public void Run(IContext ctx)
{
resultSet = new DataSet();
DataTable pTable = new DataTable() { TableName = "Probability" };
DataTable K_Table = ctx.Data.Tables["Coefficient"];
pTable.Columns.Add("k_srednee");
pTable.Columns.Add("k_sigma");
pTable.Columns.Add("k_cv");
pTable.Columns.Add("k_cs");
pTable.Columns.Add("k_eta");
RepresentationCheck represent = new RepresentationCheck();
DataTable paramsTable = data.Tables["params"];
var attrs = typeof(Probability).GetCustomAttributes<ParameterAttribute>();
int resN = (int)attrs.First((param) => { return param.Name == "resN"; }).DefaultValue;
foreach (DataRow row in paramsTable.Rows)
{
switch (row["Name"].ToString())
{
case "resN":
resN = int.Parse(row["Value"].ToString());
break;
}
}
for (int i = 0; i < resN; i++)
{
DataRow row = pTable.NewRow();
pTable.Rows.Add(row);
}
for (int type_k = 1; type_k < 6; type_k++)
{
statistics stat = new statistics();
double[] k = represent.TableValues(K_Table, type_k);
k = stat.sort_shell(k, k.Length);
for (int i = 0; i < resN; i++)
{
pTable.Rows[i][type_k-1] = k[(int)Math.Round((double)i * ((double)(k.Length - 1)) / ((double)(resN - 1)))];
}
}
resultSet.Tables.Add(pTable);
}
public void Run(IContext ctx)
{
resultSet = new DataSet();
DataTable K_Table = ctx.GetData("RepresentationCheck").Tables["Coefficient"];
DataTable SigmaTable = new DataTable() { TableName = "Standarts" };
SigmaTable.Columns.Add("n");
SigmaTable.Columns.Add("Sigma_k_srednee");
SigmaTable.Columns.Add("Sigma_k_sigma");
SigmaTable.Columns.Add("Sigma_k_cv");
SigmaTable.Columns.Add("Sigma_k_cs");
SigmaTable.Columns.Add("Sigma_k_eta");
DataTable paramsTable = data.Tables["params"];
var attrs = typeof(Standarts).GetCustomAttributes<ParameterAttribute>();
int n = (int)attrs.First((param) => { return param.Name == "n"; }).DefaultValue;
foreach (DataRow row in paramsTable.Rows)
{
switch (row["Name"].ToString())
{
case "n":
n = int.Parse(row["Value"].ToString());
break;
}
}
RepresentationCheck represent = new RepresentationCheck();
statistics stat = new statistics();
DataRow rows = SigmaTable.NewRow();
rows["n"] = n;
double[] k = represent.TableValues(K_Table, 1);
double sigma = stat.standart(k);
rows["Sigma_k_srednee"] = sigma;
k = represent.TableValues(K_Table, 2);
sigma = stat.standart(k);
rows["Sigma_k_sigma"] = sigma;
k = represent.TableValues(K_Table, 3);
sigma = stat.standart(k);
rows["Sigma_k_cv"] = sigma;
k = represent.TableValues(K_Table, 4);
sigma = stat.standart(k);
rows["Sigma_k_cs"] = sigma;
k = represent.TableValues(K_Table, 5);
sigma = stat.standart(k);
rows["Sigma_k_eta"] = sigma;
SigmaTable.Rows.Add(rows);
resultSet.Tables.Add(SigmaTable);
}
List<double> Coef_Eta(DataTable X_Table, statistics stat, int n)
{
double[] q = new double[n];
double[] x = TableValues(X_Table, 0);
List<double> k5 = new List<double>();
double cv = stat.Variation(x, 0, x.Length);
double cs = stat.Asimmetria(x, 0, x.Length);
double eta = cs / cv;
// int index = 0;
for (int i = 0; i <= x.Length - n; i++)
{
k5.Add(stat.Asimmetria(x, i, n ) / stat.Variation(x, i, n ) / eta);
}
return k5;
}
List<double> Coef_Cv(DataTable X_Table, statistics stat, int n)
{
double[] x = TableValues(X_Table, 0);
List<double> k3 = new List<double>();
double cv = stat.Variation(x, 0, x.Length);
for (int i = 0; i <= x.Length - n; i++)
{
k3.Add(stat.Variation(x, i, n) / cv);
}
return k3;
}
List<double> Coef_Cs(DataTable X_Table, statistics stat, int n)
{
double[] q = new double[n];
double[] x = TableValues(X_Table, 0);
List<double> k4 = new List<double>();
double cs = stat.Asimmetria(x, 0, x.Length);
for (int i = 0; i <= x.Length - n; i++)
{
k4.Add(stat.Asimmetria(x, i, n ) / cs);
}
return k4;
}
List<double> CoefSigma(DataTable X_Table, statistics stat, int n)
{
double[] x = TableValues(X_Table, 0);
List<double> k2 = new List<double>();
double sigma = stat.Sigma(x, 0, x.Length);
for (int i = 0; i <= x.Length - n; i++)
{
k2.Add(stat.Sigma(x, i, n ) / sigma);
}
return k2;
}
List<double> CoefAvg(DataTable X_Table, statistics stat, int n)
{
double[] x = TableValues(X_Table, 0);
List<double> k1 = new List<double>();
double avg = stat.Average(x, 0, x.Length);
for (int i = 0; i <= x.Length - n; i++)
{
k1.Add(stat.Average(x, i, n) / avg);
}
return k1;
}