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ClusterEqualWeighting=function(Cls,Data,MinClusterSize){
# V = ClusterEqualWeighting(Cls)
# V = ClusterEqualWeighting(Cls,Data)
# balance clusters such that their sizes are the same by subsampling the larger cluster
#
# INPUTC
# Cls vector of cluster identifiers can be integers or
# NaN's, need not be consecutive nor positive
# OPTIONAL
# Data
#
# OUTPUT
# BalancedCls Vector of Cls such that all classes have the same sizes
# BalancedInd index such that BalancedCls = Cls(BalancedInd);
#
# BalancedData = Data(BalancedInd,:)
# author: ALU, reimplemented from matlab by mct
if(length(Cls)!=nrow(Data)){
stop("ClusterEqualWeighting: length of Cls does not equal the number of cases in data.")
}
V= ClusterCount(Cls)
UniqueClusters=V$UniqueClusters
CountPerCluster=V$CountPerCluster
NumberOfClusters=V$NumberOfClusters
if(missing(MinClusterSize))
MinClusterSize = min(CountPerCluster,na.rm = T)
BalancedInd= c()
for(i in 1:NumberOfClusters){
Current = UniqueClusters[i] # die fragliche klasse
ClusterInd = which(Cls==Current) # index der Mitgieder in der Klasse
if(CountPerCluster[i] > MinClusterSize){ # Kuerzung notwendig
Ind = sample(1:CountPerCluster[i],size = MinClusterSize,replace = F) # subsample
ClusterInd = ClusterInd[Ind] # Kuerzung auf MinClsAnz
} else if(CountPerCluster[i] == MinClusterSize){
Ind = 1:CountPerCluster[i] # no sample
ClusterInd = ClusterInd[Ind] # Kuerzung auf MinClsAnz
}else{
Ind = sample(1:CountPerCluster[i],size = MinClusterSize - CountPerCluster[i],replace = T) # subsample
Ind=c(1:CountPerCluster[i],Ind)
ClusterInd = ClusterInd[Ind] # Kuerzung auf MinClsAnz
}
BalancedInd= c(BalancedInd,ClusterInd)# Aufsammeln des index
}; # for i
BalancedCls = Cls[BalancedInd]
if(!missing(Data)) BalancedData = Data[BalancedInd,] else BalancedData=NULL
return(list(BalancedCls=BalancedCls,BalancedInd=BalancedInd,BalancedData=BalancedData))
}
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