Merged TODO and FILEFORMAT into doxygen documentation, switched Makefile.win to GNU...
[libdai.git] / include / dai / factorgraph.h
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
4
5 This file is part of libDAI.
6
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.
11
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.
16
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 */
21
22
23 /// \file
24 /// \brief Defines the FactorGraph class
25 /// \todo Improve documentation
26
27
28 #ifndef __defined_libdai_factorgraph_h
29 #define __defined_libdai_factorgraph_h
30
31
32 #include <iostream>
33 #include <map>
34 #include <dai/bipgraph.h>
35 #include <dai/factor.h>
36
37
38 namespace dai {
39
40
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;
73
74 /// Shorthand for BipartiteGraph::Neighbor
75 typedef BipartiteGraph::Neighbor Neighbor;
76
77 /// Shorthand for BipartiteGraph::Neighbors
78 typedef BipartiteGraph::Neighbors Neighbors;
79
80 /// Shorthand for BipartiteGraph::Edge
81 typedef BipartiteGraph::Edge Edge;
82
83 private:
84 std::vector<Var> _vars;
85 std::vector<Factor> _factors;
86 std::map<size_t,Factor> _backup;
87
88 public:
89 /// Default constructor
90 FactorGraph() : G(), _vars(), _factors(), _backup() {}
91
92 /// Copy constructor
93 FactorGraph(const FactorGraph & x) : G(x.G), _vars(x._vars), _factors(x._factors), _backup(x._backup) {}
94
95 /// Assignment operator
96 FactorGraph & operator=(const FactorGraph & x) {
97 if( this != &x ) {
98 G = x.G;
99 _vars = x._vars;
100 _factors = x._factors;
101 _backup = x._backup;
102 }
103 return *this;
104 }
105
106 /// Constructs a FactorGraph from a vector of factors
107 FactorGraph(const std::vector<Factor> &P);
108
109 /// Constructs a FactorGraph from given factor and variable iterators
110 /** \tparam FactorInputIterator Iterator with value_type Factor
111 * \tparam VarInputIterator Iterator with value_type Var
112 * \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)
113 */
114 template<typename FactorInputIterator, typename VarInputIterator>
115 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 );
116
117 /// Destructor
118 virtual ~FactorGraph() {}
119
120 /// Clone *this (virtual copy constructor)
121 virtual FactorGraph* clone() const { return new FactorGraph(); }
122
123 /// Create (virtual default constructor)
124 virtual FactorGraph* create() const { return new FactorGraph(*this); }
125
126 /// Returns const reference to i'th variable
127 const Var & var(size_t i) const { return _vars[i]; }
128 /// Returns const reference to all factors
129 const std::vector<Var> & vars() const { return _vars; }
130 /// Returns reference to I'th factor
131 Factor & factor(size_t I) { return _factors[I]; }
132 /// Returns const reference to I'th factor
133 const Factor & factor(size_t I) const { return _factors[I]; }
134 /// Returns const reference to all factors
135 const std::vector<Factor> & factors() const { return _factors; }
136
137 /// Returns number of variables
138 size_t nrVars() const { return vars().size(); }
139 /// Returns number of factors
140 size_t nrFactors() const { return factors().size(); }
141 /// Calculates number of edges
142 size_t nrEdges() const { return G.nrEdges(); }
143
144 /// Provides read access to neighbors of variable
145 const Neighbors & nbV( size_t i ) const { return G.nb1(i); }
146 /// Provides full access to neighbors of variable
147 Neighbors & nbV( size_t i ) { return G.nb1(i); }
148 /// Provides read access to neighbors of factor
149 const Neighbors & nbF( size_t I ) const { return G.nb2(I); }
150 /// Provides full access to neighbors of factor
151 Neighbors & nbF( size_t I ) { return G.nb2(I); }
152 /// Provides read access to neighbor of variable
153 const Neighbor & nbV( size_t i, size_t _I ) const { return G.nb1(i)[_I]; }
154 /// Provides full access to neighbor of variable
155 Neighbor & nbV( size_t i, size_t _I ) { return G.nb1(i)[_I]; }
156 /// Provides read access to neighbor of factor
157 const Neighbor & nbF( size_t I, size_t _i ) const { return G.nb2(I)[_i]; }
158 /// Provides full access to neighbor of factor
159 Neighbor & nbF( size_t I, size_t _i ) { return G.nb2(I)[_i]; }
160
161 /// Returns the index of a particular variable
162 size_t findVar( const Var & n ) const {
163 size_t i = find( vars().begin(), vars().end(), n ) - vars().begin();
164 assert( i != nrVars() );
165 return i;
166 }
167
168 /// Returns a set of indexes corresponding to a set of variables
169 std::set<size_t> findVars( VarSet &ns ) const {
170 std::set<size_t> indexes;
171 for( VarSet::const_iterator n = ns.begin(); n != ns.end(); n++ )
172 indexes.insert( findVar( *n ) );
173 return indexes;
174 }
175
176 /// Returns index of the first factor that depends on the variables
177 size_t findFactor(const VarSet &ns) const {
178 size_t I;
179 for( I = 0; I < nrFactors(); I++ )
180 if( factor(I).vars() == ns )
181 break;
182 assert( I != nrFactors() );
183 return I;
184 }
185
186 /// Return all variables that occur in a factor involving the i'th variable, itself included
187 VarSet Delta( unsigned i ) const;
188
189 /// Return all variables that occur in a factor involving some variable in ns, ns itself included
190 VarSet Delta( const VarSet &ns ) const;
191
192 /// Return all variables that occur in a factor involving the i'th variable, n itself excluded
193 VarSet delta( unsigned i ) const;
194
195 /// Return all variables that occur in a factor involving some variable in ns, ns itself excluded
196 VarSet delta( const VarSet & ns ) const {
197 return Delta( ns ) / ns;
198 }
199
200 /// Set the content of the I'th factor and make a backup of its old content if backup == true
201 virtual void setFactor( size_t I, const Factor &newFactor, bool backup = false ) {
202 assert( newFactor.vars() == factor(I).vars() );
203 if( backup )
204 backupFactor( I );
205 _factors[I] = newFactor;
206 }
207
208 /// Set the contents of all factors as specified by facs and make a backup of the old contents if backup == true
209 virtual void setFactors( const std::map<size_t, Factor> & facs, bool backup = false ) {
210 for( std::map<size_t, Factor>::const_iterator fac = facs.begin(); fac != facs.end(); fac++ ) {
211 if( backup )
212 backupFactor( fac->first );
213 setFactor( fac->first, fac->second );
214 }
215 }
216
217 /// Clamp variable n to value i (i.e. multiply with a Kronecker delta \f$\delta_{x_n, i}\f$);
218 /// If backup == true, make a backup of all factors that are changed
219 virtual void clamp( const Var & n, size_t i, bool backup = false );
220
221 /// Set all factors interacting with the i'th variable 1
222 virtual void makeCavity( unsigned i, bool backup = false );
223
224 /// Backup the factors specified by indices in facs
225 virtual void backupFactors( const std::set<size_t> & facs );
226
227 /// Restore all factors to the backup copies
228 virtual void restoreFactors();
229
230 /// Returns true if the FactorGraph is connected
231 bool isConnected() const { return G.isConnected(); }
232
233 /// Returns true if the FactorGraph is a tree
234 bool isTree() const { return G.isTree(); }
235
236 /// Returns true if each factor depends on at most two variables
237 bool isPairwise() const;
238
239 /// Returns true if each variable has only two possible values
240 bool isBinary() const;
241
242 /// Reads a FactorGraph from a file
243 void ReadFromFile(const char *filename);
244
245 /// Writes a FactorGraph to a file
246 void WriteToFile(const char *filename) const;
247
248 /// Writes a FactorGraph to a GraphViz .dot file
249 void printDot( std::ostream& os ) const;
250
251 /// Returns the cliques in this FactorGraph
252 std::vector<VarSet> Cliques() const;
253
254 /// Clamp variable v_i to value state (i.e. multiply with a Kronecker delta \f$\delta_{x_{v_i},x}\f$);
255 /** This version changes the factor graph structure and thus returns a newly constructed FactorGraph
256 * and keeps the current one constant, contrary to clamp()
257 */
258 FactorGraph clamped( const Var & v_i, size_t x ) const;
259
260 /// Returns a copy of *this, where all factors that are subsumed by some larger factor are merged with the larger factors.
261 FactorGraph maximalFactors() const;
262
263 /// Makes a backup of the I'th Factor
264 void restoreFactor( size_t I );
265
266 /// Restores the I'th Factor from the backup (it should be backed up first)
267 void backupFactor( size_t I );
268
269 /// Makes a backup of all factors connected to a set of variables
270 void backupFactors( const VarSet &ns );
271 /// Restores all factors connected to a set of variables from their backups
272 void restoreFactors( const VarSet &ns );
273
274 // Friends
275 friend std::ostream& operator << (std::ostream& os, const FactorGraph& fg);
276 friend std::istream& operator >> (std::istream& is, FactorGraph& fg);
277
278 private:
279 /// Part of constructors (creates edges, neighbors and adjacency matrix)
280 void constructGraph( size_t nrEdges );
281 };
282
283
284 template<typename FactorInputIterator, typename VarInputIterator>
285 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() {
286 // add factors
287 size_t nrEdges = 0;
288 _factors.reserve( nr_fact_hint );
289 for( FactorInputIterator p2 = fact_begin; p2 != fact_end; ++p2 ) {
290 _factors.push_back( *p2 );
291 nrEdges += p2->vars().size();
292 }
293
294 // add variables
295 _vars.reserve( nr_var_hint );
296 for( VarInputIterator p1 = var_begin; p1 != var_end; ++p1 )
297 _vars.push_back( *p1 );
298
299 // create graph structure
300 constructGraph( nrEdges );
301 }
302
303
304 } // end of namespace dai
305
306
307 #endif