include/dai/bbp.h

   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) 2009  Frederik Eaton [frederik at ofb dot net]
   8  */
   9
  10
  11 /// \file
  12 /// \brief Defines class BBP [\ref EaG09]
  13 /// \todo Improve documentation
  14
  15
  16 #ifndef ___defined_libdai_bbp_h
  17 #define ___defined_libdai_bbp_h
  18
  19
  20 #include <vector>
  21 #include <utility>
  22
  23 #include <dai/prob.h>
  24 #include <dai/daialg.h>
  25 #include <dai/factorgraph.h>
  26 #include <dai/enum.h>
  27 #include <dai/bp_dual.h>
  28
  29
  30 namespace dai {
  31
  32
  33 /// Computes the adjoint of the unnormed probability vector from the normalizer and the adjoint of the normalized probability vector @see eqn. (13) in [\ref EaG09]
  34 Prob unnormAdjoint( const Prob &w, Real Z_w, const Prob &adj_w );
  35
  36 /// Runs Gibbs sampling for \a iters iterations on ia.fg(), and returns state
  37 std::vector<size_t> getGibbsState( const InfAlg &ia, size_t iters );
  38
  39
  40 /// Implements BBP (Back-Belief-Propagation) [\ref EaG09]
  41 class BBP {
  42     protected:
  43         /// @name Inputs
  44         //@{
  45         BP_dual _bp_dual;
  46         const FactorGraph *_fg;
  47         const InfAlg *_ia;
  48         //@}
  49
  50         /// Number of iterations done
  51         size_t _iters;
  52
  53         /// @name Outputs
  54         //@{
  55         /// Variable factor adjoints
  56         std::vector<Prob> _adj_psi_V;
  57         /// Factor adjoints
  58         std::vector<Prob> _adj_psi_F;
  59         /// Variable->factor message adjoints (indexed [i][_I])
  60         std::vector<std::vector<Prob> > _adj_n;
  61         /// Factor->variable message adjoints (indexed [i][_I])
  62         std::vector<std::vector<Prob> > _adj_m;
  63         /// Normalized variable belief adjoints
  64         std::vector<Prob> _adj_b_V;
  65         /// Normalized factor belief adjoints
  66         std::vector<Prob> _adj_b_F;
  67         //@}
  68
  69         /// @name Helper quantities computed from the BP messages
  70         //@{
  71         /// _T[i][_I] (see eqn. (41) in [\ref EaG09])
  72         std::vector<std::vector<Prob > > _T;
  73         /// _U[I][_i] (see eqn. (42) in [\ref EaG09])
  74         std::vector<std::vector<Prob > > _U;
  75         /// _S[i][_I][_j] (see eqn. (43) in [\ref EaG09])
  76         std::vector<std::vector<std::vector<Prob > > > _S;
  77         /// _R[I][_i][_J] (see eqn. (44) in [\ref EaG09])
  78         std::vector<std::vector<std::vector<Prob > > > _R;
  79         //@}
  80
  81         /// Unnormalized variable belief adjoints
  82         std::vector<Prob> _adj_b_V_unnorm;
  83         /// Unnormalized factor belief adjoints
  84         std::vector<Prob> _adj_b_F_unnorm;
  85
  86         /// Initial variable factor adjoints
  87         std::vector<Prob> _init_adj_psi_V;
  88         /// Initial factor adjoints
  89         std::vector<Prob> _init_adj_psi_F;
  90
  91         /// Unnormalized variable->factor message adjoint (indexed [i][_I])
  92         std::vector<std::vector<Prob> > _adj_n_unnorm;
  93         /// Unnormalized factor->variable message adjoint (indexed [i][_I])
  94         std::vector<std::vector<Prob> > _adj_m_unnorm;
  95         /// Updated normalized variable->factor message adjoint (indexed [i][_I])
  96         std::vector<std::vector<Prob> > _new_adj_n;
  97         /// Updated normalized factor->variable message adjoint (indexed [i][_I])
  98         std::vector<std::vector<Prob> > _new_adj_m;
  99
 100         /// @name Optimized indexing (for performance)
 101         //@{
 102         /// Calculates _indices, which is a cache of IndexFor @see bp.cpp
 103         void RegenerateInds();
 104
 105         /// Index type
 106         typedef std::vector<size_t>  _ind_t;
 107         /// Cached indices (indexed [i][_I])
 108         std::vector<std::vector<_ind_t> >  _indices;
 109         /// Returns an index from the cache
 110         const _ind_t& _index(size_t i, size_t _I) const { return _indices[i][_I]; }
 111         //@}
 112
 113         /// @name Initialization
 114         //@{
 115         /// Calculate T values; see eqn. (41) in [\ref EaG09]
 116         void RegenerateT();
 117         /// Calculate U values; see eqn. (42) in [\ref EaG09]
 118         void RegenerateU();
 119         /// Calculate S values; see eqn. (43) in [\ref EaG09]
 120         void RegenerateS();
 121         /// Calculate R values; see eqn. (44) in [\ref EaG09]
 122         void RegenerateR();
 123         /// Calculate _adj_b_V_unnorm and _adj_b_F_unnorm from _adj_b_V and _adj_b_F
 124         void RegenerateInputs();
 125         /// Initialise members for factor adjoints (call after RegenerateInputs)
 126         void RegeneratePsiAdjoints();
 127         /// Initialise members for message adjoints (call after RegenerateInputs) for parallel algorithm
 128         void RegenerateParMessageAdjoints();
 129         /// Initialise members for message adjoints (call after RegenerateInputs) for sequential algorithm
 130         /** Same as RegenerateMessageAdjoints, but calls sendSeqMsgN rather
 131          *  than updating _adj_n (and friends) which are unused in the sequential algorithm.
 132          */
 133         void RegenerateSeqMessageAdjoints();
 134         //@}
 135
 136         /// Returns reference to T value; see eqn. (41) in [\ref EaG09]
 137         Prob & T(size_t i, size_t _I) { return _T[i][_I]; }
 138         /// Returns constant reference to T value; see eqn. (41) in [\ref EaG09]
 139         const Prob & T(size_t i, size_t _I) const { return _T[i][_I]; }
 140         /// Returns reference to U value; see eqn. (42) in [\ref EaG09]
 141         Prob & U(size_t I, size_t _i) { return _U[I][_i]; }
 142         /// Returns constant reference to U value; see eqn. (42) in [\ref EaG09]
 143         const Prob & U(size_t I, size_t _i) const { return _U[I][_i]; }
 144         /// Returns reference to S value; see eqn. (43) in [\ref EaG09]
 145         Prob & S(size_t i, size_t _I, size_t _j) { return _S[i][_I][_j]; }
 146         /// Returns constant reference to S value; see eqn. (43) in [\ref EaG09]
 147         const Prob & S(size_t i, size_t _I, size_t _j) const { return _S[i][_I][_j]; }
 148         /// Returns reference to R value; see eqn. (44) in [\ref EaG09]
 149         Prob & R(size_t I, size_t _i, size_t _J) { return _R[I][_i][_J]; }
 150         /// Returns constant reference to R value; see eqn. (44) in [\ref EaG09]
 151         const Prob & R(size_t I, size_t _i, size_t _J) const { return _R[I][_i][_J]; }
 152
 153         /// @name Parallel algorithm
 154         //@{
 155         /// Calculates new variable->factor message adjoint
 156         /** Increases variable factor adjoint according to eqn. (27) in [\ref EaG09] and
 157          *  calculates the new variable->factor message adjoint according to eqn. (29) in [\ref EaG09].
 158          */
 159         void calcNewN( size_t i, size_t _I );
 160         /// Calculates new factor->variable message adjoint
 161         /** Increases factor adjoint according to eqn. (28) in [\ref EaG09] and
 162          *  calculates the new factor->variable message adjoint according to the r.h.s. of eqn. (30) in [\ref EaG09].
 163          */
 164         void calcNewM( size_t i, size_t _I );
 165         /// Calculates unnormalized variable->factor message adjoint from the normalized one
 166         void calcUnnormMsgN( size_t i, size_t _I );
 167         /// Calculates unnormalized factor->variable message adjoint from the normalized one
 168         void calcUnnormMsgM( size_t i, size_t _I );
 169         /// Updates (un)normalized variable->factor message adjoints
 170         void upMsgN( size_t i, size_t _I );
 171         /// Updates (un)normalized factor->variable message adjoints
 172         void upMsgM( size_t i, size_t _I );
 173         /// Do one parallel update of all message adjoints
 174         void doParUpdate();
 175         //@}
 176
 177         /// @name Sequential algorithm
 178         //@{
 179         /// Helper function for sendSeqMsgM: increases factor->variable message adjoint by p and calculates the corresponding unnormalized adjoint
 180         void incrSeqMsgM( size_t i, size_t _I, const Prob& p );
 181         //  DISABLED BECAUSE IT IS BUGGY:
 182         //  void updateSeqMsgM( size_t i, size_t _I );
 183         /// Sets normalized factor->variable message adjoint and calculates the corresponding unnormalized adjoint
 184         void setSeqMsgM( size_t i, size_t _I, const Prob &p );
 185         /// Implements routine Send-n in Figure 5 in [\ref EaG09]
 186         void sendSeqMsgN( size_t i, size_t _I, const Prob &f );
 187         /// Implements routine Send-m in Figure 5 in [\ref EaG09]
 188         void sendSeqMsgM( size_t i, size_t _I );
 189         //@}
 190
 191         /// Calculates averaged L-1 norm of unnormalized message adjoints
 192         Real getUnMsgMag();
 193         /// Calculates averaged L-1 norms of current and new normalized message adjoints
 194         void getMsgMags( Real &s, Real &new_s );
 195
 196         /// Sets all vectors _adj_b_F to zero
 197         void zero_adj_b_F() {
 198             _adj_b_F.clear();
 199             _adj_b_F.reserve( _fg->nrFactors() );
 200             for( size_t I = 0; I < _fg->nrFactors(); I++ )
 201                 _adj_b_F.push_back( Prob( _fg->factor(I).states(), Real( 0.0 ) ) );
 202         }
 203
 204         /// Returns indices and magnitude of the largest normalized factor->variable message adjoint
 205         void getArgmaxMsgM( size_t &i, size_t &_I, Real &mag );
 206         /// Returns magnitude of the largest (in L1-norm) normalized factor->variable message adjoint
 207         Real getMaxMsgM();
 208         /// Calculates sum of L1 norms of all normalized factor->variable message adjoints
 209         Real getTotalMsgM();
 210         /// Calculates sum of L1 norms of all updated normalized factor->variable message adjoints
 211         Real getTotalNewMsgM();
 212         /// Calculates sum of L1 norms of all normalized variable->factor message adjoints
 213         Real getTotalMsgN();
 214
 215     public:
 216         /// Called by \a init, recalculates intermediate values
 217         void Regenerate();
 218
 219         /// Constructor
 220         BBP( const InfAlg *ia, const PropertySet &opts ) : _bp_dual(ia), _fg(&(ia->fg())), _ia(ia) {
 221             props.set(opts);
 222         }
 223
 224         /// Returns a vector of Probs (filled with zeroes) with state spaces corresponding to the factors in the factor graph fg
 225         std::vector<Prob> getZeroAdjF( const FactorGraph &fg );
 226         /// Returns a vector of Probs (filled with zeroes) with state spaces corresponding to the variables in the factor graph fg
 227         std::vector<Prob> getZeroAdjV( const FactorGraph &fg );
 228
 229         /// Initializes belief adjoints and initial factor adjoints and regenerates
 230         void init( const std::vector<Prob> &adj_b_V, const std::vector<Prob> &adj_b_F, const std::vector<Prob> &adj_psi_V, const std::vector<Prob> &adj_psi_F ) {
 231             _adj_b_V = adj_b_V;
 232             _adj_b_F = adj_b_F;
 233             _init_adj_psi_V = adj_psi_V;
 234             _init_adj_psi_F = adj_psi_F;
 235             Regenerate();
 236         }
 237
 238         /// Initializes belief adjoints and with zero initial factor adjoints and regenerates
 239         void init( const std::vector<Prob> &adj_b_V, const std::vector<Prob> &adj_b_F ) {
 240             init( adj_b_V, adj_b_F, getZeroAdjV(*_fg), getZeroAdjF(*_fg) );
 241         }
 242
 243         /// Initializes variable belief adjoints (and sets factor belief adjoints to zero) and with zero initial factor adjoints and regenerates
 244         void init( const std::vector<Prob> &adj_b_V ) {
 245             init(adj_b_V, getZeroAdjF(*_fg));
 246         }
 247
 248         /// Run until change is less than given tolerance
 249         void run();
 250
 251         /// Return number of iterations done so far
 252         size_t doneIters() { return _iters; }
 253
 254         /// Returns reference to variable factor adjoint
 255         Prob& adj_psi_V(size_t i) { return _adj_psi_V[i]; }
 256         /// Returns constant reference to variable factor adjoint
 257         const Prob& adj_psi_V(size_t i) const { return _adj_psi_V[i]; }
 258         /// Returns reference to factor adjoint
 259         Prob& adj_psi_F(size_t I) { return _adj_psi_F[I]; }
 260         /// Returns constant reference to factor adjoint
 261         const Prob& adj_psi_F(size_t I) const { return _adj_psi_F[I]; }
 262         /// Returns reference to variable belief adjoint
 263         Prob& adj_b_V(size_t i) { return _adj_b_V[i]; }
 264         /// Returns constant reference to variable belief adjoint
 265         const Prob& adj_b_V(size_t i) const { return _adj_b_V[i]; }
 266         /// Returns reference to factor belief adjoint
 267         Prob& adj_b_F(size_t I) { return _adj_b_F[I]; }
 268         /// Returns constant reference to factor belief adjoint
 269         const Prob& adj_b_F(size_t I) const { return _adj_b_F[I]; }
 270
 271      protected:
 272         /// Returns reference to variable->factor message adjoint
 273         Prob& adj_n(size_t i, size_t _I) { return _adj_n[i][_I]; }
 274         /// Returns constant reference to variable->factor message adjoint
 275         const Prob& adj_n(size_t i, size_t _I) const { return _adj_n[i][_I]; }
 276         /// Returns reference to factor->variable message adjoint
 277         Prob& adj_m(size_t i, size_t _I) { return _adj_m[i][_I]; }
 278         /// Returns constant reference to factor->variable message adjoint
 279         const Prob& adj_m(size_t i, size_t _I) const { return _adj_m[i][_I]; }
 280
 281      public:
 282         /// Parameters of this algorithm
 283         /* PROPERTIES(props,BBP) {
 284            /// Enumeration of possible update schedules
 285            DAI_ENUM(UpdateType,SEQ_FIX,SEQ_MAX,SEQ_BP_REV,SEQ_BP_FWD,PAR);
 286
 287            /// Verbosity
 288            size_t verbose;
 289
 290            /// Maximum number of iterations
 291            size_t maxiter;
 292
 293            /// Tolerance (not used for updates = SEQ_BP_REV, SEQ_BP_FWD)
 294            Real tol;
 295
 296            /// Damping constant (0 for none); damping = 1 - lambda where lambda is the damping constant used in [\ref EaG09]
 297            Real damping;
 298
 299            /// Update schedule
 300            UpdateType updates;
 301
 302            // DISABLED BECAUSE IT IS BUGGY:
 303            // bool clean_updates;
 304         }
 305         */
 306 /* {{{ GENERATED CODE: DO NOT EDIT. Created by
 307     ./scripts/regenerate-properties include/dai/bbp.h src/bbp.cpp
 308 */
 309         struct Properties {
 310             /// Enumeration of possible update schedules
 311             DAI_ENUM(UpdateType,SEQ_FIX,SEQ_MAX,SEQ_BP_REV,SEQ_BP_FWD,PAR);
 312             /// Verbosity
 313             size_t verbose;
 314             /// Maximum number of iterations
 315             size_t maxiter;
 316             /// Tolerance (not used for updates = SEQ_BP_REV, SEQ_BP_FWD)
 317             Real tol;
 318             /// Damping constant (0 for none); damping = 1 - lambda where lambda is the damping constant used in [\ref EaG09]
 319             Real damping;
 320             /// Update schedule
 321             UpdateType updates;
 322
 323             /// Set members from PropertySet
 324             void set(const PropertySet &opts);
 325             /// Get members into PropertySet
 326             PropertySet get() const;
 327             /// Convert to a string which can be parsed as a PropertySet
 328             std::string toString() const;
 329         } props;
 330 /* }}} END OF GENERATED CODE */
 331 };
 332
 333
 334 /// Enumeration of several cost functions that can be used with BBP.
 335 DAI_ENUM(bbp_cfn_t,CFN_GIBBS_B,CFN_GIBBS_B2,CFN_GIBBS_EXP,CFN_GIBBS_B_FACTOR,CFN_GIBBS_B2_FACTOR,CFN_GIBBS_EXP_FACTOR,CFN_VAR_ENT,CFN_FACTOR_ENT,CFN_BETHE_ENT);
 336
 337 /// Initialise BBP using InfAlg, cost function, and stateP
 338 /** Calls bbp.init with adjoints calculated from ia.beliefV and
 339  *  ia.beliefF. stateP is a Gibbs state and can be NULL, it will be
 340  *  initialised using a Gibbs run of 2*fg.Iterations() iterations.
 341  */
 342 void initBBPCostFnAdj( BBP &bbp, const InfAlg &ia, bbp_cfn_t cfn_type, const std::vector<size_t> *stateP );
 343
 344 /// Answers question: does the given cost function depend on having a Gibbs state?
 345 bool needGibbsState( bbp_cfn_t cfn );
 346
 347 /// Calculate actual value of cost function (cfn_type, stateP)
 348 /** This function returns the actual value of the cost function whose
 349  *  gradient with respect to singleton beliefs is given by
 350  *  gibbsToB1Adj on the same arguments
 351  */
 352 Real getCostFn( const InfAlg &fg, bbp_cfn_t cfn_type, const std::vector<size_t> *stateP );
 353
 354 /// Function to test the validity of adjoints computed by BBP given a state for each variable using numerical derivatives.
 355 /** Factors containing a variable are multiplied by psi_1 adjustments to verify accuracy of _adj_psi_V.
 356  *  \param bp BP object.
 357  *  \param state Global state of all variables.
 358  *  \param bbp_props BBP Properties.
 359  *  \param cfn Cost function to be used.
 360  *  \param h controls size of perturbation.
 361  */
 362 Real numericBBPTest( const InfAlg &bp, const std::vector<size_t> *state, const PropertySet &bbp_props, bbp_cfn_t cfn, Real h );
 363
 364
 365 } // end of namespace dai
 366
 367
 368 #endif