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