/* 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) 2008-2010 Joris Mooij [joris dot mooij at libdai dot org] */ #include <dai/factorgraph.h> #include <iostream> #include <fstream> using namespace std; using namespace dai; int main() { // This example program illustrates how to construct a factorgraph // by means of the sprinkler network example discussed at // http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html Var C(0, 2); // Define binary variable Cloudy (with label 0) Var S(1, 2); // Define binary variable Sprinkler (with label 1) Var R(2, 2); // Define binary variable Rain (with label 2) Var W(3, 2); // Define binary variable Wetgrass (with label 3) // Define probability distribution for C Factor P_C( C ); P_C.set(0, 0.5); // C = 0 P_C.set(1, 0.5); // C = 1 // Define conditional probability of S given C Factor P_S_given_C( VarSet( S, C ) ); P_S_given_C.set(0, 0.5); // C = 0, S = 0 P_S_given_C.set(1, 0.9); // C = 1, S = 0 P_S_given_C.set(2, 0.5); // C = 0, S = 1 P_S_given_C.set(3, 0.1); // C = 1, S = 1 // Define conditional probability of R given C Factor P_R_given_C( VarSet( R, C ) ); P_R_given_C.set(0, 0.8); // C = 0, R = 0 P_R_given_C.set(1, 0.2); // C = 1, R = 0 P_R_given_C.set(2, 0.2); // C = 0, R = 1 P_R_given_C.set(3, 0.8); // C = 1, R = 1 // Define conditional probability of W given S and R Factor P_W_given_S_R( VarSet( S, R ) | W ); P_W_given_S_R.set(0, 1.0); // S = 0, R = 0, W = 0 P_W_given_S_R.set(1, 0.1); // S = 1, R = 0, W = 0 P_W_given_S_R.set(2, 0.1); // S = 0, R = 1, W = 0 P_W_given_S_R.set(3, 0.01); // S = 1, R = 1, W = 0 P_W_given_S_R.set(4, 0.0); // S = 0, R = 0, W = 1 P_W_given_S_R.set(5, 0.9); // S = 1, R = 0, W = 1 P_W_given_S_R.set(6, 0.9); // S = 0, R = 1, W = 1 P_W_given_S_R.set(7, 0.99); // S = 1, R = 1, W = 1 // Build factor graph consisting of those four factors vector<Factor> SprinklerFactors; SprinklerFactors.push_back( P_C ); SprinklerFactors.push_back( P_R_given_C ); SprinklerFactors.push_back( P_S_given_C ); SprinklerFactors.push_back( P_W_given_S_R ); FactorGraph SprinklerNetwork( SprinklerFactors ); // Write factorgraph to a file SprinklerNetwork.WriteToFile( "sprinkler.fg" ); cout << "Sprinkler network written to sprinkler.fg" << endl; // Output some information about the factorgraph cout << SprinklerNetwork.nrVars() << " variables" << endl; cout << SprinklerNetwork.nrFactors() << " factors" << endl; // Calculate joint probability of all four variables Factor P; for( size_t I = 0; I < SprinklerNetwork.nrFactors(); I++ ) P *= SprinklerNetwork.factor( I ); // P.normalize(); // Not necessary: a Bayesian network is already normalized by definition // Calculate some probabilities Real denom = P.marginal( W )[1]; cout << "P(W=1) = " << denom << endl; cout << "P(S=1 | W=1) = " << P.marginal( VarSet( S, W ) )[3] / denom << endl; cout << "P(R=1 | W=1) = " << P.marginal( VarSet( R, W ) )[3] / denom << endl; return 0; }