Description Usage Arguments Value References Examples
EP-means is a variant of k-means algorithm adapted to cluster multiple empirical cumulative distribution functions under metric structure induced by Earth Mover's Distance.
1 | epmeans(elist, k = 2)
|
elist |
a length N list of either vector or |
k |
the number of clusters. |
a named list containing
an integer vector indicating the cluster to which each ecdf
is allocated.
a length k list of centroid ecdf
objects.
henderson_epmeans_2015maotai
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 | ## two sets of 1d samples, 10 each and add some noise
# set 1 : mixture of two gaussians
# set 2 : single gamma distribution
# generate data
elist = list()
for (i in 1:10){
elist[[i]] = stats::ecdf(c(rnorm(100, mean=-2), rnorm(50, mean=2)))
}
for (j in 11:20){
elist[[j]] = stats::ecdf(rgamma(100,1) + rnorm(100, sd=sqrt(0.5)))
}
# run EP-means with k clusters
# change the value below to see different settings
myk = 2
epout = epmeans(elist, k=myk)
# visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,myk))
for (k in 1:myk){
idk = which(epout$cluster==k)
for (i in 1:length(idk)){
if (i<2){
pm = paste("class ",k," (size=",length(idk),")",sep="")
plot(elist[[idk[i]]], verticals=TRUE, lwd=0.25, do.points=FALSE, main=pm)
} else {
plot(elist[[idk[i]]], add=TRUE, verticals=TRUE, lwd=0.25, do.points=FALSE)
}
plot(epout$centers[[k]], add=TRUE, verticals=TRUE, lwd=2, col="red", do.points=FALSE)
}
}
par(opar)
|
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