public void Run(double[] data, double[] target)
{
training = true;
// Increment into FuzzyART A
ArtA.Vigilance = BasedVigilanceA;
ArtA.Run(data);
// Increment into FuzzyART B
ArtB.Vigilance = BasedVigilanceB;
ArtB.Run(target);
Process();
}
private void Process()
{
bool isSuccess = false;
while (!isSuccess)
{
// Select target weight
int winnerNeuronIndex = ArtA.WinningNeuronPos;
Neuron weightNeuron = null;
if (winnerNeuronIndex >= Map.Count)
{
// Create new weight neuron in mapfield.
weightNeuron = Map.Neurons(
Map.AddNeuron(FuzzyLogicUtil.MakeDummyNeuron(ArtB.InputSize)));
}
else
{
weightNeuron = Map.Neurons(winnerNeuronIndex);
}
if (training)
{
// Check vigilance condition
double[] artBValues = artB.Output;
weightNeuron.Input(artBValues);
double normOfSimilarity = weightNeuron.Output();
double testVigilance = normOfSimilarity / FuzzyLogicUtil.Norm(artBValues);
if (testVigilance >= Vigilance)
{
output = FuzzyLogicUtil.Intersect(weightNeuron.Weights.ToArray(), artBValues, ArtB.InputSize);
// Modify new mapfield weight.
for (int i = 0; i < weightNeuron.Weights.Count; ++i)
{
weightNeuron.Weights[i] = (Beta * output[i]) + ((1 - Beta) * weightNeuron.Weights[i]);
}
// Call ArtA to adjust its winner neuron weight.
bool isWeightAdjusted = false;
if (FastCommitedSlowLearningOption)
{
if (IsUnCommited(ArtA.F2.Neurons(ArtA.WinningNeuronPos)))
{
double beta = ArtA.Beta;
ArtA.Beta = 1.0;
artA.AdjustWeight();
ArtA.Beta = beta;
isWeightAdjusted = true;
}
}
if (!isWeightAdjusted)
{
ArtA.AdjustWeight();
}
// Call ArtB to adjust its winner neuron weight.
ArtB.AdjustWeight();
isSuccess = true;
}
else
{
double newVigilance = ArtA.WinningVigilance + ArtA.Choice;
ArtA.Vigilance = newVigilance > 1 ? 1.0 : newVigilance;
ArtA.Reset(winnerNeuronIndex);
}
}
else
{
output = weightNeuron.Weights.ToArray();
isSuccess = true;
}
}
training = false;
}
