/// <summary>
/// Create an annealing 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 BasicNetwork))
{
throw new TrainingError(
"Invalid method type, requires BasicNetwork");
}
ICalculateScore score = new TrainingSetScore(training);
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLTrainFactory.PropertyPopulationSize, false, 5000);
IMLTrain train = new MLMethodGeneticAlgorithm(() =>
{
IMLMethod result = (IMLMethod)ObjectCloner.DeepCopy(method);
((IMLResettable)result).Reset();
return(result);
}, score, populationSize);
return(train);
}
/// <summary>
/// Create an annealing 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 BasicNetwork))
{
throw new TrainingError(
"Invalid method type, requires BasicNetwork");
}
ICalculateScore score = new TrainingSetScore(training);
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
double startTemp = holder.GetDouble(
MLTrainFactory.PropertyTemperatureStart, false, 10);
double stopTemp = holder.GetDouble(
MLTrainFactory.PropertyTemperatureStop, false, 2);
int cycles = holder.GetInt(MLTrainFactory.Cycles, false, 100);
IMLTrain train = new NeuralSimulatedAnnealing(
(BasicNetwork)method, score, startTemp, stopTemp, cycles);
return(train);
}
/// <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 SVM 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(
"SVM Train training cannot be used on a method of type: "
+ method.GetType().FullName);
}
double defaultGamma = 1.0d / ((SupportVectorMachine)method).InputCount;
double defaultC = 1.0d;
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
double gamma = holder.GetDouble(MLTrainFactory.PropertyGamma,
false, defaultGamma);
double c = holder.GetDouble(MLTrainFactory.PropertyC, false,
defaultC);
var result = new SVMTrain((SupportVectorMachine)method, training);
result.Gamma = gamma;
result.C = c;
return(result);
}
/// <summary>
/// Create an annealing 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 BasicNetwork))
{
throw new TrainingError(
"Invalid method type, requires BasicNetwork");
}
ICalculateScore score = new TrainingSetScore(training);
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLTrainFactory.PropertyPopulationSize, false, 5000);
double mutation = holder.GetDouble(
MLTrainFactory.PropertyMutation, false, 0.1d);
double mate = holder.GetDouble(MLTrainFactory.PropertyMate,
false, 0.25d);
IMLTrain train = new NeuralGeneticAlgorithm((BasicNetwork)method,
new RangeRandomizer(-1, 1), score, populationSize, mutation,
mate);
return(train);
}
/// <summary>
/// Create a quick propagation 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)
{
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
double learningRate = holder.GetDouble(
MLTrainFactory.PropertyLearningRate, false, 2.0);
return(new QuickPropagation((BasicNetwork)method, training, learningRate));
}
/// <summary>
/// Create a Nelder Mead 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)
{
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
//final double learningRate = holder.getDouble(
// MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.1);
return(new NelderMeadTraining((BasicNetwork)method, training));
}
/// <summary>
/// Create a PSO 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)
{
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
ParamsHolder holder = new ParamsHolder(args);
int particles = holder.GetInt(
MLTrainFactory.PropertyParticles, false, 20);
ICalculateScore score = new TrainingSetScore(training);
IRandomizer randomizer = new NguyenWidrowRandomizer();
IMLTrain train = new NeuralPSO((BasicNetwork)method, randomizer, score, particles);
return(train);
}
/// <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 BasicNetwork))
{
throw new EncogError(
"LMA training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
var result = new LevenbergMarquardtTraining(
(BasicNetwork) method, training);
return result;
}
/// <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);
}
/// <summary>
/// Create a SVM 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(
"SVM Train training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
new ParamsHolder(args);
var holder = new ParamsHolder(args);
double gammaStart = holder.GetDouble(
PropertyGamma1, false,
SVMSearchTrain.DefaultGammaBegin);
double cStart = holder.GetDouble(PropertyC1,
false, SVMSearchTrain.DefaultConstBegin);
double gammaStop = holder.GetDouble(
PropertyGamma2, false,
SVMSearchTrain.DefaultGammaEnd);
double cStop = holder.GetDouble(PropertyC2,
false, SVMSearchTrain.DefaultConstEnd);
double gammaStep = holder.GetDouble(
PropertyGammaStep, false,
SVMSearchTrain.DefaultGammaStep);
double cStep = holder.GetDouble(PropertyCStep,
false, SVMSearchTrain.DefaultConstStep);
var result = new SVMSearchTrain((SupportVectorMachine)method, training)
{
GammaBegin = gammaStart,
GammaEnd = gammaStop,
GammaStep = gammaStep,
ConstBegin = cStart,
ConstEnd = cStop,
ConstStep = cStep
};
return(result);
}
/// <summary>
/// Create a RPROP 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 IContainsFlat))
{
throw new EncogError(
"RPROP 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 initialUpdate = holder.GetDouble(
MLTrainFactory.PropertyInitialUpdate, false,
RPROPConst.DefaultInitialUpdate);
double maxStep = holder.GetDouble(
MLTrainFactory.PropertyMaxStep, false,
RPROPConst.DefaultMaxStep);
return(new ResilientPropagation((IContainsFlat)method, training,
initialUpdate, maxStep));
}
/// <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 BasicNetwork))
{
throw new EncogError(
"LMA training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary <String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
bool useReg = holder.GetBoolean(
MLTrainFactory.PropertyBayesianRegularization, false, false);
var result = new LevenbergMarquardtTraining(
(BasicNetwork)method, training)
{
UseBayesianRegularization = useReg
};
return(result);
}
/**
* 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);
}