public static BasicMLDataSet CreateEvaluationSetAndLoad(string @fileName, int startLine, int HowMany, int WindowSize, int outputsize)
{
List <double> Opens = QuickCSVUtils.QuickParseCSV(fileName, "Open", startLine, HowMany);
List <double> High = QuickCSVUtils.QuickParseCSV(fileName, "High", startLine, HowMany);
// List<double> Low = QuickCSVUtils.QuickParseCSV(fileName, "Low", startLine, HowMany);
List <double> Close = QuickCSVUtils.QuickParseCSV(fileName, "Close", startLine, HowMany);
List <double> Volume = QuickCSVUtils.QuickParseCSV(fileName, 5, startLine, HowMany);
double[] Ranges = NetworkUtility.CalculateRanges(Opens.ToArray(), Close.ToArray());
IMLDataPair aPairInput = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(Opens.ToArray()), NetworkUtility.CalculatePercents(Opens.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput3 = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(Close.ToArray()), NetworkUtility.CalculatePercents(Close.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput2 = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(High.ToArray()), NetworkUtility.CalculatePercents(High.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput4 = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(Volume.ToArray()), NetworkUtility.CalculatePercents(Volume.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput5 = TrainerHelper.ProcessPairs(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);
}
// ReSharper disable UnusedMember.Local
private static void CreateEvaluationSet(string @fileName)
// ReSharper restore UnusedMember.Local
{
List <double> Opens = QuickCSVUtils.QuickParseCSV(fileName, "Open", 1200, 1200);
List <double> High = QuickCSVUtils.QuickParseCSV(fileName, "High", 1200, 1200);
List <double> Low = QuickCSVUtils.QuickParseCSV(fileName, "Low", 1200, 1200);
List <double> Close = QuickCSVUtils.QuickParseCSV(fileName, "Close", 1200, 1200);
List <double> Volume = QuickCSVUtils.QuickParseCSV(fileName, 5, 1200, 1200);
double[] Ranges = NetworkUtility.CalculateRanges(Opens.ToArray(), Close.ToArray());
TemporalMLDataSet superTemportal = TrainerHelper.GenerateTrainingWithPercentChangeOnSerie(100, 1, Opens.ToArray(),
Close.ToArray(), High.ToArray(), Low.ToArray(), Volume.ToArray());
IMLDataPair aPairInput = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(Opens.ToArray()), NetworkUtility.CalculatePercents(Opens.ToArray()), 100, 1);
IMLDataPair aPairInput3 = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(Close.ToArray()), NetworkUtility.CalculatePercents(Close.ToArray()), 100, 1);
IMLDataPair aPairInput2 = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(High.ToArray()), NetworkUtility.CalculatePercents(High.ToArray()), 100, 1);
IMLDataPair aPairInput4 = TrainerHelper.ProcessPairs(NetworkUtility.CalculatePercents(Volume.ToArray()), NetworkUtility.CalculatePercents(Volume.ToArray()), 100, 1);
IMLDataPair aPairInput5 = TrainerHelper.ProcessPairs(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();
}
/// <summary>
/// Trains a random trainer.
/// </summary>
/// <param name="inputs">The inputs.</param>
/// <param name="predictWindow">The predict window.</param>
public static double RandomTrainerMethod(int inputs, int predictWindow)
{
double[] firstinput = MakeInputs(inputs);
double[] SecondInput = MakeInputs(inputs);
double[] ThirdInputs = MakeInputs(inputs);
double[] FourthInputs = MakeInputs(inputs);
double[] inp5 = MakeInputs(inputs);
double[] inp6 = MakeInputs(inputs);
var pair = TrainerHelper.ProcessPairs(firstinput, firstinput, inputs, predictWindow);
var pair2 = TrainerHelper.ProcessPairs(SecondInput, firstinput, inputs, predictWindow);
var pair3 = TrainerHelper.ProcessPairs(ThirdInputs, firstinput, inputs, predictWindow);
var pair4 = TrainerHelper.ProcessPairs(FourthInputs, firstinput, inputs, predictWindow);
var pair5 = TrainerHelper.ProcessPairs(inp5, firstinput, inputs, predictWindow);
var pair6 = TrainerHelper.ProcessPairs(inp6, firstinput, inputs, predictWindow);
BasicMLDataSet SuperSet = new BasicMLDataSet();
SuperSet.Add(pair);
SuperSet.Add(pair2);
SuperSet.Add(pair3);
SuperSet.Add(pair4);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationTANH(), true, SuperSet.InputSize));
network.AddLayer(new BasicLayer(new ActivationTANH(), false, 20));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 0));
network.AddLayer(new BasicLayer(new ActivationLinear(), true, predictWindow));
//var layer = new BasicLayer(new ActivationTANH(), true, SuperSet.InputSize);
//layer.Network = network;
network.Structure.FinalizeStructure();
network.Reset();
// var network = (BasicNetwork)CreateEval.CreateElmanNetwork(SuperSet.InputSize, SuperSet.IdealSize);
return(CreateEval.TrainNetworks(network, SuperSet));
//Lets create an evaluation.
//Console.WriteLine(@"Last error rate on random trainer:" + error);
}
