public void SanityCheck()
{
// Check that the lander will crash with the default settings
LanderSimulator.Model.Environment environment = new LanderSimulator.Model.Environment();
LanderSimulator.Model.Lander lander = new LanderSimulator.Model.Lander(environment, 100, 0, 100);
bool crashed = false;
for (int i = 0; i < 100; i++)
{
if (lander.Update() == LanderStatus.Crashed)
{
crashed = true;
break;
}
}
Assert.IsTrue(crashed);
}
/// <summary>
/// Initializes a new instance of the MainViewModel class.
/// </summary>
public MainViewModel()
{
this.environment = new LanderSimulator.Model.Environment();
this.lander = new LanderSimulator.Model.Lander(this.environment, 100, 0, 100);
timer = new Timer(10);
timer.Elapsed += new ElapsedEventHandler(UpdateLanderPosition);
timer.AutoReset = true;
this.LanderPositionX = this.lander.PositionX;
this.LanderPositionY = this.lander.PositionY;
this.MinFitnessValues = new ObservableCollection<Tuple<int, double>>();
this.MaxFitnessValues = new ObservableCollection<Tuple<int, double>>();
this.AvgFitnessValues = new ObservableCollection<Tuple<int, double>>();
this.ExecuteTrainCommand();
}
private static double Train(bool showOutput)
{
LanderIndividualSettings landerIndividualSettings = new LanderIndividualSettings();
LanderIndividualFactory landerFactory = new LanderIndividualFactory();
GeneticAlgorithm ga = new GeneticAlgorithm(landerFactory);
LanderIndividual best = null;
int currentIteration = 0;
double bestfitness = 0;
// Set up the ga
LanderSimulator.Model.Environment environment = new LanderSimulator.Model.Environment();
environment.Gravity = 2.0;
environment.WindSpeed = 0.1;
landerIndividualSettings.StartingFuel = 100;
landerIndividualSettings.StartingHeight = 100;
landerIndividualSettings.StartingHorizontal = 0;
landerIndividualSettings.LanderEnvironment = environment;
landerIndividualSettings.CrossoverAlgorithm = LanderIndividualSettings.CrossoverType.Uniform;
ga.SelectionType = GeneticAlgorithm.SelectionTypes.Tournament;
ga.TournamentSize = 5;
ga.CrossoverProbability = 0.98;
ga.MutationProbability = 1;
ga.CalculationLimit = 60000;
ga.ElitistCount = 10;
ga.PopulationSize = 500;
landerFactory.IndividualSettings = landerIndividualSettings;
// This lambda function handles the iteration events from the ga.
EventHandler<IterationEventArgs> handler = (sender, args) =>
{
if (showOutput)
{
Console.CursorLeft = 0;
Console.Write("{0,4:g}{1,12:f3}{2,12:f3}{3,12:f3}{4,12:f3}{5,12:f3}{6,12:f3}",
currentIteration,
args.MinFitness, args.AverageFitness, args.MaxFitness,
args.MinDifference, args.AverageDifference, args.MaxDifference);
}
currentIteration++;
bestfitness = args.MinFitness;
};
ga.IterationEvent += handler;
if (showOutput)
{
Console.WriteLine("{0,4}{1,12}{2,12}{3,12}{4,12}{5,12}{6,12}",
"", "Min Fit", "Avg Fit", "Max Fit",
"Min Dif", "Avg Dif", "Max Dif");
}
// Run the ga
best = (LanderIndividual)ga.Run(landerIndividualSettings);
Console.WriteLine();
neuralNetwork.SetAllWeights(best.CurrentNeuralNetwork.GetAllWeights());
ga.IterationEvent -= handler;
return bestfitness;
}
/// <summary>
/// Runs the simulation with given starting variables.
/// </summary>
/// <returns></returns>
private static LanderStatus RunOneSimulation(bool showOutput, double positionx, double positiony,
double fuel, double windspeed, double gravity)
{
// First query the neural net to see how it reacts to the current conditions.
// Then update the conditions.
bool stop = false;
if (showOutput)
{
Console.WriteLine("(X,Y,Vx,Vy,Fuel,Wind,Gravity) => (Burn,Thrust)");
}
LanderSimulator.Model.Environment env = new LanderSimulator.Model.Environment();
env.Gravity = gravity;
env.WindSpeed = windspeed;
LanderSimulator.Model.Lander lander = new LanderSimulator.Model.Lander(env, fuel, positionx, positiony);
while (stop == false)
{
// height, xPosition, Yvelocity, Xvelocity, wind, acceleration, and fuel.
List<double> inputs = new List<double>();
inputs.Add(lander.PositionX);
inputs.Add(lander.PositionY);
inputs.Add(lander.VelocityX);
inputs.Add(lander.VelocityY);
inputs.Add(env.WindSpeed);
inputs.Add(env.Gravity);
inputs.Add(lander.Fuel);
IList<double> output = neuralNetwork.Run(inputs);
lander.Burn = output[0];
lander.Thrust = output[1];
lander.Update();
if (lander.Status != LanderSimulator.Model.LanderStatus.Flying)
{
stop = true;
}
if (showOutput)
{
Console.CursorLeft = 0;
switch (lander.Status)
{
case LanderSimulator.Model.LanderStatus.Flying:
Console.BackgroundColor = ConsoleColor.Black;
Console.Write("Flying: ");
Console.BackgroundColor = ConsoleColor.Black;
break;
case LanderSimulator.Model.LanderStatus.Landed:
Console.BackgroundColor = ConsoleColor.Green;
Console.ForegroundColor = ConsoleColor.Black;
Console.Write("Landed: ");
Console.BackgroundColor = ConsoleColor.Black;
Console.ForegroundColor = ConsoleColor.Gray;
break;
case LanderSimulator.Model.LanderStatus.Crashed:
Console.BackgroundColor = ConsoleColor.Red;
Console.Write("Crashed: ");
Console.BackgroundColor = ConsoleColor.Black;
break;
}
Console.Write("({0,6:f2},{1,6:f2},{2,6:f2},{3,6:f2},{4,6:f2},{5,6:f2},{6,6:f2}) => ({7,6:f2},{8,6:f2})",
lander.PositionX, lander.PositionY, lander.VelocityX, lander.VelocityY, lander.Fuel,
env.WindSpeed, env.Gravity, output[0], output[1]);
Thread.Sleep(250);
}
env.Update();
}
if (showOutput)
{
Console.WriteLine();
Console.WriteLine("Fitness: {0:f3}", lander.CalculateFitness());
}
return lander.Status;
}
