public static GaussianKernelEstimator CreateGKClassifier(Dataset trainingSet, double kernelParameter) { DefaultDistanceMeasure distanceMeasure = new DefaultDistanceMeasure(1); GaussianKernelEstimator GKClassifier = new GaussianKernelEstimator(kernelParameter, distanceMeasure, trainingSet); return(GKClassifier); }
public static NearestClassClassifier CreateNCClassifier(Dataset trainingSet, double distanceThreshold) { DefaultDistanceMeasure distanceMeasure = new DefaultDistanceMeasure(2); NearestClassClassifier NCClassifier = new NearestClassClassifier(distanceMeasure, trainingSet, distanceThreshold); return(NCClassifier); }
public static KNearestNeighbours CreateKNNClassifier(int k, Dataset trainingSet, bool useWeightedVote) { DefaultDistanceMeasure distanceMeasure = new DefaultDistanceMeasure(2); KNearestNeighbours knn = new KNearestNeighbours(k, distanceMeasure, trainingSet, useWeightedVote); return(knn); }
public void InitilizeHeuristicInformation(Dataset dataset, bool useAttributes, bool useInstances) { if (useAttributes) { this._entropyCalculator = new EntropyHeuristicsCalculator(dataset); } if (useInstances) { DefaultDistanceMeasure measure = new DefaultDistanceMeasure(2); KNearestNeighbours knn = new KNearestNeighbours(measure, _dataset, false); this._iblCalculator = new IBLHeuristicsCalculator(dataset, knn); } }
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 EnsembleClassifier CreateGKPSOIBMinerClassifier_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(1, distanceMeasure, trainingSet); IBClassificationQualityEvaluator evaluator = new ContinuousACO.ProblemSpecifics.IBClassificationQualityEvaluator(gke, measure); evaluator.LearningSet = trainingSet; evaluator.ValidationSet = trainingSet; PSOIB psoIB = new PSOIB(problemSize, archive, maxIterations / archive, convergenceIterations, evaluator); psoIB.OnPostSwarmIteration += OnPostColonyIteration; EnsembleClassifier psogke = psoIB.CreateEnsembleClassifier(); return(psogke); }
public static EnsembleClassifier CreateNCCPSOIBMinerClassifier_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; PSOIB psoIB = new PSOIB(problemSize, archive, maxIterations / archive, convergenceIterations, evaluator); psoIB.OnPostSwarmIteration += OnPostColonyIteration; EnsembleClassifier psoncc = psoIB.CreateEnsembleClassifier(); return(psoncc); }
public static EnsembleClassifier CreateKNNPSOIBMinerClassifier_ClassBasedWeights_Ensemble(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; PSOIB psoIB = new PSOIB(problemSize, archive, maxIterations / archive, convergenceIterations, evaluator); psoIB.OnPostSwarmIteration += OnPostColonyIteration; EnsembleClassifier psoknn = psoIB.CreateEnsembleClassifier(); return(psoknn); }
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); }