tests/testthat/test-LMSquareLossIterations.R

In SixianZhang/CS499-Coding-Project-2: Linear model for regression and binary classification

library(testthat)
library(LinearModel)
data(prostate, package = "ElemStatLearn")
X.mat <- data.matrix(subset(prostate, select = -c(train, lpsa)))
y.vec <- as.vector(data.matrix(subset(prostate, select = lpsa)))
max.iteration <- 5L
step.size <- 0.5
# 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 <-
      LMSquareLossIterations(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 <- 
        LMSquareLossIterations(as.data.frame(X.mat), y.vec, max.iteration, step.size),
      "X.mat must be a numeric matrix.",
      fixed = TRUE
    )
    expect_error(
      W.mat <-
        LMSquareLossIterations(X.mat, y.vec[-1], max.iteration, step.size),
      "y.vec must be a numeric vector of the same number of rows as X.mat.",
      fixed = TRUE
    )
    expect_error(
      W.mat <-
        LMSquareLossIterations(X.mat, y.vec, as.double(max.iteration), step.size),
      "Input max.iterations must be a greater than 1 integer scalar number.",
      fixed = TRUE
    )
    expect_error(
      W.mat <-
        LMSquareLossIterations(X.mat, y.vec, max.iteration, c(rep(step.size,2))),
      "step.size must be a numeric scalar value.",
      fixed = TRUE
    )
  }
)