static void OnPostColonyIteration(object sender, EventArgs e)
{
if (sender is AntIBMiner)
{
AntIBMiner colony = sender as AntIBMiner;
Console.WriteLine("Iteration [" + colony.CurrentIteration.ToString() + "] - Fitness: " + Math.Round(colony.GlobalBest.Quality * 100, 4).ToString());
}
else
{
PSOIB swarm = sender as PSOIB;
Console.WriteLine("Iteration [" + swarm.CurrentIteration.ToString() + "] - Fitness: " + Math.Round(swarm.GlobalBest.Quality * 100, 4).ToString());
}
}
public static EnsembleClassifier CreateNCCAntIBMinerClassifier_Ensemble(Dataset trainingSet)
{
int classCount = trainingSet.Metadata.Target.Values.Length;
int attributesCount = trainingSet.Metadata.Attributes.Length;
int problemSize = attributesCount + 1;
AccuracyMeasure measure = new AccuracyMeasure();
DefaultDistanceMeasure distanceMeasure = new DefaultDistanceMeasure(2);
NearestClassClassifier ncc = new NearestClassClassifier(distanceMeasure, trainingSet);
IBClassificationQualityEvaluator evaluator = new ContinuousACO.ProblemSpecifics.IBClassificationQualityEvaluator(ncc, measure);
evaluator.LearningSet = trainingSet;
evaluator.ValidationSet = trainingSet;
Problem <double> problem = new Problem <double>(null, null, evaluator, null);
AntIBMiner antminer = new AntIBMiner(maxIterations, colonySize, convergenceIterations, problem, problemSize, archive, q, segma, trainingSet);
EnsembleClassifier aconcc = antminer.CreateEnsembleClassifier();
return(aconcc);
}
public static KNearestNeighbours CreateKNNAntIBMinerClassifier_ClassBasedWeights(Dataset trainingSet, bool useWeightedVote)
{
int classCount = trainingSet.Metadata.Target.Values.Length;
int attributesCount = trainingSet.Metadata.Attributes.Length;
int problemSize = (attributesCount * classCount) + 1;
AccuracyMeasure measure = new AccuracyMeasure();
DefaultDistanceMeasure distanceMeasure = new DefaultDistanceMeasure(2);
KNearestNeighbours knn = new KNearestNeighbours(distanceMeasure, trainingSet, useWeightedVote);
IBClassificationQualityEvaluator evaluator = new ContinuousACO.ProblemSpecifics.IBClassificationQualityEvaluator(knn, measure);
evaluator.LearningSet = trainingSet;
evaluator.ValidationSet = trainingSet;
Problem <double> problem = new Problem <double>(null, null, evaluator, null);
AntIBMiner antminer = new AntIBMiner(maxIterations, colonySize, convergenceIterations, problem, problemSize, archive, q, segma, trainingSet);
antminer.OnPostColonyIteration += OnPostColonyIteration;
KNearestNeighbours acoknn = antminer.CreateClassifier() as KNearestNeighbours;
return(acoknn);
}
public static EnsembleClassifier CreateGKAntIBMinerClassifier_ClassBaseWeights_Ensemble(Dataset trainingSet)
{
int classCount = trainingSet.Metadata.Target.Values.Length;
int attributesCount = trainingSet.Metadata.Attributes.Length;
int problemSize = (attributesCount * classCount) + 1;
AccuracyMeasure measure = new AccuracyMeasure();
DefaultDistanceMeasure distanceMeasure = new DefaultDistanceMeasure(1);
GaussianKernelEstimator gke = new GaussianKernelEstimator(0.5, distanceMeasure, trainingSet);
IBClassificationQualityEvaluator evaluator = new ContinuousACO.ProblemSpecifics.IBClassificationQualityEvaluator(gke, measure);
evaluator.LearningSet = trainingSet;
evaluator.ValidationSet = trainingSet;
Problem <double> problem = new Problem <double>(null, null, evaluator, null);
AntIBMiner antminer = new AntIBMiner(maxIterations, colonySize, convergenceIterations, problem, problemSize, archive, q, segma, trainingSet);
antminer.OnPostColonyIteration += OnPostColonyIteration;
EnsembleClassifier acogke = antminer.CreateEnsembleClassifier();
return(acogke);
}
