ISmDiscriminantAnalysis.ClassCount

Syntax

ClassCount: Integer;

Description

The ClassCount property returns the number of classes obtained as the result of method calculation.

Comments

The IDataMining.FilledDependent property returns total classification of objects.

Example

To execute the example, add links to the MathFin, Stat system assemblies.

// Main procedure
Sub UserDiscrAn;
Var
    DA: SmDiscriminantAnalysis;
    y: Array[60] Of Integer;
    x1, x2, x3, x4, x5, x6, x7, x8: Array[60] Of Double;
    Ex: ISlSeries;
    res, i, j: Integer;
    indx0: Double;
    resM: Array Of Integer;
    str: String;
Begin
    DA := New SmDiscriminantAnalysis.Create;
    // Source data: sixty objects with eight attributes
    For i := 0 To 59 Do
        x1[i] := Math.RandBetween(95, 104);
        x2[i] := Math.RandBetween(95, 104);
        x3[i] := Math.RandBetween(95, 104);
        x4[i] := Math.RandBetween(95, 104);
        x5[i] := Math.RandBetween(95, 104);
        x6[i] := Math.RandBetween(95, 104);
        x7[i] := Math.RandBetween(95, 104);
        x8[i] := Math.RandBetween(95, 104);
    End For;
    // explained series values
    For i := 0 To 39 Do
        y[i] := Math.RandBetweenI(1, 5);
    End For;
    For i := 40 To 59 Do
        y[i] := -1;
    End For;
    // Set explanatory variables
    Ex := DA.Explanatories;
    Ex.Add.Value := x1; Ex.Add.Value := x5;
    Ex.Add.Value := x2; Ex.Add.Value := x6;
    Ex.Add.Value := x3; Ex.Add.Value := x7;
    Ex.Add.Value := x4; Ex.Add.Value := x8;
    // Calculate and display results
    If DA.Execute = 0 Then
        Debug.WriteLine(" == Summary results of classification == ");
        res := DA.ClassCount;
        Debug.WriteLine("Number of classes: " + res.ToString);
        resM := DA.ClassificationSummary;
        For i := 0 To res - 1 Do
            For j := 0 To res - 1 Do
                str := str + " " + (resM[i, j] As Integer).ToString;
            End For;
            str := str + " | " + (resM[i, res] As Integer).ToString;
            Debug.WriteLine(str);
            str := "";
        End For;
        str := "- - - - - - - - ";
        Debug.WriteLine(str);
        str := "";
        For j := 0 To DA.ClassCount - 1 Do
                str := str + " " + (resM[res, j] As Integer).ToString;
            End For;
        str := str + " | " + (resM[res, res] As Integer).ToString;
        Debug.WriteLine(str);

        str := "===========================" + #13 + #10;
        Debug.WriteLine(str + " == Grand average == ");
        Print(DA.TotalAverage, DA.TotalAverage.Rank);
        Debug.WriteLine(" == Intraclass average == ");
        Print(DA.WithinAverage, DA.WithinAverage.Rank);
        Debug.WriteLine(str + " == Common standard deviation == ");
        Print(DA.TotalStdDev, DA.TotalStdDev.Rank);
        Debug.WriteLine(" == Intraclass standard deviation == ");
        Print(DA.WithinStdDev, DA.WithinStdDev.Rank);
        Debug.WriteLine(str + " == Common correlation == ");
        Print(DA.TotalCorrelation, DA.TotalCorrelation.Rank);
        Debug.WriteLine(" == Intraclass correlation == ");
        Print(DA.WithinCorrelation, DA.WithinCorrelation.Rank);
        Debug.WriteLine(" == Averaged intraclass correlation == ");
        Print(DA.PooledWithinCorrelation, DA.PooledWithinCorrelation.Rank);
        Debug.WriteLine(str + " == Common covariance == ");
        Print(DA.TotalCovariance, DA.TotalCovariance.Rank);
        Debug.WriteLine(" == Intraclass covariance == ");
        Print(DA.WithinCovariance, DA.WithinCovariance.Rank);
        Debug.WriteLine(" == Averaged intraclass covariance == ");
        Print(DA.PooledWithinCovariance, DA.PooledWithinCovariance.Rank);
        Debug.WriteLine(str + " == Canonical discriminant function coefficients == ");
        Debug.WriteLine("    = standardized = ");
        Print(DA.StdDiscriminantFuncs, DA.StdDiscriminantFuncs.Rank);
        Debug.WriteLine("    = non-standardized = ");
        Print(DA.NonStdDiscriminantFuncs, DA.NonStdDiscriminantFuncs.Rank);
Debug.WriteLine(str+"==Wilkesstatistic==");         Print(DA.WilkesStatistics, DA.WilkesStatistics.Rank);
        Debug.WriteLine(str + " == Fisher functions == ");
        Print(DA.FishersFuncs, DA.FishersFuncs.Rank);
        Debug.WriteLine(str + " == Structure matrix == ");
        Print(DA.StructuralMatrix, DA.StructuralMatrix.Rank);
        Debug.WriteLine(str + " == Eigenvalues == ");
        Print(DA.EigenValues, DA.EigenValues.Rank);
        Debug.WriteLine(str + " == Classification of variables == ");
        For i := 0 To DA.FinallyExplained.Length - 1 Do
            indx0 := DA.FinallyExplained[i];
            Debug.WriteLine(i.ToString + ".  " + indx0.ToString);
        End For;
    End If;
End Sub UserDiscrAn;
// Data output procedure
Sub Print(a: Array Of Double; d2: integer);
Var
    ci: ICultureInfo;
    i, j: integer;
    s: string;
Begin
    ci := CultureInfo.Current;
    If d2 = 2 Then
        For i := 0 To a.GetUpperBound(2) Do
            s := "";
            For j := 0 To a.GetUpperBound(1) Do
                If Double.IsNan(a[j, i]) Then
                    s := s + " " + " - ";
                Else
                    s := s + " " + ci.FormatDoublePrec(a[j, i], 2);
                End If;
            End For;
            Debug.WriteLine(s);
        End For;
    Else
        For i := 0 To a.GetUpperBound(1) Do
            If Double.IsNan(a[i]) Then
                s := s + " " + " - ";
            Else
                s := s + " " + ci.FormatDoublePrec(a[i], 2);
            End If;
        End For;
        Debug.WriteLine(s);
    End If;
End Sub Print;

After executing the main procedure, discriminatory analysis is performed for the specified data, analysis results are displayed to the console window.