context("classif_rda")

test_that("classif_rda", {
  requirePackagesOrSkip("klaR", default.method = "load")

  set.seed(getOption("mlr.debug.seed"))
  m = klaR::rda(formula=multiclass.formula, data=multiclass.train)
  p = predict(m, newdata=multiclass.test)$class


  testSimple("classif.rda", multiclass.df, multiclass.target,
    multiclass.train.inds, p)


  parset.list = list(
    list(),
    list(gamma=0.1, lambda=0.1),
    list(gamma=0.5, lambda=1),
    list(gamma=1, lambda=0)
    )

  old.predicts.list = list()
  old.probs.list = list()

  for (i in 1:length(parset.list)) {
    parset = parset.list[[i]]
    pars = list(formula=multiclass.formula, data=multiclass.train)
    pars = c(pars, parset)
    set.seed(getOption("mlr.debug.seed"))
    m = do.call(klaR::rda, pars)
    p = predict(m, newdata=multiclass.test)
    old.predicts.list[[i]] = p$class
    old.probs.list[[i]] = p$posterior
  }

  testSimpleParsets("classif.rda", multiclass.df, multiclass.target, multiclass.train.inds, old.predicts.list, parset.list)
  testProbParsets  ("classif.rda", multiclass.df, multiclass.target, multiclass.train.inds, old.probs.list, parset.list)

  tt = klaR::rda
  tp = function(model, newdata) predict(model, newdata)$class

  testCVParsets("classif.rda", multiclass.df, multiclass.target, tune.train=tt, tune.predict=tp, parset.list=parset.list)
})
