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library(foreach)
n <- 10
nrows <- 5
ncols <- 5
# vector example
set.seed(17)
x <- numeric(n)
for (i in seq(along=x))
x[i] <- rnorm(1)
set.seed(17)
y <- foreach(icount(n), .combine='c') %do%
rnorm(1)
cat('results of vector example:\n')
print(identical(x, y))
# list example
set.seed(17)
x <- vector('list', length=n)
for (i in seq(length=n))
x[i] <- list(rnorm(10))
set.seed(17)
y <- foreach(icount(n)) %do%
rnorm(10)
cat('results of list example:\n')
print(identical(x, y))
# matrix example
set.seed(17)
cols <- vector('list', length=ncols)
for (i in seq(along=cols))
cols[i] <- list(rnorm(nrows))
x <- do.call('cbind', cols)
set.seed(17)
y <- foreach(icount(ncols), .combine='cbind') %do%
rnorm(nrows)
cat('results of matrix example:\n')
dimnames(y) <- NULL
print(identical(x, y))
# another matrix example
set.seed(17)
cols <- vector('list', length=ncols)
for (i in seq(along=cols)) {
r <- numeric(nrows)
for (j in seq(along=r))
r[j] <- rnorm(1)
cols[i] <- list(r)
}
x <- do.call('cbind', cols)
set.seed(17)
y <- foreach(icount(ncols), .combine='cbind') %:%
foreach(icount(nrows), .combine='c') %do%
rnorm(1)
cat('results of another matrix example:\n')
dimnames(y) <- NULL
print(identical(x, y))
# ragged matrix example
set.seed(17)
x <- vector('list', length=ncols)
for (i in seq(along=x))
x[i] <- list(rnorm(i))
set.seed(17)
y <- foreach(i=icount(ncols)) %do%
rnorm(i)
cat('results of ragged matrix example:\n')
print(identical(x, y))
# another ragged matrix example
set.seed(17)
x <- vector('list', length=ncols)
for (i in seq(along=x)) {
r <- numeric(i)
for (j in seq(along=r))
r[j] <- rnorm(1)
x[i] <- list(r)
}
set.seed(17)
y <- foreach(i=icount(ncols)) %:%
foreach(icount(i), .combine='c') %do%
rnorm(1)
cat('results of another ragged matrix example:\n')
print(identical(x, y))
# filtering example
set.seed(17)
a <- rnorm(10)
# C-style approach
x <- numeric(length(a))
n <- 0
for (i in a) {
if (i > 0) {
n <- n + 1
x[n] <- i
}
}
length(x) <- n
# Vector approach
y <- a[a > 0]
# foreach approach
z <- foreach(i=a, .combine='c') %:% when(i > 0) %do% i
cat('results of filtering example:\n')
print(identical(x, y))
print(identical(x, z))
# Define a function that creates an iterator that returns chunks of a vecto
ivector <- function(x, chunksize) {
n <- length(x)
i <- 1
nextEl <- function() {
if (n <= 0) stop('StopIteration')
chunks <- ceiling(n / chunksize)
m <- ceiling(n / chunks)
r <- seq(i, length=m)
i <<- i + m
n <<- n - m
x[r]
}
obj <- list(nextElem=nextEl)
class(obj) <- c('abstractiter', 'iter')
obj
}
# another filtering example
set.seed(17)
a <- rnorm(10000)
# Vector approach
x <- a[a > 0]
# foreach with vectorization, limiting vector lengths to 1000
y <- foreach(a=ivector(a, 1000), .combine='c') %do%
a[a > 0]
cat('results of another filtering example:\n')
print(identical(x, y))
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