View source: R/collectDistribute.R
collectTriangularMatrix | R Documentation |
collectTriangularMatrix
retrieves a distributed symmetric or
triangular matrix from the slave
processes, reconstructing the blocks correctly on the master process.
Objects can be copied from environments, lists, and
ReferenceClass objects as well as the global environment on the slave processes.
collectTriangularMatrix(objName, objPos = '.GlobalEnv', n, h = 1)
objName |
an object name, given as a character string, giving the name of the object on the slave processes. |
objPos |
where to look for the object, given as a character string (unlike
|
n |
a positive integer, the number of rows (and columns) of the matrix. |
h |
a positive integer, the block replication factor, |
collectTriangularMatrix
returns a matrix of dimension, n
\times n
. Note that for lower triangular matrices, the upper
triangle is non-zero and is filled with the transpose of the lower
triangle, and vice versa for upper triangular matrices.
pull
collectVector
collectRectangularMatrix
collectDiagonal
distributeVector
## Not run:
if(require(fields)) {
nProc <- 3
n <- nrow(SN2011fe_subset)
inputs <- c(as.list(SN2011fe_subset), as.list(SN2011fe_newdata_subset),
nu =2)
# initialize the problem
prob <- krigeProblem$new("prob", h_n = 1, numProcesses = nProc, n = n,
meanFunction = SN2011fe_meanfunc, covFunction = SN2011fe_covfunc, inputs = inputs,
params = SN2011fe_mle$par, data = SN2011fe_subset$flux, packages =
c("fields"))
# calculate log density, primarily so Cholesky gets calculated
prob$calcLogDens()
C <- collectTriangularMatrix('C', "prob", n = n, h = 1)
L <- collectTriangularMatrix('L', "prob", n = n, h = 1)
C[1:5, 1:5]
L[1:5, 1:5]
}
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.