## Make sure you have the .Rprofile file from npRmpi/inst/ in your
## current directory or home directory. It is necessary.

## To run this on systems with OPENMPI installed and working, try
## mpirun -np 2 R CMD BATCH npcdistml_npRmpi. Check the time in the
## output file foo.Rout (the name of this file with extension .Rout),
## then try with, say, 4 processors and compare run time.

## Initialize master and slaves.

mpi.bcast.cmd(np.mpi.initialize(),
              caller.execute=TRUE)

## Turn off progress i/o as this clutters the output file (if you want
## to see search progress you can comment out this command)

mpi.bcast.cmd(options(np.messages=FALSE),
              caller.execute=TRUE)

## Load your data and broadcast it to all slave nodes

mpi.bcast.cmd(library(MASS),
              caller.execute=TRUE)

mpi.bcast.cmd(set.seed(42),
              caller.execute=TRUE)

n <- 2000

rho <- 0.25
mu <- c(0,0)
Sigma <- matrix(c(1,rho,rho,1),2,2)
data <- mvrnorm(n=n, mu, Sigma)
mydat <- data.frame(x=data[,2],y=data[,1])

mpi.bcast.Robj2slave(mydat)

## A conditional distribution estimation example. 

t <- system.time(mpi.bcast.cmd(bw <- npcdistbw(y~x,
                                               bwmethod="cv.ls",
                                               data=mydat),
                               caller.execute=TRUE))

summary(bw)

t <- t + system.time(mpi.bcast.cmd(model <- npcdist(bws=bw),
                                   caller.execute=TRUE))

summary(model)

cat("Elapsed time =", t[3], "\n")

## Clean up properly then quit()

mpi.bcast.cmd(mpi.quit(),
              caller.execute=TRUE)
