static void Evaluate()
{
var network = (BasicNetwork)EncogDirectoryPersistence.LoadObject(Config.TrainedNetworkFile);
var analyst = new EncogAnalyst();
analyst.Load(Config.AnalystFile.ToString());
var evaluationSet = EncogUtility.LoadCSV2Memory(Config.NormalizedEvaluateFile.ToString(),
network.InputCount, network.OutputCount, true, CSVFormat.English, false);
using (var file = new System.IO.StreamWriter(Config.ValidationResult.ToString()))
{
file.WriteLine("Ideal,Actual");
foreach (var item in evaluationSet)
{
// WAS 8
var NormalizedActualoutput = (BasicMLData)network.Compute(item.Input);
var Actualoutput = analyst.Script.Normalize.NormalizedFields[8].DeNormalize(NormalizedActualoutput.Data[0]);
var IdealOutput = analyst.Script.Normalize.NormalizedFields[8].DeNormalize(item.Ideal[0]);
//Write to File
var resultLine = IdealOutput.ToString() + "," + Actualoutput.ToString();
file.WriteLine(resultLine);
Console.WriteLine("Ideal : {0}, Actual : {1}", IdealOutput, Actualoutput);
}
}
}
public string Evaluate(FileInfo TrainedNetworkFile, FileInfo AnalystFile, FileInfo NormalizedEvaluateFile)
{
var network = (BasicNetwork)EncogDirectoryPersistence.LoadObject(TrainedNetworkFile);
var analyst = new EncogAnalyst();
analyst.Load(AnalystFile.ToString());
var evaluationSet = EncogUtility.LoadCSV2Memory(NormalizedEvaluateFile.ToString(),
network.InputCount, network.OutputCount, true, CSVFormat.English, false);
var career = string.Empty;
foreach (var item in evaluationSet)
{
var output = network.Compute(item.Input);
int classCount = analyst.Script.Normalize.NormalizedFields[3].Classes.Count;
double normalizationHigh = analyst.Script.Normalize.NormalizedFields[3].NormalizedHigh;
double normalizationLow = analyst.Script.Normalize.NormalizedFields[3].NormalizedLow;
var eq = new Encog.MathUtil.Equilateral(classCount, normalizationHigh, normalizationLow);
var predictedClassInt = eq.Decode(output);
career = analyst.Script.Normalize.NormalizedFields[3].Classes[predictedClassInt].Name;
}
return(career);
}
public void TestRegression()
{
FileInfo rawFile = TEMP_DIR.CreateFile("EncogCSharp/Resources/simple.csv");
FileInfo egaFile = TEMP_DIR.CreateFile("EncogCSharp/Resources/simple.ega");
FileInfo outputFile = TEMP_DIR.CreateFile("EncogCSharp/Resources/simple_output.csv");
FileUtil.CopyResource("EncogCSharp/Resources/simple.csv", rawFile);
FileUtil.CopyResource("EncogCSharp/Resources/simple-r.ega", egaFile);
EncogAnalyst analyst = new EncogAnalyst();
analyst.Load(egaFile);
analyst.ExecuteTask("task-full");
ReadCSV csv = new ReadCSV(outputFile.ToString(), true, CSVFormat.English);
while (csv.Next())
{
double diff = Math.Abs(csv.GetDouble(2) - csv.GetDouble(4));
Assert.IsTrue(diff < 1.5);
}
Assert.AreEqual(4, analyst.Script.Fields.Length);
Assert.AreEqual(3, analyst.Script.Fields[3].ClassMembers.Count);
csv.Close();
}
public void TestClassification()
{
FileInfo rawFile = TEMP_DIR.CreateFile("simple.csv");
FileInfo egaFile = TEMP_DIR.CreateFile("simple.ega");
FileInfo outputFile = TEMP_DIR.CreateFile("simple_output.csv");
FileUtil.CopyResource("simple.csv", rawFile);
FileUtil.CopyResource("simple.ega", egaFile);
EncogAnalyst analyst = new EncogAnalyst();
analyst.AddAnalystListener(new ConsoleAnalystListener());
analyst.Load(egaFile);
analyst.ExecuteTask("task-full");
ReadCSV csv = new ReadCSV(outputFile.ToString(), true, CSVFormat.English);
while (csv.Next())
{
Assert.AreEqual(csv.Get(3), csv.Get(4));
}
Assert.AreEqual(4, analyst.Script.Fields.Length);
Assert.AreEqual(3, analyst.Script.Fields[3].ClassMembers.Count);
csv.Close();
}
/// <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.</param>
/// <param name="headers">True if headers are present.</param>
/// <param name="format">The format the file is in.</param>
public void Analyze(EncogAnalyst theAnalyst,
FileInfo inputFile, bool headers, CSVFormat format)
{
InputFilename = inputFile;
ExpectInputHeaders = headers;
Format = format;
_analyst = theAnalyst;
Analyzed = true;
PerformBasicCounts();
_inputCount = _analyst.DetermineInputCount();
_outputCount = _analyst.DetermineOutputCount();
_idealCount = InputHeadings.Length - _inputCount;
if ((InputHeadings.Length != _inputCount) &&
(InputHeadings.Length != (_inputCount + _outputCount)))
{
throw new AnalystError("Invalid number of columns("
+ InputHeadings.Length + "), must match input("
+ _inputCount + ") count or input+output("
+ (_inputCount + _outputCount) + ") count.");
}
}
private static void Step3()
{
Console.WriteLine("Step 3: Normalize Training and Evaluation Data");
//Analyst
var analyst = new EncogAnalyst();
//Wizard
var wizard = new AnalystWizard(analyst);
wizard.Wizard(Config.BaseFile, true, AnalystFileFormat.DecpntComma);
//Norm for Training
var norm = new AnalystNormalizeCSV();
norm.Analyze(Config.TrainingFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(Config.NormalizedTrainingFile);
//Norm for evaluation
norm.Analyze(Config.EvaluateFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(Config.NormalizedEvaluateFile);
analyst.Save(Config.AnalystFile);
}
/**
* Generate the code from Encog Analyst.
*
* @param analyst
* The Encog Analyst object to use for code generation.
*/
public void Generate(EncogAnalyst analyst)
{
if (targetLanguage == TargetLanguage.MQL4 ||
targetLanguage == TargetLanguage.NinjaScript)
{
if (!EmbedData)
{
throw new AnalystCodeGenerationError(
"MQL4 and Ninjascript must be embedded.");
}
}
if (generator is IProgramGenerator)
{
String methodID =
analyst.Script.Properties.GetPropertyString(ScriptProperties.MlConfigMachineLearningFile);
String trainingID = analyst.Script.Properties.GetPropertyString(ScriptProperties.MlConfigTrainingFile);
FileInfo methodFile = analyst.Script.ResolveFilename(methodID);
FileInfo trainingFile = analyst.Script.ResolveFilename(trainingID);
Generate(methodFile, trainingFile);
}
else
{
((ITemplateGenerator)generator).Generate(analyst);
}
}
public static void NormalizeData()
{
var toNormalize = new FileInfo(csvPath);
var Normalized = new FileInfo(csvPathNormalized);
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Wizard(toNormalize, true, AnalystFileFormat.DecpntComma);
var norm = new AnalystNormalizeCSV();
norm.Analyze(toNormalize, true, CSVFormat.English, analyst);
foreach (AnalystField field in analyst.Script.Normalize.NormalizedFields)
{
field.NormalizedHigh = 1;
field.NormalizedLow = -1;
field.Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
if (field.Name == "cls")
{
field.Action = Encog.Util.Arrayutil.NormalizationAction.PassThrough;
}
}
norm.ProduceOutputHeaders = true;
norm.Normalize(Normalized);
}
/// <summary>
/// Process the file and cluster.
/// </summary>
///
/// <param name="outputFile">The output file.</param>
/// <param name="clusters">The number of clusters.</param>
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="iterations">The number of iterations to use.</param>
public void Process(FileInfo outputFile, int clusters,
EncogAnalyst theAnalyst, int iterations)
{
StreamWriter tw = PrepareOutputFile(outputFile);
ResetStatus();
var cluster = new KMeansClustering(clusters,
_data);
cluster.Iteration(iterations);
int clusterNum = 0;
foreach (IMLCluster cl in cluster.Clusters)
{
foreach (IMLData item in cl.Data)
{
var row = (ClusterRow)item;
int clsIndex = row.Input.Count - 1;
LoadedRow lr = row.Row;
lr.Data[clsIndex] = "" + clusterNum;
WriteRow(tw, lr);
}
clusterNum++;
}
ReportDone(false);
tw.Close();
}
/// <see cref="INetwork.Normalize"/>
public INetwork Normalize()
{
//Analyst
var analyst = new EncogAnalyst();
//Wizard
var wizard = new AnalystWizard(analyst);
wizard.Wizard(BaseFile, true, AnalystFileFormat.DecpntComma);
// Configure normalized fields
ConfigureNormalizedFields(analyst);
//Norm for Trainng
var norm = new AnalystNormalizeCSV();
norm.Analyze(TrainingFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(NormalizedTrainingFile);
//Norm of evaluation
norm.Analyze(EvaluateFile, true, CSVFormat.English, analyst);
norm.ProduceOutputHeaders = true;
norm.Normalize(NormalizedEvaluateFile);
//Save the analyst file
analyst.Save(AnalystFile);
return(this);
}
/// <see cref="INetwork.Evaluate"/>
public INetwork Evaluate()
{
var network = (BasicNetwork)EncogDirectoryPersistence.LoadObject(TrainedNetworkFile);
var analyst = new EncogAnalyst();
analyst.Load(AnalystFile.ToString());
var evaluationSet = EncogUtility.LoadCSV2Memory(NormalizedEvaluateFile.ToString(),
network.InputCount, network.OutputCount, true, CSVFormat.English, false);
int count = 0;
using (var file = new StreamWriter(EvaluateFileOutput.ToString())) {
file.WriteLine("Target, Predict");
foreach (var item in evaluationSet)
{
count++;
var output = network.Compute(item.Input);
var predict = analyst.Script.Normalize.NormalizedFields[43].DeNormalize(output[0]);
var target = analyst.Script.Normalize.NormalizedFields[43].DeNormalize(item.Ideal[0]);
PredictResult.Add(new PredictResultModel {
Target = target, Predict = predict
});
var resultLine = string.Format("{0},{1}", target, predict);
file.WriteLine(resultLine);
}
}
return(this);
}
/// <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);
}
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();
}
}
private void AnalystCommand()
{
String egaFile;
String task;
if (_cmd.Args.Count == 0)
{
Console.WriteLine(@"Must specify the EGA file to run");
return;
}
if (_cmd.Args.Count == 1)
{
egaFile = _cmd.Args[0];
task = "task-full";
}
else
{
egaFile = _cmd.Args[0];
task = _cmd.Args[1];
}
_sw.Start();
var analyst = new EncogAnalyst();
analyst.AddAnalystListener(new ConsoleAnalystListener());
analyst.Load(new FileInfo(egaFile));
analyst.ExecuteTask(task);
}
/// <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.</param>
/// <param name="headers">True if headers are present.</param>
/// <param name="format">The format the file is in.</param>
public void Analyze(EncogAnalyst theAnalyst,
FileInfo inputFile, bool headers, CSVFormat format)
{
InputFilename = inputFile;
ExpectInputHeaders = headers;
Format = format;
_analyst = theAnalyst;
Analyzed = true;
PerformBasicCounts();
_inputCount = _analyst.DetermineInputCount();
_outputCount = _analyst.DetermineOutputCount();
_idealCount = InputHeadings.Length - _inputCount;
if ((InputHeadings.Length != _inputCount)
&& (InputHeadings.Length != (_inputCount + _outputCount)))
{
throw new AnalystError("Invalid number of columns("
+ InputHeadings.Length + "), must match input("
+ _inputCount + ") count or input+output("
+ (_inputCount + _outputCount) + ") count.");
}
}
public void Evaluate(FileInfo networkFile, FileInfo analystFile, FileInfo EvaluationFile, FileInfo validationResultsFile)
{
var network = EncogDirectoryPersistence.LoadObject(networkFile) as BasicNetwork;
var analyst = new EncogAnalyst();
analyst.Load(analystFile);
var evaluationSet = EncogUtility.LoadCSV2Memory(EvaluationFile.ToString(), network.InputCount,
network.OutputCount, true, CSVFormat.English, false);
using (var file = new StreamWriter(validationResultsFile.ToString()))
{
foreach (var item in evaluationSet)
{
var normalizedActualOutput = (BasicMLData)network.Compute(item.Input);
var actualOutput = analyst.Script.Normalize.NormalizedFields[8]
.DeNormalize(normalizedActualOutput.Data[0]);
var idealOutput = analyst.Script.Normalize.NormalizedFields[8]
.DeNormalize(item.Ideal[0]);
file.WriteLine($"{idealOutput}, {actualOutput}");
Console.WriteLine($"Ideal: {idealOutput} | Actual: {actualOutput}");
}
}
}
private static void ConfigureNormalizedFields(EncogAnalyst analyst)
{
foreach (var field in analyst.Script.Normalize.NormalizedFields)
{
field.NormalizedHigh = 1;
field.NormalizedLow = 0;
}
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="theAnalyst">The analyst.</param>
/// <param name="theBackwardWindowSize">The backward window size.</param>
/// <param name="theForwardWindowSize">The forward window size.</param>
public AnalystProcess(EncogAnalyst theAnalyst, int theBackwardWindowSize, int theForwardWindowSize)
{
analyst = theAnalyst;
backwardWindowSize = theBackwardWindowSize;
forwardWindowSize = theForwardWindowSize;
StandardExtensions.CreateAll(programContext);
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="theAnalyst">The analyst.</param>
/// <param name="theBackwardWindowSize">The backward window size.</param>
/// <param name="theForwardWindowSize">The forward window size.</param>
public AnalystProcess(EncogAnalyst theAnalyst, int theBackwardWindowSize, int theForwardWindowSize)
{
analyst = theAnalyst;
backwardWindowSize = theBackwardWindowSize;
forwardWindowSize = theForwardWindowSize;
StandardExtensions.CreateAll(programContext);
}
/// <inheritdoc/>
public double[] HandleMissing(EncogAnalyst analyst, AnalystField stat)
{
var result = new double[stat.ColumnsNeeded];
double n = stat.NormalizedHigh - (stat.NormalizedHigh - stat.NormalizedLow/2);
for (int i = 0; i < result.Length; i++)
{
result[i] = n;
}
return result;
}
public AnalystTestingUtility(String theBaseDataFile)
{
_tempDir.ClearContents();
BaseDataFile = theBaseDataFile;
_rawFile = _tempDir.CreateFile("test.csv");
FileUtil.CopyResource(theBaseDataFile, _rawFile);
_analystFile = _tempDir.CreateFile("test.ega");
EncogAnalyst = new EncogAnalyst();
FileFormat = AnalystFileFormat.DecpntComma;
}
/// <inheritdoc/>
public double[] HandleMissing(EncogAnalyst analyst, AnalystField stat)
{
var result = new double[stat.ColumnsNeeded];
double n = stat.NormalizedHigh - (stat.NormalizedHigh - stat.NormalizedLow / 2);
for (int i = 0; i < result.Length; i++)
{
result[i] = n;
}
return(result);
}
static void EncogAnalyst()
{
var sourceFile = new FileInfo("RawFile.csv");
var targetFile = new FileInfo("NormalizedFile.csv");
var analyst = new EncogAnalyst();
var wizard = new AnalystWizard(analyst);
wizard.Wizard(sourceFile, true, AnalystFileFormat.DecpntComma);
var norm = new AnalystNormalizeCSV();
norm.Analyze(sourceFile, true, CSVFormat.DecimalComma, analyst);
norm.Normalize(targetFile);
}
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);
}
/// <inheritdoc/>
public double[] HandleMissing(EncogAnalyst analyst, AnalystField stat)
{
// mode?
if (stat.Classify)
{
var m = stat.DetermineMode(analyst);
return stat.Encode(m);
}
// mean
var df = analyst.Script.FindDataField(stat.Name);
var result = new double[1];
result[0] = df.Mean;
return result;
}
/// <inheritdoc />
public double[] HandleMissing(EncogAnalyst analyst, AnalystField stat)
{
// mode?
if (stat.Classify)
{
int m = stat.DetermineMode(analyst);
return(stat.Encode(m));
}
// mean
DataField df = analyst.Script.FindDataField(stat.Name);
var result = new double[1];
result[0] = df.Mean;
return(result);
}
static void Step3()
{
Console.WriteLine("Step 3: Normalize Training and Evaluation Data");
//Analyst
var analyst = new EncogAnalyst();
//Wizard
var wizard = new AnalystWizard(analyst);
wizard.Wizard(Config.BaseFile, true, AnalystFileFormat.DecpntComma);
//Cylinders
analyst.Script.Normalize.NormalizedFields[0].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral; // multivalued discreste field type
//displacement
analyst.Script.Normalize.NormalizedFields[1].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize; // continues
//HorsePower
analyst.Script.Normalize.NormalizedFields[2].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//weight
analyst.Script.Normalize.NormalizedFields[3].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//Acceleration
analyst.Script.Normalize.NormalizedFields[4].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//year
analyst.Script.Normalize.NormalizedFields[5].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral; // multivalued discreste field type
//Origin
analyst.Script.Normalize.NormalizedFields[6].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral; // multivalued discreste field type
//Name
analyst.Script.Normalize.NormalizedFields[7].Action = Encog.Util.Arrayutil.NormalizationAction.Ignore; // will not use
//mpg
analyst.Script.Normalize.NormalizedFields[8].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize; // contniues
//Norm for Trainng
var norm = new AnalystNormalizeCSV();
norm.ProduceOutputHeaders = true;
norm.Analyze(Config.TrainingFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedTrainingFile);
//Norm of evaluation
norm.Analyze(Config.EvaluateFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedEvaluateFile);
//save the analyst file
analyst.Save(Config.AnalystFile);
}
public static void DumpFieldInfo(EncogAnalyst analyst)
{
Console.WriteLine(@"Fields found in file:");
foreach (AnalystField field in analyst.Script.Normalize.NormalizedFields)
{
var line = new StringBuilder();
line.Append(field.Name);
line.Append(",action=");
line.Append(field.Action);
line.Append(",min=");
line.Append(field.ActualLow);
line.Append(",max=");
line.Append(field.ActualHigh);
Console.WriteLine(line.ToString());
}
}
/// <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);
}
private void Normalize()
{
//Analyst
var analyst = new EncogAnalyst();
//Wizard
var wizard = new AnalystWizard(analyst);
wizard.Wizard(Config.BaseFile, true, AnalystFileFormat.DecpntComma);
//Cylinders
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;
//weight
analyst.Script.Normalize.NormalizedFields[3].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//Acceleration
analyst.Script.Normalize.NormalizedFields[4].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//year
analyst.Script.Normalize.NormalizedFields[5].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral;
//Origin
analyst.Script.Normalize.NormalizedFields[6].Action = Encog.Util.Arrayutil.NormalizationAction.Equilateral;
//Name
analyst.Script.Normalize.NormalizedFields[7].Action = Encog.Util.Arrayutil.NormalizationAction.Ignore;
//mpg
analyst.Script.Normalize.NormalizedFields[8].Action = Encog.Util.Arrayutil.NormalizationAction.Normalize;
//Norm for Trainng
var norm = new AnalystNormalizeCSV();
norm.ProduceOutputHeaders = true;
norm.Analyze(Config.TrainingFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedTrainingFile);
//Norm of Cross Validation
norm.Analyze(Config.CrossValidationFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedCrossValidationFile);
//Norm of evaluation
norm.Analyze(Config.EvaluateFile, true, CSVFormat.English, analyst);
norm.Normalize(Config.NormalizedEvaluateFile);
//save the analyst file
analyst.Save(Config.AnalystFile);
}
/// <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);
}
public void Evaluate(FileInfo networkFile, FileInfo analystFile, FileInfo EvaluationFile)
{
var network = EncogDirectoryPersistence.LoadObject(networkFile) as BasicNetwork;
var analyst = new EncogAnalyst();
analyst.Load(analystFile);
var evaluationSet = EncogUtility.LoadCSV2Memory(EvaluationFile.ToString(), network.InputCount,
network.OutputCount, true, CSVFormat.English, false);
int count = 0;
int correctCount = 0;
foreach (var item in evaluationSet)
{
var sepal_l = analyst.Script.Normalize.NormalizedFields[0].DeNormalize(item.Input[0]);
var sepal_w = analyst.Script.Normalize.NormalizedFields[1].DeNormalize(item.Input[1]);
var petal_l = analyst.Script.Normalize.NormalizedFields[2].DeNormalize(item.Input[2]);
var petal_w = analyst.Script.Normalize.NormalizedFields[3].DeNormalize(item.Input[3]);
int classCount = analyst.Script.Normalize.NormalizedFields[4].Classes.Count;
double normalizationHigh = analyst.Script.Normalize.NormalizedFields[4].NormalizedHigh;
double normalizationLow = analyst.Script.Normalize.NormalizedFields[4].NormalizedLow;
var output = network.Compute(item.Input);
var resulter = new Equilateral(classCount, normalizationHigh, normalizationLow);
var predictedClassInt = resulter.Decode(output);
var predictedClass = analyst.Script.Normalize.NormalizedFields[4].Classes[predictedClassInt].Name;
var idealClassInt = resulter.Decode(item.Ideal);
var idealClass = analyst.Script.Normalize.NormalizedFields[4].Classes[idealClassInt].Name;
if (predictedClassInt == idealClassInt)
{
++correctCount;
}
Console.WriteLine($"Count: {++count} | Ideal: {idealClass} Predicted:{predictedClass}");
}
Console.WriteLine($"Total test count: {count}");
Console.WriteLine($"Total correct test count: {correctCount}");
Console.WriteLine($"% Success: {(correctCount*100.0)/count}");
}
public void Normalize(FileInfo baseFile, FileInfo trainingFile, FileInfo normalizedTrainingFile, FileInfo evaluateFile, FileInfo normalizedEvaluateFile, FileInfo analystFile)
{
var encogAnalyst = new EncogAnalyst();
var analystWizard = new AnalystWizard(encogAnalyst);
analystWizard.Wizard(baseFile, true, AnalystFileFormat.DecpntComma);
var normalizer = new AnalystNormalizeCSV();
normalizer.Analyze(trainingFile, true, CSVFormat.English, encogAnalyst);
normalizer.ProduceOutputHeaders = true;
normalizer.Normalize(normalizedTrainingFile);
normalizer.Analyze(evaluateFile, true, CSVFormat.English, encogAnalyst);
normalizer.Normalize(normalizedEvaluateFile);
encogAnalyst.Save(analystFile);
}
/// <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.</param>
/// <param name="headers">True if headers are present.</param>
/// <param name="format">The format.</param>
public void Analyze(EncogAnalyst theAnalyst,
FileInfo inputFile, bool headers, CSVFormat format)
{
InputFilename = inputFile;
ExpectInputHeaders = headers;
Format = format;
Analyzed = true;
_analyst = theAnalyst;
PerformBasicCounts();
_fileColumns = InputHeadings.Length;
_outputColumns = _analyst.DetermineOutputFieldCount();
_analystHeaders = new CSVHeaders(InputHeadings);
_series = new TimeSeriesUtil(_analyst, false,
_analystHeaders.Headers);
}
/// <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.</param>
/// <param name="headers">True if headers are present.</param>
/// <param name="format">The format.</param>
public void Analyze(EncogAnalyst theAnalyst,
FileInfo inputFile, bool headers, CSVFormat format)
{
InputFilename = inputFile;
ExpectInputHeaders = headers;
Format = format;
Analyzed = true;
_analyst = theAnalyst;
PerformBasicCounts();
_fileColumns = InputHeadings.Length;
_outputColumns = _analyst.DetermineOutputFieldCount();
_analystHeaders = new CSVHeaders(InputHeadings);
_series = new TimeSeriesUtil(_analyst, false,
_analystHeaders.Headers);
}
/// <summary>
/// Metodo responsavel por avaliar a rede neural treinada com a massa de testes criada no metodo Segregate e normalizada no metodo Normalization
/// </summary>
private static void Evaluate()
{
var network = (BasicNetwork)EncogDirectoryPersistence.LoadObject(Config.TrainedNetworkClassificationFile);
var analyst = new EncogAnalyst();
analyst.Load(Config.AnalystClassificationFile.ToString());
var evaluationSet = EncogUtility.LoadCSV2Memory(Config.NormalizedEvaluateClassificationFile.ToString(),
network.InputCount, network.OutputCount, true, CSVFormat.English, false);
int count = 0;
int CorrectCount = 0;
foreach (var item in evaluationSet)
{
count++;
var output = network.Compute(item.Input);
var sepal_l = analyst.Script.Normalize.NormalizedFields[0].DeNormalize(item.Input[0]);
var sepal_w = analyst.Script.Normalize.NormalizedFields[1].DeNormalize(item.Input[1]);
var petal_l = analyst.Script.Normalize.NormalizedFields[2].DeNormalize(item.Input[2]);
var petal_w = analyst.Script.Normalize.NormalizedFields[3].DeNormalize(item.Input[3]);
int classCount = analyst.Script.Normalize.NormalizedFields[4].Classes.Count;
double normalizationHigh = analyst.Script.Normalize.NormalizedFields[4].NormalizedHigh;
double normalizationLow = analyst.Script.Normalize.NormalizedFields[4].NormalizedLow;
var eq = new Encog.MathUtil.Equilateral(classCount, normalizationHigh, normalizationLow);
var predictedClassInt = eq.Decode(output);
var predictedClass = analyst.Script.Normalize.NormalizedFields[4].Classes[predictedClassInt].Name;
var idealClassInt = eq.Decode(item.Ideal);
var idealClass = analyst.Script.Normalize.NormalizedFields[4].Classes[idealClassInt].Name;
if (predictedClassInt == idealClassInt)
{
CorrectCount++;
}
Console.WriteLine("Count :{0} Properties [{1},{2},{3},{4}] ,Ideal : {5} Predicted : {6} ",
count, sepal_l, sepal_w, petal_l, petal_w, idealClass, predictedClass);
}
Console.WriteLine("Quantidade de itens: {0}", count);
Console.WriteLine("Quantidade de acertos: {0}", CorrectCount);
Console.WriteLine("Porcentagem de acertos: {0}", ((CorrectCount * 100.0) / count));
}
/// <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>
/// Construct the randomize command.
/// </summary>
/// <param name="analyst">The analyst to use.</param>
public CmdProcess(EncogAnalyst analyst)
: base(analyst)
{
}
/// <summary>
/// Construct the set command with the analyst.
/// </summary>
/// <param name="analyst">The analyst to use.</param>
public CmdSet(EncogAnalyst analyst)
: base(analyst)
{
}
/// <summary>
/// Construct the evaluate command.
/// </summary>
///
/// <param name="theAnalyst">The analyst to use.</param>
public CmdEvaluate(EncogAnalyst theAnalyst)
: base(theAnalyst)
{
}
/// <summary>
/// Construct this generate command.
/// </summary>
/// <param name="analyst">The analyst to use.</param>
public CmdGenerate(EncogAnalyst analyst)
: base(analyst)
{
}
/// <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;
}
/// <summary>
/// Construct the bridge object.
/// </summary>
///
/// <param name="theAnalyst">The Encog analyst to use.</param>
public AnalystReportBridge(EncogAnalyst theAnalyst)
{
_analyst = theAnalyst;
}
/// <summary>
/// Determine the mode, this is the class item that has the most instances.
/// </summary>
/// <param name="analyst">The analyst to use.</param>
/// <returns>The mode.</returns>
public int DetermineMode(EncogAnalyst analyst)
{
if (!Classify)
{
throw new AnalystError("Can only calculate the mode for a class.");
}
DataField df = analyst.Script.FindDataField(Name);
AnalystClassItem m = null;
int result = 0;
int idx = 0;
foreach (AnalystClassItem item in df.ClassMembers)
{
if (m == null || m.Count < item.Count)
{
m = item;
result = idx;
}
idx++;
}
return result;
}
/// <summary>
/// Construct the balance command.
/// </summary>
/// <param name="analyst">The analyst to use with this command.</param>
public CmdBalance(EncogAnalyst analyst)
: base(analyst)
{
}
/// <summary>
/// Process the file and cluster.
/// </summary>
/// <param name="outputFile">The output file.</param>
/// <param name="clusters">The number of clusters.</param>
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="iterations">The number of iterations to use.</param>
public void Process(FileInfo outputFile, int clusters,
EncogAnalyst theAnalyst, int iterations)
{
StreamWriter tw = PrepareOutputFile(outputFile);
ResetStatus();
var cluster = new KMeansClustering(clusters,
_data);
cluster.Iteration(iterations);
int clusterNum = 0;
foreach (IMLCluster cl in cluster.Clusters)
{
foreach (IMLData item in cl.Data)
{
int clsIndex = item.Count;
var lr = new LoadedRow(Format, item, 1);
lr.Data[clsIndex] = "" + clusterNum;
WriteRow(tw, lr);
}
clusterNum++;
}
ReportDone(false);
tw.Close();
}
/// <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;
}
/// <summary>
/// Construct the randomize command.
/// </summary>
///
/// <param name="analyst">The analyst to use.</param>
public CmdRandomize(EncogAnalyst analyst) : base(analyst)
{
}
/// <summary>
/// Perform the analysis.
/// </summary>
/// <param name="target">The Encog analyst object to analyze.</param>
public void Process(EncogAnalyst target)
{
int count = 0;
CSVFormat csvFormat = ConvertStringConst
.ConvertToCSVFormat(_format);
var csv = new ReadCSV(_filename, _headers, csvFormat);
// pass one, calculate the min/max
while (csv.Next())
{
if (_fields == null)
{
GenerateFields(csv);
}
for (int i = 0; i < csv.ColumnCount; i++)
{
if (_fields != null)
{
_fields[i].Analyze1(csv.Get(i));
}
}
count++;
}
if (count == 0)
{
throw new AnalystError("Can't analyze file, it is empty.");
}
if (_fields != null)
{
foreach (AnalyzedField field in _fields)
{
field.CompletePass1();
}
}
csv.Close();
// pass two, standard deviation
csv = new ReadCSV(_filename, _headers, csvFormat);
while (csv.Next())
{
for (int i = 0; i < csv.ColumnCount; i++)
{
if (_fields != null)
{
_fields[i].Analyze2(csv.Get(i));
}
}
}
if (_fields != null)
{
foreach (AnalyzedField field in _fields)
{
field.CompletePass2();
}
}
csv.Close();
String str = _script.Properties.GetPropertyString(
ScriptProperties.SetupConfigAllowedClasses) ?? "";
bool allowInt = str.Contains("int");
bool allowReal = str.Contains("real")
|| str.Contains("double");
bool allowString = str.Contains("string");
// remove any classes that did not qualify
foreach (AnalyzedField field in _fields)
{
if (field.Class)
{
if (!allowInt && field.Integer)
{
field.Class = false;
}
if (!allowString && (!field.Integer && !field.Real))
{
field.Class = false;
}
if (!allowReal && field.Real && !field.Integer)
{
field.Class = false;
}
}
}
// merge with existing
if ((target.Script.Fields != null)
&& (_fields.Length == target.Script.Fields.Length))
{
for (int i = 0; i < _fields.Length; i++)
{
// copy the old field name
_fields[i].Name = target.Script.Fields[i].Name;
if (_fields[i].Class)
{
IList<AnalystClassItem> t = _fields[i].AnalyzedClassMembers;
IList<AnalystClassItem> s = target.Script.Fields[i].ClassMembers;
if (s.Count == t.Count)
{
for (int j = 0; j < s.Count; j++)
{
if (t[j].Code.Equals(s[j].Code))
{
t[j].Name = s[j].Name;
}
}
}
}
}
}
// now copy the fields
var df = new DataField[_fields.Length];
for (int i_4 = 0; i_4 < df.Length; i_4++)
{
df[i_4] = _fields[i_4].FinalizeField();
}
target.Script.Fields = df;
}
/// <summary>
/// Construct the train command.
/// </summary>
/// <param name="analyst">The analyst to use.</param>
public CmdTrain(EncogAnalyst analyst)
: base(analyst)
{
}
/// <summary>
/// Construct the create command.
/// </summary>
/// <param name="theAnalyst">The analyst to use.</param>
public CmdCreate(EncogAnalyst theAnalyst) : base(theAnalyst)
{
}
/// <summary>
/// Construct the report.
/// </summary>
///
/// <param name="theAnalyst">The analyst to use.</param>
public AnalystReport(EncogAnalyst theAnalyst)
{
_analyst = theAnalyst;
}
/// <summary>
/// Construct this generate command.
/// </summary>
/// <param name="analyst">The analyst to use.</param>
public CmdCode(EncogAnalyst analyst)
: base(analyst)
{
}
public double[] HandleMissing(EncogAnalyst analyst, AnalystField stat)
{
return null;
}
/// <summary>
/// Construct an evaluate raw command.
/// </summary>
/// <param name="analyst">The analyst object to use.</param>
public CmdEvaluateRaw(EncogAnalyst analyst)
: base(analyst)
{
}
/// <summary>
/// Construct this command.
/// </summary>
///
/// <param name="theAnalyst">The analyst that this command belongs to.</param>
protected Cmd(EncogAnalyst theAnalyst)
{
_analyst = theAnalyst;
_script = _analyst.Script;
_properties = _script.Properties;
}
/// <summary>
/// Construct the normalize command.
/// </summary>
///
/// <param name="theAnalyst">The analyst to use.</param>
public CmdNormalize(EncogAnalyst theAnalyst)
: base(theAnalyst)
{
}
/// <summary>
/// Construct the segregate command.
/// </summary>
///
/// <param name="analyst">The analyst to use.</param>
public CmdSegregate(EncogAnalyst analyst)
: base(analyst)
{
}
/// <summary>
/// Construct the cluster command.
/// </summary>
///
/// <param name="analyst">The analyst object to use.</param>
public CmdCluster(EncogAnalyst analyst) : base(analyst)
{
}
/// <summary>
/// Analyze the file.
/// </summary>
///
/// <param name="inputFilename">The input file.</param>
/// <param name="expectInputHeaders">True, if input headers are present.</param>
/// <param name="inputFormat">The format.</param>
/// <param name="theAnalyst">The analyst to use.</param>
public void Analyze(FileInfo inputFilename,
bool expectInputHeaders, CSVFormat inputFormat,
EncogAnalyst theAnalyst)
{
InputFilename = inputFilename;
InputFormat = inputFormat;
ExpectInputHeaders = expectInputHeaders;
_analyst = theAnalyst;
Analyzed = true;
_analystHeaders = new CSVHeaders(inputFilename, expectInputHeaders,
inputFormat);
foreach (AnalystField field in _analyst.Script.Normalize.NormalizedFields)
{
field.Init();
}
_series = new TimeSeriesUtil(_analyst,
_analystHeaders.Headers);
}