public decimal CalculateNormalizedScore(SawNormalizationMethod method, WeightVector weightVector, decimal[] rowValues, decimal[] minVector, decimal[] maxVector)
{
var score = 0.0m;
for (int i = 0; i < NumberOfColumns; i++)
{
if (method == SawNormalizationMethod.LinearFirstType)
{
var isProfit = DecisionCriterias.First(x => x.Position == i).IsProfit;
var weight = weightVector.GetValue(i);
if (isProfit)
{
var denom = (maxVector[i] - minVector[i]);
score += denom != 0 ? (((rowValues[i] - minVector[i]) / denom) * weight) : 0;
}
else
{
var denom = (maxVector[i] - minVector[i]);
score += denom != 0 ? ((1 - ((rowValues[i] - minVector[i])) / denom) * weight) : 0;
}
}
else
{
return(0.0m);
}
}
return(score);
}
private decimal Validate(WeightVectorLabel label, decimal?value)
{
var criteria = DecisionCriterias.First(x => x.Position == (int)label);
if (!value.HasValue)
{
return(criteria.ReserveLevel);
}
if (criteria.IsProfit)
{
return(GetInRange(value.Value, criteria.ReserveLevel, criteria.AspirationLevel));
}
else
{
return(GetInRange(value.Value, criteria.AspirationLevel, criteria.ReserveLevel));
}
}
