private TreeModel[] GetTreeModelsInParallel(byte[][] points, byte[] targets, Random[] randoms)
{
TreeModel[] treeModels = new TreeModel[TreeCount];
int treesTrainedCount = 0;
var rangePartitioner = Partitioner.Create(0, TreeCount);
ParallelOptions parallelOptions = new ParallelOptions();
parallelOptions.CancellationToken = _cancellationToken;
Parallel.ForEach(rangePartitioner, parallelOptions, range =>
{
for (int i = range.Item1; i < range.Item2; ++i)
{
TreeTrainer trainer = new TreeTrainer(BandCountPerSplit, MaxTreeHeight, MinNodeSize, BootstrappingRatio, randoms[i]);
treeModels[i] = trainer.Train(points, targets, _cancellationToken);
Interlocked.Increment(ref treesTrainedCount);
_progress.Report(ClusteringProgress.StepTaken(treesTrainedCount));
parallelOptions.CancellationToken.ThrowIfCancellationRequested();
}
});
return(treeModels);
}
private RandomForestModel GetModel(byte[][] points, byte[] targets, int bandCount)
{
TreeModel[] treeModels = new TreeModel[TreeCount];
TreeTrainer trainer = new TreeTrainer(BandCountPerSplit, MaxTreeHeight, MinNodeSize, BootstrappingRatio, new Random(_random.Next()));
for (int i = 0; i < TreeCount; ++i)
{
treeModels[i] = trainer.Train(points, targets, _cancellationToken);
_progress.Report(ClusteringProgress.StepTaken(i + 1));
}
return(new RandomForestModel(treeModels, bandCount));
}