/// <inheritdoc />
public override sealed bool ExecuteCommand(String args)
{
// get filenames
String sourceID = Prop.GetPropertyString(
ScriptProperties.NormalizeConfigSourceFile);
String targetID = Prop.GetPropertyString(
ScriptProperties.NormalizeConfigTargetFile);
FileInfo sourceFile = Script.ResolveFilename(sourceID);
FileInfo targetFile = Script.ResolveFilename(targetID);
EncogLogging.Log(EncogLogging.LevelDebug, "Beginning normalize");
EncogLogging.Log(EncogLogging.LevelDebug, "source file:" + sourceID);
EncogLogging.Log(EncogLogging.LevelDebug, "target file:" + targetID);
// mark generated
Script.MarkGenerated(targetID);
// get formats
CSVFormat format = Script.DetermineFormat();
// prepare to normalize
var norm = new AnalystNormalizeCSV {Script = Script};
Analyst.CurrentQuantTask = norm;
norm.Report = new AnalystReportBridge(Analyst);
bool headers = Script.ExpectInputHeaders(sourceID);
norm.Analyze(sourceFile, headers, format, Analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(targetFile);
Analyst.CurrentQuantTask = null;
return norm.ShouldStop();
}
public void Execute(IExampleInterface app)
{
if (app.Args.Length != 2)
{
Console.WriteLine(@"Note: This example assumes that headers are present in the CSV files.");
Console.WriteLine(@"NormalizeFile [input file] [target file]");
}
else
{
var sourceFile = new FileInfo(app.Args[0]);
var targetFile = new FileInfo(app.Args[1]);
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Wizard(sourceFile, true, AnalystFileFormat.DecpntComma);
DumpFieldInfo(analyst);
var norm = new AnalystNormalizeCSV();
norm.Analyze(sourceFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(targetFile);
EncogFramework.Instance.Shutdown();
}
}
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 static void Normalise(FileOps fileOps)
{
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Wizard(fileOps.BaseFile,true,AnalystFileFormat.DecpntComma);
var norm = new AnalystNormalizeCSV{ProduceOutputHeaders = true};
norm.Analyze(fileOps.TrainingFile, true, CSVFormat.English, analyst);
norm.Normalize(fileOps.NormalisedTrainingFile);
norm.Analyze(fileOps.EvaluationFile, true, CSVFormat.English, analyst);
norm.Normalize(fileOps.NormalisedEvaluationFile);
analyst.Save(fileOps.AnalystFile);
}
private void dataNormalization(string f1, string f2)
{
var sourceFile = new FileInfo(f1);
var targetFile = new FileInfo(f2);
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Wizard(sourceFile, true, AnalystFileFormat.DecpntComma);
var norm = new AnalystNormalizeCSV();
norm.Analyze(sourceFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(targetFile);
analyst.Save(new FileInfo("stats.ega"));
analyst.Load(new FileInfo("stats.ega"));
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
Console.WriteLine("Running wizard...");
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.TargetFieldName = "field:1";
wizard.Wizard(sourceCSV,
false, AnalystFileFormat.DecpntComma);
// customer id
analyst.Script.Normalize.NormalizedFields[0].Action = Encog.Util.Arrayutil.NormalizationAction.PassThrough;
var norm = new AnalystNormalizeCSV();
norm.Report = new ConsoleStatusReportable();
Console.WriteLine("Analyze for normalize...");
norm.Analyze(sourceCSV, false, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
Console.WriteLine("Normalize...");
norm.Normalize(targetCSV);
analyst.Save(scriptEGA);
}
/// <summary>
/// Metodo responsavel por normalizar as informacoes para adequar a execucao da rede neural
/// </summary>
private static void Normalization()
{
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Wizard(Config.ClassificationFile, true, AnalystFileFormat.DecpntComma);
var norm = new AnalystNormalizeCSV();
norm.Analyze(Config.TrainingClassificationFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(Config.NormalizedTrainingClassificationFile);
norm.Analyze(Config.EvaluateClassificationFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedEvaluateClassificationFile);
analyst.Save(Config.AnalystClassificationFile);
}
public void Normalize(string outputFileName)
{
var norm = new AnalystNormalizeCSV();
var source = new FileInfo(m_fileName);
var target = new FileInfo(outputFileName);
norm.Analyze(source, true, CSVFormat.English, m_analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(target);
m_analyst.Save(String.Format("{0}.ega",
outputFileName.Split('.')[0]));
}
private static void Normalize(string sourceFile, string targetFile)
{
var sourceFileInfo = new FileInfo(sourceFile);
var targetFileInfo = new FileInfo(targetFile);
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Goal = AnalystGoal.Unknown; // Default Classification으로하면 Wizard 동작 안함
wizard.Wizard(sourceFileInfo, true, AnalystFileFormat.DecpntComma);
var norm = new AnalystNormalizeCSV();
norm.Analyze(sourceFileInfo, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(targetFileInfo);
}
/// <summary>
/// Metodo responsavel por normalizar as informacoes para adequar a execucao da rede neural
/// </summary>
private static void Normalization()
{
var analyst = new EncogAnalyst();
//Wizard
var wizard = new AnalystWizard(analyst);
wizard.Wizard(Config.RegressionFile, true, AnalystFileFormat.DecpntComma);
//Cilindros
analyst.Script.Normalize.NormalizedFields[0].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral;
//displacement
analyst.Script.Normalize.NormalizedFields[1].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//HorsePower
analyst.Script.Normalize.NormalizedFields[2].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//Peso
analyst.Script.Normalize.NormalizedFields[3].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//Aceleração
analyst.Script.Normalize.NormalizedFields[4].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//Ano
analyst.Script.Normalize.NormalizedFields[5].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral;
//Origem
analyst.Script.Normalize.NormalizedFields[6].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral;
//Nome
analyst.Script.Normalize.NormalizedFields[7].Action = Encog.Util.Arrayutil.NormalizationAction.Ignore;
//MPG
analyst.Script.Normalize.NormalizedFields[8].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
var norm = new AnalystNormalizeCSV();
norm.ProduceOutputHeaders = true;
norm.Analyze(Config.TrainingRegressionFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedTrainingRegressionFile);
//Norm of evaluation
norm.Analyze(Config.EvaluateRegressionFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedEvaluateRegressionFile);
//save the analyst file
analyst.Save(Config.AnalystRegressionFile);
}
public override sealed bool ExecuteCommand(string args)
{
string str2;
FileInfo info;
FileInfo info2;
CSVFormat format;
CSVFormat format2;
AnalystNormalizeCSV ecsv;
bool flag;
string propertyString = base.Prop.GetPropertyString("NORMALIZE:CONFIG_sourceFile");
if ((((uint) flag) + ((uint) flag)) <= uint.MaxValue)
{
str2 = base.Prop.GetPropertyString("NORMALIZE:CONFIG_targetFile");
info = base.Script.ResolveFilename(propertyString);
goto Label_017F;
}
if ((((uint) flag) | 2) != 0)
{
goto Label_017F;
}
Label_008B:
flag = base.Script.ExpectInputHeaders(propertyString);
Label_0099:
ecsv.Analyze(info, flag, format, base.Analyst);
ecsv.OutputFormat = format2;
if ((((uint) flag) | 0x7fffffff) != 0)
{
ecsv.ProduceOutputHeaders = true;
ecsv.Normalize(info2);
base.Analyst.CurrentQuantTask = null;
}
return ecsv.ShouldStop();
Label_017F:
if ((((uint) flag) | 0x7fffffff) == 0)
{
goto Label_0099;
}
do
{
AnalystNormalizeCSV ecsv2;
info2 = base.Script.ResolveFilename(str2);
EncogLogging.Log(0, "Beginning normalize");
EncogLogging.Log(0, "source file:" + propertyString);
EncogLogging.Log(0, "target file:" + str2);
base.Script.MarkGenerated(str2);
if (2 != 0)
{
format = base.Script.DetermineInputFormat(propertyString);
format2 = base.Script.DetermineOutputFormat();
ecsv2 = new AnalystNormalizeCSV();
}
ecsv2.Script = base.Script;
ecsv = ecsv2;
base.Analyst.CurrentQuantTask = ecsv;
ecsv.Report = new AnalystReportBridge(base.Analyst);
if (((uint) flag) <= uint.MaxValue)
{
goto Label_008B;
}
}
while (3 == 0);
goto Label_017F;
}
