/// <summary>
/// Create a NEAT population.
/// </summary>
/// <param name="architecture">The architecture string to use.</param>
/// <param name="input">The input count.</param>
/// <param name="output">The output count.</param>
/// <returns>The population.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
if (input <= 0)
{
throw new EncogError("Must have at least one input for NEAT.");
}
if (output <= 0)
{
throw new EncogError("Must have at least one output for NEAT.");
}
IDictionary <String, String> args = ArchitectureParse.ParseParams(architecture);
ParamsHolder holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLMethodFactory.PropertyPopulationSize, false, 1000);
int cycles = holder.GetInt(
MLMethodFactory.PropertyCycles, false, NEATPopulation.DefaultCycles);
IActivationFunction af = this.factory.Create(
holder.GetString(MLMethodFactory.PropertyAF, false, MLActivationFactory.AF_SSIGMOID));
NEATPopulation pop = new NEATPopulation(input, output, populationSize);
pop.Reset();
pop.ActivationCycles = cycles;
pop.NEATActivationFunction = af;
return(pop);
}
/// <summary>
/// Create a feed forward network.
/// </summary>
/// <param name="architecture">The architecture string to use.</param>
/// <param name="input">The input count.</param>
/// <param name="output">The output count.</param>
/// <returns>The feedforward network.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
if (input <= 0)
{
throw new EncogError("Must have at least one input for EPL.");
}
if (output <= 0)
{
throw new EncogError("Must have at least one output for EPL.");
}
IDictionary <String, String> args = ArchitectureParse.ParseParams(architecture);
var holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLMethodFactory.PropertyPopulationSize, false, 1000);
String variables = holder.GetString("vars", false, "x");
String funct = holder.GetString("funct", false, null);
var context = new EncogProgramContext();
string[] tok = variables.Split(',');
foreach (string v in tok)
{
context.DefineVariable(v);
}
if (String.Compare("numeric", funct, StringComparison.OrdinalIgnoreCase) == 0)
{
StandardExtensions.CreateNumericOperators(context);
}
var pop = new PrgPopulation(context, populationSize);
if (context.Functions.Count > 0)
{
(new RampedHalfAndHalf(context, 2, 6)).Generate(new EncogRandom(), pop);
}
return(pop);
}
/**
* Create a K2 trainer.
*
* @param method
* The method to use.
* @param training
* The training data to use.
* @param argsStr
* The arguments to use.
* @return The newly created trainer.
*/
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
ParamsHolder holder = new ParamsHolder(args);
int maxParents = holder.GetInt(
MLTrainFactory.PropertyMaxParents, false, 1);
String searchStr = holder.GetString("SEARCH", false, "k2");
String estimatorStr = holder.GetString("ESTIMATOR", false, "simple");
String initStr = holder.GetString("INIT", false, "naive");
IBayesSearch search;
IBayesEstimator estimator;
BayesianInit init;
if (string.Compare(searchStr, "k2", true) == 0)
{
search = new SearchK2();
}
else if (string.Compare(searchStr, "none", true) == 0)
{
search = new SearchNone();
}
else
{
throw new BayesianError("Invalid search type: " + searchStr);
}
if (string.Compare(estimatorStr, "simple", true) == 0)
{
estimator = new SimpleEstimator();
}
else if (string.Compare(estimatorStr, "none", true) == 0)
{
estimator = new EstimatorNone();
}
else
{
throw new BayesianError("Invalid estimator type: " + estimatorStr);
}
if (string.Compare(initStr, "simple") == 0)
{
init = BayesianInit.InitEmpty;
}
else if (string.Compare(initStr, "naive") == 0)
{
init = BayesianInit.InitNaiveBayes;
}
else if (string.Compare(initStr, "none") == 0)
{
init = BayesianInit.InitNoChange;
}
else
{
throw new BayesianError("Invalid init type: " + initStr);
}
return(new TrainBayesian((BayesianNetwork)method, training, maxParents, init, search, estimator));
}
/// <summary>
/// Create a LMA trainer.
/// </summary>
///
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
if (!(method is SupportVectorMachine))
{
throw new EncogError(
"Neighborhood training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
double learningRate = holder.GetDouble(
MLTrainFactory.PropertyLearningRate, false, 0.7d);
String neighborhoodStr = holder.GetString(
MLTrainFactory.PropertyNeighborhood, false, "rbf");
String rbfTypeStr = holder.GetString(
MLTrainFactory.PropertyRBFType, false, "gaussian");
RBFEnum t;
if (rbfTypeStr.Equals("Gaussian", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.Gaussian;
}
else if (rbfTypeStr.Equals("Multiquadric", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.Multiquadric;
}
else if (rbfTypeStr.Equals("InverseMultiquadric", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.InverseMultiquadric;
}
else if (rbfTypeStr.Equals("MexicanHat", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.MexicanHat;
}
else
{
t = RBFEnum.Gaussian;
}
INeighborhoodFunction nf = null;
if (neighborhoodStr.Equals("bubble", StringComparison.InvariantCultureIgnoreCase))
{
nf = new NeighborhoodBubble(1);
}
else if (neighborhoodStr.Equals("rbf", StringComparison.InvariantCultureIgnoreCase))
{
String str = holder.GetString(
MLTrainFactory.PropertyDimensions, true, null);
int[] size = NumberList.FromListInt(CSVFormat.EgFormat, str);
nf = new NeighborhoodRBF(size, t);
}
else if (neighborhoodStr.Equals("rbf1d", StringComparison.InvariantCultureIgnoreCase))
{
nf = new NeighborhoodRBF1D(t);
}
if (neighborhoodStr.Equals("single", StringComparison.InvariantCultureIgnoreCase))
{
nf = new NeighborhoodSingle();
}
var result = new BasicTrainSOM((SOMNetwork)method,
learningRate, training, nf);
if (args.ContainsKey(MLTrainFactory.PropertyIterations))
{
int plannedIterations = holder.GetInt(
MLTrainFactory.PropertyIterations, false, 1000);
double startRate = holder.GetDouble(
MLTrainFactory.PropertyStartLearningRate, false, 0.05d);
double endRate = holder.GetDouble(
MLTrainFactory.PropertyEndLearningRate, false, 0.05d);
double startRadius = holder.GetDouble(
MLTrainFactory.PropertyStartRadius, false, 10);
double endRadius = holder.GetDouble(
MLTrainFactory.PropertyEndRadius, false, 1);
result.SetAutoDecay(plannedIterations, startRate, endRate,
startRadius, endRadius);
}
return(result);
}
/// <summary>
/// Create a PNN network.
/// </summary>
///
/// <param name="architecture">THe architecture string to use.</param>
/// <param name="input">The input count.</param>
/// <param name="output">The output count.</param>
/// <returns>The RBF network.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
IList <String> layers = ArchitectureParse.ParseLayers(architecture);
if (layers.Count != MaxLayers)
{
throw new EncogError(
"PNN Networks must have exactly three elements, "
+ "separated by ->.");
}
ArchitectureLayer inputLayer = ArchitectureParse.ParseLayer(
layers[0], input);
ArchitectureLayer pnnLayer = ArchitectureParse.ParseLayer(
layers[1], -1);
ArchitectureLayer outputLayer = ArchitectureParse.ParseLayer(
layers[2], output);
int inputCount = inputLayer.Count;
int outputCount = outputLayer.Count;
PNNKernelType kernel;
PNNOutputMode outmodel;
if (pnnLayer.Name.Equals("c", StringComparison.InvariantCultureIgnoreCase))
{
outmodel = PNNOutputMode.Classification;
}
else if (pnnLayer.Name.Equals("r", StringComparison.InvariantCultureIgnoreCase))
{
outmodel = PNNOutputMode.Regression;
}
else if (pnnLayer.Name.Equals("u", StringComparison.InvariantCultureIgnoreCase))
{
outmodel = PNNOutputMode.Unsupervised;
}
else
{
throw new NeuralNetworkError("Unknown model: " + pnnLayer.Name);
}
var holder = new ParamsHolder(pnnLayer.Params);
String kernelStr = holder.GetString("KERNEL", false, "gaussian");
if (kernelStr.Equals("gaussian", StringComparison.InvariantCultureIgnoreCase))
{
kernel = PNNKernelType.Gaussian;
}
else if (kernelStr.Equals("reciprocal", StringComparison.InvariantCultureIgnoreCase))
{
kernel = PNNKernelType.Reciprocal;
}
else
{
throw new NeuralNetworkError("Unknown kernel: " + kernelStr);
}
var result = new BasicPNN(kernel, outmodel, inputCount,
outputCount);
return(result);
}
