1 | #include <itpp/itbase.h> |
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2 | #include "merger.h" |
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3 | #include "arx.h" |
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4 | |
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5 | vec merger::lognorm_merge ( mat &lW ) { |
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6 | int nu=lW.rows(); |
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7 | vec mu = sum ( lW ) /nu; //mean of logs |
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8 | vec lam = sum ( pow ( lW,2 ) )-nu*pow ( mu,2 ); |
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9 | double coef=0.0; |
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10 | switch ( nu ) { |
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11 | case 2: |
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12 | coef=sqrt ( beta*2 ) * ( 1-0.5*sqrt ( ( 4*beta-3 ) /beta ) ); |
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13 | return exp ( coef*sqrt ( lam ) + mu ); |
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14 | break; |
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15 | case 3://Ration of Bessel |
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16 | break; |
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17 | case 4: |
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18 | break; |
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19 | default: // Approximate conditional density |
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20 | break; |
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21 | } |
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22 | return vec ( 0 ); |
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23 | } |
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24 | |
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25 | void merger::merge ( const epdf* g0 ) { |
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26 | it_file dbg ( "merger_debug.it" ); |
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27 | |
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28 | it_assert_debug ( rv.equal ( g0->_rv() ),"Incompatible g0" ); |
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29 | //Empirical density - samples |
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30 | eEmp eSmp ( rv,Ns ); |
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31 | eSmp.set_parameters ( ones ( Ns ), g0 ); |
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32 | Array<vec> &Smp = eSmp._samples(); //aux |
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33 | vec &w = eSmp._w(); //aux |
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34 | |
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35 | mat Smp_ex =ones ( rv.count() +1,Ns ); // Extended samples for the ARX model - the last row is ones |
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36 | for ( int i=0;i<Ns;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
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37 | |
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38 | dbg << Name ( "Smp_0" ) << Smp_ex; |
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39 | |
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40 | // Stuff for merging |
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41 | vec lw_src ( Ns ); |
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42 | vec lw_mix ( Ns ); |
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43 | mat lW=zeros ( n,Ns ); |
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44 | vec vec0 ( 0 ); |
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45 | |
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46 | // Initial component in the mixture model |
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47 | mat V0=1e-8*eye ( rv.count() +1 ); |
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48 | ARX A0 ( RV ( "{th_r }", vec_1 ( rv.count() * ( rv.count() +1 ) ) ),\ |
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49 | V0, rv.count() *rv.count() +3.0 ); //initial guess of Mix: zero mean, large variance |
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50 | |
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51 | // ============= MAIN LOOP ================== |
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52 | bool converged=false; |
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53 | int niter = 0; |
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54 | char str[100]; |
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55 | |
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56 | epdf* Mpred; |
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57 | vec Mix_pdf ( Ns ); |
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58 | while ( !converged ) { |
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59 | //Re-estimate Mix |
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60 | //Re-Initialize Mixture model |
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61 | Mix.init ( &A0, Smp_ex, Nc ); |
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62 | Mix.bayesB ( Smp_ex, w*Ns ); |
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63 | Mpred = Mix.predictor ( rv ); // Allocation => must be deleted at the end!! |
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64 | |
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65 | if ( 1 ) { |
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66 | // Generate new samples |
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67 | eSmp.set_samples ( Mpred ); |
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68 | for ( int i=0;i<Ns;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
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69 | } |
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70 | else { |
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71 | for ( int ii=0;ii<10;ii++ ) { |
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72 | for ( int jj=0; jj<10; jj++ ) { |
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73 | Smp ( ii+jj*10 ) =vec_2 ( -1.0+6*ii/10.0, -1.0+6*jj/10.0 ); |
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74 | } |
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75 | } |
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76 | for ( int i=0;i<Ns;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
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77 | } |
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78 | |
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79 | sprintf ( str,"Mpdf%d",niter ); |
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80 | for ( int i=0;i<Ns;i++ ) {Mix_pdf ( i ) = Mix.logpred ( Smp_ex.get_col ( i ) );} |
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81 | dbg << Name ( str ) << Mix_pdf; |
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82 | |
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83 | sprintf ( str,"Smp%d",niter ); |
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84 | dbg << Name ( str ) << Smp_ex; |
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85 | |
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86 | //Importace weighting |
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87 | for ( int i=0;i<n;i++ ) { |
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88 | lw_src=0.0; |
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89 | //======== Same RVs =========== |
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90 | //Split according to dependency in rvs |
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91 | if ( mpdfs ( i )->_rv().count() ==rv.count() ) { |
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92 | // no need for conditioning or marginalization |
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93 | for ( int j=0;j<Ns; j++ ) { // Smp is Array<> => for cycle |
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94 | lw_src ( j ) =mpdfs ( i )->_epdf().evalpdflog ( Smp ( j ) ); |
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95 | } |
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96 | } |
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97 | else { |
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98 | // compute likelihood of marginal on the conditional variable |
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99 | if ( mpdfs ( i )->_rvc().count() >0 ) { |
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100 | // Make marginal on rvc_i |
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101 | epdf* tmp_marg = Mpred->marginal ( mpdfs ( i )->_rvc() ); |
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102 | //compute vector of lw_src |
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103 | for ( int k=0;k<Ns;k++ ) { |
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104 | lw_src ( k ) += tmp_marg->evalpdflog ( dls ( i )->get_val ( Smp ( i ) ) ); |
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105 | } |
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106 | delete tmp_marg; |
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107 | } |
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108 | // Compute likelihood of the missing variable |
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109 | if ( rv.count() > ( mpdfs ( i )->_rv().count() + mpdfs ( i )->_rvc().count() ) ) { |
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110 | /////////////// |
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111 | cout << Mpred->mean() <<endl; |
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112 | // There are variales unknown to mpdfs(i) : rvzs |
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113 | mpdf* tmp_cond = Mpred->condition ( rvzs ( i ) ); |
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114 | // Compute likelihood |
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115 | vec lw_dbg=lw_src; |
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116 | for ( int k= 0; k<Ns; k++ ) { |
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117 | lw_src ( k ) += log ( |
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118 | tmp_cond->evalcond ( |
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119 | zdls ( i )->get_val ( Smp ( k ) ), |
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120 | zdls ( i )->get_cond ( Smp ( k ) ) ) ); |
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121 | } |
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122 | delete tmp_cond; |
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123 | } |
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124 | // Compute likelihood of the partial source |
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125 | for ( int k= 0; k<Ns; k++ ) { |
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126 | mpdfs ( i )->condition ( dls ( i )->get_cond ( Smp ( k ) ) ); |
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127 | lw_src ( k ) += mpdfs ( i )->_epdf().evalpdflog ( dls ( i )->get_val ( Smp ( k ) ) ); |
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128 | } |
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129 | |
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130 | } |
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131 | lW.set_row ( i, lw_src ); // do not divide by mix |
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132 | } |
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133 | //Importance of the mixture |
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134 | for ( int j=0;j<Ns;j++ ) { |
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135 | lw_mix ( j ) =Mix.logpred ( Smp_ex.get_col ( j ) ); |
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136 | } |
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137 | sprintf ( str,"lW%d",niter ); |
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138 | dbg << Name ( str ) << lW; |
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139 | |
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140 | w = lognorm_merge ( lW ); //merge |
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141 | |
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142 | sprintf ( str,"w%d",niter ); |
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143 | dbg << Name ( str ) << w; |
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144 | sprintf ( str,"lw_m%d",niter ); |
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145 | dbg << Name ( str ) << lw_mix; |
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146 | |
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147 | //Importance weighting |
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148 | w /=exp ( lw_mix ); // hoping that it is not numerically sensitive... |
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149 | //renormalize |
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150 | w /=sum ( w ); |
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151 | |
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152 | sprintf ( str,"w_is_%d",niter ); |
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153 | dbg << Name ( str ) << w; |
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154 | |
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155 | // eSmp.resample(); // So that it can be used in bayes |
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156 | // for ( int i=0;i<Ns;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
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157 | |
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158 | sprintf ( str,"Smp_res%d",niter ); |
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159 | dbg << Name ( str ) << Smp; |
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160 | |
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161 | // ==== stopping rule === |
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162 | niter++; |
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163 | converged = ( niter>4 ); |
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164 | } |
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165 | |
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166 | } |
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