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Vamana index

Source: R/indices_graph.R
VamanaIndex.Rd

The flat graph from DiskANN: out-degree r, pruned in two passes with the relaxed-neighbour rule. Builds slower than HnswIndex at its cheapest setting, though the gap closes once you match on recall, and the index is a few per cent smaller.

References

Jayaram Subramanya S, et al. (2019). DiskANN: fast accurate billion-point nearest neighbor search on a single node. Advances in Neural Information Processing Systems, 32. https://papers.nips.cc/paper/2019/hash/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Abstract.html

Super class

AnnIndex -> VamanaIndex

Active bindings

ef_search

Integer or NULL. Beam width at query time.

Methods

Public methods

Inherited methods

VamanaIndex$new()

Build the index.

Usage

VamanaIndex$new(
  data,
  metric = c("euclidean", "sqeuclidean", "cosine", "manhattan"),
  r = 48L,
  l_build = 100L,
  alpha_pass1 = 1,
  alpha_pass2 = 1.2,
  ef_search = NULL,
  seed = 42L,
  precision = c("float", "double")
)

Arguments

data

Numeric matrix or data.frame. Samples x features. An index pointer is also accepted; load_ann_index() uses that path.

metric

String. One of c("euclidean", "sqeuclidean", "cosine", "manhattan").

r

Integer. Maximum out-degree.

l_build

Integer. Candidate list width during the build.

alpha_pass1

Numeric. Relaxed-neighbour factor on the first pruning pass. 1 is the plain rule.

alpha_pass2

Numeric. Same on the second pass. Above 1 keeps longer edges, which keeps the graph navigable from far away.

ef_search

Integer or NULL. Beam width at query time. NULL uses 75. Can be changed after the build.

seed

Integer. Fixes the entry point and pruning order.

precision

String. "float" stores the data as f32, "double" as f64.

VamanaIndex$clone()

The objects of this class are cloneable with this method.

Usage

VamanaIndex$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

x <- generate_clustered_data(1000L, 16L)$data
idx <- VamanaIndex$new(x)
res <- idx$predict(x[1:5, ], k = 10L)