/
clustering_kmeanspp.R
executable file
·87 lines (83 loc) · 3.01 KB
/
clustering_kmeanspp.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
#' K-Means++ Clustering
#'
#' Given \eqn{N} observations \eqn{X_1, X_2, \ldots, X_N \in \mathcal{M}},
#' perform k-means++ clustering algorithm using pairwise distances. The algorithm
#' was originally designed as an efficient initialization method for k-means
#' algorithm.
#'
#' @param riemobj a S3 \code{"riemdata"} class for \eqn{N} manifold-valued data.
#' @param k the number of clusters.
#' @param geometry (case-insensitive) name of geometry; either geodesic (\code{"intrinsic"}) or embedded (\code{"extrinsic"}) geometry.
#'
#' @return a named list containing\describe{
#' \item{centers}{a length-\eqn{k} vector of sampled centers' indices.}
#' \item{cluster}{a length-\eqn{N} vector of class labels (from \eqn{1:k}).}
#' }
#'
#' @examples
#' #-------------------------------------------------------------------
#' # Example on Sphere : a dataset with three types
#' #
#' # class 1 : 10 perturbed data points near (1,0,0) on S^2 in R^3
#' # class 2 : 10 perturbed data points near (0,1,0) on S^2 in R^3
#' # class 3 : 10 perturbed data points near (0,0,1) on S^2 in R^3
#' #-------------------------------------------------------------------
#' ## GENERATE DATA
#' mydata = list()
#' for (i in 1:10){
#' tgt = c(1, stats::rnorm(2, sd=0.1))
#' mydata[[i]] = tgt/sqrt(sum(tgt^2))
#' }
#' for (i in 11:20){
#' tgt = c(rnorm(1,sd=0.1),1,rnorm(1,sd=0.1))
#' mydata[[i]] = tgt/sqrt(sum(tgt^2))
#' }
#' for (i in 21:30){
#' tgt = c(stats::rnorm(2, sd=0.1), 1)
#' mydata[[i]] = tgt/sqrt(sum(tgt^2))
#' }
#' myriem = wrap.sphere(mydata)
#' mylabs = rep(c(1,2,3), each=10)
#'
#' ## K-MEANS++ WITH K=2,3,4
#' clust2 = riem.kmeanspp(myriem, k=2)
#' clust3 = riem.kmeanspp(myriem, k=3)
#' clust4 = riem.kmeanspp(myriem, k=4)
#'
#' ## MDS FOR VISUALIZATION
#' mds2d = riem.mds(myriem, ndim=2)$embed
#'
#' ## VISUALIZE
#' opar <- par(no.readonly=TRUE)
#' par(mfrow=c(2,2), pty="s")
#' plot(mds2d, pch=19, main="true label", col=mylabs)
#' plot(mds2d, pch=19, main="K=2", col=clust2$cluster)
#' plot(mds2d, pch=19, main="K=3", col=clust3$cluster)
#' plot(mds2d, pch=19, main="K=4", col=clust4$cluster)
#' par(opar)
#'
#' @references
#' \insertRef{arthur_kmeans_2007a}{Riemann}
#'
#' @concept clustering
#' @export
riem.kmeanspp <- function(riemobj, k=2, geometry=c("intrinsic","extrinsic")){
## PREPARE
DNAME = paste0("'",deparse(substitute(riemobj)),"'")
if (!inherits(riemobj,"riemdata")){
stop(paste0("* riem.kmeanspp : input ",DNAME," should be an object of 'riemdata' class."))
}
myk = max(0, round(k))
mygeom = ifelse(missing(geometry),"intrinsic",
match.arg(tolower(geometry),c("intrinsic","extrinsic")))
## COMPUTE PAIRWISE DISTANCE
distobj = stats::as.dist(basic_pdist(riemobj$name, riemobj$data, mygeom))
## RUN K-MEDOIDS
func.import = utils::getFromNamespace("hidden_kmeanspp", "maotai")
obj.plus = func.import(distobj, k=myk)
## WRAP AND RETURN
output = list()
output$centers = obj.plus$center
output$cluster = as.vector(as.integer(obj.plus$cluster))
return(output)
}