private static WeightsModel NormalizeWeights(WeightsModel weights)
{
var sum = weights.CostWeight + weights.MacroWeight + weights.PreferncesWeight + weights.PreparationTimeWeight;
return(new WeightsModel
{
MacroWeight = weights.MacroWeight / sum,
PreparationTimeWeight = weights.PreparationTimeWeight / sum,
PreferncesWeight = weights.PreferncesWeight / sum,
CostWeight = weights.CostWeight / sum,
});
}
//public static double Euclidian(GroupDietIndividual individual, double[] referencePoint)
//{
// var summarizedScores = SummarizeScores(individual);
// //macro, cost, time, preferences
// return Math.Sqrt(
// Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.Macro).Score / summarizedScores - referencePoint[0], 2) -
// Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.Cost).Score / summarizedScores - referencePoint[1], 2) -
// Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.PreparationTime).Score / summarizedScores - referencePoint[2], 2) -
// Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.Preferences).Score / summarizedScores - referencePoint[3], 2));
//}
public static double Euclidian(GroupDietIndividual individual, WeightsModel weights)
{
var summarizedScores = SummarizeScores(individual);
var normalziedWeights = NormalizeWeights(weights);
//macro, cost, time, preferences
return(Math.Sqrt(
Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.Macro).Score / summarizedScores - 1 + normalziedWeights.MacroWeight, 2) +
Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.Cost).Score / summarizedScores - 1 + normalziedWeights.CostWeight, 2) +
Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.PreparationTime).Score / summarizedScores - 1 + normalziedWeights.PreparationTimeWeight, 2) +
Math.Pow(individual.Evaluations.Single(e => e.Type == ObjectiveType.Preferences).Score / summarizedScores - 1 + normalziedWeights.PreferncesWeight, 2)));
}
public List <GroupDietIndividual> AhpSort(List <GroupDietIndividual> individuals, List <AhpModel> ahpModels)
{
var priorityVectors = ahpModels.Select(GetPriorityVector).ToList();
var averageWeightModel = new WeightsModel
{
MacroWeight = priorityVectors.Select(v => v.MacroWeight).Average(),
CostWeight = priorityVectors.Select(v => v.CostWeight).Average(),
PreferncesWeight = priorityVectors.Select(v => v.PreferncesWeight).Average(),
PreparationTimeWeight = priorityVectors.Select(v => v.PreparationTimeWeight).Average()
};
return(EuclideanSort(individuals, averageWeightModel));
}
private List <GroupDietIndividual> EuclideanSort(List <GroupDietIndividual> individuals, WeightsModel weights)
{
var macroMin = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.Macro).Score).Min();
var prepTimeMin = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.PreparationTime).Score).Min();
var costMin = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.Cost).Score).Min();
var preferencesMin = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.Preferences).Score).Min();
var macroMax = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.Macro).Score).Max();
var prepTimeMax = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.PreparationTime).Score).Max();
var costMax = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.Cost).Score).Max();
var preferencesMax = individuals.Select(i => i.Evaluations.Single(e => e.Type == ObjectiveType.Preferences).Score).Max();
var sorted = individuals.OrderBy(i => EuclidianHelper.Euclidian(new GroupDietIndividual(new GroupDiet())
{
Evaluations = new List <Evaluation>
{
Normalize(i.Evaluations.Single(e => e.Type == ObjectiveType.Macro), macroMin, macroMax),
Normalize(i.Evaluations.Single(e => e.Type == ObjectiveType.PreparationTime), prepTimeMin, prepTimeMax),
Normalize(i.Evaluations.Single(e => e.Type == ObjectiveType.Cost), costMin, costMax),
Normalize(i.Evaluations.Single(e => e.Type == ObjectiveType.Preferences), preferencesMin, preferencesMax)
}
}, weights)).ToList();
return(sorted);
}
