+++ /dev/null
-/* This file is part of libDAI - http://www.libdai.org/
- *
- * libDAI is licensed under the terms of the GNU General Public License version
- * 2, or (at your option) any later version. libDAI is distributed without any
- * warranty. See the file COPYING for more details.
- *
- * Copyright (C) 2006-2009 Joris Mooij [joris dot mooij at libdai dot org]
- * Copyright (C) 2006-2007 Radboud University Nijmegen, The Netherlands
- */
-
-
-#include <iostream>
-#include <map>
-#include <dai/alldai.h> // Include main libDAI header file
-#include <dai/jtree.h>
-#include <dai/bp.h>
-#include <dai/decmap.h>
-
-
-using namespace dai;
-using namespace std;
-
-
-int main( int argc, char *argv[] ) {
- if ( argc != 2 ) {
- cout << "Usage: " << argv[0] << " <filename.fg>" << endl << endl;
- cout << "Reads factor graph <filename.fg> and runs" << endl;
- cout << "Belief Propagation and JunctionTree on it." << endl << endl;
- return 1;
- } else {
- // Read FactorGraph from the file specified by the first command line argument
- FactorGraph fg;
- fg.ReadFromFile(argv[1]);
-
- // Set some constants
- size_t maxiter = 10000;
- Real tol = 1e-9;
- size_t verb = 1;
-
- // Store the constants in a PropertySet object
- PropertySet opts;
- opts.set("maxiter",maxiter); // Maximum number of iterations
- opts.set("tol",tol); // Tolerance for convergence
- opts.set("verbose",verb); // Verbosity (amount of output generated)
-
- // Construct a JTree (junction tree) object from the FactorGraph fg
- // using the parameters specified by opts and an additional property
- // that specifies the type of updates the JTree algorithm should perform
- JTree jt( fg, opts("updates",string("HUGIN")) );
- // Initialize junction tree algorithm
- jt.init();
- // Run junction tree algorithm
- jt.run();
-
- // Construct another JTree (junction tree) object that is used to calculate
- // the joint configuration of variables that has maximum probability (MAP state)
- JTree jtmap( fg, opts("updates",string("HUGIN"))("inference",string("MAXPROD")) );
- // Initialize junction tree algorithm
- jtmap.init();
- // Run junction tree algorithm
- jtmap.run();
- // Calculate joint state of all variables that has maximum probability
- vector<size_t> jtmapstate = jtmap.findMaximum();
-
- // Construct a BP (belief propagation) object from the FactorGraph fg
- // using the parameters specified by opts and two additional properties,
- // specifying the type of updates the BP algorithm should perform and
- // whether they should be done in the real or in the logdomain
- BP bp(fg, opts("updates",string("SEQRND"))("logdomain",false));
- // Initialize belief propagation algorithm
- bp.init();
- // Run belief propagation algorithm
- bp.run();
-
- // Construct a BP (belief propagation) object from the FactorGraph fg
- // using the parameters specified by opts and two additional properties,
- // specifying the type of updates the BP algorithm should perform and
- // whether they should be done in the real or in the logdomain
- //
- // Note that inference is set to MAXPROD, which means that the object
- // will perform the max-product algorithm instead of the sum-product algorithm
- BP mp(fg, opts("updates",string("SEQRND"))("logdomain",false)("inference",string("MAXPROD"))("damping",string("0.1")));
- // Initialize max-product algorithm
- mp.init();
- // Run max-product algorithm
- mp.run();
- // Calculate joint state of all variables that has maximum probability
- // based on the max-product result
- vector<size_t> mpstate = mp.findMaximum();
-
- // Construct a decimation algorithm object from the FactorGraph fg
- // using the parameters specified by opts and three additional properties,
- // specifying that the decimation algorithm should use the max-product
- // algorithm and should completely reinitalize its state at every step
- DecMAP decmap(fg, opts("reinit",true)("ianame",string("BP"))("iaopts",string("[damping=0.1,inference=MAXPROD,logdomain=0,maxiter=1000,tol=1e-9,updates=SEQRND,verbose=1]")) );
- decmap.init();
- decmap.run();
- vector<size_t> decmapstate = decmap.findMaximum();
-
- // Report variable marginals for fg, calculated by the junction tree algorithm
- cout << "Exact variable marginals:" << endl;
- for( size_t i = 0; i < fg.nrVars(); i++ ) // iterate over all variables in fg
- cout << jt.belief(fg.var(i)) << endl; // display the "belief" of jt for that variable
-
- // Report variable marginals for fg, calculated by the belief propagation algorithm
- cout << "Approximate (loopy belief propagation) variable marginals:" << endl;
- for( size_t i = 0; i < fg.nrVars(); i++ ) // iterate over all variables in fg
- cout << bp.belief(fg.var(i)) << endl; // display the belief of bp for that variable
-
- // Report factor marginals for fg, calculated by the junction tree algorithm
- cout << "Exact factor marginals:" << endl;
- for( size_t I = 0; I < fg.nrFactors(); I++ ) // iterate over all factors in fg
- cout << jt.belief(fg.factor(I).vars()) << endl; // display the "belief" of jt for the variables in that factor
-
- // Report factor marginals for fg, calculated by the belief propagation algorithm
- cout << "Approximate (loopy belief propagation) factor marginals:" << endl;
- for( size_t I = 0; I < fg.nrFactors(); I++ ) // iterate over all factors in fg
- cout << bp.belief(fg.factor(I).vars()) << endl; // display the belief of bp for the variables in that factor
-
- // Report log partition sum (normalizing constant) of fg, calculated by the junction tree algorithm
- cout << "Exact log partition sum: " << jt.logZ() << endl;
-
- // Report log partition sum of fg, approximated by the belief propagation algorithm
- cout << "Approximate (loopy belief propagation) log partition sum: " << bp.logZ() << endl;
-
- // Report exact MAP variable marginals
- cout << "Exact MAP variable marginals:" << endl;
- for( size_t i = 0; i < fg.nrVars(); i++ )
- cout << jtmap.belief(fg.var(i)) << endl;
-
- // Report max-product variable marginals
- cout << "Approximate (max-product) MAP variable marginals:" << endl;
- for( size_t i = 0; i < fg.nrVars(); i++ )
- cout << mp.belief(fg.var(i)) << endl;
-
- // Report exact MAP factor marginals
- cout << "Exact MAP factor marginals:" << endl;
- for( size_t I = 0; I < fg.nrFactors(); I++ )
- cout << jtmap.belief(fg.factor(I).vars()) << " == " << jtmap.beliefF(I) << endl;
-
- // Report max-product factor marginals
- cout << "Approximate (max-product) MAP factor marginals:" << endl;
- for( size_t I = 0; I < fg.nrFactors(); I++ )
- cout << mp.belief(fg.factor(I).vars()) << " == " << mp.beliefF(I) << endl;
-
- // Report exact MAP joint state
- cout << "Exact MAP state (log score = " << fg.logScore( jtmapstate ) << "):" << endl;
- for( size_t i = 0; i < jtmapstate.size(); i++ )
- cout << fg.var(i) << ": " << jtmapstate[i] << endl;
-
- // Report max-product MAP joint state
- cout << "Approximate (max-product) MAP state (log score = " << fg.logScore( mpstate ) << "):" << endl;
- for( size_t i = 0; i < mpstate.size(); i++ )
- cout << fg.var(i) << ": " << mpstate[i] << endl;
-
- // Report DecMAP joint state
- cout << "Approximate DecMAP state (log score = " << fg.logScore( decmapstate ) << "):" << endl;
- for( size_t i = 0; i < decmapstate.size(); i++ )
- cout << fg.var(i) << ": " << decmapstate[i] << endl;
- }
-
- return 0;
-}