// MPS support on the Hive methods only
#region Generate heterogenous genomes with z-stack
// MPS NOT supported by this method
private NeatGenome.NeatGenome generateMultiGenomeStack(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));
}
// set up the hidden nodes
for (uint a = 0; a < totalHiddenCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount * numberOfAgents + OutputCount * numberOfAgents, NeuronType.Hidden, activationFunction));
}
uint agent = 0;
float A = 0.0f, B = 0.0f, C = 0.0f, D = 0.0f, learningRate = 0.0f, modConnection;
foreach (float stackCoordinate in stackCoordinates)
{
coordinates[4] = stackCoordinate;
uint sourceID = uint.MaxValue, targetID = uint.MaxValue;
NeuronGroup connectedNG;
foreach (NeuronGroup ng in neuronGroups)
{
foreach (uint connectedTo in ng.ConnectedTo)
{
connectedNG = getNeuronGroup(connectedTo);
sourceCount = 0;
foreach (PointF source in ng.NeuronPositions)
{
//-----------------Get the bias of the source node
switch (ng.GroupType)
{
case 0: sourceID = (agent * InputCount) + ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + (agent * OutputCount) + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + (agent * HiddenCount) + ng.GlobalID + sourceCount; break; //Hidden
}
coordinates[0] = source.X; coordinates[1] = source.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
neurons[(int)sourceID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
//----------------------------
targetCout = 0;
foreach (PointF target in connectedNG.NeuronPositions)
{
switch (ng.GroupType)
{
case 0: sourceID = (agent * InputCount) + ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + (agent * OutputCount) + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + (agent * HiddenCount) + ng.GlobalID + sourceCount; break; //Hidden
}
switch (connectedNG.GroupType)
{
case 0: targetID = (agent * InputCount) + connectedNG.GlobalID + targetCout; break;
case 1: targetID = totalInputCount + (agent * OutputCount) + connectedNG.GlobalID + targetCout; break;
case 2: targetID = totalInputCount + totalOutputCount + (agent * HiddenCount) + connectedNG.GlobalID + targetCout; break;
}
coordinates[0] = source.X;
coordinates[1] = source.Y;
coordinates[2] = target.X;
coordinates[3] = target.Y;
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
output = network.GetOutputSignal(0);
double leo = 0.0;
if (adaptiveNetwork)
{
A = network.GetOutputSignal(2);
B = network.GetOutputSignal(3);
C = network.GetOutputSignal(4);
D = network.GetOutputSignal(5);
learningRate = network.GetOutputSignal(6);
}
if (modulatoryNet)
{
modConnection = network.GetOutputSignal(7);
}
else
{
modConnection = 0.0f;
}
if (useLeo)
{
threshold = 0.0;
leo = network.GetOutputSignal(2);
}
if (!useLeo || leo > 0.0)
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
//if (adaptiveNetwork)
//{
// //If adaptive network set weight to small value
// weight = 0.1f;
//}
connections.Add(new ConnectionGene(connectionCounter++, sourceID, targetID, weight, ref coordinates));
}
//else
//{
// Console.WriteLine("Not connected");
//}
targetCout++;
}
sourceCount++;
}
}
}
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;
}
// NOTE: Multi-Plane Substrates ARE supported by this method!
private NeatGenome.NeatGenome generateHiveBrainGenomeStack(INetwork network, List<float> stackCoordinates, bool normalizeWeights, bool adaptiveNetwork, bool modulatoryNet,bool ct)
{
//bool relativeCoordinate = false;
bool oneWay = false;
bool homogeneous = false ;
Dictionary<String, float> weights = new Dictionary<String, float>();
float timeConstantMin = 0.1f;
float timeConstantMax = 2.0f;
uint numberOfAgents = (uint)stackCoordinates.Count;
IActivationFunction activationFunction = HyperNEATParameters.substrateActivationFunction;
ConnectionGeneList connections = new ConnectionGeneList((int)(numberOfAgents * (InputCount * HiddenCount) + numberOfAgents * (HiddenCount * OutputCount))); // TODO: Perhaps get an exact count of connections in the constructor and use that value here?
float[] coordinates = new float[5]; //JUSTIN: Used to be 6 coordinates, zstack was duplicated for relativeCoordinate hyjinx. fixed it. // Inputs to the CPPN: [srcX, srcY, tgX, tgY, zstack]
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));
}
// set up the hidden nodes
for (uint a = 0; a < totalHiddenCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount * numberOfAgents + OutputCount * numberOfAgents, NeuronType.Hidden, activationFunction));
}
uint agent = 0;
float A = 0.0f, B = 0.0f, C = 0.0f, D = 0.0f, learningRate = 0.0f, modConnection;
// CPPN Outputs: [ Weights ] [ Biases ]
// When using multi-plane substrates, there will be multiple Weight and Bias outputs.
// There is a Weight output for every plane-to-plane connection (including a plane connected to itself, as in regular substrates)
// There is a Bias output for every plane
// Since "regular substrates" only have 1 plane, they only have 1 Weight and 1 Bias output. MP substrates have more. :)
int numPlanes = planes.Count;
int numPlaneConnections = planesConnected.Count;
int computedIndex;
foreach (float stackCoordinate in stackCoordinates)
{
coordinates[4] = stackCoordinate;
//coordinates[4] = homogeneous ? 0 : stackCoordinate;//-1 ? -1 : 0;//0;//stackCoordinate;
//coordinates[5] = stackCoordinate;
uint sourceID = uint.MaxValue, targetID = uint.MaxValue;
NeuronGroup connectedNG;
foreach (NeuronGroup ng in neuronGroups)
{
foreach (uint connectedTo in ng.ConnectedTo)
{
/*if (!relativeCoordinate)
coordinates[5] = stackCoordinate;
else //USE RELATIVE
coordinates[5] = 0;//*/
connectedNG = getNeuronGroup(connectedTo);
sourceCount = 0;
foreach (PointF source in ng.NeuronPositions)
{
//-----------------Get the bias of the source node
/* switch (ng.GroupType)
{
case 0: sourceID = (agent * InputCount) + ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + (agent * OutputCount) + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + (agent * HiddenCount) + ng.GlobalID + sourceCount; break; //Hidden
}
coordinates[0] = source.X; coordinates[1] = source.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
neurons[(int)sourceID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
if (ct)
{
neurons[(int)sourceID].TimeConstant = 0.01f + ((((float)network.GetOutputSignal(2) + 1.0f) / 2.0f) * .05f);
System.Diagnostics.Debug.Assert(neurons[(int)sourceID].TimeConstant > 0);
}*/
//----------------------------
targetCout = 0;
foreach (PointF target in connectedNG.NeuronPositions)
{
switch (ng.GroupType)
{
case 0: sourceID = (agent * InputCount) + ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + (agent * OutputCount) + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + (agent * HiddenCount) + ng.GlobalID + sourceCount; break; //Hidden
}
switch (connectedNG.GroupType)
{
case 0: targetID = (agent * InputCount) + connectedNG.GlobalID + targetCout; break;
case 1: targetID = totalInputCount + (agent * OutputCount) + connectedNG.GlobalID + targetCout; break;
case 2: targetID = totalInputCount + totalOutputCount + (agent * HiddenCount) + connectedNG.GlobalID + targetCout; break;
}
//-----------------Get the bias of the target node
coordinates[0] = target.X; coordinates[1] = target.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
//String s = arrayToString(coordinates);
//if (weights.ContainsKey(s))
// neurons[(int)targetID].Bias = weights[s];
//else
{
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
computedIndex = numPlaneConnections + planes.IndexOf(connectedNG.Plane);
//neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(computedIndex) * weightRange);
//weights.Add(s,neurons[(int)targetID].Bias);
}
if (ct)
{
neurons[(int)targetID].TimeConstant = timeConstantMin + ((((float)network.GetOutputSignal(2) + 1.0f) / 2.0f) * (timeConstantMax - timeConstantMin));
System.Diagnostics.Debug.Assert(neurons[(int)targetID].TimeConstant > 0);
}
//----------------------------
coordinates[0] = source.X;
coordinates[1] = source.Y;
coordinates[2] = target.X;
coordinates[3] = target.Y;
//Console.WriteLine(arrayToString(coordinates));
//if(weights.ContainsKey(s))
// output = weights[s];
//else
{
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
computedIndex = indexOfPlaneConnection(ng.Plane, connectedNG.Plane);
//output = network.GetOutputSignal(0);
output = network.GetOutputSignal(computedIndex);
//weights.Add(s, output);
}
double leo = 0.0;
if (adaptiveNetwork)
{
A = network.GetOutputSignal(2);
B = network.GetOutputSignal(3);
C = network.GetOutputSignal(4);
D = network.GetOutputSignal(5);
learningRate = network.GetOutputSignal(6);
}
if (modulatoryNet)
{
modConnection = network.GetOutputSignal(7);
}
else
{
modConnection = 0.0f;
}
if (useLeo)
{
threshold = 0.0;
leo = network.GetOutputSignal(2);
}
if (!useLeo || leo > 0.0)
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
//if (adaptiveNetwork)
//{
// //If adaptive network set weight to small value
// weight = 0.1f;
//}
connections.Add(new ConnectionGene(connectionCounter++, sourceID, targetID, weight, ref coordinates));
}
//else
//{
// Console.WriteLine("Not connected");
//}
targetCout++;
}
sourceCount++;
}
}
foreach (uint connectedTo in ng.HiveConnectedTo)
{
bool wrapAround = true;
for (uint agentConnect = 0; agentConnect < stackCoordinates.Count; agentConnect++)
{
//Make sure we're not making a recurrent connection on the same agent
//if (agentConnect == agent)
// continue;
// else if ((agent == stackCoordinates.Count - 1 && agentConnect == 0) || (agent == 0 && agentConnect == stackCoordinates.Count - 1))
// ;//agentConnect = 0;
if (agent != 0 && agent != stackCoordinates.Count - 1)
continue;
//if (agent == 1)
// continue;
//if (agentConnect != 0 )
// continue;
//Limits connections to only neighbors. Good?
//if (!((agent == 0 || agentConnect >= agent - 1) && agentConnect <= agent + 1))
// continue;
//if (agentConnect > agent + 1 || agentConnect < agent - 1)
// continue;
if (oneWay)
{
//ONE-WAY
if (agentConnect > agent + 1 || agentConnect < agent)
continue;
}
/*if (!relativeCoordinate)
//USE THE Z COORDINATE
coordinates[5] = stackCoordinates[(int)agentConnect];
else
//USE THE RELATIVE COORDINATE
coordinates[5] = agentConnect > agent ? 1 : -1;
//*/
//WRAP AROUND
/*if (agent == stackCoordinates.Count - 1 && agentConnect == 0)
coordinates[5] = 1;
else if (agent == 0 && agentConnect == stackCoordinates.Count - 1)
coordinates[5] = -1;
*/
connectedNG = getNeuronGroup(connectedTo);
sourceCount = 0;
foreach (PointF source in ng.NeuronPositions)
{
//-----------------Get the bias of the source node
/* switch (ng.GroupType)
{
case 0: sourceID = (agent * InputCount) + ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + (agent * OutputCount) + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + (agent * HiddenCount) + ng.GlobalID + sourceCount; break; //Hidden
}
coordinates[0] = source.X; coordinates[1] = source.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
neurons[(int)sourceID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
if (ct)
{
neurons[(int)sourceID].TimeConstant = 0.01f + ((((float)network.GetOutputSignal(2) + 1.0f) / 2.0f) * .05f);
System.Diagnostics.Debug.Assert(neurons[(int)sourceID].TimeConstant > 0);
}*/
//----------------------------
targetCout = 0;
foreach (PointF target in connectedNG.NeuronPositions)
{
/*if ((source.X != target.X))
{
targetCout++;
continue;
}*/
if (/*source.X!= target.X ||*/ target.X != coordinates[4])// || source.X!= coordinates[4])
{
targetCout++;
continue;
}
/* if (agent != 0 && agent != stackCoordinates.Count - 1)
{
if(agentConnect != 0 && agentConnect != stackCoordinates.Count - 1)
{
targetCout++;
continue;
}
}*/
switch (ng.GroupType)
{
case 0: sourceID = (agent * InputCount) + ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + (agent * OutputCount) + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + (agent * HiddenCount) + ng.GlobalID + sourceCount; break; //Hidden
}
switch (connectedNG.GroupType)
{
case 0: targetID = (agentConnect * InputCount) + connectedNG.GlobalID + targetCout; break;
case 1: targetID = totalInputCount + (agentConnect * OutputCount) + connectedNG.GlobalID + targetCout; break;
case 2: targetID = totalInputCount + totalOutputCount + (agentConnect * HiddenCount) + connectedNG.GlobalID + targetCout; break;
}
//-----------------Get the bias of the target node
coordinates[0] = target.X; coordinates[1] = target.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
//String s = arrayToString(coordinates);
//if (weights.ContainsKey(s))
// neurons[(int)targetID].Bias = weights[s];
//else
{
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
computedIndex = numPlaneConnections + planes.IndexOf(connectedNG.Plane);
//neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(computedIndex) * weightRange);
// weights.Add(s, neurons[(int)targetID].Bias);
}
if (ct)
{
neurons[(int)targetID].TimeConstant = timeConstantMin + ((((float)network.GetOutputSignal(2) + 1.0f) / 2.0f) * (timeConstantMax - timeConstantMin));
System.Diagnostics.Debug.Assert(neurons[(int)targetID].TimeConstant > 0);
}
//----------------------------
coordinates[0] = source.X;
coordinates[1] = source.Y;
coordinates[2] = target.X;
coordinates[3] = target.Y;
//s = arrayToString(coordinates);
//if (weights.ContainsKey(s))
// output = weights[s];
//else
{
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
computedIndex = indexOfPlaneConnection(ng.Plane, connectedNG.Plane);
//output = network.GetOutputSignal(0);
output = network.GetOutputSignal(computedIndex);
// weights.Add(s, output);
}
double leo = 0.0;
if (adaptiveNetwork)
{
A = network.GetOutputSignal(2);
B = network.GetOutputSignal(3);
C = network.GetOutputSignal(4);
D = network.GetOutputSignal(5);
learningRate = network.GetOutputSignal(6);
}
if (modulatoryNet)
{
modConnection = network.GetOutputSignal(7);
}
else
{
modConnection = 0.0f;
}
if (useLeo)
{
threshold = 0.0;
leo = network.GetOutputSignal(2);
}
if (!useLeo || leo > 0.0)
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
//if (adaptiveNetwork)
//{
// //If adaptive network set weight to small value
// weight = 0.1f;
//}
connections.Add(new ConnectionGene(connectionCounter++, sourceID, targetID, weight, ref coordinates, true));
}
//else
//{
// Console.WriteLine("Not connected");
//}
targetCout++;
}
sourceCount++;
}
}
}
}
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;
}
// NOTE: Multi-Plane Substrates ARE MAYBE supported by this method!
private NeatGenome.NeatGenome generateHomogeneousGenome(INetwork network, bool normalizeWeights, bool adaptiveNetwork,bool modulatoryNet)
{
IActivationFunction activationFunction = HyperNEATParameters.substrateActivationFunction;
ConnectionGeneList connections = new ConnectionGeneList((int)((InputCount * HiddenCount) + (HiddenCount * OutputCount)));
float[] coordinates = new float[4]; //JUSTIN: CHANGE THIS BACK TO [4]!!!
float output;
uint connectionCounter = 0;
int iterations = 2 * (network.TotalNeuronCount - (network.InputNeuronCount + network.OutputNeuronCount)) + 1;
uint totalOutputCount = OutputCount;
uint totalInputCount = InputCount;
uint totalHiddenCount = HiddenCount;
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 + OutputCount + HiddenCount));
// 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, NeuronType.Output, activationFunction));
}
// set up the hidden nodes
for (uint a = 0; a < totalHiddenCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount + OutputCount, NeuronType.Hidden, activationFunction));
}
// CPPN Outputs: [ Weights ] [ Biases ]
// When using multi-plane substrates, there will be multiple Weight and Bias outputs.
// There is a Weight output for every plane-to-plane connection (including a plane connected to itself, as in regular substrates)
// There is a Bias output for every plane
// Since "regular substrates" only have 1 plane, they only have 1 Weight and 1 Bias output. MP substrates have more. :)
int numPlanes = planes.Count;
int numPlaneConnections = planesConnected.Count;
int computedIndex;
uint sourceID = uint.MaxValue, targetID = uint.MaxValue;
NeuronGroup connectedNG;
foreach (NeuronGroup ng in neuronGroups)
{
foreach (uint connectedTo in ng.ConnectedTo)
{
connectedNG = getNeuronGroup(connectedTo);
sourceCount = 0;
foreach (PointF source in ng.NeuronPositions)
{
//-----------------Get the bias of the source node
/*switch (ng.GroupType)
{
case 0: sourceID = ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + ng.GlobalID + sourceCount; break; //Hidden
}
coordinates[0] = source.X; coordinates[1] = source.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
neurons[(int)sourceID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
//*///----------------------------
targetCout = 0;
foreach (PointF target in connectedNG.NeuronPositions)
{
switch (ng.GroupType)
{
case 0: sourceID = ng.GlobalID + sourceCount; break; //Input
case 1: sourceID = totalInputCount + ng.GlobalID + sourceCount; break; //Output
case 2: sourceID = totalInputCount + totalOutputCount + ng.GlobalID + sourceCount; break; //Hidden
}
switch (connectedNG.GroupType)
{
case 0: targetID = connectedNG.GlobalID + targetCout; break;
case 1: targetID = totalInputCount + connectedNG.GlobalID + targetCout; break;
case 2: targetID = totalInputCount + totalOutputCount + connectedNG.GlobalID + targetCout; break;
}
//-----------------Get the bias of the target node
coordinates[0] = target.X; coordinates[1] = target.Y; coordinates[2] = 0.0f; coordinates[3] = 0.0f;
//coordinates[4] = 0.0f; coordinates[5] = 0.0f; //JUSTIN: REMOVE THIS!!!
//String s = arrayToString(coordinates);
//if (weights.ContainsKey(s))
// neurons[(int)targetID].Bias = weights[s];
//else
{
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
computedIndex = numPlaneConnections + planes.IndexOf(connectedNG.Plane);
//neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(computedIndex) * weightRange);
//weights.Add(s,neurons[(int)targetID].Bias);
}
//----------------------------
coordinates[0] = source.X;
coordinates[1] = source.Y;
coordinates[2] = target.X;
coordinates[3] = target.Y;
//coordinates[4] = source.X - target.X; coordinates[5] = source.Y - target.Y; //JUSTIN: REMOVE THIS!!!
network.ClearSignals();
network.SetInputSignals(coordinates);
network.RecursiveActivation();//network.MultipleSteps(iterations);
computedIndex = indexOfPlaneConnection(ng.Plane, connectedNG.Plane);
//output = network.GetOutputSignal(0);
output = network.GetOutputSignal(computedIndex);
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
connections.Add(new ConnectionGene(connectionCounter++, sourceID, targetID, weight, ref coordinates));
}
//else
//{
// Console.WriteLine("Not connected");
//}
targetCout++;
}
sourceCount++;
}
}
}
if (normalizeWeights)
{
normalizeWeightConnections(ref connections, neurons.Count);
}
NeatGenome.NeatGenome gn = new SharpNeatLib.NeatGenome.NeatGenome(0, neurons, connections, (int)(totalInputCount), (int)(totalOutputCount));
gn.networkAdaptable = adaptiveNetwork;
gn.networkModulatory = modulatoryNet;
return gn;
}