// MPS NOT supported by this method
private NeatGenome.NeatGenome generateHomogeneousGenomeES(INetwork network, bool normalizeWeights, bool adaptiveNetwork, bool modulatoryNet)
{
if (useMultiPlaneSubstrate) throw new Exception("MPS not implemented for these parameters");
//CHECK TO SEE IF HIDDEN NEURONS ARE OUTPUT NEURONS
List<PointF> hiddenNeuronPositions = new List<PointF>();
IActivationFunction activationFunction = HyperNEATParameters.substrateActivationFunction;
ConnectionGeneList connections = new ConnectionGeneList();//(int)((InputCount * HiddenCount) + (HiddenCount * OutputCount)));
List<PointF> outputNeuronPositions = getNeuronGroupByType(1);
List<PointF> inputNeuronPositions = getNeuronGroupByType(0);
SubstrateEvolution se = new SubstrateEvolution();
se.generateConnections(inputNeuronPositions, outputNeuronPositions, network,
SubstrateEvolution.SAMPLE_WIDTH,
SubstrateEvolution.SAMPLE_TRESHOLD,
SubstrateEvolution.NEIGHBOR_LEVEL,
SubstrateEvolution.INCREASE_RESSOLUTION_THRESHOLD,
SubstrateEvolution.MIN_DISTANCE,
SubstrateEvolution.CONNECTION_TRESHOLD, //0.4. ConnectionThreshold
InputCount, OutputCount, -1.0f, -1.0f, 1.0f, 1.0f, ref connections, ref hiddenNeuronPositions);
HiddenCount = (uint)hiddenNeuronPositions.Count;
float[] coordinates = new float[5];
uint connectionCounter = (uint)connections.Count;
NeuronGeneList neurons;
// SharpNEAT requires that the neuron list be in this order: bias|input|output|hidden
neurons = new NeuronGeneList((int)(InputCount + OutputCount + HiddenCount));
// set up the input nodes
for (uint a = 0; a < InputCount; a++)
{
neurons.Add(new NeuronGene(a, NeuronType.Input, ActivationFunctionFactory.GetActivationFunction("NullFn")));
}
// set up the output nodes
for (uint a = 0; a < OutputCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount, NeuronType.Output, activationFunction));
}
// set up the hidden nodes
for (uint a = 0; a < HiddenCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount + OutputCount, NeuronType.Hidden, activationFunction));
}
uint sourceID = uint.MaxValue, targetID = uint.MaxValue;
NeuronGroup connectedNG;
uint c1, c2;
float delta = 0.15f;//2.0f / InputCount;
float minDistance, dist, sourceX = -1, sourceY = -1, targetX = -1, targetY = -1;
uint closestNodeIndex;
// int index, hiddenCount;
//Connections to input nodes
// hiddenCount = 0;
//TEST??????????????????????????????
double tolerance = 0.1;
//bool[] taken = new bool[hiddenNeuronGroup.NeuronPositions.Count];
closestNodeIndex = 0;
int ccc;
//CONNECT FROM INPUT NODES
// ConnectionGeneList addConnections = new ConnectionGeneList();
targetID = 0;
bool[] visited = new bool[neurons.Count];
List<uint> nodeList = new List<uint>();
bool[] connectedToInput = new bool[neurons.Count];
//From hidden to output
//taken = new bool[hiddenNeuronGroup.NeuronPositions.Count];
// float targetX=-1.0f, targetY=-1.0f;
targetID = 0;
// bool outputConnectedToInput;
bool[] isOutput = new bool[neurons.Count];
//float output, weight;
//bool[] connectedToInput = new bool[neurons.Count];
//bool connectToHidden;
float totalConnectionDist = 0.0f;
//Add connections between Hidden Neurons
// addConnections.AddRange(connections);
bool danglingConnection = true;
while (danglingConnection)
{
bool[] hasIncomming = new bool[neurons.Count];
foreach (ConnectionGene co in connections)
{
// if (co.SourceNeuronId != co.TargetNeuronId)
// {
hasIncomming[co.TargetNeuronId] = true;
// }
}
for (int i = 0; i < InputCount; i++)
hasIncomming[i] = true;
bool[] hasOutgoing = new bool[neurons.Count];
foreach (ConnectionGene co in connections)
{
// if (co.TargetNeuronId != co.SourceNeuronId)
// {
if (co.TargetNeuronId != co.SourceNeuronId) //neurons that only connect to themselfs don't count
{
hasOutgoing[co.SourceNeuronId] = true;
}
// }
}
//Keep output neurons
for (int i = 0; i < OutputCount; i++)
hasOutgoing[i + InputCount] = true;
danglingConnection = false;
//Check if there are still dangling connections
foreach (ConnectionGene co in connections)
{
if (!hasOutgoing[co.TargetNeuronId] || !hasIncomming[co.SourceNeuronId])
{
danglingConnection = true;
break;
}
}
connections.RemoveAll(delegate(ConnectionGene m) { return (!hasIncomming[m.SourceNeuronId]); });
connections.RemoveAll(delegate(ConnectionGene m) { return (!hasOutgoing[m.TargetNeuronId]); });
}
if (normalizeWeights)
{
normalizeWeightConnections(ref connections, neurons.Count);
}
SharpNeatLib.NeatGenome.NeatGenome gn = new SharpNeatLib.NeatGenome.NeatGenome(0, neurons, connections, (int)(InputCount), (int)(OutputCount));
// SharpNeatLib.NeatGenome.NeatGenome sng = new SharpNeatLib.NeatGenome.NeatGenome(0, neurons, connections, (int)(totalInputCount), (int)(totalOutputCount));
gn.networkAdaptable = adaptiveNetwork;
gn.networkModulatory = modulatoryNet;
return gn;
}
// MPS NOT supported by this method
private NeatGenome.NeatGenome generateMultiGenomeStackES(INetwork network, List<float> stackCoordinates, bool normalizeWeights, bool adaptiveNetwork, bool modulatoryNet)
{
if (useMultiPlaneSubstrate) throw new Exception("MPS not implemented for these parameters");
uint numberOfAgents = (uint)stackCoordinates.Count;
IActivationFunction activationFunction = HyperNEATParameters.substrateActivationFunction;
ConnectionGeneList connections = new ConnectionGeneList((int)(numberOfAgents * (InputCount * HiddenCount) + numberOfAgents * (HiddenCount * OutputCount)));
float[] coordinates = new float[5];
float output;
uint connectionCounter = 0;
float agentDelta = 2.0f / (numberOfAgents - 1);
int iterations = 2 * (network.TotalNeuronCount - (network.InputNeuronCount + network.OutputNeuronCount)) + 1;
uint totalOutputCount = OutputCount * numberOfAgents;
uint totalInputCount = InputCount * numberOfAgents;
uint totalHiddenCount = HiddenCount * numberOfAgents;
uint sourceCount, targetCout;
double weightRange = HyperNEATParameters.weightRange;
double threshold = HyperNEATParameters.threshold;
NeuronGeneList neurons;
// SharpNEAT requires that the neuron list be in this order: bias|input|output|hidden
neurons = new NeuronGeneList((int)(InputCount * numberOfAgents + OutputCount * numberOfAgents + HiddenCount * numberOfAgents));
// set up the input nodes
for (uint a = 0; a < totalInputCount; a++)
{
neurons.Add(new NeuronGene(a, NeuronType.Input, ActivationFunctionFactory.GetActivationFunction("NullFn")));
}
// set up the output nodes
for (uint a = 0; a < totalOutputCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount * numberOfAgents, NeuronType.Output, activationFunction));
}
uint agent = 0;
float A = 0.0f, B = 0.0f, C = 0.0f, D = 0.0f, learningRate = 0.0f, modConnection;
List<PointF> outputNeuronPositions = getNeuronGroupByType(1);
List<PointF> inputNeuronPositions = getNeuronGroupByType(0);
uint hiddenCount = 0;
foreach (float stackCoordinate in stackCoordinates)
{
List<PointF> hiddenNeuronPositions = new List<PointF>();
ConnectionGeneList con = new ConnectionGeneList();
SubstrateEvolution se = new SubstrateEvolution();
se.generateConnections(inputNeuronPositions, outputNeuronPositions, network,
SubstrateEvolution.SAMPLE_WIDTH,
SubstrateEvolution.SAMPLE_TRESHOLD,
SubstrateEvolution.NEIGHBOR_LEVEL,
SubstrateEvolution.INCREASE_RESSOLUTION_THRESHOLD,
SubstrateEvolution.MIN_DISTANCE,
SubstrateEvolution.CONNECTION_TRESHOLD, //0.4. ConnectionThreshold
InputCount, OutputCount, -1.0f, -1.0f, 1.0f, 1.0f, ref con, ref hiddenNeuronPositions, stackCoordinate);
// set up the hidden nodes
for (uint a = 0; a < hiddenNeuronPositions.Count; a++)
{
neurons.Add(new NeuronGene(hiddenCount + a + totalInputCount + totalOutputCount, NeuronType.Hidden, activationFunction));
}
foreach (ConnectionGene c in con)
{
if (c.SourceNeuronId < InputCount)
{
c.SourceNeuronId += agent * InputCount;
}
else if (c.SourceNeuronId < InputCount + OutputCount)
{
c.SourceNeuronId = (c.SourceNeuronId - InputCount) + totalInputCount + agent * OutputCount;
}
else
{
c.SourceNeuronId = (uint)((c.SourceNeuronId - InputCount - OutputCount) + totalInputCount + totalOutputCount + hiddenCount);
}
if (c.TargetNeuronId < InputCount)
{
c.TargetNeuronId += agent * InputCount;
}
else if (c.TargetNeuronId < InputCount + OutputCount)
{
c.TargetNeuronId = (c.TargetNeuronId - InputCount) + totalInputCount + agent * OutputCount;
}
else
{
c.TargetNeuronId = (uint)((c.TargetNeuronId - InputCount - OutputCount) + totalInputCount + totalOutputCount + hiddenCount);
}
connections.Add(new ConnectionGene(connectionCounter++, c.SourceNeuronId, c.TargetNeuronId, c.Weight, ref c.coordinates));
}
hiddenCount += (uint)hiddenNeuronPositions.Count;
agent++;
}
if (normalizeWeights)
{
normalizeWeightConnections(ref connections, neurons.Count);
}
SharpNeatLib.NeatGenome.NeatGenome sng = new SharpNeatLib.NeatGenome.NeatGenome(0, neurons, connections, (int)(totalInputCount), (int)(totalOutputCount));
sng.networkAdaptable = adaptiveNetwork;
sng.networkModulatory = modulatoryNet;
return sng;
}