private async Task OnClassifyWann()
{
try
{
var trainer = new NeatTrainer();
var unitedArray = new double[FirstClass.Vector.GetLength(0) + SecondClass.Vector.GetLength(0), 3];
var counter = 0;
for (var i = 0; i < FirstClass.Vector.GetLength(0); i++)
{
unitedArray[counter, 0] = FirstClass.Vector[i, 0];
unitedArray[counter, 1] = FirstClass.Vector[i, 1];
unitedArray[counter, 2] = 0;
counter++;
}
for (var i = 0; i < SecondClass.Vector.GetLength(0); i++)
{
unitedArray[counter, 0] = SecondClass.Vector[i, 0];
unitedArray[counter, 1] = SecondClass.Vector[i, 1];
unitedArray[counter, 2] = 1;
counter++;
}
var index = 0;
while (index < unitedArray.GetLength(0))
{
var selectedIndexFrom = random.Next(unitedArray.GetLength(0));
var x1 = unitedArray[index, 0];
var x2 = unitedArray[index, 1];
var cls = unitedArray[index, 2];
unitedArray[index, 0] = unitedArray[selectedIndexFrom, 0];
unitedArray[index, 1] = unitedArray[selectedIndexFrom, 1];
unitedArray[index, 2] = unitedArray[selectedIndexFrom, 2];
unitedArray[selectedIndexFrom, 0] = x1;
unitedArray[selectedIndexFrom, 1] = x2;
unitedArray[selectedIndexFrom, 2] = cls;
index++;
}
var trainInput = new double[unitedArray.GetLength(0) / 2][];
var trainExpected = new double[unitedArray.GetLength(0) / 2][];
for (var i = 0; i < trainInput.GetLength(0); i++)
{
trainInput[i] = new[] { unitedArray[i, 0], unitedArray[i, 1] };
trainExpected[i] = new[] { unitedArray[i, 2] };
}
var weights = new[] { -2.0, -1.0, -0.5, 0.5, 1.0, 2.0 };
trainer.Train(trainInput, trainExpected, weights, 0.1);
}
catch (Exception e)
{
MessageBox.Show(e.Message, "Error", MessageBoxButton.OK, MessageBoxImage.Error);
}
}
private static void Main()
{
var neuromonExperimentSettingsJson = File.ReadAllText(NeuromonExperimentSettingsFileName);
var evolutionAlgorithmParametersJson = File.ReadAllText(EvolutionAlgorithmParametersFileName);
var trainingGameSettingsJson = File.ReadAllText(TrainingGameSettingsFileName);
var experimentSettings = JsonConvert.DeserializeObject<ExperimentSettings>(neuromonExperimentSettingsJson);
var evolutionAlgorithmParameters =
JsonConvert.DeserializeObject<NeatEvolutionAlgorithmParameters>(evolutionAlgorithmParametersJson);
var trainingGameSettings = JsonConvert.DeserializeObject<TrainingGameSettings>(trainingGameSettingsJson);
var neatTrainer = new NeatTrainer(experimentSettings, evolutionAlgorithmParameters, trainingGameSettings);
neatTrainer.OnStatusUpdate += (generation, highestFitness, averageFitness) =>
{
if (!_quit)
{
System.Console.WriteLine($"Generation: {generation}, Best Fitness: {highestFitness:000.000}, Average Fitness: {averageFitness:000.000}");
_trainingStopped = false;
}
};
neatTrainer.OnTrainingPaused += () =>
{
System.Console.WriteLine($"Training has been paused. Press {ResumeKey} to resume.");
_trainingStopped = true;
};
if (experimentSettings.StopTrainingOnStagnationDetection)
{
neatTrainer.OnStagnationDetected += () =>
{
System.Console.WriteLine("Stagnation detected, stopping training...");
neatTrainer.StopTraining();
};
}
else
{
neatTrainer.OnStagnationDetected += () => System.Console.WriteLine("Stagnation detected.");
}
neatTrainer.OnHighestFitnessAchieved += fitness =>
{
_highestFitness = fitness;
};
neatTrainer.OnDesiredFitnessAchieved += () =>
{
System.Console.WriteLine(
$"Desired fitness of {experimentSettings.DesiredFitness} has been achieved. Training will be stopped.");
};
System.Console.WriteLine($"Press {QuitKey} to quit, {PauseKey} to pause and {ResumeKey} to resume training.");
neatTrainer.StartTraining();
var keyPressed = ResumeKey;
while (keyPressed != QuitKey)
{
keyPressed = System.Console.ReadKey().Key;
System.Console.WriteLine();
switch (keyPressed)
{
case ResumeKey:
{
if (_trainingStopped)
{
System.Console.WriteLine("Resuming training...");
neatTrainer.StartTraining();
}
else
{
System.Console.WriteLine("Already training.");
}
}
break;
case PauseKey:
{
if (!_trainingStopped)
{
System.Console.WriteLine("Stopping training...");
neatTrainer.StopTraining();
}
else
{
System.Console.WriteLine("Already stopped.");
}
}
break;
case QuitKey:
{
_quit = true;
System.Console.WriteLine("Quitting training...");
if (!_trainingStopped)
{
neatTrainer.StopTraining();
}
}
break;
}
}
System.Console.WriteLine("Saving...");
neatTrainer.SavePopulation(experimentSettings.OutputPopulationFilePath);
neatTrainer.SaveChampionGenome(experimentSettings.OutputChampionFilePath);
System.Console.WriteLine($"Saved population to {Path.GetFullPath(experimentSettings.OutputPopulationFilePath)}");
System.Console.WriteLine($"Saved champion to to {Path.GetFullPath(experimentSettings.OutputChampionFilePath)}");
System.Console.WriteLine();
System.Console.WriteLine($"Highest fitness achieved: {_highestFitness}");
}
private void ResetState()
{
if (_trainingState == TrainingState.Paused)
{
_trainingProgressBox.WriteLine("Destroying previous session...");
_sessionStatistics.Reset();
}
_neatTrainer = null;
_trainingState = TrainingState.Uninitialised;
}
private void CreateSessionButton_Click(object sender, RoutedEventArgs e)
{
if (_trainingState == TrainingState.AutoSaving)
{
_trainingProgressBox.WriteLine("Please wait for auto save to complete.");
return;
}
if (!(_trainingState == TrainingState.Paused || _trainingState == TrainingState.Uninitialised))
{
_trainingProgressBox.WriteLine("Please pause the current session before creating a new one.");
return;
}
_trainingProgressBox.Clear();
ResetState();
if (!ValidateFields())
{
_trainingProgressBox.WriteLine("Could not create session. Please fix the invalid configuration settings.");
return;
}
_neatTrainer = CreateTrainingSession();
_trainingState = TrainingState.Paused;
_trainingProgressBox.WriteLine("Created a new training session.");
}
private NeatTrainer CreateTrainingSession()
{
var neatTrainer = new NeatTrainer(TrainerViewModel.ExperimentSettings, TrainerViewModel.NeatEvolutionAlgorithmParameters, TrainerViewModel.TrainingGameSettings);
neatTrainer.OnStatusUpdate += (generation, highestFitness, averageFitness) =>
{
if (_trainingState == TrainingState.AwaitingTraining)
{
_trainingState = TrainingState.Training;
}
_sessionStatistics.CurrentGeneration = generation;
_sessionStatistics.CurrentAverageFitness = averageFitness;
_trainingProgressBox.WriteStatusUpdate(generation, highestFitness, averageFitness);
};
neatTrainer.OnTrainingPaused += () =>
{
_sessionStatistics.StopTimer();
_dispatcherTimer.Stop();
if (_trainingState == TrainingState.AutoSaving)
{
PerformAutoSave();
}
else
{
_trainingState = TrainingState.Paused;
_trainingProgressBox.WriteLine("Training paused.");
}
};
var stagnationDetectedMessage =
$"Stagnation detected: Previous {TrainerViewModel.ExperimentSettings.StagnationDetectionTriggerValue} generations had the same fitness value.";
if (TrainerViewModel.ExperimentSettings.StopTrainingOnStagnationDetection)
{
neatTrainer.OnStagnationDetected += () =>
{
OnStagnationDetected(stagnationDetectedMessage);
if (_trainingState == TrainingState.Training)
{
_trainingProgressBox.WriteLine("Auto stop enabled, training will now be paused...");
PauseTraining();
}
};
}
else
{
neatTrainer.OnStagnationDetected += () => OnStagnationDetected(stagnationDetectedMessage);
}
neatTrainer.OnHighestFitnessAchieved += fitness =>
{
_sessionStatistics.OverallHighestFitness = fitness;
};
neatTrainer.OnDesiredFitnessAchieved += () =>
{
_sessionStatistics.DesiredFitnessAchieved = true;
_trainingProgressBox.WriteLine("Desired fitness has been achieved.");
};
return neatTrainer;
}
