public void ComputeSet(IMLDataSet setToCompute)
{
IOutput writer = MyCore.Resolve <IOutput>();
foreach (IMLDataPair pair in setToCompute)
{
IMLData output = Network.Compute(pair.Input);
if (!Regression)
{
writer.Write(String.Format(@"{0},{1}, actual={2},ideal={3}", pair.Input[0], pair.Input[1], GetOutputFormat(output), GetClass(pair.Ideal)));
}
else
{
writer.Write(String.Format(@"{0}, actual={1},ideal={2}", pair.Input[0], output, pair.Ideal));
}
AddToResults(pair, output);
}
}
public void Run()
{
IOutput writer = MyCore.Resolve <IOutput>();
if (csvPath == null)
{
writer.Write("Please load training set first!");
return;
}
if (this.csvPathTest == null)
{
writer.Write("Please load test set first!");
return;
}
InitializeDataAndNeurons();
if (!UnipolarChecked)
{
Network.SetBiPolarActivation();
}
else
{
Network.SetSigmoidActivation();
}
Network.StartLearning(Iterations);
LearningProcess = Network.learningProcess;
DrawLearningRate();
Network.ComputeSet(Network.TestSet);
resultList = Network.resultList;
if (!isRegression)
{
DrawGraph();
}
else
{
DrawRegressionFunction();
}
}
public void StartLearning(int iterationCount)
{
this.resultList = new ResultsList();
this.learningProcess = new List <IterationError>();
this.Network.Structure.FinalizeStructure();
Network.Reset();
IOutput writer = MyCore.Resolve <IOutput>();
Propagation train = new Backpropagation(this.Network, this.TrainingSet, this.LearningRate, this.TheMomentum);
// train.BatchSize = 1;
int epoch = 1;
do
{
train.Iteration();
writer.Write(String.Format(@"Epoch # {0} Error: {1}", epoch, train.Error));
epoch++;
this.learningProcess.Add(new IterationError(epoch, train.Error));
} while (epoch < iterationCount);
}
