1 /* This file is part of libDAI - http://www.libdai.org/

2 *

3 * libDAI is licensed under the terms of the GNU General Public License version

4 * 2, or (at your option) any later version. libDAI is distributed without any

5 * warranty. See the file COPYING for more details.

6 *

7 * Copyright (C) 2006-2009 Joris Mooij [joris dot mooij at libdai dot org]

8 * Copyright (C) 2006-2007 Radboud University Nijmegen, The Netherlands

9 */

12 /// \file

13 /// \brief Defines the general interface for inference methods in libDAI (classes InfAlg, DaiAlg<>, DaiAlgFG and DaiAlgRG).

16 #ifndef __defined_libdai_daialg_h

17 #define __defined_libdai_daialg_h

20 #include <string>

21 #include <iostream>

22 #include <vector>

23 #include <dai/factorgraph.h>

24 #include <dai/regiongraph.h>

25 #include <dai/properties.h>

31 /// InfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI.

32 /** \idea General marginalization functions like calcMarginal() now copy a complete InfAlg object. Instead,

33 * it would make more sense that they construct a new object without copying the FactorGraph or RegionGraph.

34 * Or they can simply be made methods of the general InfAlg class.

35 * \idea Use a PropertySet as output of an InfAlg, instead of functions like maxDiff() and Iterations().

36 */

39 /// \name Constructors/destructors

40 //@{

41 /// Virtual destructor (needed because this class contains virtual functions)

44 /// Returns a pointer to a new, cloned copy of \c *this (i.e., virtual copy constructor)

46 //@}

48 /// \name Queries

49 //@{

50 /// Identifies itself for logging purposes

53 /// Returns reference to underlying FactorGraph.

56 /// Returns constant reference to underlying FactorGraph.

58 //@}

60 /// \name Inference interface

61 //@{

62 /// Initializes all data structures of the approximate inference algorithm.

63 /** \note This method should be called at least once before run() is called.

64 */

67 /// Initializes all data structures corresponding to some set of variables.

68 /** This method can be used to do a partial initialization after a part of the factor graph has changed.

69 * Instead of initializing all data structures, it only initializes those involving the variables in \a vs.

70 * \throw NOT_IMPLEMENTED if not implemented/supported

71 */

74 /// Runs the approximate inference algorithm.

75 /** \note Before run() is called the first time, init() should have been called.

76 */

79 /// Returns the (approximate) marginal probability distribution of a variable.

80 /** \note Before this method is called, run() should have been called.

81 */

84 /// Returns the (approximate) marginal probability distribution of a set of variables.

85 /** \note Before this method is called, run() should have been called.

86 * \throw NOT_IMPLEMENTED if not implemented/supported.

87 * \throw BELIEF_NOT_AVAILABLE if the requested belief cannot be calculated with this algorithm.

88 */

91 /// Returns the (approximate) marginal probability distribution of the variable with index \a i.

92 /** For some approximate inference algorithms, using beliefV() is preferred to belief() for performance reasons.

93 * \note Before this method is called, run() should have been called.

94 */

97 /// Returns the (approximate) marginal probability distribution of the variables on which factor \a I depends.

98 /** For some approximate inference algorithms, using beliefF() is preferred to belief() for performance reasons.

99 * \note Before this method is called, run() should have been called.

100 */

103 /// Returns all beliefs (approximate marginal probability distributions) calculated by the algorithm.

104 /** \note Before this method is called, run() should have been called.

105 */

108 /// Returns the logarithm of the (approximated) partition sum (normalizing constant of the factor graph).

109 /** \note Before this method is called, run() should have been called.

110 * \throw NOT_IMPLEMENTED if not implemented/supported

111 */

114 /// Returns maximum difference between single variable beliefs in the last iteration.

115 /** \throw NOT_IMPLEMENTED if not implemented/supported

116 */

119 /// Returns number of iterations done (one iteration passes over the complete factorgraph).

120 /** \throw NOT_IMPLEMENTED if not implemented/supported

121 */

123 //@}

125 /// \name Changing the factor graph

126 //@{

127 /// Clamp variable with index \a i to value \a x (i.e. multiply with a Kronecker delta \f$\delta_{x_i, x}\f$)

128 /** If \a backup == \c true, make a backup of all factors that are changed.

129 */

132 /// Sets all factors interacting with variable with index \a i to one.

133 /** If \a backup == \c true, make a backup of all factors that are changed.

134 */

136 //@}

138 /// \name Backup/restore mechanism for factors

139 //@{

140 /// Make a backup copy of factor \a I

141 /** \throw MULTIPLE_UNDO if a backup already exists

142 */

144 /// Make backup copies of all factors involving the variables in \a vs

145 /** \throw MULTIPLE_UNDO if a backup already exists

146 */

149 /// Restore factor \a I from its backup copy

151 /// Restore the factors involving the variables in \a vs from their backup copies

153 //@}

155 /// \name Managing parameters

156 //@{

157 /// Set parameters of this inference algorithm.

158 /** The parameters are set according to the PropertySet \a opts.

159 * The values can be stored either as std::string or as the type of the corresponding MF::props member.

160 */

162 /// Returns parameters of this inference algorithm converted into a PropertySet.

164 /// Returns parameters of this inference algorithm formatted as a string in the format "[key1=val1,key2=val2,...,keyn=valn]".

166 //@}

167 };

170 /// Combines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph).

171 /** Inference algorithms in libDAI directly inherit from a DAIAlg, currently either

172 * from a DAIAlg<FactorGraph> or from a DAIAlg<RegionGraph>.

173 *

174 * \tparam GRM Should be castable to FactorGraph

175 * \idea A DAIAlg should not inherit from a FactorGraph or RegionGraph, but should

176 * store a reference to the graphical model object. This prevents needless copying

177 * of (possibly large) data structures. Disadvantage: the caller must not change

178 * the graphical model between calls to the inference algorithm (maybe a smart_ptr

179 * or some locking mechanism would help here?).

180 */

184 /// \name Constructors/destructors

185 //@{

186 /// Default constructor

189 /// Construct from GRM

191 //@}

193 /// \name Queries

194 //@{

195 /// Returns reference to underlying FactorGraph.

198 /// Returns constant reference to underlying FactorGraph.

200 //@}

202 /// \name Changing the factor graph

203 //@{

204 /// Clamp variable with index \a i to value \a x (i.e. multiply with a Kronecker delta \f$\delta_{x_i, x}\f$)

205 /** If \a backup == \c true, make a backup of all factors that are changed.

206 */

209 /// Sets all factors interacting with variable with index \a i to one.

210 /** If \a backup == \c true, make a backup of all factors that are changed.

211 */

213 //@}

215 /// \name Backup/restore mechanism for factors

216 //@{

217 /// Make a backup copy of factor \a I

219 /// Make backup copies of all factors involving the variables in \a vs

222 /// Restore factor \a I from its backup copy

224 /// Restore the factors involving the variables in \a vs from their backup copies

226 //@}

227 };

230 /// Base class for inference algorithms that operate on a FactorGraph

233 /// Base class for inference algorithms that operate on a RegionGraph

237 /// Calculates the marginal probability distribution for \a vs using inference algorithm \a obj.

238 /** calcMarginal() works by clamping all variables in \a vs and calculating the partition sum for each clamped state.

239 * Therefore, it can be used in combination with any inference algorithm that can calculate/approximate partition sums.

240 * \param obj instance of inference algorithm to be used

241 * \param vs variables for which the marginal should be calculated

242 * \param reInit should be set to \c true if at least one of the possible clamped states would be invalid (leading to a factor graph with zero partition sum).

243 */

246 /// Calculates beliefs for all pairs of variables in \a vs using inference algorithm \a obj.

247 /** calcPairBeliefs() works by

248 * - clamping single variables in \a vs and calculating the partition sum and the single variable beliefs for each clamped state, if \a accurate == \c false;

249 * - clamping pairs of variables in \a vs and calculating the partition sum for each clamped state, if \a accurate == \c true.

250 *

251 * Therefore, it can be used in combination with any inference algorithm that can calculate/approximate partition sums (and single variable beliefs, if

252 * \a accurate == \c true).

253 * \param obj instance of inference algorithm to be used

254 * \param vs variables for which the pair beliefs should be calculated

255 * \param reInit should be set to \c true if at least one of the possible clamped states would be invalid (leading to a factor graph with zero partition sum).

256 * \param accurate if \c true, uses a slower but more accurate approximation algorithm

257 */

258 std::vector<Factor> calcPairBeliefs( const InfAlg& obj, const VarSet& vs, bool reInit, bool accurate=false );

264 #endif