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test_that("regr_nnet", {
requirePackagesOrSkip("nnet", default.method = "load")
# test with empty paramset
capture.output({
m = nnet::nnet(regr.formula, size = 3L, data = regr.train, linout = TRUE)
p = predict(m, newdata = regr.test)[, 1L]
print(p)
})
testSimple("regr.nnet", regr.df, regr.target, regr.train.inds, p,
parset = list())
# test with params passed
capture.output({
m = nnet::nnet(regr.formula, size = 7L, data = regr.train, linout = TRUE)
p = predict(m, newdata = regr.test)[, 1L]
print(p)
})
testSimple("regr.nnet", regr.df, regr.target, regr.train.inds, p,
parset = list(size = 7L))
# tt = function (formula, data, subset = 1:150, ...) {
# nnet::nnet(formula, data = data[subset,], size = 3L, maxit = 50L)
# }
# tp = function(model, newdata) as.factor(predict(model, newdata, type = "class"))
# testCV("regr.nnet", regr.df, regr.target, tune.train = tt, tune.predict = tp,
# parset = list(size = 3L, maxit = 50L))
})
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