public Problem(IComponentInvalidator <T> invalidator, IHeuristicsCalculator <T> calculator, ISolutionQualityEvaluator <T> evaluator, ILocalSearch <T> localSearch)
{
this._invalidator = invalidator;
this._calculator = calculator;
this._evaluator = evaluator;
this._localSearch = localSearch;
}
public static BayesianMultinetClassifier CreateGMNAntBayesianClassification(int seed, int iterations, int colonySize, int convergence, int dependencies, Dataset trainingSet, ISolutionQualityEvaluator <Edge> qualityEvaluator, IHeuristicValueCalculator <Edge> calculator, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
CyclicRelationInvalidator invalidator = new CyclicRelationInvalidator();
BackwardRemovalLocalSearch localSearch = new BackwardRemovalLocalSearch(qualityEvaluator);
Problem <Edge> problem = new Problem <Edge>(invalidator, calculator, qualityEvaluator, localSearch);
ABCMinerGMN mnabcminer = new ABCMinerGMN(iterations, colonySize, convergence, problem, dependencies, trainingSet);
if (fireEvents)
{
mnabcminer.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
mnabcminer.OnPostColonyIteration += new EventHandler(GMNabclassifier_OnPostColonyIteration);
}
mnabcminer.Work();
BayesianNetworks.Model.BayesianMultinetClassifier mnbclassifier = mnabcminer.MultinetBayesianClassifier;
return(mnbclassifier);
}
public DRLocalSearch(ISolutionQualityEvaluator <DRComponent> qualityEvaluator)
{
this._qualityEvaluator = qualityEvaluator;
}
public BackwardRemovalLocalSearch(ISolutionQualityEvaluator <Edge> qualityEvaluator)
{
this._classificationQualityEvaluator = qualityEvaluator;
}
public VariableTypeAssignmentLocalSearch(ISolutionQualityEvaluator <VariableTypeAssignment> qualityEvaluator)
{
this._classificationQualityEvaluator = qualityEvaluator;
}
public DefaultRemovalLocalSearch(ISolutionQualityEvaluator <T> qualityEvaluator)
{
this._qualityEvaluator = qualityEvaluator;
}
