protected override void addRandomPieceOfFood(AgentsEnvironment env)
{
if (regenerateFood)
{
Food food = createRandomFood(env.Width, env.Height);
env.Add(food);
owner.Dispatcher.Invoke(() =>
{
owner.Agents.Add(new FoodPresenter(food));
});
}
}
public override void notify(AgentsEnvironment env)
{
var e = getEatenFood(env);
owner.Dispatcher.Invoke(() =>
{
foreach (var a in owner.Agents.ToArray())
{
if (e.Any(f => a.agent == f))
{
owner.Agents.Remove(a);
}
}
});
base.notify(env);
}
public TokenMangementTest()
{
_env = new AgentsEnvironment();
}
public MainWindow()
{
InitializeComponent();
Agents = new ObservableCollection <AgentPresenter>();
initializeGeneticAlgorithm(5, 1, null);
const int w = 600;
const int h = 400;
environment = new AgentsEnvironment(w, h);
environment.AgentEvent += new RenderObserver(this).notify;
NeuralNetwork brain = ga.GetBest();
for (int i = 0; i < 15; i++)
{
int x = Rnd.Next(w);
int y = Rnd.Next(h);
double direction = Rnd.NextDouble() * 2 * Math.PI;
NeuralNetworkDrivenAgent agent = new NeuralNetworkDrivenAgent(x, y, direction);
agent.setBrain(brain);
Agents.Add(new ConsumerPresenter(agent));
environment.Add(agent);
}
for (int i = 0; i < 10; i++)
{
Food food = createRandomFood(w, h);
environment.Add(food);
Agents.Add(new FoodPresenter(food));
}
new Thread(() =>
{
while (true)
{
try
{
Thread.Sleep(50);
if (play)
{
environment.timeStep();
Dispatcher.Invoke(() =>
{
foreach (var pres in Agents)
{
pres.X = pres.agent.X * 1000 / w;
pres.Y = pres.agent.Y * 1000 / h;
var cons = pres as ConsumerPresenter;
if (cons != null)
{
cons.Angle = (cons.agent as NeuralNetworkDrivenAgent).Angle * 180 / Math.PI;
}
}
});
}
}
catch { break; }
}
}).Start();
}
public virtual void Interact(AgentsEnvironment env)
{
}
