Description Usage Arguments Value Examples
View source: R/summaries_hclust.R
Given multiple functional summaries Λ_1 (t), Λ_2 (t), …, Λ_N (t), perform hierarchical agglomerative clustering with L_2 distance.
1 2 3 4 5 6 |
fslist |
a length-N list of functional summaries of persistent diagrams. |
method |
agglomeration method to be used. This must be one of |
members |
|
an object of class hclust
. See hclust
for details.
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 | # ---------------------------------------------------------------------------
# K-Groups Clustering via Energy Distance
#
# We will cluster dim=0 under top-5 landscape functions with
# - Class 1 : 'iris' dataset with noise
# - Class 2 : samples from 'gen2holes()'
# - Class 3 : samples from 'gen2circles()'
# ---------------------------------------------------------------------------
## Generate Data and Diagram from VR Filtration
ndata = 10
list_rips = list()
for (i in 1:ndata){
dat1 = as.matrix(iris[,1:4]) + matrix(rnorm(150*4), ncol=4)
dat2 = gen2holes(n=100, sd=1)$data
dat3 = gen2circles(n=100, sd=1)$data
list_rips[[i]] = diagRips(dat1, maxdim=1)
list_rips[[i+ndata]] = diagRips(dat2, maxdim=1)
list_rips[[i+(2*ndata)]] = diagRips(dat3, maxdim=1)
}
list_lab = c(rep(1,ndata), rep(2,ndata), rep(3,ndata))
## Compute Persistence Landscapes from Each Diagram with k=5 Functions
list_land0 = list()
for (i in 1:(3*ndata)){
list_land0[[i]] = diag2landscape(list_rips[[i]], dimension=0, k=5)
}
## Run MDS for Visualization
embed = fsmds(list_land0, ndim=2)
## Clustering with 'single' and 'complete' linkage
hc.sing <- fshclust(list_land0, method="single")
hc.comp <- fshclust(list_land0, method="complete")
## Visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(embed, pch=19, col=list_lab, main="2-dim embedding")
plot(hc.sing, main="single linkage")
plot(hc.comp, main="complete linkage")
par(opar)
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