Random-Seeds-different-R-32bit-64-bit

# Run R code on 32 bit and 64 bit and get different results 
# You need to run the code on 32 Bit R version and then the 64 bit R (not just the code below)
# http://stackoverflow.com/questions/17881609/parallel-randomforest-with-different-results-using-dosnow
#
library(foreach)
library(doSNOW)
library(parallel)
set.seed(666)
ncores <- 4
cl <- makeCluster(ncores)
registerDoSNOW(cl)

foreach(i=1:ncores) %dopar% {
  set.seed(666)
  rnorm(1)
}

stopCluster(cl)

#---- now seed indiviual on each cluster

library(foreach)
library(doSNOW)
library(parallel)
set.seed(666)
ncores <- 4
cl <- makeCluster(ncores)
registerDoSNOW(cl)

foreach(i=1:ncores) %dopar% {
  set.seed(i)
  rnorm(1)
}

stopCluster(cl)

#-----
#   64 bit R          32 bit R
# [[1]]		         [[1]]
# [1] 0.753311	    [1] 0.8191309
# 		    
# [[2]]		         [[2]]
# [1] 0.753311	    [1] 0.8191309
# 		    
# [[3]]		         [[3]]
# [1] 0.753311	    [1] 0.8191309
# 		    
# [[4]]		         [[4]]
# [1] 0.753311	    [1] 0.8191309

#----------------------------------
#----------------------------------
#-----	     RF static seed ------

library(foreach)
library(doSNOW)
library(parallel)
library(randomForest)
set.seed(123)
ncores <- 2
cl <- makeCluster(ncores)
registerDoSNOW(cl)
nr <- 1000
x <- matrix(runif(100000), nr)
y <- gl(4, nr/4)

trainX <- x[1:800,]
trainY <- y[1:800]

testX <- x[801:nrow(x),]
testY <- y[801:length(y)]

rf <- foreach(i=1:ncores, ntree=rep(100, ncores), .packages='randomForest', .combine=combine) %dopar% {
  # seed for each node is same
  set.seed(123)
  randomForest(trainX, trainY, ntree=ntree)
}
stopCluster(cl)

pred <- predict(rf, new=testX)
pred
table(pred)

#----- RF individual seed ----------


library(foreach)
library(doSNOW)
library(parallel)
library(randomForest)
set.seed(123)
ncores <- 2
cl <- makeCluster(ncores)
registerDoSNOW(cl)
nr <- 1000
x <- matrix(runif(100000), nr)
y <- gl(4, nr/4)

trainX <- x[1:800,]
trainY <- y[1:800]

testX <- x[801:nrow(x),]
testY <- y[801:length(y)]

rf <- foreach(i=1:ncores, ntree=rep(100, ncores), .packages='randomForest', .combine=combine) %dopar% {
  # seed for each node is different
  set.seed(i)
  randomForest(trainX, trainY, ntree=ntree)
}
stopCluster(cl)

pred <- predict(rf, new=testX)
pred
table(pred)