Annoy index

Random projection forest, as in Spotify's Annoy. More trees means better recall, a larger index and a slower build. Raise search_budget for recall at query time. No Manhattan.

References

Bernhardsson E. Annoy (Approximate Nearest Neighbors Oh Yeah). https://github.com/spotify/annoy

Super class

AnnIndex -> AnnoyIndex

Active bindings

search_budget

Integer or NULL. Candidates inspected per query.

Methods

Public methods

• AnnoyIndex$new()

• AnnoyIndex$clone()

Inherited methods

• AnnIndex$predict()
• AnnIndex$print()
• AnnIndex$query_self()
• AnnIndex$save()

---

AnnoyIndex$new()

Build the index.

Usage

AnnoyIndex$new(
  data,
  metric = c("euclidean", "sqeuclidean", "cosine"),
  n_trees = 25L,
  search_budget = 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").

n_trees

Integer. Number of trees in the forest.

search_budget

Integer or NULL. Candidates inspected per query. NULL uses k * n_trees * 20. Can be changed after the build.

seed

Integer. Fixes the random hyperplanes.

precision

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

---

AnnoyIndex$clone()

The objects of this class are cloneable with this method.

Usage

AnnoyIndex$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

x <- generate_clustered_data(1000L, 16L)$data
idx <- AnnoyIndex$new(x, n_trees = 25L)
res <- idx$predict(x[1:5, ], k = 10L)