code: public static AssociateDescription<TupleDesc_F64> kdRandomForest( ConfigAssociateNearestNeighbor configNN ,
  																	  int dimension,
  																	  int maxNodesSearched ,
  																	  int numTrees ,
  																	  int numConsiderSplit ,
  																	  long randomSeed) {
  		NearestNeighbor nn = FactoryNearestNeighbor.kdRandomForest(
  				new KdTreeTuple_F64(dimension),
  				maxNodesSearched,numTrees,numConsiderSplit,randomSeed);
  
  		return associateNearestNeighbor(configNN,nn);
  	}
docs: Approximate association using multiple random K-D trees (random forest) for descriptors with a high degree of
  freedom, e.g. &gt; 20
  
  @see AssociateNearestNeighbor_ST
  @see org.ddogleg.nn.wrap.KdForestBbfSearch
  
  @param dimension Number of elements in the feature vector
  @param maxNodesSearched  Maximum number of nodes it will search.  Controls speed and accuracy.
  @param numTrees Number of trees that are considered.  Try 10 and tune.
  @param numConsiderSplit Number of nodes that are considered when generating a tree.  Must be less than the
  point's dimension.  Try 5
  @param randomSeed Seed used by random number generator
  @return Association using approximate nearest neighbor