public DataNormalization Create(double[][] outputArray)
{
IInputField a;
IInputField b;
IInputField c;
IInputField d;
IInputField e;
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = new NormalizationStorageCSV(FILENAME.ToString());
norm.AddInputField(a = new InputFieldCSV(false, FILENAME.ToString(), 0));
norm.AddInputField(b = new InputFieldCSV(false, FILENAME.ToString(), 1));
norm.AddInputField(c = new InputFieldCSV(false, FILENAME.ToString(), 2));
norm.AddInputField(d = new InputFieldCSV(false, FILENAME.ToString(), 3));
norm.AddInputField(e = new InputFieldCSV(false, FILENAME.ToString(), 4));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(c, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(d, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(e, 0.1, 0.9));
norm.Storage = new NormalizationStorageArray2D(outputArray);
return(norm);
}
/// <summary>
/// Called internally to open the CSV file.
/// </summary>
private void OpenCSV()
{
// clear out any CSV files already there
this.csvMap.Clear();
this.readCSV.Clear();
// only add each CSV once
IDictionary <String, ReadCSV> uniqueFiles = new Dictionary <String, ReadCSV>();
// find the unique files
foreach (IInputField field in this.inputFields)
{
if (field is InputFieldCSV)
{
InputFieldCSV csvField = (InputFieldCSV)field;
String file = csvField.File;
if (!uniqueFiles.ContainsKey(file))
{
ReadCSV csv = new ReadCSV(file, false,
this.csvFormat);
uniqueFiles[file] = csv;
this.readCSV.Add(csv);
}
this.csvMap[csvField] = uniqueFiles[file];
}
}
}
public void Copy(FileInfo source, FileInfo target, int start, int stop, int size)
{
var inputField = new IInputField[55];
var norm = new DataNormalization {
Report = this, Storage = new NormalizationStorageCSV(target.ToString())
};
for (int i = 0; i < 55; i++)
{
inputField[i] = new InputFieldCSV(true, source.ToString(), i);
norm.AddInputField(inputField[i]);
IOutputField outputField = new OutputFieldDirect(inputField[i]);
norm.AddOutputField(outputField);
}
// load only the part we actually want, i.e. training or eval
var segregator2 = new IndexSampleSegregator(start, stop, size);
norm.AddSegregator(segregator2);
norm.Process();
}
public void Narrow(FileInfo source, FileInfo target, int field, int count)
{
var inputField = new IInputField[55];
var norm = new DataNormalization {
Report = this, Storage = new NormalizationStorageCSV(target.ToString())
};
for (int i = 0; i < 55; i++)
{
inputField[i] = new InputFieldCSV(true, source.ToString(), i);
norm.AddInputField(inputField[i]);
IOutputField outputField = new OutputFieldDirect(inputField[i]);
norm.AddOutputField(outputField);
}
var segregator = new IntegerBalanceSegregator(inputField[field], count);
norm.AddSegregator(segregator);
norm.Process();
Console.WriteLine(@"Samples per tree type:");
Console.WriteLine(segregator.DumpCounts());
}
/// <summary>
/// Called internally to obtain the current value for an input field.
/// </summary>
/// <param name="field">The input field to determine.</param>
/// <param name="index">The current index.</param>
/// <returns>The value for this input field.</returns>
private double DetermineInputFieldValue(IInputField field,
int index)
{
double result = 0;
if (field is InputFieldCSV)
{
InputFieldCSV fieldCSV = (InputFieldCSV)field;
ReadCSV csv = this.csvMap[field];
result = csv.GetDouble(fieldCSV.Offset);
}
else if (field is InputFieldNeuralDataSet)
{
InputFieldNeuralDataSet neuralField = (InputFieldNeuralDataSet)field;
NeuralDataFieldHolder holder = this.dataSetFieldMap
[field];
INeuralDataPair pair = holder.Pair;
int offset = neuralField.Offset;
if (offset < pair.Input.Count)
{
result = pair.Input[offset];
}
else
{
offset -= pair.Input.Count;
result = pair.Ideal[offset];
}
}
else
{
result = field.GetValue(index);
}
field.CurrentValue = result;
return(result);
}
public void Execute(IExampleInterface app)
{
string inputFile = "C:\\jth\\iris.csv";
DataNormalization normalize = new DataNormalization();
IInputField a, b, c, d;
normalize.AddInputField(a = new InputFieldCSV(true, inputFile, "sepal_l"));
normalize.AddInputField(b = new InputFieldCSV(true, inputFile, "sepal_w"));
normalize.AddInputField(c = new InputFieldCSV(true, inputFile, "petal_l"));
normalize.AddInputField(d = new InputFieldCSV(true, inputFile, "petal_w"));
normalize.AddInputField(new InputFieldCSV(false, inputFile, "species"));
normalize.AddOutputField(new OutputFieldRangeMapped(a));
normalize.AddOutputField(new OutputFieldRangeMapped(b));
normalize.AddOutputField(new OutputFieldRangeMapped(c));
normalize.AddOutputField(new OutputFieldRangeMapped(d));
//normalize.AddOutputField(new OutputOneOf(1,0));
NormalizationStorageMLDataSet store = new NormalizationStorageMLDataSet(4, 0);
normalize.Storage = store;
normalize.Report = new ConsoleStatusReportable();
normalize.Process(true);
Console.WriteLine(store.DataSet.Count);
}
public void Execute(IExampleInterface app)
{
string inputFile = @"C:\Development\AI\machinelearning\encog-dotnet-core-master\encog-core-test\Resources\iris.csv";
DataNormalization normalize = new DataNormalization();
IInputField a, b, c, d;
normalize.AddInputField(a = new InputFieldCSV(true, inputFile, "sepal_l"));
normalize.AddInputField(b = new InputFieldCSV(true, inputFile, "sepal_w"));
normalize.AddInputField(c = new InputFieldCSV(true, inputFile, "petal_l"));
normalize.AddInputField(d = new InputFieldCSV(true, inputFile, "petal_w"));
normalize.AddInputField(new InputFieldCSV(false, inputFile, "species"));
normalize.AddOutputField(new OutputFieldRangeMapped(a));
normalize.AddOutputField(new OutputFieldRangeMapped(b));
normalize.AddOutputField(new OutputFieldRangeMapped(c));
normalize.AddOutputField(new OutputFieldRangeMapped(d));
//normalize.AddOutputField(new OutputOneOf(1,0));
NormalizationStorageMLDataSet store = new NormalizationStorageMLDataSet(4, 0);
normalize.Storage = store;
normalize.Report = new ConsoleStatusReportable();
normalize.Process(true);
Console.WriteLine(store.DataSet.Count + " Datasets validated");
}
public DataNormalization Step3(bool useOneOf)
{
Console.WriteLine(@"Step 3: Normalize training data");
IInputField inputElevation;
IInputField inputAspect;
IInputField inputSlope;
IInputField hWater;
IInputField vWater;
IInputField roadway;
IInputField shade9;
IInputField shade12;
IInputField shade3;
IInputField firepoint;
var wilderness = new IInputField[4];
var soilType = new IInputField[40];
IInputField coverType;
var norm = new DataNormalization
{
Report = this,
Storage = new NormalizationStorageCSV(_config.NormalizedDataFile.ToString())
};
norm.AddInputField(inputElevation = new InputFieldCSV(true, _config.BalanceFile.ToString(), 0));
norm.AddInputField(inputAspect = new InputFieldCSV(true, _config.BalanceFile.ToString(), 1));
norm.AddInputField(inputSlope = new InputFieldCSV(true, _config.BalanceFile.ToString(), 2));
norm.AddInputField(hWater = new InputFieldCSV(true, _config.BalanceFile.ToString(), 3));
norm.AddInputField(vWater = new InputFieldCSV(true, _config.BalanceFile.ToString(), 4));
norm.AddInputField(roadway = new InputFieldCSV(true, _config.BalanceFile.ToString(), 5));
norm.AddInputField(shade9 = new InputFieldCSV(true, _config.BalanceFile.ToString(), 6));
norm.AddInputField(shade12 = new InputFieldCSV(true, _config.BalanceFile.ToString(), 7));
norm.AddInputField(shade3 = new InputFieldCSV(true, _config.BalanceFile.ToString(), 8));
norm.AddInputField(firepoint = new InputFieldCSV(true, _config.BalanceFile.ToString(), 9));
for (int i = 0; i < 4; i++)
{
norm.AddInputField(wilderness[i] = new InputFieldCSV(true, _config.BalanceFile.ToString(), 10 + i));
}
for (int i = 0; i < 40; i++)
{
norm.AddInputField(soilType[i] = new InputFieldCSV(true, _config.BalanceFile.ToString(), 14 + i));
}
norm.AddInputField(coverType = new InputFieldCSV(false, _config.BalanceFile.ToString(), 54));
norm.AddOutputField(new OutputFieldRangeMapped(inputElevation));
norm.AddOutputField(new OutputFieldRangeMapped(inputAspect));
norm.AddOutputField(new OutputFieldRangeMapped(inputSlope));
norm.AddOutputField(new OutputFieldRangeMapped(hWater));
norm.AddOutputField(new OutputFieldRangeMapped(vWater));
norm.AddOutputField(new OutputFieldRangeMapped(roadway));
norm.AddOutputField(new OutputFieldRangeMapped(shade9));
norm.AddOutputField(new OutputFieldRangeMapped(shade12));
norm.AddOutputField(new OutputFieldRangeMapped(shade3));
norm.AddOutputField(new OutputFieldRangeMapped(firepoint));
for (int i = 0; i < 40; i++)
{
norm.AddOutputField(new OutputFieldDirect(soilType[i]));
}
if (useOneOf)
{
BuildOutputOneOf(norm, coverType);
}
else
{
BuildOutputEquilateral(norm, coverType);
}
norm.Process();
return(norm);
}
