/// <summary>
///homogeneous criteria->subtract values-> the smaller the better
///heterogeneous criteria->subtract values->the biger the better
///the difference: heterogeneous value - homogeneous is the return value the biger the better
/// normalize by the best possible GroupPerformanceIndex
/// (the difference of a perfect homogeneous pair of values is 0)
/// (the difference of a perfect heterogeneous pair of value is 1)
/// (the besst possible (non realistic) GroupPerformanceIndex, with resct to these rules, is the sum (of 1 to nummber of groupmembers count) * (the count of heterogen criterion) * (the sum of the count of each criterions values)
/// e.g. for a Group of 3 persons with 2 heterogen Criterion each with 4 values the best posible GroupPerformanceIndex would be (3+2+1)*2*4
/// </summary>
/// <param name="g"></param>
private float CalcNormlizedPairPerformance(Participant participant, Participant participant_2)
{
//the summed distances of all hommogeneous values
float homVal = 0;
//the summed distances of all heterogeneous values
float hetVal = 0;
//not normalized pairperformance index (hetVal - homVal)
float pairPerformanceIndex = 0;
Criterion c_2;
//distance between two Criteria
float d = 0;
// weighted distance
float wd = 0;
//normlized pair performance index
float npi = 0;
if (participant.Criteria.Count != participant_2.Criteria.Count)
{
throw new Exception("calcPairPerformance: the entries have different count of criteria!!!");
}
foreach (Criterion c in participant.Criteria)
{
//get the same kriterion of the other participant
c_2 = participant_2.Criteria.First(x => x.Name == c.Name);
//calculate Manhatan distanze for both Criteria
//and normalize the distanze over the maximal amount of dimensions so evry criterion gets a value between 0 and 1
//(otherwise the criterion will be unthought weighted )
//d = ManhattanDistance(c, c_2);
d = distanceFunktion.normalizedDistanze(c, c_2);
wd = d * c.Weight;
if (c.IsHomogeneous)
{
homVal += wd;
}
else
{
hetVal += wd;
}
}
pairPerformanceIndex = hetVal - homVal;
float MaxDist = 0;
//worst case Heterogen Kriteria is 0 and hom is 1 than the value for pairPerformanceIndex < 0. therfore the worst possible value for
// hom Kriteria is added to the pairPerformanceIndex: and the target set lies between 0 and 1
float homMaxDist = 0;
//beacuse i normalize each distance of two criterions over their highest possible value
//here i neede to normalize pairPerformanceIndex by the count of the Criterions multiplied by its weight
foreach (Criterion c in participant.Criteria)
{
if (c.IsHomogeneous)
{
homMaxDist += 1 * c.Weight;
}
MaxDist += 1 * c.Weight;
//if (c.IsHomogeneous) homMaxDist += c.Value.Length * c.Weight;
//MaxDist += c.Value.Length * c.Weight;
}
//Debug.Assert(MaxDist == 1); only work sif weights in sum = 1!
npi = (pairPerformanceIndex + homMaxDist) / MaxDist;
return(npi);
}
