Fixed tabs and trailing whitespaces

[libdai.git] / src / bp.cpp

diff --git a/src/bp.cpp b/src/bp.cpp
index 7e11658..07c54bc 100644 (file)
--- a/src/bp.cpp
+++ b/src/bp.cpp
@@ -49,7 +49,7 @@ void BP::setProperties( const PropertySet &opts ) {
     assert( opts.hasKey("maxiter") );
     assert( opts.hasKey("logdomain") );
     assert( opts.hasKey("updates") );
-    
+
     props.tol = opts.getStringAs<double>("tol");
     props.maxiter = opts.getStringAs<size_t>("maxiter");
     props.logdomain = opts.getStringAs<bool>("logdomain");
@@ -106,7 +106,7 @@ void BP::construct() {
         _edge2lut.reserve( nrVars() );
     for( size_t i = 0; i < nrVars(); ++i ) {
         _edges.push_back( vector<EdgeProp>() );
-        _edges[i].reserve( nbV(i).size() ); 
+        _edges[i].reserve( nbV(i).size() );
         if( props.updates == Properties::UpdateType::SEQMAX ) {
             _edge2lut.push_back( vector<LutType::iterator>() );
             _edge2lut[i].reserve( nbV(i).size() );
@@ -138,7 +138,7 @@ void BP::init() {
             message( i, I.iter ).fill( c );
             newMessage( i, I.iter ).fill( c );
             if( props.updates == Properties::UpdateType::SEQMAX )
-                               updateResidual( i, I.iter, 0.0 );
+                updateResidual( i, I.iter, 0.0 );
         }
     }
 }
@@ -159,16 +159,16 @@ void BP::calcNewMessage( size_t i, size_t _I ) {
 
     Factor Fprod( factor(I) );
     Prob &prod = Fprod.p();
-    if( props.logdomain ) 
+    if( props.logdomain )
         prod.takeLog();
 
     // Calculate product of incoming messages and factor I
     foreach( const Neighbor &j, nbF(I) )
         if( j != i ) { // for all j in I \ i
             // prod_j will be the product of messages coming into j
-            Prob prod_j( var(j).states(), props.logdomain ? 0.0 : 1.0 ); 
+            Prob prod_j( var(j).states(), props.logdomain ? 0.0 : 1.0 );
             foreach( const Neighbor &J, nbV(j) )
-                if( J != I ) { // for all J in nb(j) \ I 
+                if( J != I ) { // for all J in nb(j) \ I
                     if( props.logdomain )
                         prod_j += message( j, J.iter );
                     else
@@ -204,7 +204,7 @@ void BP::calcNewMessage( size_t i, size_t _I ) {
     Prob marg;
     if( !DAI_BP_FAST ) {
         /* UNOPTIMIZED (SIMPLE TO READ, BUT SLOW) VERSION */
-        if( props.inference == Properties::InfType::SUMPROD ) 
+        if( props.inference == Properties::InfType::SUMPROD )
             marg = Fprod.marginal( var(i) ).p();
         else
             marg = Fprod.maxMarginal( var(i) ).p();
@@ -213,12 +213,12 @@ void BP::calcNewMessage( size_t i, size_t _I ) {
         marg = Prob( var(i).states(), 0.0 );
         // ind is the precalculated IndexFor(i,I) i.e. to x_I == k corresponds x_i == ind[k]
         const ind_t ind = index(i,_I);
-        if( props.inference == Properties::InfType::SUMPROD ) 
+        if( props.inference == Properties::InfType::SUMPROD )
             for( size_t r = 0; r < prod.size(); ++r )
                 marg[ind[r]] += prod[r];
         else
             for( size_t r = 0; r < prod.size(); ++r )
-                if( prod[r] > marg[ind[r]] ) 
+                if( prod[r] > marg[ind[r]] )
                     marg[ind[r]] = prod[r];
         marg.normalize();
     }
@@ -245,7 +245,7 @@ double BP::run() {
 
     double tic = toc();
     Diffs diffs(nrVars(), 1.0);
-    
+
     vector<Edge> update_seq;
 
     vector<Factor> old_beliefs;
@@ -293,7 +293,7 @@ double BP::run() {
                 }
             }
         } else if( props.updates == Properties::UpdateType::PARALL ) {
-            // Parallel updates 
+            // Parallel updates
             for( size_t i = 0; i < nrVars(); ++i )
                 foreach( const Neighbor &I, nbV(i) )
                     calcNewMessage( i, I.iter );
@@ -305,7 +305,7 @@ double BP::run() {
             // Sequential updates
             if( props.updates == Properties::UpdateType::SEQRND )
                 random_shuffle( update_seq.begin(), update_seq.end() );
-            
+
             foreach( const Edge &e, update_seq ) {
                 calcNewMessage( e.first, e.second );
                 updateMessage( e.first, e.second );
@@ -343,7 +343,7 @@ double BP::run() {
 
 
 void BP::calcBeliefV( size_t i, Prob &p ) const {
-    p = Prob( var(i).states(), props.logdomain ? 0.0 : 1.0 ); 
+    p = Prob( var(i).states(), props.logdomain ? 0.0 : 1.0 );
     foreach( const Neighbor &I, nbV(i) )
         if( props.logdomain )
             p += newMessage( i, I.iter );
@@ -356,14 +356,14 @@ void BP::calcBeliefF( size_t I, Prob &p ) const {
     Factor Fprod( factor( I ) );
     Prob &prod = Fprod.p();
 
-    if( props.logdomain ) 
+    if( props.logdomain )
         prod.takeLog();
 
     foreach( const Neighbor &j, nbF(I) ) {
         // prod_j will be the product of messages coming into j
         Prob prod_j( var(j).states(), props.logdomain ? 0.0 : 1.0 );
         foreach( const Neighbor &J, nbV(j) )
-            if( J != I ) { // for all J in nb(j) \ I 
+            if( J != I ) { // for all J in nb(j) \ I
                 if( props.logdomain )
                     prod_j += newMessage( j, J.iter );
                 else
@@ -463,7 +463,7 @@ Real BP::logZ() const {
 }
 
 
-string BP::identify() const { 
+string BP::identify() const {
     return string(Name) + printProperties();
 }
 
@@ -498,12 +498,12 @@ void BP::updateMessage( size_t i, size_t _I ) {
 
 
 void BP::updateResidual( size_t i, size_t _I, double r ) {
-       EdgeProp* pEdge = &_edges[i][_I];
-       pEdge->residual = r;
-       
-       // rearrange look-up table (delete and reinsert new key)
-       _lut.erase( _edge2lut[i][_I] );
-       _edge2lut[i][_I] = _lut.insert( make_pair( r, make_pair(i, _I) ) );
+    EdgeProp* pEdge = &_edges[i][_I];
+    pEdge->residual = r;
+
+    // rearrange look-up table (delete and reinsert new key)
+    _lut.erase( _edge2lut[i][_I] );
+    _edge2lut[i][_I] = _lut.insert( make_pair( r, make_pair(i, _I) ) );
 }
 
 
@@ -516,14 +516,14 @@ std::vector<size_t> BP::findMaximum() const {
         if( visitedVars[i] )
             continue;
         visitedVars[i] = true;
-        
+
         // Maximise with respect to variable i
         Prob prod;
         calcBeliefV( i, prod );
         maximum[i] = max_element( prod.begin(), prod.end() ) - prod.begin();
-        
+
         foreach( const Neighbor &I, nbV(i) )
-            if( !visitedFactors[I] ) 
+            if( !visitedFactors[I] )
                 scheduledFactors.push(I);
 
         while( !scheduledFactors.empty() ){
@@ -532,17 +532,17 @@ std::vector<size_t> BP::findMaximum() const {
             if( visitedFactors[I] )
                 continue;
             visitedFactors[I] = true;
-            
+
             // Evaluate if some neighboring variables still need to be fixed; if not, we're done
             bool allDetermined = true;
-            foreach( const Neighbor &j, nbF(I) ) 
+            foreach( const Neighbor &j, nbF(I) )
                 if( !visitedVars[j.node] ) {
                     allDetermined = false;
                     break;
                 }
             if( allDetermined )
                 continue;
-            
+
             // Calculate product of incoming messages on factor I
             Prob prod2;
             calcBeliefF( I, prod2 );
@@ -566,7 +566,7 @@ std::vector<size_t> BP::findMaximum() const {
                     maxProb = prod2[s];
                 }
             }
-            
+
             // Decode the argmax
             foreach( const Neighbor &j, nbF(I) ) {
                 if( visitedVars[j.node] ) {
@@ -578,7 +578,7 @@ std::vector<size_t> BP::findMaximum() const {
                     visitedVars[j.node] = true;
                     maximum[j.node] = maxState( var(j.node) );
                     foreach( const Neighbor &J, nbV(j) )
-                        if( !visitedFactors[J] ) 
+                        if( !visitedFactors[J] )
                             scheduledFactors.push(J);
                 }
             }