Merge branch 'joris'
[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 /// Iterator over factors
84 typedef std::vector<Factor>::iterator iterator;
85
86 /// Const iterator over factors
87 typedef std::vector<Factor>::const_iterator const_iterator;
88
89
90 private:
91 std::vector<Var> _vars;
92 std::vector<Factor> _factors;
93 std::map<size_t,Factor> _backup;
94
95 public:
96 /// Default constructor
97 FactorGraph() : G(), _vars(), _factors(), _backup() {}
98
99 /// Copy constructor
100 FactorGraph(const FactorGraph & x) : G(x.G), _vars(x._vars), _factors(x._factors), _backup(x._backup) {}
101
102 /// Assignment operator
103 FactorGraph & operator=(const FactorGraph & x) {
104 if( this != &x ) {
105 G = x.G;
106 _vars = x._vars;
107 _factors = x._factors;
108 _backup = x._backup;
109 }
110 return *this;
111 }
112
113 /// Constructs a FactorGraph from a vector of factors
114 FactorGraph(const std::vector<Factor> &P);
115
116 /// Constructs a FactorGraph from given factor and variable iterators
117 /** \tparam FactorInputIterator Iterator with value_type Factor
118 * \tparam VarInputIterator Iterator with value_type Var
119 * \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)
120 */
121 template<typename FactorInputIterator, typename VarInputIterator>
122 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 );
123
124 /// Destructor
125 virtual ~FactorGraph() {}
126
127 /// Clone *this (virtual copy constructor)
128 virtual FactorGraph* clone() const { return new FactorGraph(); }
129
130 /// Create (virtual default constructor)
131 virtual FactorGraph* create() const { return new FactorGraph(*this); }
132
133 /// Returns const reference to i'th variable
134 const Var & var(size_t i) const { return _vars[i]; }
135 /// Returns const reference to all factors
136 const std::vector<Var> & vars() const { return _vars; }
137 /// Returns reference to I'th factor
138 Factor & factor(size_t I) { return _factors[I]; }
139 /// Returns const reference to I'th factor
140 const Factor & factor(size_t I) const { return _factors[I]; }
141 /// Returns const reference to all factors
142 const std::vector<Factor> & factors() const { return _factors; }
143 /// Returns iterator pointing to first factor
144 iterator begin() { return _factors.begin(); }
145 /// Returns const iterator pointing to first factor
146 const_iterator begin() const { return _factors.begin(); }
147 /// Returns iterator pointing beyond last factor
148 iterator end() { return _factors.end(); }
149 /// Returns const iterator pointing beyond last factor
150 const_iterator end() const { return _factors.end(); }
151
152 /// Returns number of variables
153 size_t nrVars() const { return vars().size(); }
154 /// Returns number of factors
155 size_t nrFactors() const { return factors().size(); }
156 /// Calculates number of edges
157 size_t nrEdges() const { return G.nrEdges(); }
158
159 /// Provides read access to neighbors of variable
160 const Neighbors & nbV( size_t i ) const { return G.nb1(i); }
161 /// Provides full access to neighbors of variable
162 Neighbors & nbV( size_t i ) { return G.nb1(i); }
163 /// Provides read access to neighbors of factor
164 const Neighbors & nbF( size_t I ) const { return G.nb2(I); }
165 /// Provides full access to neighbors of factor
166 Neighbors & nbF( size_t I ) { return G.nb2(I); }
167 /// Provides read access to neighbor of variable
168 const Neighbor & nbV( size_t i, size_t _I ) const { return G.nb1(i)[_I]; }
169 /// Provides full access to neighbor of variable
170 Neighbor & nbV( size_t i, size_t _I ) { return G.nb1(i)[_I]; }
171 /// Provides read access to neighbor of factor
172 const Neighbor & nbF( size_t I, size_t _i ) const { return G.nb2(I)[_i]; }
173 /// Provides full access to neighbor of factor
174 Neighbor & nbF( size_t I, size_t _i ) { return G.nb2(I)[_i]; }
175
176 /// Returns the index of a particular variable
177 size_t findVar( const Var & n ) const {
178 size_t i = find( vars().begin(), vars().end(), n ) - vars().begin();
179 assert( i != nrVars() );
180 return i;
181 }
182
183 /// Returns a set of indexes corresponding to a set of variables
184 std::set<size_t> findVars( VarSet &ns ) const {
185 std::set<size_t> indexes;
186 for( VarSet::const_iterator n = ns.begin(); n != ns.end(); n++ )
187 indexes.insert( findVar( *n ) );
188 return indexes;
189 }
190
191 /// Returns index of the first factor that depends on the variables
192 size_t findFactor(const VarSet &ns) const {
193 size_t I;
194 for( I = 0; I < nrFactors(); I++ )
195 if( factor(I).vars() == ns )
196 break;
197 assert( I != nrFactors() );
198 return I;
199 }
200
201 /// Return all variables that occur in a factor involving the i'th variable, itself included
202 VarSet Delta( unsigned i ) const;
203
204 /// Return all variables that occur in a factor involving some variable in ns, ns itself included
205 VarSet Delta( const VarSet &ns ) const;
206
207 /// Return all variables that occur in a factor involving the i'th variable, n itself excluded
208 VarSet delta( unsigned i ) const;
209
210 /// Return all variables that occur in a factor involving some variable in ns, ns itself excluded
211 VarSet delta( const VarSet & ns ) const {
212 return Delta( ns ) / ns;
213 }
214
215 /// Set the content of the I'th factor and make a backup of its old content if backup == true
216 virtual void setFactor( size_t I, const Factor &newFactor, bool backup = false ) {
217 assert( newFactor.vars() == factor(I).vars() );
218 if( backup )
219 backupFactor( I );
220 _factors[I] = newFactor;
221 }
222
223 /// Set the contents of all factors as specified by facs and make a backup of the old contents if backup == true
224 virtual void setFactors( const std::map<size_t, Factor> & facs, bool backup = false ) {
225 for( std::map<size_t, Factor>::const_iterator fac = facs.begin(); fac != facs.end(); fac++ ) {
226 if( backup )
227 backupFactor( fac->first );
228 setFactor( fac->first, fac->second );
229 }
230 }
231
232 /// Clamp variable n to value i (i.e. multiply with a Kronecker delta \f$\delta_{x_n, i}\f$);
233 /// If backup == true, make a backup of all factors that are changed
234 virtual void clamp( const Var & n, size_t i, bool backup = false );
235
236 /// Set all factors interacting with the i'th variable 1
237 virtual void makeCavity( unsigned i, bool backup = false );
238
239 /// Backup the factors specified by indices in facs
240 virtual void backupFactors( const std::set<size_t> & facs );
241
242 /// Restore all factors to the backup copies
243 virtual void restoreFactors();
244
245 /// Returns true if the FactorGraph is connected
246 bool isConnected() const { return G.isConnected(); }
247
248 /// Returns true if the FactorGraph is a tree
249 bool isTree() const { return G.isTree(); }
250
251 /// Returns true if each factor depends on at most two variables
252 bool isPairwise() const;
253
254 /// Returns true if each variable has only two possible values
255 bool isBinary() const;
256
257 /// Reads a FactorGraph from a file
258 void ReadFromFile(const char *filename);
259
260 /// Writes a FactorGraph to a file
261 void WriteToFile(const char *filename) const;
262
263 /// Writes a FactorGraph to a GraphViz .dot file
264 void printDot( std::ostream& os ) const;
265
266 /// Returns the cliques in this FactorGraph
267 std::vector<VarSet> Cliques() const;
268
269 /// Clamp variable v_i to value state (i.e. multiply with a Kronecker delta \f$\delta_{x_{v_i},x}\f$);
270 /** This version changes the factor graph structure and thus returns a newly constructed FactorGraph
271 * and keeps the current one constant, contrary to clamp()
272 */
273 FactorGraph clamped( const Var & v_i, size_t x ) const;
274
275 /// Returns a copy of *this, where all factors that are subsumed by some larger factor are merged with the larger factors.
276 FactorGraph maximalFactors() const;
277
278 /// Makes a backup of the I'th Factor
279 void restoreFactor( size_t I );
280
281 /// Restores the I'th Factor from the backup (it should be backed up first)
282 void backupFactor( size_t I );
283
284 /// Makes a backup of all factors connected to a set of variables
285 void backupFactors( const VarSet &ns );
286 /// Restores all factors connected to a set of variables from their backups
287 void restoreFactors( const VarSet &ns );
288
289 // Friends
290 friend std::ostream& operator << (std::ostream& os, const FactorGraph& fg);
291 friend std::istream& operator >> (std::istream& is, FactorGraph& fg);
292
293 private:
294 /// Part of constructors (creates edges, neighbors and adjacency matrix)
295 void constructGraph( size_t nrEdges );
296 };
297
298
299 template<typename FactorInputIterator, typename VarInputIterator>
300 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() {
301 // add factors
302 size_t nrEdges = 0;
303 _factors.reserve( nr_fact_hint );
304 for( FactorInputIterator p2 = fact_begin; p2 != fact_end; ++p2 ) {
305 _factors.push_back( *p2 );
306 nrEdges += p2->vars().size();
307 }
308
309 // add variables
310 _vars.reserve( nr_var_hint );
311 for( VarInputIterator p1 = var_begin; p1 != var_end; ++p1 )
312 _vars.push_back( *p1 );
313
314 // create graph structure
315 constructGraph( nrEdges );
316 }
317
318
319 } // end of namespace dai
320
321
322 #endif