public Tuple <double, TimeSpan> Train()
{
if (SourceMatrix == null)
{
return(null);
}
List <int> listColumns = new List <int>();
for (int i = 0; i < FeaturesAmount; i++)
{
listColumns.Add(i);
}
double[][] inputs = SourceMatrix.GetColumns(listColumns.ToArray()).ToJagged();
double[][] outputs = SourceMatrix.GetColumn(FeaturesAmount).Transpose().ToJagged();
Network = new ActivationNetwork(new BipolarSigmoidFunction(SigmoidAlphaValue), NeuronsInFirstLayer, NeuronsInHiddenLayer, 1);
BackPropagationLearning teacher = new BackPropagationLearning(Network);
teacher.LearningRate = LearningRate;
Network.Randomize();
var sw = Stopwatch.StartNew();
double error = double.PositiveInfinity;
double previous;
for (int i = 1; i <= EpochCount; i++)
{
previous = error;
error = teacher.RunEpoch(inputs, outputs);
if (error == 0)
{
break;
}
//Console.WriteLine(String.Format("Epoch={0} Error={1}", i, error));
}
sw.Stop();
return(Tuple.Create(error, sw.Elapsed));
}
void IMatrix.SetValue(int rowNumber, int columnNumber, double value)
{
SourceMatrix.SetValue(rowNumber, columnNumber, value);
}
double IMatrix.GetValue(int rowNumber, int columnNumber)
{
return(SourceMatrix.GetValue(rowNumber, columnNumber));
}
void IMatrix.SetValue(int rowNumber, int columnNumber, double value)
{
int actualColumnNumber = GetActualColumnNumber(rowNumber);
SourceMatrix.SetValue(rowNumber, actualColumnNumber, value);
}
double IMatrix.GetValue(int rowNumber, int columnNumber)
{
int actualColumnNumber = GetActualColumnNumber(columnNumber);
return(SourceMatrix.GetValue(rowNumber, actualColumnNumber));
}
