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# This is a simple parallel random forest example, using Andy Liaw's
# randomForest package.
suppressMessages(library(doMPI))
suppressMessages(library(randomForest))
# Create and register an MPI cluster
cl <- startMPIcluster()
registerDoMPI(cl)
# Define a parallel randomForest function
rforest <- function(x, y=NULL, xtest=NULL, ytest=NULL, ntree=500, ...) {
initWorkers <- function() library(randomForest)
opts <- list(initEnvir=initWorkers)
foreach(i=idiv(ntree, chunks=getDoParWorkers()),
.combine='combine', .multicombine=TRUE, .inorder=FALSE,
.options.mpi=opts) %dopar% {
randomForest:::randomForest.default(x, y, xtest, ytest, ntree=i, ...)
}
}
# Create a matrix and factor as input
m <- 200; n <- 120
x <- matrix(rnorm(m * n), m, n)
y <- gl(10, m/10)
# Execute rforest and then print the resulting model object
rfit <- rforest(x, y)
print(rfit)
# Shutdown the cluster and quit
closeCluster(cl)
mpi.quit()
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