static void forwardBackWardDemo()
{
System.Console.WriteLine("DEMO: Forward-BackWard");
System.Console.WriteLine("======================");
System.Console.WriteLine("Umbrella World");
System.Console.WriteLine("--------------");
ForwardBackward uw = new ForwardBackward(
GenericTemporalModelFactory.getUmbrellaWorldTransitionModel(),
GenericTemporalModelFactory.getUmbrellaWorld_Xt_to_Xtm1_Map(),
GenericTemporalModelFactory.getUmbrellaWorldSensorModel());
ICategoricalDistribution prior = new ProbabilityTable(new double[] {
0.5, 0.5
}, ExampleRV.RAIN_t_RV);
// Day 1
ICollection <ICollection <AssignmentProposition> > evidence = CollectionFactory.CreateQueue <ICollection <AssignmentProposition> >();
ICollection <AssignmentProposition> e1 = CollectionFactory.CreateQueue <AssignmentProposition>();
e1.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
evidence.Add(e1);
ICollection <ICategoricalDistribution> smoothed = uw.forwardBackward(evidence, prior);
System.Console.WriteLine("Day 1 (Umbrealla_t=true) smoothed:\nday 1 = " + smoothed.Get(0));
// Day 2
ICollection <AssignmentProposition> e2 = CollectionFactory.CreateQueue <AssignmentProposition>();
e2.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
evidence.Add(e2);
smoothed = uw.forwardBackward(evidence, prior);
System.Console.WriteLine("Day 2 (Umbrealla_t=true) smoothed:\nday 1 = "
+ smoothed.Get(0) + "\nday 2 = " + smoothed.Get(1));
// Day 3
ICollection <AssignmentProposition> e3 = CollectionFactory.CreateQueue <AssignmentProposition>();
e3.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, false));
evidence.Add(e3);
smoothed = uw.forwardBackward(evidence, prior);
System.Console.WriteLine("Day 3 (Umbrealla_t=false) smoothed:\nday 1 = "
+ smoothed.Get(0) + "\nday 2 = " + smoothed.Get(1)
+ "\nday 3 = " + smoothed.Get(2));
System.Console.WriteLine("======================");
}
public List <CategoricalDistribution> DoForwardBackward(object owner, IContextLookup globalVars)
{
var randomVariables = TemporalModel.TransitionModel.GetRandomVariables(owner, globalVars);
var transitionalModel = new FiniteBayesModel(TemporalModel.TransitionModel.GetNetwork(randomVariables));
randomVariables = TemporalModel.SensorModel.GetRandomVariables(owner, globalVars, randomVariables);
var sensoryModel = new FiniteBayesModel(TemporalModel.SensorModel.GetNetwork(randomVariables));
var temporalMap = TemporalModel.GetReverseTemporalMap(randomVariables);
var forwardBackwardAlgorithm = new ForwardBackward(transitionalModel, temporalMap, sensoryModel);
var objEvidences = new java.util.ArrayList(Evidences.Count);
foreach (List <PropositionInfo> propositions in Evidences)
{
var stepEvidences = new java.util.ArrayList(propositions.Count);
foreach (PropositionInfo proposition in propositions)
{
stepEvidences.add(proposition.GetProposition(owner, globalVars, randomVariables));
}
objEvidences.add(stepEvidences);
}
CategoricalDistribution objPrior = Prior.GetProbabilityTable(randomVariables);
return(forwardBackwardAlgorithm.forwardBackward(objEvidences, objPrior).toArray().Select(o => (CategoricalDistribution)o).ToList());
}
