[262] | 1 | |
---|
[176] | 2 | #include "merger.h" |
---|
[384] | 3 | #include "../estim/arx.h" |
---|
[176] | 4 | |
---|
[299] | 5 | namespace bdm |
---|
| 6 | { |
---|
[423] | 7 | |
---|
| 8 | merger_base::merger_base(const Array<mpdf*> &S, bool own) { |
---|
| 9 | DBG = false; |
---|
| 10 | dbg_file = NULL; |
---|
| 11 | set_sources(S, own); |
---|
| 12 | } |
---|
| 13 | |
---|
[404] | 14 | vec merger_base::merge_points ( mat &lW ) { |
---|
[299] | 15 | int nu=lW.rows(); |
---|
[404] | 16 | vec result; |
---|
| 17 | ivec indW; |
---|
| 18 | bool infexist; |
---|
| 19 | switch ( METHOD ) { |
---|
[384] | 20 | case ARITHMETIC: |
---|
[404] | 21 | result= log ( sum ( exp ( lW ) ) ); //ugly! |
---|
[299] | 22 | break; |
---|
[384] | 23 | case GEOMETRIC: |
---|
[404] | 24 | result= sum ( lW ) /nu; |
---|
[299] | 25 | break; |
---|
[384] | 26 | case LOGNORMAL: |
---|
[404] | 27 | vec sumlW=sum ( lW ) ; |
---|
| 28 | indW=find((sumlW<inf) & (sumlW>-inf)); |
---|
| 29 | infexist=(indW.size()<lW.cols()); |
---|
| 30 | vec mu; |
---|
| 31 | vec lam; |
---|
| 32 | if (infexist){ |
---|
| 33 | mu = sumlW(indW) /nu; //mean of logs |
---|
| 34 | // |
---|
| 35 | mat validlW=lW.get_cols(indW); |
---|
| 36 | lam = sum ( pow ( validlW-outer_product ( ones ( validlW.rows() ),mu ),2 ) ); |
---|
| 37 | } |
---|
| 38 | else { |
---|
| 39 | mu = sum ( lW ) /nu; //mean of logs |
---|
| 40 | lam = sum ( pow ( lW-outer_product ( ones ( lW.rows() ),mu ),2 ) ); |
---|
| 41 | } |
---|
| 42 | // |
---|
[384] | 43 | double coef=0.0; |
---|
| 44 | vec sq2bl=sqrt ( 2*beta*lam ); //this term is everywhere |
---|
[404] | 45 | switch ( nu ) { |
---|
[384] | 46 | case 2: |
---|
| 47 | coef= ( 1-0.5*sqrt ( ( 4.0*beta-3.0 ) /beta ) ); |
---|
[404] | 48 | result =coef*sq2bl + mu ; |
---|
[384] | 49 | break; |
---|
[404] | 50 | // case 4: == can be done similar to case 2 - is it worth it??? |
---|
| 51 | default: // see accompanying document merge_lognorm_derivation.lyx |
---|
| 52 | coef = sqrt(1-(nu+1)/(2*beta*nu)); |
---|
| 53 | result =log(besselk((nu-3)/2, sq2bl*coef))-log(besselk((nu-3)/2, sq2bl)) + mu; |
---|
[384] | 54 | break; |
---|
| 55 | } |
---|
[299] | 56 | break; |
---|
| 57 | } |
---|
[404] | 58 | if (infexist){ |
---|
| 59 | vec tmp =-inf*ones(lW.cols()); |
---|
| 60 | set_subvector(tmp, indW, result); |
---|
| 61 | return tmp; |
---|
| 62 | } |
---|
| 63 | else {return result;} |
---|
[176] | 64 | } |
---|
| 65 | |
---|
[384] | 66 | void merger_mix::merge ( ) |
---|
[299] | 67 | { |
---|
| 68 | Array<vec> &Smp = eSmp._samples(); //aux |
---|
| 69 | vec &w = eSmp._w(); //aux |
---|
[180] | 70 | |
---|
[384] | 71 | mat Smp_ex =ones ( dim +1,Npoints ); // Extended samples for the ARX model - the last row is ones |
---|
| 72 | for ( int i=0;i<Npoints;i++ ) { set_col_part ( Smp_ex,i,Smp ( i ) );} |
---|
[180] | 73 | |
---|
[384] | 74 | if ( DBG ) *dbg_file << Name ( "Smp_0" ) << Smp_ex; |
---|
[180] | 75 | |
---|
[299] | 76 | // Stuff for merging |
---|
[384] | 77 | vec lw_src ( Npoints ); // weights of the ith source |
---|
| 78 | vec lw_mix ( Npoints ); // weights of the approximating mixture |
---|
| 79 | vec lw ( Npoints ); // tmp |
---|
| 80 | mat lW=zeros ( Nsources,Npoints ); // array of weights of all sources |
---|
[299] | 81 | vec vec0 ( 0 ); |
---|
[176] | 82 | |
---|
[300] | 83 | //initialize importance weights |
---|
[384] | 84 | lw_mix = 1.0; // assuming uniform grid density -- otherwise |
---|
[300] | 85 | |
---|
[299] | 86 | // Initial component in the mixture model |
---|
| 87 | mat V0=1e-8*eye ( dim +1 ); |
---|
| 88 | ARX A0; A0.set_statistics ( dim, V0 ); //initial guess of Mix: |
---|
[176] | 89 | |
---|
[384] | 90 | Mix.init ( &A0, Smp_ex, Ncoms ); |
---|
[299] | 91 | //Preserve initial mixture for repetitive estimation via flattening |
---|
| 92 | MixEF Mix_init ( Mix ); |
---|
[197] | 93 | |
---|
[299] | 94 | // ============= MAIN LOOP ================== |
---|
| 95 | bool converged=false; |
---|
| 96 | int niter = 0; |
---|
[384] | 97 | char dbg_str[100]; |
---|
[182] | 98 | |
---|
[299] | 99 | emix* Mpred=Mix.epredictor ( ); |
---|
[384] | 100 | vec Mix_pdf ( Npoints ); |
---|
[299] | 101 | while ( !converged ) |
---|
[311] | 102 | { |
---|
| 103 | //Re-estimate Mix |
---|
| 104 | //Re-Initialize Mixture model |
---|
| 105 | Mix.flatten ( &Mix_init ); |
---|
[384] | 106 | Mix.bayesB ( Smp_ex, w*Npoints ); |
---|
[299] | 107 | delete Mpred; |
---|
| 108 | Mpred = Mix.epredictor ( ); // Allocation => must be deleted at the end!! |
---|
| 109 | Mpred->set_rv ( rv ); //the predictor predicts rv of this merger |
---|
[213] | 110 | |
---|
[299] | 111 | // This will be active only later in iterations!!! |
---|
[384] | 112 | if ( 1./sum_sqr ( w ) <effss_coef*Npoints ) |
---|
[299] | 113 | { |
---|
| 114 | // Generate new samples |
---|
| 115 | eSmp.set_samples ( Mpred ); |
---|
[384] | 116 | for ( int i=0;i<Npoints;i++ ) |
---|
[299] | 117 | { |
---|
| 118 | //////////// !!!!!!!!!!!!! |
---|
[384] | 119 | //if ( Smp ( i ) ( 2 ) <0 ) {Smp ( i ) ( 2 ) = 0.01; } |
---|
[299] | 120 | set_col_part ( Smp_ex,i,Smp ( i ) ); |
---|
[300] | 121 | //Importance of the mixture |
---|
[310] | 122 | //lw_mix ( i ) =Mix.logpred (Smp_ex.get_col(i) ); |
---|
[311] | 123 | lw_mix ( i ) = Mpred->evallog ( Smp ( i ) ); |
---|
[299] | 124 | } |
---|
[384] | 125 | if ( DBG ) |
---|
[299] | 126 | { |
---|
| 127 | cout<<"Resampling =" << 1./sum_sqr ( w ) << endl; |
---|
[310] | 128 | cout << Mix._e()->mean() <<endl; |
---|
[384] | 129 | cout << sum ( Smp_ex,2 ) /Npoints <<endl; |
---|
| 130 | cout << Smp_ex*Smp_ex.T() /Npoints << endl; |
---|
[299] | 131 | } |
---|
[204] | 132 | } |
---|
[299] | 133 | if ( DBG ) |
---|
| 134 | { |
---|
[384] | 135 | sprintf ( dbg_str,"Mpred_mean%d",niter ); |
---|
| 136 | *dbg_file << Name ( dbg_str ) << Mpred->mean(); |
---|
| 137 | sprintf ( dbg_str,"Mpred_var%d",niter ); |
---|
| 138 | *dbg_file << Name ( dbg_str ) << Mpred->variance(); |
---|
[197] | 139 | |
---|
[205] | 140 | |
---|
[384] | 141 | sprintf ( dbg_str,"Mpdf%d",niter ); |
---|
| 142 | for ( int i=0;i<Npoints;i++ ) {Mix_pdf ( i ) = Mix.logpred ( Smp_ex.get_col ( i ) );} |
---|
| 143 | *dbg_file << Name ( dbg_str ) << Mix_pdf; |
---|
[180] | 144 | |
---|
[384] | 145 | sprintf ( dbg_str,"Smp%d",niter ); |
---|
| 146 | *dbg_file << Name ( dbg_str ) << Smp_ex; |
---|
[180] | 147 | |
---|
[176] | 148 | } |
---|
[299] | 149 | //Importace weighting |
---|
[384] | 150 | for ( int i=0;i<mpdfs.length();i++ ) |
---|
[299] | 151 | { |
---|
| 152 | lw_src=0.0; |
---|
| 153 | //======== Same RVs =========== |
---|
| 154 | //Split according to dependency in rvs |
---|
| 155 | if ( mpdfs ( i )->dimension() ==dim ) |
---|
| 156 | { |
---|
| 157 | // no need for conditioning or marginalization |
---|
[461] | 158 | lw_src = mpdfs ( i )->e()->evallog_m ( Smp ); |
---|
[299] | 159 | } |
---|
| 160 | else |
---|
| 161 | { |
---|
| 162 | // compute likelihood of marginal on the conditional variable |
---|
| 163 | if ( mpdfs ( i )->dimensionc() >0 ) |
---|
| 164 | { |
---|
| 165 | // Make marginal on rvc_i |
---|
| 166 | epdf* tmp_marg = Mpred->marginal ( mpdfs ( i )->_rvc() ); |
---|
| 167 | //compute vector of lw_src |
---|
[384] | 168 | for ( int k=0;k<Npoints;k++ ) |
---|
[299] | 169 | { |
---|
| 170 | // Here val of tmp_marg = cond of mpdfs(i) ==> calling dls->get_cond |
---|
| 171 | lw_src ( k ) += tmp_marg->evallog ( dls ( i )->get_cond ( Smp ( k ) ) ); |
---|
| 172 | } |
---|
| 173 | delete tmp_marg; |
---|
[198] | 174 | |
---|
| 175 | // sprintf ( str,"marg%d",niter ); |
---|
[299] | 176 | // *dbg << Name ( str ) << lw_src; |
---|
[198] | 177 | |
---|
[299] | 178 | } |
---|
| 179 | // Compute likelihood of the missing variable |
---|
| 180 | if ( dim > ( mpdfs ( i )->dimension() + mpdfs ( i )->dimensionc() ) ) |
---|
| 181 | { |
---|
| 182 | /////////////// |
---|
| 183 | // There are variales unknown to mpdfs(i) : rvzs |
---|
| 184 | mpdf* tmp_cond = Mpred->condition ( rvzs ( i ) ); |
---|
| 185 | // Compute likelihood |
---|
| 186 | vec lw_dbg=lw_src; |
---|
[384] | 187 | for ( int k= 0; k<Npoints; k++ ) |
---|
[299] | 188 | { |
---|
| 189 | lw_src ( k ) += log ( |
---|
| 190 | tmp_cond->evallogcond ( |
---|
| 191 | zdls ( i )->pushdown ( Smp ( k ) ), |
---|
| 192 | zdls ( i )->get_cond ( Smp ( k ) ) ) ); |
---|
| 193 | if ( !std::isfinite ( lw_src ( k ) ) ) |
---|
| 194 | { |
---|
| 195 | lw_src ( k ) = -1e16; cout << "!"; |
---|
| 196 | } |
---|
[204] | 197 | } |
---|
[299] | 198 | delete tmp_cond; |
---|
[182] | 199 | } |
---|
[299] | 200 | // Compute likelihood of the partial source |
---|
[384] | 201 | for ( int k= 0; k<Npoints; k++ ) |
---|
[299] | 202 | { |
---|
| 203 | mpdfs ( i )->condition ( dls ( i )->get_cond ( Smp ( k ) ) ); |
---|
[461] | 204 | lw_src ( k ) += mpdfs ( i )->e()->evallog ( dls ( i )->pushdown ( Smp ( k ) ) ); |
---|
[299] | 205 | } |
---|
| 206 | |
---|
[182] | 207 | } |
---|
[384] | 208 | // it_assert_debug(std::isfinite(sum(lw_src)),"bad"); |
---|
[299] | 209 | lW.set_row ( i, lw_src ); // do not divide by mix |
---|
| 210 | } |
---|
[384] | 211 | lw = merger_base::merge_points ( lW ); //merge |
---|
[197] | 212 | |
---|
[299] | 213 | //Importance weighting |
---|
[384] | 214 | lw -= lw_mix; // hoping that it is not numerically sensitive... |
---|
[299] | 215 | w = exp ( lw-max ( lw ) ); |
---|
[300] | 216 | |
---|
[299] | 217 | //renormalize |
---|
| 218 | double sumw =sum ( w ); |
---|
| 219 | if ( std::isfinite ( sumw ) ) |
---|
| 220 | { |
---|
| 221 | w = w/sumw; |
---|
| 222 | } |
---|
| 223 | else |
---|
| 224 | { |
---|
| 225 | it_file itf ( "merg_err.it" ); |
---|
| 226 | itf << Name ( "w" ) << w; |
---|
| 227 | } |
---|
[180] | 228 | |
---|
[299] | 229 | if ( DBG ) |
---|
| 230 | { |
---|
[384] | 231 | sprintf ( dbg_str,"lW%d",niter ); |
---|
| 232 | *dbg_file << Name ( dbg_str ) << lW; |
---|
| 233 | sprintf ( dbg_str,"w%d",niter ); |
---|
| 234 | *dbg_file << Name ( dbg_str ) << w; |
---|
| 235 | sprintf ( dbg_str,"lw_m%d",niter ); |
---|
| 236 | *dbg_file << Name ( dbg_str ) << lw_mix; |
---|
[299] | 237 | } |
---|
| 238 | // ==== stopping rule === |
---|
| 239 | niter++; |
---|
[395] | 240 | converged = ( niter>stop_niter ); |
---|
[204] | 241 | } |
---|
[299] | 242 | delete Mpred; |
---|
| 243 | // cout << endl; |
---|
[205] | 244 | |
---|
[176] | 245 | } |
---|
| 246 | |
---|
[399] | 247 | // DEFAULTS FOR MERGER_BASE |
---|
| 248 | const MERGER_METHOD merger_base::DFLT_METHOD=LOGNORMAL; |
---|
| 249 | const double merger_base::DFLT_beta=1.2; |
---|
| 250 | // DEFAULTS FOR MERGER_MIX |
---|
| 251 | const int merger_mix::DFLT_Ncoms=10; |
---|
| 252 | const double merger_mix::DFLT_effss_coef=0.5; |
---|
| 253 | |
---|
[176] | 254 | } |
---|