public Dictionary <string, bool> evaluateNetwork(List <double> inputs)
{
//GET OUTPUT AS BOOLEAN
Inputs = inputs.ToArray(); //SET INPUTS
inputs.Add(1);
if (inputs.Count != InputsCount)
{
Console.WriteLine("Incorrect number of neural network inputs."); return(null);
}
for (int i = 0; i < InputsCount; i++)
{
neurons[i].value = inputs[i];
}
foreach (int neuron in neurons.Keys.ToList()) //LOOP NEVER INPUTS
{
double sum = 0;
for (int i = 0; i < neurons[neuron].incoming.Count; i++)
{
newGene incoming = neurons[neuron].incoming[i];
newNeuron other = neurons[incoming.into];
sum = sum + incoming.weight * other.value; //WEIGHT CALCULATION
}
if (neurons[neuron].incoming.Count > 0)
{
neurons[neuron].value = Sigmoid(sum); //SIGMOID FUNCTION
}
}
Dictionary <string, bool> outputs = new Dictionary <string, bool>();
for (int i = 0; i < OutputsCount; i++)
{
string button = OutputKeys[i]; //GET BUTTON NAMES
if (neurons[MaxNodes + i].value > 0)
{
outputs.Add(button, true); //OUTPUT IS TRUE
}
else
{
outputs.Add(button, false); //OUTPUT IS FALSE
}
Outputs[i] = neurons[MaxNodes + i].value; //SET OUTPUTS
}
//REFRESH IO PANEL
NetMain.IO.setIO(Inputs, Outputs);
NetMain.IO.SecureRefesh();
return(outputs);
}
public generateNetwork(newGenome genome)
{
//INPUTS
for (int i = 0; i < InputsCount; i++)
{
neurons[i] = new newNeuron();
}
//OUTPUTS
for (int i = 0; i < OutputsCount; i++)
{
neurons[MaxNodes + i] = new newNeuron();
}
genome.genes.Sort(); //table.sort(genome.genes, function(a, b) return (a.output < b.output) end)
for (int i = 0; i < genome.genes.Count; i++)
{
newGene gene = genome.genes[i];
if (gene.enabled)
{
if (!neurons.Keys.Contains(gene.output))
{
neurons[gene.output] = new newNeuron();
}
newNeuron neuron = neurons[gene.output];
neuron.incoming.Add(gene);
if (!neurons.Keys.Contains(gene.into))
{
neurons[gene.into] = new newNeuron();
}
}
genome.network = this;
}
}