Nothing
################################################################################
context("CROSSPROD_SELF")
set.seed(SEED)
################################################################################
# Simulating some data
N <- 101
M <- 43
x <- matrix(rnorm(N * M, 100, 5), N)
################################################################################
test_that("equality with crossprod", {
for (t in TEST.TYPES) {
X <- `if`(t == "raw", asFBMcode(x), big_copy(x, type = t))
K <- big_crossprodSelf(X, block.size = 10)
expect_equal(K[], crossprod(X[]))
if (t == "double") {
expect_equal(crossprod(X), crossprod(X[]))
} else {
expect_error(crossprod(X, "for 'double' FBMs only"))
}
}
})
################################################################################
test_that("equality with crossprod with half of the data", {
ind <- sample(M, M / 2)
for (t in TEST.TYPES) {
X <- `if`(t == "raw", asFBMcode(x), big_copy(x, type = t))
# no scaling
K <- big_crossprodSelf(X, ind.col = ind, block.size = 10)
expect_equal(K[], crossprod(X[, ind]))
# full scaling
K2 <- big_crossprodSelf(X, fun.scaling = big_scale(), ind.col = ind,
block.size = 10)
X.scaled <- scale(X[, ind])
expect_equal(K2[], crossprod(X.scaled))
expect_equal(attr(K2, "center"), attr(X.scaled, "scaled:center"))
expect_equal(attr(K2, "scale"), attr(X.scaled, "scaled:scale"))
}
})
################################################################################
test_that("equality with crossprod with half of the data", {
ind <- sample(N, N / 2)
for (t in TEST.TYPES) {
X <- `if`(t == "raw", asFBMcode(x), big_copy(x, type = t))
# no scaling
K <- big_crossprodSelf(X, ind.row = ind, block.size = 10)
expect_equal(K[], crossprod(X[ind, ]))
# full scaling
K2 <- big_crossprodSelf(X, fun.scaling = big_scale(), ind.row = ind,
block.size = 10)
X.scaled <- scale(X[ind, ])
expect_equal(K2[], crossprod(X.scaled))
expect_equal(attr(K2, "center"), attr(X.scaled, "scaled:center"))
expect_equal(attr(K2, "scale"), attr(X.scaled, "scaled:scale"))
}
})
################################################################################
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