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 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 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();
}
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 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 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;
}