private static void CreateEvaluationSet(string @fileName)
{
List <double> Opens = SuperUtils.QuickParseCSV(fileName, "Open", 1200, 1200);
List <double> High = NetworkUtility.QuickParseCSV(fileName, "High", 1200, 1200);
List <double> Low = NetworkUtility.QuickParseCSV(fileName, "Low", 1200, 1200);
List <double> Close = NetworkUtility.QuickParseCSV(fileName, "Close", 1200, 1200);
List <double> Volume = NetworkUtility.QuickParseCSV(fileName, 5, 1200, 1200);
TemporalMLDataSet superTemportal = new TemporalMLDataSet(100, 1);
double[] Ranges = NetworkUtility.CalculateRanges(Opens.ToArray(), Close.ToArray());
superTemportal = NetworkUtility.GenerateTrainingWithPercentChangeOnSerie(100, 1, Opens.ToArray(),
Close.ToArray(), High.ToArray(), Low.ToArray(), Volume.ToArray());
IMLDataPair aPairInput = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Opens.ToArray()), NetworkUtility.CalculatePercents(Opens.ToArray()), 100, 1);
IMLDataPair aPairInput3 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Close.ToArray()), NetworkUtility.CalculatePercents(Close.ToArray()), 100, 1);
IMLDataPair aPairInput2 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(High.ToArray()), NetworkUtility.CalculatePercents(High.ToArray()), 100, 1);
IMLDataPair aPairInput4 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Volume.ToArray()), NetworkUtility.CalculatePercents(Volume.ToArray()), 100, 1);
IMLDataPair aPairInput5 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Ranges.ToArray()), NetworkUtility.CalculatePercents(Ranges.ToArray()), 100, 1);
List <IMLDataPair> listData = new List <IMLDataPair>();
listData.Add(aPairInput);
listData.Add(aPairInput2);
listData.Add(aPairInput3);
listData.Add(aPairInput4);
listData.Add((aPairInput5));
var minitrainning = new BasicMLDataSet(listData);
var network = (BasicNetwork)CreateElmanNetwork(100, 1);
double normalCorrectRate = EvaluateNetworks(network, minitrainning);
double temporalErrorRate = EvaluateNetworks(network, superTemportal);
Console.WriteLine("Percent Correct with normal Data Set:" + normalCorrectRate + " Percent Correct with temporal Dataset:" +
temporalErrorRate);
Console.WriteLine("Paused , Press a key to continue to evaluation");
Console.ReadKey();
}
public static BasicMLDataSet CreateEvaluationSetAndLoad(string @fileName, int startLine, int HowMany, int WindowSize, int outputsize)
{
List <double> Opens = NetworkUtility.QuickParseCSV(fileName, "Open", startLine, HowMany);
List <double> High = NetworkUtility.QuickParseCSV(fileName, "High", startLine, HowMany);
List <double> Low = NetworkUtility.QuickParseCSV(fileName, "Low", startLine, HowMany);
List <double> Close = NetworkUtility.QuickParseCSV(fileName, "Close", startLine, HowMany);
List <double> Volume = NetworkUtility.QuickParseCSV(fileName, 5, startLine, HowMany);
TemporalMLDataSet superTemportal = new TemporalMLDataSet(WindowSize, outputsize);
double[] Ranges = NetworkUtility.CalculateRanges(Opens.ToArray(), Close.ToArray());
superTemportal = NetworkUtility.GenerateTrainingWithPercentChangeOnSerie(100, 1, Opens.ToArray(),
Close.ToArray(), High.ToArray(), Low.ToArray(), Volume.ToArray());
IMLDataPair aPairInput = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Opens.ToArray()), NetworkUtility.CalculatePercents(Opens.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput3 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Close.ToArray()), NetworkUtility.CalculatePercents(Close.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput2 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(High.ToArray()), NetworkUtility.CalculatePercents(High.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput4 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Volume.ToArray()), NetworkUtility.CalculatePercents(Volume.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput5 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Ranges.ToArray()), NetworkUtility.CalculatePercents(Ranges.ToArray()), WindowSize, outputsize);
List <IMLDataPair> listData = new List <IMLDataPair>();
listData.Add(aPairInput);
listData.Add(aPairInput2);
listData.Add(aPairInput3);
listData.Add(aPairInput4);
listData.Add((aPairInput5));
var minitrainning = new BasicMLDataSet(listData);
return(minitrainning);
}
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();
}
}
