/// <summary>
/// Расчет одного значения
/// </summary>
/// <param name="inp">Независимая переменная</param>
/// <returns>Прогноз</returns>
public double Predict(double inp)
{
Vector data = ExtensionOfFeatureSpace.GaussRBF(inp, X, sig);
double outp = GeomFunc.ScalarProduct(data, param);
return(outp);
}
private void Param()
{
A = new Matrix(n, n);
Vector[] vect = new Vector[n];
for (int i = 0; i < n; i++)
{
vect[i] = ExtensionOfFeatureSpace.GaussRBF(newX[i], newX, sigma);
}
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
A[i, j] = vect[i][j];
}
}
Kramer kram = new Kramer();
param = kram.GetAnswer(A, newY);
}
private void Param()
{
Matrix A = new Matrix(X.N, X.N);
Vector[] vect = new Vector[X.N];
for (int i = 0; i < X.N; i++)
{
vect[i] = ExtensionOfFeatureSpace.GaussRBF(X[i], X, sig);
}
for (int i = 0; i < X.N; i++)
{
for (int j = 0; j < X.N; j++)
{
A[i, j] = vect[i][j];
}
}
Kramer kram = new Kramer();
param = kram.GetAnswer(A, Y);
}
/// <summary>
/// Прогноз
/// </summary>
/// <param name="inp">Значение незав. переменной</param>
public double Predict(double inp)
{
Vector X = ExtensionOfFeatureSpace.GaussRBF(inp, newX, sigma);
return(GeomFunc.ScalarProduct(X, param));
}
