/// <summary>
/// Construct the analyst wizard.
/// </summary>
///
/// <param name="theAnalyst">The analyst to use.</param>
public AnalystWizard(EncogAnalyst theAnalyst)
{
_directClassification = false;
_taskSegregate = true;
_taskRandomize = true;
_taskNormalize = true;
_taskBalance = false;
_taskCluster = true;
_range = NormalizeRange.NegOne2One;
_analyst = theAnalyst;
_script = _analyst.Script;
_methodType = WizardMethodType.FeedForward;
_targetField = "";
_goal = AnalystGoal.Classification;
_leadWindowSize = 0;
_lagWindowSize = 0;
_includeTargetField = false;
_missing = new DiscardMissing();
}
/// <summary>
/// Extract fields from a file into a numeric array for machine learning.
/// </summary>
///
/// <param name="analyst">The analyst to use.</param>
/// <param name="headers">The headers for the input data.</param>
/// <param name="csv">The CSV that holds the input data.</param>
/// <param name="outputLength">The length of the returned array.</param>
/// <param name="skipOutput">True if the output should be skipped.</param>
/// <returns>The encoded data.</returns>
public static double[] ExtractFields(EncogAnalyst analyst,
CSVHeaders headers, ReadCSV csv,
int outputLength, bool skipOutput)
{
var output = new double[outputLength];
int outputIndex = 0;
foreach (AnalystField stat in analyst.Script.Normalize.NormalizedFields)
{
if (stat.Action == NormalizationAction.Ignore)
{
continue;
}
if (stat.Output && skipOutput)
{
continue;
}
int index = headers.Find(stat.Name);
String str = csv.Get(index);
// is this an unknown value?
if (str.Equals("?") || str.Length == 0)
{
IHandleMissingValues handler = analyst.Script.Normalize.MissingValues;
double[] d = handler.HandleMissing(analyst, stat);
// should we skip the entire row
if (d == null)
{
return(null);
}
// copy the returned values in place of the missing values
for (int i = 0; i < d.Length; i++)
{
output[outputIndex++] = d[i];
}
}
else
{
// known value
if (stat.Action == NormalizationAction.Normalize)
{
double d = csv.Format.Parse(str.Trim());
d = stat.Normalize(d);
output[outputIndex++] = d;
}
else
{
double[] d = stat.Encode(str.Trim());
foreach (double element in d)
{
output[outputIndex++] = element;
}
}
}
}
return(output);
}
public AnalystWizard(EncogAnalyst theAnalyst)
{
if (15 == 0)
{
return;
}
this._x7047063a9bee4054 = true;
Label_0071:
this._xc24b506a94383a44 = false;
this._x34231b3d9a1591be = true;
this._x9b10ace6509508c0 = NormalizeRange.NegOne2One;
this._x554f16462d8d4675 = theAnalyst;
this._x594135906c55045c = this._x554f16462d8d4675.Script;
this._xa24f4208aa2278f4 = WizardMethodType.FeedForward;
this._x0768e2edc97194de = "";
if (8 != 0)
{
this._x29c8e5bee3cb25f8 = AnalystGoal.Classification;
this._xb6540cd895237850 = 0;
if (1 != 0)
{
this._x654428e3563552e3 = 0;
this._x0236ea04f9fa4aaa = false;
this._x771edacf1be2c386 = new DiscardMissing();
}
else
{
goto Label_0071;
}
}
}
/// <summary>
/// Construct the analyst wizard.
/// </summary>
/// <param name="theAnalyst">The analyst to use.</param>
public AnalystWizard(EncogAnalyst theAnalyst)
{
_directClassification = false;
_taskSegregate = true;
_taskRandomize = true;
_taskNormalize = true;
_taskBalance = false;
_taskCluster = true;
_range = NormalizeRange.NegOne2One;
_analyst = theAnalyst;
_script = _analyst.Script;
_methodType = WizardMethodType.FeedForward;
TargetFieldName = "";
_goal = AnalystGoal.Classification;
_leadWindowSize = 0;
_lagWindowSize = 0;
_includeTargetField = false;
_missing = new DiscardMissing();
MaxError = DefaultTrainError;
NaiveBayes = false;
}
