context("weighted.ksvm")
test_that("weighted.ksvm fitting", {
library(kernlab)
set.seed(123)
n <- 40
x <- matrix(rnorm(n * 2), ncol = 2)
y <- 2 * (sin(x[,2]) ^ 2 * exp(-x[,2]) > rnorm(n, sd = 0.1) + 0.225) - 1
weights <- runif(n, max = 1.5, min = 0.5)
wk <- weighted.ksvm(x = x[1:n/2,], y = y[1:n/2], C = c(0.1, 0.5, 1),
weights = weights[1:n/2])
expect_is(wk, "wksvm")
pr <- predict(wk, newx = x[1:n/2,])
if (Sys.info()[[1]] != "windows")
{
wk <- weighted.ksvm(x = x[1:n/2,], y = y[1:n/2], C = 1,
weights = weights[1:n/2])
expect_is(wk, "wksvm")
}
expect_error(weighted.ksvm(x = x[1:(n/2+1),], y = y[1:n/2], C = c(10),
weights = weights[1:n/2]))
expect_error(weighted.ksvm(x = x[1:n/2,], y = y[1:n/2], C = c(0.1),
weights = weights[1:(n/2 + 1)]))
foldid <- sample(rep(seq(3), length = n/2))
if (Sys.info()[[1]] != "windows")
{
wk <- weighted.ksvm(x = x[1:n/2,], y = y[1:n/2], C = c(1, 3),
foldid = foldid,
weights = weights[1:n/2])
expect_is(wk, "wksvm")
expect_error(weighted.ksvm(x = x[1:(n/2),], y = y[1:(n/2)], C = c(0.1),
nfolds = 150,
weights = weights[1:(n/2)]))
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.factor(y[1:(n/2)]), C = c(1, 3),
foldid = foldid,
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
expect_error(weighted.ksvm(x = x[1:(n/2),], y = c(1:5, y[5:(n/2)]), C = c(0.1),
weights = weights[1:(n/2)]))
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 3),
foldid = foldid,
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.factor(y[1:(n/2)]), C = c(1, 3),
foldid = foldid,
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
expect_warning(weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 3),
nfolds = -5,
weights = weights[1:(n/2)]))
expect_error(weighted.ksvm(x = x[1:(n/2),], y = y[1:(n/2)]/2 + 0.5, C = c(0.1),
weights = weights[1:(n/2)]))
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 10),
foldid = foldid,
kernel = "polydot",
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.factor(y[1:(n/2)]), C = c(1, 3),
foldid = foldid,
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(10),
foldid = foldid,
kernel = "tanhdot",
weights = rep(1, (n/2)),
margin = 0.5,
bound = 10,
maxiter = 200)
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 10),
foldid = foldid,
kernel = "vanilladot",
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 10),
foldid = foldid,
kernel = "laplacedot",
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 10),
foldid = foldid,
kernel = "besseldot",
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
wk <- weighted.ksvm(x = x[1:25,], y = as.character(y[1:25]), C = c(1, 10),
kernel = "tanhdot", maxiter = 500, bound = 10,
weights = weights[1:25])
expect_is(wk, "wksvm")
summary(wk)
wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 10),
foldid = foldid,
kernel = "anovadot",
weights = weights[1:(n/2)])
expect_is(wk, "wksvm")
# wk <- weighted.ksvm(x = x[1:(n/2),], y = as.character(y[1:(n/2)]), C = c(1, 10),
# foldid = foldid,
# kernel = "splinedot",
# weights = weights[1:(n/2)],
# margin = 0.1,
# maxiter = 100)
#
# expect_is(wk, "wksvm")
}
})
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