Nothing
################################################################################
context("APPLY")
set.seed(SEED)
################################################################################
# Simulating some data
N <- 73
M <- 4300
x <- matrix(rnorm(N * M, mean = 100, sd = 5), N)
################################################################################
test_that("equality with other functions", {
for (t in TEST.TYPES) {
X <- `if`(t == "raw", asFBMcode(x), big_copy(x, type = t))
# get the means of each column
colmeans <- big_apply(X, function(x, ind) colMeans(x[, ind]),
a.combine = "c", ncores = test_cores())
expect_equal(colmeans, colMeans(X[]))
# get the norms of each column
colnorms <- big_apply(X, function(x, ind) sqrt(colSums(x[, ind]^2)),
a.combine = "c", ncores = test_cores())
expect_equal(colnorms, sqrt(colSums(X[]^2)))
# get the sums of each row
rowsums <- big_apply(X, function(x, ind) rowSums(x[, ind]),
a.combine = "plus", ncores = test_cores())
expect_equal(rowsums, rowSums(X[]))
# get the maximum element of X (in absolute value)
maxabs <- max(big_apply(X, function(x, ind) max(abs(x[, ind])),
a.combine = "c", ncores = test_cores()))
expect_equal(maxabs, max(abs(X[])))
# no combine
size <- sample(c(1, sample(M, size = 1), M), 1)
no_comb <- big_apply(X, function(x, ind) colMeans(x[, ind, drop = FALSE]),
ncores = test_cores(), block.size = size)
expect_equal(do.call(c, no_comb), colmeans)
# big_parallelize()
no_comb2 <- big_parallelize(X, function(x, ind) {
colMeans(x[, ind, drop = FALSE])
}, ncores = test_cores())
expect_equal(unlist(no_comb2), colmeans)
}
})
################################################################################
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