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 defaultValue = 1.0 / ((double) ((SupportVectorMachine) method).InputCount);
while (true)
{
double num4;
SVMTrain train;
double num2 = 1.0;
IDictionary<string, string> theParams = ArchitectureParse.ParseParams(argsStr);
ParamsHolder holder = new ParamsHolder(theParams);
double num3 = holder.GetDouble("GAMMA", false, defaultValue);
do
{
num4 = holder.GetDouble("C", false, num2);
train = new SVMTrain((SupportVectorMachine) method, training) {
Gamma = num3
};
}
while (((uint) defaultValue) > uint.MaxValue);
if ((((uint) num2) + ((uint) num3)) <= uint.MaxValue)
{
train.C = num4;
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);
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 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);
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 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);
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
if (method is IContainsFlat)
{
ParamsHolder holder = new ParamsHolder(ArchitectureParse.ParseParams(argsStr));
double initialUpdate = holder.GetDouble("INIT_UPDATE", false, 0.1);
double maxStep = holder.GetDouble("MAX_STEP", false, 50.0);
if ((((uint) initialUpdate) - ((uint) maxStep)) >= 0)
{
return new ResilientPropagation((IContainsFlat) method, training, initialUpdate, maxStep);
}
}
throw new EncogError("RPROP training cannot be used on a method of type: " + method.GetType().FullName);
}
/// <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;
}
/**
* 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 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 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;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
double num2;
int num3;
if (!(method is BasicNetwork))
{
throw new TrainingError("Invalid method type, requires BasicNetwork");
}
ICalculateScore calculateScore = new TrainingSetScore(training);
ParamsHolder holder = new ParamsHolder(ArchitectureParse.ParseParams(argsStr));
double startTemp = holder.GetDouble("startTemp", false, 10.0);
if (((((uint) num3) & 0) != 0) || ((((uint) num2) - ((uint) num2)) < 0))
{
IMLTrain train;
return train;
}
num2 = holder.GetDouble("stopTemp", false, 2.0);
return new NeuralSimulatedAnnealing((BasicNetwork) method, calculateScore, startTemp, num2, holder.GetInt("cycles", false, 100));
}
/// <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;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
int num;
double num3;
IMLTrain train;
if (!(method is BasicNetwork))
{
throw new TrainingError("Invalid method type, requires BasicNetwork");
}
ICalculateScore calculateScore = new TrainingSetScore(training);
do
{
ParamsHolder holder = new ParamsHolder(ArchitectureParse.ParseParams(argsStr));
num = holder.GetInt("population", false, 0x1388);
double mutationPercent = holder.GetDouble("mutate", false, 0.1);
num3 = holder.GetDouble("mate", false, 0.25);
train = new NeuralGeneticAlgorithm((BasicNetwork) method, new RangeRandomizer(-1.0, 1.0), calculateScore, num, mutationPercent, num3);
}
while ((((uint) num) - ((uint) num3)) < 0);
return train;
}
/// <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);
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
bool flag;
LevenbergMarquardtTraining training3;
if (method is BasicNetwork)
{
flag = new ParamsHolder(ArchitectureParse.ParseParams(argsStr)).GetBoolean("BAYES_REG", false, false);
training3 = new LevenbergMarquardtTraining((BasicNetwork) method, training);
if (3 == 0)
{
LevenbergMarquardtTraining training2;
return training2;
}
}
else if (0 == 0)
{
throw new EncogError("LMA training cannot be used on a method of type: " + method.GetType().FullName);
}
training3.UseBayesianRegularization = flag;
if (0 == 0)
{
}
return training3;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
IDictionary<string, string> dictionary;
ParamsHolder holder;
double num;
string str;
string str2;
RBFEnum mexicanHat;
INeighborhoodFunction function;
string str3;
int[] numArray;
BasicTrainSOM nsom;
int num2;
double num3;
double num4;
double num6;
if (method is SupportVectorMachine)
{
dictionary = ArchitectureParse.ParseParams(argsStr);
holder = new ParamsHolder(dictionary);
num = holder.GetDouble("LR", false, 0.7);
str = holder.GetString("NEIGHBORHOOD", false, "rbf");
if (2 != 0)
{
goto Label_03DF;
}
goto Label_039F;
}
goto Label_03F4;
Label_0083:
num4 = holder.GetDouble("END_LR", false, 0.05);
double startRadius = holder.GetDouble("START_RADIUS", false, 10.0);
if ((((uint) num4) + ((uint) num4)) > uint.MaxValue)
{
return nsom;
}
if ((((uint) num3) + ((uint) num2)) <= uint.MaxValue)
{
num6 = holder.GetDouble("END_RADIUS", false, 1.0);
nsom.SetAutoDecay(num2, num3, num4, startRadius, num6);
return nsom;
}
Label_00E4:
if (4 == 0)
{
if ((((uint) num3) + ((uint) num2)) > uint.MaxValue)
{
goto Label_0292;
}
goto Label_02F8;
}
Label_00EE:
nsom = new BasicTrainSOM((SOMNetwork) method, num, training, function);
do
{
if (dictionary.ContainsKey("ITERATIONS"))
{
do
{
num2 = holder.GetInt("ITERATIONS", false, 0x3e8);
num3 = holder.GetDouble("START_LR", false, 0.05);
}
while ((((uint) num3) | 15) == 0);
goto Label_0083;
}
}
while ((((uint) num6) | 0xff) == 0);
if (0 == 0)
{
if ((((uint) num3) + ((uint) num3)) >= 0)
{
return nsom;
}
goto Label_03F4;
}
if ((((uint) num2) - ((uint) startRadius)) <= uint.MaxValue)
{
goto Label_00E4;
}
goto Label_0083;
Label_0184:
if (!str.Equals("single", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_00EE;
}
function = new NeighborhoodSingle();
if ((((uint) num6) - ((uint) num3)) >= 0)
{
if ((((uint) num) - ((uint) startRadius)) >= 0)
{
goto Label_00E4;
}
goto Label_0324;
}
if ((((uint) num2) & 0) == 0)
{
goto Label_0233;
}
Label_01E2:
while (!str.Equals("rbf1d", StringComparison.InvariantCultureIgnoreCase))
{
if (0 == 0)
{
if ((((uint) num3) - ((uint) num6)) >= 0)
{
goto Label_0184;
}
goto Label_0233;
}
}
function = new NeighborhoodRBF1D(mexicanHat);
if ((((uint) num2) + ((uint) num)) >= 0)
{
if (((uint) num6) < 0)
{
goto Label_01E2;
}
goto Label_0184;
}
if (((uint) num2) < 0)
{
goto Label_03DF;
}
goto Label_01E2;
Label_0233:
function = new NeighborhoodRBF(numArray, mexicanHat);
goto Label_0184;
Label_0243:
if (!str.Equals("rbf", StringComparison.InvariantCultureIgnoreCase))
{
if ((((uint) num2) & 0) != 0)
{
goto Label_03DF;
}
goto Label_01E2;
}
Label_0292:
str3 = holder.GetString("DIM", true, null);
if ((((uint) num3) + ((uint) num)) > uint.MaxValue)
{
goto Label_0292;
}
numArray = NumberList.FromListInt(CSVFormat.EgFormat, str3);
if ((((uint) num6) & 0) == 0)
{
goto Label_0233;
}
goto Label_0243;
Label_02F8:
if (str.Equals("bubble", StringComparison.InvariantCultureIgnoreCase))
{
function = new NeighborhoodBubble(1);
goto Label_0184;
}
if ((((uint) num3) & 0) == 0)
{
goto Label_0243;
}
goto Label_0292;
Label_0324:
function = null;
goto Label_02F8;
Label_0362:
mexicanHat = RBFEnum.Multiquadric;
goto Label_0324;
Label_039F:
mexicanHat = RBFEnum.Gaussian;
goto Label_0324;
Label_03DF:
str2 = holder.GetString("RBF_TYPE", false, "gaussian");
if (str2.Equals("Gaussian", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_039F;
}
if (((uint) startRadius) <= uint.MaxValue)
{
if (str2.Equals("Multiquadric", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_0362;
}
if (!str2.Equals("InverseMultiquadric", StringComparison.InvariantCultureIgnoreCase) || ((((uint) num2) + ((uint) num2)) < 0))
{
if (str2.Equals("MexicanHat", StringComparison.InvariantCultureIgnoreCase))
{
mexicanHat = RBFEnum.MexicanHat;
}
else
{
mexicanHat = RBFEnum.Gaussian;
}
goto Label_0324;
}
}
else if (((uint) num3) <= uint.MaxValue)
{
goto Label_0362;
}
if ((((uint) num3) - ((uint) num3)) <= uint.MaxValue)
{
mexicanHat = RBFEnum.InverseMultiquadric;
goto Label_0324;
}
goto Label_00E4;
Label_03F4:
throw new EncogError("Neighborhood training cannot be used on a method of type: " + method.GetType().FullName);
}
/// <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 RBF 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(
"RBF Networks must have exactly three elements, "
+ "separated by ->.");
}
ArchitectureLayer inputLayer = ArchitectureParse.ParseLayer(
layers[0], input);
ArchitectureLayer rbfLayer = ArchitectureParse.ParseLayer(
layers[1], -1);
ArchitectureLayer outputLayer = ArchitectureParse.ParseLayer(
layers[2], output);
int inputCount = inputLayer.Count;
int outputCount = outputLayer.Count;
RBFEnum t;
if (rbfLayer.Name.Equals("Gaussian", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.Gaussian;
}
else if (rbfLayer.Name.Equals("Multiquadric", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.Multiquadric;
}
else if (rbfLayer.Name.Equals("InverseMultiquadric", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.InverseMultiquadric;
}
else if (rbfLayer.Name.Equals("MexicanHat", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.MexicanHat;
}
else
{
throw new NeuralNetworkError("Unknown RBF: " + rbfLayer.Name);
}
var holder = new ParamsHolder(rbfLayer.Params);
int rbfCount = holder.GetInt("C", true, 0);
var result = new RBFNetwork(inputCount, rbfCount,
outputCount, t);
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;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
double learnRate = new ParamsHolder(ArchitectureParse.ParseParams(argsStr)).GetDouble("LR", false, 0.1);
return new ManhattanPropagation((BasicNetwork) method, training, learnRate);
}
private void MenuFileOpen_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.DefaultExt = ".eg"; // Default file extension
dlg.Filter = "Encog EG Files (.EG)|*.eg"; // Filter files by extension
Nullable<bool> result = dlg.ShowDialog();
if (result == true)
{
FileInfo inf = new FileInfo(dlg.FileName);
if (inf.Directory != null)
{
var tempn= Encog.Util.NetworkUtil.NetworkUtility.LoadNetwork(inf.Directory.ToString(), dlg.FileName);
Network = tempn;
}
if (Network == null)
{
MessageBox.Show("This does not appear to be an EG file created for this example.");
return;
}
this.Util = new GatherUtil();
ParamsHolder xpa = new ParamsHolder(Network.Properties);
this.Util.EvalWindow = xpa.GetInt("eval", true, 1);
this.Util.PredictWindow = xpa.GetInt("predict", true, 1);
this.Util.EvalWindow = xpa.GetInt("eval", true, 1);
this.Util.PredictWindow = xpa.GetInt("predict", true, 1);
// this.Util.EvalWindow = Convert.ToInt16(Network.Properties["eval"]);
}
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
ParamsHolder holder = new ParamsHolder(ArchitectureParse.ParseParams(argsStr));
double learnRate = holder.GetDouble("LR", false, 0.7);
return new Backpropagation((BasicNetwork) method, training, learnRate, holder.GetDouble("MOM", false, 0.3));
}
public IMLMethod Create(string architecture, int input, int output)
{
ArchitectureLayer layer3;
int count;
int num2;
PNNKernelType reciprocal;
PNNOutputMode classification;
ParamsHolder holder;
string str;
IList<string> list = ArchitectureParse.ParseLayers(architecture);
if (list.Count != 3)
{
throw new EncogError("PNN Networks must have exactly three elements, separated by ->.");
}
ArchitectureLayer layer = ArchitectureParse.ParseLayer(list[0], input);
ArchitectureLayer layer2 = ArchitectureParse.ParseLayer(list[1], -1);
goto Label_015F;
Label_000C:
if (str.Equals("reciprocal", StringComparison.InvariantCultureIgnoreCase))
{
reciprocal = PNNKernelType.Reciprocal;
}
else
{
throw new NeuralNetworkError("Unknown kernel: " + str);
}
Label_0032:
return new BasicPNN(reciprocal, classification, count, num2);
Label_0089:
throw new NeuralNetworkError("Unknown model: " + layer2.Name);
Label_009F:
holder = new ParamsHolder(layer2.Params);
str = holder.GetString("KERNEL", false, "gaussian");
if ((((uint) num2) & 0) == 0)
{
if (str.Equals("gaussian", StringComparison.InvariantCultureIgnoreCase))
{
reciprocal = PNNKernelType.Gaussian;
if ((((uint) num2) - ((uint) input)) > uint.MaxValue)
{
goto Label_0089;
}
goto Label_0032;
}
goto Label_000C;
}
Label_015F:
layer3 = ArchitectureParse.ParseLayer(list[2], output);
count = layer.Count;
num2 = layer3.Count;
if ((((uint) input) & 0) != 0)
{
goto Label_0089;
}
if (!layer2.Name.Equals("c", StringComparison.InvariantCultureIgnoreCase))
{
if (layer2.Name.Equals("r", StringComparison.InvariantCultureIgnoreCase))
{
classification = PNNOutputMode.Regression;
if ((((uint) output) + ((uint) num2)) < 0)
{
goto Label_000C;
}
goto Label_009F;
}
if (layer2.Name.Equals("u", StringComparison.InvariantCultureIgnoreCase))
{
classification = PNNOutputMode.Unsupervised;
goto Label_009F;
}
goto Label_0089;
}
classification = PNNOutputMode.Classification;
goto Label_009F;
}
/// <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;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
ParamsHolder holder;
double num;
double num2;
double num3;
double num4;
double num5;
double num6;
SVMSearchTrain train2;
if (method is SupportVectorMachine)
{
IDictionary<string, string> theParams = ArchitectureParse.ParseParams(argsStr);
new ParamsHolder(theParams);
if ((((uint) num3) - ((uint) num4)) < 0)
{
goto Label_0053;
}
if ((((uint) num2) + ((uint) num6)) <= uint.MaxValue)
{
holder = new ParamsHolder(theParams);
num = holder.GetDouble("GAMMA1", false, -10.0);
num2 = holder.GetDouble("C1", false, -5.0);
goto Label_0101;
}
goto Label_016E;
}
goto Label_0185;
Label_0053:
train2.GammaEnd = num3;
if ((((uint) num5) + ((uint) num)) > uint.MaxValue)
{
goto Label_0185;
}
train2.GammaStep = num5;
if (((uint) num3) >= 0)
{
train2.ConstBegin = num2;
train2.ConstEnd = num4;
if ((((uint) num4) | 3) != 0)
{
train2.ConstStep = num6;
return train2;
}
}
else
{
return train2;
}
Label_0101:
num3 = holder.GetDouble("GAMMA2", false, 10.0);
num4 = holder.GetDouble("C2", false, 15.0);
Label_016E:
if (((uint) num4) <= uint.MaxValue)
{
num5 = holder.GetDouble("GAMMASTEP", false, 1.0);
num6 = holder.GetDouble("CSTEP", false, 2.0);
train2 = new SVMSearchTrain((SupportVectorMachine) method, training) {
GammaBegin = num
};
}
goto Label_0053;
Label_0185:
throw new EncogError("SVM Train training cannot be used on a method of type: " + method.GetType().FullName);
}
