public void InvertedGenerationalDistanceTestEmptyFront()
{
double[] point = new double[2];
point[0] = 0.5;
point[1] = 0.5;
double[][] front = { };
double[][] referencefront = { point };
InvertedGenerationalDistance.Calculate(front, referencefront, 1);
}
public void InvertedGenerationalDistanceTestSinglePoint()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[][] front = { point };
double[] point2 = new double[2];
point2[0] = 1;
point2[1] = 1;
double[][] referencefront = { point2 };
double dist = InvertedGenerationalDistance.Calculate(front, referencefront, 1);
Assert.AreEqual(Math.Sqrt(2), dist);
}
private void Analyze()
{
ResultsScatterPlot = new ParetoFrontScatterPlot(solutions.Select(x => x.Fitness).ToArray(), solutions.Select(x => x.Mean.ToArray()).ToArray(), ResultsScatterPlot.ParetoFront, ResultsScatterPlot.Objectives, ResultsScatterPlot.ProblemSize);
ResultsSolutions = solutions.Select(x => x.Mean.ToArray()).ToMatrix();
var problem = Problem as MultiObjectiveTestFunctionProblem;
if (problem == null)
{
return;
}
var front = NonDominatedSelect.GetDominatingVectors(solutions.Select(x => x.Fitness), problem.ReferencePoint.CloneAsArray(), Problem.Maximization, true).ToArray();
if (front.Length == 0)
{
return;
}
var bounds = problem.Bounds.CloneAsMatrix();
ResultsCrowding = Crowding.Calculate(front, bounds);
ResultsSpacing = Spacing.Calculate(front);
ResultsGenerationalDistance = problem.BestKnownFront != null?GenerationalDistance.Calculate(front, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
ResultsInvertedGenerationalDistance = problem.BestKnownFront != null?InvertedGenerationalDistance.Calculate(front, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
ResultsHypervolume = Hypervolume.Calculate(front, problem.ReferencePoint.CloneAsArray(), Problem.Maximization);
ResultsBestHypervolume = Math.Max(ResultsHypervolume, ResultsBestHypervolume);
ResultsDifferenceBestKnownHypervolume = ResultsBestKnownHypervolume - ResultsBestHypervolume;
ResultsBestHypervolumeDataLine.Values.Add(ResultsBestHypervolume);
ResultsHypervolumeDataLine.Values.Add(ResultsHypervolume);
ResultsCrowdingDataLine.Values.Add(ResultsCrowding);
ResultsGenerationalDistanceDataLine.Values.Add(ResultsGenerationalDistance);
ResultsInvertedGenerationalDistanceDataLine.Values.Add(ResultsInvertedGenerationalDistance);
ResultsSpacingDataLine.Values.Add(ResultsSpacing);
ResultsHypervolumeDifferenceDataLine.Values.Add(ResultsDifferenceBestKnownHypervolume);
Problem.Analyze(
solutions.Select(x => (Optimization.Individual) new SingleEncodingIndividual(Problem.Encoding, new Scope {
Variables = { new Variable(Problem.Encoding.Name, x.Mean) }
})).ToArray(),
solutions.Select(x => x.Fitness).ToArray(),
Results,
random);
}
public void InvertedGenerationalDistanceTestDifferentSizes()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[] point1 = new double[2];
point1[0] = 1;
point1[1] = 0.5;
double[][] front = { point, point1 };
double[] point2 = new double[2];
point2[0] = 1;
point2[1] = 0;
double[][] referencefront = { point2 };
double dist = InvertedGenerationalDistance.Calculate(front, referencefront, 1);
Assert.AreEqual(0.5, dist);
}
public void InvertedGenerationalDistanceTestQuadratic()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[] point1 = new double[2];
point1[0] = 0;
point1[1] = 1;
double[][] front = { point, point1 };
double[] point2 = new double[2];
point2[0] = 1;
point2[1] = 0;
double[] point3 = new double[2];
point3[0] = 1;
point3[1] = 1;
double[][] referencefront = { point2, point3 };
double dist = InvertedGenerationalDistance.Calculate(front, referencefront, 1);
Assert.AreEqual(1, dist);
}
public IDictionary <string, IDictionary <string, IDictionary <string, List <double> > > > GetIndicators()
{
GenerateReferenceParetoFronts();
var indicators = new Dictionary <string, IDictionary <string, IDictionary <string, List <double> > > >();
MetricsUtil utils = new MetricsUtil();
for (int i = 0, li = experiment.QualityIndicators.Count; i < li; i++)
{
string indicatorString = experiment.QualityIndicators[i].ToUpper();
double value = 0;
var problems = new Dictionary <string, IDictionary <string, List <double> > >();
indicators.Add(indicatorString, problems);
foreach (var problem in experiment.ExperimentProblems)
{
var algorithm = new Dictionary <string, List <double> >();
problems.Add(problem.Alias, algorithm);
var trueFront = utils.ReadFront(problem.ParetoFront);
foreach (var algorithmDictionary in problem.AlgorithmDictionary)
{
var indicator = new List <double>();
algorithm.Add(algorithmDictionary.Key, indicator);
foreach (var alg in algorithmDictionary.Value)
{
var solutionFront = alg.Result.GetObjectives();
switch (indicatorString)
{
case "HV":
HyperVolume hv = new HyperVolume();
value = hv.Hypervolume(solutionFront, trueFront, trueFront[0].Length);
break;
case "SPREAD":
Spread spread = new Spread();
value = spread.CalculateSpread(solutionFront, trueFront, trueFront[0].Length);
break;
case "IGD":
InvertedGenerationalDistance igd = new InvertedGenerationalDistance();
value = igd.CalculateInvertedGenerationalDistance(solutionFront, trueFront, trueFront[0].Length);
break;
case "EPSILON":
Epsilon epsilon = new Epsilon();
value = epsilon.CalcualteEpsilon(solutionFront, trueFront, trueFront[0].Length);
break;
}
indicator.Add(value);
}
}
}
}
return(indicators);
}
