public void InitializeLayer(int NumberOfInput, int NumberOfOutput, ActivationStrategy ActivationStrategy)
{
this.NumberOfInput = NumberOfInput;
this.NumberOfOutput = NumberOfOutput;
this.ActivationStrategy = ActivationStrategy;
this.Input = new double[numberOfInput];
this.Output = new double[NumberOfOutput];
this.Weights = new double[numberOfOutput, numberOfInput];
this.Gamma = new double[numberOfOutput];
this.WeightsDelta = new double[numberOfOutput, numberOfInput];
this.Error = new double[NumberOfOutput];
InitializeWeights();
}
public double[] FeedForward(double[] Input)
{
this.Input = Input;
for (int i = 0; i < NumberOfOutput; i++)
{
Output[i] = 0;
for (int j = 0; j < NumberOfInput; j++)
{
Output[i] += this.Input[j] * weights[i, j];
}
Output[i] = ActivationStrategy.Activation(Output[i]);
}
return(Output);
}
public void BackPropOutput(double[] expected)
{
for (int i = 0; i < NumberOfOutput; i++)
{
Error[i] = Output[i] - expected[i];
}
for (int i = 0; i < NumberOfOutput; i++)
{
Gamma[i] = Error[i] * ActivationStrategy.DeActivation(Output[i]);
}
for (int i = 0; i < NumberOfOutput; i++)
{
for (int j = 0; j < NumberOfInput; j++)
{
WeightsDelta[i, j] = Gamma[i] * Input[j];
}
}
}
public NeuralNetwork(int[] Layer, ActivationStrategy ActivationFunction)
{
Layers = new MLPLayer[Layer.Length - 1];
for (int i = 0; i < Layers.Length; i++)
{
Layers[i] = new MLPLayer();
Layers[i].InitializeLayer(Layer[i], Layer[i + 1], ActivationFunction);
if (i < Layers.Length - 1)
{
this.layerBuild += Layer[i] + "-";
}
else
{
this.layerBuild += Layer[i];
}
}
this.Activation = ActivationFunction;
}
public void BackPropHidden(double[] gammaForward, double[,] weightsForward)
{
for (int i = 0; i < NumberOfOutput; i++)
{
Gamma[i] = 0;
for (int j = 0; j < gammaForward.Length; j++)
{
Gamma[i] += gammaForward[j] * weightsForward[j, i];
}
Gamma[i] *= ActivationStrategy.DeActivation(Output[i]);
}
for (int i = 0; i < NumberOfOutput; i++)
{
for (int j = 0; j < NumberOfInput; j++)
{
WeightsDelta[i, j] = Gamma[i] * Input[j];
}
}
}
