public static BayesianNetworkClassifier CreateGreedyBayesianClassifier(int maxDependencies, int maxEvaluations, Dataset trainingSet, DataMining.ClassificationMeasures.IClassificationQualityMeasure qualityEvaluator, bool fireEvents)
{
CyclicRelationInvalidator invalidator = new CyclicRelationInvalidator();
invalidator.MaxDependencies = maxDependencies;
BayesianClassificationQualityEvaluator evaluator = new BayesianClassificationQualityEvaluator(qualityEvaluator);
evaluator.ValidationSet = trainingSet;
BackwardRemovalLocalSearch localSearch = new BackwardRemovalLocalSearch(evaluator);
Problem <Edge> problem = new Problem <Edge>(invalidator, null, evaluator, localSearch);
GHC hcblassifier = new GHC(0, 0, 0, problem, maxEvaluations, trainingSet, trainingSet);
if (fireEvents)
{
hcblassifier.OnPostEvaluation += new EventHandler(hcblassifier_OnPostEvaluation);
hcblassifier.OnProgress += new EventHandler(hcblassifier_OnProgress);
}
hcblassifier.Work();
BayesianNetworks.Model.BayesianNetworkClassifier bnclassifier = hcblassifier.BayesianNetworkClassifier;
return(bnclassifier);
}
private static void hcblassifier_OnPostEvaluation(object sender, EventArgs e)
{
if (sender is GHC)
{
GHC classifier = sender as GHC;
Console.WriteLine("Counter: " + classifier.EvaluationsCounter.ToString());
}
else
{
K2Algorithm classifier = sender as K2Algorithm;
Console.WriteLine("Counter: " + classifier.EvaluationsCounter.ToString());
}
}
static void hcblassifier_OnProgress(object sender, EventArgs e)
{
if (sender is GHC)
{
GHC classifier = sender as GHC;
Console.WriteLine("-----------------------------------");
Console.WriteLine("Quality: " + classifier.BestSolution.Quality);
Console.WriteLine("-----------------------------------");
}
else
{
K2Algorithm classifier = sender as K2Algorithm;
Console.WriteLine("-----------------------------------");
Console.WriteLine("Quality: " + classifier.BestSolution.Quality);
Console.WriteLine("-----------------------------------");
}
}
