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);
}