//returns a list of output controls based off an input list for a network
List <bool> EvaluateNetwork(NeuralNet network, List <double> inputs)
{
inputs.Add(1);
if (inputs.Count != Inputs)
{
Console.WriteLine("Incorrect number of neural network inputs.");
return(new List <bool>());
}
for (int i = 0; i < Inputs; i++)
{
network.neurons[i].value = inputs[i];
}
foreach (Neuron neuron in network.neurons)
{
if (neuron == null)
{
continue;
}
double sum = 0;
for (int j = 0; j < neuron.incoming.Count; j++)
{
Gene incoming = neuron.incoming[j];
Neuron other = network.neurons[incoming.into];
sum += (incoming.weight * other.value);
}
if (neuron.incoming.Count > 0)
{
neuron.value = Sigmoid(sum);
}
}
List <bool> outputs = new List <bool>();
for (int o = 0; o < Outputs; o++)
{
if (network.neurons[MaxNodes + o].value > 0)
{
outputs.Add(true);
}
else
{
outputs.Add(false);
}
}
return(outputs);
}
//creates a neural network based off a genome's connections and neurons
void GenerateNetwork(Genome genome)
{
NeuralNet network = new NeuralNet();
network.neurons = new List <Neuron>(new Neuron[MaxNodes + Outputs]);
for (int i = 0; i < MaxNodes + Outputs; i++)
{
network.neurons[i] = null;
}
for (int i = 0; i < Inputs; i++)
{
network.neurons[i] = NewNeuron();
}
for (int o = 0; o < Outputs; o++)
{
network.neurons[MaxNodes + o] = NewNeuron();
}
genome.genes = genome.genes.OrderBy(x => x.outo).ToList();
for (int i = 0; i < genome.genes.Count; i++)
{
Gene gene = genome.genes[i];
if (gene.enabled)
{
if (network.neurons[gene.outo] == null)
{
network.neurons[gene.outo] = NewNeuron();
}
Neuron neuron = network.neurons[gene.outo];
neuron.incoming.Add(gene);
if (network.neurons[gene.into] == null)
{
network.neurons[gene.into] = NewNeuron();
}
}
}
genome.network = network;
}
