static void Main(string[] args)
{
bool Kmeans = true;
bool GasSwitch = false;
bool KohonenSwitch = false;
Thread.CurrentThread.CurrentCulture = new CultureInfo("en-US");
NeuronGenerator NeuralGenerator = new NeuronGenerator();
Configuration Configuration = new Configuration();
Pointparser Parser = new Pointparser();
Parser.Filepath = "zestaw1.txt";
double InitialXRange = 2.0;
double InitialYRange = 2.0; //describes initial square (centered at 0,0) from which the neurons are generated
List <Neuron> Neurons = NeuralGenerator.GetNeurons(Configuration.GetAmountOfNeurons(), InitialXRange, InitialYRange);
List <Point> TrainingPointsList = Parser.Parse();
KohonenLearning NeuralNetworkKohonenStyle = new KohonenLearning(Neurons, TrainingPointsList, Configuration);
NeuralGas NeuralNetworkGasStyle = new NeuralGas(Neurons, TrainingPointsList, Configuration, NeuralGenerator);
VoronoiDiagramContext DiagramContext = new VoronoiDiagramContext
{
Width = 1366,
Height = 768,
MinX = TrainingPointsList.Min(point => point.XCoordinate) - 0.5,
MaxX = TrainingPointsList.Max(point => point.XCoordinate) + 0.5,
MinY = TrainingPointsList.Min(point => point.YCoordinate) - 0.5,
MaxY = TrainingPointsList.Max(point => point.YCoordinate) + 0.5,
};
if (KohonenSwitch)
{
for (int i = 0; i < 100; i++)
{
if (i % 10 == 0)
{
Console.WriteLine("Progress o 10 %");
}
NeuralNetworkKohonenStyle.Train(i);
IEnumerable <Point> Points = NeuralNetworkKohonenStyle.ReturnNeuronsAsPoints();
VoronoiDiagram.CreateImage(DiagramContext, Points, TrainingPointsList).Save("Kohonen" + i.ToString("D3") + ".png");
}
}
else if (GasSwitch)
{
for (int i = 0; i < 500; i++)
{
if (i % 50 == 0)
{
Console.WriteLine("Progress o 10 %");
}
NeuralNetworkGasStyle.Train(i);
IEnumerable <Point> Points = NeuralNetworkGasStyle.ReturnNeuronsAsPoints();
VoronoiDiagram.CreateImage(DiagramContext, Points, TrainingPointsList).Save("NeuralGas" + i.ToString("D3") + ".png");
}
}
else if (Kmeans)
{
KSrednie KMeans = new KSrednie(10, 10, 10, TrainingPointsList, 150);
Parser.ParseOut(KMeans.Clusterize(DiagramContext));
}
}
public void Output(Pointparser Parser, String BasePathfile, int iteration)
{
//neurons as points
List <Point> NeuronsAsPoints = new List <Point>();
foreach (Neuron Neuron in Neurons)
{
NeuronsAsPoints.Add(Neuron.GetAsPoint());
}
Parser.OutputFilePath = BasePathfile + "\\Iteration" + iteration.ToString() + "\\NeuronsAfterIteration.txt";
Parser.ParseOut(NeuronsAsPoints);
}
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);
}
}
