public void DifferentDimensionException()
{
EuclideanDistance distance = new EuclideanDistance();
Vector v1 = new Vector(new double[] { 1, 2, 3 });
Vector v2 = new Vector(new double[] { 1, 2, 3, 4 });
Assert.Throws <ArgumentException>(() => distance.Measure(v1, v2));
Assert.Throws <ArgumentException>(() => distance.Measure(v2, v1));
}
public void Distance()
{
EuclideanDistance distance = new EuclideanDistance();
Vector v1 = new Vector(new double[] { 4, 3, 2 });
Vector v2 = new Vector(new double[] { 2, 3, 4 });
Assert.AreEqual(Math.Sqrt(8), distance.Measure(v1, v2));
Assert.AreEqual(distance.Measure(v2, v1), distance.Measure(v1, v2));
Assert.AreEqual(0, distance.Measure(v1, v1));
Assert.AreEqual(0, distance.Measure(v2, v2));
}
public double Calculate(IEnumerable <OptimizationSolution> solutions, Bounds[] bounds, double sampleSize = 0.1)
{
OptimizationSolution[] sorted = solutions.OrderByDescending(x => x.Fitness).Select(x => Helpers.Normalize(x, bounds)).ToArray();
int numSamples = sorted.Length;
int subsetSize = (int)(sampleSize * numSamples);
//take subset of best entities
OptimizationSolution[] subset = sorted.Take(subsetSize).ToArray();
double totalDispersion = 0;
double subsetDispersion = 0;
double distance = 0;
//calculate dispersion of all points (use size - 1 to account for comparison with self)
//must use sorted as it has been normalized!
foreach (var sample in sorted)
{
distance += sorted.Sum(x => distanceMeasure.Measure(sample.Position, x.Position)) / (numSamples - 1);
}
totalDispersion = distance / numSamples;
distance = 0; //reset total distance
//calculate dispersion of best points (use size - 1 to account for comparison with self)
foreach (var sample in subset)
{
distance += subset.Sum(x => distanceMeasure.Measure(sample.Position, x.Position)) / (subsetSize - 1);
}
subsetDispersion = distance / subsetSize;
return(subsetDispersion - totalDispersion);//(Math.Sqrt(3.0 * 30) / 4.0 - 0.1);//
}
public double Calculate(IEnumerable <OptimizationSolution> solutions)
{
OptimizationSolution[] samples = solutions.ToArray();
int numSamples = samples.Length;
OptimizationSolution best = Helpers.GetBest(samples);
List <double> fitnesses = new List <double>(numSamples);
List <double> distances = new List <double>(numSamples);
double fitnessSum = 0;
double distanceSum = 0;
foreach (var sample in samples)
{
fitnessSum += sample.Fitness.Value;
fitnesses.Add(sample.Fitness.Value);
double distance = distanceMeasure.Measure(sample.Position, best.Position);
distanceSum += distance;
distances.Add(distance);
}
double avgFitness = fitnessSum / numSamples;
double avgDistance = distanceSum / numSamples;
double numerator = 0;
double lDenominator = 0;
double rDenominator = 0;
//calculate the sums used in the final calculation
for (int i = 0; i < numSamples; i++)
{
double fitnessDiff = fitnesses[i] - avgFitness;
double distanceDiff = distances[i] - avgDistance;
numerator += fitnessDiff * distanceDiff;
lDenominator += fitnessDiff * fitnessDiff;
rDenominator += distanceDiff * distanceDiff;
}
return(numerator / (Math.Sqrt(lDenominator) * Math.Sqrt(rDenominator)));
}