public static List <ResultObject> EvaluateACOMinerDR_WekaClassifier_Multi(string algorithm, string trainingSetPath, string testSetPath, Dataset trainingSet, bool useAttributes, bool useInstances)
{
Classifier classifier = WekaNETBridge.WekaClassification.GetWekaClassifier(algorithm, trainingSetPath);
DefaultDRHeuristicCalculator calculator = new DefaultDRHeuristicCalculator();
DRComponentInvalidator invalidator = new DRComponentInvalidator();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
WekaClassificationQualityEvaluator evaluator = new WekaClassificationQualityEvaluator(wekaClassification, !useAttributes);
DRLocalSearch localSearch = new DRLocalSearch(evaluator);
Problem <DRComponent> problem = new Problem <DRComponent>(invalidator, calculator, evaluator, localSearch);
ADRMiner acominer = new ADRMiner(maxIterations, colonySize, convergenceIterations, problem, useAttributes, useInstances, false, trainingSet);
acominer.OnPostAntSolutionContruction += OnPostAntSolutionContruction;
acominer.OnPostColonyIteration += OnPostColonyIteration;
acominer.Initialize();
acominer.Work();
List <ResultObject> results = new List <ResultObject>();
foreach (string algo in WekaNETBridge.WekaClassification.GetWekaAlgorithmNames())
{
Classifier currentClassifier = WekaNETBridge.WekaClassification.GetWekaClassifier(algo, trainingSetPath);
WekaNETBridge.WekaClassifier final = evaluator.CreateWekaClassifier(currentClassifier, acominer.BestAnt.Solution);
double quality = WekaNETBridge.WekaClassification.EvaluateClassifier(final, testSetPath);
double attribueReduction = acominer.BestAnt.Solution.FeatureCount() / (double)trainingSet.Metadata.Attributes.Length;
double instanceReduction = acominer.BestAnt.Solution.InstanceCount() / (double)trainingSet.Size;
ResultObject result = new ResultObject()
{
Quality = quality, AttributeReduction = attribueReduction, InstanceReduciton = instanceReduction
};
results.Add(result);
}
return(results);
}
