RcppAnnoy (version 0.0.22)

AnnoyIndex: Approximate Nearest Neighbors with Annoy

Description

Annoy is a small library written to provide fast and memory-efficient nearest neighbor lookup from a possibly static index which can be shared across processes.

Usage

a <- new(AnnoyEuclidean, vectorsz)

a$setSeed(0) a$setVerbose(0)

a$addItem(i, dv)

a$getNItems()

a$getItemsVector(i) a$getDistance(i, j)

a$build(n_trees)

a$getNNsByItem(i, n) a$getNNsByItemList(i, n, search_k, include_distances)

a$getNNsByVector(v, n) a$getNNsByVectorList(v, n, search_k, include_distances)

a$save(fn) a$load(fn) a$unload()

Details

new(Class, vectorsz) Create a new Annoy instance of type Class where Class is on of the following: AnnoyEuclidean, AnnoyAngular, AnnoyManhattan, AnnoyHamming. vectorsz denotes the length of the vectors that the Annoy instance will be indexing.

$addItem(i, v) Adds item i (any nonnegative integer) with vector v. Note that it will allocate memory for max(i) + 1 items.

$build(n_trees) Builds a forest of n_trees trees. More trees gives higher precision when querying. After calling build, no more items can be added.

$save(fn) Saves the index to disk as filename fn. After saving, no more items can be added.

$load(fn) Loads (mmaps) an index from filename fn on disk.

$unload() Unloads index.

$getDistance(i, j) Returns the distance between items i and j

$getNNsByItem(i, n) Returns the n closest items as an integer vector of indices.

$getNNsByVector(v, n) Same as $getNNsByItem, but queries by vector v rather than index i.

$getNNsByItemList(i, n, search_k = -1, include_distances = FALSE) Returns the n closest items to item i as a list. During the query it will inspect up to search_k nodes which defaults to n_trees * n if not provided. search_k gives you a run-time tradeoff between better accuracy and speed. If you set include_distances to TRUE, it will return a length 2 list with elements "item" & "distance". The "item" element contains the n closest items as an integer vector of indices. The optional "distance" element contains the corresponding distances to "item" as a numeric vector.

$getNNsByVectorList(i, n, search_k = -1, include_distances = FALSE) Same as $getNNsByItemList, but queries by vector v rather than index i

$getItemsVector(i) Returns the vector for item i that was previously added.

$getNItems() Returns the number of items in the index.

$setVerbose() If 1 then messages will be printed during processing. If 0 then messages will be suppressed during processing.

$setSeed() Set random seed for annoy (integer).

Examples

library(RcppAnnoy)

# BUILDING ANNOY INDEX ---------------------------------------------------------
vector_size <- 10
a <- new(AnnoyEuclidean, vector_size)

a$setSeed(42)

# Turn on verbose status messages (0 to turn off)
a$setVerbose(1)

# Load 100 random vectors into index
for (i in 1:100) a$addItem(i - 1, runif(vector_size)) # Annoy uses zero indexing

# Display number of items in index
a$getNItems()

# Retrieve item at postition 0 in index
a$getItemsVector(0)

# Calculate distance between items at postitions 0 & 1 in index
a$getDistance(0, 1)

# Build forest with 50 trees
a$build(50)


# PERFORMING ANNOY SEARCH ------------------------------------------------------

# Retrieve 5 nearest neighbors to item 0
# Returned as integer vector of indices
a$getNNsByItem(0, 5)

# Retrieve 5 nearest neighbors to item 0
# search_k = -1 will invoke default search_k value of n_trees * n
# Return results as list with an element for distance
a$getNNsByItemList(0, 5, -1, TRUE)

# Retrieve 5 nearest neighbors to item 0
# search_k = -1 will invoke default search_k value of n_trees * n
# Return results as list without an element for distance
a$getNNsByItemList(0, 5, -1, FALSE)


v <- runif(vector_size)
# Retrieve 5 nearest neighbors to vector v
# Returned as integer vector of indices
a$getNNsByVector(v, 5)

# Retrieve 5 nearest neighbors to vector v
# search_k = -1 will invoke default search_k value of n_trees * n
# Return results as list with an element for distance
a$getNNsByVectorList(v, 5, -1, TRUE)

# Retrieve 5 nearest neighbors to vector v
# search_k = -1 will invoke default search_k value of n_trees * n
# Return results as list with an element for distance
a$getNNsByVectorList(v, 5, -1, TRUE)


# SAVING/LOADING ANNOY INDEX ---------------------------------------------------

# Create a tempfile, replace with a local file to keep
treefile <- tempfile(pattern="annoy", fileext="tree")

# Save annoy tree to disk
a$save(treefile)

# Load annoy tree from disk
a$load(treefile)

# Unload index from memory
a$unload()