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Returns the incidence matrix of Central Similarity Proximity Catch Digraph (CS-PCD) whose vertices are the
data points in Xp
in the multiple triangle case.
CS proximity regions are defined with respect to the
Delaunay triangles based on Yp
points with expansion parameter M
will be the same
type of center for each Delaunay triangle (this conversion is not necessary when M
is
Convex hull of Yp
is partitioned by the Delaunay triangles based on Yp
points
(i.e., multiple triangles are the set of these Delaunay triangles whose union constitutes the
convex hull of Yp
points). For the incidence matrix loops are allowed,
so the diagonal entries are all equal to 1.
See (ceyhan:Phd-thesis,ceyhan:arc-density-CS,ceyhan:test2014;textualpcds) for more on CS-PCDs. Also see (okabe:2000,ceyhan:comp-geo-2010,sinclair:2016;textualpcds) for more on Delaunay triangulation and the corresponding algorithm.
IncMatCS(Xp, Yp, t, M = c(1, 1, 1))
Incidence matrix for the CS-PCD with vertices being 2D data set, Xp
.
CS proximity regions are constructed with respect to the Delaunay triangles and M
-edge regions.
A set of 2D points which constitute the vertices of the CS-PCD.
A set of 2D points which constitute the vertices of the Delaunay triangles.
A positive real number which serves as the expansion parameter in CS proximity region.
A 3D point in barycentric coordinates which serves as a center in the interior of each Delaunay
triangle, default for
Elvan Ceyhan
IncMatCStri
, IncMatCSTe
, IncMatAS
,
and IncMatPE
if (FALSE) {
#nx is number of X points (target) and ny is number of Y points (nontarget)
nx<-20; ny<-5; #try also nx<-40; ny<-10 or nx<-1000; ny<-10;
set.seed(1)
Xp<-cbind(runif(nx,0,1),runif(nx,0,1))
Yp<-cbind(runif(ny,0,.25),runif(ny,0,.25))+cbind(c(0,0,0.5,1,1),c(0,1,.5,0,1))
#try also Yp<-cbind(runif(ny,0,1),runif(ny,0,1))
M<-c(1,1,1) #try also M<-c(1,2,3)
t<-1.5 #try also t<-2
IM<-IncMatCS(Xp,Yp,t,M)
IM
dom.greedy(IM) #try also dom.exact(IM) #takes a very long time for large nx, try smaller nx
IndUBdom(IM,3) #takes a very long time for large nx, try smaller nx
}
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