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Merge pull request #13 from snoweye/master
0.1-7 on CRAN
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‎ChangeLog‎

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2015-11-01: Ver. 0.1-7
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* Change SPMD.CT$* to .pbd_env$SPMD.CT$*.
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2014-06-21: Ver. 0.1-7
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* Migrate to MS-MPI.
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* Get rid of jss.cls and jss.bst.

‎DESCRIPTION‎

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Package: pmclust
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Version: 0.1-7
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Date: 2014-05-24
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Title: Parallel Model-Based Clustering
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Date: 2016-06-07
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Title: Parallel Model-Based Clustering using
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Expectation-Gathering-Maximization Algorithm for Finite Mixture
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Gaussian Model
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Authors@R: c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
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"[email protected]"), person("George", "Ostrouchov", role = "aut"))
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Depends: R (>= 3.0.0), methods, rlecuyer, pbdMPI (>= 0.2-3), MASS,
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pbdSLAP (>= 0.1-9), pbdBASE (>= 0.3-0), pbdDMAT (>= 0.3-0)
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Depends: R (>= 3.0.0), pbdMPI (>= 0.3-1), pbdBASE (>= 0.4-3), pbdDMAT
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(>= 0.4-0)
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Imports: methods, MASS
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Enhances: MixSim
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LazyLoad: yes
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LazyData: yes
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Description: The pmclust aims to utilize model-based clustering (unsupervised)
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Description: Aims to utilize model-based clustering (unsupervised)
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for high dimensional and ultra large data, especially in a distributed
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manner. The package employs Rmpi to perform a
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expectation-gathering-maximization (EGM) algorithm
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manner. The code employs pbdMPI to perform a
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expectation-gathering-maximization algorithm
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for finite mixture Gaussian
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models. The unstructured dispersion matrices are assumed in the Gaussian
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models. The implementation is default in the single program multiple
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data (SPMD) programming model. The code can be executed through Rmpi and
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independent to most MPI applications. See the High Performance
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Statistical Computing (HPSC) website for more information, documents
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models. The unstructured dispersion matrices are assumed in the
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Gaussian models. The implementation is default in the single program
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multiple data programming model. The code can be executed
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through pbdMPI and independent to most MPI applications.
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See the High Performance
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Statistical Computing website for more information, documents
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and examples.
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License: GPL (>= 2)
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URL: http://r-pbd.org/
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BugReports: http://group.r-pbd.org/
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MailingList: Please send questions and comments regarding pbdR to
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[email protected]
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Packaged: 2015-09-17 18:39:55 UTC; snoweye
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NeedsCompilation: yes
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Packaged: 2016-01-24 20:10:34 UTC; snoweye
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Author: Wei-Chen Chen [aut, cre],
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George Ostrouchov [aut]
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Maintainer: Wei-Chen Chen <[email protected]>
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NeedsCompilation: yes

‎NAMESPACE‎

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useDynLib(pmclust)
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import(methods, rlecuyer, pbdMPI, MASS, pbdSLAP, pbdBASE, pbdDMAT)
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import(methods, MASS, pbdBASE, pbdDMAT)
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importFrom(stats,rnorm)
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importFrom(pbdMPI,spmd.allgather.integer)
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importFrom(pbdMPI,spmd.allgather.object)
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importFrom(pbdMPI,spmd.allreduce.integer)
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importFrom(pbdMPI,spmd.allreduce.double)
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importFrom(pbdMPI,spmd.bcast.integer)
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importFrom(pbdMPI,spmd.bcast.object)
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importFrom(pbdMPI,spmd.comm.rank)
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importFrom(pbdMPI,spmd.comm.size)
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importFrom(pbdMPI,spmd.isend.double)
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importFrom(pbdMPI,spmd.recv.double)
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importFrom(pbdMPI,spmd.wait)
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importFrom(pbdMPI,comm.all)
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importFrom(pbdMPI,comm.any)
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importFrom(pbdMPI,comm.cat)
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importFrom(pbdMPI,comm.size)
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importFrom(pbdMPI,comm.stop)
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importFrom(pbdMPI,get.jid)
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# exportPattern("^[[:alpha:]]+")
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# exportPattern("^[\\.]")

‎R/000_globalVariables.r‎

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### Do not delete this file and file name!!
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### This file should be loaded before all other *.r files.
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### This is to avoid the false positive messages from R CMD check.
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### "no visible binding for global variable"
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### Suggested by Prof Brian Ripley
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### ?globalVariables
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if(getRversion() >= "2.15.1"){
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utils::globalVariables(c(".pmclustEnv", ".PMC.CT"))
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}

‎R/00_pmclust.r‎

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pmclust <- function(X = NULL, K = 2, MU = NULL,
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algorithm = .PMC.CT$algorithm, RndEM.iter = .PMC.CT$RndEM.iter,
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CONTROL = .PMC.CT$CONTROL, method.own.X = .PMC.CT$method.own.X,
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rank.own.X = .SPMD.CT$rank.source, comm = .SPMD.CT$comm){
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rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm){
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if(comm.all(is.null(X))){
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# Check global matrix.
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A <- exists("X.spmd", envir = .GlobalEnv)
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pkmeans <- function(X = NULL, K = 2, MU = NULL,
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algorithm = c("kmeans", "kmeans.dmat"),
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CONTROL = .PMC.CT$CONTROL, method.own.X = .PMC.CT$method.own.X,
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rank.own.X = .SPMD.CT$rank.source, comm = .SPMD.CT$comm){
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rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm){
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if(comm.all(is.null(X))){
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# Check global matrix.
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A <- exists("X.spmd", envir = .GlobalEnv)

‎R/00_pmclust_internal.r‎

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pmclust.internal <- function(X = NULL, K = 2, MU = NULL,
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algorithm = .PMC.CT$algorithm.gbd, RndEM.iter = .PMC.CT$RndEM.iter,
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CONTROL = .PMC.CT$CONTROL, method.own.X = .PMC.CT$method.own.X,
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rank.own.X = .SPMD.CT$rank.source, comm = .SPMD.CT$comm){
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rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm){
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# Check.
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if(! (algorithm[1] %in% .PMC.CT$algorithm.gbd)){
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comm.stop("The algorithm is not supported")

‎R/00_pmclust_internal_dmat.r‎

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pmclust.internal.dmat <- function(X = NULL, K = 2, MU = NULL,
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algorithm = .PMC.CT$algorithm.dmat, RndEM.iter = .PMC.CT$RndEM.iter,
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CONTROL = .PMC.CT$CONTROL, method.own.X = .PMC.CT$method.own.X,
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rank.own.X = .SPMD.CT$rank.source, comm = .SPMD.CT$comm){
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rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm){
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# Check.
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if(! (algorithm[1] %in% .PMC.CT$algorithm.dmat)){
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comm.stop("The algorithm is not supported.")

‎R/01_as_dmat.r‎

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### Convert X.spmd to X.dmat
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as.dmat <- function(X.spmd, bldim = pbdDMAT::.BLDIM, ICTXT = pbdDMAT::.ICTXT,
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comm = .SPMD.CT$comm){
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as.dmat <- function(X.spmd, bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT,
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comm = .pbd_env$SPMD.CT$comm){
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X.spmd <- load.balance(X.spmd, comm = comm)
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N.spmd <- nrow(X.spmd)

‎R/01_as_spmd.r‎

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### Convert X.dmat to X.spmd
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as.spmd <- function(X.dmat, comm = .SPMD.CT$comm){
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as.spmd <- function(X.dmat, comm = .pbd_env$SPMD.CT$comm){
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COMM.SIZE <- comm.size(comm)
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### check data.
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### block-cyclic in context 2.
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bldim.new <- c(ceiling(nrow(X.dmat) / COMM.SIZE), ncol(X.dmat))
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X.dmat <- pbdDMAT::dmat.reblock(X.dmat, bldim = bldim.new, ICTXT = 2)
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X.dmat <- pbdDMAT::reblock(X.dmat, bldim = bldim.new, ICTXT = 2)
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### copy to spmd.
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if(pbdBASE::base.ownany(dim(X.dmat), pbdDMAT::bldim(X.dmat), ICTXT = 2)){

‎R/02_convert_data.r‎

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### For general methods.
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convert.data <- function(X, method.own.X = .PMC.CT$method.own.X,
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rank.own.X = .SPMD.CT$rank.source, comm = .SPMD.CT$comm){
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rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm){
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COMM.SIZE <- spmd.comm.size(comm)
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COMM.RANK <- spmd.comm.rank(comm)
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