GetDouble() public method

Get the specified column as a double.
public GetDouble ( String column ) : double
column String The column to read.
return double
        protected override void LoadTestData(string testFile)
        {
            ReadCSV test_csv = new ReadCSV(testFile, true, CSVFormat.DecimalPoint);

            List<double[]> test_input = new List<double[]>();
            test_input_orig = new List<double[]>();

            while (test_csv.Next())
            {
                double x = test_csv.GetDouble(0);
                double y = test_csv.GetDouble(1);

                test_input.Add(new[] { x, y });
                test_input_orig.Add(new[] { x, y });
            }

            test_csv.Close();

            //Analyze(ref test_input);
            Normalize(ref test_input, ref vmin, ref vmax);

            testData = new List<IMLData>();
            foreach (var d in test_input)
            {
                testData.Add(new BasicMLData(d));
            }
        }
Beispiel #2
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        /// <summary>
        /// Load a CSV file into a memory dataset.  
        /// </summary>
        ///
        /// <param name="format">The CSV format to use.</param>
        /// <param name="filename">The filename to load.</param>
        /// <param name="headers">True if there is a header line.</param>
        /// <param name="inputSize">The input size.  Input always comes first in a file.</param>
        /// <param name="idealSize">The ideal size, 0 for unsupervised.</param>
        /// <returns>A NeuralDataSet that holds the contents of the CSV file.</returns>
        public static IMLDataSet LoadCSVTOMemory(CSVFormat format, String filename,
                                                bool headers, int inputSize, int idealSize)
        {
            var result = new BasicMLDataSet();
            var csv = new ReadCSV(filename, headers, format);
            while (csv.Next())
            {
                BasicMLData ideal = null;
                int index = 0;

                var input = new BasicMLData(inputSize);
                for (int i = 0; i < inputSize; i++)
                {
                    double d = csv.GetDouble(index++);
                    input[i] = d;
                }

                if (idealSize > 0)
                {
                    ideal = new BasicMLData(idealSize);
                    for (int i = 0; i < idealSize; i++)
                    {
                        double d = csv.GetDouble(index++);
                        ideal[i] = d;
                    }
                }

                IMLDataPair pair = new BasicMLDataPair(input, ideal);
                result.Add(pair);
            }

            return result;
        }
        public ICollection<LoadedMarketData> ReadAndCallLoader(TickerSymbol symbol, IList<MarketDataType> neededTypes, DateTime from, DateTime to, string File)
        {
            try
            {
                //We got a file, lets load it.
                ICollection<LoadedMarketData> result = new List<LoadedMarketData>();
                ReadCSV csv = new ReadCSV(File, true, CSVFormat.English);
                csv.DateFormat = "yyyy.MM.dd HH:mm:ss";

                DateTime ParsedDate = from;


                //  Time,Open,High,Low,Close,Volume
                while (csv.Next() && ParsedDate >= from && ParsedDate <= to  )
                {
                    DateTime date = csv.GetDate("Time");
                    double Bid= csv.GetDouble("Bid");
                    double Ask = csv.GetDouble("Ask");
                    double AskVolume = csv.GetDouble("AskVolume");
                    double BidVolume= csv.GetDouble("BidVolume");
                    double _trade = ( Bid + Ask ) /2;
                    double _tradeSize = (AskVolume + BidVolume) / 2;
                    LoadedMarketData data = new LoadedMarketData(date, symbol);
                    data.SetData(MarketDataType.Trade, _trade);
                    data.SetData(MarketDataType.Volume, _tradeSize);
                    result.Add(data);

                    Console.WriteLine("Current DateTime:"+ParsedDate.ToShortDateString()+ " Time:"+ParsedDate.ToShortTimeString() +"  Start date was "+from.ToShortDateString());
                    Console.WriteLine("Stopping at date:" + to.ToShortDateString() );
                    ParsedDate = date;
                    //double open = csv.GetDouble("Open");
                    //double close = csv.GetDouble("High");
                    //double high = csv.GetDouble("Low");
                    //double low = csv.GetDouble("Close");
                    //double volume = csv.GetDouble("Volume");
                    //LoadedMarketData data = new LoadedMarketData(date, symbol);
                    //data.SetData(MarketDataType.Open, open);
                    //data.SetData(MarketDataType.High, high);
                    //data.SetData(MarketDataType.Low, low);
                    //data.SetData(MarketDataType.Close, close);
                    //data.SetData(MarketDataType.Volume, volume);
                    result.Add(data);
                }

                csv.Close();
                return result;
            }

            catch (Exception ex)
            {

                Console.WriteLine("Something went wrong reading the csv");
                Console.WriteLine("Something went wrong reading the csv:" + ex.Message);
            }

            Console.WriteLine("Something went wrong reading the csv");
            return null;
        }
        /// <summary>
        /// Reads the CSV and call loader.
        /// Used internally to load the csv and place data in the marketdataset.
        /// </summary>
        /// <param name="symbol">The symbol.</param>
        /// <param name="neededTypes">The needed types.</param>
        /// <param name="from">From.</param>
        /// <param name="to">To.</param>
        /// <param name="File">The file.</param>
        /// <returns></returns>
        ICollection<LoadedMarketData> ReadAndCallLoader(TickerSymbol symbol, IEnumerable<MarketDataType> neededTypes, DateTime from, DateTime to, string File)
        {
                //We got a file, lets load it.

                ICollection<LoadedMarketData> result = new List<LoadedMarketData>();
                ReadCSV csv = new ReadCSV(File, true, CSVFormat.English);
                //In case we want to use a different date format...and have used the SetDateFormat method, our DateFormat must then not be null..
                //We will use the ?? operator to check for nullables.
                csv.DateFormat = DateFormat ?? "yyyy-MM-dd HH:mm:ss";
                csv.TimeFormat = "HH:mm:ss";

                DateTime ParsedDate = from;
                bool writeonce = true;

                while (csv.Next())
                {
                    DateTime date = csv.GetDate(0);
                    ParsedDate = date;

                    if (writeonce)
                    {
                        Console.WriteLine(@"First parsed date in csv:" + ParsedDate.ToShortDateString());
                        Console.WriteLine(@"Stopping at date:" + to.ToShortDateString());
                        Console.WriteLine(@"Current DateTime:" + ParsedDate.ToShortDateString() + @" Time:" +
                                          ParsedDate.ToShortTimeString() + @"  Asked Start date was " +
                                          from.ToShortDateString());
                        writeonce = false;
                    }
                    if (ParsedDate >= from && ParsedDate <= to)
                    {
                        DateTime datex = csv.GetDate(0);
                        double open = csv.GetDouble(1);
                        double close = csv.GetDouble(2);
                        double high = csv.GetDouble(3);
                        double low = csv.GetDouble(4);
                        double volume = csv.GetDouble(5);
                        double range = Math.Abs(open - close);
                        double HighLowRange = Math.Abs(high - low);
                        double DirectionalRange = close - open;
                        LoadedMarketData data = new LoadedMarketData(datex, symbol);
                        data.SetData(MarketDataType.Open, open);
                        data.SetData(MarketDataType.High, high);
                        data.SetData(MarketDataType.Low, low);
                        data.SetData(MarketDataType.Close, close);
                        data.SetData(MarketDataType.Volume, volume);
                        data.SetData(MarketDataType.RangeHighLow, Math.Round(HighLowRange, 6));
                        data.SetData(MarketDataType.RangeOpenClose, Math.Round(range, 6));
                        data.SetData(MarketDataType.RangeOpenCloseNonAbsolute, Math.Round(DirectionalRange, 6));
                        result.Add(data);


                    }

                }

                csv.Close();
                return result;
        }
Beispiel #5
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        public ICollection<LoadedMarketData> ReadAndCallLoader(TickerSymbol symbol, IList<MarketDataType> neededTypes, DateTime from, DateTime to,string File)
        {
            try
            {


                        //We got a file, lets load it.

                    

                        ICollection<LoadedMarketData> result = new List<LoadedMarketData>();
                        ReadCSV csv = new ReadCSV(File, true,LoadedFormat);


                        csv.DateFormat = DateTimeFormat.Normalize();
                        //  Time,Open,High,Low,Close,Volume
                        while (csv.Next())
                        {
                            DateTime date = csv.GetDate("Time");
                            double open = csv.GetDouble("Open");
                            double close = csv.GetDouble("High");
                            double high = csv.GetDouble("Low");
                            double low = csv.GetDouble("Close");
                            double volume = csv.GetDouble("Volume");
                            LoadedMarketData data = new LoadedMarketData(date, symbol);
                            data.SetData(MarketDataType.Open, open);
                            data.SetData(MarketDataType.High, high);
                            data.SetData(MarketDataType.Low, low);
                            data.SetData(MarketDataType.Close, close);
                            data.SetData(MarketDataType.Volume, volume);
                            result.Add(data);
                        }

                        csv.Close();
                        return result;                 
                }
            
            catch (Exception ex)
            {
                
              Console.WriteLine("Something went wrong reading the csv");
              Console.WriteLine("Something went wrong reading the csv:"+ex.Message);
            }

            Console.WriteLine("Something went wrong reading the csv");
            return null;
        }
 /// <summary>
 /// parses one column of a csv and returns an array of doubles.
 /// you can only return one double array with this method.
 /// </summary>
 /// <param name="file">The file.</param>
 /// <param name="formatused">The formatused.</param>
 /// <param name="Name">The name of the column to parse..</param>
 /// <returns></returns>
 public static List<double> QuickParseCSV(string file, CSVFormat formatused, string Name)
 {
     List<double> returnedArrays = new List<double>();
     ReadCSV csv = new ReadCSV(file, true, formatused);
     while (csv.Next())
     {
         returnedArrays.Add(csv.GetDouble(Name));
     }
     return returnedArrays;
 }
        /// <summary>
        /// Load financial data from a CSV file.
        /// </summary>
        /// <param name="ticker">The ticker being loaded, ignored for a CSV load.</param>
        /// <param name="dataNeeded">The data needed.</param>
        /// <param name="from">The starting date.</param>
        /// <param name="to">The ending date.</param>
        /// <returns></returns>
        public ICollection<LoadedMarketData> Load(TickerSymbol ticker, IList<MarketDataType> dataNeeded, DateTime from,
                                                  DateTime to)
        {
            try
            {
                if (File.Exists(TheFile))
                {
                    //We got a file, lets load it.
                    TheFile = TheFile;
                    ICollection<LoadedMarketData> result = new List<LoadedMarketData>();
                    var csv = new ReadCSV(TheFile, true, CSVFormat.English);

                    //  Time,Open,High,Low,Close,Volume
                    while (csv.Next())
                    {
                        DateTime date = csv.GetDate("Time");
                        double open = csv.GetDouble("Open");
                        double close = csv.GetDouble("High");
                        double high = csv.GetDouble("Low");
                        double low = csv.GetDouble("Close");
                        double volume = csv.GetDouble("Volume");
                        var data = new LoadedMarketData(date, ticker);
                        data.SetData(MarketDataType.Open, open);
                        data.SetData(MarketDataType.Volume, close);
                        data.SetData(MarketDataType.High, high);
                        data.SetData(MarketDataType.Low, low);
                        data.SetData(MarketDataType.Volume, volume);
                        result.Add(data);
                    }

                    csv.Close();
                    return result;
                }
            }
            catch (Exception ex)
            {
                throw new LoaderError(ex);
            }

            throw new LoaderError(@"Something went wrong reading the csv");
        }
 /// <summary>
 /// parses one column of a csv and returns an array of doubles.
 /// you can only return one double array with this method.
 /// We are assuming CSVFormat english in this quick parse csv method.
 /// You can input the size (number of lines) to read.
 /// </summary>
 /// <param name="file">The file.</param>
 /// <param name="Name">The name of the column to parse.</param>
 /// <param name="size">The size.</param>
 /// <returns></returns>
 public static List<double> QuickParseCSV(string file, string Name, int size)
 {
     List<double> returnedArrays = new List<double>();
     ReadCSV csv = new ReadCSV(file, true, CSVFormat.English);
     int currentRead = 0;
     while (csv.Next() && currentRead < size)
     {
         returnedArrays.Add(csv.GetDouble(Name));
         currentRead++;
     }
     return returnedArrays;
 }
        /// <summary>
        /// Load financial data from Google.
        /// </summary>
        /// <param name="ticker">The ticker to load from.</param>
        /// <param name="dataNeeded">The data needed.</param>
        /// <param name="from">The starting time.</param>
        /// <param name="to">The ending time.</param>
        /// <returns>The loaded data.</returns>
        public ICollection<LoadedMarketData> Load(TickerSymbol ticker, IList<MarketDataType> dataNeeded, DateTime from,
                                                  DateTime to)
        {
            ICollection<LoadedMarketData> result = new List<LoadedMarketData>();
            Uri url = BuildUrl(ticker, from, to);
            WebRequest http = WebRequest.Create(url);
            var response = (HttpWebResponse) http.GetResponse();

            if (response != null)
                using (Stream istream = response.GetResponseStream())
                {
                    var csv = new ReadCSV(istream, true, CSVFormat.DecimalPoint);

                    while (csv.Next())
                    {
                        DateTime date = csv.GetDate("date");

                        double open = csv.GetDouble("open");
                        double close = csv.GetDouble("close");
                        double high = csv.GetDouble("high");
                        double low = csv.GetDouble("low");
                        double volume = csv.GetDouble("volume");

                        var data =
                            new LoadedMarketData(date, ticker);

                        data.SetData(MarketDataType.Open, open);
                        data.SetData(MarketDataType.Close, close);
                        data.SetData(MarketDataType.High, high);
                        data.SetData(MarketDataType.Low, low);
                        data.SetData(MarketDataType.Open, open);
                        data.SetData(MarketDataType.Volume, volume);
                        result.Add(data);
                    }

                    csv.Close();
                    if (istream != null) istream.Close();
                }
            return result;
        }
        /// <summary>
        /// Convert a CSV file to binary.
        /// </summary>
        /// <param name="csvFile">The CSV file to convert.</param>
        /// <param name="format">The format.</param>
        /// <param name="binFile">The binary file.</param>
        /// <param name="input">The input.</param>
        /// <param name="ideal">The ideal.</param>
        /// <param name="headers">True, if headers are present.</param>
        public static void ConvertCSV2Binary(FileInfo csvFile, CSVFormat format,
                                             FileInfo binFile, int[] input, int[] ideal, bool headers)
        {
            binFile.Delete();
            var csv = new ReadCSV(csvFile.ToString(), headers, format);

            var buffer = new BufferedMLDataSet(binFile.ToString());
            buffer.BeginLoad(input.Length, ideal.Length);
            while (csv.Next())
            {
                var inputData = new BasicMLData(input.Length);
                var idealData = new BasicMLData(ideal.Length);

                // handle input data
                for (int i = 0; i < input.Length; i++)
                {
                    inputData[i] = csv.GetDouble(input[i]);
                }

                // handle input data
                for (int i = 0; i < ideal.Length; i++)
                {
                    idealData[i] = csv.GetDouble(ideal[i]);
                }

                // add to dataset

                buffer.Add(inputData, idealData);
            }
            buffer.EndLoad();
        }
        /// <summary>
        /// Used to calibrate the training file. 
        /// </summary>
        /// <param name="file">The file to consider.</param>
        protected void CalibrateFile(string file)
        {
            var csv = new ReadCSV(file, true, CSVFormat.English);
            while (csv.Next())
            {
                var a = new double[1];
                double close = csv.GetDouble(1);

                const int fastIndex = 2;
                const int slowIndex = fastIndex + Config.InputWindow;
                a[0] = close;
                for (int i = 0; i < Config.InputWindow; i++)
                {
                    double fast = csv.GetDouble(fastIndex + i);
                    double slow = csv.GetDouble(slowIndex + i);

                    if (!double.IsNaN(fast) && !double.IsNaN(slow))
                    {
                        double diff = (fast - slow)/Config.PipSize;
                        _minDifference = Math.Min(_minDifference, diff);
                        _maxDifference = Math.Max(_maxDifference, diff);
                    }
                }
                _window.Add(a);

                if (_window.IsFull())
                {
                    double max = (_window.CalculateMax(0, Config.InputWindow) - close)/Config.PipSize;
                    double min = (_window.CalculateMin(0, Config.InputWindow) - close)/Config.PipSize;

                    double o = Math.Abs(max) > Math.Abs(min) ? max : min;

                    _maxPiPs = Math.Max(_maxPiPs, (int) o);
                    _minPiPs = Math.Min(_minPiPs, (int) o);
                }
            }
        }
        /// <summary>
        /// Process the individual training file. 
        /// </summary>
        /// <param name="file">The training file to process.</param>
        /// <param name="output">The data set to output to.</param>
        protected void ProcessFile(string file, BufferedMLDataSet output)
        {
            var inputData = new BasicMLData(output.InputSize);
            var idealData = new BasicMLData(output.IdealSize);

            var csv = new ReadCSV(file, true, CSVFormat.English);
            while (csv.Next())
            {
                var a = new double[Config.InputWindow + 1];
                double close = csv.GetDouble(1);

                const int fastIndex = 2;
                const int slowIndex = fastIndex + Config.InputWindow;

                a[0] = close;
                for (int i = 0; i < 3; i++)
                {
                    double fast = csv.GetDouble(fastIndex + i);
                    double slow = csv.GetDouble(slowIndex + i);
                    double diff = _fieldDifference.Normalize((fast - slow)/Config.PipSize);
                    a[i + 1] = diff;
                }
                _window.Add(a);

                if (_window.IsFull())
                {
                    double max = (_window.CalculateMax(0, Config.InputWindow) - close)/Config.PipSize;
                    double min = (_window.CalculateMin(0, Config.InputWindow) - close)/Config.PipSize;

                    double o = Math.Abs(max) > Math.Abs(min) ? max : min;

                    a = _window.GetLast();
                    for (int i = 0; i < 3; i++)
                    {
                        inputData[i] = a[i + 1];
                    }

                    o = _fieldOutcome.Normalize(o);
                    idealData[0] = o;

                    output.Add(inputData, idealData);
                }
            }
        }
        /// <summary>
        /// Reads and parses CSV data from file
        /// </summary>
        /// <param name="ticker">Ticker associated with CSV file</param>
        /// <param name="neededTypes">Columns to parse (headers)</param>
        /// <param name="from">DateTime from</param>
        /// <param name="to">DateTime to</param>
        /// <param name="File">Filepath to CSV</param>
        /// <returns>Marketdata</returns>
        public ICollection<LoadedMarketData> ReadAndCallLoader(TickerSymbol ticker, IList<MarketDataType> neededTypes, DateTime from, DateTime to, string File)
        {
            try
            {
                LoadedFile = File;
                Console.WriteLine("Loading instrument: " + ticker.Symbol + " from: " + File);
                //We got a file, lets load it.
                ICollection<LoadedMarketData> result = new List<LoadedMarketData>();
                ReadCSV csv = new ReadCSV(File, true, LoadedFormat);
                if (DateTimeDualColumn)
                {
                    csv.DateFormat = DateFormat;
                    csv.TimeFormat =  TimeFormat;
                }
                else
                {
                    csv.DateFormat = DateFormat;
                }

                //"Date","Time","Open","High","Low","Close","Volume"
                while (csv.Next())
                {
                    string datetime = "";
                    if (DateTimeDualColumn)
                    {
                        datetime = csv.GetDate("Date").ToShortDateString() + " " +
                                          csv.GetTime("Time").ToShortTimeString();
                    }
                    else
                    {
                        datetime = csv.GetDate("Date").ToShortDateString();
                    }
                    DateTime date = DateTime.Parse(datetime);
                    if (date > from && date < to)
                    {
                        // CSV columns
                        double open = csv.GetDouble("Open");
                        double high = csv.GetDouble("High");
                        double low = csv.GetDouble("Low");
                        double close = csv.GetDouble("Close");
                        double volume = csv.GetDouble("Volume");

                        LoadedMarketData data = new LoadedMarketData(date, ticker);
                        foreach (MarketDataType marketDataType in neededTypes)
                        {
                            switch (marketDataType.ToString())
                            {
                                case "Open":
                                    data.SetData(MarketDataType.Open, open);
                                    break;
                                case "High":
                                    data.SetData(MarketDataType.High, high);
                                    break;
                                case "Low":
                                    data.SetData(MarketDataType.Low, low);
                                    break;
                                case "Close":
                                    data.SetData(MarketDataType.Close, close);
                                    break;
                                case "Volume":
                                    data.SetData(MarketDataType.Volume, volume);
                                    break;
                                case "RangeHighLow":
                                    data.SetData(MarketDataType.RangeHighLow, Math.Round(Math.Abs(high - low), 6));
                                    break;
                                case "RangeOpenClose":
                                    data.SetData(MarketDataType.RangeOpenClose, Math.Round(Math.Abs(close - open), 6));
                                    break;
                                case "RangeOpenCloseNonAbsolute":
                                    data.SetData(MarketDataType.RangeOpenCloseNonAbsolute, Math.Round(close - open, 6));
                                    break;
                                case "Weighted":
                                    data.SetData(MarketDataType.Weighted, Math.Round((high + low + 2 * close) / 4, 6));
                                    break;
                            }
                        }
                        result.Add(data);
                    }
                }
                csv.Close();
                return result;
            }

            catch (Exception ex)
            {
                Console.WriteLine("Something went wrong reading the csv: " + ex.Message);
            }
            return null;
        }
 /// <summary>
 /// parses one column of a csv and returns an array of doubles.
 /// you can only return one double array with this method.
 /// We are assuming CSVFormat english in this quick parse csv method.
 /// You can input the size (number of lines) to read , and the number of lines you want to skip start from the first line.
 /// </summary>
 /// <param name="file">The file.</param>
 /// <param name="columnNumber">The column number to get.</param>
 /// <param name="size">The size.</param>
 /// <param name="startLine">The start line (how many lines you want to skip from the first line.</param>
 /// <returns></returns>
 public static List<double> QuickParseCSV(string file, int columnNumber, int size, int startLine)
 {
     List<double> returnedArrays = new List<double>();
     ReadCSV csv = new ReadCSV(file, true, CSVFormat.English);
     int currentRead = 0;
     int currentLine = 0;
     while (csv.Next())
     {
         if (currentRead < size && currentLine > startLine)
         {
             returnedArrays.Add(csv.GetDouble(columnNumber));
             currentRead++;
         }
         currentLine++;
     }
     return returnedArrays;
 }
Beispiel #15
0
 public void loadSP500(String sp500Filename)
 {
     var csv = new ReadCSV(sp500Filename, true, CSVFormat.English);
     while (csv.Next())
     {
         var date = csv.GetDate("date");
         double amount = csv.GetDouble("adj close");
         var sample = new FinancialSample();
         sample.setAmount(amount);
         sample.setDate(date);
         samples.Add(sample);
     }
     csv.Close();
     samples.Sort();
 }
Beispiel #16
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        public void loadPrime(String primeFilename)
        {
            var csv = new ReadCSV(primeFilename, true, CSVFormat.English);

            while (csv.Next())
            {
                var date = csv.GetDate("date");
                double rate = csv.GetDouble("prime");
                var ir = new InterestRate(date, rate);
                rates.Add(ir);
            }

            csv.Close();
            rates.Sort();
        }
Beispiel #17
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        public void TestModel(string testDataPath, BasicNetwork model, ProblemType problem, ActivationType activation)
        {
            TestDataPath = testDataPath;
            var csvReader = new ReadCSV(testDataPath, true, CSVFormat.DecimalPoint);

            var values = new List<double[]>();
            var originalValues = new List<double[]>();

            while (csvReader.Next())
            {
                values.Add(ProblemType.Classification == problem
                    ? new[] {csvReader.GetDouble(0), csvReader.GetDouble(1)}
                    : new[] {csvReader.GetDouble(0)});

                originalValues.Add(ProblemType.Classification == problem
                    ? new[] { csvReader.GetDouble(0), csvReader.GetDouble(1) }
                    : new[] { csvReader.GetDouble(0) });
            }

            csvReader.Close();

            Normalize(values, _valuesMins, _valuesMaxes, activation);

            var answers = new List<double>();
            foreach (var value in values)
            {
                var answer = new double[LastLayerSize];
                model.Compute(value, answer);
                answers.Add(problem == ProblemType.Regression ? DenormalizeAnswer(answer[0], activation) : GetClassFromAnswer(answer));
            }

            AnswerPath = Path.GetFullPath(TestDataPath) + ".solved";

            var lines = new List<string>();
            lines.Add(problem == ProblemType.Classification ? "x,y,clc" : "x,y");

            lines.AddRange(answers.Select((t, i) =>
                problem == ProblemType.Regression
                ? originalValues[i][0].ToString(CultureInfo.InvariantCulture) + "," + t.ToString(CultureInfo.InvariantCulture)
                : originalValues[i][0].ToString(CultureInfo.InvariantCulture) + "," + originalValues[i][1].ToString(CultureInfo.InvariantCulture) + "," + t.ToString(CultureInfo.InvariantCulture)));

            File.WriteAllLines(AnswerPath, lines);
        }
Beispiel #18
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        public void LoadTrainingData(string trainingDataPath, ProblemType problem, ActivationType activation)
        {
            TrainingDataPath = trainingDataPath;

            var csvReader = new ReadCSV(trainingDataPath, true, CSVFormat.DecimalPoint);

            var values = new List<double[]>();
            var answers = new List<double[]>();

            while (csvReader.Next())
            {
                if (ProblemType.Classification == problem)
                {
                    values.Add(new []{csvReader.GetDouble(0), csvReader.GetDouble(1)});
                    answers.Add(new []{csvReader.GetDouble(2)});
                }
                else
                {
                    values.Add(new[] { csvReader.GetDouble(0)});
                    answers.Add(new[] { csvReader.GetDouble(1) });

                    _originalRegressionValues.Add(values.Last()[0]);
                    _originalRegressionAnswers.Add(answers.Last()[0]);
                }
            }

            csvReader.Close();

            if (problem == ProblemType.Classification)
            {
                answers = SpreadClassificationAnswers(answers, activation);
                FirstLayerSize = 2;
            }
            else
                LastLayerSize = FirstLayerSize = 1;

            AnalizeValues(problem, values);
            Normalize(values, _valuesMins, _valuesMaxes, activation);

            if (problem == ProblemType.Regression)
            {
                AnalizeAnswers(answers);
                Normalize(answers, _answersMins, _answersMaxes, activation);
            }

            values.StableShuffle();
            answers.StableShuffle();
            ListExtensions.ResetStableShuffle();

            var trainingSetSize = (int)(values.Count * 0.85);

            TrainingDataSet = new BasicMLDataSet(values.Take(trainingSetSize).ToArray(), answers.Take(trainingSetSize).ToArray());
            ValidationDataSet = new BasicMLDataSet(values.Skip(trainingSetSize).ToArray(), answers.Skip(trainingSetSize).ToArray());
        }
Beispiel #19
0
        protected override void LoadTrainData(string trainingFile)
        {
            ReadCSV train_csv = new ReadCSV(trainingFile, true, CSVFormat.DecimalPoint);

            List<double[]> train_input = new List<double[]>();
            List<double[]> train_ideal = new List<double[]>();

            train_input_orig = new List<Results>();

            while (train_csv.Next())
            {
                double x = train_csv.GetDouble(0);
                double y = train_csv.GetDouble(1);

                train_input.Add(new double[] { x });
                train_ideal.Add(new double[] { y });
                train_input_orig.Add(new Results(x, y, 0));
            }

            train_csv.Close();

            Analyze(ref train_input, ref vmin, ref vmax);
            Normalize(ref train_input, ref vmin, ref vmax);

            Analyze(ref train_ideal, ref vmino, ref vmaxo);
            Normalize(ref train_ideal, ref vmino, ref vmaxo);

            Randomize(ref train_input, ref train_ideal);

            int validation_size = train_input.Count / 10;

            validationData = new BasicMLDataSet(train_input.Take(validation_size).ToArray(), train_ideal.Take(validation_size).ToArray());
            trainingData = new BasicMLDataSet(train_input.Skip(validation_size).ToArray(), train_ideal.Skip(validation_size).ToArray());
        }