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 void Test2()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
IEnsembleClassificationStrategy majorityVote = new MajorityVoteStrategy();
IEnsembleClassificationStrategy weightedVote = new WeightedVoteStrategy();
DataMining.Data.Dataset[] tables = BatchTest2.LoadTrainingAndTestingData("audiology", 0);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
//EnsembleClassifier ensemble = SingleTest.CreateGKAntIBMinerClassifier_ClassBaseWeights_Ensemble(trainingSet);
GaussianKernelEstimator GCE = new GaussianKernelEstimator(-0.5, new DefaultDistanceMeasure(1), trainingSet);
double quality = 0;
quality = SingleTest.TestClassifier(GCE, testingSet, accuracyMeasure);
Console.WriteLine("Accuracy=" + quality);
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
public static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double[] ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
Random RandomNumber = new Random();
double[] predicted = new double[ground_truth.Length];
for (int i = 0; i < ground_truth.Length; i++)
{
predicted[i] = RandomNumber.NextDouble();
}
BinaryLabels ground_truth_labels = new BinaryLabels(ground_truth);
BinaryLabels predicted_labels = new BinaryLabels(predicted);
ContingencyTableEvaluation base_evaluator = new ContingencyTableEvaluation();
base_evaluator.evaluate(predicted_labels, ground_truth_labels);
AccuracyMeasure evaluator1 = new AccuracyMeasure();
double accuracy = evaluator1.evaluate(predicted_labels, ground_truth_labels);
ErrorRateMeasure evaluator2 = new ErrorRateMeasure();
double errorrate = evaluator2.evaluate(predicted_labels, ground_truth_labels);
BALMeasure evaluator3 = new BALMeasure();
double bal = evaluator3.evaluate(predicted_labels, ground_truth_labels);
WRACCMeasure evaluator4 = new WRACCMeasure();
double wracc = evaluator4.evaluate(predicted_labels, ground_truth_labels);
F1Measure evaluator5 = new F1Measure();
double f1 = evaluator5.evaluate(predicted_labels, ground_truth_labels);
CrossCorrelationMeasure evaluator6 = new CrossCorrelationMeasure();
double crosscorrelation = evaluator6.evaluate(predicted_labels, ground_truth_labels);
RecallMeasure evaluator7 = new RecallMeasure();
double recall = evaluator7.evaluate(predicted_labels, ground_truth_labels);
PrecisionMeasure evaluator8 = new PrecisionMeasure();
double precision = evaluator8.evaluate(predicted_labels, ground_truth_labels);
SpecificityMeasure evaluator9 = new SpecificityMeasure();
double specificity = evaluator9.evaluate(predicted_labels, ground_truth_labels);
Console.Write("{0}, {1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}\n", accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity);
modshogun.exit_shogun();
}
public static void Test1()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
IEnsembleClassificationStrategy majorityVote = new MajorityVoteStrategy();
IEnsembleClassificationStrategy weightedVote = new WeightedVoteStrategy();
DataMining.Data.Dataset[] tables = BatchTest2.LoadTrainingAndTestingData("audiology", 0);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
//EnsembleClassifier ensemble = SingleTest.CreateGKAntIBMinerClassifier_ClassBaseWeights_Ensemble(trainingSet);
EnsembleClassifier ensemble = SingleTest.CreateNCCAntIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet);
//EnsembleClassifier ensemble = SingleTest.CreateKNNAntIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet,false);
double quality1 = 0;
double quality2 = 0;
double quality3 = 0;
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
ensemble.Stratgy = majorityVote;
quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
quality1 = Math.Round((quality1 / 1) * 100, 2);
quality2 = Math.Round((quality2 / 1) * 100, 2);
quality3 = Math.Round((quality3 / 1) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-GKC-CB-CB: Accuracy=" + quality1);
Console.WriteLine("ACO-GKC-CB-ens-MV: Accuracy=" + quality2);
Console.WriteLine("ACO-GKC-CB-ens-WV: Accuracy=" + quality3);
//Console.WriteLine(((KNearestNeighbours)ensemble[0]).KNeighbours.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
public static void RunACOIBL_WeightOutputs()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
foreach (string dataset in GetDatasetFolds("datasets.txt"))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
#region ACO-KNN-CB
//try
{
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
KNearestNeighbours knnclassifier = SingleTest.CreateKNNAntIBMinerClassifier_ClassBasedWeights(trainingSet, false);
//------------------------------------------------------------------
Console.WriteLine("ACO-KNN-CB: " + dataset);
SaveWeights(trainingSet, knnclassifier);
}
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
//catch (Exception ex)
{
//LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
}
}
public static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double[] ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
Random RandomNumber = new Random();
double[] predicted = new double[ground_truth.Length];
for (int i = 0; i < ground_truth.Length; i++) {
predicted[i] = RandomNumber.NextDouble();
}
BinaryLabels ground_truth_labels = new BinaryLabels(ground_truth);
BinaryLabels predicted_labels = new BinaryLabels(predicted);
ContingencyTableEvaluation base_evaluator = new ContingencyTableEvaluation();
base_evaluator.evaluate(predicted_labels,ground_truth_labels);
AccuracyMeasure evaluator1 = new AccuracyMeasure();
double accuracy = evaluator1.evaluate(predicted_labels,ground_truth_labels);
ErrorRateMeasure evaluator2 = new ErrorRateMeasure();
double errorrate = evaluator2.evaluate(predicted_labels,ground_truth_labels);
BALMeasure evaluator3 = new BALMeasure();
double bal = evaluator3.evaluate(predicted_labels,ground_truth_labels);
WRACCMeasure evaluator4 = new WRACCMeasure();
double wracc = evaluator4.evaluate(predicted_labels,ground_truth_labels);
F1Measure evaluator5 = new F1Measure();
double f1 = evaluator5.evaluate(predicted_labels,ground_truth_labels);
CrossCorrelationMeasure evaluator6 = new CrossCorrelationMeasure();
double crosscorrelation = evaluator6.evaluate(predicted_labels,ground_truth_labels);
RecallMeasure evaluator7 = new RecallMeasure();
double recall = evaluator7.evaluate(predicted_labels,ground_truth_labels);
PrecisionMeasure evaluator8 = new PrecisionMeasure();
double precision = evaluator8.evaluate(predicted_labels,ground_truth_labels);
SpecificityMeasure evaluator9 = new SpecificityMeasure();
double specificity = evaluator9.evaluate(predicted_labels,ground_truth_labels);
Console.Write("{0}, {1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}\n", accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity);
modshogun.exit_shogun();
}
static void TestBackProbagation(Dataset dataset)
{
IActivationFunction activationFunction = new SigmoidActivationFunction();
//int hiddenUnitCount = dataset.Metadata.Attributes.Length * dataset.Metadata.Target.Length;
int hiddenUnitCount = (dataset.Metadata.Attributes.Length + dataset.Metadata.Target.Length) / 2;
NeuralNetwork network = SingleTest.CreateNeuralNet_BP(dataset, hiddenUnitCount, 0.9, 0.01, 1000, activationFunction);
AccuracyMeasure measure = new AccuracyMeasure();
double quality = SingleTest.TestClassifier(network, dataset, measure);
Console.WriteLine(measure.ToString() + ":" + Math.Round(quality * 100, 2));
Console.WriteLine("Size:" + network.Size);
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
DoubleMatrix ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
DoubleMatrix predicted = randn(1, ground_truth.Length);
Labels ground_truth_labels = new Labels(ground_truth);
Labels predicted_labels = new Labels(predicted);
ContingencyTableEvaluation base_evaluator = new ContingencyTableEvaluation();
base_evaluator.evaluate(predicted_labels,ground_truth_labels);
AccuracyMeasure evaluator1 = new AccuracyMeasure();
double accuracy = evaluator1.evaluate(predicted_labels,ground_truth_labels);
ErrorRateMeasure evaluator2 = new ErrorRateMeasure();
double errorrate = evaluator2.evaluate(predicted_labels,ground_truth_labels);
BALMeasure evaluator3 = new BALMeasure();
double bal = evaluator3.evaluate(predicted_labels,ground_truth_labels);
WRACCMeasure evaluator4 = new WRACCMeasure();
double wracc = evaluator4.evaluate(predicted_labels,ground_truth_labels);
F1Measure evaluator5 = new F1Measure();
double f1 = evaluator5.evaluate(predicted_labels,ground_truth_labels);
CrossCorrelationMeasure evaluator6 = new CrossCorrelationMeasure();
double crosscorrelation = evaluator6.evaluate(predicted_labels,ground_truth_labels);
RecallMeasure evaluator7 = new RecallMeasure();
double recall = evaluator7.evaluate(predicted_labels,ground_truth_labels);
PrecisionMeasure evaluator8 = new PrecisionMeasure();
double precision = evaluator8.evaluate(predicted_labels,ground_truth_labels);
SpecificityMeasure evaluator9 = new SpecificityMeasure();
double specificity = evaluator9.evaluate(predicted_labels,ground_truth_labels);
Console.Write("{0:F}, {1:F}, {2:F}, {3:F}, {4:F}, {5:F}, {6:F}, {7:F}, {8:F}\n", accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity);
modshogun.exit_shogun();
}
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 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 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 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 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);
}
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 void RunANNMiner_QEM()
{
AccuracyMeasure testMeasure = new AccuracyMeasure();
foreach (string dataset in GetDatasetFolds("datasets.txt"))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
foreach (IClassificationMeasure measure in GetMeasures())
{
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine(dataset + " - Fold:" + _currentFold.ToString() + " - " + measure.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
double quality_before = 0.0;
double quality_final = 0.0;
double size_before = 0.0;
double size_final = 0.0;
ILearningMethod learningMethod = new BackPropagation(0.05, 10, 0.9, false);
int hiddenUnitCount = (trainingSet.Metadata.Attributes.Length + trainingSet.Metadata.Target.Length);
IActivationFunction activationFunction = new SigmoidActivationFunction();
NNClassificationQualityEvaluator evaluator = new NNClassificationQualityEvaluator(measure, learningMethod, hiddenUnitCount, activationFunction);
NNConnectionHeuristicCalculator calculator = new NNConnectionHeuristicCalculator(0.7);
DefaultRemovalLocalSearch <ConnectionDC> localSearch = new DefaultRemovalLocalSearch <ConnectionDC>(evaluator);
NNConnectorInvalidator invalidator = new NNConnectorInvalidator();
Problem <ConnectionDC> problem = new Problem <ConnectionDC>(invalidator, calculator, evaluator, localSearch);
NeuralNetwork network_before = null;
try
{
NeuralNetwork network_final = SingleTest.CreateNeuralNet_ANNMiner(problem, hiddenUnitCount, true, false, trainingSet, ref network_before);
quality_before = SingleTest.TestClassifier(network_before, testingSet, testMeasure);
quality_before = Math.Round(quality_before * 100, 2);
quality_final = SingleTest.TestClassifier(network_final, testingSet, testMeasure);
quality_final = Math.Round(quality_final * 100, 2);
size_before = network_before.Size;
size_final = network_final.Size;
//----------------------------------------
Console.WriteLine("ANNMiner - before:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + measure.ToString() + ":" + quality_before.ToString());
Console.WriteLine("ANNMiner - final:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + measure.ToString() + ":" + quality_final.ToString());
Console.WriteLine("---------------------------------------------------");
//----------------------------------------
SaveResults(dataset, "ANNMiner - before", measure.ToString(), quality_before.ToString(), size_before.ToString());
SaveResults(dataset, "ANNMiner - final", measure.ToString(), quality_final.ToString(), size_final.ToString());
}
catch (Exception ex)
{
LogError(ex);
break;
}
}
}
Console.WriteLine("---------------------------------------------------");
Console.WriteLine("---------------------------------------------------");
Console.WriteLine("---------------------------------------------------");
}
}
public static void RunGHCNN()
{
foreach (string dataset in GetDatasetFolds("datasets.txt"))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
double avgQualityBefore = 0;
double avgSizeBefore = 0;
double avgQulityAfter = 0;
double avgSizeAfter = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
double quality_before = 0.0;
double quality_final = 0.0;
double size_before = 0.0;
double size_final = 0.0;
IClassificationMeasure testMeasure = new AccuracyMeasure();
ILearningMethod learningMethod = new BackPropagation(_acoLearningRateNW, _acoEpochsNW, 0.9, false);
//int hiddenUnitCount = trainingSet.Metadata.Attributes.Length * trainingSet.Metadata.Target.Length;
int hiddenUnitCount = (trainingSet.Metadata.Attributes.Length + trainingSet.Metadata.Target.Length);
IActivationFunction activationFunction = new SigmoidActivationFunction();
IClassificationMeasure trainingMeasure = new QLFunction();
NNClassificationQualityEvaluator evaluator = new NNClassificationQualityEvaluator(trainingMeasure, learningMethod, hiddenUnitCount, activationFunction);
NNConnectionHeuristicCalculator calculator = new NNConnectionHeuristicCalculator(0.7);
DefaultRemovalLocalSearch <ConnectionDC> localSearch = new DefaultRemovalLocalSearch <ConnectionDC>(evaluator);
NNConnectorInvalidator invalidator = new NNConnectorInvalidator();
Problem <ConnectionDC> problem = new Problem <ConnectionDC>(invalidator, calculator, evaluator, localSearch);
NeuralNetwork network_before = null;
try
{
stopWatch.Reset();
stopWatch.Start();
NeuralNetwork network_final = SingleTest.CreateNeuralNet_GHCNN(problem, hiddenUnitCount, true, false, trainingSet, ref network_before);
stopWatch.Stop();
quality_before = SingleTest.TestClassifier(network_before, testingSet, testMeasure);
quality_before = Math.Round(quality_before * 100, 2);
avgQualityBefore += quality_before;
quality_final = SingleTest.TestClassifier(network_final, testingSet, testMeasure);
quality_final = Math.Round(quality_final * 100, 2);
avgQulityAfter += quality_final;
size_before = network_before.Size;
size_final = network_final.Size;
avgSizeBefore += size_before;
avgSizeAfter += avgSizeAfter;
//----------------------------------------
Console.WriteLine("GHCNN - before:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + testMeasure.ToString() + ":" + quality_before.ToString());
Console.WriteLine("GHCNN - final:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + testMeasure.ToString() + ":" + quality_final.ToString());
Console.WriteLine("---------------------------------------------------");
//----------------------------------------
}
catch (Exception ex)
{
LogError(ex);
break;
}
}
avgQualityBefore /= _folds;
avgQulityAfter /= _folds;
avgSizeBefore /= _folds;
avgSizeAfter /= _folds;
SaveResults(dataset, "GHCNN - before", avgQualityBefore.ToString(), avgSizeBefore.ToString(), stopWatch.ElapsedMilliseconds.ToString());
SaveResults(dataset, "GHCNN - final", avgQulityAfter.ToString(), avgSizeAfter.ToString(), stopWatch.ElapsedMilliseconds.ToString());
Console.WriteLine("---------------------------------------------------");
Console.WriteLine("---------------------------------------------------");
Console.WriteLine("---------------------------------------------------");
}
}
public static void RunBackPropagation()
{
foreach (string dataset in GetDatasetFolds("datasets.txt"))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
double avgQuality = 0;
double avgSize = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
double quality = 0.0;
double size = 0.0;
AccuracyMeasure testMeasure = new AccuracyMeasure();
IActivationFunction activationFunction = new SigmoidActivationFunction();
//int hiddenUnitCount = trainingSet.Metadata.Attributes.Length * trainingSet.Metadata.Target.Length;
int hiddenUnitCount = (trainingSet.Metadata.Attributes.Length + trainingSet.Metadata.Target.Length);
try
{
stopWatch.Reset();
stopWatch.Start();
NeuralNetwork network = SingleTest.CreateNeuralNet_BP(trainingSet, hiddenUnitCount, 0.9, _bpLearningRate, _bpEpochs, activationFunction);
stopWatch.Stop();
quality = SingleTest.TestClassifier(network, testingSet, testMeasure);
quality = Math.Round(quality * 100, 2);
size = network.Size;
avgQuality += quality;
avgSize += size;
//----------------------------------------
Console.WriteLine("Backprop:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + testMeasure.ToString() + ":" + quality.ToString());
Console.WriteLine("---------------------------------------------------");
//----------------------------------------
}
catch (Exception ex)
{
LogError(ex);
break;
}
}
avgQuality /= _folds;
avgSize /= _folds;
SaveResults(dataset, "BackProp", avgQuality.ToString(), avgSize.ToString(), stopWatch.ElapsedMilliseconds.ToString());
Console.WriteLine("---------------------------------------------------");
Console.WriteLine("---------------------------------------------------");
Console.WriteLine("---------------------------------------------------");
}
}
public static void RunACOIBL()
{
int k = 9;
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
foreach (string dataset in GetDatasetFolds("datasets.txt"))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
//Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
Dataset datasetFull = Dataset.Merge(trainingSet, testingSet);
double quality = 0;
try
{
{
KNearestNeighbours knn = SingleTest.CreateKNNClassifier(k, datasetFull, false);
quality = SingleTest.TestClassifier(knn, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("KNN: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "KNN", k.ToString(), quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
KNearestNeighbours knnWV = SingleTest.CreateKNNClassifier(k, datasetFull, true);
quality = SingleTest.TestClassifier(knnWV, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("KNN-WV: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "KNN-WV", k.ToString(), quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
NearestClassClassifier ncc = SingleTest.CreateNCClassifier(datasetFull);
quality = SingleTest.TestClassifier(ncc, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("NNC: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "NNC", quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
KNearestNeighbours knn = SingleTest.CreateKNNAntIBMinerClassifier(k, datasetFull, false);
quality = SingleTest.TestClassifier(knn, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-KNN: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-KNN", k.ToString(), quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
KNearestNeighbours knn = SingleTest.CreateKNNAntIBMinerClassifier(k, datasetFull, true);
quality = SingleTest.TestClassifier(knn, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-KNN-WV: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-KNN-WV", k.ToString(), quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
KNearestNeighbours knn = SingleTest.CreateKNNAntIBMinerClassifier_ClassBasedWeights(k, datasetFull, false);
quality = SingleTest.TestClassifier(knn, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-KNN-CB: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-KNN-CB", k.ToString(), quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
KNearestNeighbours knn = SingleTest.CreateKNNAntIBMinerClassifier_ClassBasedWeights(k, datasetFull, true);
quality = SingleTest.TestClassifier(knn, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-KNN-CB-WV: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-KNN-CB-WV", k.ToString(), quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
NearestClassClassifier ncc = SingleTest.CreateNCCAntIBMinerClassifier(datasetFull);
quality = SingleTest.TestClassifier(ncc, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-NCC: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-NCC", quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
NearestClassClassifier ncc = SingleTest.CreateNCCAntIBMinerClassifier_ClassBasedWeights(datasetFull);
quality = SingleTest.TestClassifier(ncc, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-NCC-CB: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-NCC-CB", quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
GaussianKernelEstimator GKC = SingleTest.CreateGKAntIBMinerClassifier(datasetFull);
quality = SingleTest.TestClassifier(GKC, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-GKC: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-GKC", quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
{
GaussianKernelEstimator GKC = SingleTest.CreateGKAntIBMinerClassifier_ClassBaseWeights(datasetFull);
quality = SingleTest.TestClassifier(GKC, datasetFull, accuracyMeasure);
quality = Math.Round(quality * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-GKC-CB: " + dataset + " - Accuracy=" + quality);
SaveResults(dataset, "ACO-GKC-CB", quality.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
}
catch (Exception ex)
{
LogError(ex);
// Console.WriteLine(ex.Message);
}
}
}
}
internal static HandleRef getCPtr(AccuracyMeasure obj) {
return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr;
}
public static void RunPSOIBL()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
IEnsembleClassificationStrategy majorityVote = new MajorityVoteStrategy();
IEnsembleClassificationStrategy weightedVote = new WeightedVoteStrategy();
foreach (string dataset in GetDatasetFolds(DatasetNamesFile))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
#region PSO-KNN-CB
try
{
double quality1 = 0;
//double quality2 = 0;
double quality3 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
EnsembleClassifier ensemble = SingleTest.CreateKNNPSOIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet, false);
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
ensemble.Stratgy = majorityVote;
//quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
// quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("PSO-KNN-CB: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "PSO-KNN-CB", quality1.ToString());
//Console.WriteLine("PSO-KNN-CB-ens-MV: " + dataset + " - Accuracy=" + quality2);
//SaveResults(dataset, "PSO-KNN-CB-ens-MV",quality2.ToString());
Console.WriteLine("PSO-KNN-CB-ens-WV: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "PSO-KNN-CB-ens-WV", quality3.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
catch (Exception ex)
{
// LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
#region PSO-KNN-CB-WV
//try
//{
// double quality1 = 0;
// double quality2 = 0;
// double quality3 = 0;
// for (_currentFold = 0; _currentFold < _folds; _currentFold++)
// {
// //----------------------------------------
// Console.WriteLine("Fold:" + _currentFold.ToString());
// //----------------------------------------
// DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
// DataMining.Data.Dataset trainingSet = tables[0];
// DataMining.Data.Dataset testingSet = tables[1];
// EnsembleClassifier ensemble = SingleTest.CreateKNNPSOIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet, true);
// quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
// ensemble.Stratgy = majorityVote;
// quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
// ensemble.Stratgy = weightedVote;
// quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
// }
// quality1 = Math.Round((quality1 / _folds) * 100, 2);
// quality2 = Math.Round((quality2 / _folds) * 100, 2);
// quality3 = Math.Round((quality3 / _folds) * 100, 2);
// //------------------------------------------------------------------
// Console.WriteLine("PSO-KNN-CB-WV: " + dataset + " - Accuracy=" + quality1);
// SaveResults(dataset, "PSO-KNN-CB-WV", k.ToString(), quality1.ToString());
// Console.WriteLine("PSO-KNN-CB-WV-ens-MV: " + dataset + " - Accuracy=" + quality2);
// SaveResults(dataset, "PSO-KNN-CB-WV-ens-MV", k.ToString(), quality2.ToString());
// Console.WriteLine("PSO-KNN-CB-WV-ens-WV: " + dataset + " - Accuracy=" + quality3);
// SaveResults(dataset, "PSO-KNN-CB-WV-ens-WV", k.ToString(), quality3.ToString());
// Console.WriteLine("-------------------------------------------");
// Console.WriteLine("-------------------------------------------");
// Console.WriteLine("-------------------------------------------");
//}
//catch (Exception ex)
//{
// LogError(ex);
// // Console.WriteLine(ex.Message);
//}
#endregion
#region PSO-NCC-CB
try
{
double quality1 = 0;
//double quality2 = 0;
double quality3 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
EnsembleClassifier ensemble = SingleTest.CreateNCCPSOIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet);
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
//ensemble.Stratgy = majorityVote;
//quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
//quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("PSO-NCC-CB: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "PSO-NNC-CB", quality1.ToString());
//Console.WriteLine("PSO-NNC-CB-ens-MV: " + dataset + " - Accuracy=" + quality2);
//SaveResults(dataset, "PSO-NNC-CB-ens-MV", quality2.ToString());
Console.WriteLine("PSO-NNC-CB-ens-WV: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "PSO-NNC-CB-ens-WV", quality3.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
catch (Exception ex)
{
LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
#region PSO-GKC-CB
try
{
double quality1 = 0;
//double quality2 = 0;
double quality3 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
EnsembleClassifier ensemble = SingleTest.CreateGKPSOIBMinerClassifier_ClassBaseWeights_Ensemble(trainingSet);
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
//ensemble.Stratgy = majorityVote;
//quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
//quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("PSO-GKC-CB-CB: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "PSO-GKC-CB", quality1.ToString());
//Console.WriteLine("PSO-GKC-CB-ens-MV: " + dataset + " - Accuracy=" + quality2);
//SaveResults(dataset, "PSO-GKC-CB-ens-MV", quality2.ToString());
Console.WriteLine("PSO-GKC-CB-ens-WV: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "PSO-GKC-CB-ens-WV", quality3.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
catch (Exception ex)
{
LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
}
}
internal static HandleRef getCPtr(AccuracyMeasure obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
public static void RunConventional()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
foreach (string dataset in GetDatasetFolds(DatasetNamesFile))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
//try
{
double quality1 = 0;
double quality2 = 0;
double quality3 = 0;
double quality4 = 0;
double quality5 = 0;
double quality6 = 0;
double quality7 = 0;
double quality8 = 0;
double quality9 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
//Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
KNearestNeighbours knn1 = SingleTest.CreateKNNClassifier(1, trainingSet, false);
quality1 += SingleTest.TestClassifier(knn1, testingSet, accuracyMeasure);
//------------------------------------------------------------------
KNearestNeighbours knn11 = SingleTest.CreateKNNClassifier(11, trainingSet, false);
quality2 += SingleTest.TestClassifier(knn11, testingSet, accuracyMeasure);
//------------------------------------------------------------------
KNearestNeighbours knn21 = SingleTest.CreateKNNClassifier(21, trainingSet, false);
quality3 += SingleTest.TestClassifier(knn21, testingSet, accuracyMeasure);
//------------------------------------------------------------------
//------------------------------------------------------------------
//------------------------------------------------------------------
NearestClassClassifier ncc0 = SingleTest.CreateNCClassifier(trainingSet, 0);
quality4 += SingleTest.TestClassifier(ncc0, testingSet, accuracyMeasure);
//------------------------------------------------------------------
NearestClassClassifier ncc5 = SingleTest.CreateNCClassifier(trainingSet, 0.5);
quality5 += SingleTest.TestClassifier(ncc5, testingSet, accuracyMeasure);
//------------------------------------------------------------------
NearestClassClassifier ncc1 = SingleTest.CreateNCClassifier(trainingSet, 0.9);
quality6 += SingleTest.TestClassifier(ncc1, testingSet, accuracyMeasure);
////------------------------------------------------------------------
////------------------------------------------------------------------
////------------------------------------------------------------------
GaussianKernelEstimator gcc0 = SingleTest.CreateGKClassifier(trainingSet, 0);
quality7 += SingleTest.TestClassifier(gcc0, testingSet, accuracyMeasure);
//------------------------------------------------------------------
GaussianKernelEstimator gcc5 = SingleTest.CreateGKClassifier(trainingSet, 0.25);
quality8 += SingleTest.TestClassifier(gcc5, testingSet, accuracyMeasure);
//------------------------------------------------------------------
GaussianKernelEstimator gcc1 = SingleTest.CreateGKClassifier(trainingSet, 0.5);
quality9 += SingleTest.TestClassifier(gcc1, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
quality4 = Math.Round((quality4 / _folds) * 100, 2);
quality5 = Math.Round((quality5 / _folds) * 100, 2);
quality6 = Math.Round((quality6 / _folds) * 100, 2);
quality7 = Math.Round((quality7 / _folds) * 100, 2);
quality8 = Math.Round((quality8 / _folds) * 100, 2);
quality9 = Math.Round((quality9 / _folds) * 100, 2);
Console.WriteLine("1NN: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "1NN", quality1.ToString());
Console.WriteLine("11NN: " + dataset + " - Accuracy=" + quality2);
SaveResults(dataset, "11NN", quality2.ToString());
Console.WriteLine("21NN: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "21NN", quality3.ToString());
Console.WriteLine("NCC-0: " + dataset + " - Accuracy=" + quality4);
SaveResults(dataset, "NCC-0", quality4.ToString());
Console.WriteLine("NCC-0.5: " + dataset + " - Accuracy=" + quality5);
SaveResults(dataset, "NCC-0.5", quality5.ToString());
Console.WriteLine("NCC-1: " + dataset + " - Accuracy=" + quality6);
SaveResults(dataset, "NCC-1", quality6.ToString());
Console.WriteLine("GKE-0: " + dataset + " - Accuracy=" + quality7);
SaveResults(dataset, "GKE-0", quality7.ToString());
Console.WriteLine("GKE-0.25: " + dataset + " - Accuracy=" + quality8);
SaveResults(dataset, "GKE-0.25", quality8.ToString());
Console.WriteLine("GKE-0.5: " + dataset + " - Accuracy=" + quality9);
SaveResults(dataset, "GKE-0.5", quality9.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
//catch (Exception ex)
{
//LogError(ex);
// Console.WriteLine(ex.Message);
}
}
}