private double ComputeVigilance(double[] input, double[] category)
{
double vigilance = FuzzyLogicUtil.Norm(FuzzyLogicUtil.Intersect(input, category, 2 * inputSize))
/ FuzzyLogicUtil.Norm(input);
return(vigilance);
}
public void AdjustWeight()
{
Neuron winnerNeuron = F2.Neurons(winningNeuronPos);
double[] intersect = FuzzyLogicUtil.
Intersect(InputData, winnerNeuron.Weights.ToArray(), InputSize);
// Modify new art a weight.
for (int i = 0; i < this.InputSize; ++i)
{
winnerNeuron.Weights[i] = (Beta * intersect[i]) + ((1 - Beta) * winnerNeuron.Weights[i]);
}
}
private double[] ComputeChoicingValues(double[] input, double[][] categories,
int numberOfCategories, double choicingParam)
{
double[] choicingValues = new double[numberOfCategories];
for (int i = 0; i < numberOfCategories; ++i)
{
choicingValues[i] =
FuzzyLogicUtil.Norm(FuzzyLogicUtil.Intersect(input, categories[i], 2 * inputSize)) /
(choicingParam + FuzzyLogicUtil.Norm(categories[i]));
}
return(choicingValues);
}
private void Process()
{
bool isSuccess = false;
while (!isSuccess)
{
int max = FuzzyLogicUtil.FindMax(ChoiceValues.ToArray());
double normOfSimilarity = 0;
if (max != -1)
{
normOfSimilarity = F2.Neurons(max).Output();
}
else
{
// Indicate that there is no eligible neuron
// as winner, so create a dummy neuron in F2 layer.
max = F2.AddNeuron(FuzzyLogicUtil.MakeDummyNeuron(InputSize));
ChoiceValues.Add(
FuzzyLogicUtil.ChoicingValue(normOfInputs, 1.0 * InputSize, Choice));
normOfSimilarity = normOfInputs;
}
// Calculate and compare with vigilance
winningVigilance = normOfSimilarity / normOfInputs;
if (winningVigilance >= Vigilance)
{
winningNeuronPos = max;
isSuccess = true;
}
else
{
ChoiceValues[max] = 0;
}
}
// Adjust neuron weight automatically. if it is set.
if (AutoAdjustWeight)
{
AdjustWeight();
}
// Calculate output.
output = FuzzyLogicUtil.Intersect(F2.Neurons(WinningNeuronPos).Weights.ToArray(),
InputData, InputSize);
}
public void Run(double[] data)
{
input = data;
// Compute norm of input values.
normOfInputs = FuzzyLogicUtil.Norm(data);
// Transfer input values.
f1.Input(data);
// Calculate initial choicing value
ChoiceValues.Clear();
for (int i = 0; i < f2.Count; ++i)
{
double normOfWeight = FuzzyLogicUtil.Norm(F2.Neurons(i).Weights.ToArray());
ChoiceValues.Add(FuzzyLogicUtil.ChoicingValue(F2.Neurons(i).Output(), normOfWeight, Choice));
}
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;
}
