private double S0ij(InitialAnalysMultidimensionalData X, InitialAnalysMultidimensionalData Y, int i, int j, double N1, double N2)
{
double rez = 0;
double xij = 0,
yij = 0,
xi = 0,
xj = 0,
yi = 0,
yj = 0;
xi = X.ISA[i].unsortl.Sum();
yi = Y.ISA[i].unsortl.Sum();
xj = X.ISA[j].unsortl.Sum();
yj = Y.ISA[j].unsortl.Sum();
for (int L = 0; L < N1; L++)
{
xij += X.ISA[i].unsortl[L] * X.ISA[j].unsortl[L];
}
for (int L = 0; L < N2; L++)
{
yij += Y.ISA[i].unsortl[L] * Y.ISA[j].unsortl[L];
}
rez = -(xi + yi) * (xj + yj) / (N1 + N2);
rez += xij + yij;
rez /= N1 + N2 - 2;
return(rez);
}
private double[,] xdF(InitialAnalysMultidimensionalData IAMD)
{
double[,] rez = new double[1, IAMD.n];
for (int i = 0; i < IAMD.Ex.Length; i++)
{
rez[0, i] = IAMD.Ex[i];
}
return(rez);
}
public Data SravnSredF(InitialAnalysMultidimensionalData X, InitialAnalysMultidimensionalData Y)
{
Data rez = new Data();
double[,] S0 = S0F(X, Y);
double[,] S1 = S1F(X, Y);
rez.Name = "Рівність багатомірних середніх при рівності DC матриці";
rez.Q = -(N1 + N2 - 2 - n / 2) * Math.Log(Matrix.Determinant(S1) / Matrix.Determinant(S0));
rez.QKvant = Hi.HIF(X.ISA[0].alf.Q, n);
rez.H = rez.Q <= rez.QKvant;
return(rez);
}
private double[,] S0F(InitialAnalysMultidimensionalData X, InitialAnalysMultidimensionalData Y)
{
int n = X.ISA.Count;
double[,] rez = new double[n, n];
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
rez[i, j] = S0ij(X, Y, i, j, X.ISA[0].unsortl.Length, Y.ISA[0].unsortl.Length);
}
}
return(rez);
}
static public double Normalf(InitialAnalysMultidimensionalData IAM, double[] X)
{
double[,] Xm = new double[1, IAM.n];
double[,] E = new double[1, IAM.n];
for (int c = 0; c < IAM.n; c++)
{
E[0, c] = IAM.Ex[c];
Xm[0, c] = X[c] - IAM.Ex[c];
}
double znam = Math.Sqrt(Math.Pow(2 * Math.PI, IAM.n) * Matrix.Determinant(IAM.DC));
double[,] XEX = Matrix.MultiplicNumber(Matrix.MultiplicMatrix(
Matrix.MultiplicMatrix(Xm, Matrix.InverseMatrix(IAM.DC)), Matrix.TranspMatrix(Xm))
, -0.5);
return(Math.Exp(XEX[0, 0]) / znam);
}
private double[,] SdF(InitialAnalysMultidimensionalData IAMD)
{
double[,] rez;
double[,] X = new double[IAMD.N, IAMD.n];
double[,] XT;
double[,] Ex = xdF(IAMD);
for (int i = 0; i < IAMD.n; i++)
{
for (int l = 0; l < IAMD.N; l++)
{
X[l, i] = IAMD.ISA[i].unsortl[l] - Ex[0, i];
}
}
XT = Matrix.TranspMatrix(X);
rez = Matrix.MultiplicMatrix(XT, X);
rez = Matrix.MultiplicNumber(rez, 1.0 / (IAMD.N - 1));
return(rez);
}
public UniformityDoubleMultidimensionalData(List <InitialStatisticalAnalys> ISA, List <List <int> > ind)
{
X = new InitialAnalysMultidimensionalData(ISA, ind[0], 0);
Y = new InitialAnalysMultidimensionalData(ISA, ind[1], 0);
n = ind[0].Count;
N1 = ISA[ind[0][0]].unsortl.Length;
N2 = ISA[ind[1][0]].unsortl.Length;
for (int i = 1; i < n; i++)
{
if (N1 != ISA[ind[0][i]].unsortl.Length || N2 != ISA[ind[0][i]].unsortl.Length)
{
MessageBox.Show("Not correct input data", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
}
Refresh();
}
