static public bool KorelationZnach(double Korelation, int N, double alf) { bool Znach = false; double T = Korelation * Math.Sqrt((N - 2) / (1 - Math.Pow(Korelation, 2))); double TQv = Distributions.StudentQuantile(1 - alf / 2, N - 2); Znach = Math.Abs(T) <= TQv; return(!Znach); }
public void Refresh() { Min.Q = l[0]; Max.Q = l[l.Count - 1]; Len.Q = Max.Q - Min.Q; T = Distributions.StudentQuantile(alf.Q / 2, (int)m.Q - 1); f = new List <double>(); Y2 = new List <double>(); F = new List <double>(); if (l[l.Count - 1] - l[0] == 0) { Step.Q = 1; } else { Step.Q = 1.0001 * ((l[l.Count - 1] - l[0]) / m.Q); } MxFound(l); DxFound(); X_2.Q = StartMoment(l, 2); Mx_rang.Q = Mx.Q - (l[0] + l[l.Count - 1]) / l.Count; Mediana.Q = MEDFound(l); MADFound(); MODFound(); CoefEcscecFound(l); CoefAsimFound(l); QuantileFound(); AverYolshaFound(); CutData(); CoefVarPirson.Q = Gx.Q / Mx.Q; W.Q = MAD.Q / Mediana.Q; PredictionintervalFound(l); A = Mx.Q - Math.Sqrt(3 * (X_2.Q - Mx.Q * Mx.Q)); B = Mx.Q + Math.Sqrt(3 * (X_2.Q - Mx.Q * Mx.Q)); KrAbbe.Q = KrAbbeFound(l, unsortl); KrAbbe.QKvant = Distributions.NormalQuantile(1 - alf.Q / 2); DataRound(); }
public SimpleClass(List <InitialStatisticalAnalys> ML, List <int> MSelectGR, double BinSim) { this.ML = ML; this.MSelectGR = MSelectGR; N = NFound(ML, MSelectGR); Nezal = Nezalegni(); r = rFound(ML, MSelectGR); Sm2 = Sm2Found(ML, MSelectGR); Sv2 = Sv2Found(ML, MSelectGR); VarSv2Sm2[0] = Sm2 / Sv2; VarSv2Sm2[1] = Distributions.FisherQuantile(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1, (int)(N - MSelectGR.Count)); var tyui = Distributions.FisherQuantile(ML[MSelectGR[0]].alf.Q, 10, 10); if (Nezal == true) { KrKohrena[0] = KrKohrenaFound(ML, MSelectGR, BinSim); KrKohrena[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1); T = Distributions.StudentQuantile(1 - ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count - 2); } if (MSelectGR.Count == 2) { Doubl = true; if (Nezal == true) { f = fFound(ML[MSelectGR[0]], ML[MSelectGR[1]], 15, 7); Korelation[0] = KorelationFound(ML, MSelectGR); KorelationVidnoh[0] = KorelationVidnohFound(ML, MSelectGR); RangKorelation[0] = RangKorelationFound(ML, MSelectGR); RangKorelation[1] = RangKorelation[0] * Math.Sqrt((ML[MSelectGR[0]].l.Count - 2) / (1 - Math.Pow(RangKorelation[0], 2))); X2f[0] = X2fFound(ML[MSelectGR[0]], ML[MSelectGR[1]], f); X2f[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, ML[MSelectGR[0]].l.Count - 2); RangKoefKend[0] = RangKoefKendFound(ML, MSelectGR);//slow RangKoefKend[1] = 3 * RangKoefKend[0] * Math.Sqrt((ML[MSelectGR[0]].l.Count * (ML[MSelectGR[0]].l.Count - 1)) / (2 * (2 * ML[MSelectGR[0]].l.Count + 5))); RangKoefKend[2] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2); Nij = NijFound(ML, MSelectGR, BinSim); int ti = (int)(12 * ML[MSelectGR[1]].Dx.Q / ML[MSelectGR[0]].Dx.Q); try { if (Math.Abs(ti) > 20) { ti = 20; } } catch { ti = 20; } TablPerTab(ML, MSelectGR, fFound(ML[MSelectGR[0]], ML[MSelectGR[1]], 12, ti)); RegresParam = RegresParamFound(); NachYslovRegAnal(ML[MSelectGR[0]], ML[MSelectGR[1]]); KoefDeterm = Math.Pow(Korelation[0], 2) * 100; Szal = SzalFound(AB, ML[MSelectGR[0]], ML[MSelectGR[1]]); AB[2] = Szal * Math.Sqrt(1.0 / ML[MSelectGR[0]].l.Count + Math.Pow(ML[MSelectGR[0]].Mx.Q, 2) / (ML[MSelectGR[0]].Dx.Q * (ML[MSelectGR[0]].l.Count - 1))); AB[3] = Szal / (ML[MSelectGR[0]].Gx.Q * Math.Sqrt(ML[MSelectGR[0]].l.Count - 1)); ABTailFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); double op = Korelation[0] * (1 - Math.Pow(Korelation[0], 2)) / (2 * ML[MSelectGR[0]].l.Count); double oi = (1 - Math.Pow(Korelation[0], 2)) / Math.Sqrt(ML[MSelectGR[0]].l.Count - 1); double rn = Korelation[0] + op - Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2) * oi; double rv = Korelation[0] + op + Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2) * oi; if (rn < -1) { rn = 1; } if (rv > 1) { rv = 1; } Korelation[1] = rn; Korelation[2] = rv; Korelation[2] = op; SravnSred[1] = Distributions.StudentQuantile(ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count + ML[MSelectGR[1]].l.Count - 2); } else { SravnSred[1] = Distributions.StudentQuantile(ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count - 2); } SravnSred[0] = SimpleMx(ML[MSelectGR[0]], ML[MSelectGR[1]]); SravnDisper[0] = SimpleS(ML[MSelectGR[0]], ML[MSelectGR[1]]); SravnDisper[1] = Distributions.FisherQuantile(ML[MSelectGR[0]].alf.Q, ML[MSelectGR[0]].l.Count - 1, ML[MSelectGR[1]].l.Count - 1); KrSmirnKolmag[0] = 1.0 - LzFound(ZFound(ML[MSelectGR[0]], ML[MSelectGR[1]])); KrSmirnKolmag[1] = ML[MSelectGR[0]].alf.Q; KrsumRangVils[0] = KrsumRangVilsFound(r, ML[MSelectGR[0]], ML[MSelectGR[1]]); KrsumRangVils[1] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2.0); KrUMannaUit[0] = KrUMannaUitFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); KrUMannaUit[1] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2.0); RizSerRangVib[0] = RizSerRangVibFound(r, ML[MSelectGR[0]], ML[MSelectGR[1]]); RizSerRangVib[1] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2.0); KrZnakiv[0] = KrZnakivFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); } else { Doubl = false; SravnSred[0] = SimpleMx(ML, MSelectGR); SravnDisper[0] = SimpleS(ML, MSelectGR); SravnDisper[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1); KrKruskalaUolisa[0] = KrKruskalaUolisaFound(r, ML, MSelectGR); KrKruskalaUolisa[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1); } Round(); }
public UniformityCriteria(List <InitialStatisticalAnalys> ML, List <int> MSelectGR) { this.ML = ML; this.MSelectGR = MSelectGR; N = NFound(ML, MSelectGR); { double NezCount = ML[MSelectGR[0]].l.Count; for (int i = 0; i < MSelectGR.Count; i++) { if (ML[MSelectGR[i]].l.Count != NezCount) { Nezal = false; } } } rDvomFound(ML[MSelectGR[0]].unsortl); r = rFound(ML, MSelectGR); Sm2 = Sm2Found(ML, MSelectGR); Sv2 = Sv2Found(ML, MSelectGR); VarSv2Sm2[0] = Sm2 / Sv2; VarSv2Sm2[1] = Distributions.FisherQuantile(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1, (int)(N - MSelectGR.Count)); var tyui = Distributions.FisherQuantile(ML[MSelectGR[0]].alf.Q, 10, 10); if (Nezal == true) { KrKohrena[0] = KrKohrenaFound(ML, MSelectGR); KrKohrena[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1); T = Distributions.StudentQuantile(1 - ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count - 2); } if (MSelectGR.Count == 2) { Doubl = true; if (Nezal == true) { SravnSred[1] = Distributions.StudentQuantile(ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count + ML[MSelectGR[1]].l.Count - 2); } else { SravnSred[1] = Distributions.StudentQuantile(ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count - 2); } SravnSred[0] = SimpleMx(ML[MSelectGR[0]], ML[MSelectGR[1]]); SravnDisper[0] = SimpleS(ML[MSelectGR[0]], ML[MSelectGR[1]]); SravnDisper[1] = Distributions.FisherQuantile(ML[MSelectGR[0]].alf.Q, ML[MSelectGR[0]].l.Count - 1, ML[MSelectGR[1]].l.Count - 1); KrSmirnKolmag[0] = 1.0 - LzFound(ZFound(ML[MSelectGR[0]], ML[MSelectGR[1]])); KrSmirnKolmag[1] = ML[MSelectGR[0]].alf.Q; KrsumRangVils[0] = KrsumRangVilsFound(r, ML[MSelectGR[0]], ML[MSelectGR[1]]); KrsumRangVils[1] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2.0); KrUMannaUit[0] = KrUMannaUitFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); KrUMannaUit[1] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2.0); RizSerRangVib[0] = RizSerRangVibFound(r, ML[MSelectGR[0]], ML[MSelectGR[1]]); RizSerRangVib[1] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2.0); KrZnakiv[0] = KrZnakivFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); } else { Doubl = false; SravnSred[0] = SimpleMx(ML, MSelectGR); SravnDisper[0] = SimpleS(ML, MSelectGR); SravnDisper[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1); KrKruskalaUolisa[0] = KrKruskalaUolisaFound(r, ML, MSelectGR); KrKruskalaUolisa[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, MSelectGR.Count - 1); } Round(); }
public Correlation_RegressionAnalysis(List <InitialStatisticalAnalys> ML, List <int> MSelectGR, string RegresTypeVib) { this.ML = ML; this.MSelectGR = MSelectGR; this.RegresTypeVib = RegresTypeVib; N = NFound(ML, MSelectGR); { double NezCount = ML[MSelectGR[0]].l.Count; for (int i = 0; i < MSelectGR.Count; i++) { if (ML[MSelectGR[i]].l.Count != NezCount) { Nezal = false; } } } T = Distributions.StudentQuantile(1 - ML[MSelectGR[0]].alf.Q / 2, ML[MSelectGR[0]].l.Count - 2); if (MSelectGR.Count == 2) { Doubl = true; if (Nezal == true) { f = fFound(ML[MSelectGR[0]], ML[MSelectGR[1]], 15, 7); Korelation[0] = KorelationFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); KorelationVidnoh[0] = KorelationVidnohFound(ML, MSelectGR); RangKorelation[0] = RangKorelationFound(ML, MSelectGR); RangKorelation[1] = RangKorelation[0] * Math.Sqrt((ML[MSelectGR[0]].l.Count - 2) / (1 - Math.Pow(RangKorelation[0], 2))); X2f[0] = X2fFound(ML[MSelectGR[0]], ML[MSelectGR[1]], f); X2f[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, ML[MSelectGR[0]].l.Count - 2); RangKoefKend[0] = RangKoefKendFound(ML, MSelectGR);//slow RangKoefKend[1] = 3 * RangKoefKend[0] * Math.Sqrt((ML[MSelectGR[0]].l.Count * (ML[MSelectGR[0]].l.Count - 1)) / (2 * (2 * ML[MSelectGR[0]].l.Count + 5))); RangKoefKend[2] = Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2); Nij = NijFound(ML, MSelectGR); int ti = (int)(12 * ML[MSelectGR[1]].Dx.Q / ML[MSelectGR[0]].Dx.Q); try { if (Math.Abs(ti) > 20) { ti = 20; } } catch { ti = 20; } TablPerTab(ML, MSelectGR, fFound(ML[MSelectGR[0]], ML[MSelectGR[1]], 12, ti)); Q = RegresParamFound(ML[MSelectGR[0]], ML[MSelectGR[1]], Korelation[0], RegresTypeVib, ref Szal2); Szal = SzalF(ML[MSelectGR[0]], ML[MSelectGR[1]], Q, RegresTypeVib);/* * AB[1] = Korelation[0] * ML[MSelectGR[1]].Gx.Q / ML[MSelectGR[0]].Gx.Q; * AB[0] = ML[MSelectGR[1]].Mx.Q - AB[1] * ML[MSelectGR[0]].Mx.Q;*/ NachYslovRegAnal(ML[MSelectGR[0]], ML[MSelectGR[1]]); // Szal = SzalFound(AB, ML[MSelectGR[0]], ML[MSelectGR[1]]); /*AB[2] = Szal * Math.Sqrt(1.0 / ML[MSelectGR[0]].l.Count + Math.Pow(ML[MSelectGR[0]].Mx.Q, 2) / * (ML[MSelectGR[0]].Dx.Q * (ML[MSelectGR[0]].l.Count - 1))); * AB[3] = Szal / (ML[MSelectGR[0]].Gx.Q * Math.Sqrt(ML[MSelectGR[0]].l.Count - 1));*/ ABTailFound(ML[MSelectGR[0]], ML[MSelectGR[1]]); double op = Korelation[0] * (1 - Math.Pow(Korelation[0], 2)) / (2 * ML[MSelectGR[0]].l.Count); double oi = (1 - Math.Pow(Korelation[0], 2)) / Math.Sqrt(ML[MSelectGR[0]].l.Count - 1); double rn = Korelation[0] + op - Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2) * oi; double rv = Korelation[0] + op + Distributions.NormalQuantile(1 - ML[MSelectGR[0]].alf.Q / 2) * oi; if (rn < -1) { rn = 1; } if (rv > 1) { rv = 1; } Korelation[1] = rn; Korelation[2] = rv; Korelation[3] = op; if (RegresTypeVib == RegresTypeName.ParabRegresion) { KoefDeterm = (1 - Math.Pow(Szal2, 2) / ML[MSelectGR[1]].Dx.Q) * 100; } else { KoefDeterm = Math.Pow(Korelation[0], 2) * 100; } } } else { Doubl = false; } Round(); }
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); }