public virtual NeatGenome.NeatGenome GenerateGenome(NeuralNetwork.INetwork network)
{
int maxIterations = 2 * (network.TotalNeuronCount - (network.InputNeuronCount + network.OutputNeuronCount)) + 1;
// TODO:
maxIterations = Math.Min(maxIterations, 4);
double epsilon = 0.0;
double threshold = HyperNEATParameters.threshold;
double weightRange = HyperNEATParameters.weightRange;
// store constant ids for later references
uint biasid = 0u;
uint inputsStart = m_useBias ? 1u : 0u;
uint inputsEnd = (uint)(inputsStart + (m_rows * m_cols));
uint outputsStart = inputsEnd;
uint outputsEnd = (uint)(outputsStart + (m_rows * m_cols));
float[] coordinates = new float[4];
float output;
uint connectionCounter = 0;
ConnectionGeneList connections = new ConnectionGeneList();
if (m_useBias)
{
// we use the bias neuron on the center, and use the
// 2nd output of the CPPN to compute its weight
coordinates[0] = 0.0f;
coordinates[1] = 0.0f;
// add the bias link to all output neurons
for (int ni = 0, ncount = m_rows * m_cols; ni < ncount; ni++)
{
int row = (ni / m_cols);
int col = (ni % m_cols);
coordinates[2] = (-1.0f) + (m_colDelta * (col + 0.5f));
coordinates[3] = (-1.0f) + (m_rowDelta * (row + 0.5f));
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RelaxNetwork(maxIterations, epsilon);
output = network.GetOutputSignal(m_cppnOutIndex + 1);
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
connections.Add(new ConnectionGene(connectionCounter++, biasid, (uint)(outputsStart + ni), weight));
}
}
}
// now add possible connections between all input output neuron pairs
for (int inpi = 0, inpcount = m_rows * m_cols; inpi < inpcount; inpi++)
{
for (int outi = 0, outcount = m_rows * m_cols; outi < outcount; outi++)
{
int inrow = (inpi / m_cols); int incol = (inpi % m_cols);
int outrow = (outi / m_cols); int outcol = (outi % m_cols);
coordinates[0] = (-1.0f) + (m_colDelta * (incol + 0.5f));
coordinates[1] = (-1.0f) + (m_rowDelta * (inrow + 0.5f));
coordinates[2] = (-1.0f) + (m_colDelta * (outcol + 0.5f));
coordinates[3] = (-1.0f) + (m_rowDelta * (outrow + 0.5f));
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RelaxNetwork(maxIterations, epsilon);
output = network.GetOutputSignal(m_cppnOutIndex);
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
connections.Add(new ConnectionGene(connectionCounter++,
(uint)(inputsStart + inpi), (uint)(outputsStart + outi), weight));
}
}
}
return(new SharpNeatLib.NeatGenome.NeatGenome(0, m_neurons, connections, m_rows * m_cols, m_rows * m_cols));
}
public NeuralNetwork.INetwork GenerateNetwork(NeuralNetwork.INetwork CPPN)
{
return(GenerateGenome(CPPN).Decode(null));
}