Finished integrating Gibbs sampler by Frederik Eaton into libDAI

diff --git a/include/dai/gibbs.h b/include/dai/gibbs.h
index 6b63bdf..9e66b52 100644 (file)
--- a/include/dai/gibbs.h
+++ b/include/dai/gibbs.h
@@ -1,4 +1,5 @@
-/*  Copyright (C) 2008  Frederik Eaton [frederik at ofb dot net]
+/*  Copyright (C) 2008  Frederik Eaton  [frederik at ofb dot net],
+                        Joris Mooij  [joris dot mooij at tuebingen dot mpg dot de]
 
     This file is part of libDAI.
 
@@ -18,6 +19,11 @@
 */
 
 
+/// \file
+/// \brief Defines class Gibbs
+/// \todo Improve documentation
+
+
 #ifndef __defined_libdai_gibbs_h
 #define __defined_libdai_gibbs_h
 
@@ -30,7 +36,17 @@
 namespace dai {
 
 
+/// Approximate inference algorithm "Gibbs sampling"
 class Gibbs : public DAIAlgFG {
+    private:
+        typedef std::vector<size_t> _count_t;
+        typedef std::vector<size_t> _state_t;
+
+        size_t _sample_count;
+        std::vector<_count_t> _var_counts;
+        std::vector<_count_t> _factor_counts;
+        _state_t _state;
+
     public:
         /// Parameters of this inference algorithm
         struct Properties {
@@ -44,36 +60,17 @@ class Gibbs : public DAIAlgFG {
         /// Name of this inference algorithm
         static const char *Name;
 
-    protected:
-        typedef std::vector<size_t> _count_t;
-        typedef std::vector<size_t> _state_t;
-
-        size_t _sample_count;
-        std::vector<_count_t> _var_counts;
-        std::vector<_count_t> _factor_counts;
-        _state_t _state;
-
-        void updateCounts();
-        void randomizeState();
-        Prob getVarDist( size_t i );
-        void resampleVar( size_t i );
-        size_t getFactorEntry( size_t I );
-        size_t getFactorEntryDiff( size_t I, size_t i );
-
     public:
-        // default constructor
+        /// Default constructor
         Gibbs() : DAIAlgFG(), _sample_count(0), _var_counts(), _factor_counts(), _state() {}
-        // copy constructor
+
+        /// Copy constructor
         Gibbs(const Gibbs & x) : DAIAlgFG(x), _sample_count(x._sample_count), _var_counts(x._var_counts), _factor_counts(x._factor_counts), _state(x._state) {}
-        // construct Gibbs object from FactorGraph
-        Gibbs( const FactorGraph & fg, const PropertySet &opts ) : DAIAlgFG(fg) {
-            setProperties( opts );
-            construct();
-        }
-        // assignment operator
-        Gibbs & operator=(const Gibbs & x) {
-            if(this!=&x) {
-                DAIAlgFG::operator=(x);
+
+        /// Assignment operator
+        Gibbs& operator=( const Gibbs &x ) {
+            if( this != &x ) {
+                DAIAlgFG::operator=( x );
                 _sample_count = x._sample_count;
                 _var_counts = x._var_counts;
                 _factor_counts = x._factor_counts;
@@ -81,7 +78,16 @@ class Gibbs : public DAIAlgFG {
             }
             return *this;
         }
-        
+
+        /// Construct from FactorGraph fg and PropertySet opts
+        Gibbs( const FactorGraph &fg, const PropertySet &opts ) : DAIAlgFG(fg), _sample_count(0), _var_counts(), _factor_counts(), _state() {
+            setProperties( opts );
+            construct();
+        }
+
+
+        /// @name General InfAlg interface
+        //@{
         virtual Gibbs* clone() const { return new Gibbs(*this); }
         virtual Gibbs* create() const { return new Gibbs(); }
         virtual std::string identify() const { return std::string(Name) + printProperties(); }
@@ -94,9 +100,22 @@ class Gibbs : public DAIAlgFG {
         virtual double run();
         virtual double maxDiff() const { DAI_THROW(NOT_IMPLEMENTED); return 0.0; }
         virtual size_t Iterations() const { return props.iters; }
+        //@}
 
+
+        /// @name Additional interface specific for BP
+        //@{
         Factor beliefV( size_t i ) const;
         Factor beliefF( size_t I ) const;
+        void randomizeState();
+        //@}
+
+    private:
+        void updateCounts();
+        Prob getVarDist( size_t i );
+        void resampleVar( size_t i );
+        size_t getFactorEntry( size_t I );
+        size_t getFactorEntryDiff( size_t I, size_t i );
 
         void construct();
         /// Set Props according to the PropertySet opts, where the values can be stored as std::strings or as the type of the corresponding Props member

diff --git a/include/dai/index.h b/include/dai/index.h
index dc45789..608b665 100644 (file)
--- a/include/dai/index.h
+++ b/include/dai/index.h
@@ -264,11 +264,11 @@ class Permute {
             return *this;
         }
 
-        /// Converts the linear index li to a vector index
-        /// corresponding with the dimensions in _dims,
-        /// permutes it according to sigma, 
-        /// and converts it back to a linear index
-        /// according to the permuted dimensions.
+        /// Calculates a permuted linear index.
+        /** Converts the linear index li to a vector index
+         *  corresponding with the dimensions in _dims, permutes it according to sigma, 
+         *  and converts it back to a linear index  according to the permuted dimensions.
+         */
         size_t convert_linear_index( size_t li ) {
             size_t N = _dims.size();
 
@@ -333,8 +333,7 @@ class State {
             return( state );
         }
 
-        /// Return state of variable n,
-        /// or zero if n is not in this State
+        /// Return state of variable n, or zero if n is not in this State
         size_t operator() ( const Var &n ) const {
             assert( valid() );
             states_type::const_iterator entry = states.find( n );
@@ -344,8 +343,7 @@ class State {
                 return entry->second;
         }
 
-        /// Return linear state of variables in varset,
-        /// setting them to zero if they are not in this State
+        /// Return linear state of variables in varset, setting them to zero if they are not in this State
         size_t operator() ( const VarSet &vs ) const {
             assert( valid() );
             size_t vs_state = 0;

diff --git a/include/dai/prob.h b/include/dai/prob.h
index 4d6ea84..70c8777 100644 (file)
--- a/include/dai/prob.h
+++ b/include/dai/prob.h
@@ -82,7 +82,7 @@ template <typename T> class TProb {
         /** \tparam Iterator Iterates over instances that can be cast to T.
          *  \param begin Points to first instance to be added.
          *  \param end Points just beyond last instance to be added.
-         *  \param sizeHint For efficiency, the number of elements can be speficied by sizeHint.
+         *  \param sizeHint For efficiency, the number of entries can be speficied by sizeHint.
          */
         template <typename Iterator>
         TProb( Iterator begin, Iterator end, size_t sizeHint=0 ) : _p() {

diff --git a/src/bp.cpp b/src/bp.cpp
index 3a730fd..0deb7d2 100644 (file)
--- a/src/bp.cpp
+++ b/src/bp.cpp
@@ -224,7 +224,7 @@ double BP::run() {
     if( props.verbose >= 1 )
         cout << "Starting " << identify() << "...";
     if( props.verbose >= 3)
-       cout << endl; 
+        cout << endl;
 
     double tic = toc();
     Diffs diffs(nrVars(), 1.0);

diff --git a/src/gibbs.cpp b/src/gibbs.cpp
index 96a8a17..940b73b 100644 (file)
--- a/src/gibbs.cpp
+++ b/src/gibbs.cpp
@@ -1,4 +1,5 @@
-/*  Copyright (C) 2008  Frederik Eaton [frederik at ofb dot net]
+/*  Copyright (C) 2008  Frederik Eaton  [frederik at ofb dot net],
+                        Joris Mooij  [joris dot mooij at tuebingen dot mpg dot de]
 
     This file is part of libDAI.
 
@@ -185,7 +186,7 @@ double Gibbs::run() {
     }
     
     if( props.verbose >= 3 )
-        cout << "Gibbs::run:  ran " << props.iters << " passes (" << toc() - tic << " clocks)." << endl;
+        cout << Name << "::run:  ran " << props.iters << " passes (" << toc() - tic << " clocks)." << endl;
 
     return 0.0;
 }
@@ -208,9 +209,9 @@ Factor Gibbs::belief( const Var &n ) const {
 
 vector<Factor> Gibbs::beliefs() const {
     vector<Factor> result;
-    for( size_t i = 0; i < nrVars(); i++ )
+    for( size_t i = 0; i < nrVars(); ++i )
         result.push_back( beliefV(i) );
-    for( size_t I = 0; I < nrFactors(); I++ )
+    for( size_t I = 0; I < nrFactors(); ++I )
         result.push_back( beliefF(I) );
     return result;
 }