Nothing
test_that("on input of sufficient size, beta/||beta|| is estimated accurately enough",
{
n <- 100000
d <- 2
K <- 2
p <- 1/2
betas_ref <- array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) )
for (i in 1:(dim(betas_ref)[3]))
{
mu_ref <- normalize(betas_ref[,,i])
for (link in c("logit","probit"))
{
cat("\n\n",link," :\n",sep="")
io <- generateSampleIO(n, p, betas_ref[,,i], rep(0,K), link)
mu <- computeMu(io$X, io$Y, list(K=K))
mu_aligned <- alignMatrices(list(mu), ref=mu_ref, ls_mode="exact")[[1]]
#Some traces: 0 is not well estimated, but others are OK
cat("Reference normalized matrix:\n")
print(mu_ref)
cat("Estimated normalized matrix:\n")
print(mu_aligned)
cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
diff_norm <- norm(mu_ref - mu_aligned)
cat(diff_norm,"\n")
# NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
expect_lt( diff_norm, 0.5 )
}
}
})
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