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#' @export
makeRLearner.regr.ksvm = function() {
makeRLearnerRegr(
cl = "regr.ksvm",
package = "kernlab",
par.set = makeParamSet(
makeLogicalLearnerParam(id = "scaled", default = TRUE),
makeDiscreteLearnerParam(id = "type", default = "eps-svr", values = c("eps-svr", "nu-svr", "eps-bsvr")),
makeDiscreteLearnerParam(id = "kernel", default = "rbfdot",
values = c("vanilladot", "polydot", "rbfdot", "tanhdot", "laplacedot", "besseldot", "anovadot", "splinedot")),
makeNumericLearnerParam(id = "C",
lower = 0, default = 1, requires = quote(type %in% c("eps-svr", "eps-bsvr"))),
makeNumericLearnerParam(id = "nu",
lower = 0, default = 0.2, requires = quote(type == "nu-svr")),
makeNumericLearnerParam(id = "epsilon", lower = 0, default = 0.1,
requires = quote(type %in% c("eps-svr", "nu-svr", "eps-bsvr"))),
makeNumericLearnerParam(id = "sigma",
lower = 0, requires = quote(kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"))),
makeIntegerLearnerParam(id = "degree", default = 3L, lower = 1L,
requires = quote(kernel %in% c("polydot", "anovadot", "besseldot"))),
makeNumericLearnerParam(id = "scale", default = 1, lower = 0,
requires = quote(kernel %in% c("polydot", "tanhdot"))),
makeNumericLearnerParam(id = "offset", default = 1,
requires = quote(kernel %in% c("polydot", "tanhdot"))),
makeIntegerLearnerParam(id = "order", default = 1L,
requires = quote(kernel == "besseldot")),
makeNumericLearnerParam(id = "tol", default = 0.001, lower = 0),
makeLogicalLearnerParam(id = "shrinking", default = TRUE),
makeLogicalLearnerParam(id = "fit", default = TRUE, tunable = FALSE),
makeIntegerLearnerParam(id = "cache", default = 40L, lower = 1L)
),
par.vals = list(fit = FALSE),
properties = c("numerics", "factors"),
name = "Support Vector Machines",
short.name = "ksvm",
note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `ksvm`. Note that `fit` has been set to `FALSE` by default for speed.",
callees = "ksvm"
)
}
#' @export
trainLearner.regr.ksvm = function(.learner, .task, .subset, .weights = NULL, degree, offset, scale, sigma, order, length, lambda, ...) {
kpar = learnerArgsToControl(list, degree, offset, scale, sigma, order, length, lambda)
f = getTaskFormula(.task)
# difference in missing(kpar) and kpar = list()!
if (base::length(kpar)) {
kernlab::ksvm(f, data = getTaskData(.task, .subset), kpar = kpar, ...)
} else {
kernlab::ksvm(f, data = getTaskData(.task, .subset), ...)
}
}
#' @export
predictLearner.regr.ksvm = function(.learner, .model, .newdata, ...) {
kernlab::predict(.model$learner.model, newdata = .newdata, ...)[, 1L]
}
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