The kdtools package can be used to search for multidimensional points in a boxed region and find nearest neighbors in 1 to 9 dimensions. The package uses binary search on a sorted sequence of values. The current package is limited to matrices of real values. If you are interested in using string or mixed types in different dimensions, see the methods vignette.
Using kdtools is straightforward. There are four steps:
library(kdtools)
x = matrix(runif(3e3), nc = 3)
y = matrix_to_tuples(x)
y[1:3, c(1, 3)]
#> [,1] [,2]
#> [1,] 0.31760246 0.26673670
#> [2,] 0.91873475 0.83353354
#> [3,] 0.06588832 0.05998507The arrayvec object can be manipulated as if it were a matrix.
kd_sort(y, inplace = TRUE, parallel = TRUE)
#> [,1] [,2] [,3]
#> [1,] 0.100212932 0.03737731 0.013635131
#> [2,] 0.073604020 0.06123243 0.024205234
#> [3,] 0.038431772 0.07905850 0.074724742
#> [4,] 0.008127881 0.11080106 0.054616788
#> [5,] 0.014519467 0.22525169 0.006341059
#> (continues for 995 more rows)rq = kd_range_query(y, c(0, 0, 0), c(1/4, 1/4, 1/4)); rq
#> [,1] [,2] [,3]
#> [1,] 0.228518003 0.15631139 0.12409162
#> [2,] 0.100212932 0.03737731 0.01363513
#> [3,] 0.073604020 0.06123243 0.02420523
#> [4,] 0.038431772 0.07905850 0.07472474
#> [5,] 0.008127881 0.11080106 0.05461679
#> (continues for 16 more rows)
i = kd_nearest_neighbor(y, c(0, 0, 0)); y[i, ]
#> [1] 0.07360402 0.06123243 0.02420523
nns = kd_nearest_neighbors(y, c(0, 0, 0), 100); nns
#> [,1] [,2] [,3]
#> [1,] 0.08993964 0.2667603 0.49467104
#> [2,] 0.09539294 0.5210289 0.20716790
#> [3,] 0.17432820 0.1044617 0.53108661
#> [4,] 0.14192878 0.5455333 0.03875645
#> [5,] 0.09197128 0.5459574 0.10459673
#> (continues for 95 more rows)
nni = kd_nn_indices(y, c(0, 0, 0), 10); nni
#> [1] 8 9 6 10 5 16 4 3 1 2The kd_nearest_neighbor and kd_nn_indices functions return row-indices. The other functions return arrayvec objects.
head(tuples_to_matrix(rq))
#> [,1] [,2] [,3]
#> [1,] 0.228518003 0.15631139 0.124091624
#> [2,] 0.100212932 0.03737731 0.013635131
#> [3,] 0.073604020 0.06123243 0.024205234
#> [4,] 0.038431772 0.07905850 0.074724742
#> [5,] 0.008127881 0.11080106 0.054616788
#> [6,] 0.014519467 0.22525169 0.006341059
head(tuples_to_matrix(nns))
#> [,1] [,2] [,3]
#> [1,] 0.08993964 0.2667603 0.49467104
#> [2,] 0.09539294 0.5210289 0.20716790
#> [3,] 0.17432820 0.1044617 0.53108661
#> [4,] 0.14192878 0.5455333 0.03875645
#> [5,] 0.09197128 0.5459574 0.10459673
#> [6,] 0.43326809 0.2484376 0.26075469If you pass a matrix instead of an arrayvec object to any of the functions, it will be converted to an arrayvec object internally and results will be returned as matrices. This is slower and provided for convenience.