# A unit test for nnetar.R
test_that("Tests for nnetar", {
  oilnnet <- nnetar(airmiles, lambda = 0.15)
  woolyrnqnnet <- nnetar(woolyrnq, repeats = 10)
  expect_output(print(woolyrnqnnet), regexp = "Series: woolyrnq")
  expect_length(forecast(oilnnet)$mean, 10)
  expect_length(forecast(woolyrnqnnet)$mean, 2 * frequency(woolyrnq))
  #
  # Test with single-column xreg (which might be a vector)
  uscnnet <- nnetar(woolyrnq, xreg = seq_along(woolyrnq))
  expect_shape(uscnnet$xreg, dim = c(119, 1))
  expect_length(forecast(uscnnet, xreg = 120:130)$mean, 11)
  # Test default size with and without xreg
  uscnnet <- nnetar(woolyrnq, p = 2, P = 2)
  expect_output(
    print(uscnnet),
    regexp = "NNAR(2,2,2)",
    fixed = TRUE
  )
  expect_output(
    print(uscnnet),
    regexp = "4-2-1 network",
    fixed = TRUE
  )
  expect_true(uscnnet$size == 2)
  uscnnet <- nnetar(woolyrnq, p = 2, P = 2, xreg = 1:119, repeats = 10)
  expect_output(
    print(uscnnet),
    regexp = "NNAR(2,2,3)",
    fixed = TRUE
  )
  expect_output(
    print(uscnnet),
    regexp = "5-3-1 network",
    fixed = TRUE
  )
  expect_true(uscnnet$size == 3)
  # Test default size for models with only seasonal lags, with and without xreg
  seasonal_only_lags_nnet <- nnetar(woolyrnq, p = 0, P = 3)
  expect_output(
    print(seasonal_only_lags_nnet),
    regexp = "NNAR(0,3,2)",
    fixed = TRUE
  )
  expect_output(
    print(seasonal_only_lags_nnet),
    regexp = "3-2-1 network",
    fixed = TRUE
  )

  seasonal_only_lags_xreg_nnet <- nnetar(
    woolyrnq,
    p = 0,
    P = 3,
    xreg = cbind(1:119, 119:1)
  )
  expect_output(
    print(seasonal_only_lags_xreg_nnet),
    regexp = "NNAR(0,3,3)",
    fixed = TRUE
  )
  expect_output(
    print(seasonal_only_lags_xreg_nnet),
    regexp = "5-3-1 network",
    fixed = TRUE
  )

  # Test P=0 when m>1
  uscnnet <- nnetar(woolyrnq, p = 4, P = 0)
  expect_true(uscnnet$size == 2)
  expect_output(print(uscnnet), regexp = "NNAR(4,2)", fixed = TRUE)
  # Test overlapping p & P
  uscnnet <- nnetar(woolyrnq, p = 4, P = 2)
  expect_true(uscnnet$size == 3)
  expect_output(
    print(uscnnet),
    regexp = "NNAR(4,2,3)",
    fixed = TRUE
  )
  expect_output(
    print(uscnnet),
    regexp = "5-3-1 network",
    fixed = TRUE
  )
  # Test that p = 0 & P = 0 is not permitted
  expect_error(
    nnetar(woolyrnq, p = 0, P = 0)
  )
  # Test with multiple-column xreg
  creditnnet <- nnetar(
    wineind,
    xreg = cbind(bizdays(wineind), fourier(wineind, 1))
  )
  expect_warning(
    expect_length(forecast(creditnnet, h = 2, xreg = matrix(2, 2, 3))$mean, 2L),
    "different column names",
    fixed = TRUE
  )
  # Test if h doesn't match xreg
  expect_warning(
    expect_length(forecast(creditnnet, h = 5, xreg = matrix(2, 2, 3))$mean, 2L),
    "different column names",
    fixed = TRUE
  )
  # Test that P is ignored if m=1
  expect_warning(
    creditnnet <- nnetar(WWWusage, p = 2, P = 4, xreg = seq_along(WWWusage))
  )
  expect_output(
    print(creditnnet),
    regexp = "NNAR(2,2)",
    fixed = TRUE
  )
  # Test fixed size
  creditnnet <- nnetar(
    WWWusage,
    p = 1,
    P = 1,
    xreg = seq_along(WWWusage),
    size = 12
  )
  expect_true(uscnnet$size == 3)
  expect_output(print(creditnnet), regexp = "NNAR(1,12)", fixed = TRUE)
  # Test passing arguments to nnet
  expect_warning(
    creditnnet <- nnetar(
      WWWusage,
      p = 2,
      P = 4,
      xreg = seq_along(WWWusage),
      decay = 0.1
    )
  )
  expect_output(
    print(creditnnet),
    regexp = "decay=0.1",
    fixed = TRUE
  )
  ## Test output format correct
  oilnnet <- nnetar(
    airmiles,
    p = 1,
    size = 0,
    skip = TRUE,
    Wts = c(0, 1),
    maxit = 0,
    repeats = 10
  )
  expect_true(all.equal(oilnnet$fitted[-1], airmiles[-length(airmiles)]))
  ## Test output format correct when NAs present
  oilna <- airmiles
  oilna[12] <- NA
  suppressWarnings(
    oilnnet <- nnetar(
      oilna,
      p = 1,
      size = 0,
      skip = TRUE,
      Wts = c(0, 1),
      maxit = 0
    )
  )
  expect_true(all.equal(
    oilnnet$fitted[-c(1, 12, 13)],
    oilna[-c(11, 12, length(oilna))]
  ))
  ## Test model argument
  fit1 <- nnetar(
    WWWusage,
    xreg = seq_along(WWWusage),
    lambda = 2,
    decay = 0.5,
    maxit = 25,
    repeats = 7
  )
  fit2 <- nnetar(WWWusage, xreg = seq_along(WWWusage), model = fit1)
  # Check some model parameters
  expect_identical(fit1$p, fit2$p)
  expect_identical(fit1$lambda, fit2$lambda)
  expect_identical(fit1$nnetargs, fit2$nnetargs)
  # Check fitted values are all the same
  expect_identical(fitted(fit1), fitted(fit2))
  # Check residuals all the same
  expect_identical(residuals(fit1), residuals(fit2))
  # Check number of neural nets
  expect_length(fit1$model, length(fit2$model))
  # Check neural network weights all the same
  expect_identical(fit1$model[[1]]$wts, fit2$model[[1]]$wts)
  expect_identical(fit1$model[[7]]$wts, fit2$model[[7]]$wts)
  # Check subset argument
  oilnnet <- nnetar(airmiles, subset = 11:20)
  expect_identical(which(!is.na(fitted(oilnnet))), 11:20)
  oilnnet <- nnetar(
    airmiles,
    subset = c(rep(F, 10), rep(T, 10), rep(F, length(airmiles) - 20))
  )
  expect_identical(which(!is.na(fitted(oilnnet))), 11:20)
  ## Check short and constant data
  expect_warning(
    nnetfit <- nnetar(
      rep(1, 10),
      p = 2,
      P = 0,
      size = 1,
      repeats = 1,
      lambda = 0.1
    ),
    "Constant data"
  )
  expect_true(nnetfit$p == 1)
  expect_null(nnetfit$lambda)
  expect_null(nnetfit$scalex)
  expect_error(
    nnetfit <- nnetar(rnorm(2), p = 1, P = 0, size = 1, repeats = 1),
    "Not enough data"
  )
  expect_silent(
    nnetfit <- nnetar(rnorm(3), p = 1, P = 0, size = 1, repeats = 1)
  )
  expect_true(nnetfit$p == 1)
  expect_silent(
    nnetfit <- nnetar(rnorm(3), p = 2, P = 0, size = 1, repeats = 1)
  )
  expect_true(nnetfit$p == 2)
  expect_warning(
    nnetfit <- nnetar(rnorm(3), p = 3, P = 0, size = 1, repeats = 1),
    "short series"
  )
  expect_true(nnetfit$p == 2)
  expect_warning(
    nnetfit <- nnetar(rnorm(3), p = 4, P = 0, size = 1, repeats = 1),
    "short series"
  )
  expect_true(nnetfit$p == 2)
  expect_warning(
    nnetfit <- nnetar(
      rnorm(10),
      xreg = rep(1, 10),
      p = 2,
      P = 0,
      size = 1,
      repeats = 1,
      lambda = 0.1
    ),
    "Constant xreg"
  )
  expect_null(nnetfit$scalexreg)
  expect_warning(
    nnetfit <- nnetar(
      rnorm(3),
      xreg = matrix(c(1, 2, 3, 1, 1, 1), ncol = 2),
      p = 1,
      P = 0,
      size = 1,
      repeats = 1,
      lambda = 0.1
    ),
    "Constant xreg"
  )
  expect_null(nnetfit$scalexreg)
})