getUmatrix4Projection {DatabionicSwarm}

depricated! see GeneralizedUmatrix() Generalisierte U-Matrix fuer Projektionsverfahren

Description

depricated! see GeneralizedUmatrix()

Usage

getUmatrix4Projection(Data,ProjectedPoints,
PlotIt=TRUE,Cls=NULL,toroid=T,Tiled=F,ComputeInR=F)

Arguments

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

Value

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

Author(s)

Michael Thrun

References

[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.

Examples

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)