Controls for NND algorithm used in the package (see rnnd_build and rnnd_query for details).
control_nnd(
k_build = 30,
use_alt_metric = FALSE,
init = "tree",
n_trees = NULL,
leaf_size = NULL,
max_tree_depth = 200,
margin = "auto",
n_iters = NULL,
delta = 0.001,
max_candidates = NULL,
low_memory = TRUE,
n_search_trees = 1,
pruning_degree_multiplier = 1.5,
diversify_prob = 1,
weight_by_degree = FALSE,
prune_reverse = FALSE,
progress = "bar",
obs = "R",
max_search_fraction = 1,
epsilon = 0.1,
...
)Returns a list with parameters.
Number of nearest neighbors to build the index for.
If TRUE, use faster metrics that maintain the ordering of distances internally (e.g. squared Euclidean distances if using metric = "euclidean"),
then apply a correction at the end.
Name of the initialization strategy or initial data neighbor graph to optimize.
The number of trees to use in the RP forest.
Only used if init = "tree".
The maximum number of items that can appear in a leaf.
Only used if init = "tree".
The maximum depth of the tree to build (default = 200).
Only used if init = "tree".
A character string specifying the method used to assign points to one side of the hyperplane or the other.
Number of iterations of nearest neighbor descent to carry out.
The minimum relative change in the neighbor graph allowed before early stopping. Should be a value between 0 and 1. The smaller the value, the smaller the amount of progress between iterations is allowed.
Maximum number of candidate neighbors to try for each item in each iteration.
If TRUE, use a lower memory, but more computationally expensive approach to index construction. If set to FALSE, you should see a noticeable speed improvement, especially when using a smaller number of threads, so this is worth trying if you have the memory to spare.
The number of trees to keep in the search forest as part of index preparation. The default is 1.
How strongly to truncate the final neighbor list for each item.
The degree of diversification of the search graph by removing unnecessary edges through occlusion pruning.
If TRUE, then candidates for the local join are weighted according to their in-degree,
so that if there are more than max_candidates in a candidate list, candidates with a smaller degree are favored for retention.
If TRUE, prune the reverse neighbors of each item before the reverse graph diversification step using pruning_degree_multiplier.
Determines the type of progress information logged during the nearest neighbor descent stage.
set to C to indicate that the input data orientation stores each observation as a column.
The default R means that observations are stored in each row.
Maximum fraction of the reference data to search.
Controls trade-off between accuracy and search cost.
Additional arguments.