[Benjamin Piwowarski] Renamed "foreach" macro into "bforeach" to avoid conflicts...

diff --git a/ChangeLog b/ChangeLog
index 5076086..21c0ad4 100644 (file)
--- a/ChangeLog
+++ b/ChangeLog
@@ -1,5 +1,6 @@
 git master
 ----------
+* [Benjamin Piwowarski] Renamed "foreach" macro into "bforeach" to avoid conflicts with newer Boost library versions
 * Optimized ClusterGraph( const FactorGraph&, bool) constructor
 * Fixed "typename" bug in src/alldai.cpp which resulted in FTBFS for older g++ compilers
 * Fixed memory leak in alldai.cpp and removed the function builtinInfAlgs()

diff --git a/examples/example_bipgraph.cpp b/examples/example_bipgraph.cpp
index 9df17fc..223f66e 100644 (file)
--- a/examples/example_bipgraph.cpp
+++ b/examples/example_bipgraph.cpp
@@ -32,7 +32,7 @@ int main() {
     for( size_t n1 = 0; n1 < G.nrNodes1(); n1++ ) {
         cout << "Node " << n1 << " of type 1 has " << G.nb1(n1).size() << " neighbors:" << endl;
         // Iterate over all neighbors n2 of n1
-        foreach( const Neighbor &n2, G.nb1(n1) ) {
+        bforeach( const Neighbor &n2, G.nb1(n1) ) {
             // The n2.iter'th neighbor of n1 is n2:
             DAI_ASSERT( G.nb1(n1)[n2.iter] == n2 );
 

diff --git a/include/dai/bipgraph.h b/include/dai/bipgraph.h
index 4db3fe4..569f815 100644 (file)
--- a/include/dai/bipgraph.h
+++ b/include/dai/bipgraph.h
@@ -308,10 +308,10 @@ class BipartiteGraph {
             for( size_t n1 = 0; n1 < nrNodes1(); n1++ ) {
                 if( nb1(n1).size() != x.nb1(n1).size() )
                     return false;
-                foreach( const Neighbor &n2, nb1(n1) )
+                bforeach( const Neighbor &n2, nb1(n1) )
                     if( !x.hasEdge( n1, n2 ) )
                         return false;
-                foreach( const Neighbor &n2, x.nb1(n1) )
+                bforeach( const Neighbor &n2, x.nb1(n1) )
                     if( !hasEdge( n1, n2 ) )
                         return false;
             }

diff --git a/include/dai/clustergraph.h b/include/dai/clustergraph.h
index 22886d3..594fae0 100644 (file)
--- a/include/dai/clustergraph.h
+++ b/include/dai/clustergraph.h
@@ -100,7 +100,7 @@ namespace dai {
             /// Returns union of clusters that contain the \a i 'th variable
             VarSet Delta( size_t i ) const {
                 VarSet result;
-                foreach( const Neighbor& I, _G.nb1(i) )
+                bforeach( const Neighbor& I, _G.nb1(i) )
                     result |= _clusters[I];
                 return result;
             }
@@ -115,7 +115,7 @@ namespace dai {
                 if( i1 == i2 )
                     return false;
                 bool result = false;
-                foreach( const Neighbor& I, _G.nb1(i1) )
+                bforeach( const Neighbor& I, _G.nb1(i1) )
                     if( find( _G.nb2(I).begin(), _G.nb2(I).end(), i2 ) != _G.nb2(I).end() ) {
                         result = true;
                         break;
@@ -129,8 +129,8 @@ namespace dai {
                 const VarSet & clI = _clusters[I];
                 bool maximal = true;
                 // The following may not be optimal, since it may repeatedly test the same cluster *J
-                foreach( const Neighbor& i, _G.nb2(I) ) {
-                    foreach( const Neighbor& J, _G.nb1(i) )
+                bforeach( const Neighbor& i, _G.nb2(I) ) {
+                    bforeach( const Neighbor& J, _G.nb1(i) )
                         if( (J != I) && (clI << _clusters[J]) ) {
                             maximal = false;
                             break;

diff --git a/include/dai/dag.h b/include/dai/dag.h
index 2aa8c94..fbfb828 100644 (file)
--- a/include/dai/dag.h
+++ b/include/dai/dag.h
@@ -266,10 +266,10 @@ class DAG {
             for( size_t n1 = 0; n1 < nrNodes(); n1++ ) {
                 if( pa(n1).size() != x.pa(n1).size() )
                     return false;
-                foreach( const Neighbor &n2, pa(n1) )
+                bforeach( const Neighbor &n2, pa(n1) )
                     if( !x.hasEdge( n2, n1 ) )
                         return false;
-                foreach( const Neighbor &n2, x.pa(n1) )
+                bforeach( const Neighbor &n2, x.pa(n1) )
                     if( !hasEdge( n2, n1 ) )
                         return false;
             }

diff --git a/include/dai/graph.h b/include/dai/graph.h
index 019fea1..ab6857a 100644 (file)
--- a/include/dai/graph.h
+++ b/include/dai/graph.h
@@ -61,7 +61,7 @@ namespace dai {
  *  \code
  *  for( size_t i = 0; i < nrNodes(); ++i ) {
  *      size_t _j = 0;
- *      foreach( const Neighbor &j, nb(i) ) {
+ *      bforeach( const Neighbor &j, nb(i) ) {
  *          assert( j == nb(i,j.iter) );
  *          assert( nb(j.node,j.dual).node == i );
  *          assert( _j = j.iter );
@@ -282,10 +282,10 @@ class GraphAL {
             for( size_t n1 = 0; n1 < nrNodes(); n1++ ) {
                 if( nb(n1).size() != x.nb(n1).size() )
                     return false;
-                foreach( const Neighbor &n2, nb(n1) )
+                bforeach( const Neighbor &n2, nb(n1) )
                     if( !x.hasEdge( n1, n2 ) )
                         return false;
-                foreach( const Neighbor &n2, x.nb(n1) )
+                bforeach( const Neighbor &n2, x.nb(n1) )
                     if( !hasEdge( n1, n2 ) )
                         return false;
             }

diff --git a/include/dai/properties.h b/include/dai/properties.h
index 64ac5b0..cdabd8c 100644 (file)
--- a/include/dai/properties.h
+++ b/include/dai/properties.h
@@ -98,7 +98,7 @@ class PropertySet : private std::map<PropertyKey, PropertyValue> {
         /// Set properties according to \a newProps, overriding properties that already exist with new values
         PropertySet& set( const PropertySet& newProps ) {
             const std::map<PropertyKey, PropertyValue> *m = &newProps;
-            foreach(value_type i, *m)
+            bforeach(value_type i, *m)
                 set( i.first, i.second );
             return *this;
         }

diff --git a/include/dai/util.h b/include/dai/util.h
index 8ca3073..18a9eb8 100644 (file)
--- a/include/dai/util.h
+++ b/include/dai/util.h
@@ -40,8 +40,8 @@
 #endif
 
 
-/// An alias to the BOOST_FOREACH macro from the boost::foreach library
-#define foreach BOOST_FOREACH
+/// An alias to the BOOST_FOREACH macro from the boost::bforeach library
+#define bforeach BOOST_FOREACH
 
 #ifdef DAI_DEBUG
 /// \brief "Print variable". Prints the text of an expression, followed by its value (only if DAI_DEBUG is defined)

diff --git a/include/dai/weightedgraph.h b/include/dai/weightedgraph.h
index edb1422..1546715 100644 (file)
--- a/include/dai/weightedgraph.h
+++ b/include/dai/weightedgraph.h
@@ -22,14 +22,15 @@
 #include <set>
 #include <limits>
 #include <climits>   // Work-around for bug in boost graph library
-#include <dai/util.h>
-#include <dai/exceptions.h>
-#include <dai/graph.h>
 
 #include <boost/graph/adjacency_list.hpp>
 #include <boost/graph/prim_minimum_spanning_tree.hpp>
 #include <boost/graph/kruskal_min_spanning_tree.hpp>
 
+#include <dai/util.h>
+#include <dai/exceptions.h>
+#include <dai/graph.h>
+
 
 namespace dai {
 
@@ -123,7 +124,7 @@ class GraphEL : public std::set<UEdge> {
         /// Construct from GraphAL
         GraphEL( const GraphAL& G ) {
             for( size_t n1 = 0; n1 < G.nrNodes(); n1++ )
-                foreach( const Neighbor n2, G.nb(n1) )
+                bforeach( const Neighbor n2, G.nb(n1) )
                     if( n1 < n2 )
                         insert( UEdge( n1, n2 ) );
         }

diff --git a/src/bbp.cpp b/src/bbp.cpp
index 9a91fd9..b651c15 100644 (file)
--- a/src/bbp.cpp
+++ b/src/bbp.cpp
@@ -149,7 +149,7 @@ void BBP::RegenerateInds() {
     for( size_t i = 0; i < _fg->nrVars(); i++ ) {
         _indices[i].clear();
         _indices[i].reserve( _fg->nbV(i).size() );
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             _ind_t index;
             index.reserve( _fg->factor(I).nrStates() );
             for( IndexFor k(_fg->var(i), _fg->factor(I).vars()); k.valid(); ++k )
@@ -165,9 +165,9 @@ void BBP::RegenerateT() {
     _Tmsg.resize( _fg->nrVars() );
     for( size_t i = 0; i < _fg->nrVars(); i++ ) {
         _Tmsg[i].resize( _fg->nbV(i).size() );
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             Prob prod( _fg->var(i).states(), 1.0 );
-            foreach( const Neighbor &J, _fg->nbV(i) )
+            bforeach( const Neighbor &J, _fg->nbV(i) )
                 if( J.node != I.node )
                     prod *= _bp_dual.msgM( i, J.iter );
             _Tmsg[i][I.iter] = prod;
@@ -181,9 +181,9 @@ void BBP::RegenerateU() {
     _Umsg.resize( _fg->nrFactors() );
     for( size_t I = 0; I < _fg->nrFactors(); I++ ) {
         _Umsg[I].resize( _fg->nbF(I).size() );
-        foreach( const Neighbor &i, _fg->nbF(I) ) {
+        bforeach( const Neighbor &i, _fg->nbF(I) ) {
             Prob prod( _fg->factor(I).nrStates(), 1.0 );
-            foreach( const Neighbor &j, _fg->nbF(I) )
+            bforeach( const Neighbor &j, _fg->nbF(I) )
                 if( i.node != j.node ) {
                     Prob n_jI( _bp_dual.msgN( j, j.dual ) );
                     const _ind_t &ind = _index( j, j.dual );
@@ -202,12 +202,12 @@ void BBP::RegenerateS() {
     _Smsg.resize( _fg->nrVars() );
     for( size_t i = 0; i < _fg->nrVars(); i++ ) {
         _Smsg[i].resize( _fg->nbV(i).size() );
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             _Smsg[i][I.iter].resize( _fg->nbF(I).size() );
-            foreach( const Neighbor &j, _fg->nbF(I) )
+            bforeach( const Neighbor &j, _fg->nbF(I) )
                 if( i != j ) {
                     Factor prod( _fg->factor(I) );
-                    foreach( const Neighbor &k, _fg->nbF(I) ) {
+                    bforeach( const Neighbor &k, _fg->nbF(I) ) {
                         if( k != i && k.node != j.node ) {
                             const _ind_t &ind = _index( k, k.dual );
                             Prob p( _bp_dual.msgN( k, k.dual ) );
@@ -230,12 +230,12 @@ void BBP::RegenerateR() {
     _Rmsg.resize( _fg->nrFactors() );
     for( size_t I = 0; I < _fg->nrFactors(); I++ ) {
         _Rmsg[I].resize( _fg->nbF(I).size() );
-        foreach( const Neighbor &i, _fg->nbF(I) ) {
+        bforeach( const Neighbor &i, _fg->nbF(I) ) {
             _Rmsg[I][i.iter].resize( _fg->nbV(i).size() );
-            foreach( const Neighbor &J, _fg->nbV(i) ) {
+            bforeach( const Neighbor &J, _fg->nbV(i) ) {
                 if( I != J ) {
                     Prob prod( _fg->var(i).states(), 1.0 );
-                    foreach( const Neighbor &K, _fg->nbV(i) )
+                    bforeach( const Neighbor &K, _fg->nbV(i) )
                         if( K.node != I && K.node != J.node )
                             prod *= _bp_dual.msgM( i, K.iter );
                     _Rmsg[I][i.iter][J.iter] = prod;
@@ -264,7 +264,7 @@ void BBP::RegeneratePsiAdjoints() {
     for( size_t i = 0; i < _fg->nrVars(); i++ ) {
         Prob p( _adj_b_V_unnorm[i] );
         DAI_ASSERT( p.size() == _fg->var(i).states() );
-        foreach( const Neighbor &I, _fg->nbV(i) )
+        bforeach( const Neighbor &I, _fg->nbV(i) )
             p *= _bp_dual.msgM( i, I.iter );
         p += _init_adj_psi_V[i];
         _adj_psi_V.push_back( p );
@@ -274,7 +274,7 @@ void BBP::RegeneratePsiAdjoints() {
     for( size_t I = 0; I < _fg->nrFactors(); I++ ) {
         Prob p( _adj_b_F_unnorm[I] );
         DAI_ASSERT( p.size() == _fg->factor(I).nrStates() );
-        foreach( const Neighbor &i, _fg->nbF(I) ) {
+        bforeach( const Neighbor &i, _fg->nbF(I) ) {
             Prob n_iI( _bp_dual.msgN( i, i.dual ) );
             const _ind_t& ind = _index( i, i.dual );
             // multiply prod with n_jI
@@ -303,11 +303,11 @@ void BBP::RegenerateParMessageAdjoints() {
         _adj_m_unnorm[i].resize( n_i );
         _new_adj_n[i].resize( n_i );
         _new_adj_m[i].resize( n_i );
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             { // calculate adj_n
                 Prob prod( _fg->factor(I).p() );
                 prod *= _adj_b_F_unnorm[I];
-                foreach( const Neighbor &j, _fg->nbF(I) )
+                bforeach( const Neighbor &j, _fg->nbF(I) )
                     if( i != j ) {
                         Prob n_jI( _bp_dual.msgN( j, j.dual ) );
                         const _ind_t &ind = _index( j, j.dual );
@@ -326,7 +326,7 @@ void BBP::RegenerateParMessageAdjoints() {
             { // calculate adj_m
                 Prob prod( _adj_b_V_unnorm[i] );
                 DAI_ASSERT( prod.size() == _fg->var(i).states() );
-                foreach( const Neighbor &J, _fg->nbV(i) )
+                bforeach( const Neighbor &J, _fg->nbV(i) )
                     if( J.node != I.node )
                         prod *= _bp_dual.msgM(i,J.iter);
                 _new_adj_m[i][I.iter] = prod;
@@ -347,11 +347,11 @@ void BBP::RegenerateSeqMessageAdjoints() {
         _adj_m[i].resize( n_i );
         _adj_m_unnorm[i].resize( n_i );
         _new_adj_m[i].resize( n_i );
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             // calculate adj_m
             Prob prod( _adj_b_V_unnorm[i] );
             DAI_ASSERT( prod.size() == _fg->var(i).states() );
-            foreach( const Neighbor &J, _fg->nbV(i) )
+            bforeach( const Neighbor &J, _fg->nbV(i) )
                 if( J.node != I.node )
                     prod *= _bp_dual.msgM( i, J.iter );
             _adj_m[i][I.iter] = prod;
@@ -360,11 +360,11 @@ void BBP::RegenerateSeqMessageAdjoints() {
         }
     }
     for( size_t i = 0; i < _fg->nrVars(); i++ ) {
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             // calculate adj_n
             Prob prod( _fg->factor(I).p() );
             prod *= _adj_b_F_unnorm[I];
-            foreach( const Neighbor &j, _fg->nbF(I) )
+            bforeach( const Neighbor &j, _fg->nbF(I) )
                 if( i != j ) {
                     Prob n_jI( _bp_dual.msgN( j, j.dual) );
                     const _ind_t& ind = _index( j, j.dual );
@@ -403,7 +403,7 @@ void BBP::calcNewN( size_t i, size_t _I ) {
     Prob &new_adj_n_iI = _new_adj_n[i][_I];
     new_adj_n_iI = Prob( _fg->var(i).states(), 0.0 );
     size_t I = _fg->nbV(i)[_I];
-    foreach( const Neighbor &j, _fg->nbF(I) )
+    bforeach( const Neighbor &j, _fg->nbF(I) )
         if( j != i ) {
             const Prob &p = _Smsg[i][_I][j.iter];
             const Prob &_adj_m_unnorm_jI = _adj_m_unnorm[j][j.dual];
@@ -432,7 +432,7 @@ void BBP::calcNewM( size_t i, size_t _I ) {
     */
 
     _new_adj_m[i][_I] = Prob( _fg->var(i).states(), 0.0 );
-    foreach( const Neighbor &J, _fg->nbV(i) )
+    bforeach( const Neighbor &J, _fg->nbV(i) )
         if( J != I )
             _new_adj_m[i][_I] += _Rmsg[I][I.dual][J.iter] * _adj_n_unnorm[i][J.iter];
 }
@@ -462,12 +462,12 @@ void BBP::upMsgM( size_t i, size_t _I ) {
 
 void BBP::doParUpdate() {
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             calcNewM( i, I.iter );
             calcNewN( i, I.iter );
         }
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             upMsgM( i, I.iter );
             upMsgN( i, I.iter );
         }
@@ -534,7 +534,7 @@ void BBP::sendSeqMsgN( size_t i, size_t _I, const Prob &f ) {
         DAI_PV(_fg->factor(I).p());
     }
 #endif
-    foreach( const Neighbor &J, _fg->nbV(i) ) {
+    bforeach( const Neighbor &J, _fg->nbV(i) ) {
         if( J.node != I.node ) {
 #if 0
             if(f_unnorm.sumAbs() > pv_thresh) {
@@ -578,7 +578,7 @@ void BBP::sendSeqMsgM( size_t j, size_t _I ) {
 //     DAI_PV(I);
 //     DAI_PV(_I);
 //     DAI_PV(_fg->nbF(I).size());
-    foreach( const Neighbor &i, _fg->nbF(I) ) {
+    bforeach( const Neighbor &i, _fg->nbF(I) ) {
         if( i.node != j ) {
             const Prob &S = _Smsg[i][i.dual][_j];
             Prob msg( _fg->var(i).states(), 0.0 );
@@ -633,7 +633,7 @@ Real BBP::getUnMsgMag() {
     Real s = 0.0;
     size_t e = 0;
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             s += _adj_m_unnorm[i][I.iter].sumAbs();
             s += _adj_n_unnorm[i][I.iter].sumAbs();
             e++;
@@ -647,7 +647,7 @@ void BBP::getMsgMags( Real &s, Real &new_s ) {
     new_s = 0.0;
     size_t e = 0;
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             s += _adj_m[i][I.iter].sumAbs();
             s += _adj_n[i][I.iter].sumAbs();
             new_s += _new_adj_m[i][I.iter].sumAbs();
@@ -679,7 +679,7 @@ void BBP::getMsgMags( Real &s, Real &new_s ) {
 void BBP::getArgmaxMsgM( size_t &out_i, size_t &out__I, Real &mag ) {
     bool found = false;
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) ) {
+        bforeach( const Neighbor &I, _fg->nbV(i) ) {
             Real thisMag = _adj_m[i][I.iter].sumAbs();
             if( !found || mag < thisMag ) {
                 found = true;
@@ -703,7 +703,7 @@ Real BBP::getMaxMsgM() {
 Real BBP::getTotalMsgM() {
     Real mag = 0.0;
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) )
+        bforeach( const Neighbor &I, _fg->nbV(i) )
             mag += _adj_m[i][I.iter].sumAbs();
     return mag;
 }
@@ -712,7 +712,7 @@ Real BBP::getTotalMsgM() {
 Real BBP::getTotalNewMsgM() {
     Real mag = 0.0;
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) )
+        bforeach( const Neighbor &I, _fg->nbV(i) )
             mag += _new_adj_m[i][I.iter].sumAbs();
     return mag;
 }
@@ -721,7 +721,7 @@ Real BBP::getTotalNewMsgM() {
 Real BBP::getTotalMsgN() {
     Real mag = 0.0;
     for( size_t i = 0; i < _fg->nrVars(); i++ )
-        foreach( const Neighbor &I, _fg->nbV(i) )
+        bforeach( const Neighbor &I, _fg->nbV(i) )
             mag += _adj_n[i][I.iter].sumAbs();
     return mag;
 }
@@ -925,10 +925,10 @@ void BBP::run() {
                     break;
 
                 for( size_t i = 0; i < _fg->nrVars(); i++ )
-                    foreach( const Neighbor &I, _fg->nbV(i) )
+                    bforeach( const Neighbor &I, _fg->nbV(i) )
                         sendSeqMsgM( i, I.iter );
 /*                for( size_t i = 0; i < _fg->nrVars(); i++ )
-                    foreach( const Neighbor &I, _fg->nbV(i) )
+                    bforeach( const Neighbor &I, _fg->nbV(i) )
                         updateSeqMsgM( i, I.iter );*/
             } while( mag > tol && _iters < props.maxiter );
 
@@ -1047,7 +1047,7 @@ Real numericBBPTest( const InfAlg &bp, const std::vector<size_t> *state, const P
         // for each variable i
         for(size_t i=0; i<bp_dual.nrVars(); i++) {
             // for each factor I ~ i
-            foreach(size_t I, bp_dual.nbV(i)) {
+            bforeach(size_t I, bp_dual.nbV(i)) {
                 vector<Real> adj_n_est;
                 // for each value xi
                 for(size_t xi=0; xi<bp_dual.var(i).states(); xi++) {

diff --git a/src/bipgraph.cpp b/src/bipgraph.cpp
index 8c97622..70c3566 100644 (file)
--- a/src/bipgraph.cpp
+++ b/src/bipgraph.cpp
@@ -21,7 +21,7 @@ BipartiteGraph& BipartiteGraph::addEdge( size_t n1, size_t n2, bool check ) {
     bool exists = false;
     if( check ) {
         // Check whether the edge already exists
-        foreach( const Neighbor &nb2, nb1(n1) )
+        bforeach( const Neighbor &nb2, nb1(n1) )
             if( nb2 == n2 ) {
                 exists = true;
                 break;
@@ -118,7 +118,7 @@ void BipartiteGraph::eraseEdge( size_t n1, size_t n2 ) {
 
 SmallSet<size_t> BipartiteGraph::nb1Set( size_t n1 ) const {
     SmallSet<size_t> result;
-    foreach( const Neighbor &n2, nb1(n1) )
+    bforeach( const Neighbor &n2, nb1(n1) )
         result |= n2;
     return result;
 }
@@ -126,7 +126,7 @@ SmallSet<size_t> BipartiteGraph::nb1Set( size_t n1 ) const {
 
 SmallSet<size_t> BipartiteGraph::nb2Set( size_t n2 ) const {
     SmallSet<size_t> result;
-    foreach( const Neighbor &n1, nb2(n2) )
+    bforeach( const Neighbor &n1, nb2(n2) )
         result |= n1;
     return result;
 }
@@ -135,8 +135,8 @@ SmallSet<size_t> BipartiteGraph::nb2Set( size_t n2 ) const {
 SmallSet<size_t> BipartiteGraph::delta1( size_t n1, bool include ) const {
     // get all second-order neighbors
     SmallSet<size_t> result;
-    foreach( const Neighbor &n2, nb1(n1) )
-        foreach( const Neighbor &m1, nb2(n2) )
+    bforeach( const Neighbor &n2, nb1(n1) )
+        bforeach( const Neighbor &m1, nb2(n2) )
             if( include || (m1 != n1) )
                 result |= m1;
     return result;
@@ -146,8 +146,8 @@ SmallSet<size_t> BipartiteGraph::delta1( size_t n1, bool include ) const {
 SmallSet<size_t> BipartiteGraph::delta2( size_t n2, bool include ) const {
     // store all second-order neighbors
     SmallSet<size_t> result;
-    foreach( const Neighbor &n1, nb2(n2) )
-        foreach( const Neighbor &m2, nb1(n1) )
+    bforeach( const Neighbor &n1, nb2(n2) )
+        bforeach( const Neighbor &m2, nb1(n1) )
             if( include || (m2 != n2) )
                 result |= m2;
     return result;
@@ -169,7 +169,7 @@ bool BipartiteGraph::isConnected() const {
         vector<E> edges;
         edges.reserve( nrEdges() );
         for( size_t n1 = 0; n1 < nrNodes1(); n1++ )
-            foreach( const Neighbor &n2, nb1(n1) )
+            bforeach( const Neighbor &n2, nb1(n1) )
                 edges.push_back( E( n1, n2.node + N ) );
         boostGraph g( edges.begin(), edges.end(), nrNodes1() + nrNodes2() );
 
@@ -191,7 +191,7 @@ bool BipartiteGraph::isConnected() const {
             // For all nodes of type 2, check if they are connected with the (growing) component
             for( size_t n2 = 0; n2 < nrNodes2(); n2++ )
                 if( !incomponent2[n2] ) {
-                    foreach( const Neighbor &n1, nb2(n2) ) {
+                    bforeach( const Neighbor &n1, nb2(n2) ) {
                         if( incomponent1[n1] ) {
                             found_new_nodes = true;
                             incomponent2[n2] = true;
@@ -203,7 +203,7 @@ bool BipartiteGraph::isConnected() const {
             // For all nodes of type 1, check if they are connected with the (growing) component
             for( size_t n1 = 0; n1 < nrNodes1(); n1++ )
                 if( !incomponent1[n1] ) {
-                    foreach( const Neighbor &n2, nb1(n1) ) {
+                    bforeach( const Neighbor &n2, nb1(n1) ) {
                         if( incomponent2[n2] ) {
                             found_new_nodes = true;
                             incomponent1[n1] = true;
@@ -249,13 +249,13 @@ bool BipartiteGraph::isTree() const {
                 newLevel.ind1 = vector<size_t>( 1, n1 );
                 // add all neighbors of n1 to ind2
                 newLevel.ind2.reserve( nb1(n1).size() );
-                foreach( const Neighbor &n2, nb1(n1) )
+                bforeach( const Neighbor &n2, nb1(n1) )
                     newLevel.ind2.push_back( n2 );
             } else {
                 const levelType &prevLevel = levels.back();
                 // build newLevel.ind1
-                foreach( size_t n2, prevLevel.ind2 ) { // for all n2 in the previous level
-                    foreach( const Neighbor &n1, nb2(n2) ) { // for all neighbors n1 of n2
+                bforeach( size_t n2, prevLevel.ind2 ) { // for all n2 in the previous level
+                    bforeach( const Neighbor &n1, nb2(n2) ) { // for all neighbors n1 of n2
                         if( find( prevLevel.ind1.begin(), prevLevel.ind1.end(), n1 ) == prevLevel.ind1.end() ) { // n1 not in previous level
                             if( find( newLevel.ind1.begin(), newLevel.ind1.end(), n1 ) != newLevel.ind1.end() )
                                 foundCycle = true; // n1 already in new level: we found a cycle
@@ -269,8 +269,8 @@ bool BipartiteGraph::isTree() const {
                         break;
                 }
                 // build newLevel.ind2
-                foreach( size_t n1, newLevel.ind1 ) { // for all n1 in this level
-                    foreach( const Neighbor &n2, nb1(n1) ) { // for all neighbors n2 of n1
+                bforeach( size_t n1, newLevel.ind1 ) { // for all n1 in this level
+                    bforeach( const Neighbor &n2, nb1(n1) ) { // for all neighbors n2 of n1
                         if( find( prevLevel.ind2.begin(), prevLevel.ind2.end(), n2 ) == prevLevel.ind2.end() ) { // n2 not in previous level
                             if( find( newLevel.ind2.begin(), newLevel.ind2.end(), n2 ) != newLevel.ind2.end() )
                                 foundCycle = true; // n2 already in new level: we found a cycle
@@ -305,7 +305,7 @@ void BipartiteGraph::printDot( std::ostream& os ) const {
     for( size_t n2 = 0; n2 < nrNodes2(); n2++ )
         os << "\ty" << n2 << ";" << endl;
     for( size_t n1 = 0; n1 < nrNodes1(); n1++ )
-        foreach( const Neighbor &n2, nb1(n1) )
+        bforeach( const Neighbor &n2, nb1(n1) )
             os << "\tx" << n1 << " -- y" << n2 << ";" << endl;
     os << "}" << endl;
 }
@@ -316,7 +316,7 @@ void BipartiteGraph::checkConsistency() const {
     size_t N2 = nrNodes2();
     for( size_t n1 = 0; n1 < N1; n1++ ) {
         size_t iter = 0;
-        foreach( const Neighbor &n2, nb1(n1) ) {
+        bforeach( const Neighbor &n2, nb1(n1) ) {
             DAI_ASSERT( n2.iter == iter );
             DAI_ASSERT( n2.node < N2 );
             DAI_ASSERT( n2.dual < nb2(n2).size() );
@@ -326,7 +326,7 @@ void BipartiteGraph::checkConsistency() const {
     }
     for( size_t n2 = 0; n2 < N2; n2++ ) {
         size_t iter = 0;
-        foreach( const Neighbor &n1, nb2(n2) ) {
+        bforeach( const Neighbor &n1, nb2(n2) ) {
             DAI_ASSERT( n1.iter == iter );
             DAI_ASSERT( n1.node < N1 );
             DAI_ASSERT( n1.dual < nb1(n1).size() );

diff --git a/src/bp.cpp b/src/bp.cpp
index 4c3b951..1fd9857 100644 (file)
--- a/src/bp.cpp
+++ b/src/bp.cpp
@@ -101,7 +101,7 @@ void BP::construct() {
             _edge2lut.push_back( vector<LutType::iterator>() );
             _edge2lut[i].reserve( nbV(i).size() );
         }
-        foreach( const Neighbor &I, nbV(i) ) {
+        bforeach( const Neighbor &I, nbV(i) ) {
             EdgeProp newEP;
             newEP.message = Prob( var(i).states() );
             newEP.newMessage = Prob( var(i).states() );
@@ -133,7 +133,7 @@ void BP::construct() {
     _updateSeq.clear();
     _updateSeq.reserve( nrEdges() );
     for( size_t I = 0; I < nrFactors(); I++ )
-        foreach( const Neighbor &i, nbF(I) )
+        bforeach( const Neighbor &i, nbF(I) )
             _updateSeq.push_back( Edge( i, i.dual ) );
 }
 
@@ -141,7 +141,7 @@ void BP::construct() {
 void BP::init() {
     Real c = props.logdomain ? 0.0 : 1.0;
     for( size_t i = 0; i < nrVars(); ++i ) {
-        foreach( const Neighbor &I, nbV(i) ) {
+        bforeach( const Neighbor &I, nbV(i) ) {
             message( i, I.iter ).fill( c );
             newMessage( i, I.iter ).fill( c );
             if( props.updates == Properties::UpdateType::SEQMAX )
@@ -168,11 +168,11 @@ Prob BP::calcIncomingMessageProduct( size_t I, bool without_i, size_t i ) const
         prod.takeLog();
 
     // Calculate product of incoming messages and factor I
-    foreach( const Neighbor &j, nbF(I) )
+    bforeach( const Neighbor &j, nbF(I) )
         if( !(without_i && (j == 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) )
+            bforeach( const Neighbor &J, nbV(j) )
                 if( J != I ) { // for all J in nb(j) \ I
                     if( props.logdomain )
                         prod_j += message( j, J.iter );
@@ -274,7 +274,7 @@ Real BP::run() {
             if( _iters == 0 ) {
                 // do the first pass
                 for( size_t i = 0; i < nrVars(); ++i )
-                  foreach( const Neighbor &I, nbV(i) )
+                  bforeach( const Neighbor &I, nbV(i) )
                       calcNewMessage( i, I.iter );
             }
             // Maximum-Residual BP [\ref EMK06]
@@ -286,9 +286,9 @@ Real BP::run() {
 
                 // I->i has been updated, which means that residuals for all
                 // J->j with J in nb[i]\I and j in nb[J]\i have to be updated
-                foreach( const Neighbor &J, nbV(i) ) {
+                bforeach( const Neighbor &J, nbV(i) ) {
                     if( J.iter != _I ) {
-                        foreach( const Neighbor &j, nbF(J) ) {
+                        bforeach( const Neighbor &j, nbF(J) ) {
                             size_t _J = j.dual;
                             if( j != i )
                                 calcNewMessage( j, _J );
@@ -299,18 +299,18 @@ Real BP::run() {
         } else if( props.updates == Properties::UpdateType::PARALL ) {
             // Parallel updates
             for( size_t i = 0; i < nrVars(); ++i )
-                foreach( const Neighbor &I, nbV(i) )
+                bforeach( const Neighbor &I, nbV(i) )
                     calcNewMessage( i, I.iter );
 
             for( size_t i = 0; i < nrVars(); ++i )
-                foreach( const Neighbor &I, nbV(i) )
+                bforeach( const Neighbor &I, nbV(i) )
                     updateMessage( i, I.iter );
         } else {
             // Sequential updates
             if( props.updates == Properties::UpdateType::SEQRND )
                 random_shuffle( _updateSeq.begin(), _updateSeq.end(), rnd );
 
-            foreach( const Edge &e, _updateSeq ) {
+            bforeach( const Edge &e, _updateSeq ) {
                 calcNewMessage( e.first, e.second );
                 updateMessage( e.first, e.second );
             }
@@ -354,7 +354,7 @@ Real BP::run() {
 
 void BP::calcBeliefV( size_t i, Prob &p ) const {
     p = Prob( var(i).states(), props.logdomain ? 0.0 : 1.0 );
-    foreach( const Neighbor &I, nbV(i) )
+    bforeach( const Neighbor &I, nbV(i) )
         if( props.logdomain )
             p += newMessage( i, I.iter );
         else
@@ -430,7 +430,7 @@ Real BP::logZ() const {
 void BP::init( const VarSet &ns ) {
     for( VarSet::const_iterator n = ns.begin(); n != ns.end(); ++n ) {
         size_t ni = findVar( *n );
-        foreach( const Neighbor &I, nbV( ni ) ) {
+        bforeach( const Neighbor &I, nbV( ni ) ) {
             Real val = props.logdomain ? 0.0 : 1.0;
             message( ni, I.iter ).fill( val );
             newMessage( ni, I.iter ).fill( val );

diff --git a/src/bp_dual.cpp b/src/bp_dual.cpp
index 4638f75..1f1faf0 100644 (file)
--- a/src/bp_dual.cpp
+++ b/src/bp_dual.cpp
@@ -65,16 +65,16 @@ void BP_dual::calcMessages() {
     // calculate 'n' messages from "factor marginal / factor"
     for( size_t I = 0; I < fg().nrFactors(); I++ ) {
         Factor f = _ia->beliefF(I) / fg().factor(I);
-        foreach( const Neighbor &i, fg().nbF(I) )
+        bforeach( const Neighbor &i, fg().nbF(I) )
             msgN(i, i.dual) = f.marginal( fg().var(i) ).p();
     }
     // calculate 'm' messages and normalizers from 'n' messages
     for( size_t i = 0; i < fg().nrVars(); i++ )
-        foreach( const Neighbor &I, fg().nbV(i) )
+        bforeach( const Neighbor &I, fg().nbV(i) )
             calcNewM( i, I.iter );
     // recalculate 'n' messages and normalizers from 'm' messages
     for( size_t i = 0; i < fg().nrVars(); i++ )
-        foreach( const Neighbor &I, fg().nbV(i) )
+        bforeach( const Neighbor &I, fg().nbV(i) )
             calcNewN(i, I.iter);
 }
 
@@ -83,7 +83,7 @@ void BP_dual::calcNewM( size_t i, size_t _I ) {
     // calculate updated message I->i
     const Neighbor &I = fg().nbV(i)[_I];
     Prob prod( fg().factor(I).p() );
-    foreach( const Neighbor &j, fg().nbF(I) )
+    bforeach( const Neighbor &j, fg().nbF(I) )
         if( j != i ) { // for all j in I \ i
             Prob &n = msgN(j,j.dual);
             IndexFor ind( fg().var(j), fg().factor(I).vars() );
@@ -106,7 +106,7 @@ void BP_dual::calcNewN( size_t i, size_t _I ) {
     // calculate updated message i->I
     const Neighbor &I = fg().nbV(i)[_I];
     Prob prod( fg().var(i).states(), 1.0 );
-    foreach( const Neighbor &J, fg().nbV(i) )
+    bforeach( const Neighbor &J, fg().nbV(i) )
         if( J.node != I.node ) // for all J in i \ I
             prod *= msgM(i,J.iter);
     _msgs.Zn[i][_I] = prod.normalize();
@@ -124,7 +124,7 @@ void BP_dual::calcBeliefs() {
 
 void BP_dual::calcBeliefV( size_t i ) {
     Prob prod( fg().var(i).states(), 1.0 );
-    foreach( const Neighbor &I, fg().nbV(i) )
+    bforeach( const Neighbor &I, fg().nbV(i) )
         prod *= msgM(i,I.iter);
     _beliefs.Zb1[i] = prod.normalize();
     _beliefs.b1[i] = prod;
@@ -133,7 +133,7 @@ void BP_dual::calcBeliefV( size_t i ) {
 
 void BP_dual::calcBeliefF( size_t I ) {
     Prob prod( fg().factor(I).p() );
-    foreach( const Neighbor &j, fg().nbF(I) ) {
+    bforeach( const Neighbor &j, fg().nbF(I) ) {
         IndexFor ind( fg().var(j), fg().factor(I).vars() );
         Prob n( msgN(j,j.dual) );
         for( size_t x = 0; ind.valid(); x++, ++ind )

diff --git a/src/cbp.cpp b/src/cbp.cpp
index 8f441e6..c649fe4 100644 (file)
--- a/src/cbp.cpp
+++ b/src/cbp.cpp
@@ -188,10 +188,10 @@ void CBP::runRecurse( InfAlg *bp, Real orig_logZ, vector<size_t> clamped_vars_li
         *_clamp_ofstream << choose_count << "\t" << clamped_vars_list.size() << "\t" << i << "\t" << xis[0] << endl;
 
     if( clampingVar )
-        foreach( size_t xi, xis )
+        bforeach( size_t xi, xis )
             DAI_ASSERT(/*0<=xi &&*/ xi < var(i).states() );
     else
-        foreach( size_t xI, xis )
+        bforeach( size_t xI, xis )
             DAI_ASSERT(/*0<=xI &&*/ xI < factor(i).nrStates() );
     // - otherwise, clamp and recurse, saving margin estimates for each
     // clamp setting. afterwards, combine estimates.

diff --git a/src/clustergraph.cpp b/src/clustergraph.cpp
index f5458dd..f899c4d 100644 (file)
--- a/src/clustergraph.cpp
+++ b/src/clustergraph.cpp
@@ -22,7 +22,7 @@ using namespace std;
 ClusterGraph::ClusterGraph( const std::vector<VarSet> & cls ) : _G(), _vars(), _clusters() {
     // construct vars, clusters and edge list
     vector<Edge> edges;
-    foreach( const VarSet &cl, cls ) {
+    bforeach( const VarSet &cl, cls ) {
         if( find( clusters().begin(), clusters().end(), cl ) == clusters().end() ) {
             // add cluster
             size_t n2 = nrClusters();
@@ -53,7 +53,7 @@ ClusterGraph::ClusterGraph( const FactorGraph& fg, bool onlyMaximal ) : _G( fg.n
             if( fg.isMaximal( I ) ) {
                 _clusters.push_back( fg.factor(I).vars() );
                 size_t clind = _G.addNode2();
-                foreach( const Neighbor &i, fg.nbF(I) )
+                bforeach( const Neighbor &i, fg.nbF(I) )
                     _G.addEdge( i, clind, true );
             }
     } else {

diff --git a/src/dag.cpp b/src/dag.cpp
index 04506e8..977652f 100644 (file)
--- a/src/dag.cpp
+++ b/src/dag.cpp
@@ -21,7 +21,7 @@ DAG& DAG::addEdge( size_t n1, size_t n2, bool check ) {
     bool allowed = true;
     if( check ) {
         // Check whether the edge already exists
-        foreach( const Neighbor& n, ch(n1) )
+        bforeach( const Neighbor& n, ch(n1) )
             if( n == n2 ) {
                 allowed = false;
                 break;
@@ -115,7 +115,7 @@ void DAG::eraseEdge( size_t n1, size_t n2 ) {
 
 SmallSet<size_t> DAG::paSet( size_t n ) const {
     SmallSet<size_t> result;
-    foreach( const Neighbor &p, pa(n) )
+    bforeach( const Neighbor &p, pa(n) )
         result |= p;
     return result;
 }
@@ -123,7 +123,7 @@ SmallSet<size_t> DAG::paSet( size_t n ) const {
 
 SmallSet<size_t> DAG::chSet( size_t n ) const {
     SmallSet<size_t> result;
-    foreach( const Neighbor &c, ch(n) )
+    bforeach( const Neighbor &c, ch(n) )
         result |= c;
     return result;
 }
@@ -141,7 +141,7 @@ std::set<size_t> DAG::ancestors( size_t n ) const {
         if( node != n )
             anc.insert( node );
         // Add all parents of node to curParents
-        foreach( const Neighbor& p, pa(node) )
+        bforeach( const Neighbor& p, pa(node) )
             curParents.insert( p.node );
         // Erase node from curParents
         curParents.erase( node );
@@ -163,7 +163,7 @@ std::set<size_t> DAG::descendants( size_t n ) const {
         if( node != n )
             desc.insert( node );
         // Add all children of node to curChildren
-        foreach( const Neighbor& c, ch(node) )
+        bforeach( const Neighbor& c, ch(node) )
             curChildren.insert( c.node );
         // Erase node from curChildren
         curChildren.erase( node );
@@ -183,7 +183,7 @@ bool DAG::existsDirectedPath( size_t n1, size_t n2 ) const {
         if( node == n2 )
             return true;
         // Add all children of node to curChildren
-        foreach( const Neighbor& c, ch(node) )
+        bforeach( const Neighbor& c, ch(node) )
             curChildren.insert( c.node );
         // Erase node from curChildren
         curChildren.erase( node );
@@ -206,7 +206,7 @@ bool DAG::isConnected() const {
             // For all nodes, check if they are connected with the (growing) component
             for( size_t n1 = 0; n1 < nrNodes(); n1++ ) {
                 if( !incomponent[n1] ) {
-                    foreach( const Neighbor &n2, pa(n1) )
+                    bforeach( const Neighbor &n2, pa(n1) )
                         if( incomponent[n2] ) {
                             found_new_nodes = true;
                             incomponent[n1] = true;
@@ -214,7 +214,7 @@ bool DAG::isConnected() const {
                         }
                 }
                 if( !incomponent[n1] ) {
-                    foreach( const Neighbor &n2, ch(n1) )
+                    bforeach( const Neighbor &n2, ch(n1) )
                         if( incomponent[n2] ) {
                             found_new_nodes = true;
                             incomponent[n1] = true;
@@ -241,7 +241,7 @@ void DAG::printDot( std::ostream& os ) const {
     for( size_t n = 0; n < nrNodes(); n++ )
         os << "\tx" << n << ";" << endl;
     for( size_t n1 = 0; n1 < nrNodes(); n1++ )
-        foreach( const Neighbor &n2, ch(n1) )
+        bforeach( const Neighbor &n2, ch(n1) )
             os << "\tx" << n1 << " -> x" << n2 << ";" << endl;
     os << "}" << endl;
 }
@@ -251,7 +251,7 @@ void DAG::checkConsistency() const {
     size_t N = nrNodes();
     for( size_t n1 = 0; n1 < N; n1++ ) {
         size_t iter = 0;
-        foreach( const Neighbor &n2, pa(n1) ) {
+        bforeach( const Neighbor &n2, pa(n1) ) {
             DAI_ASSERT( n2.iter == iter );
             DAI_ASSERT( n2.node < N );
             DAI_ASSERT( n2.dual < ch(n2).size() );
@@ -259,7 +259,7 @@ void DAG::checkConsistency() const {
             iter++;
         }
         iter = 0;
-        foreach( const Neighbor &n2, ch(n1) ) {
+        bforeach( const Neighbor &n2, ch(n1) ) {
             DAI_ASSERT( n2.iter == iter );
             DAI_ASSERT( n2.node < N );
             DAI_ASSERT( n2.dual < pa(n2).size() );
@@ -269,7 +269,7 @@ void DAG::checkConsistency() const {
     }
     // Check acyclicity
     for( size_t n1 = 0; n1 < N; n1++ )
-        foreach( const Neighbor& n2, ch(n1) )
+        bforeach( const Neighbor& n2, ch(n1) )
             DAI_ASSERT( !existsDirectedPath( n2, n1 ) );
 }
 

diff --git a/src/daialg.cpp b/src/daialg.cpp
index 99533e7..7ddb495 100644 (file)
--- a/src/daialg.cpp
+++ b/src/daialg.cpp
@@ -233,7 +233,7 @@ std::vector<size_t> findMaximum( const InfAlg& obj ) {
             // First, calculate whether this state is consistent with variables that
             // have been assigned already
             bool allowedState = true;
-            foreach( const Neighbor &j, obj.fg().nbF(I) )
+            bforeach( const Neighbor &j, obj.fg().nbF(I) )
                 if( visitedVars[j.node] && maximum[j.node] != s(obj.fg().var(j.node)) ) {
                     allowedState = false;
                     break;
@@ -253,7 +253,7 @@ std::vector<size_t> findMaximum( const InfAlg& obj ) {
         DAI_ASSERT( obj.fg().factor(I).p()[maxState] != 0.0 );
 
         // Decode the argmax
-        foreach( const Neighbor &j, obj.fg().nbF(I) ) {
+        bforeach( const Neighbor &j, obj.fg().nbF(I) ) {
             if( visitedVars[j.node] ) {
                 // We have already visited j earlier - hopefully our state is consistent
                 if( maximum[j.node] != maxState( obj.fg().var(j.node) ) )
@@ -262,7 +262,7 @@ std::vector<size_t> findMaximum( const InfAlg& obj ) {
                 // We found a consistent state for variable j
                 visitedVars[j.node] = true;
                 maximum[j.node] = maxState( obj.fg().var(j.node) );
-                foreach( const Neighbor &J, obj.fg().nbV(j) )
+                bforeach( const Neighbor &J, obj.fg().nbV(j) )
                     if( !visitedFactors[J] )
                         scheduledFactors.push(J);
             }

diff --git a/src/decmap.cpp b/src/decmap.cpp
index 1d40edb..78ec919 100644 (file)
--- a/src/decmap.cpp
+++ b/src/decmap.cpp
@@ -143,7 +143,7 @@ Real DecMAP::run() {
         vector<size_t> Istate(1,0);
         Istate[0] = clamped->beliefF(bestI).p().argmax().first;
         map<Var, size_t> Istatemap = calcState( factor(bestI).vars(), Istate[0] );
-        foreach( size_t i, bipGraph().nb2Set(bestI) & freeVars ) {
+        bforeach( size_t i, bipGraph().nb2Set(bestI) & freeVars ) {
             varsToInit |= var(i);
             varsToClamp |= i;
             _state[i] = Istatemap[var(i)];
@@ -151,7 +151,7 @@ Real DecMAP::run() {
         }
 
         // clamp all variables scheduled for clamping
-        foreach( size_t i, varsToClamp )
+        bforeach( size_t i, varsToClamp )
             clamped->clamp( i, _state[i], false );
 
         // initialize clamped for the next run

diff --git a/src/factorgraph.cpp b/src/factorgraph.cpp
index 2094931..045a46a 100644 (file)
--- a/src/factorgraph.cpp
+++ b/src/factorgraph.cpp
@@ -203,8 +203,8 @@ std::istream& operator>> ( std::istream& is, FactorGraph &fg ) {
 VarSet FactorGraph::Delta( size_t i ) const {
     // calculate Markov Blanket
     VarSet Del;
-    foreach( const Neighbor &I, nbV(i) ) // for all neighboring factors I of i
-        foreach( const Neighbor &j, nbF(I) ) // for all neighboring variables j of I
+    bforeach( const Neighbor &I, nbV(i) ) // for all neighboring factors I of i
+        bforeach( const Neighbor &j, nbF(I) ) // for all neighboring variables j of I
             Del |= var(j);
 
     return Del;
@@ -222,7 +222,7 @@ VarSet FactorGraph::Delta( const VarSet &ns ) const {
 void FactorGraph::makeCavity( size_t i, bool backup ) {
     // fills all Factors that include var(i) with ones
     map<size_t,Factor> newFacs;
-    foreach( const Neighbor &I, nbV(i) ) // for all neighboring factors I of i
+    bforeach( const Neighbor &I, nbV(i) ) // for all neighboring factors I of i
         newFacs[I] = Factor( factor(I).vars(), (Real)1 );
     setFactors( newFacs, backup );
 }
@@ -260,7 +260,7 @@ void FactorGraph::printDot( std::ostream &os ) const {
     for( size_t I = 0; I < nrFactors(); I++ )
         os << "\tf" << I << ";" << endl;
     for( size_t i = 0; i < nrVars(); i++ )
-        foreach( const Neighbor &I, nbV(i) )  // for all neighboring factors I of i
+        bforeach( const Neighbor &I, nbV(i) )  // for all neighboring factors I of i
             os << "\tv" << var(i).label() << " -- f" << I << ";" << endl;
     os << "}" << endl;
 }
@@ -269,8 +269,8 @@ void FactorGraph::printDot( std::ostream &os ) const {
 GraphAL FactorGraph::MarkovGraph() const {
     GraphAL G( nrVars() );
     for( size_t i = 0; i < nrVars(); i++ )
-        foreach( const Neighbor &I, nbV(i) )
-            foreach( const Neighbor &j, nbF(I) )
+        bforeach( const Neighbor &I, nbV(i) )
+            bforeach( const Neighbor &j, nbF(I) )
                 if( i < j )
                     G.addEdge( i, j, true );
     return G;
@@ -288,8 +288,8 @@ bool FactorGraph::isMaximal( size_t I ) const {
                     return false;
         return true;
     } else {
-        foreach( const Neighbor& i, nbF(I) ) {
-            foreach( const Neighbor& J, nbV(i) ) {
+        bforeach( const Neighbor& i, nbF(I) ) {
+            bforeach( const Neighbor& J, nbV(i) ) {
                 if( J != I )
                     if( (factor(J).vars() >> I_vars) && (factor(J).vars().size() != I_size) )
                         return false;
@@ -311,8 +311,8 @@ size_t FactorGraph::maximalFactor( size_t I ) const {
                     return maximalFactor( J );
         return I;
     } else {
-        foreach( const Neighbor& i, nbF(I) ) {
-            foreach( const Neighbor& J, nbV(i) ) {
+        bforeach( const Neighbor& i, nbF(I) ) {
+            bforeach( const Neighbor& J, nbV(i) ) {
                 if( J != I )
                     if( (factor(J).vars() >> I_vars) && (factor(J).vars().size() != I_size) )
                         return maximalFactor( J );
@@ -359,7 +359,7 @@ void FactorGraph::clamp( size_t i, size_t x, bool backup ) {
     mask.set( x, (Real)1 );
 
     map<size_t, Factor> newFacs;
-    foreach( const Neighbor &I, nbV(i) )
+    bforeach( const Neighbor &I, nbV(i) )
         newFacs[I] = factor(I) * mask;
     setFactors( newFacs, backup );
 
@@ -371,13 +371,13 @@ void FactorGraph::clampVar( size_t i, const vector<size_t> &is, bool backup ) {
     Var n = var(i);
     Factor mask_n( n, (Real)0 );
 
-    foreach( size_t i, is ) {
+    bforeach( size_t i, is ) {
         DAI_ASSERT( i <= n.states() );
         mask_n.set( i, (Real)1 );
     }
 
     map<size_t, Factor> newFacs;
-    foreach( const Neighbor &I, nbV(i) )
+    bforeach( const Neighbor &I, nbV(i) )
         newFacs[I] = factor(I) * mask_n;
     setFactors( newFacs, backup );
 }
@@ -387,7 +387,7 @@ void FactorGraph::clampFactor( size_t I, const vector<size_t> &is, bool backup )
     size_t st = factor(I).nrStates();
     Factor newF( factor(I).vars(), (Real)0 );
 
-    foreach( size_t i, is ) {
+    bforeach( size_t i, is ) {
         DAI_ASSERT( i <= st );
         newF.set( i, factor(I)[i] );
     }

diff --git a/src/fbp.cpp b/src/fbp.cpp
index ca54eb6..d87f8aa 100644 (file)
--- a/src/fbp.cpp
+++ b/src/fbp.cpp
@@ -27,7 +27,7 @@ Real FBP::logZ() const {
     }
     for( size_t i = 0; i < nrVars(); ++i ) {
         Real c_i = 0.0;
-        foreach( const Neighbor &I, nbV(i) )
+        bforeach( const Neighbor &I, nbV(i) )
             c_i += Weight(I);
         if( c_i != 1.0 )
             sum += (1.0 - c_i) * beliefV(i).entropy();  // FBP
@@ -50,12 +50,12 @@ Prob FBP::calcIncomingMessageProduct( size_t I, bool without_i, size_t i ) const
         prod ^= (1.0 / c_I); // FBP
 
     // Calculate product of incoming messages and factor I
-    foreach( const Neighbor &j, nbF(I) )
+    bforeach( const Neighbor &j, nbF(I) )
         if( !(without_i && (j == i)) ) {
             // prod_j will be the product of messages coming into j
             // FBP: corresponds to messages n_jI
             Prob prod_j( var(j).states(), props.logdomain ? 0.0 : 1.0 );
-            foreach( const Neighbor &J, nbV(j) )
+            bforeach( const Neighbor &J, nbV(j) )
                 if( J != I ) { // for all J in nb(j) \ I
                     if( props.logdomain )
                         prod_j += message( j, J.iter );

diff --git a/src/gibbs.cpp b/src/gibbs.cpp
index 23fe11a..3dca7e7 100644 (file)
--- a/src/gibbs.cpp
+++ b/src/gibbs.cpp
@@ -141,7 +141,7 @@ Prob Gibbs::getVarDist( size_t i ) {
     Prob i_given_MB( i_states, 1.0 );
 
     // use Markov blanket of var(i) to calculate distribution
-    foreach( const Neighbor &I, nbV(i) ) {
+    bforeach( const Neighbor &I, nbV(i) ) {
         const Factor &f_I = factor(I);
         size_t I_skip = getFactorEntryDiff( I, i );
         size_t I_entry = getFactorEntry(I) - (_state[i] * I_skip);

diff --git a/src/graph.cpp b/src/graph.cpp
index 113cb35..ca7fd3a 100644 (file)
--- a/src/graph.cpp
+++ b/src/graph.cpp
@@ -21,7 +21,7 @@ GraphAL& GraphAL::addEdge( size_t n1, size_t n2, bool check ) {
     bool exists = false;
     if( check ) {
         // Check whether the edge already exists
-        foreach( const Neighbor &n, nb(n1) )
+        bforeach( const Neighbor &n, nb(n1) )
             if( n == n2 ) {
                 exists = true;
                 break;
@@ -95,7 +95,7 @@ void GraphAL::eraseEdge( size_t n1, size_t n2 ) {
 
 SmallSet<size_t> GraphAL::nbSet( size_t n ) const {
     SmallSet<size_t> result;
-    foreach( const Neighbor &m, nb(n) )
+    bforeach( const Neighbor &m, nb(n) )
         result |= m;
     return result;
 }
@@ -115,7 +115,7 @@ bool GraphAL::isConnected() const {
             // For all nodes, check if they are connected with the (growing) component
             for( size_t n1 = 0; n1 < nrNodes(); n1++ )
                 if( !incomponent[n1] ) {
-                    foreach( const Neighbor &n2, nb(n1) ) {
+                    bforeach( const Neighbor &n2, nb(n1) ) {
                         if( incomponent[n2] ) {
                             found_new_nodes = true;
                             incomponent[n1] = true;
@@ -142,7 +142,7 @@ bool GraphAL::isConnected() const {
         vector<E> edges;
         edges.reserve( nrEdges() );
         for( size_t n1 = 0; n1 < nrNodes(); n1++ )
-            foreach( const Neighbor &n2, nb(n1) )
+            bforeach( const Neighbor &n2, nb(n1) )
                 if( n1 < n2 )
                     edges.push_back( E( n1, n2 ) );
         boostGraphAL g( edges.begin(), edges.end(), nrNodes() );
@@ -175,7 +175,7 @@ bool GraphAL::isTree() const {
             // (without backtracking), aborting if a cycle is detected
             for( size_t e = 0; e < prevLevel.size(); e++ ) {
                 size_t n2 = prevLevel[e].first; // for all nodes n2 in the previous level
-                foreach( const Neighbor &n1, nb(n2) ) { // for all neighbors n1 of n2
+                bforeach( const Neighbor &n1, nb(n2) ) { // for all neighbors n1 of n2
                     if( n1 != prevLevel[e].second ) { // no backtracking allowed
                         for( size_t l = 0; l < levels.size() && !foundCycle; l++ )
                             for( size_t f = 0; f < levels[l].size() && !foundCycle; f++ )
@@ -208,7 +208,7 @@ void GraphAL::printDot( std::ostream& os ) const {
     for( size_t n = 0; n < nrNodes(); n++ )
         os << "\tx" << n << ";" << endl;
     for( size_t n1 = 0; n1 < nrNodes(); n1++ )
-        foreach( const Neighbor &n2, nb(n1) )
+        bforeach( const Neighbor &n2, nb(n1) )
             if( n1 < n2 )
                 os << "\tx" << n1 << " -- x" << n2 << ";" << endl;
     os << "}" << endl;
@@ -219,7 +219,7 @@ void GraphAL::checkConsistency() const {
     size_t N = nrNodes();
     for( size_t n1 = 0; n1 < N; n1++ ) {
         size_t iter = 0;
-        foreach( const Neighbor &n2, nb(n1) ) {
+        bforeach( const Neighbor &n2, nb(n1) ) {
             DAI_ASSERT( n2.iter == iter );
             DAI_ASSERT( n2.node < N );
             DAI_ASSERT( n2.dual < nb(n2).size() );

diff --git a/src/hak.cpp b/src/hak.cpp
index ef1b613..4b79981 100644 (file)
--- a/src/hak.cpp
+++ b/src/hak.cpp
@@ -133,7 +133,7 @@ void HAK::construct() {
         _muba.push_back( vector<Factor>() );
         _muab[alpha].reserve( nbOR(alpha).size() );
         _muba[alpha].reserve( nbOR(alpha).size() );
-        foreach( const Neighbor &beta, nbOR(alpha) ) {
+        bforeach( const Neighbor &beta, nbOR(alpha) ) {
             _muab[alpha].push_back( Factor( IR(beta) ) );
             _muba[alpha].push_back( Factor( IR(beta) ) );
         }
@@ -280,7 +280,7 @@ void HAK::init( const VarSet &ns ) {
                 _Qb[beta].fill( 1.0 );
             else
                 _Qb[beta].randomize();
-            foreach( const Neighbor &alpha, nbIR(beta) ) {
+            bforeach( const Neighbor &alpha, nbIR(beta) ) {
                 size_t _beta = alpha.dual;
                 if( props.init == Properties::InitType::UNIFORM ) {
                     muab( alpha, _beta ).fill( 1.0 );
@@ -311,7 +311,7 @@ void HAK::init() {
             _Qb[beta].randomize();
 
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        foreach( const Neighbor &beta, nbOR(alpha) ) {
+        bforeach( const Neighbor &beta, nbOR(alpha) ) {
             size_t _beta = beta.iter;
             if( props.init == Properties::InitType::UNIFORM ) {
                 muab( alpha, _beta ).setUniform();
@@ -351,7 +351,7 @@ Real HAK::doGBP() {
     Real maxDiff = INFINITY;
     for( _iters = 0; _iters < props.maxiter && maxDiff > props.tol; _iters++ ) {
         for( size_t beta = 0; beta < nrIRs(); beta++ ) {
-            foreach( const Neighbor &alpha, nbIR(beta) ) {
+            bforeach( const Neighbor &alpha, nbIR(beta) ) {
                 size_t _beta = alpha.dual;
                 muab( alpha, _beta ) = _Qa[alpha].marginal(IR(beta)) / muba(alpha,_beta);
                 /* TODO: INVESTIGATE THIS PROBLEM
@@ -368,7 +368,7 @@ Real HAK::doGBP() {
             }
 
             Factor Qb_new;
-            foreach( const Neighbor &alpha, nbIR(beta) ) {
+            bforeach( const Neighbor &alpha, nbIR(beta) ) {
                 size_t _beta = alpha.dual;
                 Qb_new *= muab(alpha,_beta) ^ (1 / (nbIR(beta).size() + IR(beta).c()));
             }
@@ -389,7 +389,7 @@ Real HAK::doGBP() {
             else
                 _Qb[beta] = (Qb_new^(1.0 - props.damping)) * (_Qb[beta]^props.damping);
 
-            foreach( const Neighbor &alpha, nbIR(beta) ) {
+            bforeach( const Neighbor &alpha, nbIR(beta) ) {
                 size_t _beta = alpha.dual;
                 muba(alpha,_beta) = _Qb[beta] / muab(alpha,_beta);
 
@@ -400,7 +400,7 @@ Real HAK::doGBP() {
                  */
 
                 Factor Qa_new = OR(alpha);
-                foreach( const Neighbor &gamma, nbOR(alpha) )
+                bforeach( const Neighbor &gamma, nbOR(alpha) )
                     Qa_new *= muba(alpha,gamma.iter);
                 Qa_new ^= (1.0 / OR(alpha).c());
                 Qa_new.normalize();
@@ -502,7 +502,7 @@ Real HAK::doDoubleLoop() {
         // Calculate new outer regions
         for( size_t alpha = 0; alpha < nrORs(); alpha++ ) {
             OR(alpha) = org_ORs[alpha];
-            foreach( const Neighbor &beta, nbOR(alpha) )
+            bforeach( const Neighbor &beta, nbOR(alpha) )
                 OR(alpha) *= _Qb[beta] ^ ((IR(beta).c() - org_IR_cs[beta]) / nbIR(beta).size());
         }
 

diff --git a/src/jtree.cpp b/src/jtree.cpp
index 4fb5e42..18a0982 100644 (file)
--- a/src/jtree.cpp
+++ b/src/jtree.cpp
@@ -97,7 +97,7 @@ JTree::JTree( const FactorGraph &fg, const PropertySet &opts, bool automatic ) :
 
         // Estimate memory needed (rough upper bound)
         BigInt memneeded = 0;
-        foreach( const VarSet& cl, ElimVec )
+        bforeach( const VarSet& cl, ElimVec )
             memneeded += cl.nrStates();
         memneeded *= sizeof(Real) * fudge;
         if( props.verbose >= 1 ) {
@@ -211,7 +211,7 @@ void JTree::GenerateJT( const FactorGraph &fg, const std::vector<VarSet> &cl ) {
     for( size_t alpha = 0; alpha < nrORs(); alpha++ ) {
         _mes.push_back( vector<Factor>() );
         _mes[alpha].reserve( nbOR(alpha).size() );
-        foreach( const Neighbor &beta, nbOR(alpha) )
+        bforeach( const Neighbor &beta, nbOR(alpha) )
             _mes[alpha].push_back( Factor( IR(beta), 1.0 ) );
     }
 
@@ -317,7 +317,7 @@ void JTree::runShaferShenoy() {
         size_t _e = nbIR(e)[0].dual;
 
         Factor msg = OR(i);
-        foreach( const Neighbor &k, nbOR(i) )
+        bforeach( const Neighbor &k, nbOR(i) )
             if( k != e )
                 msg *= message( i, k.iter );
         if( props.inference == Properties::InfType::SUMPROD )
@@ -334,7 +334,7 @@ void JTree::runShaferShenoy() {
         size_t _e = nbIR(e)[1].dual;
 
         Factor msg = OR(i);
-        foreach( const Neighbor &k, nbOR(i) )
+        bforeach( const Neighbor &k, nbOR(i) )
             if( k != e )
                 msg *= message( i, k.iter );
         if( props.inference == Properties::InfType::SUMPROD )
@@ -346,7 +346,7 @@ void JTree::runShaferShenoy() {
     // Calculate beliefs
     for( size_t alpha = 0; alpha < nrORs(); alpha++ ) {
         Factor piet = OR(alpha);
-        foreach( const Neighbor &k, nbOR(alpha) )
+        bforeach( const Neighbor &k, nbOR(alpha) )
             piet *= message( alpha, k.iter );
         if( nrIRs() == 0 ) {
             _logZ += log( piet.normalize() );
@@ -603,7 +603,7 @@ std::vector<size_t> JTree::findMaximum() const {
             // First, calculate whether this state is consistent with variables that
             // have been assigned already
             bool allowedState = true;
-            foreach( const Var& j, OR(alpha).vars() ) {
+            bforeach( const Var& j, OR(alpha).vars() ) {
                 size_t j_index = findVar(j);
                 if( visitedVars[j_index] && maximum[j_index] != s(j) ) {
                     allowedState = false;
@@ -624,7 +624,7 @@ std::vector<size_t> JTree::findMaximum() const {
         DAI_ASSERT( Qa[alpha][maxState] != 0.0 );
 
         // Decode the argmax
-        foreach( const Var& j, OR(alpha).vars() ) {
+        bforeach( const Var& j, OR(alpha).vars() ) {
             size_t j_index = findVar(j);
             if( visitedVars[j_index] ) {
                 // We have already visited j earlier - hopefully our state is consistent
@@ -634,8 +634,8 @@ std::vector<size_t> JTree::findMaximum() const {
                 // We found a consistent state for variable j
                 visitedVars[j_index] = true;
                 maximum[j_index] = maxState( j );
-                foreach( const Neighbor &beta, nbOR(alpha) )
-                    foreach( const Neighbor &alpha2, nbIR(beta) )
+                bforeach( const Neighbor &beta, nbOR(alpha) )
+                    bforeach( const Neighbor &alpha2, nbIR(beta) )
                         if( !visitedORs[alpha2] )
                             scheduledORs.push(alpha2);
             }

diff --git a/src/lc.cpp b/src/lc.cpp
index 50efdb1..153df27 100644 (file)
--- a/src/lc.cpp
+++ b/src/lc.cpp
@@ -94,7 +94,7 @@ LC::LC( const FactorGraph & fg, const PropertySet &opts ) : DAIAlgFG(fg), _panca
     for( size_t i = 0; i < nrVars(); i++ ) {
         _phis.push_back( vector<Factor>() );
         _phis[i].reserve( nbV(i).size() );
-        foreach( const Neighbor &I, nbV(i) )
+        bforeach( const Neighbor &I, nbV(i) )
             _phis[i].push_back( Factor( factor(I).vars() / var(i) ) );
     }
 
@@ -203,7 +203,7 @@ long LC::SetCavityDists( std::vector<Factor> &Q ) {
 
 void LC::init() {
     for( size_t i = 0; i < nrVars(); ++i )
-        foreach( const Neighbor &I, nbV(i) )
+        bforeach( const Neighbor &I, nbV(i) )
             if( props.updates == Properties::UpdateType::SEQRND )
                 _phis[i][I.iter].randomize();
             else
@@ -257,7 +257,7 @@ Real LC::run() {
     for( size_t i = 0; i < nrVars(); i++ ) {
         _pancakes[i] = _cavitydists[i];
 
-        foreach( const Neighbor &I, nbV(i) ) {
+        bforeach( const Neighbor &I, nbV(i) ) {
             _pancakes[i] *= factor(I);
             if( props.updates == Properties::UpdateType::SEQRND )
               _pancakes[i] *= _phis[i][I.iter];
@@ -287,7 +287,7 @@ Real LC::run() {
     vector<Edge> update_seq;
     update_seq.reserve( nredges );
     for( size_t i = 0; i < nrVars(); ++i )
-        foreach( const Neighbor &I, nbV(i) )
+        bforeach( const Neighbor &I, nbV(i) )
             update_seq.push_back( Edge( i, I.iter ) );
 
     // do several passes over the network until maximum number of iterations has

diff --git a/src/mf.cpp b/src/mf.cpp
index 21cee6b..cc98406 100644 (file)
--- a/src/mf.cpp
+++ b/src/mf.cpp
@@ -91,9 +91,9 @@ void MF::init() {
 
 Factor MF::calcNewBelief( size_t i ) {
     Factor result;
-    foreach( const Neighbor &I, nbV(i) ) {
+    bforeach( const Neighbor &I, nbV(i) ) {
         Factor belief_I_minus_i;
-        foreach( const Neighbor &j, nbF(I) ) // for all j in I \ i
+        bforeach( const Neighbor &j, nbF(I) ) // for all j in I \ i
             if( j != i )
                 belief_I_minus_i *= _beliefs[j];
         Factor f_I = factor(I);
@@ -128,7 +128,7 @@ Real MF::run() {
         random_shuffle( update_seq.begin(), update_seq.end(), rnd );
 
         maxDiff = -INFINITY;
-        foreach( const size_t &i, update_seq ) {
+        bforeach( const size_t &i, update_seq ) {
             Factor nb = calcNewBelief( i );
 
             if( nb.hasNaNs() ) {
@@ -198,7 +198,7 @@ Real MF::logZ() const {
         s -= beliefV(i).entropy();
     for( size_t I = 0; I < nrFactors(); I++ ) {
         Factor henk;
-        foreach( const Neighbor &j, nbF(I) )  // for all j in I
+        bforeach( const Neighbor &j, nbF(I) )  // for all j in I
             henk *= _beliefs[j];
         henk.normalize();
         Factor piet;

diff --git a/src/mr.cpp b/src/mr.cpp
index 103b48d..7270279 100644 (file)
--- a/src/mr.cpp
+++ b/src/mr.cpp
@@ -171,7 +171,7 @@ void MR::propagateCavityFields() {
     size_t maxruns = 1000;
 
     for( size_t i = 0; i < G.nrNodes(); i++ )
-        foreach( const Neighbor &j, G.nb(i) )
+        bforeach( const Neighbor &j, G.nb(i) )
             M[i][j.iter] = 0.1;
 
     size_t run=0;
@@ -179,7 +179,7 @@ void MR::propagateCavityFields() {
         maxdev=0.0;
         run++;
         for( size_t i = 0; i < G.nrNodes(); i++ ) {
-            foreach( const Neighbor &j, G.nb(i) ) {
+            bforeach( const Neighbor &j, G.nb(i) ) {
                 size_t _j = j.iter;
                 size_t _i = G.findNb(j,i);
                 DAI_ASSERT( G.nb(j,_i) == i );
@@ -291,7 +291,7 @@ Real MR::calcCavityCorrelations() {
             bpcav.run();
 
             BBP bbp( &bpcav, PropertySet()("verbose",(size_t)0)("tol",(Real)1.0e-9)("maxiter",(size_t)10000)("damping",(Real)0.0)("updates",string("SEQ_MAX")) );
-            foreach( const Neighbor &j, G.nb(i) ) {
+            bforeach( const Neighbor &j, G.nb(i) ) {
                 // Create weights for magnetization of some spin
                 Prob p( 2, 0.0 );
                 p.set( 0, -1.0 );
@@ -310,7 +310,7 @@ Real MR::calcCavityCorrelations() {
                 // run BBP to estimate adjoints
                 bbp.run();
 
-                foreach( const Neighbor &k, G.nb(i) ) {
+                bforeach( const Neighbor &k, G.nb(i) ) {
                     if( k != j )
                         cors[i][j.iter][k.iter] = (bbp.adj_psi_V(k)[1] - bbp.adj_psi_V(k)[0]);
                     else
@@ -459,7 +459,7 @@ MR::MR( const FactorGraph &fg, const PropertySet &opts ) : DAIAlgFG(fg), support
         }
     }
     for( size_t i = 0; i < N; i++ )
-        foreach( const Neighbor &j, G.nb(i) )
+        bforeach( const Neighbor &j, G.nb(i) )
             tJ[i][j.iter] = tanh( tJ[i][j.iter] );
 
     // construct M

diff --git a/src/regiongraph.cpp b/src/regiongraph.cpp
index 2542550..5bf683c 100644 (file)
--- a/src/regiongraph.cpp
+++ b/src/regiongraph.cpp
@@ -30,7 +30,7 @@ void RegionGraph::construct( const FactorGraph &fg, const std::vector<VarSet> &o
     // Construct outer regions (giving them counting number 1.0)
     _ORs.clear();
     _ORs.reserve( ors.size() );
-    foreach( const VarSet &alpha, ors )
+    bforeach( const VarSet &alpha, ors )
         _ORs.push_back( FRegion(Factor(alpha, 1.0), 1.0) );
 
     // For each factor, find an outer region that subsumes that factor.
@@ -146,7 +146,7 @@ void RegionGraph::calcCVMCountingNumbers() {
                         has_unassigned_ancestor = true;
                 if( !has_unassigned_ancestor ) {
                     Real c = 1.0;
-                    foreach( const Neighbor &alpha, nbIR(beta) )
+                    bforeach( const Neighbor &alpha, nbIR(beta) )
                         c -= OR(alpha).c();
                     for( vector<size_t>::const_iterator beta2 = ancestors[beta].begin(); beta2 != ancestors[beta].end(); beta2++ )
                         c -= IR(*beta2).c();
@@ -224,7 +224,7 @@ ostream & operator << (ostream & os, const RegionGraph & rg) {
     for( size_t beta = 0; beta < rg.nrIRs(); beta++ )
         os << "\tb" << beta << " [label=\"b" << beta << ": " << (VarSet)rg.IR(beta) << ", c=" << rg.IR(beta).c() << "\"];" << endl;
     for( size_t alpha = 0; alpha < rg.nrORs(); alpha++ )
-        foreach( const Neighbor &beta, rg.nbOR(alpha) )
+        bforeach( const Neighbor &beta, rg.nbOR(alpha) )
             os << "\ta" << alpha << " -> b" << beta << ";" << endl;
     os << "}" << endl;
     return os;

diff --git a/src/treeep.cpp b/src/treeep.cpp
index ed34613..2a3a47e 100644 (file)
--- a/src/treeep.cpp
+++ b/src/treeep.cpp
@@ -84,13 +84,13 @@ TreeEP::TreeEP( const FactorGraph &fg, const PropertySet &opts ) : JTree(fg, opt
             for( size_t i = 0; i < fg.nrVars(); i++ ) {
                 SmallSet<size_t> delta_i = fg.bipGraph().delta1( i, false );
                 const Var& v_i = fg.var(i);
-                foreach( size_t j, delta_i ) 
+                bforeach( size_t j, delta_i ) 
                     if( i < j ) {
                         const Var& v_j = fg.var(j);
                         VarSet v_ij( v_i, v_j );
                         SmallSet<size_t> nb_ij = fg.bipGraph().nb1Set( i ) | fg.bipGraph().nb1Set( j );
                         Factor piet;
-                        foreach( size_t I, nb_ij ) {
+                        bforeach( size_t I, nb_ij ) {
                             const VarSet& Ivars = fg.factor(I).vars();
                             if( props.type == Properties::TypeType::ORG ) {
                                 if( (Ivars == v_i) || (Ivars == v_j) )

diff --git a/src/trwbp.cpp b/src/trwbp.cpp
index 6bfbcaf..e277527 100644 (file)
--- a/src/trwbp.cpp
+++ b/src/trwbp.cpp
@@ -54,7 +54,7 @@ Real TRWBP::logZ() const {
     }
     for( size_t i = 0; i < nrVars(); ++i ) {
         Real c_i = 0.0;
-        foreach( const Neighbor &I, nbV(i) )
+        bforeach( const Neighbor &I, nbV(i) )
             c_i += Weight(I);
         if( c_i != 1.0 )
             sum += (1.0 - c_i) * beliefV(i).entropy();  // TRWBP/FBP
@@ -76,13 +76,13 @@ Prob TRWBP::calcIncomingMessageProduct( size_t I, bool without_i, size_t i ) con
         prod ^= (1.0 / c_I); // TRWBP
 
     // Calculate product of incoming messages and factor I
-    foreach( const Neighbor &j, nbF(I) )
+    bforeach( const Neighbor &j, nbF(I) )
         if( !(without_i && (j == i)) ) {
             const Var &v_j = var(j);
             // prod_j will be the product of messages coming into j
             // TRWBP: corresponds to messages n_jI
             Prob prod_j( v_j.states(), props.logdomain ? 0.0 : 1.0 );
-            foreach( const Neighbor &J, nbV(j) ) {
+            bforeach( const Neighbor &J, nbV(j) ) {
                 Real c_J = Weight(J);  // TRWBP
                 if( J != I ) { // for all J in nb(j) \ I
                     if( props.logdomain )
@@ -125,7 +125,7 @@ Prob TRWBP::calcIncomingMessageProduct( size_t I, bool without_i, size_t i ) con
 // This code has been copied from bp.cpp, except where comments indicate TRWBP-specific behaviour
 void TRWBP::calcBeliefV( size_t i, Prob &p ) const {
     p = Prob( var(i).states(), props.logdomain ? 0.0 : 1.0 );
-    foreach( const Neighbor &I, nbV(i) ) {
+    bforeach( const Neighbor &I, nbV(i) ) {
         Real c_I = Weight(I);
         if( props.logdomain )
             p += newMessage( i, I.iter ) * c_I;

diff --git a/tests/unit/graph_test.cpp b/tests/unit/graph_test.cpp
index a87afee..10fd00e 100644 (file)
--- a/tests/unit/graph_test.cpp
+++ b/tests/unit/graph_test.cpp
@@ -227,7 +227,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
         BOOST_CHECK( G.isConnected() );
         BOOST_CHECK_EQUAL( G.isTree(), N < 3 );
         for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-            foreach( const Neighbor &n2, G.nb(n1) ) {
+            bforeach( const Neighbor &n2, G.nb(n1) ) {
                 BOOST_CHECK( G.hasEdge( n1, n2 ) );
                 BOOST_CHECK( G.hasEdge( n2, n1 ) );
             }
@@ -251,7 +251,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
             BOOST_CHECK( G.isConnected() );
             BOOST_CHECK_EQUAL( G.isTree(), (N1 <= 1) || (N2 <= 1) );
             for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-                foreach( const Neighbor &n2, G.nb(n1) ) {
+                bforeach( const Neighbor &n2, G.nb(n1) ) {
                     BOOST_CHECK( G.hasEdge( n1, n2 ) );
                     BOOST_CHECK( G.hasEdge( n2, n1 ) );
                 }
@@ -274,7 +274,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
             BOOST_CHECK( G.isConnected() );
             BOOST_CHECK_EQUAL( G.isTree(), (G.nrNodes() <= 2) );
             for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-                foreach( const Neighbor &n2, G.nb(n1) ) {
+                bforeach( const Neighbor &n2, G.nb(n1) ) {
                     BOOST_CHECK( G.hasEdge( n1, n2 ) );
                     BOOST_CHECK( G.hasEdge( n2, n1 ) );
                 }
@@ -299,7 +299,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
                 BOOST_CHECK( G.isConnected() );
                 BOOST_CHECK_EQUAL( G.isTree(), (G.nrNodes() == 0) || (N1 <= 1 && N2 <= 1) || (N1 <= 1 && N3 <= 1) || (N2 <= 1 && N3 <= 1) );
                 for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-                    foreach( const Neighbor &n2, G.nb(n1) ) {
+                    bforeach( const Neighbor &n2, G.nb(n1) ) {
                         BOOST_CHECK( G.hasEdge( n1, n2 ) );
                         BOOST_CHECK( G.hasEdge( n2, n1 ) );
                     }
@@ -322,7 +322,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
                 BOOST_CHECK( G.isConnected() );
                 BOOST_CHECK_EQUAL( G.isTree(), (G.nrNodes() <= 2) );
                 for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-                    foreach( const Neighbor &n2, G.nb(n1) ) {
+                    bforeach( const Neighbor &n2, G.nb(n1) ) {
                         BOOST_CHECK( G.hasEdge( n1, n2 ) );
                         BOOST_CHECK( G.hasEdge( n2, n1 ) );
                     }
@@ -350,7 +350,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
         BOOST_CHECK( G.isConnected() );
         BOOST_CHECK_EQUAL( G.isTree(), N <= 2 );
         for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-            foreach( const Neighbor &n2, G.nb(n1) ) {
+            bforeach( const Neighbor &n2, G.nb(n1) ) {
                 BOOST_CHECK( G.hasEdge( n1, n2 ) );
                 BOOST_CHECK( G.hasEdge( n2, n1 ) );
             }
@@ -373,7 +373,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
         BOOST_CHECK( G.isConnected() );
         BOOST_CHECK( G.isTree() );
         for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
-            foreach( const Neighbor &n2, G.nb(n1) ) {
+            bforeach( const Neighbor &n2, G.nb(n1) ) {
                 BOOST_CHECK( G.hasEdge( n1, n2 ) );
                 BOOST_CHECK( G.hasEdge( n2, n1 ) );
             }
@@ -397,7 +397,7 @@ BOOST_AUTO_TEST_CASE( QueriesAndCreationTest ) {
                 BOOST_CHECK_EQUAL( G.nrEdges(), d * N / 2 );
                 for( size_t n1 = 0; n1 < G.nrNodes(); n1++ ) {
                     BOOST_CHECK_EQUAL( G.nb(n1).size(), d );
-                    foreach( const Neighbor &n2, G.nb(n1) ) {
+                    bforeach( const Neighbor &n2, G.nb(n1) ) {
                         BOOST_CHECK( G.hasEdge( n1, n2 ) );
                         BOOST_CHECK( G.hasEdge( n2, n1 ) );
                     }

diff --git a/utils/createfg.cpp b/utils/createfg.cpp
index 1e6a803..a77142a 100644 (file)
--- a/utils/createfg.cpp
+++ b/utils/createfg.cpp
@@ -79,7 +79,7 @@ FactorGraph createFG( const GraphAL &G, FactorType ft, size_t states, const Prop
     factors.reserve( G.nrEdges() + N );
     // Pairwise factors
     for( size_t i = 0; i < N; i++ )
-        foreach( const Neighbor &j, G.nb(i) )
+        bforeach( const Neighbor &j, G.nb(i) )
             if( i < j ) {
                 if( ft == FactorType::POTTS )
                     factors.push_back( createFactorPotts( vars[i], vars[j], beta ) );