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);
}
public void MixtureGumbelTest1()
{
var mixture = new MixtureDistribution(
new[] { new GumbelDistribution(1, 2), new GumbelDistribution(3, 4), new GumbelDistribution(5, 6) },
new[] { 0.2, 0.3, 0.5 }
);
for (double p = 0; p < 1.0; p += 0.01)
{
Assert.Equal(p, mixture.Cdf(mixture.Quantile(p)), Comparer);
}
}
public void MixtureExponentialTest1()
{
var exp = new ExponentialDistribution();
var mixture = new MixtureDistribution(exp, new ShiftedDistribution(exp, 10));
var comparer = new AbsoluteEqualityComparer(1e-4);
for (double p = 0; p < 1.0; p += 0.01)
{
double q = mixture.Quantile(p);
output.WriteLine($"{p.ToStringInvariant("N2")}: {q.ToStringInvariant()}");
Assert.Equal(p, mixture.Cdf(q), comparer);
}
}
void SetupUncertaintySatisfaction()
{
foreach (var o in _model.LeafObstacles())
{
try {
var satRates = _model.satisfactionRateRepository
.GetObstacleSatisfactionRates(o.Identifier);
if (satRates != null)
{
var estimates = satRates.Where(x => x.ExpertIdentifier != null);
if (estimates.Count() > 0)
{
var dm = ef.Fit(o.Identifier);
if (dm is ExpertOpinionSharp.Distributions.QuantileDistribution quantileD)
{
var distribution = new QuantileDistribution(quantileD.probabilities, quantileD.quantiles);
_model.satisfactionRateRepository.AddObstacleSatisfactionRate(o.Identifier, distribution);
}
else if (dm is ExpertOpinionSharp.Distributions.MixtureDistribution mixtureD)
{
var distribution = new MixtureDistribution(mixtureD.cummulativeWeight, mixtureD.distributions.Select(x => new QuantileDistribution(x.probabilities, x.quantiles)).ToArray());
_model.satisfactionRateRepository.AddObstacleSatisfactionRate(o.Identifier, distribution);
}
}
else
{
Console.WriteLine($"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 void MixtureDistributionUniformTest1()
{
var mixture = new MixtureDistribution(new UniformDistribution(0, 1), new UniformDistribution(1, 2));
Assert.Equal(1, mixture.Mean, Comparer);
Assert.Equal(1, mixture.Median, Comparer);
Assert.Equal(1.0 / 3, mixture.Variance, Comparer);
Assert.Equal(Math.Sqrt(1.0 / 3), mixture.StandardDeviation, Comparer);
Assert.Equal(0, mixture.Cdf(0), Comparer);
Assert.Equal(0.25, mixture.Cdf(0.5), Comparer);
Assert.Equal(0.5, mixture.Cdf(1), Comparer);
Assert.Equal(0.75, mixture.Cdf(1.5), Comparer);
Assert.Equal(1, mixture.Cdf(2), Comparer);
for (double p = 0; p < 1.0; p += 0.01)
{
Assert.Equal(p, mixture.Cdf(mixture.Quantile(p)), Comparer);
}
}
