examples/example_sprinkler.cpp

   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) 2008-2009  Joris Mooij  [joris dot mooij at libdai dot org]
   8  */
   9
  10
  11 #include <dai/factorgraph.h>
  12 #include <iostream>
  13
  14 using namespace std;
  15 using namespace dai;
  16
  17 int main() {
  18     // This example program illustrates how to construct a factorgraph
  19     // by means of the sprinkler network example discussed at
  20     // http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
  21
  22     Var C(0, 2);  // Define binary variable Cloudy (with label 0)
  23     Var S(1, 2);  // Define binary variable Sprinkler (with label 1)
  24     Var R(2, 2);  // Define binary variable Rain (with label 2)
  25     Var W(3, 2);  // Define binary variable Wetgrass (with label 3)
  26
  27     // Define probability distribution for C
  28     Factor P_C( C );
  29     P_C[0] = 0.5;   // C = 0
  30     P_C[1] = 0.5;   // C = 1
  31
  32     // Define conditional probability of S given C
  33     Factor P_S_given_C( VarSet( S, C ) );
  34     P_S_given_C[0] = 0.5;   // C = 0, S = 0
  35     P_S_given_C[1] = 0.9;   // C = 1, S = 0
  36     P_S_given_C[2] = 0.5;   // C = 0, S = 1
  37     P_S_given_C[3] = 0.1;   // C = 1, S = 1
  38
  39     // Define conditional probability of R given C
  40     Factor P_R_given_C( VarSet( R, C ) );
  41     P_R_given_C[0] = 0.8;   // C = 0, R = 0
  42     P_R_given_C[1] = 0.2;   // C = 1, R = 0
  43     P_R_given_C[2] = 0.2;   // C = 0, R = 1
  44     P_R_given_C[3] = 0.8;   // C = 1, R = 1
  45
  46     // Define conditional probability of W given S and R
  47     Factor P_W_given_S_R( VarSet( S, R ) | W );
  48     P_W_given_S_R[0] = 1.0;  // S = 0, R = 0, W = 0
  49     P_W_given_S_R[1] = 0.1;  // S = 1, R = 0, W = 0
  50     P_W_given_S_R[2] = 0.1;  // S = 0, R = 1, W = 0
  51     P_W_given_S_R[3] = 0.01; // S = 1, R = 1, W = 0
  52     P_W_given_S_R[4] = 0.0;  // S = 0, R = 0, W = 1
  53     P_W_given_S_R[5] = 0.9;  // S = 1, R = 0, W = 1
  54     P_W_given_S_R[6] = 0.9;  // S = 0, R = 1, W = 1
  55     P_W_given_S_R[7] = 0.99; // S = 1, R = 1, W = 1
  56
  57     // Build factor graph consisting of those four factors
  58     vector<Factor> SprinklerFactors;
  59     SprinklerFactors.push_back( P_C );
  60     SprinklerFactors.push_back( P_R_given_C );
  61     SprinklerFactors.push_back( P_S_given_C );
  62     SprinklerFactors.push_back( P_W_given_S_R );
  63     FactorGraph SprinklerNetwork( SprinklerFactors );
  64
  65     // Write factorgraph to a file
  66     SprinklerNetwork.WriteToFile( "sprinkler.fg" );
  67
  68     // Reread the factorgraph from the file
  69     SprinklerNetwork.ReadFromFile( "sprinkler.fg" );
  70
  71     // Output some information about the factorgraph
  72     cout << SprinklerNetwork.nrVars() << " variables" << endl;
  73     cout << SprinklerNetwork.nrFactors() << " factors" << endl;
  74
  75     // Calculate joint probability of all four variables
  76     Factor P;
  77     for( size_t I = 0; I < SprinklerNetwork.nrFactors(); I++ ) {
  78         P *= SprinklerNetwork.factor( I );
  79     }
  80     // P.normalize();  // Not necessary: a Bayesian network is already normalized by definition
  81
  82     // Calculate some probabilities
  83     double denom = P.marginal( W )[1];
  84     cout << "P(W=1) = " << denom << endl;
  85     cout << "P(S=1 | W=1) = " << P.marginal( VarSet( S, W ) )[3] / denom << endl;
  86     cout << "P(R=1 | W=1) = " << P.marginal( VarSet( R, W ) )[3] / denom << endl;
  87
  88     return 0;
  89 }