/** * Returns the Bayesian network at Time 0. * @return a {@link BayesianNetwork} object. */ public BayesianNetwork toBayesianNetworkTime0(){ DAG dagTime0 = this.getDynamicDAG().toDAGTime0(); BayesianNetwork bnTime0 = new BayesianNetwork(dagTime0); for (Variable dynamicVar : this.getDynamicVariables()) { Variable staticVar = dagTime0.getVariables().getVariableByName(dynamicVar.getName()); ConditionalDistribution deepCopy = Serialization.deepCopy(this.getConditionalDistributionTime0(dynamicVar)); deepCopy.setVar(staticVar); List<Variable> newParents = deepCopy.getConditioningVariables().stream().map(var -> dagTime0.getVariables().getVariableByName(var.getName())).collect(Collectors.toList()); deepCopy.setConditioningVariables(newParents); bnTime0.setConditionalDistribution(staticVar,deepCopy); } return bnTime0; }
/** * Returns the Bayesian network at Time T. * @return a {@link BayesianNetwork} object. */ public BayesianNetwork toBayesianNetworkTimeT(){ DAG dagTimeT = this.getDynamicDAG().toDAGTimeT(); BayesianNetwork bnTimeT = new BayesianNetwork(dagTimeT); for (Variable dynamicVar : this.getDynamicVariables()) { Variable staticVar = dagTimeT.getVariables().getVariableByName(dynamicVar.getName()); ConditionalDistribution deepCopy = Serialization.deepCopy(this.getConditionalDistributionTimeT(dynamicVar)); deepCopy.setVar(staticVar); List<Variable> newParents = deepCopy.getConditioningVariables().stream().map(var -> dagTimeT.getVariables().getVariableByName(var.getName())).collect(Collectors.toList()); deepCopy.setConditioningVariables(newParents); bnTimeT.setConditionalDistribution(staticVar,deepCopy); } return bnTimeT; }
cdist.getConditioningVariables().stream().forEachOrdered(cdvar -> parentList.add(variables.getVariableByName(cdvar.getName() + "_t0"))); cdist.getConditioningVariables().stream().forEachOrdered(cdvar -> { parentList.add(((cdvar.isInterfaceVariable() || (cdvar.getName().contains("_t"))) ? variables.getVariableByName(cdvar.getName().replace("_Interface", "_t" + Integer.toString(final_i - 1))) :
@Override public DynamicBayesianNetwork getLearntDynamicBayesianNetwork() { BayesianNetwork bnTime0 = this.parallelVBTime0.getLearntBayesianNetwork(); BayesianNetwork bnTimeT = this.svbTimeT.getLearntBayesianNetwork(); DynamicVariables dynamicVariables = this.dynamicDAG.getDynamicVariables(); DynamicBayesianNetwork dbn = new DynamicBayesianNetwork(this.dynamicDAG); for (Variable dynamicVariable : dynamicVariables) { ConditionalDistribution dynamicDist = dbn.getConditionalDistributionTime0(dynamicVariable); ConditionalDistribution staticDist = bnTime0.getConditionalDistribution(bnTime0.getVariables().getVariableByName(dynamicVariable.getName())); staticDist.setVar(dynamicVariable); staticDist.setConditioningVariables(dynamicDist.getConditioningVariables()); dbn.setConditionalDistributionTime0(dynamicVariable,staticDist); } for (Variable dynamicVariable : dynamicVariables) { ConditionalDistribution dynamicDist = dbn.getConditionalDistributionTimeT(dynamicVariable); ConditionalDistribution staticDist = bnTimeT.getConditionalDistribution(bnTimeT.getVariables().getVariableByName(dynamicVariable.getName())); staticDist.setVar(dynamicVariable); staticDist.setConditioningVariables(dynamicDist.getConditioningVariables()); dbn.setConditionalDistributionTimeT(dynamicVariable,staticDist); } return dbn; }
List<Variable> thisVarParents = conditionalDistribution.getConditioningVariables(); thisVarParents = thisVarParents.stream().map(parent -> variables.getVariableByName(parent.getName() + "_t0")).collect(Collectors.toList()); ConditionalDistribution conditionalDistribution1 = Serialization.deepCopy(model.getConditionalDistributionTimeT(dynVariable)); Variable staticVar2 = variables.getVariableByName(dynVariable.getName() + "_t" + Integer.toString(i)); List<Variable> thisVarParents1 = conditionalDistribution1.getConditioningVariables(); thisVarParents1 = thisVarParents1.stream().map(parent -> { if(parent.getName().contains("_Interface")) {