/// <summary>
/// 二阶线性回归预测结果
/// </summary>
public double[] SecResult()
{
double[,] X = new double[length, 3];
double[] B = new double[3];
for (int i = 0; i < length; i++)
{
X[i, 0] = 1;
X[i, 1] = x1[i];
X[i, 2] = x2[i];
}
Matrix matr1 = new Matrix(X);
Matrix matr2 = new Matrix(length, 1, y);
Matrix A1 = new Matrix(3, 3, matr1.Transpose() * matr1);
Matrix A2 = new Matrix(3, length, A1.InvertGaussJordan() * matr1.Transpose());
Matrix A = A2 * matr2;
B[0] = A.GetElement(0, 0);
B[1] = A.GetElement(1, 0);
B[2] = A.GetElement(2, 0);
Res = new double[length + n];
for (int i = 0; i < length + n; i++)
{
Res[i] = B[0] + B[1] * x1[i] + B[2] * x2[i];
}
return(Res);
}
public double[] Result()
{
this.AGO1();
double[,] B = new double[length - 1, 2];
double[] Yn = new double[length - 1];
X1 = new double[length + n]; //预测的AGO1数列，分配内存
X = new double[length + n]; //预测的结果，分配内存
for (int i = 0; i < length - 1; i++)
{
B[i, 0] = -(x1[i] + x1[i + 1]) / 2;
B[i, 1] = 1;
}
//return B;
for (int i = 1; i < length; i++)
{
Yn[i - 1] = x[i];
}
//return Yn;
Matrix matr1 = new Matrix(B);
Matrix matr2 = new Matrix(length - 1, 1, Yn);
Matrix A1 = new Matrix(2, 2, matr1.Transpose() * matr1);
Matrix A2 = new Matrix(2, length - 1, A1.InvertGaussJordan() * matr1.Transpose());
Matrix A = A2 * matr2;
double a = A.GetElement(0, 0);
double u = A.GetElement(1, 0);
X1[0] = x[0];
for (int i = 0; i < (length + n - 1); i++)
{
X1[i + 1] = (x[0] - u / a) * Math.Pow(Math.E, -a * (i + 1)) + u / a;
}
X[0] = x[0];
for (int i = length + n - 1; i > 0; i--)
{
X[i] = X1[i] - X1[i - 1];
}
return(X);
}