1 /* Copyright (C) 2006-2008 Joris Mooij [joris dot mooij at tuebingen dot mpg dot de]
2 Radboud University Nijmegen, The Netherlands /
3 Max Planck Institute for Biological Cybernetics, Germany
5 This file is part of libDAI.
7 libDAI is free software; you can redistribute it and/or modify
8 it under the terms of the GNU General Public License as published by
9 the Free Software Foundation; either version 2 of the License, or
10 (at your option) any later version.
12 libDAI is distributed in the hope that it will be useful,
13 but WITHOUT ANY WARRANTY; without even the implied warranty of
14 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
15 GNU General Public License for more details.
17 You should have received a copy of the GNU General Public License
18 along with libDAI; if not, write to the Free Software
19 Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
20 */
23 /// \file
24 /// \brief Defines the FactorGraph class
25 /// \todo Improve documentation
28 #ifndef __defined_libdai_factorgraph_h
29 #define __defined_libdai_factorgraph_h
32 #include <iostream>
33 #include <map>
34 #include <dai/bipgraph.h>
35 #include <dai/factor.h>
38 namespace dai {
41 /// Represents a factor graph.
42 /** Both Bayesian Networks and Markov random fields can be represented in a
43 * unifying representation, called <em>factor graph</em> [\ref KFL01],
44 * implemented in libDAI by the FactorGraph class.
45 *
46 * Consider a probability distribution over \f$N\f$ discrete random variables
47 * \f$x_0,x_1,\dots,x_N\f$ that factorizes as a product of factors, each of
48 * which depends on some subset of the variables:
49 * \f[
50 * P(x_0,x_1,\dots,x_N) = \frac{1}{Z} \prod_{I=0}^M f_I(x_I), \qquad
51 * Z = \sum_{x_0}\dots\sum_{x_N} \prod_{I=0}^M f_I(X_I).
52 * \f]
53 * Each factor \f$f_I\f$ is a function from an associated subset
54 * of variables \f$X_I \subset \{x_0,x_1,\dots,x_N\}\f$ to the nonnegative
55 * real numbers.
56 *
57 * For a Bayesian network, each factor corresponds to a (conditional)
58 * probability table, whereas for a Markov random field, each factor
59 * corresponds to a maximal clique of the undirected graph.
60 *
61 * Factor graphs explicitly express the factorization structure of the
62 * corresponding probability distribution.
63 *
64 * \todo Alternative implementation of undo factor changes: the only things that have to be
65 * undone currently are setting a factor to 1 and setting a factor to a Kronecker delta. This
66 * could also be implemented in the TFactor itself, which could maintain its state
67 * (ones/delta/full) and act accordingly.
68 */
69 class FactorGraph {
70 public:
71 /// Stores the neighborhood structure
72 BipartiteGraph G;
74 /// Shorthand for BipartiteGraph::Neighbor
75 typedef BipartiteGraph::Neighbor Neighbor;
77 /// Shorthand for BipartiteGraph::Neighbors
78 typedef BipartiteGraph::Neighbors Neighbors;
80 /// Shorthand for BipartiteGraph::Edge
81 typedef BipartiteGraph::Edge Edge;
83 /// Iterator over factors
84 typedef std::vector<Factor>::iterator iterator;
86 /// Const iterator over factors
87 typedef std::vector<Factor>::const_iterator const_iterator;
90 private:
91 std::vector<Var> _vars;
92 std::vector<Factor> _factors;
93 std::map<size_t,Factor> _backup;
95 public:
96 /// Default constructor
97 FactorGraph() : G(), _vars(), _factors(), _backup() {}
99 /// Constructs a FactorGraph from a vector of factors
100 FactorGraph(const std::vector<Factor> &P);
102 /// Constructs a FactorGraph from given factor and variable iterators
103 /** \tparam FactorInputIterator Iterator with value_type Factor
104 * \tparam VarInputIterator Iterator with value_type Var
105 * \pre Assumes that the set of variables in [var_begin,var_end) is the union of the variables in the factors in [fact_begin, fact_end)
106 */
107 template<typename FactorInputIterator, typename VarInputIterator>
108 FactorGraph(FactorInputIterator fact_begin, FactorInputIterator fact_end, VarInputIterator var_begin, VarInputIterator var_end, size_t nr_fact_hint = 0, size_t nr_var_hint = 0 );
110 /// Destructor
111 virtual ~FactorGraph() {}
113 /// Clone *this (virtual copy constructor)
114 virtual FactorGraph* clone() const { return new FactorGraph(); }
116 /// Returns const reference to i'th variable
117 const Var & var(size_t i) const { return _vars[i]; }
118 /// Returns const reference to all factors
119 const std::vector<Var> & vars() const { return _vars; }
120 /// Returns reference to I'th factor
121 Factor & factor(size_t I) { return _factors[I]; }
122 /// Returns const reference to I'th factor
123 const Factor & factor(size_t I) const { return _factors[I]; }
124 /// Returns const reference to all factors
125 const std::vector<Factor> & factors() const { return _factors; }
126 /// Returns iterator pointing to first factor
127 iterator begin() { return _factors.begin(); }
128 /// Returns const iterator pointing to first factor
129 const_iterator begin() const { return _factors.begin(); }
130 /// Returns iterator pointing beyond last factor
131 iterator end() { return _factors.end(); }
132 /// Returns const iterator pointing beyond last factor
133 const_iterator end() const { return _factors.end(); }
135 /// Returns number of variables
136 size_t nrVars() const { return vars().size(); }
137 /// Returns number of factors
138 size_t nrFactors() const { return factors().size(); }
139 /// Calculates number of edges
140 size_t nrEdges() const { return G.nrEdges(); }
143 const Neighbors & nbV( size_t i ) const { return G.nb1(i); }
144 /// Provides full access to neighbors of variable
145 Neighbors & nbV( size_t i ) { return G.nb1(i); }
147 const Neighbors & nbF( size_t I ) const { return G.nb2(I); }
148 /// Provides full access to neighbors of factor
149 Neighbors & nbF( size_t I ) { return G.nb2(I); }
151 const Neighbor & nbV( size_t i, size_t _I ) const { return G.nb1(i)[_I]; }
152 /// Provides full access to neighbor of variable
153 Neighbor & nbV( size_t i, size_t _I ) { return G.nb1(i)[_I]; }
155 const Neighbor & nbF( size_t I, size_t _i ) const { return G.nb2(I)[_i]; }
156 /// Provides full access to neighbor of factor
157 Neighbor & nbF( size_t I, size_t _i ) { return G.nb2(I)[_i]; }
159 /// Returns the index of a particular variable
160 size_t findVar( const Var & n ) const {
161 size_t i = find( vars().begin(), vars().end(), n ) - vars().begin();
162 assert( i != nrVars() );
163 return i;
164 }
166 /// Returns a set of indexes corresponding to a set of variables
167 std::set<size_t> findVars( VarSet &ns ) const {
168 std::set<size_t> indexes;
169 for( VarSet::const_iterator n = ns.begin(); n != ns.end(); n++ )
170 indexes.insert( findVar( *n ) );
171 return indexes;
172 }
174 /// Returns index of the first factor that depends on the variables
175 size_t findFactor(const VarSet &ns) const {
176 size_t I;
177 for( I = 0; I < nrFactors(); I++ )
178 if( factor(I).vars() == ns )
179 break;
180 assert( I != nrFactors() );
181 return I;
182 }
184 /// Return all variables that occur in a factor involving the i'th variable, itself included
185 VarSet Delta( unsigned i ) const;
187 /// Return all variables that occur in a factor involving some variable in ns, ns itself included
188 VarSet Delta( const VarSet &ns ) const;
190 /// Return all variables that occur in a factor involving the i'th variable, n itself excluded
191 VarSet delta( unsigned i ) const;
193 /// Return all variables that occur in a factor involving some variable in ns, ns itself excluded
194 VarSet delta( const VarSet & ns ) const {
195 return Delta( ns ) / ns;
196 }
198 /// Set the content of the I'th factor and make a backup of its old content if backup == true
199 virtual void setFactor( size_t I, const Factor &newFactor, bool backup = false ) {
200 assert( newFactor.vars() == factor(I).vars() );
201 if( backup )
202 backupFactor( I );
203 _factors[I] = newFactor;
204 }
206 /// Set the contents of all factors as specified by facs and make a backup of the old contents if backup == true
207 virtual void setFactors( const std::map<size_t, Factor> & facs, bool backup = false ) {
208 for( std::map<size_t, Factor>::const_iterator fac = facs.begin(); fac != facs.end(); fac++ ) {
209 if( backup )
210 backupFactor( fac->first );
211 setFactor( fac->first, fac->second );
212 }
213 }
215 /// Clamp variable n to value i (i.e. multiply with a Kronecker delta \f$\delta_{x_n, i}\f$)
216 /** If backup == true, make a backup of all factors that are changed
217 */
218 virtual void clamp( const Var & n, size_t i, bool backup = false );
220 /// Clamp a variable in a factor graph to have one out of a list of values
221 /** If backup == true, make a backup of all factors that are changed
222 */
223 void clampVar( size_t i, const std::vector<size_t> &xis, bool backup = false );
225 /// Clamp a factor in a factor graph to have one out of a list of values
226 /** If backup == true, make a backup of all factors that are changed
227 */
228 void clampFactor( size_t I, const std::vector<size_t> &xIs, bool backup = false );
230 /// Set all factors interacting with the i'th variable 1
231 virtual void makeCavity( unsigned i, bool backup = false );
233 /// Backup the factors specified by indices in facs
234 virtual void backupFactors( const std::set<size_t> & facs );
236 /// Restore all factors to the backup copies
237 virtual void restoreFactors();
239 /// Returns true if the FactorGraph is connected
240 bool isConnected() const { return G.isConnected(); }
242 /// Returns true if the FactorGraph is a tree
243 bool isTree() const { return G.isTree(); }
245 /// Returns true if each factor depends on at most two variables
246 bool isPairwise() const;
248 /// Returns true if each variable has only two possible values
249 bool isBinary() const;
251 /// Reads a FactorGraph from a file
252 void ReadFromFile(const char *filename);
254 /// Writes a FactorGraph to a file
255 void WriteToFile(const char *filename, size_t precision=15) const;
257 /// Writes a FactorGraph to a GraphViz .dot file
258 void printDot( std::ostream& os ) const;
260 /// Returns the cliques in this FactorGraph
261 std::vector<VarSet> Cliques() const;
263 /// Clamp variable v_i to value state (i.e. multiply with a Kronecker delta \f$\delta_{x_{v_i},x}\f$);
264 /** This version changes the factor graph structure and thus returns a newly constructed FactorGraph
265 * and keeps the current one constant, contrary to clamp()
266 */
267 FactorGraph clamped( const Var & v_i, size_t x ) const;
269 /// Returns a copy of *this, where all factors that are subsumed by some larger factor are merged with the larger factors.
270 FactorGraph maximalFactors() const;
272 /// Makes a backup of the I'th Factor
273 void restoreFactor( size_t I );
275 /// Restores the I'th Factor from the backup (it should be backed up first)
276 void backupFactor( size_t I );
278 /// Makes a backup of all factors connected to a set of variables
279 void backupFactors( const VarSet &ns );
280 /// Restores all factors connected to a set of variables from their backups
281 void restoreFactors( const VarSet &ns );
283 // Friends
284 friend std::ostream& operator << (std::ostream& os, const FactorGraph& fg);
285 friend std::istream& operator >> (std::istream& is, FactorGraph& fg);
287 /// @name Backwards compatibility layer (to be removed soon)
288 //@{
289 size_t VV2E(size_t n1, size_t n2) const { return G.VV2E(n1,n2); }
290 const Edge& edge(size_t e) const { return G.edge(e); }
291 void indexEdges() { G.indexEdges(); }
292 size_t nr_edges() const { return G.nr_edges(); }
293 const std::vector<Edge>& edges() const { return G.edges(); }
294 //}@
296 private:
297 /// Part of constructors (creates edges, neighbors and adjacency matrix)
298 void constructGraph( size_t nrEdges );
299 };
302 template<typename FactorInputIterator, typename VarInputIterator>
303 FactorGraph::FactorGraph(FactorInputIterator fact_begin, FactorInputIterator fact_end, VarInputIterator var_begin, VarInputIterator var_end, size_t nr_fact_hint, size_t nr_var_hint ) : G(), _backup() {
304 // add factors
305 size_t nrEdges = 0;
306 _factors.reserve( nr_fact_hint );
307 for( FactorInputIterator p2 = fact_begin; p2 != fact_end; ++p2 ) {
308 _factors.push_back( *p2 );
309 nrEdges += p2->vars().size();
310 }
312 // add variables
313 _vars.reserve( nr_var_hint );
314 for( VarInputIterator p1 = var_begin; p1 != var_end; ++p1 )
315 _vars.push_back( *p1 );
317 // create graph structure
318 constructGraph( nrEdges );
319 }
322 } // end of namespace dai
325 #endif