public void TestIntegerBalance()
{
DataNormalization norm = CreateIntegerBalance();
norm.Process();
Check(norm, 3);
}
public void TestRangeSegregate()
{
DataNormalization norm = CreateRangeSegregate();
norm.Process();
Check(norm, 1);
}
public void TestIndexSegregate()
{
DataNormalization norm = CreateIndexSegregate();
norm.Process();
Check(norm, 6);
}
private void Check(DataNormalization norm, int req)
{
ISegregator s = norm.Segregators[0];
double[][] arrayOutput = ((NormalizationStorageArray2D)norm.Storage).GetArray();
Assert.AreEqual(req, arrayOutput.Length);
}
private void Generate()
{
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 InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddInputField(c = new InputFieldArray2D(false, ARRAY_2D, 2));
norm.AddInputField(d = new InputFieldArray2D(false, ARRAY_2D, 3));
norm.AddInputField(e = new InputFieldArray2D(false, ARRAY_2D, 4));
norm.AddOutputField(new OutputFieldDirect(a));
norm.AddOutputField(new OutputFieldDirect(b));
norm.AddOutputField(new OutputFieldDirect(c));
norm.AddOutputField(new OutputFieldDirect(d));
norm.AddOutputField(new OutputFieldDirect(e));
norm.Storage = new NormalizationStorageCSV(FILENAME.ToString());
norm.Process();
}
public void TestSampleSegregate()
{
DataNormalization norm = CreateSampleSegregate();
norm.Process();
Check(norm, 6);
}
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);
}
public void TestAbsolute()
{
DataNormalization norm = Create();
norm.Process();
Check(norm);
}
private void Check(DataNormalization norm)
{
double[][] arrayOutput = ((NormalizationStorageArray2D)norm.Storage).GetArray();
Assert.AreEqual(arrayOutput[0][0], 0.1, 0.1);
Assert.AreEqual(arrayOutput[1][0], 0.9, 0.1);
Assert.AreEqual(arrayOutput[0][1], 0.1, 0.1);
Assert.AreEqual(arrayOutput[1][1], 0.9, 0.1);
}
private void Check(DataNormalization norm)
{
double[][] arrayOutput = ((NormalizationStorageArray2D) norm.Storage).GetArray();
Assert.AreEqual(arrayOutput[0][0], 0.1, 0.1);
Assert.AreEqual(arrayOutput[1][0], 0.9, 0.1);
Assert.AreEqual(arrayOutput[0][1], 0.1, 0.1);
Assert.AreEqual(arrayOutput[1][1], 0.9, 0.1);
}
public void TestOutputFieldEncode()
{
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 2);
DataNormalization norm = Create(arrayOutput);
norm.Process();
Check(arrayOutput);
}
public void TestAbsoluteSerial()
{
DataNormalization norm = Create();
norm = (DataNormalization)SerializeRoundTrip.RoundTrip(norm);
norm.Process();
Check(norm);
}
public void TestArray2D()
{
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 2);
DataNormalization norm = Create2D(arrayOutput);
norm.Process();
Check2D(arrayOutput);
}
public void TestArray1D()
{
var arrayOutput = new double[5];
DataNormalization norm = Create1D(arrayOutput);
norm.Process();
Check1D(arrayOutput);
}
public void TestGenerateAndLoad()
{
double[][] outputArray = EngineArray.AllocateDouble2D(2, 5);
Generate();
DataNormalization norm = Create(outputArray);
norm.Process();
Check(norm);
}
public void TestArray2DSerial()
{
var arrayOutput = EngineArray.AllocateDouble2D(2, 2);
DataNormalization norm = Create2D(arrayOutput);
norm = (DataNormalization)SerializeRoundTrip.RoundTrip(norm);
arrayOutput = ((NormalizationStorageArray2D)norm.Storage).GetArray();
norm.Process();
Check2D(arrayOutput);
}
public void TestGenerateAndLoadSerial()
{
double[][] outputArray = EngineArray.AllocateDouble2D(2, 5);
Generate(FILENAME2.ToString());
DataNormalization norm = Create(FILENAME2.ToString(), outputArray);
norm = (DataNormalization)SerializeRoundTrip.RoundTrip(norm);
norm.Process();
Check(norm);
}
public void TestGenerateAndLoad()
{
var outputArray = EngineArray.AllocateDouble2D(2, 5);
Generate(FILENAME1.ToString());
DataNormalization norm = Create(FILENAME1.ToString(), outputArray);
norm.Process();
Check(norm);
}
public void TestOutputFieldEncodeSerialize()
{
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 2);
DataNormalization norm = Create(arrayOutput);
norm = (DataNormalization)SerializeRoundTrip.RoundTrip(norm);
arrayOutput = ((NormalizationStorageArray2D)norm.Storage).GetArray();
norm.Process();
Check(arrayOutput);
}
public void TestArray1DSerial()
{
var arrayOutput = new double[5];
DataNormalization norm = Create1D(arrayOutput);
norm = (DataNormalization)SerializeRoundTrip.RoundTrip(norm);
arrayOutput = ((NormalizationStorageArray1D)norm.Storage).GetArray();
norm.Process();
Check1D(arrayOutput);
}
public void TestAbsolute()
{
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 3);
DataNormalization norm = Create(arrayOutput);
norm.Process();
for (int i = 0; i < arrayOutput[0].Length; i++)
{
Assert.AreEqual(arrayOutput[0][i], arrayOutput[1][i], 0.01);
}
}
public void BuildOutputOneOf(DataNormalization norm, IInputField coverType)
{
var outType = new OutputOneOf();
outType.AddItem(coverType, 1);
outType.AddItem(coverType, 2);
outType.AddItem(coverType, 3);
outType.AddItem(coverType, 4);
outType.AddItem(coverType, 5);
outType.AddItem(coverType, 6);
outType.AddItem(coverType, 7);
norm.AddOutputField(outType, true);
}
public void TestAbsoluteSerial()
{
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 3);
DataNormalization norm = Create(arrayOutput);
norm = (DataNormalization)SerializeRoundTrip.RoundTrip(norm);
arrayOutput = ((NormalizationStorageArray2D)norm.Storage).GetArray();
norm.Process();
for (int i = 0; i < arrayOutput[0].Length; i++)
{
Assert.AreEqual(arrayOutput[0][i], arrayOutput[1][i], 0.01);
}
}
private void Check(DataNormalization norm)
{
double[][] arrayOutput = ((NormalizationStorageArray2D)norm.Storage).GetArray();
Assert.AreEqual(-5.0, arrayOutput[0][0]);
Assert.AreEqual(2.5, arrayOutput[0][1]);
Assert.AreEqual(7.5, arrayOutput[0][2]);
Assert.AreEqual(0.0, arrayOutput[0][3]);
Assert.AreEqual(-1.0, arrayOutput[1][0]);
Assert.AreEqual(0.5, arrayOutput[1][1]);
Assert.AreEqual(1.5, arrayOutput[1][2]);
Assert.AreEqual(0.0, arrayOutput[1][3]);
}
private void Check(DataNormalization norm)
{
double[][] arrayOutput = ((NormalizationStorageArray2D) norm.Storage).GetArray();
Assert.AreEqual(-5.0, arrayOutput[0][0]);
Assert.AreEqual(2.5, arrayOutput[0][1]);
Assert.AreEqual(7.5, arrayOutput[0][2]);
Assert.AreEqual(0.0, arrayOutput[0][3]);
Assert.AreEqual(-1.0, arrayOutput[1][0]);
Assert.AreEqual(0.5, arrayOutput[1][1]);
Assert.AreEqual(1.5, arrayOutput[1][2]);
Assert.AreEqual(0.0, arrayOutput[1][3]);
}
private DataNormalization Create1D(double[] arrayOutput)
{
IInputField a;
var target = new NormalizationStorageArray1D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray1D(false, ARRAY_1D));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
return norm;
}
private DataNormalization Create1D(double[] arrayOutput)
{
IInputField a;
var target = new NormalizationStorageArray1D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray1D(false, ARRAY_1D));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
return(norm);
}
private void Check(DataNormalization norm)
{
IInputField a = norm.InputFields[0];
IInputField b = norm.InputFields[1];
Assert.AreEqual(1.0, a.Min, 0.1);
Assert.AreEqual(6.0, a.Max, 0.1);
Assert.AreEqual(2.0, b.Min, 0.1);
Assert.AreEqual(7.0, b.Max, 0.1);
double[][] outputArray = ((NormalizationStorageArray2D)norm.Storage).GetArray();
for (int i = 0; i < 5; i++)
{
Assert.AreEqual(0.1, outputArray[0][i], 0.1);
Assert.AreEqual(0.9, outputArray[1][i], 0.1);
}
}
public DataNormalization CreateIndexSegregate()
{
IInputField a, b;
double[][] arrayOutput = EngineArray.AllocateDouble2D(6, 2);
var target = new NormalizationStorageArray2D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
norm.AddSegregator(new IndexRangeSegregator(0, 3));
return norm;
}
private DataNormalization Create()
{
IInputField a, b;
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 2);
var dataset = new BasicMLDataSet(ARRAY_2D, null);
var target = new NormalizationStorageArray2D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldMLDataSet(false, dataset, 0));
norm.AddInputField(b = new InputFieldMLDataSet(false, dataset, 1));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
return norm;
}
private DataNormalization CreateSampleSegregate()
{
IInputField a, b;
var arrayOutput = EngineArray.AllocateDouble2D(6, 2);
var target = new NormalizationStorageArray2D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
norm.AddSegregator(new IndexSampleSegregator(0, 3, 2));
return(norm);
}
public DataNormalization Create(double[][] arrayOutput)
{
IInputField a;
IInputField b;
IInputField c;
var target = new NormalizationStorageArray2D(arrayOutput);
var group = new MultiplicativeGroup();
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, SAMPLE1, 0));
norm.AddInputField(b = new InputFieldArray2D(false, SAMPLE1, 1));
norm.AddInputField(c = new InputFieldArray2D(false, SAMPLE1, 2));
norm.AddOutputField(new OutputFieldMultiplicative(group, a));
norm.AddOutputField(new OutputFieldMultiplicative(group, b));
norm.AddOutputField(new OutputFieldMultiplicative(group, c));
return norm;
}
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());
}
private DataNormalization Create(double[][] arrayOutput)
{
IInputField a, b;
var target = new NormalizationStorageArray2D(arrayOutput);
OutputFieldEncode a1;
OutputFieldEncode b1;
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddOutputField(a1 = new OutputFieldEncode(a));
norm.AddOutputField(b1 = new OutputFieldEncode(b));
a1.AddRange(1.0, 2.0, 0.1);
b1.AddRange(0, 100, 0.2);
return norm;
}
public DataNormalization Create(double[][] arrayOutput)
{
IInputField a;
IInputField b;
IInputField c;
var target = new NormalizationStorageArray2D(arrayOutput);
var group = new MultiplicativeGroup();
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, SAMPLE1, 0));
norm.AddInputField(b = new InputFieldArray2D(false, SAMPLE1, 1));
norm.AddInputField(c = new InputFieldArray2D(false, SAMPLE1, 2));
norm.AddOutputField(new OutputFieldMultiplicative(group, a));
norm.AddOutputField(new OutputFieldMultiplicative(group, b));
norm.AddOutputField(new OutputFieldMultiplicative(group, c));
return(norm);
}
private DataNormalization Create()
{
IInputField a, b;
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 2);
var dataset = new BasicMLDataSet(ARRAY_2D, null);
var target = new NormalizationStorageArray2D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldMLDataSet(false, dataset, 0));
norm.AddInputField(b = new InputFieldMLDataSet(false, dataset, 1));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
return(norm);
}
public DataNormalization Create()
{
IInputField a;
IInputField b;
IInputField c;
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 4);
var target = new NormalizationStorageArray2D(arrayOutput);
var group = new ZAxisGroup();
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, SAMPLE1, 0));
norm.AddInputField(b = new InputFieldArray2D(false, SAMPLE1, 1));
norm.AddInputField(c = new InputFieldArray2D(false, SAMPLE1, 2));
norm.AddOutputField(new OutputFieldZAxis(group, a));
norm.AddOutputField(new OutputFieldZAxis(group, b));
norm.AddOutputField(new OutputFieldZAxis(group, c));
norm.AddOutputField(new OutputFieldZAxisSynthetic(group));
return norm;
}
private DataNormalization Create(double[][] arrayOutput)
{
IInputField a, b;
var target = new NormalizationStorageArray2D(arrayOutput);
OutputFieldEncode a1;
OutputFieldEncode b1;
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddOutputField(a1 = new OutputFieldEncode(a));
norm.AddOutputField(b1 = new OutputFieldEncode(b));
a1.AddRange(1.0, 2.0, 0.1);
b1.AddRange(0, 100, 0.2);
return(norm);
}
private DataNormalization CreateRangeSegregate()
{
IInputField a, b;
double[][] arrayOutput = EngineArray.AllocateDouble2D(1, 2);
RangeSegregator s;
var target = new NormalizationStorageArray2D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
norm.AddSegregator(s = new RangeSegregator(a, false));
s.AddRange(2, 2, true);
return(norm);
}
public DataNormalization Create()
{
IInputField a;
IInputField b;
IInputField c;
double[][] arrayOutput = EngineArray.AllocateDouble2D(2, 4);
var target = new NormalizationStorageArray2D(arrayOutput);
var group = new ZAxisGroup();
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, SAMPLE1, 0));
norm.AddInputField(b = new InputFieldArray2D(false, SAMPLE1, 1));
norm.AddInputField(c = new InputFieldArray2D(false, SAMPLE1, 2));
norm.AddOutputField(new OutputFieldZAxis(group, a));
norm.AddOutputField(new OutputFieldZAxis(group, b));
norm.AddOutputField(new OutputFieldZAxis(group, c));
norm.AddOutputField(new OutputFieldZAxisSynthetic(group));
return(norm);
}
private void Generate()
{
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 InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddInputField(c = new InputFieldArray2D(false, ARRAY_2D, 2));
norm.AddInputField(d = new InputFieldArray2D(false, ARRAY_2D, 3));
norm.AddInputField(e = new InputFieldArray2D(false, ARRAY_2D, 4));
norm.AddOutputField(new OutputFieldDirect(a));
norm.AddOutputField(new OutputFieldDirect(b));
norm.AddOutputField(new OutputFieldDirect(c));
norm.AddOutputField(new OutputFieldDirect(d));
norm.AddOutputField(new OutputFieldDirect(e));
norm.Storage = new NormalizationStorageCSV(FILENAME.ToString());
norm.Process();
}
public DataNormalization Create(string filename, 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;
}
private void Check(DataNormalization norm, int req)
{
ISegregator s = norm.Segregators[0];
double[][] arrayOutput = ((NormalizationStorageArray2D) norm.Storage).GetArray();
Assert.AreEqual(req, arrayOutput.Length);
}
public void Init(DataNormalization normalization)
{
this._normalization = normalization;
}
private void Check(DataNormalization norm)
{
IInputField a = norm.InputFields[0];
IInputField b = norm.InputFields[1];
Assert.AreEqual(1.0, a.Min, 0.1);
Assert.AreEqual(6.0, a.Max, 0.1);
Assert.AreEqual(2.0, b.Min, 0.1);
Assert.AreEqual(7.0, b.Max, 0.1);
double[][] outputArray = ((NormalizationStorageArray2D) norm.Storage).GetArray();
for (int i = 0; i < 5; i++)
{
Assert.AreEqual(0.1, outputArray[0][i], 0.1);
Assert.AreEqual(0.9, outputArray[1][i], 0.1);
}
}
/// <summary>
/// Saves a normalization to the specified folder with the specified name.
/// </summary>
/// <param name="directory">The directory.</param>
/// <param name="file">The file.</param>
/// <param name="normTosave">The norm tosave.</param>
public static void SaveNormalization(string directory, string file, DataNormalization normTosave)
{
SerializeObject.Save(directory + file, normTosave);
}
private DataNormalization CreateRangeSegregate()
{
IInputField a, b;
double[][] arrayOutput = EngineArray.AllocateDouble2D(1, 2);
RangeSegregator s;
var target = new NormalizationStorageArray2D(arrayOutput);
var norm = new DataNormalization();
norm.Report = new NullStatusReportable();
norm.Storage = target;
norm.AddInputField(a = new InputFieldArray2D(false, ARRAY_2D, 0));
norm.AddInputField(b = new InputFieldArray2D(false, ARRAY_2D, 1));
norm.AddOutputField(new OutputFieldRangeMapped(a, 0.1, 0.9));
norm.AddOutputField(new OutputFieldRangeMapped(b, 0.1, 0.9));
norm.AddSegregator(s = new RangeSegregator(a, false));
s.AddRange(2, 2, true);
return norm;
}
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;
}
