public static void TestKmeanClusteringBMN()
{
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;
int k = 8;
for (int i = 0; i < 10; 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();
DataMining.Algorithms.IClusteringAlgorithm kmeans = new DataMining.Algorithms.KMeans(trainingSet, k, similarityMeasure, 100, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, k, trainingSet, similarityMeasure, accuracy, kmeans, naive, false);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
avgQualiy += quality;
}
Console.WriteLine(avgQualiy / 10);
Console.WriteLine("End");
}
public static void TestLBMNABC()
{
Console.WriteLine("Start");
string dsname = "car";
string trainingSetPath = @"C:\0 - Khalid\Academics\Datasets\" + dsname + @"\TR0_" + dsname + ".arff";
string testingSetPath = @"C:\0 - Khalid\Academics\Datasets\" + dsname + @"\TS0_" + dsname + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(trainingSetPath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(testingSetPath);
DataMining.ClassificationMeasures.AccuracyMeasure qualityEvaluator = new DataMining.ClassificationMeasures.AccuracyMeasure();
ISolutionQualityEvaluator <Edge> trainingQualityEvaluator = new likelihoodQualityEvaluator(trainingSet);
IHeuristicValueCalculator <Edge> calculator = new MICalculator();
int seed = (int)DateTime.Now.Ticks;
BayesianMultinetClassifier lmnabclassifier = SingleTest.CreateLMNAntBayesianClassification(seed, 10, 5, 10, 3, trainingSet, trainingQualityEvaluator, calculator, true);
double quality = SingleTest.TestClassifier(lmnabclassifier, testingSet, qualityEvaluator);
quality = Math.Round(quality * 100, 2);
Console.WriteLine("LMNABC Quality: " + quality.ToString());
Console.WriteLine("End");
//Console.ReadLine();
}
public static void TestNaive()
{
Console.WriteLine("Start");
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
DataMining.ClassificationMeasures.IClassificationQualityMeasure qualityEvaluator = new DataMining.ClassificationMeasures.AccuracyMeasure();
BayesianNetworkClassifier naive = SingleTest.CreateNaiveBayesianClassifier(trainingSet);
double quality = SingleTest.TestClassifier(naive, testingSet, qualityEvaluator);
quality = Math.Round(quality * 100, 2);
Console.WriteLine("Naive Quality: " + quality.ToString());
Console.WriteLine("End");
}
public static void TestANTClustBMN_IB()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
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();
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateAntClustBMNClassifier_IB(seed, trainingSet, 1, 1, 10, 3, similarityMeasure, accuracy, naive, true);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
}
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");
}