Nothing
context("parallelComputeDistMat")
test_that("Parallel computeDistMat returns the same values as unparallelized version", {
set.seed(123)
dat = matrix(rnorm(n = 20), 5, byrow = TRUE)
a1 = computeDistMat(dat)
a2 = parallelComputeDistMat(dat, batches = 2)
expect_true(all.equal(as.vector(a1), as.vector(a2)))
b1 = computeDistMat(dat, method = "max")
b2 = parallelComputeDistMat(dat, method = "max", batches = 5)
expect_true(all.equal(as.vector(b1), as.vector(b2)))
})
test_that("Works with kernel / knn", {
set.seed(123)
trn = matrix(rnorm(n = 20), 5, byrow = TRUE)
tst = matrix(rnorm(n = 40), 10, byrow = TRUE)
mod = classiKnn(c(1, 1, 2, 2, 1), fdata = trn, knn = 1L)
pred = predict(mod, newdata = tst)
pred2 = predict(mod, newdata = tst, parallel = TRUE, batches = 2)
expect_true(all.equal(pred, pred2))
mod = classiKnn(c(1, 1, 2, 2, 1), fdata = trn, knn = 1L, metric = "shortEuclidean",
dmin = 0, dmax = 1/2)
pred = predict(mod, newdata = tst)
pred2 = predict(mod, newdata = tst, parallel = TRUE, batches = 2)
expect_true(all.equal(pred, pred2))
if (require("dtw")) {
mod = classiKnn(c(1, 1, 2, 2, 1), fdata = trn, knn = 2L, metric = "dtw")
pred = predict(mod, newdata = tst)
expect_message({pred2 = predict(mod, newdata = tst, parallel = TRUE, batches = 3)}, "Loading")
expect_true(all.equal(pred, pred2))
}
})
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