public static void TestABCMiner()
{
Console.WriteLine("Start");
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure1 = new DataMining.ClassificationMeasures.MicroAccuracyMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure2 = new DataMining.ClassificationMeasures.ProbabilityAccuracyMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure3 = new DataMining.ClassificationMeasures.ReducedErrorMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure4 = new DataMining.ClassificationMeasures.ProbabilityReducedErrorMeasure();
IHeuristicValueCalculator <Edge> calculator = new CMICalculator();
int seed = (int)DateTime.Now.Ticks;
BayesianNetworkClassifier abclassifier = SingleTest.CreateABCMinerClassifier(seed, 100, 10, 10, 3, trainingSet, measure1, calculator, false, true);
//double quality1 = SingleTest.TestClassifier(abclassifier, testingSet, measure1);
//quality1 = Math.Round(quality1 * 100, 2);
//double quality2 = SingleTest.TestClassifier(abclassifier, testingSet, measure2);
//quality2 = Math.Round(quality2 * 100, 2);
//double quality3 = SingleTest.TestClassifier(abclassifier, testingSet, measure3);
//quality3 = Math.Round(quality3 * 100, 2);
double quality4 = SingleTest.TestClassifier(abclassifier, testingSet, measure4);
quality4 = Math.Round(quality4 * 100, 2);
//Console.WriteLine("ABC Quality1: " + quality1.ToString());
//Console.WriteLine("ABC Quality2: " + quality2.ToString());
//Console.WriteLine("ABC Quality3: " + quality3.ToString());
Console.WriteLine("ABC Quality4: " + quality4.ToString());
Console.WriteLine("End");
string xml = BayesianNetworks.Utilities.GraphExporter.ExportToGaphSharpXml(abclassifier);
System.IO.File.WriteAllText(@"C:\0 - Khalid\Academics\" + datasetName + "1.xml", xml);
Console.ReadLine();
}
public static void TestABCMinerPlusI()
{
Console.WriteLine("Start");
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure1 = new DataMining.ClassificationMeasures.MicroAccuracyMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure2 = new DataMining.ClassificationMeasures.ProbabilityAccuracyMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure3 = new DataMining.ClassificationMeasures.ReducedErrorMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure4 = new DataMining.ClassificationMeasures.ProbabilityReducedErrorMeasure();
int seed = (int)DateTime.Now.Ticks;
BayesianNetworkClassifier abcMinerPlusclassifier = SingleTest.CreateABCMinerPlusIClassifier(seed, 10, 1, 5, 10, 2, trainingSet, measure1, false, true);
double quality1 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure1);
quality1 = Math.Round(quality1 * 100, 2);
double quality2 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure2);
quality2 = Math.Round(quality2 * 100, 2);
double quality3 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure3);
quality3 = Math.Round(quality3 * 100, 2);
double quality4 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure4);
quality4 = Math.Round(quality4 * 100, 2);
Console.WriteLine("ABCMinerPlusI Quality1: " + quality1.ToString());
Console.WriteLine("ABCMinerPlusI Quality2: " + quality2.ToString());
Console.WriteLine("ABCMinerPlusI Quality3: " + quality3.ToString());
Console.WriteLine("ABCMinerPlusI Quality4: " + quality4.ToString());
Console.WriteLine("End");
Console.WriteLine("End");
Console.ReadLine();
}
