double[] GetInterpolatedDistribution(CalibrationVariable v, ExpertEstimate estimate, double[] quantiles)
{
var res = new List <double> ();
double lowerBound, upperBound;
GetBounds(v, out lowerBound, out upperBound);
Func <double, double, double> interpolate = (x, y) => 1.0d * (y - x) / (upperBound - lowerBound);
res.Add(interpolate(lowerBound, estimate.GetQuantile(quantiles[0])));
var quantilesCount = quantiles.Count();
for (int i = 1; i < quantilesCount; i++)
{
var l0 = estimate.GetQuantile(quantiles[i - 1]);
var l1 = estimate.GetQuantile(quantiles[i]);
res.Add(interpolate(l0, l1));
}
var lastQuantile = quantiles[quantilesCount - 1];
res.Add(interpolate(estimate.GetQuantile(lastQuantile), upperBound));
return(res.ToArray());
}
public static void ExampleBook(string[] args)
{
Console.WriteLine("Hello World!");
var item1 = new CalibrationVariable("Item 1", 27);
var item2 = new CalibrationVariable("Item 2", 45);
var item3 = new CalibrationVariable("Item 3", 60);
var item4 = new CalibrationVariable("Item 4", 28);
var expert1 = new Expert("Expert 1");
expert1.AddEstimate(item1, new PERTEstimate(25, 32, 37, .05));
expert1.AddEstimate(item2, new PERTEstimate(35, 42, 47, .05));
expert1.AddEstimate(item3, new PERTEstimate(50, 57, 62, .05));
expert1.AddEstimate(item4, new PERTEstimate(25, 32, 37, .05));
var expert2 = new Expert("Expert 2");
expert2.AddEstimate(item1, new PERTEstimate(0, 45, 78, .05));
expert2.AddEstimate(item2, new PERTEstimate(0, 45, 78, .05));
expert2.AddEstimate(item3, new PERTEstimate(0, 45, 78, .05));
expert2.AddEstimate(item4, new PERTEstimate(0, 45, 78, .05));
var expert3 = new Expert("Expert 3");
expert3.AddEstimate(item1, new PERTEstimate(40, 45, 52, .05));
expert3.AddEstimate(item2, new PERTEstimate(35, 40, 47, .05));
expert3.AddEstimate(item3, new PERTEstimate(39, 44, 51, .05));
expert3.AddEstimate(item4, new PERTEstimate(41, 46, 53, .05));
var manager = new ExpertManager();
manager.AddExpert(expert1);
manager.AddExpert(expert2);
manager.AddExpert(expert3);
manager.SetQuantiles(new [] { 0.05, .5, .95 });
Console.WriteLine("-- Calibration");
Console.WriteLine(string.Join("\n", manager.GetCalibrationScores().Select(x => "I(" + x.Item1.Name + ") = " + x.Item2)));
Console.WriteLine();
Console.WriteLine("-- Information");
Console.WriteLine(string.Join("\n", manager.GetInformationScores().Select(x => "C(" + x.Item1.Name + ") = " + x.Item2)));
Console.WriteLine();
Console.WriteLine("-- Weight");
Console.WriteLine(string.Join("\n", manager.GetWeights().Select(x => "W(" + x.Item1.Name + ") = " + x.Item2)));
Console.WriteLine();
}
/// <summary>
/// Gets the information score for the specified variable <c>v</c> and specified expert <c>e</c>.
/// </summary>
/// <returns>The information score.</returns>
/// <param name="v">The variable.</param>
/// <param name="e">The expert.</param>
public double GetInformationScore(CalibrationVariable v, Expert e)
{
var expertOpinion = e.Estimates[v];
var p = GetInterquantileRanges(Quantiles);
var r = GetInterpolatedDistribution(v, expertOpinion, Quantiles);
var score = 0d;
for (int i = 0; i < p.Length; i++)
{
var lscore = (p[i] * Math.Log(p[i] / r[i]));
score += lscore;
}
return(score);
}
