tests/testthat/test-math_bug-ds.rUnif.R

In datashield/dsBaseClient: DataSHIELD Client Functions

#-------------------------------------------------------------------------------
# Copyright (c) 2019-2022 University of Newcastle upon Tyne. All rights reserved.
#
# This program and the accompanying materials
# are made available under the terms of the GNU Public License v3.0.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#-------------------------------------------------------------------------------

source("connection_to_datasets/init_testing_datasets.R")
source("definition_tests/def-ds.rUnif.R")

random.number <- as.integer(as.POSIXct(Sys.time(), "GMT"))

context("ds.rUnif::math::mean_variance::single")
test_that("mean_variance", 
{
  connect.dataset.1()
   seed <- random.number/1000
  .test.range.values(1,2,"uniform_1",seed)
  .test.range.values(-1,2,"uniform_2",seed)
  .test.range.values(-200,-100,"uniform_3",seed)
})

context("ds.rUnif::math::distributions comparison::single")
test_that("changes in distribution",
{
  connect.dataset.3()
  seed <- random.number/1000
  .test.dispersions.stats.same.distribution(0,1,seed)
  .test.dispersions.stats.same.distribution(0,100,seed)
  .test.dispersions.stats.same.distribution(50,100,seed)
  .test.dispersions.stats.diff.distribution(0,1,seed,100,200,seed)
  .test.dispersions.stats.diff.distribution(0,1,seed,1,2, seed)
  .test.dispersions.stats.diff.distribution(100,200, seed, 1000,2000, seed)
})


context("ds.rUnif::math::distributions comparison::multiple")
test_that("changes in distribution",
{
   connect.all.datasets()
   seed <- random.number/1000
   .test.dispersions.stats.same.distribution(0,1,seed)
   .test.dispersions.stats.same.distribution(0,100,seed)
   .test.dispersions.stats.same.distribution(50,100,seed)
   .test.dispersions.stats.diff.distribution(0,1,seed, 100,200,seed)
   .test.dispersions.stats.diff.distribution(0,1,seed, 1,2, seed)
   .test.dispersions.stats.diff.distribution(100,200, seed, 1000,2000, seed)
})