library (lasvmR)
context("LASVM Parametertest")
test_that("walltime works roughly as expected", {
# generate synthetic data set (see some stack overflow question)
# generate original model via ./la_svm -o 0 -t 2 -s 1 -p 5 -g 2 -c 2 ./synthetical.sparse.train lasvm.model
# and then predictions via ./la_test ./synthetical.sparse.test ./lasvm.model predictions
# NOTE: the original LASVM (v1.1) has a bug, and will not save the predictions. you just need to insert
# a fprintf (fp, "%d\n", (int)y); at the end of the loop in the predict routine.
# generate 2 clusters
set.seed(101)
qx = rnorm(100, mean = -3, sd = 1) - 1
qy = rnorm(100, mean = -3, sd = 1) - 1
px = rnorm(100, mean = 3, sd = 1) + 1
py = rnorm(100, mean = 3, sd = 1) + 1
traindata = rbind( cbind(px, py), cbind(qx, qy) )
trainlabel = sign (traindata[,1])
set.seed(102)
n = 333
qx = rnorm(n, mean = -3, sd = 1) - 1
qy = rnorm(n, mean = -3, sd = 1) - 1
px = rnorm(n, mean = 3, sd = 1) + 1
py = rnorm(n, mean = 3, sd = 1) + 1
testdata = rbind( cbind(px, py), cbind(qx, qy) )
testlabel = sign (testdata[,1])
mT = system.time( lasvmTrain (x = traindata, y = trainlabel, gamma = 1, cost = 1, epochs = 99999999,
termination = 2,
sample = 5, # only 5 seconds for each iteration
kernel = 2,
verbose = FALSE))
expect_less_than (mT[3], 10)
})
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