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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).

  data, k, ...,
  query = NULL,
  distance = c("euclidean", "cosine", "rankcor"),
  sym = TRUE)

The result will be a list containing

(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!

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knn.covertree documentation built on Oct. 30, 2019, 11:37 a.m.