private InternalTreeEnsemble GetBinaryEnsemble(int classID)
{
var res = new InternalTreeEnsemble();
for (int i = classID; i < TrainedEnsemble.NumTrees; i += _numClass)
{
// Ignore dummy trees.
if (TrainedEnsemble.GetTreeAt(i).NumLeaves > 1)
{
res.AddTree(TrainedEnsemble.GetTreeAt(i));
}
}
return(res);
}
private Ensemble GetBinaryEnsemble(int classID)
{
var numClass = Objective.NumClass;
Ensemble res = new Ensemble();
for (int i = classID; i < TrainedEnsemble.NumTrees; i += numClass)
{
// Ignore dummy trees
if (TrainedEnsemble.GetTreeAt(i).NumLeaves > 1)
{
res.AddTree(TrainedEnsemble.GetTreeAt(i));
}
}
return(res);
}
public ValueMapper <VBuffer <Float>, VBuffer <Float> > GetMapper(Float[] quantiles)
{
return
((ref VBuffer <Float> src, ref VBuffer <Float> dst) =>
{
// REVIEW: Should make this more efficient - it repeatedly allocates too much stuff.
Float[] weights = null;
var distribution = TrainedEnsemble.GetDistribution(ref src, _quantileSampleCount, out weights);
var qdist = new QuantileStatistics(distribution, weights);
var values = dst.Values;
if (Utils.Size(values) < quantiles.Length)
{
values = new Float[quantiles.Length];
}
for (int i = 0; i < quantiles.Length; i++)
{
values[i] = qdist.GetQuantile((Float)quantiles[i]);
}
dst = new VBuffer <Float>(quantiles.Length, values, dst.Indices);
});
}