examples/example.cpp

   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 #include <iostream>
  24 #include <dai/alldai.h>  // Include main libDAI header file
  25
  26
  27 using namespace dai;
  28 using namespace std;
  29
  30
  31 int main( int argc, char *argv[] ) {
  32     if ( argc != 2 ) {
  33         cout << "Usage: " << argv[0] << " <filename.fg>" << endl << endl;
  34         cout << "Reads factor graph <filename.fg> and runs" << endl;
  35         cout << "Belief Propagation and JunctionTree on it." << endl << endl;
  36         return 1;
  37     } else {
  38         // Read FactorGraph from the file specified by the first command line argument
  39         FactorGraph fg;
  40         fg.ReadFromFile(argv[1]);
  41
  42         // Set some constants
  43         size_t  maxiter = 10000;
  44         double  tol = 1e-9;
  45         size_t  verb = 1;
  46
  47         // Store the constants in a PropertySet object
  48         PropertySet opts;
  49         opts.Set("maxiter",maxiter);  // Maximum number of iterations
  50         opts.Set("tol",tol);          // Tolerance for convergence
  51         opts.Set("verbose",verb);     // Verbosity (amount of output generated)
  52
  53         // Construct a JTree (junction tree) object from the FactorGraph fg
  54         // using the parameters specified by opts and an additional property
  55         // that specifies the type of updates the JTree algorithm should perform
  56         JTree jt( fg, opts("updates",string("HUGIN")) );
  57         // Initialize junction tree algoritm
  58         jt.init();
  59         // Run junction tree algorithm
  60         jt.run();
  61
  62         // Construct a BP (belief propagation) object from the FactorGraph fg
  63         // using the parameters specified by opts and two additional properties,
  64         // specifying the type of updates the BP algorithm should perform and
  65         // whether they should be done in the real or in the logdomain
  66         BP bp(fg, opts("updates",string("SEQFIX"))("logdomain",false));
  67         // Initialize belief propagation algorithm
  68         bp.init();
  69         // Run belief propagation algorithm
  70         bp.run();
  71
  72         // Report single-variable marginals for fg, calculated by the junction tree algorithm
  73         cout << "Exact single-variable marginals:" << endl;
  74         for( size_t i = 0; i < fg.nrVars(); i++ ) // iterate over all variables in fg
  75             cout << jt.belief(fg.var(i)) << endl; // display the "belief" of jt for that variable
  76
  77         // Report single-variable marginals for fg, calculated by the belief propagation algorithm
  78         cout << "Approximate (loopy belief propagation) single-variable marginals:" << endl;
  79         for( size_t i = 0; i < fg.nrVars(); i++ ) // iterate over all variables in fg
  80             cout << bp.belief(fg.var(i)) << endl; // display the belief of bp for that variable
  81
  82         // Report factor marginals for fg, calculated by the junction tree algorithm
  83         cout << "Exact factor marginals:" << endl;
  84         for( size_t I = 0; I < fg.nrFactors(); I++ ) // iterate over all factors in fg
  85             cout << jt.belief(fg.factor(I).vars()) << endl;  // display the "belief" of jt for the variables in that factor
  86
  87         // Report factor marginals for fg, calculated by the belief propagation algorithm
  88         cout << "Approximate (loopy belief propagation) factor marginals:" << endl;
  89         for( size_t I = 0; I < fg.nrFactors(); I++ ) // iterate over all factors in fg
  90             cout << bp.belief(fg.factor(I).vars()) << endl; // display the belief of bp for the variables in that factor
  91
  92         // Report log partition sum (normalizing constant) of fg, calculated by the junction tree algorithm
  93         cout << "Exact log partition sum: " << jt.logZ() << endl;
  94
  95         // Report log partition sum of fg, approximated by the belief propagation algorithm
  96         cout << "Approximate (loopy belief propagation) log partition sum: " << bp.logZ() << endl;
  97     }
  98
  99     return 0;
 100 }