/// <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, InputFormat);
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(InputFormat.Format(double.Parse(GetColumnData(FileData.Open, csv)), Precision));
line.Append(";");
line.Append(InputFormat.Format(double.Parse(GetColumnData(FileData.High, csv)), Precision));
line.Append(";");
line.Append(InputFormat.Format(double.Parse(GetColumnData(FileData.Low, csv)), Precision));
line.Append(";");
line.Append(InputFormat.Format(double.Parse(GetColumnData(FileData.Close, csv)), Precision));
line.Append(";");
line.Append(InputFormat.Format(double.Parse(GetColumnData(FileData.Volume, csv)), Precision));
tw.WriteLine(line.ToString());
}
ReportDone(false);
csv.Close();
tw.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, InputFormat);
if (method.InputCount > _inputCount)
{
throw new AnalystError("This machine learning method has "
+ method.InputCount
+ " inputs, however, the data has " + _inputCount
+ " inputs.");
}
IMLData 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 = InputFormat.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++] = InputFormat.Format(d, Precision);
}
WriteRow(tw, row);
}
ReportDone(false);
tw.Close();
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, IMLMethod method)
{
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, InputFormat);
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++] = InputFormat
.Format(n, Precision);
}
}
}
}
}
WriteRow(tw, row);
}
ReportDone(false);
tw.Close();
csv.Close();
}
/// <summary>
/// Write the CSV.
/// </summary>
///
/// <param name="filename">The target filename.</param>
private void WriteCSV(FileInfo filename)
{
StreamWriter tw = null;
try
{
ResetStatus();
tw = new StreamWriter(filename.Create());
// write the headers
if (ExpectInputHeaders)
{
var line = new StringBuilder();
foreach (BaseCachedColumn column in Columns)
{
if (column.Output)
{
if (line.Length > 0)
{
line.Append(InputFormat.Separator);
}
line.Append("\"");
line.Append(column.Name);
line.Append("\"");
}
}
tw.WriteLine(line.ToString());
}
// starting and ending index
int beginningIndex = BeginningIndex;
int endingIndex = EndingIndex;
// write the file data
for (int row = beginningIndex; row <= endingIndex; row++)
{
UpdateStatus("Writing data");
var line_0 = new StringBuilder();
foreach (BaseCachedColumn column_1 in Columns)
{
if (column_1.Output)
{
if (line_0.Length > 0)
{
line_0.Append(InputFormat.Separator);
}
double d = column_1.Data[row];
line_0.Append(InputFormat.Format(d, Precision));
}
}
tw.WriteLine(line_0.ToString());
}
}
catch (IOException e)
{
throw (new QuantError(e));
}
finally
{
if (tw != null)
{
tw.Close();
}
}
}