/*
* Pre-condition:
* X (XMtx) and Y (XMtx) values are initialized
*
* Post-condition:
* Calculate Beta by using LS method
* and set it to the ResultLs
*/
public void CalcLs()
{
(ResultLs = new LsResult()).
// β = ((X^T) ∙ X)^(-1) ∙ (X^T) ∙ Y
Beta = Parameters.XMtx.TransposeThisAndMultiply(Parameters.XMtx).Inverse().Multiply(Parameters.XMtx.Transpose()).Multiply(Parameters.YVtr);
CalcResult(ResultLs);
}
/*
* Finding the coefficient of determination
*/
private double CalcRp2(LsResult result)
{
var yAvg = Parameters.YVtr.Average();
return
(1
- (result.E.Sum((i) => i * i))
/ (Parameters.YVtr.Sum((i) => Math.Pow(i - yAvg, 2))));
}
private void DrawSeriesResults(string seriesName, LsResult result, Color color)
{
if (!result.IsCalculated)
{
return;
}
DrawSeries(seriesName, result.YRes);
chart.Series[seriesName].Color = color;
}
public DataTableWindow(LsData lsData, LsResult lsResult, string filePath)
{
InitializeComponent();
this.lsData = lsData;
this.lsResult = lsResult;
this.filePath = filePath;
InitializeComponents();
}
/*
* Pre-condition:
* X (XMtx) and Y (XMtx) values are initialized
*
* Post-condition:
* Calculate Beta by using ILS method
* and set it to the ResultIls
*/
public void CalcIls()
{
CalcMCrit();
CalcAMtx();
CalcBVtr();
(ResultIls = new LsResult()).Beta = AMtx.Inverse().Multiply(BVtr);
CalcResult(ResultIls);
}
/*
* This section makes analysis of
* LS or ILS method and set it to Result
*/
/*
* Pre-condition:
* Takes Result with calculated Beta vector
*
* Post-condition:
* Makes analysis of Beta
*/
private void CalcResult(LsResult result)
{
result.YRes = CalcYVtrRes(result.Beta);
result.E = CalcEVtr(result.YRes);
result.Delta = CalcDeltaVtr(result.E);
result.Tbeta = CalcTbetaVtr();
result.SigmaE = CalcSigmaE(result);
result.Rp2 = CalcRp2(result);
result.R = CalcR(result);
result.IsCalculated = true;
}
private static void WriteResults(Excel.Workbook xlWorkbook, LsResult lsResult, string sheetName)
{
if (!lsResult.IsCalculated)
{
return;
}
try
{
xlWorkbook.Worksheets[sheetName].Delete();
}
catch (Exception) { }
CreateReslutWorksheet(xlWorkbook.Worksheets.Add(), lsResult, sheetName);
}
private static void CreateReslutWorksheet(Excel.Worksheet sheet, LsResult result, string sheetName)
{
sheet.Name = sheetName;
sheet.Columns[(new XlPosition()).Column].ColumnWidth = 2.5;
// Setting result values such as:
// Beta, e, Delta, Tbeta
// Sigma, R, R^2, F, F table
// Headers
SetResultHeaders(sheet);
// Values
SetResultValues(sheet, result);
ReleaseMemory(sheet);
}
private static void SetResultValues(Excel.Worksheet sheet, LsResult result)
{
SetColumnValues(sheet, result.Beta, resBValsSp);
SetColumnValues(sheet, result.E, resEValsSp);
SetColumnValues(sheet, result.Delta, resDltValsSp);
SetColumnValues(sheet, result.Tbeta, resTbValsSp);
double[] singleResultValues =
{
result.BetaAvg,
result.SigmaE,
result.R,
result.Rp2,
result.F,
result.Ftable
};
SetColumnValues(sheet, Vector <double> .Build.Dense(singleResultValues), resSglValsSp);
}
public LsData(LsParameters parameters)
{
Parameters = parameters;
ResultLs = new LsResult();
ResultIls = new LsResult();
}
private double CalcR(LsResult result)
{
return(Math.Sqrt(result.R));
}
/*
* Finding the mean square error variance disturbances
* √((∑ e[i]^2 ) / (NumbOfTests - NumbOfXVal - 1))
*/
private double CalcSigmaE(LsResult result)
{
return(Math.Sqrt(
result.E.Sum(
(i) => i * i / (Parameters.NumbOfTests - Parameters.NumbOfXVal - 1))));
}
