public void SetRightAdjacentLayer(Layer al)
{
for (int i = 0; i < al.neurons.Count; i++)
{
al.neurons[i].AddConnections(this.neurons);
}
}
public MLP(Settings Configuration)
{
this.settings = Configuration;
layers = new List<Layer>();
try
{
this.EpochToStop = (int)Configuration["EpochToStop"];
this.ErrorToStop = (double)Configuration["ErrorToStop"];
this.Momentum = (double)Configuration["Momentum"];
this.LerningRateDownStep = (double)Configuration["LearningRateDownStep"];
this.LerningRateUpStep = (double)Configuration["LearningRateUpStep"];
this.ErrorThresholdPercent = (int)Configuration["ErrorThresholdPercent"];
this.EpochPreventOverfitting = (int)Configuration["EpochPreventOverfitting"];
ITransferFunction tranferFunc = (ITransferFunction)Assembly.
GetExecutingAssembly().CreateInstance("MLP.Network.TransferFunctions."
+ Configuration["TransferFunction"]);
int NumberOfLayers = (int)Configuration["NumberOfLayers"];
for (int i = 1; i <= NumberOfLayers; i++)
{
int NumberOfNeurons = (int)Configuration["NeuronsOfLayer[" + i + "]"];
Layer l = new Layer(NumberOfNeurons, tranferFunc);
this.layers.Add(l);
}
for (int i = 0; i < NumberOfLayers - 1; i++)
{
layers[i].SetRightAdjacentLayer(layers[i + 1]);
}
Eta = (double)Configuration["LearningRate"];//1.0E-3; //1.0x10^-3
}
catch (Exception e)
{
throw e;
}
_errorEachEpoch = new List<double>();
_errorOnValidation = new List<double>();
}
