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 PF_H |
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14 | #define PF_H |
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15 | |
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16 | #include <itpp/itbase.h> |
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17 | #include "../stat/libEF.h" |
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18 | #include "../math/libDC.h" |
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19 | |
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20 | using namespace itpp; |
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21 | |
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22 | //UGLY HACK |
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23 | extern double PF_SSAT;//used for StrSim:06 test... if length>0 the value is written. |
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24 | |
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25 | /*! |
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26 | * \brief Trivial particle filter with proposal density equal to parameter evolution model. |
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27 | |
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28 | Posterior density is represented by a weighted empirical density (\c eEmp ). |
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29 | */ |
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30 | |
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31 | class PF : public BM { |
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32 | protected: |
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33 | //!number of particles; |
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34 | int n; |
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35 | //!posterior density |
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36 | eEmp est; |
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37 | //! pointer into \c eEmp |
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38 | vec &_w; |
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39 | //! pointer into \c eEmp |
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40 | Array<vec> &_samples; |
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41 | //! Parameter evolution model |
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42 | mpdf ∥ |
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43 | //! Observation model |
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44 | mpdf &obs; |
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45 | public: |
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46 | //! Default constructor |
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47 | PF ( const RV &rv0, mpdf &par0, mpdf &obs0, int n0 ) :BM ( rv0 ), |
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48 | n ( n0 ),est ( rv0,n ),_w ( est._w() ),_samples ( est._samples() ), |
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49 | par ( par0 ), obs ( obs0 ) {}; |
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50 | |
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51 | //! Set posterior density by sampling from epdf0 |
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52 | void set_est ( const epdf &epdf0 ); |
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53 | void bayes ( const vec &dt ); |
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54 | }; |
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55 | |
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56 | /*! |
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57 | \brief Marginalized Particle filter |
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58 | |
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59 | Trivial version: proposal $=$ parameter evolution, observation model is not used. (it is assumed to be part of BM). |
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60 | */ |
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61 | |
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62 | template<class BM_T> |
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63 | |
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64 | class MPF : public PF { |
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65 | BM_T* Bms[10000]; |
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66 | |
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67 | //! internal class for MPDF providing composition of eEmp with external components |
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68 | |
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69 | class mpfepdf : public epdf { |
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70 | protected: |
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71 | eEmp &E; |
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72 | vec &_w; |
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73 | Array<epdf*> Coms; |
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74 | public: |
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75 | mpfepdf ( eEmp &E0, const RV &rvc ) : |
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76 | epdf ( RV( ) ), E ( E0 ), _w ( E._w() ), |
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77 | Coms ( _w.length() ) { |
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78 | rv.add ( E._rv() ); |
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79 | rv.add ( rvc ); |
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80 | }; |
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81 | |
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82 | void set_elements ( int &i, double wi, epdf* ep ) |
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83 | {_w ( i ) =wi; Coms ( i ) =ep;}; |
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84 | |
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85 | vec mean() const { |
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86 | // ugly |
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87 | vec pom=zeros ( ( Coms ( 0 )->_rv() ).count() ); |
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88 | |
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89 | for ( int i=0; i<_w.length(); i++ ) {pom += Coms ( i )->mean() * _w ( i );} |
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90 | |
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91 | return concat ( E.mean(),pom ); |
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92 | } |
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93 | |
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94 | vec sample() const {it_error ( "Not implemented" );return 0;} |
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95 | |
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96 | double evalpdflog ( const vec &val ) const {it_error ( "not implemented" ); return 0.0;} |
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97 | }; |
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98 | |
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99 | //! estimate joining PF.est with conditional |
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100 | mpfepdf jest; |
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101 | |
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102 | public: |
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103 | //! Default constructor. |
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104 | MPF ( const RV &rvlin, const RV &rvpf, mpdf &par0, mpdf &obs0, int n, const BM_T &BMcond0 ) : PF ( rvpf ,par0,obs0,n ),jest ( est,rvlin ) { |
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105 | // |
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106 | //TODO test if rv and BMcond.rv are compatible. |
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107 | rv.add ( rvlin ); |
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108 | // |
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109 | |
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110 | if ( n>10000 ) it_error ( "increase 10000 here!" ); |
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111 | |
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112 | for ( int i=0;i<n;i++ ) { |
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113 | Bms[i] = new BM_T ( BMcond0 ); //copy constructor |
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114 | epdf& pom=Bms[i]->_epdf(); |
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115 | jest.set_elements ( i,1.0/n,&pom ); |
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116 | } |
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117 | }; |
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118 | |
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119 | ~MPF() { |
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120 | } |
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121 | |
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122 | void bayes ( const vec &dt ); |
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123 | epdf& _epdf() {return jest;} |
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124 | //! Set postrior of \c rvc to samples from epdf0. Statistics of Bms are not re-computed! Use only for initialization! |
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125 | void set_est ( const epdf& epdf0 ) { |
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126 | PF::set_est ( epdf0 ); // sample params in condition |
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127 | // copy conditions to BMs |
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128 | |
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129 | for ( int i=0;i<n;i++ ) {Bms[i]->condition ( _samples ( i ) );} |
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130 | } |
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131 | |
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132 | //SimStr: |
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133 | double SSAT; |
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134 | }; |
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135 | |
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136 | template<class BM_T> |
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137 | void MPF<BM_T>::bayes ( const vec &dt ) { |
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138 | int i; |
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139 | vec lls ( n ); |
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140 | vec llsP ( n ); |
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141 | ivec ind; |
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142 | double mlls=-std::numeric_limits<double>::infinity(); |
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143 | |
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144 | // StrSim:06 |
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145 | double sumLWL=0.0; |
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146 | double sumL2WL=0.0; |
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147 | double WL = 0.0; |
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148 | |
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149 | for ( i=0;i<n;i++ ) { |
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150 | //generate new samples from paramater evolution model; |
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151 | _samples ( i ) = par.samplecond ( _samples ( i ), llsP ( i ) ); |
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152 | Bms[i]->condition ( _samples ( i ) ); |
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153 | Bms[i]->bayes ( dt ); |
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154 | lls ( i ) = Bms[i]->_ll(); // lls above is also in proposal her must be lls(i) =, not +=!! |
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155 | |
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156 | if ( lls ( i ) >mlls ) mlls=lls ( i ); //find maximum likelihood (for numerical stability) |
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157 | } |
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158 | |
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159 | if ( true ) { |
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160 | for ( i=0;i<n;i++ ) { |
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161 | WL = _w ( i ) *exp ( llsP ( i ) ); //using old weights! |
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162 | sumLWL += exp ( lls ( i ) ) *WL; |
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163 | sumL2WL += exp ( 2*lls ( i ) ) *WL; |
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164 | } |
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165 | SSAT = sumL2WL/(sumLWL*sumLWL); |
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166 | } |
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167 | |
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168 | // compute weights |
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169 | for ( i=0;i<n;i++ ) { |
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170 | _w ( i ) *= exp ( lls ( i ) - mlls ); // multiply w by likelihood |
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171 | } |
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172 | |
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173 | _w /=sum ( _w ); //? |
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174 | |
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175 | |
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176 | |
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177 | double eff = 1.0/ ( _w*_w ); |
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178 | if ( eff < ( 0.1*n ) ) { |
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179 | ind = est.resample(); |
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180 | // Resample Bms! |
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181 | |
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182 | for ( i=0;i<n;i++ ) { |
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183 | if ( ind ( i ) !=i ) {//replace the current Bm by a new one |
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184 | //fixme this would require new assignment operator |
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185 | // *Bms[i] = *Bms[ind ( i ) ]; |
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186 | |
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187 | // poor-man's solution: replicate constructor here |
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188 | // copied from MPF::MPF |
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189 | delete Bms[i]; |
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190 | Bms[i] = new BM_T ( *Bms[ind ( i ) ] ); //copy constructor |
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191 | epdf& pom=Bms[i]->_epdf(); |
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192 | jest.set_elements ( i,1.0/n,&pom ); |
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193 | } |
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194 | }; |
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195 | cout << '.'; |
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196 | } |
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197 | } |
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198 | |
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199 | #endif // KF_H |
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200 | |
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