public ClusterBMN(int clustersNumber, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure classificationMeasure, IClusteringAlgorithm clusteringAlgorithm, IClassificationAlgorithm classificationAlgorithm)
{
this._similarityMeasure = similarityMeasure;
this._clustersNumber = clustersNumber;
this._classificationMeasure = classificationMeasure;
this._clusteringAlgorithm = clusteringAlgorithm;
this._classificationAlgorithm = classificationAlgorithm;
}
public ClusterBMN(DataMining.Data.Dataset trainingset, int clustersNumber, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure classificationMeasure, IClusteringAlgorithm clusteringAlgorithm, IClassificationAlgorithm classificationAlgorithm)
{
this._trainingset = trainingset;
this._similarityMeasure = similarityMeasure;
this._clustersNumber = clustersNumber;
this._classificationMeasure = classificationMeasure;
this._clusteringAlgorithm = clusteringAlgorithm;
this._classificationAlgorithm = classificationAlgorithm;
}
public LocalPerNodeClassificationAlgorithm(List <Dataset> trainingSets, IClassificationAlgorithm algorithm, EnsembleStrategy.IEnsembleClassificationStrategy ensembleStrategy, IClassificationQualityMeasure evaluator, bool serialize, bool fireEvents)
{
this._algorithm = algorithm;
this._trainingSets = trainingSets;
this._ensembleStrategy = ensembleStrategy;
this._evaluator = evaluator;
this._serialize = serialize;
this._fireEvents = fireEvents;
}
public static BayesianClusterMultinetClassifier CreateAntClustBMNClassifier_MB(int seed, Dataset dataset, int maxIterations, int colonySize, int convergence, int clustersNumber, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure classificationMeasure, IClassificationAlgorithm algorithm, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
DefaultHeuristicCalculator <int> calculator = new DefaultHeuristicCalculator <int>();
ClusteringMBInvalidator invalidator = new ClusteringMBInvalidator();
ClusteringClassificationQualityEvaluator evaluator = new ClusteringClassificationQualityEvaluator(classificationMeasure, algorithm);
evaluator.Dataset = dataset;
KMeansLocalSearch localSearch = new KMeansLocalSearch(dataset, 1, similarityMeasure, evaluator);
Problem <int> problem = new Problem <int>(invalidator, calculator, evaluator, localSearch);
AntClustBMN_MB antClustBMN = new AntClustBMN_MB(maxIterations, colonySize, convergence, problem, clustersNumber, similarityMeasure, dataset, true, algorithm, classificationMeasure);
antClustBMN.OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
return(antClustBMN.CreateClassifier() as BayesianClusterMultinetClassifier);
}
public static BayesianClusterMultinetClassifier CreateClusteringBMNClassifier(int seed, int clusterNumber, Dataset dataset, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure accuracy, IClusteringAlgorithm algorithm, IClassificationAlgorithm naive, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
if (fireEvents)
{
if (algorithm is ACOClustering_IB)
{
((ACOClustering_IB)algorithm).OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
}
if (algorithm is ACOClustering_MB)
{
((ACOClustering_MB)algorithm).OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
}
}
ClusterBMN cBMN = new ClusterBMN(dataset, clusterNumber, similarityMeasure, accuracy, algorithm, naive);
return(cBMN.CreateClassifier() as BayesianClusterMultinetClassifier);
}
public static IHierarchicalClassifier CreateLocalPerNodeHierarchicalClassifier(IClassificationAlgorithm algorithm, List <DataMining.Data.Dataset> dataRepresentations, IEnsembleClassificationStrategy ensembleStrategy, IClassificationQualityMeasure evaluator, bool serialize, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize();
LocalPerNodeClassificationAlgorithm hClassificatoinAlgorithm = new LocalPerNodeClassificationAlgorithm(dataRepresentations, algorithm, ensembleStrategy, evaluator, serialize, fireEvents);
if (fireEvents)
{
hClassificatoinAlgorithm.onPostClassifierConstruction += new EventHandler(hClassificatoinAlgorithm_onPostClassifierConstruction);
hClassificatoinAlgorithm.onPostNodeProcessing += new EventHandler(hClassificatoinAlgorithm_onPostNodeProcessing);
}
return(hClassificatoinAlgorithm.CreateClassifier());
}
public AntClustBMN_IB(int maxIterations, int colonySize, int convergenceIterations, Problem <ClusterExampleAssignment> problem, int clustersNumber, DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure, bool performLocalSearch, IClassificationAlgorithm algorithm, IClassificationQualityMeasure qualityMeasure)
: base(maxIterations, colonySize, convergenceIterations, problem, clustersNumber, similarityMeasure, performLocalSearch)
{
this._classificationAlgorithm = algorithm;
this._classificationQualityMeasure = qualityMeasure;
}
public ClusteringClassificationQualityEvaluator(DataMining.ClassificationMeasures.IClassificationQualityMeasure measure, IClassificationAlgorithm algorithm)
: base(null)
{
this._classificationAlgorithm = algorithm;
this._measure = measure;
}
