public void RunBest()
{
Time.timeScale = 1;
NeatGenome genome = null;
// Try to load the genome from the XML document.
try
{
using (XmlReader xr = XmlReader.Create(champFileSavePath))
genome = NeatGenomeXmlIO.ReadCompleteGenomeList(xr, false, (NeatGenomeFactory)experiment.CreateGenomeFactory())[0];
}
catch (Exception e1)
{
Debug.Log(" Error loading genome from file! Loading aborted.\n" + e1.Message);
return;
}
// Genomedecoders convert genomes to pheonomes
var genomeDecoder = experiment.CreateGenomeDecoder();
// Decode into a network
var phenome = genomeDecoder.Decode(genome);
GameObject obj = Instantiate(Unit, transform.position, Unit.transform.rotation) as GameObject;
obj.AddComponent <Creature>();
UnitController controller = obj.GetComponent <UnitController>();
ControllerMap.Add(phenome, controller);
controller.Activate(phenome);
}
public void RunBest()
{
Time.timeScale = 1;
NeatGenome genome = null;
// Try to load the genome from the XML document.
try
{
using (XmlReader xr = XmlReader.Create(champFileSavePath))
genome = NeatGenomeXmlIO.ReadCompleteGenomeList(xr, false, (NeatGenomeFactory)experiment.CreateGenomeFactory())[0];
}
catch (Exception e1)
{
// print(champFileLoadPath + " Error loading genome from file!\nLoading aborted.\n"
// + e1.Message + "\nJoe: " + champFileLoadPath);
return;
}
// Get a genome decoder that can convert genomes to phenomes.
var genomeDecoder = experiment.CreateGenomeDecoder();
// Decode the genome into a phenome (neural network).
var phenome = genomeDecoder.Decode(genome);
GameObject obj = Instantiate(Unit, Unit.transform.position, Unit.transform.rotation) as GameObject;
UnitController controller = obj.GetComponent <UnitController>();
ControllerMap.Add(phenome, controller);
controller.Activate(phenome);
}
/// <summary>
/// Refresh/update the view with the provided genome.
/// </summary>
public override void RefreshView(object genome)
{
NeatGenome neatGenome = genome as NeatGenome;
if (null == neatGenome)
{
return;
}
// Decode genome.
IBlackBox box = _genomeDecoder.Decode(neatGenome);
// Update plot points.
double x = _xMin;
for (int i = 0; i < _sampleCount; i++, x += _xIncr)
{
box.ResetState();
box.InputSignalArray[0] = x;
box.Activate();
_plotPointListResponse[i].Y = box.OutputSignalArray[0];
}
// Trigger graph to redraw.
zed.AxisChange();
Refresh();
}
private bool TryGetConnectionInner(NeatGenome <T> parent, out DirectedConnection conn, out int insertIdx)
{
int inputCount = _metaNeatGenome.InputNodeCount;
int outputCount = _metaNeatGenome.OutputNodeCount;
int hiddenCount = parent.HiddenNodeIdArray.Length;
// Select a source node at random.
// Note. this can be any node (input, output or hidden).
int totalNodeCount = parent.MetaNeatGenome.InputOutputNodeCount + hiddenCount;
int srcId = GetNodeIdFromIndex(parent, _rng.Next(totalNodeCount));
// Select a target node at random.
// Note. This cannot be an input node (so must be a hidden or output node).
int outputHiddenCount = outputCount + hiddenCount;
int tgtId = GetNodeIdFromIndex(parent, inputCount + _rng.Next(outputHiddenCount));
// Test if the chosen connection already exists.
// Note. Connection genes are always sorted by sourceId then targetId, so we can use a binary search to
// find an existing connection in O(log(n)) time.
conn = new DirectedConnection(srcId, tgtId);
if ((insertIdx = Array.BinarySearch(parent.ConnectionGenes._connArr, conn)) < 0)
{
// The proposed new connection does not already exist, therefore we can use it.
// Get the position in parent.ConnectionGeneArray that the new connection should be inserted at (to maintain sort order).
insertIdx = ~insertIdx;
return(true);
}
conn = default(DirectedConnection);
insertIdx = default(int);
return(false);
}
public static GenomeList CreateGenomeListAddedInputs(NeatGenome seedGenome, int length, NeatParameters neatParameters, IdGenerator idGenerator)
{
//Build the list.
GenomeList genomeList = new GenomeList();
// Use the seed directly just once.
NeatGenome newGenome = new NeatGenome(seedGenome, idGenerator.NextGenomeId);
//genomeList.Add(newGenome);
// For the remainder we alter the weights.
for (int i = 0; i < length; i++)
{
newGenome = new NeatGenome(seedGenome, idGenerator.NextGenomeId);
// Reset the connection weights
foreach (ConnectionGene connectionGene in newGenome.ConnectionGeneList)
{
connectionGene.Weight = (Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0;
}
newGenome.ConnectionGeneList.Add(new ConnectionGene(idGenerator.NextInnovationId, 5, newGenome.NeuronGeneList[Utilities.Next(newGenome.NeuronGeneList.Count - 7) + 7].InnovationId, (Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0));
newGenome.ConnectionGeneList.Add(new ConnectionGene(idGenerator.NextInnovationId, 6, newGenome.NeuronGeneList[Utilities.Next(newGenome.NeuronGeneList.Count - 7) + 7].InnovationId, (Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0));
genomeList.Add(newGenome);
}
//
return(genomeList);
}
/// <summary>
/// Gets the species with a centroid closest to the given genome.
/// If multiple species are equally close then we return all of the those species.
/// </summary>
public static List <Species <T> > GetNearestSpeciesList <T>(
NeatGenome <T> genome,
Species <T>[] speciesArr,
IDistanceMetric <T> distanceMetric)
where T : struct
{
var nearestSpeciesList = new List <Species <T> >(4);
nearestSpeciesList.Add(speciesArr[0]);
double nearestDistance = distanceMetric.CalcDistance(genome.ConnectionGenes, speciesArr[0].Centroid);
for (int i = 1; i < speciesArr.Length; i++)
{
double distance = distanceMetric.CalcDistance(genome.ConnectionGenes, speciesArr[i].Centroid);
if (distance < nearestDistance)
{
nearestSpeciesList.Clear();
nearestSpeciesList.Add(speciesArr[i]);
nearestDistance = distance;
}
else if (distance == nearestDistance)
{
nearestSpeciesList.Add(speciesArr[i]);
}
}
return(nearestSpeciesList);
}
private static void TestGenome(IGenomeDecoder<NeatGenome, IBlackBox> decoder, NeatGenome genome)
{
// Decode the genome into a phenome (neural network).
IBlackBox phenome = decoder.Decode(genome);
List<string> lines = new List<string>();
double v = 0f;
while (v < Math.PI * 2f)
{
phenome.ResetState();
phenome.InputSignalArray[0] = v;
phenome.Activate();
double result = phenome.OutputSignalArray[0];
lines.Add(string.Format("{0}\t{1}", v, result));
v += 0.1d;
}
System.IO.File.WriteAllLines(@"output.csv", lines);
}
/// <summary>
///
/// </summary>
/// <param name="doc"></param>
/// <param name="genome"></param>
/// <param name="activationFn">Not strictly part of a genome. But it is useful to document which function
/// the genome is supposed to run against when decoded into a network.</param>
public static void Write(XmlNode parentNode, NeatGenome genome, IActivationFunction activationFn)
{
//----- Start writing. Create document root node.
XmlElement xmlGenome = XmlUtilities.AddElement(parentNode, "genome");
XmlUtilities.AddAttribute(xmlGenome, "id", genome.GenomeId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "species-id", genome.SpeciesId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "age", genome.GenomeAge.ToString());
XmlUtilities.AddAttribute(xmlGenome, "fitness", genome.Fitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "objective-fitness", genome.ObjectiveFitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "activation-fn-id", activationFn.FunctionId);
//----- Write neurons.
XmlElement xmlNeurons = XmlUtilities.AddElement(xmlGenome, "neurons");
foreach (NeuronGene neuronGene in genome.NeuronGeneList)
{
WriteNeuron(xmlNeurons, neuronGene);
}
//----- Write Connections.
XmlElement xmlConnections = XmlUtilities.AddElement(xmlGenome, "connections");
foreach (ConnectionGene connectionGene in genome.ConnectionGeneList)
{
WriteConnectionGene(xmlConnections, connectionGene);
}
}
public static GenomeList CreateGenomeList(Population seedPopulation, int length, NeatParameters neatParameters, IdGenerator idGenerator)
{
//Build the list.
GenomeList genomeList = new GenomeList();
int seedIdx = 0;
for (int i = 0; i < length; i++)
{
NeatGenome newGenome = new NeatGenome((NeatGenome)seedPopulation.GenomeList[seedIdx], idGenerator.NextGenomeId);
// Reset the connection weights
foreach (ConnectionGene connectionGene in newGenome.ConnectionGeneList)
{
connectionGene.Weight = (Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0;
}
genomeList.Add(newGenome);
if (++seedIdx >= seedPopulation.GenomeList.Count)
{ // Back to first genome.
seedIdx = 0;
}
}
return(genomeList);
}
public void GenerateMesh(NeatGenome genome)
{
// Perform analysis
AcyclicNetworkDepthAnalysis depthAnalysis = new AcyclicNetworkDepthAnalysis();
NetworkDepthInfo depthInfo = depthAnalysis.CalculateNodeDepths(genome);
int maxDepth = depthInfo._networkDepth;
int[] nodesPerDepthLevel = new int[maxDepth];
Dictionary <int, List <int> > nodesByDepth = new Dictionary <int, List <int> >();
Dictionary <uint, int> nodeIdxById = new Dictionary <uint, int>();
int mostNodesPerLayer = 0;
for (int i = 0; i < genome.NodeList.Count; i++)
{
int depth = depthInfo._nodeDepthArr[i];
List <int> nodes;
if (!nodesByDepth.TryGetValue(depth, out nodes))
{
nodes = new List <int>();
nodesByDepth.Add(depth, nodes);
}
nodes.Add(i);
mostNodesPerLayer = Mathf.Max(++nodesPerDepthLevel[depth], mostNodesPerLayer);
}
List <Vector3> vertices;
var neuronsMesh = GenerateNeuronsMesh(genome, nodeIdxById, maxDepth, mostNodesPerLayer, nodesByDepth, out vertices);
neuronObject.GetComponent <MeshFilter>().mesh = neuronsMesh;
var connectionMesh = GenerateConnectionsMesh(genome, nodeIdxById, vertices);
connectionObject.GetComponent <MeshFilter>().mesh = connectionMesh;
}
private Mesh GenerateConnectionsMesh(NeatGenome genome, Dictionary <uint, int> nodeIdxById, List <Vector3> nodeVertices)
{
Mesh mesh = new Mesh();
List <Vector3> vertices = new List <Vector3>();
List <Color> colors = new List <Color>();
List <int> pointIndices = new List <int>();
// Connections
int idx = 0;
foreach (var connection in genome.ConnectionList)
{
int sourceIdx = nodeIdxById[connection.SourceNodeId];
int targetIdx = nodeIdxById[connection.TargetNodeId];
var sourcePos = nodeVertices[sourceIdx];
var targetPos = nodeVertices[targetIdx];
vertices.Add(new Vector3((float)connection.Weight, 0, 0));
colors.Add(new Color(sourcePos.x, sourcePos.y, targetPos.x, targetPos.y));
pointIndices.Add(idx++);
//Debug.Log($"[{genome.Id}] {connection.SourceNodeId} - {connection.TargetNodeId}: {connection.Weight}");
}
mesh.subMeshCount = 1;
mesh.SetVertices(vertices);
mesh.SetColors(colors);
mesh.SetIndices(pointIndices.ToArray(), MeshTopology.Points, 0);
return(mesh);
}
public static IBlackBox LoadBrain(string filePath, INeatExperiment experiment)
{
NeatGenome genome = null;
// Try to load the genome from the XML document.
try
{
using (XmlReader xr = XmlReader.Create(filePath))
genome = NeatGenomeXmlIO.ReadCompleteGenomeList(xr, false, (NeatGenomeFactory)experiment.CreateGenomeFactory())[0];
}
catch (Exception e1)
{
print(filePath + " Error loading genome from file!\nLoading aborted.\n"
+ e1.Message + "\nJoe: " + filePath);
return(null);
}
// Get a genome decoder that can convert genomes to phenomes.
var genomeDecoder = experiment.CreateGenomeDecoder();
// Decode the genome into a phenome (neural network).
var phenome = genomeDecoder.Decode(genome);
return(phenome);
}
private NeatGenome loadGenome(string filename)
{
NeatGenome genome = null;
DirectoryInfo dir = new DirectoryInfo(Application.persistentDataPath + string.Format("/{0}", folder_prefix));
if (dir.Exists)
{
FileInfo[] info = dir.GetFiles(filename + ".xml");
print(info.Length);
foreach (FileInfo f in info)
{
try {
using (XmlReader xr = XmlReader.Create(Application.persistentDataPath + string.Format("/{0}/{1}", folder_prefix, f.Name)))
genome = NeatGenomeXmlIO.ReadCompleteGenomeList(xr, false, (NeatGenomeFactory)experiment.CreateGenomeFactory()) [0];
} catch (Exception e1) {
print(" Error loading genome from file!\nLoading aborted.\n" + e1.Message);
continue;
}
}
// Debug.Log ("Filled map with " + map + " elites.");
// }
}
return(genome);
}
private static void Backprop(NeatGenome genome, IGenomeDecoder<NeatGenome, IBlackBox> decoder, double learningRate)
{
var phenome = decoder.Decode(genome);
var network = (FastCyclicNetwork)phenome;
network.Momentum = 0;
network.BackpropLearningRate = learningRate;
//Console.WriteLine("Weights Before:");
//PrintWeights(network);
//Console.WriteLine();
network.Train(new double[] { 0.3, 0.3 }, new double[] { 0.9 });
Console.WriteLine("Weights:");
PrintWeights(network);
Console.WriteLine();
network.Train(new double[] { 0.3, 0.3 }, new double[] { 0.9 });
Console.WriteLine("Weights:");
PrintWeights(network);
Console.WriteLine();
}
private NeatGenome <double> CreateGenome1(MetaNeatGenome <double> metaNeatGenome)
{
var genomeBuilder = NeatGenomeBuilderFactory <double> .Create(metaNeatGenome);
// Define a genome that matches the one defined in example1.genome.
var connGenes = new ConnectionGenes <double>(12);
connGenes[0] = (0, 5, 0.5);
connGenes[1] = (0, 7, 0.7);
connGenes[2] = (0, 3, 0.3);
connGenes[3] = (1, 5, 1.5);
connGenes[4] = (1, 7, 1.7);
connGenes[5] = (1, 3, 1.3);
connGenes[6] = (1, 6, 1.6);
connGenes[7] = (1, 8, 1.8);
connGenes[8] = (1, 4, 1.4);
connGenes[9] = (2, 6, 2.6);
connGenes[10] = (2, 8, 2.8);
connGenes[11] = (2, 4, 2.4);
// Ensure the connections are sorted correctly.
connGenes.Sort();
// Wrap in a genome.
NeatGenome <double> genome = genomeBuilder.Create(0, 0, connGenes);
return(genome);
}
public void SaveAndLoadGenome()
{
var metaNeatGenome = new MetaNeatGenome <double>(3, 2, true, new ReLU());
var genomeBuilder = NeatGenomeBuilderFactory <double> .Create(metaNeatGenome);
// Simple acyclic graph.
var connGenes = new ConnectionGenes <double>(6);
connGenes[0] = (0, 3, 0.123);
connGenes[1] = (1, 3, 1.234);
connGenes[2] = (2, 3, -0.5835);
connGenes[3] = (2, 4, 5.123456789);
connGenes[4] = (2, 5, 2.5);
connGenes[5] = (5, 4, 5.4);
// Wrap in a genome.
NeatGenome <double> genome = genomeBuilder.Create(0, 0, connGenes);
// Create a memory stream to save the genome into.
using (MemoryStream ms = new(1024))
{
// Save the genome.
NeatGenomeSaver <double> .Save(genome, ms);
// Load the genome.
ms.Position = 0;
NeatGenomeLoader <double> loader = NeatGenomeLoaderFactory.CreateLoaderDouble(metaNeatGenome);
NeatGenome <double> genomeLoaded = loader.Load(ms);
// Compare the original genome with the loaded genome.
IOTestUtils.CompareGenomes(genome, genomeLoaded);
}
}
/// <param name="activationFn">Not strictly part of a genome. But it is useful to document which function
/// the genome is supposed to run against when decoded into a network.</param>
public static void Write(XmlNode parentNode, NeatGenome genome, IActivationFunction activationFn)
{
//----- Start writing. Create document root node.
XmlElement xmlGenome = XmlUtilities.AddElement(parentNode, "genome");
XmlUtilities.AddAttribute(xmlGenome, "id", genome.GenomeId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "species-id", genome.SpeciesId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "age", genome.GenomeAge.ToString());
XmlUtilities.AddAttribute(xmlGenome, "fitness", genome.Fitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "activation-fn-id", activationFn.FunctionId);
//----- Write neurons.
XmlElement xmlNeurons = XmlUtilities.AddElement(xmlGenome, "neurons");
foreach(NeuronGene neuronGene in genome.NeuronGeneList)
WriteNeuron(xmlNeurons, neuronGene);
//----- Write modules.
XmlElement xmlModules = XmlUtilities.AddElement(xmlGenome, "modules");
foreach (ModuleGene moduleGene in genome.ModuleGeneList)
WriteModule(xmlModules, moduleGene);
//----- Write Connections.
XmlElement xmlConnections = XmlUtilities.AddElement(xmlGenome, "connections");
foreach(ConnectionGene connectionGene in genome.ConnectionGeneList)
WriteConnectionGene(xmlConnections, connectionGene);
//----- Write beahavior
if(genome.Behavior!=null)
{
if(genome.Behavior.behaviorList!=null)
{
XmlElement xmlBehavior = XmlUtilities.AddElement(xmlGenome, "behavior");
WriteBehavior(xmlBehavior,genome.Behavior);
}
}
}
private bool TryGetConnectionInner(NeatGenome <T> parent, out DirectedConnection conn, out int insertIdx)
{
int inputCount = _metaNeatGenome.InputNodeCount;
int outputCount = _metaNeatGenome.OutputNodeCount;
int hiddenCount = parent.HiddenNodeIdArray.Length;
// Select a source node at random.
// Note. Valid source nodes are input and hidden nodes. Output nodes are not source node candidates
// for acyclic nets, because that can prevent future connections from targeting the output if it would
// create a cycle.
int inputHiddenCount = inputCount + hiddenCount;
int srcIdx = _rng.Next(inputHiddenCount);
if (srcIdx >= inputCount)
{
srcIdx += outputCount;
}
int srcId = GetNodeIdFromIndex(parent, srcIdx);
// Select a target node at random.
// Note. Valid target nodes are all hidden and output nodes (cannot be an input node).
int outputHiddenCount = outputCount + hiddenCount;
int tgtId = GetNodeIdFromIndex(parent, inputCount + _rng.Next(outputHiddenCount));;
// Test for simplest cyclic connectivity - node connects to itself.
if (srcId == tgtId)
{
conn = default(DirectedConnection);
insertIdx = default(int);
return(false);
}
// Test if the chosen connection already exists.
// Note. Connection genes are always sorted by sourceId then targetId, so we can use a binary search to
// find an existing connection in O(log(n)) time.
conn = new DirectedConnection(srcId, tgtId);
if ((insertIdx = Array.BinarySearch(parent.ConnectionGenes._connArr, conn)) >= 0)
{
// The proposed new connection already exists.
conn = default(DirectedConnection);
insertIdx = default(int);
return(false);
}
// Test if the connection will form a cycle in the wider network.
int totalNodeCount = _metaNeatGenome.InputOutputNodeCount + hiddenCount;
if (_cyclicTest.IsConnectionCyclic(parent.ConnectionGenes._connArr, parent.NodeIndexByIdMap, totalNodeCount, conn))
{
conn = default(DirectedConnection);
insertIdx = default(int);
return(false);
}
// Get the position in parent.ConnectionGeneArray that the new connection should be inserted at (to maintain sort order).
insertIdx = ~insertIdx;
return(true);
}
public void TestAddNode3()
{
var pop = CreateNeatPopulation();
var genomeBuilder = NeatGenomeBuilderFactory <double> .Create(pop.MetaNeatGenome);
var genome = pop.GenomeList[0];
var strategy = new AddNodeStrategy <double>(
pop.MetaNeatGenome, genomeBuilder,
pop.GenomeIdSeq, pop.InnovationIdSeq, pop.GenerationSeq,
pop.AddedNodeBuffer);
IRandomSource rng = RandomDefaults.CreateRandomSource();
CircularBuffer <NeatGenome <double> > genomeRing = new CircularBuffer <NeatGenome <double> >(10);
genomeRing.Enqueue(genome);
for (int i = 0; i < 5000; i++)
{
NeatGenome <double> childGenome = CreateAndTestChildGenome(genome, strategy, rng);
// Add the new child genome to the ring.
genomeRing.Enqueue(childGenome);
// Take the genome at the tail of the ring for the next parent.
genome = genomeRing[0];
}
}
public void calcFitness(string genomeFile, int type)
{
XmlDocument doc = new XmlDocument();
doc.Load(genomeFile);
NeatGenome genome = XmlNeatGenomeReaderStatic.Read(doc);
INetworkEvaluator eval;
if (type == 0)
{
eval = new CTRNNNetworkEvaluator(4, 12, 12);
}
else if (type == 1)
{
eval = new SUPGNetworkEvaluator(4, 12, 12);
}
else
{
doClune = true;
eval = new CluneNetworkEvaluator(20, 20, 20);
}
var tempNet = genome.Decode(null);
MessageBox.Show(eval.threadSafeEvaluateNetwork(tempNet)[0].ToString());
}
private void RedrawGraph(NeatGenome genome)
{
IBlackBox box = _experiment.GetBlackBox(genome);
Color trueColor = panPlot.TrueColor;
Color falseColor = panPlot.FalseColor;
var samples = Enumerable.Range(0, 1000).
Select(o =>
{
Vector3D pos = Math3D.GetRandomVector(new Vector3D(0, 0, 0), new Vector3D(1, 1, 1));
box.ResetState();
//box.InputSignalArray.CopyFrom()
box.InputSignalArray[0] = pos.X;
box.InputSignalArray[1] = pos.Y;
box.InputSignalArray[2] = pos.Z;
box.Activate();
double percent = box.OutputSignalArray[0];
Color color = UtilityWPF.AlphaBlend(trueColor, falseColor, percent);
return(Tuple.Create(pos.ToPoint(), color));
});
panPlot.ClearFrame();
panPlot.AddDots(samples, .01, false);
}
/// <summary>
/// Determine the set of hidden node IDs that have been deleted as a result of a connection deletion.
/// I.e. a node only exists if a connection connects to it, therefore if there are no other connections
/// referring to a node then it has been deleted, with the exception of input and output nodes that
/// always exist.
/// </summary>
private static (int?, int?) GetDeletedNodeIds(
NeatGenome <T> parent, int deleteIdx,
DirectedConnection[] childConnArr)
{
// Get the two node IDs referred to by the deleted connection.
int?nodeId1 = parent.ConnectionGenes._connArr[deleteIdx].SourceId;
int?nodeId2 = parent.ConnectionGenes._connArr[deleteIdx].TargetId;
// Set IDs to null for input/output nodes (these nodes are fixed and therefore cannot be deleted).
// Also, for cyclic networks nodeId1 and 2 could refer to the same node (a cyclic connection on the same node), if
// so then we don't need to set nodeId2.
if (nodeId1.Value < parent.MetaNeatGenome.InputOutputNodeCount)
{
nodeId1 = null;
}
if (nodeId2.Value < parent.MetaNeatGenome.InputOutputNodeCount || nodeId1 == nodeId2)
{
nodeId2 = null;
}
if (!nodeId1.HasValue && !nodeId2.HasValue)
{
return(null, null);
}
if (!nodeId1.HasValue)
{
// 'Shuffle up' nodeId2 into nodeId1.
nodeId1 = nodeId2;
nodeId2 = null;
}
if (!nodeId2.HasValue)
{
if (!IsNodeConnectedTo(childConnArr, nodeId1 !.Value))
{
return(nodeId1.Value, null);
}
return(null, null);
}
(bool, bool)isConnectedTuple = AreNodesConnectedTo(childConnArr, nodeId1 !.Value, nodeId2.Value);
if (!isConnectedTuple.Item1 && !isConnectedTuple.Item2)
{
return(nodeId1.Value, nodeId2.Value);
}
if (!isConnectedTuple.Item1)
{
return(nodeId1.Value, null);
}
if (!isConnectedTuple.Item2)
{
return(nodeId2.Value, null);
}
return(null, null);
}
public void SetBrain(INetwork network, bool useSUPG = false, NeatGenome genome = null, INetwork cppn = null, int[] triggerMap = null)
{
if (useSUPG)
{
supgOutputs = new float[network.TotalNeuronCount /*- (network.InputNeuronCount + network.OutputNeuronCount)*/, wavelength]; // need at least as many rows as the number of hidden neurons
// set all supgOutputs to min value to signal they have not been cached yet
for (int i = 0; i < network.TotalNeuronCount /*- (network.InputNeuronCount + network.OutputNeuronCount)*/; i++)
{
for (int j = 0; j < wavelength; j++)
{
supgOutputs[i, j] = float.MinValue;
}
}
}
this.network = network;
this.useSUPG = useSUPG;
this.genome = genome;
this.cppn = cppn;
this.triggerMap = triggerMap;
bool[] useSUPGArray = new bool[genome.NeuronGeneList.Count];
useSUPGArray[7] = true;
useSUPGArray[8] = true;
if (useSUPG)
{
((FloatFastConcurrentNetwork)network).UseSUPG = false;
((FloatFastConcurrentNetwork)network).useSUPGArray = useSUPGArray;
}
brain = network;
}
public RobotIndividual(Robot robot, Individual[] parents)
{
Robot = robot;
Genome = robot.Genome;
Parents = parents;
_distanceMetric = new EuclideanDistanceMetric();
}
private NeatGenome <T> CreateGenomeInner(
NeatGenome <T> parent1,
NeatGenome <T> parent2,
IRandomSource rng)
{
// Resolve a flag that determines if *all* disjoint genes from the secondary parent will be included in the child genome, or not.
// This approach is from SharpNEAT v2.x and is preserved to act as baseline in v4.x, but better strategies may exist.
bool includeSecondaryParentGene = DiscreteDistribution.SampleBernoulli(rng, _secondaryParentGeneProbability);
// Enumerate over the connection genes in both parents.
foreach (var geneIndexPair in EnumerateParentGenes(parent1.ConnectionGenes, parent2.ConnectionGenes))
{
// Create a connection gene based on the current position in both parents.
ConnectionGene <T>?connGene = CreateConnectionGene(
parent1.ConnectionGenes, parent2.ConnectionGenes,
geneIndexPair.Item1, geneIndexPair.Item2,
includeSecondaryParentGene, rng);
if (connGene.HasValue)
{ // Attempt to add the gene to the child genome we are building.
_connGeneListBuilder.TryAddGene(connGene.Value);
}
}
// Convert the genes to the structure required by NeatGenome.
ConnectionGenes <T> connGenes = _connGeneListBuilder.ToConnectionGenes();
// Create and return a new genome.
return(_genomeBuilder.Create(
_genomeIdSeq.Next(),
_generationSeq.Peek,
connGenes));
}
/// <summary>
/// Create a new child genome from a given parent genome.
/// </summary>
/// <param name="parent">The parent genome.</param>
/// <param name="rng">Random source.</param>
/// <returns>A new child genome.</returns>
public NeatGenome <T> CreateChildGenome(NeatGenome <T> parent, IRandomSource rng)
{
// Clone the parent's connection weight array.
var weightArr = (T[])parent.ConnectionGenes._weightArr.Clone();
// Apply mutation to the connection weights.
_weightMutationScheme.MutateWeights(weightArr, rng);
// Create the child genome's ConnectionGenes object.
// Note. The parent genome's connection arrays are re-used; these remain unchanged because we are mutating
// connection *weights* only, so we can avoid the cost of cloning these arrays.
var connGenes = new ConnectionGenes <T>(
parent.ConnectionGenes._connArr,
weightArr);
// Create and return a new genome.
// Note. The parent's ConnectionIndexArray and HiddenNodeIdArray can be re-used here because the new genome
// has the same set of connections (same neural net structure).
return(_genomeBuilder.Create(
_genomeIdSeq.Next(),
_generationSeq.Peek,
connGenes,
parent.HiddenNodeIdArray,
parent.NodeIndexByIdMap,
parent.DirectedGraph,
parent.ConnectionIndexMap));
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm with a single seed genome.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(NeatGenome seed)
{
// Create evolution algorithm.
return(CreateEvolutionAlgorithm(_populationSize, seed.BirthGeneration, new List <NeatGenome> {
seed
}));
}
/// <summary>
/// Creates an instance of the current champion
/// </summary>
public void RunBest()
{
champRunning = true;
NeatGenome genome = LoadChampion();
// Get a genome decoder that can convert genomes to phenomes.
var genomeDecoder = experiment.CreateGenomeDecoder();
// Decode the genome into a phenome (neural network).
var phenome = genomeDecoder.Decode(genome);
InstantiateCandidate(phenome);
GameObject bestInstance = ControllerMap[phenome].gameObject;
// Special tag so we can selectively actuate on these units.
bestInstance.tag = "BestUnit";
// Also removes the tag from child components (but no granchildren. This
// is currently ok since these elements have no coliders and champions
// are destroyed before entering evolution).
// These cannot use "BestUnit" or we will be in trouble when we destroy
// the unit!
foreach (Transform child in bestInstance.transform)
{
child.gameObject.tag = "Untagged";
}
// This is called if we want the champions to be evaluated, mostly for
// research reasons.
if (false)
{
Coroutiner.StartCoroutine(EvaluateChamp(phenome));
}
}
private void saveBestGenomeToolStripMenuItem_Click(object sender, EventArgs e)
{
// Get the current best genome.
NeatGenome <double> bestGenome = _neatPop?.BestGenome;
if (bestGenome is null)
{
return;
}
// Ask the user to select a file path and name to save to.
string filepath = SelectFileToSave("Save best genome", "genome", "(*.genome)|*.genome");
if (string.IsNullOrEmpty(filepath))
{
return;
}
// Save the genome.
try
{
NeatGenomeSaver <double> .Save(bestGenome, filepath);
}
catch (Exception ex)
{
__log.ErrorFormat("Error saving genome; [{0}]", ex.Message);
}
}
/// <summary>
/// Load a population from a zip archive file containing one or more genome file entries (with a .genome file extension).
/// </summary>
/// <param name="path">Path to the zip file to load.</param>
/// <returns>A list of the loaded genomes.</returns>
public List <NeatGenome <T> > LoadFromZipArchive(string path)
{
if (!File.Exists(path))
{
throw new IOException($"File does not exist [{path}]");
}
using (ZipArchive zipArchive = ZipFile.OpenRead(path))
{
// Alloc genome list with an appropriate capacity.
List <NeatGenome <T> > genomeList = new List <NeatGenome <T> >(zipArchive.Entries.Count);
// Loop the genome file entries, loading each in turn.
foreach (ZipArchiveEntry zipEntry in zipArchive.Entries)
{
// Skip non-genome files.
if (Path.GetExtension(zipEntry.Name) != ".genome")
{
continue;
}
using (Stream zipEntryStream = zipEntry.Open())
{
NeatGenome <T> genome = _genomeLoader.Load(zipEntryStream);
genomeList.Add(genome);
}
}
return(genomeList);
}
}
void Update ()
{
if (Input.GetKey(KeyCode.Space) || Input.GetKeyDown(KeyCode.UpArrow))
{
currentGenome = evolutionHelper.MutateGenome(currentGenome);
//Debug.Log("Current generation: " + currentGenome.BirthGeneration);
//var byteCount = System.Text.ASCIIEncoding.ASCII.GetByteCount(NeatGenomeXmlIO.Save(currentGenome, true).OuterXml);
//Debug.LogWarning("Byte count: " + byteCount);
var phenome = genomeDecoder.Decode(currentGenome);
Mesh mesh = ArtefactEvaluator.Evaluate(phenome, m_voxelVolume, out evaluationInfo);
mesh.RecalculateNormals();
//The diffuse shader wants uvs so just fill with a empty array, they're not actually used
mesh.uv = new Vector2[mesh.vertices.Length];
// destroy mesh object to free up memory
GameObject.DestroyImmediate(m_meshGameObject.GetComponent<MeshFilter>().mesh);
m_meshGameObject.GetComponent<MeshFilter>().mesh = mesh;
//m_meshGameObject.GetComponent<Renderer>().material.color = ArtefactEvaluator.artefactColor;
SaveGenome();
}
}
/// <summary>
/// Refresh/update the view with the provided genome.
/// </summary>
public override void RefreshView(object genome)
{
NeatGenome neatGenome = genome as NeatGenome;
if (null == neatGenome)
{
return;
}
// Decode genome.
IBlackBox box = _genomeDecoder.Decode(neatGenome);
// Probe the black box.
_blackBoxProbe.Probe(box, _yArrTarget);
// Update plot points.
double[] xArr = _paramSamplingInfo._xArr;
for (int i = 0; i < xArr.Length; i++)
{
if (!_generativeMode)
{
box.ResetState();
box.InputSignalArray[0] = xArr[i];
}
box.Activate();
_plotPointListResponse[i].Y = _yArrTarget[i];
}
// Trigger graph to redraw.
zed.AxisChange();
Refresh();
}
private NeatGenome <T> CreateGenomeInner(NeatGenome <T> parent1, NeatGenome <T> parent2)
{
// Randomly select one parent as being the primary parent.
if (_rng.NextBool())
{
VariableUtils.Swap(ref parent1, ref parent2);
}
// Enumerate over the connection genes in both parents.
foreach (var geneIndexPair in EnumerateParentGenes(parent1.ConnectionGenes, parent2.ConnectionGenes))
{
// Create a connection gene based on the current position in both parents.
ConnectionGene <T>?connGene = CreateConnectionGene(
parent1.ConnectionGenes, parent2.ConnectionGenes,
geneIndexPair.Item1, geneIndexPair.Item2,
out bool isSecondaryGene);
if (connGene.HasValue)
{
// Attempt to add the gene to the child genome we are building.
_builder.TryAddGene(connGene.Value, isSecondaryGene);
}
}
// Convert the genes to the structure required by NeatGenome.
var connGenes = _builder.ToConnectionGenes();
// Create and return a new genome.
return(_genomeBuilder.Create(
_genomeIdSeq.Next(),
_generationSeq.Peek,
connGenes));
}
/**
* Assigns an AI brain to this player. This is used when
* loading a brain from an existing neural network xml champion file.
*/
public AIFighter getFighterBrain()
{
//try to load a champion file. If any issues, use default AI.
try
{
// Initialise log4net (log to console).
XmlConfigurator.Configure(new FileInfo("log4net.properties"));
// Experiment classes encapsulate much of the nuts and bolts of setting up a NEAT search.
AIExperiment experiment = new AIExperiment(new AIFighterEvaluatorFactory());
// Load config XML.
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load("ai.config.xml");
experiment.Initialize("AI", xmlConfig.DocumentElement);
// assign genome decoder for reading champion files
IGenomeDecoder <NeatGenome, IBlackBox> decoder = experiment.CreateGenomeDecoder();
//load existing champion file into a genome
string championFileLocation = "coevolution_champion.xml";
XmlReader xr = XmlReader.Create(championFileLocation);
NeatGenome genome = NeatGenomeXmlIO.ReadCompleteGenomeList(xr, false)[0];
//decode genome into a usable AIFighter brain.
return(new AIFighter(decoder.Decode(genome)));
}
catch (System.Exception e)
{
Debug.Log("Unable to load CHAMPION xml file. It may not exist.\n" + e);
return(null);
}
}
public void CacheChampion(NeatGenome championGenome)
{
_championGenomeMemoryStream.SetLength(0);
using (var xmlWriter = XmlWriter.Create(_championGenomeMemoryStream, _xmlWriterSettings))
{
_neatExperiment.SavePopulation(xmlWriter, new List<NeatGenome> { championGenome });
}
}
/// <summary>
/// Writes a single NeatGenome to XML within a containing 'Root' element and the activation function
/// library that the genome is associated with.
/// The XML is returned as a newly created XmlDocument.
/// </summary>
/// <param name="genome">The genome to save.</param>
/// <param name="nodeFnIds">Indicates if node activation function IDs should be emitted. They are required
/// for HyperNEAT genomes but not for NEAT.</param>
public static XmlDocument SaveComplete(NeatGenome genome, bool nodeFnIds)
{
XmlDocument doc = new XmlDocument();
using(XmlWriter xw = doc.CreateNavigator().AppendChild())
{
WriteComplete(xw, genome, nodeFnIds);
}
return doc;
}
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>
/// For saving and vizualization, we somtimes need to change the genome
/// BackProp for instance, happens on the phenotype and so if we want to see/save those changes
/// we need to send the data back to NeatGenome
/// </summary>
/// <param name="genomeToWrite">The genome whose weights are being changed</param>
/// <param name="connections">we set the genome's weights to the weights of these connections</param>
/// <returns></returns>
public static NeatGenome ChangeGenomeWeights(NeatGenome genomeToWrite, FastConnection[] connections)
{
IConnectionList connectionList = genomeToWrite.ConnectionList;
int connectionCount = connectionList.Count;
for (int i = 0; i < connectionCount; i++)
{
genomeToWrite.ConnectionGeneList[i].Weight = connections[i]._weight;
}
return genomeToWrite;
}
public static void Write(XmlNode parentNode, NeatGenome genome)
{
//----- Start writing. Create document root node.
XmlElement xmlGenome = XmlUtilities.AddElement(parentNode, "genome");
XmlUtilities.AddAttribute(xmlGenome, "id", genome.GenomeId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "species-id", genome.SpeciesId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "age", genome.GenomeAge.ToString());
XmlUtilities.AddAttribute(xmlGenome, "fitness", genome.Fitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "realfitness", genome.RealFitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "adaptable", genome.networkAdaptable.ToString());
XmlUtilities.AddAttribute(xmlGenome, "modulated", genome.networkModulatory.ToString());
//----- Write neurons.
XmlElement xmlNeurons = XmlUtilities.AddElement(xmlGenome, "neurons");
foreach (NeuronGene neuronGene in genome.NeuronGeneList)
WriteNeuron(xmlNeurons, neuronGene);
//----- Write modules.
XmlElement xmlModules = XmlUtilities.AddElement(xmlGenome, "modules");
foreach (ModuleGene moduleGene in genome.ModuleGeneList)
WriteModule(xmlModules, moduleGene);
//----- Write connections.
XmlElement xmlConnections = XmlUtilities.AddElement(xmlGenome, "connections");
foreach(ConnectionGene connectionGene in genome.ConnectionGeneList)
WriteConnectionGene(xmlConnections, connectionGene);
//----- Write beahavior
if(genome.Behavior!=null)
{
if(genome.Behavior.behaviorList!=null)
{
XmlElement xmlBehavior = XmlUtilities.AddElement(xmlGenome, "behavior");
WriteBehavior(xmlBehavior,genome.objectives,genome.Behavior);
}
}
//----- JUSTIN: Write grid coords
if (genome.GridCoords != null)
{
XmlElement xmlGridCoords = XmlUtilities.AddElement(xmlGenome, "grid");
WriteGridCoords(xmlGridCoords, genome.GridCoords);
}
//----- JUSTIN: Write trajectory
if (genome.Behavior.trajectory != null && genome.Behavior.trajectory.Count > 0)
{
XmlElement xmlTrajectory = XmlUtilities.AddElement(xmlGenome, "trajectory");
WriteTrajectory(xmlTrajectory, genome.Behavior.trajectory);
}
}
/// <summary>
/// Serializes a NEAT genome into a string.
/// </summary>
/// <param name="neatGenome">The NEAT genome object to serialize.</param>
/// <returns>The serialized NEAT genome string.</returns>
public static string GetGenomeXml(NeatGenome neatGenome)
{
// Create a new string writer into which to write the genome XML
StringWriter genomeStringWriter = new StringWriter();
// Serialize the genome to XML, but write it into a string writer instead of outputting to a file
if (neatGenome != null)
{
using (XmlTextWriter genomeTextWriter = new XmlTextWriter(genomeStringWriter))
{
WriteComplete(genomeTextWriter, neatGenome, false);
}
}
// Convert to a string representation and return
return genomeStringWriter.ToString();
}
void Start ()
{
evolutionHelper = new EvolutionHelper(k_numberOfInputs, k_numberOfOutputs);
currentGenome = evolutionHelper.CreateInitialGenome();
genomeDecoder = new NeatGenomeDecoder(NetworkActivationScheme.CreateAcyclicScheme());
ArtefactEvaluator.DefaultInputType = InputType;
SaveGenome();
Sine.__DefaultInstance.Curve = sineCurve;
m_meshGameObject = new GameObject("Mesh");
m_meshGameObject.AddComponent<MeshFilter>();
m_meshGameObject.AddComponent<MeshRenderer>();
m_meshGameObject.AddComponent<ProceduralMesh>();
m_meshGameObject.GetComponent<Renderer>().material = standardMaterial;
Camera.main.GetComponent<CameraMouseOrbit>().target = m_meshGameObject.transform;
}
void CreateMesh(NeatGenome genome, GameObject attachedGameObject)
{
var phenome = genomeDecoder.Decode(genome);
Mesh mesh = ArtefactEvaluator.Evaluate(phenome, m_voxelVolume, out evaluationInfo);
mesh.RecalculateNormals();
//The diffuse shader wants uvs so just fill with a empty array, they're not actually used
mesh.uv = new Vector2[mesh.vertices.Length];
// destroy mesh object to free up memory
GameObject.DestroyImmediate(attachedGameObject.GetComponent<MeshFilter>().mesh);
attachedGameObject.GetComponent<MeshFilter>().mesh = mesh;
var meshCollider = attachedGameObject.GetComponent<MeshCollider>();
if (meshCollider != null)
Destroy(meshCollider);
attachedGameObject.AddComponent<MeshCollider>();
}
public static void Write(XmlNode parentNode, NeatGenome genome)
{
//----- Start writing. Create document root node.
XmlElement xmlGenome = XmlUtilities.AddElement(parentNode, "genome");
XmlUtilities.AddAttribute(xmlGenome, "id", genome.GenomeId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "species-id", genome.SpeciesId.ToString());
XmlUtilities.AddAttribute(xmlGenome, "age", genome.GenomeAge.ToString());
XmlUtilities.AddAttribute(xmlGenome, "fitness", genome.Fitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "realfitness", genome.RealFitness.ToString("0.00"));
XmlUtilities.AddAttribute(xmlGenome, "adaptable", genome.networkAdaptable.ToString());
XmlUtilities.AddAttribute(xmlGenome, "modulated", genome.networkModulatory.ToString());
// Schrum: Extra properties necessary for multimodal networks to make sense
XmlUtilities.AddAttribute(xmlGenome, "outputsperpolicy", genome.OutputsPerPolicy.ToString());
//----- Write neurons.
XmlElement xmlNeurons = XmlUtilities.AddElement(xmlGenome, "neurons");
foreach (NeuronGene neuronGene in genome.NeuronGeneList)
WriteNeuron(xmlNeurons, neuronGene);
//----- Write modules.
XmlElement xmlModules = XmlUtilities.AddElement(xmlGenome, "modules");
foreach (ModuleGene moduleGene in genome.ModuleGeneList)
WriteModule(xmlModules, moduleGene);
//----- Write connections.
XmlElement xmlConnections = XmlUtilities.AddElement(xmlGenome, "connections");
foreach(ConnectionGene connectionGene in genome.ConnectionGeneList)
WriteConnectionGene(xmlConnections, connectionGene);
//----- Write beahavior
if(genome.Behavior!=null)
{
if(genome.Behavior.behaviorList!=null)
{
XmlElement xmlBehavior = XmlUtilities.AddElement(xmlGenome, "behavior");
WriteBehavior(xmlBehavior,genome.Behavior);
}
}
}
private static void BackpropEpochs(NeatGenome genome, IGenomeDecoder<NeatGenome, IBlackBox> decoder, double learningRate, int epochs)
{
var phenome = decoder.Decode(genome);
var network = (FastCyclicNetwork)phenome;
network.Momentum = 0;
network.BackpropLearningRate = learningRate;
//Console.WriteLine("Weights Before:");
//PrintWeights(network);
//Console.WriteLine();
//double[][] inputs = TrainXOR(epochs, network);
for(int i = 0; i < epochs; i++)
network.Train(new double[] { 0.3, 0.3 }, new double[] { 0.9 });
Console.WriteLine("Weights After:");
PrintWeights(network);
Console.WriteLine();
//RunXor(network, inputs);
}
/// <summary>
/// Add a ConnectionGene to the builder, but only if the connection is not already present (as determined by it's neuron ID endpoints).
/// </summary>
/// <param name="connectionGene">The connection to add.</param>
/// <param name="parentGenome">The conenction's parent genome. This is used to obtain NeuronGene(s) for the connection endpoints.</param>
/// <param name="overwriteExisting">A flag that indicates if this connection should take precedence oevr an existing connection with
/// the same endpoints.</param>
public void TryAddGene(ConnectionGene connectionGene, NeatGenome parentGenome, bool overwriteExisting)
{
// Check if a matching gene has already been added.
ConnectionEndpointsStruct connectionKey = new ConnectionEndpointsStruct(connectionGene.SourceNodeId, connectionGene.TargetNodeId);
ConnectionGene existingConnectionGene;
if(!_connectionGeneDictionary.TryGetValue(connectionKey, out existingConnectionGene))
{ // Add new connection gene.
ConnectionGene connectionGeneCopy = new ConnectionGene(connectionGene);
_connectionGeneDictionary.Add(connectionKey, connectionGeneCopy);
// Insert connection gene into a list. Use more efficient approach (append to end) if we know the gene belongs at the end.
if(connectionGeneCopy.InnovationId > _highestConnectionGeneId) {
_connectionGeneList.Add(connectionGeneCopy);
_highestConnectionGeneId = connectionGeneCopy.InnovationId;
} else {
_connectionGeneList.InsertIntoPosition(connectionGeneCopy);
}
// Add neuron genes (if not already added).
// Source neuron.
NeuronGene srcNeuronGene;
if(!_neuronDictionary.TryGetValue(connectionGene.SourceNodeId, out srcNeuronGene))
{
srcNeuronGene = parentGenome.NeuronGeneList.GetNeuronById(connectionGene.SourceNodeId);
srcNeuronGene = new NeuronGene(srcNeuronGene, false); // Make a copy.
_neuronDictionary.Add(srcNeuronGene.Id, srcNeuronGene);
}
// Target neuron.
NeuronGene tgtNeuronGene;
if(!_neuronDictionary.TryGetValue(connectionGene.TargetNodeId, out tgtNeuronGene))
{
tgtNeuronGene = parentGenome.NeuronGeneList.GetNeuronById(connectionGene.TargetNodeId);
tgtNeuronGene = new NeuronGene(tgtNeuronGene, false); // Make a copy.
_neuronDictionary.Add(tgtNeuronGene.Id, tgtNeuronGene);
}
// Register connectivity with each neuron.
srcNeuronGene.TargetNeurons.Add(tgtNeuronGene.Id);
tgtNeuronGene.SourceNeurons.Add(srcNeuronGene.Id);
}
else if(overwriteExisting)
{ // The genome we are building already has a connection with the same neuron endpoints as the one we are
// trying to add. It didn't match up during correlation because it has a different innovation number, this
// is possible because the innovation history buffers throw away old innovations in a FIFO manner in order
// to prevent them from bloating.
// Here the 'overwriteExisting' flag is set so the gene we are currently trying to add is probably from the
// fitter parent, and therefore we want to use its connection weight in place of the existing gene's weight.
existingConnectionGene.Weight = connectionGene.Weight;
}
}
void SpawnParentAndSeeds(NeatGenome parent)
{
CreateMesh(parent, parentGameObject);
seeds.Clear();
for (int i = 0; i < numberOfChildren; i++)
{
var child = evolutionHelper.MutateGenome(parent);
seeds.Add(child);
CreateMesh(child, seedsGameObjects[i]);
}
}
/// <summary>
/// Writes a NeatGenome to XML.
/// </summary>
/// <param name="xw">XmlWriter to write XML to.</param>
/// <param name="genome">Genome to write as XML.</param>
/// <param name="nodeFnIds">Indicates if node activation function IDs should be emitted. They are required
/// for HyperNEAT genomes but not for NEAT.</param>
public static void Write(XmlWriter xw, NeatGenome genome, bool nodeFnIds)
{
xw.WriteStartElement(__ElemNetwork);
xw.WriteAttributeString(__AttrId, genome.Id.ToString(NumberFormatInfo.InvariantInfo));
xw.WriteAttributeString(__AttrBirthGeneration, genome.BirthGeneration.ToString(NumberFormatInfo.InvariantInfo));
xw.WriteAttributeString(__AttrFitness, genome.EvaluationInfo.Fitness.ToString("R", NumberFormatInfo.InvariantInfo));
// Emit nodes.
StringBuilder sb = new StringBuilder();
xw.WriteStartElement(__ElemNodes);
foreach(NeuronGene nGene in genome.NeuronGeneList)
{
xw.WriteStartElement(__ElemNode);
xw.WriteAttributeString(__AttrType, NetworkXmlIO.GetNodeTypeString(nGene.NodeType));
xw.WriteAttributeString(__AttrId, nGene.Id.ToString(NumberFormatInfo.InvariantInfo));
if(nodeFnIds)
{ // Write activation fn ID.
xw.WriteAttributeString(__AttrActivationFunctionId, nGene.ActivationFnId.ToString(NumberFormatInfo.InvariantInfo));
// Write aux state as comma separated list of real values.
XmlIoUtils.WriteAttributeString(xw, __AttrAuxState, nGene.AuxState);
}
xw.WriteEndElement();
}
xw.WriteEndElement();
// Emit connections.
xw.WriteStartElement(__ElemConnections);
foreach(ConnectionGene cGene in genome.ConnectionList)
{
xw.WriteStartElement(__ElemConnection);
xw.WriteAttributeString(__AttrId, cGene.InnovationId.ToString(NumberFormatInfo.InvariantInfo));
xw.WriteAttributeString(__AttrSourceId, cGene.SourceNodeId.ToString(NumberFormatInfo.InvariantInfo));
xw.WriteAttributeString(__AttrTargetId, cGene.TargetNodeId.ToString(NumberFormatInfo.InvariantInfo));
xw.WriteAttributeString(__AttrWeight, cGene.Weight.ToString("R", NumberFormatInfo.InvariantInfo));
xw.WriteEndElement();
}
xw.WriteEndElement();
// </Network>
xw.WriteEndElement();
}
/// <summary>
/// Refresh/update the view with the provided genome.
/// </summary>
public override void RefreshView(object genome)
{
_box = null;
_neatGenome = genome as NeatGenome;
if(null == _neatGenome) {
return;
}
// Decode genome.
_box = _genomeDecoder.Decode(_neatGenome);
// Generate new test case.
_testCaseField.InitTestCase(_largeBoxTestCase++ % 3);
// Paint test case and network response.
PaintView(_box);
}
/// <summary>
/// Writes a single NeatGenome to XML within a containing 'Root' element and the activation
/// function library that the genome is associated with.
/// </summary>
/// <param name="xw">XmlWriter to write XML to.</param>
/// <param name="genome">Genome to write as XML.</param>
/// <param name="nodeFnIds">Indicates if node activation function IDs should be emitted. They are required
/// for HyperNEAT genomes but not for NEAT.</param>
public static void WriteComplete(XmlWriter xw, NeatGenome genome, bool nodeFnIds)
{
// <Root>
xw.WriteStartElement(__ElemRoot);
// Write activation function library.
NetworkXmlIO.Write(xw, genome.ActivationFnLibrary);
// <Networks>
xw.WriteStartElement(__ElemNetworks);
// Write single genome.
Write(xw, genome, nodeFnIds);
// </Networks>
xw.WriteEndElement();
// </Root>
xw.WriteEndElement();
}
public NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(NeatGenome seed, IGenomeListEvaluator<NeatGenome> eval = null)
{
// Create a genome factory with our neat genome parameters object and the appropriate number of input and output neuron genes.
IGenomeFactory<NeatGenome> genomeFactory = CreateGenomeFactory();
// Create an initial population of randomly generated genomes.
List<NeatGenome> genomeList = genomeFactory.CreateGenomeList(_populationSize, 0, seed);
return CreateEvolutionAlgorithm(genomeFactory, genomeList, eval);
}
public static void WriteNeatGenome(string serializedGenomePath, NeatGenome genomeToWrite)
{
// Create a reader for the serialized genome
XmlWriter writer = XmlWriter.Create(serializedGenomePath);
NeatGenomeXmlIO.WriteComplete(writer, genomeToWrite, true);
writer.Close();
}
public NeatGenome MutateGenome(NeatGenome genome)
{
return genome.CreateOffspring(genome.BirthGeneration + 1);
}