set-up tests

[talweg.git] / pkg / tests / testthat / test.integerIndexToDate.R

diff --git a/pkg/tests/testthat/test.integerIndexToDate.R b/pkg/tests/testthat/test.integerIndexToDate.R
index 5f0008f..31cd740 100644 (file)
--- a/pkg/tests/testthat/test.integerIndexToDate.R
+++ b/pkg/tests/testthat/test.integerIndexToDate.R
@@ -1,34 +1,22 @@
-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)
 })