Type:	Package
Title:	Toolkit for Topological Data Analysis
Version:	0.1.3
Description:	Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
License:	MIT + file LICENSE
Encoding:	UTF-8
Imports:	Rcpp, Rdpack, TDAstats, T4cluster, energy, ggplot2, maotai, stats, utils
LinkingTo:	Rcpp, RcppArmadillo
RoxygenNote:	7.3.2
RdMacros:	Rdpack
NeedsCompilation:	yes
Packaged:	2025-09-21 01:40:16 UTC; kyou
Author:	Kisung You [aut, cre], Byeongsu Yu [aut]
Maintainer:	Kisung You <kisung.you@outlook.com>
Repository:	CRAN
Date/Publication:	2025-09-21 22:10:13 UTC

Convert Persistence Diagram into Persistence Landscape

Description

Persistence Landscape (PL) is a functional summary of persistent homology that is constructed given a homology object.

Usage

diag2landscape(homology, dimension = 1, k = 0, nseq = 1000)

Arguments

Value

a list object of "landscape" class containing

lambda

an (\code{nseq} \times k) landscape functions.

tseq

a length-nseq vector of domain grid.

dimension

dimension of features considered.

References

Peter Bubenik (2018). “The Persistence Landscape and Some of Its Properties.” arXiv:1810.04963.

Examples

# ---------------------------------------------------------------------------
#              Persistence Landscape of 'iris' Dataset
#
# We will extract landscapes of dimensions 0, 1, and 2.
# For each feature, only the top 5 landscape functions are plotted.
# ---------------------------------------------------------------------------
## Prepare 'iris' data
XX = as.matrix(iris[,1:4])

## Compute Persistence Diagram 
pdrips = diagRips(XX, maxdim=2)

## Convert to Landscapes of Each Dimension
land0 <- diag2landscape(pdrips, dimension=0, k=5)
land1 <- diag2landscape(pdrips, dimension=1, k=5)
land2 <- diag2landscape(pdrips, dimension=2, k=5)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(pdrips$Birth, pdrips$Death, col=as.factor(pdrips$Dimension),
     pch=19, main="persistence diagram", xlab="Birth", ylab="Death")
matplot(land0$tseq, land0$lambda, type="l", lwd=3, main="dimension 0", xlab="t")
matplot(land1$tseq, land1$lambda, type="l", lwd=3, main="dimension 1", xlab="t")
matplot(land2$tseq, land2$lambda, type="l", lwd=3, main="dimension 2", xlab="t")
par(opar)

Convert Persistence Diagram into Persistent Silhouette

Description

Persistence Silhouette (PS) is a functional summary of persistent homology that is constructed given a homology object. PS is a weighted average of landscape functions so that it becomes a uni-dimensional function.

Usage

diag2silhouette(homology, dimension = 1, p = 2, nseq = 100)

Arguments

Value

a list object of "silhouette" class containing

lambda

an (\code{nseq} \times k) landscape functions.

tseq

a length-nseq vector of domain grid.

dimension

dimension of features considered.

Examples

# ---------------------------------------------------------------------------
#              Persistence Silhouette of 'iris' Dataset
#
# We will extract silhouettes of dimensions 0, 1, and 2.
# ---------------------------------------------------------------------------
## Prepare 'iris' data
XX = as.matrix(iris[,1:4])

## Compute Persistence Diagram 
pdrips = diagRips(XX, maxdim=2)

## Convert to Silhouettes of Each Dimension
sil0 <- diag2silhouette(pdrips, dimension=0)
sil1 <- diag2silhouette(pdrips, dimension=1)
sil2 <- diag2silhouette(pdrips, dimension=2)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(pdrips$Birth, pdrips$Death, col=as.factor(pdrips$Dimension),
     pch=19, main="persistence diagram", xlab="Birth", ylab="Death")
plot(sil0$tseq, sil0$lambda, type="l", lwd=3, main="dimension 0", xlab="t")
plot(sil1$tseq, sil1$lambda, type="l", lwd=3, main="dimension 1", xlab="t")
plot(sil2$tseq, sil2$lambda, type="l", lwd=3, main="dimension 2", xlab="t")
par(opar)

Compute Vietoris-Rips Complex for Persistent Homology

Description

diagRips computes the persistent diagram of the Vietoris-Rips filtration constructed on a point cloud represented as matrix or dist object. This function is a second-hand wrapper to TDAstats's wrapping for Ripser library.

Usage

diagRips(data, maxdim = 1, threshold = Inf)

Arguments

Value

a dataframe object of S3 class "homology" with following columns

Dimension

dimension corresponding to a feature.

Birth

birth of a feature.

Death

death of a feature.

References

Raoul R. Wadhwa, Drew F.K. Williamson, Andrew Dhawan, Jacob G. Scott (2018). “TDAstats: R Pipeline for Computing Persistent Homology in Topological Data Analysis.” Journal of Open Source Software, 3(28), 860. ISSN 2475-9066.

Ulrich Bauer (2019). “Ripser: Efficient Computation of Vietoris-Rips Persistence Barcodes.” arXiv:1908.02518.

See Also

calculate_homology

Examples

# ---------------------------------------------------------------------------
# Check consistency of two types of inputs : 'matrix' and 'dist' objects
# ---------------------------------------------------------------------------
# Use 'iris' data and compute its distance matrix
XX = as.matrix(iris[,1:4])
DX = stats::dist(XX)

# Compute VR Diagram with two inputs
vr.mat = diagRips(XX)
vr.dis = diagRips(DX)

col1 = as.factor(vr.mat$Dimension)
col2 = as.factor(vr.dis$Dimension)

# Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
plot(vr.mat$Birth, vr.mat$Death, pch=19, col=col1, main="from 'matrix'")
plot(vr.dis$Birth, vr.dis$Death, pch=19, col=col2, main="from 'dist'")
par(opar)

Pairwise L_p Distance of Multiple Functional Summaries

Description

Given multiple functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), compute L_p distance in a pairwise sense.

Usage

fsdist(fslist, p = 2, as.dist = TRUE)

Arguments

Value

a S3 dist object or (N\times N) symmetric matrix of pairwise distances according to as.dist parameter.

Examples

# ---------------------------------------------------------------------------
#      Compute L_2 Distance for 3 Types of Landscapes and Silhouettes
#
# We will compare dim=0,1 with 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)
}

## Compute Persistence Landscapes from Each Diagram with k=5 Functions
#  We try to get distance in dimensions 0 and 1.
list_land0 = list()
list_land1 = list()
for (i in 1:(3*ndata)){
  list_land0[[i]] = diag2landscape(list_rips[[i]], dimension=0, k=5)
  list_land1[[i]] = diag2landscape(list_rips[[i]], dimension=1, k=5)
}

## Compute Silhouettes
list_sil0 = list()
list_sil1 = list()
for (i in 1:(3*ndata)){
  list_sil0[[i]] = diag2silhouette(list_rips[[i]], dimension=0)
  list_sil1[[i]] = diag2silhouette(list_rips[[i]], dimension=1)
}

## Compute L2 Distance Matrices
ldmat0 = fsdist(list_land0, p=2, as.dist=FALSE)
ldmat1 = fsdist(list_land1, p=2, as.dist=FALSE)
sdmat0 = fsdist(list_sil0, p=2, as.dist=FALSE)
sdmat1 = fsdist(list_sil1, p=2, as.dist=FALSE)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(ldmat0[,(3*(ndata)):1], axes=FALSE, main="Landscape : dim=0")
image(ldmat1[,(3*(ndata)):1], axes=FALSE, main="Landscape : dim=1")
image(sdmat0[,(3*(ndata)):1], axes=FALSE, main="Silhouette : dim=0")
image(sdmat1[,(3*(ndata)):1], axes=FALSE, main="Silhouette : dim=1")
par(opar)

Pairwise L_p Distance for Two Sets of Functional Summaries

Description

Given two sets of functional summaries \Lambda_1 (t), \ldots, \Lambda_M (t) and \Omega_1 (t), \ldots, \Omega_N (t), compute L_p distance across pairs.

Usage

fsdist2(fslist1, fslist2, p = 2)

Arguments

Value

an (M\times N) distance matrix.

Examples

# ---------------------------------------------------------------------------
#         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)

Multi-sample Energy Test of Equal Distributions

Description

Also known as k-sample problem, it tests whether multiple functional summaries are equally distributed or not via Energy statistics.

Usage

fseqdist(fslist, label, method = c("original", "disco"), mc.iter = 999)

Arguments

Value

a (list) object of S3 class htest containing:

method

name of the test.

statistic

a test statistic.

p.value

p-value under H_0 of equal distributions.

Examples

# ---------------------------------------------------------------------------
#         Test for Equality of Distributions via Energy Statistics
#
# We will compare dim=0's 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)
}

## 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)
}

## Create Label and Run the Test with Different Options
list_lab = c(rep(1,ndata), rep(2,ndata), rep(3,ndata))
fseqdist(list_land0, list_lab, method="original")
fseqdist(list_land0, list_lab, method="disco")

Hierarchical Agglomerative Clustering

Description

Given multiple functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), perform hierarchical agglomerative clustering with L_2 distance.

Usage

fshclust(
  fslist,
  method = c("single", "complete", "average", "mcquitty", "ward.D", "ward.D2",
    "centroid", "median"),
  members = NULL
)

Arguments

Value

an object of class hclust. See hclust for details.

Examples

# ---------------------------------------------------------------------------
#           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)

k-Groups Clustering of Multiple Functional Summaries by Energy Distance

Description

Given N functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), perform k-groups clustering by energy distance using L_2 metric.

Usage

fskgroups(fslist, k = 2, ...)

Arguments

Value

a length-N vector of class labels (from 1:k).

Examples

# ---------------------------------------------------------------------------
#           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)
}

## 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 K-Groups Clustering with different K's
label2  = fskgroups(list_land0, k=2)
label3  = fskgroups(list_land0, k=3)
label4  = fskgroups(list_land0, k=4)
truelab = rep(c(1,2,3), each=ndata)

## Run MDS & Visualization
embed = fsmds(list_land0, ndim=2)
opar  = par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
plot(embed, col=truelab, pch=19, main="true label")
plot(embed, col=label2,  pch=19, main="k=2 label")
plot(embed, col=label3,  pch=19, main="k=3 label")
plot(embed, col=label4,  pch=19, main="k=4 label")
par(opar)

K-Medoids Clustering

Description

Given N functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), perform k-medoids clustering using pairwise distances using L_2 metric.

Usage

fskmedoids(fslist, k = 2)

Arguments

Value

a length-N vector of class labels (from 1:k).

Examples

# ---------------------------------------------------------------------------
#           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)
}

## 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 K-Medoids Clustering with different K's
label2  = fskmedoids(list_land0, k=2)
label3  = fskmedoids(list_land0, k=3)
label4  = fskmedoids(list_land0, k=4)
truelab = rep(c(1,2,3), each=ndata)

## Run MDS & Visualization
embed = fsmds(list_land0, ndim=2)
opar  = par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
plot(embed, col=truelab, pch=19, main="true label")
plot(embed, col=label2,  pch=19, main="k=2 label")
plot(embed, col=label3,  pch=19, main="k=3 label")
plot(embed, col=label4,  pch=19, main="k=4 label")
par(opar)

Multidimensional Scaling

Description

Given multiple functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), apply multidimensional scaling to get low-dimensional representation in Euclidean space. Usually, ndim=2,3 is chosen for visualization.

Usage

fsmds(fslist, ndim = 2, method = c("classical", "metric"))

Arguments

Value

an (N\times ndim) matrix of embedding.

Examples

# ---------------------------------------------------------------------------
#     Multidimensional Scaling for Multiple Landscapes and Silhouettes
#
# We will compare dim=0 with top-5 landscape and silhouette 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)
}

## Compute Landscape and Silhouettes of Dimension 0
list_land = list()
list_sils = list()
for (i in 1:(3*ndata)){
  list_land[[i]] = diag2landscape(list_rips[[i]], dimension=0)
  list_sils[[i]] = diag2silhouette(list_rips[[i]], dimension=0)
}
list_lab = rep(c(1,2,3), each=ndata)

## Run Classical/Metric Multidimensional Scaling
land_cmds = fsmds(list_land, method="classical")
land_mmds = fsmds(list_land, method="metric")
sils_cmds = fsmds(list_sils, method="classical")
sils_mmds = fsmds(list_sils, method="metric")

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(land_cmds, pch=19, col=list_lab, main="Landscape+CMDS")
plot(land_mmds, pch=19, col=list_lab, main="Landscape+MMDS")
plot(sils_cmds, pch=19, col=list_lab, main="Silhouette+CMDS")
plot(sils_mmds, pch=19, col=list_lab, main="Silhouette+MMDS")
par(opar)

Mean of Multiple Functional Summaries

Description

Given multiple functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), compute the mean

\bar{\Lambda} (t) = \frac{1}{N} \sum_{n=1}^N \Lambda_n (t)

.

Usage

fsmean(fslist)

Arguments

Value

a functional summary object.

Examples

# ---------------------------------------------------------------------------
#         Mean of 10 Persistence Landscapes from '2holes' data
# ---------------------------------------------------------------------------
## Generate 10 Diagrams with 'gen2holes()' function
list_rips = list()
for (i in 1:10){
  list_rips[[i]] = diagRips(gen2holes(n=100, sd=2)$data, maxdim=1)
}

## Compute Persistence Landscapes from Each Diagram with k=5 Functions
list_land = list()
for (i in 1:10){
  list_land[[i]] = diag2landscape(list_rips[[i]], dimension=0, k=5)
}

## Compute Weighted Sum of Landscapes
ldsum = fsmean(list_land)

## Visualize
sam5  <- sort(sample(1:10, 5, replace=FALSE))
opar  <- par(no.readonly=TRUE)
par(mfrow=c(2,3), pty="s")
for (i in 1:5){
  tgt = list_land[[sam5[i]]]
  matplot(tgt$tseq, tgt$lambda[,1:5], type="l", lwd=3, main=paste("landscape no.",sam5[i]))
}
matplot(ldsum$tseq, ldsum$lambda[,1:5], type="l", lwd=3, main="weighted sum")
par(opar)

L_p Norm of a Single Functional Summary

Description

Given a functional summary \Lambda (t), compute the p-norm.

Usage

fsnorm(fsobj, p = 2)

Arguments

Value

an L_p-norm value.

Examples

## Generate Toy Data from 'gen2circles()'
dat = gen2circles(n=100)$data

## Compute PD, Landscapes, and Silhouettes
myPD  = diagRips(dat, maxdim=1)
myPL0 = diag2landscape(myPD, dimension=0)
myPL1 = diag2landscape(myPD, dimension=1)
myPS0 = diag2silhouette(myPD, dimension=0)
myPS1 = diag2silhouette(myPD, dimension=1)

## Compute 2-norm
fsnorm(myPL0, p=2)
fsnorm(myPL1, p=2)
fsnorm(myPS0, p=2)
fsnorm(myPS1, p=2)

Spectral Clustering by Zelnik-Manor and Perona (2005)

Description

Given N functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), perform spectral clustering proposed by Zelnik-Manor and Perona using a set of data-driven bandwidth parameters.

Usage

fssc05Z(fslist, k = 2, nnbd = 5)

Arguments

Value

a length-N vector of class labels (from 1:k).

References

Zelnik-manor L, Perona P (2005). “Self-Tuning Spectral Clustering.” In Saul LK, Weiss Y, Bottou L (eds.), Advances in Neural Information Processing Systems 17, 1601–1608. MIT Press.

Examples

# ---------------------------------------------------------------------------
#           Spectral Clustering 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)
}

## 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 Spectral Clustering using Different K's.
label2  = fssc05Z(list_land0, k=2)
label3  = fssc05Z(list_land0, k=3)
label4  = fssc05Z(list_land0, k=4)
truelab = rep(c(1,2,3), each=ndata)

## Run MDS & Visualization
embed = fsmds(list_land0, ndim=2)
opar  = par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
plot(embed, col=truelab, pch=19, main="true label")
plot(embed, col=label2,  pch=19, main="k=2 label")
plot(embed, col=label3,  pch=19, main="k=3 label")
plot(embed, col=label4,  pch=19, main="k=4 label")
par(opar)

Weighted Sum of Multiple Functional Summaries

Description

Given multiple functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), compute the weighted sum

\bar{\Lambda} (t) = \sum_{n=1}^N w_n \Lambda_n (t)

with a specified vector of given weights w_1,w_2,\ldots,w_N.

Usage

fssum(fslist, weight = NULL)

Arguments

Value

a functional summary object.

Examples

# ---------------------------------------------------------------------------
#     Weighted Average of 10 Persistence Landscapes from '2holes' data
# ---------------------------------------------------------------------------
## Generate 10 Diagrams with 'gen2holes()' function
list_rips = list()
for (i in 1:10){
  list_rips[[i]] = diagRips(gen2holes(n=100, sd=2)$data, maxdim=1)
}

## Compute Persistence Landscapes from Each Diagram with k=5 Functions
list_land = list()
for (i in 1:10){
  list_land[[i]] = diag2landscape(list_rips[[i]], dimension=0, k=5)
}

## Some Random Weights
wrand = abs(stats::rnorm(10))
wrand = wrand/sum(wrand)

## Compute Weighted Sum of Landscapes
ldsum = fssum(list_land, weight=wrand)

## Visualize
sam5  <- sort(sample(1:10, 5, replace=FALSE))
opar  <- par(no.readonly=TRUE)
par(mfrow=c(2,3), pty="s")
for (i in 1:5){
  tgt = list_land[[sam5[i]]]
  matplot(tgt$tseq, tgt$lambda[,1:5], type="l", lwd=3, main=paste("landscape no.",sam5[i]))
}
matplot(ldsum$tseq, ldsum$lambda[,1:5], type="l", lwd=3, main="weighted sum")
par(opar)

t-distributed Stochastic Neighbor Embedding

Description

Given N functional summaries \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), t-SNE mimicks the pattern of probability distributions over pairs of Banach-valued objects on low-dimensional target embedding space by minimizing Kullback-Leibler divergence.

Usage

fstsne(fslist, ndim = 2, ...)

Arguments

Value

a named list containing

embed

an (N\times ndim) matrix whose rows are embedded observations.

stress

discrepancy between embedded and original distances as a measure of error.

See Also

Rtsne

Examples

# ---------------------------------------------------------------------------
#     Multidimensional Scaling for Multiple Landscapes and Silhouettes
#
# We will compare dim=0 with top-5 landscape and silhouette 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)
}

## Compute Landscape and Silhouettes of Dimension 0
list_land = list()
list_sils = list()
for (i in 1:(3*ndata)){
  list_land[[i]] = diag2landscape(list_rips[[i]], dimension=0)
  list_sils[[i]] = diag2silhouette(list_rips[[i]], dimension=0)
}
list_lab = rep(c(1,2,3), each=ndata)

## Run t-SNE and Classical/Metric MDS
land_cmds = fsmds(list_land, method="classical")
land_mmds = fsmds(list_land, method="metric")
land_tsne = fstsne(list_land, perplexity=5)$embed
sils_cmds = fsmds(list_sils, method="classical")
sils_mmds = fsmds(list_sils, method="metric")
sils_tsne = fstsne(list_land, perplexity=5)$embed

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,3))
plot(land_cmds, pch=19, col=list_lab, main="Landscape+CMDS")
plot(land_mmds, pch=19, col=list_lab, main="Landscape+MMDS")
plot(land_tsne, pch=19, col=list_lab, main="Landscape+tSNE")
plot(sils_cmds, pch=19, col=list_lab, main="Silhouette+CMDS")
plot(sils_mmds, pch=19, col=list_lab, main="Silhouette+MMDS")
plot(sils_tsne, pch=19, col=list_lab, main="Silhouette+tSNE")
par(opar)

Generate Two Intersecting Circles

Description

It generates data from two intersecting circles.

Usage

gen2circles(n = 496, sd = 0)

Arguments

Value

a list containing

data

an (n\times 2) data matrix for row-stacked observations.

label

a length-n vector for class label.

Examples

## Generate Data with Different Noise Levels
nn = 200
x1 = gen2circles(n=nn, sd=0)
x2 = gen2circles(n=nn, sd=0.1)
x3 = gen2circles(n=nn, sd=0.25)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3), pty="s")
plot(x1$data, pch=19, main="sd=0.00", col=x1$label)
plot(x2$data, pch=19, main="sd=0.10", col=x2$label)
plot(x3$data, pch=19, main="sd=0.25", col=x3$label)
par(opar)

Generate Two Intertwined Holes

Description

It generates data from two intertwine circles with empty interiors(holes).

Usage

gen2holes(n = 496, sd = 0)

Arguments

Value

a list containing

data

an (n\times 2) data matrix for row-stacked observations.

label

a length-n vector for class label.

Examples

## Generate Data with Different Noise Levels
nn = 200
x1 = gen2holes(n=nn, sd=0)
x2 = gen2holes(n=nn, sd=0.1)
x3 = gen2holes(n=nn, sd=0.25)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3), pty="s")
plot(x1$data, pch=19, main="sd=0.00", col=x1$label)
plot(x2$data, pch=19, main="sd=0.10", col=x2$label)
plot(x3$data, pch=19, main="sd=0.25", col=x3$label)
par(opar)

Persistence Landscape Kernel

Description

Given multiple persistence landscapes \Lambda_1 (t), \Lambda_2 (t), \ldots, \Lambda_N (t), compute the persistence landscape kernel under the L_2 sense.

Usage

plkernel(landlist)

Arguments

Value

an (N\times N) kernel matrix.

References

Jan Reininghaus, Stefan Huber, Ulrich Bauer, and Roland Kwitt (2015). “A stable multi-scale kernel for topological machine learning.” Proc. 2015 IEEE Conf. Comp. Vision & Pat. Rec. (CVPR ’15).

Examples

# ---------------------------------------------------------------------------
#      Persistence Landscape Kernel in Dimension 0 and 1
#
# We will compare dim=0,1 with top-20 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)
}

## Compute Persistence Landscapes from Each Diagram with k=5 Functions
#  We try to get distance in dimensions 0 and 1.
list_land0 = list()
list_land1 = list()
for (i in 1:(3*ndata)){
  list_land0[[i]] = diag2landscape(list_rips[[i]], dimension=0, k=5)
  list_land1[[i]] = diag2landscape(list_rips[[i]], dimension=1, k=5)
}

## Compute Persistence Landscape Kernel Matrix
plk0 <- plkernel(list_land0)
plk1 <- plkernel(list_land1)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
image(plk0[,(3*(ndata)):1], axes=FALSE, main="Kernel : dim=0")
image(plk1[,(3*(ndata)):1], axes=FALSE, main="Kernel : dim=1")
par(opar)

Plot Persistent Homology via Barcode or Diagram

Description

Given a persistent homology of the data represented by a reconstructed complex in S3 class homology object, visualize it as either a barcode or a persistence diagram using ggplot2.

Usage

## S3 method for class 'homology'
plot(x, ...)

Arguments

Value

a ggplot2 object.

Examples

# Use 'iris' data
XX = as.matrix(iris[,1:4])

# Compute VR Diagram 
homology = diagRips(XX)

# Plot with 'barcode'
opar <- par(no.readonly=TRUE)
plot(homology, method="barcode")
par(opar)

Plot Persistence Landscape

Description

Given a persistence landscape object in S3 class landscape, visualize the landscapes using ggplot2.

Usage

## S3 method for class 'landscape'
plot(x, ...)

Arguments

Value

a ggplot2 object.

Examples

# Use 'iris' data
XX = as.matrix(iris[,1:4])

# Compute Persistence diagram and landscape of order 0 
homology  = diagRips(XX)
landscape = diag2landscape(homology, dimension=0)

# Plot with 'barcode'
opar <- par(no.readonly=TRUE)
plot(landscape)
par(opar)