library(testthat)
library(LinearModel)
data(spam, package = "ElemStatLearn")
X.mat <- data.matrix(spam[,-ncol(spam)])
y.vec <- as.vector(ifelse(spam$spam == 'spam',1,0))
max.iteration <- 5L
step.size <- 0.5L
# LMLogisticLossIterations X.mat, y.vec, max.iterations, step.size = 0.5
test_that(
"For valid inputs, your function returns an output of the expected type/dimension",
{
W.mat <-
LMLogisticLossIterations(X.mat, y.vec, max.iteration, step.size)
expect_true(is.numeric(W.mat))
expect_true(is.matrix(W.mat))
expect_equal(nrow(W.mat), ncol(cbind(1,X.mat)))
}
)
test_that(
"For an invalid input, your function stops with an informative error message.",
{
expect_error(
W.mat <-
LMLogisticLossIterations(as.data.frame(X.mat), y.vec, max.iteration, step.size),
"X.mat must be a numeric matrix",
fixed = TRUE
)
expect_error(
W.mat <-
LMLogisticLossIterations(X.mat, y.vec[-1], max.iteration, step.size),
"y.vec must be a numeric vector of length nrow(X.mat)",
fixed = TRUE
)
expect_error(
W.mat <-
LMLogisticLossIterations(X.mat, y.vec, as.double(max.iteration), step.size),
"max.iterations must be an integer scalar greater than one",
fixed = TRUE
)
expect_error(
W.mat <-
LMLogisticLossIterations(X.mat, y.vec, max.iteration, c(rep(step.size,2))),
"step.size must be a numeric scalar value.",
fixed = TRUE
)
}
)
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