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 NeuralGas(List <Neuron> ListOfNeurons, List <Point> ListOfPoints, Configuration Configuration, NeuronGenerator Generator)
{
this.Neurons = ListOfNeurons;
this.Points = ListOfPoints;
this.SetParams(Configuration);
this.Errors = new List <Point>();
ProximityFunctionType = new ProximityFunction(Metric.Euclidean, Proximity.Gauss).CalculateGasProximity;
NeuronGen = new NeuronGenerator();
}
public KSrednie(int ClusterCount, double Xrange, double Yrange, List <Point> Points, int Maxiters)
{
this.ClusterCount = ClusterCount;
this.NeurGen = new NeuronGenerator();
this.Xrange = Xrange;
this.Yrange = Yrange;
this.Points = Points;
this.Clusters = new Dictionary <int, List <Point> >();
for (int i = 0; i < ClusterCount; i++)
{
Clusters.Add(i, new List <Point>());
}
this.Error = new List <Point>();
this.MaxIters = Maxiters;
}