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test.R
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test.R
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library(testthat)
if (exists("testing")) {
setwd(cwd)
dyn.load("libKMCUDA.so")
context("K-means")
test_that("Random",{
set.seed(42)
samples <- replicate(4, runif(16000))
result = .External("kmeans_cuda", samples, 50, tolerance=0.01,
init="random", seed=777, verbosity=2)
kmcuda_asses = replicate(1, result$assignments)
attach(kmeans(samples, result$centroids, iter.max=1))
reasses = length(intersect(kmcuda_asses, cluster)) / length(cluster)
print(sprintf("Reassignments: %f", reasses))
expect_lt(reasses, 0.01)
})
test_that("SingleDeviceKmeans++Lloyd",{
set.seed(42)
samples <- replicate(4, runif(16000))
result = .External("kmeans_cuda", samples, 50, yinyang_t=0, device=1,
init="kmeans++", seed=777, verbosity=2)
kmcuda_asses = replicate(1, result$assignments)
attach(kmeans(samples, result$centroids, iter.max=1))
reasses = length(intersect(kmcuda_asses, cluster)) / length(cluster)
print(sprintf("Reassignments: %f", reasses))
expect_lt(reasses, 0.01)
})
test_that("MultiSamples",{
set.seed(42)
samples1 <- replicate(4, runif(16000))
samples2 <- replicate(4, runif(16000))
result = .External("kmeans_cuda", list(samples1, samples2), 50,
init="kmeans++", seed=777, verbosity=2)
kmcuda_asses = replicate(1, result$assignments)
expect_equal(length(kmcuda_asses), 32000)
attach(kmeans(rbind(samples1, samples2), result$centroids, iter.max=1))
reasses = length(intersect(kmcuda_asses, cluster)) / length(cluster)
print(sprintf("Reassignments: %f", reasses))
expect_lt(reasses, 0.01)
})
test_that("AFK-MC2",{
set.seed(42)
samples <- replicate(4, runif(16000))
result = .External("kmeans_cuda", samples, 50, tolerance=0.01,
init=c("afkmc2", 100), seed=777, verbosity=2)
kmcuda_asses = replicate(1, result$assignments)
attach(kmeans(samples, result$centroids, iter.max=1))
reasses = length(intersect(kmcuda_asses, cluster)) / length(cluster)
print(sprintf("Reassignments: %f", reasses))
expect_lt(reasses, 0.01)
})
test_that("ImportCentroids",{
set.seed(42)
samples <- replicate(4, runif(16000))
centroids <- replicate(4, runif(50))
result = .External("kmeans_cuda", samples, 50, tolerance=0.01,
init=centroids, seed=777, verbosity=2)
kmcuda_asses = replicate(1, result$assignments)
attach(kmeans(samples, result$centroids, iter.max=1))
reasses = length(intersect(kmcuda_asses, cluster)) / length(cluster)
print(sprintf("Reassignments: %f", reasses))
expect_lt(reasses, 0.01)
})
} else {
testing <- TRUE
cwd <- getwd()
thisFile <- function() {
cmdArgs <- commandArgs(trailingOnly=FALSE)
needle <- "--file="
match <- grep(needle, cmdArgs)
if (length(match) > 0) {
return(normalizePath(sub(needle, "", cmdArgs[match])))
} else {
return(normalizePath(sys.frames()[[1]]$ofile))
}
}
test_results <- test_dir(dirname(thisFile()), reporter="summary")
}