Nothing
## Contents:
# sot_avg_exact, sot_avg_exact_single, sot_avg_exact_list
################################################################################
# calculate the spillover table average exactly, automatic detection whether 'list' (rolling windows) or 'single' use
sot_avg_exact <- function(Sigma,A,ncores=1)
# Sigma: either a covariance matrix or a list thereof
# A: either an array consisting of MA coefficient matrices or a list thereof
# ...: one might specify
# avg_only: in case of 'single', should the value be only the average or a list of average, minimum, and maximum?
# helpers: a list delivered by helpers_avg_exact which takes some time to be calculated and depends only on the number of variables (and therefore might/should be calculated only once)
# ncores: number of cores, only used in 'list' case
# missing or 1: no parallelization
# 0: automatic detection of number of cores
# other integer: use this number
{
if (is.list(Sigma)) restructure_list(sot_avg_exact_list(Sigma,A,dim(Sigma[[1]])[1],dim(A[[1]])[3],ncores=ncores))
else sot_avg_exact_single(Sigma,A,dim(Sigma)[1],dim(A)[3])
}
################################################################################
# calculates the spillover table average exactly, single version
sot_avg_exact_single <- function(Sigma,A,N=dim(Sigma)[1],H=dim(A)[3],helpers=helpers_avg_exact(N))
{
# incomplete: handle the case when N is small
# or do that within the C function (quite unnecessary)
#if(N<5) return(VDT_DYKW(Sigma=Sigma,A=A,perm=perm)) else require(gtools)
scaling_factor <- 1/sqrt(.Call(C_fev,Sigma,A,N,H))
res <- .Call(C_SOT_avg,.Call(C_scaleSigma,Sigma,scaling_factor,N),.Call(C_scaleA,A,scaling_factor,N,H),N,H,helpers$NcK,helpers$cumpos,helpers$gensets-1L,helpers$NminusOne)
for (i in 1:9)
{
dim(res[[i]]) <- c(N,N)
dimnames(res[[i]]) <- dimnames(Sigma)
res[[i]] <- 100*res[[i]]
}
res
}
################################################################################
# calculates the spillover table average exactly, list version
sot_avg_exact_list <- function(Sigma,A,N=dim(Sigma[[1]])[1],H=dim(A[[1]])[3],helpers=helpers_avg_exact(N),ncores=1)
{
len <- length(Sigma)
res <- vector("list",len)
if ( (ncores!=1) && (!requireNamespace("parallel")) )
{
print("Parallelization not possible because package 'parallel' is not installed. Using single core version instead.")
ncores <- 1
}
if (ncores==1) # no parallelization
{
for (i in 1:len)
res[[i]] <- sot_avg_exact_single(Sigma[[i]],A[[i]],N,H,helpers)
}
else # parallelization
{
if (ncores==0)
{
ncores <- detectCores() # determine number oc cores
cat("Number of cores detected:",ncores,"\n")
}
ncores <- min(ncores,len)
splitted <- splitIndices(len,ncores) # determine how to distribute the workload
cl <- makeCluster(ncores) # create cluster
clusterEvalQ(cl, library(fastSOM)) # load package fastSOM on every core
clusterExport(cl,c("Sigma","A","N","H","helpers"),envir=environment()) # send variables to every core
tmp <- clusterApply(cl,1:ncores,function(ind) sot_avg_exact_list(Sigma[splitted[[ind]]],A[splitted[[ind]]],N,H,helpers,1)) # do parallel jobs
stopCluster(cl) # close Cluster
for (i in 1:ncores) # putting results together
{
res[splitted[[i]]] <- tmp[[i]]
}
}
names(res) <- names(Sigma)
res
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.