Fixed tabs and trailing whitespaces
[libdai.git] / 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 }