Changeset 477 for library/bdm/estim/mixtures.cpp

Timestamp:

08/05/09 14:40:03 (15 years ago)

Author:

mido

Message:

panove, vite, jak jsem peclivej na upravu kodu.. snad se vam bude libit:) konfigurace je v souboru /system/astylerc

Files:

• library/bdm/estim/mixtures.cpp (modified) (10 diffs)

library/bdm/estim/mixtures.cpp

@@ -2 +2 @@
 #include "mixtures.h"
 
-namespace bdm{
+namespace bdm {
 
 
@@ -8 +8 @@
         //prepare sizes
         Coms.set_size ( c );
-        n=c;
+        n = c;
         weights.set_parameters ( ones ( c ) ); //assume at least one observation in each comp.
         //est will be done at the end
@@ -22 +22 @@
 
         //Copy it to the rest
-        for ( i=1;i<n;i++ ) {
+        for ( i = 1; i < n; i++ ) {
                 //copy Com0 and create new rvs for them
                 Coms ( i ) =  Coms ( 0 )->_copy_ ( );
         }
         //Pick some data for each component and update it
-        for ( i=0;i<n;i++ ) {
+        for ( i = 0; i < n; i++ ) {
                 //pick one datum
-                int ind=floor(ndat*UniRNG.sample());
-                Coms ( i )->bayes ( Data.get_col ( ind ),1.0 );
+                int ind = floor ( ndat * UniRNG.sample() );
+                Coms ( i )->bayes ( Data.get_col ( ind ), 1.0 );
                 //flatten back to oringinal
-                Coms(i)->flatten(Com0);
+                Coms ( i )->flatten ( Com0 );
         }
 
@@ -42 +42 @@
 
 void MixEF::bayesB ( const mat &data , const vec &wData ) {
-        int ndat=data.cols();
-        int t,i,niter;
-        bool converged=false;
+        int ndat = data.cols();
+        int t, i, niter;
+        bool converged = false;
 
         multiBM weights0 ( weights );
 
         Array<BMEF*> Coms0 ( n );
-        for ( i=0;i<n;i++ ) {Coms0 ( i ) = ( BMEF* ) Coms ( i )->_copy_();}
+        for ( i = 0; i < n; i++ ) {
+                Coms0 ( i ) = ( BMEF* ) Coms ( i )->_copy_();
+        }
 
-        niter=0;
-        mat W=ones ( n,ndat ) / n;
-        mat Wlast=ones ( n,ndat ) / n;
+        niter = 0;
+        mat W = ones ( n, ndat ) / n;
+        mat Wlast = ones ( n, ndat ) / n;
         vec w ( n );
         vec ll ( n );
@@ -67 +69 @@
                 //
                 //#pragma omp parallel for
-                for ( t=0;t<ndat;t++ ) {
+                for ( t = 0; t < ndat; t++ ) {
                         //#pragma omp parallel for
-                        for ( i=0;i<n;i++ ) {
-                                ll ( i ) =Coms ( i )->logpred ( data.get_col ( t ) );
-                                wtmp =0.0; wtmp ( i ) =1.0;
+                        for ( i = 0; i < n; i++ ) {
+                                ll ( i ) = Coms ( i )->logpred ( data.get_col ( t ) );
+                                wtmp = 0.0;
+                                wtmp ( i ) = 1.0;
                                 ll ( i ) += weights.logpred ( wtmp );
                         }
-                        
-                        maxll = max(ll,maxi);
-                        switch (method) {
-                                case QB:
-                                        w = exp ( ll-maxll );
-                                        w/=sum(w);
-                                        break;
-                                case EM:
-                                        w = 0.0;
-                                        w(maxi) = 1.0;
-                                        break;
+
+                        maxll = max ( ll, maxi );
+                        switch ( method ) {
+                        case QB:
+                                w = exp ( ll - maxll );
+                                w /= sum ( w );
+                                break;
+                        case EM:
+                                w = 0.0;
+                                w ( maxi ) = 1.0;
+                                break;
                         }
-                        
+
                         W.set_col ( t, w );
                 }
@@ -92 +95 @@
                 // copy initial statistics
                 //#pragma omp parallel for
-                for ( i=0;i<n;i++ ) {
+                for ( i = 0; i < n; i++ ) {
                         Coms ( i )-> set_statistics ( Coms0 ( i ) );
                 }
@@ -100 +103 @@
                 // !!!!    note  wData ==> this is extra weight of the data record
                 // !!!!    For typical cases wData=1.
-                for ( t=0;t<ndat;t++ ) {
+                for ( t = 0; t < ndat; t++ ) {
                         //#pragma omp parallel for
-                        for ( i=0;i<n;i++ ) {
-                                Coms ( i )-> bayes ( data.get_col ( t ),W ( i,t ) * wData ( t ) );
+                        for ( i = 0; i < n; i++ ) {
+                                Coms ( i )-> bayes ( data.get_col ( t ), W ( i, t ) * wData ( t ) );
                         }
                         weights.bayes ( W.get_col ( t ) * wData ( t ) );
@@ -110 +113 @@
                 niter++;
                 //TODO better convergence rule.
-                converged = (niter>10);//( sumsum ( abs ( W-Wlast ) ) /n<0.1 );
+                converged = ( niter > 10 );//( sumsum ( abs ( W-Wlast ) ) /n<0.1 );
         }
 
         //Clean Coms0
-        for ( i=0;i<n;i++ ) {delete Coms0 ( i );}
+        for ( i = 0; i < n; i++ ) {
+                delete Coms0 ( i );
+        }
 }
 
@@ -128 +133 @@
 double MixEF::logpred ( const vec &dt ) const {
 
-        vec w=weights.posterior().mean();
-        double exLL=0.0;
-        for ( int i=0;i<n;i++ ) {
-                exLL+=w ( i ) *exp ( Coms ( i )->logpred ( dt ) );
+        vec w = weights.posterior().mean();
+        double exLL = 0.0;
+        for ( int i = 0; i < n; i++ ) {
+                exLL += w ( i ) * exp ( Coms ( i )->logpred ( dt ) );
         }
         return log ( exLL );
 }
 
-emix* MixEF::epredictor (  ) const {
+emix* MixEF::epredictor ( ) const {
         Array<epdf*> pC ( n );
-        for ( int i=0;i<n;i++ ) {pC ( i ) =Coms ( i )->epredictor ( );}
+        for ( int i = 0; i < n; i++ ) {
+                pC ( i ) = Coms ( i )->epredictor ( );
+        }
         emix* tmp;
-        tmp = new emix(  );
+        tmp = new emix( );
         tmp->set_parameters ( weights.posterior().mean(), pC, false );
         tmp->ownComs();
@@ -146 +153 @@
 }
 
-void MixEF::flatten(const BMEF* M2){
-        const MixEF* Mix2=dynamic_cast<const MixEF*>(M2);
-        it_assert_debug(Mix2->n==n,"Different no of coms");
+void MixEF::flatten ( const BMEF* M2 ) {
+        const MixEF* Mix2 = dynamic_cast<const MixEF*> ( M2 );
+        it_assert_debug ( Mix2->n == n, "Different no of coms" );
         //Flatten each component
-        for (int i=0; i<n;i++){
-                Coms(i)->flatten(Mix2->Coms(i));
+        for ( int i = 0; i < n; i++ ) {
+                Coms ( i )->flatten ( Mix2->Coms ( i ) );
         }
         //Flatten weights = make them equal!!
-        weights.set_statistics(&(Mix2->weights));
+        weights.set_statistics ( & ( Mix2->weights ) );
 }
 }