/// <inheritdoc/>
public Object Read(Stream mask0)
{
var result = new TrainingContinuation();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("CONT") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
foreach (String key in paras.Keys)
{
if (key.Equals("type", StringComparison.InvariantCultureIgnoreCase))
{
result.TrainingType = paras[key];
}
else
{
double[] list = section.ParseDoubleArray(paras, key);
result.Put(key, list);
}
}
}
}
return(result);
}
/// <summary>
/// Read a an object.
/// </summary>
public Object Read(Stream mask0)
{
var result = new HopfieldNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("HOPFIELD") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("HOPFIELD") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.Weights = NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Weights]));
result.SetCurrentState(NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Output])));
result.NeuronCount = EncogFileSection.ParseInt(p,
PersistConst.NeuronCount);
}
}
return(result);
}
/**
* {@inheritDoc}
*/
public Object Read(Stream istream)
{
var result = new SOMNetwork();
var reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("SOM") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> p = section.ParseParams();
EngineArray.PutAll(p, result.Properties);
}
if (section.SectionName.Equals("SOM") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.Weights = EncogFileSection.ParseMatrix(p,
PersistConst.Weights)
;
}
}
return(result);
}
/// <inheritdoc/>
public Object Read(Stream mask0)
{
var result = new BAMNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BAM") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("BAM") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.F1Count = EncogFileSection.ParseInt(p,
PersistConst.PropertyF1Count);
result.F2Count = EncogFileSection.ParseInt(p,
PersistConst.PropertyF2Count);
result.WeightsF1ToF2 = EncogFileSection.ParseMatrix(p, PersistConst.PropertyWeightsF1F2);
result.WeightsF2ToF1 = EncogFileSection.ParseMatrix(p, PersistConst.PropertyWeightsF2F1);
}
}
return(result);
}
/// <summary>
/// Load an Encog script.
/// </summary>
/// <param name="stream">The stream to load from.</param>
public void Load(Stream stream)
{
EncogReadHelper reader = null;
try
{
EncogFileSection section;
reader = new EncogReadHelper(stream);
while ((section = reader.ReadNextSection()) != null)
{
ProcessSubSection(section);
}
// init the script
_script.Init();
}
finally
{
if (reader != null)
{
reader.Close();
}
}
}
/// <inheritdoc/>
public Object Read(Stream mask0)
{
IDictionary <String, String> networkParams = null;
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
int inputCount = 0;
int instarCount = 0;
int outputCount = 0;
int winnerCount = 0;
Matrix m1 = null;
Matrix m2 = null;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("CPN") &&
section.SubSectionName.Equals("PARAMS"))
{
networkParams = section.ParseParams();
}
if (section.SectionName.Equals("CPN") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> paras = section.ParseParams();
inputCount = EncogFileSection.ParseInt(paras,
PersistConst.InputCount);
instarCount = EncogFileSection.ParseInt(paras,
PersistConst.Instar);
outputCount = EncogFileSection.ParseInt(paras,
PersistConst.OutputCount);
winnerCount = EncogFileSection.ParseInt(paras,
PropertyWinnerCount);
m1 = EncogFileSection.ParseMatrix(paras,
PropertyInputToInstar);
m2 = EncogFileSection.ParseMatrix(paras,
PropertyInstarToInput);
}
}
var result = new CPNNetwork(inputCount, instarCount, outputCount,
winnerCount);
EngineArray.PutAll(networkParams, result.Properties);
result.WeightsInputToInstar.Set(m1);
result.WeightsInstarToOutstar.Set(m2);
return(result);
}
/// <inheritdoc/>
public Object Read(Stream mask0)
{
var result = new ART1();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("ART1") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("ART1") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.A1 = EncogFileSection.ParseDouble(p,
BasicART.PropertyA1);
result.B1 = EncogFileSection.ParseDouble(p,
BasicART.PropertyB1);
result.C1 = EncogFileSection.ParseDouble(p,
BasicART.PropertyC1);
result.D1 = EncogFileSection.ParseDouble(p,
BasicART.PropertyD1);
result.F1Count = EncogFileSection.ParseInt(p,
PersistConst.PropertyF1Count);
result.F2Count = EncogFileSection.ParseInt(p,
PersistConst.PropertyF2Count);
result.NoWinner = EncogFileSection.ParseInt(p,
BasicART.PropertyNoWinner);
result.L = EncogFileSection
.ParseDouble(p, BasicART.PropertyL);
result.Vigilance = EncogFileSection.ParseDouble(p,
BasicART.PropertyVigilance);
result.WeightsF1ToF2 = EncogFileSection.ParseMatrix(p,
PersistConst.PropertyWeightsF1F2);
result.WeightsF2ToF1 = EncogFileSection.ParseMatrix(p,
PersistConst.PropertyWeightsF2F1);
}
}
return(result);
}
/// <inheritdoc/>
public Object Read(Stream mask0)
{
var result = new BoltzmannMachine();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BOLTZMANN") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("BOLTZMANN") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.Weights = NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Weights]));
result.SetCurrentState(NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Output])));
result.NeuronCount = EncogFileSection.ParseInt(p,
PersistConst.NeuronCount);
result.Threshold = NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Thresholds]));
result.AnnealCycles = EncogFileSection.ParseInt(p,
BoltzmannMachine.ParamAnnealCycles);
result.RunCycles = EncogFileSection.ParseInt(p,
BoltzmannMachine.ParamRunCycles);
result.Temperature = EncogFileSection.ParseDouble(p,
PersistConst.Temperature);
}
}
return(result);
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
BayesianNetwork result = new BayesianNetwork();
EncogReadHelper input = new EncogReadHelper(istream);
EncogFileSection section;
String queryType = "";
String queryStr = "";
String contentsStr = "";
while ((section = input.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BAYES-NETWORK") &&
section.SubSectionName.Equals("BAYES-PARAM"))
{
IDictionary <String, String> p = section.ParseParams();
queryType = p["queryType"];
queryStr = p["query"];
contentsStr = p["contents"];
}
if (section.SectionName.Equals("BAYES-NETWORK") &&
section.SubSectionName.Equals("BAYES-TABLE"))
{
result.Contents = contentsStr;
// first, define relationships (1st pass)
foreach (String line in section.Lines)
{
result.DefineRelationship(line);
}
result.FinalizeStructure();
// now define the probabilities (2nd pass)
foreach (String line in section.Lines)
{
result.DefineProbability(line);
}
}
if (section.SectionName.Equals("BAYES-NETWORK") &&
section.SubSectionName.Equals("BAYES-PROPERTIES"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
}
// define query, if it exists
if (queryType.Length > 0)
{
IBayesianQuery query = null;
if (queryType.Equals("EnumerationQuery"))
{
query = new EnumerationQuery(result);
}
else
{
query = new SamplingQuery(result);
}
if (query != null && queryStr.Length > 0)
{
result.Query = query;
result.DefineClassificationStructure(queryStr);
}
}
return(result);
}
/// <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);
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
int states = 0;
int[] items;
double[] pi = null;
Matrix transitionProbability = null;
IDictionary <String, String> properties = null;
IList <IStateDistribution> distributions = new List <IStateDistribution>();
EncogReadHelper reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("HMM") &&
section.SubSectionName.Equals("PARAMS"))
{
properties = section.ParseParams();
}
if (section.SectionName.Equals("HMM") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <String, String> p = section.ParseParams();
states = EncogFileSection.ParseInt(p, HiddenMarkovModel.TAG_STATES);
if (p.ContainsKey(HiddenMarkovModel.TAG_ITEMS))
{
items = EncogFileSection.ParseIntArray(p, HiddenMarkovModel.TAG_ITEMS);
}
pi = section.ParseDoubleArray(p, HiddenMarkovModel.TAG_PI);
transitionProbability = EncogFileSection.ParseMatrix(p, HiddenMarkovModel.TAG_TRANSITION);
}
else if (section.SectionName.Equals("HMM") &&
section.SubSectionName.StartsWith("DISTRIBUTION-"))
{
IDictionary <String, String> p = section.ParseParams();
String t = p[HiddenMarkovModel.TAG_DIST_TYPE];
if ("ContinousDistribution".Equals(t))
{
double[] mean = section.ParseDoubleArray(p, HiddenMarkovModel.TAG_MEAN);
Matrix cova = EncogFileSection.ParseMatrix(p, HiddenMarkovModel.TAG_COVARIANCE);
ContinousDistribution dist = new ContinousDistribution(mean, cova.Data);
distributions.Add(dist);
}
else if ("DiscreteDistribution".Equals(t))
{
Matrix prob = EncogFileSection.ParseMatrix(p, HiddenMarkovModel.TAG_PROBABILITIES);
DiscreteDistribution dist = new DiscreteDistribution(prob.Data);
distributions.Add(dist);
}
}
}
HiddenMarkovModel result = new HiddenMarkovModel(states);
EngineArray.PutAll(properties, result.Properties);
result.TransitionProbability = transitionProbability.Data;
result.Pi = pi;
int index = 0;
foreach (IStateDistribution dist in distributions)
{
result.StateDistributions[index++] = dist;
}
return(result);
}
/// <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 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);
}
/// <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 mask0)
{
var result = new SupportVectorMachine();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("SVM") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("SVM") &&
section.SubSectionName.Equals("SVM-PARAM"))
{
IDictionary <String, String> p = section.ParseParams();
result.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
result.Params.C = EncogFileSection.ParseDouble(p,
ParamC);
result.Params.cache_size = EncogFileSection.ParseDouble(
p, ParamCacheSize);
result.Params.coef0 = EncogFileSection.ParseDouble(p,
ParamCoef0);
result.Params.degree = EncogFileSection.ParseDouble(p,
ParamDegree);
result.Params.eps = EncogFileSection.ParseDouble(p,
ParamEps);
result.Params.gamma = EncogFileSection.ParseDouble(p,
ParamGamma);
result.Params.kernel_type = EncogFileSection.ParseInt(
p, ParamKernelType);
result.Params.nr_weight = EncogFileSection.ParseInt(
p, ParamNumWeight);
result.Params.nu = EncogFileSection.ParseDouble(p,
ParamNu);
result.Params.p = EncogFileSection.ParseDouble(p,
ParamP);
result.Params.probability = EncogFileSection.ParseInt(
p, ParamProbability);
result.Params.shrinking = EncogFileSection.ParseInt(
p, ParamShrinking);
/*result.Params.statIterations = Encog.Persist.EncogFileSection.ParseInt(
* params_0, PersistSVM.PARAM_START_ITERATIONS);*/
result.Params.svm_type = EncogFileSection.ParseInt(p,
ParamSVMType);
result.Params.weight = EncogFileSection.ParseDoubleArray(
p, ParamWeight);
result.Params.weight_label = EncogFileSection
.ParseIntArray(p, ParamWeightLabel);
}
else if (section.SectionName.Equals("SVM") &&
section.SubSectionName.Equals("SVM-MODEL"))
{
try
{
#if !SILVERLIGHT
var rdr = new StringReader(
section.LinesAsString);
TextReader br = rdr;
svm_model model = svm.svm_load_model(rdr);
result.Model = model;
br.Close();
rdr.Close();
#endif
}
catch (IOException ex)
{
throw new PersistError(ex);
}
}
}
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);
}
