R/FindCentersAutoD.R

In ethanyxu/ADPclust: Fast Clustering Using Adaptive Density Peak Detection

##' Automatically finds centers with diagonal f(x) vs delta(x) thresholds. This is used in adpclust() with ac = 2. It finds points that are above and farthest from the diagonal line in the f vs. delta plots, and label them to be centers.
##'
##' @title Automatically finds centers with diagonal f(x) vs delta(x) thresholds
##' @param f vector of local distance.
##' @param delta vector of minimal distances to higher ground.
##' @param nclust number of clusters. Can be a single integer or a vector of integers. Duplicates are silently removed.
##' @return a list of vectors. Each vector gives the locations of centers.
##' @author Ethan Xu
FindCentersAutoD <- function(f, delta, nclust){
    if(!is.numeric(nclust)) stop('arg nclust should inherit numeric. Got ', class(nclust))
    if(!all.equal(nclust, as.integer(nclust))) stop('nclust must all be integers')
    if(min(nclust) <= 0) stop('nclust must be positive integers')
    
    center.list <- list()
    nclust <- unique(nclust)

    x1 <- min(f); y1 <- max(delta)
    x2 <- max(f); y2 <- min(delta)
    pl.dist <- ((x2 - x1) * delta - (y2 - y1) * f + y2 * x1 - x2 * y1) / sqrt((y2 - y1) ^ 2 + (x2 - x1) ^ 2)
    cts <- order(pl.dist, decreasing = TRUE)[1:max(nclust)]
    for(i in seq_along(nclust)){
        centers <- cts[1:nclust[i]]
        attributes(centers) <- list(nclust = nclust[i])
        center.list <- c(center.list, list(centers))
    }
    return(center.list = center.list)
}