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) )
+ 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"))
+ μ_ref <- normalize(βs_ref[,,i])
+ for (link in c("logit","probit"))
{
- cat("\n\n",model," :\n",sep="")
+ cat("\n\n",link," :\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), link)
+ 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")
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)
+ diff_norm <- norm(μ_ref - μ_aligned)
cat(diff_norm,"\n")
#NOTE: 0.5 is loose threshold, but values around 0.3 are expected...