public override void AddScores(RegressionTree tree, DocumentPartitioning partitioning, double multiplier)
{
_k++;
double coeff = (_k - 1.0) / (_k + 2.0);
var actions = new Action[tree.NumLeaves];
Parallel.For(0, tree.NumLeaves, new ParallelOptions {
MaxDegreeOfParallelism = BlockingThreadPool.NumThreads
},
(int leaf) =>
{
int[] documents;
int begin;
int count;
partitioning.ReferenceLeafDocuments(leaf, out documents, out begin, out count);
double output = tree.LeafValue(leaf) * multiplier;
for (int i = begin; i < begin + count; ++i)
{
int doc = documents[i];
double newXK = YK[doc] + output;
double newYK = newXK + coeff * (newXK - XK[doc]);
XK[doc] = newXK;
YK[doc] = newYK;
}
});
SendScoresUpdatedMessage();
}
//Use faster method for score update with Partitioning
// suitable for TrainSet
public virtual void AddScores(RegressionTree tree, DocumentPartitioning partitioning, double multiplier)
{
Parallel.For(0, tree.NumLeaves, new ParallelOptions {
MaxDegreeOfParallelism = BlockingThreadPool.NumThreads
}, (leaf) =>
{
int[] documents;
int begin;
int count;
partitioning.ReferenceLeafDocuments(leaf, out documents, out begin, out count);
double output = tree.LeafValue(leaf) * multiplier;
for (int i = begin; i < begin + count; ++i)
{
Scores[documents[i]] += output;
}
});
SendScoresUpdatedMessage();
}