public static void TestLBMNABC()
{
Console.WriteLine("Start");
string dsname = "car";
string trainingSetPath = @"C:\0 - Khalid\Academics\Datasets\" + dsname + @"\TR0_" + dsname + ".arff";
string testingSetPath = @"C:\0 - Khalid\Academics\Datasets\" + dsname + @"\TS0_" + dsname + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(trainingSetPath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(testingSetPath);
DataMining.ClassificationMeasures.AccuracyMeasure qualityEvaluator = new DataMining.ClassificationMeasures.AccuracyMeasure();
ISolutionQualityEvaluator <Edge> trainingQualityEvaluator = new likelihoodQualityEvaluator(trainingSet);
IHeuristicValueCalculator <Edge> calculator = new MICalculator();
int seed = (int)DateTime.Now.Ticks;
BayesianMultinetClassifier lmnabclassifier = SingleTest.CreateLMNAntBayesianClassification(seed, 10, 5, 10, 3, trainingSet, trainingQualityEvaluator, calculator, true);
double quality = SingleTest.TestClassifier(lmnabclassifier, testingSet, qualityEvaluator);
quality = Math.Round(quality * 100, 2);
Console.WriteLine("LMNABC Quality: " + quality.ToString());
Console.WriteLine("End");
//Console.ReadLine();
}
private double EvaluateQuality(List <Ant <Edge> > ants)
{
BayesianMultinetClassifier classifier = this.ConstructMulitNetClassifier(ants);
double quality = this._classificationQualityEvaluator.CalculateMeasure(DataMining.ClassificationMeasures.ConfusionMatrix.GetConfusionMatrixes(classifier, this._trainingSet));
return(quality);
}
private BayesianMultinetClassifier ConstructMulitNetClassifier(List <Ant <Edge> > ants)
{
BayesianMultinetClassifier mnClassifier = new BayesianMultinetClassifier(this._trainingSet.Metadata);
for (int classIndex = 0; classIndex < this._trainingSet.Metadata.Target.Values.Length; classIndex++)
{
BayesianNetwork bayesianNetwork = new BayesianNetwork(this._datasets[classIndex].Metadata, ants[classIndex].Solution.ToList());
bayesianNetwork.LearnParameters(this._datasets[classIndex]);
mnClassifier.AddBayesianNetwork(classIndex, bayesianNetwork);
}
return(mnClassifier);
}
