/// <summary>
/// Construct a trainer to train a supercluster using the sandpit's example data.
/// </summary>
/// <param name="supercluster">Supercluster to train.</param>
/// <param name="clusterRate">Number of subcycles between clustering.</param>
/// <param name="mergingRate">Number of subcylces between merging.</param>
/// <param name="maxThreads">Max number of concurrent threads to spawn during training.</param>
public ExampleTrainer(Supercluster supercluster, uint clusterRate, uint mergingRate, int maxThreads)
{
this.Supercluster = supercluster;
this.maxThreads = (maxThreads < 1)? 1 : maxThreads;
this.Cycle = 0;
this.clusterRate = clusterRate;
this.mergingRate = mergingRate;
// Load datasets.
var testingDataset = this.LoadDatasetFromCSV("data/eg-testing-set.csv");
var positiveTrainingDataset = this.LoadDatasetFromCSV("data/eg-training-set-positive.csv");
var negativeTrainingDataset = this.LoadDatasetFromCSV("data/eg-training-set-negative.csv");
var trainingDataset = positiveTrainingDataset.Concat(negativeTrainingDataset).ToList();
// Find max values for normalisation.
var completeDataset = trainingDataset.Concat(testingDataset);
this.maxAge = completeDataset.Max(o => o.Age);
this.maxSpendCategoryA = completeDataset.Max(o => o.SpendCategoryA);
this.maxSpendCategoryB = completeDataset.Max(o => o.SpendCategoryA);
// Normalise datasets.
this.TestingDataset = this.NormaliseDataset(testingDataset);
this.TrainingDataset = this.NormaliseDataset(trainingDataset);
this.BestTestingScore = double.MinValue;
this.MaxTestingScore = this.TestingDataset.Count;
this.BestTrainingScore = double.MinValue;
this.MaxTrainingScore = this.TrainingDataset.Count;
}
static void Main(string[] args)
{
Console.WriteLine("CBANE SANDPIT");
Console.WriteLine("-------------------------");
Console.WriteLine("");
var superclusterConfig = new SuperclusterConfig(5);
var clusterConfig = new ClusterConfig(50, 0.20, 0.05);
var networkConfig = new NetworkConfig(3, 2, 3, 12);
networkConfig.InputActivation = ActivationTypes.Passthrough;
networkConfig.OutputActivation = ActivationTypes.Softmax;
networkConfig.HiddenActivation = ActivationTypes.LeakyReLU;
var supercluster = new Supercluster(superclusterConfig, clusterConfig, networkConfig);
supercluster.GenerateRandomPopulation();
Trainer = new ExampleTrainer(supercluster, 25, 50, 15);
outputStatus = ProgramStatus.IDLE;
var outputTask = Task.Run(() => ManageOutput());
Task trainingTask = null;
while (running)
{
var keyInfo = Console.ReadKey(true);
if (outputStatus == ProgramStatus.IDLE && keyInfo.Key == ConsoleKey.T)
{
outputStatus = ProgramStatus.TRAINING;
trainingTask = Task.Run(() => PerformTrainingRun());
}
else if (outputStatus == ProgramStatus.IDLE && keyInfo.Key == ConsoleKey.O)
{
outputStatus = ProgramStatus.OUTPUT;
OutputBestWeights();
outputStatus = ProgramStatus.IDLE;
}
else if (outputStatus == ProgramStatus.TRAINING && keyInfo.Key == ConsoleKey.S)
{
outputStatus = ProgramStatus.STOPPING;
Trainer.StopTraining();
if (trainingTask != null && !trainingTask.IsCompleted)
{
trainingTask.Wait();
}
outputStatus = ProgramStatus.IDLE;
}
else if (keyInfo.Key == ConsoleKey.X)
{
outputStatus = ProgramStatus.EXITING;
Trainer.StopTraining();
if (trainingTask != null && !trainingTask.IsCompleted)
{
trainingTask.Wait();
}
running = false;
outputTask.Wait();
}
}
}
