public void ProportionateParentSelection_VerifySelectionProbability()
{
var random = new DefaultRandomNumberGenerator("seed".GetHashCode());
var population = new Population(11);
var index = 0;
population.Fill(() => new TestPhenotype(index, index++));
population.Sort(EAMode.MaximizeFitness);
var selection = new ProportionateParentSelection();
var selected = selection.SelectParents(population, 275000, EAMode.MaximizeFitness, random);
var bucket = new int[population.Size];
foreach (var(a, b) in selected)
{
bucket[((TestPhenotype)a).Index]++;
bucket[((TestPhenotype)b).Index]++;
}
for (int i = 0; i < bucket.Length; i++)
{
bucket[i].Should().BeCloseTo(i * 10000, 1000);
}
}
public void Shuffle_WithoutNParameter_AllValuesCanChangePosition()
{
var rng = new DefaultRandomNumberGenerator();
var shuffle = new SortingBasedWeightedLeftShuffle <int>(rng);
Assert.That.AllValuesCanChangePositions(shuffle);
}
public void Shuffle_WithNParameter_AllNValuesCanChangePosition()
{
var rng = new DefaultRandomNumberGenerator();
var shuffle = new StochasticAcceptanceBasedWeightedLeftShuffle <int>(rng);
Assert.That.AllNValuesCanChangePositions(shuffle);
}
public override void BeginInit()
{
_children = new VisualCollection(this);
_rng = new DefaultRandomNumberGenerator();
base.BeginInit();
}
public void SigmaScalingParentSelection_VerifySelectionProbability()
{
var random = new DefaultRandomNumberGenerator("seed".GetHashCode());
var population = new Population(11);
var index = 0;
population.Fill(() => new TestPhenotype(index++, index * index));
population.Sort(EAMode.MaximizeFitness);
var selection = new SigmaScalingParentSelection();
var selected = selection.SelectParents(population, 275000, EAMode.MaximizeFitness, random);
foreach (var(a, b) in selected)
{
((TestPhenotype)a).Count++;
((TestPhenotype)b).Count++;
}
var std = population.Select(p => p.Fitness).StandardDeviation();
var avg = population.Select(p => p.Fitness).Average();
var psum = population.Sum(p => 1 + (p.Fitness - avg) / (2 * std));
foreach (TestPhenotype p in population)
{
var prob = (1 + (p.Fitness - avg) / (2 * std)) / psum;
var expectedCount = prob * 275000 * 2;
p.Count.Should().BeCloseTo((int)expectedCount, 1000);
}
}
public void Shuffle_WithNParameter_AllNValuesCanChangePosition()
{
var rng = new DefaultRandomNumberGenerator();
var shuffle = new FisherYatesShuffle <ValuePriorityPair <int> >(rng);
Assert.That.AllNValuesCanChangePositions(shuffle);
}
public void OfOfT_WithRngReturnsValuesAccoringToTheRng()
{
var rng1 = new DefaultRandomNumberGenerator(987654);
var rng2 = new DefaultRandomNumberGenerator(987654);
var value = Out.Of <char>(rng1).Values(new[] { 'a', 'b', 'c', 'd' }).PickDistinct(3).ToList();
var value2 = Out.Of <char>(rng2).Values(new[] { 'a', 'b', 'c', 'd' }).PickDistinct(3).ToList();
CollectionAssert.AreEqual(value, value2);
}
public void NullStrategyThrowsExceptionForAConstantAdjustment()
{
IBackoffStrategy decoratedStrategy = null;
IRandomNumberGenerator randomNumberGenerator = new DefaultRandomNumberGenerator();
Assert.Throws <ArgumentNullException>(() =>
{
new ConstantAdjustedBackoffStrategy(decoratedStrategy, TimeSpan.Zero);
});
}
public void NullStrategyThrowsExceptionForAPercentageAdjustment()
{
IBackoffStrategy decoratedStrategy = null;
IRandomNumberGenerator randomNumberGenerator = new DefaultRandomNumberGenerator();
Assert.Throws <ArgumentNullException>(() =>
{
new PercentageAdjustedBackoffStrategy(decoratedStrategy, 1);
});
}
public void NullStrategyThrowsException()
{
IBackoffStrategy decoratedStrategy = null;
IRandomNumberGenerator randomNumberGenerator = new DefaultRandomNumberGenerator();
Assert.Throws <ArgumentNullException>(() =>
{
new MultiplicativeJitterBackoffStrategy(decoratedStrategy, 0, 1);
});
}
public void NextDouble_WithoutSeed_ReturnsValuesBetween0And1()
{
// Arrange
var randomValues = new double[Iterations];
var rng = new DefaultRandomNumberGenerator();
// Action
for (var i = 0; i < Iterations; i++)
{
randomValues[i] = rng.NextDouble();
}
// Assert
Assert.IsTrue(randomValues.All(x => x >= 0 && x < 1));
}
public void NextInt_TwoArgumentsWithoutSeed_ReturnsEquallyDistributedValues()
{
// Arrange
var randomValues = new long[Iterations];
var rng = new DefaultRandomNumberGenerator();
const int AcceptedDeviation = 240;
// Action
for (var i = 0; i < Iterations; i++)
{
randomValues[i] = rng.NextInt(1_200, 3_600);
}
// Assert
Assert.IsTrue(randomValues.Sum() / Iterations > 2_400 - AcceptedDeviation);
Assert.IsTrue(randomValues.Sum() / Iterations < 2_400 + AcceptedDeviation);
}
public void TournamentParentSelection_VerifySelectionProbability()
{
// Test tournament selection by comparing it to the old "established" tournament selection implementation.
var random = new DefaultRandomNumberGenerator("seed".GetHashCode());
for (int tournamentSize = 2; tournamentSize <= 10; tournamentSize++)
{
var population = new Population(11);
var index = 0;
population.Fill(() => new TestPhenotype(index++, index * index));
population.Sort(EAMode.MaximizeFitness);
var config = new EAConfiguration
{
TournamentSize = 3,
TournamentProbability = 0.5
};
#pragma warning disable CS0618 // Type or member is obsolete
var oldselection = new TournamentParentSelectionOld(config);
#pragma warning restore CS0618 // Type or member is obsolete
var oldselected = oldselection.SelectParents(population, 275000, EAMode.MaximizeFitness, random);
foreach (var(a, b) in oldselected)
{
((TestPhenotype)a).Count2++;
((TestPhenotype)b).Count2++;
}
var selection = new TournamentParentSelection(config);
var selected = selection.SelectParents(population, 275000, EAMode.MaximizeFitness, random);
foreach (var(a, b) in selected)
{
((TestPhenotype)a).Count++;
((TestPhenotype)b).Count++;
}
var counts = population.Cast <TestPhenotype>().Select(p => p.Count).Zip(population.Cast <TestPhenotype>().Select(p => p.Count2), (c1, c2) => (c1, c2));
foreach (var count in counts)
{
(count.c1 - count.c2).Should().BeLessThan(1000, $"tournament size = {tournamentSize}");
}
}
}
public void NextDouble_WithoutSeed_ReturnsEquallyDistributedValues()
{
// Arrange
var randomValues = new double[Iterations];
var rng = new DefaultRandomNumberGenerator();
const int AcceptedDeviation = 1000;
// Action
for (var i = 0; i < Iterations; i++)
{
randomValues[i] = rng.NextDouble();
}
// Assert
Assert.IsTrue(randomValues.Sum() > Iterations / 2 - AcceptedDeviation);
Assert.IsTrue(randomValues.Sum() < Iterations / 2 + AcceptedDeviation);
}
public void SelectParentsShouldSelectCorrectNumber()
{
var random = new DefaultRandomNumberGenerator("seed".GetHashCode());
var population = new Population(20);
for (int i = 0; i < population.Size; i++)
{
var pmock = new Mock <IPhenotype>();
pmock.SetupGet(p => p.Fitness).Returns(i);
pmock.SetupGet(p => p.IsEvaluated).Returns(true);
population.Add(pmock.Object);
}
var selection = new ProportionateParentSelection();
var selected = selection.SelectParents(population, 20, EAMode.MaximizeFitness, random);
selected.Count().Should().Be(20);
}
public void NextInt_NoArgumentWithSeed_ReturnsAlwaysTheSameValue()
{
// Arrange
var randomValues = new int[Iterations];
var seed = new System.Random().Next();
// Action
for (var i = 0; i < Iterations; i++)
{
var rng = new DefaultRandomNumberGenerator(seed);
randomValues[i] = rng.NextInt();
var a = randomValues[i];
Console.WriteLine(randomValues[i]);
}
// Assert
Assert.IsTrue(randomValues.All(x => x == randomValues[0]));
}
public void TournamentSelection_PerfTest()
{
var random = new DefaultRandomNumberGenerator();
var config = new EAConfiguration
{
TournamentSize = 20,
TournamentProbability = 0.5
};
var popsize = 10000;
var pop = new Population(popsize);
pop.Fill(() => CreatePhenotypeMock(random.NextDouble() * 10).Object);
pop.Sort(EAMode.MaximizeFitness);
Console.WriteLine($"popsize = {popsize}, tournament size = {config.TournamentSize}");
var watch = new Stopwatch();
watch.Start();
#pragma warning disable CS0618 // Type or member is obsolete
IParentSelection selection = new TournamentParentSelectionOld(config);
#pragma warning restore CS0618 // Type or member is obsolete
foreach (var selected in selection.SelectParents(pop, popsize, EAMode.MaximizeFitness, random))
{
}
watch.Stop();
Console.WriteLine($"old: {watch.ElapsedMilliseconds} ms");
watch.Restart();
selection = new TournamentParentSelection(config);
foreach (var selected in selection.SelectParents(pop, popsize, EAMode.MaximizeFitness, random))
{
}
watch.Stop();
Console.WriteLine($"new: {watch.ElapsedMilliseconds} ms");
}
public void NextInt_NoArgumentWithoutSeed_ReturnsEquallyDistributedValues()
{
// Arrange
var bitEquals1Counters = new int[31]; // to 31 because first bit indicates +-
var rng = new DefaultRandomNumberGenerator();
const int AcceptedDeviation = Iterations / 100;
// Action
for (var i = 0; i < Iterations; i++)
{
var value = rng.NextInt();
for (var j = 0; j < 31; j++)
{
if ((value & (1 << j)) == (1 << j))
{
bitEquals1Counters[j]++;
}
}
}
// Assert
Assert.IsTrue(bitEquals1Counters.All(x => x >= Iterations / 2 - AcceptedDeviation));
Assert.IsTrue(bitEquals1Counters.All(x => x <= Iterations / 2 + AcceptedDeviation));
}
public void TestInitialize()
{
random = new DefaultRandomNumberGenerator("seed".GetHashCode());
}
