public override void EvaluateSolutionQuality(Solution <ClusterExampleAssignment> solution)
{
double quality = 0;
this.ClusteringSolution.SetClusterExampleAssignment(solution.ToList());
IClassifier classifier = BayesianNetworks.Model.BayesianClusterMultinetClassifier.ConstructClusterBMNClassifier(this.ClusteringSolution, this._classificationAlgorithm, this._dataset);
DataMining.ClassificationMeasures.ConfusionMatrix[] list = DataMining.ClassificationMeasures.ConfusionMatrix.GetConfusionMatrixes(classifier, this._dataset);
quality = _measure.CalculateMeasure(list);
solution.Quality = quality;
}
private List <ClassifierInfo> CreateEnsemble(Node current)
{
List <ClassifierInfo> ensemble = new List <ClassifierInfo>();
foreach (Dataset currentTrainingSet in this._trainingSets)
{
Dataset local = GetLocalFlatDataset(current, currentTrainingSet);
_algorithm.Dataset = local;
IClassifier currentClassifier = _algorithm.CreateClassifier();
double currentQyuality = _evaluator.CalculateMeasure(ConfusionMatrix.GetConfusionMatrixes(currentClassifier, local));
ClassifierInfo info = new ClassifierInfo()
{
Classifier = currentClassifier, Desc = current.Name + ":" + currentTrainingSet.Metadata.DatasetName, Quality = currentQyuality
};
if (_fireEvents)
{
ClassifierInfoEventArgs args = new ClassifierInfoEventArgs()
{
Info = info
};
this.onPostClassifierConstruction(current, args);
}
ensemble.Add(info);
}
return(ensemble);
}