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#' @rdname find.m
#'
#' @export
"find.cut" <-
function(
M, #matrix of a network
clu, #partition
alt.blocks="reg", #alternative block to null block
cuts="all", #maximumvnumber of evaluations at different cuts
... #other parameters to critFun
){
if(cuts=="all"){
allvals<-sort(unique(M))
# allvals<-allvals[allvals>0]
if(length(allvals)>1000) cat(length(allvals), "evaluations will be made.\n")
cuts<-allvals
}
if(is.list(clu)){
k<-sapply(clu,function(x)length(unique(x)))
clu<-lapply(clu,function(x)as.integer(factor(x)))
if(length(k)>2) {
for(i in 2:length(clu)){
clu[[i]]<-clu[[i]] + max(clu[[i-1]])
}
k2<-max(clu[[length(clu)]])
} else k2<-k
} else {
k<-length(unique(clu))
clu<-as.integer(factor(clu))
k2<-c(k,k)
}
res.IM<-array(NA,dim=c(k2[1],k2[2],length(cuts)))
res.IM[,,1]<-alt.blocks
for(i in 1:length(cuts)) res.IM[,,i]<-critFunC(M=M,clu=clu,blocks=c("nul",alt.blocks),preSpecM = cuts[i],approaches="bin",...)$IM
cut<-matrix(NA,nrow=k2[1],ncol=k2[2])
for(i in 1:k2[1]){
for(j in 1:k2[2]){
cut[i,j]<- max(cuts[which(res.IM[i,j,]==alt.blocks)])
}
}
return(cut)
}
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