public double[] FeedForward(double[] input)
{
double[] output = new double[Weights[0].Length];
for (int i = 0; i < Weights.Length; i++)
{
for (int k = 0; k < Weights[i].Length; k++)
{
if (i == Weights.Length - 1)
{
output[k] += Weights[i][k];
}
else
{
output[k] += Weights[i][k] * input[i];
}
}
}
for (int i = 0; i < output.Length; i++)
{
output[i] = NeuralMath.ReLU(output[i]);
}
return(output);
}
public void Mutate(double probability, double amount)
{
for (int i = 0; i < Weights.Length; i++)
{
for (int k = 0; k < Weights[i].Length; k++)
{
if (NeuralMath.RandomDouble() < probability)
{
Weights[i][k] = NeuralMath.RandomDouble() * (amount * 2) - amount;
}
}
}
}
public NeuralSection(UInt32 inputCount, UInt32 outputCount)
{
Weights = new double[inputCount + 1][];
for (int i = 0; i < Weights.Length; i++)
{
Weights[i] = new double[outputCount];
}
for (int i = 0; i < Weights.Length; i++)
{
for (int k = 0; k < Weights[i].Length; k++)
{
Weights[i][k] = NeuralMath.RandomDouble() - 0.5f;
}
}
}
public static NeuralNetwork Crossover(NeuralNetwork parent1, NeuralNetwork parent2, double uniformRate)
{
NeuralNetwork child = new NeuralNetwork(parent1);
for (int i = 0; i < child.Sections.Length; i++)
{
double[][] weights = child.Sections[i].Weights;
for (int k = 0; k < weights.Length; k++)
{
for (int m = 0; m < weights[k].Length; m++)
{
if (NeuralMath.RandomDouble() > uniformRate)
{
weights[k][m] = parent2.Sections[i].Weights[k][m];
}
}
}
}
return(child);
}
