public override void Train(int NumberOfIteration)
{
Random Randomizer = new Random();
ListShuffler Shuffler = new ListShuffler();
Shuffler.Shuffle(Points, Randomizer);
double CurrentError = 0;
foreach (Point TargetPoint in Points)
{
this.SetTargetPoints(TargetPoint);
LambdaParameter = MaxRange * Math.Pow(MinRange / MaxRange, NumberOfIteration / (double)EpochsCount);
Winner = this.SeekForWinner();
NeuronsToChange = this.GetNeuronsToChange();
this.SetNeuronPotential(NumberOfIteration);
CurrentError += Winner.CalculateDistanceFrom(TargetPoint);
this.UpdateWeights(NumberOfIteration, TargetPoint);
}
foreach (Neuron Neuron in Neurons)
{
Neuron.UpdatePositions(Neuron.GetWeight(0), Neuron.GetWeight(1));
}
Errors.Add(new Point(NumberOfIteration, CurrentError / Points.Count()));
}
public override void Train(int Iteration)
{
Shuffler.Shuffle <Point>(Points, Randomizer);
double error = 0.0;
for (int i = 0; i < Points.Count(); i++)
{
Point Target = Points[i];
this.SetTargetPoint(Target);
Shuffler.NeuronListSort(Neurons);
Winner = Neurons[0];
error += Neurons[0].CalculateDistanceFrom(Points[i]);
this.UpdateWeights(Iteration, Target);
Neurons[0].AddPointInNeuronArea(Target);
}
foreach (Neuron Neuron in Neurons)
{
Neuron.UpdatePositions(Neuron.GetWeight(0), Neuron.GetWeight(1));
}
Errors.Add(new Point(Iteration, error / Points.Count()));
if (Iteration == 0)
{
Pointparser Outlet = new Pointparser();
Output(Outlet, "G:\\OutputsFromGasNetwork", Iteration);
}
if (Iteration == 9)
{
Pointparser Outlet = new Pointparser();
Output(Outlet, "G:\\OutputsFromGasNetwork", Iteration);
}
if (Iteration == 49)
{
Pointparser Outlet = new Pointparser();
Output(Outlet, "G:\\OutputsFromGasNetwork", Iteration);
}
if (Iteration == 100)
{
Pointparser Outlet = new Pointparser();
Output(Outlet, "G:\\OutputsFromGasNetwork", Iteration);
}
if (Iteration == 499)
{
Pointparser Outlet = new Pointparser();
Output(Outlet, "G:\\OutputsFromGasNetwork", Iteration);
}
}
