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("---------------------------------------------------");
}
}