View source: R/getUmatrix4Projection.R
getUmatrix4Projection | R Documentation |
depricated! see GeneralizedUmatrix()
getUmatrix4Projection(Data,ProjectedPoints,
PlotIt=TRUE,Cls=NULL,toroid=T,Tiled=F,ComputeInR=F)
Data |
[1:n,1:d] Numeric matrix: 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 |
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 |
ComputeInR |
Optional, =T: Rcode, =F Cpp Code |
List with
Umatrix |
[1:Lines,1:Columns] (see |
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)
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