public int AddF2Neuron(LayerF1 f1Layer, double[] tdConnWeights, double [] prototype) // to be called from CAFuzzyART(string path) method where pattern != tdConnweights
{
F2Neuron newF2Neuron = new F2Neuron(f1Layer.Count);
base.Add(newF2Neuron);
SynapticConnection[] connections = newF2Neuron.getConnections();
for (int i = 0; i < connections.Length; i++)
{
connections[i].setF1Neuron((F1Neuron)f1Layer[i]);
connections[i].setF2Neuron(newF2Neuron);
((F1Neuron)f1Layer[i]).AddSynapticConnection(connections[i]);
connections[i].setWeight(tdConnWeights[i]);
}
newF2Neuron.setProtoTypeCluster(prototype);
return(base.Count - 1);
}
public ConSelfART(int inputVectorLength, int categoryCountLimit, double rho, double alpha, double beta, bool cc)
{
this.CategoryCountLimit = categoryCountLimit;
this.rho = rho;
this.alpha = alpha;
this.beta = beta;
//F2 = new LayerF2();
complementCoding = cc;
if (cc)
{
i = new double[inputVectorLength * 2];
F1 = new LayerF1(inputVectorLength * 2);
}
else
{
i = new double[inputVectorLength];
F1 = new LayerF1(inputVectorLength);
}
}
double beta; // Learning Rate
public ConSelfART(int inputVectorLength, double rho, double rhoInc, double alpha, double beta, bool cc)
{
this.rho = rho;
this.epsilon = rhoInc;
this.alpha = alpha;
this.beta = beta;
//F2 = new LayerF2();
complementCoding = cc;
if (cc)
{
i = new double[inputVectorLength * 2];
F1 = new LayerF1(inputVectorLength * 2);
}
else
{
i = new double[inputVectorLength];
F1 = new LayerF1(inputVectorLength);
}
}
