epmedoids: Medoids

In kyoustat/T4ecdf: Tools for Inference with Empirical CDF

Description

Medoids

Usage

epmedoids(elist, k = 2, type = c("KS", "Lp", "Wasserstein"), p = 2)

Examples

## 3 sets of 1d samples, 10 each and add some noise
#    set 1 : mixture of two gaussians
#    set 2 : single gamma distribution
#    set 3 : mixture of gaussian and gamma

# generate data
myn   = 50
elist = list()
for (i in 1:10){
   elist[[i]] = stats::ecdf(c(rnorm(myn, mean=-2), rnorm(myn, mean=2)))
}
for (i in 11:20){
   elist[[i]] = stats::ecdf(rgamma(2*myn,1))
}
for (i in 21:30){
   elist[[i]] = stats::ecdf(rgamma(myn,1) + rnorm(myn, mean=3))
}

# run EP-medoids with k clusters with k=2,3,4
ep2 = epmedoids(elist, k=2)
ep3 = epmedoids(elist, k=3)
ep4 = epmedoids(elist, k=4)

# run EP-medoids with k=3 clusters 
epout = epmedoids(elist, k=3)

# 2d embedding using mds
dmat = T4ecdf::pdist(elist, type="wasserstein", as.dist=TRUE)
ebd2 = stats::cmdscale(dmat, 2)

## visualize
#  (1) show ECDF for three types of data
opar = par(mfrow=c(3,3))
plot(elist[[10]], cex=0.1, main="2 Gaussians")
plot(elist[[20]], cex=0.1, main="Gamma")
plot(elist[[30]], cex=0.1, main="Gaussian+Gamma")

#  (2) per-class ECDFs
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)
  }
}

#  (3) 2d embedding colored class labels
plot(ebd2, col=ep2$cluster, main="k=2 means", pch=19)
plot(ebd2, col=ep3$cluster, main="k=3 means", pch=19)
plot(ebd2, col=ep4$cluster, main="k=4 means", pch=19)
par(opar)

kyoustat/T4ecdf documentation built on Dec. 15, 2019, 10:12 p.m.