public MlpNeuron(List<Connection> inputs, List<Connection> outputs,
FunctionType functionType, ITunableParameterService paramService)
{
Inputs = inputs;
Outputs = outputs;
_functionType = functionType;
_sigmoidAlpha = paramService.SigmoidAlpha;
}
public FunctionApproximator(ITunableParameterService paramService)
{
_iterations = paramService.Iterations;
_trainingSetSize = paramService.TrainingSetSize;
_testSetSize = paramService.TestSetSize;
_printMovingAverage = paramService.PrintMovingAverage;
_averageWindowSize = paramService.AverageWindowSize;
_lowerBound = paramService.LowerBound;
_upperBound = paramService.UpperBound;
_printNetworkWeights = paramService.PrintNetworkWeights;
_printWeightsFrequency = paramService.PrintWeightsFrequency;
}
public MultiLayerPerceptron(int numInputs, int numOutputs, int numHiddenLayers, int numNeuronsPerHiddenLayer, ITunableParameterService paramService)
: base(numInputs, numOutputs)
{
_numHiddenLayers = numHiddenLayers;
_numNeuronsPerHiddenLayer = numNeuronsPerHiddenLayer;
_isMomentumUsed = paramService.IsMomentumUsed;
_momentumAmount = paramService.MomentumAmount;
_learningRate = paramService.LearningRate;
_sigmoidAlpha = paramService.SigmoidAlpha;
_isAnnealingUsed = paramService.IsAnnealingUsed;
_annealingValue = paramService.AnnealingValue;
BuildNetwork();
}
public RadialBasisFunctionNetwork(int numInputs, int numOutputs, ITunableParameterService paramService)
: base(numInputs, numOutputs)
{
_numCenters = paramService.NumberOfCenters;
_numDimensions = numInputs;
_lowerBound = paramService.LowerBound;
_upperBound = paramService.UpperBound;
_learningRate = paramService.LearningRate;
_isMomentumUsed = paramService.IsMomentumUsed;
_momentumAmount = paramService.MomentumAmount;
_isAnnealingUsed = paramService.IsAnnealingUsed;
_annealingValue = paramService.AnnealingValue;
CalculateSpacingAndSpread();
BuildNetwork();
}