void btnOk_Click(object sender, EventArgs e)
{
try
{
Sample[] arrSX = new Sample[matrX.GetLength(1)];
for (int j = 0; j < matrX.GetLength(1); j++)
{
double[] arrX = new double[matrX.GetLength(0)];
for (int i = 0; i < matrX.GetLength(0); i++)
{
arrX[i] = matrX[i, j];
}
arrSX[j] = new TranSample(new Sample(arrXName[j],
string.Format("x<sub>{0}</sub>", j + 1), arrX),
dgv.Rows[j].Cells[1].Value.ToString());
}
Regression[] arrReg = new Regression[matrY.GetLength(1)];
rep = "ПАРАМЕТРИЧЕСКАЯ ИДЕНТИФИКАЦИЯ<br>";
for (int j = 0; j < matrY.GetLength(1); j++)
{
double[] arrY = new double[matrY.GetLength(0)];
for (int i = 0; i < matrY.GetLength(0); i++)
{
arrY[i] = matrY[i, j];
}
arrReg[j] = new Regression(new Sample("y", string.Format("y<sub>{0}</sub>", j + 1), arrY), arrSX);
rep += arrReg[j].GetRegReport() + "<br>";
}
Quality.GenErr(matrX, matrY, arrReg, int.Parse(tbN.Text), double.Parse(tbA.Text),
double.Parse(tbB.Text), out arrI, out matrXM, out matrYM);
rep += string.Format("ПАРАМЕТРЫ ГЕНЕРАЦИИ<br>n = {0}<br>A = {1}<br>B = {2}<br><br>",
tbN.Text, tbA.Text, tbB.Text);
rep += "ПЕРЕЧЕНЬ ИЗМЕНЕННЫХ НАБЛЮДЕНИЙ<br>";
foreach (int i in arrI)
{
rep += string.Format("{0} ", i + 1);
}
WBForm wbf = new WBForm("Параметрическая идентификация и генерация наблюдений", rep);
DialogResult = DialogResult.OK;
Close();
wbf.ShowDialog();
}
catch
{
MessageBox.Show("Ошибка генерации");
}
}
void btnOk_Click(object sender, EventArgs e)
{
try
{
Regression[] arrReg = new Regression[matrY.GetLength(1)];
repSmp = repHSmp = repId = repHId = repCorr = repHCorr = repGen = "";
Sample[] arrSX = new Sample[matrX.GetLength(1)];
for (int j = 0; j < arrSX.Length; j++)
{
double[] arrX = new double[matrX.GetLength(0)];
for (int i = 0; i < matrX.GetLength(0); i++)
{
arrX[i] = matrX[i, j];
}
arrSX[j] = new Sample(arrXName[j], string.Format("x<sub>{0}</sub>", j + 1), arrX);
arrSX[j].Calculate();
repSmp += arrSX[j].GetReport();
repHSmp += arrSX[j].GetHypReport();
}
repId = "ПАРАМЕТРИЧЕСКАЯ ИДЕНТИФИКАЦИЯ<br>";
Sample[] arrSY = new Sample[matrY.GetLength(1)];
for (int j = 0; j < matrY.GetLength(1); j++)
{
Sample[] arrTSX = new Sample[matrX.GetLength(1)];
for (int k = 0; k < matrX.GetLength(1); k++)
{
arrTSX[k] = new TranSample(arrSX[k],
dgv.Rows[k].Cells[j + 1].Value.ToString());
}
double[] arrY = new double[matrY.GetLength(0)];
for (int i = 0; i < matrY.GetLength(0); i++)
{
arrY[i] = matrY[i, j];
}
Sample smpY = new Sample(arrYName[j], string.Format("y<sub>{0}</sub>", j + 1), arrY);
smpY.Calculate();
arrSY[j] = smpY;
repSmp += smpY.GetReport();
repHSmp += smpY.GetHypReport();
arrReg[j] = new Regression(smpY, arrTSX);
repId += arrReg[j].GetRegReport() + "<br>";
repCorr += arrReg[j].GetCorrReport();
if (cbH.Checked)
{
arrReg[j].CheckHypothesises(double.Parse(tbAlpha.Text));
repHId += arrReg[j].GetHypRegrReport();
repHCorr += arrReg[j].GetHypCorrReport();
}
}
Sample[] arrSYX = new Sample[matrX.GetLength(1) + matrY.GetLength(1) - 1];
for (int i = 0; i < arrSY.Length - 1; i++)
{
arrSYX[i] = arrSY[i + 1];
}
for (int i = 0; i < arrSX.Length; i++)
{
arrSYX[arrSY.Length - 1 + i] = arrSX[i];
}
Regression reg = new Regression(arrSY[0], arrSYX);
repCorrXY = reg.GetCorrReport().Replace("<P>ПОКАЗАТЕЛЬ: y<sub>1</sub><BR>", "");
if (cbG.Checked)
{
Quality.GenErr(matrX, matrY, arrReg, int.Parse(tbN.Text), double.Parse(tbA.Text),
double.Parse(tbB.Text), out arrI, out matrXM, out matrYM);
repGen += string.Format("ПАРАМЕТРЫ ГЕНЕРАЦИИ<br>n = {0}<br>A = {1}<br>B = {2}<br><br>",
tbN.Text, tbA.Text, tbB.Text);
repGen += "ПЕРЕЧЕНЬ ИЗМЕНЕННЫХ НАБЛЮДЕНИЙ<br>";
foreach (int i in arrI)
{
repGen += string.Format("{0} ", i + 1);
}
}
else
{
matrXM = matrX;
matrYM = matrY;
}
DialogResult = DialogResult.OK;
Close();
}
catch
{
MessageBox.Show("Ошибка генерации");
}
}
void btnOpt_Click(object sender, EventArgs e)
{
string rep = "";
Variable[] arrQ, arrU;
Import(out arrQ, out arrU);
Sample[] arrSmp = new Sample[arrU.Length];
double[][] arrCoeff = new double[arrQ.Length][];
for (int i = 0; i < arrU.Length; i++)
{
arrSmp[i] = new Sample(arrU[i].name, arrU[i].id, arrU[i].arr);
}
for (int i = 0; i < arrQ.Length; i++)
{
Sample[] arrTSmp = new Sample[arrSmp.Length];
for (int j = 0; j < arrTSmp.Length; j++)
{
arrTSmp[j] = new TranSample(arrSmp[j], dgvU[i + 2, j].Value.ToString());
}
Sample smp = new Sample(arrQ[i].name, arrQ[i].id, arrQ[i].arr);
Regression r = new Regression(smp, arrSmp);
arrCoeff[i] = r.arrB;
rep += r.GetRegReport() + "<br>";
}
string[] arrStage = new string[dgvStage.Rows.Count];
for (int i = 0; i < arrStage.Length; i++)
{
arrStage[i] = dgvStage[0, i].Value.ToString();
}
StageForm sf = new StageForm(arrStage);
sf.ShowDialog();
List <string> lNU = new List <string>(), lU = new List <string>();
int k;
for (k = 0; k < arrStage.Length; k++)
{
if (sf.stage == arrStage[k])
{
break;
}
else
{
lNU.AddRange(dgvStage[1, k].Value.ToString().Split(new char[] { ' ', ',' },
StringSplitOptions.RemoveEmptyEntries));
}
}
ExpForm ef = new ExpForm(lNU.ToArray(), arrU);
ef.ShowDialog();
for (int i = 0; i < arrU.Length; i++)
{
if (!lNU.Contains(string.Format("{0}", i + 1)))
{
lU.Add(arrU[i].name);
}
}
int p = lU.Count, n = arrQ.Length;
int[] arrI = new int[p], arrNI = new int[arrU.Length - p];
for (int i = 0; i < p; i++)
{
for (int j = 0; j < arrU.Length; j++)
{
if (arrU[j].name == lU[i])
{
arrI[i] = j;
break;
}
}
}
for (int i = 0; i < arrNI.Length; i++)
{
for (int j = 0; j < arrU.Length; j++)
{
if (arrU[j].name == lNU[i])
{
arrNI[i] = j;
break;
}
}
}
string[,] matrFunc = new string[p, n];
for (int i = 0; i < p; i++)
{
for (int j = 0; j < n; j++)
{
matrFunc[i, j] = dgvU[j + 2, arrI[i]].Value.ToString();
}
}
double[][] arrC = new double[n][];
for (int j = 0; j < n; j++)
{
arrC[j] = new double[p + 1];
double s = arrCoeff[j][0];
for (int i = 0; i < arrU.Length; i++)
{
for (k = 0; k < p; k++)
{
if (arrI[k] == i)
{
break;
}
}
if (k < p)
{
arrC[j][k + 1] = arrCoeff[j][i + 1];
}
else
{
for (k = 0; k < p; k++)
{
if (arrNI[k] == i)
{
break;
}
}
s += arrCoeff[j][i + 1] * F(ef.arr[k], dgvU[j + 2, i].Value.ToString());
}
}
arrC[j][0] = s;
}
double[] arrUMin = new double[p], arrUMax = new double[p], arrUSMin = new double[p];
for (int i = 0; i < p; i++)
{
arrUMin[i] = double.Parse(dgvUParam[1, arrI[i]].Value.ToString());
arrUMax[i] = double.Parse(dgvUParam[2, arrI[i]].Value.ToString());
arrUSMin[i] = double.Parse(dgvUParam[3, arrI[i]].Value.ToString());
}
double[] arrYOpt = new double[n], arrYMin = new double[n], arrYMax = new double[n], arrAlpha = new double[n];
for (int i = 0; i < n; i++)
{
arrYOpt[i] = double.Parse(dgvQParam[4, i].Value.ToString());
arrYMin[i] = double.Parse(dgvQParam[2, i].Value.ToString());
arrYMax[i] = double.Parse(dgvQParam[3, i].Value.ToString());
arrAlpha[i] = double.Parse(dgvQParam[1, i].Value.ToString());
}
double R = double.Parse(tbR.Text);
HJInitialParams init = new HJInitialParams(double.Parse(tbDU.Text), double.Parse(tbDF.Text),
arrCoeff, matrFunc, arrUMin, arrUMax, arrUSMin, arrYOpt, arrYMin, arrYMax, arrAlpha, R);
int iterNum = 0;
List <HJIteration> lIter = new List <HJIteration>();
double[] arrX = new double[2 * p], arrXDelta = new double[2 * p];
double xEps = double.Parse(tbDU.Text);
for (int i = 0; i < 2 * p; i++)
{
arrX[i] = 0.5;
arrXDelta[i] = xEps;
}
HJIteration it = new HJIteration(arrX, arrXDelta);
HJOptimizer opt = new HJOptimizer();
opt.Initialize(init);
//double C = double.Parse(tbC.Text);
double f = double.MaxValue, fEps = double.Parse(tbDF.Text);
rep += "ОПТИМИЗАЦИЯ МЕТОДОМ ХУКА-ДЖИВСА<br><table border = 1 cellspacing = 0><tr>";
for (int i = 0; i < p; i++)
{
rep += "<td>" + lU[i];
}
rep += "<td>Целевая функция";
rep += it.ToHtml(init, 3);
//do
{
do
{
it = (HJIteration)opt.DoIteration(it);
if (it == null)
{
break;
}
lIter.Add(it);
iterNum++;
rep += it.ToHtml(init, 3);
}while (iterNum < 1000);
//double FNext = lIter.Last().fRes;
//if (Math.Abs(FNext - f) < fEps)
// break;
//f = FNext;
//R *= C;
//init = new HJInitialParams(double.Parse(tbDU.Text), double.Parse(tbDF.Text),
// arrCoeff, matrFunc, arrUMin, arrUMax, arrUSMin, arrYOpt, arrYMin, arrYMax, arrAlpha, R);
//it = new HJIteration(lIter.Last().arrX, arrXDelta);
}
//while (true);
rep += "</table><br>";
rep += "РЕЗУЛЬТАТЫ ОПТИМИЗАЦИИ<br>";
for (int i = 0; i < p; i++)
{
rep += string.Format("m<sub>{0}</sub> = {1:f4}<br>", i + 1, lIter.Last().arrX[i]);
}
Random rnd = new Random();
for (int i = 0; i < p; i++)
{
rep += string.Format("s<sub>{0}</sub> = {1:f4}<br>", i + 1, lIter.Last().arrX[i + p]);
}
rep += string.Format("<br>Целевая функция:<br>F = {0:f4}<br>", init.GetFuncValue(lIter.Last().arrX));
rep += "<br>Значения параметров распределений показателей качества<br>";
for (int i = 0; i < n; i++)
{
rep += string.Format("mu<sub>{0}</sub> = {1:f4}<br>", i + 1, init.arrMu[i]);
rep += string.Format("sigma<sub>{0}</sub> = {1:f4}<br>", i + 1, init.arrSigma[i]);
}
rep += "<br>Значения показателей качества<br>";
for (int i = 0; i < n; i++)
{
rep += string.Format("y<sub>{0}</sub> = {1:f4}<br>", i + 1, init.arrY[i]);
}
rep += "<br>Технологические факторы до и после оптимизации<br>";
rep += "Нормированные средние значения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>Среднее до оптимизации<td>Среднее после оптимизации";
for (int i = 0; i < p; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrU[i].name, 0,
lIter.Last().arrX[i]);
}
rep += "</table><br>Средние значения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>Среднее до оптимизации<td>Среднее после оптимизации";
for (int i = 0; i < p; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrU[i].name, arrU[i].Inv(0),
arrU[i].Inv(lIter.Last().arrX[i]));
}
rep += "</table><br>Нормированные средние квадратические отклонения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>СКО до оптимизации<td>СКО после оптимизации";
for (int i = 0; i < p; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrU[i].name, 1,
lIter.Last().arrX[p + i]);
}
rep += "</table><br>Средние квадратические отклонения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>СКО до оптимизации<td>СКО после оптимизации";
for (int i = 0; i < p; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrU[i].name, arrU[i].sigma,
arrU[i].sigma * lIter.Last().arrX[p + i]);
}
rep += "</table>";
rep += "<br>Показатели качества до и после оптимизации<br>";
rep += "Нормированные средние значения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>Среднее до оптимизации<td>Среднее после оптимизации";
for (int i = 0; i < n; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrQ[i].name, 0,
init.arrMu[i]);
}
rep += "</table><br>Средние значения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>Среднее до оптимизации<td>Среднее после оптимизации";
for (int i = 0; i < n; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrQ[i].name, arrQ[i].Inv(0),
arrQ[i].Inv(init.arrMu[i]));
}
rep += "</table><br>Нормированные средние квадратические отклонения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>СКО до оптимизации<td>СКО после оптимизации";
for (int i = 0; i < n; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrQ[i].name, 1,
init.arrSigma[i]);
}
rep += "</table><br>Средние квадратические отклонения до и после оптимизации<table border = 1 cellspacing = 0><tr>" +
"<td>Величина<td>СКО до оптимизации<td>СКО после оптимизации";
for (int i = 0; i < n; i++)
{
rep += string.Format("<tr><td>{0}<td>{1:f4}<td>{2:f4}", arrQ[i].name, arrQ[i].sigma,
arrQ[i].sigma * init.arrSigma[i]);
}
rep += "</table>";
List <Sample> lSmp = new List <Sample>();
for (int i = 1; i < arrQ.Length; i++)
{
lSmp.Add(new Sample(arrQ[i].name, string.Format("y{0}", i + 1), arrQ[i].arr));
}
lSmp.AddRange(arrSmp);
Regression reg = new Regression(new Sample(arrQ[0].name, "y0", arrQ[0].arr), lSmp.ToArray());
rep += reg.GetCorrReport();
ResForm rf = new ResForm(rep);
rf.Show();
List <double[]> lArr = new List <double[]>();
List <string> lName = new List <string>();
List <double> lMu = new List <double>(), lSigma = new List <double>();
for (int i = 0; i < arrU.Length; i++)
{
lName.Add(arrU[i].name);
lMu.Add(arrU[i].Inv(lIter.Last().arrX[i]));
lSigma.Add(arrU[i].sigma * lIter.Last().arrX[i + p]);
double[] arr = new double[arrU[i].arr.Length];
for (int j = 0; j < arr.Length; j++)
{
arr[j] = arrU[i].Inv(arrU[i].arr[j]);
}
lArr.Add(arr);
}
for (int i = 0; i < arrQ.Length; i++)
{
lName.Add(arrQ[i].name);
lMu.Add(arrQ[i].Inv(init.arrMu[i]));
lSigma.Add(arrQ[i].sigma * init.arrSigma[i]);
double[] arr = new double[arrQ[i].arr.Length];
for (int j = 0; j < arr.Length; j++)
{
arr[j] = arrQ[i].Inv(arrQ[i].arr[j]);
}
lArr.Add(arr);
}
DistForm df = new DistForm(lName.ToArray(), lArr.ToArray(), lMu.ToArray(), lSigma.ToArray());
df.Show();
}
void btnOpt_Click(object sender, EventArgs e)
{
Variable[] arrQ, arrU;
Import(out arrQ, out arrU);
Sample[] arrSmp = new Sample[arrU.Length];
double[][] arrCoeff = new double[arrQ.Length][];
for (int i = 0; i < arrU.Length; i++)
{
arrSmp[i] = new Sample(arrU[i].name, arrU[i].id, arrU[i].arr);
}
for (int i = 0; i < arrQ.Length; i++)
{
Sample[] arrTSmp = new Sample[arrSmp.Length];
for (int j = 0; j < arrTSmp.Length; j++)
{
arrTSmp[j] = new TranSample(arrSmp[j], dgvU[i + 1, j].Value.ToString());
}
Sample smp = new Sample(arrQ[i].name, arrQ[i].id, arrQ[i].arr);
Regression r = new Regression(smp, arrSmp);
arrCoeff[i] = r.arrB;
}
string[][] arrF = new string[arrQ.Length][];
for (int i = 0; i < arrF.Length; i++)
{
arrF[i] = new string[arrU.Length];
for (int j = 0; j < arrU.Length; j++)
{
arrF[i][j] = dgvU[i + 1, j].Value.ToString();
}
}
int[] arrN = new int[arrQ.Length + arrU.Length];
for (int i = 0; i < arrN.Length; i++)
{
arrN[i] = int.Parse(dgvParam[1, i].Value.ToString());
}
double[,] mData = Data(arrQ, arrU, arrN);
int iQ = int.Parse(tbIQ.Text), N = int.Parse(tbN.Text);
double eMin = double.Parse(tbEMin.Text), eMax = double.Parse(tbEMax.Text);
DataForm df = new DataForm(mData, arrU.Length, iQ, arrU, arrQ[iQ]);
df.ShowDialog();
int iOpt = df.listBox1.SelectedIndex;
List <double[]> listArrE = new List <double[]>(), listArrV = new List <double[]>();
List <int> lIndex = new List <int>();
for (int i = 0; i < N; i++)
{
double[] arr1, arr2;
lIndex.Add(Calc(arrCoeff, arrF, mData, iOpt, iQ, eMin, eMax, out arr1, out arr2));
listArrE.Add(arr1);
listArrV.Add(arr2);
}
ResDataForm rf = new ResDataForm(mData, arrU.Length, iQ, arrU, arrQ[iQ], iOpt, lIndex.ToArray(), listArrE, listArrV);
rf.ShowDialog();
List <Variable> listV = new List <Variable>(arrU);
listV.AddRange(arrQ);
HistForm hf = new HistForm(listV.ToArray(), mData, lIndex.ToArray(), arrN);
hf.ShowDialog();
/*string rep = "";
* Variable[] arrQ, arrU;
* Import(out arrQ, out arrU);
* Sample[] arrSmp = new Sample[arrU.Length];
* double[][] arrCoeff = new double[arrQ.Length][];
* for (int i = 0; i < arrU.Length; i++)
* arrSmp[i] = new Sample(arrU[i].name, arrU[i].id, arrU[i].arr);
* for (int i = 0; i < arrQ.Length; i++)
* {
* Sample[] arrTSmp = new Sample[arrSmp.Length];
* for (int j = 0; j < arrTSmp.Length; j++)
* arrTSmp[j] = new TranSample(arrSmp[j], dgvU[i + 1, j].Value.ToString());
* Sample smp = new Sample(arrQ[i].name, arrQ[i].id, arrQ[i].arr);
* Regression r = new Regression(smp, arrSmp);
* arrCoeff[i] = r.arrB;
* rep += r.GetRegReport() + "<br>";
* }
* string[] arrStage = new string[dgvStage.Rows.Count - 1];
* for (int i = 0; i < arrStage.Length; i++)
* arrStage[i] = dgvStage[0, i].Value.ToString();
* StageForm sf = new StageForm(arrStage);
* sf.ShowDialog();
* List<string> lNU = new List<string>(), lU = new List<string>();
* int k;
* for (k = 0; k < arrStage.Length; k++)
* if (sf.stage == arrStage[k])
* break;
* else
* lNU.AddRange(dgvStage[1, k].Value.ToString().Split(new char[] { ' ', ',' },
* StringSplitOptions.RemoveEmptyEntries));
* ExpForm ef = new ExpForm(lNU.ToArray());
* ef.ShowDialog();
* for (int i = 0; i < arrU.Length; i++)
* if (!lNU.Contains(arrU[i].name))
* lU.Add(arrU[i].name);
* int p = lU.Count, n = arrQ.Length;
* int[] arrI = new int[p], arrNI = new int[arrU.Length - p];
* for (int i = 0; i < p; i++)
* for (int j = 0; j < arrU.Length; j++)
* if (arrU[j].name == lU[i])
* {
* arrI[i] = j;
* break;
* }
* for (int i = 0; i < arrNI.Length; i++)
* for (int j = 0; j < arrU.Length; j++)
* if (arrU[j].name == lNU[i])
* {
* arrNI[i] = j;
* break;
* }
* string[,] matrFunc = new string[p, n];
* for (int i = 0; i < p; i++)
* for (int j = 0; j < n; j++)
* matrFunc[i, j] = dgvU[j + 1, arrI[i]].Value.ToString();
* double[][] arrC = new double[n][];
* for (int j = 0; j < n; j++)
* {
* arrC[j] = new double[p + 1];
* double s = arrCoeff[j][0];
* for (int i = 0; i < arrU.Length; i++)
* {
* for (k = 0; k < p; k++)
* if (arrI[k] == i)
* break;
* if (k < p)
* arrC[j][k + 1] = arrCoeff[j][i + 1];
* else
* {
* for (k = 0; k < p; k++)
* if (arrNI[k] == i)
* break;
* s += arrCoeff[j][i + 1] * F(ef.arr[k], dgvU[j + 1, i].Value.ToString());
* }
* }
* arrC[j][0] = s;
* }
* double[] arrUMin = new double[p], arrUMax = new double[p], arrUSMin = new double[p];
* for (int i = 0; i < p; i++)
* {
* arrUMin[i] = double.Parse(dgvParam[1, arrI[i]].Value.ToString());
* arrUMax[i] = double.Parse(dgvParam[2, arrI[i]].Value.ToString());
* arrUSMin[i] = double.Parse(dgvParam[3, arrI[i]].Value.ToString());
* }
* double[] arrMuOpt = new double[n], arrSigmaOpt = new double[n], arrAlpha = new double[n];
* for (int i = 0; i < n; i++)
* {
* arrMuOpt[i] = double.Parse(dgvQParam[1, i].Value.ToString());
* arrSigmaOpt[i] = double.Parse(dgvQParam[2, i].Value.ToString());
* arrAlpha[i] = double.Parse(dgvQParam[3, i].Value.ToString());
* }
* double R = double.Parse(tbN.Text);
* HJInitialParams init = new HJInitialParams(double.Parse(tbEMax.Text), double.Parse(tbDF.Text),
* arrCoeff, matrFunc, arrUMin, arrUMax, arrUSMin, arrMuOpt, arrSigmaOpt, arrAlpha, R);
* int iterNum = 0;
* List<HJIteration> lIter = new List<HJIteration>();
* double[] arrX = new double[2 * p], arrXDelta = new double[2 * p];
* double xEps = double.Parse(tbEMax.Text);
* for (int i = 0; i < 2 * p; i++)
* {
* arrX[i] = 0.5;
* arrXDelta[i] = xEps;
* }
* HJIteration it = new HJIteration(arrX, arrXDelta);
* HJOptimizer opt = new HJOptimizer();
* opt.Initialize(init);
* double C = double.Parse(tbEMin.Text);
* double f = double.MaxValue, fEps = double.Parse(tbDF.Text);
* rep += "ОПТИМИЗАЦИЯ МЕТОДОМ ХУКА-ДЖИВСА<br>";
* do
* {
* do
* {
* it = (HJIteration)opt.DoIteration(it);
* if (it == null)
* break;
* lIter.Add(it);
* iterNum++;
* rep += it.ToHtml(init, 3);
* }
* while (iterNum < 100);
* double FNext = lIter.Last().fRes;
* if (Math.Abs(FNext - f) < fEps)
* break;
* f = FNext;
* R *= C;
* init = new HJInitialParams(double.Parse(tbEMax.Text), double.Parse(tbDF.Text),
* arrCoeff, matrFunc, arrUMin, arrUMax, arrUSMin, arrMuOpt, arrSigmaOpt, arrAlpha, R);
* it = new HJIteration(lIter.Last().arrX, arrXDelta);
* }
* while (true);
* rep += "РЕЗУЛЬТАТЫ ОПТИМИЗАЦИИ<br>";
* for (int i = 0; i < p; i++)
* rep += string.Format("m{0} = {1:g4}<br>", i + 1, lIter.Last().arrX[i]);
* Random rnd = new Random();
* for (int i = 0; i < p; i++)
* rep += string.Format("s{0} = {1:g4}<br>", i + 1, Math.Abs(lIter.Last().arrX[i + p]));
* rep += string.Format("<br>Целевая функция:<br>F = {0:g4}", init.GetFuncValue(lIter.Last().arrX));//rnd.Next(100) / 100.0);
*
* rep += "<br>Значения параметров распределений показателей качества<br>";
* for (int i = 0; i < n; i++)
* {
* rep += string.Format("mu{0} = {1:g4}<br>", i + 1, arrMuOpt[i] + rnd.Next(0, 100) / 1000.0);
* rep += string.Format("sigma{0} = {1:g4}<br>", i + 1, arrSigmaOpt[i] + rnd.Next(0, 100) / 1000.0);
* }
* ResForm rf = new ResForm(rep);
* rf.Show();*/
}
