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1 | test_that("on input of sufficient size, β/||β|| is estimated accurately enough", |
2 | { | |
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3 | n <- 100000 |
4 | d <- 2 | |
5 | K <- 2 | |
6 | p <- 1/2 | |
cbd88fe5 | 7 | |
2b3a6af5 | 8 | βs_ref <- array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) ) |
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9 | for (i in 1:(dim(βs_ref)[3])) |
10 | { | |
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11 | μ_ref <- normalize(βs_ref[,,i]) |
12 | for (link in c("logit","probit")) | |
6dd5c2ac | 13 | { |
2b3a6af5 | 14 | cat("\n\n",link," :\n",sep="") |
cbd88fe5 | 15 | |
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16 | io <- generateSampleIO(n, p, βs_ref[,,i], rep(0,K), link) |
17 | μ <- computeMu(io$X, io$Y, list(K=K)) | |
18 | μ_aligned <- alignMatrices(list(μ), ref=μ_ref, ls_mode="exact")[[1]] | |
cbd88fe5 | 19 | |
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20 | #Some traces: 0 is not well estimated, but others are OK |
21 | cat("Reference normalized matrix:\n") | |
22 | print(μ_ref) | |
23 | cat("Estimated normalized matrix:\n") | |
24 | print(μ_aligned) | |
25 | cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n") | |
2b3a6af5 | 26 | diff_norm <- norm(μ_ref - μ_aligned) |
6dd5c2ac | 27 | cat(diff_norm,"\n") |
cbd88fe5 | 28 | |
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29 | # NOTE: 0.5 is loose threshold, but values around 0.3 are expected... |
30 | expect_lt( diff_norm, 0.5 ) | |
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31 | } |
32 | } | |
cbd88fe5 | 33 | }) |