pkg/tests/testthat/test-computeMu.R

   1 context("computeMu")
   2
   3 test_that("on input of sufficient size, β/||β|| is estimated accurately enough",
   4 {
   5     n = 100000
   6     d = 2
   7     K = 2
   8     p = 1/2
   9
  10     βs_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) )
  11     for (i in 1:(dim(βs_ref)[3]))
  12     {
  13         μ_ref = normalize(βs_ref[,,i])
  14         for (model in c("logit","probit"))
  15         {
  16             cat("\n\n",model," :\n",sep="")
  17
  18             io = generateSampleIO(n, p, βs_ref[,,i], rep(0,K), model)
  19             μ = computeMu(io$X, io$Y, list(K=K))
  20             μ_aligned = alignMatrices(list(μ), ref=μ_ref, ls_mode="exact")[[1]]
  21
  22             #Some traces: 0 is not well estimated, but others are OK
  23             cat("Reference normalized matrix:\n")
  24             print(μ_ref)
  25             cat("Estimated normalized matrix:\n")
  26             print(μ_aligned)
  27             cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
  28             diff_norm = norm(μ_ref - μ_aligned)
  29             cat(diff_norm,"\n")
  30
  31             #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
  32             expect_that( diff_norm, is_less_than(0.5) )
  33         }
  34     }
  35 })