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); }