/// <summary>
/// Create a genome with the provided internal state/definition data/objects.
/// Overridable method to allow alternative NeatGenome sub-classes to be used.
/// </summary>
public virtual NeatGenome CreateGenome(uint id,
uint birthGeneration,
NeuronGeneList neuronGeneList,
ConnectionGeneList connectionGeneList,
int inputNeuronCount,
int outputNeuronCount,
bool rebuildNeuronGeneConnectionInfo)
{
return(new NeatGenome(this, id, birthGeneration, neuronGeneList, connectionGeneList,
inputNeuronCount, outputNeuronCount, rebuildNeuronGeneConnectionInfo));
}
/// <summary>
/// Sort connection genes into ascending order by their innovation IDs.
///
/// Sorts the non-protected connections in the active module. The
/// protected connections are sorted by construction, and we do not want
/// to interfere with older modules.
/// </summary>
public void SortByInnovationId()
{
// Fist we get only the active part in a new List.
ConnectionGeneList littleList = new ConnectionGeneList(Count -
_firstActiveIndex);
for (int i = _firstActiveIndex; i < Count; ++i)
{
littleList.Add(this[i]);
}
// Now we sort only the active part...
littleList.Sort(__connectionGeneComparer);
// And we copy back these values.
int small_indx = 0;
for (int i = _firstActiveIndex; i < Count; ++i)
{
this[i] = littleList[small_indx];
++small_indx;
}
}
/// <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 possible 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.
SortUtils.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)NumericsUtils.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);
// Read fitness attribute if present. Otherwise default to zero.
string fitnessStr = xr.GetAttribute(__AttrFitness);
double fitness;
double.TryParse(fitnessStr, out fitness);
// Find <Nodes>.
XmlIoUtils.MoveToElement(xr, true, __ElemNodes);
// Create a reader over the <Nodes> sub-tree.
int inputNodeCount = 0;
int outputNodeCount = 0;
int regulatoryCount = 0;
int localInCount = 0;
int localOutCount = 0;
// Used to count local input and output neurons (which are not
// found in the base module = 0)
int activeModule = 1;
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 = GetFunctionId(neuronType);
int module = XmlIoUtils.ReadAttributeAsInt(xrSubtree, __AttrModule);
int pandemonium;
// If we have a regulatory neuron, we read its pandemonium label
if (neuronType == NodeType.Regulatory)
{
pandemonium = XmlIoUtils.ReadAttributeAsInt(xrSubtree, __AttrPandemonium);
}
// Otherwise it is simply -1
else
{
pandemonium = -1;
}
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,
module, pandemonium, auxState);
nGeneList.Add(nGene);
// Track the number of input and output nodes.
switch (neuronType)
{
case NodeType.Input:
++inputNodeCount;
break;
case NodeType.Output:
++outputNodeCount;
break;
case NodeType.Regulatory:
++regulatoryCount;
break;
case NodeType.Local_Input:
if (module == activeModule)
{
++localInCount;
}
else
{
// Found a new module, discard previous count
activeModule = module;
localInCount = 1;
localOutCount = 0;
}
break;
case NodeType.Local_Output:
// Here we do not care about the correct module,
// because that has been considered in the local input
// count (and local input always comes first).
++localOutCount;
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);
int module = XmlIoUtils.ReadAttributeAsInt(xrSubtree, __AttrModule);
bool protect = XmlIoUtils.ReadAttributeAsBool(xrSubtree, __AttrProtected);
ConnectionGene cGene = new ConnectionGene(id, srcId, tgtId,
weight, module,
protect);
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. We construct it first
// so we can access its properties before leaving.
bool rebuildConnectivity = true;
// Integrity will fail if we attempt to create the genome before
// updating some of the statistics (counts for each type of neurons,
// etc). It can be done after that step!
bool shouldAssertIntegrity = false;
NeatGenome genome = new NeatGenome(null, genomeId, birthGen, nGeneList,
cGeneList, rebuildConnectivity,
shouldAssertIntegrity);
if (genomeFactory != null)
{
// Note genomeFactory is not fully initialized yet, but it is needed to create
// an EvaluationInfo structure.
genome.GenomeFactory = genomeFactory;
genome.EvaluationInfo.SetFitness(fitness);
}
// We update count variables. While it is true most are static
// variables, loading genomes is done only once, so we can afford
// to count them for each genome.
genome.Input = inputNodeCount;
genome.Output = outputNodeCount;
genome.Regulatory = regulatoryCount;
genome.LocalIn = localInCount;
genome.LocalOut = localOutCount;
// We use base for neurons InHiddenModules
genome.NeuronGeneList.LocateLastBase();
genome.InHiddenModulesFromLoad();
genome.ActiveConnectionsFromLoad();
// Before this was done only once after creating all genomes. However,
// we need these values if we want to perform an integrity check.
// The performance overhead is negligible (specially in a one-time method)
// and reliability is increased this way.
genome.NeuronGeneList.LocateLastBase();
genome.NeuronGeneList.LocateFirstIndex();
genome.ConnectionGeneList.LocateFirstId();
Debug.Assert(genome.PerformIntegrityCheck());
return(genome);
}
/// <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 separated 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));
}
