1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
|
#' @export
makeRLearner.classif.rda = function() {
makeRLearnerClassif(
cl = "classif.rda",
package = "klaR",
par.set = makeParamSet(
makeNumericLearnerParam(id = "lambda", lower = 0, upper = 1),
makeNumericLearnerParam(id = "gamma", lower = 0, upper = 1),
makeLogicalLearnerParam(id = "crossval", default = TRUE),
makeIntegerLearnerParam(id = "fold", default = 10L, lower = 1L),
makeNumericLearnerParam(id = "train.fraction", default = 0.5, lower = 0, upper = 1),
makeLogicalLearnerParam(id = "output", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "schedule", default = 2L, values = 1:2, requires = quote(simAnn == TRUE)),
makeNumericLearnerParam(id = "T.start", default = 0.1, lower = 0, requires = quote(simAnn == TRUE)),
makeNumericLearnerParam(id = "halflife", default = 50, lower = 0, requires = quote(simAnn == TRUE && schedule == 1)),
makeNumericLearnerParam(id = "zero.temp", default = 0.01, lower = 0, requires = quote(simAnn == TRUE && schedule == 1)),
makeNumericLearnerParam(id = "alpha", default = 2, lower = 1, requires = quote(simAnn == TRUE && schedule == 2)),
makeIntegerLearnerParam(id = "K", default = 100L, lower = 1L, requires = quote(simAnn == TRUE && schedule == 2)),
makeLogicalLearnerParam(id = "trafo", default = TRUE),
makeLogicalLearnerParam(id = "simAnn", default = FALSE),
makeLogicalLearnerParam(id = "estimate.error", default = TRUE)
),
par.vals = list(estimate.error = FALSE),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
name = "Regularized Discriminant Analysis",
short.name = "rda",
note = "`estimate.error` has been set to `FALSE` by default for speed.",
callees = "rda"
)
}
#' @export
trainLearner.classif.rda = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
klaR::rda(f, data = getTaskData(.task, .subset), ...)
}
#' @export
predictLearner.classif.rda = function(.learner, .model, .newdata, ...) {
p = predict(.model$learner.model, newdata = .newdata, ...)
if (.learner$predict.type == "response") {
return(p$class)
}
return(p$posterior)
}
|
