+ # Expected output: 50-3-10 series of type 2, then 23 series of type 3 (closest next)
+ expect_identical(length(f$neighb_indices), as.integer(60))
+ expect_identical(length(f$colors), as.integer(60))
+ expect_equal(f$index, 143)
+ expect_true(all(I(f$neighb_indices) >= 2))
+ for (i in 1:37)
+ {
+ expect_equal(I(f$neighb_indices[i]), 2)
+ expect_match(f$colors[i], f$colors[1])
+ }
+ for (i in 38:60)
+ {
+ expect_equal(I(f$neighb_indices[i]), 3)
+ expect_match(f$colors[i], f$colors[38])
+ }
+ expect_match(f$colors[1], "#1*")
+ expect_match(f$colors[38], "#E*")
+
+ # index 142 : serie type 1
+ pred = computeForecast(data, 142, "Neighbors", "Zero",
+ horizon=length(data$getSerie(1)), simtype="endo", h_window=1)
+ f = computeFilaments(data, pred, 1, limit=50, plot=FALSE)
+
+ # Expected output: 50-10-3 series of type 1, then 13 series of type 3 (closest next)
+ # NOTE: -10 because only past days with no-NAs tomorrow => exclude type 1 in [60,90[
+ expect_identical(length(f$neighb_indices), as.integer(50))
+ expect_identical(length(f$colors), as.integer(50))
+ expect_equal(f$index, 142)
+ expect_true(all(I(f$neighb_indices) != 2))
+ for (i in 1:37)
+ {
+ expect_equal(I(f$neighb_indices[i]), 1)
+ expect_match(f$colors[i], f$colors[1])
+ }
+ for (i in 38:50)
+ {
+ expect_equal(I(f$neighb_indices[i]), 3)
+ expect_match(f$colors[i], f$colors[38])