public void Run(IContext ctx)
{
resultSet = new DataSet();
Statistics stat = new Statistics();
DataTable kTable = new DataTable() { TableName = "FlowSequence" };
kTable.Columns.Add("K");
DataTable Option = new DataTable() { TableName = "OptionsSequence" };
Option.Columns.Add("Type");
Option.Columns.Add("Srednee");
Option.Columns.Add("Sigma");
Option.Columns.Add("Cv");
Option.Columns.Add("Cs");
Option.Columns.Add("Eta");
Option.Columns.Add("r");
DataTable paramsTable = data.Tables["params"];
var attrs = typeof(FlowSequenceGeneration).GetCustomAttributes<ParameterAttribute>();
double eps = (double)attrs.First((param) => { return param.Name == "eps"; }).DefaultValue;
int n = (int)attrs.First((param) => { return param.Name == "N"; }).DefaultValue;
double r = (double)attrs.First((param) => { return param.Name == "r"; }).DefaultValue;
double cv = (double)attrs.First((param) => { return param.Name == "CV"; }).DefaultValue;
double eta = (double)attrs.First((param) => { return param.Name == "ETA"; }).DefaultValue;
foreach(DataRow row in paramsTable.Rows) {
switch (row["Name"].ToString())
{
case "eps":
eps = double.Parse(row["Value"].ToString(), CultureInfo.InvariantCulture);
break;
case "N":
n = int.Parse(row["Value"].ToString());
break;
case "r":
r = double.Parse(row["Value"].ToString(), CultureInfo.InvariantCulture);
break;
case "CV":
cv = double.Parse(row["Value"].ToString(), CultureInfo.InvariantCulture);
break;
case "ETA":
eta = double.Parse(row["Value"].ToString(), CultureInfo.InvariantCulture);
break;
}
}
DataTable optSource = ctx.InitialData.Tables["optsource_big"];
double[] kArray = Sequence(optSource, stat, n, r, cv, eta,eps);
for (int i = 0; i < kArray.Length; i++)
{
DataRow row = kTable.NewRow();
row["K"] = kArray[i];
kTable.Rows.Add(row);
}
resultSet.Tables.Add(kTable);
double[] x = BigOptSource(optSource, cv, eta);
DataRow row0 = Option.NewRow();
row0["Type"] = "K_teor";
row0["Srednee"] = stat.Average(x, 0, x.Length-1);
row0["Sigma"] = stat.Sigma(x, 0, x.Length-1);
// row0["Cv"] = stat.Variation(x, 0, x.Length - 1);
// row0["Cs"] = stat.Asimmetria(x, 0, x.Length - 1);
// row0["Eta"] = stat.Asimmetria(x, 0, x.Length - 1) / stat.Variation(x, 0, x.Length - 1);
// row0["r"] = stat.Correlation(x, 0, x.Length - 1);
row0["Cv"] = cv;
row0["Cs"] = cv*eta;
row0["Eta"] = eta;
row0["r"] = r;
Option.Rows.Add(row0);
DataRow row1 = Option.NewRow();
row1["Type"] = "K_practic";
row1["Srednee"] = stat.Average(kArray, 0, n-1);
row1["Sigma"] = stat.Sigma(kArray, 0, n-1);
row1["Cv"] = stat.Variation(kArray, 0, n-1);
row1["Cs"] = stat.Asimmetria(kArray, 0, n-1);
row1["Eta"] = stat.Asimmetria(kArray, 0, n) / stat.Variation(kArray, 0, n-1);
row1["r"] = stat.Correlation(kArray, 0, n-1);
Option.Rows.Add(row1);
DataRow row2 = Option.NewRow();
row2["Type"] = "P";
row2["Srednee"] = Average_p;
row2["Sigma"] = Sigma_p;
Option.Rows.Add(row2);
resultSet.Tables.Add(Option);
}
