public void DrawMesh(int X, int Y)
{
for (int index1 = 0; index1 < this.crqUrCA6J; ++index1)
{
for (int index2 = 0; index2 < this.R00zPKwYO; ++index2)
{
TKohonenNeuron tkohonenNeuron1 = this.C4gBBYR9YW[index1, index2];
if (index1 < this.crqUrCA6J - 1)
{
TKohonenNeuron tkohonenNeuron2 = this.C4gBBYR9YW[index1 + 1, index2];
new TLine(tkohonenNeuron1.GetWeight(X).Weight, tkohonenNeuron1.GetWeight(Y).Weight, tkohonenNeuron2.GetWeight(X).Weight, tkohonenNeuron2.GetWeight(Y).Weight)
{
Color = Color.Blue
}.Draw();
}
if (index2 < this.R00zPKwYO - 1)
{
TKohonenNeuron tkohonenNeuron2 = this.C4gBBYR9YW[index1, index2 + 1];
new TLine(tkohonenNeuron1.GetWeight(X).Weight, tkohonenNeuron1.GetWeight(Y).Weight, tkohonenNeuron2.GetWeight(X).Weight, tkohonenNeuron2.GetWeight(Y).Weight)
{
Color = Color.Blue
}.Draw();
}
new TMarker(tkohonenNeuron1.GetWeight(X).Weight, tkohonenNeuron1.GetWeight(Y).Weight, 20.0)
{
Color = Color.Red
}.Draw();
}
}
}
public void Update(TKohonenNeuron Neuron)
{
double neighborhoodFunction = this.GetNeighborhoodFunction(Neuron);
foreach (TNeuralWeight tneuralWeight in this.weights)
{
tneuralWeight.Weight = tneuralWeight.Weight + this.vOHKkfFcp.LearningRate * neighborhoodFunction * (tneuralWeight.GetInput() - tneuralWeight.Weight);
}
}
public double GetDistance(TKohonenNeuron Neuron)
{
if (Neuron == this)
{
return(0.0);
}
else
{
return(Math.Sqrt((double)((this.WKXIlHax4 - Neuron.Col) * (this.WKXIlHax4 - Neuron.Col) + (this.uiQQufIMH - Neuron.Row) * (this.uiQQufIMH - Neuron.Row))));
}
}
public Histogram2D GetMapHist()
{
this.NZFB8Hsyyk = new Histogram2D(this.fName, this.fTitle, this.crqUrCA6J, 0.0, (double)this.crqUrCA6J, this.R00zPKwYO, 0.0, (double)this.R00zPKwYO);
for (int index1 = 0; index1 < this.crqUrCA6J; ++index1)
{
for (int index2 = 0; index2 < this.R00zPKwYO; ++index2)
{
this.C4gBBYR9YW[index1, index2].ResetWin();
}
}
for (int Seek = 0; Seek < this.en8Br1nJcm.GetNData(); ++Seek)
{
TKohonenData tkohonenData = (TKohonenData)this.en8Br1nJcm.GetData(Seek);
double[] input = tkohonenData.Input;
double[] output = tkohonenData.Output;
this.ProcessInput(input);
TKohonenNeuron winner = this.GetWinner();
if (output != null)
{
if (output[0] != 0.0)
{
winner.AddWin(output[0]);
}
}
else
{
winner.AddWin(1.0);
winner.SetNWins(1);
}
tkohonenData.X = winner.Col;
tkohonenData.Y = winner.Row;
}
for (int index1 = 0; index1 < this.crqUrCA6J; ++index1)
{
for (int index2 = 0; index2 < this.R00zPKwYO; ++index2)
{
TKohonenNeuron tkohonenNeuron = this.C4gBBYR9YW[index1, index2];
if (tkohonenNeuron.GetNWins() != 0)
{
this.NZFB8Hsyyk.Set((double)tkohonenNeuron.Col, (double)tkohonenNeuron.Row, tkohonenNeuron.GetWin() / (double)tkohonenNeuron.GetNWins());
}
else
{
this.NZFB8Hsyyk.Set((double)tkohonenNeuron.Col, (double)tkohonenNeuron.Row, 0.0);
}
}
}
return(this.NZFB8Hsyyk);
}
public double GetNeighborhoodFunction(TKohonenNeuron Neuron)
{
if (Neuron == this)
return 1.0;
double distance = this.GetDistance(Neuron);
double neighborhoodRadius = this.vOHKkfFcp.NeighborhoodRadius;
switch (this.vOHKkfFcp.Kernel)
{
case EKohonenKernel.Bubble:
return distance <= neighborhoodRadius ? 1.0 : 0.0;
case EKohonenKernel.Gauss:
return Math.Exp(-distance * distance / (neighborhoodRadius * neighborhoodRadius));
default:
return 0.0;
}
}
public void TrainThread()
{
this.fNPattern = 0;
for (int index = 0; (double)index < this.fSParameter; ++index)
{
this.fInput = this.en8Br1nJcm.GetData().Input;
this.ProcessInput(this.fInput);
this.oGkBVEIgUD = this.GetWinner();
this.Update();
TKohonenMap tkohonenMap = this;
int num1 = tkohonenMap.fNPattern + 1;
tkohonenMap.fNPattern = num1;
double num2 = (double)this.fNPattern / this.fSParameter;
this.fLearningRate = this.g5ykpFFS7 * (1.0 - num2);
this.L2kBAuWI5g = 1.0 + this.yem372767 * (1.0 - num2);
}
Console.WriteLine(this.fNPattern);
}
public TKohonenNeuron GetWinner()
{
TKohonenNeuron tkohonenNeuron = this.C4gBBYR9YW[0, 0];
double num = tkohonenNeuron.GetError();
for (int index1 = 0; index1 < this.crqUrCA6J; ++index1)
{
for (int index2 = 0; index2 < this.R00zPKwYO; ++index2)
{
double error = this.C4gBBYR9YW[index1, index2].GetError();
if (error < num)
{
num = error;
tkohonenNeuron = this.C4gBBYR9YW[index1, index2];
}
}
}
return(tkohonenNeuron);
}
public double GetNeighborhoodFunction(TKohonenNeuron Neuron)
{
if (Neuron == this)
{
return(1.0);
}
double distance = this.GetDistance(Neuron);
double neighborhoodRadius = this.vOHKkfFcp.NeighborhoodRadius;
switch (this.vOHKkfFcp.Kernel)
{
case EKohonenKernel.Bubble:
return(distance <= neighborhoodRadius ? 1.0 : 0.0);
case EKohonenKernel.Gauss:
return(Math.Exp(-distance * distance / (neighborhoodRadius * neighborhoodRadius)));
default:
return(0.0);
}
}
public void Update(TKohonenNeuron Neuron)
{
double neighborhoodFunction = this.GetNeighborhoodFunction(Neuron);
foreach (TNeuralWeight tneuralWeight in this.weights)
tneuralWeight.Weight = tneuralWeight.Weight + this.vOHKkfFcp.LearningRate * neighborhoodFunction * (tneuralWeight.GetInput() - tneuralWeight.Weight);
}
public double GetDistance(TKohonenNeuron Neuron)
{
if (Neuron == this)
return 0.0;
else
return Math.Sqrt((double) ((this.WKXIlHax4 - Neuron.Col) * (this.WKXIlHax4 - Neuron.Col) + (this.uiQQufIMH - Neuron.Row) * (this.uiQQufIMH - Neuron.Row)));
}
public void TrainThread()
{
this.fNPattern = 0;
for (int index = 0; (double) index < this.fSParameter; ++index)
{
this.fInput = this.en8Br1nJcm.GetData().Input;
this.ProcessInput(this.fInput);
this.oGkBVEIgUD = this.GetWinner();
this.Update();
TKohonenMap tkohonenMap = this;
int num1 = tkohonenMap.fNPattern + 1;
tkohonenMap.fNPattern = num1;
double num2 = (double) this.fNPattern / this.fSParameter;
this.fLearningRate = this.g5ykpFFS7 * (1.0 - num2);
this.L2kBAuWI5g = 1.0 + this.yem372767 * (1.0 - num2);
}
Console.WriteLine( this.fNPattern);
}
