static EvaluatedData Evaluate(Trained[,] trained, TrainingData refineData, int have)
{
if (refineData.level != have) return null;
EvaluatedData e = new EvaluatedData() { level = refineData.level, Label = refineData.Label, DocCount = refineData.DocCount };
for (int stop = 0; stop <= have; stop++)
{
if (trained[stop, have] == null) e.Score[stop] = float.MaxValue;
else e.Score[stop] = trained[stop, have].predictor.Predict(new TrainingData(refineData, stop)).Score;
}
return e;
}
static bool Score(EvaluatedData ev, float[] current, out float cost, out float origCost)
{
origCost = ev.DocCount;
float end = ev.DocCount;
for (int i = 0; i <=ev.level; i++)
{
if (ev.Score[i] <= current[i])
{
end = TrainingData.Range[0];
break;
}
}
cost = (float)Math.Min(end, origCost);
return end >= ev.Label;
}
