public Triangular(IDistribution d)
{
this._distribution = d;
_sampling = _distribution.GetSampling();
Center = (Xmax + Xmin) / 2;
DeltaX = (Xmax - Xmin) / 2;
}
/// <summary>
/// Вычисление градиента только для ядра гаусса
/// </summary>
/// <param name="distribution"></param>
public static double Grad(IDistribution distribution, GaussianKernal kernal)
{
double min = distribution.GetSampling().Min();
double max = distribution.GetSampling().Max();
double x0 = new Random().Next((int)min, (int)max);
double xi = x0;
double step = 0.0;
double YMin = kernal.getDerivative(xi);
double xNext = xi - EPSSILON * kernal.getDerivative(xi);
while (kernal.getDerivative(xNext) - kernal.getDerivative(xi) > C)
{
step = xi - EPSSILON * kernal.getDerivative(xi) + xi;
YMin = kernal.getDerivative(xi);
xi = xNext;
xNext = xi + step;
}
return YMin;
}
/// <summary>
/// Применить корректировку)(getWeight) и поработать с нормой .
/// </summary>
/// <param name="distribution"></param>
/// <param name="kernal"></param>
public void Learm(IDistribution distribution, IKernal kernal)
{
double norma = 0.0;
List<double> ErrFunctionValues = new List<double>();
LearmSampling = distribution.GetSampling().ToList();
Signals = new List<double>();
List<double> faktOj = new List<double>();//Подать на выход новой сети разность между фактическим и ошидаемым значениями
do
{
foreach (double sample in LearmSampling)
{
Signals.Add(kernal.getDensityFunctionValue(sample));//выходные сигналы сети ска
}
for (int i = 0; i < LearmSampling.Count; i++)
{
ErrFunctionValues.Add(this.ErrFunction(LearmSampling.ElementAt(i), Signals.ElementAt(i)));
}
List<double> BaskSignals = new List<double>();//Замена функций их производными
for (int i = 0; i < Signals.Count; i++)//2
{
BaskSignals.Add(kernal.getDerivative(LearmSampling.ElementAt(i)));
}
// DeltaWeight = -N*GetGradirnt()
}
while (norma > C);
//Weights//вектор весов
}
public EpanichnikovKernal(IDistribution distributiob)
{
this._distribution = distributiob;
_sampling = _distribution.GetSampling();
r = new Random();
}
public GaussianKernal(IDistribution distibution)
{
this._distribution = distibution;
_sampling = _distribution.GetSampling();
}
public QuarticKernal(IDistribution distr)
{
this._distribution = distr;
_sampling = _distribution.GetSampling();
}