/// <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); }