# --- Test Suite for quantile functionality ---

test_that("AddiVortes fit 1 with quantile check", {
  set.seed(111333)
  X <- matrix(rnorm(100), 10, 10)
  Y <- rnorm(10)
  X_test <- matrix(rnorm(100), 10, 10)
  Y_test <- rnorm(10)

  results <- AddiVortes(Y, X, 10,
    90, 10,
    6, 0.85, 3, 0.8, 3, 25,
    IntialSigma = "Linear"
  )
  predictions <- predict(
    results,
    X_test,
    "quantile",
    c(0.5, 0.75)
  )

  # Check ordering of all predictions
  expect_true(all(predictions[, 1] <= predictions[, 2]))
  expect_equal(
    round(as.numeric(predictions[1, 2]), 3),
    0.356
  )
  expect_equal(
    round(as.numeric(predictions[10, 1]), 3),
    -0.133
  )
})

test_that("AddiVortes fit 2 with reverse quantile check", {
  set.seed(1789)
  X <- matrix(runif(500), 100, 5)
  Y <- rnorm(100, -5, 3)
  X_test <- matrix(runif(100), 20, 5)
  Y_test <- rnorm(20, -5, 3)

  results <- AddiVortes(Y, X, 5,
    150, 50,
    6, 0.85, 3, 0.8, 3, 25,
    IntialSigma = "Linear"
  )
  predictions <- predict(
    results,
    X_test,
    "quantile",
    c(0.95, 0.75)
  )

  # Check ordering of all predictions
  expect_true(all(predictions[, 1] >= predictions[, 2]))
})

test_that("AddiVortes fit 3 with quantile count", {
  set.seed(1234)
  X <- matrix(rnorm(10000), 1000, 10)
  Y <- runif(1000, -1, 3)
  X_test <- matrix(rnorm(1000), 100, 10)
  Y_test <- runif(100, -1, 3)

  results <- AddiVortes(Y, X, 10,
    100, 30,
    6, 0.85, 3, 0.8, 3, 25,
    IntialSigma = "Linear"
  )
  predictions <- predict(
    results,
    X_test,
    "quantile",
    c(0.01, 0.2, 0.3, 0.4, 0.95, 0.96, 0.97)
  )

  expect_equal(ncol(predictions), 7)
})