1 | /*! |
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2 | \file |
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3 | \brief Bayesian Filtering using stochastic sampling (Particle Filters) |
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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 PARTICLES_H |
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14 | #define PARTICLES_H |
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15 | |
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16 | |
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17 | #include "../estim/arx_ext.h" |
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18 | #include "../stat/emix.h" |
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19 | |
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20 | namespace bdm { |
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21 | |
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22 | //! class used in PF |
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23 | class MarginalizedParticle : public BM{ |
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24 | protected: |
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25 | //! discrte particle |
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26 | dirac est_emp; |
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27 | //! internal Bayes Model |
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28 | shared_ptr<BM> bm; |
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29 | //! pdf with for transitional par |
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30 | shared_ptr<pdf> par; // pdf for non-linear part |
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31 | //! link from this to bm |
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32 | shared_ptr<datalink_part> cond2bm; |
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33 | //! link from cond to par |
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34 | shared_ptr<datalink_part> cond2par; |
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35 | //! link from emp 2 par |
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36 | shared_ptr<datalink_part> emp2bm; |
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37 | //! link from emp 2 par |
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38 | shared_ptr<datalink_part> emp2par; |
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39 | |
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40 | //! custom posterior - product |
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41 | class eprod_2:public eprod_base { |
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42 | protected: |
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43 | MarginalizedParticle ∓ |
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44 | public: |
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45 | eprod_2(MarginalizedParticle &m):mp(m){} |
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46 | const epdf* factor(int i) const {return (i==0) ? &mp.bm->posterior() : &mp.est_emp;} |
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47 | const int no_factors() const {return 2;} |
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48 | } est; |
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49 | |
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50 | public: |
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51 | MarginalizedParticle():est(*this){}; |
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52 | BM* _copy() const{return new MarginalizedParticle(*this);}; |
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53 | void bayes(const vec &dt, const vec &cond){ |
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54 | vec par_cond(par->dimensionc()); |
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55 | cond2par->filldown(cond,par_cond); // copy ut |
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56 | emp2par->filldown(est_emp._point(),par_cond); // copy xt-1 |
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57 | |
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58 | //sample new particle |
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59 | est_emp.set_point(par->samplecond(par_cond)); |
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60 | //if (evalll) |
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61 | vec bm_cond(bm->dimensionc()); |
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62 | cond2bm->filldown(cond, bm_cond);// set e.g. ut |
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63 | emp2bm->filldown(est_emp._point(), bm_cond);// set e.g. ut |
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64 | bm->bayes(dt, bm_cond); |
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65 | ll=bm->_ll(); |
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66 | } |
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67 | const eprod_2& posterior() const {return est;} |
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68 | |
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69 | void set_prior(const epdf *pdf0){ |
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70 | const eprod *ep=dynamic_cast<const eprod*>(pdf0); |
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71 | if (ep){ // full prior |
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72 | bdm_assert(ep->no_factors()==2,"Incompatible prod"); |
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73 | bm->set_prior(ep->factor(0)); |
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74 | est_emp.set_point(ep->factor(1)->sample()); |
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75 | } else { |
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76 | // assume prior is only for emp; |
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77 | est_emp.set_point(pdf0->sample()); |
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78 | } |
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79 | } |
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80 | |
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81 | /*! parse structure |
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82 | \code |
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83 | class = "BootstrapParticle"; |
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84 | parameter_pdf = {class = 'epdf_offspring', ...}; |
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85 | observation_pdf = {class = 'epdf_offspring',...}; |
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86 | \endcode |
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87 | If rvs are set, then it checks for compatibility. |
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88 | */ |
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89 | void from_setting(const Setting &set){ |
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90 | BM::from_setting ( set ); |
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91 | par = UI::build<pdf> ( set, "parameter_pdf", UI::compulsory ); |
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92 | bm = UI::build<BM> ( set, "bm", UI::compulsory ); |
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93 | } |
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94 | void validate(){ |
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95 | yrv = bm->_rv(); |
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96 | dimy = bm->dimension(); |
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97 | set_rv( par->_rv()); |
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98 | set_dim( par->dimension()); |
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99 | |
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100 | rvc = par->_rvc().subt(par->_rv().copy_t(-1)); |
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101 | rvc.add(bm->_rvc()); // |
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102 | |
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103 | cond2bm=new datalink_part; |
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104 | cond2par=new datalink_part; |
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105 | emp2bm =new datalink_part; |
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106 | emp2par =new datalink_part; |
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107 | cond2bm->set_connection(bm->_rvc(), rvc); |
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108 | cond2par->set_connection(par->_rvc(), rvc); |
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109 | emp2bm->set_connection(bm->_rvc(), _rv()); |
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110 | emp2par->set_connection(par->_rvc(), _rv().copy_t(-1)); |
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111 | |
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112 | dimc = rvc._dsize(); |
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113 | }; |
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114 | }; |
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115 | UIREGISTER(MarginalizedParticle); |
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116 | |
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117 | //! class used in PF |
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118 | class BootstrapParticle : public BM{ |
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119 | dirac est; |
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120 | shared_ptr<pdf> par; |
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121 | shared_ptr<pdf> obs; |
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122 | shared_ptr<datalink_part> cond2par; |
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123 | shared_ptr<datalink_part> cond2obs; |
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124 | shared_ptr<datalink_part> xt2obs; |
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125 | shared_ptr<datalink_part> xtm2par; |
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126 | public: |
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127 | BM* _copy() const{return new BootstrapParticle(*this);}; |
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128 | void bayes(const vec &dt, const vec &cond){ |
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129 | vec par_cond(par->dimensionc()); |
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130 | cond2par->filldown(cond,par_cond); // copy ut |
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131 | xtm2par->filldown(est._point(),par_cond); // copy xt-1 |
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132 | |
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133 | //sample new particle |
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134 | est.set_point(par->samplecond(par_cond)); |
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135 | //if (evalll) |
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136 | vec obs_cond(obs->dimensionc()); |
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137 | cond2obs->filldown(cond, obs_cond);// set e.g. ut |
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138 | xt2obs->filldown(est._point(), obs_cond);// set e.g. ut |
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139 | ll=obs->evallogcond(dt,obs_cond); |
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140 | } |
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141 | const dirac& posterior() const {return est;} |
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142 | |
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143 | void set_prior(const epdf *pdf0){est.set_point(pdf0->sample());} |
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144 | |
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145 | /*! parse structure |
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146 | \code |
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147 | class = "BootstrapParticle"; |
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148 | parameter_pdf = {class = 'epdf_offspring', ...}; |
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149 | observation_pdf = {class = 'epdf_offspring',...}; |
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150 | \endcode |
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151 | If rvs are set, then it checks for compatibility. |
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152 | */ |
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153 | void from_setting(const Setting &set){ |
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154 | BM::from_setting ( set ); |
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155 | par = UI::build<pdf> ( set, "parameter_pdf", UI::compulsory ); |
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156 | obs = UI::build<pdf> ( set, "observation_pdf", UI::compulsory ); |
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157 | } |
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158 | void validate(){ |
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159 | yrv = obs->_rv(); |
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160 | dimy = obs->dimension(); |
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161 | set_rv( par->_rv()); |
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162 | set_dim( par->dimension()); |
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163 | |
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164 | rvc = par->_rvc().subt(par->_rv().copy_t(-1)); |
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165 | rvc.add(obs->_rvc()); // |
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166 | |
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167 | cond2obs=new datalink_part; |
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168 | cond2par=new datalink_part; |
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169 | xt2obs =new datalink_part; |
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170 | xtm2par =new datalink_part; |
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171 | cond2obs->set_connection(obs->_rvc(), rvc); |
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172 | cond2par->set_connection(par->_rvc(), rvc); |
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173 | xt2obs->set_connection(obs->_rvc(), _rv()); |
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174 | xtm2par->set_connection(par->_rvc(), _rv().copy_t(-1)); |
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175 | |
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176 | dimc = rvc._dsize(); |
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177 | }; |
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178 | }; |
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179 | UIREGISTER(BootstrapParticle); |
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180 | |
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181 | |
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182 | /*! |
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183 | * \brief Trivial particle filter with proposal density equal to parameter evolution model. |
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184 | |
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185 | Posterior density is represented by a weighted empirical density (\c eEmp ). |
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186 | */ |
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187 | |
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188 | class PF : public BM { |
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189 | //! \var log_level_enums weights |
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190 | //! all weightes will be logged |
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191 | |
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192 | //! \var log_level_enums samples |
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193 | //! all samples will be logged |
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194 | LOG_LEVEL(PF,weights,samples); |
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195 | |
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196 | class pf_mix: public emix_base{ |
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197 | Array<BM*> &bms; |
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198 | public: |
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199 | pf_mix(vec &w0, Array<BM*> &bms0):emix_base(w0),bms(bms0){} |
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200 | const epdf* component(const int &i)const{return &(bms(i)->posterior());} |
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201 | int no_coms() const {return bms.length();} |
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202 | }; |
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203 | protected: |
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204 | //!number of particles; |
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205 | int n; |
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206 | //!posterior density |
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207 | pf_mix est; |
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208 | //! weights; |
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209 | vec w; |
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210 | //! particles |
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211 | Array<BM*> particles; |
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212 | //! internal structure storing loglikelihood of predictions |
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213 | vec lls; |
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214 | |
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215 | //! which resampling method will be used |
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216 | RESAMPLING_METHOD resmethod; |
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217 | //! resampling threshold; in this case its meaning is minimum ratio of active particles |
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218 | //! For example, for 0.5 resampling is performed when the numebr of active aprticles drops belo 50%. |
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219 | double res_threshold; |
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220 | |
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221 | //! \name Options |
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222 | //!@{ |
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223 | //!@} |
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224 | |
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225 | public: |
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226 | //! \name Constructors |
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227 | //!@{ |
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228 | PF ( ) : est(w,particles) { }; |
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229 | |
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230 | void set_parameters ( int n0, double res_th0 = 0.5, RESAMPLING_METHOD rm = SYSTEMATIC ) { |
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231 | n = n0; |
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232 | res_threshold = res_th0; |
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233 | resmethod = rm; |
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234 | }; |
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235 | void set_model ( const BM *particle0, const epdf *prior) { |
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236 | if (n>0){ |
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237 | particles.set_length(n); |
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238 | for (int i=0; i<n;i++){ |
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239 | particles(i) = particle0->_copy(); |
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240 | particles(i)->set_prior(prior); |
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241 | } |
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242 | } |
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243 | // set values for posterior |
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244 | est.set_rv ( particle0->posterior()._rv() ); |
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245 | }; |
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246 | void set_statistics ( const vec w0, const epdf &epdf0 ) { |
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247 | //est.set_statistics ( w0, epdf0 ); |
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248 | }; |
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249 | /* void set_statistics ( const eEmp &epdf0 ) { |
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250 | bdm_assert_debug ( epdf0._rv().equal ( par->_rv() ), "Incompatible input" ); |
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251 | est = epdf0; |
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252 | };*/ |
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253 | //!@} |
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254 | |
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255 | //! bayes compute weights of the |
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256 | virtual void bayes_weights(); |
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257 | //! important part of particle filtering - decide if it is time to perform resampling |
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258 | virtual bool do_resampling() { |
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259 | double eff = 1.0 / ( w * w ); |
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260 | return eff < ( res_threshold*n ); |
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261 | } |
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262 | void bayes ( const vec &yt, const vec &cond ); |
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263 | //!access function |
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264 | vec& _lls() { |
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265 | return lls; |
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266 | } |
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267 | //!access function |
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268 | RESAMPLING_METHOD _resmethod() const { |
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269 | return resmethod; |
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270 | } |
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271 | //! return correctly typed posterior (covariant return) |
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272 | const pf_mix& posterior() const { |
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273 | return est; |
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274 | } |
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275 | |
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276 | /*! configuration structure for basic PF |
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277 | \code |
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278 | parameter_pdf = pdf_class; // parameter evolution pdf |
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279 | observation_pdf = pdf_class; // observation pdf |
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280 | prior = epdf_class; // prior probability density |
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281 | --- optional --- |
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282 | n = 10; // number of particles |
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283 | resmethod = 'systematic', or 'multinomial', or 'stratified' |
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284 | // resampling method |
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285 | res_threshold = 0.5; // resample when active particles drop below 50% |
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286 | \endcode |
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287 | */ |
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288 | void from_setting ( const Setting &set ) { |
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289 | BM::from_setting ( set ); |
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290 | |
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291 | shared_ptr<BM> bm0 = UI::build<BM>(set, "particle",UI::compulsory); |
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292 | |
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293 | shared_ptr<epdf> pri = UI::build<epdf> ( set, "prior", UI::compulsory ); |
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294 | n =0; |
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295 | UI::get(n,set,"n",UI::optional);; |
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296 | if (n>0){ |
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297 | particles.set_length(n); |
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298 | for(int i=0;i<n;i++){particles(i)=bm0->_copy();} |
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299 | w = ones(n)/n; |
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300 | } |
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301 | set_prior(pri.get()); |
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302 | // set resampling method |
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303 | resmethod_from_set ( set ); |
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304 | //set drv |
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305 | |
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306 | set_yrv ( bm0->_rv() ); |
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307 | rvc = bm0->_rvc(); |
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308 | BM::set_rv(bm0->_rv()); |
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309 | yrv=bm0->_yrv(); |
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310 | } |
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311 | |
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312 | void set_prior(const epdf *pri){ |
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313 | const emix_base *emi=dynamic_cast<const emix_base*>(pri); |
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314 | if (emi) { |
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315 | bdm_assert(particles.length()>0, "initial particle is not assigned"); |
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316 | n = emi->_w().length(); |
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317 | int old_n = particles.length(); |
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318 | if (n!=old_n){ |
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319 | particles.set_length(n,true); |
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320 | } |
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321 | for(int i=old_n;i<n;i++){particles(i)=particles(0)->_copy();} |
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322 | |
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323 | for (int i =0; i<n; i++){ |
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324 | particles(i)->set_prior(emi->_com(i)); |
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325 | } |
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326 | } else { |
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327 | // try to find "n" |
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328 | bdm_assert(n>0, "Field 'n' must be filled when prior is not of type emix"); |
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329 | for (int i =0; i<n; i++){ |
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330 | particles(i)->set_prior(pri); |
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331 | } |
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332 | |
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333 | } |
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334 | } |
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335 | //! auxiliary function reading parameter 'resmethod' from configuration file |
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336 | void resmethod_from_set ( const Setting &set ) { |
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337 | string resmeth; |
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338 | if ( UI::get ( resmeth, set, "resmethod", UI::optional ) ) { |
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339 | if ( resmeth == "systematic" ) { |
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340 | resmethod = SYSTEMATIC; |
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341 | } else { |
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342 | if ( resmeth == "multinomial" ) { |
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343 | resmethod = MULTINOMIAL; |
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344 | } else { |
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345 | if ( resmeth == "stratified" ) { |
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346 | resmethod = STRATIFIED; |
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347 | } else { |
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348 | bdm_error ( "Unknown resampling method" ); |
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349 | } |
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350 | } |
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351 | } |
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352 | } else { |
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353 | resmethod = SYSTEMATIC; |
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354 | }; |
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355 | if ( !UI::get ( res_threshold, set, "res_threshold", UI::optional ) ) { |
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356 | res_threshold = 0.9; |
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357 | } |
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358 | //validate(); |
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359 | } |
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360 | |
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361 | void validate() { |
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362 | BM::validate(); |
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363 | est.validate(); |
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364 | bdm_assert ( n>0, "empty particle pool" ); |
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365 | n = w.length(); |
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366 | lls = zeros ( n ); |
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367 | |
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368 | if ( particles(0)->_rv()._dsize() > 0 ) { |
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369 | bdm_assert ( particles(0)->_rv()._dsize() == est.dimension(), "Mismatch of RV and dimension of posterior" ); |
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370 | } |
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371 | } |
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372 | //! resample posterior density (from outside - see MPF) |
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373 | void resample ( ) { |
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374 | ivec ind = zeros_i ( n ); |
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375 | bdm::resample(w,ind,resmethod); |
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376 | // copy the internals according to ind |
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377 | for (int i = 0; i < n; i++ ) { |
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378 | if ( ind ( i ) != i ) { |
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379 | particles( i ) = particles( ind ( i ) )->_copy(); |
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380 | } |
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381 | w ( i ) = 1.0 / n; |
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382 | } |
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383 | } |
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384 | //! access function |
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385 | Array<BM*>& _particles() { |
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386 | return particles; |
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387 | } |
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388 | |
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389 | }; |
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390 | UIREGISTER ( PF ); |
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391 | |
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392 | /*! |
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393 | \brief Marginalized Particle filter |
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394 | |
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395 | A composition of particle filter with exact (or almost exact) bayesian models (BMs). |
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396 | The Bayesian models provide marginalized predictive density. Internaly this is achieved by virtual class MPFpdf. |
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397 | */ |
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398 | |
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399 | // class MPF : public BM { |
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400 | // //! Introduces new option |
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401 | // //! \li means - meaning TODO |
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402 | // LOG_LEVEL(MPF,means); |
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403 | // protected: |
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404 | // //! particle filter on non-linear variable |
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405 | // shared_ptr<PF> pf; |
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406 | // //! Array of Bayesian models |
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407 | // Array<BM*> BMs; |
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408 | // |
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409 | // //! internal class for pdf providing composition of eEmp with external components |
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410 | // |
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411 | // class mpfepdf : public epdf { |
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412 | // //! pointer to particle filter |
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413 | // shared_ptr<PF> &pf; |
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414 | // //! pointer to Array of BMs |
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415 | // Array<BM*> &BMs; |
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416 | // public: |
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417 | // //! constructor |
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418 | // mpfepdf ( shared_ptr<PF> &pf0, Array<BM*> &BMs0 ) : epdf(), pf ( pf0 ), BMs ( BMs0 ) { }; |
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419 | // //! a variant of set parameters - this time, parameters are read from BMs and pf |
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420 | // void read_parameters() { |
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421 | // rv = concat ( pf->posterior()._rv(), BMs ( 0 )->posterior()._rv() ); |
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422 | // dim = pf->posterior().dimension() + BMs ( 0 )->posterior().dimension(); |
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423 | // bdm_assert_debug ( dim == rv._dsize(), "Wrong name " ); |
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424 | // } |
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425 | // vec mean() const; |
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426 | // |
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427 | // vec variance() const; |
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428 | // |
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429 | // void qbounds ( vec &lb, vec &ub, double perc = 0.95 ) const; |
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430 | // |
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431 | // vec sample() const NOT_IMPLEMENTED(0); |
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432 | // |
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433 | // double evallog ( const vec &val ) const NOT_IMPLEMENTED(0); |
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434 | // }; |
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435 | // |
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436 | // //! Density joining PF.est with conditional parts |
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437 | // mpfepdf jest; |
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438 | // |
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439 | // //! datalink from global yt and cond (Up) to BMs yt and cond (Down) |
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440 | // datalink_m2m this2bm; |
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441 | // //! datalink from global yt and cond (Up) to PFs yt and cond (Down) |
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442 | // datalink_m2m this2pf; |
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443 | // //!datalink from PF part to BM |
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444 | // datalink_part pf2bm; |
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445 | // |
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446 | // public: |
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447 | // //! Default constructor. |
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448 | // MPF () : jest ( pf, BMs ) {}; |
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449 | // //! set all parameters at once |
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450 | // void set_pf ( shared_ptr<pdf> par0, int n0, RESAMPLING_METHOD rm = SYSTEMATIC ) { |
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451 | // if (!pf) pf=new PF; |
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452 | // pf->set_model ( par0, par0 ); // <=== nasty!!! |
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453 | // pf->set_parameters ( n0, rm ); |
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454 | // pf->set_rv(par0->_rv()); |
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455 | // BMs.set_length ( n0 ); |
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456 | // } |
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457 | // //! set a prototype of BM, copy it to as many times as there is particles in pf |
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458 | // void set_BM ( const BM &BMcond0 ) { |
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459 | // |
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460 | // int n = pf->__w().length(); |
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461 | // BMs.set_length ( n ); |
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462 | // // copy |
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463 | // //BMcond0 .condition ( pf->posterior()._sample ( 0 ) ); |
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464 | // for ( int i = 0; i < n; i++ ) { |
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465 | // BMs ( i ) = (BM*) BMcond0._copy(); |
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466 | // } |
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467 | // }; |
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468 | // |
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469 | // void bayes ( const vec &yt, const vec &cond ); |
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470 | // |
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471 | // const epdf& posterior() const { |
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472 | // return jest; |
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473 | // } |
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474 | // |
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475 | // //!Access function |
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476 | // const BM* _BM ( int i ) { |
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477 | // return BMs ( i ); |
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478 | // } |
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479 | // PF& _pf() {return *pf;} |
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480 | // |
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481 | // |
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482 | // virtual double logpred ( const vec &yt ) const NOT_IMPLEMENTED(0); |
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483 | // |
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484 | // virtual epdf* epredictor() const NOT_IMPLEMENTED(NULL); |
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485 | // |
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486 | // virtual pdf* predictor() const NOT_IMPLEMENTED(NULL); |
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487 | // |
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488 | // |
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489 | // /*! configuration structure for basic PF |
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490 | // \code |
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491 | // BM = BM_class; // Bayesian filtr for analytical part of the model |
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492 | // parameter_pdf = pdf_class; // transitional pdf for non-parametric part of the model |
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493 | // prior = epdf_class; // prior probability density |
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494 | // --- optional --- |
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495 | // n = 10; // number of particles |
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496 | // resmethod = 'systematic', or 'multinomial', or 'stratified' |
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497 | // // resampling method |
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498 | // \endcode |
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499 | // */ |
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500 | // void from_setting ( const Setting &set ) { |
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501 | // BM::from_setting( set ); |
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502 | // |
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503 | // shared_ptr<pdf> par = UI::build<pdf> ( set, "parameter_pdf", UI::compulsory ); |
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504 | // |
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505 | // pf = new PF; |
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506 | // // prior must be set before BM |
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507 | // pf->prior_from_set ( set ); |
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508 | // pf->resmethod_from_set ( set ); |
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509 | // pf->set_model ( par, par ); // too hackish! |
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510 | // |
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511 | // shared_ptr<BM> BM0 = UI::build<BM> ( set, "BM", UI::compulsory ); |
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512 | // set_BM ( *BM0 ); |
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513 | // |
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514 | // //set drv |
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515 | // //??set_yrv(concat(BM0->_yrv(),u) ); |
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516 | // set_yrv ( BM0->_yrv() ); |
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517 | // rvc = BM0->_rvc().subt ( par->_rv() ); |
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518 | // //find potential input - what remains in rvc when we subtract rv |
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519 | // RV u = par->_rvc().subt ( par->_rv().copy_t ( -1 ) ); |
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520 | // rvc.add ( u ); |
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521 | // dimc = rvc._dsize(); |
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522 | // validate(); |
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523 | // } |
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524 | // |
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525 | // void validate() { |
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526 | // BM::validate(); |
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527 | // try { |
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528 | // pf->validate(); |
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529 | // } catch ( std::exception ) { |
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530 | // throw UIException ( "Error in PF part of MPF:" ); |
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531 | // } |
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532 | // jest.read_parameters(); |
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533 | // this2bm.set_connection ( BMs ( 0 )->_yrv(), BMs ( 0 )->_rvc(), yrv, rvc ); |
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534 | // this2pf.set_connection ( pf->_yrv(), pf->_rvc(), yrv, rvc ); |
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535 | // pf2bm.set_connection ( BMs ( 0 )->_rvc(), pf->posterior()._rv() ); |
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536 | // } |
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537 | // }; |
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538 | // UIREGISTER ( MPF ); |
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539 | |
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540 | /*! ARXg for estimation of state-space variances |
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541 | */ |
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542 | // class MPF_ARXg :public BM{ |
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543 | // protected: |
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544 | // shared_ptr<PF> pf; |
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545 | // //! pointer to Array of BMs |
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546 | // Array<ARX*> BMso; |
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547 | // //! pointer to Array of BMs |
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548 | // Array<ARX*> BMsp; |
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549 | // //!parameter evolution |
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550 | // shared_ptr<fnc> g; |
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551 | // //!observation function |
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552 | // shared_ptr<fnc> h; |
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553 | // |
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554 | // public: |
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555 | // void bayes(const vec &yt, const vec &cond ); |
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556 | // void from_setting(const Setting &set) ; |
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557 | // void validate() { |
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558 | // bdm_assert(g->dimensionc()==g->dimension(),"not supported yet"); |
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559 | // bdm_assert(h->dimensionc()==g->dimension(),"not supported yet"); |
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560 | // } |
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561 | // |
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562 | // double logpred(const vec &cond) const NOT_IMPLEMENTED(0.0); |
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563 | // epdf* epredictor() const NOT_IMPLEMENTED(NULL); |
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564 | // pdf* predictor() const NOT_IMPLEMENTED(NULL); |
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565 | // const epdf& posterior() const {return pf->posterior();}; |
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566 | // |
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567 | // void log_register( logger &L, const string &prefix ){ |
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568 | // BM::log_register(L,prefix); |
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569 | // logrec->ids.set_size ( 3 ); |
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570 | // logrec->ids(1)= L.add_vector(RV("Q",dimension()*dimension()), prefix+L.prefix_sep()+"Q"); |
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571 | // logrec->ids(2)= L.add_vector(RV("R",dimensiony()*dimensiony()), prefix+L.prefix_sep()+"R"); |
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572 | // |
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573 | // }; |
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574 | // void log_write() const { |
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575 | // BM::log_write(); |
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576 | // mat mQ=zeros(dimension(),dimension()); |
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577 | // mat pom=zeros(dimension(),dimension()); |
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578 | // mat mR=zeros(dimensiony(),dimensiony()); |
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579 | // mat pom2=zeros(dimensiony(),dimensiony()); |
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580 | // mat dum; |
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581 | // const vec w=pf->posterior()._w(); |
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582 | // for (int i=0; i<w.length(); i++){ |
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583 | // BMsp(i)->posterior().mean_mat(dum,pom); |
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584 | // mQ += w(i) * pom; |
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585 | // BMso(i)->posterior().mean_mat(dum,pom2); |
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586 | // mR += w(i) * pom2; |
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587 | // |
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588 | // } |
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589 | // logrec->L.log_vector ( logrec->ids ( 1 ), cvectorize(mQ) ); |
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590 | // logrec->L.log_vector ( logrec->ids ( 2 ), cvectorize(mR) ); |
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591 | // |
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592 | // } |
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593 | // }; |
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594 | // UIREGISTER(MPF_ARXg); |
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595 | |
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596 | |
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597 | } |
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598 | #endif // KF_H |
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599 | |
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