[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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| 29 | y_t = \theta_1 \psi_1 + \theta_2 + \psi_2 +\ldots + \theta_n \psi_n + r e_t |
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| 30 | \f] |
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| 31 | where unknown parameters \c rv are \f$[\theta r]\f$, 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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| 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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[384] | 41 | \todo 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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[625] | 45 | //! switch if constant is modelled or not |
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| 46 | bool have_constant; |
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[679] | 47 | //! vector of dyadic update |
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| 48 | vec dyad; |
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[979] | 49 | //! RV of regressor |
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| 50 | RV rgr; |
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[990] | 51 | //! length of the regressor (without optional constant) |
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[964] | 52 | int rgrlen; |
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[679] | 53 | //! posterior density |
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| 54 | egiw est; |
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[639] | 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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[270] | 58 | //! \name Constructors |
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| 59 | //!@{ |
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[964] | 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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| 63 | ARX* _copy() const; |
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| 64 | |
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[796] | 65 | void set_frg ( double frg0 ) { |
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[477] | 66 | frg = frg0; |
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| 67 | } |
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[649] | 68 | void set_constant ( bool const0 ) { |
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[737] | 69 | have_constant = const0; |
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[649] | 70 | } |
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[679] | 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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[878] | 73 | est.validate(); |
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[477] | 74 | last_lognc = est.lognc(); |
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[679] | 75 | dimy = dimy0; |
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[477] | 76 | } |
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[270] | 77 | //!@} |
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[170] | 78 | |
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[145] | 79 | //! Set sufficient statistics |
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[170] | 80 | void set_statistics ( const BMEF* BM0 ); |
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[625] | 81 | |
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[270] | 82 | //!\name Mathematical operations |
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| 83 | //!@{ |
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| 84 | |
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| 85 | //! Weighted Bayes \f$ dt = [y_t psi_t] \f$. |
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[737] | 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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[477] | 89 | }; |
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[1009] | 90 | double logpred ( const vec &yt, const vec &cond ) const; |
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[1013] | 91 | void flatten ( const BMEF* B , double weight ); |
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[262] | 92 | //! Conditioned version of the predictor |
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[270] | 93 | enorm<ldmat>* epredictor ( const vec &rgr ) const; |
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[262] | 94 | //! conditional version of the predictor |
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[723] | 95 | template<class sq_T> |
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| 96 | shared_ptr<mlnorm<sq_T> > ml_predictor() const; |
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[737] | 97 | //! fast version of predicto |
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[723] | 98 | template<class sq_T> |
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[737] | 99 | void ml_predictor_update ( mlnorm<sq_T> &pred ) const; |
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[270] | 100 | mlstudent* predictor_student() const; |
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[896] | 101 | //! Brute force structure estimation.\return indices of accepted regressors. |
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[1003] | 102 | ivec structure_est ( const egiw &Eg0 ); |
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[896] | 103 | //! Smarter structure estimation by Ludvik Tesar.\return indices of accepted regressors. |
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[1003] | 104 | ivec structure_est_LT ( const egiw &Eg0 ); |
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[1009] | 105 | //! reduce structure to the given ivec of matrix V |
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[996] | 106 | void reduce_structure(ivec &inds_in_V){ |
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| 107 | ldmat V = posterior()._V(); |
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| 108 | if (max(inds_in_V)>=V.rows()) {bdm_error("Incompatible structure");} |
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| 109 | |
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| 110 | ldmat newV(V,inds_in_V); |
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| 111 | est.set_parameters(dimy,newV, posterior()._nu()); |
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[1009] | 112 | |
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| 113 | if (have_constant){ |
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| 114 | ivec rgr_elem= find(inds_in_V<(V.rows()-1)); // < -- find non-constant |
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| 115 | rgr = rgr.subselect(rgr_elem); |
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| 116 | rgrlen = rgr_elem.length(); |
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| 117 | } else{ |
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| 118 | rgr = rgr.subselect(inds_in_V); |
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| 119 | } |
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[996] | 120 | validate(); |
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| 121 | } |
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[270] | 122 | //!@} |
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| 123 | |
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| 124 | //!\name Access attributes |
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| 125 | //!@{ |
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[737] | 126 | //! return correctly typed posterior (covariant return) |
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| 127 | const egiw& posterior() const { |
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[477] | 128 | return est; |
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| 129 | } |
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[270] | 130 | //!@} |
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| 131 | |
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[357] | 132 | /*! UI for ARX estimator |
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| 133 | |
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| 134 | \code |
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[625] | 135 | class = 'ARX'; |
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[964] | 136 | yrv = RV({names_of_dt} ) // description of output variables |
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[625] | 137 | rgr = RV({names_of_regressors}, [-1,-2]} // description of regressor variables |
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[631] | 138 | constant = 1; // 0/1 switch if the constant term is modelled or not |
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[357] | 139 | |
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[625] | 140 | --- optional --- |
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[665] | 141 | prior = {class='egiw',...}; // Prior density, when given default is used instead |
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| 142 | alternative = {class='egiw',...}; // Alternative density in stabilized estimation, when not given prior is used |
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[737] | 143 | |
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[625] | 144 | frg = 1.0; // forgetting, default frg=1.0 |
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| 145 | |
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[964] | 146 | rv = RV({names_of_parameters}} // description of parametetr names |
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| 147 | // default: [""] |
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[357] | 148 | \endcode |
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| 149 | */ |
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[477] | 150 | void from_setting ( const Setting &set ); |
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[357] | 151 | |
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[625] | 152 | void validate() { |
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[850] | 153 | BMEF::validate(); |
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[964] | 154 | est.validate(); |
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[990] | 155 | |
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[996] | 156 | // When statistics is defined, it has priority |
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| 157 | if(posterior()._dimx()>0) {//statistics is assigned |
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[1003] | 158 | dimy = posterior()._dimx(); |
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[996] | 159 | rgrlen=posterior()._V().rows() - dimy - int ( have_constant == true ); |
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| 160 | dimc = rgrlen; |
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| 161 | } else{ // statistics is not assigned - build it from dimy and rgrlen |
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| 162 | bdm_assert(dimy>0,"No way to validate egiw: empty statistics and empty dimy"); |
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[990] | 163 | est.set_parameters(dimy, zeros(dimy+rgrlen+int(have_constant==true))); |
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| 164 | set_prior_default(est); |
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[679] | 165 | } |
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[990] | 166 | if (alter_est.dimension()==0) alter_est=est; |
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[737] | 167 | |
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[990] | 168 | dyad = ones ( est._V().rows() ); |
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[625] | 169 | } |
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[737] | 170 | //! function sets prior and alternative density |
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[973] | 171 | void set_prior ( const epdf *prior ); |
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[665] | 172 | //! build default prior and alternative when all values are set |
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[737] | 173 | void set_prior_default ( egiw &prior ) { |
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| 174 | //assume |
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| 175 | vec dV0 ( prior._V().rows() ); |
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| 176 | dV0.set_subvector ( 0, prior._dimx() - 1, 1.0 ); |
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[810] | 177 | if (dV0.length()>prior._dimx()) |
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| 178 | dV0.set_subvector ( prior._dimx(), dV0.length() - 1, 1e-5 ); |
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[878] | 179 | |
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[737] | 180 | prior.set_parameters ( prior._dimx(), ldmat ( dV0 ) ); |
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[878] | 181 | prior.validate(); |
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[665] | 182 | } |
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[746] | 183 | |
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| 184 | void to_setting ( Setting &set ) const |
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| 185 | { |
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[796] | 186 | BMEF::to_setting( set ); // takes care of rv, yrv, rvc |
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[979] | 187 | UI::save(rgr, set, "rgr"); |
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[796] | 188 | int constant = have_constant ? 1 : 0; |
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| 189 | UI::save(constant, set, "constant"); |
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| 190 | UI::save(&alter_est, set, "alternative"); |
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[979] | 191 | UI::save(&posterior(), set, "posterior"); |
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[796] | 192 | |
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[746] | 193 | } |
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[1009] | 194 | //! access function |
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| 195 | RV & _rgr() {return rgr;} |
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| 196 | bool _have_constant() {return have_constant;} |
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| 197 | int _rgrlen() {return rgrlen;} |
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[97] | 198 | }; |
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[477] | 199 | UIREGISTER ( ARX ); |
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[529] | 200 | SHAREDPTR ( ARX ); |
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[357] | 201 | |
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[639] | 202 | /*! ARX model conditined by knowledge of the forgetting factor |
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| 203 | \f[ f(\theta| d_1 \ldots d_t , \phi_t) \f] |
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[700] | 204 | |
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| 205 | The symbol \f$ \phi \f$ is assumed to be the last of the conditioning variables. |
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[639] | 206 | */ |
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[737] | 207 | class ARXfrg : public ARX { |
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| 208 | public: |
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| 209 | ARXfrg() : ARX() {}; |
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| 210 | //! copy constructor |
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| 211 | ARXfrg ( const ARXfrg &A0 ) : ARX ( A0 ) {}; |
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[766] | 212 | virtual ARXfrg* _copy() const { |
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[737] | 213 | ARXfrg *A = new ARXfrg ( *this ); |
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| 214 | return A; |
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| 215 | } |
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[700] | 216 | |
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[737] | 217 | void bayes ( const vec &val, const vec &cond ) { |
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[990] | 218 | bdm_assert_debug(cond.size()>rgrlen, "ARXfrg: Insufficient conditioning, frg not given."); |
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| 219 | frg = cond ( rgrlen); // the first part after rgrlen |
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| 220 | ARX::bayes ( val, cond.left(rgrlen) ); |
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[639] | 221 | } |
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[700] | 222 | void validate() { |
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| 223 | ARX::validate(); |
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[737] | 224 | rvc.add ( RV ( "{phi }", vec_1 ( 1 ) ) ); |
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| 225 | dimc += 1; |
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[700] | 226 | } |
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[639] | 227 | }; |
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[737] | 228 | UIREGISTER ( ARXfrg ); |
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[723] | 229 | |
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| 230 | |
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[996] | 231 | |
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[723] | 232 | //////////////////// |
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| 233 | template<class sq_T> |
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| 234 | shared_ptr< mlnorm<sq_T> > ARX::ml_predictor ( ) const { |
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| 235 | shared_ptr< mlnorm<sq_T> > tmp = new mlnorm<sq_T> ( ); |
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[737] | 236 | tmp->set_rv ( yrv ); |
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| 237 | tmp->set_rvc ( _rvc() ); |
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| 238 | |
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| 239 | ml_predictor_update ( *tmp ); |
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[723] | 240 | tmp->validate(); |
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| 241 | return tmp; |
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| 242 | } |
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| 243 | |
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| 244 | template<class sq_T> |
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[737] | 245 | void ARX::ml_predictor_update ( mlnorm<sq_T> &pred ) const { |
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[723] | 246 | mat mu ( dimy, posterior()._V().rows() - dimy ); |
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| 247 | mat R ( dimy, dimy ); |
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[737] | 248 | |
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[723] | 249 | est.mean_mat ( mu, R ); //mu = |
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| 250 | mu = mu.T(); |
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| 251 | //correction for student-t -- TODO check if correct!! |
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[737] | 252 | |
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| 253 | if ( have_constant ) { // constant term |
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[723] | 254 | //Assume the constant term is the last one: |
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| 255 | pred.set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), sq_T ( R ) ); |
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| 256 | } else { |
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[737] | 257 | pred.set_parameters ( mu, zeros ( dimy ), sq_T ( R ) ); |
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[723] | 258 | } |
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| 259 | } |
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| 260 | |
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[639] | 261 | }; |
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[97] | 262 | #endif // AR_H |
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| 263 | |
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