/// <summary>
/// Handle loading segregation info.
/// </summary>
/// <param name="section">The section being loaded.</param>
private void HandleSegregateFiles(EncogFileSection section)
{
IList <AnalystSegregateTarget> nfs = new List <AnalystSegregateTarget>();
bool first = true;
foreach (String line in section.Lines)
{
if (!first)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
String filename = cols[0];
int percent = Int32.Parse(cols[1]);
var nf = new AnalystSegregateTarget(
filename, percent);
nfs.Add(nf);
}
else
{
first = false;
}
}
var array = new AnalystSegregateTarget[nfs.Count];
for (int i = 0; i < array.Length; i++)
{
array[i] = nfs[i];
}
_script.Segregate.SegregateTargets = array;
}
/// <summary>
/// Handle normalization ranges.
/// </summary>
/// <param name="section">The section being loaded.</param>
private void HandleNormalizeRange(EncogFileSection section)
{
_script.Normalize.NormalizedFields.Clear();
bool first = true;
foreach (String line in section.Lines)
{
if (!first)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
bool isOutput = cols[1].ToLower()
.Equals("output");
int timeSlice = Int32.Parse(cols[2]);
String action = cols[3];
double high = CSVFormat.EgFormat.Parse(cols[4]);
double low = CSVFormat.EgFormat.Parse(cols[5]);
NormalizationAction des;
if (action.Equals("range"))
{
des = NormalizationAction.Normalize;
}
else if (action.Equals("ignore"))
{
des = NormalizationAction.Ignore;
}
else if (action.Equals("pass"))
{
des = NormalizationAction.PassThrough;
}
else if (action.Equals("equilateral"))
{
des = NormalizationAction.Equilateral;
}
else if (action.Equals("single"))
{
des = NormalizationAction.SingleField;
}
else if (action.Equals("oneof"))
{
des = NormalizationAction.OneOf;
}
else
{
throw new AnalystError("Unknown field type:" + action);
}
var nf = new AnalystField(name, des, high, low)
{
TimeSlice = timeSlice, Output = isOutput
};
_script.Normalize.NormalizedFields.Add(nf);
}
else
{
first = false;
}
}
}
/// <summary>
/// Handle loading data stats.
/// </summary>
/// <param name="section">The section being loaded.</param>
private void HandleDataStats(EncogFileSection section)
{
IList <DataField> dfs = new List <DataField>();
bool first = true;
foreach (String line in section.Lines)
{
if (!first)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
bool isclass = Int32.Parse(cols[1]) > 0;
bool iscomplete = Int32.Parse(cols[2]) > 0;
bool isint = Int32.Parse(cols[ColumnThree]) > 0;
bool isreal = Int32.Parse(cols[ColumnFour]) > 0;
double amax = CSVFormat.EgFormat.Parse(cols[5]);
double amin = CSVFormat.EgFormat.Parse(cols[6]);
double mean = CSVFormat.EgFormat.Parse(cols[7]);
double sdev = CSVFormat.EgFormat.Parse(cols[8]);
String source = "";
// source was added in Encog 3.2, so it might not be there
if (cols.Count > 9)
{
source = cols[9];
}
var df = new DataField(name)
{
Class = isclass,
Complete = iscomplete,
Integer = isint,
Real = isreal,
Max = amax,
Min = amin,
Mean = mean,
StandardDeviation = sdev,
Source = source
};
dfs.Add(df);
}
else
{
first = false;
}
}
var array = new DataField[dfs.Count];
for (int i = 0; i < array.Length; i++)
{
array[i] = dfs[i];
}
_script.Fields = array;
}
private void LoadOpcodes(EncogFileSection section)
{
bool first = true;
foreach (string line in section.Lines)
{
if (!first)
{
IList <string> cols = EncogFileSection.SplitColumns(line);
string name = cols[0];
int childCount = int.Parse(cols[1]);
var opcode = new ScriptOpcode(name, childCount);
_script.Opcodes.Add(opcode);
}
else
{
first = false;
}
}
}
private void HandleProcessFields(EncogFileSection section)
{
IList <ProcessField> fields = _script.Process.Fields;
bool first = true;
fields.Clear();
foreach (string line in section.Lines)
{
if (!first)
{
IList <string> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
String command = cols[1];
var pf = new ProcessField(name, command);
fields.Add(pf);
}
else
{
first = false;
}
}
}
/// <inheritdoc/>
public Object Read(Stream mask0)
{
var result = new RBFNetwork();
var flat = (FlatNetworkRBF)result.Flat;
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("RBF-NETWORK") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("RBF-NETWORK") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
flat.BeginTraining = EncogFileSection.ParseInt(p,
BasicNetwork.TagBeginTraining);
flat.ConnectionLimit = EncogFileSection.ParseDouble(p,
BasicNetwork.TagConnectionLimit);
flat.ContextTargetOffset = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagContextTargetOffset);
flat.ContextTargetSize = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagContextTargetSize);
flat.EndTraining = EncogFileSection.ParseInt(p,
BasicNetwork.TagEndTraining);
flat.HasContext = EncogFileSection.ParseBoolean(p,
BasicNetwork.TagHasContext);
flat.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
flat.LayerCounts = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerCounts);
flat.LayerFeedCounts = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerFeedCounts);
flat.LayerContextCount = EncogFileSection.ParseIntArray(p, BasicNetwork.TagLayerContextCount);
flat.LayerIndex = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerIndex);
flat.LayerOutput = section.ParseDoubleArray(p,
PersistConst.Output);
flat.LayerSums = new double[flat.LayerOutput.Length];
flat.OutputCount = EncogFileSection.ParseInt(p, PersistConst.OutputCount);
flat.WeightIndex = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagWeightIndex);
flat.Weights = section.ParseDoubleArray(p,
PersistConst.Weights);
flat.BiasActivation = section.ParseDoubleArray(p, BasicNetwork.TagBiasActivation);
}
else if (section.SectionName.Equals("RBF-NETWORK") &&
section.SubSectionName.Equals("ACTIVATION"))
{
int index = 0;
flat.ActivationFunctions = new IActivationFunction[flat.LayerCounts.Length];
foreach (String line in section.Lines)
{
IActivationFunction af;
IList <String> cols = EncogFileSection
.SplitColumns(line);
String name = ReflectionUtil.AfPath
+ cols[0];
try
{
af = (IActivationFunction)ReflectionUtil.LoadObject(name);
}
catch (Exception e)
{
throw new PersistError(e);
}
for (int i = 0; i < af.ParamNames.Length; i++)
{
af.Params[i] = CSVFormat.EgFormat.Parse(cols[i + 1]);
}
flat.ActivationFunctions[index++] = af;
}
}
else if (section.SectionName.Equals("RBF-NETWORK") &&
section.SubSectionName.Equals("RBF"))
{
int index = 0;
int hiddenCount = flat.LayerCounts[1];
int inputCount = flat.LayerCounts[2];
flat.RBF = new IRadialBasisFunction[hiddenCount];
foreach (String line in section.Lines)
{
IRadialBasisFunction rbf;
IList <String> cols = EncogFileSection
.SplitColumns(line);
String name = ReflectionUtil.RBFPath + cols[0];
try
{
rbf = (IRadialBasisFunction)ReflectionUtil.LoadObject(name);
}
catch (TypeLoadException ex)
{
throw new PersistError(ex);
}
catch (TargetException ex)
{
throw new PersistError(ex);
}
catch (MemberAccessException ex)
{
throw new PersistError(ex);
}
rbf.Width = CSVFormat.EgFormat.Parse(cols[1]);
rbf.Peak = CSVFormat.EgFormat.Parse(cols[2]);
rbf.Centers = new double[inputCount];
for (int i = 0; i < inputCount; i++)
{
rbf.Centers[i] = CSVFormat.EgFormat.Parse(cols[i + 3]);
}
flat.RBF[index++] = rbf;
}
}
}
return(result);
}
/// <summary>
/// Handle loading the data classes.
/// </summary>
/// <param name="section">The section being loaded.</param>
private void HandleDataClasses(EncogFileSection section)
{
var map = new Dictionary <String, List <AnalystClassItem> >();
bool first = true;
foreach (String line in section.Lines)
{
if (!first)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (cols.Count < ColumnFour)
{
throw new AnalystError("Invalid data class: " + line);
}
String field = cols[0];
String code = cols[1];
String name = cols[2];
int count = Int32.Parse(cols[3]);
DataField df = _script.FindDataField(field);
if (df == null)
{
throw new AnalystError(
"Attempting to add class to unknown field: " + name);
}
List <AnalystClassItem> classItems;
if (!map.ContainsKey(field))
{
classItems = new List <AnalystClassItem>();
map[field] = classItems;
}
else
{
classItems = map[field];
}
classItems.Add(new AnalystClassItem(code, name, count));
}
else
{
first = false;
}
}
foreach (DataField field in _script.Fields)
{
if (field.Class)
{
List <AnalystClassItem> classList = map[field.Name];
if (classList != null)
{
classList.Sort();
field.ClassMembers.Clear();
foreach (AnalystClassItem item in classList)
{
field.ClassMembers.Add(item);
}
}
}
}
}
/// <inheritdoc />
public Object Read(Stream istream)
{
var context = new EncogProgramContext();
var result = new PrgPopulation(context, 0);
var reader = new EncogReadHelper(istream);
EncogFileSection section;
int count = 0;
ISpecies lastSpecies = null;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <string, string> prms = section.ParseParams();
EngineArray.PutAll(prms, result.Properties);
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("EPL-POPULATION"))
{
foreach (string line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
Population = result,
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (cols[0].Equals("p"))
{
double score;
double adjustedScore;
if (String.Compare(cols[1], "nan", StringComparison.OrdinalIgnoreCase) == 0 ||
String.Compare(cols[2], "nan", StringComparison.OrdinalIgnoreCase) == 0)
{
score = Double.NaN;
adjustedScore = Double.NaN;
}
else
{
score = CSVFormat.EgFormat.Parse(cols[1]);
adjustedScore = CSVFormat.EgFormat.Parse(cols[2]);
}
String code = cols[3];
var prg = new EncogProgram(context);
prg.CompileEPL(code);
prg.Score = score;
prg.Species = lastSpecies;
prg.AdjustedScore = adjustedScore;
if (lastSpecies == null)
{
throw new EncogError(
"Have not defined a species yet");
}
lastSpecies.Add(prg);
count++;
}
}
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("EPL-OPCODES"))
{
foreach (String line in section.Lines)
{
IList <string> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
int args = int.Parse(cols[1]);
result.Context.Functions.AddExtension(name, args);
}
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("EPL-SYMBOLIC"))
{
bool first = true;
foreach (string line in section.Lines)
{
if (!first)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
String t = cols[1];
var vt = EPLValueType.Unknown;
if (string.Compare(t, "f", true) == 0)
{
vt = EPLValueType.FloatingType;
}
else if (string.Compare(t, "b", true) == 0)
{
vt = EPLValueType.BooleanType;
}
else if (string.Compare(t, "i", true) == 0)
{
vt = EPLValueType.IntType;
}
else if (string.Compare(t, "s", true) == 0)
{
vt = EPLValueType.StringType;
}
else if (string.Compare(t, "e", true) == 0)
{
vt = EPLValueType.EnumType;
}
int enumType = int.Parse(cols[2]);
int enumCount = int.Parse(cols[3]);
var mapping = new VariableMapping(
name, vt, enumType, enumCount);
if (mapping.Name.Length > 0)
{
result.Context.DefineVariable(mapping);
}
else
{
result.Context.Result = mapping;
}
}
else
{
first = false;
}
}
}
}
result.PopulationSize = count;
// set the best genome, should be the first genome in the first species
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
// set the leaders
foreach (ISpecies sp in result.Species)
{
if (sp.Members.Count > 0)
{
sp.Leader = sp.Members[0];
}
}
}
return(result);
}
/// <summary>
/// Read the object.
/// </summary>
/// <param name="mask0">The stream to read the object from.</param>
/// <returns>The object that was loaded.</returns>
public virtual Object Read(Stream mask0)
{
var result = new NEATPopulation();
var innovationList = new NEATInnovationList {
Population = result
};
result.Innovations = innovationList;
var ins0 = new EncogReadHelper(mask0);
IDictionary <Int32, ISpecies> speciesMap = new Dictionary <Int32, ISpecies>();
IDictionary <ISpecies, Int32> leaderMap = new Dictionary <ISpecies, Int32>();
IDictionary <Int32, IGenome> genomeMap = new Dictionary <Int32, IGenome>();
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("INNOVATIONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
var innovation = new NEATInnovation
{
InnovationID = Int32.Parse(cols[0]),
InnovationType = StringToInnovationType(cols[1]),
NeuronType = StringToNeuronType(cols[2]),
SplitX = CSVFormat.EgFormat.Parse(cols[3]),
SplitY = CSVFormat.EgFormat.Parse(cols[4]),
NeuronID = Int32.Parse(cols[5]),
FromNeuronID = Int32.Parse(cols[6]),
ToNeuronID = Int32.Parse(cols[7])
};
result.Innovations.Add(innovation);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("SPECIES"))
{
foreach (String line in section.Lines)
{
String[] cols = line.Split(',');
var species = new BasicSpecies
{
SpeciesID = Int32.Parse(cols[0]),
Age = Int32.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
GensNoImprovement = Int32.Parse(cols[3]),
SpawnsRequired = CSVFormat.EgFormat
.Parse(cols[4])
};
species.SpawnsRequired = CSVFormat.EgFormat
.Parse(cols[5]);
leaderMap[(species)] = (Int32.Parse(cols[6]));
result.Species.Add(species);
speciesMap[((int)species.SpeciesID)] = (species);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("GENOMES"))
{
NEATGenome lastGenome = null;
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (cols[0].Equals("g", StringComparison.InvariantCultureIgnoreCase))
{
lastGenome = new NEATGenome
{
NeuronsChromosome = new Chromosome(),
LinksChromosome = new Chromosome()
};
lastGenome.Chromosomes.Add(lastGenome.NeuronsChromosome);
lastGenome.Chromosomes.Add(lastGenome.LinksChromosome);
lastGenome.GenomeID = Int32.Parse(cols[1]);
lastGenome.SpeciesID = Int32.Parse(cols[2]);
lastGenome.AdjustedScore = CSVFormat.EgFormat
.Parse(cols[3]);
lastGenome.AmountToSpawn = CSVFormat.EgFormat
.Parse(cols[4]);
lastGenome.NetworkDepth = Int32.Parse(cols[5]);
lastGenome.Score = CSVFormat.EgFormat.Parse(cols[6]);
result.Add(lastGenome);
genomeMap[(int)lastGenome.GenomeID] = lastGenome;
}
else if (cols[0].Equals("n", StringComparison.InvariantCultureIgnoreCase))
{
var neuronGene = new NEATNeuronGene
{
Id = Int32.Parse(cols[1]),
NeuronType = StringToNeuronType(cols[2]),
Enabled = Int32.Parse(cols[3]) > 0,
InnovationId = Int32.Parse(cols[4]),
ActivationResponse = CSVFormat.EgFormat
.Parse(cols[5]),
SplitX = CSVFormat.EgFormat.Parse(cols[6]),
SplitY = CSVFormat.EgFormat.Parse(cols[7])
};
lastGenome.Neurons.Add(neuronGene);
}
else if (cols[0].Equals("l", StringComparison.InvariantCultureIgnoreCase))
{
var linkGene = new NEATLinkGene();
linkGene.Id = Int32.Parse(cols[1]);
linkGene.Enabled = Int32.Parse(cols[2]) > 0;
linkGene.Recurrent = Int32.Parse(cols[3]) > 0;
linkGene.FromNeuronID = Int32.Parse(cols[4]);
linkGene.ToNeuronID = Int32.Parse(cols[5]);
linkGene.Weight = CSVFormat.EgFormat.Parse(cols[6]);
linkGene.InnovationId = Int32.Parse(cols[7]);
lastGenome.Links.Add(linkGene);
}
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <String, String> paras = section.ParseParams();
result.NeatActivationFunction = EncogFileSection
.ParseActivationFunction(paras,
NEATPopulation.PropertyNEATActivation);
result.OutputActivationFunction = EncogFileSection
.ParseActivationFunction(paras,
NEATPopulation.PropertyOutputActivation);
result.Snapshot = EncogFileSection.ParseBoolean(paras,
PersistConst.Snapshot);
result.InputCount = EncogFileSection.ParseInt(paras,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(paras,
PersistConst.OutputCount);
result.OldAgePenalty = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertyOldAgePenalty);
result.OldAgeThreshold = EncogFileSection.ParseInt(paras,
PopulationConst.PropertyOldAgeThreshold);
result.PopulationSize = EncogFileSection.ParseInt(paras,
PopulationConst.PropertyPopulationSize);
result.SurvivalRate = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertySurvivalRate);
result.YoungBonusAgeThreshhold = EncogFileSection.ParseInt(
paras, PopulationConst.PropertyYoungAgeThreshold);
result.YoungScoreBonus = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertyYoungAgeBonus);
result.GenomeIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextGenomeID);
result.InnovationIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextInnovationID);
result.GeneIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextGeneID);
result.SpeciesIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextSpeciesID);
}
}
// now link everything up
// first put all the genomes into correct species
foreach (IGenome genome in result.Genomes)
{
var neatGenome = (NEATGenome)genome;
var speciesId = (int)neatGenome.SpeciesID;
if (speciesMap.ContainsKey(speciesId))
{
ISpecies s = speciesMap[speciesId];
s.Members.Add(neatGenome);
}
neatGenome.InputCount = result.InputCount;
neatGenome.OutputCount = result.OutputCount;
}
// set the species leader links
foreach (ISpecies species in leaderMap.Keys)
{
int leaderID = leaderMap[species];
IGenome leader = genomeMap[leaderID];
species.Leader = leader;
((BasicSpecies)species).Population = result;
}
return(result);
}
/// <summary>
/// Read an object.
/// </summary>
public Object Read(Stream mask0)
{
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
var samples = new BasicMLDataSet();
IDictionary <String, String> networkParams = null;
PNNKernelType kernel = default(PNNKernelType) /* was: null */;
PNNOutputMode outmodel = default(PNNOutputMode) /* was: null */;
int inputCount = 0;
int outputCount = 0;
double error = 0;
double[] sigma = null;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("PNN") &&
section.SubSectionName.Equals("PARAMS"))
{
networkParams = section.ParseParams();
}
if (section.SectionName.Equals("PNN") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> paras = section.ParseParams();
inputCount = EncogFileSection.ParseInt(paras,
PersistConst.InputCount);
outputCount = EncogFileSection.ParseInt(paras,
PersistConst.OutputCount);
kernel = StringToKernel(paras[PersistConst.Kernel]);
outmodel = StringToOutputMode(paras[PropertyOutputMode]);
error = EncogFileSection
.ParseDouble(paras, PersistConst.Error);
sigma = section.ParseDoubleArray(paras, PersistConst.Sigma);
}
if (section.SectionName.Equals("PNN") &&
section.SubSectionName.Equals("SAMPLES"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection
.SplitColumns(line);
int index = 0;
var inputData = new BasicMLData(inputCount);
for (int i = 0; i < inputCount; i++)
{
inputData[i] =
CSVFormat.EgFormat.Parse(cols[index++]);
}
var idealData = new BasicMLData(inputCount);
idealData[0] = CSVFormat.EgFormat.Parse(cols[index++]);
IMLDataPair pair = new BasicMLDataPair(inputData,
idealData);
samples.Add(pair);
}
}
}
var result = new BasicPNN(kernel, outmodel, inputCount,
outputCount);
if (networkParams != null)
{
EngineArray.PutAll(networkParams, result.Properties);
}
result.Samples = samples;
result.Error = error;
if (sigma != null)
{
EngineArray.ArrayCopy(sigma, result.Sigma);
}
return(result);
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
long nextInnovationId = 0;
long nextGeneId = 0;
var result = new NEATPopulation();
var innovationList = new NEATInnovationList {
Population = result
};
result.Innovations = innovationList;
var reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("INNOVATIONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection
.SplitColumns(line);
var innovation = new NEATInnovation();
var innovationId = int.Parse(cols[1]);
innovation.InnovationId = innovationId;
innovation.NeuronId = int.Parse(cols[2]);
result.Innovations.Innovations[cols[0]] = innovation;
nextInnovationId = Math.Max(nextInnovationId, innovationId + 1);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("SPECIES"))
{
NEATGenome lastGenome = null;
BasicSpecies lastSpecies = null;
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Population = result,
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (String.Compare(cols[0], "g", StringComparison.OrdinalIgnoreCase) == 0)
{
bool isLeader = lastGenome == null;
lastGenome = new NEATGenome
{
InputCount = result.InputCount,
OutputCount = result.OutputCount,
Species = lastSpecies,
AdjustedScore = CSVFormat.EgFormat.Parse(cols[1]),
Score = CSVFormat.EgFormat.Parse(cols[2]),
BirthGeneration = int.Parse(cols[3])
};
lastSpecies.Add(lastGenome);
if (isLeader)
{
lastSpecies.Leader = lastGenome;
}
}
else if (String.Compare(cols[0], "n", StringComparison.OrdinalIgnoreCase) == 0)
{
var neuronGene = new NEATNeuronGene();
int geneId = int.Parse(cols[1]);
neuronGene.Id = geneId;
IActivationFunction af = EncogFileSection.ParseActivationFunction(cols[2]);
neuronGene.ActivationFunction = af;
neuronGene.NeuronType = PersistNEATPopulation.StringToNeuronType(cols[3]);
neuronGene.InnovationId = int.Parse(cols[4]);
lastGenome.NeuronsChromosome.Add(neuronGene);
nextGeneId = Math.Max(geneId + 1, nextGeneId);
}
else if (String.Compare(cols[0], "l", StringComparison.OrdinalIgnoreCase) == 0)
{
var linkGene = new NEATLinkGene
{
Id = int.Parse(cols[1]),
Enabled = (int.Parse(cols[2]) > 0),
FromNeuronId = int.Parse(cols[3]),
ToNeuronId = int.Parse(cols[4]),
Weight = CSVFormat.EgFormat.Parse(cols[5]),
InnovationId = int.Parse(cols[6])
};
lastGenome.LinksChromosome.Add(linkGene);
}
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <string, string> prm = section.ParseParams();
string afStr = prm[NEATPopulation.PropertyNEATActivation];
if (String.Compare(afStr, TypeCppn, StringComparison.OrdinalIgnoreCase) == 0)
{
HyperNEATGenome.BuildCPPNActivationFunctions(result.ActivationFunctions);
}
else
{
result.NEATActivationFunction = EncogFileSection.ParseActivationFunction(prm,
NEATPopulation.PropertyNEATActivation);
}
result.ActivationCycles = EncogFileSection.ParseInt(prm,
PersistConst.ActivationCycles);
result.InputCount = EncogFileSection.ParseInt(prm,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(prm,
PersistConst.OutputCount);
result.PopulationSize = EncogFileSection.ParseInt(prm,
NEATPopulation.PropertyPopulationSize);
result.SurvivalRate = EncogFileSection.ParseDouble(prm,
NEATPopulation.PropertySurvivalRate);
result.ActivationCycles = EncogFileSection.ParseInt(prm,
NEATPopulation.PropertyCycles);
}
}
// set factories
if (result.IsHyperNEAT)
{
result.GenomeFactory = new FactorHyperNEATGenome();
result.CODEC = new HyperNEATCODEC();
}
else
{
result.GenomeFactory = new FactorNEATGenome();
result.CODEC = new NEATCODEC();
}
// set the next ID's
result.InnovationIDGenerate.CurrentID = nextInnovationId;
result.GeneIdGenerate.CurrentID = nextGeneId;
// find first genome, which should be the best genome
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
}
return(result);
}
/// <summary>
/// Read the object.
/// </summary>
/// <param name="mask0">The stream to read from.</param>
/// <returns>The loaded object.</returns>
public virtual Object Read(Stream mask0)
{
var result = new NEATNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
IDictionary <Int32, NEATNeuron> neuronMap = new Dictionary <Int32, NEATNeuron>();
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
foreach (String key in paras.Keys)
{
result.Properties.Add(key, paras[key]);
}
}
if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(p,
PersistConst.OutputCount);
result.ActivationFunction = EncogFileSection
.ParseActivationFunction(p,
PersistConst.ActivationFunction);
result.OutputActivationFunction = EncogFileSection
.ParseActivationFunction(p,
NEATPopulation.PropertyOutputActivation);
result.NetworkDepth = EncogFileSection.ParseInt(p,
PersistConst.Depth);
result.Snapshot = EncogFileSection.ParseBoolean(p,
PersistConst.Snapshot);
}
else if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("NEURONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
long neuronID = Int32.Parse(cols[0]);
NEATNeuronType neuronType = PersistNEATPopulation
.StringToNeuronType(cols[1]);
double activationResponse = CSVFormat.EgFormat
.Parse(cols[2]);
double splitY = CSVFormat.EgFormat
.Parse(cols[3]);
double splitX = CSVFormat.EgFormat
.Parse(cols[4]);
var neatNeuron = new NEATNeuron(neuronType,
neuronID, splitY, splitX, activationResponse);
result.Neurons.Add(neatNeuron);
neuronMap[((int)neuronID)] = (neatNeuron);
}
}
else if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("LINKS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
int fromID = Int32.Parse(cols[0]);
int toID = Int32.Parse(cols[1]);
bool recurrent = Int32.Parse(cols[2]) > 0;
double weight = CSVFormat.EgFormat.Parse(cols[3]);
NEATNeuron fromNeuron = (neuronMap[fromID]);
NEATNeuron toNeuron = (neuronMap[toID]);
var neatLink = new NEATLink(weight, fromNeuron,
toNeuron, recurrent);
fromNeuron.OutputboundLinks.Add(neatLink);
toNeuron.InboundLinks.Add(neatLink);
}
}
}
return(result);
}