public void Learn_3statefullyconnectedgraph_aHMMfittedtothedata()
{
// Arrange
int[] symbols = Enumerable.Range(0, 41).ToArray();
List<int[]> obs = new List<int[]>();
Random rnd = new Random();
for (int i = 0; i < 10; i++)
{
obs.Add(symbols.OrderBy(x => rnd.Next()).ToArray());
}
SequenceData sd = new SequenceData(0);
sd.AddSequences(obs);
sd.SaveAddedSequences();
PadawanLearner pwl = new PadawanLearner();
pwl.Initialise(null, 0);
// Act
pwl.Learn(sd, null, null);
//pwl.Learn(sd);
double result = pwl.CalculateProbability(obs[2]);
// Assert
Assert.IsTrue(0.9 < result && result < 1.0);
}
public Tuple<SequenceData, SequenceData> RandomSplit(int trainingDataSize, int validationDataSize, int randomSeed)
{
SequenceData trainingData = new SequenceData(NumSymbols);
SequenceData validaitonData = new SequenceData(NumSymbols);
List<int[]> shuffled = sequence_list.ToList();
Utilities.Shuffle(shuffled, randomSeed);
trainingData.AddSequences(sequence_list.Take(trainingDataSize));
validaitonData.AddSequences(sequence_list.Skip(sequence_list.Count - validationDataSize).Take(validationDataSize));
trainingData.SaveAddedSequences();
validaitonData.SaveAddedSequences();
return new Tuple<SequenceData, SequenceData>(trainingData, validaitonData);
}