snow integration

How to use the <pkg>bnlearn</pkg> package with the parallel computing
  environment provided by the <pkg>snow</pkg> package.

internal

Bayesian network structure learning
  via constraint-based (also known as 'conditional
  independence') and score-based algorithms.
  This package implements the Grow-Shrink (GS)
  algorithm, the Incremental Association (IAMB)
  algorithm, the Interleaved-IAMB (Inter-IAMB)
  algorithm, the Fast-IAMB (Fast-IAMB) algorithm
  and the Hill-Climbing (HC) greedy search
  algorithm for both discrete and gaussian networks,
  along with many score functions and conditional
  independence tests.
  Some utility functions (model comparison and
  manipulation, random data generation, arc
  orientation testing) are also included.

Marco Scutari

bnlearn

Bayesian network structure learning

snow integration function

# load snow, bnlearn and rsprng (for parallel random number
# generation, just in case it's needed); start LAM/MPI via
# lamboot if using an MPI cluster.
> library(snow)
> library(bnlearn)
> library(rsprng)
# initialize the cluster ("socket" and "PVM" clusters are fine, too).
> cl &lt;- makeCluster(2, type = "MPI")
Loading required package: Rmpi
        2 slaves are spawned successfully. 0 failed.
> clusterSetupSPRNG(cl)
# load the data.
> data(learning.test)
# call a learning function passing the cluster object (the
# return value of the previous makeCluster() call) as a
# parameter.
> res = gs(learning.test, cluster = cl)
# note that the number of test is evenly divided between
# the two nodes of the cluster.
> clusterEvalQ(cl, .test.counter)
[[1]]
[1] 46<p></p><p>[[2]]
[1] 40
# a few tests are still executed by this process.
> .test.counter
[1] 4
# stop the cluster.
> stopCluster(cl)
[1] 1</p>

snow integration: bnlearn - snow package integration

Description

Arguments

Parallel computing for constraint-based algorithms