public ConstructiveTrainer(double learnRate = 0.05, double minError = 0.01,
double momentum = 0.01, int maxEpochs = 100, int show = 10, int maxHiddenLayers = 2,
IPerformanceFunction pFunc = null, TrainingModes tMode = TrainingModes.OffLine)
: base(learnRate, minError, momentum, maxEpochs, show, pFunc, tMode)
{
this.maxHiddenLayers = maxHiddenLayers;
subTrainer = new PerceptronTrainer(learnRate, minError, momentum, maxEpochs, show, pFunc, tMode);
}
public Trainer(double learnRate = 0.05, double minError = 0.01,
double momentum = 0.01, int maxEpochs = 100,
int show = 10, IPerformanceFunction pFunc = null,
TrainingModes tMode = TrainingModes.OffLine)
{
this.learnRate = learnRate;
this.minError = minError;
this.momentum = momentum;
this.maxEpochs = maxEpochs;
this.show = show;
this.tMode = tMode;
if (pFunc != null)
{
this.pFunc = pFunc;
}
}
private Tuple <Type, object> EvaluateCreateTrainer(ParseTreeNode node)
{
var trainerClassName = node.ChildNodes[0];
var trainerParams = EvaluateKeyValuePair(node.ChildNodes[1]);
double learnRate = 0.05;
double minError = 0.01;
int maxEpochs = 100;
int maxHiddenLayers = 2;
int show = 10;
IPerformanceFunction pFunc = null;
TrainingModes tMode = TrainingModes.OnLine;
if (trainerParams.ContainsKey("learnRate"))
{
learnRate = double.Parse(trainerParams["learnRate"]);
}
if (trainerParams.ContainsKey("minError"))
{
minError = double.Parse(trainerParams["minError"]);
}
if (trainerParams.ContainsKey("maxEpochs"))
{
maxEpochs = int.Parse(trainerParams["maxEpochs"]);
}
if (trainerParams.ContainsKey("maxHiddenLayers"))
{
maxHiddenLayers = int.Parse(trainerParams["maxHiddenLayers"]);
}
if (trainerParams.ContainsKey("show"))
{
show = int.Parse(trainerParams["show"]);
}
if (trainerParams.ContainsKey("performanceFunction"))
{
pFunc = (IPerformanceFunction)(typeof(PerformanceFunctions)
.GetField(trainerParams["performanceFunction"])
.GetValue(null));
}
if (trainerParams.ContainsKey("mode"))
{
tMode = (TrainingModes)Enum.Parse(typeof(TrainingModes), trainerParams["mode"]);
}
Trainer trainer = null;
switch (trainerClassName.Token.Text)
{
case "BackPropagationTrainer":
trainer = new BackPropagationTrainer(learnRate, minError, 0.01, maxEpochs, show, pFunc, tMode);
break;
case "PerceptronTrainer":
trainer = new PerceptronTrainer(learnRate, minError, 0.01, maxEpochs, show, pFunc, tMode);
break;
case "ConstructiveTrainer":
trainer = new ConstructiveTrainer(learnRate, minError, 0.01, maxEpochs, show, maxHiddenLayers,
pFunc, tMode);
break;
default:
throw new Exception("Trainer of kind " + trainerClassName.Token.Text + " does not exist.");
}
return(new Tuple <Type, object>(typeof(Trainer), trainer));
}
public PerceptronTrainer(double learnRate = 0.05, double minError = 0.01,
double momentum = 0.01, int maxEpochs = 100, int show = 10,
IPerformanceFunction pFunc = null, TrainingModes tMode = TrainingModes.OffLine)
: base(learnRate, minError, momentum, maxEpochs, show, pFunc, tMode)
{
}