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 double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
Example example = null;
double[] errors = new double[dataset.Size];
for (int i = 0; i < dataset.Size; i++)
{
example = dataset[i];
int actual = example.Label;
Prediction prediction = classifier.Classify(example);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
errors[i] = Math.Abs(1 - prediction.Probabilities[actual]);
}
Array.Sort(errors);
double MedAE = errors[errors.Length / 2];
return(MedAE);
}
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 double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
double MAE = 0;
Example example = null;
for (int i = 0; i < dataset.Size; i++)
{
example = dataset[i];
int actual = example.Label;
Prediction prediction = classifier.Classify(example);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
MAE += Math.Abs(1 - prediction.Probabilities[actual]);
}
MAE = MAE / dataset.Size;
return(MAE);
}
public double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
double QLE = 0;
Example example = null;
for (int i = 0; i < dataset.Size; i++)
{
example = dataset[i];
int actual = example.Label;
Prediction prediction = classifier.Classify(example);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
QLE += Math.Log(1 + Math.Exp(-probability));
}
QLE = QLE / dataset.Size;
return(QLE);
}
public double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
double MSE = 0;
Instance instance = null;
for (int i = 0; i < dataset.Size; i++)
{
instance = dataset[i];
int actual = instance.Label;
Prediction prediction = classifier.Classify(instance);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
MSE += Math.Pow(1 - prediction.Probabilities[actual], 2);
}
MSE = Math.Sqrt(MSE) / dataset.Size;
return(MSE);
}
public double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
double QLF = 0;
Example example = null;
for (int i = 0; i < dataset.Size; i++)
{
example = dataset[i];
int actual = example.Label;
Prediction prediction = classifier.Classify(example);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
double value = 0;
for (int index = 0; index < dataset.Metadata.Target.Length; index++)
{
if (index == actual)
{
value -= 2 * probability;
}
else
{
value += Math.Pow(prediction.Probabilities[index], 2);
}
}
QLF += (2 + value) / 3;
}
QLF = QLF / dataset.Size;
return(QLF);
}
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");
}
public double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
double BIReward = 0;
double[] classProbabilities = new double[dataset.Metadata.Target.Length];
for (int i = 0; i < classProbabilities.Length; i++)
{
classProbabilities[i] = (double)dataset.Filter(i).Count / (double)dataset.Size;
}
Example example = null;
for (int i = 0; i < dataset.Size; i++)
{
example = dataset[i];
int actual = example.Label;
Prediction prediction = classifier.Classify(example);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
if (predicted == actual)
{
BIReward += Math.Log(probability / classProbabilities[actual]);
}
else
{
BIReward += Math.Log((1 - probability) / (1 - classProbabilities[actual]));
}
}
BIReward = (BIReward / dataset.Size) + 0.5;
return(BIReward);
}
public static double TestClassifier(DataMining.Model.IClassifier classifier, Dataset testingSet, DataMining.ClassificationMeasures.IClassificationQualityMeasure qualityTester)
{
return(qualityTester.CalculateMeasure(DataMining.ClassificationMeasures.ConfusionMatrix.GetConfusionMatrixes(classifier, testingSet)));
}
public double CalculateMeasure(DataMining.Model.IClassifier classifier, Data.Dataset dataset)
{
return(this.CalculateMeasure(ConfusionMatrix.ComputeConfusionMatrixes(classifier, dataset)));
}
