landmark
Calculate landmark sets for finite metric spaces using the maxmin procedure (for fixed-radius balls) or an adaptation of it for rank data (for roughly fixed-cardinality nearest neighborhoods).
(x <- matrix(c(-1, -.5, 0, .75, .875, 1), dimnames = list(letters[1:6], "x")))
#> x
#> a -1.000
#> b -0.500
#> c 0.000
#> d 0.750
#> e 0.875
#> f 1.000
plot(cbind(x, 0), asp = 1, pch = 16)
text(cbind(x, .05), labels = rownames(x))
maxmin
procedure
The original maxmin
procedure produces a landmark set for covering a
point cloud with either of two minimal ball covers:
- a minimum number of balls of fixed uniform radius
- a fixed number of balls of minimum uniform radius
x[landmarks_maxmin(x, radius = 0.5, engine = "C++"), , drop = FALSE]
#> x
#> a -1
#> f 1
#> c 0
x[landmarks_maxmin(x, radius = 0.25, engine = "C++"), , drop = FALSE]
#> x
#> a -1.0
#> f 1.0
#> c 0.0
#> b -0.5
x[landmarks_maxmin(x, radius = 0.125, engine = "C++"), , drop = FALSE]
#> x
#> a -1.00
#> f 1.00
#> c 0.00
#> b -0.50
#> d 0.75
x[landmarks_maxmin(x, num = 6L, engine = "C++"), , drop = FALSE]
#> x
#> a -1.000
#> f 1.000
#> c 0.000
#> b -0.500
#> d 0.750
#> e 0.875
landmarks_maxmin(x, num = 4L, engine = "R", cover = TRUE)
#> landmark cover_set
#> 1 1 1
#> 2 6 4, 5, 6
#> 3 3 3
#> 4 2 2
landmarks_maxmin(x, radius = 0.5, engine = "R", cover = TRUE)
#> landmark cover_set
#> 1 1 1, 2
#> 2 6 4, 5, 6
#> 3 3 2, 3
landmarks_maxmin(x, radius = 1.5, engine = "R", cover = TRUE)
#> landmark cover_set
#> 1 1 1, 2, 3
#> 2 6 2, 3, 4,....
landmarks_maxmin(x, radius = 3.5, engine = "R", cover = TRUE)
#> landmark cover_set
#> 1 1 1, 2, 3,....
lastfirst
procedure
An adaptation of maxmin
to ranked distances will produce a landmark
set for covering a point cloud with either of two minimal neighborhood
covers:
- a minimum number of neighborhoods of fixed (approximately) uniform cardinality
- a fixed number of neighborhoods of minimal (approximately) uniform cardinality
Cardinality is only exact up to ties, which may be handled different ways and will result in cover sets of different cardinalities.
x[landmarks_lastfirst(x, cardinality = 3L, seed_index = 6L), , drop = FALSE]
#> x
#> f 1
#> a -1
x[landmarks_lastfirst(x, cardinality = 2L, seed_index = 6L), , drop = FALSE]
#> x
#> f 1.00
#> a -1.00
#> c 0.00
#> d 0.75
x[landmarks_lastfirst(x, num = 4L, seed_index = 6L), , drop = FALSE]
#> x
#> f 1.00
#> a -1.00
#> c 0.00
#> d 0.75
x[landmarks_lastfirst(x, cardinality = 1L, seed_index = 6L), , drop = FALSE]
#> x
#> f 1.000
#> a -1.000
#> c 0.000
#> d 0.750
#> b -0.500
#> e 0.875
landmarks_lastfirst(x, cardinality = 1L, seed_index = 6L, engine = "C++", cover = TRUE)
#> landmark cover_set
#> 1 6 6
#> 2 1 1
#> 3 3 3
#> 4 4 4
#> 5 2 2
#> 6 5 5
landmarks_lastfirst(x, num = 4L, seed_index = 6L, engine = "C++", cover = TRUE)
#> landmark cover_set
#> 1 6 5, 6
#> 2 1 1, 2
#> 3 3 2, 3
#> 4 4 4, 5
landmarks_lastfirst(x, cardinality = 3L, seed_index = 6L, engine = "C++", cover = TRUE)
#> landmark cover_set
#> 1 6 4, 5, 6
#> 2 1 1, 2, 3
landmarks_lastfirst(x, cardinality = 5L, seed_index = 6L, engine = "C++", cover = TRUE)
#> landmark cover_set
#> 1 6 2, 3, 4,....
#> 2 1 1, 2, 3,....
references
This package was spun off from the Mapper package.
A rigorous mathematical treatment is underway at this Overleaf project.