/// <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);
}
public void Execute(IExampleInterface app)
{
this.app = app;
FileInfo dataDir = new FileInfo(Environment.CurrentDirectory);
if (String.Compare(app.Args[0], "randomtrainer", true) == 0)
{
if (app.Args.Length > 1)
{
Encog.Examples.RangeandMarket.RandomTrainer.RandomTrainerMethod(Convert.ToInt16(app.Args[1]), Convert.ToInt16(app.Args[2]));
}
else
{
Console.WriteLine(@"You didn't input enough args in your request, will default to 3000 inputs , and 50 prediction size");
Encog.Examples.RangeandMarket.RandomTrainer.RandomTrainerMethod(3000, 50);
MakeAPause();
}
}
if (String.Compare(app.Args[0], "eval", true) == 0)
{
if (app.Args.Length > 0)
{
//We have enough arguments, lets test them.
if (File.Exists(app.Args[1]))
{
BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1], CONFIG.EvalHowMany, CONFIG.EvalStartFrom, CONFIG.Inputs,
CONFIG.Outputs);
//create our network.
BasicNetwork network =
(BasicNetwork)SuperUtils.LoadNetwork(CONFIG.DIRECTORY, CONFIG.NetWorkFile);
CreateEval.EvaluateNetworks(network, set);
MakeAPause();
app.Exit();
}
}
}
if (String.Compare(app.Args[0], "prune", true) == 0)
{
//Start pruning.
Console.WriteLine("Starting the pruning process....");
Prunes.Incremental(new FileInfo(CONFIG.DIRECTORY), CONFIG.NetWorkFile,
CONFIG.TrainingFile);
MakeAPause();
app.Exit();
}
if (String.Compare(app.Args[0], "train", true) == 0)
{
if (app.Args.Length > 0)
{
//We have enough arguments, lets test them.
if (File.Exists(app.Args[1]))
{
//the file exits lets build the training.
//create our basic ml dataset.
BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1], CONFIG.HowMany, CONFIG.StartFrom, CONFIG.Inputs,
CONFIG.Outputs);
//create our network.
BasicNetwork network = (BasicNetwork)CreateEval.CreateElmanNetwork(CONFIG.Inputs, CONFIG.Outputs);
//Train it..
double LastError = CreateEval.TrainNetworks(network, set);
Console.WriteLine("NetWork Trained to :" + LastError);
SuperUtils.SaveTraining(CONFIG.DIRECTORY, CONFIG.TrainingFile, set);
SuperUtils.SaveNetwork(CONFIG.DIRECTORY, CONFIG.NetWorkFile, network);
Console.WriteLine("Network Saved to :" + CONFIG.DIRECTORY + " File Named :" +
CONFIG.NetWorkFile);
Console.WriteLine("Training Saved to :" + CONFIG.DIRECTORY + " File Named :" +
CONFIG.TrainingFile);
MakeAPause();
}
else
{
Console.WriteLine("Couldnt find the file :" + app.Args[2].ToString());
Console.WriteLine("Exiting");
MakeAPause();
app.Exit();
}
}
}
else
{
Console.WriteLine("Couldnt understand your command..");
Console.WriteLine(
"Valid commands are : RandomTrainer or Randomtrainer [inputs] [output] , or Train [File]");
Console.WriteLine(
"Valid commands are : Range Prune, to prune your network.");
Console.WriteLine(
"Valid commands are : Range eval , to evaluate your network.");
MakeAPause();
app.Exit();
}
}
