find_knn {destiny} | R Documentation

## kNN search

### Description

Approximate k nearest neighbor search with flexible distance function.

### Usage

    find_knn(data, k, ..., query = NULL, distance = c("euclidean",
      "cosine", "rankcor", "l2"), method = c("covertree", "hnsw"),
      sym = TRUE, verbose = FALSE)

### Arguments

`data` | Data matrix
`k` | Number of nearest neighbors
`...` | Parameters passed to `hnsw_knn`
`query` | Query matrix. Leave it out to use `data` as query
`distance` | Distance metric to use. Allowed measures: Euclidean distance (default), cosine distance (1-corr(c_1, c_2)) or rank correlation distance (1-corr(rank(c_1), rank(c_2)))
`method` | Method to use. `'hnsw'` is tunable with `...` but generally less exact than `'covertree'` (default: 'covertree')
`sym` | Return a symmetric matrix (as long as query is NULL)?
`verbose` | Show a progressbar? (default: FALSE)

### Value

A `list` with the entries:

`index`

  A nrow(data) \times k integer matrix containing the indices of the k nearest neighbors for each cell.

`dist`

  A nrow(data) \times k double matrix containing the distances to the k nearest neighbors for each cell.

`dist_mat`

  A `dgCMatrix` if `sym == TRUE`, else a `dsCMatrix` (nrow(query) \times nrow(data)). Any zero in the matrix (except for the diagonal) indicates that the cells in the corresponding pair are close neighbors.

[Package destiny version 3.1.0 Index]