Nothing
R/kmBlock.r
In kmBlock: k-Means Like Blockmodeling of One-Mode and Linked Networks
Defines functions
.onAttach
kmBlockORPC
critFunKmeans
kmBlockC
unlistPar
Documented in
critFunKmeans
kmBlockC
kmBlockORPC
unlistPar<- function(part){
if(is.list(part)){
part<-sapply(part,paste,collapse=" ")
part<-paste(paste("\nMode ", 1:length(part),":",sep=""), part,collapse="",sep=" ")
}
part
}
#' Function that performs k-means like one-mode blockmodeling. If \code{clu} is a list, the method for linked/multilevel networks is applied
#'
#' @param M A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation.
#' @param clu A partition. Each unique value represents one cluster. If the network is one-mode, than this should be a vector, else a list of vectors, one for each mode. Similarly, if units are comprised of several sets, clu should be the list containing one vector for each set.
#' @param weights The weights for each cell in the matrix/array. A matrix or an array with the same dimensions as \code{M}.
#' @param diagonal How should the diagonal values be treated. Possible values are:
#' \itemize{
#' \item ignore - diagonal values are ignored
#' \item seperate - diagonal values are treated separately
#' \item same - diagonal values are treated the same as all other values
#' }
#' @param limits If \code{diagonal} is \code{"ignore"} or \code{"same"}, an array with dimensions equal to:
#' \itemize{
#' \item number of clusters (of all types)
#' \item number of clusters (of all types)
#' \item number of relations
#' \item 2 - the first is lower limit and the second is upper limit
#' }
#' If \code{diagonal} is \code{"seperate"}, a list of two array. The first should be as described above, representing limits for off diagonal values. The second should be similar with only 3 dimensions, as one of the first two must be omitted.
#' @param limitType What do the limits represent, on which "side" of this limits should the values lie. Possible values: "none","inside","outside"
#' @return A list similar to optParC in package \code{blockmodeling}.
#' @seealso \code{\link{kmBlockORPC}}
#' @import Rcpp
#' @importFrom Rcpp evalCpp
#' @useDynLib kmBlock, .registration = TRUE
#' @export
kmBlockC<-function(M,
clu,
weights=NULL,
diagonal = c("ignore","seperate","same"),
limitType=c("none","inside","outside"),
limits=NULL){
n<-dim(M)[1]
diagonal<-match.arg(diagonal)
limitType<-match.arg(limitType)
if(is.null(weights)){
weights<-M
weights[,]<-1
} else if(any(dim(weights)!=dim(M))) stop("Weights have wrong dim!")
w<-weights
nMode<-ifelse(is.list(clu),length(clu),1)
if(nMode>1){
tmN<-sapply(clu,length)
clu<-lapply(clu,function(x)as.integer(factor(x)))
tmNclu<-sapply(clu,max)
for(iMode in 2:nMode){
clu[[iMode ]]<-clu[[iMode ]]+sum(tmNclu[1:(iMode -1)])
}
clu<-unlist(clu)
} else{
clu<-as.integer(factor(clu))
tmNclu<-max(clu)
tmN<-length(clu)
}
clu <- clu - 1
if(length(dim(M))==2) M<-array(M,dim=c(dim(M),1))
if(length(dim(w))==2) w<-array(w,dim=c(dim(w),1))
if(is.null(limits)){
bordersMatLower <- bordersMatUpper <- bordersSeperateLower <- bordersSeperateUpper<-NULL
if(limitType!="none"){
limitType<-"none"
warning("limitType is set to 'none' as limits are NULL!")
}
} else {
if(diagonal %in% c("ignore","same")){
bordersSeperateLower <- bordersSeperateUpper<-NULL
if(is.list(limits)){
limits<-limits[[1]]
}
if(!is.array(limits)) stop("'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
dl<-dim(limits)
if(length(dl)!=4) stop("'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
dim(bordersMatLower)<-dim(limits)[1:3]
bordersMatUpper <- limits[,,,2]
dim(bordersMatUpper)<-dim(limits)[1:3]
}
} else {
if(is.list(limits) & length(limits)==2){
diagLimits<-limits[[2]]
limits<-limits[[1]]
} else stop("If diagonal is 'seperate', limits must be a list of length 2")
if(!is.array(limits)) stop("First element of 'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
dl<-dim(limits)
if(length(dl)!=4) stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
bordersMatUpper <- limits[,,,2]
}
if(!is.array(diagLimits)) stop("Second element of 'limits' must be specified as an array!")
dl<-dim(diagLimits)
if(length(dl)==2 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1],1,dl[2]))
dl<-dim(diagLimits)
if(length(dl)!=3) stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(diagLimits)!=c(sum(tmNclu),dim(M)[3],2))){
stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersSeperateLower <- diagLimits[,,1]
bordersSeperateUpper <- diagLimits[,,2]
}
}
}
res<-kmBlock(M=M, clu=clu, weights=w, n=tmN, nClu=tmNclu, diagonal = diagonal, sBorders = limitType, bordersMatLower = bordersMatLower, bordersMatUpper = bordersMatUpper, bordersSeperateLower = bordersSeperateLower, bordersSeperateUpper = bordersSeperateUpper)
res<-list(M=M, clu=blockmodeling::splitClu(res$bestClu,tmN), IM=res$IM, err=res$bestCf, best=list(list(M=M, clu=res$bestClu, IM=res$IM)))
#return(res)
class(res)<-"opt.par"
return(res)
}
#' Function that computes criterion function used in k-means like one-mode blockmodeling. If \code{clu} is a list, the method for linked/multilevel networks is applied
#'
#' @param M A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation.
#' @param clu A partition. Each unique value represents one cluster. If the network is one-mode, than this should be a vector, else a list of vectors, one for each mode. Similarly, if units are comprised of several sets, clu should be the list containing one vector for each set.
#' @param weights The weights for each cell in the matrix/array. A matrix or an array with the same dimensions as \code{M}.
#' @param diagonal How should the diagonal values be treated. Possible values are:
#' \itemize{
#' \item ignore - diagonal values are ignored
#' \item seperate - diagonal values are treated separately
#' \item same - diagonal values are treated the same as all other values
#' }
#' @param limits If \code{diagonal} is \code{"ignore"} or \code{"same"}, an array with dimensions equal to:
#' \itemize{
#' \item number of clusters (of all types)
#' \item number of clusters (of all types)
#' \item number of relations
#' \item 2 - the first is lower limit and the second is upper limit
#' }
#' If \code{diagonal} is \code{"seperate"}, a list of two array. The first should be as described above, representing limits for off diagonal values. The second should be similar with only 3 dimensions, as one of the first two must be omitted.
#' @param limitType What do the limits represent, on which "side" of this limits should the values lie. Possible values: "none","inside","outside"
#' @return A list similar to optParC in package \code{blockmodeling}.
#' @export
critFunKmeans<-function(M,
clu,
weights=NULL,
diagonal = c("ignore","seperate","same"),
limitType=c("none","inside","outside"),
limits=NULL){
#n<-dim(M)[1]
diagonal<-match.arg(diagonal)
limitType<-match.arg(limitType)
if(is.null(weights)){
weights<-M
weights[]<-1
} else if(any(dim(weights)!=dim(M))) stop("Weights have wrong dim!")
w<-weights
nMode<-ifelse(is.list(clu),length(clu),1)
if(nMode>1){
tmN<-sapply(clu,length)
clu<-lapply(clu,function(x)as.integer(factor(x)))
tmNclu<-sapply(clu,max)
for(iMode in 2:nMode){
clu[[iMode ]]<-clu[[iMode ]]+sum(tmNclu[1:(iMode -1)])
}
clu<-unlist(clu)
} else{
clu<-as.integer(factor(clu))
tmNclu<-max(clu)
tmN<-length(clu)
}
clu <- clu - 1
if(length(dim(M))==2) M<-array(M,dim=c(dim(M),1))
if(length(dim(w))==2) w<-array(w,dim=c(dim(w),1))
if(is.null(limits)){
bordersMatLower <- bordersMatUpper <- bordersSeperateLower <- bordersSeperateUpper<-NULL
if(limitType!="none"){
limitType<-"none"
warning("limitType is set to 'none' as limits are NULL!")
}
} else {
if(diagonal %in% c("ignore","same")){
bordersSeperateLower <- bordersSeperateUpper
if(is.list(limits)){
limits<-limits[[1]]
}
if(!is.array(limits)) stop("'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
if(dim(limits)!=4) stop("'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
bordersMatUpper <- limits[,,,2]
}
} else {
if(is.list(limits) & length(limits)==2){
diagLimits<-limits[[2]]
limits<-limits[[1]]
} else stop("If diagonal is 'seperate', limits must be a list of length 2")
if(!is.array(limits)) stop("First element of 'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
if(dim(limits)!=4) stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
bordersMatUpper <- limits[,,,2]
}
if(!is.array(diagLimits)) stop("Second element of 'limits' must be specified as an array!")
dl<-dim(diagLimits)
if(length(dl)==2 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1],1,dl[2]))
if(dim(diagLimits)!=3) stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(diagLimits)!=c(sum(tmNclu),dim(M)[3],2))){
stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersSeperateLower <- diagLimits[,,,1]
bordersSeperateUpper <- diagLimits[,,,2]
}
}
}
res<-critFunction(M=M, clu=clu, weights=w, dimensions=tmNclu, n=tmN, diagonal = diagonal, sBorders = limitType, bordersMatLower = bordersMatLower, bordersMatUpper = bordersMatUpper, bordersSeperateLower = bordersSeperateLower, bordersSeperateUpper = bordersSeperateUpper)
return(res)
# res<-list(M=M, clu=clu, IM=IM, err=err, best=list(list(M=M, clu=clu, IM=IM)))
# return(res)
}
#' A function for optimizing multiple random partitions using k-means one-mode and linked blockmodeling. Calls \code{kmBlockC} for optimizing individual random partitions.
#'
#' @import parallel
#' @import foreach
#' @import doParallel
#' @import doRNG
#' @import blockmodeling
#'
#' @param M A square matrix giving the adjaciency relationg between the network's nodes (aka vertexes)
#' @param k The number of clusters used in the generation of partitions.
#' @param rep The number of repetitions/different starting partitions to check.
#' @param save.initial.param Should the inital parameters(\code{approaches}, ...) of using \code{kmBlockC} be saved. The default value is \code{TRUE}.
#' @param deleteMs Delete networks/matrices from the results of to save space. Defaults to \code{TRUE}.
#' @param max.iden Maximum number of results that should be saved (in case there are more than \code{max.iden} results with minimal error, only the first \code{max.iden} will be saved).
#' @param return.all If \code{FALSE}, solution for only the best (one or more) partition/s is/are returned.
#' @param return.err Should the error for each optimized partition be returned. Defaults to \code{TRUE}.
#' @param seed Optional. The seed for random generation of partitions.
#' @param parGenFun The function (object) that will generate random partitions. The default function is \code{\link{genRandomPar}}. The function has to accept the following parameters: \code{k} (number o of partitions by modes, \code{n} (number of units by modes), \code{seed} (seed value for random generation of partition), \code{addParam} (a list of additional parameters).
#' @param mingr Minimal allowed group size.
#' @param maxgr Maximal allowed group size.
#' @param addParam A list of additional parameters for function specified above. In the usage section they are specified for the default function \code{\link{genRandomPar}}.
#' @param maxTriesToFindNewPar The maximum number of partition try when trying to find a new partition to optimize that was not yet checked before - the default value is \code{rep * 1000}.
#' @param skip.par The partitions that are not allowed or were already checked and should therefore be skipped.
#' @param printRep Should some information about each optimization be printed.
#' @param n The number of units by "modes". It is used only for generating random partitions. It has to be set only if there are more than two modes or if there are two modes, but the matrix representing the network is one mode (both modes are in rows and columns).
#' @param nCores Number of cores to be used. Value \code{0} means all available cores. It can also be a cluster object.
#' @param useParLapply Should \code{parLapplyLB} be used (otherwise \code{foreach} is used). Defaults to true as it needs less dependencies. It might be removed in future releases and only allow the use of parLapplyLB.
#' @param cl The cluster to use (if formed beforehand). Defaults to \code{NULL}.
#' @param stopcl Should the cluster be stopped after the function finishes. Defaults to \code{is.null(cl)}.
#' @param \dots Arguments passed to other functions, see \code{\link{kmBlockC}}.
#'
#' @return A list of class "opt.more.par" containing:
#' \item{M}{The one- or multi-mode matrix of the network analyzed}
#' \item{res}{If \code{return.all = TRUE} - A list of results the same as \code{best} - one \code{best} for each partition optimized.}
#' \item{best}{A list of results from \code{kmBlockC}, only without \code{M}.}
#' \item{err}{If \code{return.err = TRUE} - The vector of errors or inconsistencies = -log-likelihoods.}
#' \item{ICL}{Integrated classification likelihood for the best partition.}
#' \item{checked.par}{If selected - A list of checked partitions. If \code{merge.save.skip.par} is \code{TRUE}, this list also includes the partitions in \code{skip.par}.}
#' \item{call}{The call to this function.}
#' \item{initial.param}{If selected - The initial parameters are used.}
#' \item{Random.seed}{.Random.seed at the end of the function.}
#' \item{cl}{Cluster used for parallel computations if supplied as an input parameter.}
#'
#' @section Warning:
#' It should be noted that the time needed to optimize the partition depends on the number of units (aka nodes) in the networks as well as the number of clusters
#' due to the underlying algorithm. Hence, partitioning networks with several hundred units and large number of blocks (e.g., >5) can take a long time (from 20 minutes to a few hours or even days).
#'
#' @references Žiberna, Aleš (2020). k-means-based algorithm for blockmodeling linked networks. Social Networks 32(1), 105-126, \doi{10.1016/j.socnet.2019.10.006}.
#'
#' @author \enc{Aleš, Žiberna}{Ales Ziberna}
#' @seealso \code{\link{kmBlockC}}
#'
#' @examples
#'# Simple one-mode network
#'library(blockmodeling)
#'k<-2
#'blockSizes<-rep(20,k)
#'IM<-matrix(c(0.8,.4,0.2,0.8), nrow=2)
#'if(any(dim(IM)!=c(k,k))) stop("invalid dimensions")
#'
#'set.seed(2021)
#'clu<-rep(1:k, times=blockSizes)
#'n<-length(clu)
#'M<-matrix(rbinom(n*n,1,IM[clu,clu]),ncol=n, nrow=n)
#'diag(M)<-0
#'plotMat(M)
#'
#'resORP<-kmBlockORPC(M,k=2, rep=10, return.all = TRUE)
#'plot(resORP)
#'clu(resORP)
#'
#'
#'# Linked network
#'library(blockmodeling)
#'set.seed(2021)
#'IM<-matrix(c(0.8,.4,0.2,0.8), nrow=2)
#'clu<-rep(1:2, each=20)
#'n<-length(clu)
#'nClu<-length(unique(clu))
#'M1<-matrix(rbinom(n^2,1,IM[clu,clu]),ncol=n, nrow=n)
#'M2<-matrix(rbinom(n^2,1,IM[clu,clu]),ncol=n, nrow=n)
#'M12<-diag(n)
#'nn<-c(n,n)
#'k<-c(2,2)
#'Ml<-matrix(0, nrow=sum(nn),ncol=sum(nn))
#'Ml[1:n,1:n]<-M1
#'Ml[n+1:n,n+1:n]<-M2
#'Ml[n+1:n, 1:n]<-M12
#'plotMat(Ml)
#'
#'resMl<-kmBlockORPC(M=Ml, k=k, n=nn, rep=10)
#'plot(resMl)
#'clu(resMl)
#'
#' @author \enc{Aleš, Žiberna}{Ales Ziberna}
#'
#' @export
kmBlockORPC<-function(M, #a square matrix
k,#number of clusters/groups
rep,#number of repetitions/different starting partitions to check
save.initial.param=TRUE, #save the initial parametrs of this call
deleteMs=TRUE, #delete networks/matrices from results of optParC or optParMultiC to save space
max.iden=10, #the maximum number of results that should be saved (in case there are more than max.iden results with minimal error, only the first max.iden will be saved)
return.all=FALSE,#if 'FALSE', solution for only the best (one or more) partition/s is/are returned
return.err=TRUE,#if 'FALSE', only the resoults of crit.fun are returned (a list of all (best) soulutions including errors), else the resoult is list
seed=NULL,#the seed for random generation of partitions
parGenFun = blockmodeling::genRandomPar, #The function that will generate random partitions. It should accept argumetns: k (number of partitions by modes, n (number of units by modes), seed (seed value for random generation of partition), addParam (a list of additional parametres)
mingr=NULL, #minimal alowed group size (defaults to c(minUnitsRowCluster,minUnitsColCluster) if set, else to 1) - only used for parGenFun function
maxgr=NULL, #maximal alowed group size (default to c(maxUnitsRowCluster,maxUnitsColCluster) if set, else to Inf) - only used for parGenFun function
addParam=list( #list of additional parameters for gerenrating partitions. Here they are specified for dthe default function "genRandomPar"
genPajekPar = TRUE, #Should the partitions be generated as in Pajek (the other options is completly random)
probGenMech = NULL), #Here the probabilities for different mechanizems for specifying the partitions are set. If not set this is determined based on the previous parameter.
maxTriesToFindNewPar=rep*10, #The maximum number of partition try when trying to find a new partition to optimize that was not yet checked before
skip.par = NULL, #partitions to be skiped
printRep= ifelse(rep<=10,1,round(rep/10)), #should some information about each optimization be printed
n=NULL, #the number of units by "modes". It is used only for generating random partitions. It has to be set only if there are more than two modes or if there are two modes, but the matrix representing the network is onemode (both modes are in rows and columns)
nCores=1, #number of cores to be used 0 -means all available cores, can also be a cluster object,
useParLapply=TRUE, #should ply be used instead of foreach
cl = NULL, #the cluster to use (if formed beforehand)
stopcl = is.null(cl), # should the cluster be stoped
... #paramters to kmBlockC
){
dots<-list(...)
if(save.initial.param)initial.param<-c(tryCatch(lapply(as.list(sys.frame(sys.nframe())),eval),error=function(...)return("error")),dots=list(...))#saves the inital parameters
if(is.null(mingr)){
if(is.null(dots$minUnitsRowCluster)){
mingr<-1
} else {
mingr<-c(dots$minUnitsRowCluster,dots$minUnitsColCluster)
}
}
if(is.null(maxgr)){
if(is.null(dots$maxUnitsRowCluster)){
maxgr<-Inf
} else {
maxgr<-c(dots$maxUnitsRowCluster,dots$maxUnitsColCluster)
}
}
nmode<-length(k)
res<-list(NULL)
err<-NULL
dots<-list(...)
if(save.initial.param)initial.param<-c(tryCatch(lapply(as.list(sys.frame(sys.nframe())),eval),error=function(...)return("error")),dots=list(...))#saves the inital parameters
if(is.null(n)) if(nmode==1){
n<-dim(M)[1]
} else if(nmode==2){
n<-dim(M)[1:2]
} else warning("Number of nodes by modes can not be determined. Parameter 'n' must be supplied!!!")
if(!is.null(seed))set.seed(seed)
on.exit({
res1 <- res[which(err==min(err, na.rm = TRUE))]
best<-NULL
best.clu<-NULL
for(i in 1:length(res1)){
for(j in 1:length(res1[[i]]$best)){
if(
ifelse(is.null(best.clu),
TRUE,
if(nmode==1){
!any(sapply(best.clu,blockmodeling::rand2,clu2=res1[[i]]$clu)==1)
} else {
!any(sapply(best.clu,function(x,clu2)blockmodeling::rand2(unlist(x),clu2),clu2=unlist(res1[[i]]$clu))==1)
}
)
){
best<-c(best,res1[i])
best.clu<-c(best.clu,list(res1[[i]]$clu))
}
if(length(best)>=max.iden) {
warning("Only the first ",max.iden," solutions out of ",length(stats::na.omit(err))," solutions with minimal sum of square deviations will be saved.\n")
break
}
}
}
names(best)<-paste("best",1:length(best),sep="")
if(any(stats::na.omit(err)==-Inf) || blockmodeling::ss(stats::na.omit(err))!=0 || length(stats::na.omit(err))==1){
message("\n\nOptimization of all partitions completed\n",
length(best)," solution(s) with minimal sum of square deviations = ", min(err,na.rm=TRUE), " found.","\n")
}else {
message("\n\nOptimization of all partitions completed\n",
"All ",length(stats::na.omit(err))," solutions have sum of square deviations ",err[1],"\n")
}
call<-list(call=match.call())
best<-list(best=best)
checked.par<-list(checked.par=skip.par)
if(return.all) res<-list(res=res) else res<-NULL
if(return.err) err<-list(err=err) else err<-NULL
if(!exists("initial.param")){
initial.param<-NULL
} else initial.param=list(initial.param)
res<-c(list(M=M),res,best,err,checked.par,call,initial.param=initial.param, list(Random.seed=.Random.seed, cl=cl))
class(res)<-"opt.more.par"
return(res)
})
if(nCores==1||!requireNamespace("parallel")){
if(nCores!=1) {
warning("Only single core is used as package 'parallel' is not available", immediate. = TRUE)
}
for(i in 1:rep){
if(printRep & (i%%printRep==0)) message("\n\nStarting optimization of the partiton ",i," of ",rep," partitions.\n")
find.unique.par<-TRUE
ununiqueParTested=0
while(find.unique.par){
temppar<-parGenFun(n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam)
find.unique.par<-
ifelse(is.null(skip.par),
FALSE,
if(nmode==1) {
any(sapply(skip.par,blockmodeling::rand2,clu2=temppar)==1)
} else any(sapply(skip.par,function(x,clu2)blockmodeling::rand2(unlist(x),clu2),clu2=unlist(temppar))==1)
)
ununiqueParTested=ununiqueParTested+1
endFun<-ununiqueParTested>=maxTriesToFindNewPar
if(endFun) {
break
} else if(ununiqueParTested%%10==0) message(ununiqueParTested," partitions tested for unique partition\n")
}
if(endFun) break
skip.par<-c(skip.par,list(temppar))
if(printRep==1) message("Starting partition: ",unlistPar(temppar),"\n")
res[[i]]<-kmBlockC(M=M, clu=temppar, ...)
if(deleteMs){
res[[i]]$M<-NULL
}
res[[i]]$best<-NULL
err[i]<-res[[i]]$err
if(printRep==1){
message("Final sum of square deviations: ",err[i],"\n")
message("Final partition: ",unlistPar(res[[i]]$clu),"\n")
}
}
} else {
oneRep<-function(i,M,n,k,mingr,maxgr,addParam,rep,...){
temppar<-parGenFun(n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam)
#skip.par<-c(skip.par,list(temppar))
tres <- try(kmBlockC(M=M, clu=temppar, ...))
if(inherits(x = tres,what = "try-error")){
tres<-list("try-error"=tres, err=Inf, startPart=temppar)
}
if(deleteMs){
tres$M<-NULL
}
tres$best<-NULL
return(list(tres))
}
if(!useParLapply) if(!requireNamespace("doParallel")|!requireNamespace("doRNG")) useParLapply<-TRUE
if(nCores==0){
nCores<-parallel::detectCores()-1
}
pkgName<-utils::packageName()
if(is.null(pkgName)) pkgName<-utils::packageName(environment(fun.by.blocks))
if(useParLapply) {
if(is.null(cl)) cl<-parallel::makeCluster(nCores)
parallel::clusterSetRNGStream(cl)
nC<-nCores
#parallel::clusterExport(cl, varlist = c("kmBlock","kmBlockORP"))
#parallel::clusterExport(cl, varlist = "kmBlock")
parallel::clusterExport(cl, varlist = "pkgName", envir=environment())
parallel::clusterEvalQ(cl, expr={requireNamespace(pkgName,character.only = TRUE)})
res<-parallel::parLapplyLB(cl = cl,1:rep, fun = oneRep, M=M,n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam,rep=rep,...)
if(stopcl) parallel::stopCluster(cl)
res<-lapply(res,function(x)x[[1]])
} else {
requireNamespace("doParallel")
requireNamespace("doRNG")
if(!getDoParRegistered()|(getDoParWorkers()!=nCores)){
if(!is.null(cl)) {
#cl<-parallel::makeCluster(nCores)
registerDoParallel(cl)
} else registerDoParallel(nCores)
}
nC<-getDoParWorkers()
res<-foreach::foreach(i=1:rep,.combine=c, .packages=pkgName) %dorng% oneRep(i=i,M=M,n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam,rep=rep,...)
if(!is.null(cl) & stopcl) {
registerDoSEQ()
parallel::stopCluster(cl)
}
}
err<-sapply(res,function(x)x$err)
}
}
.onAttach <- function(libname, pkgname) {
packageStartupMessage('To cite \"', pkgname, '\" in publications please use package citation and the following article:\n\n\n\n',
'\u017Diberna, Ale\u0161 (2020). k-means-based algorithm for blockmodeling linked networks. Social Networks 32(1), 105-126.\n\n\n\n',
'To see these entries in BibTeX format, use "print(<citation>, bibtex=TRUE)", "toBibtex(.)", or set "options(citation.bibtex.max=999)".')
}
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R Package Documentation
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Defines functions .onAttach kmBlockORPC critFunKmeans kmBlockC unlistPar
Documented in critFunKmeans kmBlockC kmBlockORPC
unlistPar<- function(part){
if(is.list(part)){
part<-sapply(part,paste,collapse=" ")
part<-paste(paste("\nMode ", 1:length(part),":",sep=""), part,collapse="",sep=" ")
}
part
}
#' Function that performs k-means like one-mode blockmodeling. If \code{clu} is a list, the method for linked/multilevel networks is applied
#'
#' @param M A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation.
#' @param clu A partition. Each unique value represents one cluster. If the network is one-mode, than this should be a vector, else a list of vectors, one for each mode. Similarly, if units are comprised of several sets, clu should be the list containing one vector for each set.
#' @param weights The weights for each cell in the matrix/array. A matrix or an array with the same dimensions as \code{M}.
#' @param diagonal How should the diagonal values be treated. Possible values are:
#' \itemize{
#' \item ignore - diagonal values are ignored
#' \item seperate - diagonal values are treated separately
#' \item same - diagonal values are treated the same as all other values
#' }
#' @param limits If \code{diagonal} is \code{"ignore"} or \code{"same"}, an array with dimensions equal to:
#' \itemize{
#' \item number of clusters (of all types)
#' \item number of clusters (of all types)
#' \item number of relations
#' \item 2 - the first is lower limit and the second is upper limit
#' }
#' If \code{diagonal} is \code{"seperate"}, a list of two array. The first should be as described above, representing limits for off diagonal values. The second should be similar with only 3 dimensions, as one of the first two must be omitted.
#' @param limitType What do the limits represent, on which "side" of this limits should the values lie. Possible values: "none","inside","outside"
#' @return A list similar to optParC in package \code{blockmodeling}.
#' @seealso \code{\link{kmBlockORPC}}
#' @import Rcpp
#' @importFrom Rcpp evalCpp
#' @useDynLib kmBlock, .registration = TRUE
#' @export
kmBlockC<-function(M,
clu,
weights=NULL,
diagonal = c("ignore","seperate","same"),
limitType=c("none","inside","outside"),
limits=NULL){
n<-dim(M)[1]
diagonal<-match.arg(diagonal)
limitType<-match.arg(limitType)
if(is.null(weights)){
weights<-M
weights[,]<-1
} else if(any(dim(weights)!=dim(M))) stop("Weights have wrong dim!")
w<-weights
nMode<-ifelse(is.list(clu),length(clu),1)
if(nMode>1){
tmN<-sapply(clu,length)
clu<-lapply(clu,function(x)as.integer(factor(x)))
tmNclu<-sapply(clu,max)
for(iMode in 2:nMode){
clu[[iMode ]]<-clu[[iMode ]]+sum(tmNclu[1:(iMode -1)])
}
clu<-unlist(clu)
} else{
clu<-as.integer(factor(clu))
tmNclu<-max(clu)
tmN<-length(clu)
}
clu <- clu - 1
if(length(dim(M))==2) M<-array(M,dim=c(dim(M),1))
if(length(dim(w))==2) w<-array(w,dim=c(dim(w),1))
if(is.null(limits)){
bordersMatLower <- bordersMatUpper <- bordersSeperateLower <- bordersSeperateUpper<-NULL
if(limitType!="none"){
limitType<-"none"
warning("limitType is set to 'none' as limits are NULL!")
}
} else {
if(diagonal %in% c("ignore","same")){
bordersSeperateLower <- bordersSeperateUpper<-NULL
if(is.list(limits)){
limits<-limits[[1]]
}
if(!is.array(limits)) stop("'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
dl<-dim(limits)
if(length(dl)!=4) stop("'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
dim(bordersMatLower)<-dim(limits)[1:3]
bordersMatUpper <- limits[,,,2]
dim(bordersMatUpper)<-dim(limits)[1:3]
}
} else {
if(is.list(limits) & length(limits)==2){
diagLimits<-limits[[2]]
limits<-limits[[1]]
} else stop("If diagonal is 'seperate', limits must be a list of length 2")
if(!is.array(limits)) stop("First element of 'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
dl<-dim(limits)
if(length(dl)!=4) stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
bordersMatUpper <- limits[,,,2]
}
if(!is.array(diagLimits)) stop("Second element of 'limits' must be specified as an array!")
dl<-dim(diagLimits)
if(length(dl)==2 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1],1,dl[2]))
dl<-dim(diagLimits)
if(length(dl)!=3) stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(diagLimits)!=c(sum(tmNclu),dim(M)[3],2))){
stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersSeperateLower <- diagLimits[,,1]
bordersSeperateUpper <- diagLimits[,,2]
}
}
}
res<-kmBlock(M=M, clu=clu, weights=w, n=tmN, nClu=tmNclu, diagonal = diagonal, sBorders = limitType, bordersMatLower = bordersMatLower, bordersMatUpper = bordersMatUpper, bordersSeperateLower = bordersSeperateLower, bordersSeperateUpper = bordersSeperateUpper)
res<-list(M=M, clu=blockmodeling::splitClu(res$bestClu,tmN), IM=res$IM, err=res$bestCf, best=list(list(M=M, clu=res$bestClu, IM=res$IM)))
#return(res)
class(res)<-"opt.par"
return(res)
}
#' Function that computes criterion function used in k-means like one-mode blockmodeling. If \code{clu} is a list, the method for linked/multilevel networks is applied
#'
#' @param M A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation.
#' @param clu A partition. Each unique value represents one cluster. If the network is one-mode, than this should be a vector, else a list of vectors, one for each mode. Similarly, if units are comprised of several sets, clu should be the list containing one vector for each set.
#' @param weights The weights for each cell in the matrix/array. A matrix or an array with the same dimensions as \code{M}.
#' @param diagonal How should the diagonal values be treated. Possible values are:
#' \itemize{
#' \item ignore - diagonal values are ignored
#' \item seperate - diagonal values are treated separately
#' \item same - diagonal values are treated the same as all other values
#' }
#' @param limits If \code{diagonal} is \code{"ignore"} or \code{"same"}, an array with dimensions equal to:
#' \itemize{
#' \item number of clusters (of all types)
#' \item number of clusters (of all types)
#' \item number of relations
#' \item 2 - the first is lower limit and the second is upper limit
#' }
#' If \code{diagonal} is \code{"seperate"}, a list of two array. The first should be as described above, representing limits for off diagonal values. The second should be similar with only 3 dimensions, as one of the first two must be omitted.
#' @param limitType What do the limits represent, on which "side" of this limits should the values lie. Possible values: "none","inside","outside"
#' @return A list similar to optParC in package \code{blockmodeling}.
#' @export
critFunKmeans<-function(M,
clu,
weights=NULL,
diagonal = c("ignore","seperate","same"),
limitType=c("none","inside","outside"),
limits=NULL){
#n<-dim(M)[1]
diagonal<-match.arg(diagonal)
limitType<-match.arg(limitType)
if(is.null(weights)){
weights<-M
weights[]<-1
} else if(any(dim(weights)!=dim(M))) stop("Weights have wrong dim!")
w<-weights
nMode<-ifelse(is.list(clu),length(clu),1)
if(nMode>1){
tmN<-sapply(clu,length)
clu<-lapply(clu,function(x)as.integer(factor(x)))
tmNclu<-sapply(clu,max)
for(iMode in 2:nMode){
clu[[iMode ]]<-clu[[iMode ]]+sum(tmNclu[1:(iMode -1)])
}
clu<-unlist(clu)
} else{
clu<-as.integer(factor(clu))
tmNclu<-max(clu)
tmN<-length(clu)
}
clu <- clu - 1
if(length(dim(M))==2) M<-array(M,dim=c(dim(M),1))
if(length(dim(w))==2) w<-array(w,dim=c(dim(w),1))
if(is.null(limits)){
bordersMatLower <- bordersMatUpper <- bordersSeperateLower <- bordersSeperateUpper<-NULL
if(limitType!="none"){
limitType<-"none"
warning("limitType is set to 'none' as limits are NULL!")
}
} else {
if(diagonal %in% c("ignore","same")){
bordersSeperateLower <- bordersSeperateUpper
if(is.list(limits)){
limits<-limits[[1]]
}
if(!is.array(limits)) stop("'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
if(dim(limits)!=4) stop("'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
bordersMatUpper <- limits[,,,2]
}
} else {
if(is.list(limits) & length(limits)==2){
diagLimits<-limits[[2]]
limits<-limits[[1]]
} else stop("If diagonal is 'seperate', limits must be a list of length 2")
if(!is.array(limits)) stop("First element of 'limits' must be specified as an array!")
dl<-dim(limits)
if(length(dl)==3 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1:2],1,dl[3]))
if(dim(limits)!=4) stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(limits)!=c(sum(tmNclu),sum(tmNclu),dim(M)[3],2))){
stop("First element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersMatLower <- limits[,,,1]
bordersMatUpper <- limits[,,,2]
}
if(!is.array(diagLimits)) stop("Second element of 'limits' must be specified as an array!")
dl<-dim(diagLimits)
if(length(dl)==2 & dim(M)[3]==1) limits<-array(limits, dim=c(dl[1],1,dl[2]))
if(dim(diagLimits)!=3) stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
if(all(dim(diagLimits)!=c(sum(tmNclu),dim(M)[3],2))){
stop("Second element of 'limits' has wrong dimensions (see help for correct dimensions)")
} else{
bordersSeperateLower <- diagLimits[,,,1]
bordersSeperateUpper <- diagLimits[,,,2]
}
}
}
res<-critFunction(M=M, clu=clu, weights=w, dimensions=tmNclu, n=tmN, diagonal = diagonal, sBorders = limitType, bordersMatLower = bordersMatLower, bordersMatUpper = bordersMatUpper, bordersSeperateLower = bordersSeperateLower, bordersSeperateUpper = bordersSeperateUpper)
return(res)
# res<-list(M=M, clu=clu, IM=IM, err=err, best=list(list(M=M, clu=clu, IM=IM)))
# return(res)
}
#' A function for optimizing multiple random partitions using k-means one-mode and linked blockmodeling. Calls \code{kmBlockC} for optimizing individual random partitions.
#'
#' @import parallel
#' @import foreach
#' @import doParallel
#' @import doRNG
#' @import blockmodeling
#'
#' @param M A square matrix giving the adjaciency relationg between the network's nodes (aka vertexes)
#' @param k The number of clusters used in the generation of partitions.
#' @param rep The number of repetitions/different starting partitions to check.
#' @param save.initial.param Should the inital parameters(\code{approaches}, ...) of using \code{kmBlockC} be saved. The default value is \code{TRUE}.
#' @param deleteMs Delete networks/matrices from the results of to save space. Defaults to \code{TRUE}.
#' @param max.iden Maximum number of results that should be saved (in case there are more than \code{max.iden} results with minimal error, only the first \code{max.iden} will be saved).
#' @param return.all If \code{FALSE}, solution for only the best (one or more) partition/s is/are returned.
#' @param return.err Should the error for each optimized partition be returned. Defaults to \code{TRUE}.
#' @param seed Optional. The seed for random generation of partitions.
#' @param parGenFun The function (object) that will generate random partitions. The default function is \code{\link{genRandomPar}}. The function has to accept the following parameters: \code{k} (number o of partitions by modes, \code{n} (number of units by modes), \code{seed} (seed value for random generation of partition), \code{addParam} (a list of additional parameters).
#' @param mingr Minimal allowed group size.
#' @param maxgr Maximal allowed group size.
#' @param addParam A list of additional parameters for function specified above. In the usage section they are specified for the default function \code{\link{genRandomPar}}.
#' @param maxTriesToFindNewPar The maximum number of partition try when trying to find a new partition to optimize that was not yet checked before - the default value is \code{rep * 1000}.
#' @param skip.par The partitions that are not allowed or were already checked and should therefore be skipped.
#' @param printRep Should some information about each optimization be printed.
#' @param n The number of units by "modes". It is used only for generating random partitions. It has to be set only if there are more than two modes or if there are two modes, but the matrix representing the network is one mode (both modes are in rows and columns).
#' @param nCores Number of cores to be used. Value \code{0} means all available cores. It can also be a cluster object.
#' @param useParLapply Should \code{parLapplyLB} be used (otherwise \code{foreach} is used). Defaults to true as it needs less dependencies. It might be removed in future releases and only allow the use of parLapplyLB.
#' @param cl The cluster to use (if formed beforehand). Defaults to \code{NULL}.
#' @param stopcl Should the cluster be stopped after the function finishes. Defaults to \code{is.null(cl)}.
#' @param \dots Arguments passed to other functions, see \code{\link{kmBlockC}}.
#'
#' @return A list of class "opt.more.par" containing:
#' \item{M}{The one- or multi-mode matrix of the network analyzed}
#' \item{res}{If \code{return.all = TRUE} - A list of results the same as \code{best} - one \code{best} for each partition optimized.}
#' \item{best}{A list of results from \code{kmBlockC}, only without \code{M}.}
#' \item{err}{If \code{return.err = TRUE} - The vector of errors or inconsistencies = -log-likelihoods.}
#' \item{ICL}{Integrated classification likelihood for the best partition.}
#' \item{checked.par}{If selected - A list of checked partitions. If \code{merge.save.skip.par} is \code{TRUE}, this list also includes the partitions in \code{skip.par}.}
#' \item{call}{The call to this function.}
#' \item{initial.param}{If selected - The initial parameters are used.}
#' \item{Random.seed}{.Random.seed at the end of the function.}
#' \item{cl}{Cluster used for parallel computations if supplied as an input parameter.}
#'
#' @section Warning:
#' It should be noted that the time needed to optimize the partition depends on the number of units (aka nodes) in the networks as well as the number of clusters
#' due to the underlying algorithm. Hence, partitioning networks with several hundred units and large number of blocks (e.g., >5) can take a long time (from 20 minutes to a few hours or even days).
#'
#' @references Žiberna, Aleš (2020). k-means-based algorithm for blockmodeling linked networks. Social Networks 32(1), 105-126, \doi{10.1016/j.socnet.2019.10.006}.
#'
#' @author \enc{Aleš, Žiberna}{Ales Ziberna}
#' @seealso \code{\link{kmBlockC}}
#'
#' @examples
#'# Simple one-mode network
#'library(blockmodeling)
#'k<-2
#'blockSizes<-rep(20,k)
#'IM<-matrix(c(0.8,.4,0.2,0.8), nrow=2)
#'if(any(dim(IM)!=c(k,k))) stop("invalid dimensions")
#'
#'set.seed(2021)
#'clu<-rep(1:k, times=blockSizes)
#'n<-length(clu)
#'M<-matrix(rbinom(n*n,1,IM[clu,clu]),ncol=n, nrow=n)
#'diag(M)<-0
#'plotMat(M)
#'
#'resORP<-kmBlockORPC(M,k=2, rep=10, return.all = TRUE)
#'plot(resORP)
#'clu(resORP)
#'
#'
#'# Linked network
#'library(blockmodeling)
#'set.seed(2021)
#'IM<-matrix(c(0.8,.4,0.2,0.8), nrow=2)
#'clu<-rep(1:2, each=20)
#'n<-length(clu)
#'nClu<-length(unique(clu))
#'M1<-matrix(rbinom(n^2,1,IM[clu,clu]),ncol=n, nrow=n)
#'M2<-matrix(rbinom(n^2,1,IM[clu,clu]),ncol=n, nrow=n)
#'M12<-diag(n)
#'nn<-c(n,n)
#'k<-c(2,2)
#'Ml<-matrix(0, nrow=sum(nn),ncol=sum(nn))
#'Ml[1:n,1:n]<-M1
#'Ml[n+1:n,n+1:n]<-M2
#'Ml[n+1:n, 1:n]<-M12
#'plotMat(Ml)
#'
#'resMl<-kmBlockORPC(M=Ml, k=k, n=nn, rep=10)
#'plot(resMl)
#'clu(resMl)
#'
#' @author \enc{Aleš, Žiberna}{Ales Ziberna}
#'
#' @export
kmBlockORPC<-function(M, #a square matrix
k,#number of clusters/groups
rep,#number of repetitions/different starting partitions to check
save.initial.param=TRUE, #save the initial parametrs of this call
deleteMs=TRUE, #delete networks/matrices from results of optParC or optParMultiC to save space
max.iden=10, #the maximum number of results that should be saved (in case there are more than max.iden results with minimal error, only the first max.iden will be saved)
return.all=FALSE,#if 'FALSE', solution for only the best (one or more) partition/s is/are returned
return.err=TRUE,#if 'FALSE', only the resoults of crit.fun are returned (a list of all (best) soulutions including errors), else the resoult is list
seed=NULL,#the seed for random generation of partitions
parGenFun = blockmodeling::genRandomPar, #The function that will generate random partitions. It should accept argumetns: k (number of partitions by modes, n (number of units by modes), seed (seed value for random generation of partition), addParam (a list of additional parametres)
mingr=NULL, #minimal alowed group size (defaults to c(minUnitsRowCluster,minUnitsColCluster) if set, else to 1) - only used for parGenFun function
maxgr=NULL, #maximal alowed group size (default to c(maxUnitsRowCluster,maxUnitsColCluster) if set, else to Inf) - only used for parGenFun function
addParam=list( #list of additional parameters for gerenrating partitions. Here they are specified for dthe default function "genRandomPar"
genPajekPar = TRUE, #Should the partitions be generated as in Pajek (the other options is completly random)
probGenMech = NULL), #Here the probabilities for different mechanizems for specifying the partitions are set. If not set this is determined based on the previous parameter.
maxTriesToFindNewPar=rep*10, #The maximum number of partition try when trying to find a new partition to optimize that was not yet checked before
skip.par = NULL, #partitions to be skiped
printRep= ifelse(rep<=10,1,round(rep/10)), #should some information about each optimization be printed
n=NULL, #the number of units by "modes". It is used only for generating random partitions. It has to be set only if there are more than two modes or if there are two modes, but the matrix representing the network is onemode (both modes are in rows and columns)
nCores=1, #number of cores to be used 0 -means all available cores, can also be a cluster object,
useParLapply=TRUE, #should ply be used instead of foreach
cl = NULL, #the cluster to use (if formed beforehand)
stopcl = is.null(cl), # should the cluster be stoped
... #paramters to kmBlockC
){
dots<-list(...)
if(save.initial.param)initial.param<-c(tryCatch(lapply(as.list(sys.frame(sys.nframe())),eval),error=function(...)return("error")),dots=list(...))#saves the inital parameters
if(is.null(mingr)){
if(is.null(dots$minUnitsRowCluster)){
mingr<-1
} else {
mingr<-c(dots$minUnitsRowCluster,dots$minUnitsColCluster)
}
}
if(is.null(maxgr)){
if(is.null(dots$maxUnitsRowCluster)){
maxgr<-Inf
} else {
maxgr<-c(dots$maxUnitsRowCluster,dots$maxUnitsColCluster)
}
}
nmode<-length(k)
res<-list(NULL)
err<-NULL
dots<-list(...)
if(save.initial.param)initial.param<-c(tryCatch(lapply(as.list(sys.frame(sys.nframe())),eval),error=function(...)return("error")),dots=list(...))#saves the inital parameters
if(is.null(n)) if(nmode==1){
n<-dim(M)[1]
} else if(nmode==2){
n<-dim(M)[1:2]
} else warning("Number of nodes by modes can not be determined. Parameter 'n' must be supplied!!!")
if(!is.null(seed))set.seed(seed)
on.exit({
res1 <- res[which(err==min(err, na.rm = TRUE))]
best<-NULL
best.clu<-NULL
for(i in 1:length(res1)){
for(j in 1:length(res1[[i]]$best)){
if(
ifelse(is.null(best.clu),
TRUE,
if(nmode==1){
!any(sapply(best.clu,blockmodeling::rand2,clu2=res1[[i]]$clu)==1)
} else {
!any(sapply(best.clu,function(x,clu2)blockmodeling::rand2(unlist(x),clu2),clu2=unlist(res1[[i]]$clu))==1)
}
)
){
best<-c(best,res1[i])
best.clu<-c(best.clu,list(res1[[i]]$clu))
}
if(length(best)>=max.iden) {
warning("Only the first ",max.iden," solutions out of ",length(stats::na.omit(err))," solutions with minimal sum of square deviations will be saved.\n")
break
}
}
}
names(best)<-paste("best",1:length(best),sep="")
if(any(stats::na.omit(err)==-Inf) || blockmodeling::ss(stats::na.omit(err))!=0 || length(stats::na.omit(err))==1){
message("\n\nOptimization of all partitions completed\n",
length(best)," solution(s) with minimal sum of square deviations = ", min(err,na.rm=TRUE), " found.","\n")
}else {
message("\n\nOptimization of all partitions completed\n",
"All ",length(stats::na.omit(err))," solutions have sum of square deviations ",err[1],"\n")
}
call<-list(call=match.call())
best<-list(best=best)
checked.par<-list(checked.par=skip.par)
if(return.all) res<-list(res=res) else res<-NULL
if(return.err) err<-list(err=err) else err<-NULL
if(!exists("initial.param")){
initial.param<-NULL
} else initial.param=list(initial.param)
res<-c(list(M=M),res,best,err,checked.par,call,initial.param=initial.param, list(Random.seed=.Random.seed, cl=cl))
class(res)<-"opt.more.par"
return(res)
})
if(nCores==1||!requireNamespace("parallel")){
if(nCores!=1) {
warning("Only single core is used as package 'parallel' is not available", immediate. = TRUE)
}
for(i in 1:rep){
if(printRep & (i%%printRep==0)) message("\n\nStarting optimization of the partiton ",i," of ",rep," partitions.\n")
find.unique.par<-TRUE
ununiqueParTested=0
while(find.unique.par){
temppar<-parGenFun(n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam)
find.unique.par<-
ifelse(is.null(skip.par),
FALSE,
if(nmode==1) {
any(sapply(skip.par,blockmodeling::rand2,clu2=temppar)==1)
} else any(sapply(skip.par,function(x,clu2)blockmodeling::rand2(unlist(x),clu2),clu2=unlist(temppar))==1)
)
ununiqueParTested=ununiqueParTested+1
endFun<-ununiqueParTested>=maxTriesToFindNewPar
if(endFun) {
break
} else if(ununiqueParTested%%10==0) message(ununiqueParTested," partitions tested for unique partition\n")
}
if(endFun) break
skip.par<-c(skip.par,list(temppar))
if(printRep==1) message("Starting partition: ",unlistPar(temppar),"\n")
res[[i]]<-kmBlockC(M=M, clu=temppar, ...)
if(deleteMs){
res[[i]]$M<-NULL
}
res[[i]]$best<-NULL
err[i]<-res[[i]]$err
if(printRep==1){
message("Final sum of square deviations: ",err[i],"\n")
message("Final partition: ",unlistPar(res[[i]]$clu),"\n")
}
}
} else {
oneRep<-function(i,M,n,k,mingr,maxgr,addParam,rep,...){
temppar<-parGenFun(n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam)
#skip.par<-c(skip.par,list(temppar))
tres <- try(kmBlockC(M=M, clu=temppar, ...))
if(inherits(x = tres,what = "try-error")){
tres<-list("try-error"=tres, err=Inf, startPart=temppar)
}
if(deleteMs){
tres$M<-NULL
}
tres$best<-NULL
return(list(tres))
}
if(!useParLapply) if(!requireNamespace("doParallel")|!requireNamespace("doRNG")) useParLapply<-TRUE
if(nCores==0){
nCores<-parallel::detectCores()-1
}
pkgName<-utils::packageName()
if(is.null(pkgName)) pkgName<-utils::packageName(environment(fun.by.blocks))
if(useParLapply) {
if(is.null(cl)) cl<-parallel::makeCluster(nCores)
parallel::clusterSetRNGStream(cl)
nC<-nCores
#parallel::clusterExport(cl, varlist = c("kmBlock","kmBlockORP"))
#parallel::clusterExport(cl, varlist = "kmBlock")
parallel::clusterExport(cl, varlist = "pkgName", envir=environment())
parallel::clusterEvalQ(cl, expr={requireNamespace(pkgName,character.only = TRUE)})
res<-parallel::parLapplyLB(cl = cl,1:rep, fun = oneRep, M=M,n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam,rep=rep,...)
if(stopcl) parallel::stopCluster(cl)
res<-lapply(res,function(x)x[[1]])
} else {
requireNamespace("doParallel")
requireNamespace("doRNG")
if(!getDoParRegistered()|(getDoParWorkers()!=nCores)){
if(!is.null(cl)) {
#cl<-parallel::makeCluster(nCores)
registerDoParallel(cl)
} else registerDoParallel(nCores)
}
nC<-getDoParWorkers()
res<-foreach::foreach(i=1:rep,.combine=c, .packages=pkgName) %dorng% oneRep(i=i,M=M,n=n,k=k,mingr=mingr,maxgr=maxgr,addParam=addParam,rep=rep,...)
if(!is.null(cl) & stopcl) {
registerDoSEQ()
parallel::stopCluster(cl)
}
}
err<-sapply(res,function(x)x$err)
}
}
.onAttach <- function(libname, pkgname) {
packageStartupMessage('To cite \"', pkgname, '\" in publications please use package citation and the following article:\n\n\n\n',
'\u017Diberna, Ale\u0161 (2020). k-means-based algorithm for blockmodeling linked networks. Social Networks 32(1), 105-126.\n\n\n\n',
'To see these entries in BibTeX format, use "print(<citation>, bibtex=TRUE)", "toBibtex(.)", or set "options(citation.bibtex.max=999)".')
}
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