Nothing
## ---- echo = FALSE-------------------------------------------------------
library(liquidSVM)
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval=T)
set.seed(123)
options(digits=3)
myOwnCache <- function(name, envir=parent.frame(),vignette_dir="."){
filename <- paste0(vignette_dir,'/demo_cache/',name,".R")
if(exists(name, envir=envir)){
dput(get(name, envir=envir), file=filename)
}else if(file.exists(filename)){
#message("Loading")
assign(name,dget(filename),envir=envir)
}else{
warning(paste0("Did not have or load ",name))
}
}
## ------------------------------------------------------------------------
compilationInfo()
## ----eval=F--------------------------------------------------------------
# library(parallel)
# ## how big should the cluster be
# workers <- 2
# cl <- makeCluster(workers)
# ## how many threads should each worker use
# threads <- 2
#
# sml <- liquidData('reg-1d')
# clusterExport(cl, c("sml","threads","workers"))
# obj <- parLapply(cl, 1:workers, function(i) {
# library(liquidSVM)
# ## to make it interesting use disjoint parts of sml$train
# data <- sml$train[ seq(i,nrow(sml$train),workers) , ]
# ## the second argument to threads sets the offset of cores
# model <- lsSVM(Y~., data, threads=c(threads,threads*(i-1)) )
# ## finally return the serialized solution
# serialize.liquidSVM(model)
# })
# for(i in 1:workers){
# ## get the solution in the master session
# model <- unserialize.liquidSVM(obj[[i]])
# print(errors(test(model,sml$test)))
# }
# #> val_error
# #> 0.00542
# #> val_error
# #> 0.00583
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