| public static getUmbrellaWorldNetwork ( ) : DynamicBayesianNetwork | ||
| return | DynamicBayesianNetwork | |
public static DynamicBayesianNetwork getUmbrellaWorldNetwork()
{
FiniteNode prior_rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] {0.5, 0.5});
BayesNet priorNetwork = new BayesNet(prior_rain_tm1);
// Prior belief state
FiniteNode rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] {0.5, 0.5});
// Transition Model
FiniteNode rain_t = new FullCPTNode(ExampleRV.RAIN_t_RV, new double[]
{
// R_t-1 = true, R_t = true
0.7,
// R_t-1 = true, R_t = false
0.3,
// R_t-1 = false, R_t = true
0.3,
// R_t-1 = false, R_t = false
0.7
}, rain_tm1);
// Sensor Model
FiniteNode umbrealla_t = new FullCPTNode(ExampleRV.UMBREALLA_t_RV,
new double[]
{
// R_t = true, U_t = true
0.9,
// R_t = true, U_t = false
0.1,
// R_t = false, U_t = true
0.2,
// R_t = false, U_t = false
0.8
}, rain_t);
Map<RandomVariable, RandomVariable> X_0_to_X_1 = new HashMap<RandomVariable, RandomVariable>();
X_0_to_X_1.put(ExampleRV.RAIN_tm1_RV, ExampleRV.RAIN_t_RV);
Set<RandomVariable> E_1 = new HashSet<RandomVariable>();
E_1.add(ExampleRV.UMBREALLA_t_RV);
return new DynamicBayesNet(priorNetwork, X_0_to_X_1, E_1, rain_tm1);
}
}