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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::lognorm_merge ( mat &lW ) |
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| 8 | { |
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| 9 | int nu=lW.rows(); |
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| 10 | vec mu = sum ( lW ) /nu; //mean of logs |
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| 11 | // vec lam = sum ( pow ( lW,2 ) )-nu*pow ( mu,2 ); ======= numerically unsafe! |
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| 12 | vec lam = sum ( pow ( lW-outer_product ( ones ( lW.rows() ),mu ),2 ) ); |
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| 13 | double coef=0.0; |
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| 14 | vec sq2bl=sqrt ( 2*beta*lam ); //this term is everywhere |
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| 15 | switch ( nu ) |
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| 16 | { |
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| 17 | case 2: |
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| 18 | coef= ( 1-0.5*sqrt ( ( 4.0*beta-3.0 ) /beta ) ); |
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| 19 | return coef*sq2bl + mu ; |
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| 20 | break; |
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| 21 | case 3://Ratio of Bessel |
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| 22 | coef = sqrt ( ( 3*beta-2 ) /3*beta ); |
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| 23 | return log ( besselk ( 0,sq2bl*coef ) ) - log ( besselk ( 0,sq2bl ) ) + mu; |
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| 24 | break; |
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| 25 | case 4: |
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| 26 | break; |
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| 27 | default: // Approximate conditional density |
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| 28 | break; |
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| 29 | } |
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| 30 | return vec ( 0 ); |
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| 31 | } |
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| 32 | |
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| 33 | void merger::merge ( const epdf* g0 ) |
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| 34 | { |
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| 35 | |
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| 36 | it_assert_debug ( rv.equal ( g0->_rv() ),"Incompatible g0" ); |
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| 37 | //Empirical density - samples |
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| 38 | if ( !fix_smp ) |
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| 39 | { |
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| 40 | eSmp.set_statistics ( ones ( Ns ), g0 ); |
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| 41 | } |
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| 42 | |
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| 43 | Array<vec> &Smp = eSmp._samples(); //aux |
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| 44 | vec &w = eSmp._w(); //aux |
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| 45 | |
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| 46 | mat Smp_ex =ones ( dim +1,Ns ); // Extended samples for the ARX model - the last row is ones |
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| 47 | for ( int i=0;i<Ns;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
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| 48 | |
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| 49 | if ( DBG ) *dbg << Name ( "Smp_0" ) << Smp_ex; |
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| 50 | |
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| 51 | // Stuff for merging |
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| 52 | vec lw_src ( Ns ); |
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| 53 | vec lw_mix ( Ns ); |
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| 54 | vec lw ( Ns ); |
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| 55 | mat lW=zeros ( n,Ns ); |
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| 56 | vec vec0 ( 0 ); |
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| 57 | |
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| 58 | //initialize importance weights |
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| 59 | if ( !fix_smp ) |
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| 60 | for ( int i=0;i<Ns;i++ ) |
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| 61 | { |
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| 62 | lw_mix ( i ) =g0->evallog ( Smp ( i ) ); |
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| 63 | } |
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| 64 | |
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| 65 | // Initial component in the mixture model |
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| 66 | mat V0=1e-8*eye ( dim +1 ); |
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| 67 | ARX A0; A0.set_statistics ( dim, V0 ); //initial guess of Mix: |
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| 68 | |
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| 69 | Mix.init ( &A0, Smp_ex, Nc ); |
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| 70 | //Preserve initial mixture for repetitive estimation via flattening |
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| 71 | MixEF Mix_init ( Mix ); |
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| 72 | |
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| 73 | // ============= MAIN LOOP ================== |
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| 74 | bool converged=false; |
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| 75 | int niter = 0; |
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| 76 | char str[100]; |
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| 77 | |
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| 78 | emix* Mpred=Mix.epredictor ( ); |
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| 79 | vec Mix_pdf ( Ns ); |
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| 80 | while ( !converged ) |
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| 81 | { |
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| 82 | //Re-estimate Mix |
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| 83 | //Re-Initialize Mixture model |
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| 84 | Mix.flatten ( &Mix_init ); |
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| 85 | Mix.bayesB ( Smp_ex, w*Ns ); |
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| 86 | delete Mpred; |
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| 87 | Mpred = Mix.epredictor ( ); // Allocation => must be deleted at the end!! |
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| 88 | Mpred->set_rv ( rv ); //the predictor predicts rv of this merger |
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| 89 | |
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| 90 | // This will be active only later in iterations!!! |
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| 91 | if ( ( !fix_smp ) & ( 1./sum_sqr ( w ) <effss_coef*Ns ) ) |
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| 92 | { |
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| 93 | // Generate new samples |
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| 94 | eSmp.set_samples ( Mpred ); |
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| 95 | for ( int i=0;i<Ns;i++ ) |
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| 96 | { |
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| 97 | //////////// !!!!!!!!!!!!! |
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| 98 | if ( Smp ( i ) ( 2 ) <0 ) {Smp ( i ) ( 2 ) = 0.01; } |
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| 99 | set_col_part ( Smp_ex,i,Smp ( i ) ); |
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| 100 | //Importance of the mixture |
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| 101 | //lw_mix ( i ) =Mix.logpred (Smp_ex.get_col(i) ); |
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| 102 | lw_mix ( i ) = Mpred->evallog ( Smp ( i ) ); |
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| 103 | } |
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| 104 | if ( 0 ) |
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| 105 | { |
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| 106 | cout<<"Resampling =" << 1./sum_sqr ( w ) << endl; |
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| 107 | cout << Mix._e()->mean() <<endl; |
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| 108 | cout << sum ( Smp_ex,2 ) /Ns <<endl; |
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| 109 | cout << Smp_ex*Smp_ex.T() /Ns << endl; |
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| 110 | } |
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| 111 | } |
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| 112 | if ( DBG ) |
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| 113 | { |
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| 114 | sprintf ( str,"Mpred_mean%d",niter ); |
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| 115 | *dbg << Name ( str ) << Mpred->mean(); |
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| 116 | sprintf ( str,"Mpred_var%d",niter ); |
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| 117 | *dbg << Name ( str ) << Mpred->variance(); |
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| 118 | |
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| 119 | |
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| 120 | sprintf ( str,"Mpdf%d",niter ); |
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| 121 | for ( int i=0;i<Ns;i++ ) {Mix_pdf ( i ) = Mix.logpred ( Smp_ex.get_col ( i ) );} |
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| 122 | *dbg << Name ( str ) << Mix_pdf; |
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| 123 | |
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| 124 | sprintf ( str,"Smp%d",niter ); |
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| 125 | *dbg << Name ( str ) << Smp_ex; |
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| 126 | |
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| 127 | } |
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| 128 | //Importace weighting |
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| 129 | for ( int i=0;i<n;i++ ) |
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| 130 | { |
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| 131 | lw_src=0.0; |
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| 132 | //======== Same RVs =========== |
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| 133 | //Split according to dependency in rvs |
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| 134 | if ( mpdfs ( i )->dimension() ==dim ) |
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| 135 | { |
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| 136 | // no need for conditioning or marginalization |
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| 137 | for ( int j=0;j<Ns; j++ ) // Smp is Array<> => for cycle |
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| 138 | { |
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| 139 | lw_src ( j ) =mpdfs ( i )->_epdf().evallog ( Smp ( j ) ); |
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| 140 | } |
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| 141 | } |
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| 142 | else |
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| 143 | { |
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| 144 | // compute likelihood of marginal on the conditional variable |
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| 145 | if ( mpdfs ( i )->dimensionc() >0 ) |
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| 146 | { |
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| 147 | // Make marginal on rvc_i |
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| 148 | epdf* tmp_marg = Mpred->marginal ( mpdfs ( i )->_rvc() ); |
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| 149 | //compute vector of lw_src |
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| 150 | for ( int k=0;k<Ns;k++ ) |
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| 151 | { |
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| 152 | // Here val of tmp_marg = cond of mpdfs(i) ==> calling dls->get_cond |
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| 153 | lw_src ( k ) += tmp_marg->evallog ( dls ( i )->get_cond ( Smp ( k ) ) ); |
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| 154 | } |
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| 155 | delete tmp_marg; |
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| 156 | |
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| 157 | // sprintf ( str,"marg%d",niter ); |
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| 158 | // *dbg << Name ( str ) << lw_src; |
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| 159 | |
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| 160 | } |
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| 161 | // Compute likelihood of the missing variable |
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| 162 | if ( dim > ( mpdfs ( i )->dimension() + mpdfs ( i )->dimensionc() ) ) |
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| 163 | { |
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| 164 | /////////////// |
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| 165 | // There are variales unknown to mpdfs(i) : rvzs |
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| 166 | mpdf* tmp_cond = Mpred->condition ( rvzs ( i ) ); |
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| 167 | // Compute likelihood |
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| 168 | vec lw_dbg=lw_src; |
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| 169 | for ( int k= 0; k<Ns; k++ ) |
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| 170 | { |
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| 171 | lw_src ( k ) += log ( |
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| 172 | tmp_cond->evallogcond ( |
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| 173 | zdls ( i )->pushdown ( Smp ( k ) ), |
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| 174 | zdls ( i )->get_cond ( Smp ( k ) ) ) ); |
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| 175 | if ( !std::isfinite ( lw_src ( k ) ) ) |
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| 176 | { |
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| 177 | lw_src ( k ) = -1e16; cout << "!"; |
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| 178 | } |
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| 179 | } |
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| 180 | delete tmp_cond; |
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| 181 | } |
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| 182 | // Compute likelihood of the partial source |
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| 183 | for ( int k= 0; k<Ns; k++ ) |
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| 184 | { |
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| 185 | mpdfs ( i )->condition ( dls ( i )->get_cond ( Smp ( k ) ) ); |
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| 186 | lw_src ( k ) += mpdfs ( i )->_epdf().evallog ( dls ( i )->pushdown ( Smp ( k ) ) ); |
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| 187 | } |
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| 188 | |
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| 189 | } |
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| 190 | // it_assert_debug(std::isfinite(sum(lw_src)),"bad"); |
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| 191 | lW.set_row ( i, lw_src ); // do not divide by mix |
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| 192 | } |
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| 193 | lw = lognorm_merge ( lW ); //merge |
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| 194 | |
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| 195 | //Importance weighting |
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| 196 | if ( !fix_smp ) |
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| 197 | lw -= lw_mix; // hoping that it is not numerically sensitive... |
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| 198 | w = exp ( lw-max ( lw ) ); |
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| 199 | |
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| 200 | //renormalize |
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| 201 | double sumw =sum ( w ); |
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| 202 | if ( std::isfinite ( sumw ) ) |
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| 203 | { |
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| 204 | w = w/sumw; |
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| 205 | } |
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| 206 | else |
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| 207 | { |
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| 208 | it_file itf ( "merg_err.it" ); |
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| 209 | itf << Name ( "w" ) << w; |
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| 210 | } |
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| 211 | |
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| 212 | if ( DBG ) |
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| 213 | { |
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| 214 | sprintf ( str,"lW%d",niter ); |
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| 215 | *dbg << Name ( str ) << lW; |
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| 216 | sprintf ( str,"w%d",niter ); |
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| 217 | *dbg << Name ( str ) << w; |
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| 218 | sprintf ( str,"lw_m%d",niter ); |
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| 219 | *dbg << Name ( str ) << lw_mix; |
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| 220 | } |
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| 221 | // ==== stopping rule === |
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| 222 | niter++; |
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| 223 | converged = ( niter>40 ); |
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| 224 | } |
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| 225 | delete Mpred; |
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| 226 | // cout << endl; |
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| 227 | |
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| 228 | } |
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| 229 | |
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| 230 | } |
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