,
Erik Bernhardsson [aut] (Principal author of Annoy)| Title: | 'Rcpp' Bindings for 'Annoy', a Library for Approximate Nearest Neighbors |
|---|---|
| Description: | 'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license. |
| Authors: | Dirk Eddelbuettel [aut, cre] ,
Erik Bernhardsson [aut] (Principal author of Annoy) |
| Maintainer: | Dirk Eddelbuettel <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.0.22.1 |
| Built: | 2024-12-07 18:25:13 UTC |
| Source: | https://github.com/eddelbuettel/rcppannoy |
Annoy is a small library written to provide fast and memory-efficient nearest neigbor lookup from a possibly static index which can be shared across processes.
Details about Annoy are available at the reference listed below.
Dirk Eddelbuettel for the R interface; Erik Bernhardsson for Annoy itself.
Maintainer: Dirk Eddelbuettel <[email protected]>
https://github.com/spotify/annoy
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.
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()
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.
$setSeed()
Set random seed for annoy (integer).
library(RcppAnnoy) # BUILDING ANNOY INDEX --------------------------------------------------------- vector_size <- 10 a <- new(AnnoyEuclidean, vector_size) a$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$getNNsByVectorList(v, 5, -1, TRUE) # SAVING/LOADING ANNOY INDEX --------------------------------------------------- # Create a tempfile, replace with a local file to keep treefile <- tempfile(pattern="annoy", fileext=".tree") # Save annoy tree to disk a$save(treefile) # Load annoy tree from disk a$load(treefile) # Unload index from memory a$unload()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) # 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()
Get the version of the Annoy C++ library that RcppAnnoy was compiled with.
getAnnoyVersion(compact = FALSE)getAnnoyVersion(compact = FALSE)
compact |
Logical scalar indicating whether a compact
|
An integer vector containing the major, minor and patch version numbers;
or if compact=TRUE, a package_version object.
Aaron Lun
Report CPU Architecture and Compiler
getArchictectureStatus()getArchictectureStatus()
A constant direct created at compile-time describing the extent of AVX instructions (512 bit, 128 bit, or none) and compiler use where currently recognised are MSC (unlikely for R), GCC, Clang, or ‘other’.