/// <summary>
/// Save the script to a stream.
/// </summary>
///
/// <param name="stream">The output stream.</param>
public void Save(Stream stream)
{
var xout = new EncogWriteHelper(stream);
SaveSubSection(xout, "HEADER", "DATASOURCE");
SaveConfig(xout);
if (_script.Fields != null)
{
SaveData(xout);
SaveNormalize(xout);
}
SaveSubSection(xout, "RANDOMIZE", "CONFIG");
SaveSubSection(xout, "CLUSTER", "CONFIG");
SaveSubSection(xout, "BALANCE", "CONFIG");
if (_script.Segregate.SegregateTargets != null)
{
SaveSegregate(xout);
}
SaveSubSection(xout, "GENERATE", "CONFIG");
SaveMachineLearning(xout);
SaveTasks(xout);
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var art1 = (ART1)obj;
xout.AddSection("ART1");
xout.AddSubSection("PARAMS");
xout.AddProperties(art1.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(BasicART.PropertyA1, art1.A1);
xout.WriteProperty(BasicART.PropertyB1, art1.B1);
xout.WriteProperty(BasicART.PropertyC1, art1.C1);
xout.WriteProperty(BasicART.PropertyD1, art1.D1);
xout.WriteProperty(PersistConst.PropertyF1Count, art1.F1Count);
xout.WriteProperty(PersistConst.PropertyF2Count, art1.F2Count);
xout.WriteProperty(BasicART.PropertyNoWinner, art1.NoWinner);
xout.WriteProperty(BasicART.PropertyL, art1.L);
xout.WriteProperty(BasicART.PropertyVigilance, art1.Vigilance);
xout.WriteProperty(PersistConst.PropertyWeightsF1F2,
art1.WeightsF1ToF2);
xout.WriteProperty(PersistConst.PropertyWeightsF2F1,
art1.WeightsF2ToF1);
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var net = (RBFNetwork)obj;
var flat = (FlatNetworkRBF)net.Flat;
xout.AddSection("RBF-NETWORK");
xout.AddSubSection("PARAMS");
xout.AddProperties(net.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(BasicNetwork.TagBeginTraining,
flat.BeginTraining);
xout.WriteProperty(BasicNetwork.TagConnectionLimit,
flat.ConnectionLimit);
xout.WriteProperty(BasicNetwork.TagContextTargetOffset,
flat.ContextTargetOffset);
xout.WriteProperty(BasicNetwork.TagContextTargetSize,
flat.ContextTargetSize);
xout.WriteProperty(BasicNetwork.TagEndTraining, flat.EndTraining);
xout.WriteProperty(BasicNetwork.TagHasContext, flat.HasContext);
xout.WriteProperty(PersistConst.InputCount, flat.InputCount);
xout.WriteProperty(BasicNetwork.TagLayerCounts, flat.LayerCounts);
xout.WriteProperty(BasicNetwork.TagLayerFeedCounts,
flat.LayerFeedCounts);
xout.WriteProperty(BasicNetwork.TagLayerContextCount,
flat.LayerContextCount);
xout.WriteProperty(BasicNetwork.TagLayerIndex, flat.LayerIndex);
xout.WriteProperty(PersistConst.Output, flat.LayerOutput);
xout.WriteProperty(PersistConst.OutputCount, flat.OutputCount);
xout.WriteProperty(BasicNetwork.TagWeightIndex, flat.WeightIndex);
xout.WriteProperty(PersistConst.Weights, flat.Weights);
xout.WriteProperty(BasicNetwork.TagBiasActivation,
flat.BiasActivation);
xout.AddSubSection("ACTIVATION");
foreach (IActivationFunction af in flat.ActivationFunctions)
{
xout.AddColumn(af.GetType().Name);
foreach (double t in af.Params)
{
xout.AddColumn(t);
}
xout.WriteLine();
}
xout.AddSubSection("RBF");
foreach (IRadialBasisFunction rbf in flat.RBF)
{
xout.AddColumn(rbf.GetType().Name);
xout.AddColumn(rbf.Width);
xout.AddColumn(rbf.Peak);
foreach (double t in rbf.Centers)
{
xout.AddColumn(t);
}
xout.WriteLine();
}
xout.Flush();
}
/// <summary>
/// Write a link.
/// </summary>
/// <param name="xout">The output file.</param>
/// <param name="link">The link.</param>
private static void WriteLink(EncogWriteHelper xout, NEATLink link)
{
xout.AddColumn((int)link.FromNeuron.NeuronID);
xout.AddColumn((int)link.ToNeuron.NeuronID);
xout.AddColumn(link.Recurrent);
xout.AddColumn(link.Weight);
xout.WriteLine();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var output = new EncogWriteHelper(os);
var pop = (NEATPopulation)obj;
output.AddSection("NEAT-POPULATION");
output.AddSubSection("CONFIG");
output.WriteProperty(PersistConst.ActivationCycles, pop.ActivationCycles);
if (pop.IsHyperNEAT)
{
output.WriteProperty(NEATPopulation.PropertyNEATActivation, TypeCppn);
}
else
{
IActivationFunction af = pop.ActivationFunctions.Contents[0].obj;
output.WriteProperty(NEATPopulation.PropertyNEATActivation, af);
}
output.WriteProperty(PersistConst.InputCount, pop.InputCount);
output.WriteProperty(PersistConst.OutputCount, pop.OutputCount);
output.WriteProperty(NEATPopulation.PropertyCycles, pop.ActivationCycles);
output.WriteProperty(NEATPopulation.PropertyPopulationSize, pop.PopulationSize);
output.WriteProperty(NEATPopulation.PropertySurvivalRate, pop.SurvivalRate);
output.AddSubSection("INNOVATIONS");
if (pop.Innovations != null)
{
foreach (string key in pop.Innovations.Innovations.Keys)
{
NEATInnovation innovation = pop.Innovations.Innovations[key];
output.AddColumn(key);
output.AddColumn(innovation.InnovationId);
output.AddColumn(innovation.NeuronId);
output.WriteLine();
}
}
output.AddSubSection("SPECIES");
// make sure the best species goes first
ISpecies bestSpecies = pop.DetermineBestSpecies();
if (bestSpecies != null)
{
SaveSpecies(output, bestSpecies);
}
// now write the other species, other than the best one
foreach (ISpecies species in pop.Species)
{
if (species != bestSpecies)
{
SaveSpecies(output, species);
}
}
output.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var svm2 = (SupportVectorMachine)obj;
xout.AddSection("SVM");
xout.AddSubSection("PARAMS");
xout.AddProperties(svm2.Properties);
xout.AddSubSection("SVM-PARAM");
xout.WriteProperty(PersistConst.InputCount, svm2.InputCount);
xout.WriteProperty(ParamC, svm2.Params.C);
xout.WriteProperty(ParamCacheSize,
svm2.Params.cache_size);
xout.WriteProperty(ParamCoef0, svm2.Params.coef0);
xout.WriteProperty(ParamDegree, svm2.Params.degree);
xout.WriteProperty(ParamEps, svm2.Params.eps);
xout.WriteProperty(ParamGamma, svm2.Params.gamma);
xout.WriteProperty(ParamKernelType,
svm2.Params.kernel_type);
xout.WriteProperty(ParamNumWeight,
svm2.Params.nr_weight);
xout.WriteProperty(ParamNu, svm2.Params.nu);
xout.WriteProperty(ParamP, svm2.Params.p);
xout.WriteProperty(ParamProbability,
svm2.Params.probability);
xout.WriteProperty(ParamShrinking,
svm2.Params.shrinking);
/* xout.WriteProperty(PersistSVM.PARAM_START_ITERATIONS,
* svm2.Params.statIterations); */
xout.WriteProperty(ParamSVMType, svm2.Params.svm_type);
xout.WriteProperty(ParamWeight, svm2.Params.weight);
xout.WriteProperty(ParamWeightLabel,
svm2.Params.weight_label);
if (svm2.Model != null)
{
xout.AddSubSection("SVM-MODEL");
try
{
#if !SILVERLIGHT
var ba = new MemoryStream();
var w = new StreamWriter(ba);
svm.svm_save_model(w, svm2.Model);
var enc = new ASCIIEncoding();
xout.Write(enc.GetString(ba.ToArray()));
w.Close();
ba.Close();
#endif
}
catch (IOException ex)
{
throw new PersistError(ex);
}
}
xout.Flush();
}
/// <summary>
/// Save the data fields.
/// </summary>
/// <param name="xout">The output file.</param>
private void SaveData(EncogWriteHelper xout)
{
SaveSubSection(xout, "DATA", "CONFIG");
xout.AddSubSection("STATS");
xout.AddColumn("name");
xout.AddColumn("isclass");
xout.AddColumn("iscomplete");
xout.AddColumn("isint");
xout.AddColumn("isreal");
xout.AddColumn("amax");
xout.AddColumn("amin");
xout.AddColumn("mean");
xout.AddColumn("sdev");
xout.AddColumn("source");
xout.WriteLine();
foreach (DataField field in _script.Fields)
{
xout.AddColumn(field.Name);
xout.AddColumn(field.Class);
xout.AddColumn(field.Complete);
xout.AddColumn(field.Integer);
xout.AddColumn(field.Real);
xout.AddColumn(field.Max);
xout.AddColumn(field.Min);
xout.AddColumn(field.Mean);
xout.AddColumn(field.StandardDeviation);
xout.AddColumn(field.Source);
xout.WriteLine();
}
xout.Flush();
xout.AddSubSection("CLASSES");
xout.AddColumn("field");
xout.AddColumn("code");
xout.AddColumn("name");
xout.WriteLine();
foreach (DataField field in _script.Fields)
{
if (field.Class)
{
foreach (AnalystClassItem col in field.ClassMembers)
{
xout.AddColumn(field.Name);
xout.AddColumn(col.Code);
xout.AddColumn(col.Name);
xout.AddColumn(col.Count);
xout.WriteLine();
}
}
}
}
/// <summary>
/// Save the normalization data.
/// </summary>
///
/// <param name="xout">The output file.</param>
private void SaveNormalize(EncogWriteHelper xout)
{
SaveSubSection(xout, "NORMALIZE", "CONFIG");
xout.AddSubSection("RANGE");
xout.AddColumn("name");
xout.AddColumn("io");
xout.AddColumn("timeSlice");
xout.AddColumn("action");
xout.AddColumn("high");
xout.AddColumn("low");
xout.WriteLine();
foreach (AnalystField field in _script.Normalize.NormalizedFields)
{
xout.AddColumn(field.Name);
xout.AddColumn(field.Input ? "input" : "output");
xout.AddColumn(field.TimeSlice);
switch (field.Action)
{
case NormalizationAction.Ignore:
xout.AddColumn("ignore");
break;
case NormalizationAction.Normalize:
xout.AddColumn("range");
break;
case NormalizationAction.PassThrough:
xout.AddColumn("pass");
break;
case NormalizationAction.OneOf:
xout.AddColumn("oneof");
break;
case NormalizationAction.Equilateral:
xout.AddColumn("equilateral");
break;
case NormalizationAction.SingleField:
xout.AddColumn("single");
break;
default:
throw new AnalystError("Unknown action: " + field.Action);
}
xout.AddColumn(field.NormalizedHigh);
xout.AddColumn(field.NormalizedLow);
xout.WriteLine();
}
}
/// <summary>
///
/// </summary>
///
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var hopfield = (HopfieldNetwork)obj;
xout.AddSection("HOPFIELD");
xout.AddSubSection("PARAMS");
xout.AddProperties(hopfield.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(PersistConst.Weights, hopfield.Weights);
xout.WriteProperty(PersistConst.Output, hopfield.CurrentState.Data);
xout.WriteProperty(PersistConst.NeuronCount, hopfield.NeuronCount);
xout.Flush();
}
/**
* {@inheritDoc}
*/
public void Save(Stream os, Object obj)
{
var writer = new EncogWriteHelper(os);
var som = (SOMNetwork)obj;
writer.AddSection("SOM");
writer.AddSubSection("PARAMS");
writer.AddProperties(som.Properties);
writer.AddSubSection("NETWORK");
writer.WriteProperty(PersistConst.Weights, som.Weights);
writer.WriteProperty(PersistConst.InputCount, som.InputCount);
writer.WriteProperty(PersistConst.OutputCount, som.OutputCount);
writer.Flush();
}
/// <summary>
/// Save segregate info.
/// </summary>
/// <param name="xout">The output file.</param>
private void SaveSegregate(EncogWriteHelper xout)
{
SaveSubSection(xout, "SEGREGATE", "CONFIG");
xout.AddSubSection("FILES");
xout.AddColumn("file");
xout.AddColumn("percent");
xout.WriteLine();
foreach (AnalystSegregateTarget target in _script.Segregate.SegregateTargets)
{
xout.AddColumn(target.File);
xout.AddColumn(target.Percent);
xout.WriteLine();
}
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var net = (BasicNetwork)obj;
FlatNetwork flat = net.Structure.Flat;
xout.AddSection("BASIC");
xout.AddSubSection("PARAMS");
xout.AddProperties(net.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(BasicNetwork.TagBeginTraining,
flat.BeginTraining);
xout.WriteProperty(BasicNetwork.TagConnectionLimit,
flat.ConnectionLimit);
xout.WriteProperty(BasicNetwork.TagContextTargetOffset,
flat.ContextTargetOffset);
xout.WriteProperty(BasicNetwork.TagContextTargetSize,
flat.ContextTargetSize);
xout.WriteProperty(BasicNetwork.TagEndTraining, flat.EndTraining);
xout.WriteProperty(BasicNetwork.TagHasContext, flat.HasContext);
xout.WriteProperty(PersistConst.InputCount, flat.InputCount);
xout.WriteProperty(BasicNetwork.TagLayerCounts, flat.LayerCounts);
xout.WriteProperty(BasicNetwork.TagLayerFeedCounts,
flat.LayerFeedCounts);
xout.WriteProperty(BasicNetwork.TagLayerContextCount,
flat.LayerContextCount);
xout.WriteProperty(BasicNetwork.TagLayerIndex, flat.LayerIndex);
xout.WriteProperty(PersistConst.Output, flat.LayerOutput);
xout.WriteProperty(PersistConst.OutputCount, flat.OutputCount);
xout.WriteProperty(BasicNetwork.TagWeightIndex, flat.WeightIndex);
xout.WriteProperty(PersistConst.Weights, flat.Weights);
xout.WriteProperty(BasicNetwork.TagBiasActivation,
flat.BiasActivation);
xout.AddSubSection("ACTIVATION");
foreach (IActivationFunction af in flat.ActivationFunctions)
{
xout.AddColumn(af.GetType().Name);
for (int i = 0; i < af.Params.Length; i++)
{
xout.AddColumn(af.Params[i]);
}
xout.WriteLine();
}
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var cont = (TrainingContinuation)obj;
xout.AddSection("CONT");
xout.AddSubSection("PARAMS");
xout.WriteProperty("type", cont.TrainingType);
foreach (String key in cont.Contents.Keys)
{
var list = (double[])cont.Get(key);
xout.WriteProperty(key, list);
}
xout.Flush();
}
/// <summary>
/// Save the config info.
/// </summary>
/// <param name="xout">THe output file.</param>
private void SaveConfig(EncogWriteHelper xout)
{
SaveSubSection(xout, "SETUP", "CONFIG");
xout.AddSubSection("FILENAMES");
IList <String> list = _script.Properties.Filenames;
foreach (string key in list)
{
String v = _script.Properties.GetFilename(key);
var f = new FileInfo(v);
xout.WriteProperty(key,
f.DirectoryName.Equals(_script.BasePath, StringComparison.InvariantCultureIgnoreCase)
? f.Name
: v);
}
}
/// <summary>
/// Save the tasks.
/// </summary>
///
/// <param name="xout">The output file.</param>
private void SaveTasks(EncogWriteHelper xout)
{
xout.AddSection("TASKS");
var list = _script.Tasks.Keys.ToList();
list.Sort();
foreach (String key in list)
{
AnalystTask task = _script.GetTask(key);
xout.AddSubSection(task.Name);
foreach (String line in task.Lines)
{
xout.AddLine(line);
}
}
}
/// <summary>
///
/// </summary>
///
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var bam = (BAMNetwork)obj;
xout.AddSection("BAM");
xout.AddSubSection("PARAMS");
xout.AddProperties(bam.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(PersistConst.PropertyF1Count, bam.F1Count);
xout.WriteProperty(PersistConst.PropertyF2Count, bam.F2Count);
xout.WriteProperty(PersistConst.PropertyWeightsF1F2,
bam.WeightsF1ToF2);
xout.WriteProperty(PersistConst.PropertyWeightsF2F1,
bam.WeightsF2ToF1);
xout.Flush();
}
/// <summary>
/// Save the object.
/// </summary>
/// <param name="os">The output stream.</param>
/// <param name="obj">The object to save.</param>
public virtual void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var neat = (NEATNetwork)obj;
xout.AddSection("NEAT");
xout.AddSubSection("PARAMS");
xout.AddProperties(neat.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(PersistConst.InputCount, neat.InputCount);
xout.WriteProperty(PersistConst.OutputCount, neat.OutputCount);
xout.WriteProperty(PersistConst.ActivationFunction,
neat.ActivationFunction);
xout.WriteProperty(NEATPopulation.PropertyOutputActivation,
neat.OutputActivationFunction);
xout.WriteProperty(PersistConst.Depth, neat.NetworkDepth);
xout.WriteProperty(PersistConst.Snapshot, neat.Snapshot);
xout.AddSubSection("NEURONS");
foreach (NEATNeuron neatNeuron in neat.Neurons)
{
xout.AddColumn((int)neatNeuron.NeuronID);
xout.AddColumn(PersistNEATPopulation.NeuronTypeToString(neatNeuron.NeuronType));
xout.AddColumn(neatNeuron.ActivationResponse);
xout.AddColumn(neatNeuron.SplitX);
xout.AddColumn(neatNeuron.SplitY);
xout.WriteLine();
}
xout.AddSubSection("LINKS");
foreach (NEATNeuron neatNeuron in neat.Neurons)
{
foreach (NEATLink link in neatNeuron.OutputboundLinks)
{
WriteLink(xout, link);
}
}
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var boltz = (BoltzmannMachine)obj;
xout.AddSection("BOLTZMANN");
xout.AddSubSection("PARAMS");
xout.AddProperties(boltz.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(PersistConst.Weights, boltz.Weights);
xout.WriteProperty(PersistConst.Output, boltz.CurrentState.Data);
xout.WriteProperty(PersistConst.NeuronCount, boltz.NeuronCount);
xout.WriteProperty(PersistConst.Thresholds, boltz.Threshold);
xout.WriteProperty(BoltzmannMachine.ParamAnnealCycles,
boltz.AnnealCycles);
xout.WriteProperty(BoltzmannMachine.ParamRunCycles, boltz.RunCycles);
xout.WriteProperty(PersistConst.Temperature, boltz.Temperature);
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var cpn = (CPNNetwork)obj;
xout.AddSection("CPN");
xout.AddSubSection("PARAMS");
xout.AddProperties(cpn.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(PersistConst.InputCount, cpn.InputCount);
xout.WriteProperty(PersistConst.Instar, cpn.InstarCount);
xout.WriteProperty(PersistConst.OutputCount, cpn.OutputCount);
xout.WriteProperty(PropertyInputToInstar,
cpn.WeightsInputToInstar);
xout.WriteProperty(PropertyInstarToInput,
cpn.WeightsInstarToOutstar);
xout.WriteProperty(PropertyWinnerCount, cpn.WinnerCount);
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
EncogWriteHelper writer = new EncogWriteHelper(os);
HiddenMarkovModel net = (HiddenMarkovModel)obj;
writer.AddSection("HMM");
writer.AddSubSection("PARAMS");
writer.AddProperties(net.Properties);
writer.AddSubSection("CONFIG");
writer.WriteProperty(HiddenMarkovModel.TAG_STATES, net.StateCount);
if (net.Items != null)
{
writer.WriteProperty(HiddenMarkovModel.TAG_ITEMS, net.Items);
}
writer.WriteProperty(HiddenMarkovModel.TAG_PI, net.Pi);
writer.WriteProperty(HiddenMarkovModel.TAG_TRANSITION, new Matrix(net.TransitionProbability));
for (int i = 0; i < net.StateCount; i++)
{
writer.AddSubSection("DISTRIBUTION-" + i);
IStateDistribution sd = net.StateDistributions[i];
writer.WriteProperty(HiddenMarkovModel.TAG_DIST_TYPE, sd.GetType().Name);
if (sd is ContinousDistribution)
{
ContinousDistribution cDist = (ContinousDistribution)sd;
writer.WriteProperty(HiddenMarkovModel.TAG_MEAN, cDist.Mean);
writer.WriteProperty(HiddenMarkovModel.TAG_COVARIANCE, cDist.Covariance);
}
else if (sd is DiscreteDistribution)
{
DiscreteDistribution dDist = (DiscreteDistribution)sd;
writer.WriteProperty(HiddenMarkovModel.TAG_PROBABILITIES, new Matrix(dDist.Probabilities));
}
}
writer.Flush();
}
/// <summary>
/// Save a subsection.
/// </summary>
///
/// <param name="xout">The output file.</param>
/// <param name="section">The section.</param>
/// <param name="subSection">The subsection.</param>
private void SaveSubSection(EncogWriteHelper xout,
String section, String subSection)
{
if (!section.Equals(xout.CurrentSection))
{
xout.AddSection(section);
}
xout.AddSubSection(subSection);
List <PropertyEntry> list = PropertyConstraints.Instance
.GetEntries(section, subSection);
list.Sort();
foreach (PropertyEntry entry in list)
{
String key = section + ":" + subSection + "_"
+ entry.Name;
String v = _script.Properties.GetPropertyString(
key);
xout.WriteProperty(entry.Name, v ?? "");
}
}
private void SaveSpecies(EncogWriteHelper output, ISpecies species)
{
output.AddColumn("s");
output.AddColumn(species.Age);
output.AddColumn(species.BestScore);
output.AddColumn(species.GensNoImprovement);
output.WriteLine();
foreach (IGenome genome in species.Members)
{
var neatGenome = (NEATGenome)genome;
output.AddColumn("g");
output.AddColumn(neatGenome.AdjustedScore);
output.AddColumn(neatGenome.Score);
output.AddColumn(neatGenome.BirthGeneration);
output.WriteLine();
foreach (NEATNeuronGene neatNeuronGene in neatGenome.NeuronsChromosome)
{
output.AddColumn("n");
output.AddColumn(neatNeuronGene.Id);
output.AddColumn(neatNeuronGene.ActivationFunction);
output.AddColumn(NeuronTypeToString(neatNeuronGene.NeuronType));
output.AddColumn(neatNeuronGene.InnovationId);
output.WriteLine();
}
foreach (NEATLinkGene neatLinkGene in neatGenome.LinksChromosome)
{
output.AddColumn("l");
output.AddColumn(neatLinkGene.Id);
output.AddColumn(neatLinkGene.Enabled);
output.AddColumn(neatLinkGene.FromNeuronId);
output.AddColumn(neatLinkGene.ToNeuronId);
output.AddColumn(neatLinkGene.Weight);
output.AddColumn(neatLinkGene.InnovationId);
output.WriteLine();
}
}
}
/// <summary>
///
/// </summary>
///
public void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var pnn = (BasicPNN)obj;
xout.AddSection("PNN");
xout.AddSubSection("PARAMS");
xout.AddProperties(pnn.Properties);
xout.AddSubSection("NETWORK");
xout.WriteProperty(PersistConst.Error, pnn.Error);
xout.WriteProperty(PersistConst.InputCount, pnn.InputCount);
xout.WriteProperty(PersistConst.Kernel,
KernelToString(pnn.Kernel));
xout.WriteProperty(PersistConst.OutputCount, pnn.OutputCount);
xout.WriteProperty(PropertyOutputMode,
OutputModeToString(pnn.OutputMode));
xout.WriteProperty(PersistConst.Sigma, pnn.Sigma);
xout.AddSubSection("SAMPLES");
if (pnn.Samples != null)
{
foreach (IMLDataPair pair in pnn.Samples)
{
for (int i = 0; i < pair.Input.Count; i++)
{
xout.AddColumn(pair.Input[i]);
}
for (int i = 0; i < pair.Ideal.Count; i++)
{
xout.AddColumn(pair.Ideal[i]);
}
xout.WriteLine();
}
}
xout.Flush();
}
/// <inheritdoc />
public void Save(Stream ostream, Object obj)
{
var writer = new EncogWriteHelper(ostream);
var pop = (PrgPopulation)obj;
writer.AddSection("BASIC");
writer.AddSubSection("PARAMS");
writer.AddProperties(pop.Properties);
writer.AddSubSection("EPL-OPCODES");
foreach (IProgramExtensionTemplate temp in pop.Context
.Functions.OpCodes)
{
writer.AddColumn(temp.Name);
writer.AddColumn(temp.ChildNodeCount);
writer.WriteLine();
}
writer.AddSubSection("EPL-SYMBOLIC");
writer.AddColumn("name");
writer.AddColumn("type");
writer.AddColumn("enum");
writer.AddColumn("enum_type");
writer.AddColumn("enum_count");
writer.WriteLine();
// write the first line, the result
writer.AddColumn("");
writer.AddColumn(GetType(pop.Context.Result));
writer.AddColumn(pop.Context.Result.EnumType);
writer.AddColumn(pop.Context.Result.EnumValueCount);
writer.WriteLine();
// write the next lines, the variables
foreach (VariableMapping mapping in pop.Context.DefinedVariables)
{
writer.AddColumn(mapping.Name);
writer.AddColumn(GetType(mapping));
writer.AddColumn(mapping.EnumType);
writer.AddColumn(mapping.EnumValueCount);
writer.WriteLine();
}
writer.AddSubSection("EPL-POPULATION");
foreach (ISpecies species in pop.Species)
{
if (species.Members.Count > 0)
{
writer.AddColumn("s");
writer.AddColumn(species.Age);
writer.AddColumn(species.BestScore);
writer.AddColumn(species.GensNoImprovement);
writer.WriteLine();
foreach (IGenome genome in species.Members)
{
var prg = (EncogProgram)genome;
writer.AddColumn("p");
if (Double.IsInfinity(prg.Score) ||
Double.IsNaN(prg.Score))
{
writer.AddColumn("NaN");
writer.AddColumn("NaN");
}
else
{
writer.AddColumn(prg.Score);
writer.AddColumn(prg.AdjustedScore);
}
writer.AddColumn(prg.GenerateEPL());
writer.WriteLine();
}
}
}
writer.Flush();
}
/// <summary>
/// Save the object.
/// </summary>
/// <param name="os">The stream to write to.</param>
/// <param name="obj">The object to save.</param>
public virtual void Save(Stream os, Object obj)
{
var xout = new EncogWriteHelper(os);
var pop = (NEATPopulation)obj;
xout.AddSection("NEAT-POPULATION");
xout.AddSubSection("CONFIG");
xout.WriteProperty(PersistConst.Snapshot, pop.Snapshot);
xout.WriteProperty(NEATPopulation.PropertyOutputActivation,
pop.OutputActivationFunction);
xout.WriteProperty(NEATPopulation.PropertyNEATActivation,
pop.NeatActivationFunction);
xout.WriteProperty(PersistConst.InputCount, pop.InputCount);
xout.WriteProperty(PersistConst.OutputCount, pop.OutputCount);
xout.WriteProperty(PopulationConst.PropertyOldAgePenalty,
pop.OldAgePenalty);
xout.WriteProperty(PopulationConst.PropertyOldAgeThreshold,
pop.OldAgeThreshold);
xout.WriteProperty(PopulationConst.PropertyPopulationSize,
pop.PopulationSize);
xout.WriteProperty(PopulationConst.PropertySurvivalRate,
pop.SurvivalRate);
xout.WriteProperty(PopulationConst.PropertyYoungAgeThreshold,
pop.YoungBonusAgeThreshold);
xout.WriteProperty(PopulationConst.PropertyYoungAgeBonus,
pop.YoungScoreBonus);
xout.WriteProperty(PopulationConst.PropertyNextGenomeID, pop.GenomeIDGenerate.CurrentID);
xout.WriteProperty(PopulationConst.PropertyNextInnovationID, pop.InnovationIDGenerate.CurrentID);
xout.WriteProperty(PopulationConst.PropertyNextGeneID, pop.GeneIDGenerate.CurrentID);
xout.WriteProperty(PopulationConst.PropertyNextSpeciesID, pop.SpeciesIDGenerate.CurrentID);
xout.AddSubSection("INNOVATIONS");
if (pop.Innovations != null)
{
foreach (IInnovation innovation in pop.Innovations.Innovations)
{
var neatInnovation = (NEATInnovation)innovation;
xout.AddColumn(neatInnovation.InnovationID);
xout.AddColumn(InnovationTypeToString(neatInnovation.InnovationType));
xout.AddColumn(NeuronTypeToString(neatInnovation.NeuronType));
xout.AddColumn(neatInnovation.SplitX);
xout.AddColumn(neatInnovation.SplitY);
xout.AddColumn(neatInnovation.NeuronID);
xout.AddColumn(neatInnovation.FromNeuronID);
xout.AddColumn(neatInnovation.ToNeuronID);
xout.WriteLine();
}
}
xout.AddSubSection("GENOMES");
foreach (IGenome genome in pop.Genomes)
{
var neatGenome = (NEATGenome)genome;
xout.AddColumn("g");
xout.AddColumn(neatGenome.GenomeID);
xout.AddColumn(neatGenome.SpeciesID);
xout.AddColumn(neatGenome.AdjustedScore);
xout.AddColumn(neatGenome.AmountToSpawn);
xout.AddColumn(neatGenome.NetworkDepth);
xout.AddColumn(neatGenome.Score);
xout.WriteLine();
foreach (IGene neuronGene in neatGenome.Neurons.Genes)
{
var neatNeuronGene = (NEATNeuronGene)neuronGene;
xout.AddColumn("n");
xout.AddColumn(neatNeuronGene.Id);
xout.AddColumn(NeuronTypeToString(neatNeuronGene.NeuronType));
xout.AddColumn(neatNeuronGene.Enabled);
xout.AddColumn(neatNeuronGene.InnovationId);
xout.AddColumn(neatNeuronGene.ActivationResponse);
xout.AddColumn(neatNeuronGene.SplitX);
xout.AddColumn(neatNeuronGene.SplitY);
xout.WriteLine();
}
foreach (IGene linkGene in neatGenome.Links.Genes)
{
var neatLinkGene = (NEATLinkGene)linkGene;
xout.AddColumn("l");
xout.AddColumn(neatLinkGene.Id);
xout.AddColumn(neatLinkGene.Enabled);
xout.AddColumn(neatLinkGene.Recurrent);
xout.AddColumn(neatLinkGene.FromNeuronID);
xout.AddColumn(neatLinkGene.ToNeuronID);
xout.AddColumn(neatLinkGene.Weight);
xout.AddColumn(neatLinkGene.InnovationId);
xout.WriteLine();
}
}
xout.AddSubSection("SPECIES");
foreach (ISpecies species in pop.Species)
{
xout.AddColumn(species.SpeciesID);
xout.AddColumn(species.Age);
xout.AddColumn(species.BestScore);
xout.AddColumn(species.GensNoImprovement);
xout.AddColumn(species.NumToSpawn);
xout.AddColumn(species.SpawnsRequired);
xout.AddColumn(species.Leader.GenomeID);
xout.WriteLine();
}
xout.Flush();
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
EncogWriteHelper o = new EncogWriteHelper(os);
BayesianNetwork b = (BayesianNetwork)obj;
o.AddSection("BAYES-NETWORK");
o.AddSubSection("BAYES-PARAM");
String queryType = "";
String queryStr = b.ClassificationStructure;
if (b.Query != null)
{
queryType = b.Query.GetType().Name;
}
o.WriteProperty("queryType", queryType);
o.WriteProperty("query", queryStr);
o.WriteProperty("contents", b.Contents);
o.AddSubSection("BAYES-PROPERTIES");
o.AddProperties(b.Properties);
o.AddSubSection("BAYES-TABLE");
foreach (BayesianEvent e in b.Events)
{
foreach (TableLine line in e.Table.Lines)
{
if (line == null)
{
continue;
}
StringBuilder str = new StringBuilder();
str.Append("P(");
str.Append(BayesianEvent.FormatEventName(e, line.Result));
if (e.Parents.Count > 0)
{
str.Append("|");
}
int index = 0;
bool first = true;
foreach (BayesianEvent parentEvent in e.Parents)
{
if (!first)
{
str.Append(",");
}
first = false;
int arg = line.Arguments[index++];
if (parentEvent.IsBoolean)
{
if (arg == 0)
{
str.Append("+");
}
else
{
str.Append("-");
}
}
str.Append(parentEvent.Label);
if (!parentEvent.IsBoolean)
{
str.Append("=");
if (arg >= parentEvent.Choices.Count)
{
throw new BayesianError("Argument value " + arg + " is out of range for event " + parentEvent.ToString());
}
str.Append(parentEvent.GetChoice(arg));
}
}
str.Append(")=");
str.Append(line.Probability);
str.Append("\n");
o.Write(str.ToString());
}
}
o.Flush();
}
/// <summary>
/// Save the ML sections.
/// </summary>
///
/// <param name="xout">The output file.</param>
private void SaveMachineLearning(EncogWriteHelper xout)
{
SaveSubSection(xout, "ML", "CONFIG");
SaveSubSection(xout, "ML", "TRAIN");
}
