private Data SravSredF(List <InitialAnalysMultidimensionalData> IAMD)
{
Data rez = new Data()
{
Name = "Збіг середніх при розбіжності DC"
};
double[,] Ex = ExF(IAMD);
double[,] V = new double[1, 1];
for (int d = 0; d < IAMD.Count; d++)
{
double[,] Exd = xdF(IAMD[d]);
double[,] Xdx = new double[1, n];
for (int i = 0; i < n; i++)
{
Xdx[0, i] = Exd[0, i] - Ex[0, i];
}
double[,] SInv = Matrix.InverseMatrix(SdF(IAMD[d]));
V = Matrix.Addition(V, Matrix.MultiplicMatrix(Xdx, Matrix.MultiplicMatrix(SInv, Matrix.TranspMatrix(Xdx))));
}
rez.Q = V[0, 0];
rez.QKvant = Hi.HIF(IAMD[0].ISA[0].alf.Q, (int)(n * (IAMD.Count - 1)));
rez.H = rez.Q <= rez.QKvant;
return(rez);
}
private Data SravnDisperF(List <InitialAnalysMultidimensionalData> IAMD)
{
Data rez = new Data()
{
Name = "Збіг DC"
};
double Nsum = N.Sum();
double V = 0;
double[,] S = new double[n, n];
List <double[, ]> Sd = new List <double[, ]>();
for (int d = 0; d < IAMD.Count; d++)
{
Sd.Add(SdF(IAMD[d]));
}
for (int d = 0; d < IAMD.Count; d++)
{
S = Matrix.Addition(S, Matrix.MultiplicNumber(Sd[d], N[d] - 1));
}
S = Matrix.MultiplicNumber(S, 1.0 / (Nsum - IAMD.Count));
double detS = Matrix.Determinant(S);
for (int d = 0; d < IAMD.Count; d++)
{
V += ((N[d] - 1) / 2) * Math.Log(detS / Matrix.Determinant(Sd[d]));
}
rez.Q = V;
rez.QKvant = Hi.HIF(IAMD[0].ISA[0].alf.Q, n * (n + 1) * (IAMD.Count - 1) / 2);
rez.H = rez.Q <= rez.QKvant;
return(rez);
}
public Data SravnSredF(InitialAnalysMultidimensionalData X, InitialAnalysMultidimensionalData Y)
{
Data rez = new Data();
double[,] S0 = S0F(X, Y);
double[,] S1 = S1F(X, Y);
rez.Name = "Рівність багатомірних середніх при рівності DC матриці";
rez.Q = -(N1 + N2 - 2 - n / 2) * Math.Log(Matrix.Determinant(S1) / Matrix.Determinant(S0));
rez.QKvant = Hi.HIF(X.ISA[0].alf.Q, n);
rez.H = rez.Q <= rez.QKvant;
return(rez);
}
double[,] NijFound(List <InitialStatisticalAnalys> ML, List <int> MSelectGR, double BinSim)
{
double[,] rez = new double[3, 3];
for (int i = 0; i < ML[MSelectGR[0]].l.Count; i++)
{
if (ML[MSelectGR[0]].unsortl[i] > BinSim && ML[MSelectGR[1]].unsortl[i] > BinSim)
{
rez[1, 1]++;
}
else if (ML[MSelectGR[0]].unsortl[i] > BinSim && ML[MSelectGR[1]].unsortl[i] <= BinSim)
{
rez[0, 1]++;
}
else if (ML[MSelectGR[0]].unsortl[i] <= BinSim && ML[MSelectGR[1]].unsortl[i] > BinSim)
{
rez[1, 0]++;
}
else if (ML[MSelectGR[0]].unsortl[i] <= BinSim && ML[MSelectGR[1]].unsortl[i] <= BinSim)
{
rez[0, 0]++;
}
}
rez[2, 0] = rez[0, 0] + rez[0, 1]; ///N0
rez[2, 1] = rez[1, 0] + rez[1, 1]; ///N1
rez[0, 2] = rez[0, 0] + rez[1, 0]; ///M0
rez[1, 2] = rez[0, 1] + rez[1, 1]; ///M1
rez[2, 2] = rez[0, 0] + rez[0, 1] + rez[1, 0] + rez[1, 1]; ///N
double V = rez[0, 0] + rez[1, 1];
double W = rez[0, 1] + rez[1, 0];
IndexFehnera = (V - W) / (V + W);
KoefSpolF[0] = (rez[0, 0] * rez[1, 1] - rez[0, 1] * rez[1, 0]) / Math.Sqrt(rez[0, 2] * rez[1, 2] * rez[2, 0] * rez[2, 1]);
KoefSpolF[1] = Math.Pow(KoefSpolF[0], 2) * ML[MSelectGR[0]].l.Count;
KoefSpolF[2] = Hi.HIF(ML[MSelectGR[1]].alf.Q, 1);
KoefSvazYoola[0] = (rez[0, 0] * rez[1, 1] - rez[0, 1] * rez[1, 0]) / (rez[0, 0] * rez[1, 1] + rez[0, 1] * rez[1, 0]);
KoefSvazYoola[1] = (Math.Sqrt(rez[0, 0] * rez[1, 1]) - Math.Sqrt(rez[0, 1] * rez[1, 0])) /
(Math.Sqrt(rez[0, 0] * rez[1, 1]) + Math.Sqrt(rez[0, 1] * rez[1, 0]));
KoefSvazYoola[2] = KoefSvazYoola[0] / (Math.Sqrt(1.0 / rez[0, 0] + 1.0 / rez[1, 0] + 1.0 / rez[0, 1] + 1.0 / rez[1, 1]) *
(1 - Math.Pow(KoefSvazYoola[0], 2)) / 2.0);
KoefSvazYoola[3] = KoefSvazYoola[1] / (Math.Sqrt(1.0 / rez[0, 0] + 1.0 / rez[1, 0] + 1.0 / rez[0, 1] + 1.0 / rez[1, 1]) *
(1 - Math.Pow(KoefSvazYoola[1], 2)) / 4.0);
return(rez);
}
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();
}
void TablPerTab(List <InitialStatisticalAnalys> ML, List <int> MSelectGR, double[,] f)
{
double[] n = new double[f.GetLength(1)];
double[] m = new double[f.GetLength(0)];
for (int i = 0; i < f.GetLength(0); i++)
{
for (int j = 0; j < f.GetLength(1); j++)
{
m[i] += f[i, j] * ML[MSelectGR[0]].l.Count;
}
}
for (int i = 0; i < f.GetLength(1); i++)
{
for (int j = 0; j < f.GetLength(0); j++)
{
n[i] += f[j, i] * ML[MSelectGR[1]].l.Count;
}
}
for (int i = 0; i < f.GetLength(1); i++)
{
for (int j = 0; j < f.GetLength(0); j++)
{
NezXY[0] += Math.Pow(f[j, i] * ML[MSelectGR[0]].l.Count - n[i] * m[j] / ML[MSelectGR[0]].l.Count, 2) / (n[i] * m[j] / ML[MSelectGR[0]].l.Count);
}
}
NezXY[1] = Hi.HIF(ML[MSelectGR[0]].alf.Q, (f.GetLength(1) - 1) * (f.GetLength(0) - 1));
KoefSpolPirs[0] = Math.Sqrt(NezXY[0] / (NezXY[0] + ML[MSelectGR[0]].l.Count));
double P = 0, Q = 0;
double Stsig = 0;
for (int i = 0; i < f.GetLength(1); i++)
{
for (int j = 0; j < f.GetLength(0); j++)
{
double t1 = 0, t2 = 0, A1 = 0, B1 = 0, A2 = 0, B2 = 0;
for (int k = i; k < f.GetLength(1); k++)
{
for (int l = j; l < f.GetLength(0); l++)
{
t1 += f[l, k] * ML[MSelectGR[1]].l.Count;
A1 += f[l, k] * ML[MSelectGR[1]].l.Count;
A2 += f[l - j, k - i] * ML[MSelectGR[1]].l.Count;
B1 += f[l, k - i] * ML[MSelectGR[1]].l.Count;
B2 += f[l - j, k] * ML[MSelectGR[1]].l.Count;
}
for (int l = 0; l < j; l++)
{
t2 += f[l, k] * ML[MSelectGR[1]].l.Count;
}
}
P += f[j, i] * t1 * ML[MSelectGR[1]].l.Count;
Q += f[j, i] * t2 * ML[MSelectGR[1]].l.Count;
Stsig += f[j, i] * ML[MSelectGR[1]].l.Count * Math.Pow(A1 + A2 - B1 - B2, 2);
}
}
if (f.GetLength(0) == f.GetLength(1))
{
MeraZvazKendallaStatStyardName = "Міра зв'язку Кендалла";
double T1 = 0, T2 = 0;
for (int j = 0; j < f.GetLength(1); j++)
{
T1 += n[j] * (n[j] - 1);
}
for (int j = 0; j < f.GetLength(0); j++)
{
T2 += m[j] * (m[j] - 1);
}
T1 /= 2;
T2 /= 2;
MeraZvazKendallaStatStyard[0] = (P - Q) / Math.Sqrt((ML[MSelectGR[1]].l.Count * (ML[MSelectGR[1]].l.Count - 1) / 2.0 - T1) *
(ML[MSelectGR[1]].l.Count * (ML[MSelectGR[1]].l.Count - 1) / 2.0 - T2));
MeraZvazKendallaStatStyard[1] = Math.Sqrt((f.GetLength(1) * 4.0 + 10) / (9.0 * (Math.Pow(f.GetLength(1), 2) - f.GetLength(1))));
}
else
{
MeraZvazKendallaStatStyardName = "Статистика Стюарда";
MeraZvazKendallaStatStyard[0] = 2 * (P - Q) * Math.Min(f.GetLength(0), f.GetLength(1)) /
(Math.Pow(ML[MSelectGR[1]].l.Count, 2) * (Math.Min(f.GetLength(0), f.GetLength(1)) - 1));
MeraZvazKendallaStatStyard[1] = 2 * Math.Min(f.GetLength(0), f.GetLength(1)) *
Math.Sqrt(Math.Pow(ML[MSelectGR[1]].l.Count, 2) * Stsig - 4.0 * ML[MSelectGR[1]].l.Count * (P - Q)) /
(Math.Pow(ML[MSelectGR[1]].l.Count, 3) * (Math.Min(f.GetLength(0), f.GetLength(1)) - 1));
}
double RKS = 0;
for (int i = 0; i < f.GetLength(1); i++)
{
for (int j = 0; j < f.GetLength(0); j++)
{
double S1 = 0, S2 = 0;
for (int k = 0; k < i; k++)
{
S1 += 1.5 * n[k];
}
for (int l = 0; l < j; l++)
{
S2 += 1.5 * m[l];
}
RKS += f[j, i] * ML[MSelectGR[1]].l.Count * (S1 - ML[MSelectGR[1]].l.Count / 2.0) * (S2 - ML[MSelectGR[1]].l.Count / 2.0);
}
}
double Mi = 0, Ni = 0;
for (int i = 0; i < f.GetLength(1); i++)
{
Ni += Math.Pow(n[i], 3) - n[i];
}
for (int i = 0; i < f.GetLength(0); i++)
{
Mi += Math.Pow(m[i], 3) - m[i];
}
KoefRKorSpir[0] = 12.0 * RKS / Math.Sqrt((Math.Pow(ML[MSelectGR[1]].l.Count, 3) - f.GetLength(1) - Ni) *
(Math.Pow(ML[MSelectGR[1]].l.Count, 3) - f.GetLength(1) - Mi));
KoefRKorSpir[1] = (1 - Math.Pow(KoefRKorSpir[0], 2)) / (Math.Sqrt(ML[MSelectGR[1]].l.Count - 2));
}
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();
}