/// <summary>
/// Обучить нейрон-победитель.
/// </summary>
/// <param name="neuronWinner">Нейрон-победитель.</param>
/// <param name="inputEntity">Входной вектор.</param>
/// <param name="learningRate">Скорость обучения.</param>
public void StudyNeuron(KohonenNeuron neuronWinner, NetworkDataEntity inputEntity, double learningRate)
{
for (int i = 0; i < neuronWinner.Weights.Count(); i++)
{
neuronWinner.Weights[i] = neuronWinner.Weights[i] +
learningRate * (inputEntity.AttributeValues[i].GetNormalizedValue(NormalizationType) - neuronWinner.Weights[i]);
}
}
/// <summary>
/// Обучить входной вектор (провести итерацию обучения).
/// </summary>
/// <param name="inputEntity">Входной вектор.</param>
/// <param name="currentIteration">Номер текущей итерации обучения.</param>
/// <param name="iterationsCount">Общее количество итераций обучения.</param>
/// <returns>Ошибка обучения.</returns>
public override double StudyInputEntity(NetworkDataEntity inputEntity, int currentIteration, int iterationsCount)
{
var neuronWinner = GetNeuronWinner(inputEntity);
var learningRate = GetLearningRate(currentIteration, iterationsCount);
StudyNeuron(neuronWinner, inputEntity, learningRate);
var attributeValues = inputEntity.AttributeValues.Select(v => v.GetNormalizedValue(NormalizationType));
double error = GetEuclideanDistance(neuronWinner.Weights, attributeValues);
return(error);
}
/// <summary>
/// Получить нейрон-победитель.
/// </summary>
/// <param name="inputEntity">Входной вектор.</param>
/// <returns>Нейрон-победитель для входного вектора.</returns>
public override KohonenNeuron GetNeuronWinner(NetworkDataEntity inputEntity)
{
var attributeValues = inputEntity.AttributeValues.Select(v => v.GetNormalizedValue(NormalizationType));
double minDistance = GetEuclideanDistance(Neurons[0].Weights, attributeValues);
KohonenNeuron neuronWinner = Neurons[0];
for (int i = 1; i < Neurons.Count; i++)
{
double currentDistance = GetEuclideanDistance(Neurons[i].Weights, attributeValues);
if (currentDistance < minDistance)
{
minDistance = currentDistance;
neuronWinner = Neurons[i];
}
}
return(neuronWinner);
}
