private static void WriteConnection(XmlElement xmlConnections, ConnectionGene connectionGene)
{
XmlElement xmlConnection = XmlUtilities.AddElement(xmlConnections, "connection");
XmlUtilities.AddAttribute(xmlConnection, "src-id", connectionGene.SourceNeuronId.ToString() );
XmlUtilities.AddAttribute(xmlConnection, "tgt-id", connectionGene.TargetNeuronId.ToString());
XmlUtilities.AddAttribute(xmlConnection, "weight", connectionGene.Weight.ToString("R"));
}
public NewNeuronGeneStruct( NeuronGene newNeuronGene,
ConnectionGene newConnectionGene_Input,
ConnectionGene newConnectionGene_Output)
{
this.NewNeuronGene = newNeuronGene;
this.NewConnectionGene_Input = newConnectionGene_Input;
this.NewConnectionGene_Output = newConnectionGene_Output;
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public ConnectionGene(ConnectionGene copyFrom)
{
this.innovationId = copyFrom.innovationId;
this.sourceNeuronId = copyFrom.sourceNeuronId;
this.targetNeuronId = copyFrom.targetNeuronId;
// this.enabled = copyFrom.enabled;
this.weight = copyFrom.weight;
this.fixedWeight = copyFrom.fixedWeight;
}
private void MutateConnectionWeight(ConnectionGene connectionGene, NeatParameters np, ConnectionMutationParameterGroup paramGroup)
{
switch(paramGroup.PerturbationType)
{
case ConnectionPerturbationType.JiggleEven:
{
connectionGene.Weight += (Utilities.NextDouble()*2-1.0) * paramGroup.PerturbationFactor;
// Cap the connection weight. Large connections weights reduce the effectiveness of the search.
connectionGene.Weight = Math.Max(connectionGene.Weight, -np.connectionWeightRange/2.0);
connectionGene.Weight = Math.Min(connectionGene.Weight, np.connectionWeightRange/2.0);
break;
}
case ConnectionPerturbationType.JiggleND:
{
connectionGene.Weight += RandLib.gennor(0, paramGroup.Sigma);
// Cap the connection weight. Large connections weights reduce the effectiveness of the search.
connectionGene.Weight = Math.Max(connectionGene.Weight, -np.connectionWeightRange/2.0);
connectionGene.Weight = Math.Min(connectionGene.Weight, np.connectionWeightRange/2.0);
break;
}
case ConnectionPerturbationType.Reset:
{
// TODO: Precalculate connectionWeightRange / 2.
connectionGene.Weight = (Utilities.NextDouble()*np.connectionWeightRange) - np.connectionWeightRange/2.0;
break;
}
default:
{
throw new Exception("Unexpected ConnectionPerturbationType");
}
}
}
private void RemoveSimpleNeuron(uint neuronId, EvolutionAlgorithm ea)
{
// Create new connections that connect all of the incoming and outgoing neurons
// that currently exist for the simple neuron.
NeuronConnectionLookup lookup = (NeuronConnectionLookup)neuronConnectionLookupTable[neuronId];
foreach(ConnectionGene incomingConnection in lookup.incomingList)
{
foreach(ConnectionGene outgoingConnection in lookup.outgoingList)
{
if(TestForExistingConnection(incomingConnection.SourceNeuronId, outgoingConnection.TargetNeuronId))
{ // Connection already exists.
continue;
}
// Test for matching connection within NewConnectionGeneTable.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(incomingConnection.SourceNeuronId,
outgoingConnection.TargetNeuronId);
ConnectionGene existingConnection = (ConnectionGene)ea.NewConnectionGeneTable[connectionKey];
ConnectionGene newConnectionGene;
if(existingConnection==null)
{ // No matching connection found. Create a connection with a new ID.
newConnectionGene = new ConnectionGene(ea.NextInnovationId,
incomingConnection.SourceNeuronId,
outgoingConnection.TargetNeuronId,
(Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange) - ea.NeatParameters.connectionWeightRange/2.0);
// Register the new ID with NewConnectionGeneTable.
ea.NewConnectionGeneTable.Add(connectionKey, newConnectionGene);
// Add the new gene to the genome.
connectionGeneList.Add(newConnectionGene);
}
else
{ // Matching connection found. Re-use its ID.
newConnectionGene = new ConnectionGene(existingConnection.InnovationId,
incomingConnection.SourceNeuronId,
outgoingConnection.TargetNeuronId,
(Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange) - ea.NeatParameters.connectionWeightRange/2.0);
// Add the new gene to the genome. Use InsertIntoPosition() to ensure we don't break the sort
// order of the connection genes.
connectionGeneList.InsertIntoPosition(newConnectionGene);
}
}
}
// Delete the old connections.
foreach(ConnectionGene incomingConnection in lookup.incomingList)
connectionGeneList.Remove(incomingConnection);
foreach(ConnectionGene outgoingConnection in lookup.outgoingList)
{
// Filter out recurrent connections - they will have already been
// deleted in the loop through 'lookup.incomingList'.
if(outgoingConnection.TargetNeuronId != neuronId)
connectionGeneList.Remove(outgoingConnection);
}
// Delete the simple neuron - it no longer has any connections to or from it.
neuronGeneList.Remove(neuronId);
}
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);
}
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);
}
/// <summary>
/// Add a new node to the Genome. We do this by removing a connection at random and inserting
/// a new node and two new connections that make the same circuit as the original connection.
///
/// This way the new node is properly integrated into the network from the outset.
/// </summary>
/// <param name="ea"></param>
private void Mutate_AddNode(EvolutionAlgorithm ea)
{
if(connectionGeneList.Count==0)
return;
// Select a connection at random.
int connectionToReplaceIdx = (int)Math.Floor(Utilities.NextDouble() * connectionGeneList.Count);
ConnectionGene connectionToReplace = connectionGeneList[connectionToReplaceIdx];
// Delete the existing connection. JOEL: Why delete old connection?
//connectionGeneList.RemoveAt(connectionToReplaceIdx);
// Check if this connection has already been split on another genome. If so then we should re-use the
// neuron ID and two connection ID's so that matching structures within the population maintain the same ID.
object existingNeuronGeneStruct = ea.NewNeuronGeneStructTable[connectionToReplace.InnovationId];
NeuronGene newNeuronGene;
ConnectionGene newConnectionGene1;
ConnectionGene newConnectionGene2;
IActivationFunction actFunct;
if(existingNeuronGeneStruct==null)
{ // No existing matching structure, so generate some new ID's.
//TODO: DAVID proper random activation function
// Replace connectionToReplace with two new connections and a neuron.
actFunct=ActivationFunctionFactory.GetRandomActivationFunction(ea.NeatParameters);
//newNeuronGene = new NeuronGene(ea.NextInnovationId, NeuronType.Hidden, actFunct);
newNeuronGene = new NeuronGene(null, ea.NextInnovationId, (neuronGeneList.GetNeuronById(connectionToReplace.SourceNeuronId).Layer + neuronGeneList.GetNeuronById(connectionToReplace.TargetNeuronId).Layer) / 2.0, NeuronType.Hidden, actFunct);
newConnectionGene1 = new ConnectionGene(ea.NextInnovationId, connectionToReplace.SourceNeuronId, newNeuronGene.InnovationId, 1.0);
newConnectionGene2 = new ConnectionGene(ea.NextInnovationId, newNeuronGene.InnovationId, connectionToReplace.TargetNeuronId, connectionToReplace.Weight);
// Register the new ID's with NewNeuronGeneStructTable.
ea.NewNeuronGeneStructTable.Add(connectionToReplace.InnovationId,
new NewNeuronGeneStruct(newNeuronGene, newConnectionGene1, newConnectionGene2));
}
else
{ // An existing matching structure has been found. Re-use its ID's
//TODO: DAVID proper random activation function
// Replace connectionToReplace with two new connections and a neuron.
actFunct = ActivationFunctionFactory.GetRandomActivationFunction(ea.NeatParameters);
NewNeuronGeneStruct tmpStruct = (NewNeuronGeneStruct)existingNeuronGeneStruct;
//newNeuronGene = new NeuronGene(tmpStruct.NewNeuronGene.InnovationId, NeuronType.Hidden, actFunct);
newNeuronGene = new NeuronGene(null, tmpStruct.NewNeuronGene.InnovationId, tmpStruct.NewNeuronGene.Layer, NeuronType.Hidden, actFunct);
newConnectionGene1 = new ConnectionGene(tmpStruct.NewConnectionGene_Input.InnovationId, connectionToReplace.SourceNeuronId, newNeuronGene.InnovationId, 1.0);
newConnectionGene2 = new ConnectionGene(tmpStruct.NewConnectionGene_Output.InnovationId, newNeuronGene.InnovationId, connectionToReplace.TargetNeuronId, connectionToReplace.Weight);
}
// Add the new genes to the genome.
neuronGeneList.Add(newNeuronGene);
connectionGeneList.InsertIntoPosition(newConnectionGene1);
connectionGeneList.InsertIntoPosition(newConnectionGene2);
}
public CorrelationItem(CorrelationItemType correlationItemType, ConnectionGene connectionGene1, ConnectionGene connectionGene2)
{
this.correlationItemType = correlationItemType;
this.connectionGene1 = connectionGene1;
this.connectionGene2 = connectionGene2;
}
// Schrum: Simple form of Module Mutation, MM(P)
private void Module_Mutation_Previous(EvolutionAlgorithm ea)
{
// Push all output neurons together
this.neuronGeneList.SortByNeuronOrder();
int numModules = this.outputNeuronCount / this.outputsPerPolicy; // Should evenly divide
int randomModule = Utilities.Next(numModules);
// Because outputs come after inputs.
// Although list is 0-indexed, the +1 is needed because the bias does not count as an input
double outputLayer = neuronGeneList[1 + inputNeuronCount].Layer;
// Create the new module
for (int i = 0; i < outputsPerPolicy; i++)
{
IActivationFunction outputActFunction = ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid");
NeuronGene newNeuronGene = new NeuronGene(null, ea.NextInnovationId, outputLayer, NeuronType.Output, outputActFunction);
neuronGeneList.Add(newNeuronGene);
// Link to the new neuron: bias, then inputs, then appropriate module, then neuron within that module
uint sourceNeuron = neuronGeneList[1 + inputNeuronCount + (randomModule * outputsPerPolicy) + i].InnovationId;
ConnectionGene connection = new ConnectionGene(ea.NextInnovationId, sourceNeuron, newNeuronGene.InnovationId, 1.0);
connectionGeneList.InsertIntoPosition(connection);
this.outputNeuronCount++; // Increase number of outputs
}
// Schrum: Debugging
//Console.WriteLine("MM(P): outputNeuronCount=" + outputNeuronCount);
// Schrum: More debugging
/*
this.neuronGeneList.SortByInnovationId();
XmlDocument doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)this);
FileInfo oFileInfo = new FileInfo("MMPNet.xml");
doc.Save(oFileInfo.FullName);
*/
}
public NewConnectionGeneStruct(NeatGenome.NeatGenome owningGenome, ConnectionGene newConnectionGene)
{
this.OwningGenome = owningGenome;
this.NewConnectionGene = newConnectionGene;
}
private void RemoveSimpleNeuron(long neuronId, NeatParameters neatParameters, IdGenerator idGen, Hashtable NewConnectionGeneTable)
{
WINManager win = WINManager.SharedWIN;
//WINNode node;
WINConnection connection;
// Create new connections that connect all of the incoming and outgoing neurons
// that currently exist for the simple neuron.
NeuronConnectionLookup lookup = (NeuronConnectionLookup)neuronConnectionLookupTable[neuronId];
foreach(ConnectionGene incomingConnection in lookup.incomingList)
{
foreach(ConnectionGene outgoingConnection in lookup.outgoingList)
{
if(TestForExistingConnection(incomingConnection.SourceNeuronId, outgoingConnection.TargetNeuronId))
{ // Connection already exists.
continue;
}
// Test for matching connection within NewConnectionGeneTable.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(incomingConnection.SourceNeuronId,
outgoingConnection.TargetNeuronId);
ConnectionGene existingConnection = (ConnectionGene)NewConnectionGeneTable[connectionKey];
ConnectionGene newConnectionGene;
if(existingConnection==null)
{
// No matching connection found. Create a connection with a new ID.
//create our newest conncetion, noting the source,target and weight
connection = win.createWINConnection(
WINConnection.ConnectionWithProperties(incomingConnection.SourceNeuronId, outgoingConnection.TargetNeuronId), idGen);
// Create a new connection with a new ID and add it to the Genome.
newConnectionGene = new ConnectionGene(connection.UniqueID, connection.SourceID, connection.TargetID,
(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0
);
//newConnectionGene = new ConnectionGene(idGen.NextInnovationId,
// incomingConnection.SourceNeuronId,
// outgoingConnection.TargetNeuronId,
// (Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange/2.0);
// Register the new ID with NewConnectionGeneTable.
NewConnectionGeneTable.Add(connectionKey, newConnectionGene);
// Add the new gene to the genome.
connectionGeneList.Add(newConnectionGene);
}
else
{
//WIN should acknowledge change in individual here
// Matching connection found. Re-use its ID.
newConnectionGene = new ConnectionGene(existingConnection.InnovationId,
incomingConnection.SourceNeuronId,
outgoingConnection.TargetNeuronId,
(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange/2.0);
// Add the new gene to the genome. Use InsertIntoPosition() to ensure we don't break the sort
// order of the connection genes.
connectionGeneList.InsertIntoPosition(newConnectionGene);
}
}
}
// Delete the old connections.
foreach(ConnectionGene incomingConnection in lookup.incomingList)
connectionGeneList.Remove(incomingConnection);
foreach(ConnectionGene outgoingConnection in lookup.outgoingList)
{
// Filter out recurrent connections - they will have already been
// deleted in the loop through 'lookup.incomingList'.
if(outgoingConnection.TargetNeuronId != neuronId)
connectionGeneList.Remove(outgoingConnection);
}
// Delete the simple neuron - it no longer has any connections to or from it.
neuronGeneList.Remove(neuronId);
}
/// <summary>
/// Add a new node to the Genome. We do this by removing a connection at random and inserting
/// a new node and two new connections that make the same circuit as the original connection.
///
/// This way the new node is properly integrated into the network from the outset.
/// </summary>
/// <param name="ea"></param>
private void Mutate_AddNode(NeatParameters neatParameters, IdGenerator idGen, Hashtable NewNeuronGeneStructTable)
{
if(connectionGeneList.Count==0)
return;
// Select a connection at random.
int connectionToReplaceIdx = (int)Math.Floor(Utilities.NextDouble() * connectionGeneList.Count);
ConnectionGene connectionToReplace = connectionGeneList[connectionToReplaceIdx];
// Delete the existing connection. JOEL: Why delete old connection?
//connectionGeneList.RemoveAt(connectionToReplaceIdx);
// Check if this connection has already been split on another genome. If so then we should re-use the
// neuron ID and two connection ID's so that matching structures within the population maintain the same ID.
object existingNeuronGeneStruct = NewNeuronGeneStructTable[connectionToReplace.InnovationId];
NeuronGene newNeuronGene;
ConnectionGene newConnectionGene1;
ConnectionGene newConnectionGene2;
IActivationFunction actFunct;
WINManager win = WINManager.SharedWIN;
WINNode node;
WINConnection connection;
if(existingNeuronGeneStruct==null)
{ // No existing matching structure, so generate some new ID's.
//TODO: DAVID proper random activation function
// Replace connectionToReplace with two new connections and a neuron.
actFunct=ActivationFunctionFactory.GetRandomActivationFunction(neatParameters);
//newNeuronGene = new NeuronGene(ea.NextInnovationId, NeuronType.Hidden, actFunct);
//we should be making a call to WIN anytime we call for a new innovationID
//here we create a new node, and two new connections
//we don't send in a genomeID here, so we get it set for us!
node = win.createWINNode(new PropertyObject() { { WINNode.SNodeString, NeuronType.Hidden.ToString()}});
newNeuronGene = new NeuronGene(null, node.UniqueID, (neuronGeneList.GetNeuronById(connectionToReplace.SourceNeuronId).Layer + neuronGeneList.GetNeuronById(connectionToReplace.TargetNeuronId).Layer) / 2.0, NeuronType.Hidden, actFunct);
//we don't send in any uniqueIDs so they are generated for us, and we update our idGen object
connection = win.createWINConnection(WINConnection.ConnectionWithProperties(connectionToReplace.SourceNeuronId, newNeuronGene.InnovationId), idGen);
newConnectionGene1 = new ConnectionGene(connection.UniqueID, connection.SourceID, connection.TargetID , 1.0);
//we don't send in any uniqueIDs so they are generated for us, and we update our idGen object
connection = win.createWINConnection(WINConnection.ConnectionWithProperties(newNeuronGene.InnovationId, connectionToReplace.TargetNeuronId), idGen);
newConnectionGene2 = new ConnectionGene(connection.UniqueID, connection.SourceID, connection.TargetID, connectionToReplace.Weight);
// Register the new ID's with NewNeuronGeneStructTable.
NewNeuronGeneStructTable.Add(connectionToReplace.InnovationId,
new NewNeuronGeneStruct(newNeuronGene, newConnectionGene1, newConnectionGene2));
}
else
{ // An existing matching structure has been found. Re-use its ID's
//Since we don't call idGen.nextinnovationID, we don't have to create anything new
//however, we need to note the change in weights override previous weight values
//this should be documented in WIN - either explicitly (calling a function to note changes) or implicitly (by scanning the changes in a saved genome)
//Probably explicitly, calling a mutate function for a SingleStep (since we are in the SingleStep process of creating new individuals)
//TODO: DAVID proper random activation function
// Replace connectionToReplace with two new connections and a neuron.
actFunct = ActivationFunctionFactory.GetRandomActivationFunction(neatParameters);
NewNeuronGeneStruct tmpStruct = (NewNeuronGeneStruct)existingNeuronGeneStruct;
//newNeuronGene = new NeuronGene(tmpStruct.NewNeuronGene.InnovationId, NeuronType.Hidden, actFunct);
newNeuronGene = new NeuronGene(null, tmpStruct.NewNeuronGene.InnovationId, tmpStruct.NewNeuronGene.Layer, NeuronType.Hidden, actFunct);
newConnectionGene1 = new ConnectionGene(tmpStruct.NewConnectionGene_Input.InnovationId, connectionToReplace.SourceNeuronId, newNeuronGene.InnovationId, 1.0);
newConnectionGene2 = new ConnectionGene(tmpStruct.NewConnectionGene_Output.InnovationId, newNeuronGene.InnovationId, connectionToReplace.TargetNeuronId, connectionToReplace.Weight);
}
// Add the new genes to the genome.
neuronGeneList.Add(newNeuronGene);
connectionGeneList.InsertIntoPosition(newConnectionGene1);
connectionGeneList.InsertIntoPosition(newConnectionGene2);
}
private void Mutate_AddModule(NeatParameters neatParameters, IdGenerator idGen, Hashtable NewConnectionGeneTable)
{
// 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();
WINManager win = WINManager.SharedWIN;
WINNode node;
WINConnection connection;
// 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<long> inputDummies = new List<long>(func.InputCount);
for (int i = 0; i < func.InputCount; i++) {
//we are calling nextinnovationID, this is the place for WIN!
//in reality, win should know the activation function as well, but that's not currently implemented
//here we create a new node, and two new connections
//we don't send in a genomeID here, so we get it set for us!
//do we need to inform it of the activation function? I think so?
node = win.createWINNode(new PropertyObject() { { WINNode.SNodeString, NeuronType.Hidden.ToString()}});
NeuronGene newNeuronGene = new NeuronGene(node.UniqueID, NeuronType.Hidden, inputFunction);
neuronGeneList.Add(newNeuronGene);
long sourceId = potentialInputs[Utilities.Next(potentialInputs.Count)].InnovationId;
long targetId = newNeuronGene.InnovationId;
inputDummies.Add(targetId);
//aha! we must call the innovationID again, we check against win
//we don't send in any uniqueIDs so they are generated for us, and we update our idGen object
connection = win.createWINConnection(
WINConnection.ConnectionWithProperties(sourceId, targetId), idGen);
// Create a new connection with a new ID and add it to the Genome.
ConnectionGene newConnectionGene = new ConnectionGene(connection.UniqueID, connection.SourceID, connection.TargetID,
(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0
);
// Register the new connection with NewConnectionGeneTable.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(sourceId, targetId);
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<long> outputDummies = new List<long>(func.OutputCount);
for (int i = 0; i < func.OutputCount; i++) {
node = win.createWINNode(new PropertyObject() { { WINNode.SNodeString, NeuronType.Hidden.ToString() } });
NeuronGene newNeuronGene = new NeuronGene(node.UniqueID, NeuronType.Hidden, outputFunction);
neuronGeneList.Add(newNeuronGene);
long sourceId = newNeuronGene.InnovationId;
long targetId = potentialOutputs[Utilities.Next(potentialOutputs.Count)].InnovationId;
outputDummies.Add(sourceId);
connection = win.createWINConnection(
WINConnection.ConnectionWithProperties(sourceId, targetId), idGen);
// Create a new connection with a new ID and add it to the Genome.
ConnectionGene newConnectionGene = new ConnectionGene(connection.UniqueID, connection.SourceID, connection.TargetID,
(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0
);
//new ConnectionGene(idGen.NextInnovationId, sourceId, targetId,
//(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0);
// Register the new connection with NewConnectionGeneTable.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(sourceId, targetId);
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.
//TODO: Paul check win conditions here
//this is confusing from a WIN perspective, I don't know if I'm going to support modules
//I can create a generic NextInnovationID object, but don't know if it's worth it
ModuleGene newModule = new ModuleGene(idGen.NextInnovationId, func, inputDummies, outputDummies);
moduleGeneList.Add(newModule);
}
/// <summary>
/// Add a new node to the Genome. We do this by removing a connection at random and inserting
/// a new node and two new connections that make the same circuit as the original connection.
///
/// This way the new node is properly integrated into the network from the outset.
/// </summary>
/// <param name="ea"></param>
private void Mutate_AddNode(EvolutionAlgorithm ea)
{
if(connectionGeneList.Count==0)
return;
for (int attempts = 0; attempts < 5; attempts++)
{
// Select a connection at random.
int connectionToReplaceIdx = (int)Math.Floor(Utilities.NextDouble() * connectionGeneList.Count);
ConnectionGene connectionToReplace = connectionGeneList[connectionToReplaceIdx];
// Delete the existing connection. JOEL: Why delete old connection?
connectionGeneList.RemoveAt(connectionToReplaceIdx);
// Check if this connection has already been split on another genome. If so then we should re-use the
// neuron ID and two connection ID's so that matching structures within the population maintain the same ID.
object existingNeuronGeneStruct = ea.NewNeuronGeneStructTable[connectionToReplace.InnovationId];
NeuronGene newNeuronGene;
ConnectionGene newConnectionGene1;
ConnectionGene newConnectionGene2;
IActivationFunction actFunct;
// JUSTIN: Calculate the layer for this new neuron as halfway between the source and the target neuron layers
float sourceLayer = neuronGeneList.GetNeuronById(connectionToReplace.SourceNeuronId).Layer;
float targetLayer = neuronGeneList.GetNeuronById(connectionToReplace.TargetNeuronId).Layer;
float newLayer = ((sourceLayer + targetLayer) / 2);
if (existingNeuronGeneStruct == null)
{ // No existing matching structure, so generate some new ID's.
//TODO: DAVID proper random activation function
// Replace connectionToReplace with two new connections and a neuron.
// JUSTIN: If using neatBrain, then do NOT get a random actv. function!!
actFunct = ea.neatBrain ? ActivationFunctionFactory.GetActivationFunction(HyperNEATParameters.substrateActivationFunction.FunctionId) : ActivationFunctionFactory.GetRandomActivationFunction(ea.NeatParameters);
newNeuronGene = new NeuronGene(ea.NextInnovationId, NeuronType.Hidden, actFunct, newLayer);
newConnectionGene1 = new ConnectionGene(ea.NextInnovationId, connectionToReplace.SourceNeuronId, newNeuronGene.InnovationId, 1.0);
newConnectionGene2 = new ConnectionGene(ea.NextInnovationId, newNeuronGene.InnovationId, connectionToReplace.TargetNeuronId, connectionToReplace.Weight);
// Register the new ID's with NewNeuronGeneStructTable.
ea.NewNeuronGeneStructTable.Add(connectionToReplace.InnovationId,
new NewNeuronGeneStruct(newNeuronGene, newConnectionGene1, newConnectionGene2));
}
else
{ // An existing matching structure has been found. Re-use its ID's
//DAVID: Trying to add a node where one already exists, reject, is this good?
if (neuronGeneList.GetNeuronById(((NewNeuronGeneStruct)existingNeuronGeneStruct).NewNeuronGene.InnovationId) != null)
{
continue;
}
//TODO: DAVID proper random activation function
// Replace connectionToReplace with two new connections and a neuron.
// JUSTIN: If using neatBrain, then do NOT get a random actv. function!!
actFunct = ea.neatBrain ? ActivationFunctionFactory.GetActivationFunction(HyperNEATParameters.substrateActivationFunction.FunctionId) : ActivationFunctionFactory.GetRandomActivationFunction(ea.NeatParameters);
NewNeuronGeneStruct tmpStruct = (NewNeuronGeneStruct)existingNeuronGeneStruct;
newNeuronGene = new NeuronGene(tmpStruct.NewNeuronGene.InnovationId, NeuronType.Hidden, actFunct, newLayer);
newConnectionGene1 = new ConnectionGene(tmpStruct.NewConnectionGene_Input.InnovationId, connectionToReplace.SourceNeuronId, newNeuronGene.InnovationId, (Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange) - ea.NeatParameters.connectionWeightRange / 2.0);
newConnectionGene2 = new ConnectionGene(tmpStruct.NewConnectionGene_Output.InnovationId, newNeuronGene.InnovationId, connectionToReplace.TargetNeuronId, (Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange / 8.0) - ea.NeatParameters.connectionWeightRange / 16.0);
}
// Add the new genes to the genome.
//Debug.Assert();
neuronGeneList.Add(newNeuronGene);
connectionGeneList.InsertIntoPosition(newConnectionGene1);
connectionGeneList.InsertIntoPosition(newConnectionGene2);
break;
}
}
private void BuildNeuronConnectionLookupTable_NewOutgoingConnection(uint neuronId, ConnectionGene connectionGene)
{
// Is this neuron already known to the lookup table?
NeuronConnectionLookup lookup = (NeuronConnectionLookup)neuronConnectionLookupTable[neuronId];
if(lookup==null)
{ // Creae a new lookup entry for this neuron Id.
lookup = new NeuronConnectionLookup();
lookup.neuronGene = (NeuronGene)neuronGeneTable[neuronId];
neuronConnectionLookupTable.Add(neuronId, lookup);
}
// Register the connection with the NeuronConnectionLookup object.
lookup.outgoingList.Add(connectionGene);
}
/// <summary>
/// Adds a connection to the list that will eventually be copied into a child of this genome during sexual reproduction.
/// A helper function that is only called by CreateOffspring_Sexual_ProcessCorrelationItem().
/// </summary>
/// <param name="connectionGene">Specifies the connection to add to this genome.</param>
/// <param name="overwriteExisting">If there is already a connection from the same source to the same target,
/// that connection is replaced when overwriteExisting is true and remains (no change is made) when overwriteExisting is false.</param>
private void CreateOffspring_Sexual_AddGene(ConnectionGene connectionGene, bool overwriteExisting)
{
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(
connectionGene.SourceNeuronId,
connectionGene.TargetNeuronId);
// Check if a matching gene has already been added.
object oIdx = newConnectionGeneTable[connectionKey];
if(oIdx==null)
{ // No matching gene has been added.
// Register this new gene with the newConnectionGeneTable - store its index within newConnectionGeneList.
newConnectionGeneTable[connectionKey] = newConnectionGeneList.Count;
// Add the gene to the list.
newConnectionGeneList.Add(connectionGene);
}
else if(overwriteExisting)
{
// Overwrite the existing matching gene with this one. In fact only the weight value differs between two
// matching connection genes, so just overwrite the existing genes weight value.
// Remember that we stored the gene's index in newConnectionGeneTable. So use it here.
newConnectionGeneList[(int)oIdx].Weight = connectionGene.Weight;
}
}
// Schrum: Module Mutation Random creates a new module with
// completely random incoming links.
private void Module_Mutation_Random(EvolutionAlgorithm ea)
{
// Push all output neurons together
this.neuronGeneList.SortByNeuronOrder();
int numModules = this.outputNeuronCount / this.outputsPerPolicy; // Should evenly divide
int randomModule = Utilities.Next(numModules);
// Because outputs come after inputs.
// Although list is 0-indexed, the +1 is needed because the bias does not count as an input
double outputLayer = neuronGeneList[1 + inputNeuronCount].Layer;
// Create the new module one neuron per loop iteration
for (int i = 0; i < outputsPerPolicy; i++)
{
// The activation function for the output layer
IActivationFunction outputActFunction = ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid");
NeuronGene newNeuronGene = new NeuronGene(null, ea.NextInnovationId, outputLayer, NeuronType.Output, outputActFunction);
neuronGeneList.Add(newNeuronGene);
// Count links to random output neuron: bias, then inputs, then random module, then neuron within that module
uint randomModuleInnovation = neuronGeneList[1 + inputNeuronCount + (randomModule * outputsPerPolicy) + i].InnovationId;
int numIncoming = 0;
foreach (ConnectionGene cg in this.ConnectionGeneList)
{
// Count the link
if (cg.TargetNeuronId == randomModuleInnovation)
numIncoming++;
}
// Give the new module (up to) the same number of links as some other module
for (int j = 0; j < numIncoming; j++) // Will always create ay least one link
{
uint randomSource = NeuronGeneList[Utilities.Next(NeuronGeneList.Count)].InnovationId;
// Magic equation stolen from Mutate_AddConnection below
double randomWeight = (Utilities.NextDouble() * ea.NeatParameters.connectionWeightRange/4.0) - ea.NeatParameters.connectionWeightRange/8.0;
if (!TestForExistingConnection(randomSource, newNeuronGene.InnovationId)) // Only create each connection once
{
ConnectionGene connection = new ConnectionGene(ea.NextInnovationId, randomSource, newNeuronGene.InnovationId, randomWeight);
connectionGeneList.InsertIntoPosition(connection);
}
}
this.outputNeuronCount++; // Increase number of outputs
}
}
// Schrum: Module Mutation Duplicate creates a new module with
// links copying those of another module.
private void Module_Mutation_Duplicate(EvolutionAlgorithm ea)
{
// Push all output neurons together
this.neuronGeneList.SortByNeuronOrder();
int numModules = this.outputNeuronCount / this.outputsPerPolicy; // Should evenly divide
int randomModule = Utilities.Next(numModules); // Duplicate this module
// Because outputs come after inputs.
// Although list is 0-indexed, the +1 is needed because the bias does not count as an input
double outputLayer = neuronGeneList[1 + inputNeuronCount].Layer;
// Create the new module one neuron per loop iteration
for (int i = 0; i < outputsPerPolicy; i++)
{
// The activation function for the output layer
IActivationFunction outputActFunction = ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid");
NeuronGene newNeuronGene = new NeuronGene(null, ea.NextInnovationId, outputLayer, NeuronType.Output, outputActFunction);
neuronGeneList.Add(newNeuronGene);
uint randomModuleInnovation = neuronGeneList[1 + inputNeuronCount + (randomModule * outputsPerPolicy) + i].InnovationId;
// Copy each connection to the new module neuron
int originalLength = ConnectionGeneList.Count; // Don't need to check the newly added connections
for (int j = 0; j < originalLength; j++)
{
ConnectionGene cg = ConnectionGeneList[j];
if (cg.TargetNeuronId == randomModuleInnovation)
{
// Copy the link
ConnectionGene connection = new ConnectionGene(ea.NextInnovationId, cg.SourceNeuronId, newNeuronGene.InnovationId, cg.Weight);
connectionGeneList.InsertIntoPosition(connection);
}
}
this.outputNeuronCount++; // Increase number of outputs
}
}
public CorrelationItem(CorrelationItemType correlationItemType, ConnectionGene connectionGene1, ConnectionGene connectionGene2)
{
this.correlationItemType = correlationItemType;
this.connectionGene1 = connectionGene1;
this.connectionGene2 = connectionGene2;
}
