public Regression(DoubleSeries series1, DoubleSeries series2, double alpha, double beta)
{
this.graph = new Graph();
this.series1 = series1;
this.series2 = series2;
this.graph.LineEnabled = false;
this.graph.MarkerSize = 1;
}
public Graph GetCorrelogram(int lag1, int lag2)
{
Graph graph = new Graph();
for (int i = lag1; i <= lag2; ++i)
graph.Add((double)i, this.GetAutoCorrelation(i));
return graph;
}
public TKohonenMap() : base()
{
this.en8Br1nJcm = (TNeuralDataSet) null;
this.NZFB8Hsyyk = (Histogram2D) null;
this.lHWBj2LFHb = (Graph) null;
this.L2kBAuWI5g = this.yem372767;
}
public void DrawData(int X, int Y)
{
if (this.lHWBj2LFHb == null)
{
this.lHWBj2LFHb = new Graph("graph");
this.lHWBj2LFHb.Style = EGraphStyle.Scatter;
this.lHWBj2LFHb.MarkerSize = 1;
}
for (int Seek = 0; Seek < this.en8Br1nJcm.GetNData(); ++Seek)
{
double[] input = this.en8Br1nJcm.GetInput(Seek);
this.lHWBj2LFHb.Add(input[X], input[Y]);
}
this.lHWBj2LFHb.Draw();
}
private void P2odmXxb0()
{
this.x0pBGNZNEv = new TInputNeuron[this.fNInput];
for (int index = 0; index < this.fNInput; ++index)
this.x0pBGNZNEv[index] = new TInputNeuron();
this.C4gBBYR9YW = new TKohonenNeuron[this.crqUrCA6J, this.R00zPKwYO];
for (int Col = 0; Col < this.crqUrCA6J; ++Col)
{
for (int Row = 0; Row < this.R00zPKwYO; ++Row)
this.C4gBBYR9YW[Col, Row] = new TKohonenNeuron(this, Col, Row);
}
for (int index1 = 0; index1 < this.fNInput; ++index1)
{
for (int index2 = 0; index2 < this.crqUrCA6J; ++index2)
{
for (int index3 = 0; index3 < this.R00zPKwYO; ++index3)
this.C4gBBYR9YW[index2, index3].Connect((TNeuron) this.x0pBGNZNEv[index1]);
}
}
this.NZFB8Hsyyk = (Histogram2D) null;
this.lHWBj2LFHb = (Graph) null;
this.M7SYu5gxf = EKohonenTopology.Rectangular;
this.a8fxHLKhg = EKohonenKernel.Bubble;
this.g5ykpFFS7 = 0.05;
this.yem372767 = 10.0;
}
public void Configure()
{
this.fInputLayer = (TNeuralLayer) new TInputNeuralLayer(this.fNInput, true);
TNeuralLayer Layer = (TNeuralLayer) new THiddenNeuralLayer(this.kB5uny9D9);
Layer.Connect(this.fInputLayer);
this.rCHgEckPw = new ArrayList();
this.HnCyxyTcM = 1;
this.rCHgEckPw.Add((object) Layer);
this.fOutputLayer = (TNeuralLayer) new TOutputNeuralLayer(this.fNOutput, this.wAeTg5Tjg);
this.fOutputLayer.Connect(Layer);
this.fOutput = new double[this.fNOutput];
this.cGqCj2YUM = new double[this.fNOutput];
this.fMaxNX = Math.Max(this.fNInput + 1, this.fNOutput);
this.fMaxNX = Math.Max(this.fMaxNX, this.kB5uny9D9 + 1);
this.fMaxNY = 3;
this.fNPattern = 0;
this.wm8s050QY = 0.0;
this.MSg9cpEpW = 0.0;
this.V8laR9xb7 = new Graph();
this.V8laR9xb7.Title = "title";
this.V8laR9xb7.LineColor = Color.Red;
this.XOwvCEen2 = new Graph();
this.XOwvCEen2.Title = "title";
this.XOwvCEen2.LineColor = Color.Blue;
this.x1tDaZE2v = false;
}
public void ResetError()
{
this.V8laR9xb7 = new Graph();
this.V8laR9xb7.Title = "title";
this.V8laR9xb7.LineColor = Color.Red;
this.XOwvCEen2 = new Graph();
this.XOwvCEen2.Title = "title";
this.XOwvCEen2.LineColor = Color.Blue;
}
