context("Test exchange methods")
library("anticlust")

test_that("fast exchange and exchange functions yield the same results - kmeans", {
  # for K = 2
  clusters <- rep(1:2, 5)
  features <- matrix(rnorm(20), ncol = 2)
  ac <- anticlustering(features, K = clusters, objective = "variance")
  ac_fast <- fast_anticlustering(features, clusters, exchange_partners = rep(list(1:10), 10))
  expect_equal(all(ac == ac_fast), TRUE)

  ## for K = 3
  clusters <- rep(1:3, 5)
  features <- matrix(rnorm(30), ncol = 2)
  ac <- anticlustering(features, K = clusters, objective = "variance")
  ac_fast <- fast_anticlustering(features, clusters, exchange_partners = rep(list(1:15), 15))
  expect_equal(all(ac == ac_fast), TRUE)

  ## for K = 4
  clusters <- rep(1:4, 5)
  features <- matrix(rnorm(20), ncol = 1)
  ac <- anticlustering(features, K = clusters, objective = "variance")
  ac_fast <- fast_anticlustering(features, clusters, exchange_partners = rep(list(1:20), 20))
  expect_equal(all(ac == ac_fast), TRUE)
})


test_that("fast exchange and exchange functions yield the same results - anticluster editing", {
  # for K = 2
  K <- 2
  clusters <- rep(1:K, 5)
  features <- matrix(rnorm(K * 10), ncol = 2)
  ac <- anticlustering(features, K = clusters, objective = diversity_objective)
  ac_fast <- fast_exchange_dist(as.matrix(dist(features)), clusters, NULL)
  expect_equal(all(ac == ac_fast), TRUE)

  # for K = 3
  K <- 3
  clusters <- rep(1:K, 5)
  features <- matrix(rnorm(K * 10), ncol = 2)
  ac <- anticlustering(features, K = clusters, objective = diversity_objective)
  ac_fast <- fast_exchange_dist(as.matrix(dist(features)), clusters, NULL)
  expect_equal(all(ac == ac_fast), TRUE)

  # for K = 4
  K <- 4
  clusters <- rep(1:K, 5)
  features <- matrix(rnorm(K * 10), ncol = 2)
  ac <- anticlustering(features, K = clusters, objective = diversity_objective)
  ac_fast <- fast_exchange_dist(as.matrix(dist(features)), clusters, NULL)
  expect_equal(all(ac == ac_fast), TRUE)

  ## Test preclustering restrictions
  features <- schaper2019[, 3:6]
  clusters <- categorical_sampling(matching(features, 2), K = 2)
  ac <- anticlustering(features, K = clusters, objective = diversity_objective)
  ac_fast <- anticlustering(features, K = clusters, objective = "distance")
  expect_equal(all(ac == ac_fast), TRUE)

  ## Test categorical restrictions
  features <- schaper2019[, 3:6]
  ac <- anticlustering(features, K = rep_len(1:2, nrow(schaper2019)),
                       objective = diversity_objective,
                       categories = schaper2019$room)
  ac_fast <- anticlustering(features, K = rep_len(1:2, nrow(schaper2019)),
                            objective = "distance",
                            categories = schaper2019$room)
  expect_equal(all(ac == ac_fast), TRUE)

})


test_that("same results for local updating and recomputing - different group sizes", {
  N <- 60 
  M <- 2
  clusters <- rep(1:3, c(30, 15, 15))
  features <- matrix(rnorm(N * M), ncol = M)
  ac1 <- anticlustering(features, K = clusters, objective = "variance")
  ac2 <- fast_anticlustering(features, clusters, exchange_partners = rep(list(1:N), N))
  ac3 <- anticlustering(features, clusters, objective = "variance")
  expect_true(all(ac1 == ac2))
  expect_true(all(ac2 == ac3))
  
  ac4 <- anticlustering(features, K = clusters, objective = diversity_objective)
  ac5 <- anticlustering(features, clusters)
  expect_true(all(ac4 == ac5))
  
})