-context("Check that getParamsDirs behaves as expected")
+context("Check that integerIndexToDate behaves as expected")
-test_that("on input of sufficient size, beta is estimated accurately enough", {
- n = 100000
- d = 2
- K = 2
- Pr = c(0.5, 0.5)
+test_that("date matches index in data",
+{
+ #TODO: with and without shift at origin (so series values at least forst ones are required)
- 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]))
+ n = 1500
+ 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)]
-
- 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")
-
- #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")
-
- #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
- expect_that( diff_norm, is_less_than(0.5) )
+ index = (i%%3) + 1
+ level = mean(s[[index]])
+ serie = s[[index]] - level + rnorm(L,sd=0.05)
+ # 10 series with NAs for index 2
+ if (index == 2 && i >= 60 && i<= 90)
+ serie[sample(seq_len(L),1)] = NA
+ series[[i]] = list("level"=level,"serie"=serie) #no need for more :: si : time !!!
}
+ data = new("Data", data=series)
+
+ integerIndexToDate = function(index, data)
})