/// <summary>
/// 找到一组数中第k小元素 最坏时间复杂度O^2 期望时间复杂度O(n)
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="a"></param>
/// <param name="k"></param>
/// <returns></returns>
public static T Select <T>(T[] a, int k)
{
RandomAlgorithm.Shuffle <T>(a);
int lo = 0, hi = a.Length - 1;
while (hi > lo)
{
int j = QuickSort <T> .Partition(a, lo, hi);
if (j == k)
{
return(a[k]);
}
else if (j > k)
{
hi = j - 1;
}
else if (j < k)
{
lo = j + 1;
}
}
return(a[k]);
}
public async Task <ActionResult <IEnumerable <Film> > > GetRandomFilms()
{
RandomAlgorithm alg = new RandomAlgorithm(db);
//В конструктор передается пустой аккаунт просто как заглушка потому, что в интерфейсе IFilmSelection есть сигнатура метода с параметром
return(await alg.GetFilmsAsync(new Account()));
}
protected override void SolveInstance(IGH_DataAccess DA)
{
Interval range = new Interval(0, 0);
if (!DA.GetData(0, ref range))
{
return;
}
var randomSolution = new RandomAlgorithm(range);
CreateAndSetAlgorithm(DA, randomSolution.Solve);
}
public void RandomAlgorithm_ReturnsMove()
{
var generator = new TestCase1.Generator();
var applier = new TestCase1.Applier();
var algorithm = new RandomAlgorithm <TestCase1.State, TestCase1.Move>(generator);
var initState = new TestCase1.State(1, 0);
var move1 = algorithm.Calculate(initState);
var state2 = applier.Apply(initState, move1);
var move2 = algorithm.Calculate(state2);
Assert.NotNull(move1);
Assert.NotNull(move2);
}
private IAlgorithm <ChessRepresentation, BaseMove> GetAlgorithm(Type algorithmType, Type evaluatorType)
{
var generator = new MoveGenerator(_mechanism);
var applier = new MoveApplier(_mechanism);
var evaluator = (IEvaluator <ChessRepresentation>)Activator.CreateInstance(evaluatorType, new[] { _mechanism });
// TODO : A bit hacky, refactor later!
if (algorithmType == typeof(MinimaxAlgorithm <ChessRepresentation, BaseMove>))
{
var minimax = new MinimaxAlgorithm <ChessRepresentation, BaseMove>(evaluator, generator, applier)
{
MaxDepth = (int)numericUpDown1.Value
};
return(minimax);
}
if (algorithmType == typeof(AlphaBetaAlgorithm <ChessRepresentation, BaseMove>))
{
var ab = new AlphaBetaAlgorithm <ChessRepresentation, BaseMove>(evaluator, generator, applier)
{
MaxDepth = (int)numericUpDown1.Value
};
return(ab);
}
if (algorithmType == typeof(MinimaxAverageAlgorithm <ChessRepresentation, BaseMove>))
{
var minimaxAvg = new MinimaxAverageAlgorithm <ChessRepresentation, BaseMove>(evaluator, generator, applier)
{
MaxDepth = (int)numericUpDown1.Value
};
return(minimaxAvg);
}
if (algorithmType == typeof(GreedyAlgorithm <ChessRepresentation, BaseMove>))
{
var greedy = new GreedyAlgorithm <ChessRepresentation, BaseMove>(evaluator, generator, applier);
return(greedy);
}
if (algorithmType == typeof(RandomAlgorithm <ChessRepresentation, BaseMove>))
{
var randomAlgorithm = new RandomAlgorithm <ChessRepresentation, BaseMove>(generator);
return(randomAlgorithm);
}
throw new ArgumentOutOfRangeException(nameof(algorithmType));
}
/// <summary>
/// Generates a random list based on a given list (default random algorithm uses modified Fisher-Yates
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="list">list to be randomized</param>
/// <param name="algorithm">type of algorithm used for randomization</param>
/// <returns></returns>
public static List <T> GenerateRandomList <T>(List <T> list, RandomAlgorithm algorithm = RandomAlgorithm.FisherYatesShuffle)
{
if (list.Count == 0)
{
return(new List <T>());
}
List <T> randomList = new List <T>();
switch (algorithm)
{
case RandomAlgorithm.FisherYatesShuffle:
randomList = FisherYatesShuffleList(list);
break;
default:
break;
}
return(randomList);
}
public async Task InvokeAsync(HttpContext context)
{
var balancerAlgorithmSetting = LoadBalancerSettings.Current.BalancingAlgorithm.ToLowerInvariant().Replace(" ", "");
BalancingAlgorithm balancerAlgorithm = null;
if (balancerAlgorithmSetting == "roundrobin")
{
balancerAlgorithm = new RoundRobinAlgorithm();
}
else if (balancerAlgorithmSetting == "random")
{
balancerAlgorithm = new RandomAlgorithm();
}
else if (balancerAlgorithmSetting == "connectioncount")
{
balancerAlgorithm = new ConnectionCountAlgorithm();
}
context.Items["destination"] = balancerAlgorithm.GetInstance();
await _next.Invoke(context);
}
public static void Sort(T[] a)
{
RandomAlgorithm.Shuffle <T>(a);
Sort(a, 0, a.Length - 1);
}
static void Main(string[] args)
{
QAPAlgorithm test = null;
string command = "";
while (command != "exit")
{
command = Console.ReadLine();
switch (command)
{
case "gen":
//Console.WriteLine("nOfGenerations: ");
//int nOfGenerations = Int32.Parse(Console.ReadLine());
//Console.WriteLine("p_crossover: ");
//double p_crossover = Double.Parse(Console.ReadLine());
//Console.WriteLine("p_mutation: ");
//double p_mutation = Double.Parse(Console.ReadLine());
//Console.WriteLine("tourn_size: ");
//int tourn_size = Int32.Parse(Console.ReadLine());
//Console.WriteLine("pop_size: ");
//int pop_size = Int32.Parse(Console.ReadLine());
//Console.WriteLine("test_case: ");
//int gen_test_case = Int32.Parse(Console.ReadLine());
//base
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
//test selection
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Roulette, 1, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 1, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 2, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 10, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 25, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 100, $"input{12}.txt"); test.Run();
//test mutation
test = new GeneticAlgorithm(100, 100, 0.7, 0.00, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.02, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.05, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.10, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.20, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.50, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 1.00, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
//test crossover
test = new GeneticAlgorithm(100, 100, 0.0, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.2, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.4, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.5, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.6, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.8, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 1.0, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
//test pop-size
test = new GeneticAlgorithm(100, 10, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 20, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 50, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 200, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 500, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
//test gen-count
test = new GeneticAlgorithm(10, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(20, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(50, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(100, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(200, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
test = new GeneticAlgorithm(500, 100, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
//test both
test = new GeneticAlgorithm(500, 500, 0.7, 0.01, SelectionMethod.Tournament, 5, $"input{12}.txt"); test.Run();
Console.WriteLine("done!");
break;
case "grd":
Console.WriteLine("starting_point: ");
int starting_point = Int32.Parse(Console.ReadLine());
Console.WriteLine("test_case: ");
int grd_test_case = Int32.Parse(Console.ReadLine());
test = new GreedyAlgorithm(starting_point, $"input{grd_test_case}.txt");
break;
case "rnd":
Console.WriteLine("best_of: ");
int best_of = Int32.Parse(Console.ReadLine());
Console.WriteLine("test_case: ");
int rnd_test_case = Int32.Parse(Console.ReadLine());
test = new RandomAlgorithm(best_of, $"input{rnd_test_case}.txt");
break;
default:
Console.WriteLine("wut");
break;
}
if (test != null)
{
test.Input.ReadFile();
Console.WriteLine(test.Evaluate(test.Run()));
}
}
}
