public void PopulationSelector_Ctor()
{
PopulationSelector selector = new PopulationSelector();
Assert.Null(selector.SelectedPopulation);
Assert.Equal(-1, selector.SelectedPopulationIndex);
Assert.Null(selector.Environment);
}
public void PopulationSelector_OnEnvironmentChanged_NoPopulation()
{
PopulationSelector selector = new PopulationSelector
{
Environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>())
};
Assert.Null(selector.SelectedPopulation);
}
public void PopulationSelector_SelectedPopulationIndexChanged_NoEnvironment()
{
PopulationSelector selector = new PopulationSelector
{
SelectedPopulationIndex = 1
};
Assert.Null(selector.SelectedPopulation);
}
public void Agent0x1Simulation_AgentTrajectories1()
{
Console.WriteLine("Agent0x1Simulation_AgentTrajectories1");
LoggerInitialization.SetThreshold(typeof(sim_tests), LogLevel.Debug);
LoggerInitialization.SetThreshold(typeof(Agent0x0), LogLevel.Info);
int dim = 0;
string [] names = new string [0];
double [] mins = new double [0];
double [] maxs = new double [0];
IBlauSpace s = BlauSpace.create(dim, names, mins, maxs);
IBlauPoint mean = new BlauPoint(s);
IBlauPoint std = new BlauPoint(s);
IDistribution d = new Distribution_Gaussian(s, mean, std);
IAgentFactory afact = new Agent0x0_Factory(d);
int NUMAGENTS = 10;
IPopulation pop = PopulationFactory.Instance().create(afact, NUMAGENTS);
foreach (IAgent ag in pop)
{
SingletonLogger.Instance().DebugLog(typeof(metrics_tests), "agent: " + ag);
}
string PATH = "" + ApplicationConfig.EXECDIR + "orderbooks/orderbook.csv";
AgentOrderbookLoader loader = MakeAgentOrderbookLoader(PATH);
pop.addAgent(loader);
IOrderbook_Observable ob = new Orderbook();
// 1 hours
ISimulation sim = new Simulation(pop, ob, 0.0, 3600.0);
IAgent agent = PopulationSelector.Select(pop);
ITrajectoryFactory agTF = new TrajectoryFactory_AgentOrders(agent, 10.0, 0.0);
sim.add(agTF);
ITrajectoryFactory agTF2 = new TrajectoryFactory_AgentNamedMetric(agent, "NetWorth", 10.0, 0.0);
sim.add(agTF2);
ITrajectoryFactory agTF3 = new TrajectoryFactory_AgentNamedMetric(agent, "NetWorth", 10.0, 0.99);
sim.add(agTF3);
SingletonLogger.Instance().DebugLog(typeof(sim_tests), "Running Simulation");
ISimulationResults res = sim.run();
SingletonLogger.Instance().DebugLog(typeof(sim_tests), "Stopping Simulation");
SingletonLogger.Instance().DebugLog(typeof(sim_tests), "Results\n" + res.ToStringLong());
Assert.AreEqual(res.Valid, true);
}
public void PopulationSelector_OnEnvironmentChanged_WithPopulation()
{
PopulationSelector selector = new PopulationSelector();
GeneticEnvironment environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>());
Population population = Mock.Of <Population>();
environment.Populations.Add(population);
selector.Environment = environment;
Assert.Same(population, selector.SelectedPopulation);
}
public void PopulationSelector_InitialPopulationAdded()
{
PopulationSelector selector = new PopulationSelector();
GeneticEnvironment environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>());
selector.Environment = environment;
Population population = Mock.Of <Population>();
environment.Populations.Add(population);
DispatcherHelper.DoEvents();
Assert.Same(population, selector.SelectedPopulation);
}
public void PopulationSelector_SelectedPopulationRemoved()
{
PopulationSelector selector = new PopulationSelector();
GeneticEnvironment environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>());
Population population = Mock.Of <Population>();
environment.Populations.Add(population);
selector.Environment = environment;
environment.Populations.RemoveAt(0);
DispatcherHelper.DoEvents();
Assert.Null(selector.SelectedPopulation);
}
public void PopulationSelector_SelectedPopulationIndexChanged_GreaterThanAllowed()
{
PopulationSelector selector = new PopulationSelector();
GeneticEnvironment environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>());
Population population = Mock.Of <Population>();
environment.Populations.Add(population);
selector.Environment = environment;
Assert.Same(population, selector.SelectedPopulation);
selector.SelectedPopulationIndex = 1;
Assert.Null(selector.SelectedPopulation);
}
public void PopulationSelector_SelectedPopulationUpdatedOnRemoval_NonDefaultIndex()
{
PopulationSelector selector = new PopulationSelector();
GeneticEnvironment environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>());
Population population = Mock.Of <Population>();
environment.Populations.Add(population);
Population population2 = Mock.Of <Population>();
environment.Populations.Add(population2);
selector.Environment = environment;
selector.SelectedPopulationIndex = 1;
environment.Populations.RemoveAt(1);
DispatcherHelper.DoEvents();
Assert.Same(population, selector.SelectedPopulation);
}
public void PopulationSelector_OnEnvironmentChanged_RemoveEnvironment()
{
PopulationSelector selector = new PopulationSelector();
GeneticEnvironment environment = new GeneticEnvironment(Mock.Of <GeneticAlgorithm>());
Population population = Mock.Of <Population>();
environment.Populations.Add(population);
selector.Environment = environment;
selector.Environment = null;
Assert.Null(selector.SelectedPopulation);
Assert.Equal(-1, selector.SelectedPopulationIndex);
// Verify the control doesn't respond to a population being added to removed environment
environment.Populations.Add(Mock.Of <Population>());
Assert.Null(selector.SelectedPopulation);
Assert.Equal(-1, selector.SelectedPopulationIndex);
}
public static void Main(string[] args)
{
Random rand = new Random();
ItemSetController itemSetController = new ItemSetController(rand, ITERATION_COUNT);
PopulationSelector populationSelector = new PopulationSelector(1, 106, rand, ITERATION_COUNT);
CrossoverManager crossoverManager = new CrossoverManager(rand, ITERATION_COUNT);
SelectionManager selectionManager = new SelectionManager(rand);
MutationManager mutationManager = new MutationManager(rand, ITERATION_COUNT);
for (int i = 0; i < ITERATION_COUNT; i++)
{
itemSetController.addItem(new Item(rand.Next(1, 30), rand.Next(1, 30)), i);
}
itemSetController.ShowItems();
itemSetController.IniSets();
itemSetController.ShowSets();
string pairSelection;
do
{
Console.WriteLine("Выберите тип выборки пары: 1 - Лучшая пара 2 - Случайный");
pairSelection = Console.ReadLine();
} while (pairSelection != "1" && pairSelection != "2");
string crossover;
do
{
Console.WriteLine("Выберите вид кроссовера: 1 - Двухточечный(Случайный) 2 - Одноточечный классический");
crossover = Console.ReadLine();
} while (crossover != "1" && crossover != "2");
string mutation;
do
{
Console.WriteLine("Выберите вид мутации: 1 - изменеие бита на противоположный 2 - инверсия в случайном интервале");
mutation = Console.ReadLine();
} while (mutation != "1" && mutation != "2");
string selection;
do
{
Console.WriteLine("Выберите вид селекции: 1 - Бинарный турнир 2 - Пропорциональная");
selection = Console.ReadLine();
} while (selection != "1" && selection != "2");
do
{
if (pairSelection == "1")
{
populationSelector.ChooseBestPair(itemSetController.getSets());
}
else
{
populationSelector.ChoosePair(itemSetController.getSets());
}
Set[] childrens = new Set[CHILDS_COUNT];
int dividerPoint = rand.Next(1, ITERATION_COUNT - 1);
Set[] bestPair = populationSelector.getPair();
for (int i = 0; i < childrens.Length; i++)
{
childrens[i] = crossover == "1" ? crossoverManager.DoubleCross(bestPair, itemSetController.getItems()) :
crossoverManager.ClassicCross(bestPair, itemSetController.getItems(), dividerPoint);
if (mutation == "1")
{
mutationManager.RandomMutation(childrens[i]);
}
else
{
mutationManager.InversionMutation(childrens[i]);
}
}
populationSelector.NewPopulation(itemSetController.getSets(), selection == "1" ?
selectionManager.BinaryTournament(childrens, CHILDS_COUNT / 2) :
selectionManager.ProportionallySelection(childrens, CHILDS_COUNT / 2));
Console.WriteLine("Новая популяция:");
itemSetController.ShowSets();
populationSelector.ShowBestOf(itemSetController.getSets());
} while (!populationSelector.End(itemSetController.getSets(), 85) && populationSelector.getAge() != 10);
Console.ReadKey();
}
/// <summary>
/// Creates a new <see cref="RouletteWheelSelection{TProgram}" /> with the given arguments.
/// </summary>
/// <param name="selector">The scheme used to attribute proportion of selection.</param>
/// <param name="fitnessFunction">The function used to evaluate the programs' fitness.</param>
public RouletteWheelSelection(PopulationSelector selector, IFitnessFunction <TProgram> fitnessFunction)
{
this._fitnessFunction = fitnessFunction;
this._selector = selector;
}
public static MathExpressionTree FirstOrNew(this IEnumerable <MathExpressionTree> selected, PopulationSelector populationSelector) => selected.FirstOrDefault() ?? populationSelector.GenerateIndividual();
