/// <summary>
/// Gets the information scores for all experts, sorted by score.
/// </summary>
/// <returns>The information scores.</returns>
public List <Tuple <Expert, double> > GetInformationScores()
{
var scores = Experts.Select(x => new Tuple <Expert, double> (x, GetInformationScore(x)))
.ToList();
scores.Sort((x, y) => y.Item2.CompareTo(x.Item2));
return(scores);
}
/// <summary>
/// Gets the weights for all experts, sorted.
/// </summary>
/// <returns>The weights.</returns>
public IEnumerable <Tuple <Expert, double> > GetWeights()
{
var scores = Experts.Select(x => new Tuple <Expert, double> (x, GetCalibrationScore(x) * GetInformationScore(x)))
.ToList();
scores.Sort((x, y) => y.Item2.CompareTo(x.Item2));
var scaling = scores.Sum(x => x.Item2);
return(scores.Select(x => new Tuple <Expert, double> (x.Item1, x.Item2 / scaling)));
}
/// <summary>
/// Gets the weights for all experts, sorted.
/// </summary>
/// <returns>The weights.</returns>
public IEnumerable <Tuple <Expert, double> > GetWeights(double alpha = 0)
{
var scores = Experts.Select(x => {
var c = GetCalibrationScore(x);
var i = GetInformationScore(x);
return(new Tuple <Expert, double> (x, ((c >= alpha) ? c : 0) * i));
}).ToList();
scores.Sort((x, y) => y.Item2.CompareTo(x.Item2));
var scaling = scores.Sum(x => x.Item2);
return(!(scaling > 0) ? scores : scores.Select(x => new Tuple <Expert, double> (x.Item1, x.Item2 / scaling)));
}
private void RefreshAggregatedVectorMethod()
{
var norm = Sqrt(Experts.Select(e => e.Weight).Sum(x => x * x));
var vectors = Experts.Select(
e => new Vector(
e.GlobalPriorityVector.GetColumn(0).Select(x => Pow(x, e.Weight / norm)).ToArray()
)
).ToList();
var temp = new Matrix(vectors);
var res = new Vector(temp.N);
for (int i = 0; i < temp.N; ++i)
{
res[i] = Mult(temp.GetRow(i).X);
}
AggregatedVector = new Matrix(res);
SelectedExpert.RefreshGlobalPriorityVector();
}
