https://github.com/flying-sheep/knn.covertree

A not-too-fast but accurate kNN implementation supporting multiple distance measures
https://github.com/flying-sheep/knn.covertree

Last synced: about 1 month ago
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A not-too-fast but accurate kNN implementation supporting multiple distance measures

• Host: GitHub
• URL: https://github.com/flying-sheep/knn.covertree
• Owner: flying-sheep
• License: gpl-3.0
• Created: 2019-08-06T17:11:43.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-09T13:40:05.000Z (about 5 years ago)
• Last Synced: 2023-11-20T16:23:21.016Z (about 1 year ago)
• Language: C++
• Homepage:
• Size: 56.6 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
[![CRAN badge][]](https://cran.r-project.org/package=knn.covertree) [![Workflow badge][]](https://github.com/flying-sheep/knn.covertree/commits/master)

[CRAN badge]: https://www.r-pkg.org/badges/version/knn.covertree

[Workflow badge]: https://github.com/flying-sheep/knn.covertree/workflows/Build%20R%20package/badge.svg

knn.covertree

=============

A package for precise approximative nearest neighbor search in more than just euclidean space.

Its only exported function `find_knn` computes the `k` nearest neighbors of the rows of the `query` matrix in the `data` matrix.

If no `query` matrix is passed, the nearest neighbors for all rows in the data will be returned (i.e. `data` will be used as `query`).

[generic]: https://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/dgCMatrix-class.html

[symmetric]: https://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/dsCMatrix-class.html

```r

find_knn(

  data, k, ...,

  query = NULL,

  distance = c("euclidean", "cosine", "rankcor"),

  sym = TRUE)

```

The result will be a list containing

- `index`, a `nrow(query)` × `k` integer matrix containing the row indices into `data` that are the nearest neighbors.

- `dist`, a `nrow(query)` × `k` double matrix containing the `distance`s to those neighbors.

- `dist_mat`, a `nrow(query)` × `nrow(data)` a `Matrix::dSparseMatrix`,

  [generic][] if `!sym` or `!is.null(query)`, and [symmetric][] if `sym` and `is.null(query)`.

  Zeros in this matrix mean “not a knn”, and if `sym` is set, the matrix will be post processed to be symmetric.

  

  (Without post processing, the matrix will likely be asymmetric as `r1∈kNN(r2)` does not imply `r2∈knn(r1)`)

This package was separated from [destiny][] as it might prove helpful in other contexts.

It provides more distance metrics than [FNN][] and is more precise than [RcppHNSW][], but slower than both.

If anyone knows a faster and similarly precise kNN search in cosine (=rank correlation) space, please tell me!

[destiny]: http://bioconductor.org/packages/destiny/

[FNN]: https://CRAN.R-project.org/package=FNN

[RcppHNSW]: https://github.com/jlmelville/rcpphnsw