public static double [] PolynomialRegression(double[] X, double[] Y, int degree_of_a_polynomial)
{
var quantity = X.Length;
double[][] A = Matrix.MatrixCreate(degree_of_a_polynomial, degree_of_a_polynomial);
double[] C = new double[degree_of_a_polynomial];
for (int i = 1; i <= degree_of_a_polynomial; i++)
{
for (int j = 1; j <= degree_of_a_polynomial; j++)
{
double Sum = 0;
for (int r = 1; r <= quantity; r++)
{
Sum += 1.00 / quantity * Math.Pow(X[r - 1], i + j - 2);
}
A[i - 1][j - 1] = Sum;
}
}
for (int j = 1; j <= degree_of_a_polynomial; j++)
{
double Sum = 0;
for (int r = 1; r <= quantity; r++)
{
Sum += 1.00 / quantity * Math.Pow(X[r - 1], j - 1) * Y[r - 1];
}
C[j - 1] = Sum;
}
var XX = CrautMethod.LU(A, C);
return(XX);
}
public static double[] TranscendentalRegression(double[] X, double[] Y, Func <double, double> [] functions)
{
var functionsLength = functions.Length;
var quantity = X.Length;
double[][] A = Matrix.MatrixCreate(functionsLength, functionsLength);
double[] C = new double[functionsLength];
for (int i = 1; i <= functionsLength; i++)
{
for (int j = 1; j <= functionsLength; j++)
{
double Sum = 0;
for (int r = 1; r <= quantity; r++)
{
//!!!
Sum += 1.00 / quantity * functions[i - 1](X[r - 1]) * functions[j - 1](X[r - 1]);
}
A[i - 1][j - 1] = Sum;
}
}
for (int j = 1; j <= functionsLength; j++)
{
double Sum = 0;
for (int r = 1; r <= quantity; r++)
{
Sum += 1.00 / quantity * functions[j - 1](X[r - 1]) * Y[r - 1];
}
C[j - 1] = Sum;
}
var XX = CrautMethod.LU(A, C);
return(XX);
}
