1 | #include "arx.h" |
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2 | namespace bdm { |
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3 | |
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4 | void ARX::bayes ( const vec &dt, const double w ) { |
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5 | double lnc; |
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6 | |
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7 | if ( frg < 1.0 ) { |
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8 | est.pow ( frg ); |
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9 | |
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10 | //stabilize |
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11 | ldmat V0(eye(V.rows())); |
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12 | V0*=(1-frg)*1e-3; |
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13 | V += V0; //stabilization |
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14 | nu +=(1-frg)*(0.1 + V.rows() + 1* dimx + 2); |
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15 | |
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16 | // recompute loglikelihood of "prior" |
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17 | if ( evalll ) { |
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18 | last_lognc = est.lognc(); |
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19 | } |
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20 | } |
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21 | if (have_constant) { |
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22 | _dt.set_subvector(0,dt); |
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23 | V.opupdt ( _dt, w ); |
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24 | } else { |
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25 | V.opupdt ( dt, w ); |
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26 | } |
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27 | nu += w; |
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28 | |
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29 | // log(sqrt(2*pi)) = 0.91893853320467 |
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30 | if ( evalll ) { |
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31 | lnc = est.lognc(); |
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32 | ll = lnc - last_lognc - 0.91893853320467; |
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33 | last_lognc = lnc; |
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34 | } |
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35 | } |
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36 | |
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37 | double ARX::logpred ( const vec &dt ) const { |
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38 | egiw pred ( est ); |
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39 | ldmat &V = pred._V(); |
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40 | double &nu = pred._nu(); |
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41 | |
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42 | double lll; |
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43 | |
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44 | if ( frg < 1.0 ) { |
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45 | pred.pow ( frg ); |
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46 | lll = pred.lognc(); |
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47 | } else//should be save: last_lognc is changed only by bayes; |
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48 | if ( evalll ) { |
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49 | lll = last_lognc; |
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50 | } else { |
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51 | lll = pred.lognc(); |
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52 | } |
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53 | |
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54 | V.opupdt ( dt, 1.0 ); |
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55 | nu += 1.0; |
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56 | // log(sqrt(2*pi)) = 0.91893853320467 |
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57 | return pred.lognc() - lll - 0.91893853320467; |
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58 | } |
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59 | |
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60 | ARX* ARX::_copy_ ( ) const { |
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61 | ARX* Tmp = new ARX ( *this ); |
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62 | return Tmp; |
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63 | } |
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64 | |
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65 | void ARX::set_statistics ( const BMEF* B0 ) { |
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66 | const ARX* A0 = dynamic_cast<const ARX*> ( B0 ); |
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67 | |
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68 | bdm_assert_debug ( V.rows() == A0->V.rows(), "ARX::set_statistics Statistics differ" ); |
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69 | set_statistics ( A0->dimx, A0->V, A0->nu ); |
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70 | } |
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71 | |
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72 | enorm<ldmat>* ARX::epredictor ( const vec &rgr ) const { |
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73 | int dim = dimx;//est.dimension(); |
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74 | mat mu ( dim, V.rows() - dim ); |
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75 | mat R ( dim, dim ); |
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76 | |
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77 | enorm<ldmat>* tmp; |
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78 | tmp = new enorm<ldmat> ( ); |
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79 | //TODO: too hackish |
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80 | if ( drv._dsize() > 0 ) { |
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81 | } |
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82 | |
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83 | est.mean_mat ( mu, R ); //mu = |
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84 | //correction for student-t -- TODO check if correct!! |
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85 | //R*=nu/(nu-2); |
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86 | mat p_mu = mu.T() * rgr; //the result is one column |
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87 | tmp->set_parameters ( p_mu.get_col ( 0 ), ldmat ( R ) ); |
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88 | return tmp; |
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89 | } |
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90 | |
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91 | mlnorm<ldmat>* ARX::predictor ( ) const { |
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92 | int dim = est.dimension(); |
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93 | |
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94 | mat mu ( dim, V.rows() - dim ); |
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95 | mat R ( dim, dim ); |
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96 | mlnorm<ldmat>* tmp; |
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97 | tmp = new mlnorm<ldmat> ( ); |
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98 | |
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99 | est.mean_mat ( mu, R ); //mu = |
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100 | mu = mu.T(); |
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101 | //correction for student-t -- TODO check if correct!! |
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102 | |
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103 | if ( have_constant) { // constant term |
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104 | //Assume the constant term is the last one: |
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105 | tmp->set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ) ); |
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106 | } else { |
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107 | tmp->set_parameters ( mu, zeros ( dim ), ldmat ( R ) ); |
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108 | } |
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109 | return tmp; |
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110 | } |
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111 | |
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112 | mlstudent* ARX::predictor_student ( ) const { |
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113 | int dim = est.dimension(); |
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114 | |
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115 | mat mu ( dim, V.rows() - dim ); |
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116 | mat R ( dim, dim ); |
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117 | mlstudent* tmp; |
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118 | tmp = new mlstudent ( ); |
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119 | |
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120 | est.mean_mat ( mu, R ); // |
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121 | mu = mu.T(); |
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122 | |
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123 | int xdim = dimx; |
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124 | int end = V._L().rows() - 1; |
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125 | ldmat Lam ( V._L() ( xdim, end, xdim, end ), V._D() ( xdim, end ) ); //exp val of R |
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126 | |
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127 | |
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128 | if ( have_constant) { // no constant term |
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129 | //Assume the constant term is the last one: |
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130 | if ( mu.cols() > 1 ) { |
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131 | tmp->set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ), Lam ); |
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132 | } else { |
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133 | tmp->set_parameters ( mat ( dim, 0 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ), Lam ); |
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134 | } |
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135 | } else { |
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136 | // no constant term |
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137 | tmp->set_parameters ( mu, zeros ( xdim ), ldmat ( R ), Lam ); |
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138 | } |
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139 | return tmp; |
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140 | } |
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141 | |
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142 | |
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143 | |
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144 | /*! \brief Return the best structure |
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145 | @param Eg a copy of GiW density that is being examined |
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146 | @param Eg0 a copy of prior GiW density before estimation |
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147 | @param Egll likelihood of the current Eg |
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148 | @param indeces current indeces |
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149 | \return best likelihood in the structure below the given one |
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150 | */ |
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151 | double egiw_bestbelow ( egiw Eg, egiw Eg0, double Egll, ivec &indeces ) { //parameter Eg is a copy! |
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152 | ldmat Vo = Eg._V(); //copy |
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153 | ldmat Vo0 = Eg._V(); //copy |
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154 | ldmat& Vp = Eg._V(); // pointer into Eg |
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155 | ldmat& Vp0 = Eg._V(); // pointer into Eg |
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156 | int end = Vp.rows() - 1; |
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157 | int i; |
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158 | mat Li; |
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159 | mat Li0; |
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160 | double maxll = Egll; |
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161 | double tmpll = Egll; |
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162 | double belll = Egll; |
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163 | |
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164 | ivec tmpindeces; |
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165 | ivec maxindeces = indeces; |
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166 | |
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167 | |
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168 | cout << "bb:(" << indeces << ") ll=" << Egll << endl; |
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169 | |
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170 | //try to remove only one rv |
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171 | for ( i = 0; i < end; i++ ) { |
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172 | //copy original |
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173 | Li = Vo._L(); |
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174 | Li0 = Vo0._L(); |
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175 | //remove stuff |
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176 | Li.del_col ( i + 1 ); |
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177 | Li0.del_col ( i + 1 ); |
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178 | Vp.ldform ( Li, Vo._D() ); |
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179 | Vp0.ldform ( Li0, Vo0._D() ); |
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180 | tmpll = Eg.lognc() - Eg0.lognc(); // likelihood is difference of norm. coefs. |
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181 | |
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182 | cout << "i=(" << i << ") ll=" << tmpll << endl; |
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183 | |
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184 | // |
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185 | if ( tmpll > Egll ) { //increase of the likelihood |
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186 | tmpindeces = indeces; |
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187 | tmpindeces.del ( i ); |
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188 | //search for a better match in this substructure |
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189 | belll = egiw_bestbelow ( Eg, Eg0, tmpll, tmpindeces ); |
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190 | if ( belll > maxll ) { //better match found |
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191 | maxll = belll; |
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192 | maxindeces = tmpindeces; |
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193 | } |
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194 | } |
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195 | } |
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196 | indeces = maxindeces; |
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197 | return maxll; |
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198 | } |
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199 | |
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200 | ivec ARX::structure_est ( egiw est0 ) { |
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201 | ivec ind = linspace ( 1, est.dimension() - 1 ); |
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202 | egiw_bestbelow ( est, est0, est.lognc() - est0.lognc(), ind ); |
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203 | return ind; |
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204 | } |
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205 | |
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206 | |
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207 | |
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208 | ivec ARX::structure_est_LT ( egiw est0 ) { |
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209 | //some stuff with beliefs etc. |
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210 | // ivec ind = bdm::straux1(V,nu, est0._V(), est0._nu()); |
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211 | return ivec();//ind; |
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212 | } |
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213 | |
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214 | void ARX::from_setting ( const Setting &set ) { |
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215 | shared_ptr<RV> yrv = UI::build<RV> ( set, "rv", UI::compulsory ); |
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216 | shared_ptr<RV> rrv = UI::build<RV> ( set, "rgr", UI::compulsory ); |
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217 | int ylen = yrv->_dsize(); |
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218 | // rgrlen - including constant!!! |
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219 | int rgrlen = rrv->_dsize(); |
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220 | |
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221 | set_rv ( *yrv, *rrv ); |
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222 | |
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223 | string opt; |
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224 | if ( UI::get(opt, set, "options", UI::optional) ) { |
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225 | BM::set_options(opt); |
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226 | } |
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227 | int constant; |
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228 | if (!UI::get(constant, set, "constant", UI::optional)){ |
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229 | have_constant=true; |
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230 | } else { |
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231 | have_constant=constant>0; |
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232 | } |
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233 | if (have_constant) {rgrlen++;_dt=ones(rgrlen+ylen);} |
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234 | |
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235 | //init |
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236 | mat V0; |
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237 | vec dV0; |
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238 | if (!UI::get(V0, set, "V0",UI::optional)){ |
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239 | if ( !UI::get ( dV0, set, "dV0" ) ) |
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240 | dV0 = concat ( 1e-3 * ones ( ylen ), 1e-5 * ones ( rgrlen ) ); |
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241 | V0 = diag ( dV0 ); |
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242 | } |
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243 | double nu0; |
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244 | if ( !UI::get ( nu0, set, "nu0" ) ) |
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245 | nu0 = rgrlen + ylen + 2; |
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246 | |
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247 | double frg; |
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248 | if ( !UI::get ( frg, set, "frg" ) ) |
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249 | frg = 1.0; |
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250 | |
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251 | set_parameters ( frg ); |
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252 | set_statistics ( ylen, V0, nu0 ); |
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253 | |
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254 | //name results (for logging) |
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255 | shared_ptr<RV> rv_par=UI::build<RV>(set, "rv_param",UI::optional ); |
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256 | if (!rv_par){ |
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257 | est.set_rv ( RV ( "{theta r }", vec_2 ( ylen*rgrlen, ylen*ylen ) ) ); |
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258 | } else { |
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259 | est.set_rv ( *rv_par ); |
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260 | } |
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261 | validate(); |
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262 | } |
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263 | |
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264 | } |
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