public NetworkTrainer(BackPropagationNetwork network, DataPointCollection dataSet)
{
Nudge = true;
NudgeTolerance = 0.0001;
NudgeScale = 0.4;
NudgeWindow = 100;
TargetError = 0.001;
MaxIterations = 100000;
TrainingRate = 0.15;
Momentum = 0.10;
Network = network;
DataSet = dataSet;
Initialize();
}
public BasicNeuralNetworkTrainerWrapper(DataPointCollection dataSet)
{
DataSet = dataSet;
int[] layerSizes = new int[10] { DataSet.DataPointBound, 5, 5, 5, 5, 5, 5, 5, 3, 1 };
TransferFunction[] transferFunctions = new TransferFunction[10]
{
TransferFunction.None,
TransferFunction.RationalSigmoid,
TransferFunction.RationalSigmoid,
TransferFunction.Sigmoid,
TransferFunction.Sigmoid,
TransferFunction.Sigmoid,
TransferFunction.Sigmoid,
TransferFunction.Gaussian,
TransferFunction.Gaussian,
TransferFunction.Linear
};
Network.Initialize(layerSizes, transferFunctions);
_network = new BackPropagationNetwork(layerSizes, transferFunctions);
_networkTrainer = new SimpleNetworkTrainer(_network, DataSet);
}
public void Initialize(int[] layerSizes, TransferFunction[] layerFunctions)
{
_network = new BackPropagationNetwork(layerSizes, layerFunctions);
}
public SimpleNetworkTrainer(BackPropagationNetwork network, DataPointCollection dataSet)
: base(network, dataSet)
{
}
public AutoRegressiveTrainer(BackPropagationNetwork network, DataPointCollection dataSet)
: base(network, dataSet)
{
lastOutput = new double[dataSet.Count];
}
