1 | #include "particles.h" |
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2 | |
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3 | namespace bdm { |
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4 | |
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5 | using std::endl; |
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6 | |
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7 | void PF::bayes_gensmp ( const vec &ut ) { |
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8 | if ( ut.length() > 0 ) { |
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9 | vec cond ( par->dimensionc() ); |
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10 | cond.set_subvector ( par->dimension(), ut ); |
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11 | #pragma parallel for |
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12 | for ( int i = 0; i < n; i++ ) { |
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13 | cond.set_subvector ( 0, _samples ( i ) ); |
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14 | _samples ( i ) = par->samplecond ( cond ); |
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15 | lls ( i ) = 0; |
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16 | } |
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17 | } else { |
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18 | #pragma parallel for |
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19 | for ( int i = 0; i < n; i++ ) { |
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20 | _samples ( i ) = par->samplecond ( _samples ( i ) ); |
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21 | lls ( i ) = 0; |
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22 | } |
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23 | } |
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24 | } |
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25 | |
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26 | void PF::bayes_weights() { |
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27 | // |
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28 | double mlls = max ( lls ); |
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29 | // compute weights |
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30 | for ( int i = 0; i < n; i++ ) { |
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31 | _w ( i ) *= exp ( lls ( i ) - mlls ); // multiply w by likelihood |
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32 | } |
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33 | |
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34 | //renormalize |
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35 | double sw = sum ( _w ); |
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36 | if ( !std::isfinite ( sw ) ) { |
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37 | for ( int i = 0; i < n; i++ ) { |
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38 | if ( !std::isfinite ( _w ( i ) ) ) { |
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39 | _w ( i ) = 0; |
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40 | } |
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41 | } |
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42 | sw = sum ( _w ); |
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43 | if ( !std::isfinite ( sw ) || sw == 0.0 ) { |
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44 | bdm_error ( "Particle filter is lost; no particle is good enough." ); |
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45 | } |
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46 | } |
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47 | _w /= sw; |
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48 | } |
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49 | |
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50 | void PF::bayes ( const vec &yt, const vec &cond ) { |
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51 | const vec &ut = cond; //todo |
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52 | |
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53 | int i; |
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54 | // generate samples - time step |
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55 | bayes_gensmp ( ut ); |
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56 | // weight them - data step |
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57 | for ( i = 0; i < n; i++ ) { |
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58 | lls ( i ) += obs->evallogcond ( yt, _samples ( i ) ); //+= because lls may have something from gensmp! |
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59 | } |
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60 | |
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61 | bayes_weights(); |
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62 | |
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63 | if ( do_resampling() ) { |
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64 | est.resample ( resmethod ); |
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65 | } |
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66 | |
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67 | } |
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68 | |
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69 | // void PF::set_est ( const epdf &epdf0 ) { |
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70 | // int i; |
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71 | // |
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72 | // for ( i=0;i<n;i++ ) { |
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73 | // _samples ( i ) = epdf0.sample(); |
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74 | // } |
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75 | // } |
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76 | |
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77 | void MPF::bayes ( const vec &yt, const vec &cond ) { |
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78 | // follows PF::bayes in most places!!! |
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79 | int i; |
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80 | int n = pf->__w().length(); |
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81 | vec &lls = pf->_lls(); |
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82 | Array<vec> &samples = pf->__samples(); |
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83 | |
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84 | // generate samples - time step |
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85 | pf->bayes_gensmp ( vec ( 0 ) ); |
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86 | // weight them - data step |
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87 | #pragma parallel for |
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88 | for ( i = 0; i < n; i++ ) { |
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89 | vec bm_cond ( BMs ( i )->dimensionc() ); |
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90 | this2bm.fill_cond ( yt, cond, bm_cond ); |
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91 | pf2bm.filldown ( samples ( i ), bm_cond ); |
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92 | BMs ( i ) -> bayes ( this2bm.pushdown ( yt ), bm_cond ); |
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93 | lls ( i ) += BMs ( i )->_ll(); |
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94 | } |
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95 | |
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96 | pf->bayes_weights(); |
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97 | |
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98 | ivec ind; |
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99 | if ( pf->do_resampling() ) { |
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100 | pf->resample ( ind ); |
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101 | |
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102 | #pragma omp parallel for |
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103 | for ( i = 0; i < n; i++ ) { |
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104 | if ( ind ( i ) != i ) {//replace the current Bm by a new one |
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105 | delete BMs ( i ); |
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106 | BMs ( i ) = BMs ( ind ( i ) )->_copy_(); //copy constructor |
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107 | } |
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108 | }; |
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109 | } |
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110 | }; |
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111 | |
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112 | |
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113 | } |
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114 | //MPF::MPF:{} |
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