public static void TrainElmhanNetwork(ref IExampleInterface app)
{
BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1], CONFIG.STARTING_YEAR,
CONFIG.TRAIN_END,
CONFIG.INPUT_WINDOW,
CONFIG.PREDICT_WINDOW);
//create our network.
BasicNetwork network =
(BasicNetwork)CreateEval.CreateElmanNetwork(CONFIG.INPUT_WINDOW, CONFIG.PREDICT_WINDOW);
//Train it..
double LastError = CreateEval.TrainNetworks(network, set);
Console.WriteLine("NetWork Trained to :" + LastError);
SuperUtils.SaveTraining(CONFIG.DIRECTORY, CONFIG.TRAINING_FILE, set);
SuperUtils.SaveNetwork(CONFIG.DIRECTORY, CONFIG.NETWORK_FILE, network);
Console.WriteLine("Network Saved to :" + CONFIG.DIRECTORY + " File Named :" +
CONFIG.NETWORK_FILE);
Console.WriteLine("Training Saved to :" + CONFIG.DIRECTORY + " File Named :" +
CONFIG.TRAINING_FILE);
MakeAPause();
}
public static void TrainSVMNetwork(ref IExampleInterface app)
{
//BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1],1000,500,CONFIG.INPUT_WINDOW,CONFIG.PREDICT_WINDOW);
TemporalMLDataSet Tempo = CreateEval.GenerateATemporalSet(app.Args[1], 1000, 500, CONFIG.INPUT_WINDOW, CONFIG.PREDICT_WINDOW);
SupportVectorMachine machine = createNetwork();
//Train it..
double error = TrainNetworks(machine, Tempo);
Console.WriteLine(@"SVM NetWork Trained to :" + error);
SuperUtils.SaveTraining(CONFIG.DIRECTORY, CONFIG.SVMTRAINING_FILE, Tempo);
SuperUtils.SaveNetwork(CONFIG.DIRECTORY, CONFIG.SVMNETWORK_FILE, machine);
Console.WriteLine(@"Network Saved to :" + CONFIG.DIRECTORY + @" File Named :" +
CONFIG.SVMNETWORK_FILE);
Console.WriteLine(@"Training Saved to :" + CONFIG.DIRECTORY + @" File Named :" +
CONFIG.SVMTRAINING_FILE);
MakeAPause();
}
/// <summary>
/// Trains a random trainer.
/// </summary>
/// <param name="inputs">The inputs.</param>
/// <param name="predictWindow">The predict window.</param>
public static void RandomTrainerMethod(int inputs, int predictWindow)
{
double[] firstinput = MakeInputs(inputs);
double[] SecondInput = MakeInputs(inputs);
double[] ThirdInputs = MakeInputs(inputs);
double[] FourthInputs = MakeInputs(inputs);
var pair = SuperUtils.ProcessPair(firstinput, FourthInputs, inputs, predictWindow);
var pair2 = SuperUtils.ProcessPair(SecondInput, FourthInputs, inputs, predictWindow);
var pair3 = SuperUtils.ProcessPair(ThirdInputs, FourthInputs, inputs, predictWindow);
var pair4 = SuperUtils.ProcessPair(FourthInputs, FourthInputs, inputs, predictWindow);
BasicMLDataSet SuperSet = new BasicMLDataSet();
SuperSet.Add(pair);
SuperSet.Add(pair2);
SuperSet.Add(pair3);
SuperSet.Add(pair4);
var network = (BasicNetwork)CreateEval.CreateElmanNetwork(SuperSet.InputSize, SuperSet.IdealSize);
double error = CreateEval.TrainNetworks(network, SuperSet);
//Lets create an evaluation.
Console.WriteLine(@"Last error rate on random trainer:" + error);
}
