public static IMLDataSet LoadAndNormalizeData(FileInfo fileInfo, AnalystGoal problemType, NormalizationAction normalizationType, bool randomize = true)
{
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Goal = problemType;
wizard.Wizard(fileInfo, true, AnalystFileFormat.DecpntComma);
var fields = analyst.Script.Normalize.NormalizedFields;
if (problemType == AnalystGoal.Classification)
fields[fields.Count - 1].Action = normalizationType;
var norm = new AnalystNormalizeCSV();
norm.Analyze(fileInfo, true, CSVFormat.DecimalPoint, analyst);
var normalizedDataFileInfo = new FileInfo("temp/temp.csv");
norm.Normalize(normalizedDataFileInfo);
var inputNeurons = fields.Count - 1;
int outputNeurons;
if (problemType == AnalystGoal.Classification)
outputNeurons = fields.Last().Classes.Count - (normalizationType == NormalizationAction.Equilateral ? 1 : 0);
else
outputNeurons = fields.Count - inputNeurons;
var result = CSVHelper.LoadCSVToDataSet(normalizedDataFileInfo, inputNeurons, outputNeurons, randomize);
normalizedDataFileInfo.Delete();
return result;
}
public void Wizard(AnalystGoal goal, WizardMethodType methodType, bool headers)
{
EncogAnalyst.MaxIteration = MaxIterations;
var wiz = new AnalystWizard(EncogAnalyst)
{
Goal = goal, MethodType = methodType, EvidenceSegements = 3
};
wiz.Wizard(_rawFile, headers, FileFormat);
EncogAnalyst.Save(_analystFile);
EncogAnalyst.Load(_analystFile);
}
/// <summary>
/// Set a property.
/// </summary>
/// <param name="name">The name.</param>
/// <param name="v">The value.</param>
public void SetProperty(String name, AnalystGoal v)
{
switch (v)
{
case AnalystGoal.Classification:
_data[name] = "classification";
break;
case AnalystGoal.Regression:
_data[name] = "regression";
break;
default:
_data[name] = "";
break;
}
}
/// <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>
/// Set a property.
/// </summary>
/// <param name="name">The name.</param>
/// <param name="v">The value.</param>
public void SetProperty(String name, AnalystGoal v)
{
switch (v)
{
case AnalystGoal.Classification:
_data[name] = "classification";
break;
case AnalystGoal.Regression:
_data[name] = "regression";
break;
default:
_data[name] = "";
break;
}
}
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;
}
public void Wizard(AnalystGoal goal, WizardMethodType methodType, bool headers)
{
EncogAnalyst.MaxIteration = MaxIterations;
var wiz = new AnalystWizard(EncogAnalyst) {Goal = goal, MethodType = methodType, EvidenceSegements = 3};
wiz.Wizard(_rawFile, headers, FileFormat);
EncogAnalyst.Save(_analystFile);
EncogAnalyst.Load(_analystFile);
}
public void SetProperty(string name, AnalystGoal v)
{
switch (v)
{
case AnalystGoal.Regression:
this._x4a3f0a05c02f235f[name] = "regression";
return;
case AnalystGoal.Classification:
this._x4a3f0a05c02f235f[name] = "classification";
return;
}
this._x4a3f0a05c02f235f[name] = "";
}
