public SelfOrganizingMapTrainer(SelfOrganizingMap network, double[][] trainingSet, LearningMethod learningMethod, double learningRate)
{
ReductionFactor = 0.99;
TotalError = 1.0;
Network = network;
TrainingSet = trainingSet;
LearningMethod = learningMethod;
LearningRate = learningRate;
TotalError = 1.0;
_outputNeuronCount = network.OutputNeuronCount;
_inputNeuronCount = network.InputNeuronCount;
for (int i = 0; i < trainingSet.Length; i++)
{
if (Matrix.CreateColumnMatrix(trainingSet[i]).VectorLength() < MinValue)
{
throw new Exception("Multiplicative normalization has null training case");
}
}
BestNetwork = new SelfOrganizingMap(_inputNeuronCount, _outputNeuronCount, network.NormalizationType);
_neuronWinCounts = new int[_outputNeuronCount];
_correctionMatrix = new Matrix(_outputNeuronCount, _inputNeuronCount + 1);
_workMatrix = LearningMethod == LearningMethod.Additive ? new Matrix(1, _inputNeuronCount + 1) : null;
Initialize();
BestError = Double.MaxValue;
}
public SelfOrganizingMapTrainer(SelfOrganizingMap network, double[][] trainingSet, LearningMethod learningMethod, double learningRate)
{
ReductionFactor = 0.99;
TotalError = 1.0;
Network = network;
TrainingSet = trainingSet;
LearningMethod = learningMethod;
LearningRate = learningRate;
TotalError = 1.0;
outputNeuronCount = network.OutputNeuronCount;
inputNeuronCount = network.InputNeuronCount;
for (int i = 0; i < trainingSet.Length; i++)
{
if (Matrix.CreateColumnMatrix(trainingSet[i]).VectorLength() < MinValue)
throw new Exception("Multiplicative normalization has null training case");
}
BestNetwork = new SelfOrganizingMap(inputNeuronCount, outputNeuronCount, network.NormalizationType);
neuronWinCounts = new int[outputNeuronCount];
correctionMatrix = new Matrix(outputNeuronCount, inputNeuronCount + 1);
workMatrix = LearningMethod == LearningMethod.Additive ? new Matrix(1, inputNeuronCount + 1) : null;
Initialize();
BestError = Double.MaxValue;
}
