test_that("on input of sufficient size, β/||β|| is estimated accurately enough",
{
- n = 100000
- d = 2
- K = 2
- p = 1/2
+ n = 100000
+ d = 2
+ K = 2
+ p = 1/2
- βs_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) )
- for (i in 1:(dim(βs_ref)[3]))
- {
- μ_ref = normalize(βs_ref[,,i])
- for (model in c("logit","probit"))
- {
- cat("\n\n",model," :\n",sep="")
+ βs_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) )
+ for (i in 1:(dim(βs_ref)[3]))
+ {
+ μ_ref = normalize(βs_ref[,,i])
+ for (model in c("logit","probit"))
+ {
+ cat("\n\n",model," :\n",sep="")
- io = generateSampleIO(n, p, βs_ref[,,i], rep(0,K), model)
- μ = computeMu(io$X, io$Y, list(K=K))
- μ_aligned = alignMatrices(list(μ), ref=μ_ref, ls_mode="exact")[[1]]
+ io = generateSampleIO(n, p, βs_ref[,,i], rep(0,K), model)
+ μ = computeMu(io$X, io$Y, list(K=K))
+ μ_aligned = alignMatrices(list(μ), ref=μ_ref, ls_mode="exact")[[1]]
- #Some traces: 0 is not well estimated, but others are OK
- cat("Reference normalized matrix:\n")
- print(μ_ref)
- cat("Estimated normalized matrix:\n")
- print(μ_aligned)
- cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
- diff_norm = norm(μ_ref - μ_aligned)
- cat(diff_norm,"\n")
+ #Some traces: 0 is not well estimated, but others are OK
+ cat("Reference normalized matrix:\n")
+ print(μ_ref)
+ cat("Estimated normalized matrix:\n")
+ print(μ_aligned)
+ cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
+ diff_norm = norm(μ_ref - μ_aligned)
+ 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) )
- }
- }
+ #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
+ expect_that( diff_norm, is_less_than(0.5) )
+ }
+ }
})