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 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();
}
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);
}
}
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();
}
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();
}
