/// <summary>
/// Gets maximum of all gaussian distributions for specified song
/// </summary>
/// <param name="data">Song</param>
public double GetGaussianValue(DataElement data)
{
if (!AfterLearning)
{
throw new NotSupportedException("Could not get GMM value before learning phase!");
}
if (!data.HasFeaturesExtracted())
{
data.GetFeatures();
}
double max = em.GetGaussianMixtureMax(data.MfccCoefficients);
return(max);
}
public override SongGenre GetClass(DataElement testedData)
{
base.GetClass(testedData);
if (!testedData.HasFeaturesExtracted())
{
testedData.GetFeatures();
}
for (int i = 0; i < TrainingData.Count; i++)
{
TrainingData[i].GetDistance(testedData);
}
TrainingData.Sort((data1, data2) => data1.NeighbourDistance.CompareTo(data2.NeighbourDistance));
List <int> labelHistogram = new int[Enum.GetNames(typeof(SongGenre)).Length].ToList();
List <DataElement> nearestNeighbours = TrainingData.GetRange(0, K);
foreach (DataElement neighbour in nearestNeighbours)
{
SongGenre genre = neighbour.GetClass();
labelHistogram[(int)genre]++;
}
int max = labelHistogram.Max();
List <int> nearestGenre = new List <int>();
for (int i = 0; i < labelHistogram.Count; i++)
{
if (labelHistogram[i] == max)
{
nearestGenre.Add(i);
}
}
nearestNeighbours.RemoveAll(data => !nearestGenre.Contains((int)data.GetClass()));
Random r = new Random();
testedData.Probability = 1.0;
return(nearestNeighbours[r.Next(nearestNeighbours.Count)].GetClass());
}