View source: R/collectDistribute.R
collectRectangularMatrix | R Documentation |
collectRectangularMatrix
retrieves a distributed rectangular 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. WARNING: do not use with a distributed symmetric square matrix; instead
use collectTriangularMatrix
.
collectRectangularMatrix(objName, objPos = '.GlobalEnv', n1, n2, h1 = 1, h2 = 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
|
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 relevant for the rows of the matrix. |
h2 |
a positive integer, the block replication factor relevant for the columns of the matrix. |
collectRectangularMatrix
returns a matrix of dimension, n1
\times n2
.
pull
collectVector
collectTriangularMatrix
collectDiagonal
distributeVector
## Not run:
if(require(fields)) {
nProc <- 3
n <- nrow(SN2011fe_subset)
m <- nrow(SN2011fe_newdata_subset)
inputs <- c(as.list(SN2011fe_subset), as.list(SN2011fe_newdata_subset),
nu =2)
# initialize the problem
prob <- krigeProblem$new("prob", h_n = 1, h_m = 1, numProcesses =
nProc, n = n, m = m,
meanFunction = SN2011fe_meanfunc, predMeanFunction = SN2011fe_predmeanfunc,
covFunction = SN2011fe_covfunc, crossCovFunction = SN2011fe_crosscovfunc,
predCovFunction = SN2011fe_predcovfunc, params = SN2011fe_mle$par,
inputs = inputs, data = SN2011fe_subset$flux, packages = c("fields"))
# do predictions, primarily so cross-covariance gets calculated
pred <- prob$predict(ret = TRUE, verbose = TRUE)
crossC <- collectRectangularMatrix('crossC', "prob", n1 = n, n2 = m,
h1 = 1, h2 = 1)
crossC[1:5, 1:5]
}
## End(Not run)
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