/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public ModuleGene(ModuleGene copyFrom)
{
this.InnovationId = copyFrom.InnovationId;
this.Function = copyFrom.Function;
this.InputIds = copyFrom.InputIds;
this.OutputIds = copyFrom.OutputIds;
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public ModuleGene(ModuleGene copyFrom)
{
this.InnovationId = copyFrom.InnovationId;
this.Function = copyFrom.Function;
this.InputIds = copyFrom.InputIds;
this.OutputIds = copyFrom.OutputIds;
}
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);
}
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);
}