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plotSwarm=function(Points,Cls=rep(1,nrow(Points)),xlab='X',ylab='Y',main="DataBots"){
X=Points[,1]
Y=Points[,2]
#ColorSymbSequence <- DefaultColorSymbSequence()
PlotSymbol <- 20#ColorSymbSequence[1]
#DefaultColorSeq <- DefaultColorSequence()
# if (missing(ColorSequence))
ColorSequence <- DatabionicSwarm::DefaultColorSequence
NormalizeCls_hlp <- function(Cls) {
#E<-NormalizeCls(Cls);
#NormalizedCls <- E$normalizedCls # Cls consistently recoded to positive consecutive integers
#NormalizedClasses<- E$normalizedClasses # the different class numbers in NormalizedCls
#UniqueCls <- E$uniqueClasses # the different class numbers in Cls such that
#AnzClasses <- E$numberOfClasses # the number of different classes
#
# Values in Cls are consistently recoded to positive consecutive integers
# INPUT
# Cls vector of class identifiers can be integers or
# NaN's, need not be consecutive nor positive
# OUTPUT list of
# normalizedCls Cls consistently recoded to positive consecutive integers
# normalizedClasses the different class numbers in NormalizedCls
# uniqueClasses the different class numbers in Cls such that
# NormalizedCls(i) <-> UniqueCls(i)
# numberOfClasses the number of different classes
# ALU 2014
# 1.Editor:MT 2016
uniqueClasses <- sort(na.last=T,unique(Cls))
numberOfClasses <- length(uniqueClasses)
unique2Cls <- NULL # initializing the vector
for (i in 1:length(Cls) ) { # calculating the indexes of elements of Cls in uniqueClasses
unique2Cls <- c( unique2Cls, which(uniqueClasses == Cls[i]))
}
if (numberOfClasses > 0) {
normalizedClasses <- c(1: numberOfClasses)
normalizedCls <- normalizedClasses[unique2Cls]
}
else {
normalizedClasses <- Cls
}
return(list(normalizedCls = normalizedCls, normalizedClasses = normalizedClasses, uniqueClasses = uniqueClasses, numberOfClasses = numberOfClasses))
}
E <- NormalizeCls_hlp(Cls)
NormalizedCls <- E$normalizedCls
UniqueCls <- E$uniqueClasses
AnzClasses <- E$numberOfClasses
AnzColors = length(ColorSequence)
ColorNR = c(1:AnzColors)
if (AnzClasses > AnzColors) {
ColorNR = (c(0:AnzClasses)%%AnzColors) + 1
}
MinX = min(X,na.rm=T)
MaxX = max(X,na.rm=T)
MinY = min(Y,na.rm=T)
MaxY = max(Y,na.rm=T)
xlim=c((MinX-abs(0.1*MinX)),(MaxX+abs(0.1*MaxX)))
ylim=c((MinY-abs(0.1*MinY)),(MaxY+abs(0.1*MaxY)))
#Initialisierungsplot
plot.new()
#par(usr=c(MinX,MaxX,MinY,MaxY))
par(usr=c(xlim,ylim))
par(xaxs='i')
par(yaxs='i')
# Plot der Punkte
for (i in 1:AnzClasses) {
C = UniqueCls[i]
Ind = which(C == NormalizedCls)
points(X[Ind], Y[Ind], pch = PlotSymbol,
col = ColorSequence[ColorNR[i]], new = TRUE)
}
axis(1,xlim=xlim,col="black",las=1) #x-Achse
axis(2,ylim=ylim,col="black",las=1) #y-Achse
title(xlab=xlab,ylab=ylab,main=main)
box()
}
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