| 1 | |
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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 | namespace bdm |
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| 6 | { |
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| 7 | vec merger_base::merge_points ( mat &lW ) |
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| 8 | { |
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| 9 | int nu=lW.rows(); |
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| 10 | switch ( METHOD ) |
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| 11 | { |
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| 12 | case ARITHMETIC: |
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| 13 | return log ( sum ( exp ( lW ) ) ); //ugly! |
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| 14 | break; |
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| 15 | case GEOMETRIC: |
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| 16 | return sum ( lW ) /nu; |
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| 17 | break; |
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| 18 | case LOGNORMAL: |
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| 19 | vec mu = sum ( lW ) /nu; //mean of logs |
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| 20 | // vec lam = sum ( pow ( lW,2 ) )-nu*pow ( mu,2 ); ======= numerically unsafe! |
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| 21 | vec lam = sum ( pow ( lW-outer_product ( ones ( lW.rows() ),mu ),2 ) ); |
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| 22 | double coef=0.0; |
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| 23 | vec sq2bl=sqrt ( 2*beta*lam ); //this term is everywhere |
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| 24 | switch ( nu ) |
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| 25 | { |
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| 26 | case 2: |
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| 27 | coef= ( 1-0.5*sqrt ( ( 4.0*beta-3.0 ) /beta ) ); |
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| 28 | return coef*sq2bl + mu ; |
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| 29 | break; |
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| 30 | case 3://Ratio of Bessel |
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| 31 | coef = sqrt ( ( 3*beta-2 ) /3*beta ); |
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| 32 | return log ( besselk ( 0,sq2bl*coef ) ) - log ( besselk ( 0,sq2bl ) ) + mu; |
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| 33 | break; |
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| 34 | case 4: |
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| 35 | break; |
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| 36 | default: // Approximate conditional density |
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| 37 | break; |
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| 38 | } |
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| 39 | break; |
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| 40 | } |
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| 41 | return vec ( 0 ); |
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| 42 | } |
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| 43 | |
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| 44 | void merger_mix::merge ( ) |
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| 45 | { |
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| 46 | Array<vec> &Smp = eSmp._samples(); //aux |
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| 47 | vec &w = eSmp._w(); //aux |
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| 48 | |
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| 49 | mat Smp_ex =ones ( dim +1,Npoints ); // Extended samples for the ARX model - the last row is ones |
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| 50 | for ( int i=0;i<Npoints;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
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| 51 | |
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| 52 | if ( DBG ) *dbg_file << Name ( "Smp_0" ) << Smp_ex; |
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| 53 | |
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| 54 | // Stuff for merging |
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| 55 | vec lw_src ( Npoints ); // weights of the ith source |
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| 56 | vec lw_mix ( Npoints ); // weights of the approximating mixture |
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| 57 | vec lw ( Npoints ); // tmp |
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| 58 | mat lW=zeros ( Nsources,Npoints ); // array of weights of all sources |
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| 59 | vec vec0 ( 0 ); |
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| 60 | |
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| 61 | //initialize importance weights |
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| 62 | lw_mix = 1.0; // assuming uniform grid density -- otherwise |
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| 63 | |
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| 64 | // Initial component in the mixture model |
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| 65 | mat V0=1e-8*eye ( dim +1 ); |
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| 66 | ARX A0; A0.set_statistics ( dim, V0 ); //initial guess of Mix: |
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| 67 | |
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| 68 | Mix.init ( &A0, Smp_ex, Ncoms ); |
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| 69 | //Preserve initial mixture for repetitive estimation via flattening |
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| 70 | MixEF Mix_init ( Mix ); |
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| 71 | |
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| 72 | // ============= MAIN LOOP ================== |
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| 73 | bool converged=false; |
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| 74 | int niter = 0; |
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| 75 | char dbg_str[100]; |
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| 76 | |
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| 77 | emix* Mpred=Mix.epredictor ( ); |
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| 78 | vec Mix_pdf ( Npoints ); |
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| 79 | while ( !converged ) |
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| 80 | { |
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| 81 | //Re-estimate Mix |
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| 82 | //Re-Initialize Mixture model |
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| 83 | Mix.flatten ( &Mix_init ); |
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| 84 | Mix.bayesB ( Smp_ex, w*Npoints ); |
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| 85 | delete Mpred; |
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| 86 | Mpred = Mix.epredictor ( ); // Allocation => must be deleted at the end!! |
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| 87 | Mpred->set_rv ( rv ); //the predictor predicts rv of this merger |
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| 88 | |
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| 89 | // This will be active only later in iterations!!! |
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| 90 | if ( 1./sum_sqr ( w ) <effss_coef*Npoints ) |
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| 91 | { |
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| 92 | // Generate new samples |
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| 93 | eSmp.set_samples ( Mpred ); |
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| 94 | for ( int i=0;i<Npoints;i++ ) |
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| 95 | { |
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| 96 | //////////// !!!!!!!!!!!!! |
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| 97 | //if ( Smp ( i ) ( 2 ) <0 ) {Smp ( i ) ( 2 ) = 0.01; } |
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| 98 | set_col_part ( Smp_ex,i,Smp ( i ) ); |
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| 99 | //Importance of the mixture |
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| 100 | //lw_mix ( i ) =Mix.logpred (Smp_ex.get_col(i) ); |
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| 101 | lw_mix ( i ) = Mpred->evallog ( Smp ( i ) ); |
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| 102 | } |
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| 103 | if ( DBG ) |
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| 104 | { |
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| 105 | cout<<"Resampling =" << 1./sum_sqr ( w ) << endl; |
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| 106 | cout << Mix._e()->mean() <<endl; |
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| 107 | cout << sum ( Smp_ex,2 ) /Npoints <<endl; |
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| 108 | cout << Smp_ex*Smp_ex.T() /Npoints << endl; |
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| 109 | } |
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| 110 | } |
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| 111 | if ( DBG ) |
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| 112 | { |
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| 113 | sprintf ( dbg_str,"Mpred_mean%d",niter ); |
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| 114 | *dbg_file << Name ( dbg_str ) << Mpred->mean(); |
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| 115 | sprintf ( dbg_str,"Mpred_var%d",niter ); |
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| 116 | *dbg_file << Name ( dbg_str ) << Mpred->variance(); |
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| 117 | |
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| 118 | |
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| 119 | sprintf ( dbg_str,"Mpdf%d",niter ); |
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| 120 | for ( int i=0;i<Npoints;i++ ) {Mix_pdf ( i ) = Mix.logpred ( Smp_ex.get_col ( i ) );} |
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| 121 | *dbg_file << Name ( dbg_str ) << Mix_pdf; |
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| 122 | |
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| 123 | sprintf ( dbg_str,"Smp%d",niter ); |
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| 124 | *dbg_file << Name ( dbg_str ) << Smp_ex; |
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| 125 | |
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| 126 | } |
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| 127 | //Importace weighting |
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| 128 | for ( int i=0;i<mpdfs.length();i++ ) |
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| 129 | { |
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| 130 | lw_src=0.0; |
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| 131 | //======== Same RVs =========== |
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| 132 | //Split according to dependency in rvs |
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| 133 | if ( mpdfs ( i )->dimension() ==dim ) |
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| 134 | { |
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| 135 | // no need for conditioning or marginalization |
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| 136 | for ( int j=0;j<Npoints; j++ ) // Smp is Array<> => for cycle |
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| 137 | { |
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| 138 | lw_src ( j ) =mpdfs ( i )->_epdf().evallog ( Smp ( j ) ); |
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| 139 | } |
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| 140 | } |
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| 141 | else |
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| 142 | { |
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| 143 | // compute likelihood of marginal on the conditional variable |
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| 144 | if ( mpdfs ( i )->dimensionc() >0 ) |
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| 145 | { |
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| 146 | // Make marginal on rvc_i |
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| 147 | epdf* tmp_marg = Mpred->marginal ( mpdfs ( i )->_rvc() ); |
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| 148 | //compute vector of lw_src |
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| 149 | for ( int k=0;k<Npoints;k++ ) |
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| 150 | { |
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| 151 | // Here val of tmp_marg = cond of mpdfs(i) ==> calling dls->get_cond |
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| 152 | lw_src ( k ) += tmp_marg->evallog ( dls ( i )->get_cond ( Smp ( k ) ) ); |
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| 153 | } |
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| 154 | delete tmp_marg; |
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| 155 | |
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| 156 | // sprintf ( str,"marg%d",niter ); |
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| 157 | // *dbg << Name ( str ) << lw_src; |
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| 158 | |
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| 159 | } |
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| 160 | // Compute likelihood of the missing variable |
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| 161 | if ( dim > ( mpdfs ( i )->dimension() + mpdfs ( i )->dimensionc() ) ) |
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| 162 | { |
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| 163 | /////////////// |
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| 164 | // There are variales unknown to mpdfs(i) : rvzs |
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| 165 | mpdf* tmp_cond = Mpred->condition ( rvzs ( i ) ); |
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| 166 | // Compute likelihood |
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| 167 | vec lw_dbg=lw_src; |
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| 168 | for ( int k= 0; k<Npoints; k++ ) |
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| 169 | { |
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| 170 | lw_src ( k ) += log ( |
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| 171 | tmp_cond->evallogcond ( |
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| 172 | zdls ( i )->pushdown ( Smp ( k ) ), |
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| 173 | zdls ( i )->get_cond ( Smp ( k ) ) ) ); |
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| 174 | if ( !std::isfinite ( lw_src ( k ) ) ) |
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| 175 | { |
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| 176 | lw_src ( k ) = -1e16; cout << "!"; |
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| 177 | } |
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| 178 | } |
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| 179 | delete tmp_cond; |
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| 180 | } |
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| 181 | // Compute likelihood of the partial source |
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| 182 | for ( int k= 0; k<Npoints; k++ ) |
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| 183 | { |
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| 184 | mpdfs ( i )->condition ( dls ( i )->get_cond ( Smp ( k ) ) ); |
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| 185 | lw_src ( k ) += mpdfs ( i )->_epdf().evallog ( dls ( i )->pushdown ( Smp ( k ) ) ); |
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| 186 | } |
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| 187 | |
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| 188 | } |
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| 189 | // it_assert_debug(std::isfinite(sum(lw_src)),"bad"); |
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| 190 | lW.set_row ( i, lw_src ); // do not divide by mix |
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| 191 | } |
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| 192 | lw = merger_base::merge_points ( lW ); //merge |
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| 193 | |
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| 194 | //Importance weighting |
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| 195 | lw -= lw_mix; // hoping that it is not numerically sensitive... |
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| 196 | w = exp ( lw-max ( lw ) ); |
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| 197 | |
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| 198 | //renormalize |
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| 199 | double sumw =sum ( w ); |
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| 200 | if ( std::isfinite ( sumw ) ) |
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| 201 | { |
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| 202 | w = w/sumw; |
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| 203 | } |
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| 204 | else |
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| 205 | { |
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| 206 | it_file itf ( "merg_err.it" ); |
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| 207 | itf << Name ( "w" ) << w; |
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| 208 | } |
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| 209 | |
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| 210 | if ( DBG ) |
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| 211 | { |
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| 212 | sprintf ( dbg_str,"lW%d",niter ); |
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| 213 | *dbg_file << Name ( dbg_str ) << lW; |
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| 214 | sprintf ( dbg_str,"w%d",niter ); |
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| 215 | *dbg_file << Name ( dbg_str ) << w; |
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| 216 | sprintf ( dbg_str,"lw_m%d",niter ); |
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| 217 | *dbg_file << Name ( dbg_str ) << lw_mix; |
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| 218 | } |
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| 219 | // ==== stopping rule === |
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| 220 | niter++; |
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| 221 | converged = ( niter>40 ); |
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| 222 | } |
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| 223 | delete Mpred; |
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| 224 | // cout << endl; |
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| 225 | |
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| 226 | } |
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| 227 | |
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| 228 | } |
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