examples/example_sprinkler.cpp

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