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());
}
public CategoricalDistribution AskBayesianModel(object owner, IContextLookup globalVars)
{
var randomVariables = BayesianNetwork.GetRandomVariables(owner, globalVars);
var network = BayesianNetwork.GetNetwork(randomVariables);
var inference = InferenceType.GetInference();
var model = new FiniteBayesModel(network, inference);
return(Ask.AskBayesianModel(owner, globalVars, randomVariables, model));
}
