/// <summary>
/// 計算穩定狀態下的參數組(單一觀測值)，使用一般的 Variance-Covariance Matrix 去計算，而非 Sullivan-Woodall
/// </summary>
/// <param name="data">欲使用的資料表</param>
/// <returns></returns>
public TsquareParameters Execute(Algebra.Matrix <double> data, Mtb.Project proj)
{
//確認基本資訊能否計算
int p = data.ColumnCount;
int m = data.RowCount;
if (m - p <= 0)
{
throw new Exception(string.Format("樣本數不可低於{0}，請重新確認。", p));
}
//組合管制界線指令並計算 Phase1 的 limit
double ucl;
StringBuilder cmnd = new StringBuilder();
cmnd.AppendLine("invCdf 0.9973 k1;");
cmnd.AppendFormat("Beta {0} {1}.\r\n", (double)p / 2, (double)(m - p - 1) / 2);
cmnd.AppendFormat("let k1=(({0}-1)**2)/{0}*k1\r\n", m);
try
{
Mtb.Worksheet ws = proj.Worksheets.Add(1);
string fpath = Mtblib.Tools.MtbTools.BuildTemporaryMacro("mymacro.mtb", cmnd.ToString());
proj.ExecuteCommand(string.Format("Exec \"{0}\" 1", fpath), ws);
ucl = ws.Constants.Item("k1").GetData();
ws.Delete();
proj.Commands.Delete();
}
catch (Exception e)
{
throw new Exception(e.Message);
}
double miss = Mtblib.Tools.MtbTools.MISSINGVALUE;
var M = Algebra.Matrix <double> .Build;
//計算 Mean vector & Covariance matrix
IEnumerable <Algebra.Vector <double> > cols = data.EnumerateColumns();
var mean = M.DenseOfColumnMajor(1, data.ColumnCount, cols.Select(x => x.Where(o => o < miss).Average()));
var subData = M.DenseOfRows((data.EnumerateRows().Where(x => x.All(y => y < miss)).ToArray()));
var diff = subData - M.Dense(subData.RowCount, 1, (i, j) => 1).Multiply(mean);
var cov = diff.Transpose().Multiply(diff) / (subData.RowCount - 1);
var invS = cov.Inverse();
TsquareParameters tsquarePara = new TsquareParameters()
{
Mean = mean,
Covariance = cov,
SampleSize = m,
SubgroupSize = 1
};
//計算出資料的Tsquare value
List <double> t2 = new List <double>();
for (int i = 0; i < data.RowCount; i++)
{
if (data.Row(i).ToArray().All(x => x < miss))
{
t2.Add(Tool.CalculateTSquare(data.Row(i).ToRowMatrix(), mean, invS));
}
else
{
t2.Add(miss);
}
}
if (t2.Any(x => x >= ucl && x < miss))
{
int[] oocRow = t2.Select((x, i) => new { Tsquare = x, Index = i }).Where(x => x.Tsquare >= ucl && x.Tsquare < miss).Select(x => x.Index).OrderByDescending(x => x).ToArray();
subData = data.Clone();
//逐列移除OOC的資料列
for (int i = 0; i < oocRow.Length; i++)
{
subData = subData.RemoveRow(oocRow[i]);
}
tsquarePara = Execute(subData, proj);
}
return(tsquarePara);
}
/// <summary>
/// 判斷individaul T2管制圖的參數是否合法? 如果合法不回發生任何事，若不合法則回傳例外。
/// </summary>
/// <param name="paraSet"></param>
/// <returns></returns>
private void ParaValidation(ParaSetInfo paraSet)
{
DataTable mean = paraSet.Mean;
DataTable cov = paraSet.Cov;
int? ssiz = paraSet.SampleSize;
if (mean == null || cov == null)
{
throw new ArgumentNullException("尚未輸入平均數向量或共變異數矩陣");
}
if (SampleSize == null || SampleSize <= 0)
{
throw new Exception("樣本數內容不正確");
}
//確認輸入的數據是否正確(數量、共變異矩陣對稱、對角項大於0、正定等特性)
int r = cov.Columns.Count;
int c = cov.Rows.Count;
double result;
if (mean.Rows.Cast <DataRow>().Any(x => x.ItemArray.Any(y => !Double.TryParse(y.ToString(), out result))))
{
throw new Exception("平均數欄位內容必須為數字");
}
if (cov.Rows.Cast <DataRow>().Any(x => x.ItemArray.Any(y => !Double.TryParse(y.ToString(), out result))))
{
throw new Exception("共變異數內容必須為數字");
}
//將共變異數矩陣轉到Minitab中判斷是否合法
double[] covArray = new double[r * c];
for (int cCnt = 0; cCnt < c; cCnt++)
{
for (int rCnt = cCnt; rCnt < r; rCnt++)
{
if (rCnt == cCnt)
{
double value = double.Parse(cov.Rows[rCnt][cCnt].ToString());
if (value <= 0)
{
throw new Exception("共變異數矩陣對角項小於等於0");
}
covArray[cCnt * r + rCnt] = value;
}
else
{
if (cov.Rows[rCnt][cCnt].ToString() != cov.Rows[cCnt][rCnt].ToString())
{
throw new Exception("共變異數矩陣不對稱");
}
double value = double.Parse(CovarianceMatrix.Rows[rCnt][cCnt].ToString());
covArray[cCnt * r + rCnt] = value;
covArray[rCnt * r + cCnt] = value;
}
}
}
Mtb.Worksheet ws = Project.Worksheets.Add(1);
Mtb.Matrix mat = ws.Matrices.Add(Quantity: 1);
Mtb.Column col = ws.Columns.Add(Quantity: 1);
mat.SetData(covArray, 1, 1);
Project.ExecuteCommand(string.Format("Eigen {0} {1}.\r\n", mat.SynthesizedName, col.SynthesizedName), ws);
double[] eigvalue = col.GetData();
if (eigvalue.Any(x => x <= 0))
{
throw new Exception("共變異數矩陣不為正定");
}
ws.Delete();
Project.Commands.Delete();
}
