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 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);
}
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);
}
private void DataSet(List <InitialStatisticalAnalys> ISA, List <int> IND, int k)
{
this.k = k;
this.ISA = new List <InitialStatisticalAnalys>();
Ex = new double[IND.Count];
for (int i = 0; i < IND.Count; i++)
{
this.ISA.Add(ISA[IND[i]]);
Ex[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);
KTurnOn = new bool[n];
for (int i = 0; i < KTurnOn.Length; i++)
{
KTurnOn[i] = true;
}
if (k >= 0)
{
Y = YFound(this.ISA, n, N, k);
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 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);
}
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]));
}
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);
}
}
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);
}
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)
{
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);
}
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);
}