depricated! see GeneralizedUmatrix() Generalisierte U-Matrix fuer Projektionsverfahren
depricated! see GeneralizedUmatrix()
getUmatrix4Projection(Data,ProjectedPoints, PlotIt=TRUE,Cls=NULL,toroid=T,Tiled=F,ComputeInR=F)
Data
: [1:n,1:d] array of data: n cases in rows, d variables in columns
ProjectedPoints
: [1:n,2]n by 2 matrix containing coordinates of the Projection: A matrix of the fitted configuration.
PlotIt
: Optional,bool, defaut=FALSE, if =TRUE: U-Marix of every current Position of Databots will be shown
Cls
: Optional, For plotting, see plotUmatrix
in package Umatrix
toroid
: Optional, Default=FALSE,
==FALSE planar computation
==TRUE: toroid borderless computation, set so only if projection method is also toroidal
Tiled
: Optional,For plotting see plotUmatrix
in package Umatrix
ComputeInR
: Optional, =T: Rcode, =F Cpp Code
List with - Umatrix: [1:Lines,1:Columns] (see ReadUMX
in package DataIO)
EsomNeurons: [Lines,Columns,weights] 3-dimensional numeric array (wide format), not wts (long format)
Bestmatches: [1:n,OutputDimension] GridConverted Projected Points information converted by convertProjectionProjectedPoints() to predefined Grid by Lines and Columns
gplotres: Ausgabe von ggplot
unbesetztePositionen: Umatrix[unbesetztePositionen] =NA
Michael Thrun
[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, ISBN: 978-3-658-20539-3, Heidelberg, 2018.
data("Lsun3D") Data=Lsun3D$Data Cls=Lsun3D$Cls InputDistances=as.matrix(dist(Data)) res=cmdscale(d=InputDistances, k = 2, eig = TRUE, add = FALSE, x.ret = FALSE) ProjectedPoints=as.matrix(res$points) # Stress = KruskalStress(InputDistances, as.matrix(dist(ProjectedPoints))) #resUmatrix=GeneralizedUmatrix(Data,ProjectedPoints) #plotTopographicMap(resUmatrix$Umatrix,resUmatrix$Bestmatches,Cls)