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ClassifyByDecisionBoundaries=function(Data,DecisionBoundaries,ClassLabels){
# Cls = ClassifyByDecisionBoundaries(Data,DecisionBoundaries)
# Classify Data according to decision Boundaries
#
# INPUT
# Data(1:n,1) vector of Data
# DecisionBoundaries(1:L) decision boundaries
# OPTIONAL
# ClassLabels(1:L+1) numbered class labels that are assigned to the classes. default (1:L)
# OUTPUT
# Cls(1:n,1:d) classiffication of Data
# Author MT 04/2015
if(!is.vector(Data)){
warning('Data converted to vector')
Data=as.vector(Data)
}
if(is.list(DecisionBoundaries)){
DecisionBoundaries=as.vector(DecisionBoundaries$DecisionBoundaries)
print('DecisionBoundaries was a list, assuming usage of BayesDecisionBoundaries()')
}
AnzBounds= length(DecisionBoundaries)
if(missing(ClassLabels)){
ClassLabels=seq(from=1,by=1,to=(AnzBounds+1))
}
Cls=rep(1,length(Data)) # default alles in Klasse 1
nonan=which(is.finite(Data))
if(length(nonan)!=length(Data)){
warning('Datavector contains NaN. These values cannot be classified.')
names(Cls)=1:length(Data)
ClsTmp=Cls[nonan]
DataTmp=Data[nonan]
for(b in 1:AnzBounds){
ind=DataTmp>DecisionBoundaries[b]
ClsTmp[ind] = rep(ClassLabels[b+1],sum(ind))
} # for c
Cls[as.numeric(names(ClsTmp))]=ClsTmp
Cls[setdiff(1:length(Cls),as.numeric(names(ClsTmp)))]=NaN
}else{
for(b in 1:AnzBounds){
ind=Data>DecisionBoundaries[b]
Cls[ind] = rep(ClassLabels[b+1],sum(ind))
} # for c
}
return(Cls)
}
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