private double[] TypeRFound() { double[] TypeR = new double[3]; double i2 = -0.5; for (int i = 0; i < l.Count - 1; i++, i2 += (double)1 / l.Count) { double v1 = Distributions.NormalFobrFound(i2); double Y_X = ((this.l[i] - Min.Q) / Len.Q); //double l = Math.Abs((ML[i] - ML[0]) / ((ML[ML.Count - 1] - ML[0])) - ((v1 * gr.Gx + gr.Mx.Q - ML[0]) / (ML[ML.Count - 1] - ML[0]))); double li = Math.Abs((l[i] - v1 * Gx.Q - Mx.Q) / Len.Q); TypeR[0] += Math.Pow(li, 2); TypeR[1] += Math.Pow((this.F[i]) - (1 - Math.Pow(2.73, -(1 / this.Mx.Q) * (this.l[i] - this.Min.Q))), 2); TypeR[2] += Math.Pow((this.F[i]) - Y_X, 2); } TypeR[0] /= (this.l.Count - 1); TypeR[1] /= (this.l.Count - 1); TypeR[2] /= (this.l.Count - 1); if (TypeR[0] < TypeR[1] && TypeR[0] < TypeR[2]) { AvtoType = "Нормальний"; } else if (TypeR[1] < TypeR[0] && TypeR[1] < TypeR[2]) { AvtoType = "Експоненціальний"; } else if (TypeR[2] < TypeR[1] && TypeR[2] < TypeR[0]) { AvtoType = "Рівномірний"; } return(TypeR); }
private void AddNewData_Click(object sender, EventArgs e) { try { double[] X = new double[n]; double[] p = new double[Classters.Count]; for (int c = 0; c < n; c++) { X[c] = Convert.ToDouble(NewDataDataGridView[c, 0].Value); } for (int c = 0; c < Classters.Count; c++) { p[c] = Convert.ToDouble(pDataGridView[c, 0].Value); } double[] f = new double[Classters.Count]; double[] pf = new double[Classters.Count]; for (int i = 0; i < Classters.Count; i++) { f[i] = Distributions.Normalf(Classters[i].IAM, X); pf[i] = f[i] * p[i]; } ShowP(pf, f); } catch { } }
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)); }
private void HToolStripMenuItem_Click(object sender, EventArgs e) { bool[] KTurnOn = KTurnOnFound(); int w = 0; foreach (bool b in KTurnOn) { if (!b) { w++; } } double[,] SubK = InitialAnalysMultidimensionalData.SubK(IAM.K, KTurnOn); double[,] T = TFound(SubK, KTurnOn); double T_alf_v = Distributions.StudentQuantile(1 - IAM.ISA[0].alf.Q / 2, IAM.N - w - 2); bool[,] H = new bool[T.GetLength(0), T.GetLength(1)]; for (int i = 0; i < H.GetLength(0); i++) { for (int j = 0; j < H.GetLength(1); j++) { if (Math.Abs(T[i, j]) <= T_alf_v) { H[i, j] = true; } } } WraitH(CorelDataGridView, H, KTurnOn); }
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); }
static public double StudentQuantile(double alf, int m) { double T = 0; double u_alf = Math.Abs(Distributions.NormalQuantile(alf /*1 - 0.05 / 2*/)); double g1 = (Math.Pow(u_alf, 3) + u_alf) / 4; double g2 = (5 * Math.Pow(u_alf, 5) + 16 * Math.Pow(u_alf, 3) + 3 * u_alf) / 96; double g3 = (34 * Math.Pow(u_alf, 7) + 19 * Math.Pow(u_alf, 5) + 17 * Math.Pow(u_alf, 3) - 15 * u_alf) / 384; double g4 = (79 * Math.Pow(u_alf, 9) + 779 * Math.Pow(u_alf, 7) + 1482 * Math.Pow(u_alf, 5) - 1920 * Math.Pow(u_alf, 3) - 945 * u_alf) / 92160; T = u_alf + g1 / (m) + g2 / Math.Pow(m, 2) + g3 / Math.Pow(m, 3) + g4 / Math.Pow(m, 4); return(T); }
public InitialAnalysMultidimensionalData(List <InitialStatisticalAnalys> ISA, List <int> IND, int k) { this.k = k; this.ISA = new List <InitialStatisticalAnalys>(); E = new double[IND.Count]; for (int i = 0; i < IND.Count; i++) { this.ISA.Add(ISA[IND[i]]); E[i] = ISA[IND[i]].Mx.Q; } n = this.ISA.Count; N = this.ISA[0].unsortl.Length; DC = DCFound(ISA, IND); K = KFound(ISA, IND); X = XFound(this.ISA, n, N, k); Y = XFound(this.ISA, n, N, k); KTurnOn = new bool[n]; for (int i = 0; i < KTurnOn.Length; i++) { KTurnOn[i] = true; } if (k >= 0) { CMC = new Data[n]; for (int i = 0; i < n; i++) { CMC[i] = new Data(); CMC[i].Name = "Коеф. багатов. корел."; CMC[i].Q = CMCFound(ISA, K, i); CMC[i].QKvant = (N - n - 1) * (Math.Pow(CMC[i].Q, 2)) / (n * (1 - Math.Pow(CMC[i].Q, 2))); CMC[i].Q0 = Distributions.FisherQuantile(1 - ISA[0].alf.Q / 2, n, N - n - 1); } SigmKv = Math.Sqrt(this.ISA[k].Dx.Q * (1 - Math.Pow(CMC[k].Q, 2)) * (N - 1) / (N - n - 1)); CoefDeter = new Data(); CoefDeter.Name = "Коефіціент Детермінації = "; CoefDeter.Q = Math.Pow(CMC[k].Q, 2); ZnachRegres = new Data(); ZnachRegres.Name = "Перевірка значущості від. регресії"; ZnachRegres.QKvant = (N - n) * ((1 / (1 - CoefDeter.Q)) - 1) / (n - 1); ZnachRegres.Q0 = Distributions.FisherQuantile(1 - ISA[0].alf.Q / 2, n - 1, N - n - 2); C = CFound(n, X); A = MuliplRegresFound(this.ISA, k); Astandart = AstndF(this.ISA, A, k); Szal = SzalFound(this.ISA, k, A); } }
private void CorelatioDataGridView_ColumnHeaderMouseDoubleClick(object sender, DataGridViewCellMouseEventArgs e) { bool[] b = new bool[CorelDataGridView.ColumnCount]; int size = 0; for (int i = 0; i < CorelDataGridView.ColumnCount; i++) { if (CorelDataGridView[i, i].Style.BackColor != Color.Red) { size++; b[i] = true; } } if (CorelDataGridView[e.ColumnIndex, 0].Style.BackColor != Color.Red) { b[e.ColumnIndex] = false; size--; if (size < 2) { return; } for (int i = 0; i < CorelDataGridView.ColumnCount; i++) { CorelDataGridView[i, e.ColumnIndex].Style.BackColor = Color.Red; CorelDataGridView[e.ColumnIndex, i].Style.BackColor = Color.Red; CorelDataGridView[i, e.ColumnIndex].Value = ""; CorelDataGridView[e.ColumnIndex, i].Value = ""; } } else { b[e.ColumnIndex] = true; size++; for (int i = 0; i < CorelDataGridView.ColumnCount; i++) { if (b[i]) { CorelDataGridView[i, e.ColumnIndex].Style.BackColor = Color.Green; CorelDataGridView[e.ColumnIndex, i].Style.BackColor = Color.Green; } } } CorelDataGridView.TopLeftHeaderCell.Value = Math.Round(Distributions.StudentQuantile(1 - IAM.ISA[0].alf.Q / 2, IAM.N - (b.Length - size) - 2), 4).ToString(); double[,] SubK = InitialAnalysMultidimensionalData.SubK(IAM.K, b); WraitK(CorelDataGridView, SubK, b); }
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); }
private void UppperValueToolStripMenuItem_Click(object sender, EventArgs e) { bool[] KTurnOn = KTurnOnFound(); int w = 0; foreach (bool b in KTurnOn) { if (!b) { w++; } } double[,] SubK = InitialAnalysMultidimensionalData.SubK(IAM.K, KTurnOn); double[,] V2 = new double[SubK.GetLength(0), SubK.GetLength(1)]; double[,] BV = new double[SubK.GetLength(0), SubK.GetLength(1)]; double U = Distributions.NormalQuantile(1 - IAM.ISA[0].alf.Q / 2); U /= (IAM.N - w - 3); for (int col = 0; col < SubK.GetLength(1); col++) { for (int row = 0; row < SubK.GetLength(0); row++) { if (KTurnOn[col] && KTurnOn[row]) { V2[row, col] = Math.Log((1 + SubK[row, col]) / (1 - SubK[row, col])) / 2 + U; double exp = Math.Exp(2 * V2[row, col]); if (double.IsInfinity(exp)) { BV[row, col] = 1; } else { BV[row, col] = (exp - 1.0) / (exp + 1.0); } } } } WraitK(CorelDataGridView, BV, KTurnOn); }
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(); }
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 == Distributions.Normal) { l2 = Distributions.NormalfFound(gr1.l[i], gr2.Mx.Q, gr2.Gx.Q); } else if (gr2.AvtoType == Distributions.Exp) { l2 = Distributions.ExpFound(gr1.l[i], gr2.Mx.Q); } else if (gr2.AvtoType == Distributions.Line) { 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); } }
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); }
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(); }
static public void Repaint(InitialStatisticalAnalys gr, InitialStatisticalAnalys gr2, Correlation_RegressionAnalysis CRA, Chart chart1, Chart chart2) { chart1.ChartAreas[0].AxisX.LabelStyle.Format = "###,##0.000"; chart1.ChartAreas[0].AxisY.LabelStyle.Format = "###,##0.000"; chart2.ChartAreas[0].AxisX.LabelStyle.Format = "###,##0.000"; chart2.ChartAreas[0].AxisY.LabelStyle.Format = "###,##0.000"; ///++++++++++++++++++++++++++++++++++++++++++++++ ///GISTOGAMA_PAINT ///++++++++++++++++++++++++++++++++++++++++++++++ chart1.Series.Clear(); chart2.Series.Clear(); if (CRA.f == null) { return; } chart1.Series.Add(CorelPaint(gr.unsortl, gr2.unsortl, Color.DarkBlue, "f")); chart1.ChartAreas[0].AxisX.Minimum = gr.Min.Q - gr.Len.Q * 0.05; chart1.ChartAreas[0].AxisX.Maximum = gr.Max.Q + gr.Len.Q * 0.05; chart1.ChartAreas[0].AxisX.Interval = Math.Round(gr.Len.Q * 0.1, 3); chart1.ChartAreas[0].AxisY.Interval = Math.Round(gr2.Len.Q * 0.1, 3); chart1.ChartAreas[0].AxisY.Minimum = gr2.Min.Q - gr2.Len.Q * 0.05; chart1.ChartAreas[0].AxisY.Maximum = gr2.Max.Q + gr2.Len.Q * 0.05; chart2.ChartAreas[0].AxisX.Minimum = chart2.ChartAreas[0].AxisY.Minimum = 0; chart2.ChartAreas[0].AxisX.Maximum = CRA.f.GetLength(0); chart2.ChartAreas[0].AxisY.Maximum = CRA.f.GetLength(1); chart2.ChartAreas[0].AxisX.Interval = chart2.ChartAreas[0].AxisY.Interval = 100; chart1.Titles[0].Text = "y"; chart1.Titles[1].Text = "x"; chart2.Titles[0].Text = "x"; chart2.Titles[1].Text = "y"; chart2.ChartAreas[0].AxisY.CustomLabels.Clear(); /*for (int j = 0; j < CRA.f.GetLength(0); j++) * { * string str2 = ""; * for (int k = 0; k < CRA.f.GetLength(1); k++) * { * str2 = "f" + j.ToString() + "_" + k.ToString(); * chart2.Series.Add(str2); * chart2.Series[str2].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Point; * chart2.Series[str2].MarkerStyle = MarkerStyle.Square; * chart2.Series[str2].IsVisibleInLegend = false; * chart2.Series[str2].MarkerSize = (chart2.Size.Width - 20 + k * 10) / CRA.f.GetLength(0); * //chart2.Series[str2]["PixelPointWidth"] = "200";//PointWidth * double z = CRA.f[j, k]; * if (z > 0.07) * chart2.Series[str2].Color = Color.Black; * else * { * int r = 255 - (int)(255 * CRA.f[j, k] / 0.07); * chart2.Series[str2].Color = Color.FromArgb(r, r, r); * } * //chart2.Series[str2].Color = Color.FromArgb(r, r, r); * //chart2.Series[str2]["StackedGroupName"] = "Group" + j.ToString(); * chart2.Series[str2].Points.AddXY(j + 0.5, * k + 0.5); * } * }*/ Image d = PaintData.Paintf(CRA, 1000, 1000); NamedImage ni = new NamedImage("backimage", d); chart2.Images.Add(ni); chart2.ChartAreas[0].BackImageAlignment = ChartImageAlignmentStyle.Center; chart2.ChartAreas[0].BackImageWrapMode = ChartImageWrapMode.Scaled; chart2.ChartAreas[0].BackImage = "backimage"; if (CRA.Doubl == true && CRA.Nezal == true) { chart1.Series.Add("Лін Рег" + ":" + CRA.RegresTypeVib); chart1.Series["Лін Рег" + ":" + CRA.RegresTypeVib].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line; chart1.Series["Лін Рег" + ":" + CRA.RegresTypeVib].Color = Color.Yellow; chart1.Series["Лін Рег" + ":" + CRA.RegresTypeVib].BorderWidth = 3; if (CRA.RegresTypeVib == RegresTypeName.LineRegresion) { //CRA.ABTeil ///teilor chart1.Series.Add("Лін Рег Тейл"); chart1.Series["Лін Рег Тейл"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line; chart1.Series["Лін Рег Тейл"].Color = Color.Green; chart1.Series["Лін Рег Тейл"].Points.AddXY(gr.Min.Q, CRA.ABTeil[0] + gr.Min.Q * CRA.ABTeil[1]); chart1.Series["Лін Рег Тейл"].BorderWidth = 3; chart1.Series["Лін Рег Тейл"].Points.AddXY(gr.Max.Q, CRA.ABTeil[0] + gr.Max.Q * CRA.ABTeil[1]); } chart1.Series.Add("Тол меж"); chart1.Series["Тол меж"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line; chart1.Series["Тол меж"].Color = Color.DarkRed; chart1.Series["Тол меж"].BorderWidth = 2; chart1.Series.Add("Тол меж2"); chart1.Series["Тол меж2"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line; chart1.Series["Тол меж2"].Color = chart1.Series["Тол меж"].Color; chart1.Series["Тол меж2"].IsVisibleInLegend = false; chart1.Series["Тол меж2"].BorderWidth = 2; chart1.Series.Add("Дов інтр"); chart1.Series["Дов інтр"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line; chart1.Series["Дов інтр"].Color = Color.DarkMagenta; chart1.Series["Дов інтр"].BorderWidth = 2; chart1.Series.Add("Дов інтр2"); chart1.Series["Дов інтр2"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line; chart1.Series["Дов інтр2"].Color = chart1.Series["Дов інтр"].Color; chart1.Series["Дов інтр2"].IsVisibleInLegend = false; chart1.Series["Дов інтр2"].BorderWidth = 2; if (CRA.RegresTypeVib != RegresTypeName.ParabRegresion) { for (double x0 = gr.Min.Q; x0 <= gr.Max.Q; x0 += gr.Len.Q * 0.005) { double Sx0 = Math.Sqrt(CRA.Szal * (1 + 1.0 / gr.l.Count) + CRA.Q[1].QSigma * Math.Pow(x0 - gr.Mx.Q, 2)); if (CRA.RegresTypeVib == RegresTypeName.LineRegresion) { chart1.Series["Дов інтр"].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib) - CRA.T * Sx0); chart1.Series["Дов інтр2"].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib) + CRA.T * Sx0); } chart1.Series["Тол меж"].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib) - CRA.T * CRA.Szal); chart1.Series["Тол меж2"].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib) + CRA.T * CRA.Szal); } for (double x0 = gr.Min.Q; x0 <= gr.Max.Q; x0 += gr.Len.Q * 0.005) { chart1.Series["Лін Рег" + ":" + CRA.RegresTypeVib].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib)); } } else if (CRA.RegresTypeVib == RegresTypeName.ParabRegresion) { double x2 = InitialStatisticalAnalys.StartMoment(gr.l, 2); double x3 = InitialStatisticalAnalys.StartMoment(gr.l, 3); double x4 = InitialStatisticalAnalys.StartMoment(gr.l, 4); double Tt = Distributions.StudentQuantile(1 - gr.alf.Q / 2, gr.unsortl.Length - 3); for (double x0 = gr.Min.Q; x0 <= gr.Max.Q; x0 += gr.Len.Q * 0.005) { double Sx0 = Math.Sqrt(Math.Pow(CRA.Szal2, 2) * (1 + 1.0 / gr.l.Count) + Math.Pow(CRA.Q[4].QSigma * Correlation_RegressionAnalysis.fi1F(x0, gr.Mx.Q), 2) + Math.Pow(CRA.Q[5].QSigma * Correlation_RegressionAnalysis.fi2F(x0, gr.Dx.Q, gr.Mx.Q, x2, x3), 2)); /* * chart1.Series["Дов інтр"].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib) - CRA.T * Sx0); * chart1.Series["Дов інтр2"].Points.AddXY(x0, RegresType.Model(x0, CRA.Q, CRA.RegresTypeVib) + CRA.T * Sx0);*/ chart1.Series["Дов інтр"].Points.AddXY(x0, CRA.Q[3].Q + CRA.Q[4].Q * Correlation_RegressionAnalysis.fi1F(x0, gr.Mx.Q) + CRA.Q[5].Q * Correlation_RegressionAnalysis.fi2F(x0, gr.Dx.Q, gr.Mx.Q, x2, x3) - Tt * Sx0); chart1.Series["Дов інтр2"].Points.AddXY(x0, CRA.Q[3].Q + CRA.Q[4].Q * Correlation_RegressionAnalysis.fi1F(x0, gr.Mx.Q) + CRA.Q[5].Q * Correlation_RegressionAnalysis.fi2F(x0, gr.Dx.Q, gr.Mx.Q, x2, x3) + Tt * Sx0); chart1.Series["Тол меж"].Points.AddXY(x0, CRA.Q[3].Q + CRA.Q[4].Q * Correlation_RegressionAnalysis.fi1F(x0, gr.Mx.Q) + CRA.Q[5].Q * Correlation_RegressionAnalysis.fi2F(x0, gr.Dx.Q, gr.Mx.Q, x2, x3) - Tt * CRA.Szal2); chart1.Series["Тол меж2"].Points.AddXY(x0, CRA.Q[3].Q + CRA.Q[4].Q * Correlation_RegressionAnalysis.fi1F(x0, gr.Mx.Q) + CRA.Q[5].Q * Correlation_RegressionAnalysis.fi2F(x0, gr.Dx.Q, gr.Mx.Q, x2, x3) + Tt * CRA.Szal2); } for (double x0 = gr.Min.Q; x0 <= gr.Max.Q; x0 += gr.Len.Q * 0.005) { chart1.Series["Лін Рег" + ":" + CRA.RegresTypeVib].Points.AddXY(x0, CRA.Q[3].Q + CRA.Q[4].Q * Correlation_RegressionAnalysis.fi1F(x0, gr.Mx.Q) + CRA.Q[5].Q * Correlation_RegressionAnalysis.fi2F(x0, gr.Dx.Q, gr.Mx.Q, x2, x3)); } } } }
private TabPage TapPageCreate() { CorelDataGridView = new DataGridView(); CorelDataGridView.Location = new Point(0, 0); CorelDataGridView.Dock = DockStyle.Fill; CorelDataGridView.AllowUserToAddRows = false; CorelDataGridView.ColumnHeaderMouseDoubleClick += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.CorelatioDataGridView_ColumnHeaderMouseDoubleClick); CorelDataGridView.TopLeftHeaderCell.Value = Math.Round(Distributions.StudentQuantile(1 - IAM.ISA[0].alf.Q / 2, IAM.N - 2), 4).ToString(); CorelDataGridView.RowHeadersWidth = 60; // // BottomValueToolStripMenuItem // this.BottomValueToolStripMenuItem = new System.Windows.Forms.ToolStripMenuItem(); this.BottomValueToolStripMenuItem.Name = "BottomValueToolStripMenuItem"; this.BottomValueToolStripMenuItem.Size = new System.Drawing.Size(223, 22); this.BottomValueToolStripMenuItem.Text = "Нижній довірчий інтервал"; this.BottomValueToolStripMenuItem.Click += new System.EventHandler(this.BottomValueToolStripMenuItem_Click); // // UppperValueToolStripMenuItem // this.UppperValueToolStripMenuItem = new System.Windows.Forms.ToolStripMenuItem(); this.UppperValueToolStripMenuItem.Name = "UppperValueToolStripMenuItem"; this.UppperValueToolStripMenuItem.Size = new System.Drawing.Size(223, 22); this.UppperValueToolStripMenuItem.Text = "Верхній довірчий інтервал"; this.UppperValueToolStripMenuItem.Click += new System.EventHandler(this.UppperValueToolStripMenuItem_Click); // // ValueToolStripMenuItem // this.ValueToolStripMenuItem = new System.Windows.Forms.ToolStripMenuItem(); this.ValueToolStripMenuItem.Name = "ValueToolStripMenuItem"; this.ValueToolStripMenuItem.Size = new System.Drawing.Size(223, 22); this.ValueToolStripMenuItem.Text = "Оцінка кореляції"; this.ValueToolStripMenuItem.Click += new System.EventHandler(this.ValueToolStripMenuItem_Click); // // TToolStripMenuItem // this.TToolStripMenuItem = new System.Windows.Forms.ToolStripMenuItem(); this.TToolStripMenuItem.Name = "TToolStripMenuItem"; this.TToolStripMenuItem.Size = new System.Drawing.Size(223, 22); this.TToolStripMenuItem.Text = "t "; this.TToolStripMenuItem.Click += new System.EventHandler(this.TToolStripMenuItem_Click); // // HToolStripMenuItem // this.HToolStripMenuItem = new System.Windows.Forms.ToolStripMenuItem(); this.HToolStripMenuItem.Name = "HToolStripMenuItem"; this.HToolStripMenuItem.Size = new System.Drawing.Size(223, 22); this.HToolStripMenuItem.Text = "Значущість (= 0)"; this.HToolStripMenuItem.Click += new System.EventHandler(this.HToolStripMenuItem_Click); // // CorelContextMenuStrip // CorelContextMenuStrip = new ContextMenuStrip(); CorelDataGridView.ContextMenuStrip = CorelContextMenuStrip; this.CorelContextMenuStrip.Items.AddRange(new System.Windows.Forms.ToolStripItem[] { this.UppperValueToolStripMenuItem, this.ValueToolStripMenuItem, this.BottomValueToolStripMenuItem, this.TToolStripMenuItem, this.HToolStripMenuItem }); this.CorelContextMenuStrip.Name = "contextMenuStrip1"; this.CorelContextMenuStrip.Size = new System.Drawing.Size(224, 158); System.Windows.Forms.TabPage tabPagenew = new TabPage(); tabPagenew.Controls.Add(CorelDataGridView); tabPagenew.Location = new System.Drawing.Point(4, 22); tabPagenew.Name = "tabPage3"; tabPagenew.Padding = new System.Windows.Forms.Padding(3); tabPagenew.Size = new System.Drawing.Size(1005, 273); tabPagenew.TabIndex = 0; tabPagenew.Text = "Кореляція"; tabPagenew.UseVisualStyleBackColor = true; tabPagenew.ResumeLayout(false); return(tabPagenew); }
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(); }
private bool NachYslovRegAnal(InitialStatisticalAnalys gr1, InitialStatisticalAnalys gr2) { 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 - Q[0].Q - Q[1].Q * ((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); }
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(); }