double SimpleMx(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
if (gr1.l.Count != gr2.l.Count)
{
if (gr1.l.Count + gr2.l.Count <= 25)
{
double A = gr1.Mx.Q - gr2.Mx.Q;
double B = Math.Sqrt((gr1.l.Count * gr2.l.Count) / (gr1.l.Count + gr2.l.Count));
double C = (gr1.l.Count - 1) * gr1.Dx.Q + (gr2.l.Count - 1) * gr2.Dx.Q;
C /= gr1.l.Count + gr2.l.Count - 2;
C = Math.Sqrt(C);
return(A * B / C);
}
return((gr1.Mx.Q - gr2.Mx.Q) / Math.Sqrt((gr1.Dx.Q / gr1.l.Count) + (gr2.Dx.Q / gr2.l.Count)));
}
for (int i = 0; i < gr1.l.Count; i++)
{
Z.Add(gr1.unsortl[i] - gr2.unsortl[i]);
}
InitialStatisticalAnalys TG = new InitialStatisticalAnalys(Z);
if (TG.Mx.Q == TG.Gx.Q && TG.Mx.Q == 0)
{
return(0);
}
return(TG.Mx.Q * Math.Sqrt(TG.l.Count) / TG.Gx.Q);
}
static public string TypeRegresFound(InitialStatisticalAnalys ISAX, InitialStatisticalAnalys ISAY)
{
string[] Type = Regex.Split(RegresTypeName.TypeRegresion, "\n");
double[] Szalmas = new double[Type.Length];
double Korel = KorelationFound(ISAX, ISAY);
for (int i = 0; i < Type.Length; i++)
{
double td = 0;
List <Data> q = RegresParamFound(ISAX, ISAY, Korel, Type[i], ref td);
Szalmas[i] = SzalF(ISAX, ISAY, q, Type[i]);
}
double min = Szalmas[0];
int index = 0;
for (int i = 0; i < Type.Length; i++)
{
if (min != double.NaN && Szalmas[i] <= min)
{
min = Szalmas[i];
index = i;
}
}
return(Type[index]);
}
static public double HiSqurdFound(InitialStatisticalAnalys gr, int l)
{
double Hi = 0;
List <double> Y4 = new List <double>();
double L = (double)1 / gr.Mx.Q;
for (double i = gr.l[0], v = 0; v < gr.Y2.Count; i += gr.Step.Q, v++)
{
if (l == 0)
{
Y4.Add(Distributions.NormalFFound(((i + gr.Step.Q) - gr.Mx.Q) / gr.Gx.Q) - Distributions.NormalFFound((i - gr.Mx.Q) / gr.Gx.Q));
}
else if (l == 1)
{
double F2 = 1 - Math.Exp(-(1 / gr.Mx.Q) * (gr.Min.Q + gr.Step.Q * (v + 1)));
double F1 = 1 - Math.Exp(-(1 / gr.Mx.Q) * (gr.Min.Q + gr.Step.Q * (v)));
Y4.Add(F2 - F1);
}
else if (l == 2)
{
Y4.Add(gr.Step.Q / gr.Len.Q);
}
}
for (int i = 0; i < gr.m.Q; i++)
{
Hi += (Math.Pow(Y4[i] - gr.f[i], 2) / Y4[i]) * gr.l.Count;
}
return(Math.Round(Hi, 4));
}
#pragma warning disable IDE1006
double[,] fFound(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2, int Ncol, int Nrow)
#pragma warning restore IDE1006
{
double[,] rez = new double[Ncol, Nrow];
for (int i = 0; i < gr1.l.Count; i++)
{
for (int j = 0; j < Ncol; j++)
{
for (int k = 0; k < Nrow; k++)
{
if (gr1.unsortl[i] <= (j + 1) * gr1.Len.Q / Ncol + gr1.Min.Q && gr1.unsortl[i] >= (j * gr1.Len.Q / Ncol + gr1.Min.Q) &&
gr2.unsortl[i] <= ((k + 1) * gr2.Len.Q / Nrow + gr2.Min.Q) && gr2.unsortl[i] >= (k * gr2.Len.Q / Nrow + gr2.Min.Q))
{
rez[j, k] += 1.0 / gr1.l.Count;
goto t1;
}
}
}
t1 :;
}
/*for (int j = 0; j < Ncol; j++)
* for (int k = 0; k < Nrow; k++)
* if (rez[j, k] <= gr1.alf)
* rez[j, k] = 0;*/
return(rez);
}
#pragma warning disable IDE1006
static public double[] fi1F(InitialStatisticalAnalys ISA)
{
double[] rez = new double[ISA.unsortl.Length]; ///page 108
for (int i = 0; i < rez.Length; i++)
{
rez[i] = ISA.unsortl[i] - ISA.Mx.Q;
}
return(rez);
//return element - mx;
}
double SimpleS(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
if (gr1.Dx.Q >= gr2.Dx.Q)
{
return(gr1.Dx.Q / gr2.Dx.Q);
}
else
{
return(gr2.Dx.Q / gr1.Dx.Q);
}
}
static public double SzalF(InitialStatisticalAnalys ISAX, InitialStatisticalAnalys ISAY, List <Data> Q, string RegresTypeN)
{
double Szal = 0;
for (int i = 0; i < ISAX.unsortl.Length; i++)
{
Szal += Math.Pow(ISAY.unsortl[i] - RegresType.Model(ISAX.unsortl[i], Q, RegresTypeN), 2);
}
Szal /= (ISAX.unsortl.Length - Q.Count);
return(Math.Sqrt(Szal));
}
private double SzalFound(double[] AB, InitialStatisticalAnalys X, InitialStatisticalAnalys Y)
{
double rez = 0;
for (int i = 0; i < Y.l.Count; i++)
{
rez += Math.Abs(Y.unsortl[i] - AB[0] - AB[1] * X.unsortl[i]);
}
rez /= X.l.Count;
return(rez);
}
static public double[] fi2F(InitialStatisticalAnalys ISA)
{
double[] rez = new double[ISA.unsortl.Length]; ///page 108
double x3 = InitialStatisticalAnalys.StartMoment(ISA.l, 3);
double x2 = InitialStatisticalAnalys.StartMoment(ISA.l, 2);
for (int i = 0; i < rez.Length; i++)
{
rez[i] = Math.Pow(ISA.unsortl[i], 2) - (x3 - x2 * ISA.Mx.Q) * (ISA.unsortl[i] - ISA.Mx.Q) / ISA.Dx.Q - x2;
}
return(rez);
}
double KrsumRangVilsFound(double[,] rang, InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
double KrsumRangVils = 0, Mw = 0, Dw = 0;
for (int i = 0; i < N; i++)
{
if (rang[i, 1] == 0)
{
KrsumRangVils += rang[i, 0];
}
}
Mw = gr1.l.Count * (N + 1) / 2;
Dw = gr1.l.Count * gr2.l.Count * (N + 1) / 12;
return((KrsumRangVils - Mw) / Math.Sqrt(Dw));
}
public static double KorelationFound(InitialStatisticalAnalys ISAX, InitialStatisticalAnalys ISAY)
{
double Mxy = 0;
double sx = 0, sy = 0;
for (int i = 0; i < ISAX.l.Count; i++)
{
sx += ISAX.unsortl[i];
sy += ISAY.unsortl[i];
Mxy += ISAX.unsortl[i] * ISAY.unsortl[i];
}
var ty = Mxy / Math.Sqrt(sx * sy);
Mxy /= ISAX.l.Count;
return((ISAX.l.Count / (ISAX.l.Count - 1)) *
((Mxy - ISAX.Mx.Q * ISAY.Mx.Q) / (ISAX.Gx.Q * ISAY.Gx.Q)));
}
private double X2fFound(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2, double[,] f)
{
double rez = 0;
for (int i = 0; i < f.GetLength(0); i++)
{
for (int j = 0; j < f.GetLength(1); j++)
{
double f1 = Distributions.NormalDoublefFound(
((i + 0.5) * gr1.Len.Q / f.GetLength(0) + gr1.Min.Q), gr1.Mx.Q, gr1.Gx.Q,
((j + 0.5) * gr2.Len.Q / f.GetLength(1) + gr2.Min.Q), gr2.Mx.Q, gr2.Gx.Q, Korelation[0]) *
(gr2.Len.Q / f.GetLength(1)) * (gr1.Len.Q / f.GetLength(0));
if (Math.Round(f1, 4) != 0)
{
rez += Math.Pow(f[i, j] - f1, 2) / f1;
}
}
}
return(rez);
}
double RizSerRangVibFound(double[,] rang, InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
double Mrx = 0, Mry = 0;
for (int i = 0; i < N; i++)
{
if (rang[i, 1] == 0)
{
Mrx += rang[i, 0];
}
else if (rang[i, 1] == 1)
{
Mry += rang[i, 0];
}
}
Mrx /= gr1.l.Count;
Mry /= gr2.l.Count;
double v = (Mrx - Mry) / (N * Math.Sqrt((N + 1) / (12.0 * gr1.l.Count * gr2.l.Count)));
return(v);
}
private void ABTailFound(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
double B = 0;
double A = 0;
List <double> b = new List <double>();
for (int i = 1; i < gr1.l.Count; i++)
{
for (int j = 0; j < i; j++)
{
b.Add((gr2.unsortl[j] - gr2.unsortl[i]) / (gr1.unsortl[j] - gr1.unsortl[i]));
}
}
b.Sort();
if (b.Count % 2 == 0)
{
B = (b[b.Count / 2 - 1] + b[b.Count / 2]) / 2;
}
else
{
B = b[(b.Count - 1) / 2];
}
List <double> a = new List <double>();
for (int i = 0; i < gr1.l.Count; i++)
{
a.Add(gr2.unsortl[i] - B * gr1.unsortl[i]);
}
a.Sort();
if (a.Count % 2 == 0)
{
A = (a[a.Count / 2 - 1] + a[a.Count / 2]) / 2;
}
else
{
A = a[(a.Count - 1) / 2];
}
ABTeil[0] = A;
ABTeil[1] = B;
}
/// <summary>
///
/// </summary>
/// <param name="ML"></param>
/// <param name="gr"></param>
/// <param name="type"></param>
/// <returns>P</returns>
static public double KolmagorovFound(List <double> ML, InitialStatisticalAnalys gr, int type, double Mx, double Gx)
{
double D = DFound(ML, gr, type, Mx, Gx);
double Z = Math.Sqrt(ML.Count) * D;
double rez = 0;
double A1 = (double)1 / (18 * ML.Count);
for (int k = 1; k < 75; k++)
{
double f1 = k * k - 0.5 * (1 - Math.Pow(-1, k));
double f2 = 5 * k * k + 22 - 7.5 * (1 - Math.Pow(-1, k));
//double O = Math.Pow(Z, 13) / Math.Pow(ML.Count,2);
double C1 = (f1 - 4 * (f1 + 3)) * Math.Pow(Z * k, 2) + 8 * Math.Pow(k * Z, 4);
double C2 = (f2 * f2 / 5 - 4 * (f2 + 45) * Math.Pow(Z * k, 2) / (15) + 8 * Math.Pow(k * Z, 4));
double B1 = 1 - 2 * k * k * Z / (3 * Math.Sqrt(ML.Count));
double A2 = k * k * Z / (27 * Math.Pow(ML.Count, 1.5));
double G = Math.Pow(-1, k) * Math.Exp((double)-2 * Math.Pow(k * Z, 2));
rez += G * (B1 - A1 * C1 + A2 * C2);
}
rez *= 2;
return(-rez);
}
static private double DFound(List <double> ML, InitialStatisticalAnalys gr, int Type, double Mx, double Gx)
{
double[] D = new double[2];
for (int i = 0; i < gr.l.Count - 1; i++)
{
double l = 0;
double lmin = 0;
if (Type == 0)
{
double v1 = Distributions.NormalFFound((gr.l[i] - Mx) / Gx);
l = Math.Abs(gr.F[i] - v1);
if (i > 0)
{
v1 = Distributions.NormalFFound((gr.l[i - 1] - Mx) / Gx);
lmin = Math.Abs(gr.F[i] - v1);
}
}
else if (Type == 1)
{
l = Math.Abs(gr.F[i] - 1 + Math.Pow(2.73, -(1 / (Mx - gr.Min.Q)) * (gr.l[i] - gr.Min.Q)));
if (i > 0)
{
lmin = Math.Abs(gr.F[i] - 1 + Math.Pow(2.73, -(1 / (Mx - gr.Min.Q)) * (gr.l[i - 1] - gr.l[0])));
}
}
else if (Type == 2)
{
l = Math.Abs(gr.F[i] - ((gr.l[i] - gr.l[0]) / gr.Len.Q));
if (i > 0)
{
lmin = Math.Abs(gr.F[i] - ((gr.l[i - 1] - ML[0]) / gr.Len.Q));
}
}
D[1] = Math.Max(l, D[1]);
D[0] = Math.Max(lmin, D[0]);
}
return(Math.Max(D[0], D[1]));
}
private double ZFound(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
double D = 0;
for (int i = 0; i < gr1.l.Count; i++)
{
double l2 = 0;
if (gr2.AvtoType == "Нормальний")
{
l2 = Distributions.NormalfFound(gr1.l[i], gr2.Mx.Q, gr2.Gx.Q);
}
else if (gr2.AvtoType == "Еспоненціальний")
{
l2 = Distributions.ExpFound(gr1.l[i], gr2.Mx.Q);
}
else if (gr2.AvtoType == "Рівномірний")
{
l2 = (gr1.l[i] - gr2.Min.Q) * gr2.Len.Q;
}
D = Math.Max(Math.Abs(gr1.F[i] - l2), D);
}
return(D);
}
double KrZnakivFound(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
double S = 0;
double hN = gr1.l.Count;
List <double> Z = new List <double>();
for (int i = 0; i < gr1.l.Count && i < gr2.l.Count; i++)
{
Z.Add(gr1.unsortl[i] - gr2.unsortl[i]);
if (gr1.unsortl[i] - gr2.unsortl[i] > 0)
{
S++;
}
if (gr1.unsortl[i] - gr2.unsortl[i] == 0)
{
hN--;
}
}
if (hN <= 15)
{
double alf0 = 0;
for (int i = 0; i <= hN - S; i++)
{
alf0 += CFound(i, hN);
}
alf0 *= Math.Pow(2, -hN);
KrZnakiv[1] = gr1.alf.Q;
return(alf0);
}
else
{
double Sn = (S - 0.5 - hN / 2) / Math.Sqrt(hN / 4);
KrZnakiv[1] = Distributions.NormalQuantile(1 - gr1.alf.Q / 2);
return(Sn);
}
}
double KrUMannaUitFound(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
double u = 0, Mu = 0, Du = 0;
double[,] z = new double[gr1.l.Count, gr2.l.Count];
for (int i = 0; i < gr1.l.Count; i++)
{
for (int j = 0; j < gr2.l.Count; j++)
{
if (gr1.unsortl[i] > gr2.unsortl[j])
{
z[i, j] = 1;
u++;
}
else
{
z[i, j] = 0;
}
}
}
Mu = gr1.l.Count * gr2.l.Count / 2;
Du = Mu * (N + 1) / 6;
return((u - Mu) / Math.Sqrt(Du));
}
double cov(InitialStatisticalAnalys ISAe1, InitialStatisticalAnalys ISAe2)
{
return(ISAe1.Gx.Q * ISAe2.Gx.Q * Correlation_RegressionAnalysis.KorelationFound(ISAe1, ISAe2));
}
/// <summary>
///
/// </summary>
/// <param name="ML"></param>
/// <param name="gr"></param>
/// <param name="type"></param>
/// <returns>P</returns>
static public double KolmagorovFound(List <double> ML, InitialStatisticalAnalys gr, int type)
{
return(KolmagorovFound(ML, gr, type, gr.Mx.Q, gr.Gx.Q));
}
static public List <Data> RegresParamFound(InitialStatisticalAnalys ISAX, InitialStatisticalAnalys ISAY, double Korelation, string TypeRegVib, ref double Szal2)
{
List <Data> Qm = new List <Data>();
if (TypeRegVib == RegresTypeName.ParabRegresion)
{
Data a = new Data(), b = new Data(), c = new Data();
Qm.Add(a);
Qm.Add(b);
Qm.Add(c);
a.Name = "a";
b.Name = "b";
c.Name = "c";
double n1 = 0;//107 page andan
double x2 = InitialStatisticalAnalys.StartMoment(ISAX.l, 2);
double x3 = InitialStatisticalAnalys.StartMoment(ISAX.l, 3);
double x4 = InitialStatisticalAnalys.StartMoment(ISAX.l, 4);
for (int i = 0; i < ISAX.unsortl.Length; i++)
{
n1 += (ISAY.unsortl[i] - ISAY.Mx.Q) * (Math.Pow(ISAX.unsortl[i], 2) - x2);
}
n1 /= ISAX.unsortl.Length;
c.Q = ISAX.Dx.Q * n1 - (x3 - x2 * ISAX.Mx.Q) * Korelation * ISAX.Gx.Q * ISAY.Gx.Q;
c.Q /= ISAX.Dx.Q * (x4 - Math.Pow(x2, 2)) - Math.Pow(x3 - x2 * ISAX.Mx.Q, 2);
b.Q = (x4 - Math.Pow(x2, 2)) * Korelation * ISAX.Gx.Q * ISAY.Gx.Q - (x3 - x2 * ISAX.Mx.Q) * n1;
b.Q /= ISAX.Dx.Q * (x4 - Math.Pow(x2, 2)) - Math.Pow(x3 - x2 * ISAX.Mx.Q, 2);
a.Q = ISAY.Mx.Q - b.Q * ISAX.Mx.Q - c.Q * ISAX.X_2.Q;
Data a2 = new Data(), b2 = new Data(), c2 = new Data();
Qm.Add(a2);
Qm.Add(b2);
Qm.Add(c2);
a2.Name = "a2";
b2.Name = "b2";
c2.Name = "c2";
a2.Q = ISAY.Mx.Q;
double Mfi2scv = 0;
{
double TD = 0;
for (int i = 0; i < ISAX.l.Count; i++)
{
Mfi2scv += Math.Pow(fi2F(ISAX.unsortl[i], ISAX.Dx.Q, ISAX.Mx.Q, x2, x3), 2);
b2.Q += (ISAX.unsortl[i] - ISAX.Mx.Q) * ISAY.unsortl[i];
c2.Q += fi2F(ISAX.unsortl[i], ISAX.Dx.Q, ISAX.Mx.Q, x2, x3) * ISAY.unsortl[i];
TD += Math.Pow(fi2F(ISAX.unsortl[i], ISAX.Dx.Q, ISAX.Mx.Q, x2, x3), 2);
}
c2.Q /= TD;
}
Mfi2scv /= ISAX.l.Count;
b2.Q /= ISAX.l.Count;
b2.Q /= ISAX.Dx.Q;
Szal2 = 0;
for (int i = 0; i < ISAX.l.Count; i++)
{
Szal2 += Math.Pow(ISAY.unsortl[i] - a2.Q - b2.Q * fi1F(ISAX.unsortl[i], ISAX.Mx.Q) - c2.Q * fi2F(ISAX.unsortl[i], ISAX.Dx.Q, ISAX.Mx.Q, x2, x3), 2);
}
Szal2 /= ISAX.l.Count - 3;
Szal2 = Math.Sqrt(Szal2);
a2.QSigma = Szal2 / Math.Sqrt(ISAX.unsortl.Length);
b2.QSigma = Szal2 / (ISAX.Dx.Q * Math.Sqrt(ISAX.unsortl.Length));
c2.QSigma = Szal2 / Math.Sqrt(ISAX.unsortl.Length * Mfi2scv);
double Tt = Distributions.StudentQuantile(1 - ISAX.alf.Q / 2, ISAX.unsortl.Length - 3);
a2.QButton = a2.Q - Tt * a2.QSigma;
a2.QUpper = a2.Q + Tt * a2.QSigma;
b2.QButton = b2.Q - Tt * b2.QSigma;
b2.QUpper = b2.Q + Tt * b2.QSigma;
c2.QButton = c2.Q - Tt * c2.QSigma;
c2.QUpper = c2.Q + Tt * c2.QSigma;
double at = ISAY.Mx.Q - b.Q * ISAX.Mx.Q - c.Q * Math.Pow(ISAX.Mx.Q, 2) - a.Q;
Data ta = new Data(), tb = new Data(), tc = new Data();
}
else
{
Data a = new Data(), b = new Data();
Qm.Add(a);
Qm.Add(b);
a.Name = "a";
b.Name = "b";
List <double> t = new List <double>();
List <double> z = new List <double>();
for (int i = 0; i < ISAX.unsortl.Length; i++)
{
t.Add(RegresType.FiX(ISAX.unsortl[i], TypeRegVib));
z.Add(RegresType.FiY(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib));
}
/*double Mfx = 0,Mfxfx=0, Mfy = 0, Mfxfy = 0,W = 0;
* for (int i = 0; i < ISAX.unsortl.Length; i++)
* {
* Mfx += RegresType.FiX(ISAX.unsortl[i], TypeRegVib) *
* RegresType.W(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib);
* Mfxfx += Math.Pow(RegresType.FiX(ISAX.unsortl[i], TypeRegVib),2) *
* RegresType.W(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib);
* Mfy += RegresType.FiY(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib) *
* RegresType.W(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib);
* Mfxfy += RegresType.FiX(ISAX.unsortl[i], TypeRegVib) * RegresType.FiY(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib) *
* RegresType.W(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib);
* W += RegresType.W(ISAY.unsortl[i], ISAX.unsortl[i], TypeRegVib);
*
* }
* Mfx /= W;
* Mfxfx /= W;
* Mfxfy /= W;
* Mfy /= W;*/
// Qm[1].Q = (Mfxfy - Mfx * Mfy) / (Mfxfx - Math.Pow(Mfx, 2));
//Qm[0].Q = Mfy - Qm[1].Q * Mfx;
InitialStatisticalAnalys ISAt = new InitialStatisticalAnalys(t);
InitialStatisticalAnalys ISAz = new InitialStatisticalAnalys(z);
double Kor_tz = Correlation_RegressionAnalysis.KorelationFound(ISAt, ISAz);
Qm[1].Q = Kor_tz * ISAz.Gx.Q / ISAt.Gx.Q;
Qm[0].Q = RegresType.A(ISAz.Mx.Q - Qm[1].Q * ISAt.Mx.Q, TypeRegVib);
double Szal = SzalF(ISAX, ISAY, Qm, TypeRegVib);
Qm[0].QSigma = RegresType.A(Szal * Math.Sqrt(1.0 / ISAX.unsortl.Length + Math.Pow(ISAt.Mx.Q, 2) / (ISAt.Dx.Q * (ISAX.unsortl.Length - 1))), TypeRegVib);
Qm[1].QSigma = Szal / (ISAX.unsortl.Length - 1);
Qm[0].QButton = RegresType.A(Qm[0].Q - Distributions.StudentQuantile(1 - ISAX.alf.Q / 2, ISAX.unsortl.Length - 2) * Qm[0].QSigma, TypeRegVib);
Qm[0].QUpper = RegresType.A(Qm[0].Q + Distributions.StudentQuantile(1 - ISAX.alf.Q / 2, ISAX.unsortl.Length - 2) * Qm[0].QSigma, TypeRegVib);
Qm[1].QButton = Qm[1].Q - Distributions.StudentQuantile(1 - ISAX.alf.Q / 2, ISAX.unsortl.Length - 2) * Qm[1].QSigma;
Qm[1].QUpper = Qm[1].Q + Distributions.StudentQuantile(1 - ISAX.alf.Q / 2, ISAX.unsortl.Length - 2) * Qm[1].QSigma;
}
return(Qm);
}
private bool NachYslovRegAnal(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2)
{
AB[1] = Korelation[0] * gr2.Gx.Q / gr1.Gx.Q;
AB[0] = gr2.Mx.Q - AB[1] * gr1.Mx.Q;
bool rez = true;
if (!(gr1.AvtoType == gr2.AvtoType && gr2.AvtoType == "Нормальний"))
{
rez = false;
}
int M = 18;
List <double>[] yi = new List <double> [M];
for (int i = 0; i < M; i++)
{
yi[i] = new List <double>();
}
for (int i = 0; i < ML[MSelectGR[0]].l.Count; i++)
{
for (int j = 0; j < M; j++)
{
if (gr1.unsortl[i] < (j + 1.0001) * gr1.Len.Q / M + gr1.Min.Q && gr1.unsortl[i] >= (j * gr1.Len.Q / M + gr1.Min.Q))
{
yi[j].Add(gr2.unsortl[i]);
break;
}
}
}
///ProvRegr
double t1 = 0, t2 = 0;
///
double[] Sxj = new double[M];
double S = 0;
for (int i = 0; i < M; i++)
{
double ysr = yi[i].Sum() / yi[i].Count;
if (ysr > 0)
{
for (int j = 0; j < yi[i].Count; j++)
{
t2 += Math.Pow(yi[i][j] - ysr, 2);
Sxj[i] += Math.Pow(yi[i][j] - ysr, 2);
}
if (yi[i].Count > 1)
{
Sxj[i] /= yi[i].Count - 1;
}
t1 += yi[i].Count * Math.Pow(ysr - AB[0] - AB[1] * ((i + 0.5) * gr1.Len.Q / M + gr1.Min.Q), 2);
}
}
ProvRegrs[0] = (gr1.l.Count - M) * t1 / ((M - 1) * t2);
ProvRegrs[1] = Distributions.FisherQuantile(gr1.alf.Q, M - 1, gr1.l.Count - M);
double C = 0;
for (int i = 0; i < M; i++)
{
S += (yi[i].Count - 1) * Sxj[i];
if (yi[i].Count != 0)
{
C += 1.0 / yi[i].Count - 1.0 / gr1.l.Count;
}
}
S /= gr1.l.Count - M;
//C -= 1 / gr1.l.Count;
C /= 3.0 * (M - 1);
C++;
for (int i = 0; i < M; i++)
{
if (Sxj[i] != 0)
{
KriterBarkleta[0] += yi[i].Count * Math.Log(Sxj[i] / S);
}
}
KriterBarkleta[0] /= -C;
KriterBarkleta[1] = Hi.HIF(gr1.alf.Q, M - 1);
return(rez);
}
