protected override void DoTrain()
{
base.DoTrain();
var classes = Classes.ToList();
for (int i = 0; i < classes.Count; i++)
{
var any = classes.Any(c => (int)c.Value == i);
if (!any)
{
throw new MLException(string.Format("Class values must be enumerated from 0 to {0}", classes.Count));
}
}
m_ClassHist = new int[classes.Count];
m_PriorProbs = new double[classes.Count];
m_DataCount = TrainingSample.Count;
m_DataDim = TrainingSample.GetDimension();
foreach (var pData in TrainingSample)
{
var cls = pData.Value;
m_ClassHist[cls.Value] += 1;
}
foreach (var cls in classes)
{
var penalty = (ClassLosses == null) ? 1 : ClassLosses[cls.Value];
m_PriorProbs[cls.Value] = Math.Log(penalty * m_ClassHist[cls.Value] / (double)m_DataCount);
}
TrainImpl();
}
/// <summary>
/// Estimated proximity of given point to given classes
/// </summary>
public override double CalculateClassScore(double[] obj, Class cls)
{
var dim = TrainingSample.GetDimension();
var p = PriorProbs[cls.Value];
var ds = m_DistributionParameters[cls.Value];
foreach (var pData in TrainingSample.Where(d => d.Value.Equals(cls)))
{
var data = pData.Key;
for (int i = 0; i < dim; i++)
{
m_Distribution.Params = ds[i];
var value = m_Distribution.LogValue(obj[i]);
p += value;
}
}
return(p);
}