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@@ -59,18 +59,18 @@ patternMatch4Parallel <- function(Ptot, nSets, cnt, minNS=NULL, maxNS=NULL,
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cc<-corcut(Ptot,minNS,cnt,cluster.method)
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### split by maxNS
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62
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- indx<-unlist(sapply(cc$PByClust,function(x) which(dim(x)[1]>maxNS)))
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+ indx<-which(unlist(lapply(cc$PByClust,function(x) dim(x)[1]>maxNS)))
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while(length(indx)>0){
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icc<-corcut(cc$PByClust[[indx[1]]],minNS,2,cluster.method)
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65
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- cc$PByClust[[indx[1]]]<-icc[[2]][[2]]
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66
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- cc$RtoMeanPattern[[indx[1]]]<-icc[[1]][[2]]
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67
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- if(is.null(icc[[2]][[1]])){
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68
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- indx<-unlist(sapply(cc$PByClust,function(x) which(dim(x)[1]>maxNS)))
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+ cc$PByClust[[indx[1]]]<-icc[[2]][[1]]
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+ cc$RtoMeanPattern[[indx[1]]]<-icc[[1]][[1]]
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+ if(is.null(icc[[2]][[2]])){
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+ indx<-which(unlist(lapply(cc$PByClust,function(x) dim(x)[1]>maxNS)))
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next
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} else {
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- cc$PByClust<-append(cc$PByClust,icc[[2]][1])
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- cc$RtoMeanPattern<-append(cc$PByClust,icc[[1]][[1]])
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- indx<-unlist(sapply(cc$PByClust,function(x) which(dim(x)[1]>maxNS)))
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+ cc$PByClust<-append(cc$PByClust,icc[[2]][2])
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+ cc$RtoMeanPattern<-append(cc$PByClust,icc[[1]][[2]])
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+ indx<-which(unlist(lapply(cc$PByClust,function(x) dim(x)[1]>maxNS)))
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}
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}
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