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#' @export
makeRLearner.regr.brnn = function() {
makeRLearnerRegr(
cl = "regr.brnn",
package = "brnn",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "neurons", default = 2L, lower = 1L),
makeLogicalLearnerParam(id = "normalize", default = TRUE),
makeIntegerLearnerParam(id = "epochs", default = 1000L, lower = 1L),
makeNumericLearnerParam(id = "mu", default = 0.005, lower = .Machine$double.eps),
makeNumericLearnerParam(id = "mu_dec", default = 0.1, lower = .Machine$double.eps),
makeNumericLearnerParam(id = "mu_inc", default = 10, lower = .Machine$double.eps),
makeNumericLearnerParam(id = "mu_max", default = 1e10, lower = .Machine$double.eps),
makeNumericLearnerParam(id = "min_grad", default = 1e-10),
makeNumericLearnerParam(id = "change", default = 0.001, lower = .Machine$double.eps),
makeIntegerLearnerParam(id = "cores", default = 1L, lower = 1L),
makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "Monte_Carlo", default = FALSE),
makeNumericLearnerParam(id = "tol", default = 1e-06, lower = .Machine$double.eps),
makeIntegerLearnerParam(id = "samples", default = 40L, lower = 1L),
makeUntypedLearnerParam(id = "contrasts")
),
properties = c("numerics", "factors"),
name = "Bayesian regularization for feed-forward neural networks",
short.name = "brnn",
callees = "brnn"
)
}
#' @export
trainLearner.regr.brnn = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
brnn::brnn(f, data = getTaskData(.task, .subset), ...)
}
#' @export
predictLearner.regr.brnn = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, ...)
}
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