adapt Bruno method into package, add 'operational' mode

[talweg.git] / pkg / tests / testthat / test-computeFilaments.R

diff --git a/pkg/tests/testthat/test-computeFilaments.R b/pkg/tests/testthat/test-computeFilaments.R
index 355d58d..0c58c69 100644 (file)
--- a/pkg/tests/testthat/test-computeFilaments.R
+++ b/pkg/tests/testthat/test-computeFilaments.R
@@ -1,85 +1,54 @@
 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*")