Description Usage Arguments Value Examples
View source: R/summaries_dist2.R
Given two sets of functional summaries Λ_1 (t), …, Λ_M (t) and Ω_1 (t), …, Ω_N (t), compute L_p distance across pairs.
1 | fsdist2(fslist1, fslist2, p = 2)
|
fslist1 |
a length-M list of functional summaries of persistent diagrams. |
fslist2 |
a length-N list of functional summaries of persistent diagrams. |
p |
an exponent in [1,∞) (default: 2). |
an (M\times N) distance matrix.
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 | # ---------------------------------------------------------------------------
# Compute L1 and L2 Distance for Two Sets of Landscapes
#
# First set consists of {Class 1, Class 2}, while
# Second set consists of {Class 1, Class 3} where
#
# - 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_rips1 = list()
list_rips2 = list()
for (i in 1:ndata){
dat1 = as.matrix(iris[,1:4]) + matrix(rnorm(150*4, sd=4), ncol=4)
dat2 = gen2holes(n=100, sd=1)$data
dat3 = as.matrix(iris[,1:4]) + matrix(rnorm(150*4, sd=4), ncol=4)
dat4 = gen2circles(n=100, sd=1)$data
list_rips1[[i]] = diagRips(dat1, maxdim=1)
list_rips1[[i+ndata]] = diagRips(dat2, maxdim=1)
list_rips2[[i]] = diagRips(dat3, maxdim=1)
list_rips2[[i+ndata]] = diagRips(dat4, maxdim=1)
}
## Compute Persistence Landscapes from Each Diagram with k=10 Functions
# We try to get distance in dimension 1 only for faster comparison.
list_pset1 = list()
list_pset2 = list()
for (i in 1:(2*ndata)){
list_pset1[[i]] = diag2landscape(list_rips1[[i]], dimension=1, k=10)
list_pset2[[i]] = diag2landscape(list_rips2[[i]], dimension=1, k=10)
}
## Compute L1 and L2 Distance Matrix
dmat1 = fsdist2(list_pset1, list_pset2, p=1)
dmat2 = fsdist2(list_pset1, list_pset2, p=2)
## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
image(dmat1[,(2*ndata):1], axes=FALSE, main="distance for p=1")
image(dmat2[,(2*ndata):1], axes=FALSE, main="distance for p=2")
par(opar)
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