remoteConstructRnormVector: Create Distributed Vector or Matrix of Random Normals

In bigGP: Distributed Gaussian Process Calculations

Create Distributed Vector or Matrix of Random Normals

Description

remoteConstructRnormVector constructs a distributed vector of standard normal random variables, while remoteConstructRnormMatrix constructs a distributed matrix. The output object can both be contained within environments or ReferenceClass objects as well as the global environment on the slave processes.

Usage

remoteConstructRnormVector(objName, objPos = ".GlobalEnv", n, h = 1)
 remoteConstructRnormMatrix(objName, objPos = ".GlobalEnv", n1, n2, h1 = 1, h2 = 1)

Arguments

objName	the name to use for the vector or matrix, on the slave processes.
objPos	where to do the assignment of the output matrix or vector, given as a character string (unlike assign). This can indicate an environment or a ReferenceClass object.
n	a positive integer, the length of the vector
h	a positive integer, the block replication factor, h, relevant for the vector
n1	a positive integer, the number of rows of the matrix.
n2	a positive integer, the number of columns of the matrix.
h1	a positive integer, the block replication factor, h, relevant for the rows of the matrix.
h2	a positive integer, the block replication factor, h, relevant for the columns of the matrix.

Warning

Note that a vector and a one-column matrix are stored differently, with padded columns included for the matrix. For other distributed computation functions, providing the argument n2 = NULL indicates the input is a vector, while n2 = 1 indicates a one-column matrix.

See Also

bigGP

Examples

## Not run: 
if(require(fields)) {
  SN2011fe <- SN2011fe_subset
  SN2011fe_newdata <- SN2011fe_newdata_subset
  SN2011fe_mle <- SN2011fe_mle_subset
  nProc <- 3
n <- nrow(SN2011fe)
m <- nrow(SN2011fe_newdata)
nu <- 2
inputs <- c(as.list(SN2011fe), as.list(SN2011fe_newdata), nu = nu)
prob <- krigeProblem$new("prob", numProcesses = nProc, n = n, m = m,
predMeanFunction = SN2011fe_predmeanfunc, crossCovFunction = SN2011fe_crosscovfunc,  
predCovFunction = SN2011fe_predcovfunc, meanFunction = SN2011fe_meanfunc, 
covFunction = SN2011fe_covfunc,  inputs = inputs, params = SN2011fe_mle$par, 
data = SN2011fe$flux, packages = c("fields"))
remoteCalcChol(matName = 'C', cholName = 'L', matPos = 'prob',
  cholPos = 'prob', n = n, h = prob$h_n)
remoteConstructRnormVector('z', n = n, h = prob$h_n)
remoteMultChol(cholName = 'L', inputName = 'z', outputName = 'result',
cholPos = 'prob', n1 = n, h1 = prob$h_n)
realiz <- collectVector('result', n = n, h = prob$h_n)

r = 10
remoteConstructRnormMatrix('z2', n1 = n, n2 = r, h1 = prob$h_n, h2 = 1)
remoteMultChol(cholName = 'L', inputName = 'z2', outputName = 'result2',
cholPos = 'prob', n1 = n, n2 = r, h1 = prob$h_n, h2 = 1)
realiz2 <- collectRectangularMatrix('result2', n1 = prob$n, n2 = r, h1
= prob$h_n, h2 = 1)
}

## End(Not run)

bigGP documentation built on April 12, 2025, 1:22 a.m.