public void SelectsRandomDnaWeightedByFitnessExcluding()
{
// Arrange
float totalFitness = 0f;
List <Dna> parentPool = Enumerable.Range(0, 5)
.Select((_) => Dna.GenerateRandomDnaEncoding(1, new int[] { 1 }, 1, ActivationType.BinaryStep, false))
.Select((dna, index) =>
{
dna.RawFitnessRating = Mathf.Pow(index, 2);
totalFitness += dna.RawFitnessRating;
return(dna);
})
.ToList();
Dna excludedDna = parentPool[3]; // second fitess dna
totalFitness -= excludedDna.RawFitnessRating;
// List<Dna> remainingCandidatePool = parentPool.FindAll(dna => dna != excludedDna);
Dictionary <Dna, float> expectedProportions = parentPool.ToDictionary(
dna => dna,
dna => dna == excludedDna ? 0 : dna.RawFitnessRating / totalFitness
);
// Act
int sampleSize = 100000;
Dictionary <Dna, int> selectionResults = parentPool.ToDictionary(dna => dna, dna => 0);
for (int i = 0; i < sampleSize; i++)
{
selectionResults[Darwin.SelectRandomBasedOnFitness(parentPool, excludedDna)]++;
}
// Assert
Dictionary <Dna, float> actualProportions = selectionResults.ToDictionary(
kvp => kvp.Key,
kvp => (float)kvp.Value / (float)sampleSize
);
selectionResults[excludedDna].Should().Be(0, "excluded was never selected");
foreach (Dna dna in parentPool)
{
actualProportions[dna].Should().BeApproximately(expectedProportions[dna], 0.01f, "proportion was within 1% of expected value");
}
}
