Search a nearest neighbor index

Find neighbors in an existing index for each query in X.

Usage

hnsw_search(
  X,
  ann,
  k,
  ef = 10,
  verbose = FALSE,
  progress = "bar",
  n_threads = 0,
  grain_size = 1,
  byrow = TRUE
)

Arguments

X

Numeric query matrix with the same number of dimensions as the indexed data. Each row is a query, or each column when byrow = FALSE.

ann

An index from hnsw_build() or a Module constructor.

k

Number of neighbors per query. A positive whole number no larger than the number of undeleted items. ann$size() includes deleted items.

ef

Candidate-list size during search. Increase it to improve recall at the cost of search time. Must be a positive whole number; the effective value is at least k and is not capped at the index size.

verbose

If TRUE, log messages to the console.

progress

Unused; retained for compatibility.

n_threads

Maximum number of threads for batch insertion or search. Zero (the default) and one run serially.

grain_size

Minimum number of items per thread. Larger values limit threading overhead for small batches. Zero is treated as one.

byrow

If TRUE, items are rows of X; otherwise, they are columns. Results follow the same orientation.

Value

A list with matrices idx (one-based labels in ann) and dist (distances), ordered by increasing distance for each query. Both are n × k when byrow = TRUE, or k × n otherwise, where n is the number of queries in X.

Details

The index's distance measure is used for all queries. Search is approximate; with inner-product distance, an item need not be its own nearest neighbor. Coordinates are stored in single precision; see RcppHnsw-package for numeric limits.

This call also updates ann's ef, n_threads, and grain_size settings. If you then use the Module methods directly, they will use these settings.

See also

hnsw_build() to create an index, hnsw_knn() to build and search in one call.

Examples

irism <- as.matrix(iris[, -5])
ann <- hnsw_build(irism[1:100, ])
neighbors <- hnsw_search(irism[101:150, ], ann, k = 5)