context("Test heuristic methods for (anti)clustering")
library("anticlust")

test_that("heuristic clustering works for different inputs", {
  for (m in 1:4) {
    m_features <- m
    ## vary number of anticlusters
    for (p in 2:5) {
      n_elements <- p * 3 # n-elements must be multiplier of p
      features <- matrix(rnorm(n_elements * m_features), ncol = m_features)
      clusters <- balanced_clustering(features, K = p)
      expect_equal(legal_number_of_clusters(features, clusters), NULL)
      clusters <- balanced_clustering(dist(features), K = p)
      expect_equal(legal_number_of_clusters(features, clusters), NULL)
    }
  }
})

test_that("heuristic anticlustering produces expected output", {
  conditions <- expand.grid(m = 1:4, p = 2:4, objective = c("distance", "variance"),
                            preclustering = c(TRUE, FALSE))
  for (i in 1:nrow(conditions)) {
    m_features <- conditions[i, "m"]
    p_anticlusters <- conditions[i, "p"]
    n_elements <- p_anticlusters * 5 # n must be multiplier of p
    features <- matrix(rnorm(n_elements * m_features), ncol = m_features)

    ## First: heuristic preclustering
    preclusters <- NULL
    if (conditions$preclustering[i] == TRUE) {
      n_preclusters <- n_elements / p_anticlusters
      preclusters <- balanced_clustering(features, K = n_preclusters)
      ## Legal number of preclusters?
      expect_equal(legal_number_of_clusters(features, preclusters), NULL)
      ## Expected number of preclusters?
      expect_equal(as.numeric(table(preclusters)[1]), n_elements / n_preclusters)
    }

    ## Now anticlustering:
    obj_function <- ifelse(conditions$objective[i] == "distance",
                           diversity_objective, variance_objective)
    anticlusters <- anticlustering(
      features,
      K = p_anticlusters,
      categories = preclusters,
      objective = obj_function
    )
    ## Legal number of anticlusters?
    expect_equal(legal_number_of_clusters(features, anticlusters), NULL)
    ## Expected number of anticlusters?
    expect_equal(as.numeric(table(anticlusters)[1]), n_elements / p_anticlusters)
    if (conditions$preclustering[i] == TRUE) {
      # Are no preclustered elements together?
      tab <- table(anticlusters, preclusters)
      expect_equal(all(tab == 1), TRUE)
    }
  }
})