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#' @export
makeRLearner.classif.clusterSVM = function() {
makeRLearnerClassif(
cl = "classif.clusterSVM",
package = c("SwarmSVM", "LiblineaR"),
par.set = makeParamSet(
makeIntegerLearnerParam(id = "centers", default = 2, lower = 1),
# FIXME default for centers of clusterSVM is NULL
makeUntypedLearnerParam(id = "cluster.object", default = NULL, requires = quote(is.null(centers))),
makeNumericLearnerParam(id = "lambda", default = 1, lower = 0),
makeLogicalLearnerParam(id = "sparse", default = TRUE),
makeUntypedLearnerParam(id = "valid.x", default = NULL),
makeUntypedLearnerParam(id = "valid.y", default = NULL),
makeUntypedLearnerParam(id = "valid.metric", default = NULL),
makeDiscreteLearnerParam(id = "type", default = 1, values = c(1, 2, 3, 5)),
makeNumericLearnerParam(id = "cost", default = 1, lower = 0),
# better default epsilon is dependent on type ( = NULL, see docs), but we cannot store this
makeNumericLearnerParam(id = "epsilon", lower = 0),
makeLogicalLearnerParam(id = "bias", default = TRUE),
makeNumericVectorLearnerParam(id = "wi", len = NA_integer_),
makeDiscreteLearnerParam(id = "verbose", default = 1, values = c(0, 1, 2)),
makeNumericLearnerParam(id = "seed"),
makeDiscreteLearnerParam(id = "cluster.method", default = "kmeans", values = c("kmeans", "kernkmeans")),
makeFunctionLearnerParam(id = "cluster.fun"),
makeFunctionLearnerParam(id = "cluster.predict")
),
par.vals = list(centers = 2),
properties = c("twoclass", "numerics"),
name = "Clustered Support Vector Machines",
short.name = "clusterSVM",
note = "`centers` set to `2` by default.",
callees = "clusterSVM"
)
}
#' @export
trainLearner.classif.clusterSVM = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
pname = "SwarmSVM"
asNamespace(pname)$clusterSVM(x = d$data, y = d$target, ...)
}
#' @export
predictLearner.classif.clusterSVM = function(.learner, .model, .newdata, ...) {
as.factor(predict(.model$learner.model, newdata = .newdata, ...)$predictions)
}
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