private WTAMLModel GetNewWeight(WTAMLModel data, WinningNeuronModel winningNeuron, List <double> iteration)
{
var weight = data.Neurons.Find(p => p.Id == winningNeuron.Id).InputsWeight.ToList();
for (int i = 0; i < weight.Count(); i++)
{
weight[i] = Math.Round(weight[i] + (0.5 * (iteration[i] - weight[i])), 3);
}
data.Neurons.Find(p => p.Id == winningNeuron.Id).InputsWeight = weight;
return(data);
}
public void Calc(WTAMLModel data)
{
_outputService.InitInformation(data.Neurons);
for (var it = 0; it < data.Iterations.Count; it++)
{
var iteration = data.Iterations[it];
_outputService.WriteIterationData(iteration, it + 1);
WinningNeuronModel winningNeuron = this.winningNeuron(data.Neurons, iteration);
_outputService.WriteWinningNeuron(winningNeuron.Id + 1);
data = GetNewWeight(data, winningNeuron, iteration);
_outputService.WriteNeuronState(data.Neurons);
}
;
}
private WinningNeuronModel winningNeuron(List <WTANeuronModel> neurons, List <double> iteration)
{
WinningNeuronModel winningNeuron = new WinningNeuronModel()
{
Value = null, Id = 0
};
neurons.ForEach(n =>
{
var d = SimiliarityCalc(n, iteration);
_outputService.SimilarityLog(n.Id + 1, d);
if (winningNeuron.Value == null || d < winningNeuron.Value)
{
winningNeuron.Value = d;
winningNeuron.Id = n.Id;
}
});
return(winningNeuron);
}
