AnnoyIndex {RcppAnnoy} | R Documentation |
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()