#' @title Learner for nonparametric classification for functional data.
#'
#' @description
#' Learner for Nonparametric Supervised Classification.
#'
#' @export
makeRLearner.classif.fdausc.np = function() {
makeRLearnerClassif(
cl = "classif.fdausc.np",
package = "fda.usc",
par.set = makeParamSet(
makeIntegerVectorLearnerParam(id = "h", default = NULL, special.vals = list(NULL)),
makeDiscreteLearnerParam(id = "Ker", default = "AKer.norm", values = list("AKer.norm", "AKer.cos", "AKer.epa", "AKer.tri", "AKer.quar", "AKer.unif")),
makeDiscreteLearnerParam(id = "metric", default = "metric.lp", values = list("metric.lp", "metric.kl", "metric.hausdorff", "metric.dist")),
makeDiscreteLearnerParam(id = "type.CV", default = "GCV.S", values = c("GCV.S", "CV.S", "GCCV.S")),
makeDiscreteLearnerParam(id = "type.S", default = "S.NW", values = list("S.NW", "S.LLR", "S.KNN")),
makeNumericLearnerParam(id = "trim", lower = 0L, upper = 1L, default = 0L),
makeLogicalLearnerParam(id = "draw", default = TRUE, tunable = FALSE)
),
par.vals = list(draw = FALSE),
properties = c("twoclass", "multiclass", "prob", "single.functional"),
name = "Nonparametric classification on FDA",
short.name = "fdausc.np",
note = "Argument draw=FALSE is used as default. Additionally, mod$C[[1]] is set to quote(classif.np)"
)
}
#' @export
trainLearner.classif.fdausc.np = function(.learner, .task, .subset, .weights = NULL, trim, draw, ...) {
# Get and transform functional data
d = getTaskData(.task, subset = .subset, target.extra = TRUE, functionals.as = "matrix")
fd = getFunctionalFeatures(d$data)
# transform the data into fda.usc:fdata class type.
data.fdclass = fda.usc::fdata(mdata = as.matrix(fd))
par.cv = learnerArgsToControl(list, trim, draw)
mod = fda.usc::classif.np(group = d$target, fdataobj = data.fdclass, par.CV = par.cv,
par.S = list(w = .weights), ...)
# Fix a bug in the package
mod$C[[1]] = quote(classif.np)
return(mod)
}
#' @export
predictLearner.classif.fdausc.np = function(.learner, .model, .newdata, ...) {
# transform the data into fda.usc:fdata class type.
fd = getFunctionalFeatures(.newdata)
nd = fda.usc::fdata(mdata = as.matrix(fd))
# predict according to predict.type
type = ifelse(.learner$predict.type == "prob", "probs", "class")
if (type == "probs") {
predict(.model$learner.model, nd, type = type)$prob.group
} else {
predict(.model$learner.model, nd, type = type)
}
}
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