/// <summary>
/// Analyze the data. This counts the records and prepares the data to be
/// processed.
/// </summary>
///
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="inputFile">The input file to analyze.</param>
/// <param name="headers">True, if the input file has headers.</param>
/// <param name="format">The format of the input file.</param>
public void Analyze(EncogAnalyst theAnalyst,
FileInfo inputFile, bool headers, CSVFormat format)
{
InputFilename = inputFile;
ExpectInputHeaders = headers;
InputFormat = format;
Analyzed = true;
_analyst = theAnalyst;
if (OutputFormat == null)
{
OutputFormat = InputFormat;
}
_data = new BasicMLDataSet();
ResetStatus();
int recordCount = 0;
int outputLength = _analyst.DetermineTotalColumns();
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, InputFormat);
ReadHeaders(csv);
_analystHeaders = new CSVHeaders(InputHeadings);
while (csv.Next() && !ShouldStop())
{
UpdateStatus(true);
var row = new LoadedRow(csv, 1);
double[] inputArray = AnalystNormalizeCSV.ExtractFields(
_analyst, _analystHeaders, csv, outputLength, true);
var input = new ClusterRow(inputArray, row);
_data.Add(input);
recordCount++;
}
RecordCount = recordCount;
Count = csv.ColumnCount;
ReadHeaders(csv);
csv.Close();
ReportDone(true);
}
/// <summary>
/// Construct the time-series utility.
/// </summary>
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="includeOutput">Should output fields be included.</param>
/// <param name="headings">The column headings.</param>
public TimeSeriesUtil(EncogAnalyst theAnalyst, bool includeOutput,
IEnumerable <string> headings)
{
_buffer = new List <double[]>();
_headingMap = new Dictionary <String, Int32>();
_analyst = theAnalyst;
_lagDepth = _analyst.LagDepth;
_leadDepth = _analyst.LeadDepth;
_totalDepth = _lagDepth + _leadDepth + 1;
_inputSize = includeOutput ? _analyst.DetermineTotalColumns() : _analyst.DetermineTotalInputFieldCount();
_outputSize = _analyst.DetermineInputCount()
+ _analyst.DetermineOutputCount();
int headingIndex = 0;
foreach (String column in headings)
{
_headingMap[column.ToUpper()] = headingIndex++;
}
}
/// <summary>
/// Analyze the data. This counts the records and prepares the data to be
/// processed.
/// </summary>
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="inputFile">The input file to analyze.</param>
/// <param name="headers">True, if the input file has headers.</param>
/// <param name="format">The format of the input file.</param>
public void Analyze(EncogAnalyst theAnalyst,
FileInfo inputFile, bool headers, CSVFormat format)
{
InputFilename = inputFile;
ExpectInputHeaders = headers;
Format = format;
Analyzed = true;
_analyst = theAnalyst;
_data = new BasicMLDataSet();
ResetStatus();
int recordCount = 0;
int outputLength = _analyst.DetermineTotalColumns();
var csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
ReadHeaders(csv);
_analystHeaders = new CSVHeaders(InputHeadings);
while (csv.Next() && !ShouldStop())
{
UpdateStatus(true);
double[] inputArray = AnalystNormalizeCSV.ExtractFields(
_analyst, _analystHeaders, csv, outputLength, true);
IMLData input = new BasicMLData(inputArray);
_data.Add(new BasicMLDataPair(input));
recordCount++;
}
RecordCount = recordCount;
Count = csv.ColumnCount;
ReadHeaders(csv);
csv.Close();
ReportDone(true);
}
/// <summary>
/// Normalize the input file. Write to the specified file.
/// </summary>
///
/// <param name="file">The file to write to.</param>
public void Normalize(FileInfo file)
{
if (_analyst == null)
{
throw new EncogError(
"Can't normalize yet, file has not been analyzed.");
}
ReadCSV csv = null;
StreamWriter tw = null;
try
{
csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, InputFormat);
file.Delete();
tw = new StreamWriter(file.OpenWrite());
// write headers, if needed
if (ProduceOutputHeaders)
{
WriteHeaders(tw);
}
ResetStatus();
int outputLength = _analyst.DetermineTotalColumns();
// write file contents
while (csv.Next() && !ShouldStop())
{
UpdateStatus(false);
double[] output = ExtractFields(
_analyst, _analystHeaders, csv, outputLength,
false);
if (_series.TotalDepth > 1)
{
output = _series.Process(output);
}
if (output != null)
{
var line = new StringBuilder();
NumberList.ToList(OutputFormat, line, output);
tw.WriteLine(line);
}
}
}
catch (IOException e)
{
throw new QuantError(e);
}
finally
{
ReportDone(false);
if (csv != null)
{
try
{
csv.Close();
}
catch (Exception ex)
{
EncogLogging.Log(ex);
}
}
if (tw != null)
{
try
{
tw.Close();
}
catch (Exception ex)
{
EncogLogging.Log(ex);
}
}
}
}
/// <summary>
/// Construct the time-series utility.
/// </summary>
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="includeOutput">Should output fields be included.</param>
/// <param name="headings">The column headings.</param>
public TimeSeriesUtil(EncogAnalyst theAnalyst, bool includeOutput,
IEnumerable<string> headings)
{
_buffer = new List<double[]>();
_headingMap = new Dictionary<String, Int32>();
_analyst = theAnalyst;
_lagDepth = _analyst.LagDepth;
_leadDepth = _analyst.LeadDepth;
_totalDepth = _lagDepth + _leadDepth + 1;
_inputSize = includeOutput ? _analyst.DetermineTotalColumns() : _analyst.DetermineTotalInputFieldCount();
_outputSize = _analyst.DetermineInputCount()
+ _analyst.DetermineOutputCount();
int headingIndex = 0;
foreach (String column in headings)
{
_headingMap[column.ToUpper()] = headingIndex++;
}
}
