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; }