Controls for the NND algorithm — control

Controls for NND algorithm used in the package (see rnnd_build and rnnd_query for details).

Usage

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,
  ...
)

Arguments

k_build: Number of nearest neighbors to build the index for.
use_alt_metric: 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.
init: Name of the initialization strategy or initial data neighbor graph to optimize.
n_trees: The number of trees to use in the RP forest. Only used if init = "tree".
leaf_size: The maximum number of items that can appear in a leaf. Only used if init = "tree".
max_tree_depth: The maximum depth of the tree to build (default = 200). Only used if init = "tree".
margin: A character string specifying the method used to assign points to one side of the hyperplane or the other.
n_iters: Number of iterations of nearest neighbor descent to carry out.
delta: 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.
max_candidates: Maximum number of candidate neighbors to try for each item in each iteration.
low_memory: 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.
n_search_trees: The number of trees to keep in the search forest as part of index preparation. The default is 1.
pruning_degree_multiplier: How strongly to truncate the final neighbor list for each item.
diversify_prob: The degree of diversification of the search graph by removing unnecessary edges through occlusion pruning.
weight_by_degree: 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.
prune_reverse: If TRUE, prune the reverse neighbors of each item before the reverse graph diversification step using pruning_degree_multiplier.
progress: Determines the type of progress information logged during the nearest neighbor descent stage.
obs: 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.
max_search_fraction: Maximum fraction of the reference data to search.
epsilon: Controls trade-off between accuracy and search cost.
...: Additional arguments.

Value

Returns a list with parameters.