-#TODO: toy dataset, check that indices returned are correct + colors
+context("Check that computeFilaments behaves as expected")
-context("Check that getParamsDirs behaves as expected")
+#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3
+I = function(i)
+ (i-1) %% 3 + 1
-test_that("on input of sufficient size, beta is estimated accurately enough", {
- n = 100000
- d = 2
- K = 2
- Pr = c(0.5, 0.5)
-
- betas_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(2,2,2) )
- for (i in 1:(dim(betas_ref)[3]))
+#MOCK data; NOTE: could be in inst/testdata as well
+getDataTest = function(n)
+{
+ data = Data$new()
+ x = seq(0,10,0.1)
+ L = length(x)
+ s1 = cos(x)
+ s2 = sin(x)
+ s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+ #sum((s1-s2)^2) == 97.59381
+ #sum((s1-s3)^2) == 57.03051
+ #sum((s2-s3)^2) == 40.5633
+ s = list( s1, s2, s3 )
+ series = list()
+ for (i in seq_len(n))
{
- beta_ref = betas_ref[,,i]
- #all parameters are supposed to be of norm 1: thus, normalize beta_ref
- norm2 = sqrt(colSums(beta_ref^2))
- beta_ref = beta_ref / norm2[col(beta_ref)]
+ serie = s[[I(i)]] + rnorm(L,sd=0.01)
+ level = mean(serie)
+ serie = serie - level
+ # 10 series with NAs for index 2
+ if (I(i) == 2 && i >= 60 && i<= 90)
+ serie[sample(seq_len(L),1)] = NA
+ data$append(c(), serie, level, c(), c()) #no need for more
+ }
+ data
+}
- io = generateSampleIO(n, d, K, Pr, beta_ref)
- beta = getParamsDirs(io$X, io$Y, K)
- betas = .labelSwitchingAlign(
- array( c(beta_ref,beta), dim=c(d,K,2) ), compare_to="first", ls_mode="exact")
+test_that("output is as expected on simulated series",
+{
+ data = getDataTest(150)
- #Some traces: 0 is not well estimated, but others are OK
- cat("\n\nReference parameter matrix:\n")
- print(beta_ref)
- cat("Estimated parameter matrix:\n")
- print(betas[,,2])
- cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
- diff_norm = norm(beta_ref - betas[,,2])
- cat(diff_norm,"\n")
+ # index 143 : serie type 2
+ f = computeFilaments(data, 143, limit=60, plot=FALSE)
- #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
- expect_that( diff_norm, is_less_than(0.5) )
+ # 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
+ f = computeFilaments(data, 142, 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])
}
+ expect_match(f$colors[1], "#1*")
+ expect_match(f$colors[38], "#E*")
})