Replaced all "protected:" by "private:" or "public:"

diff --git a/Makefile b/Makefile
index 64a6ab9..94e8723 100644 (file)
--- a/Makefile
+++ b/Makefile
@@ -55,7 +55,7 @@ BOOSTLIBS=-lboost_program_options
 CC=g++
 
 # Flags for the C++ compiler
-CCFLAGS=-Wall -W -Wextra -fpic -I./include -Llib -O3 #-pg #-static #-DVERBOSE
+CCFLAGS=-Wno-deprecated -Wall -W -Wextra -fpic -I./include -Llib -O3 #-pg #-static #-DVERBOSE
 ifdef DEBUG
 CCFLAGS:=$(CCFLAGS) -g -DDAI_DEBUG
 endif

diff --git a/include/dai/factor.h b/include/dai/factor.h
index 62b0943..6332f20 100644 (file)
--- a/include/dai/factor.h
+++ b/include/dai/factor.h
@@ -49,7 +49,7 @@ template<typename T> std::ostream&  operator<< (std::ostream& os, const TFactor<
         
 // T should be castable from and to double
 template <typename T> class TFactor {
-    protected:
+    private:
         VarSet      _vs;
         TProb<T>    _p;
 

diff --git a/include/dai/hak.h b/include/dai/hak.h
index 159c2f7..01373b5 100644 (file)
--- a/include/dai/hak.h
+++ b/include/dai/hak.h
@@ -35,7 +35,7 @@ namespace dai {
 
 /// HAK provides an implementation of the single and double-loop algorithms by Heskes, Albers and Kappen
 class HAK : public DAIAlgRG {
-    protected:
+    private:
         std::vector<Factor>                _Qa;
         std::vector<Factor>                _Qb;
         std::vector<std::vector<Factor> >  _muab;

diff --git a/include/dai/jtree.h b/include/dai/jtree.h
index eff98de..032d84d 100644 (file)
--- a/include/dai/jtree.h
+++ b/include/dai/jtree.h
@@ -39,14 +39,14 @@ namespace dai {
 
 
 class JTree : public DAIAlgRG {
-    protected:
-        DEdgeVec             _RTree;     // rooted tree
-        std::vector<Factor>  _Qa;
-        std::vector<Factor>  _Qb;
+    private:
         std::vector<std::vector<Factor> >  _mes;
         double               _logZ;
 
     public:
+        DEdgeVec             RTree;     // rooted tree
+        std::vector<Factor>  Qa;
+        std::vector<Factor>  Qb;
         struct Properties {
             size_t verbose;
             DAI_ENUM(UpdateType,HUGIN,SHSH)
@@ -57,13 +57,13 @@ class JTree : public DAIAlgRG {
 
     public:
         /// Default constructor
-        JTree() : DAIAlgRG(), _RTree(), _Qa(), _Qb(), _mes(), _logZ(), props() {}
+        JTree() : DAIAlgRG(), _mes(), _logZ(), RTree(), Qa(), Qb(), props() {}
 
         /// Construct from FactorGraph fg and PropertySet opts
         JTree( const FactorGraph &fg, const PropertySet &opts, bool automatic=true );
 
         /// Copy constructor
-        JTree( const JTree &x ) : DAIAlgRG(x), _RTree(x._RTree), _Qa(x._Qa), _Qb(x._Qb), _mes(x._mes), _logZ(x._logZ), props(x.props) {}
+        JTree( const JTree &x ) : DAIAlgRG(x), _mes(x._mes), _logZ(x._logZ), RTree(x.RTree), Qa(x.Qa), Qb(x.Qb), props(x.props) {}
 
         /// Clone *this (virtual copy constructor)
         virtual JTree* clone() const { return new JTree(*this); }
@@ -75,11 +75,11 @@ class JTree : public DAIAlgRG {
         JTree& operator=( const JTree &x ) {
             if( this != &x ) {
                 DAIAlgRG::operator=( x );
-                _RTree  = x._RTree;
-                _Qa     = x._Qa;
-                _Qb     = x._Qb;
                 _mes    = x._mes;
                 _logZ   = x._logZ;
+                RTree   = x.RTree;
+                Qa      = x.Qa;
+                Qb      = x.Qb;
                 props   = x.props;
             }
             return *this;

diff --git a/include/dai/lc.h b/include/dai/lc.h
index b49da41..4f31426 100644 (file)
--- a/include/dai/lc.h
+++ b/include/dai/lc.h
@@ -35,7 +35,7 @@ namespace dai {
 
 
 class LC : public DAIAlgFG {
-    protected:
+    private:
         std::vector<Factor>      _pancakes;      // used by all LC types (psi_I is stored in the pancake)
         std::vector<Factor>      _cavitydists;   // used by all LC types to store the approximate cavity distribution
         /// _phis[i][_I] corresponds to \f$ \phi^{\setminus i}_I(x_{I \setminus i}) \f$

diff --git a/include/dai/mf.h b/include/dai/mf.h
index 6daef4b..90e5a4f 100644 (file)
--- a/include/dai/mf.h
+++ b/include/dai/mf.h
@@ -33,7 +33,7 @@ namespace dai {
 
 
 class MF : public DAIAlgFG {
-    protected:
+    private:
         std::vector<Factor>  _beliefs;
         /// Maximum difference encountered so far
         double _maxdiff;

diff --git a/include/dai/prob.h b/include/dai/prob.h
index b0466d2..53c4598 100644 (file)
--- a/include/dai/prob.h
+++ b/include/dai/prob.h
@@ -50,7 +50,7 @@ template<typename T> TProb<T> max( const TProb<T> &a, const TProb<T> &b );
 /// TProb<T> implements a probability vector of type T.
 /// T should be castable from and to double.
 template <typename T> class TProb {
-    protected:
+    private:
         /// The entries
         std::vector<T> _p;
 

diff --git a/include/dai/regiongraph.h b/include/dai/regiongraph.h
index 9d0d924..887515b 100644 (file)
--- a/include/dai/regiongraph.h
+++ b/include/dai/regiongraph.h
@@ -34,7 +34,7 @@ namespace dai {
 
 /// A Region is a set of variables with a counting number
 class Region : public VarSet {
-    protected:
+    private:
         /// Counting number
         double          _c;
 
@@ -66,7 +66,7 @@ class Region : public VarSet {
 
 /// A FRegion is a factor with a counting number
 class FRegion : public Factor {
-    protected:
+    private:
         /// Counting number
         double _c;
 
@@ -103,7 +103,6 @@ class RegionGraph : public FactorGraph {
         std::vector<FRegion>    ORs;
         std::vector<Region>     IRs;
 
-    protected:
         /// Give back the OR index that corresponds to a factor index
         std::vector<size_t>     fac2OR;
 

diff --git a/include/dai/treeep.h b/include/dai/treeep.h
index fd039a6..d98d02f 100644 (file)
--- a/include/dai/treeep.h
+++ b/include/dai/treeep.h
@@ -40,7 +40,7 @@ namespace dai {
 
 
 class TreeEP : public JTree {
-    protected:
+    private:
         /// Maximum difference encountered so far
         double                  _maxdiff;
         /// Number of iterations needed
@@ -57,14 +57,14 @@ class TreeEP : public JTree {
         /// Name of this inference method
         static const char *Name;
 
-    protected:
+    private:
         class TreeEPSubTree {
-            protected:
+            private:
                 std::vector<Factor>  _Qa;
                 std::vector<Factor>  _Qb;
                 DEdgeVec             _RTree;
-                std::vector<size_t>  _a;        // _Qa[alpha]  <->  superTree._Qa[_a[alpha]]
-                std::vector<size_t>  _b;        // _Qb[beta]   <->  superTree._Qb[_b[beta]]
+                std::vector<size_t>  _a;        // _Qa[alpha]  <->  superTree.Qa[_a[alpha]]
+                std::vector<size_t>  _b;        // _Qb[beta]   <->  superTree.Qb[_b[beta]]
                                                 // _Qb[beta]   <->  _RTree[beta]    
                 const Factor *       _I;
                 VarSet               _ns;

diff --git a/src/jtree.cpp b/src/jtree.cpp
index 0f28d5b..3f16a28 100644 (file)
--- a/src/jtree.cpp
+++ b/src/jtree.cpp
@@ -58,7 +58,7 @@ string JTree::printProperties() const {
 }
 
 
-JTree::JTree( const FactorGraph &fg, const PropertySet &opts, bool automatic ) : DAIAlgRG(fg), _RTree(), _Qa(), _Qb(), _mes(), _logZ(), props() {
+JTree::JTree( const FactorGraph &fg, const PropertySet &opts, bool automatic ) : DAIAlgRG(fg), _mes(), _logZ(), RTree(), Qa(), Qb(), props() {
     setProperties( opts );
 
     if( !isConnected() ) 
@@ -102,7 +102,7 @@ void JTree::GenerateJT( const std::vector<VarSet> &Cliques ) {
         }
     
     // Construct maximal spanning tree using Prim's algorithm
-    _RTree = MaxSpanningTreePrims( JuncGraph );
+    RTree = MaxSpanningTreePrims( JuncGraph );
 
     // Construct corresponding region graph
 
@@ -125,29 +125,29 @@ void JTree::GenerateJT( const std::vector<VarSet> &Cliques ) {
     RecomputeORs();
 
     // Create inner regions and edges
-    IRs.reserve( _RTree.size() );
+    IRs.reserve( RTree.size() );
     vector<Edge> edges;
-    edges.reserve( 2 * _RTree.size() );
-    for( size_t i = 0; i < _RTree.size(); i++ ) {
-        edges.push_back( Edge( _RTree[i].n1, nrIRs() ) );
-        edges.push_back( Edge( _RTree[i].n2, nrIRs() ) );
+    edges.reserve( 2 * RTree.size() );
+    for( size_t i = 0; i < RTree.size(); i++ ) {
+        edges.push_back( Edge( RTree[i].n1, nrIRs() ) );
+        edges.push_back( Edge( RTree[i].n2, nrIRs() ) );
         // inner clusters have counting number -1
-        IRs.push_back( Region( Cliques[_RTree[i].n1] & Cliques[_RTree[i].n2], -1.0 ) );
+        IRs.push_back( Region( Cliques[RTree[i].n1] & Cliques[RTree[i].n2], -1.0 ) );
     }
 
     // create bipartite graph
     G.construct( nrORs(), nrIRs(), edges.begin(), edges.end() );
 
     // Create messages and beliefs
-    _Qa.clear();
-    _Qa.reserve( nrORs() );
+    Qa.clear();
+    Qa.reserve( nrORs() );
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        _Qa.push_back( OR(alpha) );
+        Qa.push_back( OR(alpha) );
 
-    _Qb.clear();
-    _Qb.reserve( nrIRs() );
+    Qb.clear();
+    Qb.reserve( nrIRs() );
     for( size_t beta = 0; beta < nrIRs(); beta++ ) 
-        _Qb.push_back( Factor( IR(beta), 1.0 ) );
+        Qb.push_back( Factor( IR(beta), 1.0 ) );
 
     _mes.clear();
     _mes.reserve( nrORs() );
@@ -174,17 +174,17 @@ string JTree::identify() const {
 
 Factor JTree::belief( const VarSet &ns ) const {
     vector<Factor>::const_iterator beta;
-    for( beta = _Qb.begin(); beta != _Qb.end(); beta++ )
+    for( beta = Qb.begin(); beta != Qb.end(); beta++ )
         if( beta->vars() >> ns )
             break;
-    if( beta != _Qb.end() )
+    if( beta != Qb.end() )
         return( beta->marginal(ns) );
     else {
         vector<Factor>::const_iterator alpha;
-        for( alpha = _Qa.begin(); alpha != _Qa.end(); alpha++ )
+        for( alpha = Qa.begin(); alpha != Qa.end(); alpha++ )
             if( alpha->vars() >> ns )
                 break;
-        assert( alpha != _Qa.end() );
+        assert( alpha != Qa.end() );
         return( alpha->marginal(ns) );
     }
 }
@@ -193,9 +193,9 @@ Factor JTree::belief( const VarSet &ns ) const {
 vector<Factor> JTree::beliefs() const {
     vector<Factor> result;
     for( size_t beta = 0; beta < nrIRs(); beta++ )
-        result.push_back( _Qb[beta] );
+        result.push_back( Qb[beta] );
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        result.push_back( _Qa[alpha] );
+        result.push_back( Qa[alpha] );
     return result;
 }
 
@@ -208,38 +208,38 @@ Factor JTree::belief( const Var &n ) const {
 // Needs no init
 void JTree::runHUGIN() {
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        _Qa[alpha] = OR(alpha);
+        Qa[alpha] = OR(alpha);
 
     for( size_t beta = 0; beta < nrIRs(); beta++ )
-        _Qb[beta].fill( 1.0 );
+        Qb[beta].fill( 1.0 );
 
     // CollectEvidence
     _logZ = 0.0;
-    for( size_t i = _RTree.size(); (i--) != 0; ) {
-//      Make outer region _RTree[i].n1 consistent with outer region _RTree[i].n2
-//      IR(i) = seperator OR(_RTree[i].n1) && OR(_RTree[i].n2)
-        Factor new_Qb = _Qa[_RTree[i].n2].partSum( IR( i ) );
+    for( size_t i = RTree.size(); (i--) != 0; ) {
+//      Make outer region RTree[i].n1 consistent with outer region RTree[i].n2
+//      IR(i) = seperator OR(RTree[i].n1) && OR(RTree[i].n2)
+        Factor new_Qb = Qa[RTree[i].n2].partSum( IR( i ) );
         _logZ += log(new_Qb.normalize());
-        _Qa[_RTree[i].n1] *= new_Qb.divided_by( _Qb[i] ); 
-        _Qb[i] = new_Qb;
+        Qa[RTree[i].n1] *= new_Qb.divided_by( Qb[i] ); 
+        Qb[i] = new_Qb;
     }
-    if( _RTree.empty() )
-        _logZ += log(_Qa[0].normalize() );
+    if( RTree.empty() )
+        _logZ += log(Qa[0].normalize() );
     else
-        _logZ += log(_Qa[_RTree[0].n1].normalize());
+        _logZ += log(Qa[RTree[0].n1].normalize());
 
     // DistributeEvidence
-    for( size_t i = 0; i < _RTree.size(); i++ ) {
-//      Make outer region _RTree[i].n2 consistent with outer region _RTree[i].n1
-//      IR(i) = seperator OR(_RTree[i].n1) && OR(_RTree[i].n2)
-        Factor new_Qb = _Qa[_RTree[i].n1].marginal( IR( i ) );
-        _Qa[_RTree[i].n2] *= new_Qb.divided_by( _Qb[i] ); 
-        _Qb[i] = new_Qb;
+    for( size_t i = 0; i < RTree.size(); i++ ) {
+//      Make outer region RTree[i].n2 consistent with outer region RTree[i].n1
+//      IR(i) = seperator OR(RTree[i].n1) && OR(RTree[i].n2)
+        Factor new_Qb = Qa[RTree[i].n1].marginal( IR( i ) );
+        Qa[RTree[i].n2] *= new_Qb.divided_by( Qb[i] ); 
+        Qb[i] = new_Qb;
     }
 
     // Normalize
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        _Qa[alpha].normalize();
+        Qa[alpha].normalize();
 }
 
 
@@ -248,11 +248,11 @@ void JTree::runShaferShenoy() {
     // First pass
     _logZ = 0.0;
     for( size_t e = nrIRs(); (e--) != 0; ) {
-        // send a message from _RTree[e].n2 to _RTree[e].n1
-        // or, actually, from the seperator IR(e) to _RTree[e].n1
+        // send a message from RTree[e].n2 to RTree[e].n1
+        // or, actually, from the seperator IR(e) to RTree[e].n1
 
-        size_t i = nbIR(e)[1].node; // = _RTree[e].n2
-        size_t j = nbIR(e)[0].node; // = _RTree[e].n1
+        size_t i = nbIR(e)[1].node; // = RTree[e].n2
+        size_t j = nbIR(e)[0].node; // = RTree[e].n1
         size_t _e = nbIR(e)[0].dual;
         
         Factor piet = OR(i);
@@ -265,8 +265,8 @@ void JTree::runShaferShenoy() {
 
     // Second pass
     for( size_t e = 0; e < nrIRs(); e++ ) {
-        size_t i = nbIR(e)[0].node; // = _RTree[e].n1
-        size_t j = nbIR(e)[1].node; // = _RTree[e].n2
+        size_t i = nbIR(e)[0].node; // = RTree[e].n1
+        size_t j = nbIR(e)[1].node; // = RTree[e].n2
         size_t _e = nbIR(e)[1].dual;
         
         Factor piet = OR(i);
@@ -283,17 +283,17 @@ void JTree::runShaferShenoy() {
             piet *= message( alpha, k.iter );
         if( nrIRs() == 0 ) {
             _logZ += log( piet.normalize() );
-            _Qa[alpha] = piet;
-        } else if( alpha == nbIR(0)[0].node /*_RTree[0].n1*/ ) {
+            Qa[alpha] = piet;
+        } else if( alpha == nbIR(0)[0].node /*RTree[0].n1*/ ) {
             _logZ += log( piet.normalize() );
-            _Qa[alpha] = piet;
+            Qa[alpha] = piet;
         } else
-            _Qa[alpha] = piet.normalized();
+            Qa[alpha] = piet.normalized();
     }
 
     // Only for logZ (and for belief)...
     for( size_t beta = 0; beta < nrIRs(); beta++ ) 
-        _Qb[beta] = _Qa[nbIR(beta)[0].node].marginal( IR(beta) );
+        Qb[beta] = Qa[nbIR(beta)[0].node].marginal( IR(beta) );
 }
 
 
@@ -309,10 +309,10 @@ double JTree::run() {
 Real JTree::logZ() const {
     Real sum = 0.0;
     for( size_t beta = 0; beta < nrIRs(); beta++ )
-        sum += IR(beta).c() * _Qb[beta].entropy();
+        sum += IR(beta).c() * Qb[beta].entropy();
     for( size_t alpha = 0; alpha < nrORs(); alpha++ ) {
-        sum += OR(alpha).c() * _Qa[alpha].entropy();
-        sum += (OR(alpha).log0() * _Qa[alpha]).totalSum();
+        sum += OR(alpha).c() * Qa[alpha].entropy();
+        sum += (OR(alpha).log0() * Qa[alpha]).totalSum();
     }
     return sum;
 }
@@ -332,7 +332,7 @@ size_t JTree::findEfficientTree( const VarSet& ns, DEdgeVec &Tree, size_t Previo
 
     // grow new tree
     Graph oldTree;
-    for( DEdgeVec::const_iterator e = _RTree.begin(); e != _RTree.end(); e++ )
+    for( DEdgeVec::const_iterator e = RTree.begin(); e != RTree.end(); e++ )
         oldTree.insert( UEdge(e->n1, e->n2) );
     DEdgeVec newTree = GrowRootedTree( oldTree, maxalpha );
     
@@ -421,17 +421,17 @@ size_t JTree::findEfficientTree( const VarSet& ns, DEdgeVec &Tree, size_t Previo
 // assumes that run() has been called already
 Factor JTree::calcMarginal( const VarSet& ns ) {
     vector<Factor>::const_iterator beta;
-    for( beta = _Qb.begin(); beta != _Qb.end(); beta++ )
+    for( beta = Qb.begin(); beta != Qb.end(); beta++ )
         if( beta->vars() >> ns )
             break;
-    if( beta != _Qb.end() )
+    if( beta != Qb.end() )
         return( beta->marginal(ns) );
     else {
         vector<Factor>::const_iterator alpha;
-        for( alpha = _Qa.begin(); alpha != _Qa.end(); alpha++ )
+        for( alpha = Qa.begin(); alpha != Qa.end(); alpha++ )
             if( alpha->vars() >> ns )
                 break;
-        if( alpha != _Qa.end() )
+        if( alpha != Qa.end() )
             return( alpha->marginal(ns) );
         else {
             // Find subtree to do efficient inference
@@ -442,26 +442,26 @@ Factor JTree::calcMarginal( const VarSet& ns ) {
             VarSet nsrem = ns / OR(T.front().n1).vars();
             Factor Pns (ns, 0.0);
             
-            // Save _Qa and _Qb on the subtree
-            map<size_t,Factor> _Qa_old;
-            map<size_t,Factor> _Qb_old;
+            // Save Qa and Qb on the subtree
+            map<size_t,Factor> Qa_old;
+            map<size_t,Factor> Qb_old;
             vector<size_t> b(Tsize, 0);
             for( size_t i = Tsize; (i--) != 0; ) {
                 size_t alpha1 = T[i].n1;
                 size_t alpha2 = T[i].n2;
                 size_t beta;
                 for( beta = 0; beta < nrIRs(); beta++ )
-                    if( UEdge( _RTree[beta].n1, _RTree[beta].n2 ) == UEdge( alpha1, alpha2 ) )
+                    if( UEdge( RTree[beta].n1, RTree[beta].n2 ) == UEdge( alpha1, alpha2 ) )
                         break;
                 assert( beta != nrIRs() );
                 b[i] = beta;
 
-                if( !_Qa_old.count( alpha1 ) )
-                    _Qa_old[alpha1] = _Qa[alpha1];
-                if( !_Qa_old.count( alpha2 ) )
-                    _Qa_old[alpha2] = _Qa[alpha2];
-                if( !_Qb_old.count( beta ) )
-                    _Qb_old[beta] = _Qb[beta];
+                if( !Qa_old.count( alpha1 ) )
+                    Qa_old[alpha1] = Qa[alpha1];
+                if( !Qa_old.count( alpha2 ) )
+                    Qa_old[alpha2] = Qa[alpha2];
+                if( !Qb_old.count( beta ) )
+                    Qb_old[beta] = Qb[beta];
             }
                 
             // For all states of nsrem
@@ -473,28 +473,28 @@ Factor JTree::calcMarginal( const VarSet& ns ) {
                 // IR(i) = seperator OR(T[i].n1) && OR(T[i].n2)
 
                     for( VarSet::const_iterator n = nsrem.begin(); n != nsrem.end(); n++ )
-                        if( _Qa[T[i].n2].vars() >> *n ) {
+                        if( Qa[T[i].n2].vars() >> *n ) {
                             Factor piet( *n, 0.0 );
                             piet[s(*n)] = 1.0;
-                            _Qa[T[i].n2] *= piet; 
+                            Qa[T[i].n2] *= piet; 
                         }
 
-                    Factor new_Qb = _Qa[T[i].n2].partSum( IR( b[i] ) );
+                    Factor new_Qb = Qa[T[i].n2].partSum( IR( b[i] ) );
                     logZ += log(new_Qb.normalize());
-                    _Qa[T[i].n1] *= new_Qb.divided_by( _Qb[b[i]] ); 
-                    _Qb[b[i]] = new_Qb;
+                    Qa[T[i].n1] *= new_Qb.divided_by( Qb[b[i]] ); 
+                    Qb[b[i]] = new_Qb;
                 }
-                logZ += log(_Qa[T[0].n1].normalize());
+                logZ += log(Qa[T[0].n1].normalize());
 
                 Factor piet( nsrem, 0.0 );
                 piet[s] = exp(logZ);
-                Pns += piet * _Qa[T[0].n1].partSum( ns / nsrem );      // OPTIMIZE ME
+                Pns += piet * Qa[T[0].n1].partSum( ns / nsrem );      // OPTIMIZE ME
 
                 // Restore clamped beliefs
-                for( map<size_t,Factor>::const_iterator alpha = _Qa_old.begin(); alpha != _Qa_old.end(); alpha++ )
-                    _Qa[alpha->first] = alpha->second;
-                for( map<size_t,Factor>::const_iterator beta = _Qb_old.begin(); beta != _Qb_old.end(); beta++ )
-                    _Qb[beta->first] = beta->second;
+                for( map<size_t,Factor>::const_iterator alpha = Qa_old.begin(); alpha != Qa_old.end(); alpha++ )
+                    Qa[alpha->first] = alpha->second;
+                for( map<size_t,Factor>::const_iterator beta = Qb_old.begin(); beta != Qb_old.end(); beta++ )
+                    Qb[beta->first] = beta->second;
             }
 
             return( Pns.normalized() );

diff --git a/src/treeep.cpp b/src/treeep.cpp
index ce7d167..d21ed5f 100644 (file)
--- a/src/treeep.cpp
+++ b/src/treeep.cpp
@@ -269,7 +269,7 @@ void TreeEP::ConstructRG( const DEdgeVec &tree ) {
         }
     
     // Construct maximal spanning tree using Prim's algorithm
-    _RTree = MaxSpanningTreePrims( JuncGraph );
+    RTree = MaxSpanningTreePrims( JuncGraph );
 
     // Construct corresponding region graph
 
@@ -296,14 +296,14 @@ void TreeEP::ConstructRG( const DEdgeVec &tree ) {
     RecomputeORs();
 
     // Create inner regions and edges
-    IRs.reserve( _RTree.size() );
+    IRs.reserve( RTree.size() );
     vector<Edge> edges;
-    edges.reserve( 2 * _RTree.size() );
-    for( size_t i = 0; i < _RTree.size(); i++ ) {
-        edges.push_back( Edge( _RTree[i].n1, IRs.size() ) );
-        edges.push_back( Edge( _RTree[i].n2, IRs.size() ) );
+    edges.reserve( 2 * RTree.size() );
+    for( size_t i = 0; i < RTree.size(); i++ ) {
+        edges.push_back( Edge( RTree[i].n1, IRs.size() ) );
+        edges.push_back( Edge( RTree[i].n2, IRs.size() ) );
         // inner clusters have counting number -1
-        IRs.push_back( Region( Cliques[_RTree[i].n1] & Cliques[_RTree[i].n2], -1.0 ) );
+        IRs.push_back( Region( Cliques[RTree[i].n1] & Cliques[RTree[i].n2], -1.0 ) );
     }
 
     // create bipartite graph
@@ -313,15 +313,15 @@ void TreeEP::ConstructRG( const DEdgeVec &tree ) {
     Check_Counting_Numbers();
     
     // Create messages and beliefs
-    _Qa.clear();
-    _Qa.reserve( nrORs() );
+    Qa.clear();
+    Qa.reserve( nrORs() );
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        _Qa.push_back( OR(alpha) );
+        Qa.push_back( OR(alpha) );
 
-    _Qb.clear();
-    _Qb.reserve( nrIRs() );
+    Qb.clear();
+    Qb.reserve( nrIRs() );
     for( size_t beta = 0; beta < nrIRs(); beta++ ) 
-        _Qb.push_back( Factor( IR(beta), 1.0 ) );
+        Qb.push_back( Factor( IR(beta), 1.0 ) );
 
     // DIFF with JTree::GenerateJT:  no messages
     
@@ -338,7 +338,7 @@ void TreeEP::ConstructRG( const DEdgeVec &tree ) {
             //subTree.resize( subTreeSize );  // FIXME
 //          cout << "subtree " << I << " has size " << subTreeSize << endl;
 
-            TreeEPSubTree QI( subTree, _RTree, _Qa, _Qb, &factor(I) );
+            TreeEPSubTree QI( subTree, RTree, Qa, Qb, &factor(I) );
             _Q[I] = QI;
         }
     // Previous root of first off-tree factor should be the root of the last off-tree factor
@@ -350,7 +350,7 @@ void TreeEP::ConstructRG( const DEdgeVec &tree ) {
             //subTree.resize( subTreeSize ); // FIXME
 //          cout << "subtree " << I << " has size " << subTreeSize << endl;
 
-            TreeEPSubTree QI( subTree, _RTree, _Qa, _Qb, &factor(I) );
+            TreeEPSubTree QI( subTree, RTree, Qa, Qb, &factor(I) );
             _Q[I] = QI;
             break;
         }
@@ -395,9 +395,9 @@ double TreeEP::run() {
     for( _iters=0; _iters < props.maxiter && diffs.maxDiff() > props.tol; _iters++ ) {
         for( size_t I = 0; I < nrFactors(); I++ )
             if( offtree(I) ) {  
-                _Q[I].InvertAndMultiply( _Qa, _Qb );
-                _Q[I].HUGIN_with_I( _Qa, _Qb );
-                _Q[I].InvertAndMultiply( _Qa, _Qb );
+                _Q[I].InvertAndMultiply( Qa, Qb );
+                _Q[I].HUGIN_with_I( Qa, Qb );
+                _Q[I].InvertAndMultiply( Qa, Qb );
             }
 
         // calculate new beliefs and compare with old ones
@@ -435,18 +435,18 @@ Real TreeEP::logZ() const {
 
     // entropy of the tree
     for( size_t beta = 0; beta < nrIRs(); beta++ )
-        sum -= _Qb[beta].entropy();
+        sum -= Qb[beta].entropy();
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        sum += _Qa[alpha].entropy();
+        sum += Qa[alpha].entropy();
 
     // energy of the on-tree factors
     for( size_t alpha = 0; alpha < nrORs(); alpha++ )
-        sum += (OR(alpha).log0() * _Qa[alpha]).totalSum();
+        sum += (OR(alpha).log0() * Qa[alpha]).totalSum();
 
     // energy of the off-tree factors
     for( size_t I = 0; I < nrFactors(); I++ )
         if( offtree(I) )
-            sum += (_Q.find(I))->second.logZ( _Qa, _Qb );
+            sum += (_Q.find(I))->second.logZ( Qa, Qb );
     
     return sum;
 }