clusterApply.gdsn {gdsfmt} | R Documentation

## Apply functions over matrix margins in parallel

### Description

Return a vector or list of values obtained by applying a function to margins of a GDS matrix in parallel.

### Usage

    clusterApply.gdsn(cl, gds.fn, node.name, margin, FUN, selection=NULL,
        as.is=c("list", "none", "integer", "double", "character", "logical", "raw"),
        var.index=c("none", "relative", "absolute"), .useraw=FALSE,
        .value=NULL, .substitute=NULL, ...)

### Arguments

`cl` | a cluster object, created by this package or by the package parallel
`gds.fn` | the file name of a GDS file
`node.name` | a character vector indicating GDS node path
`margin` | an integer giving the subscripts which the function will be applied over. E.g., for a matrix 1 indicates rows, 2 indicates columns
`FUN` | the function to be applied
`selection` | a list or NULL; if a list, it is a list of logical vectors according to dimensions indicating selection; if NULL, uses all data
`as.is` | returned value: a list, an integer vector, etc
`var.index` | if `"none"`, call `FUN(x, ...)` without an index; if `"relative"` or `"absolute"`, add an argument to the user-defined function `FUN` like `FUN(index, x, ...)` where `index` in the function is an index starting from 1: `"relative"` for indexing in the selection defined by `selection`, `"absolute"` for indexing with respect to all data
`.useraw` | use R RAW storage mode if integers can be stored in a byte, to reduce memory usage
`.value` | a vector of values to be replaced in the original data array, or NULL for nothing
`.substitute` | a vector of values after replacing, or NULL for nothing; `length(.substitute)` should be one or `length(.value)`; if `length(.substitute)` = `length(.value)`, it is a mapping from `.value` to `.substitute`
`...` | optional arguments to `FUN`

### Details

The algorithm of applying is optimized by blocking the computations to exploit the high-speed memory instead of disk.

### Value

A vector or list of values.

### Author(s)

Xiuwen Zheng

### References

http://github.com/zhengxwen/gdsfmt

### See Also

`apply.gdsn`

### Examples

    ###########################################################
    # prepare a GDS file

    # cteate a GDS file
    f <- createfn.gds("test1.gds")

    (n <- add.gdsn(f, "matrix", val=matrix(1:(10*6), nrow=10)))
    read.gdsn(index.gdsn(f, "matrix"))

    closefn.gds(f)

    # cteate the GDS file "test2.gds"
    (f <- createfn.gds("test2.gds"))

    X <- matrix(1:50, nrow=10)
    Y <- matrix((1:50)/100, nrow=10)
    Z1 <- factor(c(rep(c("ABC", "DEF", "ETD"), 3), "TTT"))
    Z2 <- c(TRUE, FALSE, TRUE, FALSE, TRUE)

    node.X <- add.gdsn(f, "X", X)
    node.Y <- add.gdsn(f, "Y", Y)
    node.Z1 <- add.gdsn(f, "Z1", Z1)
    node.Z2 <- add.gdsn(f, "Z2", Z2)
    f

    closefn.gds(f)

    ###########################################################
    # apply in parallel

    library(parallel)

    # Use option cl.core to choose an appropriate cluster size.
    cl <- makeCluster(getOption("cl.cores", 2L))

    # Apply functions over rows or columns of matrix

    clusterApply.gdsn(cl, "test1.gds", "matrix", margin=1, FUN=function(x) x)

    clusterApply.gdsn(cl, "test1.gds", "matrix", margin=2, FUN=function(x) x)

    clusterApply.gdsn(cl, "test1.gds", "matrix", margin=1,
        selection = list(rep(c(TRUE, FALSE), 5), rep(c(TRUE, FALSE), 3)),
        FUN=function(x) x)

    clusterApply.gdsn(cl, "test1.gds", "matrix", margin=2,
        selection = list(rep(c(TRUE, FALSE), 5), rep(c(TRUE, FALSE), 3)),
        FUN=function(x) x)

    # Apply functions over rows or columns of multiple data sets

    clusterApply.gdsn(cl, "test2.gds", c("X", "Y", "Z1"), margin=c(1, 1, 1),
        FUN=function(x) x)

    # with variable names
    clusterApply.gdsn(cl, "test2.gds", c(X="X", Y="Y", Z="Z2"), margin=c(2, 2, 1),
        FUN=function(x) x)

    # stop clusters
    stopCluster(cl)

    # delete the temporary file
    unlink(c("test1.gds", "test2.gds"), force=TRUE)

[Package gdsfmt version 1.30.0 Index]