context("computeFilaments")
-#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3
-I = function(i)
- (i-1) %% 3 + 1
-
-#MOCK data; NOTE: could be in inst/testdata as well
-getDataTest = function(n)
-{
- data = Data$new()
- x = seq(0,9.5,0.1)
- L = length(x) #96 1/4h
- s1 = cos(x)
- s2 = sin(x)
- s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
- #sum((s1-s2)^2) == 96
- #sum((s1-s3)^2) == 58
- #sum((s2-s3)^2) == 38
- s = list(s1, s2, s3)
- series = list()
- for (i in seq_len(n))
- {
- serie = s[[I(i)]] + rnorm(L,sd=0.01)
- level = mean(serie)
- serie = serie - level
- # 10 series with NAs for index 2
- if (I(i) == 2 && i >= 60 && i<= 90)
- serie[sample(seq_len(L),1)] = NA
- time = as.POSIXct(i*15*60, origin="2007-01-01", tz="GMT")
- exo = runif(4)
- exo_hat = runif(4)
- data$append(time, serie, level, exo, exo_hat)
- }
- data
-}
-
test_that("output is as expected on simulated series",
{
data = getDataTest(150)
- # index 143 : serie type 2
- pred = computeForecast(data, 143, "Neighbors", "Zero",
- horizon=length(data$getSerie(1)), simtype="endo", h_window=1)
- f = computeFilaments(data, pred, 1, limit=60, plot=FALSE)
+ # index 144 : serie type 3, yersteday type 2
+ pred = computeForecast(data, 144, "Neighbors", "Zero", predict_from=1,
+ horizon=length(data$getSerie(1)), simtype="endo", local=FALSE, window=1, opera=TRUE)
+ f = computeFilaments(data, pred, 1, 8, limit=60, plot=FALSE)
- # Expected output: 50-3-10 series of type 2, then 23 series of type 3 (closest next)
+ # Expected output: 50-3-10 series of type 2+1 = 3,
+ # then 23 series of type 3+1 %% 3 = 1 (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))
+ expect_equal(f$index, 144)
+ expect_true(all(I(f$neighb_indices) != 2))
for (i in 1:37)
{
- expect_equal(I(f$neighb_indices[i]), 2)
+ expect_equal(I(f$neighb_indices[i]), 3)
expect_match(f$colors[i], f$colors[1])
}
for (i in 38:60)
{
- expect_equal(I(f$neighb_indices[i]), 3)
+ expect_equal(I(f$neighb_indices[i]), 1)
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$getSerie(1)), simtype="endo", h_window=1)
- f = computeFilaments(data, pred, 1, limit=50, plot=FALSE)
+ # index 143 : serie type 2
+ pred = computeForecast(data, 143, "Neighbors", "Zero", predict_from=1,
+ horizon=length(data$getSerie(1)), simtype="endo", local=FALSE, window=1, opera=TRUE)
+ f = computeFilaments(data, pred, 1, 8, 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[
+ # Expected output: 50-10-3 series of type 1+1=2,
+ # then 13 series of type 3+1 %% 3 = 1 (closest next)
+ # NOTE: -10 because only past tomorrows with no-NAs yerstedays
+ # => exclude type 2 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))
+ expect_equal(f$index, 143)
+ expect_true(all(I(f$neighb_indices) <= 2))
for (i in 1:37)
{
- expect_equal(I(f$neighb_indices[i]), 1)
+ expect_equal(I(f$neighb_indices[i]), 2)
expect_match(f$colors[i], f$colors[1])
}
for (i in 38:50)
{
- expect_equal(I(f$neighb_indices[i]), 3)
+ expect_equal(I(f$neighb_indices[i]), 1)
expect_match(f$colors[i], f$colors[38])
}
expect_match(f$colors[1], "#1*")