public override ExpertOpinionSharp.Distributions.IDistribution Fit(string variableName)
{
var variable = Variables.Single(x => x.Name == variableName);
var bounds = GetBounds(variable);
var w = new double[Experts.Count() + 1];
var d = new QuantileDistribution[Experts.Count()];
w [0] = 0;
w [w.Length - 1] = 1;
var weights = UseOptimalAlpha ? GetOptimalWeights() : GetWeights(_alpha);
int i = 0;
foreach (var kv in weights)
{
var p = Estimates [kv.Item1, variable];
var t = new [] { bounds.Item1, p [0], p [1], p [2], bounds.Item2 };
d [i] = new QuantileDistribution(QuantileVector, t);
w [i + 1] = w [i] + kv.Item2;
i++;
}
var dist = new MixtureDistribution(w, d);
return(dist);
}
void SetupUncertaintySatisfaction()
{
foreach (var o in _model.LeafObstacles())
{
try {
var satRates = _model.satisfactionRateRepository
.GetObstacleSatisfactionRates(o.Identifier);
if (satRates != null)
{
var estimates = satRates.Where(x => x.ExpertIdentifier != null);
if (estimates.Count() > 0)
{
var dm = ef.Fit(o.Identifier);
if (dm is ExpertOpinionSharp.Distributions.QuantileDistribution quantileD)
{
var distribution = new QuantileDistribution(quantileD.probabilities, quantileD.quantiles);
_model.satisfactionRateRepository.AddObstacleSatisfactionRate(o.Identifier, distribution);
}
else if (dm is ExpertOpinionSharp.Distributions.MixtureDistribution mixtureD)
{
var distribution = new MixtureDistribution(mixtureD.cummulativeWeight, mixtureD.distributions.Select(x => new QuantileDistribution(x.probabilities, x.quantiles)).ToArray());
_model.satisfactionRateRepository.AddObstacleSatisfactionRate(o.Identifier, distribution);
}
}
else
{
Console.WriteLine($"Obstacle '{o.Identifier}' not estimated by experts. Using 'probability' value.");
}
}
else
{
Console.WriteLine($"Obstacle '{o.Identifier}' not estimated. Using 0 as satisfaction rate.");
_model.satisfactionRateRepository.AddObstacleSatisfactionRate(o.Identifier, new DoubleSatisfactionRate(0));
}
} catch (Exception e) {
//throw (e);
Console.WriteLine("Error with " + o.FriendlyName);
Console.WriteLine(e.Message);
Console.WriteLine("---");
Console.WriteLine(e);
return;
}
}
}
public override IDistribution Fit(string variableName)
{
/* **** */
orderedExpertNames = new string[Experts.Count()];
int j = 0;
foreach (var e in Experts)
{
orderedExpertNames [j] = e.Name;
j++;
}
/* ---- */
Buildz();
BuildZ();
BuildS();
BuildPr();
/* **** */
var variable = Variables.Single(x => x.Name == variableName);
var bounds = GetBounds(variable);
var min = bounds.Item1;
var max = bounds.Item2;
var xx = Estimates.Get(variable);
// var xx = expertEstimates.SelectMany(t => t);
var xlist = new List <double> (xx);
xlist.Add(min);
xlist.Add(max);
xlist.Sort();
var probabilities = new double[xlist.Count - 1];
for (int i = 0; i < xlist.Count - 1; i++)
{
var v = xlist[i];
probabilities[i] = 1;
var h = 0;
// for getting the correct h, we need to compute it in the opposite
// direction; as h = ((...) * (m + 1) + j ) * (m + 1) + k for [...,j,k]
for (int l = Experts.Count() - 1; l >= 0; l--)
{
var currentExpert = Experts.Single(x => x.Name == orderedExpertNames [l]);
var ll = Estimates [currentExpert, variable].ToList();
// expertEstimates[l].ToList();
var index = ll.FindIndex(y => y > v);
if (index < 0)
{
index = NbQuantiles;
}
h = (h * (NbQuantiles + 1)) + index;
}
probabilities[i] *= Pr[h];
}
var sum = probabilities.Sum();
probabilities = probabilities.Select(t => t / sum).ToArray();
var stops = xlist.ToArray();
var quantiles = new double [probabilities.Length + 1];
quantiles [0] = 0;
int ij;
for (ij = 1; ij < probabilities.Length; ij++)
{
quantiles [ij] = quantiles[ij - 1] + probabilities[ij - 1];
}
quantiles [ij] = 1;
var dist = new QuantileDistribution(quantiles, stops);
return(dist);
}
