Find Nearest-Neighbor Pathways Through Matrices
pathway finds a pathway of observations between two points in a matrix.
You can install pathway from GitHub with:
# install.packages("devtools")
devtools::install_github("mdlincoln/pathway")
library(pathway)
set.seed(34)
m <- matrix(rnorm(1000), nrow = 500, ncol = 2)
p1 <- 2L
p2 <- 11L
p <- pathway(m, p1, p2, n = 5)
# Returns both the ideal points between p1 and p2
p$line
#> [,1] [,2]
#> [1,] 0.9326974 -0.5290643
#> [2,] 0.6655819 -0.3696614
#> [3,] 0.3984664 -0.2102584
#> [4,] 0.1313509 -0.0508555
#> [5,] -0.1357646 0.1085474
# as well as the indices of the nearest neighbors
p$i
#> [1] 227 495 419 479 451
plot_pathway(m, p)
It is also possible to place conditional restraints on the solution thatpathway
finds by using navigate_
functions. For example,navigate_unique
will not revisit the same point along a path, andnavigate_ordered
will only look at points that occurr in later rows in
the matrix.
p_ordered <- pathway(m, 5, 380, n = 5, navigator = navigate_ordered)
p_ordered$i
#> [1] 75 279 362 367 370
plot_pathway(m, p_ordered)
To use your own predicate function, define a function that returns a
vector of indices to search and call it with navigator =
navigate(f)
obs_types <- sample(c("setosa", "versicolor", "virginica"), 500, replace = TRUE)
# A custom predicate function must take the original matrix, the list of
# previously-selected pathway points, along with p1 and p2.
different_species <- function(x, pi, p1, p2, obs_types) {
if (is.null(pi)) {
search_space <- 1:nrow(x)
} else {
# Only search observations that do not have the same species as the immediately previous one.
prev_type <- obs_types[tail(pi, 1)]
search_space <- which(obs_types != prev_type)
}
# Don't forget to exclude p1 and p2
setdiff(search_space, c(p1, p2))
}
p_species <- pathway(m, p1, p2, n = 8, navigator = navigate(different_species, obs_types))
obs_types[p_species$i]
#> [1] "versicolor" "setosa" "virginica" "versicolor" "setosa"
#> [6] "versicolor" "virginica" "setosa"