public void ActivationTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(new ActivationTANH(), TemporalDataDescription.Type.Raw,
true, false));
temporal.AddDescription(new TemporalDataDescription(new ActivationTANH(), TemporalDataDescription.Type.Raw,
true, false));
temporal.AddDescription(new TemporalDataDescription(new ActivationTANH(), TemporalDataDescription.Type.Raw,
false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp[0] = 1.0 + (i*3);
tp[1] = 2.0 + (i*3);
tp[2] = 3.0 + (i*3);
}
temporal.Generate();
IEnumerator<IMLDataPair> itr = temporal.GetEnumerator();
// set 0
itr.MoveNext();
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(0.75, Math.Round(pair.Input[0]*4.0)/4.0);
Assert.AreEqual(1.0, Math.Round(pair.Input[1]*4.0)/4.0);
Assert.AreEqual(1.0, Math.Round(pair.Input[2]*4.0)/4.0);
Assert.AreEqual(1.0, Math.Round(pair.Input[3]*4.0)/4.0);
}
public void ActivationTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(new ActivationTANH(), TemporalDataDescription.Type.Raw,
true, false));
temporal.AddDescription(new TemporalDataDescription(new ActivationTANH(), TemporalDataDescription.Type.Raw,
true, false));
temporal.AddDescription(new TemporalDataDescription(new ActivationTANH(), TemporalDataDescription.Type.Raw,
false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp[0] = 1.0 + (i * 3);
tp[1] = 2.0 + (i * 3);
tp[2] = 3.0 + (i * 3);
}
temporal.Generate();
IEnumerator <IMLDataPair> itr = temporal.GetEnumerator();
// set 0
itr.MoveNext();
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(0.75, Math.Round(pair.Input[0] * 4.0) / 4.0);
Assert.AreEqual(1.0, Math.Round(pair.Input[1] * 4.0) / 4.0);
Assert.AreEqual(1.0, Math.Round(pair.Input[2] * 4.0) / 4.0);
Assert.AreEqual(1.0, Math.Round(pair.Input[3] * 4.0) / 4.0);
}
private static void CreateEvaluationSet(string @fileName)
{
List<double> Opens = SuperUtils.QuickParseCSV(fileName, "Open", 1200, 1200);
List<double> High = NetworkUtility.QuickParseCSV(fileName, "High", 1200, 1200);
List<double> Low = NetworkUtility.QuickParseCSV(fileName, "Low", 1200, 1200);
List<double> Close = NetworkUtility.QuickParseCSV(fileName, "Close", 1200, 1200);
List<double> Volume = NetworkUtility.QuickParseCSV(fileName, 5, 1200, 1200);
TemporalMLDataSet superTemportal = new TemporalMLDataSet(100, 1);
double[] Ranges = NetworkUtility.CalculateRanges(Opens.ToArray(), Close.ToArray());
superTemportal = NetworkUtility.GenerateTrainingWithPercentChangeOnSerie(100, 1, Opens.ToArray(),
Close.ToArray(), High.ToArray(), Low.ToArray(), Volume.ToArray());
IMLDataPair aPairInput = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Opens.ToArray()), NetworkUtility.CalculatePercents(Opens.ToArray()), 100, 1);
IMLDataPair aPairInput3 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Close.ToArray()), NetworkUtility.CalculatePercents(Close.ToArray()), 100, 1);
IMLDataPair aPairInput2 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(High.ToArray()), NetworkUtility.CalculatePercents(High.ToArray()), 100, 1);
IMLDataPair aPairInput4 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Volume.ToArray()), NetworkUtility.CalculatePercents(Volume.ToArray()), 100, 1);
IMLDataPair aPairInput5 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Ranges.ToArray()), NetworkUtility.CalculatePercents(Ranges.ToArray()), 100, 1);
List<IMLDataPair> listData = new List<IMLDataPair>();
listData.Add(aPairInput);
listData.Add(aPairInput2);
listData.Add(aPairInput3);
listData.Add(aPairInput4);
listData.Add((aPairInput5));
var minitrainning = new BasicMLDataSet(listData);
var network = (BasicNetwork)CreateElmanNetwork(100, 1);
double normalCorrectRate = EvaluateNetworks(network, minitrainning);
double temporalErrorRate = EvaluateNetworks(network, superTemportal);
Console.WriteLine("Percent Correct with normal Data Set:" + normalCorrectRate + " Percent Correct with temporal Dataset:" +
temporalErrorRate);
Console.WriteLine("Paused , Press a key to continue to evaluation");
Console.ReadKey();
}
public void HiLowTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp[0] = 1.0 + (i * 3);
tp[1] = 2.0 + (i * 3);
tp[2] = 3.0 + (i * 3);
}
temporal.HighSequence = 8;
temporal.LowSequence = 2;
temporal.Generate();
Assert.AreEqual(10, temporal.InputNeuronCount);
Assert.AreEqual(1, temporal.OutputNeuronCount);
Assert.AreEqual(7, temporal.CalculateActualSetSize());
IEnumerator <IMLDataPair> itr = temporal.GetEnumerator();
itr.MoveNext();
// set 0
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(7.0, pair.Input[0]);
Assert.AreEqual(8.0, pair.Input[1]);
Assert.AreEqual(10.0, pair.Input[2]);
Assert.AreEqual(11.0, pair.Input[3]);
Assert.AreEqual(13.0, pair.Input[4]);
Assert.AreEqual(14.0, pair.Input[5]);
Assert.AreEqual(16.0, pair.Input[6]);
Assert.AreEqual(17.0, pair.Input[7]);
Assert.AreEqual(19.0, pair.Input[8]);
Assert.AreEqual(20.0, pair.Input[9]);
Assert.AreEqual(24.0, pair.Ideal[0]);
}
public void FormatTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.DeltaChange, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.PercentChange, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp[0] = 1.0 + (i * 3);
tp[1] = 2.0 + (i * 3);
tp[2] = 3.0 + (i * 3);
}
temporal.Generate();
IEnumerator <IMLDataPair> itr = temporal.GetEnumerator();
itr.MoveNext();
// set 0
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(3.0, pair.Input[0]);
Assert.AreEqual(1.5, pair.Input[1]);
Assert.AreEqual(3.0, pair.Input[2]);
Assert.AreEqual(0.6, pair.Input[3]);
Assert.AreEqual(3.0, pair.Input[4]);
Assert.AreEqual(0.375, pair.Input[5]);
Assert.AreEqual(3.0, pair.Input[6]);
Assert.AreEqual(0.25, Math.Round(pair.Input[7] * 4.0) / 4.0);
Assert.AreEqual(3.0, pair.Input[8]);
Assert.AreEqual(0.25, Math.Round(pair.Input[9] * 4.0) / 4.0);
Assert.AreEqual(18.0, pair.Ideal[0]);
}
/// <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)
{
var result = new TemporalMLDataSet(windowsize, predictsize);
var desc = new TemporalDataDescription(TemporalDataDescription.Type.PercentChange, true, true);
result.AddDescription(desc);
for (int index = 0; index < inputserie.Length - 1; index++)
{
var point = new TemporalPoint(1) {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 year = TrainStart; year < TrainEnd; year++)
{
var point = new TemporalPoint(1) { 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 void BasicTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp[0] = 1.0 + (i*3);
tp[1] = 2.0 + (i*3);
tp[2] = 3.0 + (i*3);
}
temporal.Generate();
Assert.AreEqual(10, temporal.InputNeuronCount);
Assert.AreEqual(1, temporal.OutputNeuronCount);
Assert.AreEqual(10, temporal.CalculateActualSetSize());
IEnumerator<IMLDataPair> itr = temporal.GetEnumerator();
itr.MoveNext();
// set 0
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(1.0, pair.Input[0]);
Assert.AreEqual(2.0, pair.Input[1]);
Assert.AreEqual(4.0, pair.Input[2]);
Assert.AreEqual(5.0, pair.Input[3]);
Assert.AreEqual(7.0, pair.Input[4]);
Assert.AreEqual(8.0, pair.Input[5]);
Assert.AreEqual(10.0, pair.Input[6]);
Assert.AreEqual(11.0, pair.Input[7]);
Assert.AreEqual(13.0, pair.Input[8]);
Assert.AreEqual(14.0, pair.Input[9]);
Assert.AreEqual(18.0, pair.Ideal[0]);
// set 1
itr.MoveNext();
pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(4.0, pair.Input[0]);
Assert.AreEqual(5.0, pair.Input[1]);
Assert.AreEqual(7.0, pair.Input[2]);
Assert.AreEqual(8.0, pair.Input[3]);
Assert.AreEqual(10.0, pair.Input[4]);
Assert.AreEqual(11.0, pair.Input[5]);
Assert.AreEqual(13.0, pair.Input[6]);
Assert.AreEqual(14.0, pair.Input[7]);
Assert.AreEqual(16.0, pair.Input[8]);
Assert.AreEqual(17.0, pair.Input[9]);
Assert.AreEqual(21.0, pair.Ideal[0]);
// set 2
itr.MoveNext();
pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(7.0, pair.Input[0]);
Assert.AreEqual(8.0, pair.Input[1]);
Assert.AreEqual(10.0, pair.Input[2]);
Assert.AreEqual(11.0, pair.Input[3]);
Assert.AreEqual(13.0, pair.Input[4]);
Assert.AreEqual(14.0, pair.Input[5]);
Assert.AreEqual(16.0, pair.Input[6]);
Assert.AreEqual(17.0, pair.Input[7]);
Assert.AreEqual(19.0, pair.Input[8]);
Assert.AreEqual(20.0, pair.Input[9]);
Assert.AreEqual(24.0, pair.Ideal[0]);
// set 3
itr.MoveNext();
pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(10.0, pair.Input[0]);
Assert.AreEqual(11.0, pair.Input[1]);
Assert.AreEqual(13.0, pair.Input[2]);
Assert.AreEqual(14.0, pair.Input[3]);
Assert.AreEqual(16.0, pair.Input[4]);
Assert.AreEqual(17.0, pair.Input[5]);
Assert.AreEqual(19.0, pair.Input[6]);
Assert.AreEqual(20.0, pair.Input[7]);
Assert.AreEqual(22.0, pair.Input[8]);
Assert.AreEqual(23.0, pair.Input[9]);
Assert.AreEqual(27.0, pair.Ideal[0]);
}
public void FormatTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.DeltaChange, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.PercentChange, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp[0] = 1.0 + (i*3);
tp[1] = 2.0 + (i*3);
tp[2] = 3.0 + (i*3);
}
temporal.Generate();
IEnumerator<IMLDataPair> itr = temporal.GetEnumerator();
itr.MoveNext();
// set 0
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(3.0, pair.Input[0]);
Assert.AreEqual(1.5, pair.Input[1]);
Assert.AreEqual(3.0, pair.Input[2]);
Assert.AreEqual(0.6, pair.Input[3]);
Assert.AreEqual(3.0, pair.Input[4]);
Assert.AreEqual(0.375, pair.Input[5]);
Assert.AreEqual(3.0, pair.Input[6]);
Assert.AreEqual(0.25, Math.Round(pair.Input[7]*4.0)/4.0);
Assert.AreEqual(3.0, pair.Input[8]);
Assert.AreEqual(0.25, Math.Round(pair.Input[9]*4.0)/4.0);
Assert.AreEqual(18.0, pair.Ideal[0]);
}
public static BasicMLDataSet CreateEvaluationSetAndLoad(string @fileName, int startLine, int HowMany, int WindowSize, int outputsize)
{
List<double> Opens = NetworkUtility.QuickParseCSV(fileName, "Open", startLine, HowMany);
List<double> High = NetworkUtility.QuickParseCSV(fileName, "High", startLine, HowMany);
List<double> Low = NetworkUtility.QuickParseCSV(fileName, "Low", startLine, HowMany);
List<double> Close = NetworkUtility.QuickParseCSV(fileName, "Close", startLine, HowMany);
List<double> Volume = NetworkUtility.QuickParseCSV(fileName, 5, startLine, HowMany);
TemporalMLDataSet superTemportal = new TemporalMLDataSet(WindowSize, outputsize);
double[] Ranges = NetworkUtility.CalculateRanges(Opens.ToArray(), Close.ToArray());
superTemportal = NetworkUtility.GenerateTrainingWithPercentChangeOnSerie(100, 1, Opens.ToArray(),
Close.ToArray(), High.ToArray(), Low.ToArray(), Volume.ToArray());
IMLDataPair aPairInput = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Opens.ToArray()), NetworkUtility.CalculatePercents(Opens.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput3 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Close.ToArray()), NetworkUtility.CalculatePercents(Close.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput2 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(High.ToArray()), NetworkUtility.CalculatePercents(High.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput4 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Volume.ToArray()), NetworkUtility.CalculatePercents(Volume.ToArray()), WindowSize, outputsize);
IMLDataPair aPairInput5 = SuperUtils.ProcessPair(NetworkUtility.CalculatePercents(Ranges.ToArray()), NetworkUtility.CalculatePercents(Ranges.ToArray()), WindowSize, outputsize);
List<IMLDataPair> listData = new List<IMLDataPair>();
listData.Add(aPairInput);
listData.Add(aPairInput2);
listData.Add(aPairInput3);
listData.Add(aPairInput4);
listData.Add((aPairInput5));
var minitrainning = new BasicMLDataSet(listData);
return minitrainning;
}
public void HiLowTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp.Data[0] = 1.0 + (i*3);
tp.Data[1] = 2.0 + (i*3);
tp.Data[2] = 3.0 + (i*3);
}
temporal.HighSequence = 8;
temporal.LowSequence = 2;
temporal.Generate();
Assert.AreEqual(10, temporal.InputNeuronCount);
Assert.AreEqual(1, temporal.OutputNeuronCount);
Assert.AreEqual(7, temporal.CalculateActualSetSize());
IEnumerator<IMLDataPair> itr = temporal.GetEnumerator();
itr.MoveNext();
// set 0
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(7.0, pair.Input.Data[0]);
Assert.AreEqual(8.0, pair.Input.Data[1]);
Assert.AreEqual(10.0, pair.Input.Data[2]);
Assert.AreEqual(11.0, pair.Input.Data[3]);
Assert.AreEqual(13.0, pair.Input.Data[4]);
Assert.AreEqual(14.0, pair.Input.Data[5]);
Assert.AreEqual(16.0, pair.Input.Data[6]);
Assert.AreEqual(17.0, pair.Input.Data[7]);
Assert.AreEqual(19.0, pair.Input.Data[8]);
Assert.AreEqual(20.0, pair.Input.Data[9]);
Assert.AreEqual(24.0, pair.Ideal.Data[0]);
}
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 void BasicTemporal()
{
var temporal = new TemporalMLDataSet(5, 1);
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, true, false));
temporal.AddDescription(new TemporalDataDescription(TemporalDataDescription.Type.Raw, false, true));
for (int i = 0; i < 10; i++)
{
TemporalPoint tp = temporal.CreatePoint(i);
tp.Data[0] = 1.0 + (i * 3);
tp.Data[1] = 2.0 + (i * 3);
tp.Data[2] = 3.0 + (i * 3);
}
temporal.Generate();
Assert.AreEqual(10, temporal.InputNeuronCount);
Assert.AreEqual(1, temporal.OutputNeuronCount);
Assert.AreEqual(10, temporal.CalculateActualSetSize());
IEnumerator <IMLDataPair> itr = temporal.GetEnumerator();
itr.MoveNext();
// set 0
IMLDataPair pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(1.0, pair.Input.Data[0]);
Assert.AreEqual(2.0, pair.Input.Data[1]);
Assert.AreEqual(4.0, pair.Input.Data[2]);
Assert.AreEqual(5.0, pair.Input.Data[3]);
Assert.AreEqual(7.0, pair.Input.Data[4]);
Assert.AreEqual(8.0, pair.Input.Data[5]);
Assert.AreEqual(10.0, pair.Input.Data[6]);
Assert.AreEqual(11.0, pair.Input.Data[7]);
Assert.AreEqual(13.0, pair.Input.Data[8]);
Assert.AreEqual(14.0, pair.Input.Data[9]);
Assert.AreEqual(18.0, pair.Ideal[0]);
// set 1
itr.MoveNext();
pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(4.0, pair.Input.Data[0]);
Assert.AreEqual(5.0, pair.Input.Data[1]);
Assert.AreEqual(7.0, pair.Input.Data[2]);
Assert.AreEqual(8.0, pair.Input.Data[3]);
Assert.AreEqual(10.0, pair.Input.Data[4]);
Assert.AreEqual(11.0, pair.Input.Data[5]);
Assert.AreEqual(13.0, pair.Input.Data[6]);
Assert.AreEqual(14.0, pair.Input.Data[7]);
Assert.AreEqual(16.0, pair.Input.Data[8]);
Assert.AreEqual(17.0, pair.Input.Data[9]);
Assert.AreEqual(21.0, pair.Ideal.Data[0]);
// set 2
itr.MoveNext();
pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(7.0, pair.Input.Data[0]);
Assert.AreEqual(8.0, pair.Input.Data[1]);
Assert.AreEqual(10.0, pair.Input.Data[2]);
Assert.AreEqual(11.0, pair.Input.Data[3]);
Assert.AreEqual(13.0, pair.Input.Data[4]);
Assert.AreEqual(14.0, pair.Input.Data[5]);
Assert.AreEqual(16.0, pair.Input.Data[6]);
Assert.AreEqual(17.0, pair.Input.Data[7]);
Assert.AreEqual(19.0, pair.Input.Data[8]);
Assert.AreEqual(20.0, pair.Input.Data[9]);
Assert.AreEqual(24.0, pair.Ideal.Data[0]);
// set 3
itr.MoveNext();
pair = itr.Current;
Assert.AreEqual(10, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(10.0, pair.Input.Data[0]);
Assert.AreEqual(11.0, pair.Input.Data[1]);
Assert.AreEqual(13.0, pair.Input.Data[2]);
Assert.AreEqual(14.0, pair.Input.Data[3]);
Assert.AreEqual(16.0, pair.Input.Data[4]);
Assert.AreEqual(17.0, pair.Input.Data[5]);
Assert.AreEqual(19.0, pair.Input.Data[6]);
Assert.AreEqual(20.0, pair.Input.Data[7]);
Assert.AreEqual(22.0, pair.Input.Data[8]);
Assert.AreEqual(23.0, pair.Input.Data[9]);
Assert.AreEqual(27.0, pair.Ideal.Data[0]);
}
