@Test(dataProvider = "alterProbabilityParameters")
public void alterProbability(
final Integer ngenes, final Integer nchromosomes, final Integer npopulation, final Double p) {
final Population<DoubleGene, Double> population =
newDoubleGenePopulation(ngenes, nchromosomes, npopulation);
// The mutator to test.
final SinglePointCrossover<DoubleGene, Double> crossover = new SinglePointCrossover<>(p);
final long nallgenes = ngenes * nchromosomes * npopulation;
final long N = 200;
final double mean = crossover.getOrder() * npopulation * p;
final long min = 0;
final long max = nallgenes;
final Range<Long> domain = new Range<>(min, max);
final Histogram<Long> histogram = Histogram.ofLong(min, max, 10);
final LongMomentStatistics variance = new LongMomentStatistics();
for (int i = 0; i < N; ++i) {
final long alterations = crossover.alter(population, 1);
histogram.accept(alterations);
variance.accept(alterations);
}
// Normal distribution as approximation for binomial distribution.
System.out.println(histogram);
// TODO: Implement test
// assertDistribution(histogram, new NormalDistribution<>(domain, mean,
// variance.getVariance()));
}
@Test(invocationCount = 20, successPercentage = 95)
public void newInstanceDistribution() {
using(
new Random(12345),
r -> {
final CharSeq characters = new CharSeq("0123456789");
final Factory<CharacterGene> factory = CharacterGene.of(characters);
final Histogram<Long> histogram = Histogram.ofLong(0L, 10L, 10);
final int samples = 100000;
for (int i = 0; i < samples; ++i) {
final CharacterGene g1 = factory.newInstance();
final CharacterGene g2 = factory.newInstance();
Assert.assertNotSame(g1, g2);
histogram.accept(Long.valueOf(g1.getAllele().toString()));
histogram.accept(Long.valueOf(g2.getAllele().toString()));
}
assertUniformDistribution(histogram);
});
}