private static void GMNabclassifier_OnPostColonyIteration(object sender, EventArgs e)
{
ABCMinerGMN miner = sender as ABCMinerGMN;
Console.WriteLine("------------------------");
Console.WriteLine("Iteration [" + miner.CurrentIteration.ToString() + "]");
Console.WriteLine("------------------------");
Console.WriteLine("Iteration Best: " + Math.Round(miner.IterationBestQuality, 5).ToString());
Console.WriteLine("Global Best: " + Math.Round(miner.BestQuality, 5).ToString());
Console.WriteLine("------------------------");
}
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);
}