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test_that("classif_mlp", {
requirePackagesOrSkip("RSNNS", default.method = "load")
# test with empty paramset
capture.output({
# neuralnet is not dealing with formula with `.` well
x = data.matrix(binaryclass.train[, -ncol(binaryclass.train)])
y = RSNNS::decodeClassLabels(binaryclass.train[, ncol(binaryclass.train)])
m = RSNNS::mlp(x = x, y = y)
p = predict(m, data.matrix(binaryclass.test[, -ncol(binaryclass.test)]))
p = max.col(p)
p = factor(p, labels = binaryclass.class.levs)
})
testSimple("classif.mlp", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p, parset = list())
# test with params passed
capture.output({
# neuralnet is not dealing with formula with `.` well
x = data.matrix(binaryclass.train[, -ncol(binaryclass.train)])
y = RSNNS::decodeClassLabels(binaryclass.train[, ncol(binaryclass.train)])
m = RSNNS::mlp(x = x, y = y, size = 7, maxit = 100)
p = predict(m, data.matrix(binaryclass.test[, -ncol(binaryclass.test)]))
p = max.col(p)
p = factor(p, labels = binaryclass.class.levs)
})
testSimple("classif.mlp", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list(size = 7, maxit = 100))
})
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