IVF index — IvfIndex • annsearchR

Inverted file over k-means Voronoi cells. Cheap to build and easy to tune: nlist sets how finely the space is cut, nprobe how many cells a query visits. The smallest of the unquantised approximate indices. No Manhattan.

References

Sivic J, Zisserman A (2003). Video Google: a text retrieval approach to object matching in videos. Proceedings Ninth IEEE International Conference on Computer Vision, 1470-1477. doi:10.1109/ICCV.2003.1238663

Jégou H, Douze M, Schmid C (2011). Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 117-128. doi:10.1109/TPAMI.2010.57

Super class

AnnIndex -> IvfIndex

Active bindings

nprobe: Integer or NULL. Cells visited per query.

Methods

Inherited methods

`IvfIndex$new()`

Build the index.

Usage

IvfIndex$new(
  data,
  metric = c("euclidean", "sqeuclidean", "cosine"),
  nlist = NULL,
  nprobe = NULL,
  kmeans_iters = NULL,
  kmeans_balanced = FALSE,
  seed = 42L,
  precision = c("float", "double"),
  .verbose = FALSE
)

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").
nlist: Integer or NULL. Number of Voronoi cells. NULL uses sqrt(n).
nprobe: Integer or NULL. Cells visited per query. NULL uses sqrt(nlist). Can be changed after the build.
kmeans_iters: Integer or NULL. Lloyd iterations. NULL uses 30.
kmeans_balanced: Boolean. Reseed starved centroids each iteration.
seed: Integer. Fixes the k-means initialisation.
precision: String. "float" stores the data as f32, "double" as f64.
.verbose: Boolean. Print build progress from Rust.

`IvfIndex$clone()`

The objects of this class are cloneable with this method.

Usage

IvfIndex$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

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