public NeatGenome(uint genomeId,
NeuronGeneList neuronGeneList,
List<ModuleGene> moduleGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount)
{
this.genomeId = genomeId;
this.neuronGeneList = neuronGeneList;
this.moduleGeneList = moduleGeneList;
this.connectionGeneList = connectionGeneList;
this.inputNeuronCount = inputNeuronCount;
this.inputAndBiasNeuronCount = inputNeuronCount+1;
this.outputNeuronCount = outputNeuronCount;
this.inputBiasOutputNeuronCount = inputAndBiasNeuronCount + outputNeuronCount;
this.inputBiasOutputNeuronCountMinus2 = inputBiasOutputNeuronCount-2;
//justin
objectives = new double[6];
nearestNeighbors = 0;
localGenomeNovelty = 0;
competition = 0;
geneticDiversity = 0;
locality = 0;
Fitness = 0;
RealFitness = 0;
Debug.Assert(connectionGeneList.IsSorted(), "ConnectionGeneList is not sorted by innovation ID");
}
public NeatGenome(long genomeId,
NeuronGeneList neuronGeneList,
List<ModuleGene> moduleGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount)
{
this.genomeId = genomeId;
this.neuronGeneList = neuronGeneList;
this.moduleGeneList = moduleGeneList;
this.connectionGeneList = connectionGeneList;
this.inputNeuronCount = inputNeuronCount;
this.inputAndBiasNeuronCount = inputNeuronCount+1;
this.outputNeuronCount = outputNeuronCount;
this.inputBiasOutputNeuronCount = inputAndBiasNeuronCount + outputNeuronCount;
this.inputBiasOutputNeuronCountMinus2 = inputBiasOutputNeuronCount-2;
//all genomes must be catalogued by the evolution manager
EvolutionManager.SharedEvolutionManager.GenomeCreated(this);
if (!connectionGeneList.IsSorted())
{
connectionGeneList.IsSorted();
}
Debug.Assert(connectionGeneList.IsSorted(), "ConnectionGeneList is not sorted by innovation ID");
}
// Schrum: Added
public NeatGenome(uint genomeId,
NeuronGeneList neuronGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount,
int outputsPerPolicy) // Schrum: Added required parameter
: this(genomeId, neuronGeneList, new List<ModuleGene>(), connectionGeneList, inputNeuronCount, outputNeuronCount, outputsPerPolicy) { }
public Substrate(uint input, uint output, uint hidden, IActivationFunction function)
{
weightRange = HyperNEATParameters.weightRange;
threshold = HyperNEATParameters.threshold;
inputCount = input;
outputCount = output;
hiddenCount = hidden;
activationFunction = function;
inputDelta = 2.0f / (inputCount);
if (hiddenCount != 0)
hiddenDelta = 2.0f / (hiddenCount);
else
hiddenDelta = 0;
outputDelta = 2.0f / (outputCount);
//SharpNEAT requires that the neuronlist be input|bias|output|hidden
neurons=new NeuronGeneList((int)(inputCount + outputCount+ hiddenCount));
//setup the inputs
for (uint a = 0; a < inputCount; a++)
{
neurons.Add(new NeuronGene(a, NeuronType.Input, activationFunction));
}
//setup the outputs
for (uint a = 0; a < outputCount; a++)
{
neurons.Add(new NeuronGene(a + inputCount, NeuronType.Output, activationFunction));
}
for (uint a = 0; a < hiddenCount; a++)
{
neurons.Add(new NeuronGene(a + inputCount+outputCount, NeuronType.Hidden, activationFunction));
}
}
public NeatGenome(uint genomeId,
NeuronGeneList neuronGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount)
: this(genomeId, neuronGeneList, new List<ModuleGene>(), connectionGeneList, inputNeuronCount,
// Schrum: Added new constructor that assumes there is only one output module (default NEAT), so total outputs = outputs per policy
outputNeuronCount, outputNeuronCount) { }
public NeatGenome(long genomeId,
NeuronGeneList neuronGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount)
: this(genomeId, neuronGeneList, new List<ModuleGene>(), connectionGeneList, inputNeuronCount, outputNeuronCount)
{
}
public static NeatGenome Read(XmlElement xmlGenome)
{
int inputNeuronCount=0;
int outputNeuronCount=0;
uint id = uint.Parse(XmlUtilities.GetAttributeValue(xmlGenome, "id"));
//--- Read neuron genes into a list.
NeuronGeneList neuronGeneList = new NeuronGeneList();
XmlNodeList listNeuronGenes = xmlGenome.SelectNodes("neurons/neuron");
foreach(XmlElement xmlNeuronGene in listNeuronGenes)
{
NeuronGene neuronGene = ReadNeuronGene(xmlNeuronGene);
// Count the input and output neurons as we go.
switch(neuronGene.NeuronType)
{
case NeuronType.Input:
inputNeuronCount++;
break;
case NeuronType.Output:
outputNeuronCount++;
break;
}
neuronGeneList.Add(neuronGene);
}
//--- Read module genes into a list.
List<ModuleGene> moduleGeneList = new List<ModuleGene>();
XmlNodeList listModuleGenes = xmlGenome.SelectNodes("modules/module");
foreach (XmlElement xmlModuleGene in listModuleGenes) {
moduleGeneList.Add(ReadModuleGene(xmlModuleGene));
}
//--- Read connection genes into a list.
ConnectionGeneList connectionGeneList = new ConnectionGeneList();
XmlNodeList listConnectionGenes = xmlGenome.SelectNodes("connections/connection");
foreach(XmlElement xmlConnectionGene in listConnectionGenes)
connectionGeneList.Add(ReadConnectionGene(xmlConnectionGene));
//return new NeatGenome(id, neuronGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount);
NeatGenome g = new NeatGenome(id, neuronGeneList, moduleGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount);
g.Behavior = ReadBehavior(xmlGenome.SelectSingleNode("behavior"));
g.Behavior.objectives = new double[6];
g.objectives = new double[6];
// JUSTIN: Read grid/trajectory info
g.GridCoords = ReadGrid(xmlGenome.SelectSingleNode("grid"));
g.Behavior.trajectory = ReadTrajectory(xmlGenome.SelectSingleNode("trajectory"));
return g;
}
/// <summary>
/// Create a genome with the provided internal state/definition data/objects.
/// Overridable method to allow alternative NeatGenome sub-classes to be used.
/// </summary>
public override NeatGenome CreateGenome(uint id,
uint birthGeneration,
NeuronGeneList neuronGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount,
bool rebuildNeuronGeneConnectionInfo)
{
return new NeatGenome(this, id, birthGeneration, neuronGeneList, connectionGeneList,
inputNeuronCount, outputNeuronCount, rebuildNeuronGeneConnectionInfo, false);
}
public static NeatGenome Read(XmlElement xmlGenome)
{
int inputNeuronCount=0;
int outputNeuronCount=0;
uint id = uint.Parse(XmlUtilities.GetAttributeValue(xmlGenome, "id"));
// Schrum: Retrieve this new property, which is saved to xml files now
int outputsPerPolicy = int.Parse(XmlUtilities.GetAttributeValue(xmlGenome, "outputsperpolicy"));
//--- Read neuron genes into a list.
NeuronGeneList neuronGeneList = new NeuronGeneList();
XmlNodeList listNeuronGenes = xmlGenome.SelectNodes("neurons/neuron");
foreach(XmlElement xmlNeuronGene in listNeuronGenes)
{
NeuronGene neuronGene = ReadNeuronGene(xmlNeuronGene);
// Count the input and output neurons as we go.
switch(neuronGene.NeuronType)
{
case NeuronType.Input:
inputNeuronCount++;
break;
case NeuronType.Output:
outputNeuronCount++;
break;
}
neuronGeneList.Add(neuronGene);
}
//--- Read module genes into a list.
List<ModuleGene> moduleGeneList = new List<ModuleGene>();
XmlNodeList listModuleGenes = xmlGenome.SelectNodes("modules/module");
foreach (XmlElement xmlModuleGene in listModuleGenes) {
moduleGeneList.Add(ReadModuleGene(xmlModuleGene));
}
//--- Read connection genes into a list.
ConnectionGeneList connectionGeneList = new ConnectionGeneList();
XmlNodeList listConnectionGenes = xmlGenome.SelectNodes("connections/connection");
foreach(XmlElement xmlConnectionGene in listConnectionGenes)
connectionGeneList.Add(ReadConnectionGene(xmlConnectionGene));
//return new NeatGenome(id, neuronGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount);
//return new NeatGenome(id, neuronGeneList, moduleGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount);
// Schrum: Changed to include the outputs per policy
return new NeatGenome(id, neuronGeneList, moduleGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount, outputsPerPolicy);
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public NeatGenome(NeatGenome copyFrom, uint genomeId)
{
this.genomeId = genomeId;
// No need to loop the arrays to clone each element because NeuronGene and ConnectionGene are
// value data types (structs).
neuronGeneList = new NeuronGeneList(copyFrom.neuronGeneList);
connectionGeneList = new ConnectionGeneList(copyFrom.connectionGeneList);
inputNeuronCount = copyFrom.inputNeuronCount;
inputAndBiasNeuronCount = copyFrom.inputNeuronCount+1;
outputNeuronCount = copyFrom.outputNeuronCount;
inputBiasOutputNeuronCount = copyFrom.inputBiasOutputNeuronCount;
inputBiasOutputNeuronCountMinus2 = copyFrom.inputBiasOutputNeuronCountMinus2;
Debug.Assert(connectionGeneList.IsSorted(), "ConnectionGeneList is not sorted by innovation ID");
}
/// <summary>
/// Default constructor.
/// </summary>
public NeatGenome( uint genomeId,
NeuronGeneList neuronGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount)
{
this.genomeId = genomeId;
this.neuronGeneList = neuronGeneList;
this.connectionGeneList = connectionGeneList;
this.inputNeuronCount = inputNeuronCount;
this.inputAndBiasNeuronCount = inputNeuronCount+1;
this.outputNeuronCount = outputNeuronCount;
this.inputBiasOutputNeuronCount = inputAndBiasNeuronCount + outputNeuronCount;
this.inputBiasOutputNeuronCountMinus2 = inputBiasOutputNeuronCount-2;
Debug.Assert(connectionGeneList.IsSorted(), "ConnectionGeneList is not sorted by innovation ID");
}
public Substrate(uint biasCount, uint inputCount, uint outputCount, uint hiddenCount, IActivationFunction function)
{
this.biasCount = biasCount;
this.inputCount = inputCount;
this.outputCount = outputCount;
this.hiddenCount = hiddenCount;
this.activationFunction = function;
weightRange = HyperNEATParameters.weightRange;
threshold = HyperNEATParameters.threshold;
if (inputCount != 0)
inputDelta = 2.0f / (inputCount);
if (hiddenCount != 0)
hiddenDelta = 2.0f / (hiddenCount);
if (outputCount != 0)
outputDelta = 2.0f / (outputCount);
// SharpNEAT requires that the neuron list be in this order: bias|input|output|hidden
neurons = new NeuronGeneList((int)(inputCount + outputCount + hiddenCount));
// set up the bias nodes
for (uint a = 0; a < biasCount; a++) {
neurons.Add(new NeuronGene(a, NeuronType.Bias, ActivationFunctionFactory.GetActivationFunction("NullFn")));
}
// set up the input nodes
for (uint a = 0; a < inputCount; a++) {
neurons.Add(new NeuronGene(a + biasCount, NeuronType.Input, ActivationFunctionFactory.GetActivationFunction("NullFn")));
}
// set up the output nodes
for (uint a = 0; a < outputCount; a++) {
neurons.Add(new NeuronGene(a + biasCount + inputCount, NeuronType.Output, activationFunction));
}
// set up the hidden nodes
for (uint a = 0; a < hiddenCount; a++) {
neurons.Add(new NeuronGene(a + biasCount + inputCount + outputCount, NeuronType.Hidden, activationFunction));
}
}
public static NeatGenome Read(XmlElement xmlGenome)
{
int inputNeuronCount=0;
int outputNeuronCount=0;
uint id = uint.Parse(XmlUtilities.GetAttributeValue(xmlGenome, "id"));
//--- Read neuron genes into a list.
NeuronGeneList neuronGeneList = new NeuronGeneList();
XmlNodeList listNeuronGenes = xmlGenome.SelectNodes("neurons/neuron");
foreach(XmlElement xmlNeuronGene in listNeuronGenes)
{
NeuronGene neuronGene = ReadNeuronGene(xmlNeuronGene);
// Count the input and output neurons as we go.
switch(neuronGene.NeuronType)
{
case NeuronType.Input:
inputNeuronCount++;
break;
case NeuronType.Output:
outputNeuronCount++;
break;
}
neuronGeneList.Add(neuronGene);
}
//--- Read connection genes into a list.
ConnectionGeneList connectionGeneList = new ConnectionGeneList();
XmlNodeList listConnectionGenes = xmlGenome.SelectNodes("connections/connection");
foreach(XmlElement xmlConnectionGene in listConnectionGenes)
connectionGeneList.Add(ReadConnectionGene(xmlConnectionGene));
return new NeatGenome(id, neuronGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount);
}
public override IGenome CreateOffspring_Sexual(EvolutionAlgorithm ea, IGenome parent)
{
NeatGenome otherParent = parent as NeatGenome;
if (otherParent == null)
return null;
// Build a list of connections in either this genome or the other parent.
CorrelationResults correlationResults = CorrelateConnectionGeneLists(connectionGeneList, otherParent.connectionGeneList);
Debug.Assert(correlationResults.PerformIntegrityCheck(), "CorrelationResults failed integrity check.");
//----- Connection Genes.
// We will temporarily store the offspring's genes in newConnectionGeneList and keeping track of which genes
// exist with newConnectionGeneTable. Here we ensure these objects are created, and if they already existed
// then ensure they are cleared. Clearing existing objects is more efficient that creating new ones because
// allocated memory can be re-used.
// Key = connection key, value = index in newConnectionGeneList.
if(newConnectionGeneTable==null)
{ // Provide a capacity figure to the new Hashtable. The offspring will be the same length (or thereabouts).
newConnectionGeneTable = new Hashtable(connectionGeneList.Count);
}
else
{
newConnectionGeneTable.Clear();
}
//TODO: No 'capacity' constructor on CollectionBase. Create modified/custom CollectionBase.
// newConnectionGeneList must be constructed on each call because it is passed to a new NeatGenome
// at construction time and a permanent reference to the list is kept.
newConnectionGeneList = new ConnectionGeneList(ConnectionGeneList.Count);
// A switch that stores which parent is fittest 1 or 2. Chooses randomly if both are equal. More efficient to calculate this just once.
byte fitSwitch;
if(Fitness > otherParent.Fitness)
fitSwitch = 1;
else if(Fitness < otherParent.Fitness)
fitSwitch = 2;
else
{ // Select one of the parents at random to be the 'master' genome during crossover.
if(Utilities.NextDouble() < 0.5)
fitSwitch = 1;
else
fitSwitch = 2;
}
bool combineDisjointExcessFlag = Utilities.NextDouble() < ea.NeatParameters.pDisjointExcessGenesRecombined;
// Loop through the correlationResults, building a table of ConnectionGenes from the parents that will make it into our
// new [single] offspring. We use a table keyed on connection end points to prevent passing connections to the offspring
// that may have the same end points but a different innovation number - effectively we filter out duplicate connections.
int idxBound = correlationResults.CorrelationItemList.Count;
for(int i=0; i<idxBound; i++)
{
CreateOffspring_Sexual_ProcessCorrelationItem((CorrelationItem)correlationResults.CorrelationItemList[i], fitSwitch, combineDisjointExcessFlag, ea.NeatParameters);
}
//----- Neuron Genes.
// Build a neuronGeneList by analysing each connection's neuron end-point IDs.
// This strategy has the benefit of eliminating neurons that are no longer connected too.
// Remember to always keep all input, output and bias neurons though!
NeuronGeneList newNeuronGeneList = new NeuronGeneList(neuronGeneList.Count);
// Keep a table of the NeuronGene ID's keyed by ID so that we can keep track of which ones have been added.
// Key = innovation ID, value = null for some reason.
if(newNeuronGeneTable==null)
newNeuronGeneTable = new Hashtable(neuronGeneList.Count);
else
newNeuronGeneTable.Clear();
// Get the input/output neurons from this parent. All Genomes share these neurons, they do not change during a run.
idxBound = neuronGeneList.Count;
for(int i=0; i<idxBound; i++)
{
if(neuronGeneList[i].NeuronType != NeuronType.Hidden)
{
newNeuronGeneList.Add(new NeuronGene(neuronGeneList[i]));
newNeuronGeneTable.Add(neuronGeneList[i].InnovationId, null);
}
else
{ // No more bias, input or output nodes. break the loop.
break;
}
}
// Now analyse the connections to determine which NeuronGenes are required in the offspring.
// Loop through every connection in the child, and add to the child those hidden neurons that are sources or targets of the connection.
idxBound = newConnectionGeneList.Count;
for(int i=0; i<idxBound; i++)
{
NeuronGene neuronGene;
ConnectionGene connectionGene = newConnectionGeneList[i];
if(!newNeuronGeneTable.ContainsKey(connectionGene.SourceNeuronId))
{
//TODO: DAVID proper activation function
// We can safely assume that any missing NeuronGenes at this point are hidden heurons.
neuronGene = this.neuronGeneList.GetNeuronById(connectionGene.SourceNeuronId);
if (neuronGene != null)
newNeuronGeneList.Add(new NeuronGene(neuronGene));
else
newNeuronGeneList.Add(new NeuronGene(otherParent.NeuronGeneList.GetNeuronById(connectionGene.SourceNeuronId)));
//newNeuronGeneList.Add(new NeuronGene(connectionGene.SourceNeuronId, NeuronType.Hidden, ActivationFunctionFactory.GetActivationFunction("SteepenedSigmoid")));
newNeuronGeneTable.Add(connectionGene.SourceNeuronId, null);
}
if(!newNeuronGeneTable.ContainsKey(connectionGene.TargetNeuronId))
{
//TODO: DAVID proper activation function
// We can safely assume that any missing NeuronGenes at this point are hidden heurons.
neuronGene = this.neuronGeneList.GetNeuronById(connectionGene.TargetNeuronId);
if (neuronGene != null)
newNeuronGeneList.Add(new NeuronGene(neuronGene));
else
newNeuronGeneList.Add(new NeuronGene(otherParent.NeuronGeneList.GetNeuronById(connectionGene.TargetNeuronId)));
//newNeuronGeneList.Add(new NeuronGene(connectionGene.TargetNeuronId, NeuronType.Hidden, ActivationFunctionFactory.GetActivationFunction("SteepenedSigmoid")));
newNeuronGeneTable.Add(connectionGene.TargetNeuronId, null);
}
}
// Determine which modules to pass on to the child in the same way.
// For each module in this genome or in the other parent, if it was referenced by even one connection add it and all its dummy neurons to the child.
List<ModuleGene> newModuleGeneList = new List<ModuleGene>();
// Build a list of modules the child might have, which is a union of the parents' module lists, but they are all copies so we can't just do a union.
List<ModuleGene> unionParentModules = new List<ModuleGene>(moduleGeneList);
foreach (ModuleGene moduleGene in otherParent.moduleGeneList) {
bool alreadySeen = false;
foreach (ModuleGene match in unionParentModules) {
if (moduleGene.InnovationId == match.InnovationId) {
alreadySeen = true;
break;
}
}
if (!alreadySeen) {
unionParentModules.Add(moduleGene);
}
}
foreach (ModuleGene moduleGene in unionParentModules) {
// Examine each neuron in the child to determine whether it is part of a module.
foreach (List<uint> dummyNeuronList in new List<uint>[] { moduleGene.InputIds, moduleGene.OutputIds }) {
foreach (uint dummyNeuronId in dummyNeuronList) {
if (newNeuronGeneTable.ContainsKey(dummyNeuronId)) {
goto childHasModule;
}
}
}
continue; // the child does not contain this module, so continue the loop and check for the next module.
childHasModule: // the child does contain this module, so make sure the child gets all the nodes the module requires to work.
// Make sure the child has all the neurons in the given module.
newModuleGeneList.Add(new ModuleGene(moduleGene));
foreach (List<uint> dummyNeuronList in new List<uint>[] { moduleGene.InputIds, moduleGene.OutputIds }) {
foreach (uint dummyNeuronId in dummyNeuronList) {
if (!newNeuronGeneTable.ContainsKey(dummyNeuronId)) {
newNeuronGeneTable.Add(dummyNeuronId, null);
NeuronGene neuronGene = this.neuronGeneList.GetNeuronById(dummyNeuronId);
if (neuronGene != null) {
newNeuronGeneList.Add(new NeuronGene(neuronGene));
} else {
newNeuronGeneList.Add(new NeuronGene(otherParent.NeuronGeneList.GetNeuronById(dummyNeuronId)));
}
}
}
}
}
// TODO: Inefficient code?
newNeuronGeneList.SortByInnovationId();
// Schrum: Need to calculate this because of Module Mutation adding extra outputs
int revisedOutputCount = 0;
foreach(NeuronGene n in newNeuronGeneList) {
if (n.NeuronType == NeuronType.Output)
revisedOutputCount++;
}
// newConnectionGeneList is already sorted because it was generated by passing over the list returned by
// CorrelateConnectionGeneLists() - which is always in order.
// Schrum: Modified to add outputsPerPolicy as a parameter, and use revisedOutputCount
return new NeatGenome(ea.NextGenomeId, newNeuronGeneList, newModuleGeneList, newConnectionGeneList, inputNeuronCount, revisedOutputCount, outputsPerPolicy);
}
// 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;
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public NeatGenome(NeatGenome copyFrom, uint genomeId)
{
this.genomeId = genomeId;
this.parent = copyFrom;
// No need to loop the arrays to clone each element because NeuronGene and ConnectionGene are
// value data types (structs).
neuronGeneList = new NeuronGeneList(copyFrom.neuronGeneList);
moduleGeneList = new List<ModuleGene>(copyFrom.moduleGeneList);
connectionGeneList = new ConnectionGeneList(copyFrom.connectionGeneList);
//joel
if(copyFrom.Behavior!=null)
Behavior = new SharpNeatLib.BehaviorType(copyFrom.Behavior);
inputNeuronCount = copyFrom.inputNeuronCount;
// Schrum: Removed (not used)
//inputAndBiasNeuronCount = copyFrom.inputNeuronCount+1;
outputNeuronCount = copyFrom.outputNeuronCount;
// Schrum: removed (not used)
//inputBiasOutputNeuronCount = copyFrom.inputBiasOutputNeuronCount;
//inputBiasOutputNeuronCountMinus2 = copyFrom.inputBiasOutputNeuronCountMinus2;
// Schrum: Added
outputsPerPolicy = copyFrom.outputsPerPolicy;
Debug.Assert(connectionGeneList.IsSorted(), "ConnectionGeneList is not sorted by innovation ID");
}
private void Mutate_AddConnection(EvolutionAlgorithm ea)
{
// We are always guaranteed to have enough neurons to form connections - because the input/output neurons are
// fixed. Any domain that doesn't require input/outputs is a bit nonsensical!
// Make a fixed number of attempts at finding a suitable connection to add.
if(neuronGeneList.Count>1)
{ // At least 2 neurons, so we have a chance at creating a connection.
for(int attempts=0; attempts<5; attempts++)
{
// Select candidate source and target neurons. Any neuron can be used as the source. Input neurons
// should not be used as a target
int srcNeuronIdx;
int tgtNeuronIdx;
/* Here's some code for adding connections that attempts to avoid any recursive conenctions
* within a network by only linking to neurons with innovation id's greater than the source neuron.
* Unfortunately this doesn't work because new neurons with large innovations ID's are inserted
* randomly through a network's topology! Hence this code remains here in readyness to be resurrected
* as part of some future work to support feedforward nets.
// if(ea.NeatParameters.feedForwardOnly)
// {
// /* We can ensure that all networks are feedforward only by only adding feedforward connections here.
// * Feed forward connections fall into one of the following categories. All references to indexes
// * are indexes within this genome's neuronGeneList:
// * 1) Source neuron is an input or hidden node, target is an output node.
// * 2) Source is an input or hidden node, target is a hidden node with an index greater than the source node's index.
// * 3) Source is an output node, target is an output node with an index greater than the source node's index.
// *
// * These rules are easier to understand if you understand how the different types if neuron are arranged within
// * the neuronGeneList array. Neurons are arranged in the following order:
// *
// * 1) A single bias neuron is always first.
// * 2) Experiment specific input neurons.
// * 3) Output neurons.
// * 4) Hidden neurons.
// *
// * The quantity and innovationID of all neurons within the first 3 categories remains fixed throughout the life
// * of an experiment, hence we always know where to find the bias, input and output nodes. The number of hidden nodes
// * can vary as ne nodes are created, pruned away or perhaps dropped during crossover, however they are always arranged
// * newest to oldest, or in other words sorted by innovation idea, lowest ID first.
// *
// * If output neurons were at the end of the list with hidden nodes in the middle then generating feedforward
// * connections would be as easy as selecting a target neuron with a higher index than the source neuron. However, that
// * type of arrangement is not conducive to the operation of other routines, hence this routine is a little bit more
// * complicated as a result.
// */
//
// // Ok, for a source neuron we can pick any neuron except the last output neuron.
// int neuronIdxCount = neuronGeneList.Count;
// int neuronIdxBound = neuronIdxCount-1;
//
// // Generate count-1 possibilities and avoid the last output neuron's idx.
// srcNeuronIdx = (int)Math.Floor(Utilities.NextDouble() * neuronIdxBound);
// if(srcNeuronIdx>inputBiasOutputNeuronCountMinus2) srcNeuronIdx++;
//
//
// // Now generate a target idx depending on what type of neuron srcNeuronIdx is pointing to.
// if(srcNeuronIdx<inputAndBiasNeuronCount)
// { // Source is a bias or input neuron. Target can be any output or hidden neuron.
// tgtNeuronIdx = inputAndBiasNeuronCount + (int)Math.Floor(Utilities.NextDouble() * (neuronIdxCount-inputAndBiasNeuronCount));
// }
// else if(srcNeuronIdx<inputBiasOutputNeuronCount)
// { // Source is an output neuron, but not the last output neuron. Target can be any output neuron with an index
// // greater than srcNeuronIdx.
// tgtNeuronIdx = (inputAndBiasNeuronCount+1) + (int)Math.Floor(Utilities.NextDouble() * ((inputBiasOutputNeuronCount-1)-srcNeuronIdx));
// }
// else
// { // Source is a hidden neuron. Target can be any hidden neuron after srcNeuronIdx or any output neuron.
// tgtNeuronIdx = (int)Math.Floor(Utilities.NextDouble() * ((neuronIdxBound-srcNeuronIdx)+outputNeuronCount));
//
// if(tgtNeuronIdx<outputNeuronCount)
// { // Map to an output neuron idx.
// tgtNeuronIdx += inputAndBiasNeuronCount;
// }
// else
// {
// // Map to one of the hidden neurons after srcNeuronIdx.
// tgtNeuronIdx = (tgtNeuronIdx-outputNeuronCount)+srcNeuronIdx+1;
// }
// }
// }
// // Source neuron can by any neuron. Target neuron is any neuron except input neurons.
// srcNeuronIdx = (int)Math.Floor(Utilities.NextDouble() * neuronGeneList.Count);
// tgtNeuronIdx = inputAndBiasNeuronCount + (int)Math.Floor(Utilities.NextDouble() * (neuronGeneList.Count-inputAndBiasNeuronCount));
//
// NeuronGene sourceNeuron = neuronGeneList[srcNeuronIdx];
// NeuronGene targetNeuron = neuronGeneList[tgtNeuronIdx];
// Find all potential inputs, or quit if there are not enough.
// Neurons cannot be inputs if they are dummy input nodes of a module.
NeuronGeneList potentialInputs = new NeuronGeneList();
foreach (NeuronGene n in neuronGeneList) {
if (!(n.ActivationFunction is ModuleInputNeuron)) {
potentialInputs.Add(n);
}
}
if (potentialInputs.Count < 1)
return;
// Find all potential outputs, or quit if there are not enough.
// Neurons cannot be outputs if they are dummy input or output nodes of a module, or network input or bias nodes.
NeuronGeneList potentialOutputs = new NeuronGeneList();
foreach (NeuronGene n in neuronGeneList) {
if (n.NeuronType != NeuronType.Bias && n.NeuronType != NeuronType.Input
&& !(n.ActivationFunction is ModuleInputNeuron)
&& !(n.ActivationFunction is ModuleOutputNeuron)) {
potentialOutputs.Add(n);
}
}
if (potentialOutputs.Count < 1)
return;
NeuronGene sourceNeuron = potentialInputs[Utilities.Next(potentialInputs.Count)];
NeuronGene targetNeuron = potentialOutputs[Utilities.Next(potentialOutputs.Count)];
// Check if a connection already exists between these two neurons.
uint sourceId = sourceNeuron.InnovationId;
uint targetId = targetNeuron.InnovationId;
if(!TestForExistingConnection(sourceId, targetId))
{
// Check if a matching mutation has already occured on another genome.
// If so then re-use the connection ID.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(sourceId, targetId);
ConnectionGene existingConnection = (ConnectionGene)ea.NewConnectionGeneTable[connectionKey];
ConnectionGene newConnectionGene;
if(existingConnection==null)
{ // Create a new connection with a new ID and add it to the Genome.
newConnectionGene = new ConnectionGene(ea.NextInnovationId, sourceId, targetId,
(Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange/4.0) - ea.NeatParameters.connectionWeightRange/8.0);
// Register the new connection with NewConnectionGeneTable.
ea.NewConnectionGeneTable.Add(connectionKey, newConnectionGene);
// Add the new gene to this genome. We have a new ID so we can safely append the gene to the end
// of the list without risk of breaking the innovation ID order.
connectionGeneList.Add(newConnectionGene);
}
else
{ // Create a new connection, re-using the ID from existingConnection, and add it to the Genome.
newConnectionGene = new ConnectionGene(existingConnection.InnovationId, sourceId, targetId,
(Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange/4.0) - ea.NeatParameters.connectionWeightRange/8.0);
// Add the new gene to this genome. We are re-using an ID so we must ensure the connection gene is
// inserted into the correct position (sorted by innovation ID).
connectionGeneList.InsertIntoPosition(newConnectionGene);
}
return;
}
}
}
// We couldn't find a valid connection to create. Instead of doing nothing lets perform connection
// weight mutation.
Mutate_ConnectionWeights(ea);
}
// 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;
}
private void Mutate_AddModule(EvolutionAlgorithm ea)
{
// Find all potential inputs, or quit if there are not enough.
// Neurons cannot be inputs if they are dummy input nodes created for another module.
NeuronGeneList potentialInputs = new NeuronGeneList();
foreach (NeuronGene n in neuronGeneList) {
if (!(n.ActivationFunction is ModuleInputNeuron)) {
potentialInputs.Add(n);
}
}
if (potentialInputs.Count < 1)
return;
// Find all potential outputs, or quit if there are not enough.
// Neurons cannot be outputs if they are dummy input or output nodes created for another module, or network input or bias nodes.
NeuronGeneList potentialOutputs = new NeuronGeneList();
foreach (NeuronGene n in neuronGeneList) {
if (n.NeuronType != NeuronType.Bias && n.NeuronType != NeuronType.Input
&& !(n.ActivationFunction is ModuleInputNeuron)
&& !(n.ActivationFunction is ModuleOutputNeuron)) {
potentialOutputs.Add(n);
}
}
if (potentialOutputs.Count < 1)
return;
// Pick a new function for the new module.
IModule func = ModuleFactory.GetRandom();
// Choose inputs uniformly at random, with replacement.
// Create dummy neurons to represent the module's inputs.
// Create connections between the input nodes and the dummy neurons.
IActivationFunction inputFunction = ActivationFunctionFactory.GetActivationFunction("ModuleInputNeuron");
List<uint> inputDummies = new List<uint>(func.InputCount);
for (int i = 0; i < func.InputCount; i++) {
NeuronGene newNeuronGene = new NeuronGene(ea.NextInnovationId, NeuronType.Hidden, inputFunction);
neuronGeneList.Add(newNeuronGene);
uint sourceId = potentialInputs[Utilities.Next(potentialInputs.Count)].InnovationId;
uint targetId = newNeuronGene.InnovationId;
inputDummies.Add(targetId);
// Create a new connection with a new ID and add it to the Genome.
ConnectionGene newConnectionGene = new ConnectionGene(ea.NextInnovationId, sourceId, targetId,
(Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange) - ea.NeatParameters.connectionWeightRange / 2.0);
// Register the new connection with NewConnectionGeneTable.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(sourceId, targetId);
ea.NewConnectionGeneTable.Add(connectionKey, newConnectionGene);
// Add the new gene to this genome. We have a new ID so we can safely append the gene to the end
// of the list without risk of breaking the innovation ID order.
connectionGeneList.Add(newConnectionGene);
}
// Choose outputs uniformly at random, with replacement.
// Create dummy neurons to represent the module's outputs.
// Create connections between the output nodes and the dummy neurons.
IActivationFunction outputFunction = ActivationFunctionFactory.GetActivationFunction("ModuleOutputNeuron");
List<uint> outputDummies = new List<uint>(func.OutputCount);
for (int i = 0; i < func.OutputCount; i++) {
NeuronGene newNeuronGene = new NeuronGene(ea.NextInnovationId, NeuronType.Hidden, outputFunction);
neuronGeneList.Add(newNeuronGene);
uint sourceId = newNeuronGene.InnovationId;
uint targetId = potentialOutputs[Utilities.Next(potentialOutputs.Count)].InnovationId;
outputDummies.Add(sourceId);
// Create a new connection with a new ID and add it to the Genome.
ConnectionGene newConnectionGene = new ConnectionGene(ea.NextInnovationId, sourceId, targetId,
(Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange) - ea.NeatParameters.connectionWeightRange / 2.0);
// Register the new connection with NewConnectionGeneTable.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(sourceId, targetId);
ea.NewConnectionGeneTable.Add(connectionKey, newConnectionGene);
// Add the new gene to this genome. We have a new ID so we can safely append the gene to the end
// of the list without risk of breaking the innovation ID order.
connectionGeneList.Add(newConnectionGene);
}
// Pick a new ID for the new module and create it.
// Modules do not participate in history comparisons, so we will always create a new innovation ID.
// We can change this here if it becomes a problem.
ModuleGene newModule = new ModuleGene(ea.NextInnovationId, func, inputDummies, outputDummies);
moduleGeneList.Add(newModule);
}
public static IGenome CreateGenomePreserveID(NeatParameters neatParameters, IdGenerator idGenerator, int inputNeuronCount, int outputNeuronCount, float connectionProportion)
{
IActivationFunction actFunct;
NeuronGene neuronGene; // temp variable.
NeuronGeneList inputNeuronGeneList = new NeuronGeneList(); // includes bias neuron.
NeuronGeneList outputNeuronGeneList = new NeuronGeneList();
NeuronGeneList neuronGeneList = new NeuronGeneList();
ConnectionGeneList connectionGeneList = new ConnectionGeneList();
int nodeCount = 0;
WINManager win = WINManager.SharedWIN;
// IMPORTANT NOTE: The neurons must all be created prior to any connections. That way all of the genomes
// will obtain the same innovation ID's for the bias,input and output nodes in the initial population.
// Create a single bias neuron.
//TODO: DAVID proper activation function change to NULL?
actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
//neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Bias, actFunct);
WINNode neuronNode = win.findOrInsertNodeWithProperties(idGenerator,
WINNode.NodeWithProperties(nodeCount++, NeuronType.Bias)
);
neuronGene = new NeuronGene(null, neuronNode.UniqueID, NeuronGene.INPUT_LAYER, NeuronType.Bias, actFunct);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
// Create input neuron genes.
actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
for (int i = 0; i < inputNeuronCount; i++)
{
//TODO: DAVID proper activation function change to NULL?
//neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Input, actFunct);
neuronNode = win.findOrInsertNodeWithProperties(idGenerator, WINNode.NodeWithProperties(nodeCount++, NeuronType.Input));
neuronGene = new NeuronGene(null, neuronNode.UniqueID, NeuronGene.INPUT_LAYER, NeuronType.Input, actFunct);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Create output neuron genes.
//actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
for (int i = 0; i < outputNeuronCount; i++)
{
actFunct = ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid");
//actFunct = ActivationFunctionFactory.GetRandomActivationFunction(neatParameters);
//TODO: DAVID proper activation function
//neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Output, actFunct);
neuronNode = win.findOrInsertNodeWithProperties(idGenerator, WINNode.NodeWithProperties(nodeCount++, NeuronType.Output));
neuronGene = new NeuronGene(null, neuronNode.UniqueID, NeuronGene.OUTPUT_LAYER, NeuronType.Output, actFunct);
outputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
int currentConnCount = 0;
WINConnection winConn;
// Loop over all possible connections from input to output nodes and create a number of connections based upon
// connectionProportion.
foreach (NeuronGene targetNeuronGene in outputNeuronGeneList)
{
foreach (NeuronGene sourceNeuronGene in inputNeuronGeneList)
{
// Always generate an ID even if we aren't going to use it. This is necessary to ensure connections
// between the same neurons always have the same ID throughout the generated population.
//PAUL NOTE:
//instead of generating and not using and id, we use the target and connection properties to uniquely identify a connection in WIN
//uint connectionInnovationId = idGenerator.NextInnovationId;
if (Utilities.NextDouble() < connectionProportion)
{
// Ok lets create a connection.
//first we search or create the winconnection object
winConn = win.findOrInsertConnectionWithProperties(idGenerator,
WINConnection.ConnectionWithProperties(currentConnCount, sourceNeuronGene.InnovationId, targetNeuronGene.InnovationId));
//our winconn will have our innovationID, and our weight like normal
//this will also respect the idgenerator, since it gets sent in as well, for legacy purposes
connectionGeneList.Add(new ConnectionGene(winConn.UniqueID,
sourceNeuronGene.InnovationId,
targetNeuronGene.InnovationId,
(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0)
);
//(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0)); // Weight 0 +-5
}
currentConnCount++;
}
}
//WIN will eventually be in control of all the genomes that are created as well, but not quite yet!
//TODO: WIN should be generating genomeIDs explicitly
// Don't create any hidden nodes at this point. Fundamental to the NEAT way is to start minimally!
return new NeatGenome(idGenerator.NextGenomeId, neuronGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount);
}
// 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;
}
void QueryConnection(INetwork network, ConnectionGeneList connections, uint connectionCounter, uint neuron1id, uint neuron2id, int moduleI, NeuronGeneList newNeurons)
{
int iterations = 2 * (network.TotalNeuronCount - (network.InputNeuronCount + network.OutputNeuronCount)) + 1;
network.ClearSignals();
//network.SetInputSignal(0, 1);
network.SetInputSignal(0, newNeurons[(int)neuron1id].XValue);
network.SetInputSignal(1, newNeurons[(int)neuron1id].YValue);
network.SetInputSignal(2, newNeurons[(int)neuron2id].XValue);
network.SetInputSignal(3, newNeurons[(int)neuron2id].YValue);
network.SetInputSignal(4, 1);
network.MultipleSteps(iterations);
float output = network.GetOutputSignal(moduleI);
if (Math.Abs(output) > threshold)
{
float weight = (float)(((Math.Abs(output) - (threshold)) / (1 - threshold)) * weightRange * Math.Sign(output));
connections.Add(new ConnectionGene(connectionCounter, neuron1id, neuron2id, weight));
}
}
public NeatGenome MakeGenome(INetwork network, int moduleI)
{
// copy the neuron list to a new list and update the x/y values
NeuronGeneList newNeurons = new NeuronGeneList(neurons);
// set the x and y value of the SUPGs
foreach (NeuronGene neuron in newNeurons)
{
Point point = GetCustomPos(neuron.InnovationId);
neuron.XValue = point.X;
neuron.YValue = point.Y;
/*if (neuron.NeuronType != NeuronType.Input) {
* neuron.ActivationFunction = new SteepenedSigmoid();
* }*/
//neuron.TimeConstant = 1;
neuron.NeuronBias = GetNeuronBias(network, neuron, moduleI + 1);
}
ConnectionGeneList connections = new ConnectionGeneList((int)((inputCount * hiddenCount) + (hiddenCount * outputCount)));
float[] coordinates = new float[5];
float output;
uint connectionCounter = 0;
int iterations = 2 * (network.TotalNeuronCount - (network.InputNeuronCount + network.OutputNeuronCount)) + 1;
// Connections from input to hidden
for (uint source = 0; source < inputCount; source++)
{
QueryConnection(network, connections, connectionCounter++, source, 11, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, source, 12, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, source, 13, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, source, 14, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, source, 15, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, source, 16, moduleI, newNeurons);
// output
/*QueryConnection(network, connections, connectionCounter++, source, 6, moduleI, newNeurons);
* QueryConnection(network, connections, connectionCounter++, source, 7, moduleI, newNeurons);
* QueryConnection(network, connections, connectionCounter++, source, 8, moduleI, newNeurons);
* QueryConnection(network, connections, connectionCounter++, source, 9, moduleI, newNeurons);*/
// Special Output
//QueryConnection(network, connections, connectionCounter++, source, 10, moduleI+2, newNeurons);
}
//QueryConnection(network, connections, connectionCounter++, 0, 10, 2, newNeurons);
//QueryConnection(network, connections, connectionCounter++, 5, 10, 2, newNeurons);
// Connection from input to output
/*QueryConnection(network, connections, connectionCounter++, 5, 6, newNeurons);
* QueryConnection(network, connections, connectionCounter++, 5, 7, newNeurons);*/
// Connections from hidden to output
for (uint source = 0; source < hiddenCount; source++)
{
uint tmpSource = source + inputCount + outputCount;
QueryConnection(network, connections, connectionCounter++, tmpSource, 6, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, tmpSource, 7, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, tmpSource, 8, moduleI, newNeurons);
QueryConnection(network, connections, connectionCounter++, tmpSource, 9, moduleI, newNeurons);
// Special Output
//QueryConnection(network, connections, connectionCounter++, tmpSource, 10, moduleI + 2, newNeurons);
}
return(new SharpNeatLib.NeatGenome.NeatGenome(0, newNeurons, connections, (int)inputCount, (int)outputCount));
}
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];
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));
}
bool[] biasCalculated = new bool[totalHiddenCount + totalOutputCount + totalInputCount];
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)
{
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;
}
//calculate bias of target node
if (!biasCalculated[targetID])
{
coordinates[0] = 0.0f; coordinates[1] = 0.0f; coordinates[2] = target.X; coordinates[3] = target.Y;
network.ClearSignals();
network.SetInputSignals(coordinates);
((ModularNetwork)network).RecursiveActivation();
neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
biasCalculated[targetID] = true;
}
coordinates[0] = source.X;
coordinates[1] = source.Y;
coordinates[2] = target.X;
coordinates[3] = target.Y;
network.ClearSignals();
network.SetInputSignals(coordinates);
((ModularNetwork)network).RecursiveActivation();
//network.MultipleSteps(iterations);
output = network.GetOutputSignal(0);
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, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f));
}
//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;
}
public NeatGenome(uint genomeId,
NeuronGeneList neuronGeneList,
List<ModuleGene> moduleGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount,
int outputsPerPolicy) // Schrum: Added
{
this.genomeId = genomeId;
this.neuronGeneList = neuronGeneList;
this.moduleGeneList = moduleGeneList;
this.connectionGeneList = connectionGeneList;
this.inputNeuronCount = inputNeuronCount;
// Schrum: Removed (not used)
//this.inputAndBiasNeuronCount = inputNeuronCount+1;
this.outputNeuronCount = outputNeuronCount;
// Schrum: Removed (not used)
//this.inputBiasOutputNeuronCount = inputAndBiasNeuronCount + outputNeuronCount;
//this.inputBiasOutputNeuronCountMinus2 = inputBiasOutputNeuronCount-2;
// Schrum: Added
this.outputsPerPolicy = outputsPerPolicy;
Debug.Assert(connectionGeneList.IsSorted(), "ConnectionGeneList is not sorted by innovation ID");
}
/// <summary>
/// TODO
/// </summary>
/// <param name="birthGeneration">
/// The current evolution algorithm generation.
/// Assigned to the new genome as its birth generation.
/// </param>
public override NeatGenome CreateGenome(uint birthGeneration)
{
NeuronGeneList neuronGeneList = new NeuronGeneList(_inputNeuronCount + _outputNeuronCount);
NeuronGeneList inputNeuronGeneList = new NeuronGeneList(_inputNeuronCount); // includes single bias neuron.
NeuronGeneList outputNeuronGeneList = new NeuronGeneList(_outputNeuronCount);
// Create a single bias neuron.
uint biasNeuronId = _innovationIdGenerator.NextId;
if (0 != biasNeuronId)
{ // The ID generator must be reset before calling this method so that all generated genomes use the
// same innovation ID for matching neurons and structures.
throw new SharpNeatException("IdGenerator must be reset before calling CreateGenome(uint)");
}
// Note. Genes within nGeneList must always be arranged according to the following layout plan.
// Bias - single neuron. Innovation ID = 0
// Input neurons.
// Output neurons.
// Hidden neurons.
NeuronGene neuronGene = CreateNeuronGene(biasNeuronId, NodeType.Bias);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
// Create input neuron genes.
for (int i = 0; i < _inputNeuronCount; i++)
{
neuronGene = CreateNeuronGene(_innovationIdGenerator.NextId, NodeType.Input);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Create output neuron genes.
for (int i = 0; i < _outputNeuronCount; i++)
{
neuronGene = CreateNeuronGene(_innovationIdGenerator.NextId, NodeType.Output);
outputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Define all possible connections between the input and output neurons (fully interconnected).
int srcCount = inputNeuronGeneList.Count;
int tgtCount = outputNeuronGeneList.Count;
ConnectionDefinition[] connectionDefArr = new ConnectionDefinition[srcCount * tgtCount];
for (int srcIdx = 0, i = 0; srcIdx < srcCount; srcIdx++)
{
for (int tgtIdx = 0; tgtIdx < tgtCount; tgtIdx++)
{
connectionDefArr[i++] = new ConnectionDefinition(_innovationIdGenerator.NextId, srcIdx, tgtIdx);
}
}
// Shuffle the array of possible connections.
Utilities.Shuffle(connectionDefArr, _rng);
// Select connection definitions from the head of the list and convert them to real connections.
// We want some proportion of all possible connections but at least one (Connectionless genomes are not allowed).
int connectionCount = (int)Utilities.ProbabilisticRound(
(double)connectionDefArr.Length * _neatGenomeParamsComplexifying.InitialInterconnectionsProportion,
_rng);
connectionCount = Math.Max(1, connectionCount);
// Create the connection gene list and populate it.
ConnectionGeneList connectionGeneList = new ConnectionGeneList(connectionCount);
#region Add connection to bisas short connections
NeuronGene srcNeuronGeneACBias = inputNeuronGeneList[0];
if (!srcNeuronGeneACBias.TargetNeurons.Contains(outputNeuronGeneList[2].InnovationId))
{
NeuronGene tgtNeuronGeneAC = outputNeuronGeneList[2];
ConnectionGene biasGene = new ConnectionGene(_innovationIdGenerator.NextId,
srcNeuronGeneACBias.InnovationId,
tgtNeuronGeneAC.InnovationId,
Math.Abs(GenerateRandomConnectionWeight()));
connectionGeneList.Add(biasGene);
// Register connection with endpoint neurons.
srcNeuronGeneACBias.TargetNeurons.Add(biasGene.TargetNodeId);
tgtNeuronGeneAC.SourceNeurons.Add(biasGene.SourceNodeId);
}
double conW = GenerateRandomConnectionWeight();
for (int i = 5; i <= 6; i++)
{
NeuronGene srcNeuronGeneAC = inputNeuronGeneList[i];
if (!srcNeuronGeneAC.TargetNeurons.Contains(outputNeuronGeneList[2].InnovationId))
{
NeuronGene tgtNeuronGeneAC = outputNeuronGeneList[2];
ConnectionGene biasGene = new ConnectionGene(_innovationIdGenerator.NextId,
srcNeuronGeneAC.InnovationId,
tgtNeuronGeneAC.InnovationId,
-Math.Abs(conW));
connectionGeneList.Add(biasGene);
// Register connection with endpoint neurons.
srcNeuronGeneAC.TargetNeurons.Add(biasGene.TargetNodeId);
tgtNeuronGeneAC.SourceNeurons.Add(biasGene.SourceNodeId);
}
srcNeuronGeneAC = inputNeuronGeneList[i];
if (!srcNeuronGeneAC.TargetNeurons.Contains(outputNeuronGeneList[5].InnovationId))
{
NeuronGene tgtNeuronGeneAC = outputNeuronGeneList[5];
ConnectionGene biasGene = new ConnectionGene(_innovationIdGenerator.NextId,
srcNeuronGeneAC.InnovationId,
tgtNeuronGeneAC.InnovationId,
-Math.Abs(conW));
connectionGeneList.Add(biasGene);
// Register connection with endpoint neurons.
srcNeuronGeneAC.TargetNeurons.Add(biasGene.TargetNodeId);
tgtNeuronGeneAC.SourceNeurons.Add(biasGene.SourceNodeId);
}
}
#endregion
#region Add connection to bisas connection strength based on distance
for (int i = 5; i <= 6; i++)
{
NeuronGene srcNeuronGeneAC = inputNeuronGeneList[i];
if (!srcNeuronGeneAC.TargetNeurons.Contains(outputNeuronGeneList[0].InnovationId))
{
NeuronGene tgtNeuronGeneAC = outputNeuronGeneList[0];
ConnectionGene biasGene = new ConnectionGene(_innovationIdGenerator.NextId,
srcNeuronGeneAC.InnovationId,
tgtNeuronGeneAC.InnovationId,
Math.Abs(GenerateRandomConnectionWeight()));
connectionGeneList.Add(biasGene);
// Register connection with endpoint neurons.
srcNeuronGeneAC.TargetNeurons.Add(biasGene.TargetNodeId);
tgtNeuronGeneAC.SourceNeurons.Add(biasGene.SourceNodeId);
}
}
#endregion
for (int i = 0; i < connectionCount; i++)
{
ConnectionDefinition def = connectionDefArr[i];
NeuronGene srcNeuronGene = inputNeuronGeneList[def._sourceNeuronIdx];
NeuronGene tgtNeuronGene = outputNeuronGeneList[def._targetNeuronIdx];
ConnectionGene cGene = new ConnectionGene(def._innovationId,
srcNeuronGene.InnovationId,
tgtNeuronGene.InnovationId,
GenerateRandomConnectionWeight());
if (!srcNeuronGene.TargetNeurons.Contains(cGene.TargetNodeId))
{
connectionGeneList.Add(cGene);
// Register connection with endpoint neurons.
srcNeuronGene.TargetNeurons.Add(cGene.TargetNodeId);
tgtNeuronGene.SourceNeurons.Add(cGene.SourceNodeId);
}
}
// Ensure connections are sorted.
connectionGeneList.SortByInnovationId();
// Create and return the completed genome object.
return CreateGenome(_genomeIdGenerator.NextId, birthGeneration,
neuronGeneList, connectionGeneList,
_inputNeuronCount, _outputNeuronCount, false);
}
// Schrum: Added this intermediate constructor to lead to my modified one below
public NeatGenome(uint genomeId,
NeuronGeneList neuronGeneList,
List<ModuleGene> moduleGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount)
: this(genomeId, neuronGeneList, moduleGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount, outputNeuronCount) { }
// 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;
}
public NeatGenome.NeatGenome generateMultiGenomeStack(INetwork network, List<float> stackCoordinates, bool normalizeWeights, bool adaptiveNetwork, bool modulatoryNet, bool dirComm)
{
uint numberOfAgents = (uint)stackCoordinates.Count;
IActivationFunction activationFunction = HyperNEATParameters.substrateActivationFunction;
ConnectionGeneList connections = new ConnectionGeneList((int)(numberOfAgents * (InputCount * HiddenCount) + numberOfAgents * (HiddenCount * OutputCount) +
numberOfAgents * (ReceiveCount * HiddenCount) + numberOfAgents * (HiddenCount * TransCount)));
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 totalTransCount = TransCount * numberOfAgents;
uint totalReceiveCount = ReceiveCount * 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|receive|transmit
neurons = new NeuronGeneList((int)(InputCount * numberOfAgents + OutputCount * numberOfAgents + HiddenCount * numberOfAgents + ReceiveCount * numberOfAgents + TransCount * 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));
}
// set up the receive nodes
for (uint a = 0; a < totalReceiveCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount * numberOfAgents + OutputCount * numberOfAgents + HiddenCount * numberOfAgents, NeuronType.Receive, ActivationFunctionFactory.GetActivationFunction("NullFn")));
}
// set up the transmit nodes
for (uint a = 0; a < totalTransCount; a++)
{
neurons.Add(new NeuronGene(a + InputCount * numberOfAgents + OutputCount * numberOfAgents + HiddenCount * numberOfAgents + ReceiveCount * numberOfAgents, NeuronType.Transmit, activationFunction));
}
bool[] biasCalculated = new bool[totalHiddenCount + totalOutputCount + totalInputCount + totalReceiveCount + totalTransCount];
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)
{
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
case 3: sourceID = totalInputCount + totalOutputCount + totalHiddenCount + (agent * ReceiveCount) + ng.GlobalID + sourceCount; break; //Receive
case 4: sourceID = totalInputCount + totalOutputCount + totalHiddenCount + totalReceiveCount + (agent * TransCount) + ng.GlobalID + sourceCount; break; //Transmit
}
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;
case 3: targetID = totalInputCount + totalOutputCount + totalHiddenCount + (agent * ReceiveCount) + connectedNG.GlobalID + targetCout; break;
case 4: targetID = totalInputCount + totalOutputCount + totalHiddenCount + totalReceiveCount + (agent * TransCount) + connectedNG.GlobalID + targetCout; break;
}
//target node bias
if (!biasCalculated[targetID])
{
coordinates[0] = 0.0f; coordinates[1] = 0.0f; coordinates[2] = target.X; coordinates[3] = target.Y;
network.ClearSignals();
network.SetInputSignals(coordinates);
((ModularNetwork)network).RecursiveActivation();
neurons[(int)targetID].Bias = (float)(network.GetOutputSignal(1) * weightRange);
biasCalculated[targetID] = true;
}
coordinates[0] = source.X;
coordinates[1] = source.Y;
coordinates[2] = target.X;
coordinates[3] = target.Y;
network.ClearSignals();
network.SetInputSignals(coordinates);
((ModularNetwork)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, A, B, C, D, modConnection, learningRate));
}
//else
//{
// Console.WriteLine("Not connected");
//}
targetCout++;
}
sourceCount++;
}
}
}
agent++;
}
if (normalizeWeights)
{
normalizeWeightConnections(ref connections, neurons.Count);
}
//Add Direct Communication connections
if (dirComm)
{
uint numConnected = ReceiveCount / TransCount;
agent = 0;
foreach (float stackCoordinate in stackCoordinates)
{
SortedList<float, uint> closestAgents = new SortedList<float, uint>();
uint i = 0;
foreach (float otherCoordinate in stackCoordinates)
{
if (i == agent) continue;
float delta = Math.Abs(stackCoordinate - otherCoordinate);
closestAgents.Add(delta, i);
i++;
}
uint[] orderedAgents = new uint[numberOfAgents];
closestAgents.Values.CopyTo(orderedAgents, 0);
uint[] connectedAgents = new uint[numConnected];
for (uint j = 0; j < numConnected; j++)
{
connectedAgents[j] = orderedAgents[j];
}
foreach (NeuronGroup ng in neuronGroups)
{
if (ng.GroupType != 4) continue;
sourceCount = 0;
foreach (PointF source in ng.NeuronPositions)
{
uint sourceID = totalInputCount + totalOutputCount + totalHiddenCount + totalReceiveCount + (agent * TransCount) + ng.GlobalID + sourceCount;
foreach (uint connectedAgent in connectedAgents)
{
uint targetID = totalInputCount + totalOutputCount + totalHiddenCount + (connectedAgent * ReceiveCount) + (ng.GlobalID * numConnected) + agent;
connections.Add(new ConnectionGene(connectionCounter++, sourceID, targetID, 1.0));
}
sourceCount++;
}
}
agent++;
}
}
SharpNeatLib.NeatGenome.NeatGenome sng = new SharpNeatLib.NeatGenome.NeatGenome(0, neurons, connections, (int)(totalInputCount), (int)(totalOutputCount));
sng.networkAdaptable = adaptiveNetwork;
sng.networkModulatory = modulatoryNet;
return sng;
}
/// <summary>
/// Create a default minimal genome that describes a NN with the given number of inputs and outputs.
/// </summary>
/// <returns></returns>
public static IGenome CreateGenome(NeatParameters neatParameters, IdGenerator idGenerator, int inputNeuronCount, int outputNeuronCount, int outputsPerPolicy, float connectionProportion)
{
IActivationFunction actFunct;
NeuronGene neuronGene; // temp variable.
NeuronGeneList inputNeuronGeneList = new NeuronGeneList(); // includes bias neuron.
NeuronGeneList outputNeuronGeneList = new NeuronGeneList();
NeuronGeneList neuronGeneList = new NeuronGeneList();
ConnectionGeneList connectionGeneList = new ConnectionGeneList();
// IMPORTANT NOTE: The neurons must all be created prior to any connections. That way all of the genomes
// will obtain the same innovation ID's for the bias,input and output nodes in the initial population.
// Create a single bias neuron.
//TODO: DAVID proper activation function change to NULL?
actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
//neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Bias, actFunct);
neuronGene = new NeuronGene(null, idGenerator.NextInnovationId, NeuronGene.INPUT_LAYER, NeuronType.Bias, actFunct);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
// Create input neuron genes.
actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
for(int i=0; i<inputNeuronCount; i++)
{
//TODO: DAVID proper activation function change to NULL?
//neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Input, actFunct);
neuronGene = new NeuronGene(null, idGenerator.NextInnovationId, NeuronGene.INPUT_LAYER, NeuronType.Input, actFunct);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Create output neuron genes.
//actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
for(int i=0; i<outputNeuronCount; i++)
{
actFunct = ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid");
//actFunct = ActivationFunctionFactory.GetRandomActivationFunction(neatParameters);
//TODO: DAVID proper activation function
//neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Output, actFunct);
neuronGene = new NeuronGene(null, idGenerator.NextInnovationId, NeuronGene.OUTPUT_LAYER, NeuronType.Output, actFunct);
outputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Loop over all possible connections from input to output nodes and create a number of connections based upon
// connectionProportion.
foreach(NeuronGene targetNeuronGene in outputNeuronGeneList)
{
foreach(NeuronGene sourceNeuronGene in inputNeuronGeneList)
{
// Always generate an ID even if we aren't going to use it. This is necessary to ensure connections
// between the same neurons always have the same ID throughout the generated population.
uint connectionInnovationId = idGenerator.NextInnovationId;
if(Utilities.NextDouble() < connectionProportion)
{ // Ok lets create a connection.
connectionGeneList.Add( new ConnectionGene(connectionInnovationId,
sourceNeuronGene.InnovationId,
targetNeuronGene.InnovationId,
(Utilities.NextDouble() * neatParameters.connectionWeightRange ) - neatParameters.connectionWeightRange/2.0)); // Weight 0 +-5
}
}
}
// Don't create any hidden nodes at this point. Fundamental to the NEAT way is to start minimally!
// Schrum: Added outputsPerPolicy: If outputsPerPolicy == outputNeuronCount, then behaves like default NEAT
return new NeatGenome(idGenerator.NextGenomeId, neuronGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount, outputsPerPolicy);
}
private NeatGenome.NeatGenome generateHomogeneousGenomeES(INetwork network, bool normalizeWeights, bool adaptiveNetwork, bool modulatoryNet)
{
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);
EvolvableSubstrate se = new EvolvableSubstrate();
se.generateConnections(inputNeuronPositions, outputNeuronPositions, network,
HyperNEATParameters.initialRes,
(float)HyperNEATParameters.varianceThreshold,
(float)HyperNEATParameters.bandingThreshold,
(int)HyperNEATParameters.ESIterations,
(float)HyperNEATParameters.divisionThreshold,
HyperNEATParameters.maximumRes,
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));
}
bool[] visited = new bool[neurons.Count];
List<uint> nodeList = new List<uint>();
bool[] connectedToInput = new bool[neurons.Count];
bool[] isOutput = new bool[neurons.Count];
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;
}
