public static BayesianNetworkClassifier CreateABCMinerPlusIClassifier(int seed, int iterations, int colonySize, int localColonySize, int convergence, int maxParents, Dataset trainingSet, DataMining.ClassificationMeasures.IClassificationQualityMeasure measure, bool performLocalSearch, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
VariableTypeAssigmentInvalidator invalidator1 = new VariableTypeAssigmentInvalidator();
BayesianClassificationQualityEvaluator evaluator = new BayesianClassificationQualityEvaluator(measure);
SimpleVariableTypeAssignmentLocalSearch localSearch1 = new SimpleVariableTypeAssignmentLocalSearch(evaluator);
DefaultHeuristicCalculator <VariableTypeAssignment> calculator1 = new DefaultHeuristicCalculator <VariableTypeAssignment>();
Problem <VariableTypeAssignment> problem1 = new Problem <VariableTypeAssignment>(invalidator1, calculator1, evaluator, localSearch1);
CyclicRelationInvalidator invalidator2 = new CyclicRelationInvalidator();
BackwardRemovalLocalSearch localSearch2 = new BackwardRemovalLocalSearch(evaluator);
CMICalculator calculator = new CMICalculator();
Problem <Edge> problem2 = new Problem <Edge>(invalidator2, calculator, evaluator, localSearch2);
ABCMinerPlusI abcMinerPlusI = new ABCMinerPlusI(iterations, colonySize, localColonySize, convergence, problem2, problem1, maxParents, trainingSet, performLocalSearch);
if (fireEvents)
{
abcMinerPlusI.ABCAlgorithm.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abcMinerPlusI.ABCAlgorithm.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
abcMinerPlusI.ABCMinerAlgorithm.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abcMinerPlusI.ABCMinerAlgorithm.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
}
BayesianNetworks.Model.BayesianNetworkClassifier bnclassifier = abcMinerPlusI.CreateClassifier() as BayesianNetworkClassifier;
return(bnclassifier);
}
static void TestANNMiner(Dataset dataset)
{
IClassificationMeasure measure = new AccuracyMeasure();
ILearningMethod learningMethod = new BackPropagation(0.1, 10, 0.9, false);
int hiddenUnitCount = dataset.Metadata.Attributes.Length * dataset.Metadata.Target.Length;
IActivationFunction activationFunction = new SigmoidActivationFunction();
ISolutionQualityEvaluator <ConnectionDC> evaluator = new NNClassificationQualityEvaluator(measure, learningMethod, hiddenUnitCount, activationFunction);
IHeuristicsCalculator <ConnectionDC> calculator = new DefaultHeuristicCalculator <ConnectionDC>();
ILocalSearch <ConnectionDC> localSearch = new DefaultRemovalLocalSearch <ConnectionDC>(evaluator);
IComponentInvalidator <ConnectionDC> invalidator = new NNConnectorInvalidator();
Problem <ConnectionDC> problem = new Problem <ConnectionDC>(invalidator, calculator, evaluator, localSearch);
NeuralNetwork network_before = null;
NeuralNetwork network_final = SingleTest.CreateNeuralNet_ANNMiner(problem, hiddenUnitCount, true, false, dataset, ref network_before);
double quilty_before = SingleTest.TestClassifier(network_before, dataset, measure);
double quilty_final = SingleTest.TestClassifier(network_final, dataset, measure);
Console.WriteLine("ANN -" + quilty_before);
Console.WriteLine("ANN -" + quilty_final);
}
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 ClusteringSolution CreateACOClusters_IB(int seed, Dataset dataset, int clustersNumber, ISimilarityMeasure similarityMeasure, int maxIterations, int colonySize, int convergenceIterations, bool fireEvents, bool performLocalSearch)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
DefaultHeuristicCalculator <ClusterExampleAssignment> calculator = new DefaultHeuristicCalculator <ClusterExampleAssignment>();
ClusteringIBInvalidator invalidator = new ClusteringIBInvalidator();
DataMining.ProximityMeasures.IClusteringQualityMeasure measure = new CohesionClusteringMeasure();
ClusteringQualityEvaluator cohesionEvaluator = new ClusteringQualityEvaluator(measure);
KMeansLocalSearch localSearch = new KMeansLocalSearch(dataset, 1, similarityMeasure, cohesionEvaluator);
ACO.ProblemSpecifics.ISolutionQualityEvaluator <DataMining.Model.ClusterExampleAssignment> evaluator = new ClusteringQualityEvaluator(measure);
Problem <DataMining.Model.ClusterExampleAssignment> problem = new Problem <DataMining.Model.ClusterExampleAssignment>(invalidator, calculator, evaluator, localSearch);
ACOClustering_IB antClustering = new ACOClustering_IB(maxIterations, colonySize, convergenceIterations, problem, clustersNumber, similarityMeasure, dataset, performLocalSearch);
antClustering.OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
return(antClustering.CreateClusters());
}
public static ResultObject EvaluateRandDR_WekaClassifier(string algorithm, string trainingSetPath, string testSetPath, Dataset trainingSet, bool useAttributes, bool useInstances)
{
Classifier classifier = WekaNETBridge.WekaClassification.GetWekaClassifier(algorithm, trainingSetPath);
//DefaultDRHeuristicCalculator calculator = new DefaultDRHeuristicCalculator();
DefaultHeuristicCalculator <DRComponent> calculator = new DefaultHeuristicCalculator <DRComponent>(trainingSet);
DRComponentInvalidator invalidator = new DRComponentInvalidator();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
WekaClassificationQualityEvaluator evaluator = new WekaClassificationQualityEvaluator(wekaClassification, true);
DRLocalSearch localSearch = new DRLocalSearch(evaluator);
Problem <DRComponent> problem = new Problem <DRComponent>(invalidator, calculator, evaluator, localSearch);
RandomDR random = new RandomDR(maxIterations, colonySize, 50, problem, useAttributes, useInstances, false, trainingSet);
random.OnPostAntSolutionContruction += OnPostAntSolutionContruction;
random.OnPostColonyIteration += OnPostColonyIteration;
WekaNETBridge.WekaClassifier wekcaClassifier = random.CreateWekaClassifier();
double quality = WekaNETBridge.WekaClassification.EvaluateClassifier(wekcaClassifier, testSetPath);
double attribueReduction = random.BestAnt.Solution.FeatureCount() / (double)trainingSet.Metadata.Attributes.Length;
double instanceReduction = random.BestAnt.Solution.InstanceCount() / (double)trainingSet.Size;
ResultObject result = new ResultObject()
{
Quality = quality, AttributeReduction = attribueReduction, InstanceReduciton = instanceReduction
};
return(result);
}
public static BayesianNetworkClassifier CreateABCClassifier(int seed, int iterations, int colonySize, int convergence, Dataset trainingSet, DataMining.ClassificationMeasures.IClassificationQualityMeasure measure, bool performLocalSearch, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
VariableTypeAssigmentInvalidator invalidator = new VariableTypeAssigmentInvalidator();
BayesianClassificationQualityEvaluator evaluator = new BayesianClassificationQualityEvaluator(measure);
VariableTypeAssignmentLocalSearch localSearch = new VariableTypeAssignmentLocalSearch(evaluator);
DefaultHeuristicCalculator <VariableTypeAssignment> calculator = new DefaultHeuristicCalculator <VariableTypeAssignment>();
Problem <VariableTypeAssignment> problem = new Problem <VariableTypeAssignment>(invalidator, calculator, evaluator, localSearch);
ABC abc = new ABC(iterations, colonySize, convergence, problem, trainingSet, performLocalSearch);
if (fireEvents)
{
abc.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abc.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
}
BayesianNetworks.Model.BayesianNetworkClassifier bnclassifier = abc.CreateClassifier() as BayesianNetworkClassifier;
return(bnclassifier);
}
public static void TestACOCluster_IBThenBMN()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
double avgQualiy = 0;
for (int i = 0; i < 1; i++)
{
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
//DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DefaultHeuristicCalculator <ClusterExampleAssignment> calculator = new DefaultHeuristicCalculator <ClusterExampleAssignment>();
ClusteringIBInvalidator invalidator = new ClusteringIBInvalidator();
DataMining.ProximityMeasures.IClusteringQualityMeasure measure = new CohesionClusteringMeasure();
ClusteringQualityEvaluator cohesionEvaluator = new ClusteringQualityEvaluator(measure);
KMeansLocalSearch localSearch = new KMeansLocalSearch(trainingSet, 1, similarityMeasure, cohesionEvaluator);
ACO.ProblemSpecifics.ISolutionQualityEvaluator <DataMining.Model.ClusterExampleAssignment> evaluator = new ClusteringQualityEvaluator(measure);
Problem <DataMining.Model.ClusterExampleAssignment> problem = new Problem <DataMining.Model.ClusterExampleAssignment>(invalidator, calculator, evaluator, localSearch);
DataMining.Algorithms.IClusteringAlgorithm AntClustering = new ACOClustering_IB(1000, 10, 10, problem, 10, similarityMeasure, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, 10, trainingSet, similarityMeasure, accuracy, AntClustering, naive, true);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
avgQualiy += quality;
}
Console.WriteLine(avgQualiy / 10);
Console.WriteLine("End");
}
