context("regr_fnn")

test_that("regr_fnn", {
  requirePackagesOrSkip("FNN", default.method = "load")

  parset.list = list(
    list(),
    list(k=1),
    list(k=4),
    list(k=10)
  )

  rdf = regr.df[,-4]
  rtrain = regr.train[,-4]
  rtest = regr.test[,-4]
  rtask = makeRegrTask("regrtask", data=rdf, target="medv")

  old.predicts.list1 = list()

  for (i in 1:length(parset.list)) {
    parset = parset.list[[i]]
    j = which(colnames(rtrain) == regr.target)
    pars = list(train=rtrain[,-j], test=rtest[,-j], y=rtrain[,j])
    pars = c(pars, parset)
    set.seed(getOption("mlr.debug.seed"))
    old.predicts.list1[[i]] = do.call(FNN::knn.reg, pars)$pred
  }

  testSimpleParsets("regr.fnn", rdf, regr.target, regr.train.inds, old.predicts.list1, parset.list)

  tt = function (formula, data, k=3) {
    j = which(colnames(data) == as.character(formula)[2])
    list(train=data[,-j], y=data[,j], k=k, target=j)
  }
  tp = function(model, newdata) {
    newdata = newdata[, -model$target]
    FNN::knn.reg(train=model$train, test=newdata, y=model$y, k=model$k)$pred
  }

  testCVParsets("regr.fnn", rdf, regr.target, tune.train=tt, tune.predict=tp, parset.list=parset.list)
})
