public static IMLDataSet GenerateTrainingSet(double[][] inputData, double[][] outputData, int leadWindowSize, int lagWindowSize)
{
var temporalDataSet = new TemporalMLDataSet(lagWindowSize, leadWindowSize);
int inputCount = inputData[0].Length;
int outputCount = outputData[0].Length;
for (int i = 0; i < inputCount; i++)
{
var desc = new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false);
temporalDataSet.AddDescription(desc);
}
for (int i = 0; i < outputCount; i++)
{
var desc = new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true);
temporalDataSet.AddDescription(desc);
}
for (int i = 0; i < inputData.Length; i++)
{
var point = temporalDataSet.CreatePoint(i);
point.Data = inputData[i].Concat(outputData[i]).ToArray();
}
temporalDataSet.Generate();
return(temporalDataSet);
}
/// <summary>
/// Generates a temporal data set with a given double serie.
/// uses Type percent change.
/// </summary>
/// <param name="inputserie">The inputserie.</param>
/// <param name="windowsize">The windowsize.</param>
/// <param name="predictsize">The predictsize.</param>
/// <returns></returns>
public static TemporalMLDataSet GenerateTrainingWithPercentChangeOnSerie(double[] inputserie, int windowsize, int predictsize)
{
TemporalMLDataSet result = new TemporalMLDataSet(windowsize, predictsize);
TemporalDataDescription desc = new TemporalDataDescription(TemporalDataDescription.Type.PercentChange, true, true);
result.AddDescription(desc);
for (int index = 0; index < inputserie.Length - 1; index++)
{
TemporalPoint point = new TemporalPoint(1);
point.Sequence = index;
point.Data[0] = inputserie[index];
result.Points.Add(point);
}
result.Generate();
return(result);
}
public IMLDataSet GenerateTraining()
{
var result = new TemporalMLDataSet(WindowSize, 1);
var desc = new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, true);
result.AddDescription(desc);
for (int i = WindowSize; i < _normalizedTrainingData.Length; i++)
{
var point = new TemporalPoint(1)
{
Sequence = i
};
point.Data[0] = _normalizedTrainingData[i];
result.Points.Add(point);
}
result.Generate();
return(result);
}
public IMLDataSet GenerateTraining()
{
var result = new TemporalMLDataSet(WindowSize, 1);
var desc = new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, true);
result.AddDescription(desc);
for (var year = TrainStart; year < TrainEnd; year++)
{
var point = new TemporalPoint(1)
{
Sequence = year
};
point.Data[0] = _normalizedForexPair[year];
result.Points.Add(point);
}
result.Generate();
return(result);
}
public static IMLDataSet GenerateTraining()
{
TemporalMLDataSet result = new TemporalMLDataSet(WindowSize, 1);
TemporalDataDescription desc =
new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, true);
result.AddDescription(desc);
for (int year = TrainStart; year < TrainEnd; year++)
{
TemporalPoint point = new TemporalPoint(1);
point.Sequence = year;
point.Data[0] = _normalizedSunspots[year];
result.Points.Add(point);
}
result.Generate();
return(result);
}
/// <summary>
/// Generates a temporal data set with a given double serie or a any number of double series , making your inputs.
/// uses Type percent change.
/// </summary>
/// <param name="windowsize">The windowsize.</param>
/// <param name="predictsize">The predictsize.</param>
/// <param name="inputserie">The inputserie.</param>
/// <returns></returns>
public static TemporalMLDataSet GenerateTrainingWithPercentChangeOnSerie(int windowsize, int predictsize, params double[][] inputserie)
{
TemporalMLDataSet result = new TemporalMLDataSet(windowsize, predictsize);
TemporalDataDescription desc = new TemporalDataDescription(TemporalDataDescription.Type.PercentChange, true,
true);
result.AddDescription(desc);
foreach (double[] t in inputserie)
{
for (int j = 0; j < t.Length; j++)
{
TemporalPoint point = new TemporalPoint(1);
point.Sequence = j;
point.Data[0] = t[j];
result.Points.Add(point);
}
result.Generate();
return(result);
}
return(null);
}
public IMLDataSet GenerateTraining()
{
var result = new TemporalMLDataSet(WindowSize, 1);
var desc = new TemporalDataDescription(
TemporalDataDescription.Type.Raw, true, true);
result.AddDescription(desc);
for (int day = TrainStart; day < TrainEnd; day++)
{
var point = new TemporalPoint(1)
{
Sequence = day,
Data = { [0] = _normalizedArray[day] }
};
result.Points.Add(point);
}
result.Generate();
return(result);
}
public IMLDataSet GenerateTraining(double[] normalizedData)
{
var result = new TemporalMLDataSet(WindowSize, 1);
TemporalDataDescription desc = new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, true);
result.AddDescription(desc);
int TrainStart = 0;
int TrainEnd = normalizedData.Length;
for (int index = TrainStart; index < TrainEnd; index++)
{
TemporalPoint point = new TemporalPoint(1)
{
Sequence = index
};
point.Data[0] = normalizedData[index];
result.Points.Add(point);
}
result.Generate();
return(result);
}
