/// <summary>
/// Creates a single randomly initialised genome.
/// A random set of connections are made form the input to the output neurons, the number of
/// connections made is based on the NeatGenomeParameters.InitialInterconnectionsProportion
/// which specifies the proportion of all posssible input-output connections to be made in
/// initial genomes.
///
/// The connections that are made are allocated innovation IDs in a consistent manner across
/// the initial population of genomes. To do this we allocate IDs sequentially to all possible
/// interconnections and then randomly select some proportion of connections for inclusion in the
/// genome. In addition, for this scheme to work the innovation ID generator must be reset to zero
/// prior to each call to CreateGenome(), and a test is made to ensure this is the case.
///
/// The consistent allocation of innovation IDs ensure that equivalent connections in different
/// genomes have the same innovation ID, and although this isn't strictly necessary it is
/// required for sexual reproduction to work effectively - like structures are detected by comparing
/// innovation IDs only.
/// </summary>
/// <param name="birthGeneration">The current evolution algorithm generation.
/// Assigned to the new genome as its birth generation.</param>
public NeatGenome CreateGenome(uint birthGeneration)
{
NeuronGeneList neuronGeneList = new NeuronGeneList(_inputNeuronCount + _outputNeuronCount);
NeuronGeneList inputNeuronGeneList = new NeuronGeneList(_inputNeuronCount); // includes single bias neuron.
NeuronGeneList outputNeuronGeneList = new NeuronGeneList(_outputNeuronCount);
// Create a single bias neuron.
uint biasNeuronId = _innovationIdGenerator.NextId;
if (0 != biasNeuronId)
{ // The ID generator must be reset before calling this method so that all generated genomes use the
// same innovation ID for matching neurons and structures.
throw new SharpNeatException("IdGenerator must be reset before calling CreateGenome(uint)");
}
// Note. Genes within nGeneList must always be arranged according to the following layout plan.
// Bias - single neuron. Innovation ID = 0
// Input neurons.
// Output neurons.
// Hidden neurons.
NeuronGene neuronGene = CreateNeuronGene(biasNeuronId, NodeType.Bias);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
// Create input neuron genes.
for (int i = 0; i < _inputNeuronCount; i++)
{
neuronGene = CreateNeuronGene(_innovationIdGenerator.NextId, NodeType.Input);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Create output neuron genes.
for (int i = 0; i < _outputNeuronCount; i++)
{
neuronGene = CreateNeuronGene(_innovationIdGenerator.NextId, NodeType.Output);
outputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Define all possible connections between the input and output neurons (fully interconnected).
int srcCount = inputNeuronGeneList.Count;
int tgtCount = outputNeuronGeneList.Count;
ConnectionDefinition[] connectionDefArr = new ConnectionDefinition[srcCount * tgtCount];
for (int srcIdx = 0, i = 0; srcIdx < srcCount; srcIdx++)
{
for (int tgtIdx = 0; tgtIdx < tgtCount; tgtIdx++)
{
connectionDefArr[i++] = new ConnectionDefinition(_innovationIdGenerator.NextId, srcIdx, tgtIdx);
}
}
// Shuffle the array of possible connections.
Utilities.Shuffle(connectionDefArr, _rng);
// Select connection definitions from the head of the list and convert them to real connections.
// We want some proportion of all possible connections but at least one (Connectionless genomes are not allowed).
int connectionCount = (int)Utilities.ProbabilisticRound(
(double)connectionDefArr.Length * _neatGenomeParamsComplexifying.InitialInterconnectionsProportion,
_rng);
connectionCount = Math.Max(1, connectionCount);
// Create the connection gene list and populate it.
ConnectionGeneList connectionGeneList = new ConnectionGeneList(connectionCount);
for (int i = 0; i < connectionCount; i++)
{
ConnectionDefinition def = connectionDefArr[i];
NeuronGene srcNeuronGene = inputNeuronGeneList[def._sourceNeuronIdx];
NeuronGene tgtNeuronGene = outputNeuronGeneList[def._targetNeuronIdx];
ConnectionGene cGene = new ConnectionGene(def._innovationId,
srcNeuronGene.InnovationId,
tgtNeuronGene.InnovationId,
GenerateRandomConnectionWeight());
connectionGeneList.Add(cGene);
// Register connection with endpoint neurons.
srcNeuronGene.TargetNeurons.Add(cGene.TargetNodeId);
tgtNeuronGene.SourceNeurons.Add(cGene.SourceNodeId);
}
// Ensure connections are sorted.
connectionGeneList.SortByInnovationId();
// Create and return the completed genome object.
return(CreateGenome(_genomeIdGenerator.NextId, birthGeneration,
neuronGeneList, connectionGeneList,
_inputNeuronCount, _outputNeuronCount, false));
}
/// <summary>
/// Reads a NeatGenome from XML.
/// </summary>
/// <param name="xr">The XmlReader to read from.</param>
/// <param name="nodeFnIds">Indicates if node activation function IDs should be read. They are required
/// for HyperNEAT genomes but not for NEAT</param>
public static NeatGenome ReadGenome(XmlReader xr, bool nodeFnIds)
{
// Find <Network>.
XmlIoUtils.MoveToElement(xr, false, __ElemNetwork);
int initialDepth = xr.Depth;
// Read genome ID attribute if present. Otherwise default to zero; it's the caller's responsibility to
// check IDs are unique and in-line with the genome factory's ID generators.
string genomeIdStr = xr.GetAttribute(__AttrId);
uint genomeId;
uint.TryParse(genomeIdStr, out genomeId);
// Read birthGeneration attribute if present. Otherwise default to zero.
string birthGenStr = xr.GetAttribute(__AttrBirthGeneration);
uint birthGen;
uint.TryParse(birthGenStr, out birthGen);
// Find <Nodes>.
XmlIoUtils.MoveToElement(xr, true, __ElemNodes);
// Create a reader over the <Nodes> sub-tree.
int inputNodeCount = 0;
int outputNodeCount = 0;
NeuronGeneList nGeneList = new NeuronGeneList();
using (XmlReader xrSubtree = xr.ReadSubtree())
{
// Re-scan for the root <Nodes> element.
XmlIoUtils.MoveToElement(xrSubtree, false);
// Move to first node elem.
XmlIoUtils.MoveToElement(xrSubtree, true, __ElemNode);
// Read node elements.
do
{
NodeType neuronType = NetworkXmlIO.ReadAttributeAsNodeType(xrSubtree, __AttrType);
uint id = XmlIoUtils.ReadAttributeAsUInt(xrSubtree, __AttrId);
int functionId = 0;
double[] auxState = null;
if (nodeFnIds)
{ // Read activation fn ID.
functionId = XmlIoUtils.ReadAttributeAsInt(xrSubtree, __AttrActivationFunctionId);
// Read aux state as comma seperated list of real values.
auxState = XmlIoUtils.ReadAttributeAsDoubleArray(xrSubtree, __AttrAuxState);
}
NeuronGene nGene = new NeuronGene(id, neuronType, functionId, auxState);
nGeneList.Add(nGene);
// Track the number of input and output nodes.
switch (neuronType)
{
case NodeType.Input:
inputNodeCount++;
break;
case NodeType.Output:
outputNodeCount++;
break;
}
}while(xrSubtree.ReadToNextSibling(__ElemNode));
}
// Find <Connections>.
XmlIoUtils.MoveToElement(xr, false, __ElemConnections);
// Create a reader over the <Connections> sub-tree.
ConnectionGeneList cGeneList = new ConnectionGeneList();
using (XmlReader xrSubtree = xr.ReadSubtree())
{
// Re-scan for the root <Connections> element.
XmlIoUtils.MoveToElement(xrSubtree, false);
// Move to first connection elem.
string localName = XmlIoUtils.MoveToElement(xrSubtree, true);
if (localName == __ElemConnection)
{ // We have at least one connection.
// Read connection elements.
do
{
uint id = XmlIoUtils.ReadAttributeAsUInt(xrSubtree, __AttrId);
uint srcId = XmlIoUtils.ReadAttributeAsUInt(xrSubtree, __AttrSourceId);
uint tgtId = XmlIoUtils.ReadAttributeAsUInt(xrSubtree, __AttrTargetId);
double weight = XmlIoUtils.ReadAttributeAsDouble(xrSubtree, __AttrWeight);
ConnectionGene cGene = new ConnectionGene(id, srcId, tgtId, weight);
cGeneList.Add(cGene);
}while(xrSubtree.ReadToNextSibling(__ElemConnection));
}
}
// Move the reader beyond the closing tags </Connections> and </Network>.
do
{
if (xr.Depth <= initialDepth)
{
break;
}
}while(xr.Read());
// Construct and return loaded NeatGenome.
return(new NeatGenome(null, genomeId, birthGen, nGeneList, cGeneList, inputNodeCount, outputNodeCount, true));
}
