025] | 264 | \li 0 means that the parameter doesn't change, |
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| 265 | \li 1 means that the parameter varies. |
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| 266 | |
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| 267 | The combinations of parameters are described binary: |
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[1036] | 268 | \f{bmatrix}[ |
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| 269 | 0 & 0 & 0 & \ldots \\ |
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| 270 | 1 & 0 & 0 & \ldots \\ |
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| 271 | 0 & 1 & 0 & \ldots \\ |
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| 272 | 1 & 1 & 0 & \ldots \\ |
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[1064] | 273 | \vdots & \vdots & \vdots & \vdots |
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[1036] | 274 | \f{bmatrix}] |
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| 275 | Notice, that each n-th column has altering n-tuples of 1's and 0's, n = 0,...,number of params. Hence, the first forg. factor relates to the situation when no parameter changes, the second one when the first parameter changes etc. |
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[1025] | 276 | |
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| 277 | See ARX class for more information about ARX. |
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| 278 | */ |
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| 279 | class ARXpartialforg : public ARX { |
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| 280 | public: |
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[1064] | 281 | ARXpartialforg() : ARX(1.0) {}; |
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| 282 | //! copy constructor |
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| 283 | ARXpartialforg ( const ARXpartialforg &A0 ) : ARX ( A0 ) {}; |
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| 284 | virtual ARXpartialforg* _copy() const { |
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| 285 | ARXpartialforg *A = new ARXpartialforg ( *this ); |
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| 286 | return A; |
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| 287 | } |
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[1025] | 288 | |
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[1064] | 289 | void bayes ( const vec &val, const vec &cond ); |
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[1063] | 290 | |
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[1064] | 291 | void validate() { |
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| 292 | ARX::validate(); |
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| 293 | int philen = 1 << (est._V().cols() - 1); |
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| 294 | rvc.add ( RV ( "{phi }", vec_1(philen) ) ); // pocet 2^parametru |
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| 295 | dimc += philen; |
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| 296 | } |
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[1025] | 297 | }; |
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| 298 | UIREGISTER ( ARXpartialforg ); |
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| 299 | |
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| 300 | |
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[723] | 301 | //////////////////// |
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| 302 | template<class sq_T> |
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| 303 | shared_ptr< mlnorm<sq_T> > ARX::ml_predictor ( ) const { |
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[1064] | 304 | shared_ptr< mlnorm<sq_T> > tmp = new mlnorm<sq_T> ( ); |
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| 305 | tmp->set_rv ( yrv ); |
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| 306 | tmp->set_rvc ( _rvc() ); |
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[737] | 307 | |
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[1064] | 308 | ml_predictor_update ( *tmp ); |
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| 309 | tmp->validate(); |
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| 310 | return tmp; |
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[723] | 311 | } |
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| 312 | |
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| 313 | template<class sq_T> |
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[737] | 314 | void ARX::ml_predictor_update ( mlnorm<sq_T> &pred ) const { |
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[1064] | 315 | mat mu ( dimy, posterior()._V().rows() - dimy ); |
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| 316 | mat R ( dimy, dimy ); |
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[737] | 317 | |
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[1064] | 318 | est.mean_mat ( mu, R ); //mu = |
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| 319 | mu = mu.T(); |
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| 320 | //correction for student-t -- TODO check if correct!! |
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[737] | 321 | |
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[1064] | 322 | if ( have_constant ) { // constant term |
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| 323 | //Assume the constant term is the last one: |
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| 324 | pred.set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), sq_T ( R ) ); |
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| 325 | } else { |
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| 326 | pred.set_parameters ( mu, zeros ( dimy ), sq_T ( R ) ); |
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| 327 | } |
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[723] | 328 | } |
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| 329 | |
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[639] | 330 | }; |
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[97] | 331 | #endif // AR_H |
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| 332 | |
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