C# (CSharp) TrainerHelper.GenerateTrainingWithPercentChangeOnSerie Exemples

Exemple #1

        public static TemporalMLDataSet GenerateATemporalSet(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);

            return(TrainerHelper.GenerateTrainingWithPercentChangeOnSerie(WindowSize, outputsize, Opens.ToArray(), Close.ToArray(), High.ToArray(), Low.ToArray(), Volume.ToArray()));
        }

Exemple #2

// 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();
        }