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#' @export
makeRLearner.classif.gaterSVM = function() {
makeRLearnerClassif(
cl = "classif.gaterSVM",
package = "SwarmSVM",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "m", default = 3, lower = 1),
makeNumericLearnerParam(id = "c", default = 1, lower = 0),
makeIntegerLearnerParam(id = "max.iter", default = 1, lower = 1),
# FIXME m and max.iter seem to have no default in gaterSVM. If they have, par.vals is redundant.
makeIntegerLearnerParam(id = "hidden", default = 5, lower = 0),
makeNumericLearnerParam(id = "learningrate", default = 0.01, lower = 0),
makeNumericLearnerParam(id = "threshold", default = 0.01, lower = 0),
makeIntegerLearnerParam(id = "stepmax", default = 100, lower = 1),
makeNumericLearnerParam(id = "seed"),
makeUntypedLearnerParam(id = "valid.x", default = NULL),
makeUntypedLearnerParam(id = "valid.y", default = NULL),
makeUntypedLearnerParam(id = "valid.metric", default = NULL),
makeLogicalLearnerParam(id = "verbose", default = FALSE)
),
par.vals = list(m = 3, max.iter = 1),
properties = c("twoclass", "numerics"),
name = "Mixture of SVMs with Neural Network Gater Function",
short.name = "gaterSVM",
note = "`m` set to `3` and `max.iter` set to `1` by default.",
callees = "gaterSVM"
)
}
#' @export
trainLearner.classif.gaterSVM = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
pname = "SwarmSVM"
asNamespace(pname)$gaterSVM(x = d$data, y = d$target, ...)
}
#' @export
predictLearner.classif.gaterSVM = function(.learner, .model, .newdata, ...) {
factor(predict(.model$learner.model, newdata = .newdata, ...),
levels = c(-1, 1),
labels = .model$factor.levels[[1]])
}
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