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);
test_input.Add(new[] { x });
test_input_orig.Add(new[] { x });
}
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));
}
}
/// <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;
}
/// <summary>
/// Process the file and output to the target file.
/// </summary>
/// <param name="target">The target file to write to.</param>
public void Process(string target)
{
var csv = new ReadCSV(InputFilename.ToString(), ExpectInputHeaders, Format);
TextWriter tw = new StreamWriter(target);
ResetStatus();
while (csv.Next())
{
var line = new StringBuilder();
UpdateStatus(false);
line.Append(GetColumnData(FileData.Date, csv));
line.Append(" ");
line.Append(GetColumnData(FileData.Time, csv));
line.Append(";");
line.Append(Format.Format(double.Parse(GetColumnData(FileData.Open, csv)), Precision));
line.Append(";");
line.Append(Format.Format(double.Parse(GetColumnData(FileData.High, csv)), Precision));
line.Append(";");
line.Append(Format.Format(double.Parse(GetColumnData(FileData.Low, csv)), Precision));
line.Append(";");
line.Append(Format.Format(double.Parse(GetColumnData(FileData.Close, csv)), Precision));
line.Append(";");
line.Append(Format.Format(double.Parse(GetColumnData(FileData.Volume, csv)), Precision));
tw.WriteLine(line.ToString());
}
ReportDone(false);
csv.Close();
tw.Close();
}
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;
}
/// <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>
/// 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;
}
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>
/// 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>
/// 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>
/// Construct the object.
/// </summary>
///
/// <param name="filename">The filename.</param>
/// <param name="headers">False if headers are not extended.</param>
/// <param name="format">The CSV format.</param>
public CSVHeaders(FileInfo filename, bool headers,
CSVFormat format)
{
_headerList = new List<String>();
_columnMapping = new Dictionary<String, Int32>();
ReadCSV csv = null;
try
{
csv = new ReadCSV(filename.ToString(), headers, format);
if (csv.Next())
{
if (headers)
{
foreach (String str in csv.ColumnNames)
{
_headerList.Add(str);
}
}
else
{
for (int i = 0; i < csv.ColumnCount; i++)
{
_headerList.Add("field:" + (i + 1));
}
}
}
Init();
}
finally
{
if (csv != null)
{
csv.Close();
}
}
}
/// <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 and balance the data.
/// </summary>
/// <param name="outputFile">The output file to write data to.</param>
/// <param name="targetField"></param>
/// <param name="countPer">The desired count per class.</param>
public void Process(FileInfo outputFile, int targetField,
int countPer)
{
ValidateAnalyzed();
StreamWriter tw = PrepareOutputFile(outputFile);
_counts = new Dictionary<String, Int32>();
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
ResetStatus();
while (csv.Next() && !ShouldStop())
{
var row = new LoadedRow(csv);
UpdateStatus(false);
String key = row.Data[targetField];
int count;
if (!_counts.ContainsKey(key))
{
count = 0;
}
else
{
count = _counts[key];
}
if (count < countPer)
{
WriteRow(tw, row);
count++;
}
_counts[key] = count;
}
ReportDone(false);
csv.Close();
tw.Close();
}
/// <summary>
/// Load the buffer from the underlying file.
/// </summary>
///
/// <param name="csv">The CSV file to load from.</param>
private void LoadBuffer(ReadCSV csv)
{
for (int i = 0; i < _buffer.Length; i++)
{
_buffer[i] = null;
}
int index = 0;
while (csv.Next() && (index < _bufferSize) && !ShouldStop())
{
var row = new LoadedRow(csv);
_buffer[index++] = row;
}
_remaining = index;
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
// Download the data that we will attempt to model.
string filename = DownloadData(app.Args);
// Define the format of the data file.
// This area will change, depending on the columns and
// format of the file that you are trying to model.
var format = new CSVFormat('.', ' '); // decimal point and space separated
IVersatileDataSource source = new CSVDataSource(filename, false, format);
var data = new VersatileMLDataSet(source);
data.NormHelper.Format = format;
ColumnDefinition columnMPG = data.DefineSourceColumn("mpg", 0, ColumnType.Continuous);
ColumnDefinition columnCylinders = data.DefineSourceColumn("cylinders", 1, ColumnType.Ordinal);
// It is very important to predefine ordinals, so that the order is known.
columnCylinders.DefineClass(new[] {"3", "4", "5", "6", "8"});
data.DefineSourceColumn("displacement", 2, ColumnType.Continuous);
ColumnDefinition columnHorsePower = data.DefineSourceColumn("horsepower", 3, ColumnType.Continuous);
data.DefineSourceColumn("weight", 4, ColumnType.Continuous);
data.DefineSourceColumn("acceleration", 5, ColumnType.Continuous);
ColumnDefinition columnModelYear = data.DefineSourceColumn("model_year", 6, ColumnType.Ordinal);
columnModelYear.DefineClass(new[]
{"70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82"});
data.DefineSourceColumn("origin", 7, ColumnType.Nominal);
// Define how missing values are represented.
data.NormHelper.DefineUnknownValue("?");
data.NormHelper.DefineMissingHandler(columnHorsePower, new MeanMissingHandler());
// Analyze the data, determine the min/max/mean/sd of every column.
data.Analyze();
// Map the prediction column to the output of the model, and all
// other columns to the input.
data.DefineSingleOutputOthersInput(columnMPG);
// Create feedforward neural network as the model type. MLMethodFactory.TYPE_FEEDFORWARD.
// You could also other model types, such as:
// MLMethodFactory.SVM: Support Vector Machine (SVM)
// MLMethodFactory.TYPE_RBFNETWORK: RBF Neural Network
// MLMethodFactor.TYPE_NEAT: NEAT Neural Network
// MLMethodFactor.TYPE_PNN: Probabilistic Neural Network
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward);
// Send any output to the console.
model.Report = new ConsoleStatusReportable();
// Now normalize the data. Encog will automatically determine the correct normalization
// type based on the model you chose in the last step.
data.Normalize();
// Hold back some data for a final validation.
// Shuffle the data into a random ordering.
// Use a seed of 1001 so that we always use the same holdback and will get more consistent results.
model.HoldBackValidation(0.3, true, 1001);
// Choose whatever is the default training type for this model.
model.SelectTrainingType(data);
// Use a 5-fold cross-validated train. Return the best method found.
var bestMethod = (IMLRegression) model.Crossvalidate(5, true);
// Display the training and validation errors.
Console.WriteLine(@"Training error: " + model.CalculateError(bestMethod, model.TrainingDataset));
Console.WriteLine(@"Validation error: " + model.CalculateError(bestMethod, model.ValidationDataset));
// Display our normalization parameters.
NormalizationHelper helper = data.NormHelper;
Console.WriteLine(helper.ToString());
// Display the final model.
Console.WriteLine("Final model: " + bestMethod);
// Loop over the entire, original, dataset and feed it through the model.
// This also shows how you would process new data, that was not part of your
// training set. You do not need to retrain, simply use the NormalizationHelper
// class. After you train, you can save the NormalizationHelper to later
// normalize and denormalize your data.
source.Close();
var csv = new ReadCSV(filename, false, format);
var line = new String[7];
IMLData input = helper.AllocateInputVector();
while (csv.Next())
{
var result = new StringBuilder();
line[0] = csv.Get(1);
line[1] = csv.Get(2);
line[2] = csv.Get(3);
line[3] = csv.Get(4);
line[4] = csv.Get(5);
line[5] = csv.Get(6);
line[6] = csv.Get(7);
String correct = csv.Get(0);
helper.NormalizeInputVector(line, ((BasicMLData) input).Data, false);
IMLData output = bestMethod.Compute(input);
String irisChosen = helper.DenormalizeOutputVectorToString(output)[0];
result.Append(line);
result.Append(" -> predicted: ");
result.Append(irisChosen);
result.Append("(correct: ");
result.Append(correct);
result.Append(")");
Console.WriteLine(result.ToString());
}
csv.Close();
// Delete data file and shut down.
File.Delete(filename);
EncogFramework.Instance.Shutdown();
}
private void x08af8e36ac9914b5()
{
ReadCSV dcsv = null;
try
{
int num;
double num2;
dcsv = new ReadCSV(base.InputFilename.ToString(), base.ExpectInputHeaders, base.InputFormat);
goto Label_006B;
Label_0021:
num++;
if ((((uint) num2) & 0) == 0)
{
}
Label_005E:
while (dcsv.Next())
{
if (!base.ShouldStop())
{
goto Label_0075;
}
if ((((uint) num2) + ((uint) num)) <= uint.MaxValue)
{
break;
}
}
return;
Label_006B:
base.ResetStatus();
num = 0;
goto Label_005E;
Label_0075:
base.UpdateStatus("Reading data");
using (IEnumerator<BaseCachedColumn> enumerator = base.Columns.GetEnumerator())
{
BaseCachedColumn column;
FileData data;
Label_008F:
if (enumerator.MoveNext() || ((((uint) num) + ((uint) num)) > uint.MaxValue))
{
goto Label_011D;
}
goto Label_0021;
Label_00BD:
if (column.Input)
{
goto Label_00D8;
}
goto Label_008F;
Label_00C7:
if (0 == 0)
{
}
goto Label_008F;
Label_00CC:
if (column is FileData)
{
goto Label_00BD;
}
goto Label_00C7;
Label_00D8:
data = (FileData) column;
string str = dcsv.Get(data.Index);
num2 = base.InputFormat.Parse(str);
data.Data[num] = num2;
goto Label_008F;
Label_0111:
if (0 == 0)
{
goto Label_00CC;
}
goto Label_00BD;
Label_011D:
column = enumerator.Current;
goto Label_0111;
}
}
finally
{
base.ReportDone("Reading data");
if (dcsv != null)
{
dcsv.Close();
}
}
}
/// <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;
}
public void Process(EncogAnalyst target)
{
string text1;
int num;
int num2;
string str;
bool flag;
bool flag2;
bool flag3;
AnalyzedField field3;
int num3;
IList<AnalystClassItem> analyzedClassMembers;
IList<AnalystClassItem> classMembers;
int num4;
DataField[] fieldArray;
int num5;
AnalyzedField[] fieldArray2;
int num6;
AnalyzedField[] fieldArray3;
int num7;
AnalyzedField[] fieldArray4;
int num8;
CSVFormat format = ConvertStringConst.ConvertToCSVFormat(this._x5786461d089b10a0);
ReadCSV dcsv = new ReadCSV(this._xb41a802ca5fde63b, this._x94e6ca5ac178dbd0, format);
Label_0676:
if (dcsv.Next())
{
if (this._xa942970cc8a85fd4 == null)
{
this.xd2a854890d89a856(dcsv);
}
num = 0;
while (num < dcsv.ColumnCount)
{
if (this._xa942970cc8a85fd4 != null)
{
this._xa942970cc8a85fd4[num].Analyze1(dcsv.Get(num));
}
num++;
}
if (((uint) num2) >= 0)
{
goto Label_0676;
}
}
else if (this._xa942970cc8a85fd4 != null)
{
fieldArray2 = this._xa942970cc8a85fd4;
}
else
{
if ((((uint) num8) & 0) == 0)
{
goto Label_05F5;
}
goto Label_05D0;
}
if ((((uint) num2) - ((uint) flag2)) >= 0)
{
for (num6 = 0; num6 < fieldArray2.Length; num6++)
{
fieldArray2[num6].CompletePass1();
}
goto Label_05F5;
}
goto Label_05D0;
Label_0011:
num5++;
if (((uint) num2) < 0)
{
goto Label_0251;
}
Label_002C:
if (num5 < fieldArray.Length)
{
fieldArray[num5] = this._xa942970cc8a85fd4[num5].FinalizeField();
if ((((uint) num6) + ((uint) num)) <= uint.MaxValue)
{
goto Label_0011;
}
if ((((uint) num8) | 3) != 0)
{
goto Label_00E8;
}
}
else
{
if (((uint) flag2) > uint.MaxValue)
{
goto Label_0336;
}
target.Script.Fields = fieldArray;
return;
}
Label_00A6:
if (this._xa942970cc8a85fd4.Length == target.Script.Fields.Length)
{
num3 = 0;
goto Label_00EE;
}
if ((((uint) flag3) & 0) != 0)
{
goto Label_0248;
}
Label_00D7:
fieldArray = new DataField[this._xa942970cc8a85fd4.Length];
if ((((uint) num6) + ((uint) num4)) >= 0)
{
num5 = 0;
goto Label_002C;
}
goto Label_0011;
Label_00E8:
num3++;
Label_00EE:
if (num3 < this._xa942970cc8a85fd4.Length)
{
this._xa942970cc8a85fd4[num3].Name = target.Script.Fields[num3].Name;
if (!this._xa942970cc8a85fd4[num3].Class)
{
goto Label_00E8;
}
analyzedClassMembers = this._xa942970cc8a85fd4[num3].AnalyzedClassMembers;
classMembers = target.Script.Fields[num3].ClassMembers;
if (classMembers.Count != analyzedClassMembers.Count)
{
goto Label_00E8;
}
num4 = 0;
if (((uint) num2) > uint.MaxValue)
{
goto Label_0341;
}
goto Label_0195;
}
goto Label_00D7;
Label_018F:
num4++;
Label_0195:
if (num4 < classMembers.Count)
{
if (analyzedClassMembers[num4].Code.Equals(classMembers[num4].Code))
{
analyzedClassMembers[num4].Name = classMembers[num4].Name;
}
goto Label_018F;
}
goto Label_00E8;
Label_0238:
if (num8 < fieldArray4.Length)
{
field3 = fieldArray4[num8];
if ((((uint) num) & 0) != 0)
{
goto Label_02FF;
}
if (field3.Class)
{
if (flag)
{
goto Label_0350;
}
if (((uint) num4) <= uint.MaxValue)
{
goto Label_03E6;
}
goto Label_040F;
}
goto Label_0251;
}
if (target.Script.Fields != null)
{
goto Label_00A6;
}
if ((((uint) num8) + ((uint) num5)) > uint.MaxValue)
{
goto Label_0341;
}
goto Label_00D7;
Label_0248:
if (field3.Integer && (field3.AnalyzedClassMembers.Count <= 2))
{
if ((((uint) num6) - ((uint) num5)) >= 0)
{
if ((((uint) num7) | 4) == 0)
{
goto Label_059B;
}
field3.Class = false;
}
else
{
if ((((uint) flag2) + ((uint) num8)) >= 0)
{
goto Label_0350;
}
goto Label_02FF;
}
}
Label_0251:
num8++;
goto Label_0238;
Label_02FF:
if (!flag2 && (field3.Real && !field3.Integer))
{
field3.Class = false;
}
goto Label_0248;
Label_030B:
if ((((uint) flag3) - ((uint) num2)) >= 0)
{
goto Label_02FF;
}
Label_0341:
while (!field3.Real)
{
field3.Class = false;
goto Label_02FF;
Label_0336:
if (field3.Integer)
{
goto Label_030B;
}
}
goto Label_0368;
Label_0350:
if (flag3)
{
goto Label_02FF;
}
if ((((uint) num5) & 0) == 0)
{
goto Label_0336;
}
Label_0368:
if ((((uint) flag) - ((uint) num4)) >= 0)
{
goto Label_02FF;
}
goto Label_030B;
Label_03E6:
if (!field3.Integer)
{
goto Label_0350;
}
Label_040F:
field3.Class = false;
if ((((uint) num7) + ((uint) flag)) < 0)
{
goto Label_05F5;
}
if ((((uint) num3) + ((uint) flag)) > uint.MaxValue)
{
goto Label_04D6;
}
if ((((uint) num3) & 0) == 0)
{
goto Label_0350;
}
goto Label_03E6;
Label_04A8:
dcsv.Close();
Label_04AE:
text1 = this._x594135906c55045c.Properties.GetPropertyString("SETUP:CONFIG_allowedClasses");
if (text1 != null)
{
str = text1;
}
else
{
if (0 != 0)
{
goto Label_02FF;
}
str = "";
}
flag = str.Contains("int");
if (str.Contains("real"))
{
}
flag2 = true;
flag3 = str.Contains("string");
fieldArray4 = this._xa942970cc8a85fd4;
num8 = 0;
goto Label_0238;
Label_04D6:
if (num7 >= fieldArray3.Length)
{
}
AnalyzedField field2 = fieldArray3[num7];
if (((uint) num4) < 0)
{
goto Label_04AE;
}
field2.CompletePass2();
num7++;
if ((((uint) num4) + ((uint) num2)) < 0)
{
goto Label_018F;
}
if ((((uint) num3) + ((uint) flag)) >= 0)
{
goto Label_04D6;
}
Label_0554:
if (((uint) flag) > uint.MaxValue)
{
goto Label_059B;
}
goto Label_04D6;
if ((((uint) num2) | 0x7fffffff) != 0)
{
if ((((uint) flag2) - ((uint) num3)) >= 0)
{
goto Label_04A8;
}
goto Label_05D0;
}
Label_058B:
num2++;
Label_0591:
if (num2 < dcsv.ColumnCount)
{
goto Label_05D0;
}
Label_059B:
if (dcsv.Next())
{
num2 = 0;
goto Label_0591;
}
if (this._xa942970cc8a85fd4 != null)
{
fieldArray3 = this._xa942970cc8a85fd4;
num7 = 0;
goto Label_0554;
}
goto Label_04A8;
Label_05D0:
if (this._xa942970cc8a85fd4 != null)
{
this._xa942970cc8a85fd4[num2].Analyze2(dcsv.Get(num2));
}
goto Label_058B;
Label_05F5:
dcsv.Close();
if (((uint) num) >= 0)
{
}
dcsv = new ReadCSV(this._xb41a802ca5fde63b, this._x94e6ca5ac178dbd0, format);
goto Label_059B;
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
ErrorCalculation.Mode = ErrorCalculationMode.RMS;
// Download the data that we will attempt to model.
string filename = DownloadData(app.Args);
// Define the format of the data file.
// This area will change, depending on the columns and
// format of the file that you are trying to model.
var format = new CSVFormat('.', ' '); // decimal point and
// space separated
IVersatileDataSource source = new CSVDataSource(filename, true,
format);
var data = new VersatileMLDataSet(source);
data.NormHelper.Format = format;
ColumnDefinition columnSSN = data.DefineSourceColumn("SSN",
ColumnType.Continuous);
ColumnDefinition columnDEV = data.DefineSourceColumn("DEV",
ColumnType.Continuous);
// Analyze the data, determine the min/max/mean/sd of every column.
data.Analyze();
// Use SSN & DEV to predict SSN. For time-series it is okay to have
// SSN both as
// an input and an output.
data.DefineInput(columnSSN);
data.DefineInput(columnDEV);
data.DefineOutput(columnSSN);
// Create feedforward neural network as the model type.
// MLMethodFactory.TYPE_FEEDFORWARD.
// You could also other model types, such as:
// MLMethodFactory.SVM: Support Vector Machine (SVM)
// MLMethodFactory.TYPE_RBFNETWORK: RBF Neural Network
// MLMethodFactor.TYPE_NEAT: NEAT Neural Network
// MLMethodFactor.TYPE_PNN: Probabilistic Neural Network
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward);
// Send any output to the console.
model.Report = new ConsoleStatusReportable();
// Now normalize the data. Encog will automatically determine the
// correct normalization
// type based on the model you chose in the last step.
data.Normalize();
// Set time series.
data.LeadWindowSize = 1;
data.LagWindowSize = WindowSize;
// Hold back some data for a final validation.
// Do not shuffle the data into a random ordering. (never shuffle
// time series)
// Use a seed of 1001 so that we always use the same holdback and
// will get more consistent results.
model.HoldBackValidation(0.3, false, 1001);
// Choose whatever is the default training type for this model.
model.SelectTrainingType(data);
// Use a 5-fold cross-validated train. Return the best method found.
// (never shuffle time series)
var bestMethod = (IMLRegression) model.Crossvalidate(5,
false);
// Display the training and validation errors.
Console.WriteLine(@"Training error: "
+ model.CalculateError(bestMethod,
model.TrainingDataset));
Console.WriteLine(@"Validation error: "
+ model.CalculateError(bestMethod,
model.ValidationDataset));
// Display our normalization parameters.
NormalizationHelper helper = data.NormHelper;
Console.WriteLine(helper.ToString());
// Display the final model.
Console.WriteLine(@"Final model: " + bestMethod);
// Loop over the entire, original, dataset and feed it through the
// model. This also shows how you would process new data, that was
// not part of your training set. You do not need to retrain, simply
// use the NormalizationHelper class. After you train, you can save
// the NormalizationHelper to later normalize and denormalize your
// data.
source.Close();
var csv = new ReadCSV(filename, true, format);
var line = new String[2];
// Create a vector to hold each time-slice, as we build them.
// These will be grouped together into windows.
var slice = new double[2];
var window = new VectorWindow(WindowSize + 1);
IMLData input = helper.AllocateInputVector(WindowSize + 1);
// Only display the first 100
int stopAfter = 100;
while (csv.Next() && stopAfter > 0)
{
var result = new StringBuilder();
line[0] = csv.Get(2); // ssn
line[1] = csv.Get(3); // dev
helper.NormalizeInputVector(line, slice, false);
// enough data to build a full window?
if (window.IsReady())
{
window.CopyWindow(((BasicMLData) input).Data, 0);
String correct = csv.Get(2); // trying to predict SSN.
IMLData output = bestMethod.Compute(input);
String predicted = helper
.DenormalizeOutputVectorToString(output)[0];
result.Append(line);
result.Append(" -> predicted: ");
result.Append(predicted);
result.Append("(correct: ");
result.Append(correct);
result.Append(")");
Console.WriteLine(result.ToString());
}
// Add the normalized slice to the window. We do this just after
// the after checking to see if the window is ready so that the
// window is always one behind the current row. This is because
// we are trying to predict next row.
window.Add(slice);
stopAfter--;
}
csv.Close();
// Delete data file and shut down.
File.Delete(filename);
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Process the input file.
/// </summary>
///
/// <param name="outputFile">The output file to write to.</param>
public void Process(FileInfo outputFile)
{
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, InputFormat);
StreamWriter tw = PrepareOutputFile(outputFile);
_filteredCount = 0;
ResetStatus();
while (csv.Next() && !ShouldStop())
{
UpdateStatus(false);
var row = new LoadedRow(csv);
if (ShouldProcess(row))
{
WriteRow(tw, row);
_filteredCount++;
}
}
ReportDone(false);
tw.Close();
csv.Close();
}
public void Process()
{
ReadCSV dcsv;
this.x461c3bf969128260();
Label_0006:
dcsv = new ReadCSV(base.InputFilename.ToString(), base.ExpectInputHeaders, base.InputFormat);
base.ResetStatus();
using (IEnumerator<SegregateTargetPercent> enumerator = this._x2ea7a1eff81ae7c0.GetEnumerator())
{
SegregateTargetPercent percent;
StreamWriter writer;
goto Label_0044;
Label_0038:
if (0 != 0)
{
goto Label_00D1;
}
Label_003E:
writer.Close();
Label_0044:
if (enumerator.MoveNext())
{
goto Label_00D1;
}
if (0 == 0)
{
goto Label_00EE;
}
if (0 == 0)
{
goto Label_00D1;
}
if (0 == 0)
{
goto Label_00BC;
}
if (0 == 0)
{
goto Label_0098;
}
goto Label_003E;
Label_0067:
if (percent.NumberRemaining > 0)
{
goto Label_0086;
}
if (0 == 0)
{
goto Label_00B9;
}
goto Label_0098;
Label_0075:
percent.NumberRemaining--;
Label_0083:
if (0 == 0)
{
goto Label_0067;
}
Label_0086:
if (!dcsv.Next() || base.ShouldStop())
{
goto Label_003E;
}
Label_0098:
base.UpdateStatus(false);
LoadedRow row = new LoadedRow(dcsv);
base.WriteRow(writer, row);
if (4 == 0)
{
goto Label_0083;
}
goto Label_0075;
Label_00B9:
if (0 == 0)
{
goto Label_00CE;
}
Label_00BC:
writer = base.PrepareOutputFile(percent.Filename);
goto Label_0067;
Label_00CE:
if (0 == 0)
{
goto Label_0038;
}
Label_00D1:
percent = enumerator.Current;
goto Label_00BC;
}
Label_00EE:
base.ReportDone(false);
if (0 != 0)
{
goto Label_0006;
}
dcsv.Close();
}
/// <summary>
/// Process the file.
/// </summary>
///
/// <param name="outputFile">The output file.</param>
/// <param name="method">THe method to use.</param>
public void Process(FileInfo outputFile, IMLMethod method)
{
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
IMLData output;
int outputLength = _analyst.DetermineTotalInputFieldCount();
StreamWriter tw = PrepareOutputFile(outputFile);
ResetStatus();
while (csv.Next())
{
UpdateStatus(false);
var row = new LoadedRow(csv, _outputColumns);
double[] inputArray = AnalystNormalizeCSV.ExtractFields(_analyst,
_analystHeaders, csv, outputLength, true);
if (_series.TotalDepth > 1)
{
inputArray = _series.Process(inputArray);
}
if (inputArray != null)
{
IMLData input = new BasicMLData(inputArray);
// evaluation data
if ((method is IMLClassification)
&& !(method is IMLRegression))
{
// classification only?
output = new BasicMLData(1);
output[0] =
((IMLClassification) method).Classify(input);
}
else
{
// regression
output = ((IMLRegression) method).Compute(input);
}
// skip file data
int index = _fileColumns;
int outputIndex = 0;
// display output
foreach (AnalystField field in _analyst.Script.Normalize.NormalizedFields)
{
if (_analystHeaders.Find(field.Name) != -1)
{
if (field.Output)
{
if (field.Classify)
{
// classification
ClassItem cls = field.DetermineClass(
outputIndex, output.Data);
outputIndex += field.ColumnsNeeded;
if (cls == null)
{
row.Data[index++] = "?Unknown?";
}
else
{
row.Data[index++] = cls.Name;
}
}
else
{
// regression
double n = output[outputIndex++];
n = field.DeNormalize(n);
row.Data[index++] = Format
.Format(n, Precision);
}
}
}
}
}
WriteRow(tw, row);
}
ReportDone(false);
tw.Close();
csv.Close();
}
/// <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>
/// Perform a basic analyze of the file. This method is used mostly
/// internally.
/// </summary>
public void PerformBasicCounts()
{
ResetStatus();
int rc = 0;
var csv = new ReadCSV(_inputFilename.ToString(),
_expectInputHeaders, _format);
while (csv.Next() && !_cancel)
{
UpdateStatus(true);
rc++;
}
_recordCount = rc;
_columnCount = csv.ColumnCount;
ReadHeaders(csv);
csv.Close();
ReportDone(true);
}
/// <summary>
/// Private constructor.
/// </summary>
private PropertyConstraints()
{
_data = new Dictionary<String, List<PropertyEntry>>();
try
{
Stream mask0 = ResourceLoader.CreateStream("Encog.Resources.analyst.csv");
var csv = new ReadCSV(mask0, false, CSVFormat.EgFormat);
while (csv.Next())
{
String sectionStr = csv.Get(0);
String nameStr = csv.Get(1);
String typeStr = csv.Get(2);
// determine type
PropertyType t;
if ("boolean".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeBoolean;
}
else if ("real".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeDouble;
}
else if ("format".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeFormat;
}
else if ("int".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeInteger;
}
else if ("list-string".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeListString;
}
else if ("string".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeString;
}
else
{
throw new AnalystError("Unknown type constraint: "
+ typeStr);
}
var entry = new PropertyEntry(t, nameStr,
sectionStr);
List<PropertyEntry> list;
if (_data.ContainsKey(sectionStr))
{
list = _data[sectionStr];
}
else
{
list = new List<PropertyEntry>();
_data[sectionStr] = list;
}
list.Add(entry);
}
csv.Close();
mask0.Close();
}
catch (IOException e)
{
throw new EncogError(e);
}
}
/// <summary>
/// Read the input file.
/// </summary>
///
private void ReadInputFile()
{
ResetStatus();
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, InputFormat);
while (csv.Next() && !ShouldStop())
{
UpdateStatus("Reading input file");
var row = new LoadedRow(csv);
_data.Add(row);
}
Count = csv.ColumnCount;
if (ExpectInputHeaders)
{
InputHeadings = new String[csv.ColumnCount];
for (int i = 0; i < csv.ColumnCount; i++)
{
InputHeadings[i] = csv.ColumnNames[i];
}
}
csv.Close();
}
/// <summary>
/// Process the input file and segregate into the output files.
/// </summary>
public void Process()
{
Validate();
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
ResetStatus();
foreach (SegregateTargetPercent target in _targets)
{
StreamWriter tw = PrepareOutputFile(target.Filename);
while ((target.NumberRemaining > 0) && csv.Next()
&& !ShouldStop())
{
UpdateStatus(false);
var row = new LoadedRow(csv);
WriteRow(tw, row);
target.NumberRemaining = target.NumberRemaining - 1;
}
tw.Close();
}
ReportDone(false);
csv.Close();
}
/// <summary>
/// Read the CSV file.
/// </summary>
private void ReadFile()
{
ReadCSV csv = null;
try
{
csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
ResetStatus();
int row = 0;
while (csv.Next() && !ShouldStop())
{
UpdateStatus("Reading data");
foreach (BaseCachedColumn column in Columns)
{
if (column is FileData)
{
if (column.Input)
{
var fd = (FileData) column;
String str = csv.Get(fd.Index);
double d = Format.Parse(str);
fd.Data[row] = d;
}
}
}
row++;
}
}
finally
{
ReportDone("Reading data");
if (csv != null)
{
csv.Close();
}
}
}
/// <summary>
/// Process the file.
/// </summary>
///
/// <param name="outputFile">The output file.</param>
/// <param name="method">The method to use.</param>
public void Process(FileInfo outputFile, IMLRegression method)
{
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
if (method.InputCount > _inputCount)
{
throw new AnalystError("This machine learning method has "
+ method.InputCount
+ " inputs, however, the data has " + _inputCount
+ " inputs.");
}
var input = new BasicMLData(method.InputCount);
StreamWriter tw = AnalystPrepareOutputFile(outputFile);
ResetStatus();
while (csv.Next())
{
UpdateStatus(false);
var row = new LoadedRow(csv, _idealCount);
int dataIndex = 0;
// load the input data
for (int i = 0; i < _inputCount; i++)
{
String str = row.Data[i];
double d = Format.Parse(str);
input[i] = d;
dataIndex++;
}
// do we need to skip the ideal values?
dataIndex += _idealCount;
// compute the result
IMLData output = method.Compute(input);
// display the computed result
for (int i = 0; i < _outputCount; i++)
{
double d = output[i];
row.Data[dataIndex++] = Format.Format(d, Precision);
}
WriteRow(tw, row);
}
ReportDone(false);
tw.Close();
csv.Close();
}
/// <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();
}