public KLayer(int inputsCount, int neuronsCount)
{
_Neurons = new KNeuron[neuronsCount];
_Output = new double[neuronsCount];
for (int i = 0; i < _Neurons.Length; i++)
_Neurons[i] = new KNeuron(inputsCount);
}
public KLayer(int inputsCount, int neuronsCount)
{
_Neurons = new KNeuron[neuronsCount];
_Output = new double[neuronsCount];
for (int i = 0; i < _Neurons.Length; i++)
{
_Neurons[i] = new KNeuron(inputsCount);
}
}
public double Run(KLearnData sample)
{
double error = 0.0;
_Network.Compute(sample._Input);
int winner = _Network.GetWinner();
// get layer of the network
KLayer layer = _Network[0];
layer[winner].AddSymbol(sample._Symbol);
// update weight of the winner only
// check learning radius
if (_LearningRadius == 0)
{
KNeuron neuron = layer[winner];
for (int i = 0, n = neuron._Weights.Length; i < n; i++)
{
neuron[i] += (sample._Input[i] - neuron[i]) * 0.01;
}
}
else
{
// winner's X and Y
int wx = winner % _Width;
int wy = winner / _Width;
// walk through all neurons of the layer
for (int j = 0, m = layer._Neurons.Length; j < m; j++)
{
KNeuron neuron = layer[j];
int dx = (j % _Width) - wx;
int dy = (j / _Width) - wy;
// update factor ( Gaussian based )
double factor = Math.Exp(-(double)(dx * dx + dy * dy) / _SquaredRadius2);
// update weight of the neuron
for (int i = 0, n = neuron._Weights.Length; i < n; i++)
{
// calculate the error
double e = (sample._Input[i] - neuron[i]) * factor;
error += Math.Abs(e);
// update weight
neuron[i] += e * _LearningRate;
}
}
}
return(0);
}
