# MixtComp version 4 - july 2019
# Copyright (C) Inria - Université de Lille - CNRS

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>


# @author Quentin Grimonprez
Sys.setenv(MC_DETERMINISTIC = 42)

context("Launch hierarchical MixtComp")

# These tests are skipped on CRAN to keep elapsed time < 10min

test_that("simple case", {
  skip_on_cran()
  set.seed(42, kind = "Mersenne-Twister", normal.kind = "Inversion")
  data <- matrix(nrow = 200, ncol = 2)
  data[, 1] <- c(rnorm(50, 1, 0.5), rnorm(50, -4, 0.5), rnorm(30, 3, 0.5), rnorm(70, -1, 0.4))
  data[, 2] <- c(rnorm(100, -2, 1), rnorm(100, 3, 0.4))
  colnames(data) <- c("gauss1", "gauss2")
  truePartition <- rep(1:4, c(50, 50, 30, 70))

  model <- list(gauss1 = "Gaussian", gauss2 = "Gaussian")

  algo <- list(
    nbBurnInIter = 50,
    nbIter = 50,
    nbGibbsBurnInIter = 50,
    nbGibbsIter = 50,
    nInitPerClass = 10,
    nSemTry = 20,
    ratioStableCriterion = 0.95,
    nStableCriterion = 10,
    confidenceLevel = 0.95
  )

  expect_warning(
    res <- hierarchicalMixtCompLearn(data, model, algo, nClass = 4, criterion = "BIC", minClassSize = 5, nCore = 1),
    regexp = NA
  )

  partition <- res$variable$data$z_class$completed

  expect_true(is.null(res$warnLog))
  expect_gt(RMixtCompIO:::rand.index(partition, truePartition), 0.9)
})

Sys.unsetenv("MC_DETERMINISTIC")