-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]))
- {
- 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) )
- }
+test_that("computed integer index is correct; predict_at == 0",
+{
+ data0 = getData(ts_data="pm10_mesures_H_loc.csv", exo_data="meteo_extra_noNAs.csv",
+ input_tz="Europe/Paris",working_tz="Europe/Paris", predict_at=0, limit=200)
+ expect_identical( dateIndexToInteger("2008-12-10",data), 1 )
+ expect_identical( dateIndexToInteger("2008-12-11",data), 2 )
+ expect_identical( dateIndexToInteger("2008-12-20",data), 11 )
+ expect_identical( dateIndexToInteger("2009-02-01",data), 53 )
+ expect_identical( dateIndexToInteger("2009-03-01",data), 81 )
+ expect_identical( dateIndexToInteger("2009-05-31",data), 172 )