tests/testthat/test-max_lambda.R

In gujyjean/discretecdAlgorithm: Coordinate-Descent Algorithm for Learning Sparse Discrete Bayesian Networks

context("max_lambda")

# set up input variable
data <- matrix(c(1, 1, 0, 0, 1, 1,
                 1, 1, 0, 1, 1, 1,
                 0, 0, 1, 0, 0, 1,
                 0, 0, 1, 0, 0, 1,
                 0, 0, 0, 1, 1, 0,
                 0, 0, 0, 1, 1, 1,
                 1, 1, 1, 1, 0, 0,
                 1, 0, 1, 1, 0, 1,
                 0, 0, 0, 0, 1, 0,
                 1, 1, 1, 1, 0, 1,
                 1, 1, 0, 1, 1, 1,
                 0, 0, 1, 0, 0, 1,
                 1, 1, 0, 1, 0, 0,
                 1, 0, 1, 1, 0, 1,
                 1, 1, 1, 1, 1, 0,
                 1, 0, 1, 1, 1, 1,
                 0, 0, 1, 0, 0, 0,
                 1, 1, 0, 1, 1, 1,
                 1, 1, 1, 0, 0, 0,
                 1, 1, 1, 1, 0, 0,
                 0, 0, 0, 0, 1, 0,
                 0, 0, 1, 0, 0, 0,
                 1, 0, 0, 0, 1, 1,
                 0, 0, 1, 0, 0, 0,
                 1, 0, 1, 1, 0, 1,
                 0, 0, 0, 1, 1, 0,
                 0, 0, 0, 0, 0, 0,
                 0, 0, 1, 0, 1, 0,
                 0, 0, 1, 0, 0, 0,
                 0, 0, 1, 0, 0, 0), byrow = TRUE, ncol = 6)
dataSize <- dim(data)[1]
node <- dim(data)[2]
ivn <- vector("list", length = dataSize)
ivn <- lapply(ivn, function(x){return(as.integer(0))})
databn <- sparsebnUtils::sparsebnData(data, ivn = ivn, type = "discrete")

# test
test_that("Testing behaviour of max_lambda", {
  ### no error with default setting
  expect_error(max_lambda(databn), NA)

  ### no error with mannual settings
  weights <- matrix(1, nrow = node, ncol = node)
  gamma = 1.0
  upperbound = 100
  expect_error(max_lambda(databn, weights, gamma, upperbound), NA)
})